Metagenomic Analysis of gut flora Structure and Function in Neonates Respiratory Distress Syndrome

Metagenomic Analysis of gut flora Structure and Function in Neonates Respiratory Distress Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metagenomic Analysis of gut flora Structure and Function in Neonates Respiratory Distress Syndrome Faqun Liu, Jinghua Luo, Lihua Feng, Xiangxiang Chen, Yunping Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6051340/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neonatal respiratory distress syndrome (NRDS) is a prevalent respiratory condition in newborns that significantly impacts their health and survival rates. In recent years, the potential role of the gut-lung axis in NRDS has garnered increasing attention; however, its specific contributions remain unclear. In this study, we conducted a metagenomics analysis of fecal samples obtained from an observational cohort including NRDS (n = 25) and healthy controls (n = 15). The results indicated alterations in both the structure and function of the gut flora in NRDS. Specifically, the NRDS group exhibited significantly greater relative abundances of Bacillota and Nematoda compared to the control group, while the relative abundances of Pseudomonadota, Actinomycetota, Bacteroidota, and Fusobacteriota were significantly lower. At the genus level, the NRDS group demonstrated greater abundances of Klebsiella, Escherichia, Enterococcus, and Staphylococcus and reduced abundances of Bifidobacterium and Enterobacter. Functional changes included the upregulation of the Staphylococcus aureus infection and the phosphotransferase system (PTS) and the downregulation of metabolic pathways, such as butanoate metabolism and glutathione metabolism. Additionally, both synergistic and mutually exclusive relationships between gut microbiota as well as between gut microbiota and clinical factors were observed. These results increase our overall comprehension of NRDS pathogenesis and offer important resources for identifying potential biomarkers. Biological sciences/Microbiology/Clinical microbiology Health sciences/Medical research/Paediatric research NRDS Gut flora Gut-lung axis Metagenomic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Neonatal respiratory distress syndrome (NRDS) is a common and serious condition that affects the breathing of newborns, which poses significant risk to their lives 1 . The primary pathophysiological mechanism involves the synthesis, secretion, or dysfunction of pulmonary surfactant (PS). This dysfunction results in alveolar collapse and decreased lung compliance. NRDS is clinically characterized by the onset and progressive worsening of respiratory distress shortly after birth. The causes of NRDS are intricate and can be categorized into direct lung damage, including pulmonary bleeding, severe pulmonary infections, aspiration of amniotic fluid or meconium, and indirect lung damage, such as sepsis, necrotizing enterocolitis, and asphyxia 2 . Recent multicenter cohort research has indicated that mortality rates for NRDS can reach as high as 17–24%, establishing it as a major factor contributing to neonatal mortality and disability 3 . Despite progress in clinical therapies, the frequency of this condition continues to increase each year. The fundamental pathological processes are not fully understood, and the condition is linked to prolonged health complications, such as chronic lung disease and neurodevelopmental disorders 4 . It is essential to gain insights into the mechanisms driving NRDS, improve diagnostic tools and identify therapeutic targets. Microbial communities, often referred to as “endocrine organs” within the human body, play a vital role in the integration of microbial signals and immune reactions that help sustain homeostasis 5 – 7 . Recent findings contest that the lungs are devoid of microorganisms through the revelation of microbial communities, primarily composed of the genera Prevotella, Streptococcus, Veillonella, Fusobacterium, and Haemophilus, with densities ranging from 10³ to 10⁵ per gram of tissue. These communities are significantly linked to various respiratory illnesses 8 – 11 . Damage to lung tissue can modify the composition of lung microbial communities, amplify inflammation, hinder immune defenses, and initiate a feedback loop that exacerbates acute lung injury 12 – 14 . The gastrointestinal tract, which is the largest reservoir of microbes, plays a crucial role in influencing lung diseases via the “gut-lung axis” 15 . In contrast to older children and adults, newborns display unique structures of microbial communities, which are affected by elements such as gestational timing and mode of delivery 16 . Consequently, it is crucial to explore the impact of the neonatal lung-gut microbiota on the progression of NRDS. We used metagenomics to investigate alterations in the structure and function of the intestinal microbiota in children with NRDS and analyze the correlations among microbial communities. This approach will improve our understanding of the pathogenesis of NRDS, facilitate the development of improved identification tools, and identify potential therapeutic targets. 2. Material and methods 2.1 Subjects This study has been approved by the local ethics committee [The Second Affiliated Hospital of Nanchang University, approval no. 2024 (90)]. Consent was obtained from the guardians of every participant. The NRDS group and the healthy control group (CON) were studied, comprising children diagnosed with NRDS and healthy newborns undergoing physical examinations at our hospital, respectively, from November 2023 to August 2024. Additionally, the basic data of both the children and their mothers were recorded. Inclusion criteria for the NRDS group: (1) All children must meet the diagnostic criteria for NRDS 17 : a. Gradual onset of breathing difficulties, characterized by signs like difficulty in breathing, cyanosis, and the observation of three indentations during inhalation, usually appears a few hours post-delivery, resulting in significant hypoxic respiratory failure; b. Lung X-ray findings reveal a general decrease in transparency across both lung fields, with ground-glass opacities, reticular granular shadows, air bronchograms, and blurred margins of the heart and diaphragm. In severe cases, the lungs may appear completely white. (2) The clinical data for all patients were complete; (3) Informed consent forms were signed by family members. Exclusion criteria included: (1) severe infection or sepsis; (2) severe hereditary or congenital diseases; (3) patients diagnosed with pulmonary tuberculosis; (4) incomplete clinical data; (5) refusal to participate by guardians. Additionally, we recruited healthy newborns for physical examination to serve as the CON. 2.2 Sample collection and DNA extraction Immediately after the newborn defecates, use a sterile cotton swab to collect 1–2 grams of fresh, intermediate internal stool specimens. Place the specimens in a 2 ml sterile EP tube and transfer the tube to a -80°C refrigerator for storage until DNA extraction. A stool sample weighing 0.5g was used to extract genomic DNA with the FastPure Stool DNA Isolation Kit (Magnetic bead) (MJYH, Shanghai, China), adhering to the manufacturer's instructions. To determine the concentration and purity of the extracted DNA, assessments were conducted using Synergy HTX and NanoDrop2000, respectively. The DNA quality was further analyzed by electrophoresis on a 1% agarose gel. 2.3 Metagenomic sequencing The DNA extract was processed to reach an approximate length of 400 base pairs using the Covaris M220 (Gene Company Limited, China) for creating a paired-end library. The library was developed with the help of NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Sequencing of the end pairs was performed on the Illumina NovaSeq™ X Plus (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), in accordance with the recommendations of the manufacturer for the NovaSeq X Series 25B Reagent Kit ( www.illumina.com ). 2.4 Processing of metagenome sequencing data The data analysis was conducted utilizing the free online resource called Majorbio Cloud Platform ( www.majorbio.com ). To summarize, adapters were removed from the raw sequencing reads, and low-quality reads—defined as those shorter than 50 bp, possessing a quality value under 20, or containing N bases—were discarded using fastp 18 ( https://github.com/OpenGene/fastp , version 0.20.0). Following this, the reads were aligned to the human genome through the use of BWA 19 ( http://bio-bwa.sourceforge.net , version 0.7.17), and any related hits corresponding to the reads and their mate reads were eliminated. The dataset that met the quality criteria was compiled using MEGAHIT 20 ( https://github.com/voutcn/megahit , version 1.1.2). For the final assembly output, contigs with a minimum length of 300 bp were selected. Open reading frames (ORFs) associated with each assembled contig were identified via Prodigal 21 ( https://github.com/hyattpd/Prodigal , version 2.6.3), and ORFs of a length of at least 100 bp were chosen. A non-redundant gene catalog was created using CD-HIT 22 ( http://weizhongli-lab.org/cd-hit/ , version 4.7) at a 90% sequence identity level and 90% coverage. The abundance of genes for a particular sample was assessed with SOAPaligner 23 ( https://github.com/ShujiaHuang/SOAPaligner , version soap2.21release) applying a 95% identity threshold. 2.5 Taxonomic and functional annotation The classification of the highest-ranking non-redundant genes was ascertained by aligning these genes with the NCBI NR database through DIAMOND 24 ( http://ab.inf.uni-tuebingen.de/software/diamond/ , version 2.0.13) with an e-value cutoff of 1e-5. Similarly, the functional annotation of non-redundant genes was obtained. By integrating the taxonomic and functional annotations with the abundance profile of the non-redundant genes, a differential analysis was carried out across different levels—be it taxonomic, functional, or at the gene level—using the Kruskal-Wallis test. 2.6 Statistical analysis The data statistics for this study were analyzed using SPSS version 25.0 software (SPSS Inc., Chicago, Illinois, USA). Results are expressed as means ± standard deviation (SD) or medians and interquartile ranges, or percentages, as appropriate. Additionally, the Majorbio ISanger Cloud platform ( https://www.majorbio.com/ ) was used for statistical analysis. 3. Results 3.1 Basic Characteristics of the NRDS and CON This research included a total of 40 newborns, between 2 and 6 days of age, with 25 participants in the NRDS group and 15 in the CON group. There were no notable differences in sex, age, stool characteristics, or maternal age between these two groups (P > 0.05). In addition, the results of routine blood tests and inflammatory index evaluation in the NRDS group were collected, etc. Table 1. Characteristics of the study subjects. Characteristics NRDS group CON group p N = 25 N = 15 Female (n, %) 13 (52.0) 7 (46.7) 0.744 Age (days) 4 (3, 5) 5 (3, 5) 0.356 GA (days) 215.92 ± 22.87 274.60 ± 8.31 0.000 ** Weight (Kg) 1.67 ± 0.59 3.27 ± 0.29 0.000 ** VB (n, %) 0 (0.0) 15 (100.0) 0.000 ** BM (n, %) 0 (0.0) 15 (100.0) 0.000 ** ST (n, %) 19 (76.0) 13 (86.7) 0.615 APC (n, %) 10 (40.0) 0 (0.0) 0.000 ** AC (n, %) 10 (40) 0 (0.0) 0.000 ** MH (n, %) 25 (100) 4 (27) 0.000 ** Mother's age (years) 30.72 ± 5.23 29.00 ± 4.84 0.307 Father's age (years) 30.50 ± 4.50 – – LOS (days) 31.67 ± 12.81 – – A1 (scores) 6.5 (1.25, 8.00) – – A5 (scores) 8 (7, 9) – – A10 (scores) 9 (8, 9) – – GRT (s) 1.25 (1, 1.5) – – GAS (scores) 5.875 ± 2.071 – – PF (n, %) 17 (68) – – PCT 0.34 (0.24, 0.73) – – CRP 5 (5, 7) – – MONO.1 10.37 ± 3.47 – – WBC 8.14 (6.21, 11.26) – – EOSIN 0.23 (0.11, 0.35) – – LYM 3.54 ± 1.68 – – NEUT.1 43.1 (32.08, 50.95) – – RBC 283.54 ± 96.64 – – BASO 0.03 (0.01, 0.04) – – Hb 134.63 ± 30.84 – – MCV 15.76 ± 1.94 – – HCT 40.80 ± 9.37 – – MCH 34.89 ± 3.53 – – RDW 15.55 (13.9, 17.03) – – MCHC 329.96 ± 12.12 – – PLT 283.54 ± 96.64 – – PCT 0.28 ± 0.10 – – MPV 10.09 ± 1.01 – – PDW 16.15 (11.4, 16.6) – – VB, Vaginal Birth; BM, Breast Milk; ST, soft stool; AC, Antenatal Corticosteroids; MH, Maternal complications during pregnancy; LOS, Length of hospitalization; A1/5/10, Apgar scoring 1/5/10 minutes; CRT, Capillary refill time; GAS, gestational age score; PF, Porcine Lung Phospholipid; PCT, Procalcitonin; CRP, C-reactive protein; MONO.1, monocyte percentage; WBC, white blood cell count;; EOSIN, eosinophilia LYM, lymphocyte; NEUT.1, neutrophil percentage; RBC, Red blood cell count; BASO, basophil count; Hb, hemoglobin; MCV, Mean red blood cell volume; HCT, Hematocrit; MCH, mean cell hemoglobin; RDW, Red blood cell distribution width; MCHC, mean cell hemoglobin concentration; PLT, platelet count; PCT, plateletocrit; MPV, mean platelet volume; PDW; Results are presented as mean ± SD or as median with lower and upper quartiles, * p < 0.05, ** p < 0.01 ( P 0.05, no difference); The symbol “-” indicates that the information is not available. 3.2 Microbial community composition and differences A metagenomic approach was utilized to investigate the microbial communities and compositions present in the NRDS and CON groups. Following quality control filtering, 98–99% of the sequences were classified as high-quality. The ACE index for the NRDS group was significantly lower than that of the CON group, indicating that the CON group possessed a greater total number of species. The α-diversity indices, including the Shannon and Simpson, demonstrated relatively comparable values between the two groups (Table 2 ). This finding suggests that, although there has been a shift in the overall species composition of intestinal microorganisms within the NRDS group, the alterations in community structure remain minimal. Table 2 α diversity analysis of intestinal microbe and metagenomic information statistics represent the significance of differences in different samples. Samples NRDS CON P ACE Total 507.00(210.3, 652.0) 620.00(388.0-735.0) 0.214 Bacteria 471.00(191.0, 537.0) 550.00(352.0-693.0) 0.132 Fungi 0.21 ± 0.59 0.27 ± 0.46 0.746 Viruses 61.46 ± 54.50 46.33 ± 36.25 0.305 Eukaryota 3.48 ± 2.76 3.07 ± 3.06 0.662 Archaea 0.13 ± 0.34 0.07 ± 0.26 0.571 Shannon Total 1.762(1.4, 2.1) 1.882(1.5–2.4) 0.326 Bacteria 1.638(1.4, 2.1) 1.857(1.4–2.3) 0.303 Fungi 0.00(0.0, 0.0) 0.00(0.0–0.0) 0.257 Viruses 1.869(0.9, 3.0) 0.932(0.7–1.3) 0.021* Eukaryota 0.481(0.2, 0.9) 1.021(0.6–1.3) 0.005** Archaea 0.00(0.0, 0.0) 0.00(0.0–0.0) 2.00 Simpson Total 0.293(2.0, 4.0) 0.250(0.2–0.4) 0.237 Bacteria 0.312(0.2, 0.4) 0.276(0.2–0.4) 0.289 Fungi 0.10 ± 0.27 0.40 ± 0.51 0.046* Viruses 0.338(0.1, 0.5) 0.536(0.4–0.7) 0.021* Eukaryota 0.724(0.5, 0.9) 0.449(0.3–0.7) 0.005** Archaea 0.33 ± 0.48 0.13 ± 0.35 0.144 Coverage Total 1.00(1.0, 1.0) 1.00(1.0–1.0) 2.00 Sequence information Clean reads 43875714.00(42914441.0, 45311492.0) 43656982.00(42481660.0-44009956.0) 0.235 Clean base(bp) 6605852969.00(6454467809.5, 6826353626.0) 6575541948.00(6400291465.0-6631072261.0) 0.308 Percent in raw reads (%) 99.30 (99.2, 99.4) 99.37 (99.3–99.5) 0.224 Percent in raw bases (%) 99.03 (98.8, 99.2) 99.16 (98.9–99.2) 0.086 Results are presented as mean ± SD or as median with lower and upper quartiles, * p < 0.05, ** p < 0.01, (NRDS n = 25; CON, n = 15). This study revealed that the species accumulation curve tends to flatten, suggesting that an increase in sample size will not yield a significant number of new species and that the current sample size is adequate for analysis (Fig. 1A). The microorganisms detected through metagenomics in the NRDS and CON groups were classified into 5 domains, 11 kingdoms, 43 phyla, 74 classes, 130 orders, 223 families, 621 genera, and 3030 species (Fig. 1B). The Venn diagram illustrated that a total of 1,108 species were common to both the NRDS group and the CON group. The NRDS group included 637 unique species, whereas the CON group included 1,285 unique species (Fig. 1C). To investigate the specific alterations in the microbial community, we assessed the relative prevalence of dominant taxa within both the NRDS and CON groups. Our research focused on the variations in microbial community structure between the two groups, with an emphasis on microbiome diversity. We found no significant differences in α-diversity (P = 0.3335, Wilcoxon rank-sum test, Fig. 2A). However, β-diversity exhibited a statistically significant difference (P = 0.001, PERMANOVA, Fig. 2B). This suggests that, in contrast to the CON group, patients with NRDS exhibited significant alterations in their gut microbiome composition, even though the species richness of the microbial community remained unchanged. Hierarchical clustering analysis revealed pronounced differences in gut microbiota colonization among the various groups (Fig. 2C). Enterotype analysis was employed to evaluate the overall changes in the gut microbiome. The intestinal microorganisms in the NRDS and CON groups were classified into nine distinct enterotype categories. Notably, enterotypes 1 and 2 were present in both groups, with type 1 being significantly more abundant in the CON group, whereas type 2 was dominant in NRDS patients. These findings suggested that the composition of the intestinal microbiome in NRDS patients differed significantly from that of the CON group (Fig. 2D-E). Subsequent analysis revealed that, at the phylum level, patients with NRDS exhibited a greater abundance of Bacillota (Firmicutes) and a reduced presence of Pseudomonadota, Actinomycetota, and Bacteroidota in comparison to the CON group. Similarly, at the genus level, NRDS patients displayed an increased prevalence of Klebsiella, Lacticaseibacillus, Enterococcus, Clostridium, and Staphylococcus and decreased abundances of Escherichia, Bifidobacterium, Bacteroides, and Enterobacter (Fig. 2F-G). Analysis of the differences between the NRDS group and the CON group revealed notable variations in gut flora at the phylum and genus levels, with the most significant discrepancies observed at these levels (Fig. 2H-J). To further elucidate the microbial contributions to NRDS, we compared batch-corrected ensemble microbiota data (LDA > 3) using linear discriminant effect size (LEfSe) analysis. In the NRDS group, microorganisms from Bacillota, Streptosporangiales, and Burkholderiaceae were enriched. Conversely, the microorganisms enriched in the CON group were predominantly Bacteroidota, Pseudomonadota, and Coriobacteriia within Actinomycetota (Fig. 2K). 3.3 Alterations in gut flora function We performed KEGG analysis and discovered that 2,977 of the 10,660 KEGG orthologous genes (KOs) exhibited differential enrichment between the NRDS and CON groups at KEGG level 3. Specifically, 2,000 KOs were enriched in the NRDS group, whereas 977 exhibited medium enrichment in the CON group. KO markers enriched in NRDS patients interacted primarily with protein families classified at KEGG level 3, which are associated with signaling and cellular functions. In contrast, the KO markers enriched in the CON group were more closely related to protein families typically involved in metabolic processes. At the module level, 495 of 1,829 KEGG homologous genes (KOs) were found to be differentially enriched between NRDS patients and the CON group, with 250 enriched in NRDS patients and 245 enriched in CON. This study identified a total of 386 KEGG pathways and 361 KEGG modules, of which 59 KEGG pathways (43 enriched in the NRDS group and 16 enriched in the CON group) and 65 KEGG modules (39 enriched in the NRDS group and 26 enriched in the CON group) exhibited significant differences in enrichment. Additionally, a significant difference was observed in the reporter gene score (Fig. 3A-C). An analysis of KEGG levels 2 and 3 revealed that the top five differential pathways were Signal Transduction, Glycan Biosynthesis and Metabolism, Lipid Metabolism, ABC Transporters, and the Two-Component System. The abundance of metabolic pathways associated with Staphylococcus aureus infection, the phosphotransferase system (PTS), the PPAR signaling pathway, and the Toll-like receptor signaling pathway was significantly higher in the NRDS group than the CON group. The diversity of metabolic pathways, including glutathione metabolism, vitamin B6 metabolism, and butanoate metabolism, was significantly diminished in the NRDS group in comparison to the CON group (Fig. 3D-E). 3.4 The connection between species and function 3.4.1. Butyrate metabolism pathway is less enriched in disease groups and is related to intestinal microbial abnormalities Short-chain fatty acids (SCFAs), which mainly consist of acetate, propionate, and butyrate, serve as the key metabolites produced through the fermentation of dietary fiber by bacteria present in the gastrointestinal tract 25 . Butyrate serves a vital function as the primary energy source for epithelial cells in the colon and is recognized as one of the key anti-inflammatory metabolites present in the intestine 26 , 27 . An abundance differential analysis was performed on the KEGG gene matrix, revealing significant differences in butyrate synthesis (KEGG map00650) between the NRDS and CON groups. Within the intestinal environment, the level of butyrate metabolism in NRDS patients was notably lower in comparison to the CON group (Fig. 4A). Subsequent analysis revealed a notable decrease in the abundance of key enzymes related to the metabolic pathways within the NRDS group (Fig. 4B). Additionally, in the NRDS group, the metagenomic classification model identified a depletion of microorganisms associated with butyric acid synthesis, including g__Bacteroides, s__Roseburia_inulinivorans and g__Butyricimonas (Fig. 4C). Thus, the ability of the intestinal microbiome in the NRDS group to metabolize or produce SCFAs is less effectual than that of the CON group. 3.4.2 Contribution analysis of species and functions. Analysis of the contributions of species and functions at KEGG Level 3 revealed that both the NRDS group and the CON group involved the top 10 species and functional KOs in total abundance, which included: Metabolic pathways (F1), Biosynthesis of secondary metabolites (F2), Microbial metabolism in diverse environments (F3), ABC transporters (F4), Biosynthesis of cofactors (F5), Biosynthesis of amino acids (F6), Two-component system (F7), Carbon metabolism (F8), Quorum sensing (F9), and Ribosome (F10). An examination of species, at the family and genus levels, and their functional roles revealed that the primary contributors to these functions included Klebsiella, Enterococcus, Lacticaseibacillus, Escherichia, Bifidobacterium, Clostridium, Streptococcus, Staphylococcus, and Bacteroides. In comparison to the CON group, the NRDS group exhibited greater contributions from Klebsiella, Enterococcus, Lactobacillus, Clostridium, and Staphylococcus. Conversely, the contributions of Escherichia, Bifidobacterium, and Bacteroides as well as the Bacillus genus were comparatively lower (Fig. 5A). A linear regression analysis was performed to investigate the relationship between functional similarity and species composition similarity across the entire microbial community in the NRDS and CON groups. The results revealed a significant relationship between functional similarity and species composition similarity in both groups (R² = 0.1637, P = 0.0106), suggesting that alterations in microbial community composition can influence the overall functional composition (Fig. 5B). 3.5 Relationship of gut flora with clinical factors of NRDS Our analysis revealed that in the NRDS and CON groups, gestational age (GA) and weight (Wt) exhibited a significant positive correlation with five potential microorganisms: Phocaeicola, Bacteroides, Escherichia, Shigella, and Salmonella. Conversely, a notable negative correlation was identified with Staphylococcus. Additionally, age showed a significant positive correlation with five candidate microorganisms: Paraclostridium, Clostridium, Terrisporobacter, Lacticaseibacillus, and Staphylococcus (Fig. 6A). In the NRDS group, abnormal inflammatory indicators, such as PCT, CRP, MONO.1, and WBC, were negatively correlated with Staphylococcus and Acinetobacter (Fig. 6B). Further analysis of the correlations between the KEGG pathway and clinical factors revealed that, in the NRDS and CON groups, age demonstrated a significant negative correlation with Galactose metabolism (Fig. 6C). In the NRDS group, indicators of abnormal inflammation, such as PCT, CRP, MONO.1, and WBC, exhibited a significant positive correlation with the biosynthesis of nucleotide sugars and homologous recombination. Conversely, a notable negative correlation was found with fatty acid metabolism. (Fig. 6D). 3.6 Diagnostic model construction and network analysis The observed microbial differences between the NRDS and CON groups prompted us to explore whether the gut microbiome can effectively differentiate between these two groups. A random forest classifier was created and assessed through the area under the curve (AUC), emphasizing the 15 most important microorganisms (Fig. 7A). Among the top 10 species biomarkers identified, the AUC reached 96% (Fig. 7B). Species correlation analysis revealed that five phylum-level microorganisms (p__Bacillota, p__Pseudomonadota, p__unclassified_d_Bacteria, p__Actinomycetota, and p__Uroviricota) were enriched in both the NRDS and CON groups. In contrast, p__Nematoda was enriched exclusively in the NRDS group, whereas p__Bacteroidota and p__Fusobacteriota were enriched only in the CON group. In the NRDS and CON groups, several species at the phylum level exhibited comparable relationships. Notably, p__Bacillota and p__Pseudomonadota displayed a negative correlation. In contrast, p__Actinomycetota was positively correlated with p_Bacillota but negatively correlated with p_Pseudomonadota. Notably, p__Bacillota and p__Pseudomonadota displayed a negative correlation. In contrast, p__Actinomycetota showed a positive correlation with p__Bacillota while demonstrating a negative correlation with p__Pseudomonadota. Notably, in the NRDS group, p__Uroviricota was negatively correlated with p__Bacillota, while there was a positive correlation between p__Uroviricota and p__Pseudomonadota. However, no correlation was observed between p__Uroviricota and p__Pseudomonadota in the CON group. Consequently, there appears to be an antagonistic or mutually exclusive relationship between the microorganisms in the NRDS and CON groups (Fig. 7C-D). An analysis of the correlations between clinical factors and species revealed that in the NRDS and CON groups, MAg, weight (Wt), and gestational age (GA) were negatively correlated with p__Bacillota but positively correlated with p__Pseudomonadota and p__Bacteroidota (Fig. 7E). In the NRDS group, degree and AC were positively correlated with p__Pseudomonadota and p__Uroviricota, whereas length of stay (LOS) was positively correlated with p__Pseudomonadota. Additionally, PF and CRP were negatively correlated with s_Enterobacter_hormaechei and s_Acinetobacter_baumannii, respectively (Fig. 7F). The results suggest that NRDS-specific microbial genes may serve as potential diagnostic markers for this disease. 4. Discussion Research indicates that the occurrence of various lung diseases is associated with alterations in gut flora. For example, although the general composition of gut microbiota in children with asthma remains stable during the initial phases, an increase in the relative abundance of Bacteroides fragilis is typically observed, accompanied by a decrease in the relative abundance of Roseburia. Research indicates that rectifying imbalanced gut microbiota through the use of probiotics can lead to a reduction in lung inflammation 28 , 29 . In individuals with chronic obstructive pulmonary disease (COPD), the relative abundance of Streptococcus increases, whereas the relative abundance of Bacteroides decreases 30 . Furthermore, supplementation with Bifidobacterium breve and Lactobacillus rhamnosus has been found to mitigate lung lesions in a mouse model of COPD 31 . These changes in gut flora are closely linked to lung diseases. Through metagenomic analysis, this study revealed that the structure and function of the gut flora in NRDS patients have undergone changes. Research indicates that the human gut flora is predominantly composed of Bacteroidetes and Firmicutes, with additional contributions from Proteobacteria and Actinobacteria 32 – 34 . In this study, the relative abundances of Firmicutes and Nematoda in the NRDS group notably increased, whereas the relative abundances of Pseudomonadota, Actinomycetota, Bacteroidota, and Fusobacteriota significantly decreased. At the genus level, the NRDS group exhibited higher abundances of Klebsiella, Escherichia, Enterococcus, Clostridium, and Staphylococcus and lower abundances of Bifidobacterium, Bacteroides, and Enterobacter. These findings indicate that NRDS influences the normal colonization of these bacteria in the human body. Within a specific range, Bacillota maintain the integrity of the intestinal mucosal barrier by producing SCFAs, thereby mitigating the body’s inflammatory response 35 – 37 . Pseudomonadota, which belongs to the Proteobacteria phylum, constitutes one of the most prevalent groups in the gut microbiota and plays a crucial role in the regulation of the immune system 38 . Previous studies have demonstrated that birth and feeding methods influence the colonization of gut flora. The gut flora of infants who are born naturally and breastfed predominantly consists of Bacteroidota 39 . In contrast, the NRDS group was exclusively born via cesarean section and was fed formula milk after birth, which delayed Bacteroidota colonization. The Bacteroidota phylum is recognized for its role in energy metabolism, providing energy to the host while also modulating the host's immune response through various pathways to maintain ecological balance within the body 40 . A rise in Firmicutes alongside a decrease in Pseudomonadota and Bacteroidota is strongly linked to inflammatory diseases 41 , 42 . Importantly, the levels of Enterococcus in the feces of individuals suffering from NRDS were markedly decreased. This genus stimulates host immune mechanisms and facilitates pathogen tolerance through its peptidoglycan structure and hydrolase functionality 43 . In conclusion, the composition of gut microbiota is modified in patients with NRDS, and additional research is necessary to understand its effects on lung disease. This research revealed that the gut microbiota in children with NRDS displayed not only changes in composition but also significant functional variations. Specifically, the enrichment of metabolic pathways beneficial to health was diminished in the NRDS group, whereas the enrichment of pathways associated with disease promotion was heightened. Furthermore, the enrichment of pathways related to biodegradation and metabolism increased, whereas the enrichment of biosynthetic pathways decreased. Within the host, the components of intestinal microbiota and their metabolites greatly affect inflammatory and immune reactions, both locally and systemically. Commensal microorganisms in the intestine, such as Bifidobacterium and Bacteroidetes, are known to induce the production of antimicrobial peptides, secretory immunoglobulin A, and proinflammatory cytokines 44 . Importantly, microbial metabolites, particularly SCFAs, can directly or indirectly modulate various immune cells, thereby influencing inflammatory responses 45 . The primary SCFAs that play crucial roles in regulating immunity, apoptosis, inflammation, and lipid metabolism include acetate, propionate, and butyrate 25 . In this study, we observed a significant reduction in the enrichment of KEGG pathways associated with butyrate metabolism in the NRDS group. Additionally, the abundance of butyrate-producing microorganisms, such as Bacteroidetes, Faecalimonas, and Inulinivorans, markedly decreased. Furthermore, the PTS pathway in the NRDS group was enriched. In pathogenic bacterial species, the PTS pathway has been shown to influence the expression of virulence genes 46 , 47 . and it was found to be enriched in the NRDS group in this study. Consequently, we speculate that the PTS may enhance the pathogenicity of related gut flora by increasing the expression of virulence genes, thereby exacerbating the destruction of the intestinal barrier in patients with NRDS. Further linear regression analysis showed that changes in microbial community composition were related to changes in overall functional composition. The "gut-lung axis" serves as a crucial mechanism through which gut microbiota influence lung diseases 9 . Factors such as the mode of birth, feeding practices, and environmental conditions play significant roles in shaping the intestinal microbiota of newborns. This microbiota typically exhibits low diversity, simplistic functions, rapid fluctuations, and substantial individual variation 48 . This study identified synergistic and mutually exclusive relationships among microorganisms as well as between microorganisms and clinical factors. For example, abnormal inflammatory markers were positively correlated with Staphylococcus and Acinetobacter. Previous studies have demonstrated that healthy gut flora can significantly increase the production of PS to a certain extent 49 . Most children with NRDS present with PS deficiency at birth, resulting in progressive dyspnea and hypoxia, which impacts microbial colonization of the distal intestine. Furthermore, the gut microbiota may exert immunomodulatory effects on the lungs by influencing gut-derived hormones 45 . However, it remains challenging to ascertain whether alterations in the gut microbiota are a cause or a consequence of the inflammatory response in children with NRDS. These two factors may interact, functioning either simultaneously or sequentially at various stages of the disease. Nevertheless, the association between inflammatory responses and imbalances in gut microbiota is evident. In recent years, there has been a growing emphasis on "bacterial therapy," particularly the use of saliva-derived Streptococcus 24SMB and oral Streptococcus 89a, in the clinical management of respiratory illnesses 50 . Consequently, targeting key gut microbes may be an effective strategy for treating patients with NRDS. The composition of human microorganisms varies significantly among individuals. Although the stringent inclusion criteria of this study resulted in a small sample size, results demonstrate that the gut flora of children with NRDS experienced alterations. The composition of the gut flora in newborns is influenced by numerous factors, with the mode of birth one of the most critical. In this study, all participants in the NRDS group were delivered via cesarean section, whereas the CON group participants were delivered vaginally, thereby eliminating the influence of birth mode on the test results within each group. Future research should consider expanding the sample size to include children with NRDS born through natural delivery and further investigate the effects of different birth methods on gut flora. Additionally, feeding methods can impact the diversity of gut flora. In this study, all participants in the NRDS group were fed formula milk, whereas those in the CON group were exclusively breastfed. Importantly, the respiratory centers of children with NRDS are immature and susceptible to apnea under various stimuli. Consequently, bronchoalveolar lavage fluid samples could not be obtained, thus the composition of cytokines and lung microorganisms in the bronchoalveolar lavage fluid was not assessed. Through metagenomic sequencing technology, this study revealed that the gut flora of children with NRDS exhibited changes not only at the taxonomic level but also in metabolic pathways, as determined through gene prediction and functional analysis. These alterations may further influence the host's disease susceptibility and severity, thereby providing a theoretical basis for the relationship between NRDS and intestinal microorganisms. While there is currently no consensus on the efficacy of probiotic treatment in alleviating the condition of children with NRDS, the observed connection between gut flora and NRDS offers novel insights for potential treatment strategies. 5. Conclusion Through metagenomic sequencing, this study demonstrated that the structure and function of the gut flora in children with NRDS underwent significant changes, characterized by an increase in pathogenic bacteria (Klebsiella, Enterococcus, Staphylococcus) and a decrease in beneficial bacteria (Bacteroidota, Bifidobacterium). Functional changes include the upregulation of pathogenic pathways associated with Staphylococcus aureus infection and biological metabolic pathways related to the degradation of valine, leucine, and isoleucine. Conversely, there is a downregulation of pathways that promote health, such as butanoate metabolism and the biosynthesis of valine, leucine, and isoleucine. These results improve the understanding of the pathogenesis of NRDS and offer important resources for identifying biomarkers. Declarations Conflicts of Interest The authors declare no conflicts of interest. Funding This work is supported by the Jiangxi Provincial Health and Health Committee Science and Technology Plan (Grant No. 202410250). Author Contribution FQL and YL contributed significantly to this work, having conceived and designed the project. JHL, LHF, XXC, YPX, and YNX were involved in DNA extraction, library construction, and macro-based group data analysis. FQL was responsible for sample collection, data analysis, and manuscript writing, while YL provided guidance on the manuscript draft. All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors would 1ike to acknowledge its support for Nanchang University Second Affiliated Hospital and Majorbio Cloud Platform ( www.majorbio.com ) for providing online platform metagenomics analysis. Data Availability Data will be made available on request References Marseglia, L. et al. Role of oxidative stress in neonatal respiratory distress syndrome. Free Radic Biol. Med. 142 , 132–137 (2019). Chi, M., Mei, Y. B. & Feng, Z. C. [a review on neonatal acute respiratory distress syndrome]. Zhongguo Dang Dai Er Ke Za Zhi Chin. J. Contemp. Pediatr. 20 , 724–728 (2018). De Luca, D. et al. Epidemiology of neonatal acute respiratory distress syndrome: prospective, multicenter, international cohort study. Pediatr. Crit. Care Med. J. Soc. Crit. Care Med. World Fed. Pediatr. Intensive Crit. Care Soc. 23 , 524–534 (2022). Wang, L. et al. The Bidirectional Gut-Lung Axis in Chronic Obstructive Pulmonary Disease. Am. J. Respir Crit. Care Med. 207 , 1145–1160 (2023). Rooks, M. 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Ahlawat, S., Sharma, K. K. & Asha & Gut–organ axis: a microbial outreach and networking. Lett. Appl. Microbiol. 72 , 636–668 (2021). Kim, B. et al. Enterococcus faecium secreted antigen a generates muropeptides to enhance host immunity and limit bacterial pathogenesis. eLife 8 , e45343 (2019). Towards the human intestinal microbiota phylogenetic core. (2009). https://enviromicro-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1462-2920 .01982.x. Lin, C. et al. Intestinal ‘infant-type’ bifidobacteria mediate immune system development in the first 1000 days of life. Nutrients 14 , 1498 (2022). Wang, Q. et al. A genome-wide screen reveals that the vibrio cholerae phosphoenolpyruvate phosphotransferase system modulates virulence gene expression. Infect. Immun. 83 , 3381–3395 (2015). Stülke, J. Regulation of virulence in bacillus anthracis: the phosphotransferase system transmits the signals. Mol. Microbiol. 63 , 626–628 (2007). Enav, H., Bäckhed, F. & Ley, R. E. The developing infant gut microbiome: a strain-level view. Cell. Host Microbe . 30 , 627–638 (2022). Li, R., Li, J. & Zhou, X. Lung microbiome: new insights into the pathogenesis of respiratory diseases. Signal. Transduct. Target. Ther. 9 , 19 (2024). Bidossi, A. et al. Probiotics streptococcus salivarius 24SMB and streptococcus oralis 89a interfere with biofilm formation of pathogens of the upper respiratory tract. BMC Infect. Dis. 18 , 653 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6051340","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":417916145,"identity":"5144a928-e7d7-49a1-8240-a66ce4ed0a19","order_by":0,"name":"Faqun Liu","email":"","orcid":"","institution":"Department of Medical Genetics, The Second Affiliated Hospital, Jiangxi Medical College, Nan-chang University, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Faqun","middleName":"","lastName":"Liu","suffix":""},{"id":417916146,"identity":"c4e0f2d3-7175-4dd3-b0e6-21d6555a3037","order_by":1,"name":"Jinghua Luo","email":"","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Jinghua","middleName":"","lastName":"Luo","suffix":""},{"id":417916147,"identity":"c93a1412-ae01-43d3-96de-07f441b7053b","order_by":2,"name":"Lihua Feng","email":"","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Feng","suffix":""},{"id":417916148,"identity":"a218eac1-6177-4b38-a434-84292cc7c53f","order_by":3,"name":"Xiangxiang Chen","email":"","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Chen","suffix":""},{"id":417916149,"identity":"fb90d430-b2b4-484c-bbfe-d4702fb06f4d","order_by":4,"name":"Yunping Xu","email":"","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Yunping","middleName":"","lastName":"Xu","suffix":""},{"id":417916150,"identity":"1cbee20b-0b7b-4554-bc65-2a08f9a65c8c","order_by":5,"name":"Yanan Xia","email":"","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Xia","suffix":""},{"id":417916151,"identity":"7ce4ae4c-3739-480a-97bf-1eeee17a5cf3","order_by":6,"name":"Yang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYNCCHxJy/OwNDAyMDcTqYOyxMJbsOQDVwkaUHraKRIMZCURq4Wc/fPBzAY9EgoHkG8MHP3cw5PHLE3CdZE9asvQMC4k8c+kcY8PeMwzFkm0EbDG4wWMgzcMjUWw5O8dMgreNIXHDMYJa+D//5mGTSNxw84z5z79ALfsJa+FhkwZrucFjxgy2hZD3gX4xs+btkQAGclqxtGybROKMYwn4tQBD7PFtnh91wKg8vPHj2zabxP7mAwSsQQAOAyAhQbRyEGB/QJLyUTAKRsEoGDkAAIi8PwUrQ/SxAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Pediatrics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang Uni-versity, Nanchang, 330000, China","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-02-18 00:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6051340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6051340/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76863175,"identity":"cfae7cdd-7569-4cd7-bfaa-d2938013050d","added_by":"auto","created_at":"2025-02-21 14:00:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69961,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community composition and difference. (A) The examination of species accumulation curves revealed that there was not a considerable rise in the quantity of core species with the enlargement of the sample size. (B) A chart depicting species statistics. (C) A Venn diagram demonstrating the species count that is both shared and distinct within the fecal samples of the NRDS and CON groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/6fa4875fbaa143a5e151c985.png"},{"id":76863138,"identity":"56db65d9-c5c4-4ad4-b60d-b9aa0d17e752","added_by":"auto","created_at":"2025-02-21 14:00:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2113875,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity and difference of fecal microorganisms between NRDS group and CON group. (A, B) α-diversity and β-diversity. (C) Hierarchical cluster analysis reveals differences in fecal microbial composition between NRDS and CON groups. (D) Distribution proportion of enterotypes in NRDS and CON. (E) Dominant species of each enterotype. (F, G) Composition of fecal microorganisms at phylum and genus levels. (H, I) Comparative analysis of the NRDS group versus the CON group revealed significant differences in gut microbiota at both the phylum and genus classifications (*p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001). (J) The LEfSe analysis demonstrated significant differences in gut flora between the NRDS and CON groups at both the phylum and genus levels (LDA \u0026gt; 4). (K) A cladogram showing various taxa that are differently enriched in the gut microbiota (LDA \u0026gt; 3). PCoA, Principal coordinates analysis; LEFse, LDA effect size analysis; LDA, linear discriminant analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/40ea4e9de40794e7c54a6537.png"},{"id":76863137,"identity":"92f6298f-8e79-4bfc-9e59-74fafb4b3fa9","added_by":"auto","created_at":"2025-02-21 14:00:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1720482,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional changes of intestinal microbiome in NRDS and CON groups. (A, B) Heat map of different pathways and modules in NRDS group and CON group. The abscissa is the reporter gene score (|reporter gene score| \u0026gt; 1.65), and if the absolute value of the reporter score is \u0026gt; 1.65, the pathway or module is shown. Blue, enriched in NRDS patients; red, enriched in CON. The size of the bubble reflects the number of genes annotated to KO. (C) Difference analysis of level 3 KEGG pathways between NRDS patients and CON. (D, E) Speculated metagenomic differences between NRDS and CON. The metagenomic relative abundance of metabolic pathways for each predicted sample was assessed using the Wilcoxon rank-sum test (***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05). KEGG, Kyoto encyclopedia of genes and genomes; KO, KEGG Orthology.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/97455abe06903cc95d545030.png"},{"id":76863136,"identity":"ad7f625d-3496-4984-b97d-08f63e9358e9","added_by":"auto","created_at":"2025-02-21 14:00:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1471865,"visible":true,"origin":"","legend":"\u003cp\u003eThe abundance of most key enzymes in the butyrate machinery was reduced (KEGG pathway map00650), indicating that the butyrate biosynthetic ability of microorganisms was reduced in the NRDS group. (A) Key enzymes are colored and annotated in the figure (decreasing green, increasing red). (B) Comparison diagram of differential testing among metabolic pathway groups, analyzing the relative abundance of key enzymes in metabolic pathways across various groups. (C) Butyrate-producing intestinal microorganisms were differentially enriched in the NRDS and CON groups.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/59610c87644f821fd01f4c74.png"},{"id":76863187,"identity":"01c7adaf-fff5-43dd-af2d-077dbe163780","added_by":"auto","created_at":"2025-02-21 14:00:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90124,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the contribution of species and functions in the NRDS and CON groups. (A) Relative contribution analysis of enriched functional attributes identified by different taxa. (B) Regression analysis of species (genus level) and function. \u003cstrong\u003eF1\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eMetabolic pathways; \u003cstrong\u003eF2\u003c/strong\u003e, Biosynthesis of secondary metabolites; \u003cstrong\u003eF3\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eMicrobial metabolism in diverse environments; \u003cstrong\u003eF4\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eABC transporters; \u003cstrong\u003eF5\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eBiosynthesis of cofactors; \u003cstrong\u003eF6\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eBiosynthesis of amino acids; \u003cstrong\u003eF7\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eTwo-component system; \u003cstrong\u003eF8\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eCarbon metabolism; \u003cstrong\u003eF9\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eQuorum sensing; \u003cstrong\u003eF10\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eRibosome.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/2d7d02819a6b89ef8134a948.png"},{"id":76863139,"identity":"abf0ec0a-b3df-46c7-81a7-c0fcc9c31e44","added_by":"auto","created_at":"2025-02-21 14:00:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1662129,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heat map of microbial species, KEGG pathways and clinical factors. (A) Spearman correlation analysis of clinical factors and microorganisms in the NRDS and CON groups. (B) Spearman correlation analysis of clinical factors and microorganisms in the NRDS group. (C) Spearman correlation analysis of clinical factors and functions between the NRDS and the CON groups. (D) Spearman correlation analysis of clinical factors and functions in the NRDS group. Red and light green represent positive correlations, blue, orange, and dark green represent negative correlations. The degree of correlation can be reflected by the intensity of the color, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05, DM, mode of delivery; FM, mode of feeding; Wt, weight; Scons, stool characteristics; SC, stool color; MAg, Mother's age.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/979e53fa5bccb545a097d935.png"},{"id":76863183,"identity":"2280affa-c0ac-4a0c-a4a3-aa29ae84cdcc","added_by":"auto","created_at":"2025-02-21 14:00:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2920158,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic model construction and network analysis. (A) Species importance ranking chart, the abscissa (Mean Decrease Accuracy) serves as an indicator of species importance, with larger values indicating greater importance of the species. The ordinate displays the species names arranged in order of their importance. (B) ROC curve of random forest (RF) model. ROC, receiver operating characteristic curve; AUC, area under the receiver operating curve; CI, confidence interval. (C, D) Species network analysis, interaction between species in NRDS and CON. (E) Analysis of the correlation network involving clinical factors and species within the NRDS and CON groups. (F) Examination of the correlation network pertaining to clinical factors and species specifically in the NRDS group. A red line indicates a positive correlation, while a blue line signifies a negative correlation; larger nodes represent greater species abundance, env: clinical factor.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/4503a6f28e7e4f4fa11dc1b0.png"},{"id":80385461,"identity":"665c627a-8e8a-46ef-9b40-8dea3a5bd6f1","added_by":"auto","created_at":"2025-04-11 10:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11227488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6051340/v1/156c5be5-8479-4b57-baf0-c15b590d7b93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic Analysis of gut flora Structure and Function in Neonates Respiratory Distress Syndrome","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeonatal respiratory distress syndrome (NRDS) is a common and serious condition that affects the breathing of newborns, which poses significant risk to their lives \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The primary pathophysiological mechanism involves the synthesis, secretion, or dysfunction of pulmonary surfactant (PS). This dysfunction results in alveolar collapse and decreased lung compliance. NRDS is clinically characterized by the onset and progressive worsening of respiratory distress shortly after birth. The causes of NRDS are intricate and can be categorized into direct lung damage, including pulmonary bleeding, severe pulmonary infections, aspiration of amniotic fluid or meconium, and indirect lung damage, such as sepsis, necrotizing enterocolitis, and asphyxia \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recent multicenter cohort research has indicated that mortality rates for NRDS can reach as high as 17\u0026ndash;24%, establishing it as a major factor contributing to neonatal mortality and disability \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite progress in clinical therapies, the frequency of this condition continues to increase each year. The fundamental pathological processes are not fully understood, and the condition is linked to prolonged health complications, such as chronic lung disease and neurodevelopmental disorders \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It is essential to gain insights into the mechanisms driving NRDS, improve diagnostic tools and identify therapeutic targets.\u003c/p\u003e \u003cp\u003eMicrobial communities, often referred to as \u0026ldquo;endocrine organs\u0026rdquo; within the human body, play a vital role in the integration of microbial signals and immune reactions that help sustain homeostasis \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Recent findings contest that the lungs are devoid of microorganisms through the revelation of microbial communities, primarily composed of the genera Prevotella, Streptococcus, Veillonella, Fusobacterium, and Haemophilus, with densities ranging from 10\u0026sup3; to 10⁵ per gram of tissue. These communities are significantly linked to various respiratory illnesses \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Damage to lung tissue can modify the composition of lung microbial communities, amplify inflammation, hinder immune defenses, and initiate a feedback loop that exacerbates acute lung injury \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The gastrointestinal tract, which is the largest reservoir of microbes, plays a crucial role in influencing lung diseases via the \u0026ldquo;gut-lung axis\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In contrast to older children and adults, newborns display unique structures of microbial communities, which are affected by elements such as gestational timing and mode of delivery \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Consequently, it is crucial to explore the impact of the neonatal lung-gut microbiota on the progression of NRDS.\u003c/p\u003e \u003cp\u003eWe used metagenomics to investigate alterations in the structure and function of the intestinal microbiota in children with NRDS and analyze the correlations among microbial communities. This approach will improve our understanding of the pathogenesis of NRDS, facilitate the development of improved identification tools, and identify potential therapeutic targets.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003e This study has been approved by the local ethics committee [The Second Affiliated Hospital of Nanchang University, approval no. 2024 (90)]. Consent was obtained from the guardians of every participant. The NRDS group and the healthy control group (CON) were studied, comprising children diagnosed with NRDS and healthy newborns undergoing physical examinations at our hospital, respectively, from November 2023 to August 2024. Additionally, the basic data of both the children and their mothers were recorded. Inclusion criteria for the NRDS group: (1) All children must meet the diagnostic criteria for NRDS \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e: a. Gradual onset of breathing difficulties, characterized by signs like difficulty in breathing, cyanosis, and the observation of three indentations during inhalation, usually appears a few hours post-delivery, resulting in significant hypoxic respiratory failure; b. Lung X-ray findings reveal a general decrease in transparency across both lung fields, with ground-glass opacities, reticular granular shadows, air bronchograms, and blurred margins of the heart and diaphragm. In severe cases, the lungs may appear completely white. (2) The clinical data for all patients were complete; (3) Informed consent forms were signed by family members. Exclusion criteria included: (1) severe infection or sepsis; (2) severe hereditary or congenital diseases; (3) patients diagnosed with pulmonary tuberculosis; (4) incomplete clinical data; (5) refusal to participate by guardians. Additionally, we recruited healthy newborns for physical examination to serve as the CON.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection and DNA extraction\u003c/h2\u003e \u003cp\u003eImmediately after the newborn defecates, use a sterile cotton swab to collect 1\u0026ndash;2 grams of fresh, intermediate internal stool specimens. Place the specimens in a 2 ml sterile EP tube and transfer the tube to a -80\u0026deg;C refrigerator for storage until DNA extraction. A stool sample weighing 0.5g was used to extract genomic DNA with the FastPure Stool DNA Isolation Kit (Magnetic bead) (MJYH, Shanghai, China), adhering to the manufacturer's instructions. To determine the concentration and purity of the extracted DNA, assessments were conducted using Synergy HTX and NanoDrop2000, respectively. The DNA quality was further analyzed by electrophoresis on a 1% agarose gel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Metagenomic sequencing\u003c/h2\u003e \u003cp\u003eThe DNA extract was processed to reach an approximate length of 400 base pairs using the Covaris M220 (Gene Company Limited, China) for creating a paired-end library. The library was developed with the help of NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Sequencing of the end pairs was performed on the Illumina NovaSeq\u0026trade; X Plus (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), in accordance with the recommendations of the manufacturer for the NovaSeq X Series 25B Reagent Kit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.illumina.com\u003c/span\u003e\u003cspan address=\"http://www.illumina.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Processing of metagenome sequencing data\u003c/h2\u003e \u003cp\u003eThe data analysis was conducted utilizing the free online resource called Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.majorbio.com\u003c/span\u003e\u003cspan address=\"http://www.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To summarize, adapters were removed from the raw sequencing reads, and low-quality reads\u0026mdash;defined as those shorter than 50 bp, possessing a quality value under 20, or containing N bases\u0026mdash;were discarded using fastp \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.20.0). Following this, the reads were aligned to the human genome through the use of BWA \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bwa.sourceforge.net\u003c/span\u003e\u003cspan address=\"http://bio-bwa.sourceforge.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.7.17), and any related hits corresponding to the reads and their mate reads were eliminated.\u003c/p\u003e \u003cp\u003eThe dataset that met the quality criteria was compiled using MEGAHIT \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/voutcn/megahit\u003c/span\u003e\u003cspan address=\"https://github.com/voutcn/megahit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 1.1.2). For the final assembly output, contigs with a minimum length of 300 bp were selected. Open reading frames (ORFs) associated with each assembled contig were identified via Prodigal\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/hyattpd/Prodigal\u003c/span\u003e\u003cspan address=\"https://github.com/hyattpd/Prodigal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.6.3), and ORFs of a length of at least 100 bp were chosen. A non-redundant gene catalog was created using CD-HIT \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://weizhongli-lab.org/cd-hit/\u003c/span\u003e\u003cspan address=\"http://weizhongli-lab.org/cd-hit/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 4.7) at a 90% sequence identity level and 90% coverage. The abundance of genes for a particular sample was assessed with SOAPaligner \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ShujiaHuang/SOAPaligner\u003c/span\u003e\u003cspan address=\"https://github.com/ShujiaHuang/SOAPaligner\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version soap2.21release) applying a 95% identity threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Taxonomic and functional annotation\u003c/h2\u003e \u003cp\u003eThe classification of the highest-ranking non-redundant genes was ascertained by aligning these genes with the NCBI NR database through DIAMOND \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ab.inf.uni-tuebingen.de/software/diamond/\u003c/span\u003e\u003cspan address=\"http://ab.inf.uni-tuebingen.de/software/diamond/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.0.13) with an e-value cutoff of 1e-5. Similarly, the functional annotation of non-redundant genes was obtained. By integrating the taxonomic and functional annotations with the abundance profile of the non-redundant genes, a differential analysis was carried out across different levels\u0026mdash;be it taxonomic, functional, or at the gene level\u0026mdash;using the Kruskal-Wallis test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data statistics for this study were analyzed using SPSS version 25.0 software (SPSS Inc., Chicago, Illinois, USA). Results are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or medians and interquartile ranges, or percentages, as appropriate. Additionally, the Majorbio ISanger Cloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.majorbio.com/\u003c/span\u003e\u003cspan address=\"https://www.majorbio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for statistical analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic Characteristics of the NRDS and CON\u003c/h2\u003e \u003cp\u003e This research included a total of 40 newborns, between 2 and 6 days of age, with 25 participants in the NRDS group and 15 in the CON group. There were no notable differences in sex, age, stool characteristics, or maternal age between these two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, the results of routine blood tests and inflammatory index evaluation in the NRDS group were collected, etc.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1. Characteristics of the study subjects.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRDS group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCON group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.92\u0026thinsp;\u0026plusmn;\u0026thinsp;22.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVB (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPC (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMH (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather's age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1 (scores)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (1.25, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5 (scores)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA10 (scores)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS (scores)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.875\u0026thinsp;\u0026plusmn;\u0026thinsp;2.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.24, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMONO.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.14 (6.21, 11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOSIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.11, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.1 (32.08, 50.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283.54\u0026thinsp;\u0026plusmn;\u0026thinsp;96.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.01, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.63\u0026thinsp;\u0026plusmn;\u0026thinsp;30.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.55 (13.9, 17.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283.54\u0026thinsp;\u0026plusmn;\u0026thinsp;96.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.15 (11.4, 16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVB, Vaginal Birth; BM, Breast Milk; ST, soft stool; AC, Antenatal Corticosteroids; MH, Maternal complications during pregnancy; LOS, Length of hospitalization; A1/5/10, Apgar scoring 1/5/10 minutes; CRT, Capillary refill time; GAS, gestational age score; PF, Porcine Lung Phospholipid; PCT, Procalcitonin; CRP, C-reactive protein; MONO.1, monocyte percentage; WBC, white blood cell count;; EOSIN, eosinophilia LYM, lymphocyte; NEUT.1, neutrophil percentage; RBC, Red blood cell count; BASO, basophil count; Hb, hemoglobin; MCV, Mean red blood cell volume; HCT, Hematocrit; MCH, mean cell hemoglobin; RDW, Red blood cell distribution width; MCHC, mean cell hemoglobin concentration; PLT, platelet count; PCT, plateletocrit; MPV, mean platelet volume; PDW; Results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or as median with lower and upper quartiles, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, significant differences; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, no difference); The symbol \u0026ldquo;-\u0026rdquo; indicates that the information is not available.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Microbial community composition and differences\u003c/h2\u003e \u003cp\u003eA metagenomic approach was utilized to investigate the microbial communities and compositions present in the NRDS and CON groups. Following quality control filtering, 98\u0026ndash;99% of the sequences were classified as high-quality. The ACE index for the NRDS group was significantly lower than that of the CON group, indicating that the CON group possessed a greater total number of species. The α-diversity indices, including the Shannon and Simpson, demonstrated relatively comparable values between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This finding suggests that, although there has been a shift in the overall species composition of intestinal microorganisms within the NRDS group, the alterations in community structure remain minimal.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eα diversity analysis of intestinal microbe and metagenomic information statistics represent the significance of differences in different samples.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507.00(210.3, 652.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e620.00(388.0-735.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471.00(191.0, 537.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550.00(352.0-693.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViruses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.46\u0026thinsp;\u0026plusmn;\u0026thinsp;54.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.33\u0026thinsp;\u0026plusmn;\u0026thinsp;36.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEukaryota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchaea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.762(1.4, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.882(1.5\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.638(1.4, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.857(1.4\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.0, 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.0\u0026ndash;0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViruses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.869(0.9, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932(0.7\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEukaryota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.481(0.2, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.021(0.6\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchaea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.0, 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.0\u0026ndash;0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293(2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.250(0.2\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.312(0.2, 0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.276(0.2\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eViruses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338(0.1, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536(0.4\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEukaryota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.724(0.5, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.449(0.3\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchaea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(1.0, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(1.0\u0026ndash;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43875714.00(42914441.0, 45311492.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43656982.00(42481660.0-44009956.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClean base(bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6605852969.00(6454467809.5, 6826353626.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6575541948.00(6400291465.0-6631072261.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent in raw reads (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.30 (99.2, 99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.37 (99.3\u0026ndash;99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent in raw bases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.03 (98.8, 99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.16 (98.9\u0026ndash;99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or as median with lower and upper quartiles, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, (NRDS n\u0026thinsp;=\u0026thinsp;25; CON, n\u0026thinsp;=\u0026thinsp;15).\u003c/p\u003e \u003cp\u003eThis study revealed that the species accumulation curve tends to flatten, suggesting that an increase in sample size will not yield a significant number of new species and that the current sample size is adequate for analysis (Fig.\u0026nbsp;1A). The microorganisms detected through metagenomics in the NRDS and CON groups were classified into 5 domains, 11 kingdoms, 43 phyla, 74 classes, 130 orders, 223 families, 621 genera, and 3030 species (Fig.\u0026nbsp;1B). The Venn diagram illustrated that a total of 1,108 species were common to both the NRDS group and the CON group. The NRDS group included 637 unique species, whereas the CON group included 1,285 unique species (Fig.\u0026nbsp;1C).\u003c/p\u003e \u003cp\u003e To investigate the specific alterations in the microbial community, we assessed the relative prevalence of dominant taxa within both the NRDS and CON groups. Our research focused on the variations in microbial community structure between the two groups, with an emphasis on microbiome diversity. We found no significant differences in α-diversity (P\u0026thinsp;=\u0026thinsp;0.3335, Wilcoxon rank-sum test, Fig.\u0026nbsp;2A). However, β-diversity exhibited a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.001, PERMANOVA, Fig.\u0026nbsp;2B). This suggests that, in contrast to the CON group, patients with NRDS exhibited significant alterations in their gut microbiome composition, even though the species richness of the microbial community remained unchanged. Hierarchical clustering analysis revealed pronounced differences in gut microbiota colonization among the various groups (Fig.\u0026nbsp;2C). Enterotype analysis was employed to evaluate the overall changes in the gut microbiome. The intestinal microorganisms in the NRDS and CON groups were classified into nine distinct enterotype categories. Notably, enterotypes 1 and 2 were present in both groups, with type 1 being significantly more abundant in the CON group, whereas type 2 was dominant in NRDS patients. These findings suggested that the composition of the intestinal microbiome in NRDS patients differed significantly from that of the CON group (Fig.\u0026nbsp;2D-E).\u003c/p\u003e \u003cp\u003eSubsequent analysis revealed that, at the phylum level, patients with NRDS exhibited a greater abundance of Bacillota (Firmicutes) and a reduced presence of Pseudomonadota, Actinomycetota, and Bacteroidota in comparison to the CON group. Similarly, at the genus level, NRDS patients displayed an increased prevalence of Klebsiella, Lacticaseibacillus, Enterococcus, Clostridium, and Staphylococcus and decreased abundances of Escherichia, Bifidobacterium, Bacteroides, and Enterobacter (Fig.\u0026nbsp;2F-G). Analysis of the differences between the NRDS group and the CON group revealed notable variations in gut flora at the phylum and genus levels, with the most significant discrepancies observed at these levels (Fig.\u0026nbsp;2H-J). To further elucidate the microbial contributions to NRDS, we compared batch-corrected ensemble microbiota data (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3) using linear discriminant effect size (LEfSe) analysis. In the NRDS group, microorganisms from Bacillota, Streptosporangiales, and Burkholderiaceae were enriched. Conversely, the microorganisms enriched in the CON group were predominantly Bacteroidota, Pseudomonadota, and Coriobacteriia within Actinomycetota (Fig.\u0026nbsp;2K).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Alterations in gut flora function\u003c/h2\u003e \u003cp\u003eWe performed KEGG analysis and discovered that 2,977 of the 10,660 KEGG orthologous genes (KOs) exhibited differential enrichment between the NRDS and CON groups at KEGG level 3. Specifically, 2,000 KOs were enriched in the NRDS group, whereas 977 exhibited medium enrichment in the CON group. KO markers enriched in NRDS patients interacted primarily with protein families classified at KEGG level 3, which are associated with signaling and cellular functions. In contrast, the KO markers enriched in the CON group were more closely related to protein families typically involved in metabolic processes. At the module level, 495 of 1,829 KEGG homologous genes (KOs) were found to be differentially enriched between NRDS patients and the CON group, with 250 enriched in NRDS patients and 245 enriched in CON. This study identified a total of 386 KEGG pathways and 361 KEGG modules, of which 59 KEGG pathways (43 enriched in the NRDS group and 16 enriched in the CON group) and 65 KEGG modules (39 enriched in the NRDS group and 26 enriched in the CON group) exhibited significant differences in enrichment. Additionally, a significant difference was observed in the reporter gene score (Fig.\u0026nbsp;3A-C).\u003c/p\u003e \u003cp\u003eAn analysis of KEGG levels 2 and 3 revealed that the top five differential pathways were Signal Transduction, Glycan Biosynthesis and Metabolism, Lipid Metabolism, ABC Transporters, and the Two-Component System. The abundance of metabolic pathways associated with Staphylococcus aureus infection, the phosphotransferase system (PTS), the PPAR signaling pathway, and the Toll-like receptor signaling pathway was significantly higher in the NRDS group than the CON group. The diversity of metabolic pathways, including glutathione metabolism, vitamin B6 metabolism, and butanoate metabolism, was significantly diminished in the NRDS group in comparison to the CON group (Fig.\u0026nbsp;3D-E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The connection between species and function\u003c/h2\u003e \u003cp\u003e3.4.1. Butyrate metabolism pathway is less enriched in disease groups and is related to intestinal microbial abnormalities\u003c/p\u003e\u003cp\u003eShort-chain fatty acids (SCFAs), which mainly consist of acetate, propionate, and butyrate, serve as the key metabolites produced through the fermentation of dietary fiber by bacteria present in the gastrointestinal tract \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Butyrate serves a vital function as the primary energy source for epithelial cells in the colon and is recognized as one of the key anti-inflammatory metabolites present in the intestine \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. An abundance differential analysis was performed on the KEGG gene matrix, revealing significant differences in butyrate synthesis (KEGG map00650) between the NRDS and CON groups. Within the intestinal environment, the level of butyrate metabolism in NRDS patients was notably lower in comparison to the CON group (Fig.\u0026nbsp;4A). Subsequent analysis revealed a notable decrease in the abundance of key enzymes related to the metabolic pathways within the NRDS group (Fig.\u0026nbsp;4B). Additionally, in the NRDS group, the metagenomic classification model identified a depletion of microorganisms associated with butyric acid synthesis, including g__Bacteroides, s__Roseburia_inulinivorans and g__Butyricimonas (Fig.\u0026nbsp;4C). Thus, the ability of the intestinal microbiome in the NRDS group to metabolize or produce SCFAs is less effectual than that of the CON group.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Contribution analysis of species and functions.\u003c/h2\u003e \u003cp\u003eAnalysis of the contributions of species and functions at KEGG Level 3 revealed that both the NRDS group and the CON group involved the top 10 species and functional KOs in total abundance, which included: Metabolic pathways (F1), Biosynthesis of secondary metabolites (F2), Microbial metabolism in diverse environments (F3), ABC transporters (F4), Biosynthesis of cofactors (F5), Biosynthesis of amino acids (F6), Two-component system (F7), Carbon metabolism (F8), Quorum sensing (F9), and Ribosome (F10). An examination of species, at the family and genus levels, and their functional roles revealed that the primary contributors to these functions included Klebsiella, Enterococcus, Lacticaseibacillus, Escherichia, Bifidobacterium, Clostridium, Streptococcus, Staphylococcus, and Bacteroides. In comparison to the CON group, the NRDS group exhibited greater contributions from Klebsiella, Enterococcus, Lactobacillus, Clostridium, and Staphylococcus. Conversely, the contributions of Escherichia, Bifidobacterium, and Bacteroides as well as the Bacillus genus were comparatively lower (Fig.\u0026nbsp;5A). A linear regression analysis was performed to investigate the relationship between functional similarity and species composition similarity across the entire microbial community in the NRDS and CON groups. The results revealed a significant relationship between functional similarity and species composition similarity in both groups (R\u0026sup2; = 0.1637, P\u0026thinsp;=\u0026thinsp;0.0106), suggesting that alterations in microbial community composition can influence the overall functional composition (Fig.\u0026nbsp;5B).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship of gut flora with clinical factors of NRDS\u003c/h2\u003e \u003cp\u003eOur analysis revealed that in the NRDS and CON groups, gestational age (GA) and weight (Wt) exhibited a significant positive correlation with five potential microorganisms: Phocaeicola, Bacteroides, Escherichia, Shigella, and Salmonella. Conversely, a notable negative correlation was identified with Staphylococcus. Additionally, age showed a significant positive correlation with five candidate microorganisms: Paraclostridium, Clostridium, Terrisporobacter, Lacticaseibacillus, and Staphylococcus (Fig.\u0026nbsp;6A). In the NRDS group, abnormal inflammatory indicators, such as PCT, CRP, MONO.1, and WBC, were negatively correlated with Staphylococcus and Acinetobacter (Fig.\u0026nbsp;6B). Further analysis of the correlations between the KEGG pathway and clinical factors revealed that, in the NRDS and CON groups, age demonstrated a significant negative correlation with Galactose metabolism (Fig.\u0026nbsp;6C). In the NRDS group, indicators of abnormal inflammation, such as PCT, CRP, MONO.1, and WBC, exhibited a significant positive correlation with the biosynthesis of nucleotide sugars and homologous recombination. Conversely, a notable negative correlation was found with fatty acid metabolism. (Fig.\u0026nbsp;6D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Diagnostic model construction and network analysis\u003c/h2\u003e \u003cp\u003eThe observed microbial differences between the NRDS and CON groups prompted us to explore whether the gut microbiome can effectively differentiate between these two groups. A random forest classifier was created and assessed through the area under the curve (AUC), emphasizing the 15 most important microorganisms (Fig.\u0026nbsp;7A). Among the top 10 species biomarkers identified, the AUC reached 96% (Fig.\u0026nbsp;7B).\u003c/p\u003e \u003cp\u003eSpecies correlation analysis revealed that five phylum-level microorganisms (p__Bacillota, p__Pseudomonadota, p__unclassified_d_Bacteria, p__Actinomycetota, and p__Uroviricota) were enriched in both the NRDS and CON groups. In contrast, p__Nematoda was enriched exclusively in the NRDS group, whereas p__Bacteroidota and p__Fusobacteriota were enriched only in the CON group. In the NRDS and CON groups, several species at the phylum level exhibited comparable relationships. Notably, p__Bacillota and p__Pseudomonadota displayed a negative correlation. In contrast, p__Actinomycetota was positively correlated with p_Bacillota but negatively correlated with p_Pseudomonadota. Notably, p__Bacillota and p__Pseudomonadota displayed a negative correlation. In contrast, p__Actinomycetota showed a positive correlation with p__Bacillota while demonstrating a negative correlation with p__Pseudomonadota. Notably, in the NRDS group, p__Uroviricota was negatively correlated with p__Bacillota, while there was a positive correlation between p__Uroviricota and p__Pseudomonadota. However, no correlation was observed between p__Uroviricota and p__Pseudomonadota in the CON group. Consequently, there appears to be an antagonistic or mutually exclusive relationship between the microorganisms in the NRDS and CON groups (Fig.\u0026nbsp;7C-D).\u003c/p\u003e \u003cp\u003eAn analysis of the correlations between clinical factors and species revealed that in the NRDS and CON groups, MAg, weight (Wt), and gestational age (GA) were negatively correlated with p__Bacillota but positively correlated with p__Pseudomonadota and p__Bacteroidota (Fig.\u0026nbsp;7E). In the NRDS group, degree and AC were positively correlated with p__Pseudomonadota and p__Uroviricota, whereas length of stay (LOS) was positively correlated with p__Pseudomonadota. Additionally, PF and CRP were negatively correlated with s_Enterobacter_hormaechei and s_Acinetobacter_baumannii, respectively (Fig.\u0026nbsp;7F). The results suggest that NRDS-specific microbial genes may serve as potential diagnostic markers for this disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eResearch indicates that the occurrence of various lung diseases is associated with alterations in gut flora. For example, although the general composition of gut microbiota in children with asthma remains stable during the initial phases, an increase in the relative abundance of \u003cem\u003eBacteroides fragilis\u003c/em\u003e is typically observed, accompanied by a decrease in the relative abundance of Roseburia. Research indicates that rectifying imbalanced gut microbiota through the use of probiotics can lead to a reduction in lung inflammation \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In individuals with chronic obstructive pulmonary disease (COPD), the relative abundance of Streptococcus increases, whereas the relative abundance of Bacteroides decreases \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, supplementation with Bifidobacterium breve and Lactobacillus rhamnosus has been found to mitigate lung lesions in a mouse model of COPD \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These changes in gut flora are closely linked to lung diseases. Through metagenomic analysis, this study revealed that the structure and function of the gut flora in NRDS patients have undergone changes.\u003c/p\u003e \u003cp\u003eResearch indicates that the human gut flora is predominantly composed of Bacteroidetes and Firmicutes, with additional contributions from Proteobacteria and Actinobacteria \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this study, the relative abundances of Firmicutes and Nematoda in the NRDS group notably increased, whereas the relative abundances of Pseudomonadota, Actinomycetota, Bacteroidota, and Fusobacteriota significantly decreased. At the genus level, the NRDS group exhibited higher abundances of Klebsiella, Escherichia, Enterococcus, Clostridium, and Staphylococcus and lower abundances of Bifidobacterium, Bacteroides, and Enterobacter. These findings indicate that NRDS influences the normal colonization of these bacteria in the human body. Within a specific range, Bacillota maintain the integrity of the intestinal mucosal barrier by producing SCFAs, thereby mitigating the body\u0026rsquo;s inflammatory response \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Pseudomonadota, which belongs to the Proteobacteria phylum, constitutes one of the most prevalent groups in the gut microbiota and plays a crucial role in the regulation of the immune system \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated that birth and feeding methods influence the colonization of gut flora. The gut flora of infants who are born naturally and breastfed predominantly consists of Bacteroidota \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In contrast, the NRDS group was exclusively born via cesarean section and was fed formula milk after birth, which delayed Bacteroidota colonization. The Bacteroidota phylum is recognized for its role in energy metabolism, providing energy to the host while also modulating the host's immune response through various pathways to maintain ecological balance within the body \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. A rise in Firmicutes alongside a decrease in Pseudomonadota and Bacteroidota is strongly linked to inflammatory diseases \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Importantly, the levels of Enterococcus in the feces of individuals suffering from NRDS were markedly decreased. This genus stimulates host immune mechanisms and facilitates pathogen tolerance through its peptidoglycan structure and hydrolase functionality \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In conclusion, the composition of gut microbiota is modified in patients with NRDS, and additional research is necessary to understand its effects on lung disease.\u003c/p\u003e \u003cp\u003eThis research revealed that the gut microbiota in children with NRDS displayed not only changes in composition but also significant functional variations. Specifically, the enrichment of metabolic pathways beneficial to health was diminished in the NRDS group, whereas the enrichment of pathways associated with disease promotion was heightened. Furthermore, the enrichment of pathways related to biodegradation and metabolism increased, whereas the enrichment of biosynthetic pathways decreased. Within the host, the components of intestinal microbiota and their metabolites greatly affect inflammatory and immune reactions, both locally and systemically. Commensal microorganisms in the intestine, such as Bifidobacterium and Bacteroidetes, are known to induce the production of antimicrobial peptides, secretory immunoglobulin A, and proinflammatory cytokines \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Importantly, microbial metabolites, particularly SCFAs, can directly or indirectly modulate various immune cells, thereby influencing inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The primary SCFAs that play crucial roles in regulating immunity, apoptosis, inflammation, and lipid metabolism include acetate, propionate, and butyrate \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In this study, we observed a significant reduction in the enrichment of KEGG pathways associated with butyrate metabolism in the NRDS group. Additionally, the abundance of butyrate-producing microorganisms, such as Bacteroidetes, Faecalimonas, and Inulinivorans, markedly decreased. Furthermore, the PTS pathway in the NRDS group was enriched. In pathogenic bacterial species, the PTS pathway has been shown to influence the expression of virulence genes \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. and it was found to be enriched in the NRDS group in this study. Consequently, we speculate that the PTS may enhance the pathogenicity of related gut flora by increasing the expression of virulence genes, thereby exacerbating the destruction of the intestinal barrier in patients with NRDS. Further linear regression analysis showed that changes in microbial community composition were related to changes in overall functional composition.\u003c/p\u003e \u003cp\u003eThe \"gut-lung axis\" serves as a crucial mechanism through which gut microbiota influence lung diseases \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Factors such as the mode of birth, feeding practices, and environmental conditions play significant roles in shaping the intestinal microbiota of newborns. This microbiota typically exhibits low diversity, simplistic functions, rapid fluctuations, and substantial individual variation \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This study identified synergistic and mutually exclusive relationships among microorganisms as well as between microorganisms and clinical factors. For example, abnormal inflammatory markers were positively correlated with Staphylococcus and Acinetobacter. Previous studies have demonstrated that healthy gut flora can significantly increase the production of PS to a certain extent \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Most children with NRDS present with PS deficiency at birth, resulting in progressive dyspnea and hypoxia, which impacts microbial colonization of the distal intestine. Furthermore, the gut microbiota may exert immunomodulatory effects on the lungs by influencing gut-derived hormones \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, it remains challenging to ascertain whether alterations in the gut microbiota are a cause or a consequence of the inflammatory response in children with NRDS. These two factors may interact, functioning either simultaneously or sequentially at various stages of the disease. Nevertheless, the association between inflammatory responses and imbalances in gut microbiota is evident. In recent years, there has been a growing emphasis on \"bacterial therapy,\" particularly the use of saliva-derived Streptococcus 24SMB and oral Streptococcus 89a, in the clinical management of respiratory illnesses \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Consequently, targeting key gut microbes may be an effective strategy for treating patients with NRDS.\u003c/p\u003e \u003cp\u003eThe composition of human microorganisms varies significantly among individuals. Although the stringent inclusion criteria of this study resulted in a small sample size, results demonstrate that the gut flora of children with NRDS experienced alterations. The composition of the gut flora in newborns is influenced by numerous factors, with the mode of birth one of the most critical. In this study, all participants in the NRDS group were delivered via cesarean section, whereas the CON group participants were delivered vaginally, thereby eliminating the influence of birth mode on the test results within each group. Future research should consider expanding the sample size to include children with NRDS born through natural delivery and further investigate the effects of different birth methods on gut flora. Additionally, feeding methods can impact the diversity of gut flora. In this study, all participants in the NRDS group were fed formula milk, whereas those in the CON group were exclusively breastfed. Importantly, the respiratory centers of children with NRDS are immature and susceptible to apnea under various stimuli. Consequently, bronchoalveolar lavage fluid samples could not be obtained, thus the composition of cytokines and lung microorganisms in the bronchoalveolar lavage fluid was not assessed. Through metagenomic sequencing technology, this study revealed that the gut flora of children with NRDS exhibited changes not only at the taxonomic level but also in metabolic pathways, as determined through gene prediction and functional analysis. These alterations may further influence the host's disease susceptibility and severity, thereby providing a theoretical basis for the relationship between NRDS and intestinal microorganisms. While there is currently no consensus on the efficacy of probiotic treatment in alleviating the condition of children with NRDS, the observed connection between gut flora and NRDS offers novel insights for potential treatment strategies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThrough metagenomic sequencing, this study demonstrated that the structure and function of the gut flora in children with NRDS underwent significant changes, characterized by an increase in pathogenic bacteria (Klebsiella, Enterococcus, Staphylococcus) and a decrease in beneficial bacteria (Bacteroidota, Bifidobacterium). Functional changes include the upregulation of pathogenic pathways associated with Staphylococcus aureus infection and biological metabolic pathways related to the degradation of valine, leucine, and isoleucine. Conversely, there is a downregulation of pathways that promote health, such as butanoate metabolism and the biosynthesis of valine, leucine, and isoleucine. These results improve the understanding of the pathogenesis of NRDS and offer important resources for identifying biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e This work is supported by the Jiangxi Provincial Health and Health Committee Science and Technology Plan (Grant No. 202410250).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFQL and YL contributed significantly to this work, having conceived and designed the project. JHL, LHF, XXC, YPX, and YNX were involved in DNA extraction, library construction, and macro-based group data analysis. FQL was responsible for sample collection, data analysis, and manuscript writing, while YL provided guidance on the manuscript draft. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would 1ike to acknowledge its support for Nanchang University Second Affiliated Hospital and Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.majorbio.com\u003c/span\u003e\u003cspan address=\"http://www.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for providing online platform metagenomics analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarseglia, L. et al. Role of oxidative stress in neonatal respiratory distress syndrome. \u003cem\u003eFree Radic Biol. 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Dis.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 653 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NRDS, Gut flora, Gut-lung axis, Metagenomic","lastPublishedDoi":"10.21203/rs.3.rs-6051340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6051340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeonatal respiratory distress syndrome (NRDS) is a prevalent respiratory condition in newborns that significantly impacts their health and survival rates. In recent years, the potential role of the gut-lung axis in NRDS has garnered increasing attention; however, its specific contributions remain unclear. In this study, we conducted a metagenomics analysis of fecal samples obtained from an observational cohort including NRDS (n\u0026thinsp;=\u0026thinsp;25) and healthy controls (n\u0026thinsp;=\u0026thinsp;15). The results indicated alterations in both the structure and function of the gut flora in NRDS. Specifically, the NRDS group exhibited significantly greater relative abundances of Bacillota and Nematoda compared to the control group, while the relative abundances of Pseudomonadota, Actinomycetota, Bacteroidota, and Fusobacteriota were significantly lower. At the genus level, the NRDS group demonstrated greater abundances of Klebsiella, Escherichia, Enterococcus, and Staphylococcus and reduced abundances of Bifidobacterium and Enterobacter. Functional changes included the upregulation of the \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection and the phosphotransferase system (PTS) and the downregulation of metabolic pathways, such as butanoate metabolism and glutathione metabolism. Additionally, both synergistic and mutually exclusive relationships between gut microbiota as well as between gut microbiota and clinical factors were observed. These results increase our overall comprehension of NRDS pathogenesis and offer important resources for identifying potential biomarkers.\u003c/p\u003e","manuscriptTitle":"Metagenomic Analysis of gut flora Structure and Function in Neonates Respiratory Distress Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-21 14:00:42","doi":"10.21203/rs.3.rs-6051340/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06100d44-9a0e-4dd7-9c9a-d22478df8b85","owner":[],"postedDate":"February 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44556575,"name":"Biological sciences/Microbiology/Clinical microbiology"},{"id":44556576,"name":"Health sciences/Medical research/Paediatric research"}],"tags":[],"updatedAt":"2025-04-11T09:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-21 14:00:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6051340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6051340","identity":"rs-6051340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}