Abstract Background and aim Autism spectrum disorder (ASD) is a neurodevelopmental disorder that may have long-term effects on individual development, family functioning, and social integration. This study aimed to determine the gut microbiota and urine metabolomics signature and identify the regional characteristics in ASD from Southern China. Methods We conducted a cohort study of 88 well-characterized participants from Guangxi Zhuang Autonomous Region in Southern China. Gut microbiota and urine metabolomics signature was explored by 16 S rRNA sequences and untargeted metabolomic profiles respectively. Results The gut microbial α-diversity of ASD were significantly lower than healthy controls. The β-diversity analysis indicated that the community structure in ASD group was obviously distinctive. Significant microbiota enriched in 5 sensitive species, Faecalibacterium prausnitzii, Bifidobacterium catenulatum, Blautia obeum, Lachnoclostridium sp., and Blautia sp. in ASD children. In addition, functional analysis of the gut microbiota revealed that the ATP-binding cassette and ABC-2 type transport system ATP-binding protein were closely associated with ASD. Notably, microbiota showing a positive correlation with Androstenedione, Stearamide, Oleamide, Cadaverine, Hexadecanamide, Orotic acid, Linoleic acid, Palmitoleic acid, Lauric acid, suggesting a potential association with the Arginine and proline metabolism pathway. Conclusion This study found lower α-diversity, unique β-diversity, enriched species, and positive correlations between microbiota and Arginine/Proline metabolis, which demonstrated typical signature of microbiota and metabolites discriminated Zhuang ethnic group ASD children of regional characteristics. Keywords: Autism spectrum disorder, Guangxi Zhuang autonomous region, Gut microbiota, Urine metabolomics Impact * Studied gut microbiota of ASD children in Guangxi Zhuang Autonomous Region, found lower α-diversity, unique β-diversity, enriched species, and positive correlations with Arginine/Proline metabolism links. * Contributed to ASD regional specificity research, offering new insights into diversity and complexity, focusing on Guangxi Zhuang Autonomous Region. * Insights advance understanding of ASD pathogenesis, especially regional/ethnic aspects, informing targeted interventions for precision medicine. Introduction Autism Spectrum Disorder (ASD) is a diverse neuro-developmental condition marked by heterogeneity and multifaceted etiology. Prevalence rates of ASD vary widely across regions, with notable increased in the US from 6.7‰ in 2000 to 23.0‰ in 2018 [[36]1, [37]2], particularly prevalent in Korea (26.4‰) [[38]3]. Conversely, low prevalence rates have been reported, such as 1.1‰ in Quito, Ecuador [[39]4], and 2.9‰ across 8 provinces in Chinese from 2014 to 2016 [[40]5], underscoring geographical disparities. Baio J et al. [[41]6] also reported that the ASD prevalence in children of non-Hispanic whites was higher than non-Hispanic blacks. These heterogeneities may stem from complex genetic and environmental factors, as researches have revealed no single gene accounts for more than 1% of cases, and definitive biomarkers still remain elusive [[42]7–[43]9]. Yatsunenko et al. [[44]10] documented that the gut microbiota of participants from the Amazon of Venezuela, rural Malawi, and metropolitan areas of the United States was compared, revealing significant variations in both composition and functionality across different age groups and geographical populations. Additionally, significant differences in gut microbiota structure were observed among different ethnic groups, while samples from the same ethnic group clustered closely together [[45]11]. Furthermore, they highlight the importance of identifying regional specific gut microbiota for the diagnosis and treatment of regional specific diseases. For example, Wan et al. [[46]12] reported that chronological age was the main factor associated with the disturbances of gut microbiota genera in the ASD cohort. They also identified neurotransmitter biosynthesis pathways were decreased with age, which could affect the functionality of the gut microbiota in ASD children. Metabolic abnormalities have been reported in the pathogenesis of ASD. Accumulated evidence has shown the disturbances in metabolism associate with amino acids, mitochondrial dysfunction, oxidative stress, purine intermediates, and gut microbiota in ASD [[47]13, [48]14]. Urine sample, compared to other biofluids, it can be readily obtained in large quantities non-invasive and presented low risk of infection to participants. Concentrations of metabolites in the urine are often higher than that of plasma, providing a richer matrix for analysis [[49]15]. Thus, it is an attractive option for the discovery of metabolism biomarkers. A clinical study by Milagros et al. [[50]16] has shown that Homocysteine (Hcy) is altered in the urine (also in blood) of children with ASD, and the overproduction of Hcy levels is significantly associated with the severity of the disorder. Wan et al. [[51]12] reported that increased levels of N-methylnicotinic acid and N-methylnicotinamide were observer in the urine of children with ASD, which may influence tryptophan nicotinic acid metabolism. Both clinical trails and experimental animal studies have shown that an elevation in urine hydroxyl-polycyclic aromatic hydrocarbons (PAHs) were positively correlated with the severity of ASD [[52]17, [53]18]. Therefore, alteration of gut microbial composition and urine metabolomics may contribute to the pathogenesis of ASD. However, the specific change in individual were inconsistent across different cohort demography and geography. To gain a deeper understanding of the pathogenesis of ASD among ethnic minority children in the Guangxi Zhuang Autonomous Region, we conducted a comprehensive research that focused on detecting the signature and inter-correlations in gut microbiota and urinary metabolites of ASD children in this region. Materials and methods Participants 58 ASD children (ASD group) were recruited between June 2022 and June 2023 in the Departments of Pediatrics of the First Affiliated Hospital of Guangxi Medical University. ASD group based on the following inclusion criteria: (1) typical autistic features; (2) an Autism Behavior Scale (ABC) score of ≥ 53 or a Modified Checklist for Autism in Toddlers score of (M-CHAT) ≥ 7, and an Autism Rating Scale for Children (CARS) score of ≥ 30 at screening and baseline. Exclusion criteria included: (1) schizophrenia, mood disorders and other psychosis; (2) intellectual disability; (3) the use of probiotics/prebiotics, or antibiotics within 4 weeks prior to the collection of fecal samples; (4) immune deficiency; (5) organic illnesses that affected their GI function, including digestive tract malformations, Hirschsprung’s disease chronic gastrointestinal diseases, spina bifida, and hypothyroidism. 30 healthy controls (CON group) were recruited from local kindergartens. The same set of exclusion criteria that was applied to the ASD group was similarly applied to the CON group. They were excluded if they had history of speech deficits, autistic features and other neurological/psychiatric disorders. All participants were from the Guangxi Zhuang Autonomous Region and without infections, malnutrition, immunodeficiency, metabolic disorders, chronic inflammatory disease. All participants did not take any antibiotics, probiotics, or prebiotics within 1 month. Fecal and urine samples were collected. The demographics information, clinical information, obstetric histories, and imaging of participants were collected. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (NO.2021-KT-003) and parents of participants have provided written informed consent. ASD diagnostic The diagnosis of ASD was made by two specialists (neurologist and psychiatrists) on the basis of the 4th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria [[54]19], in line with China’s national conditions and conformed to the “Expert consensus on early diagnosis of autism spectrum disorder in Chinese young children" [[55]20]. According to the above expert consensus, the ABC and M-CHAT were used for parents interviews and screening, and the CARS was adopted for diagnostic assessment. The Rome IV diagnostic criterias of Childhood Functional Gastrointestinal Disorders were used for assess Gastrointestinal (GI) status [[56]21, [57]22], including stomach pain and hurt, discomfort when eating, and nausea/vomiting. Sample preparation Fecal samples were collected in the morning and stored at −80 °C with GUHE Flora Storage buffer (20190682, GUHE Laboratories, Hangzhou, China) for 16 S rRNA sequencing. Urine samples were collected by sterilized instruments on the same day and stored at −80 °C. Before Liquid Chromatography coupled with Mass Spectrometry (LC-MS) analysis, 100 µL urine sample was thawed and mixed with 300 µL methanol by vortexed for 30s. The mixture was centrifuged at 4 °C at 12,000 rpm for 15 min. 200 µL supernatant was then mixed with 5 µL internal standard (1 mg/mL, DL-o-Chlorophenylalanine) as samples pretreatment. DNA extraction and 16 S rRNA sequencing Total DNA were extracted from fecal samples using the GHFDE100 DNA isolation kit (20190952, GUHE Laboratories, Hangzhou, China). The quantity and quality of extracted DNAs were assessed using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher, USA). PCR amplification of the V4 region of 16S rRNA gene was performed using the forward primer 515 F (5’-GTGCCAGCMGCCGCGGTAA-3’) and the reverse primer 806 R (5’-GGACTACHVGGGTWTCTAAT-3’). The PCR amplicons were purified using Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN) and then sequenced at the Illlumina NovaSeq6000 platform (Hangzhou, China). 16 S rRNA sequencing raw data have been deposited to National Center for Biotechnology Information (NCBI) under the BioProject number PRJNA1277165. Gut microbiota analysis Paired-end reads were obtained and merged using Vsearch V2.18.0, followed by Operational Taxonomic Unit (OTU) picking [[58]23]. The OTUs and sequencing data were process by the Quantitative Insights Into Microbial Ecology (QIIME2, v2024.2) [[59]24]. OTU taxonomic classification was performed by aligning the representative sequences set against the SILVA138 database [[60]25]. And then OTUs were assigned for bioinformatics and statistical analysis utilizing QIIME2 and R packages (v3.2.0). The key OTUs were analyzed by 10-fold cross-validation. We standardized the sequencing depth by rarefaction. The rarefaction was applied uniformly across all samples. α-diversity was assessed by species richness indices (chao and ace) as well as species diversity indices (sobs) via t-tests. β-diversity was estimated through unifrac distance metrics-across samples. Principal component analysis (PCA), non-metric multidimensional scaling (NMDS), principal-coordinate analysis (PCoA), and partial least-squares discriminant analysis (PLS-DA), all with 7-fold cross validation, and a PERMANOVA test were employed to visually assess the overall dissimilarity and similarity of communities. Species differences inter-group was identified by Kruskal-Wallis test. DESeq2 (DESeq 1.26.0) was the primary method performed to analysis species differences between groups, then Linear discriminant analysis effect size (LEfSe, v1.1.0) and Welch’s t-test as supplementary. The P-values from DESeq2 and Welch’s t-test were FDR-adjusted to Q-values. The cladogram was graphed with linear discriminant analysis (LDA) value > 2 and P < 0.05 [[61]26]. PICRUSt 1.1.4 was utilized to predict the pathways of gut microbiota and the activity of gut-brain modules (GBMs) [[62]27]. And MetaCYC database was used to identify metabolic pathways, mostly involved in the biosynthetic pathway. LC-MS analysis The gradient elution method based on acetonitrile-water as the mobile phase was used for separation at ACQUITY HSS T3 ultra-performance liquid chromatography (UPLC) and a mass spectrometer (Thermo Scientific, USA). All samples were both analyzed in reverse phase chromatography with positive ionization methods (POS) and negative ionization conditions (NEG) for compounds. Quality control (QC) samples were prepared by mixing equal amounts of the test samples, and were analyzed before, during, and after the LC-MS injection of the test samples. LC-MS analysis raw data have been deposited to Metabolights datebase (MTBLS12605). Raw data de-noising and normalization were performed to correct variation and further obtained the three-dimensional matrices, which included the peak number, sample name, and normalized peak area. LC-MS data were then analyzed by SIMCA14.1 (Sartorius Stedim Data Analytics AB, Umea, Sweden). HMDB database was applied for the qualitative analysis of metabolites. PCA and Orthogonal projections for latent structures-discriminant analysis (OPLS-DA) were applied to obtain a higher level of separation and classification between groups. OPLS-DA model first principal component Variable Importance in the Projection (VIP) value was used to identify the differential metabolites between groups. The metabolites with VIP > 1, t test P < 0.05, and Q < 0.05 (multiple hypothesis test correction by False discovery rate (FDR)-adjusted Q-values), and |log2 fold change|>1.5 were considered as significant features. Simultaneously, MS2 score and Standard score (z-score) were used to evaluate these differential metabolites. MS2 score [0, 100] is the score for material secondary matching, the larger MS2 score, the better the differentiation. Z-score was based on the mean and standard deviation of Control group, and was used to measure the relative abundance of metabolites at the same level in various samples. MetaboAnalyst 4.0 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolomics reference library were utilized to conduct metabolite pathway analysis. We used the P value (Holm adjust) after multiple hypothesis test correction by Holm-Bonferroni method, and the P value after multiple hypothesis test correction by FDR method to reduce false positives. Statistical methods Power analysis of sample sizes was conducted using G*Power 3.1.9.7., and power > 0.8 suggested adequate statistical power. Clinical information was statistically analyzed using SPSS 26.0. Quantitative data was presented using median (interquartile range). T-tests was used for comparison between groups when the indices pass normality test and had adequate statistical power, if not, used Wilcoxon -Mann-Whitney tests. Count data was represented by frequency (%), and chi-square test analysis was used for comparison between groups when the indices had sufficient sample size and adequate statistical power, if not, used Fisher’s exact test. Spearman correlations corrected by FDR, db-RDA and CCA were used to link microbes and metabolites. All analyses showed statistically significant differences with P < 0.05 or Q < 0.05. Results Clinical characterization As shown in Table [63]1, age, gender, BMI, and antibiotic using in pregnancy resulted with power < 0.8, and other indices with power > 0.8. There were no statistically significant differences in age (Z=−0.795, P = 0.427), gender (P = 0.406), and BMI (Z=−1.585, P = 0.113) between the CON and ASD groups. As for GI problems, there was statistically significant difference in the incidence between two groups (χ^2 = 23.25, P < 0.0001), with higher proportion in the ASD group experiencing functional constipation and diarrhea. Table 1. Comparison of the characteristics of children in the CON group and the ASD group Characteristics CON(n = 30) ASD(n = 58) POWER Z/χ2 P Age(month) / 48.00(13.25) 49.00(19.75) 0.183 −0.795 0.427 Gender Male 27(90.00%) 55(94.83%) 0.137 / 0.406 Female 3(10.00%) 3(5.17%) BMI (kg/m^2) / 14.88(0.81) 14.46(3.53) 0.151 −1.585 0.113 GI problems No 22(73.33%) 17(29.31%) 1.000 23.250 < 0.0001 Functional abdominal pain ‒ not otherwise specified 5(16.67%) 5(8.62%) Functional constipation 2(6.67%) 29(50.00%) Functional diarrhea 1(3.33%) 7(12.07%) Mode of delivery Natural delivery 22(73.33%) 24(41.38%) 0.816 8.092 0.004 Caesarean section 8(26.67%) 34(58.62%) Time of delivery Premature infant 7(23.33%) 31(53.45%) 0.847 8.856 0.006 Term infant 23(76.67%) 25(43.10%) Overdue delivery 0(0.00%) 2(3.45%) Antibiotic using in pregnancy No 30(100.00%) 53(91.38%) 0.124 / 0.161 Yes 0(0.00%) 5(8.62%) Breadfed for month ≤ 12 16(53.33%) 56(96.55%) 1.000 24.827 < 0.0001 >12 14(46.67%) 2(3.45%) ABC Scales / 21.50(2.75) 75.50(7.50) 1.000 −7.674 < 0.0001 CARS Scales / 14.00(2.00) 36.00(3.00) 1.000 −7.723 < 0.0001 GDS score Gross motor 98.00(18.00) 55.00(25.00) 1.000 −7.619 < 0.0001 Fine motor 95.00(16.00) 56.00(25.25) 1.000 −7.670 < 0.0001 Adaptive behavior 92.00(14.50) 49.50(20.50) 1.000 −7.620 < 0.0001 Language function 88.00(16.00) 35.00(9.00) 1.000 −7.679 < 0.0001 Personal/social function 90.00(17.50) 38.00(11.75) 1.000 −7.686 < 0.0001 [64]Open in a new tab There was statistical difference in the delivery methods between two groups (χ^2 = 8.092, P = 0.004). Most CON group children were giving birth naturally, but most ASD group children by caesarean section. Moreover, the distribution for time of delivery between the two groups also showed statistical differences (χ^2 = 8.856, P = 0.006). The majority of CON group children were term infants, but the majority of ASD group children were premature infants. There was a statistically significant difference in the distribution of breastfeeding time between the two groups (χ^2 = 24.827, P < 0.0001), and 96.55% of ASD group breastfed for ≤ 12 months. However, there was no statistically significant difference in whether the mothers of the two groups of children took antibiotics during pregnancy (P = 0.161). The ABC and CARS scores of ASD children were significantly higher than those of the CON group (Z=−7.674, P < 0.0001, Z=−7.723, P < 0.0001). The GDS scores of ASD children were significantly lower than those of CON group (Z [Gross moto]=−7.619, P < 0.0001, Z [Fine motor]=−7.67, P < 0.0001, Z [Adaptive behavior]=−7.62, P < 0.0001, Z [Language function]=−7.679, P < 0.0001, Z [Personal/social function] =−7.686, P < 0.0001). Gut microbiota abundance and diversity The abundance and diversity of the microbial communities were determined by 16 S rRNA sequencing. The Rarefaction Curve (Fig. [65]1A) was progressively flatter, suggested the adequate amount of sequencing in this study. Rank-Abundance reflected the richness and evenness of the microbiota. The richer the microbiota, the wider the curve in the direction of the horizontal coordinate; the more even the microbiota, the flatter the curve. Results suggested low evenness in ASD group when compared with CON group (Fig. [66]1B). Fig. 1. [67]Fig. 1 [68]Open in a new tab Sample diversity analysis. The Rarefaction curves (A) and Rank-Abundance curves (B) of Autism spectrum disorder group and control group. Comparison of the indices ace, chao, and sobs for calculating community richness between the Autism spectrum disorder group and the control group (C). The distance of the sample in the Principal Component Analysis (PCA) plot (D) reflects the similarity of the sample species composition. Non metric Multidimensional Scaling (NMDS) plot (E) reflects the degree of difference between different samples through the distance between points. PCoA (Principal Coordinates Analysis) plot (F) reflects the similarity of species composition and structure through inter group sample distance. Similarity analysis (Anosim) (G) We used α-diversity indices to measure microbial abundance between groups, and found ASD group had a significant lower α-diversity than CON group (ace/chao/sobs Indices, P < 0.001, Fig. [69]1C). β-diversity analysis resulted with a clear separation that could discriminate ASD from CON group via PCA (Fig. [70]1D), NMDS (Fig. [71]1E), and PCOA (Fig. [72]1F) algorithms. These indicated the microbial composition of ASD group had been altered distinctively, as confirmed by Distances box plot (P = 0.020, Fig. [73]1G) and PERMANOVA test formally test group-level compositional differences significantly (F.Model = 7.318, R²=0.079, P = 0.001). Gut microbiota composition The Venn diagram provided a visual representation of the number of species shared between groups. As showed in Figs. [74]2A and 386 species were shared in both ASD and CON groups, while 311 and 147 species were unique to ASD and CON group respectively. Circos plot (Fig. [75]2B) revealed the abundance of Bacteroides and Faecalibacterium was beyond 5%, which were also showed visually as dominant genus in the abundance 3D bar chart (Fig. [76]3A). A Taxonomic Tree was drawn using GraPhlAn and results were shown in Fig. [77]3B, suggesting the dominant community structure included 28 genus, such as Bacteroides, Faecalibacterium and so on. Fig. 2. [78]Fig. 2 [79]Open in a new tab Community composition analysis. Venn diagram (A); Circos diagram of community composition (B) Fig. 3. [80]Fig. 3 [81]Open in a new tab Abundance analysis. The abundance 3D bar chart (A) provides a more three-dimensional observation of the dominant species or functional distribution in all samples; Taxonomic Tree (B) showed the classification of dominant species based on the taxonomy of the sample, combined with species abundance information, presented in a circular dendrogram DESeq2 tool was preformed to detect differentially abundant taxa and then RF classifiers were used to identify a gut microbiota signature for ASD. In total, 5 species (Faecalibacterium prausnitzii, Bifidobacterium catenulatum, Blautia obeum, Lachnoclostridium sp., Blautia sp., all Q < 0.05) with low error rate plus standard deviation were selected as the most important biomarkers (Fig. [82]4A). Additionally, LEfSe analysis (Fig. [83]4B) and Welch’st-test (Fig. [84]5A) results suggested the proportions of these spices were significantly upregulated in ASD group when compared with CON group (all Q < 0.05). These species had significant value as the critical microbiota to distinguish the ASD from healthy control. Fig. 4. [85]Fig. 4 [86]Open in a new tab Microbial categories. DESeq2 (A) selects the most important microbial categories for sample classification. LEfSe (Linear Discriminant Analysis Effect Size) (B): the horizontal axis represents the significant marker LDA value, and the vertical axis represents the significant marker analyzed Fig. 5. [87]Fig. 5 [88]Open in a new tab Different species analysis and functional enrichment. STAMP difference analysis (Welch's t-test) (A): The left figure shows the abundance ratios of different species classifications in two groups of samples, and the middle figure shows the proportion of differences in species classification abundance within the 95% confidence interval. The rightmost value is the P-value and Q-value, where P-value and Q-value<0.05 indicates significant differences, *represents 0.01 1, t test P < 0.05, and Q < 0.05, Table [105]3). Z-score was used to measure the relative content of these 7 metabolites. Table 3. Urine metabolic signatures MS2 name MS2 Score VIP P-velue Q-velue Fold Change LOG_Fold Change 3-Methylxanthine 95.80 1.03 0.02 0.03 0.20 −2.31 Theobromine 98.80 1.30 0.02 0.02 0.21 −2.25 9-Methyluric acid 92.30 1.42 0.02 0.02 0.23 −2.10 D-Proline 99.00 1.30 0.00 0.00 0.38 −1.38 4-GUANIDINOBUTANOATE 83.90 1.26 0.01 0.01 0.45 −1.15 LysoPC(18:0/0:0) 87.60 1.04 0.00 0.01 0.48 −1.05 Octanoyl-L-Carnitine 86.30 1.35 0.02 0.02 0.55 −0.86 Xanthurenic acid 95.30 1.38 0.00 0.00 0.55 −0.86 N-(2-Furoyl)glycine 89.10 1.51 0.04 0.04 0.56 −0.84 4-oxododecanedioic acid 79.30 1.95 0.01 0.02 0.57 −0.82 Decanoylcarnitine 94.80 1.33 0.02 0.03 0.57 −0.82 Glycyl-L-leucine 69.40 1.63 0.00 0.00 0.57 −0.82 Glycocholic Acid 89.90 1.13 0.01 0.02 0.58 −0.79 Trimethylamine N-oxide 87.80 1.40 0.01 0.01 0.59 −0.75 4-Hydroxyphenylacetic acid 96.70 1.37 0.00 0.01 0.60 −0.74 N6,N6,N6-Trimethyl-L-lysine 97.50 1.32 0.01 0.02 0.61 −0.71 Salicylic acid 65.20 1.47 0.00 0.00 0.62 −0.69 4-Pyridoxic acid 99.30 1.05 0.00 0.01 0.63 −0.68 α-Aspartylphenylalanine 86.20 1.06 0.01 0.02 0.63 −0.66 4-Hydroxybenzoic acid 86.40 1.10 0.03 0.04 0.64 −0.65 Hexanoylcarnitine 99.00 1.23 0.00 0.01 0.64 −0.63 Creatinine 99.90 1.47 0.00 0.00 0.64 −0.63 Pantothenic acid 94.80 1.05 0.01 0.02 0.65 −0.62 cis, cis-Muconic acid 77.60 1.24 0.00 0.00 0.65 −0.61 Urocanic acid 98.30 1.53 0.00 0.00 0.65 −0.61 DL-α-Aminocaprylic acid 98.70 1.29 0.01 0.02 0.66 −0.60 Kynurenic acid 99.40 1.16 0.00 0.00 0.66 −0.59 Pipecolic acid 68.00 1.40 0.00 0.00 0.66 −0.59 Prolylleucine 92.40 1.36 0.00 0.00 0.67 −0.57 2,8-Quinolinediol 79.80 1.09 0.00 0.01 0.68 −0.55 Suberic acid 77.40 1.82 0.03 0.03 0.68 −0.55 Leucylproline 99.20 1.10 0.00 0.01 0.70 −0.52 2-Pyrrolidineacetic acid 72.70 1.30 0.01 0.02 0.70 −0.52 Glycylproline 85.70 1.16 0.00 0.01 0.70 −0.51 Picolinic acid 98.00 1.20 0.00 0.01 0.71 −0.50 1-Methyladenosine 99.00 1.12 0.00 0.00 0.72 −0.48 gamma-Glutamylleucine 92.50 1.34 0.00 0.00 0.72 −0.47 N6-Methyladenine 93.80 1.10 0.00 0.00 0.72 −0.47 N6-Threonylcarbamoyladenosine 88.50 1.15 0.00 0.00 0.72 −0.47 N-Acetylornithine 61.20 1.22 0.02 0.03 0.72 −0.47 Nicotinuric acid 71.40 1.16 0.00 0.01 0.73 −0.46 3-Hydroxyanthranilic acid 92.20 1.10 0.00 0.01 0.73 −0.45 Thymine 69.50 1.10 0.00 0.00 0.74 −0.44 Proline betaine 61.40 1.32 0.01 0.01 0.75 −0.42 Hydroxyproline 82.00 1.30 0.00 0.00 0.75 −0.41 L-Pyroglutamic acid 96.30 1.35 0.00 0.00 0.76 −0.40 L-Valine 87.00 1.37 0.00 0.01 0.76 −0.39 Adenosine 99.90 1.20 0.01 0.01 0.77 −0.38 N2,N2-Dimethylguanosine 90.90 1.18 0.00 0.01 0.77 −0.38 Citrulline 62.30 1.14 0.00 0.01 0.77 −0.37 Imidazolelactic acid 96.20 1.38 0.00 0.00 0.78 −0.36 Adenine 89.20 1.12 0.01 0.01 0.78 −0.36 L-Homoserine 65.40 1.05 0.00 0.01 0.79 −0.35 2-Hydroxyhippuric acid 70.80 1.09 0.04 0.04 0.79 −0.34 Creatine 99.80 1.04 0.01 0.02 0.79 −0.33 4-Acetamidobutanoic acid 82.20 1.02 0.01 0.02 0.82 −0.29 Androstenedione 65.20 1.07 0.00 0.01 1.27 0.34 Arachidoyl Ethanolamide 70.40 1.14 0.02 0.02 1.40 0.48 Dihydrosphingosine 61.70 1.23 0.00 0.00 1.43 0.51 Stearamide 76.80 1.43 0.00 0.00 1.63 0.71 Oleamide 78.40 1.81 0.00 0.00 1.67 0.74 Cadaverine 70.70 1.92 0.00 0.00 1.76 0.81 Hexadecanamide 95.60 1.76 0.00 0.00 1.78 0.83 Erucamide 94.20 1.66 0.00 0.00 1.79 0.84 Monoethylhexyl phthalic acid 99.60 1.79 0.00 0.00 1.92 0.94 Phosphoric acid 96.80 1.42 0.00 0.00 2.03 1.02 [106]Open in a new tab Urine metabolic pathway Pathway enrichment analysis was performed to understand the functions of these differential metabolites compared with healthy controls, using the KEGG metabolic pathway database. The KEGG mtabolic pathway histogram (Table [107]4) suggested that the microbiota in the ASD group were enriched in hsa01100 Metabolic pathway (33 metabolics), hsa00330 Arginine and proline metabolism (6 metabolics) and hsa00380 Tryptophan metabolism (4 metabolics). We further screened the pathways and identify the key pathways with the highest correlation with differences by enrichment analysis and topology analysis. Metabolite mapping results suggested urine metabolic of ASD group enriched in Arginine and proline metabolism, Arginine biosynthesis, Pantothenate and CoA biosynthesis pathway (Table [108]5). Table 4. Metabolites pathway enrichment KEGG pathway Compound hsa01100 Metabolic pathways - Homo sapiens (human)(33) cpd: [109]C01104 Trimethylamine N-oxide; cpd: [110]C01672 Cadaverine; cpd: [111]C00791 Creatinine; cpd: [112]C00763 D-Proline; cpd: [113]C00183 L-Valine; cpd: [114]C00263 L-Homoserine; cpd: [115]C10164 Picolinic acid; cpd: [116]C00178 Thymine; cpd: [117]C01879 5-Oxoproline; cpd: [118]C00408 L-Pipecolate; cpd: [119]C01157 Hydroxyproline; cpd: [120]C00300 Creatine; cpd: [121]C00147 Adenine; cpd: [122]C00805 Salicylate; cpd: [123]C00156 4-Hydroxybenzoate; cpd: [124]C00785 Urocanate; cpd: [125]C02480 cis, cis-Muconate; cpd: [126]C02946 4-Acetamidobutanoate; cpd: [127]C01035 4-Guanidinobutanoate; cpd: [128]C00642 4-Hydroxyphenylacetate; cpd: [129]C00632 3-Hydroxyanthranilate; cpd: [130]C16357 3-Methylxanthine; cpd: [131]C00437 N-Acetylornithine; cpd: [132]C00327 L-Citrulline; cpd: [133]C07480 Theobromine; cpd: [134]C00847 4-Pyridoxate; cpd: [135]C03793 N6,N6,N6-Trimethyl-L-lysine; cpd: [136]C01717 4-Hydroxy-2-quinolinecarboxylic acid; cpd: [137]C00864 Pantothenate; cpd: [138]C00212 Adenosine; cpd: [139]C00280 Androstenedione; cpd: [140]C00836 Sphinganine; cpd: [141]C01921 Glycocholate hsa00330 Arginine and proline metabolism - Homo sapiens (human)(6) cpd: [142]C00791 Creatinine; cpd: [143]C00763 D-Proline; cpd: [144]C01157 Hydroxyproline; cpd: [145]C00300 Creatine; cpd: [146]C02946 4-Acetamidobutanoate; cpd: [147]C01035 4-Guanidinobutanoate hsa00380 Tryptophan metabolism - Homo sapiens (human)(4) cpd: [148]C10164 Picolinic acid; cpd: [149]C00632 3-Hydroxyanthranilate; cpd: [150]C01717 4-Hydroxy-2-quinolinecarboxylic acid; cpd: [151]C02470 Xanthurenic acid [152]Open in a new tab Table 5. Metabolite mapping results Metabolic Pathway Arginine and proline metabolism Arginine biosynthesis Pantothenate and CoA biosynthesis Total 38 14 19 Hits 5 2 2 Raw P 0.0049 0.0650 0.1110 -ln(P) 5.3208 2.7339 2.1981 Holm adjust 0.4107 1 1 FDR 0.4107 1 1 Impact 0.0731 0.2284 0.0071 Hits Cpd Creatine cpd: [153]C00300; D-Proline cpd: [154]C00763; Hydroxyproline cpd: [155]C01157; 4-Guanidinobutanoate cpd: [156]C01035; 4-Acetamidobutanoate cpd: [157]C02946 N-Acetylornithine cpd: [158]C00437; L-Citrulline cpd: [159]C00327 Pantothenate cpd: [160]C00864; L-Valine cpd: [161]C00183 [162]Open in a new tab Associations of gut microbiota and urine metabolites To obtain the metabolic potential of gut microbiota in the ASD group, we calculated Spearman correlation coefficient of altered gut microbiota and differentially accumulated urine metabolites at specie level. The permutation test of orthogonal projections to latent structures-discriminant analysis (PLS-DA) and OPLS-DA model for group ASD vs. CON revealed a separation of positive and negative urine metabolites representing 95% confidence intervals (Fig. [163]6B, C). Volcano plot of metabolites of ASD patients compared to healthy controls was showen in Fig. [164]7A. Z-score analysis showed metabolites expression in the ASD and controls (Fig. [165]7B). Fig. 7. [166]Fig. 7 [167]Open in a new tab Metabolic profiles in the positive ion mode of ASD patients and healthy controls. Volcano plot (A) of metabolites of ASD patients compared to healthy controls. The y-axis representsP-value converted to negative log[10] (Scale) and the x-axis represents log[2] (Fold change). Up regulated significant metabolites were highlighted in red. Down-regulated significant metabolites were highlighted in blue. Z-score analysis (B) showing metabolites expression in the ASD and controls Results revealed that 11 metabolites had strong correlations with the microbes of ASD patients. These metabolites involved lipids and lipid-like molecules, organic acids and derivatives and polyketides, and organic oxygen compounds, among others (Fig. [168]8A-C). Specifically, we found the 9 metabolites (Androstenedione, Stearamide, Oleamide, Cadaverine, Hexadecanamide, Orotic acid, Linoleic acid, Palmitoleic acid, Lauric acid) positively correlated with microbiotas (Blautia obeum, Lachnoclostridium sp., Blautia sp., Roseburia inulinivorans, Bacteroidefragilis, Bacteroidethetaiotaomicron, Akkermansia sp., Erysipelotrichaceae UCG-003 sp., Bacteroidexylanisolvens, Collinsella stercoris, Tuzzerella sp.) and played promoted role in neurodevelopment dysfunction and up-regulated in ASD. Fig. 8. [169]Fig. 8 [170]Open in a new tab Correlations analysisi between metabolites and microbes. Spearman correlations corrected by FDR (A); db-RDA (B) and CCA (C) were used to link microbes and metabolites Disscusion Currently, there is still no specific diagnostic approach for diagnosing ASD. So, it is imperative to conduct a comprehensive evaluation during the diagnosis process. Though the ADI-R and ADOS-2 are internationally recognized standardized diagnostic tools for ASD [[171]28], studies on the reliability of these two tools reveal that their specificity is limited. The diagnostic tools commonly employed are intricate, time-consuming, and lack specificity. Consequently, there is a need for simpler screening methods to address the challenge of delayed diagnosis. In this cohort, a total of 88 participants were included, we examined gut microbiota composition and urine metabolomics to discover the signature for discriminating ASD children. Considering the regional host and environment, we focused on patients were from Guangxi Zhuang Autonomous Region. We found the frequency of gastrointestinal problems in ASD group was high to 70.69%, with a higher incidence of constipation and diarrhea. This was consistent with previous reports that have found ASD children have an increased rate of gastrointestinal disorders in general [[172]29]. It is possible that ASD children is attributable to the behavioral features of ritualistic tendencies and insistence on sameness, which more likely to manifest gastrointestinal symptoms [[173]30]. Most ASD children in our study were delivered by caesarean section and were premature infant. Curran EA et al. [[174]31] found that children born by caesarean section are approximately 20% more likely to be diagnosed as having ASD. Chen M et al. [[175]32] conducted a meta-analysis revealed caesarean section was a risk factor for ASD in offspring compared with vaginal delivery. A longitudinal study results suggested an estimated prevalence of ASD in the very premature population of 18.46% [[176]33]. Brito A et al. [[177]34] indicated that breastfeeding might act as a protective factor for ASD children whose mothers took antibiotics during pregnancy. A longer period of exclusive breastfeeding was associated with a subsequent reduced likelihood of ASD diagnosis [[178]35]. However, 96.55% ASD children in our study were breastfed for ≤ 12 months, and most of them had higher ABC and CARS scores, lower GDS scores. Therefor, it is very necessary to pay close attention to care for premature infants who have undergone cesarean section, and to improve the breastfeeding period and nutrition. The high prevalence of GI symptoms, C-section delivery, and prematurity in the ASD group were potential confounders that could independently affect microbiome and metabolome profiles. Limited by the sample size of sub-groups in this study, we couldn’t independently statistically analyzed these influence factors. Age, gender, BMI, and antibiotic using in pregnancy also might be considered insufficient evidence due to insufficient efficacy, they need to be verified in the future. Here, we found that a distinct gut microbiota signature could identify ASD from Guangxi geographical cohorts, and discovered the alterations in the characteristics of urine metabolites in children with ASD. Our study showed the gut microbial α-diversity of ASD was significantly lower than healthy controls. It has been reported that α-diversity usually down-regulated in various diseases, such as overweight, cancer, neurological and psychiatric disorders [[179]36–[180]38]. The β-diversity analysis showed that a large proportion of the ASD group did not cluster with the CON group, indicating that the community structure was obviously distinctive. ASD children showed a significantly increased specie abundance of Faecalibacterium prausnitzii, Bifidobacterium catenulatum, Blautia obeum, Lachnoclostridium sp., and Blautia sp.. Coretti L et al. also found an increase of Faecalibacterium prausnitzi in ASD children from Department of Translational Medical Science-Pediatric Section, University of Naples Federico II, Naples, Italy [[181]39]. Guangxi’s climate is similar to that of Naples, being consistently warm and humid. Such climatic conditions might allow beneficial bacteria in the environment to provide Faecalibacterium prausnitzi with the nutrients needed for growth, which is conducive to the growth and reproduction of microorganisms, promoting the growth of Faecalibacterium prausnitzi and other microorganisms in the intestines of children with ASD. In addition, Faecalibacterium prausnitzi is a late colonizer of human gut and a major butyrate producer [[182]40]. High levels of Faecalibacterium prausnitzi were associated to increase of fecal butyrate levels within normal range [[183]41]. Guangxi Zhuang Autonomous Regioni has many characteristic sour fermented food, such as sour bamboo shoots, kimchi, fermented bean products and so on. These fermented foods are often made using natural fermentation, which relies on microbial communities in the local environment to participate in the fermentation process. These might have a symbiotic or synergistic relationship with Faecalibacterium prausnitzi. Yap CX et al. [[184]42] found that the limited dietary diversity and high preference for high-carbohydrate, high-fat foods (UPFs) among ASD children were associated with specific ASD characteristics. In Guangxi, where fermented foods were widely consumed, children with autism might frequently consume these fermented foods, which could alter the gut microbiome, stimulating the growth and proliferation of Faecalibacterium prausnitzi, leading to an increase in its population. As for Blautia obeum, Li Y et al. [[185]43] showed a reducing Blautia obeum level in ASD children. However, Blautia obeum showed significantly increased relative abundance in Parkinson’s disease and Colorectal Cancer [[186]44, [187]45]. The studies of Yang C et al. [[188]46] and Ding X et al. [[189]47] exhibited elevated levels of Lachnoclostridium at the genus level in ASD children, while the studies of Ma B et al. [[190]48] with a decrease in the relative abundance. There are few studies about Bifidobacterium catenulatum and Blautia sp in ASD. More in-depth research is needed in the future to better discover the patterns of specific microbiota communities in children with ASD. It was confirmed that dietary preferences in different regions affects