Abstract

Objective

   Type 1 diabetes (T1D) progression is affected by circulating glutamic
   acid decarboxylase antibody (GADA) that persist for many years. This
   study aimed at investigating whether and how the gut microbiome and its
   correlated metabolites change in T1D with the presence of GADA.

Methods

   We used a radiobinding assay to measure GADA titers and identify the 49
   T1D patients with GADA+ and 52 T1D patients with GADA-. The fresh feces
   and serum were analyzed using 16S rRNA gene sequencing and GC/MS. Then
   gut microbiome and serum metabolites were compared between the GADA+
   patients and the GADA- patients. The association between gut microbial
   community and metabolites was assessed using the Spearman’s rank
   correlation.

Results

   The gut microbiome in diversity, composition, and function differed
   between these two groups. The abundance of genus Alistipes,
   Ruminococcus significantly increased in patients with GADA+ compared to
   that observed in the samples of GADA-. There were 54 significantly
   altered serum metabolites associated with tryptophan metabolism,
   phenylalanine, and tyrosine biosynthesis in individuals with GADA+
   compared with those of GADA-For the serum metabolites, compared with
   those of GADA-, there were 54 significantly different metabolites with
   tryptophan metabolism, phenylalanine, and tyrosine and tryptophan
   biosynthesis decreased in individuals with GADA+. The abundance of
   Alistipes was positively correlated with altered metabolites involved
   in tryptophan metabolism.

Conclusion

   We demonstrate that T1D patients with GADA+ are characterised by
   aberrant profiles of gut microbiota and serum metabolites. The
   abundance of Alistipes disturbances may participate in the development
   of T1D patients with GADA by modulating the host’s tryptophan
   metabolism. These findings extend our insights into the association
   between the gut microbiota and tryptophan metabolism and GADA and might
   be targeted for preventing the development of T1D.

   Keywords: T1D (type 1 diabetes), GADA, gut microbiome, serum
   metabolites, tryptophan metabolism

Introduction

   Type 1 diabetes mellitus (T1D) is an autoimmune disease characterized
   by pancreatic β-cells being destroyed ([43]1). Autoimmunity is
   heterogeneous in the pathogenesis of T1D, and the presence of
   autoantibodies is considered a potential feature for classification
   ([44]2, [45]3). Among the autoantibodies, glutamic acid decarboxylase
   antibody (GADA) is known for its early appearance, highest positivity
   rate, and most extended duration, and is often used in β-cell function
   prediction ([46]4). During the follow-up of several longitudinal
   cohorts of T1D patients, GADA titers are shown to be associated with
   various persistent autoimmunity activity levels and disease development
   ([47]5, [48]6). Patients who are GADA positive are more likely to have
   multiple autoimmune diseases, such as Hashimoto’s thyroiditis and
   gastritis ([49]7, [50]8). However, the underlying biological mechanisms
   of T1D patients with different GADA phenotypes is not completely
   understood.

   There has been an interest that the gut microbiota is an important
   environmental factor contributing to patients with autoimmune diseases,
   including rheumatoid arthritis (RA) and T1D ([51]9, [52]10). Previous
   studies have shown that alpha-diversity decreased, accompanied by
   spikes in inflammation-favoring organisms and subsequent E. coli
   depletion prior to seroconversion was observed in patients with type 1
   diabetes during disease progression ([53]11, [54]12). Besides, the
   human gut microbiome composition and metabolic pathways may influence
   host metabolism and inflammation ([55]13, [56]14). Given that the gut
   microbes have implications for host metabolic pathways, beta diversity
   plasma concentrations of indoxyl sulphate and L-citrulline differed in
   T1D without and with stratification by albuminuria ([57]15).. The
   changes of these gut microbiota and the disturbance of serum metabolism
   provide a new understanding of the pathogenesis of T1D. Accumulating
   evidence indicates gut microbiota composition and function change might
   be involved in the progression from β-cell autoimmunity to clinical
   disease emerge after the appearance of autoantibodies ([58]16, [59]17).
   However, the variation in how microbiota and serum metabolites may link
   to the metabolism of T1D with different immunotypes (without/with GADA)
   has not been investigated.

   Here, we aimed to examine the gut microbiome composition and serum
   metabolites in individuals in different GADA subgroups. Furthermore, we
   looked into the interaction between GADA-associated microbiota and
   metabolism, and also their potential effect on the pathogenesis of T1D.

Materials and methods

Study population and clinical measures

   All participants were recruited from the Third Affiliated Hospital of
   Sun Yat-sen University (Guangzhou, Guangdong, China). All patients with
   T1D were participants of the T1D China Registry Study
   (ChiCTR2000034642) ([60]18). Inclusion criteria were patients who were
   diagnosed with T1D according to the previous report ([61]18) and aged
   were >18 years ([62]19). Exclusion criteria were: (1) use of
   antibiotics in the three months before study enrollment; (2)
   significant acute or other chronic illnesses including diseases of the
   digestive tract, severe psychiatric diseases; (3) abdominal surgery
   history; (4) dietary supplements including prebiotic, probiotic, or
   fiber-rich supplements within four weeks before the sample collection;
   and (5) dietary practices that are known to affect the gut microbiota,
   such as a weight-loss diet or a diet that is entirely composed of
   vegetables. Healthy controls were age- and sex-matched volunteers
   recruited from public advertisements.

   GADA were detected from fasting serum with radio binding assay in
   duplicate following the standards of the Islet Autoantibody
   Standardization Program. The GADA positivity cutoff value was set at
   0.05, which means that a test value over 0.05 was considered positive
   ([63]20). We collected all participants’ information from the
   questionnaires and clinical examination, including but not limited to
   demographic and clinically relevant data. All diabetic patients were
   given structuralized diabetes education and were required to follow a
   diabetes diet. A food frequency questionnaire was collected to
   determine the dietary intake history. The study protocol conformed to
   the 1975 Declaration of Helsinki ethical guidelines, and all
   participating universities acquired Institutional Review Board
   approval, and the study received signed informed consent.

DNA preparation and sequencing

   Fresh feces and plasma samples were collected from participants in our
   hospital. The sample tubes were frozen and stored at −80°C immediately
   until further analysis. The microbial DNA was extracted from
   approximately 220 mg of feces using the QIAamp DNA Stool Mini Kit
   (Qiagen, Germany) following the manufacturer’s instructions. PCR using
   primers, 515F (5’-GTGCCAGCMGCCGCGGTAA-3’) and 806R
   (5’-GGACTACHVGGGTWTCTAAT-3’) were used to amplify the regions V4 of
   bacterial 16S rRNA gene. The PCR reactions were 50 μl reaction in
   triplicate. The PCR products were purified using Agencourt AMPure XP
   beads and eluted in an elution buffer. Purified amplicons were
   quantified using the Agilent Technologies 2100 bioanalyser and paired
   end sequenced (2 × 250) following the standard pipelines on an Illumina
   MiSeq platform (BGI, Shenzhen, China).

16s rRNA gene sequencing and bioinformatics analysis

   Raw fastq files procession and analysis were conducted using the
   Quantitative Insights Into Microbial Ecology (QIIME2) platform
   ([64]21). At each location with more than three consecutive bases, the
   250 bp readings were clipped, resulting in an average quality score of
   20. The Fast Length Adjustment of Short Reads (FLASH, v1.2.11) program
   was used to add paired-end reads to tags ([65]22). Using UPARSE
   (v7.0.1090) with a cutoff value of 97%, the tags were grouped into
   OTUs, and chimera sequences were validated with UCHIME (v4.2.40) and
   excluded from further analysis. The remaining high-quality sequences
   were categorized into operational amplicon sequence variants (ASVs) at
   100% similarity by DADA2 algorithm ([66]23). The α-diversity was
   calculated at the gene abundance, including community richness indexes
   (Ace and Chao1) and community diversity indexes (Shannon and Simpson).
   The Bray-Curtis distance method was used to estimate β-diversity, which
   was reported using the principal coordinate analysis (PCoA); the
   difference between groups was examined using the permutational
   multivariate analysis of variance (PERMANOVA). On the normalized taxon
   composition, Linear discriminant analysis effect size (LEfSe) analysis
   was performed on a web server
   ([67]http://huttenhower.sph.harvard.edu/galaxy) using linear
   discriminant analysis (LDA) score ([68]24). KEGG functional predictions
   were generated using the PICRUSt algorithm ([69]25).

Serum metabolome analysis

   The serum metabolite extracts were analyzed on LC-MS/MS technology.
   High-resolution mass spectrometer Q Exactive (Thermo Fisher Scientific,
   USA) collected positive and negative ions data. The raw LC-MS data were
   handled with The Compound Discoverer 3.1 (Thermo Fisher Scientific,
   USA) software, and multidimensional statistical analysis was performed
   PCA and OPLS-DA analyses. The variable importance projection (VIP)
   value, the fold change, and the Student’s test were used to screen for
   differential metabolites. The Enrichment analysis of the KEGG metabolic
   pathway based on the significantly altered metabolites was performed
   with MetaboAnalyst 5.0 ([70]26). The three databases were searched to
   identify further and validate the differential metabolites, including
   Human Metabolome Database (HMDB), Kyoto Encyclopedia of Genes and
   Genomes (KEGG), and mzCloud.

Statistical analysis

   χ^2 test for categorical variables, or a one-way analysis of variance
   (ANOVA) for continuous variables, were performed in comparisons among
   groups. And unpaired Student’s t-test was adopted in multiple
   comparisons. The Kruskal–Wallis test was performed in comparisons
   between groups for non-normally distributed variables. Data are
   presented as the mean ± SEM. The differential abundance of metabolites
   and genera was tested by Kruskal–Wallis test The network plot of
   metabolites and microbes was based on M^2IA ([71]27). The correlation
   between the metabolites and microbiota was estimated using Spearman’s
   rank correlation was performed in R version 4.0.2. P-value < 0.05 were
   considered statistically significant.

Results

Clinical characteristics in participants

   We collected the serum samples from Chinese participants, including 101
   T1D patients and 38 healthy controls. The clinical characteristics of
   the T1D patients are summarized in [72]Table 1 , and the detailed
   information including GADA titers among the three groups is shown in
   [73]Supplementary Table S1 . There were no significant differences in
   age and body mass index (BMI) between patients with GADA+ (age 27.10 ±
   11.34 years; BMI, 20.44 ± 2.65 kg/m^2), and patients with GADA- (age
   26.62 ± 12.05 years; BMI, 20.35 ± 2.57 kg/m^2). Moreover, there were no
   group differences in serum levels (mmol/L) of TC, TG, LDL-C, HDL-C, and
   glycemic control. However, patients with GADA+ had lower concentrations
   of fasting C-peptide than those in the patients with GADA- group (0.026
   ± 0.29 vs 0.048 ± 0.06, pmol/L; p < 0.002).

Table 1.

   Characteristics of the participants.
                                          T1D              HCs (n=38)   P-value
                              GADA- (n=52)  GADA+ (n=49)
   Sex (Women, %)              (29,55.77%)   (30,61.22%)   (24,63.16%)   0.578
   Age, years                 26.62 (12.05) 27.10 (11.34) 27.03 (11.30)  0.835
   Diabetes Duration, years   10.21 (6.10)  11.42 (5.82)        ─        0.237
   BMI,kg/m2                  20.35 (2.57)  20.44 (2.65)  21.58 (23.48)  0.852
   HbA1c (%)                   7.70 (2.04)   7.56 (1.48)   5.24 (0.31)   0.676
   TC (mmol/L)                 4.63 (0.86)   4.80 (0.82)   4.66 (0.80)   0.294
   TG (mmol/L)                 0.76 (0.33)   0.75 (0.32)   0.96 (0.50)   0.786
   LDL-C (mmol/L)              2.69 (0.80)   2.85 (0.66)   2.78 (0.70)   0.183
   HDL-C (mmol/L)              1.50 (0.25)   1.57 (0.32)   1.29 (0.24)   0.227
   Fasting C-peptide (pmol/L) 0.048 (0.06)  0.026 (0.29)        ─        0.002
   Insulin regimen                                              ─        1.000
    MDI                         25 (48.1)     23 (46.9)         ─
    CSII                        26 (50.0)     25 (51.0)         ─
    BD                          1 (1.92)      1 (2.04)          ─
   Insulin dosage (u/kg)       0.75 (0.27)   0.73 (0.19)                 0.665
   [74]Open in a new tab

   Data are presented as mean (SD) or n (%).MDI: basal bolus, long-acting
   plus rapid insulin with meals; CSII: continuous subcutaneous insulin
   infusion; BD: twice-daily insulin (breakfast and evening meal).
   P-values were compared by the two groups (GADA-; GADA+). HCs, Healthy
   Controls; BMI, body mass index; TC, total cholesterol; TG,
   triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C,
   high-density lipoprotein cholesterol; T test/ANOVA notes needed.

The gut microbiome community profiling in T1D patients

   We assessed the α-diversity and the β-diversity to investigate whether
   the gut microbial dysbiosis differed between HCs and T1D patients with
   GADA- or GADA+ in diversity and structure. HCs showed the highest level
   of microbial community diversity in contrast to the T1D patients, which
   was reflected in the Chao1 index ([75] Figure 1A ). However, the
   Shannon and Simpson indexes result did not reach the statistically
   significant threshold ([76] Figure 1B , [77]Figure S1 ). The Fisher
   indexes suggested that the gut microbiome community richness and
   species number differed between the GADA-, GADA+, and HCs groups ([78]
   Figure S1 ). Next, we performed β-diversity based on the Bray-Curtis
   dissimilarity to explore the microbial composition among three groups
   ([79] Figure 1D ). No statistically significant differences were
   observed in the β-diversity of the gut microbiota among the GADA-,
   GADA+, and HCs groups.

Figure 1.

   [80]Figure 1
   [81]Open in a new tab

   Results of diversity and structure profile. (A, B) Box plot of
   differences in α-diversity of the gut microbiota of Chao1 (A) and
   Shannon index (B) respectively, the difference of Chao1 index (ANOVA,
   p-value: 0.0148, F-value: 4.349), the difference of Shannon index
   (ANOVA, p-value: 0.439, F-value: 0.827) among the three study groups.
   (C) The stacked bar plots at the family level. (D) PCoA plot of
   β-diversity of the Bray–Curtis index shows microbial communities’
   distance. (Permutational multivariate analysis of variance (PERMANOVA),
   F= 1.3449, R-squared= 0.019364; p< 0.128]. G0, GADA-; G1, GADA+.

   We calculated all taxonomic abundance to analyze the abundance of
   similarity in bacterial taxonomy among the three groups.
   Actinobacteriota, Bacteroidetes, Desulfobacterota, Firmicutes, and
   Proteobacteria constituted most of the individuals’ gut microbiota at
   the phylum level ([82] Table S2 ). As shown in [83]Figure 1C , the top
   10 abundant gut microbiota at the family level, which are different
   panels among the three groups. Twelve
   genera(Erysipelotrichaceae_UCG_003, Coprococcus, Anaerostipes,
   Streptococcus Ruminococcus_torques, Blautia, Oscillibacter, Sutterella,
   Intestinibacter, Ruminococcus_gnavus, Veillonella, and Dorea) were
   enriched in T1D patients compared with HC individuals, of which the
   majority pertained to the Ruminococcus, and three genera
   (Oscillospiraceae, Colidextribacter, and Butyricicoccus) were depleted
   in individuals with type 1 diabetes. The relative abundance of
   Alistipes, Ruminococcus was significantly enriched in the GADA+ group
   compared with the GADA- (p < 0.05), but the relative abundance of these
   genera did not differ between GADA- and HC individuals ([84] Figure S2
   ). These findings revealed gut microbial diversity and structure
   differences among T1D patients and HCs.

Functional characterization of the gut microbiome in T1D patients with GADA

   Among the individuals with type 1 diabetes, we performed LEfSe analysis
   to determine different microbial compositions at all taxonomic levels
   between the two groups. In patients with GADA+, there were 22 bacterial
   taxonomic clades (20 increased and 2 decreased) detected by LDA score
   showing statistically significant differences, compared with those in
   GADA- patients ([85] Figure 2A , [86]Table S3 ). Compared with patients
   with GADA-, patients with GADA+ showed higher abundances of
   Ruminococcus, Alistipes, Dialister, Coprobacter, Eubacterium,
   Victivallis, Enterococcus, Oxalobacter, Intestinimonas, and
   Mogibacterium and lower abundances of the Clostridium_XVIII at the
   genus level ([87] Figure 2A , [88]Table S3 ). The abundant bacteria at
   the family level in GADA+ patients included Rikenellaceae, a family of
   Bacteroidetes; and Ruminococcaceae, Eubacteriaceae, and
   Enterococcaceae, belonging to Firmicutes ([89] Figure 2B ). We used the
   SparCC algorithm to explore the relationships between dominating
   microbiota at the ecological dimension. In an altered genus-genus
   co-occurrence network, Bacteroides, Blautia, Phascolarctobacterium,
   Ruminococcus, and Lachnoclostridium exhibited positive interactions in
   T1D patients ([90] Figure 2C ). In the T1D patients with GADA+, we
   observed that Alispties were key genera that play essential roles in
   the composition and function of the intestinal micro-environment.

Figure 2.

   [91]Figure 2
   [92]Open in a new tab

   Results of gut microbial characteristics and correlations. (A)
   Histogram of LDA value distribution, list ranked by LDA score. (B)The
   cladogram shows different taxonomic levels between GADA+ or GADA-
   group. (C) Correlation among the differentially abundant microbiota in
   T1D patients. Nodes with correlations between circles represent
   positive (represented by red lines) and negative correlation
   (represented by blue lines). (Circle with red part: G1; circle with
   green part). (D) Wilcoxon test of pathway differences between GADA+ and
   GADA-groups. The histogram shows the relative abundance for pathway in
   each group. The middle panel is a histogram of the log2 value of the
   mean relative abundance ratio in the two groups of the same pathway.
   The P and FDR values obtained by the Wilcoxon test are shown on the
   right panel.

   We conducted the KEGG pathway analysis using the PICRUSt2 algorithm to
   explore the link between the dominating microbiota and the functional
   profile of a microbial community. Compared with patients with GADA-,
   the abundance of pathways linked with cell growth and death,
   transcription, and nucleotide metabolism were significantly increased
   in patients with GADA+ ([93] Figure 2D , [94]Table S4 ). Further
   analysis found that the microbial gene function related to bile acid
   biosynthesis, and phenylalanine metabolism was decreased in patients
   with GADA+ ([95] Figure S3 , [96]Table S4 ).

OPLS−DA and metabolites identification in serum from T1D patients

   We used LC-MS to compare the metabolic signatures of patients with
   GADA+ or GADA- to investigate how the gut microbiota modulates host
   metabolic pathways. The metabolites profile indicating global changes
   to serum metabolite composition in patients with GADA+ were
   significantly different from patients with GADA- ([97] Figures 3A, B ).
   The relative concentration of 54 significantly different serum
   metabolites (Fold change > 1.2, Fold change < 0.83, and p < 0.05) were
   identified by using the three databases ([98] Table S5 ). 7
   up-regulated and 47 down-regulated metabolites differed significantly
   in concentration between patients with GADA+ and GADA-.

Figure 3.

   Figure 3
   [99]Open in a new tab

   Profiles of serum metabolites. (A, B) The OPLS−DA score plots of serum
   samples from T1D patients in (A) negative ion mode and (B) positive ion
   mode. (C) KEGG pathway enrichment analysis of significantly altered
   metabolites. The size and color of each circle were obtained by pathway
   impact value and p-value, respectively.

   Three metabolites related to tryptophan metabolism, including
   3-hydroxyanthranilic acid, L-phenylalanine, and L-kynurenine, were
   lower in patients with GADA+ than those with GADA-. The KEGG pathway
   analysis showed that the altered serum metabolites were linked with
   tryptophan metabolism, phenylalanine, tyrosine and tryptophan
   biosynthesis and phenylalanine metabolism ([100] Figure 3C ). Most of
   the metabolites involved in tryptophan metabolism also be involved in
   phenylalanine, tyrosine, and tryptophan biosynthesis. For example,
   L-phenylalanine is a key node between the two metabolic pathways,
   broadly associated with tryptophan metabolism. Notably, we noticed a
   reduction of two metabolites involved in the pathway the reduced levels
   of tryptophan metabolism, namely L-phenylalanine and
   5-hydroxyindoleacetate, in the T1D patients with GADA+. ([101] Figure
   S4 ).

Co-occurrence network analysis of altered gut microbiota and serum
metabolites

   We constructed a co-occurrence network to explore potential
   associations between altered gut microbiota and serum metabolites. We
   found that Alistipes, Ruminococcus, Butyricimonas formed strong
   co-occurring relationships with steroids and steroids derivatives
   (3a,7a-dihydroxycholanoic acid, 3b-hydroxy-5-cholenoic acid), fatty
   acyls (3-(acetylsulfanyl)-2-(sulfanylmethyl) propanoic acid), and
   sulfamic acid derivatives (cyclamic acid) ([102] Figure 4A ).
   Additionally, we used the top changed metabolites for spearman
   correlation analysis with genus abundance. As shown in [103]Figure 4B ,
   the change in Ruminococcus was positively associated with the change in
   pipecolic acid (r=0.34, p<0.001), whereas the change in Alistipes was
   negatively associated with the changes in 5-hydroxyindoleacetate
   (r=-0.35, p<0.001), which was involved in tryptophan metabolism ([104]
   Figure 4B , [105]Table S4 ).

Figure 4.

   Figure 4
   [106]Open in a new tab

   Associations between serum metabolites and altered gut microbiota. (A)
   Network of the Spearman’s rank correlation coefficient between
   metabolites class and gut microbes. (red circles: metabolites; blue
   ovals: microbes; red lines: positive correlation; green lines: negative
   correlation). (B) Heatmap of correlation between altered metabolites
   and gut bacteria at the genus level. The colors in the heatmap
   represent the positive (represented by pink) or negative correlation
   (represented by blue). The Benjamini-Hochberg false discovery rate was
   used to correct p values for multiple testing. *p<0.05, **p<0.01,
   ***p<0.001.

Discussion

   In the present study, we included 49 T1D patients with GADA+ and 52 T1D
   patients with GADA-. We examined gut microbiome composition and serum
   metabolites to explore the potential association of microbiota and
   metabolite in T1D patient with different GADA levels. Among individuals
   with type 1 diabetes, we found that the gut microbiome and serum
   metabolites profile of GADA+ is distinct from GADA-. The abundance of
   Alistipes was negatively associated with serum metabolites involved in
   tryptophan metabolism. Integrated analysis of the microbiome-metabolome
   shows the correlation between microbiome and metabolites in T1D
   patients, driving or perpetuating the progression.

   Small studies in humans have found the c-peptide levels drop after a
   diagnosis of (T1D), indicating that β cell function is deteriorating
   ([107]28). In our study, focusing on the β cell function of T1D
   patients with different levels of GADA, we found that GADA+ patients
   have lower C-peptide concentrations than GADA- patients.

   The gut microbiota may increase gut permeability and regulate
   metabolism through disturbing immune system to impact the pathogenesis
   of T1D ([108]3, [109]29). Previous studies have reported that T1D
   patients had significant disturbances in Bacteroidetes/Firmicutes,
   Lactobacillus, Blautia, and Prevotella, relative to healthy controls
   ([110]17, [111]30–[112]32). We found that 22 bacterial taxonomic clades
   (20 increased and 2 decreased) of gut bacteria differed significantly
   between individuals with type 1 diabetes stratified by levels of GADA.
   We found that the lower abundance of Clostridium_XVIII in GADA+
   patients. Clostridium represent intestinal commensal bacteria, ferment
   a variety of benefit metabolites, especially butyrate, possess
   anti-inflammation effects, and maintain the intestinal immune system
   ([113]33).

   Differences in the first appearing autoantibody related to the average
   longitudinal abundance of Ruminococcus may be characterized by distinct
   microbial configurations ([114]16). Moreover, GADA explains variation
   in the gut microbiota and strongly correlates with microbiota
   composition and structure ([115]31). Our study found that patients with
   GADA+ showed a significant increase in the abundance of Alistipes,
   Ruminococcus. Inflammation correlated with an increased abundance of
   Alistipes and Ruminococcus([116]34). These interconnections might play
   vital roles in shaping the gut microbiome in T1D patients with GADA+.
   The studies of genus Alistipes that have provided conflicting findings
   have been protective effects for liver fibrosis, and cardiovascular
   disease via product short fatty acids (SCFA), whereas as potential
   drivers of intestinal barrier dysfunction and inflammation affect
   inflammatory bowel disease (IBD) ([117]35). Some genus abundance and
   gene function changes may differ from previous studies because the
   microbiome is influenced by various factors, such as ethnicity, age,
   geography, and cultural differences. Together, these results suggest
   gut microbial dysbiosis may affect the host immune progression in T1D
   patients with GADA+.

   We found that GADA+ patients were characterized by disturbances of gut
   microbiota and serum metabolites. Among the 54 differentially expressed
   serum metabolites, the reduced levels of 3-hydroxyanthranilic acid,
   L-phenylalanine, and L-kynurenine belong to tryptophan-related
   metabolites in T1D patients with GADA+. Tryptophan metabolism has
   become a vital part of cellular and organismal communication
   strategies, regulating immunity and intestinal homeostasis ([118]36).
   It was reported that autoimmune diseases including IBD, Crohn’s disease
   (CD), and T1D were associated with a lower ratio of tryptophan
   metabolites ([119]37–[120]39).

   An aberrant gut microbiota in individuals with GADA may contribute to
   lower tryptophan-related metabolites, which promotes progression of the
   disease. The tryptophan metabolites binding to the aryl hydrocarbon
   receptor (AHR), which promotes IL-22 secretion, improves the intestinal
   epithelial barrier, stimulates gastrointestinal motility, has
   anti-inflammatory properties, and may modulate gut microbial
   composition ([121]40–[122]42). Indole-3-acetic-acid, as a bacterial
   tryptophan metabolite, regulates intestinal immune function in
   combination with activation of AHR. In our study we found Alistipes and
   Clostridium XlVa was positively correlated with 5-hydroxyindoleacetate
   and Indole-3-acetic-acid (r=0.19, p=0.06). It is possible that this
   depletion of tryptophan metabolism in T1D patients with GADA+ by a
   T1D-associated dysbiosis microbiota ([123]40). These findings suggest
   the possible association between gut microbial dysbiosis and tryptophan
   metabolism, which may provide another dimension to understanding the
   immune progression of T1D.

   This study has some limitations. First, our study design was a
   cross-sectional analysis; we can only identify associations. It would
   also be interesting to systematically study the progression of disease
   dynamics in T1D patients by longitudinal multi-omics. In addition, we
   used 16S rDNA amplicon sequencing; a deep shotgun sequencing can give
   more insights into the function and specific species of gut microbiomes
   than 16S rDNA amplicon sequencing. We cannot conclusively determine
   whether the gut microbiome and metabolites dysbiosis is the etiology in
   T1D, which fecal transplantation experiments should further confirm. We
   expect future model experiments and longitudinal cohort studies of an
   altered gut microbiome and tryptophan metabolites in T1D to move
   towards mechanistic studies.

   In conclusion, we provided the gut microbiome and serum metabolites
   profiles in T1D patients without and with GADA. In light of our
   results, we used multi-omics data and characterized the network of
   interactions between the microbes and serum metabolites that may play a
   critical role in the development of T1D. We found that gut microbiome
   disturbances may modulate the host’s tryptophan metabolism in T1D
   patients with GADA. We expect our exploratory study to spark
   longitudinal cohorts to confirm and expand intervention in human T1D
   heterogeneity.

Data availability statement

   The datasets presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found below: [124]https://www.ncbi.nlm.nih.gov/,
   PRJNA766410.

Author contributions

   Conception and design of the study: XZ, TY, SL, and JW. Acquisition of
   data was performed by XZ, TY, and SL. Collection of data: ZL, JY, DY,
   WX, WJ. Analysis and interpretation of data: TY, SL, MX, YD. All
   authors contributed to the article and approved the submitted version.

Funding

   This study was supported by grants from the National Natural Science
   Foundation of China (Grant No. 82100822), the Anhui Provincial Natural
   Science Foundation (2008085MH248, 2008085MH278), the Program for
   Innovative Research Team of The First Affiliated Hospital of USTC
   (CXGG02), and the Fundamental Research Funds for the Central
   Universities.

Acknowledgments