Microbiome-derived bile acid signatures in early life and their association with islet autoimmunity

Domain: earth_science
Published: 2025-12-03
Editorial outcome: minor_revision

Abstract
Emerging studies reveal that gut microbes can conjugate diverse amino acids to bile acids, known as microbially conjugated bile acids. However, their regulation and health effects remain unclear. Here, we analyzed early-life microbially conjugated bile acid patterns and their link to islet autoimmunity. We quantified 110 microbial bile acids in 303 stool samples collected longitudinally (3–36 months) from children who developed one or more islet autoantibodies and controls who remained autoantibody-negative. We identified distinct age-dependent trajectories of these bile acid amidates and correlated them with gut microbiome composition. We found that altered levels of ursodeoxycholic and deoxycholic acid conjugates were linked to islet autoimmunity as well as modulated monocyte activation in response to immunostimulatory lipopolysaccharide and Th17/Treg cell balance. These findings suggest that microbially conjugated bile acids influence immune development and type 1 diabetes risk.

Introduction
Bile acids (BAs) are cholesterol-derived surfactants produced from cholesterol in the liver, stored in the gallbladder, and released into the small intestine upon meal consumption1–3. The primary BAs produced in the hepatocytes consist of cholic acid (CA), chenodeoxycholic acid (CDCA), and their amidates (i.e., either glycine or taurine conjugates)4. Gut microbes modify these host-derived primary BAs to a broad range of secondary BAs via a series of reactions, including deconjugation, dehydroxylation, dehydrogenation, oxidation, and epimerization5. Almost 95% of these BAs (both primary and secondary) are recirculated to the liver via the enterohepatic circulation across the distal small intestine6. The remaining 5% still undergo a wide range of transformations throughout the hind gastrointestinal tract2,3,7,8. Traditionally, it was believed that BAs undergo amino acid conjugation only in the liver, mediated by a human enzyme known as bile acid-CoA:amino acid N-acyltransferase9,10. However, recent studies demonstrate that in addition to the host, gut microbes can reconjugate BAs via bile salt hydrolase (BSHS) N-acyltransferase activity11,12. Therefore, hundreds of microbially conjugated amino acid amidates have been identified and termed as microbially conjugated bile acids (MCBAs)2,3,7,8.

BAs are known as emulsifiers, facilitating the absorption of lipids in the gut. They are also important signaling molecules and antimicrobial compounds that influence human health. BAs act as ligands to activate various receptors such as farnesoid X receptor (FXR), pregnane X receptor (PXR), Takeda G-protein coupled receptor 5 (TGR5), and sphingosine-1-phosphate receptors (S1PRs)13–15. These receptors regulate cellular growth and differentiation, host energy metabolism, and modulate glucose and lipid homeostasis. BAs also play a crucial role in the regulation of the immune system by suppressing proinflammatory cytokine production16–18. Dysregulated BA levels are associated with multiple diseases, including type 2 diabetes, inflammatory bowel disease, steatotic liver disease, and various immune-mediated disorders19.

We previously reported that, at an early age, systemic BAs, including those derived from the secondary BA pathways, are associated with progression to islet autoimmunity and the onset of type 1 diabetes (T1D)20. Recently, MCBAs—particularly phenylalanine-, tyrosine- and leucine-conjugated to CA—have been associated with inflammatory bowel disease and cystic fibrosis7,8. However, their early-life dynamics and potential role in the development of islet autoimmunity and T1D remain unexplored.

Herein, in a longitudinal cohort study of young children (ages 3–36 months), we investigate the trajectories of MCBAs during early life, including how these microbial BAs are regulated in children who later progressed to islet autoimmunity. The study included children who developed multiple islet autoantibodies (P2Ab) during follow-up and were thus at high risk for progression to T1D later in life21, the children who developed a single islet autoantibody (P1Ab) but did not progress to T1D during follow-up, and the controls (CTRs), i.e., children who remained islet autoantibody (AAb) negative during the follow-up. We also investigate how the MCBAs modulate the innate and adaptive immune responses, and how they associate with profiles of gut bacterial communities in a prospective series of samples.

Results
Presence of MCBAs in early life
We prospectively analyzed MCBAs in a longitudinal series of stool samples (n=303) from 74 children across three study groups: P1Ab, P2Ab, and CTR. For each participant, stool samples were collected at up to six time points for the analysis, corresponding to ages 3, 6, 12, 18, 24, or 36 months (Fig. 1). At the time of sample collection, none of the children had been diagnosed with T1D. We analyzed a total of 110 MCBAs, including BAs (CA, Chenodeoxycholic Acid (CDCA), Deoxycholic Acid (DCA), Ursodeoxycholic Acid (UDCA), and Hyodeoxycholic Acid (HDCA)) conjugated to 22 different amino acids (L-DOPA, Cys, Pro, Met, Asn, Citrulline, Ala, Ser, Arg, Gln, Trp, Val, Tyr, Thr, Lys, His, Phe, Asp, Leu, Ile, Glu, Ornithine), using a targeted LC-MS/MS assay. Mixed BA standards and a pooled quality control (QC) sample to match retention times and MS/MS spectra were analyzed alongside longitudinal stool extracts, to obtain a level 1 annotation.

Among the 110 MCBAs analyzed, 78 were present in at least one of the 303 fecal samples (Fig. 2). Specifically, MCBAs formed from primary BAs, CA, and CDCA, were present in the highest number of fecal samples, while those formed from secondary BAs, i.e., DCA, UDCA, and HDCA, were found in fewer samples. BAs conjugated to Pro, Phe, Glu, His, Ile/Leu, Thr, Trp, Asp, and Tyr were detected in a higher number of samples. In most samples, BA amidates were detected more than once, except for ornithine conjugated to UDCA and L-DOPA conjugated to CA, which were detected in only one fecal sample, respectively. Notably, Ile/Leu isomers were not chromatographically separated. Among all the MCBAs, HDCA conjugates were observed in the lowest number of samples.

MCBA trajectories in early life
To explore the influences of various factors on early-life MCBA profiles, we conducted multivariate analyses using linear mixed-effect models in samples where breastfeeding information was available (n=242). Age, sex, case status (P1Ab, P2Ab, or CTR), and duration of breastfeeding were treated as fixed effects, with random effects within individual samples/subjects considered. Age emerged as the most significant determinant of MCBA stool profiles compared to sex, case status, and breastfeeding duration. We identified 48 MCBAs that were influenced by age (p<0.05; Fig. 3 and Supplementary Table S1). Additionally, ten MCBAs varied across case groups, nine showed differences between sexes, and three were associated with breastfeeding duration (Supplementary Tables S2–S4). Specifically, primary BA amidates (i.e., CA and CDCA amidates) decreased after the first year of life (Fig. 3a, b). Conversely, MCBAs formed from secondary BAs (DCA and UDCA amidates) exhibited an opposing trend, gradually increasing during the first 18 months of life and remaining stable (DCA-conjugated) or slightly decreasing (UDCA conjugates) at the ages of 24 and 36 months (Fig. 3c, d). Regarding HDCA conjugates, a mixed trend was observed. HDCA-Asp and HDCA-Tyr decreased, while HDCA-Glu increased with age (Supplementary Fig. S1). Here, UDCA-Val, HDCA-Citrulline, CDCA-Phe, CA-His, UDCA-Glu, CA-Ile, CA-Leu, UDCA-Tyr, and UDCA-Phe were affected by sex. Among the 78 detected MCBAs, CDCA-Ala, CA-Pro, and CDCA-Tyr were influenced by breastfeeding status.

MCBAs in the development of islet autoimmunity
Concentrations of ten MCBAs in stool were found to be different in the children who developed single or multiple autoantibodies during follow-up (P1Ab or P2Ab) when compared to the children in the CTR group who were antibody negative (p<0.05; Fig. 4 and Supplementary Table S2). This included two CA conjugates (CA-Citrulline and CA-Cys), three CDCA conjugates (CDCA-Ala, CDCA-Ser, and CDCA-Tyr), four DCA conjugates (DCA-Ile, DCA-Leu, DCA-Pro, and DCA-Val), and one UDCA conjugate, UDCA-Asn (Fig. 3a–d and Supplementary Table S2). Figure 4 shows the beta coefficients from the linear model. We found that all of the DCA conjugates were lower in the P1Ab group when compared to the CTR, while CDCA-Tyr showed the opposing trend (Fig. 4a, c, d). Likewise, the BA conjugates UDCA-Asn, CDCA-Ser, CA-Citrulline, and CDCA-Ala remained downregulated in the P2Ab group when compared to the CTR group, with the exception of CA-Cys (Fig. 4b–f).

We also compared the MCBA stool profiles separately in different age cohorts. There was no consistent pattern in the same MCBA differences between the study groups (P1Ab, P2Ab, or CTR) when analyzed at individual time points. However, we found that 22 conjugates (12 CA-conjugates, five CDCA-conjugates, four DCA-conjugates, one UDCA-conjugate) remained altered between the P1Ab and CTR groups at either 6, 12, 18, or 36 months of age. Specifically, the CA-conjugates were higher in the P1Ab group (18 months of age), while the DCA-conjugates were generally lower, and other conjugates showed no consistent trend. Additionally, 9 MCBAs were different between the P2Ab and CTR at either 6, 12, or 18 months of age. Notably, UDCA-Asn remained lower in the P2Ab group compared to the CTR group at 12 months of age.

MCBA concentrations in the stool are associated with the specific gut microbes
Gut microbes have the ability to conjugate amino acids to BAs. Thus, our next objective was to determine if the microbial conjugated BA profiles, which differed between the study groups (Fig. 4), were associated with bacterial relative abundance in the paired longitudinal metagenomes (n=110) obtained at 3, 6, 12, 18, 24, and 36 months of age from children. Using Spearman correlation analysis, we investigated associations between bacterial composition at the strain level and ten MCBA profiles that were persistently altered in the islet autoantibody group. Our analysis revealed that deoxycholic acid (DCA) conjugates exhibited the most frequent associations with the paired microbial strains (Fig. 5). In total, we identified 72 bacteria that were positively or inversely associated with at least one of the DCA conjugates. Notably, Eubacterium eligens, Subdoligranulum_unclassified, Oscillibacter, and Ruminococcus bromii remained strongly associated with DCA-Val, DCA-Leu, and DCA-Ile, while Roseburia intestinalis showed the strongest positive association with DCA-Pro, as well as with Faecalibacterium prausnitzii. Furthermore, we observed that many strains possessing BA-transforming capabilities were associated with DCA-Val, DCA-Leu, and DCA-Ile. These strains included Alistipes onderdonkii, Alistipes finegoldii, Bacteroides finegoldii, and Bacteroides xylanisolvens. Fifty of the gut bacterial strains were also associated with CDCA conjugates, including Ruminococcus gnavus, Akkermansia muciniphila, Escherichia coli, F. prausnitzii, Erysipelotrichaceae bacterium, Bifidobacterium adolescentis, Veillonella atypica, and Bacteroides fragilis (Fig. 5 and Supplementary Table S5). Meanwhile, R. gnavus, E. coli, along with three Veillonella spp., were inversely associated with DCA conjugates (Supplementary Table S5). We also found that UDCA-Asn was associated with Prevotella oralis, Actinobaculum schaalii, Haemophilus parahaemolyticus, and Morganella morganii. Among all conjugates, CA-Cys exhibited the strongest positive association with Eubacterium cylindroides, Parabacteroides goldsteinii, and Parabacteroides johnsonii, respectively (r=0.57, 0.47, and 0.47).

In addition, we leveraged microbeMASST (https://masst.gnps2.org/microbemasst/) to investigate those MCBAs that were persistently altered in the islet autoantibody group and to identify possible microbial producers22. We found that CDCA-Ala was predominantly found in cultures of Clostridium scindens ATCC 35704 and Clostridium sordelli AO32, UDCA-Asn in R. gnavus ATCC 29149. CA-Citrulline was found in a diverse number of microbial cultures, including Enterococcus faecium 513, Bifidobacterium breve HPH0326, and 21 other microbial strains (Supplementary Table S6). Likewise, DCA-Ile/Leu was detected in E. coli, B. adolescentis L2-32, Bifidobacterium angulatum F16_22, and several other strains (Supplementary Table S6). Likewise, microbial candidates for CDCA-Ser and DCA-Val are listed in Supplementary Table S6.

Based on multi-omics analysis and strain availability, we cultured nine predicted BA metabolizers that were altered in the P2Ab group (A. onderdonkii, R. bromii, Clostridium bartlettii, Bacteroides vulgatus, Bacteroides wadsworthia, Coprococcus comes, Parabacteroides distasonis, Bacteroides intestinalis, and Eggerthella lenta) in brain heart infusion media supplemented with conjugated UDCA-Asn, UDCA, and Asn, as well as in control media without supplements (Supplementary Fig. S2). After 72 h of culturing, samples were analyzed using LC-MS/MS and compared to the UDCA-Asn reference. Monocultures of C. bartlettii, B. vulgatus, and C. comes had UDCA-Asn deconjugation potential. C. bartlettii, C. comes, and R. bromii had conjugation potential. In addition, A. onderdonkii and E. lenta supplemented with UDCA-Asn, we observed an increase in the UDCA-Asn signal (Supplementary Fig. S2). These findings confirm that specific human gut microorganisms are capable of producing these conjugated MCBAs.

MCBAs modulate host immune responses
We further explored the potential of MCBAs to modulate the innate immune system, specifically examining whether the conjugated form of available UDCA and the unconjugated BAs UDCA and DCA exert immunomodulatory properties in vitro in response to lipopolysaccharide (LPS) using human whole blood cultures. Whole blood from healthy donors was diluted 1:1 in WB-STIM buffer (Cytodelics AB, Stockholm) and stimulated with a Salmonella-derived form of immunostimulatory LPS. We then measured three signaling pathways downstream of the TLR4 receptor in a time-course detecting phosphorylation of p38 (pp38) and ERK (pERK), as well as degradation of IkBa, at the single-cell level by intracellular staining and flow cytometry (Fig. 6a). To robustly compare immunomodulatory effects of different BA conjugates across the entire timecourse we calculated the area under the curve (AUC) for each signaling intermediate and compared these across pretreatment conditions and used combinations of small molecule inhibitors of ERK and p38 phosphorylation as negative controls. The different UDCA conjugates exerted variable effects (Fig. 6b). Unconjugated UDCA, Asn-UDCA, and Cit-UDCA partially inhibited LPS-induced signaling across all three pathways investigated, while Trp-UDCA, Asp-UDCA, and Glu-UDCA enhanced the LPS-induced signaling (Fig. 6b), indicating immunomodulatory potentials of these discovered MCBAs. DCA strongly inhibited LPS-induced signaling across all three signaling pathways in human blood monocytes and was even more efficient than either of the conjugated UDCAs tested (Fig. 6c).

Given the abundance of LPS in the human intestine and the previously reported role of LPS exposure early in life in relation to T1D development23 it is intriguing that BA modulation of LPS-induced responses offers an additional, previously unrecognized layer of regulation for the establishment of healthy immune-microbe interactions early in life.

Next, we sought to investigate whether MCBAs interact with the adaptive immune system. Specifically, we examined the effects of MCBAs during the early in vitro differentiation stages of human Th17 and in vitro induced Treg (iTreg) cells. Naive CD4+ CD25- T cells were isolated from human cord blood and cultured with MCBAs under conditions promoting Th17 and Treg differentiation (Fig. 7a). We screened three MCBAs—UDCA-Asn, CDCA-Tyr, and CDCA-Ser, as well as unconjugated UDCA, which we identified as altered in the P2Ab and P1Ab groups compared to the CTR (Fig. 4). Our results showed that UDCA-Asn and CDCA-Ser enhanced Th17 cell differentiation, leading to increased IL-17a secretion (Fig. 7b, c). Conversely, these compounds inhibited the differentiation of iTreg cells, as evidenced by a decrease in Foxp3 expression levels (Fig. 7f–g). However, while neither unconjugated UDCA nor CDCA-Tyr affected the iTreg population (Fig. 7h, i), both compounds reduced IL-17a secretion, thereby impeding Th17 differentiation (Fig. 7d, e). Further, we assessed RORC, as well as RORA expression in T cells treated with Asn-UDCA, Ser-UDCA, Tyr-CDCA, and UDCA. Notably, we observed that RORA expression positively correlated with IL-17A levels in Th17 cells treated with these MCBAs (Supplementary Fig. S4), suggesting a potential role for RORα in bile acid-mediated modulation of Th17 cells. However, RORC expression or correlation with IL-17A secretion remained unchanged.

Methods
Study subjects
The DIABIMMUNE study recruited 832 families in Finland (Espoo), Estonia (Tartu), and Russia (Petrozavodsk) with infants carrying HLA alleles that confer risk for islet autoimmunity and T1D. The subjects involved in the current study were chosen from the subset (n=74) of available samples (stool) in the international DIABIMMUNE study. This comprises children who progressed to at least a single AAb (P1Ab, n=24) or to multiple islet AAb (P2Ab, n=12), and controls (CTRs, n=38), i.e., the children who remained islet AAb-negative during the follow-up. Samples were collected longitudinally at 3, 6, 12, 18, 24, and 36 months from each child. Here, no prior sample-size estimation was performed. The study groups were matched for HLA-associated diabetes risk, sex, country, and period of birth.
This study was conducted according to the guidelines in the Declaration of Helsinki. The Ethics and Research Committee of the participating Universities and Hospitals approved the study protocol. All families provided written informed consent prior to sample collection.

Quantification of bile acids
The BAs were measured in fecal samples as described previously20. All fecal samples were freeze-dried prior to extraction to account for the inconsistency in the fecal water content and dry weight in the stool. The fecal homogenate was prepared using fecal samples and ethanol in 2:1 w/v ratio. The MCBAs were extracted by adding a volume of 10 μL of fecal homogenate to 200 μL of crash solution containing MeOH and 25 ppb of internal standard mix: CA-d4, CDCA-d4, UDCA-d4, DCA-d4, LCA-d4, TCA-d4, GUDCA-d4, GCDCA-d4, GDCA-d4, GCA-d4, TLCA-d4, TDCA-d4, TCDCA-d4, TUDCA-d4, TCA-d4, and GLCA-d4. The samples were filtered using a 96-well protein precipitation filter plate (Supelco). The filtrate was transferred to a glass vial and dried with N2 flow at 45 °C and resuspended in 50 μL of resuspension solution (methanol:water 4:6). For QC samples, a pooled sample was prepared in the same way.

The analyses were performed on a Sciex 6600 quadrupole time-of-flight mass spectrometer (Sciex, United States) coupled to a Sciex Exion LC system. Separation was performed on an ACQUITY PREMIERE HSS T3 (2.1 × 100 mm, 1.8 μm) column, Waters, using gradient elution. The eluents were water/methanol (7:3 v/v) with 2 mM ammonium acetate (A) and methanol with 2 mM ammonium acetate (B), the flow rate was 0.4 mL/min, the injection volume was 5 μL, and the column was kept at 45 °C. The gradient program was 0–0.5 min: 5% B, 4 min: 60% B; 11–16 min: 100% B, 16.1–17 min 5% B. The mass spectrometer was operated in negative mode. Full scan in the range 100-800 m/z was used for all samples, except QC samples, where information-dependent analysis was performed. The source parameters CUR, GS1, GS2, ISFV, and TEM were 25, 40, 40, 4500, and 650, respectively. The accumulation time was 250 ms. The mass was calibrated using sodium formate clusters between every five samples.
Identification of MCBAs was carried out by comparing retention time and accurate mass (mass error < 10 ppm) to authentic standards, which were synthesized as described previously8. Further, for the QC samples, the fragmentation pattern was compared with the authentic standards. Data was processed using SciexOS (3.0). Results were reported as the peak area of the MCBAs normalized with the corresponding internal standard. For QC, we randomized the order of samples and injected (1) a pooled QC, (2) a blank sample, and (3) a known standard every ten samples. In addition to that, the samples were blinded to the person preparing and running the experiments.

Analysis of islet autoantibodies
Four diabetes-associated autoantibodies were analyzed from each serum sample with specific radiobinding assays: insulin autoantibodies (IAA), glutamic acid decarboxylase antibodies (GADA), islet antigen-2 antibodies (IA-2A), and zinc transporter 8 antibodies (ZnT8A) as described previously in ref. 20. The cut-off values for autoantibody positivity were based on the 99th percentile in non-diabetic children and were 2.80 relative units (RUs) for IAA, 5.36 RU for GADA, 0.78 RU for IA-2A, and 0.61 RU for ZnT8A.

Gut microbiome analysis by shotgun metagenomics sequencing
Metagenomic shotgun sequencing and data processing were conducted as previously described in refs. 20, 23, 32–34. Raw metagenomic sequencing data were retrieved from (https://diabimmune.broadinstitute.org/) (NCBI BioProject ID: PRJNA231909). Stool samples (n=110) were common between the published metagenomics data and the stool BAs measured in the present study. Metagenomic data from the matched samples (n=110) were considered for further analysis. As stated earlier20, host genome‒contaminated reads and low-quality reads are already removed from the raw sequencing data using kneadData v0.4. Taxonomic microbiome profiles were determined using MetaPhlAn2 using default parameters as described20.

Bacterial cultures screening
All bacterial cultures were started from glycerol stock and incubated at 37 °C for three days in an anaerobic chamber (10% CO2, 7.5% H2, 82.5% N2) in a filtered BHI medium at a pH adjusted to 7.2 using 5 N NaOH. After the cultures were normalized at OD600 = 0.02, 200 μL of bacterial suspension was added in triplicate in a 96-well plate with and without 100 μM of Asn-UDCA and incubated for 72 h at 37 °C. Bacterial cultures were also extracted at the start of the experiment (0 h) to establish a baseline. Following bacterial growth, 200 μL of culture was transferred to a new 2 mL deep-well plate, 600 μL of 50% MeOH/H2O was added, and incubated overnight at 4 °C. Samples were centrifuged at 2000 RPM for 10 min, and 200 μL was transferred into a deep-well plate and dried overnight in a CentriVap and stored at −80 °C until LC-MS/MS analysis.

LC-MS/MS analysis of bacterial culture
The samples were resuspended in 200 μL of 50% MeOH/H2O with sulfadimethoxine as internal standard and incubated overnight at −20 °C. Samples were centrifuged at 2000 RPM for 10 min, and 150 μL was transferred to a shallow 96-well plate. A pooled sample was created for QC. Samples were randomized and analyzed using a Vanquish UHPLC (ultra-high performance liquid chromatography) system coupled to an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific). The chromatographic separation was achieved by using a Phenomenex polar C18 column (2.6 µm particle size, 100 × 2.1 mm), and the mobile phase consisted of H2O + 0.1% FA (solvent A) and ACN + 0.1% FA (solvent B). Five microliters of samples were injected and eluted using the following gradient: 0–0.5 min 5% B, 0.5–1.1 min 5% B, 1.1–7.5 min 40% B, 7.5–8.5 min 99% B, 8.5–9.5 min 99% B, 9.5–10 min 5% B, 10–10.5 min 5% B, 10.5–10.75 99% B, 10.75–11.25 99% B, 11.25–12 min 5% B. Data-dependent acquisition (DDA) mode was used to acquire the MS/MS data using positive electrospray ionization (ESI+). Sheath gas was set to 50 L/min, aux gas flow rate was set at 10 L/min, and sweep gas was set to 1. The spray voltage was 3.5 kV, ion transfer tube 325 °C, and vaporizer temperature 350 °C. The AcquireX Deep Scan method was enabled, and an exclusion list was created by injecting four times the pooled sample. MS/MS scan range was set to 100–1000 m/z, RF lens (%) was 70, a resolution of 60,000 with 1 microscans, a charge state of 1, the expected peak width was 6 s, and advanced peak determination was enabled. The automatic gain control (AGC) target was set to standard, and with a maximum injection time set to auto. Dynamic exclusion was set to custom, and the following parameters were used: Exclude after number of times: 2, if it occurs within 3 s, with a duration of 4 s. Isotopes were excluded. Up to 10 scans per MS1 were collected with a resolution at 200 m/z of 22,500 with 1 microscans. The isolation window was set to 1 m/z. The AGC target was set to custom with 200% the normalized AGC target, with a maximum injection time set to Auto. The scan range mode was set to Auto. The collision energies were set to a stepwise increase of 25, 40, and 60 eV.

LC-MS/MS data processing
Thermo RAW files were converted to .mzML using the ProteoWizard MSconvert software. Feature detection and extraction were performed using MZmine 4.4.335. The batch file used for feature extraction (.mzbatch) can be found on the GitHub webpage. The data was imported using the MS1 and MS2 detectors using the factor of the lowest signal of 3 and 2, respectively. Mass detection was also performed using the above parameters. For the chromatogram builder, the minimum consecutive scans was set to 4, intensity to 3E4, height to 1.5E5, and 10 ppm for m/z tolerance. Smoothing was applied using the Savitzky–Golay algorithm before applying the local minimum feature resolver, which had the following parameters: chromatographic threshold set to 90%, minimum search range retention time of 0.05 min, minimum ratio of peak top/edge of 2, and a minimum of 4 scans. Then, the 13C isotope filter and isotope finder were applied using a m/z tolerance of 3 ppm and retention time tolerance of 0.04 min. Features were aligned using the join aligner module at a m/z tolerance of 10 ppm and retention time tolerance set to 0.07 min. The feature list rows filter was applied using two samples or 10%. Peak finder was set to an intensity tolerance of 20%, a m/z tolerance of 10 ppm, retention time tolerance of 0.05 min, and three minimum data points before removing duplicates with a m/z tolerance of 1.5 ppm and retention time tolerance of 0.04 min. Meta Correlate and ion identity networking were performed before exporting the final files. GNPS, SIRIUS, and feature information (legacy MZmine 2) modules were used to generate the feature table containing peak areas, .mgf files, and the content of each feature, respectively, which were necessary for downstream analysis.

Data analysis
All tables generated using MZmine software were imported into R 4.4.1 for downstream analysis.

Synthesis of Asn-UDCA
Solid ursodeoxycholic acid (1.27 mmol, 500 mg, 3 eq.) and 5 mL of DMF were added to a 20 mL glass vial with a stir bar. Next, solid EDC (1.27 mmol, 244 mg, 3 eq.) and neat DIPEA (4.25 mmol, 740 μL, 10 eq.) were subsequently added, and the solution was stirred at room temperature. After 15 min, asparagine (845 µmol, 112 mg, 2 eq.), DMAP were added, and the reaction was stirred overnight. The mixture was then concentrated in vacuo and purified by CombiFlash NextGen 300+ using reversed phase column C18 15.5 g Gold at a flow rate 13 mL per min with H2O (Solvent A) and ACN (solvent B) using the following gradient: 0–6 min, 5% B; 6–10 min, 20% B; 10–17 min 20% B; 17–20 min, 30% B; 20–30 min, 30% B; 30–34 min, 40% B; 34–42 min, 40% B; 42–50 min, 80% B; 50–55 min, 80% B. Asn-UDCA eluted at 34 min, 40% B.

LPS stimulation experiment
Whole blood pre-treatment and stimulation ex vivo for phospho-Flow cytometry analysis. A blood sample was obtained from healthy adult volunteers in BD Vacutainer Heparin Plasma Tubes. The blood sample was mixed in an equal ratio with WB-STIM buffer (Cytodelics AB) at room temperature. The sample was then split into groups: (i) non-treated, (ii) vehicle control treated with 0.25% dimethyl sulfoxide (DMSO; Sigma-Aldrich), (iii) treated with each metabolite (50 μM conjugated UDCAs, collaborator, Finland), (iv) treated with kinase inhibitors Losmapimod (1500 nM, p38 inhibitor) and Selumetinib (1000 nM, ERK inhibitor), or (v) with combinations of both kinase, used as positive control. Metabolite-treated samples were incubated for 3 h, and kinase inhibitor-treated samples were incubated for 45 min at 37 °C and 5% CO2. After incubation, samples were stimulated ex vivo with LPS (1 ng/mL) derived from Salmonella enterica for 5 time points (3, 7, 15, 30, and 60 min) at 37 °C and 5% CO2. Whole blood samples were then fixed, red blood cells lysed, and samples washed using a Whole blood processing kit (Cytodelics AB)36 according to the manufacturer’s instructions. Completely fixed and processed white blood cells (WBCs) (0.5–1×106 cells per sample) were plated and cryogenically preserved using CRYO#20 buffer (Cytodelics AB).

Detecting intracellular phosphorylated protein using flow cytometry. Cryopreserved cells were quickly thawed at 37 °C and then kept on ice while counted using Cellaca MX (Nexcelom). The 0.5–1×106 cells were plated into each well in a 96-well U-bottom plate. Next, the cells were stained overnight at 4 °C with antibodies targeting intracellular antigens (Table 1), washed, and acquired using a Symphony A3 analyzer equipped with an HTS system.

CD4+CD25− T cell isolation and Th17/iTreg differentiation
Studies with primary human CD4+ T cells were approved by the Finnish Ethics Committee. Oral informed consent was obtained from all donors prior to the onset of the study. Primary human mononuclear cells were isolated from the umbilical cord blood of healthy neonates (Turku University Central Hospital, Turku, Finland) using Ficoll-Paque PLUS (Cytiva, Cat# 17144003) density gradient centrifugation. CD4+ T cells were further enriched using CD4+ Dynal positive selection beads (Invitrogen, Cat# 11331D) followed by CD25+ T cell depletion using the CD25 Microbeads II kit (Miltenyi Biotec, Cat# 130-092-983), according to the manufacturer’s instructions. Prior to activation, naïve CD4+CD25− T cells from different donors, which were highly positive for CD45RA (FITC anti-CD45RA, BD Biosciences, Cat# 555488, RRID:AB_395879) and negative for CD45RO (PE anti-CD45RO, BD Biosciences, Cat# 555493, RRID:AB_395884), were characterized by flow cytometry and were pooled.
Th17 cell differentiation was performed as described previously in refs. 37, 38. In brief, CD4+CD25− T cells were activated with plate-bound anti-CD3 (3.75 µg/mL; Beckman Coulter, Cat# IM1304; RRID:AB_131612) and soluble anti-CD28 (1 µg/mL; Beckman Coulter, Cat# IM1376; RRID:AB_131624) in X-vivo 20 serum-free medium (Lonza), supplemented with L-glutamine (2 mM, Sigma-Aldrich) and antibiotics (50 U/mL penicillin plus 50 μg/mL streptomycin; Sigma-Aldrich). Th17 cells were cultured in the presence of IL6 (20 ng/mL; Roche, Cat# 7270-IL), IL1β (10 ng/mL; R&D Systems, Cat# 201-LB) and TGFβ (10 ng/mL; R&D Systems, Cat# 240-B), in the presence of neutralizing anti-IFNγ (1 μg/mL; R&D Systems, Cat# MAB285; RRID:AB_2123306) and anti-IL4 (1 μg/mL; R&D Systems, Cat# MAB204; RRID:AB_2126745) to block Th1 and Th2 differentiation, respectively.
iTreg cells were cultured as described earlier in refs. 38, 39, with minor changes. Briefly, CD4+CD25− T cells were activated with plate-bound anti-CD3 (2.5 µg/mL, Beckman Coulter, Cat# IM1304; RRID:AB_131612) and soluble anti-CD28 (0.5 µg/mL, Beckman Coulter, Cat# IM1376; RRID:AB_131624) in X-vivo 15 serum-free medium (Lonza), supplemented with L-glutamine (2 mM), penicillin (50 U), and streptomycin (50 μg/ml) (all from Biowest). Treg cell differentiation was induced in the presence of TGF-β (10 ng/mL; R&D Systems) and IL-2 (12 ng/mL; R&D Systems). The dilution for each antibody was 1/100.
To study the effect of MCBAs on human Th17 and iTreg cell differentiation, 100 µM of either Asn-UDCA, Ser-CDCA, Tyr-CDCA, or UDCA (in DMSO), and DMSO as a control, were added to the Th17 and iTreg cell culture media at day 0, and cultured for 72 h. After differentiation, secreted IL-17a levels were determined from Th17 cell culture supernatants at 72 h using the human IL-17a DuoSet ELISA kit (R&D Systems, Cat# DY317-05, DY008). Expression of RORC and RORA in Th17 cells at 72 h of differentiation was assessed by quantitative real-time PCR, as described previously in ref. 37. For iTreg, intracellular staining of Foxp3 was performed using the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set (Thermo Scientific, Cat# 00-5523-00), according to the manufacturer’s protocol. Cells were stained with PE-conjugated Foxp3 antibody (Thermo Fisher Scientific, clone PCH101, Cat# 12-4776-42, RRID:_AB151878) or corresponding isotype control antibody (Thermo Fisher Scientific Cat# 12-4321-42, RRID: AB_1518773). Cells were incubated with fluorochrome-labeled antibody for 30 min at 4 °C. After staining, the cells were washed twice, resuspended in flow buffer (2% FBS/0.1% Na-azide/PBS), and acquired on BD LSRFortessa (BD Biosciences). The data was analyzed with FlowJo software (FlowJo LLC).

Statistical methods
The metabolites data values were log-transformed prior to analysis. The difference in the lipidome and metabolome between the study groups was compared using a multivariate linear model. For longitudinal samples, linear mixed effects models were regressed with fixed effect (~sex+case+age+breastfeeding duration) and random effect ~(1|Subject). For age-wise comparisons, the metabolites were regressed with various factors such as sex, and disease conditions (e.g., P1Ab vs CTR) using the MaAsLin2 package in R (BAs~sex+case). To subsequently visualize the metabolite level, forest plots from the ggplot2 R package were used. We used the paired t-test to analyze IL-17A and Foxp3 expression data, and the figures were plotted with GraphPad Prism 8 software.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, Lamichhane et al presented an investigation on early life microbiome derived bile acids on islet autoimmunity.\nWhile the study design is unique with repeated fecal sample collection up to six time points, there are many limitations\ndownplaying our enthusiasm to support the publication in this present form.\nMajor comments:\n(1) Bile acids were measured only in fecal samples without a paired measurement in blood. While conjugated BAs are\nknown as microbial related, their systemic bioavailability in blood matters, especially concerning their role in peripheral\nimmune modulation. I would encourage the authors to show their data in blood to verify their main findings.\n(2) The longitudinal analysis includes only 74 children, though a total of 303 samples were provided. This sample size might\nbe too small in terms of the multiple comparisons conducted in this work. However, the authors didn’t correct any false\ndiscoveries in both human and experiment analyses.\n(3) The proposed trajectory analyses of microbial conjugated bile acids (MCBAs) are thoughtful. But the current analysis is\nlimited to individual BAs rather than the overall BA profiles. The biological contributions of BAs to islet autoimmunity might\nbe results of multiple BAs congeners rather than single BAs. I would suggest the authors exploring the trajectory patterns of\nall BAs and determining if any specific latent patterns exist. Meanwhile, maternal (e.g, diet, history of T1D or autoimmune\ndiseases) and offspring feeding related factors (e.g., solid food introduction and other diets after breastfeeding stops) are not\ncontrolled in the analyses of MCBAs trajectory and differences across case status.\n(4) While the in vitro immune modulation experiments are compelling, the study does not mechanistically link MCBA\nalterations to in vivo immune dysfunction in progression to autoimmunity. For example: Are MCBA changes causally linked\nto autoantibody development, or are they correlative biomarkers? And How do MCBA levels interact with HLA risk alleles or\nother genetic factors? In addition, the overlap between MCBA-associated microbes and case status-linked taxa warrants\ndeeper exploration but is currently only described via correlation.\n(5) It is unclear which time points with available gut microbiome data.\n(6) MCBAs showed sex-specific differences, but this observation is not explored further in experimental setting. Do the\nauthors believe that the MCBAs examined do not contribute to the known sex disparities in autoimmune risk (e.g., female\nbias in T1D)?\nMinor comments:\n(1) Inconsistent usage of \"MCBAs\" vs. \"MBCAs\" (e.g., Abstract vs. Methods). Standardize to \"MCBAs\" throughout.\n(2) Figure 4: Clarify whether error bars represent standard deviation or standard errors in forest plots. The overlaps of error\nbars and dots are quite confusing.\n(3) It is unclear how the missing values of MCBAs during six time points were handled in analyses. Meanwhile, the\ntransformations of MCBAs/microbiota should be clarified.\n(4) Please add LOD/LOQ data for the MCBAs quantifications.\n(5) Please highlight how the batch effects were adjusted for the longitudinal sample processing and omics data profiling.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "The study investigated alterations in fecal microbially conjugated bile acids (MCBAs) in children who developed one or\nmore islet autoantibodies, compared to age-matched controls who remained seronegative, over the period from 3 to 36\nmonths of age. A total of 330 fecal samples were collected, and 110 MCBAs were profiled alongside gut microbiome\ncomposition analyses. Perturbations in the levels of ursodeoxycholic acid (UDCA) and\ndeoxycholic acid (DCA) derivatives were observed. Their potential immunomodulatory roles\nwere evaluated through in vitro assays using T cells polarized toward either Th17 or\nregulatory T cell (Treg) phenotypes.\nGeneral Comments\nThe study presents an intriguing dataset; however, the manuscript remains predominantly descriptive. Below are specific\npoints of concern:\nIntroduction and Mechanistic Insight:\nThe introduction notes that MCBAs may function as ligands for nuclear and membrane receptors such as FXR, GPBAR1,\nand PXR. However, the manuscript does not provide experimental data to support receptor binding or activation by UDCA or\nDCA derivatives. This constitutes a critical gap in linking bile acid alterations to immune signaling.\nFigure 6 – Incomplete Mechanistic Elucidation:\nThe data presented in Figure 6 appear preliminary. There is insufficient mechanistic explanation regarding how the tested\nbile acids influence intracellular signaling cascades—specifically the phosphorylation of p38, ERK, and degradation of IκBα\nin LPS-stimulated monocytes.\nRelevance to Th17 Biology – RORγt Modulation:\nIt is well established that several secondary bile acids can function as inverse agonists of RORγt, a transcription factor key to\nTh17 lineage commitment. The study should assess whether Asn-UDCA, Ser-UDCA, Tyr-CDCA, and UDCA modulate\nRORγt activity in T cells, which would substantiate claims related to Th17 polarization.\nFoxp3 Modulation – Unexplored Mechanism:\nThe mechanisms by which the tested bile acids influence Foxp3 expression and Treg differentiation, are not explored in\nsufficient detail (Figure 7). Clarification of this mechanism is necessary to support the proposed functional conclusions.\nOverinterpretation of Non-significant Findings (Figure 7):\nSeveral panels in Figure 7 (specifically b, c, d, f, h, and i) do not reach statistical significance. These data should be\ninterpreted with caution, and conclusions must reflect the actual statistical weight of the findings.\nOn the same line the in vitro modulation do not provide any insight on how these MCBA could modulate immunity in\nchildren.", "reviewer_4": "The authors have used stool samples from an existing longitudinal observational, case-control study involving young\nchildren that did or did not develop one or more islet autoantibodies. They hypothesized that MCBA’s formed by the gut\nmicrobiome can affect the occurrence of auto-immunity resulting in type 1 diabetes. They measured MCBA’s in stool\nsamples at 6 timepoints (3-36 months) and associated these to microbiome composition. To gain more mechanistic insights, they assed immune-modulatory properties of the MCBA’s. They identified clear differences in stool MCBA’s in children with single or multiple auto-antibodies and those with no antibodies and associated these differences to specific gut microbes.\nAdditionally they identified immuno-modulatory properties of MBCA’s on both innate and adaptive immune cells that might\nplay a role in auto-immunity. The authors conclude that MCBAs influence immune development and type 1 diabetes risk.\nOverall this is a comprehensive study that indeed provides evidence that MCBAs produced by the gut microbiome have\nimmunomodulatory affects and are possibly involved in developing type 1 diabetes.\nStrengths include: the unique study cohort consisting of young children, the longitudinal study design and the state-of-the-art\ntechniques used. Weaknesses include the rather low study power and potential selection issues.\nA number of questions and comments:\n1. Study subjects: 74 subjects were selected based on sample availability of infants at risk of type 1 diabetes. How were\nthese 74 subjects selected, where these all available samples or a subset? Line 319 states that samples were collected\nlongitudinally for each child but in figure 1 the largest number of samples for a timepoint is 55. This suggests several missing\nsamples per timepoint, how was accounted for these missing samples? Specifically in the results presented in figure 4.\n2. Results:\na. Are the data depicted in figure 4a an average of all timepoints?\nb. I understand the authors choose to depict the unadjusted p-value in this figure as the groups are quite small. However, I\nwould prefer to also include the adjusted p-value in the results section describing these data (line 143-153).\nc. I suggest to include a statement on the relatively low power of this study in the discussion.\n3. Results: Authors find a modulatory effect of MCBAs on LPS-induced signaling in human monocytes. These experiments\nwere performed in whole blood samples, therefore including other cell types than monocytes. What was the reason for solely\nincluding the monocyte results? As these experiments are performed in whole blood, I assume that other immune cells are\nalso measured. As T-cells are crucial in the development of type 1 diabetes it would be interesting to include information on\nthese cells. Did MCBA’s also modify LPS signaling in other immune cells such as T-cells?\n4. Statistical analysis: The linear regression model was corrected for sex, age, and breast feeding duration. Where these\nfactors also taken into account in the multivariate model (figure 4a and b)? Where these factors different between the 3\nstudie groups?\n5. Statistical analysis: A spearman correlation analysis was performed to determine the link between bacterial composition\nand MCBA levels. No levels of significance are included in the results section or depicted in figure 5. I would suggest to\ninclude this to clarify the message\n6. Statistical analysis: The statistical method section does not mention the method used for the analysis performed on IL17A\nand Foxp3 production/expression (figure 7), this could be included.\n7. Line 155 to 164 are hard to read, what is meant by: “There was no persistent trend with respect to MCBA differences…”?\nWhich is followed by a description of the differences in MCBAs at different timepoints. What do the authors mean by “no\npersistent trends”? Please clarify.\n8. Line 165: MBCA should be MCBA, line 168: microbial conjugated BA profiles should be MCBA profiles, line 171: stain\nshould be strain\n9. Figure legends: Not in all figure legends subfigures (e.g. a,b,c) are specifically mentioned, please include this.\nAdditionally the style is not consistent (for examples a. or (a)).", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Response: We agree with the reviewer that the systemic bioavailability of metabolite in\nblood is crucial. Due to lack of sample availability, we were not able to show these data in\nthe circulating metabolite. However, we performed immune assay to understand how these\nbile acids modulate host immune responses. We acknowledge some limitations to our study,\nwhich we have updated in the revised manuscript (Page 15, Line 330-335).\nResponse: We did correct for false discoveries. The p values shown in figure are nominal p\nvalues, and the adjusted ones (for multiple comparisons using Benjamini-Hochberg) are\nshown in supplementary tables. We have clarified this information, in method section,\nfigure legend and also added a discussion section in the revised manuscript relating to the\nsample size (Page 33, 751- 753 and Page 15, Line 330-335).\nResponse: We agree that diet is crucial for the bile acids, for that reason we conducted our\nanalyses using in samples where breastfeeding information was available. Here, the breast-\nfeeding duration was taken into account in our linear mixed model as suggested by the\nreviewer. We don’t have data related to maternal diet.\nResponse: Our results demonstrate that the altered MCBAs modulate monocyte activation\nin response to immunostimulatory lipopolysaccharide and influence the Th17/Treg cell\nbalance. Additionally, bacterial monoculture screening revealed that specific gut microbes\nare responsible for transforming these bile acids. Previous studies have suggested that such\nimmunomodulatory effects may influence immune development and the risk of type 1\ndiabetes (T1D). While our findings offer potentially clinically significant insights into how\nmicrobial metabolites may affect T1D progression, the causal relationship remains unclear\nand warrants further investigation, which is beyond the scope of this study. We agree the\nreviewer and therefore, modified our discussion (Page 13-15).\nResponse: These were paired longitudinal sample (110) with the microbial conjugated BA\nprofiles obtained at 6, 12, 18, 24, and 36 months of age from children. We have clarified this\ninformation in the revised manuscript (Page 8).\nResponse: The MCBAs examined may potentially contribute to the known sex disparities in\nautoimmune risk. To account for this, we included sex as a factor in our longitudinal model.\nOur analysis revealed that age was the most significant determinant of MCBA stool profiles,\ncompared to sex (male vs. female) and case status (case vs. control). These effects are\ndetailed in Supplementary Tables 1 (age), 2 (case status), and 3 (sex).\nResponse: Thank you very much for pointing this out. The has been clarified in the revised\nversion of the manuscript.\nResponse: This error bar is only plotted for the significant metabolites and this information\nhas been clarified in the revised version of the manuscript.\nResponse: The MCBAs values were imputed using half the minimum detected value. For\nthe Spearman correlation analysis, zero values in the microbiome data were retained as it is,\ngiven that Spearman is a rank-based method and not sensitive to the absolute values.\nResponse: MCBA identification was based on retention time matching using an in-house\nlibrary generated from standards synthesized and provided by the Dorrestein Laboratory at\nUCSD. The concentrations of the standards were unknown, so we were unable to determine\nthe limit of detection or limit of quantification for MCBA measurements.\nResponse: No noticeable batch effects were observed in our data.\nResponse: The code is available at\nhttps://github.com/LamichhaneSantosh/MCBA_DIABIMMUNE\nResponse: We appreciate the reviewer’s suggestion. While our introduction highlights the\npotential for MCBAs to act on nuclear and membrane receptors such as FXR, GPBAR1, and\nPXR, we acknowledge that the current study does not directly assess receptor binding or\nactivation. Our primary focus was to investigate the developmental trajectories of MCBAs\nduring early life, including how these microbially derived bile acids are regulated in children\nwho later progressed to islet autoimmunity. We aimed to identify candidate MCBAs within\nthis context and offer an initial insight into their potential downstream effects on Th17 and\nTreg differentiation, thereby laying the groundwork for future mechanistic studies. We agree\nthat defining specific receptor interactions is essential for fully elucidating the mechanisms of\nMCBA-mediated immune modulation. Future work will incorporate receptor activation\nassays (e.g., reporter systems and binding affinity studies) to determine whether these\nderivatives directly engage FXR, GPBAR1, PXR, or RORα.\nResponse: We appreciate the reviewer's valuable feedback regarding the mechanistic\nelucidation in Figure 6. Our experiments demonstrated the immunomodulatory potential of\nvarious microbially conjugated and unconjugated bile acids on LPS-induced TLR4 signaling\nin human monocytes within a physiologically relevant whole blood context, showing distinct\neffects on p38, ERK phosphorylation, and IκBα degradation. We acknowledge that the precise\nmolecular mechanisms by which these specific bile acids influence intracellular signaling\ncascades, including their direct receptors and detailed crosstalk with TLR4, are not fully\nelucidated in the current dataset. We agree that a comprehensive mechanistic dissection is a\ncritical next step. Future studies will focus on identifying specific receptors and elucidating\nthe precise molecular interference points with the TLR4 signaling pathway, which will be\ncrucial for understanding this previously unrecognized layer of immune regulation in early\nlife and T1D development. We have revised the discussion to more explicitly state these\nlimitations and future directions.\nResponse: We thank the reviewer for this insightful suggestion. As noted, certain secondary\nbile acids act as inverse agonists of RORγt, the master regulator of Th17 differentiation and\nIL-17A production (Hang et al., 2019). In line with the reviewer’s recommendation, we assessed\nRORγt expression in T cells treated with Asn-UDCA, Ser-UDCA, Tyr-CDCA, and UDCA.\nHowever, neither TaqMan qPCR nor intracellular staining revealed significant changes in\nRORγt expression or correlation with IL-17A secretion.\nWhile RORγt remains the canonical Th17 transcription factor, RORα (encoded by RORA) also\ncontributes to IL-17A expression, particularly under inflammatory conditions in both mouse\nand human systems (Yang et al., 2008; Huh et al., 2011; Castro et al., 2017). Notably, we found\nthat RORA expression positively correlated with IL-17A levels in Th17 cells treated with the\ntested bile acids (Figure below), suggesting a potential role for RORα in bile acid–mediated\nTh17 modulation. Although RORα has been studied in the context of circadian regulation and\nbile acid synthesis in mice (Ferrell et al., 2015), it is also a ligand-responsive nuclear receptor\nthat may be directly modulated by bile acid derivatives, independently of circadian cues. This\nraises the possibility that certain bile acids influence RORα directly or via upstream metabolic\nsignaling pathways, thereby promoting IL-17A expression. Our findings point to a potential\nRORα–IL-17A axis in bile acid–driven immune regulation and highlight the need for future\nstudies to define the upstream mechanisms that govern RORα activation in this context. We\nhave included RORA expression data in the revised manuscript (also shown below) and\nupdated the corresponding figure legend and main text.\nResponse: We thank the reviewer for highlighting this important point. We would like to\nclarify that the tested bile acids exert a stronger and statistically significant effect on the Th17\nphenotype, as indicated by IL-17A and RORA expression, whereas their effect on Foxp3\nexpression in Tregs showed an inverse but less pronounced trend. The mechanisms by which\nbile acids influence Foxp3 expression and Treg differentiation remain incompletely\nunderstood, with only a few studies suggesting potential pathways. For instance, emerging\nevidence indicates that certain bile acid derivatives may enhance Treg function via\nmitochondrial reactive oxygen species (mtROS)-dependent chromatin remodeling at the\nFoxp3 promoter, particularly through increased H3K27 acetylation (Hang et al., 2019;\nKiriyama & Nochi, 2023). Furthermore, the bile acid metabolite isoalloLCA has been shown\nto promote Foxp3 expression through a mechanism involving chromatin remodeling that\ncritically depends on the nuclear receptor NR4A1—highlighting a potential role for NR4A1 in\nbile acid–mediated Treg differentiation (10.1016/j.chom.2021.07.013). Although this lies\nbeyond the scope of the present study, these mechanistic pathways, particularly those\ninvolving NR4A1 and epigenetic regulation, represent promising directions for future\ninvestigation into MCBA-driven immune modulation.\nResponse: We thank the reviewer for this helpful comment. We agree that statistical\nsignificance was not achieved in all panels referenced. Our intention was to present the\nmeasured expression and secretion levels of IL-17A and Foxp3 as observed. To address inter-\ndonor variability, values were normalized to the vehicle control (DMSO), which improved\nstatistical resolution in several cases—most notably for IL-17A (Figure below, Panel A) and\nRORA expression (see above). FOXP3 expression in Tregs treated with the respective MCBAs\nshowed an inverse trend relative to IL-17A and RORA levels; however, these changes did not\nreach statistical significance (Figure below, Panel B). In response to the reviewer’s concern,\nwe have included the normalized data in Supplementary Figure S4 and clarified this\nnormalization approach in the corresponding figure legend. Additionally, we have revised the\nmain text (Page 12, Line 255-260) to more cautiously interpret these findings, ensuring that\nour conclusions align with the statistical robustness of the data.\nResponse: We have modified discussion as per the context pointed by the reviewer.\nResponse: The DIABIMMUNE project was designed to test the hygiene hypothesis and\ninvestigate its role in the development of type 1 diabetes (T1D) and other autoimmune\ndiseases in infants carrying HLA alleles that confer risk for islet autoimmunity and T1D. The\nsubjects included in the present study were selected from a subset (n = 74) of available stool\nsamples. As is common in many human longitudinal studies, it is logistically challenging to\nobtain complete data points for all participants. To address this, we employed a linear\nmixed-effects model, incorporating age as a fixed effect and subject (with repeated\nmeasures) as a random effect, an established approach in the analysis of longitudinal data.\nResponse: These are regression coefficient obtained from linear mixed effect models. We\nhave clarified this information in the legend section of the revised manuscript.\nResponse: This is relevant comment by the reviewer. The p-values shown are nominal;\nadjusted p-values (corrected for multiple comparisons using the Benjamini-Hochberg\nmethod) are available in Supplementary Table. We have clarified this information in the\nlegend section of the revised manuscript.\nResponse: We have addressed this concern of the reviewer in the discussion section of the\nmanuscript.\nResponse: We appreciate the reviewer's valuable comment regarding the scope of our\ncellular analysis in whole blood samples. Our primary focus on CD14+ monocytes in this\nexperiment was driven by their pivotal role as immediate and robust responders to LPS via\nTLR4, making them ideal for investigating rapid innate immune signaling events.\nThere are differential TLR4 responsiveness across immune cells. CD14+ monocytes express\nhigh levels of TLR4 and the co-receptor CD14, enabling a potent and rapid intracellular\nsignaling cascade upon LPS exposure. This makes them central to the initial phases of TLR4\nactivation in whole blood, functioning as primary responders. While other innate immune\ncells like neutrophils also express TLR4, their responsiveness to LPS can differ significantly\nfrom monocytes. For instance, neutrophils express lower levels of CD14 compared to\nmonocytes, which can lead to a less potent signaling response to LPS, particularly at the early\ntime points (0-60 minutes) assessed in our phospho flow assay. In contrast, T-cells are\nadaptive immune cells that do not express TLR4 as a primary LPS-sensing receptor. Their\nactivation is indirect, requiring antigen presentation and co-stimulation from innate immune\ncells (like monocytes/APCs) over a much longer timeframe (hours to days). Consequently,\nour short-duration LPS stimulation (0-60 minutes) and phospho flow assay, designed for\nrapid innate immune signaling, are not suitable for directly evaluating T-cell responses to LPS.\nAny observed T-cell phosphorylation within this short window would likely be secondary to\nmonocyte activation.\nOur phospho flow cytometry pipeline was specifically optimized to investigate rapid, robust\nintracellular signaling in innate immune populations that directly respond to LPS in whole\nblood. Performing these experiments in whole blood provides a more physiologically relevant\ncontext than isolated PBMCs or cell lines. This allows us to study monocyte signaling in the\npresence of other circulating immune cells, plasma proteins, and other soluble factors that\nexist in the natural systemic environment, capturing complex intercellular interactions and\nsoluble mediators that can influence immune responses.\nResponse: Yes, this has been taken into account in the multivariate model. Thank you very\nmuch for pointing this out.\nResponse: Taking reviewer comment into consideration, we have revised and updated the\nFigure 5 and shown the only those correlations coefficient with values with p less than 0.05.\nResponse: We thank the reviewer for pointing this out. We used the paired t-test to analyze\nIL-17A and Foxp3 expression data. This has now been clearly stated in the Statistical Analysis\nmethod section (Page 25).\nResponse: This point has been clarified in the revised manuscript.\nResponse: We have changed it as suggested by the reviewer.\nResponse: We have addressed this concern of the reviewer in the revised version of the\nmanuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors addressed most of the previous comments, but lack of blood bile acid measurements is still a major limitation.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have respondend to my questions/suggestions. I have no further comemnts.", "reviewer_4": "Overall the authors have addressed the comments sufficiently. The only concern that remains is the reporting of solely unadjusted p-values in figure 4 and figure 5 and the main text, which can result in overinterpretation of the findings.\nI suggest to mention in the results section that findings are not significant after correction for multiple testing. Please also include adjusted p-values of the correlations shown in figure 5 in the supplement (as done for the data shown in figure 4).", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We have acknowledged the lack of blood data as a limitation in the Discussion section of the\nmanuscript.\nResponse: The code is available at\nhttps://github.com/LamichhaneSantosh/MCBA_DIABIMMUNE\nResponse: N.A\nResponse: We have added adjusted p values in the Supplementary tables. Also. we have acknowledged in our\nmanuscript about the sample size. We have also clarified in the legend relating to the p and adjust values.\nResponse: “I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the\nNature Communications initiative to facilitate training in peer review and to provide appropriate recognition\nfor Early Career Researchers who co-review manuscripts.”"}]

Decision letter
Trajectories of microbiome-derived bile acids in early life – insights into the progression to
islet autoimmunity" to Nature Communications. We have now received reports from 5
reviewers and, based on their comments, we have decided to invite a revision of your work.
Your revision should address all the points raised by our reviewers (see their reports below).
In particular, we would like to see in a revised version of the manuscript in which all
technical points are addressed, with an improved statistical analysis, additional data
regarding possible mechanism linking bile acid profiles to immune modulation suggested by
#R1 and #R3, additional analyses on overall BA profiles and sex-specific differences
requested by #R1. While we do not expect a full elucidation of causal links for how bile acid
regulates immune signaling through nuclear and membrane receptor binding, we expect to
see additional data on how MCBA alterations may modulate autoimmunity and T cell
differentiation.