Biosynthetic enzyme-guided disease correlation analysis reveals a negative correlation between SoL biosynthesis and IBDWe began by systematically investigating the biosynthetic potential of SoLs from 285,835 human gut bacterial reference genomes including single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs)33. Based on sequence homology with experimentally verified SoL biosynthetic enzymes22,34,35,36 (Supplementary Fig. 1, Supplementary Data 1), we identified a total of 562,214 homologous enzyme sequences, including 469,012 cysteate synthases (CYS), 33,486 cysteate fatty acyltransferases (CFAT), and 59,716 short-chain dehydrogenases/reductases (SDR) (Supplementary Fig. 2a). Uncovering phylogenetic trends, we found that these three enzymes were widely distributed in 255,572 genomes (Supplementary Fig. 2a) across 21 phyla, with the majority belonging to Bacteroidota and Firmicutes_A (Supplementary Fig. 2b). A subset of 6.21% (15,863/255,572) of the genomes was found to encode all three putative SoL biosynthetic enzymes (Supplementary Fig. 2a). To prioritize them for further analysis, we filtered the homologs on the basis of three rules: (1) the homology of both CFAT and CYS must equal or exceed 50% sequence similarity with experimentally validated CFATs and CYSs (Supplementary Data 1), as these enzymes are the first two specific enzymes in the biosynthetic pathway of SoLs that distinguish the biosynthesis between SoLs and SLs22,34,35,36; (2) the homologous regions of CYS, CFAT, and SDR should include protein domains with Pfam IDs PF00291, PF00155, and PF00106, respectively (hit score >50); (3) a set of homologous enzymes, especially SDR enzymes that show variable sequence similarities, should come from the same genome encoding all three enzymes as all three are required for SoL biosynthesis, thus ensuring co-occurrence. Applying these rules, we prioritized 9,731 CYS (1384 unique sequences), 9740 CFAT (917 unique sequences), and 10,319 SDR enzymes (1,076 unique sequences) (Fig. 1a, Supplementary Data 2) from 9,633 bacterial genomes. The prioritized enzymes were distributed among 42 species from Bacteroidota (99.99%, 9632/9633, 95% confidence interval: 99.94% ~ 100%) and one species from Firmicutes_A (0.01%, 1/9633, 95% confidence interval: 0.0018% ~ 0.059%) (Fig. 1b, Supplementary Data 3). Of note, among the 42 species from Bacteroidota, 71% (30/42) of them belong to bacterial families that have been previously reported to produce SoLs (Fig. 1b) including Rikenellaceae (containing genera Alistipes and Alistipes_A)16,37, Marinifilaceae (containing genus Odoribacter)16, and Weeksellaceae (containing genus Chryseobacterium B)22,38.Fig. 1: The presence and expression profiles of SoL biosynthetic enzymes and the production of SoLs differ in IBD subjects versus healthy controls.a Overview of SoL biosynthetic enzymes identified in human gut bacteria. 562,214 putative SoL biosynthetic enzymes were identified across 21 bacterial phyla. 6.21% of genomes encode 3 types of SoL biosynthetic enzymes (Pie chart, sections in red and purple). Bar chart shows the number of prioritized SoL biosynthetic enzymes encoded by 9,633 genomes (highlighted in red in the pie chart). b A circular phylogenetic tree shows the prioritized SoL biosynthetic enzymes found primarily in species from Bacteroidota (highlighted in green and orange). The tree is annotated with species names and colored by taxonomic families (Rikenellaceae: green; Marinifilaceae: orange; Weeksellaceae: pink; Lachnospiraceae: gray). c Principal Coordinate Analysis (PCoA) shows differences in the presence profile of overall SoL biosynthetic enzyme subfamilies between IBD and non-IBD groups based on Jaccard distance. Statistical significance was determined using PERMANOVA; p = 0.001. d 35 SoL biosynthetic enzyme subfamilies were significantly more prevalent in healthy individuals (red dots) than in IBD groups (blue dots) with a difference of prevalence >10%. All comparisons were significant by two-sided Fisher’s exact test with p < 0.05. e PCoA shows the differences in the expression profile of overall SoL biosynthetic enzyme subfamilies between IBD and non-IBD groups based on Bray-Curtis distances. Statistical significance was determined using PERMANOVA; p = 0.001. f Expression profiles of differential SoL biosynthetic enzyme subfamilies (n = 8; two-sided Mann-Whitney U test, adjusted p < 0.05). Upper panel: bar charts showing the prevalence of differential SoL biosynthetic enzyme subfamilies across non-IBD (red) and IBD individuals (dark blue). Statistical significance for prevalence was calculated using a two-sided Fisher’s exact test. Except CYS subfamily24 (CYS_24, no significance), all were significantly higher in prevalence in non-IBD than IBD groups (p < 0.05). Lower panel: box plots displaying the abundance profiles of differential SoL biosynthetic enzyme subfamilies in non-IBD (red) and IBD individuals (dark blue). All box plots include center lines representing the median, box limits representing upper and lower quartiles, whiskers representing the 1.5x interquartile range, and points representing outliers. Significance was further determined by one-sided Mann-Whitney U test, with adjusted p-value < 0.05.To determine whether there is a link between gut microbial capacity to produce SoLs and IBD incidence, we conducted a comparative analysis of metagenomic and metatranscriptomic data obtained from the Inflammatory Bowel Disease Multi’omics Database (IBDMDB)19,39. We began by generating sequence similarity networks with a 90% sequence identity threshold to group enzymes with similar functions. Consequently, we categorized the prioritized biosynthetic enzymes into 214 subfamilies (79 CYS subfamilies; 25 CFAT subfamilies; and 110 SDR subfamilies) for the subsequent analyses (Supplementary Data 4). Looking for the presence of the prioritized 214 subfamilies in IBD cohorts, we identified 154 subfamilies in 667 metagenome samples (182 healthy samples and 485 IBD disease samples), of which 116 subfamilies were detected in ≥5% of samples (Supplementary Fig. 2c). Beta diversity of the presence of these 116 subfamily biosynthetic enzymes indicated that the overall composition of SoL biosynthetic enzyme subfamilies was significantly different between the healthy and IBD cohorts (Fig. 1c, Jaccard distance, PERMANOVA p = 0.001). Of note, 57 subfamilies had a significantly higher prevalence (two-sided Fisher’s exact test p < 0.05) in healthy individuals as compared to IBD cases (Supplementary Data 5), among which 35 subfamilies (18 CYS subfamilies, 2 CFAT subfamilies, and 15 SDR subfamilies) further show a difference of prevalence > 10% (Fig. 1d).To further examine the difference between the expression profiles of SoL biosynthetic enzymes between the IBD and healthy groups, we extended our comparative analysis to the metatranscriptomic level. We found that 132 SoL biosynthetic enzyme subfamilies were expressed in 777 metatranscriptomic samples (193 healthy samples and 584 IBD disease samples), with about 42% (55/132) detected in at least 5% of samples (Supplementary Fig. 2d). Beta diversity of the expression profiles of SoL biosynthetic enzymes suggested that the overall expression of these enzyme subfamilies is significantly different between the healthy and IBD cohorts (Fig. 1e, Bray-Curtis distance, PERMANOVA p = 0.001). To capture more detail, we compared the prevalence and abundance differences of each enzyme subfamily in the metatranscriptomic samples. Nine subfamilies had higher prevalence (two-sided Fisher’s exact test p < 0.05, varying from 9% ~ 17%) in the non-IBD group than the IBD group (Supplementary Data 6). We further identified 8 subfamilies (6 CYS, 1 CFAT, and 1 SDR) as significantly different in abundance (expression) profiles between the healthy controls and IBD cases (Fig. 1f, two-sided Mann-Whitney U test, adjusted p < 0.05). Notably, 7 of the 8 subfamilies had a higher prevalence (Fig. 1f, upper panel, two-sided Fisher’s exact test p < 0.05) and a higher abundance (Fig. 1f, lower panel, one-sided Mann-Whitney U test, adjusted p < 0.05) in the non-IBD group than in the IBD group.We finally looked for metabolomic evidence in the differences of detectable SoLs, the products of the biosynthetic enzymes mentioned above, among IBD and non-IBD groups from publicly accessible metabolomics datasets. We expected that the increased expression of SoL biosynthetic enzymes would correspond with an increased abundance of stool SoLs in non-IBD groups compared to IBD groups after possible uptake by the host. Using metabolomics data from two independent datasets (dataset 1: IBDMDB19,39, corresponding to the same dataset used for metagenomics and metatranscriptomics analysis; dataset 2: PRISM40), we identified metabolite features putatively corresponding to specific sulfonolipids16,41 (Supplementary Data 7), by exact mass comparison with mass error less than 5 ppm (Supplementary Data 8). Within each dataset individually, we indeed found that metabolomic features potentially corresponding to SoLs were decreased in stool samples of IBD groups compared to non-IBD groups. Since there were no MS/MS data available in either dataset, we utilized additional complementary approaches to confirm these features as SoLs including analysis of in-source fragmentation, correlation of co-eluting metabolomic features, and retention time matching between the dataset and data recreated using our own instrument, followed by experimental validation using targeted metabolomics with an additional set of samples from independent IBD disease case and control cohorts.To validate the presence of SoLs in these datasets, we first tried to identify SoLs using in-source fragments (ISFs) of metabolites based on an established set of criteria42. We initially identified six groups of co-eluting metabolomic features as potential ISFs which showed peak-to-peak intensities highly correlated with putative SoL features (Supplementary Fig. 3, Pearson correlation coefficients ≥0.9). We then examined the reference MS/MS spectra of our isolated and literature-reported SoLs22,41 matching putative SoL masses, which contained limited m/z values corresponding to potential ISFs we identified. However, their relatively low intensity was not conclusive enough to classify them as high-confidence ISFs42. Thus, we proceeded with a complementary correlational approach to identify the putative SoL features. Among the originally identified six groups of co-eluting metabolomic features, five members were detected in both datasets mentioned above (Supplementary Fig. 3c–f, features highlighted in bold). Based on exact mass matching, these features corresponded to SoL analogs: SL 34:1;2 O, SL 17:0;O/16:1;O, SL 33:1;2 O | SL 17:0;O/16:1;O, SL 34:1;2 O | SL 17:0;O/17:1;O, and SL 32:0;O | SL 17:0;O/15:043. In addition, these features had higher peak area-peak area correlation with each other (Pearson correlation ≥0.9; Supplementary Fig. 3c–f). Notably, SoLs are often detected in metabolomics as a series of analogs with consecutive additions of CH2 and H2 moieties within the class and with different numbers of oxygens between classes16,43. Thus, these features likely represent a series of analogs chemically modified from a common parent metabolite, or co-produced by a specific microbe, which is consistent with SoL analogs. These metabolomic features were further positively correlated with species of the prolific SoL-producing genus Alistipes: A. putredinis, A. finegoldii, A. indistinctus, A. shahii, A. onderdonkii, and Bacteroidales bacterium ph8 (which belongs to A. obesi) with Spearman correlation coefficients ≥ 0.5 (Supplementary Figs. 4–6). This positive correlation indicated that the abundance of these metabolites increased with the increase in these species, supporting that these species likely produced these molecules. Furthermore, these metabolomic features had significantly higher abundance in non-IBD groups compared to IBD groups in both IBD datasets (Fig. 2a, Supplementary Fig. 7, Wilcoxon rank sum test, p < 0.05, one-sided), which was consistent with our exact mass matching analysis.Fig. 2: SoL abundances are decreased in IBD metabolomics datasets and independent IBD patient samples.a Box plots showing the relative abundance of SoL candidates detected in dataset 1 (upper) from non-IBD (red boxes, n = 124 individuals) and IBD individuals (blue boxes, n = 348 individuals) and in dataset 2 (lower) from non-IBD (red boxes, n = 56 individuals) and IBD individuals (blue boxes, n = 164 individuals). For each feature, the corresponding SoL is noted in the bottom label. The prefixes of metabolomic feature names correspond to the detection method used: In dataset 1, HILp indicates the HILIC-positive method and HILn indicates the HILIC-negative method. In dataset 2, HILIC-pos indicates the HILIC-positive method and HILIC-neg indicates the HILIC-negative method. Details for the corresponding LCMS methods can be found in the original studies. Significance was determined using the one-sided Wilcoxon rank sum test with the hypothesis that the abundance of SoL was higher in the non-IBD than in IBD group. Exact p-values from left to right are: 1.2E-11, 3.9E-13, 2.3E-14, 1.1E-6, 1.1E-8 in dataset 1 (upper) and 3.3E-8, 6.2E-9, 1.2E-7, 4.5E-10, and 6.5E-11 in dataset 2 (lower). b Box plots showing the absolute abundance of SoLs B, C, and F measured by targeted metabolomics in an independent cohort of IBD patient stool samples. SoL B and F were found to be significantly decreased in IBD (blue boxes, n = 40) compared to non-IBD (red boxes, n = 20) samples. All box plots include center lines representing the median, box limits representing upper and lower quartiles, whiskers representing the 1.5x interquartile range, and points representing outliers. Source data are provided in the Source Data file. Significance was determined using two-sided Student’s t-test. The exact p-values for SoL B and SoL F were 0.0241 and 0.0482, respectively. For all p values: *0.01 <p < 0.05, **0.001 <p < 0.01, and ***p < 0.001.To further validate our identification of SoLs in these datasets, we acquired one of the columns used to generate the original data19,40. We then selected several standard compounds used in dataset 2 and the candidate SoL B feature that we identified by exact mass matching, and subsequently analyzed the retention times of the standard compounds alongside our own standard SoL B using our in-house HPLC-MS instrument. Due to the inherent variability of retention time between instruments44,45, we calculated the relative retention time (RRT)46,47 using each of the standard’s retention time relative to that of our SoL B standard and compared these values to the RRTs calculated using the corresponding dataset standards and candidate SoL B. We found that the RRT values using our SoL B standard and the RRT values using the candidate SoL B shared a linear relationship (Supplementary Fig. 8, R2 = 0.9915), indicating that the shift in retention time was linear and thus suggesting that the candidate SoL B feature was SoL B.Finally, to experimentally validate our informatic analysis, we obtained deidentified stool samples collected from an independent cohort of IBD patients (n = 40) and healthy controls (n = 40), and analyzed their SoL abundance by targeted HPLC-MS/MS. We detected SoLs B, C, and F as major SoLs and found that all their abundances were decreased in IBD samples compared to non-IBD samples (Fig. 2b), with SoL B and F being significantly decreased (Wilcoxon rank sum test, p < 0.05, one-sided). This independent validation was consistent with our bioinformatic analysis which also showed that major SoLs including SoL B were significantly decreased in IBD metabolomes and further supported our identification of SoLs in the metabolomics datasets as well as our chemoinformatic analysis showing decreased abundance of SoLs in stool samples of IBD.Thus, our metagenomic analysis reflected that SoL biosynthetic enzymes were more prevalent in the non-IBD group than the IBD group, metatranscriptomics suggested that genes encoding these enzymes are more actively transcribed in the non-IBD group, and chemoinformatics and metabolomics indicated that representative SoLs are in higher abundance in stool samples from the non-IBD group. We further validated the metabolomics data in an independent cohort of IBD patient samples which showed that SoL abundance was indeed significantly decreased in IBD compared to non-IBD samples. Altogether, our findings establish a negative correlation directly between SoL biosynthesis and IBD, consistent with the previously reported negative association between SoL-producers, namely Alistipes and Odoribacter, and IBD22,23.An experimental model of colitis demonstrates the negative correlation between SoL production and IBD progressionEncouraged by our informatically predicted negative correlation between SoL biosynthesis and IBD, we sought to further experimentally validate our prediction using a well-established mouse model of IBD. We used Il10-deficient (Il10–/–) mice that are genetically susceptible to developing intestinal inflammation and chronically treated them with the non-steroidal anti-inflammatory drug piroxicam, which induces the development of colitis through the disruption of the gut mucosal barrier in inflammation-susceptible hosts48,49. We selected this model due to its stability, as Il10–/– mice generally will not develop colitis when born and raised under specific pathogen-free conditions unless induced by external stimuli such as piroxicam treatment. This allowed us to more confidently ensure that the effects observed were dependent on the induction of colitis and not due to the Il10 deficiency. Stimulation of mucosal TLRs stemming from mucosal barrier breakdown was another factor in our selection of this model, as we have previously shown that SoL A suppresses LPS-induced inflammation and LPS is well-known to activate TLR signaling22,50. As has been previously reported48,49, we observed that the colonic tissues were inflamed in the piroxicam-treated (IBD) group of Il10–/– mice when compared to the control (pre-IBD) group as indicated by gross pathology and blinded histopathology analyses (Fig. 3a–c, Supplementary Data 9 and 10).Fig. 3: SoLs are decreased in a mouse model of colitis concurrent with increased expression of inflammatory markers.a Histological analysis of the mouse distal colon reveals that piroxicam treatment induced intestinal inflammation in Il10–/–- mice. b, c Histology and gross pathology scores indicate induction of colitis in Il10–/– mice treated with piroxicam (red bars, n = 7 female mice) compared to pre-IBD control Il10–/– mice (green bars, n = 4 female mice), confirming the successful establishment of the IBD model. The trends were consistent in male mice in another independent cohort using the same IBD model (Supplementary Fig. 10). Significance was determined using two-sided Mann-Whitney U test. Bars represent mean ± standard error. Exact p-values were 0.02037 and 0.01951 for Histology Score and Gross Pathology, respectively. d Total ion chromatograms (TICs) obtained from HPLC-HRMS analysis of fecal pellet extracts from control Il10–/– mice and Il10–/– + piroxicam mice reveal the presence of SoL A and SoL B. SoL abundances appear to be decreased in Il10–/– + piroxicam mice fecal pellets. e MS/MS spectra of SoLs A and B confirm their identities based on the presence of the 80 m/z fragment characteristic of sulfonate-containing compounds as well as other characteristic fragments (Supplementary Fig. 9a, b) and compared to literature fragmentation patterns16,22. f, g Peak areas were calculated using TICs obtained after MS/MS fragmentation and used to measure the abundance of SoLs A and B. Both SoLs A and B were significantly decreased in feces from inflamed mice (red bars, n = 7, female) compared to control mice (green bars, n = 4, female). Significance was determined using two-sided Student’s t-test. Bars represent mean ± standard error. Exact p values were 5.42E-5 and 0.01472 for SoL A and SoL B, respectively. h–k Gene expression of inflammatory markers TNFα, NOS2, IL-6, and IL-1β were significantly increased in the ceca of Il10–/– + piroxicam mice (red bars, n = 7, female) compared to control mice (green bars, n = 4, female). Significance was determined using two-sided Mann-Whitney U test. Bars represent mean ± standard error. Exact p values were 0.0061, 0.0061, 0.0061, and 0.0424 for TNFα, NOS2, IL-6, and IL-1β respectively. For all p values: *0.01 <p < 0.05, **0.001 <p < 0.01, and ****p < 0.0001. Source data are provided in the Source Data file.To explore the link between SoL production and IBD, we collected fecal material from piroxicam-treated Il10–/– (IBD) mice (n = 7, female) and pre-IBD control Il10–/– mice (n = 4, female), extracted metabolites, and measured the abundance of SoLs by targeted metabolomics using high-performance liquid chromatography (HPLC)-high resolution mass spectrometry (HRMS) (Fig. 3d). We detected metabolites with m/z corresponding to major SoLs, specifically SoLs A (SL 34:0;2 O | SL 17:0;O/17:0;O) and B (SL 32:0;O | SL 17:0;O/15:0), in all fecal samples tested and unambiguously determined their identities by HPLC-MS/MS (Fig. 3e, Supplementary Fig. 9a, b). We then determined that the abundances of both SoLs A and B were significantly decreased in feces from piroxicam-treated mice compared to control (Fig. 3f, g). This result confirms our above-described informatic analysis and directly establishes a negative correlation between SoL production and colitis progression in the mouse model. In addition, we also observed significantly increased expression of the NF-κB-regulated inflammatory markers TNFα, NOS2, IL-6, and IL-1β in the IBD mouse group (Mann-Whitney U test, p ≤ 0.005; Fig. 3h–k), further indicating a negative correlation between SoL production and these inflammatory markers. Given our previously observed anti-inflammatory activity of SoL A against LPS22, a natural ligand of TLR4, this negative correlation suggests a potential role of SoLs in regulating IBD that may involve suppressing TLR4-mediated NF-κB activation. To exclude any differences caused by sex, we performed another independent study with male mice using the same model and observed the same negative correlation between SoL production and IBD progression (Supplementary Fig. 10).Constant identification of SoLs’ contribution to immunomodulatory activityWe next examined the production of SoLs and their contribution to immunomodulatory activity in different human gut commensals. Unlike C. gleum F93 DSM 16776, which we experimentally investigated for its functional metabolites in relation to inflammatory activity22, the prolific SoL-producers Alistipes and Odoribacter had not yet been thoroughly chemically investigated to identify the biologically active components associated with remediation of IBD. In addition, Alistipes and Odoribacter produce a mixture of other SoLs16 and are likely to produce a multitude of other functional metabolites, both of which may complicate the potential immunomodulatory activity of these genera’s metabolites with respect to their bioinformatically predicted negative association with IBD. Thus, we conducted bioactive molecular networking of three Alistipes and two Odoribacter strains (Supplementary Data 11) to identify the constant contributor(s) to biological activity. We fractionated crude extracts of the Alistipes and Odoribacter strains and determined the biological activity of each fraction using a cell-based assay that measured the suppression of LPS-indued TNFα production (Fig. 4a). We simultaneously analyzed each fraction by untargeted HPLC-HRMS/MS to generate molecular networks using the Global Natural Products Social (GNPS) feature-based molecular networking (FBMN) pipeline51. We then correlated the relative expression of TNFα in each fraction with the relative peak area of molecular features across all fractions to generate a bioactivity score reflecting the contribution of specific features to the activity of the fractions. Bioactivity scores and relative peak areas were then mapped onto the molecular network to visualize these contributions. The resulting bioactive molecular network generated from Alistipes timonensis DSM 27924 is presented in Fig. 4b. The SoL-containing cluster contained the most abundant and most active molecular features, as indicated by the node size and color intensity compared to other clusters in the network. Additionally, this cluster contained several known SoLs but many more unannotated SoLs, suggesting that the family of biologically active SoLs is larger than what is currently known. In all other SoL-producers tested, we consistently identified SoLs as a major contributor in the active fractions of each strain (Supplementary Fig. 11). To exclude the possibility of observed SoL activity being influenced by LPS contamination, we confirmed the absence of leftover LPS in the SoL samples using a chromogenic LAL assay (Supplementary Fig. 12). Narrowing down the immunosuppressive activity of each of the strains to SoLs guided us to isolate pure SoLs A and B from A. timonensis DSM 27924 (structures confirmed by NMR spectroscopy; Supplementary Tables 1 and 2, Supplementary Figs. 13–24), as well as from each of the other Alistipes and Odoribacter strains tested. We thus reinforced the contribution of this class of lipids to the observed biological activity of Alistipes and Odoribacter.Fig. 4: Bioactive molecular networking leads to the identification of SoLs as major bioactive components of a SoL-producer.a A crude extract of A. timonensis DSM 27924 was separated based on polarity into 5 fractions. Each fraction was used in an in vitro cell-based assay (n = 3 individual wells) to measure its respective capacity to suppress LPS-induced expression of TNFα. Fraction 2 (highlighted as a green bar) was found to have the most significant anti-inflammatory effect compared to LPS. All fractions were compared to LPS for statistical significance with only fractions 1 and 2 showing significant change. Fractions 3, 4, and 5 showed no significant change. Statistical significance was determined using two-sided Student’s t-test. Bars represent mean ± standard error. Exact p values were 0.02943 and 0.0133 for LPS against LPS + Fraction 1 and LPS against LPS + Fraction 2, respectively. For all p values: *0.01 <p < 0.05. Source data are provided in the Source Data file. b Untargeted HPLC-HRMS/MS was used to construct a molecular network for each fraction through GNPS FBMN. The relative peak area of each molecular feature in fraction 2 was mapped to the color of the nodes with more abundant features increasing from white to green. Bioactivity score was mapped to the node size with larger nodes indicating stronger negative correlations. Several known SoLs were annotated in this cluster and their structural variations are illustrated, further demonstrating that SoLs as a family of molecules contribute to the observed suppression of LPS-induced TNFα expression.SoL A mediates dual immunomodulatory activity through TLR signalingWhile the causes of IBD remain largely unknown, IBD progression has been linked to aberrant TLR signaling50. TLRs are pattern recognition receptors (PRRs) that initiate a variety of host processes, especially inflammatory responses, through the recognition of pathogen-associated molecular patterns (PAMPs) and other non-pathogenic microbial factors50,52,53. Specifically, TLR2 and TLR4 are well-known to recognize PAMPs in the gut microbiome50. In addition, their expression is significantly increased in IBD pathogenesis, reflecting a state of aberrant activation50,52. Thus, we expected that SoLs may interact with TLR4 or TLR2 to mediate their immunomodulatory activity. We treated primary mouse macrophages collected from wild-type C57BL/6 mice with SoL A (as a representative of SoLs) either alone or together with LPS (an agonist of TLR4) or Pam3CSK4 (an agonist of TLR2/1) and measured the expression of three inflammatory cytokines (IL-6, TNFα, and IL-1β). By itself, SoL A exhibited a mild to moderate effect on the expression of pro-inflammatory cytokines compared to control (Fig. 5), generally consistent with our previous finding22. As expected, the TLR ligands, LPS and Pam3CSK4, both showed significant induction of all three cytokines compared to control (Student’s t-test, p ≤ 0.0001) (Fig. 5). Notably, SoL A was found to significantly suppress the expression of all three cytokines induced by LPS (p ≤ 0.0001) (Fig. 5). Together, SoL A’s mild pro-inflammatory activity by itself and strong inhibition against LPS-induced inflammation constitute its dual immunomodulatory activity. SoL A also inhibited Pam3CSK4-induced IL-6 and TNFα to a smaller extent while increasing IL-1β expression induced by Pam3CSK4 (p ≤ 0.05) (Fig. 5). This result indicates that SoL A primarily affects LPS-induced inflammation and implies that interaction with TLR4 may be involved in SoL A’s mechanism of action. Interestingly, SoL A’s partial suppression of Pam3CSK4-induced inflammation suggests that SoL A-related anti-inflammatory activity may also extend to the TLR2/1 pathway and warrants further investigation. After identifying that SoL A’s primary effect is through TLR4, we further examined the biological activity of SoL B against LPS-induced inflammation. We found that SoL B also inhibited LPS-induced inflammation albeit to a lesser extent than SoL A (Supplementary Fig. 25). This activity is consistent with previous reports that SoL B exhibits anti-inflammatory activity both in vitro and in vivo in mouse models of acute inflammation54.Fig. 5: SoL A primarily suppresses LPS-induced TLR4 activation.Mouse peritoneal macrophages were treated with SoL A (10 μM), LPS (100 ng/mL), and Pam3CSK4 (500 ng/mL), either alone or in combination for 6 h. RT-qPCR analysis revealed that SoL A induces a mild pro-inflammatory effect compared to control but significantly suppresses LPS-induced cytokine expression levels and only partially suppresses Pam3CSK4-induced cytokine expression. Bars represent mean ± standard error. Experiments were independently repeated three times. For each treatment, n = 3 individual wells. Significance was determined using two-way ANOVA. For all p values: *p < 0.05, ****p < 0.0001. Source data are provided in the Source Data file.Molecular docking and ELISA displacement assay suggest SoLs binding to TLR4/MD-2 complexLPS stimulation of TLR4 occurs through a series of interactions ultimately resulting in LPS binding to MD-2, which forms a complex with TLR4 and induces dimerization to initiate signaling55,56,57. The TLR4/MD-2 heterodimer recognizes structurally diverse LPS molecules, giving it the flexibility to detect different LPS-related PAMPs in the human gut microbiome57. Interestingly, the TLR4/MD-2 complex was recently found to recognize human sulfatides, sphingolipid derivatives which bear a sulfated saccharide head group and dual acyl chains, presumably mimicking the disaccharide core and multiple acyl chains of LPS58. Comparing the chemical structure of SoL A to those of sulfatides and lipid A (the immunogenic portion of LPS) (Fig. 6a), we noted structural similarity in the negatively charged head groups and multiple acyl chains. We thus considered if multiple molecules of SoL A might bind to MD-2 in a similar configuration as sulfatides and lipid A. Inspired by sulfatides that bind in triplicate to MD-258, we used molecular docking to model the binding of three molecules of SoL A to MD-2. Our analysis predicted three molecules of SoL A indeed bind in the hydrophobic pocket of MD-2 (Fig. 6b), where lipid A is known to bind, with a docking score of -8.9 kcal/mol, better than that of lipid A which had a docking score of -6.2 kcal/mol. Additionally, SoL A was predicted to make hydrophobic contacts with several amino acids including I117, F119, I52, and F121 (Supplementary Data 12), all of which are also reported to contact the acyl chains of lipid A57. Notably, SoL A is also predicted to contact residues including R264 and R90 (Supplementary Data 12), consistent with contacts between these residues and the phosphate groups of lipid A57. This suggests that SoL A may bind directly to the TLR4/MD-2 complex and possibly compete for binding with LPS, allowing it to suppress LPS-induced activation of the TLR4 pathway. A critical aspect of lipid A binding to MD-2 is the exclusion of one acyl chain from the hydrophobic pocket of MD-2 which forms a bridge with TLR4 and is involved in inducing dimerization57. Likewise, we observed one acyl chain of SoL A excluded from the hydrophobic pocket in our docking analysis (Fig. 6b), further suggesting that SoL A mimics LPS as a ligand for TLR4. After successfully docking SoL A we then tested SoL B which lacks an extra hydroxy group, (Fig. 6a) potentially increasing its interactions with the hydrophobic binding pocket of MD-2. Our docking analysis indeed showed that SoL B also binds to MD-2 (Fig. 6b), with similar contacts as SoL A (Supplementary Data 12) but higher affinity (docking score of −9.6 kcal/mol) as predicted.Fig. 6: SoLs bear structural similarity to both lipid A and sulfatide and bind with MD-2 to block LPS binding.a Chemical structures of immunogenic lipid A (derived from LPS), sulfatide, SoL A, and SoL B illustrating structural similarity in multiple acyl chains and negatively charged head groups. b Molecular docking of lipid A (red), sulfatide (magenta), SoL A (blue), and SoL B (orange) into the hydrophobic pocket of MD-2 in complex with TLR4. Three molecules of SoLs A and B were used in molecular docking experiments to mimic the six acyl chains of lipid A as inspired by sulfatides58. c ELISA displacement assay used to measure the binding behavior of SoLs A and B in competition with 1 ng/mL LPS, a natural ligand of the TLR4/MD-2 complex. Compounds were added either simultaneously (blue), LPS first (green), or SoL first (red). Bars indicate mean ± standard deviation. Experiments were independently repeated three times. For each treatment, n = 3 individual wells. Source data are provided in the Source Data file.To experimentally determine if SoLs A and B bind to MD-2 and to what extent the SoLs compete with LPS for binding to MD-2, we conducted an ELISA-based displacement assay. Taking advantage of biotinylated LPS, which retains the activity of unconjugated LPS59, we measured absorbance generated by an HRP-linked streptavidin probe to measure the relative amount of MD-2 which was bound with biotin-LPS as opposed to MD-2 bound with SoL A or B. We administered 0.1, 1.0, and 10 μM concentrations of SoL A or B and 1 ng/mL biotin-LPS to MD-2 in three sequences: 1) SoL first followed by LPS 1 h later, 2) LPS first followed by SoL 1 h later, and 3) both SoL A or B and LPS at the same time. After 1 h of incubation, we found that at all concentrations, when SoL A or B was added first, there was a marked decrease in percent absorbance as compared to when LPS was added first and when the two compounds were added together (Fig. 6c). This suggests that SoLs A and B both bind and occupy some sites of MD-2, preventing LPS from fully binding when it is added 1 h after SoL A or B. Furthermore, when moving from low to high concentrations of SoL A or B, we observed that the percent absorbance decreased dramatically. This indicates that with increasing concentration of SoL A or B, less LPS binds to MD-2, implying that SoLs indeed compete with LPS for binding to MD-2. Taken together, these results indicate that SoLs A and B can bind directly to MD-2 and more importantly compete with LPS for binding to this target, thus providing a potential molecular mechanism underlying SoL A’s pro-inflammatory activity by itself as well as its strong activity in suppressing LPS-induced inflammation which likely also expands to other members of the SoL family.SoLs suppress LPS-induced TLR4 signaling to regulate macrophage polarizationUpon LPS binding, TLR4 initiates downstream signaling, such as through the NF-kB and MAPK pathways, resulting in the induction of inflammatory cytokine expression52. If a SoL binds to MD-2, it will block LPS-induced activation of the TLR4 pathway. Therefore, we investigated whether the addition of SoL A or B affected the phosphorylation of TLR4-downsteam signaling molecules, ERK1/2, and p38, or the degradation of IκBα, which are all critical for LPS-induced cytokine expression52 (Fig. 7a). We treated macrophages with LPS in the presence of increasing concentrations of SoL A or B (from 0 to 20 μM), then performed western blot analysis to examine the TLR4-downstream signaling pathways. We found that both SoLs reduced LPS-induced phosphorylation of p38 and ERK1/2 in a concentration-dependent manner. At the concentration of 20 μM, SoLs A and B almost completely blocked LPS-induced phosphorylation of p38 and ERK1/2 (Fig. 7b). Western blot also showed that SoLs concentration-dependently suppressed LPS-induced IκBα degradation (Fig. 7b). These results support that SoLs exert their anti-inflammatory effect by blocking LPS-mediated phosphorylation of downstream TLR4 proteins, effectively negating LPS activation of the TLR4 pathway. Also, SoL A or B alone at the concentration of 20 μM slightly enhanced the phosphorylation of certain signaling molecules (e.g., ERK1/2) compared to control, consistent with our observations that SoLs alone induced a mild pro-inflammatory effect on cytokine expression (Fig. 5) and supporting the dual immunomodulatory activity of SoLs which provides further opportunities to regulate homeostatic immune responses.Fig. 7: SoLs suppress LPS-induced activation of TLR4 signaling pathway and macrophage M1 polarization.a Simplified pathway of TLR4 activation by LPS, highlighting proposed inhibition by SoLs competing for LPS binding. Proteins IκBα, ERK1/2, and p38 downstream of TLR4 which were selected for analysis are highlighted in red rectangles. Created in BioRender. Chen, H. (2024) BioRender.com/v71h443. b Western blot analysis of protein levels of IκBα as well as total and phosphorylated ERK1/2 and p38, after treatment with LPS (100 ng/mL) with or without various concentrations of SoL A or B. The housekeeping gene β-Actin was used as a loading control. The gel for IκBα was spliced to remove extra lanes (See Source Data for raw gel images). c, d THP-1-derived macrophages were treated with LPS + IFN-γ or IL-4 + IL-13 to polarize to M1 or M2 macrophages, respectively. Relative expression of markers IL-6, CXCL10, IL-12β, and TNFα (compared to M0) indicate that SoL A (10 μM) had a significant effect on suppressing M1 polarization (c); and relative expression of markers IL-10, CD206, and CD209 (compared to M0) indicate that SoL A had no significant effect on M2 polarization (d). Bars represent mean ± standard error. Experiments were independently repeated three times. For each treatment, n = 3 individual wells. Significance was determined using two-way ANOVA. For all p values: **0.001 <p < 0.01, ***0.0001 <p < 0.001, and ****p < 0.0001. Source data are provided in the Source Data file.Because TLR4 signaling leads to macrophage polarization which has been shown to contribute to IBD60,61,62, we also examined the effects of SoL A on macrophage polarization. We treated THP-1 monocytes with IFN-γ and LPS to induce M1 polarization or IL-4 and IL-13 to induce M2 polarization. Successful induction of M1 and M2 polarization was confirmed by morphology changes and subsequent RT-qPCR quantification of cytokine profiles. When 10 μM of SoL A was added alongside the respective inducing agents, our relative cytokine expression results showed that SoL A significantly reduced the production of M1-polarized macrophage markers IL-6, CXCL10, IL-12β, and TNFα, compared to macrophages treated without SoL A (Fig. 7c) but had a mostly non-significant effect on M2 polarization (Fig. 7d). This suggested that SoL A suppresses macrophage M1 polarization, which supports our aforementioned result that SoLs interfere with TLR4 signaling potentially leading to inhibition of TLR4-mediated IBD.
