A decrease in Flavonifractor plautii and its product, phytosphingosine, predisposes individuals with phlegm-dampness constitution to metabolic disorders

AdMaPlace March 19, 2025

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A decrease in Flavonifractor plautii and its product, phytosphingosine, predisposes individuals with phlegm-dampness constitution to metabolic disorders

Introduction

Metabolic disorders represent a group of disorders that include various interrelated pathological conditions, such as overweight/obesity, hyperlipidemia, diabetes, hypertension, and hyperuricemia¹. Over the last two decades, due to diet and lifestyle changes, the global incidence of these diseases has reached a high level^2,3,4,5, which significantly increases the morbidity and mortality rates of cardiovascular diseases. Early diagnosis and prevention of metabolic disorders are important for preventing and blocking disease processes and reducing the public health burden^6,7,8. At present, according to the consensus of experts, overweight, prediabetes, and borderline hyperlipidemia are still important primary or secondary prevention indicators⁹. However, abnormal metabolic indices indicate long-lasting changes in the physiological characteristics of the whole body¹⁰. There is an urgent need for a method that can identify precursor changes before metabolic diseases occur .

Traditional Chinese medicine (TCM) is a type of medicine that originated in China. Holism is its fundamental principle, and the preventive treatment of disease represents one of its advantages. TCM obtains systemic characteristics through four diagnostic methods (sight, hearing, questioning, and pulse) to predict changes in the pathogenesis of the body rather than relying on a single clinical indicator; thus, precursor changes are potentially identified before the clinical indicators change¹¹. With the development of TCM, diagnosis is no longer dependent on the characteristics collected using the four diagnostic methods; rather, it has gradually changed to quantification, objectification and standardization. The term “constitution” refers to the unique TCM “type“¹². On the basis of the literature and an epidemiological study of the Han Chinese population, we have defined nine constitutional groups in the population, namely, a balanced constitution (BC) group and eight unbalanced constitution groups. We have also formulated classification criteria, such as the 1–5 graded scoring methods, for the nine constitutional groups. This scoring system has been issued as the industry standard of TCM (version: ZYYXH/T157-2009)¹³. BC individuals account for less than 30% of the population¹⁴. The clinical phenotypic features of individuals with BC include high energy, loud voices, stable moods, adaptability to external changes, good sleep quality, good memory, and no other unbalanced constitutional features. These individuals are at a low risk for various diseases. People with unbalanced constitutions always suffer from or face the risk of various diseases¹⁵. The phlegm-dampness constitution (PDC) is an unbalanced constitution. Individuals with PDCs account for nearly 10% of the population¹⁴. The clinical phenotypic features of PDC are as follows: heavy body, stuffy chest, full abdomen, loose and soft abdominal skin, greasy forehead, upper eyelid edema, phlegm obstruction in the throat, sticky mouth, and thick tongue coating¹⁶. The PDC scoring system from the constitutional industry standard can be used to diagnose PDC. Meta-analyses of epidemiological and clinical investigations confirmed that PDC subjects are at high risk of metabolic disorders such as obesity, hyperlipidemia, hypertension and diabetes^17,18,19,20. Previous studies have shown that PDC individuals, even those with normal metabolic indices, exhibit gene expression changes related to metabolic disorders²¹. However, the biological mechanism of these precursor changes is still unclear.

From the perspective of TCM, people’s various tissues and organs are not independent but rather interconnected and together influence the whole-body physiological state and pathological changes. Recently, many studies have shown that the gut microbiota can connect various tissues and organs, which is an important biological basis for affecting systemic physiological and pathological states²². Thus, the gut microbiota has emerged as a new avenue for elucidating TCM principles²³. The gut microbiota participates in host metabolism via its derived metabolites. Previous studies have shown that microbial metabolites play a critical role in the development of metabolic disorders^{24,25,26,27,28}. Our team and others have reported that individuals with PDC have a distinctive gut microbial structure^29,30,31. However, it remains to be revealed whether gut bacteria and related metabolites contribute to gene expression changes related to metabolic disorders in PDC individuals.

Here, we applied 16S rRNA gene sequencing and metabolic analyses to investigate how the gut microbiota and related metabolite characteristics contribute to the susceptibility of PDC to metabolic disorders. Through comparisons with BC subjects, correlation analysis and a series of follow-up experiments, such as metagenome sequencing and qPCR verification of bacteria, in vitro bacterial anaerobic culture experiments, in vivo bacteria-colonized germ-free mouse experiments, and metabolite-targeted monitoring of serum and culture media, we highlight Flavonifractor plautii and its product, phytosphingosine, which are negatively correlated with PDC scores. Both F. plautii and phytosphingosine decreased in PDC individuals with normal metabolic indices. Fecal transplantation from these individuals accelerated the development of metabolic disorders in mice. Supplementation with F. plautii and phytosphingosine improved metabolic disorders induced by fecal transplantation from PDC subjects with normal metabolic indices and a high-fat diet (HFD) by increasing phytosphingosine levels in the gut‒hepatic axis. Mechanistic investigations confirmed that phytosphingosine can directly bind to hepatic PPARα and activate its nuclear transcription activity, thereby regulating downstream gene expression. In particular, these findings provide an explanation for the gene expression changes related to metabolic disorders in PDC individuals and may be beneficial for the early detection and intervention of metabolic disorders.

Results

Clinical characteristics of the study population

A total of 209 subjects (167 PDC subjects and 42 BC subjects) were selected according to the TCM constitution determination industry criteria (Version: ZYYXH/T157-2009) from a pool of 528 volunteers in 5 communities in Beijing (China) over a period of 5 months (Jan–Jun 2019) (Fig. 1a). To understand the clinical characteristics of PDC subjects compared with those of BC subjects we collected comprehensive clinical metadata, including detailed demographic data, as well as clinical data such as weight, lipid, blood glucose, uric acid and blood pressure data. Compared with the BC group, the PDC group presented elevated body weight, body mass index (BMI), waist‒to‒hip ratio (WHR), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), fasting insulin, uric acid, systolic blood pressure (SBP), and diastolic blood pressure (DBP) (Supplementary Table S1). According to the clinical diagnostic criteria for metabolic disorders, only ~ 14.29% of the BC subjects exhibited metabolic disorders (6 subjects, Fig. 1b, c). Approximately 92.22% of the PDC subjects had one or more metabolic disorders (154 subjects), with overweight/obesity accounting for the highest proportion (148 subjects), followed by dyslipidemia (94 subjects) and hyperuricemia (44 subjects) (Fig. 1b, d). Furthermore, Spearman’s correlation analysis revealed that the PDC scores were significantly positively correlated with BMI (r = 0.492, p < 0.001), WHR (r = 0.396, p < 0.001), serum TG (r = 0.385, p < 0.001), serum TC (r = 0.202, p = 0.003), serum LDLC (r = 0.264, p < 0.001), serum high-density lipoprotein cholesterol (HDLC) (r = –0.290, p < 0.001), serum apolipoprotein A1 (ApoA1) (r = –0.177, p = 0.01), serum apolipoprotein B (ApoB) (r = 0.223, p = 0.001), fasting blood glucose (r = 0.097, p = 0.162), 2 h postprandial blood glucose (2 h-PG) (r = 0.187, p = 0.007), fasting insulin (r = 0.399, p < 0.001), serum uric acid (r = 0.301, p < 0.001), SBP (r = 0.244, p < 0.001), and DBP (r = 0.279, p < 0.001) (Fig. 1e–l and Supplementary Fig. S1a–f). Collectively, these results suggest that the PDC score can represent the degree of metabolic disorders.

Shifts in the gut microbiota in the PDC group and BC group

To characterize the microbiome profile of the PDC group, we collected fecal samples from all the subjects in the clinical cohort for gut microbiota analysis (Fig. 1m). We investigated the changes in the gut microbiome between the PDC group and BC group by sequencing the fecal 16S rRNA V3‒V4 region. No significant differences in the richness or diversity of the gut microbiota were found between the PDC group and the BC group (Supplementary Fig. S2a, b). Principal coordinate analysis (PCoA) calculated from the Bray‒Curtis distance was performed to display the variation in community composition, which significantly differed between the PDC group and the BC group (p = 0.009; Fig. 1n). Notably, we found that the PDC scores can explain the differences along the first principal coordinate (p = 0.001; Fig. 1o). The gut microbial community consisted of 187 genera belonging to 10 phyla. At both the phylum and genus levels, the microbial composition of the PDC group differed from that of the BC group (Fig. 1p, q and Supplementary Fig. S3). To further identify which bacterial taxa were distinct between the PDC group and BC group, we compared the mean relative abundances of more than 0.1% of the genera. Nineteen genera showed significant differences, and Flavonifractor, Bifidobacterium, Faecalicatena, Eggerthella, and Bacteroides (the top 5 genera differed the most) were depleted in the PDC group compared with those in the BC group (Fig. 1q).

Shifts in the serum metabolic profiles of the PDC group and BC group

To characterize the metabolic profile of the PDC group, untargeted metabolome profiling was performed on fasting serum samples from all the subjects via liquid chromatography–tandem mass spectrometry (LC–MS/MS) (Fig. 2a). A total of 1822 metabolites were captured via untargeted metabolomics, and significant differences were detected between the PDC group and the BC group via orthogonal partial least squares discriminant analysis (OPLS‒DA) (Fig. 2b). The volcano plots revealed that a total of 123 serum metabolites were changed in the PDC group compared with those in the BC group, of which 70 metabolites were significantly upregulated and 53 metabolites were significantly downregulated (screening criteria were p < 0.05 and variable importance in projection (VIP) > 1.0) (Supplementary Fig. S4). We focused on the 10 differentially abundant metabolites with the largest VIP values, either upregulated or downregulated, in the PDC group compared with those in the BC group. The results revealed that the differentially abundant metabolites included mainly phytosphingosine, PCs, LysoPCs, and C16 sphinganine (Fig. 2c).

**Fig. 2: PDC subjects had different serum metabolite structures than BC subjects did, and phytosphingosine was significantly correlated with PDC scores and metabolic indices.**

A decrease in phytosphingosine was associated with higher PDC scores

Next, we analyzed the correlation between the differentially abundant metabolites and the PDC scores. We focused on the metabolite phytosphingosine, which was most negatively correlated with the PDC scores (Fig. 2d). Moreover, the level of phytosphingosine was also significantly correlated with metabolic indices (Fig. 2e–m and Supplementary Fig. S5) related to obesity (BMI (r = −0.242, p < 0.001), WHR (r = −0.222, p = 0.001)), lipid metabolism (serum TG (r = −0.183, p = 0.008), TC (r = −0.205, p = 0.003), LDLC (r = −0.232, p = 0.001), and uric acid (r = −0.221, p = 0.001)). We next developed a machine learning classifier based on a random forest algorithm to distinguish individuals with either PDC from those with BC via the value of phytosphingosine. Receiver operating characteristic curve analysis revealed that the model had good prediction power, with a mean cross-validation area under the curve (AUC) of 0.85 for differentiating PDC subjects and BC subjects (Fig. 2n). These data indicated that a decrease in phytosphingosine was associated with higher PDC scores, which might increase susceptibility to metabolic disorders in PDC individuals.

F. plautii biosynthesized phytosphingosine, which was negatively correlated with the PDC score

The main source of phytosphingosine in the host is the gut microbiota. Thus, a correlation analysis between phytosphingosine and various genera was performed to explore the microbial source of phytosphingosine. We reported that Flavonifractor had the strongest positive correlation with phytosphingosine, suggesting that these bacteria may contribute to phytosphingosine alterations (Fig. 3a). Interestingly, Flavonifractor also had the strongest positive correlation with the PDC score (Fig. 3b). These results indicated that Flavonifractor was closely related to PDC and might substantially contribute to serum phytosphingosine levels. To further identify which gut bacteria contribute to phytosphingosine at the species level, we selected fecal samples from 40 PDC subjects and 10 age- and sex-matched BC subjects for metagenomic sequencing, and the results of the metagenomic sequencing analysis were verified by qPCR in all the fecal samples (Fig. 3c and Supplementary Table S2). The results revealed no significant differences in species richness or diversity between the PDC group and BC group (Fig. 3d), but significant differences in microbial community composition were detected via PCoA (p = 0.010, Fig. 3e), which was consistent with the results of 16S rRNA gene sequencing. Our investigation focused on Flavonifractor species. Our findings revealed that F. plautii was the major species of Flavonifractor. Moreover, there was a significant decrease in the relative abundance of F. plautii in the PDC group compared with those in the BC group (p < 0.001, Fig. 3f). Furthermore, the results of the qPCR quantification confirmed that the level of F. plautii in the PDC group was lower than that in the BC group (p < 0.001, Fig. 3g and Supplementary Fig. S6). More importantly, F. plautii had a positive correlation with phytosphingosine (r = 0.222, p = 0.001; Fig. 3h) and a negative correlation with PDC scores (r = −0.395, p < 0.001; Fig. 3i). Moreover, ROC curve analysis revealed that F. plautii was a good predictor for distinguishing PDC subjects from BC subjects (AUC = 0.85, Fig. 3j).

**Fig. 3: *F. plautii*, which was significantly correlated with the phytosphingosine and PDC scores, can biosynthesize phytosphingosine in vitro and in germ-free mice.**

To investigate whether F. plautii can biosynthesize phytosphingosine, we performed in vitro experiments in F. plautii anaerobic culture and in vivo experiments in F. plautii-colonized germ-free mice. In vitro, we conducted a growth curve analysis of cultured F. plautii in modified brain heart infusion (BHI) broth (Fig. 3k). Our findings indicated that the organism was capable of producing phytosphingosine within a relatively short period (Fig. 3l, m). In vivo, 8-week-old germ-free (GF) mice were gavaged with phosphate-buffered saline (PBS) containing F. plautii or lacking F. plautii and then maintained on a normal diet for 10 days (Fig. 3n). Compared with those of the mice that were not colonized with F. plautii, the phytosphingosine levels in the feces, serum, and liver of the mice colonized with F. plautii were greater (Fig. 3o–q). These data suggested that F. plautii could biosynthesize phytosphingosine. F. plautii was negatively correlated with the PDC score. Its reduction might increase susceptibility to metabolic disorders in PDC individuals.

Fig. 4: PDC subjects presented lower *F. plautii* and phytosphingosine levels, even those with normal metabolic indices, and fecal transplantation of these subjects accelerated the development of metabolic disorders in mice.

The F. plautii and phytosphingosine levels decreased in PDC subjects, including those with normal metabolic indices

Based on the diagnostic criteria of metabolic disorders (one or more of the following: overweight/obesity, dyslipidemia, prediabetes, hypertension, or hyperuricemia), approximately 85.71% of the BC subjects presented normal metabolic indices (BC subjects with normal metabolic indices, BCN, n = 36), and 14.29% presented abnormal metabolic indices or metabolic disorders (BC subjects with metabolic disorders, BCD, n = 6). In addition, ~ 7.78% of PDC subjects exhibited normal metabolic indices (PDC subjects with normal metabolic indices, PDN, n = 13), and 92.22% exhibited abnormal metabolic indices or metabolic disorders (PDC subjects with metabolic disorders, PDD, n = 154) (Fig. 4a). These findings support the idea that individuals with BC are at a low risk for various diseases, whereas individuals with PDC are at high risk of metabolic disorders. PCoA calculated from the Bray‒Curtis distance was performed to display the variation in community composition. The results revealed a significant difference between the PDN group and the BCN group (p = 0.04, Fig. 4b) but no difference between the PDN group and the PDD group (Supplementary Fig. S7a). The metabolite structure also supported this conclusion (Fig. 4c and Supplementary Fig. S7b). More importantly, the levels of F. plautii and phytosphingosine were significantly lower in the PDN group than in the BCN group (Fig. 4d, e). BCN and PDN (AUC = 0.87 or 0.89) can be effectively distinguished via the F. plautii or phytosphingosine classifier model (Fig. 4f, g). These data showed that PDC subjects with normal metabolic indices also tend to have metabolic disorders. These individuals have relatively low levels of F. plautii and serum phytosphingosine, which might be the biological basis for their susceptibility to metabolic disorders.

Fecal transplantation from PDC subjects with normal metabolic indices accelerated the development of metabolic disorders in mice

Furthermore, we administered fecal transplantation from the PDN group (PDN-F) and fecal transplantation from the BCN group (BCN-F) to antibiotic-treated mice (Fig. 4h and Supplementary Table S3). At 5 weeks posttransplantation, the visible lethargy scores and greasy fur scores of the PDN-F group were greater than those of the BCN-F group (p < 0.05, Fig. 4i, j), indicating that fecal transplantation from the PDC group worsened the PDC phenotypes. In terms of obesity phenotypes, compared with the BCN-F group, the PDN-F group presented significantly greater weight gain (p < 0.01, Fig. 4k) with no difference in food intake (p > 0.05, Supplementary Fig. S8a). Moreover, the amounts of inguinal fat, epididymal fat, and perirenal fat markedly increased in the PDN-F group (p < 0.01, Fig. 4l), and hematoxylin‒eosin (H&E) staining revealed considerably increased adipocyte size in the epididymal fat of the PDN-F group (p < 0.001, Fig. 4m). In terms of glucose metabolism phenotypes, compared with those in the BCN-F group, fasting serum glucose (p < 0.01, Fig. 4n) levels, fasting serum insulin (p < 0.001, Fig. 4o) levels, and homeostasis model assessment — insulin resistance (HOMA-IR, p < 0.001, Supplementary Fig. S8b) levels increased. In terms of uric acid metabolism phenotypes, serum uric acid levels were markedly increased in the PDN-F group (p < 0.05, Supplementary Fig. S8c). With respect to lipid metabolism phenotypes, dramatically higher serum TC, LDLC, and HDLC levels were observed in the PDN-F group (p < 0.01, Fig. 4p). The liver TC (p < 0.001) and TG (p < 0.001) contents increased markedly, and the liver alanine transaminase (p < 0.05, ALT) and aspartate aminotransferase (p < 0.001, AST) contents decreased markedly in the PDN-F group (Fig. 4q, r and Supplementary Fig. S8d, e). Moreover, H&E staining of liver tissue revealed a considerable increase in lipid accumulation in the liver tissue of the PDN-F group (Fig. 4s). These results show that multiple metabolic disorders are aggravated by fecal transplantation from PDC subjects with normal metabolic indices. Notably, F. plautii demonstrated a similar trend between recipient mice and fecal donor individuals, with a significant decrease in F. plautii abundance in the PDN-F group (p < 0.01, Fig. 4t). The levels of phytosphingosine in the serum (p < 0.01), and liver (p < 0.05) of the mice also decreased in the PDN-F group (Fig. 4u, v). These results suggest that the levels of F. plautii and phytosphingosine are correlated with the degree of metabolic disorders. In addition, the reduction in F. plautii and phytosphingosine serves as a precursor of metabolic disorders, representing one of the biological bases for PDN individuals to be prone to metabolic disorders.

F. plautii alleviates the metabolic disorders induced by a HFD and fecal transplantation of PDC subjects with normal metabolic indices

We used PDN-F mice treated with PBS (PBS1 group) as controls and PDN-F mice treated with F. plautii (F. plautii group) to investigate the effects of F. plautii on metabolic disorders (Fig. 5a). First, the key gut microbiota and metabolite phenotypes of the recipient mice were analyzed via qPCR and LC‒MS/MS. Daily supplementation with F. plautii for 5 weeks in PDN-F mice led to a significant increase in the abundance of F. plautii in the fecal samples (p < 0.05, Fig. 5b and Supplementary Fig. S9a). The levels of phytosphingosine in the serum (p < 0.05, Fig. 5c), liver (p < 0.05, Fig. 5d), and epididymal fat (p < 0.05, Supplementary Fig. S9b) increased after F. plautii treatment. Notably, the phytosphingosine levels in the liver were much greater than those in other tissues, indicating that phytosphingosine might be enriched in the liver. The effects of F. plautii on metabolic disorders were subsequently observed. For the PDC phenotypes, the visible lethargy scores (p < 0.05) and greasy fur scores (p < 0.01) decreased markedly after F. plautii supplementation (Fig. 5e, f). In terms of obesity phenotypes, after 5 weeks of F. plautii supplementation, body weight was significantly reduced without changes in food intake (p < 0.01, Fig. 5g and Supplementary Fig. S10a), and the amounts of inguinal fat and epididymal fat markedly decreased (p < 0.05, Supplementary Fig. S10b). Moreover, H&E staining of adipose tissue revealed a considerable decrease in adipocyte size in epididymal fat (p < 0.05, Fig. 5h). In terms of glucose metabolism phenotypes, compared with those in the PBS1 group, fasting serum glucose (p < 0.01, Fig. 5i) levels, fasting serum insulin (p < 0.001, Supplementary Fig. S10c) levels, and HOMA-IR (p < 0.001, Supplementary Fig. S10d) in the F. plautii supplementation group were markedly lower. For the uric acid metabolism phenotypes, the level of serum uric acid decreased markedly after F. plautii supplementation (p < 0.05, Supplementary Fig. S10e). With respect to lipid metabolism phenotypes, a notable reduction in serum TC (p < 0.001) and LDLC (p < 0.001) levels was observed (Fig. 5j). H&E staining of liver tissue revealed a considerable decrease in liver lipid accumulation following F. plautii supplementation (p < 0.05, Fig. 5k). Moreover, liver TC and TG markedly decreased in the F. plautii group (p < 0.01, Supplementary Fig. S10f, g). Additionally, there was no statistically significant change in the liver ALT or AST levels before or after the intervention (Supplementary Fig. S10h, i). Our results suggest that F. plautii supplementation can improve metabolic disorders induced by HFD and fecal transplantation in PDC subjects with normal metabolic indices by increasing the content of phytosphingosine in the gut‒hepatic axis.

**Fig. 5: *F. plautii* and phytosphingosine ameliorate metabolic disorders induced by HFD and fecal transplantation in PDC subjects with normal metabolic indices.**

Phytosphingosine alleviates the metabolic disorders induced by a HFD and fecal transplantation in PDC subjects with normal metabolic indices

To further investigate the effects of phytosphingosine on metabolic disorders induced by a HFD and fecal transplantation in PDC subjects with normal metabolic indices, PDN-F mice were given phytosphingosine by intragastric gavage daily for five weeks (Fig. 5l). We used PDN-F mice treated with PBS (PBS2 group) as controls and those treated with low doses (25 mg/kg) or high doses (50 mg/kg) of phytosphingosine as the PhyL group and PhyH group, respectively (Fig. 5l). In terms of the PDC phenotypes, the visible lethargy scores (p < 0.05) and greasy fur scores (p < 0.05) of the PhyH group were markedly lower than those of the PBS1 group (Fig. 5m, n). With respect to obesity phenotypes, we detected a significant reduction in body weight gain in the PhyH (p < 0.05) and PhyL (p < 0.05) groups (Fig. 5o) without changes in food intake (Supplementary Fig. S11a). Moreover, the amounts of inguinal fat (p < 0.01) and epididymal fat (p < 0.01) were markedly decreased in the PhyH groups (Supplementary Fig. S11b), and H&E staining demonstrated a considerable decrease in adipocyte size in the epididymal fat of the PhyH (p < 0.01) and PhyL (p < 0.05) groups (Fig. 5p). Compared with those in the PBS2 group, the fasting serum glucose (p < 0.001 for the PhyH group and p < 0.05 for the PhyL group; Fig. 5q), fasting serum insulin (p < 0.001, Fig. 5r), and HOMA-IR (p < 0.001, Supplementary Fig. S11c) levels in the PhyH and PhyL groups were markedly lower. In addition, phytosphingosine improved oral glucose clearance and insulin sensitivity, as revealed by the GTT and ITT results in type 2 diabetic rats (Supplementary Fig. S12a–f). In terms of uric acid metabolism phenotypes, the level of serum uric acid decreased markedly in the PhyH and PhyL groups (p < 0.001, Supplementary Fig. S13a). For the lipid metabolism phenotypes, lower serum TC and LDLC and higher serum HDLC were noted in the PhyH (p < 0.001) and PhyL (p < 0.01) groups (Fig. 5s). Moreover, H&E staining of liver tissue revealed considerably decreased lipid accumulation in the liver tissue of the PhyH and PhyL groups (p < 0.05, Fig. 5t), while liver TC (p < 0.05 for the PhyH group) and TG (p < 0.01 for the PhyH group and p < 0.01 for the PhyL group) markedly decreased in the PhyH and PhyL groups (Supplementary Fig. S13b, c), and there was no statistically significant change in liver ALT and AST levels after the intervention (Supplementary Fig. S13d, e). These results indicate that phytosphingosine improves metabolic disorders induced by HFD and fecal transplantation in PDC subjects with normal metabolic indices.

Phytosphingosine activates PPARα to improve metabolic disorders

To elucidate the mechanism by which phytosphingosine improves metabolic disorders, RNA-seq and analysis were performed on liver samples from five groups (the PBS1 vs. F. plautii groups; the PBS2 vs. PhyH, or PhyL groups) (Fig. 6a). A total of 55,487 genes were identified. The 190 differentially expressed common genes (DEGs) obtained after F. plautii intervention and phytosphingosine intervention (Fig. 6a) were enriched in the PPAR pathway (Fig. 6b). Furthermore, GO enrichment of the DEGs related to the PPAR signaling pathway was performed. The main biological processes were fatty acid metabolic processes and cholesterol metabolic processes (Fig. 6c). Since PPARα is the key regulator of fatty acid metabolism and cholesterol metabolism³², we explored the expression of PPARα and its downstream genes after F. plautii or phytosphingosine intervention. The results revealed that neither F. plautii nor phytosphingosine intervention altered PPARα mRNA or protein levels in the mouse liver (Supplementary Fig. S14a, Fig. 6d; Supplementary Fig. S14b, c, Fig. 6e; Supplementary Fig. S15). However, F. plautii and phytosphingosine increased the mRNA levels of 11 genes downstream of PPARα; of these genes, acyl-CoA synthetase long chain family member 4 (Acsl4), acyl-CoA synthetase long chain family member 5 (Ascl5), acyl-CoA dehydrogenase medium chain (ACADM), acyl-CoA dehydrogenase long chain (ACADL), sterol carrier protein 2 (Scp2), enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (Ehhadh), cytochrome P450 (Cyp) 4a12a, and Cyp4a14 are related to fatty acid oxidation, and Cyp27a1, Cyp7a1, and Cyp8b1 are related to cholesterol metabolism (Supplementary Fig. S14a and Fig. 6d). Western blot analysis revealed that the changes in the protein levels were consistent with the changes in the mRNA levels of these genes (Supplementary Fig. S14b, c; Fig. 6e; Supplementary Fig. S15). Moreover, after F. plautii and phytosphingosine intervention, the levels of glucose metabolism-related proteins (p-IR/IR, p-IRS/IRS, and GLUTs) also increased (Supplementary Figs. S16 and S17). Interestingly, the results of the Western blot analysis of livers (Fig. 6f) or epididymal fat (Supplementary Fig. S18) from the mice revealed that F. plautii and phytosphingosine increased the protein nuclear‒cytoplasmic ratio of PPARα. Moreover, the immunofluorescence results in the PhyL and PhyH groups were consistent with the western blot results (Fig. 6g). On the basis of these results, we speculated that there might be molecular binding between phytosphingosine and PPARα. Surface plasmon resonance (SPR) (Fig. 6h) revealed that the KD between phytosphingosine and PPARα was 7.19 × 10⁻⁷, and the dual-luciferase reporter system revealed that phytosphingosine can activate the nuclear transcriptional activity of PPARα (Supplementary Fig. S19). To explore the effects of phytosphingosine produced by F. plautii on metabolic disorders in vitro, an insulin-resistant human hepatocellular carcinoma (HepG2) cell model and a fatty HepG2 cell model were generated and treated with two doses of phytosphingosine, which were selected based on CCK-8 test results (Supplementary Fig. S20). The results showed that phytosphingosine treatment improved the lipid and glucose metabolic status (Fig. 7a–d). Phytosphingosine intervention did not alter PPARα protein levels in insulin-resistant HepG2 cells (Fig. 7e, f). However, phytosphingosine increased the nuclear-to-cytoplasmic ratio of PPARα (Fig. 7g, h) and resulted in the elevation of key proteins involved in fatty acid oxidation, including ACADM, ACADL, and CYP4A14 (Fig. 7i–l). Furthermore, to determine whether the action of phytosphingosine is PPARα dependent, PPARα-knockdown HepG2 cells were generated. Notably, the effects of phytosphingosine on the protein levels of ACADL, ACADM, and CYP4A14 were attenuated in PPARα-knockdown cells (Fig. 7m and Supplementary Fig. S21). Therefore, both in vivo and in vitro evidence demonstrated that phytosphingosine promoted PPARα nuclear localization and thus activated PPARα-mediated fatty acid metabolism and cholesterol metabolism (Fig. 7n). In addition, SPR (Supplementary Fig. S22) and Western blot analysis (Supplementary Fig. S23) revealed that F. plautii and phytosphingosine could also act in epididymal fat and bind other PPAR receptors, such as PPARβ.

**Fig. 6: Phytosphingosine activates PPARα to ameliorate metabolic disorders in vivo.**

**Fig. 7: Phytosphingosine activates PPARα to ameliorate metabolic disorders in vitro.**

Discussion

PDC is a constitutional type closely related to metabolic disorders in TCM. Without effective intervention, individuals with PDCs are predisposed to develop metabolic disorders¹⁶. To our knowledge, this study is the first to clarify that gut bacteria and their metabolites contribute to gene expression changes related to metabolic disorders in PDC individuals and increase the predisposition of individuals with PDC to metabolic disorders (Fig. 8). First, through comparative analysis of the gut microbial and serum metabolic characteristics of 167 PDC and 42 BC subjects, we revealed that PDC subjects have distinct gut microbiota and serum metabolite profiles. Second, through correlation analysis, we determined that serum phytosphingosine has the most significant negative correlation with PDC scores. Subsequent experiments demonstrated that F. plautii could biosynthesize phytosphingosine, which was negatively correlated with the PDC score. Third, we found that both F. plautii and its product, phytosphingosine, decreased in PDC subjects with normal metabolic indices. Fecal transplantation from these individuals accelerated the development of metabolic disorders in mice. However, supplementation with F. plautii and phytosphingosine ameliorated metabolic disorders by increasing phytosphingosine levels in the gut‒hepatic axis. Finally, we verified that phytosphingosine can directly bind to hepatic PPARα and activate its nuclear transcription activity, thereby regulating downstream gene expression. These results provide a biological basis for the susceptibility of PDC subjects to metabolic disorders and suggest a new approach to prevent the onset of metabolic disorders even in the preclinical stage.

**Fig. 8: The decrease in *F. plautii* and its product, phytosphingosine, predisposes individuals with phlegm-dampness constitution to metabolic disorders.**

In this study, we found that PDC scores were significantly positively correlated with metabolic indices. The PDC subjects had a greater degree of metabolic disorders, and the proportion of metabolic diseases in the PDC subjects was greater than that in the BC subjects. These results are consistent with our previous research²⁰. However, the biological basis behind this clinical phenomenon needs to be further revealed. TCM recognizes the human body from the perspective of holism and holds that the tissues and organs of the body are not isolated but interrelated¹¹. Previous studies have shown that gut microbiota-generated metabolites can be absorbed into the blood circulation and affect host phenotypes, metabolism, and the tissues and organs of the whole body. Thus, the gut microbiota has emerged as a promising avenue for elucidating TCM principles²³. Moreover, PDC represents a physiological state in which the transport and transformation ability of the spleen and stomach are decreased, and phlegm and dampness accumulate throughout the whole body. The spleen and stomach, as defined by TCM, cover the whole digestive tract in Western medicine and are closely related to the gut microbiota. Therefore, the PDC might be closely related to the gut microbiota.

Previously, our team and others reported that individuals with PDC have a distinctive gut microbial structure^29,30,31. In this study, we further revealed that individuals with PDC have distinctive serum metabolite profiles. Furthermore, we found that the gut microbiota structure and serum metabolic profiles of PDC subjects with normal metabolic indices (~7.78% of PDC subjects) were different from those of BC subjects with normal metabolic indices but similar to those of PDC subjects with metabolic disorders. Therefore, we believe that through the TCM diagnostic method of PDC diagnosis, a population with changes in the gut microbiota and related metabolites before metabolic disorders occur could be identified. This might be related to the PDC being diagnosed by collecting PDC phenotypic characteristics via the PDC scoring system rather than relying on metabolic indices¹¹. However, the changes that occur in the gut microbiota and metabolites before the occurrence of metabolic disorders have not been described. Using correlation analysis, we highlight the metabolite phytosphingosine, which has the most significant negative correlation with PDC scores, and the bacteria F. plautii, which is closely related to it. Interestingly, F. plautii and serum phytosphingosine levels are negatively correlated with the degree of metabolic disorders, and both are reduced in PDC subjects with normal metabolic indices. Fecal transplantation from PDC individuals with normal metabolic indices can accelerate metabolic disorders. On the basis of these results, we believe that the gut microbiota and its metabolites could explain the biological basis behind the concept of TCM and link the concept of TCM with modern medicine^22,23,33. Decreases in F. plautii and serum phytosphingosine levels may represent a precursor of metabolic disorders and predispose individuals with PDC to metabolic disorders.

We subsequently explored the relationship between F. plautii and phytosphingosine. Phytosphingosine is present in certain tissues of mammals, such as the epidermis and small intestine, but is more common in the plasma membranes of bacteria and fungi. Phytosphingosine is the main sphingosine base in fermented food³⁴. In this study, we confirmed that F. plautii directs the biosynthesis of phytosphingosine both in vitro and in germ-free mice. F. plautii is the main active Flavonifractor species, and Flavonifractor is a close relative of Sphingobacteria^35,36. A previous study demonstrated that Sphingobacteria can produce sphingosine (analogs of phytosphingosine), which supports our results³⁷. F. plautii is a beneficial bacterium that can protect against increased arterial stiffness³⁸. Here, we confirmed that F. plautii can biosynthesize phytosphingosine and that it can improve various metabolic disorders.

Next, given the central role of glucose‒lipid metabolism in metabolic disorders, we explored the therapeutic effect and mechanism of phytosphingosine on glucose‒lipid metabolic disorders. Many previous studies have shown that phytosphingosine has antibacterial and protective effects on the skin^39,40. Some studies have reported that it lowers blood glucose and lipid levels^41,42, which is consistent with our findings. By interacting with the intestinal tract and adipose tissue, the liver plays a key role in various aspects of glucose‒lipid metabolism. Furthermore, we revealed that phytosphingosine can directly bind to PPARα, activating its nuclear transcription activity in the liver or epididymal fat. PPARα is a transcription factor that belongs to the nuclear receptor superfamily. As a gene regulator of metabolic pathways, PPARα is a key player in regulating energy and lipid metabolism, glucose homeostasis, adipogenesis, and inflammation^43,44,45. This may be an important mechanism by which DEGs from PDC individuals are enriched in the PPAR signaling pathway and are associated with metabolic disorders, low immunity and an inflammatory phenotype^21,46. Here, we verified that phytosphingosine activated the nuclear transcription of PPARα and improved glucose‒lipid metabolism. Moreover, F. plautii and phytosphingosine could also act on other PPAR receptors. In the future, the detailed mechanism needs to be explored.

Regarding to phytosphingosine, it is inevitable to think of sphingosine. Sphingosine and phytosphingosine are sphingoid bases and are both components of sphingolipids, but they have different physiological and pharmacological activities. Excess sphingosine is widely known to be a toxicant since it induces apoptosis and binds long-chain fatty acids to produce ceramides, which can cause insulin resistance, lipid accumulation, inflammation, and apoptosis^47,48. However, recent studies have shown that glycosylated or homoserine sphingosine and ceramides produced by the gut microbiota can regulate host sphingosine and ceramide metabolism and ameliorate metabolic disorders^49,50. Glycosylated or homoserine modifies the structure of sphingosine and ceramides, whereas phytosphingosine and sphingosine differ in the structure of the mother nucleus. These findings suggest that phytosphingosine may have a positive effect on sphingosine and ceramide metabolism, which will also be the direction of our future exploration.

In modern medicine, identifying changes in precursors before the occurrence of diseases is important, as preventing and blocking disease processes is beneficial. For example, systems biomedicine, a relatively new discipline that combines experimental observations with computer modeling to simulate disease, has been gradually applied to identify precursor changes before diseases occur^51,52. Claudio Franceschi from the University of Bologna (Bologna, Italy) established a model by collecting data related to the gut microbiota, inflammation and aging and reported that before the onset of diabetes, the gut microbiota exhibited changes related to the inflammatory response¹⁰. In this study, based on the cross-sectional clinical investigations of PDC subjects and experimental validation, we revealed that the decrease in F. plautii and its product, phytosphingosine, can contribute to metabolic disorders, which may be a precursor of metabolic diseases. Through the diagnosis of PDC, people with these precursor changes can be identified. These findings indicate that TCM diagnosis is a method that can aid in the early detection and prevention of diseases. In addition, this study confirmed that the phenotypic characteristics of PDC have a biological basis, and similar research on other constitutional types can also be performed in the future to contribute to disease prevention.

This research has several limitations that should be noted. Owing to the age distribution of PDC subjects, it is challenging to recruit matched subjects for clinical research. Moreover, owing to restrictions related to clinical specimen collection, only blood and fecal samples were collected from our cohort. The availability of tissues closely related to metabolism, such as the liver, intestine, and adipose tissue, would strengthen our conclusions. Finally, in the mouse fecal transplantation experiment, we assessed the transfer of the PDC phenotype. Owing to the limited capacity of rodents to mimic humans, we were only able to observe visible lethargy and greasy fur statuses in mice; we were unable to observe symptoms such as upper eyelid edema and thick tongue coatings in the mouse model. Future studies to address these key issues may help gain a better understanding of the pathogenesis of metabolic disorders via TCM.

Materials and methods

Key materials and software

The key materials and software were showed in Table 1.

Table 1 Key materials and software.

Full size table

Volunteer recruitment and data collection

The Institutional Review Board—Ethics Committee and the Committee for Beijing University of Chinese Medicine approved the protocols of the human study (2017BZHYLL0502). The research performed in this study complied with all relevant ethical regulations. All experiments adhered to the Declaration of Helsinki. Written informed consent was obtained from all participant. The inclusion criteria were as follows: 1. met the criteria for determining the BC/PDC constitution (ZYXH/T157-2009), 2. aged between 18 and 50 years, and 3. had at least 1 year of living in Beijing. The exclusion criteria were as follows: 1. the use of antibiotics, gastrointestinal stimulants, microecological regulators, or endocrine-affecting drugs in the past three months; 2. weight loss by any pharmacological means in the past three months; 3. a history of gastrointestinal surgery; 4. the presence of a serious illness, infectious disease or mental disorder; or 5. alcoholism.

According to the industry standard (version: ZYYXH/T157-2009; Supplementary Method Section 1), individuals were diagnosed with PDC when the PDC score was ≥ 40, and individuals were diagnosed with BC when the BC score was ≥ 60 and the unbalanced constitution score was < 30. A total of 209 subjects, including 167 PDC subjects and 42 BC subjects, were enrolled. All the clinical characteristics are shown in Supplementary Table S1. The PDC subjects were further divided into two groups according to the diagnostic criteria for metabolic disorders (Supplementary Method Section 2): the PDN group (n = 13, PDC subjects with normal metabolic indices) and the PDD group (n = 154, PDC subjects with metabolic disorders). Similarly, the PDC subjects were also further divided into two groups according to the diagnostic criteria for metabolic disorders: the BCN (n = 36, BC subjects with normal metabolic indices) group and the BCD (n = 6, BC subjects with metabolic disorders) group.

The waist circumference was measured by circling the abdomen in a horizontal direction 5 cm above the umbilicus. The hip circumference was measured at the most prominent part of the buttocks. Blood pressure was measured with a standard mercury sphygmomanometer by specially trained nurses when the subjects were in a seated position after ≥ 5 min of rest. Blood and stool samples were collected from the participants after an overnight fast. The peripheral blood samples were incubated at room temperature for at least 30 min and centrifuged at 3000× g for 5 min, after which the supernatant was purified and stored at −80 °C. The subjects were given a stool sampler and provided detailed instructions for sample collection. Immediately after collection, the fresh stool samples were transported to the laboratory and frozen at −80 °C.

In this cohort, we characterized the gut microbiome and serum metabolome and identified microbial and metabolic characteristics (Fig. 1a). The collected metadata covered demographic characteristics, health status, disease history and drug treatment, as well as the TCM constitution scale.

Untargeted metabolomics analysis

The samples were thawed on ice, and QC samples were also prepared by mixing and blending equal amounts of each sample. LC–MS/MS analyses were performed via an ultra-performance liquid chromatography (UPLC) system (Thermo Fisher Scientific, USA) with a UPLC BEH C18 column (1.7 μm, 2.1 mm × 100 mm, Waters, USA) coupled to a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) in both positive and negative ionization modes. The original LC‒MS/MS data were processed via Progenesis O1 V2.3 software (Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integral and retention time correction, peak alignment, and normalization. The extracted data were then processed to generate a data matrix consisting of a combination of the data obtained in the positive and negative ion modes. OPLS–DA was applied to discriminate the different groups. The metabolites that differed between PDC subjects and BC subjects were identified on the basis of the following criteria: VIP > 1.0, two-tailed Student’s t-test (p < 0.05), and fold change (< 0.8 or > 1.2). Metabolic pathway enrichment analysis of the differentially abundant metabolites was performed via the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

DNA extraction and 16S rRNA gene V3-V4 region sequencing

Following the instructions provided with the EZNA^® soil kit, total DNA was extracted from the stool samples. A NanoDrop2000 (Thermo Fisher Scientific, USA) was used to detect and calculate the DNA purity and concentration, and a 1% agarose gel was used to determine the quality of the extracted DNA. The 343 F (5′-TACGGRAGGCAGCAG-3′) and 798 R (5′-AGGGTATCTAATCCT-3′) primers were used for PCR amplification of the V3–V4 hypervariable region of the DNA samples. The amplicon quality was visualized by gel electrophoresis, and the amplicon was purified with AMPure XP beads and amplified through another round of PCR. After another round of purification with AMPure XP beads, the final amplicon was quantified via a Qubit dsDNA assay kit. Equal amounts of purified amplicon were pooled for subsequent sequencing.

Sequencing data analysis

Paired-end reads were generated and assigned to each sample on the basis of their barcodes and then merged via Flash (version 1.2.11) software. Through high-quality filtering, reads with ambiguous or homologous sequences or less than 200 bp were removed from the raw tags to acquire clean tags via the split_libraries (version 1.8.0) function of QIIME software (version 1.8.0). The downstream bioinformatic analyses were performed via EasyAmplicon v1.0⁵³. We then discarded low-abundance sequences (n < 8) via the derep_fullength command in VSEARCH software (v2.15). The nonredundant sequences were denoised into amplicon sequence variants (ASVs) via the -unoise3 command in USEARCH software (v10.0). The feature (ASV) table was created with Vsearch–usearch_global. We analyzed the high-quality reads via USEARCH to remove chimeric and organelle sequences, which resulted in the generation of 3076 ASVs. The representative sequences for each ASV were classified, and the Ribosomal Database Project (RDP) classifier (version 11.5) was then used to assign taxonomic data to each sequence. The sequences of all the samples were rarefied to 30000 for downstream diversity analysis. The α diversity was assessed via species richness indices and Shannon diversity indices. The β diversity was calculated via PCoA. Random forest and AUC calculations were performed on the oebiotech platform (https://cloud.oebiotech.cn/task/), a free online data analysis website.

Metagenomic sequencing and analysis

For shotgun sequencing, Illumina libraries were prepared via a Nextera DNA Sample Prep Kit (Illumina) according to the manufacturer’s protocol and sequenced via the Illumina NovaSeq platform to generate 2 × 150-bp paired-end reads. The Illumina sequence data and low-quality reads were filtered and trimmed via Trimmomatic version 0.36 with the default parameters. After valid reads were obtained, metagenome assembly was performed via SOAPdenovo2 (v2.04). Gaps inside the scaffold were used as breakpoints to break the scaffold into new contigs (ScafContig), and new contigs with a length of 200 bp were retained. The ORFs of the assembled scaffolds were predicted via Prodigal (v2.6.3), and these scaffolds were translated into amino acid sequences. Nonredundant gene sets were built for all the predicted genes via CDHIT (v4.6.7). The clustering parameters were 95% identity and 90% coverage. The longest gene was selected as a representative sequence of each gene set. The clean reads of each sample were aligned against the nonredundant gene set (95% identity) via Bowtie2 (v2.2.9), and the abundance information of the gene in the corresponding sample was obtained. The taxonomy of the species was obtained from the corresponding taxonomy database of the NR Library, and the species abundance was calculated from the corresponding abundance of the genes. To construct the abundance profile for the corresponding taxonomy level, statistical analyses of the abundance data were performed at the Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species levels. PCoA and plotting of the abundance spectrum of the species or functional abundance spectrum were conducted via R software, and the results from the equidistant PCoA were calculated and analyzed.

Preparation and in vitro culture of F. plautii

F. plautii was obtained from the Guangdong Microbial Culture Collection Center (GDMCC). All the samples were processed and cultured under anaerobic conditions in an ELECTROTEK AW 400SG workstation at 37 °C. The culture media, PBS and all other materials that were used for culturing were placed in the anaerobic cabinet 24 h before use to allow their acclimation to anaerobic conditions. F. plautii lyophilized powder was resuscitated on blood agar plates and then incubated in modified BHI broth.

For the in vitro experiment, the cultured F. plautii were diluted 1:500, inoculated in BHI broth and cultured for 24 h. F. plautii cultures were collected after 0, 2, 6, and 10 h for the phytosphingosine-targeting assay.

In vivo, the bacterial cells were pelleted by centrifugation at 3000× g and 4 °C for 5 min. The cells were then washed and resuspended in sterile reduced PBS to obtain a density of 5 × 10⁸ colony-forming units (CFUs) per 200 μL. The bacterial suspension was freshly prepared for the animal experiments.

Measurement of the phytosphingosine level

The level of phytosphingosine was analyzed via ultra-performance liquid chromatography‒tandem mass spectrometry (UPLC‒MS/MS). Briefly, the samples were extracted with extraction solution (chloroform:methanol = 9:1). After 5 min of vortexing and 10 min of centrifugation at 12,000× g, 400 μL of the supernatant was concentrated to complete dryness at 20 °C and then reconstituted with 100 μL of methanol. Phytosphingosine was separated on an ACQUITY HSS T3 column (i.e., 2.1 × 100 mm, 1.8 μm) using water and methanol as the mobile phases and detected via MRM on a QTRAP 6500 mass spectrometer (Sciex, Concord, Canada). Quantification was performed via internal and external standard curves.

Mouse experiments

C57BL/6J mice were maintained at a temperature of 30 ± 2 °C with 45%–70% humidity under a 12-h light/dark cycle (light from 7 a.m. to 7 p.m.) and standard specific-pathogen-free (SPF) or GF conditions. SPF mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. All the mice were males, aged 7 weeks and weight matched at the start of the experiments. The mice were fasted for 8 h before being killed. The weekly food consumption was measured per cage. All the mice were fed a HFD (58.6% basal feed, 15% lard, 20% sucrose sugar, 5% casein, 1.2% cholesterol, and 0.2% sodium cholate) (SiPeiFu (Beijing) Biotechnology Co., Ltd., China) during the intervention period.

Animal experiment 1. Fecal transplantation experiment

Five subjects from the BCN group and five from the PDN group who were matched with respect to age, sex and BMI were selected for the fecal transplantation experiment (Supplementary Table S2). Five hundred milligrams of fresh mixed stools obtained from the PDN group or BCN group were suspended in 5 mL of PBS buffer. The stool samples were pooled to obtain the fecal slurry. All the mice were treated with a previously described antibiotic cocktail (vancomycin, 100 mg/kg; neomycin sulfate, metronidazole, and ampicillin, 200 mg/kg) over two cycles of treatment (5 days of treatment and one day of suspension)⁵⁴. Following antibiotic treatment, the recipient mice received fecal slurry from the PDN group or BCN group daily for 14 consecutive days and were fed a HFD for 5 weeks. Blood, fecal, liver, and adipose tissue samples were harvested at the end of the experiment. The experiment was approved by the Medical Ethics Committee, Shandong University of Technology (YLX20220615).

Animal experiment 2. Treatment of a humanized animal model of PDC with F. plautii

Antibiotic-treated mice received a fecal slurry from the PDN group and were fed a HFD to establish the mouse model described above. The bacterial suspension containing F. plautii was gavaged into the model mice at a dose of 5 × 10⁸ cfu for 5 weeks (F. plautii group). Sterile PBS was used as a control (the PBS1 group). Blood, fecal, liver, and adipose tissue samples were harvested at the end of the experiment. The experiment was approved by the Medical Ethics Committee, Shandong University of Technology (YLX20220906).

Animal experiment 3. Treatment of a humanized animal model of PDC with phytosphingosine

Phytosphingosine (D136389) was obtained from Aladdin and mixed with sterile PBS to form a suspension. Antibiotic-treated mice receiving the fecal slurry from the PDN group were gavaged with PBS as the PBS2 group and gavaged with 25 or 50 mg/kg phytosphingosine as the PhyL group or PhyH group on a daily basis for 5 weeks. The mice were fasted for 8 h before being killed. Blood, liver, and adipose tissue samples were harvested at the end of the experiment. The experiment was approved by the Medical Ethics Committee, Shandong University of Technology (YLX20220905).

Animal experiment in which F. plautii colonized germ-free mice

Male germ-free C57BL/6J mice were obtained from GemPharmatech Co., Ltd. The germ-free mice (8 weeks old) fed a HFD were randomly divided into two groups: the PBS-G group (the germ-free mice were gavaged with PBS every day) or the F. plautii-G group (the germ-free mice were gavaged with 5 × 10⁸ cfu F. plautii every day). After 10 days of administration, blood, fecal, liver, and adipose tissue samples were harvested. The colonization of germ-free C57BL/6J mice with the F. plautii strain was conducted with the approval of the Institutional Animal Care and Use Committee of Gempharmatech Co., Ltd. (AP#: GPTAP20230925-4).

In both batches of experiments, no mice died during the experiment, and body weight and food intake were measured once a week. During the experiment, animal pain was alleviated using isoflurane anesthesia. After the experiment, the animals were euthanized with carbon dioxide.

Body condition scoring

For PDC phenotype scoring, the mice were scored on a scale of 0–3 in two categories as follows: (1) visible lethargy: 3 points if stationary unless stimulated, 2 points if showing mildly decreased movement, 2 points if showing moderately decreased movement, 0 points if normal; (2) greasy fur: 3 points if severely greasy with poor grooming, 2 points if mildly greasy, 1 point if moderately greasy, 0 points if normal⁵⁵. Visible lethargy and greasy fur are analogous to heavy body and greasy skin in humans, respectively.

Biochemical analyses

Fasting blood samples were centrifuged at 4000× g for 5 min at 4 °C to obtain the serum. The main tissues were immediately excised and stored in liquid nitrogen for biochemical analyses. The levels of TC, TG, LDLC, HDLC, serum glucose, serum insulin, and uric acid in the serum were measured via a Beckman-Coulter AU480 automated analyzer. The concentrations of TC, TG, AST, and ALT in the liver were determined via commercial kits (Grace Biotechnology Co. Ltd., Suzhou, China) according to the manufacturer’s instructions.

Histological analyses

For histological analyses, tissue samples were fixed in 4% paraformaldehyde solution for 24 h and then embedded in paraffin. The liver and adipose sections (thickness of ~3 μm) were stained with hematoxylin‒eosin, and the stained samples were observed at ×400 magnification. The liver and adipose lipid levels (% staining area) were analyzed via ImageJ software (https://imagej.nih.gov/ij/). For Oil Red O staining, the fixed HepG2 cells were washed with PBS and stained with Oil Red O working solution according to the kit’s instructions. Briefly, Oil Red O working solution was freshly prepared, and HepG2 cells were dyed for 30 min. Then, the cells were washed with 60% isopropyl alcohol to remove the excess dye and washed with PBS. Mayer hematoxylin was applied for 2 min to further dye the nuclei, which were subsequently washed with PBS. Finally, the stained HepG2 cells were observed with an Olympus microscope (Olympus, Tokyo, Japan) and analyzed via ImageJ software.

Immunofluorescence

For immunofluorescence analysis, the deparaffinized slides were submerged in citric acid antigen retrieval solution (Solarbio) under steam treatment for 20 min. The tissues were allowed to cool at room temperature for 30 min, subjected to three 3-min washes with PBS, and then permeabilized by incubation with 0.1% Triton-X 100 in PBS for 15 min. The sections were then blocked by incubation with 5% bovine serum albumin in PBS for 30 min at 37 °C, stained with anti-pparα antibody overnight at 4 °C in a humidification chamber, subjected to three 3-min washes with PBS, and then incubated with a fluorescein isothiocyanate-labeled secondary antibody in PBS with 1% BSA at a 1:200 dilution. The unbound secondary antibody on the sections was removed by two washes with PBS, and the tissues were then stained with DAPI for 5 min and mounted with Glycerol Jelly Mounting Medium (Solarbio). The fluorescent signals were viewed under a panoramic scanner (3DHISTECH P250 FLASH).

Oral glucose tolerance test and insulin tolerance test

Sprague-Dawley (SD) rats were maintained at a temperature of 20 ± 2 °C with 45%–70% humidity under a 12-h light/dark cycle (light from 7 a.m. to 7 p.m.) and standard SPF conditions. All the rats were males, aged 8 weeks and weight matched (180–200 g) at the start of the experiments. The type 2 diabetic rat model was established using streptozotocin and a HFD. The HFD consisted of 36.4% starch, 25.6% butter, 20% protein, 1% cholesterol, and 0.1% bile acid. The mice were injected intraperitoneally with 45 mg/kg STZ, and subsequent oral GTTs were performed on the 7th and 21st days after injection. A total of 16 rats met the standard of type 2 diabetes (FBG ≥ 7.0 mM and 2 h-PG ≥ 11.1 mM) in both GTTs. In total, 16 matched diabetic rats were selected for phytosphingosine prevention. The 8 diabetic rats fed a HFD were grouped as the DM-CON group, and another 8 diabetic rats fed a HFD were administered 50 mg/kg/d phytosphingosine by gavage for 3 weeks as the DM-PHY group (n = 8 for all groups). After phytosphingosine prevention, GTTs and ITTs were performed. For the GTTs, oral glucose (2.5 g/kg) was given to 4-h-fasted individually housed rats. All GTTs began at 1 pm, and glucose was measured in whole blood collected from the tail vein using a glucometer at 0, 15, 30, 60, 90, and 120 min after glucose gavage. For ITTs, intraperitoneal insulin (0.5 U/kg) was given to 4-h fasted individually housed mice. All ITTs began at 1 pm, and glucose was measured in whole blood that was collected from the tail vein using a glucometer at 0, 30, 60, 90, and 120 min after insulin injection.

No rats died during the experiment. During the experiment, animal pain was alleviated using isoflurane anesthesia. After the experiment, the animals were euthanized with carbon dioxide.

Cell culture and treatment

HepG2 cells were purchased from Procell Life Science & Technology Co., Ltd. HepG2 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37 °C in a 5% CO₂ atmosphere. HepG2 cells were used for insulin-resistant model or lipid-loaded model construction and phytosphingosine administration. For the insulin-resistant model, HepG2 cells were incubated with 1 μM insulin for 48 h and then changed to nonserum culture medium for another 12 h. Finally, HepG2 cells were given 1 μM insulin culture medium with or without 1 or 2.5 μM phytosphingosine in DMSO for 48 h. For the lipid-loaded model, HepG2 cells were treated with 500 μM palmitic acid or 1000 μM oleic acid for 48 h, after which 1 or 2.5 μM phytosphingosine was administered for 48 h.

Liver RNA extraction, sequencing, and processing

Total RNA was extracted from fresh frozen liver tissues via TRIzol reagent. The quantity and quality of total RNA were determined via a NanoDrop 2000 ultramicro-spectrophotometer. Poly(A)-containing mRNA was then enriched via magnetic beads with oligo (dT) and fragmented into small pieces via divalent cations at elevated temperatures. The cleaved RNA fragments were reverse transcribed with Oligo T primers to produce cDNA, and dsDNA samples were generated via DNA polymerase, RNase H enzyme, and T4 DNA ligase. These cDNA fragments were then subjected to end repair, the addition of a single ‘A’ base, and ligation with adapters. The resulting products were purified and amplified via PCR to create the library for sequencing. The Illumina HiSeq X Ten/NovaSeq 6000 platform was used for RNA sequencing. The raw RNA-seq data were trimmed via Prinseq (v0.20.4), and the read quality was assessed via FastQC (v0.11.2). The trimmed reads were aligned to the Rattus norvegicus reference genome (release 6) or the Mus musculus reference genome (release 10) from the UCSC genome browser database via STAR aligner (v2.7.9a). The number of reads per gene was quantified via HTSeq (v0.9.1). The DEGs between groups were identified via the R package DESeq2 (v1.32.0). KEGG pathway enrichment analyses via the GOseq R package (v1.44.0) were performed after annotation of each gene to the corresponding KEGG orthology based on the KEGGREST package (v1.32.0).

DNA isolation and quantitative PCR

Following the instructions provided with the E.Z.N.A.® Soil DNA Midi Kit (Omega Bio-Tek Co. Ltd., Norcross, GA, USA), total DNA was extracted from the stool samples. Briefly, 1 μL of gDNA was added to 7.5 μL of 2 × Taq Pro Universal SYBR qPCR Master Mix (Vazyme-Q712) with 0.7 μL of each forward and reverse primer pair, and molecular biology-grade H₂O was then added to obtain a final volume of 20 μL. qPCR detection was performed with the Step One Plus real-time PCR system and software (Applied Biosystems, Foster City, CA, USA) under the following conditions: 95 °C for 5 min followed by 40 cycles of 95 °C for 20 s, annealing at 55 °C for 20 s and extension at 72 °C for 40 s. The computed CT value was substituted into the standard curve formula y = −3.6206x + 31.549 as previously described⁵⁶, and the logarithm of gene copy number x was calculated. The sequences of the primers used in this study are shown in Supplementary Table S4.

RNA extraction and RT‒qPCR

Total RNA was extracted from the liver tissue of the animals via TRIzol reagent (Vazyme, Nanjing, China) according to the manufacturer’s instructions. cDNA was generated via reverse transcription of 1–2 µg of total RNA with a reverse transcription enzyme (Vazyme, Nanjing, China). The results from the gene expression analysis are presented as percentages of the expression of each gene normalized to that of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a reference. qPCR detection was performed with the Step One Plus real-time PCR system and software (Applied Biosystems, Foster City, CA, USA). Briefly, 2 μL of cDNA was added to 10 μL of 2× Taq Pro Universal SYBR qPCR Master Mix (Vazyme-Q712) with 0.4 μL of each forward and reverse primer pair, and molecular biology-grade H₂O was added to reach a final volume of 20 μL. qPCR was performed via the LightCycler 480 System (Roche Diagnostics International AG, Switzerland) with the following conditions: 95 °C for 30 s followed by 40 cycles of 95 °C for 10 s and annealing/extension at 60 °C for 30 s. The relative expression levels were calculated on the basis of the expression of GAPDH via the 2^-ΔΔCT method. The sequences of the primers used in this study are shown in Supplementary Table S4.

Western blot

Total protein from mouse liver samples or cells was extracted with RIPA buffer and quantified with a BCA protein assay kit. After the proteins were resolved via SDS‒PAGE, they were transferred to PVDF membranes, blocked with 5% bovine serum albumin at room temperature for 1 h, and incubated with specific primary antibodies at 4 °C overnight. The membranes were incubated with the corresponding secondary antibodies. Densitometric analysis was then performed with ImageJ software.

Transient dual-luciferase reporter assay

Briefly, a dual-luciferase reporter assay was performed via the transfection of a plasmid with PPRE sequences and internal control vectors (PGL6 promoter) into HEK-293 cells. Luciferase activity was detected via a dual luciferase reporter assay system (Beyotime) with an i3X spectrometer (Molecular Device SpectraMax). The expression level is expressed as the ratio of firefly luciferase activity to Renilla luciferase activity.

Surface plasmon resonance

Interactions between PPARα and phytosphingosine were analyzed via the Biacore T200 system at 25 °C. Recombinant PPARs (LBDs) were immobilized on a CM5 sensor chip. The level of immobilized PPARs (LBD) was ~14000 RU. Phytosphingosine was injected at various concentrations as an analyte, and 5% DMSO in PBS-P (5% DMSO + PBS + 0.05% P20) was used as running buffer. To explore whether phytosphingosine had any effect on the interactions with PPARs, phytosphingosine at 1.56 μM, 3.125 μM, 6.25 μM, 12.5 μM, or 25 μM was flowed through the chip with a contact time of 60 s and a dissociation time of 120 s. Data were analyzed with BiacoreTM Insight Software by curve fitting.

Transfection of small interfering RNA

Predesigned small interfering RNAs (siRNAs) against PPARα and control scrambled siRNAs were synthesized by Hanbio Biotechnology Co. Ltd. The sequences of the PPARα siRNAs used were as follows: for PPARAα, the sequences of the sense and antisense siRNAs were 5′-GGGUUUAUAACUCGUGAAUTT-3′ and 5′-AUUCACGAGUUAUAAACCCTT-3′, respectively. For the negative control, the sense and antisense siRNAs used were 5′-UUCUCCGAACGUGUCACGUTT-3′ and 5′-ACGUGACACGUUCGGAGAATT-3′, respectively. The cells were then transfected with Opti-MEM and control or PPARα siRNA together with Lipo8000 transfection reagent for 24 h. After transfection, the culture medium was changed, and the cells were incubated with phytosphingosine for 48 h. The effectiveness of PPARα silencing was verified via western blotting.

Statistical analysis

The statistical analyses were performed via IBM SPSS Statistics software (version 16.0) unless otherwise stated. The normal distribution assumption was tested with the Shapiro‒Wilk test and Q‒Q plots. The data from the human studies are presented as the means ± SDs. Comparisons of the basic characteristics were conducted with an independent Student’s t-test or one-way ANOVA with Tukey’s post hoc test for normally distributed data or the Wilcoxon rank-sum test for nonnormally distributed data. The categorical variables were analyzed with the χ² test. For the animal, cell and germ culture studies, the sample sizes were estimated on the basis of previous studies, and no statistical test was used to predetermine the sample sizes. The animal data are presented as the means ± SEM, and all analyses were performed via GraphPad Prism 9.0.

Categories: Genomic analysis, Mechanisms of disease

Tags: Genomic analysis, Mechanisms of disease

Energy metabolism in health and diseases

Energy metabolism is indispensable for sustaining physiological functions in living organisms and assumes a pivotal role across physiological and pathological conditions. This review provides an extensive overview of advancements in energy metabolism research, elucidating critical pathways such as glycolysis, oxidative phosphorylation, fatty acid metabolism, and amino acid metabolism, along with their intricate regulatory mechanisms. The homeostatic balance of these processes is crucial; however, in pathological states such as neurodegenerative diseases, autoimmune disorders, and cancer, extensive metabolic reprogramming occurs, resulting in impaired glucose metabolism and mitochondrial dysfunction, which accelerate disease progression. Recent investigations into key regulatory pathways, including mechanistic target of rapamycin, sirtuins, and adenosine monophosphate-activated protein kinase, have considerably deepened our understanding of metabolic dysregulation and opened new avenues for therapeutic innovation. Emerging technologies, such as fluorescent probes, nano-biomaterials, and metabolomic analyses, promise substantial improvements in diagnostic precision. This review critically examines recent advancements and ongoing challenges in metabolism research, emphasizing its potential for precision diagnostics and personalized therapeutic interventions. Future studies should prioritize unraveling the regulatory mechanisms of energy metabolism and the dynamics of intercellular energy interactions. Integrating cutting-edge gene-editing technologies and multi-omics approaches, the development of multi-target pharmaceuticals in synergy with existing therapies such as immunotherapy and dietary interventions could enhance therapeutic efficacy. Personalized metabolic analysis is indispensable for crafting tailored treatment protocols, ultimately providing more accurate medical solutions for patients. This review aims to deepen the understanding and improve the application of energy metabolism to drive innovative diagnostic and therapeutic strategies.

AdMaPlace March 19, 2025

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Tissue macrophages: origin, heterogenity, biological functions, diseases and therapeutic targets

Macrophages are immune cells belonging to the mononuclear phagocyte system. They play crucial roles in immune defense, surveillance, and homeostasis. This review systematically discusses the types of hematopoietic progenitors that give rise to macrophages, including primitive hematopoietic progenitors, erythro-myeloid progenitors, and hematopoietic stem cells. These progenitors have distinct genetic backgrounds and developmental processes. Accordingly, macrophages exhibit complex and diverse functions in the body, including phagocytosis and clearance of cellular debris, antigen presentation, and immune response, regulation of inflammation and cytokine production, tissue remodeling and repair, and multi-level regulatory signaling pathways/crosstalk involved in homeostasis and physiology. Besides, tumor-associated macrophages are a key component of the TME, exhibiting both anti-tumor and pro-tumor properties. Furthermore, the functional status of macrophages is closely linked to the development of various diseases, including cancer, autoimmune disorders, cardiovascular disease, neurodegenerative diseases, metabolic conditions, and trauma. Targeting macrophages has emerged as a promising therapeutic strategy in these contexts. Clinical trials of macrophage-based targeted drugs, macrophage-based immunotherapies, and nanoparticle-based therapy were comprehensively summarized. Potential challenges and future directions in targeting macrophages have also been discussed. Overall, our review highlights the significance of this versatile immune cell in human health and disease, which is expected to inform future research and clinical practice.

AdMaPlace March 19, 2025

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Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines

The successful approval of peptide-based drugs can be attributed to a collaborative effort across multiple disciplines. The integration of novel drug design and synthesis techniques, display library technology, delivery systems, bioengineering advancements, and artificial intelligence have significantly expedited the development of groundbreaking peptide-based drugs, effectively addressing the obstacles associated with their character, such as the rapid clearance and degradation, necessitating subcutaneous injection leading to increasing patient discomfort, and ultimately advancing translational research efforts. Peptides are presently employed in the management and diagnosis of a diverse array of medical conditions, such as diabetes mellitus, weight loss, oncology, and rare diseases, and are additionally garnering interest in facilitating targeted drug delivery platforms and the advancement of peptide-based vaccines. This paper provides an overview of the present market and clinical trial progress of peptide-based therapeutics, delivery platforms, and vaccines. It examines the key areas of research in peptide-based drug development through a literature analysis and emphasizes the structural modification principles of peptide-based drugs, as well as the recent advancements in screening, design, and delivery technologies. The accelerated advancement in the development of novel peptide-based therapeutics, including peptide-drug complexes, new peptide-based vaccines, and innovative peptide-based diagnostic reagents, has the potential to promote the era of precise customization of disease therapeutic schedule.

AdMaPlace March 19, 2025

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Pancreatic endocrine and exocrine signaling and crosstalk in physiological and pathological status

The pancreas, an organ with dual functions, regulates blood glucose levels through the endocrine system by secreting hormones such as insulin and glucagon. It also aids digestion through the exocrine system by secreting digestive enzymes. Complex interactions and signaling mechanisms between the endocrine and exocrine functions of the pancreas play a crucial role in maintaining metabolic homeostasis and overall health. Compelling evidence indicates direct and indirect crosstalk between the endocrine and exocrine parts, influencing the development of diseases affecting both. From a developmental perspective, the exocrine and endocrine parts share the same origin—the “tip-trunk” domain. In certain circumstances, pancreatic exocrine cells may transdifferentiate into endocrine-like cells, such as insulin-secreting cells. Additionally, several pancreatic diseases, including pancreatic cancer, pancreatitis, and diabetes, exhibit potential relevance to both endocrine and exocrine functions. Endocrine cells may communicate with exocrine cells directly through cytokines or indirectly by regulating the immune microenvironment. This crosstalk affects the onset and progression of these diseases. This review summarizes the history and milestones of findings related to the exocrine and endocrine pancreas, their embryonic development, phenotypic transformations, signaling roles in health and disease, the endocrine-exocrine crosstalk from the perspective of diseases, and potential therapeutic targets. Elucidating the regulatory mechanisms of pancreatic endocrine and exocrine signaling and provide novel insights for the understanding and treatment of diseases.

AdMaPlace March 19, 2025

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Iron homeostasis and ferroptosis in muscle diseases and disorders: mechanisms and therapeutic prospects

The muscular system plays a critical role in the human body by governing skeletal movement, cardiovascular function, and the activities of digestive organs. Additionally, muscle tissues serve an endocrine function by secreting myogenic cytokines, thereby regulating metabolism throughout the entire body. Maintaining muscle function requires iron homeostasis. Recent studies suggest that disruptions in iron metabolism and ferroptosis, a form of iron-dependent cell death, are essential contributors to the progression of a wide range of muscle diseases and disorders, including sarcopenia, cardiomyopathy, and amyotrophic lateral sclerosis. Thus, a comprehensive overview of the mechanisms regulating iron metabolism and ferroptosis in these conditions is crucial for identifying potential therapeutic targets and developing new strategies for disease treatment and/or prevention. This review aims to summarize recent advances in understanding the molecular mechanisms underlying ferroptosis in the context of muscle injury, as well as associated muscle diseases and disorders. Moreover, we discuss potential targets within the ferroptosis pathway and possible strategies for managing muscle disorders. Finally, we shed new light on current limitations and future prospects for therapeutic interventions targeting ferroptosis.

AdMaPlace March 9, 2025

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Introduction

Results

Clinical characteristics of the study population

Shifts in the gut microbiota in the PDC group and BC group

Shifts in the serum metabolic profiles of the PDC group and BC group

A decrease in phytosphingosine was associated with higher PDC scores

F. plautii biosynthesized phytosphingosine, which was negatively correlated with the PDC score

The F. plautii and phytosphingosine levels decreased in PDC subjects, including those with normal metabolic indices

Fecal transplantation from PDC subjects with normal metabolic indices accelerated the development of metabolic disorders in mice

F. plautii alleviates the metabolic disorders induced by a HFD and fecal transplantation of PDC subjects with normal metabolic indices

Phytosphingosine alleviates the metabolic disorders induced by a HFD and fecal transplantation in PDC subjects with normal metabolic indices

Phytosphingosine activates PPARα to improve metabolic disorders

Discussion

Materials and methods

Key materials and software

Volunteer recruitment and data collection

Untargeted metabolomics analysis

DNA extraction and 16S rRNA gene V3-V4 region sequencing

Sequencing data analysis

Metagenomic sequencing and analysis

Preparation and in vitro culture of F. plautii

Measurement of the phytosphingosine level

Mouse experiments

Animal experiment 1. Fecal transplantation experiment

Animal experiment 2. Treatment of a humanized animal model of PDC with F. plautii

Animal experiment 3. Treatment of a humanized animal model of PDC with phytosphingosine

Animal experiment in which F. plautii colonized germ-free mice

Body condition scoring

Biochemical analyses

Histological analyses

Immunofluorescence

Oral glucose tolerance test and insulin tolerance test

Cell culture and treatment

Liver RNA extraction, sequencing, and processing

DNA isolation and quantitative PCR

RNA extraction and RT‒qPCR

Western blot

Transient dual-luciferase reporter assay

Surface plasmon resonance

Transfection of small interfering RNA

Statistical analysis

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