Lactulose, a galactose-fructose disaccharide, is made from the milk sugar lactose by heating or isomerization processes. Lactulose is proposed to modulate gut microbiota and thus expected to be beneficial in treating inflammatory bowel disease. In the present study, we investigated the therapeutic effect of lactulose on gastrointestinal inflammation and inflammation-related tumorigenesis in a mouse model of colorectal cancer as well as its effect on gut microbiota composition. Azoxymethane (AOM)/dextran sulfate sodium (DSS) model was used in this study. Lactulose treatment was performed by feeding 2% lactulose for 14 weeks. Stool samples collected at 4 time points were used for metagenomic analysis of the microbiota. Pathological analysis was performed 21 weeks after AOM injection. AOM/DSS increased the macrophage counts, inflammatory cytokine expression, colorectal tumorigenesis, and imbalance in gut microbiota composition, as evidenced by increased pathogen abundance (e.g., Escherichia and Clostridium). Lactulose significantly inhibited the inflammatory events, and ameliorated inflammation and tumorigenesis. The composition of the intestinal microbiota was also restored upon lactulose treatment, and lactulose reduced pathogen abundance and increased the abundance of Muribaculum and Lachnospiraceae. Meanwhile, the pathways related to Crohn’s disease were downregulated after lactulose treatment. Our findings suggest that lactulose restores the structure and composition of the intestinal microbiota, mitigates inflammation, and suppresses inflammatory tumorigenesis.

Intestinal bacteria play important roles in the progression of colitis-associated carcinogenesis. Colostrum-derived Lacticaseibacillus rhamnosus Probio-M9 (Probio-M9) has shown a protective effect in a colitis-associated cancer (CAC) model, but detailed metagenomic analysis had not been performed. Here, we investigated the preventive effects of the probiotic Probio-M9 on CAC-model mice, tracking the microbiota. Feces were obtained at four time points for evaluation of gut microbiota. The effect of Probio-M9 on tight junction protein expression was evaluated in co-cultured Caco-2 cells. Probio-M9 treatment decreased the number of tumors as well as stool consistency score, spleen weight, inflammatory score, and macrophage expression in the CAC model. Probio-M9 accelerated the recovery of the structure, composition, and function of the intestinal microbiota destroyed by azoxymethane (AOM)/dextran sulfate sodium (DSS) by regulating key bacteria (including Lactobacillus murinus, Muribaculaceae bacterium DSM 103720, Muribaculum intestinale, and Lachnospiraceae bacterium A4) and pathways from immediately after administration until the end of the experiment. Probio-M9 co-culture protected against lipopolysaccharide-induced impairment of tight junctions in Caco-2 cells. This study provides valuable insight into the role of Probio-M9 in correcting gut microbiota defects associated with inflammatory bowel disease carcinogenesis and may have clinical application in the treatment of inflammatory carcinogenesis.

Background: Intestinal microbiota plays an important role in the progression of pulmonary hypertension (PH). Colostrum-derived Lacticaseibacillus rhamnosus Probio-M9 (Probio-M9) has shown protective effects against inflammation and remodeling. We investigated whether Probio-M9 supplementation could improve the pathology of PH. Methods: The monocrotaline (MCT)-induced PH model rats are created followed by Probio-M9 treatment. Microbiota and pathological analyses were performed to investigate the therapeutic effects of Probio-M9. Results: Probio-M9 significantly suppressed cardiovascular remodeling and reduced mortality in rats. Analysis of the fecal microbiota revealed that Probio-M9 significantly altered the gut microbiota of MCT model rats. Specifically, Alistipes sp009774895 and Duncaniella muris populations increased, whereas Limosilactobacillus reuteri_D, Ligilactobacillus apodeme and Monoglobus sp900542675 decreased compared to those in the MCT group. Focusing on the expression of GPNMB in macrophages and the localization of CD44, we found that the number of these cells increased in the MCT group but significantly decreased with Probio-M9 treatment. In lung tissue from PH patients, more GPNMB-positive macrophages were found than non-PH lungs, and an increase in CD44-positive cells was confirmed in the vicinity of GPNMB. Conclusions: Probio-M9 had a significant impact on the intestinal microbiota and GPNMB/CD44 positive cells in the lungs of PH rats.

(1) Background: Bifidobacterium plays a pivotal role within the gut microbiota, significantly affecting host health through its abundance and composition in the intestine. Factors such as age, gender, and living environment exert considerable influence on the gut microbiota, yet scant attention has been directed towards understanding the specific effects of these factors on the Bifidobacterium population. Therefore, this study focused on 98 adult fecal samples to conduct absolute and relative quantitative analyses of bifidobacteria. (2) Methods: Using droplet digital PCR and the PacBio Sequel II sequencing platform, this study sought to determine the influence of various factors, including living environment, age, and BMI, on the absolute content and biodiversity of intestinal bifidobacteria. (3) Results: Quantitative results indicated that the bifidobacteria content in the intestinal tract ranged from 10⁶ to 10⁹ CFU/g. Notably, the number of bifidobacteria in the intestinal tract of the school population surpassed that of the off-campus population significantly (p = 0.003). Additionally, the group of young people exhibited a significantly higher count of bifidobacteria than the middle-aged and elderly groups (p = 0.041). The normal-weight group displayed a significantly higher bifidobacteria count than the obese group (p = 0.027). Further analysis of the relative abundance of bifidobacteria under different influencing factors revealed that the living environment emerged as the primary factor affecting the intestinal bifidobacteria structure (p = 0.046, R² = 2.411). Moreover, the diversity of bifidobacteria in the intestinal tract of college students surpassed that in the out-of-school population (p = 0.034). This was characterized by a notable increase in 11 strains, including B. longum, B. bifidum, and B. pseudolongum, in the intestinal tract of college students, forming a more intricate intestinal bifidobacteria interaction network. (4) Conclusions: In summary, this study elucidated the principal factors affecting intestinal bifidobacteria and delineated their characteristics of intestinal bifidobacteria in diverse populations. By enriching the theory surrounding gut microbiota and health, this study provides essential data support for further investigations into the intricate dynamics of the gut microbiota.

Mongolians have high overweight prevalence linked to their nomadic lifestyle and diet, but gut microbiota studies in this population are scarce. This study used fecal metagenomic and serum metabolomic analyses of 96 Mongolian participants (normal-weight n = 55, overweight n = 41) to characterize gut microbiome alterations and identify weight-related biomarkers. The analyses revealed that Parabacteroides distasonis, Barnesiella intestinihominis, and Alistipes onderdonkii were significantly reduced in overweight individuals (p < 0.05). Concurrently, the metabolites such as beta-cryptoxanthin, p-cresol, and ribothymidine were significantly down-regulated in the overweight group (p < 0.05). Random forest models from the three datasets showed a strong diagnostic ability for microbial families (AUC > 0.70). A subsequent integrated multi-kingdom classifier that combined microbiota and metabolite data achieved the highest performance (AUC = 0.818). Key features with high predictive contributions were identified, including Lactobacillus crispatus, Alistipes onderdonkii, and Parabacteroides distasonis, and metabolites, such as beta-cryptoxanthin, p-cresol, and picolinic acid. These results show the random forest model has high predictive value for distinguishing normal weight and overweight individuals. In summary, this study identified specific gut microbiota and serum metabolomic profiles linked to overweight in Mongolians. Multi-omics integration established a diagnostic biomarker model, laying a theoretical basis for microbiome-targeted weight management interventions.