Page 77 - Read Online

Page 10 of 14 Tian et al. Microbiome Res Rep 2024;3:49 https://dx.doi.org/10.20517/mrr.2024.33

Figure 4. Pediococcus acidilactici CCFM6432’s effect on the fecal metabolome. (A) Differences in gut microbiota metabolites before and
after intervention in the CCFM6432 group. Fold change > 2 and P < 0.05 were set as the thresholds for biomarker screening; (B)
Differences in gut microbiota metabolites between the placebo group and the CCFM6432 intervention group at the end of treatment.
**
Fold change > 2 and P < 0.05 were set as the thresholds for biomarker screening; (C) Absolute quantification of DL-lactic acid. P < 0.01
##
in paired t-test. P < 0.01 in the unpaired t-tests; (D) Correlation analysis between changes in scale scores and changes in gut
metabolites before and after intervention. The correlation was established by DSPC network. The size of each node is proportional to the
number of connections. Red lines represent a positive correlation, and blue lines represent a negative correlation. The width of the line
represents the correlation strength. DSPC: Debiased sparse partial correlation.
depressed patients [33,34] . Patients undergoing cytokine therapy for viral infections or tumors often experience
severe depressive symptoms, accompanied by an increased risk of suicide . Notably, disruptions in gut
[35]
microbiota may be an important risk factor for inflammatory depression, as the gut hosts numerous
immune cells, such as macrophages and dendritic cells. Moreover, intestinal epithelial cells express a variety
of pattern recognition receptors, including Toll-like receptors (TLRs) and NOD-like receptors, which can