Multi-Strain Probiotics Alleviate Food Allergy-Induced Neurobehavioral Abnormalities by Regulating Gut Microbiota and Metabolites

Abstract

Background and aim: Neurobehavioral changes associated with food allergies have been reported, but the therapeutic effects of probiotics have not been fully explored. Our study aimed to investigate the impact of multi-strain probiotics on neurobehavioral outcomes and to elucidate the underlying mechanism via the microbiota-gut-brain axis. Methods: C57BL/6J Male mice were randomly divided into the following three groups: (1) control group; (2) OVA-sensitized group; (3) OVA-sensitized group treated with multi-strain probiotics (OVA + P). Anaphylactic reactions and behavioral abnormalities were assessed by histological, immunological, and behavioral analyses. To further elucidate the underlying mechanisms, the prefrontal cortex was collected for microglial morphological analysis, while serum and fecal samples were obtained for untargeted metabolomic profiling and 16S rDNA-based gut microbiota analysis, respectively. Results: Multi-strain probiotics significantly alleviated anaphylactic reactions in OVA-sensitized mice, as evidenced by reduced serum IgE levels, decreased Th2 cytokines, and reduced epithelial damage. Meanwhile, neurobehavioral symptoms were alleviated, including anxiety-like and depression-like behaviors, repetitive behaviors, social avoidance, and impaired attention. Mechanistically, probiotics administration suppressed production of inflammatory cytokines (TNF-α, IL-1β and IL-6) and inhibited activation of M1 microglia in the prefrontal cortex, which might contribute to neuron recovery. Furthermore, multi-omics analysis revealed that amino acid metabolism restoration in OVA + P mice, particularly carboxylic acids and derivatives, which was remarkably correlated with alterations in gut microbiota and behaviors related to FA. Conclusions: Gut microbiota and its amino acid metabolites mediate the therapeutic effects of multi-strain probiotics on FA-induced behavioral abnormalities. These effects occur alongside the suppression of neuroinflammation and microglial activation in the prefrontal cortex. Our findings highlight the neuroimmune regulatory role of the gut-microbiota-brain axis and support the potential use of probiotics as an intervention for FA-induced brain dysfunctions.

Keywords: amino acids metabolism; food allergy; gut microbiota; metabolomics; neurobehavior; probiotics.

Figure 1

Schematic timeline of experimental procedure.

Figure 2

Multi-strain probiotics attenuated allergic responses in OVA mice. (A) The gains of body weights. (B) Anaphylactic symptoms. (C) Rectal temperature. (D) Serum OVA-specific IgE. (E) Serum OVA-specific IgG1. (F) Serum histamine. (G) Representative images of intestinal morphology by H&E staining. (H) Serum IL-4. (I) Serum IL-5. (J) Serum IL-17. (K) Serum IL-10. n = 8–9 in each group. Data are presented as the means ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001 using Mann-Whitney U test.

Figure 3

Positive effect of multi-strain probiotics in neurobehaviors. (A–C) Open Field Test (OFT). (D,E) Elevated plus maze (EPM). (F) Forced swimming test (FST). (G) Self-grooming. (H) Marble burying test. (I,J) Non-sustained, non-sustained visual attention test (NNAT). The NNAT attention level = [(time spent exploring new object)/(total time spent exploring objects)] × 100%. (K) Representative trajectory heatmap of mice in session 1 and session 2 of three-chamber sociability test (TST). S1: Stranger mice 1. S2: Stranger mice 2. E: Empty cup. (L–N) TST in Session 1. Social index (SI) = [(time spent interaction with stranger 1)/(time spent interaction with empty cup)] × 100%. (O–Q) TST in Session 2. Social novelty index (SNI) = [(time spent interaction with stranger 2)/(time spent interaction with stranger 1)] × 100%. n = 8–12 in each group. Data are presented as the means ± SEM. ns = not significant. * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001 using Mann-Whitney U test.

Figure 4

Multi-strain probiotics alleviated neuroinflammation and neural damage in prefrontal cortex. (A) TNF-α in PFC. (B) IL-6 in PFC. (C) IL-1β in PFC. (D) Representative images displaying the morphological changes in microglia in PFC. (E–J) Sholl analysis: (E) intersecting radii, (F) sum intersection, (G) mean intersection, (H) max intersection, (I) max intersection radius and (J) ramification index. n = 5 in each group. (K) Representative images displaying the neuron changes in Nissl stain. (L) Neuron counts in PFC. (M) Nissl-positive cell counts per 20× field. n = 5 in each group. Data are presented as the means ± SEM. ns = not significant. * p < 0.05, ** p < 0.01, *** p < 0.00 using Mann-Whitney U test.

Figure 5

Imbalance of gut microbiota in OVA-induced FA mice. (A,B) Alpha diversity analysis: (A) Simpson and (B) Shannon. (C,D) Beta diversity analysis performed by (C) PCA and (D) PCoA. (E) Different gut bacterial composition at family level in three groups. (F–M) The relative abundance of 8 bacteria in three groups at family level. n = 5 in each group. Data are presented as the means ± SEM. ns = not significant. * p < 0.05, ** p < 0.01.

Figure 6

Effects of OVA-induced FA on metabolic differences in the serum. (A) The heatmap of serum metabolites level in three groups. (B–E) Unsupervised PCA was performed in serum metabolites of three groups in (B) positive mode and (C) negative mode. Supervised PCA analysis (OPLS-DA) showed differences in serum metabolism groups within three groups in (D) positive mode and (E) negative mode. (F) The involved pathway enriched by significantly differential metabolites in KEGG level 1 and level 2.

Figure 7

Heatmap obtained by Pearson’s hierarchical clustering of metabolites selected from carbohydrate metabolism and amino acid metabolism based on p value < 0.05.

Figure 8

Correlation analysis between different gut microbiota and serum metabolites with behavioral index. * p < 0.05, ** p < 0.01, *** p < 0.001.