Marine fungal metabolite butyrolactone I prevents cognitive deficits by relieving inflammation and intestinal microbiota imbalance on aluminum trichloride-injured zebrafish

Abstract

Background: Mounting evidences indicate that oxidative stress, neuroinflammation, and dysregulation of gut microbiota are related to neurodegenerative disorders (NDs). Butyrolactone I (BTL-I), a marine fungal metabolite, was previously reported as an in vitro neuroprotectant and inflammation inhibitor. However, little is known regarding its in vivo effects, whereas zebrafish (Danio rerio) could be used as a convenient in vivo model of toxicology and central nervous system (CNS) diseases.

Methods: Here, we employed in vivo and in silico methods to investigate the anti-NDs potential of BTL-I. Specifically, we established a cognitive deficit model in zebrafish by intraperitoneal (i.p.) injection of aluminum trichloride (AlCl₃) (21 μg) and assessed their behaviors in the T-maze test. The proinflammatory cytokines interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) as well as acetylcholinesterase (AChE) activity or glutathione (GSH) levels were assayed 24 h after AlCl₃ injection. The intestinal flora variation of the zebrafish was investigated by 16S rDNA high-throughput analysis. The marine fungal metabolite, butyrolactone I (BTL-I), was used to modulate zebrafish cognitive deficits evoked by AlCl₃ and evaluated about its effects on the above inflammatory, cholinergic, oxidative stress, and gut floral indicators. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) and drug-likeness properties of BTL-I were studied by the in silico tool ADMETlab.

Results: BTL-I dose-dependently ameliorated AlCl₃-induced cognitive deficits in zebrafish. While AlCl₃ treatment elevated the levels of central and peripheral proinflammatory cytokines, increased AChE activity, and lowered GSH in the brains of zebrafish, these effects, except GSH reduction, were reversed by 25-100 mg/kg BTL-I administration. Besides, 16S rDNA high-throughput sequencing of the intestinal flora of zebrafish showed that AlCl₃ decreased Gram-positive bacteria and increased proinflammatory Gram-negative bacteria, while BTL-I contributed to maintaining the predominance of beneficial Gram-positive bacteria. Moreover, the in silico analysis indicated that BTL-I exhibits acceptable drug-likeness and ADMET profiles.

Conclusions: The present findings suggest that BTL-I is a potential therapeutic agent for preventing CNS deficits caused by inflammation, neurotoxicity, and gut flora imbalance.

Keywords: Acetylcholinesterase; Butyrolactone I; Inflammation; Intestinal flora; Neurodegenerative diseases; Oxidative stress.

Fig. 1

A general diagram summarizing the experimental design used in the present study. Inset: the chemical structure of BTL-I

Fig. 2

Behavioral performance of zebrafish in the enriched chamber zone of the T-maze test. a The latency (s) of first entry into the EC zone of the T-maze test. Day 5: *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.001, vs. the control group; ^#P < 0.05, ^##P < 0.01, ^###P < 0.005, ^####P < 0.001, vs. the model group. b Average swimming speed on the fifth day (n = 6). c The number of entries to the EC zone on the fifth day (n = 6). *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.001, vs. the control group; ^#P < 0.05, ^##P < 0.01, ^###P < 0.005, ^####P < 0.001, vs. the model group

Fig. 3

Heatmaps of zebrafish activity in the T-maze on the fifth day. The X-axis and Y-axis in the figure represent the motion trajectory of zebrafish, while the Z-axis represents the residence time of zebrafish. The higher the Z-axis is, the longer the residence time of zebrafish at a certain point

Fig. 4

The results of biochemical indices of zebrafish (n = 3). a GSH content in zebrafish brain tissue. b AChE activity in zebrafish brain tissue. c–f IL-1β and TNF-α content in zebrafish brain and peripheral tissue. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.001, vs. the control group; ^#P < 0.05, ^##P < 0.01, ^###P < 0.005, ^####P < 0.001, vs. the model group

Fig. 5

Statistics of the OTUs. a Statistics of the OTUs of 15 samples. b Differences in the distribution of OTUs between groups using the Venn diagram. Group B1: model, Group B2: control, Group B3: 25 mg/kg BTL-I + AlCl₃, Group B4: 50 mg/kg BTL-I + AlCl₃, Group B5: 100 mg/kg BTL-I + AlCl₃

Fig. 6

Alpha-diversity. a The rarefaction curves. b The rank–abundance curves. c–f Alpha indices

Fig. 7

Beta diversity. a Jaccard algorithm (stress value = 0.137). Only the presence or absence of OTUs in the sample was considered, not the abundance. b Bray–Curtis algorithm (stress value = 0.096). Both the presence and absence of OTUs in the sample and the abundance were considered. c Unweighted-UniFrac algorithm (stress value = 0.064). It only considers whether the sequence was present in the community, not the abundance of the sequence. d Weighted-UniFrac algorithm (stress value = 0.069). It accounts for the abundance of sequences on the basis of unweighted UniFrac and was able to differentiate species abundance

Fig. 8

Relative abundance (a and b) and analysis of differential microorganisms (c and d). a and c Results at the phylum level. b and d Results at the genus level. *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.001, vs. the control group; ^#P < 0.05, ^##P < 0.01, ^###P < 0.005, ^####P < 0.001, vs. the model group

Fig. 9

LDA effect size analysis. a Branching diagram of the evolution of different species between the control, model and experimental groups. b Bar graph of LDA values for different species