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. 2023 Nov 19;13(12):409. doi: 10.1007/s13205-023-03831-5

Evaluation of gut microbiota composition to screening for potential biomarker in AFB1-exposed sheep

Luxi Lin 1,#, Pengfei Fu 1,#, Chaodong Zhang 1, Tingting Xu 1, Qinqin Cao 1, Aftab Shaukat 2, Ke Yue 1, Fang Liu 1, Haiju Dong 1, Shucheng Huang 1,✉, Fuchun Jian 1,✉
PMCID: PMC10657922  PMID: 37990733

Abstract

Aflatoxin B1 (AFB1) is an inevitable contaminant in animal feed and agricultural products, which seriously threatens the health of animals. However, there is currently no better diagnostic tool available than depending on clinical symptoms, pathophysiology, biochemical indicators, etc. Here, we profiled the fecal microbiomes of sheep exposed to and not exposed to AFB1 to identify potential non-invasive biomarkers of AFB1 intoxication by 16S rRNA gene sequencing technology, while measuring serum biochemical indexes. The results showed that the sheep exposed to AFB1 had significantly higher levels of the liver function indicators ALT (alanine transaminase) and AST (aspartate aminotransferase), and their microbial profiles were different from those of the CON (Control) group. In detail, the relative abundance of seven phyla and three genera were overrepresented in the AFB1 group from top 10 relative abundance. Importantly, we found that Prevotella and Bifidobacterium were significantly different in the CON and AFB1 groups (p = 0.032 and p = 0.021, respectively) based on linear discriminant analysis effect size (LEfSe) and random forest analysis. Additionally, the area under curve (AUC) of ALT was 1 (95% CI 1.00–1.00; p < 0.001) and that of Bifidobacterium was 0.95 (95% CI 0.81–1.00; p = 0.0275), suggesting that Bifidobacterium correlated with ALT (r = 0.783, p < 0.01) may be a potential biomarker for AFB1 exposure in sheep.

Keywords: Aflatoxin B1, Bifidobacterium, Biomarker, Gut microbiota

Introduction

Aflatoxins are the most known and usually contaminate grain and feed that cause disease in livestock (Benkerroum 2020; Hernández-Ramírez et al. 2020). Its derivative aflatoxin B1 (AFB1) is the most widely distributed and most toxic and carcinogenic among the known mycotoxins (Cao et al. 2021). Generally, animals ingest AFB1 by intestinal epithelial cells to enter the blood circulation, and transport to different organs, causing acute or chronic organ damage (Benkerroum 2020; Huang et al. 2023; Robert et al. 2017). Our previous study has found that AFB1 can cause a decrease in the muscle quality of mutton sheep (Cao et al. 2021). Moreover, AFB1 can also be transferred to its milk metabolite from various feedstuff, after mammals such as dairy cows, sheep or goats, ingest contaminated feeds (Jiang et al. 2021). Notably, AFB1 can be stable in food processing and can eventually remain in animal-derived food (such as meat, eggs, and milk), bringing huge safety risks to human health (Alshannaq and Yu 2017; Kumar et al. 2017).

It is common that aflatoxin poisoning in sheep is caused by feed mildew, thus an early diagnosis of AFB1 toxicity is essential for treatment (Rushing and Selim 2019). Severe AFB1 poisoning can be easily diagnosed by clinical manifestations in animals but has no therapeutic value owing to serious health problems. However, apart from relying on clinical symptoms, pathophysiology, biochemical indicators, etc., there is still no better diagnostic technology (Aleissa et al. 2020; Cao et al. 2021; Elgioushy et al. 2020; Rajput et al. 2021). The findings of recent studies revealed that microbial composition within the gastrointestinal tract mirrors the physiological and metabolic features of the organism (Xie et al. 2021). These microorganisms coexist with the host and play an essential role in the nutrient metabolism and immune homeostasis of human and animal, encompassing the emergence and development of disease (Ma et al., 2023; Pickard et al. 2017; Rowland et al. 2018). Moreover, some researches have emphasized that detecting the characteristics of the 16S rRNA gene profile of the gut microbiota provides rich clues for the diagnosis and evaluation of diseases and can be used as a forecast for therapeutic intervention or as a monitor for disease prognosis (Hao et al. 2021; Pan et al. 2020). This provides inspiration for the identification of microbial markers that can predict AFB1 intoxication in sheep.

To address these challenges, the microbial compositions of AFB1-poisoned sheep fecal samples were analyzed by 16S rRNA gene sequencing. Studies have previously have confirmed that the 16S rRNA gene sequencing is an effective strategy for discovering potential biomarkers for various diseases (Gong et al. 2020). Therefore, we have identified and screened differential fecal microbiota for AFB1 poisoning in sheep based on this full-fledged strategy for assessing the prevalence of AFB1-poisoned sheep and providing an accurate and rapid clinical diagnosis.

Materials and methods

Materials, animals and experiment design

AFB1 (purity ≥ 99.8%, #2J0G25) was purchased from Pribolab Biological Engineering Co., Ltd. (Qingdao, China) and was prepared as an experimental solution with 4% ethanol before use. All sheep experiments in this study followed the guidelines of the Institutional Animal Welfare and Research Ethics Committee of the Henan Agricultural University (Permit No: 17-0126, Zhengzhou, China).

Ten 6-month-old healthy sheep with almost similar weight were randomly divided into two groups (n = 5): the CON group was gavaged with 4% ethanol solution (20 mL), and the AFB1-exposed group was gavaged with 1.0 mg AFB1/kg body weight (dissolved in 20 mL of 4% ethanol). AFB1 using method and concentration referenced Cao et al. (2021) and Lin et al. (2022). After fasting for 24 h, collect the fresh feces of the sheep with severe AFB1 poisoning symptoms in sterile containers, liquid nitrogen frozen, immediately kept at – 80 ℃ until use. Blood was collected from the jugular vein, centrifuged and the supernatant was stored at − 20 ℃.

Detection of serum ALT and AST activity

Serum was collected for the determination of the activity levels of liver function indicators ALT and AST. The methods were consistent with previous study (Lin et al. 2022).

16S rRNA gene sequencing and analysis

The fecal microbial DNA was extracted by using the Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA), following the kit instructions. DNA libraries were constructed for subsequent analysis as described previously (Huang et al. 2022). PCR amplification procedure was referred to Cao et al. (2021). Library construction was conducted using the TruSeq DNA PCR-Free Library Preparation Kit library building kit, and after Qubit quantification and Q-PCR quantification, Hiseq 2500 high-throughput sequencing on the Illumina Corporation platform. Later, the sequencing data were processed, including its quality control, purification, redundancy removal, and finally obtained high-quality sequence data that can be used for subsequent bioinformatics analysis. Data from different samples were subsequently normalized to the amount of data at the same sequencing depth for bioinformatic analysis.

Differential microbiota analysis

Alpha diversity analysis of the gut microbiota was performed using standardized data for comparison, and community composition was counted at the phylum and genus levels (Tian et al. 2022). Differential species were screened for each group of characteristic bacteria with specificity by linear discriminant analysis (LDA) effect size (LEfSe) analysis. The distribution of LDA values shows significantly enriched species within each group by bar graphs of significantly different species and species taxonomic clade plots to show the taxonomic hierarchical distribution of marked species in each group of samples.

Statistical analysis

The t test was used for the analysis of serum indicators. Regression analysis was executed using GraphPad Prism V.8.0.2 software to construct the model and obtain the ROC curve. The area under the ROC curve (AUC) represents the ROC effect. p value < 0.05 was considered statistically significant.

Results

AFB1 exposure increases serum biochemical markers

As shown in Fig. 1, compared with the CON group, the activities of ALT and AST in the AFB1 group were obviously elevated (p < 0.001 or p < 0.01). This indicated that the liver function of sheep was damaged. It suggested that AFB1 exposure damaged the metabolic system of sheep.

Fig. 1.

Fig. 1

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AFB1 exposure increases sheep liver function enzyme levels. A The activity of alanine aminotransferase in the serum (ALT). B The activity of Serum aspartate aminotransferase in the serum (AST). n = 5, and p < 0.05 indicates a difference

Difference analysis of gut microbiota composition

Alpha diversity measures were used to identify within individual taxa richness and diversity, which characterized by Chao1, Observed species, Shannon, and Simpson indices in the present study (Fig. 2A). Non-significant difference in alpha diversity was observed between the CON group and the AFB1 group, but the Shannon and Simpson indices showed the trend of downward. This suggested that AFB1 exposure might reduce fecal microbial abundance.

Fig. 2.

Fig. 2

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AFB1 exposure alters sheep fecal microbial composition. A Alpha diversity index. Chao1 and Observed species indices characterize richness. Diversity characterized by Shannon and Simpson indexes. B Changes of intestinal microbial composition at phylum level (TOP 10). C Abundance analysis of gut microbiota at the genus level (TOP 20). D Linear discriminant analysis (LDA) effect size (LEfSe) analyzes the most diverse classification units. The vertical coordinates are the classification unit with significant differences between groups, and the horizontal coordinates indicate the log LDA score of each unit in the bar graph plane (LDA score > 2 is displayed). E Cladogram made by LEfSe reflecting the phylogenetic distribution of gut microbiota communities between CON group and AFB1 group

At the phylum level (Fig. 2B), phyla with higher abundance include Firmicutes, Bacterioides, Spirochaetes, and other phyla. And among the TOP10 phyla that Firmicutes, TM7 and Tenericutes in the AFB1 group were lower. At the same time, the relative abundance of Bacteroidetes, Spirochaetes, Proteobacteria, Actinobacteria, Verrucomicrobia, Fibrobacteres, and Deferribacteres were higher than those in the CON group (p < 0.01). At the genus level (Fig. 2C), compared with the CON group, the abundance of Clostridium, Clostridiaceae Clostridium, Bacillaceae Bacillus, CF231, Planococcaceae Bacillus, Sporosarcina, and Solibacillus in the AFB1 group were reduced. In contrast, the abundance of Treponema, Rumenococcus, and BF3111 were increased. These results indicate significant changes in gut microbiota composition in sheep after AFB1 poisoning.

The histogram of the distribution of LDA values showed the species significantly enriched within each group (Fig. 2D) with the AFB1 group significantly enriched for Bifidobacterium at the genus level, and the CON group were enriched mainly for Erysipelotrichi at the class level, RF32 at the order level and Erysipelotrichaceae at the family level (Fig. 2E).

Analysis of fecal microbial marker species

To further compare the differences in species composition among the groups, the abundance data of the top 20 genera with average abundance were used to draw the heat map for species composition analysis (Fig. 3A). Similarly, it was found that compared with the CON group, the fecal microbial composition changed significantly after AFB1 poisoning. At the genus level, the top 20 significantly differences microbiota were then screened by random forest analysis, including Prevotella, Bifidobacterium, SMB53, CF231, BF311, Bisobacteria, succinate, Mogibacterium, Clostridium, p-75-a5, Rumococcus, Anaerobic bacteria, Tremillella, Paludia, Anaerofustis, Ensoglyophila, Porphymononas, Campyomonas, Vibrio, Fibacterium (Fig. 3B).

Fig. 3.

Fig. 3

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Screening differential fecal microorganisms. A Heat map of species composition at the genus level (TOP 20). B Random Forests analysis (Top 20 in importance). C Venn diagram analysis of differential genera. The differential genera screened by LEfSe analysis and random forest analysis were analyzed by Venn plot, and the coincidence part was the potential biomarkers. D Comparison of relative abundance of Prevotella. E Comparison of relative abundance of Bifidobacterium. The two sample groups were compared by Wilcoxon rank-sum test. p value < 0.05 is significant. F–I The correlation between differential microorganisms and serum biochemical indexes was analyzed by CORREL function. The greater the |r| value, the stronger the correlation. r > 0 indicates positive correlation, r < 0 indicates negative correlation. J–M ROC analysis of Prevotella, Bifidobacterium, ALT, and AST. AUC close to 1.00 indicated high sensitivity and specificity. CI, confidence interval. Student's t test was used to analyze the relative abundance of the two differential metabolites, and p < 0.05 is significant

Venn diagram analysis of differential genera selected from LEfSe analysis and random forest analysis identified two signature genera, Prevotella and Bifidobacterium, respectively (Fig. 3C). Comparison of the relative abundances of Prevotella and Bifidobacterium genera in the two groups by Wilcoxon rank-sum test showed that the AFB1 group was significantly higher than the CON group (all p < 0.05; Fig. 3D–E).

Predictive ability of fecal microbiota

To evaluate whether the changes in fecal microorganisms can reflect the body's response after AFB1 exposure, we analyzed the correlation between differential microbiota and serum markers by CORREL function. As shown in Fig. 3F–G, Prevotella was positively correlated with ALT and negatively correlated with AST (r = 0.2106, p < 0.01; r = − 0.1645, p < 0.001). Bifidobacterium was positively correlated with ALT and AST (r = 0.7831, p < 0.01; r = 0.6353, p < 0.001; Fig. 3H–I). Notably, Bifidobacterium has a stronger correlation with serum markers.

Furthermore, the predictive power and diagnostic capability of serum biochemical indexes and the differential fecal microbiota in AFB1-exposed and normal sheep were assessed by ROC curve analysis. The results showed that the AUC of Prevotella was 0.55 (95% CI 0.89–1.00; p = 0.806; Fig. 3J), the AUC of Bifidobacterium was 0.95 (95% CI 0.81–1.00; p = 0.0275; Fig. 3K), the AUC of ALT was 1 (95% CI 1.00–1.00; p < 0.001; Fig. 3L), and the AUC of AST was 0.81 (95% CI 0.63–0.92; p = 0.009; Fig. 3M). It can be seen that the sensitivity and specificity of Bifidobacterium are higher than that of Prevotella.

Discussion

Biomarkers are crucial to developing medical-specific therapy (Califf 2018). Increasing studies have confirmed that gut microbiota has become a research hotspot in finding biomarkers (Gong et al. 2020; Pan et al. 2020; Russo 2021). Although a previous report showed that serum biochemical indexes including ALT, AST, and lactate dehydrogenase (LDH) widespread reflect AFB1 poisoning (Elgioushy et al. 2020). However, these serum enzymes are not specific for AFB1 poisoning, which can easily cause misdiagnosis and secondary damage to animals. Nevertheless, biological methods using microorganisms and their metabolites are considered a promising mycotoxin solution (Ghazvini et al. 2016; Salem et al. 2018). Lactobacillus plantarum C88 was proved to improve AFB1-induced excessive apoptosis of hepatocytes and effectively inhibit AFB1 toxicity (Huang et al. 2019, 2017). Chen et al. (2019) found that Lactobacillus bulgaricus and Lactobacillus rhamnosus also could treat AFB1 poisoning. Therefore, the study of gut microbial inhibition of AFB1-induced toxicity is of great significance.

In recent years, biomarkers discovered using 16S rRNA gene high-throughput sequencing technology have become a research hotspot for developing new clinical testing methods, conducive to measurement standardization, and are a mature strategy to identify potential disease-related markers (Gong et al. 2020; Jiao et al. 2018; Pan et al. 2020; Russo 2021). In this experiment, we used 16S rRNA high-throughput sequencing technology to analyze sheep’s fecal samples and found that some special bacteria at the phylum or genus level of fecal microbes changed significantly after AFB1 exposure. These findings are consistent with our previous research results and indicated that AFB1 exposure could cause intestinal flora disorder in mutton sheep (Cao et al. 2021; Lin et al. 2022).

LEfSe and random forest analysis are professional methods for screening biomarkers, used to identify the target biomarker by both Li et al. (2020). Our study based on LEfSe analysis and random forest analysis, we found that Prevotella and Bifidobacterium were significantly different in Control and AFB1 groups. Prevotella species is the largest single bacterial group in rumen of cattle and sheep under most dietary systems, where they help the breakdown of protein and carbohydrate foods (Flint and Duncan 2017). It is reported that Bifidobacterium can compete with other gastrointestinal bacteria and occupy a large proportion of the gastrointestinal tract flora, which may be related to its ability to use a large number of molecules to obtain energy (Schell et al. 2002). Ghazvini et al. (2016) showed that Bifidobacterium could be used as an inhibitor for the growth of Aspergillus species. Ruminants usually have stronger resistance to AFB1 than other monogastric animals, because rumen microorganisms can partially reduce AFB1 (Jean-Philippe et al. 2019). These collective findings demonstrated that Bifidobacterium might play an important role in the diagnosis of AFB1 exposure.

Conclusion

These results indicated that AFB1 could cause alterations in the structure and composition of the gut microbiota of mutton sheep, leading to gut microbiota disorder. Meanwhile, we screened Bifidobacterium by LEfSe and random forest analysis, which has the high predictive ability and may be used as a potential biomarker for the prediction and clinical diagnosis of AFB1 poisoning in mutton sheep. Our finding may provide a new potential path to developing a non-invasive tool for the early prediction of AFB1 exposure.

Acknowledgements

Thanks to Aftab Shaukat for the linguistic revisions to this manuscript.

Author contributions

LL: analysis and interpretation of data, drafting the article for important intellectual content, final approval of the version to be submitted. PF: methodology, data curation, and investigation. CZ: investigation. TX: methodology. QC: methodology. AS: revising it critically for important intellectual content. KY: methodology. FL: Supervision and resources. HD: acquisition of data. SH: conception and design of the study, supervision, resources, writing–review and editing. FJ: supervision and resources.

Funding

This study was supported by the China Postdoctoral Science Foundation (Grant No. 2023T160198); the Outstanding Talents of Henan Agricultural University (Grant No.30500421); the China Agriculture Research System of MOF and MARA (Grant No. nycyt-38); and the Open Project Program of Beijing Key Laboratory of Traditional Chinese Veterinary Medicine at Beijing University of Agriculture (TCVM-201702).

Data availability

The bacterial 16 S rRNA sequencing data obtained from the fecal have been deposited in the NCBI Sequence Read Archive (SRA) database under accession number PRJNA728434.

Declarations

Conflict of interest

The authors report there are no competing interests to declare.

Footnotes

Luxi Lin and Pengfei Fu have contributed equally to this work.

Contributor Information

Shucheng Huang, Email: huang.sc@henau.edu.cn.

Fuchun Jian, Email: jfchun2008@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The bacterial 16 S rRNA sequencing data obtained from the fecal have been deposited in the NCBI Sequence Read Archive (SRA) database under accession number PRJNA728434.

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