Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification

Background and purpose When it comes to the onset of moyamoya disease (MMD), environmental variables are crucial. Furthermore, there is confusion about the relationship between the gut microbiome, an environmental variable, and MMD. Consequently, to identify the particular bacteria that cause MMD, we examined the gut microbiome of MMD individuals and healthy controls (HC). Methods A prospective case-control investigation was performed from June 2021 to May 2022. The fecal samples of patients with MMD and HC were obtained. Typically, 16S rRNA sequencing was employed to examine their gut microbiota. The QIIME and R softwares were used to examine the data. The linear discriminant analysis effect size analysis was used to determine biomarkers. Multivariate analysis by linear models (MaAsLin)2 were used to find associations between microbiome data and clinical variables. Model performance was assessed using the receiver operating characteristic curve and the decision curve analysis. Results This investigation involved a total of 60 MMD patients and 60 HC. The MMD group’s Shannon and Chao 1 indices were substantially lower than those of the HC cohort. β-diversity was significantly different in the weighted UniFrac distances. At the phylum level, the relative abundance of Fusobacteriota/Actinobacteria was significantly higher/lower in the MMD group than that in the HC group. By MaAsLin2 analysis, the relative abundance of the 2 genera, Lachnoclostridium and Fusobacterium, increased in the MMD group, while the relative abundance of the 2 genera, Bifidobacterium and Enterobacter decreased in the MMD group. A predictive model was constructed by using these 4 genera. The area under the receiver operating characteristic curve was 0.921. The decision curve analysis indicated that the model had usefulness in clinical practice. Conclusions The gut microbiota was altered in individuals with MMD, and was characterized by increased abundance of Lachnoclostridium and Fusobacterium and decreased abundance of Bifidobacterium and Enterobacter. These 4 genera could be used as biomarkers and predictors in clinical practice.


Bacterial DNA extraction and 16S rRNA sequencing
Total genome DNA from samples was extracted using CTAB method.DNA concentration and purity was monitored on 1% agarose gels.According to the concentrations, DNA was diluted to 1ng/µL using sterile water.
Mix same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection.PCR products was mixed in equidensity ratios.Then, mixture PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany).
Sequencing libraries were generated using TruSeq DNA PCR-Free Sample Preparation Kit (Illumina, USA) following manufacture's recommendations and index codes were added.The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system.At last, the library was sequenced on an Illumina NovaSeq platform and 250 bp paired-end were generated.

Microbiome Bioinformatics
Paired-end reads was assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence.Paired-end reads were merged using FLASH (V1.2.7, http://ccb.jhu.edu/software/FLASH), a very fast and accurate analysis tool, which was designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment, and the splicing sequences were called raw tags [1].Quality filtering on the raw tags were performed under specific filtering conditions to obtain the high-quality clean tags according to the QIIME (V1.9.1, http://qiime.org/scripts/split_libraries_fastq.html)quality controlled process [2,3].
Sequences analysis were performed by Uparse software (Uparse v7.0.1001, http://drive5.com/uparse)[6].Sequences with ≥97% similarity were assigned to the same OTUs.Representative sequence for each OUT was screened for further annotation.For each representative sequence, the Silva Database (http://www.arb-silva.de/) was used based on Mothur algorithm to annotate taxonomic information [7].In order to study phylogenetic relationship of different OTUs, and the difference of the dominant species in different samples (groups), multiple sequence alignment were conducted using the MUSCLE software (Version 3.8.31,http://www.drive5.com/muscle/)[8].OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences.Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data.A, In the HC group, Genus Enterobacter was identified in 82.61% of males and 89.19% of females (p = 0.446).In the MMD group, Genus Enterobacter was identified in 19.35% of males and 24.14% of females (p = 0.653).In total, Enterobacter was identified in 46.30% of males and 60.61% of females (p = 0.118); B, In the HC group, Genus Fusobacterium was identified in 52.17% of males and 29.73% of females (p = 0.082).In the MMD group, Genus Fusobacterium was identified in 70.97% of males and 65.52% of females (p = 0.650).In total, Fusobacterium was identified in 62.96% of males and 45.45% of females (p = 0.056).

Table I. Comparison of baseline characteristics between ischemic and hemorrhagic MMD
Figure III.Envfit analysis associated to the PCoA was performed to identify whether sex, smoking, drinking, or hypertension have an effect on microbial composition distribution.

Figure IV. Comparison of microbial diversity between MMD with wild-type p.R4810K variants (GG) and heterozygous p.R4810K variants (GA).
A, There was no significant difference in α-diversity (p > 0.05); B, There was no significant difference in β-diversity (p > 0.05); C, The Venn diagram displaying the overlaps between these two groups indicated that 881 OTUs were shared among the two groups.MMD, moyamoya disease; GG_MMD, moyamoya disease with wild-type p.R4810K variants; GA_MMD, moyamoya disease with heterozygous p.R4810K variants  MMD was associated with decreased relative abundances of Enterobacter and Bifidobacterium and increased relative abundances of Lachnoclostridium and Fusobacterium.
False discovery rate adjusted p < 0.2 was considered statistically significant for taxonomic analysis.

Figure I .
Figure I. Relationship between the relative abundance of the differential genera and clinical indices.

Figure V .
Figure V.Comparison of microbial diversity between MMD with Suzuki stage of 0-2 and Suzuki stage of 3-6.

Figure VI .
Figure VI.Genera association with population characteristics.Correlation and statistical significance were determined by MaAsLin2 with multiple comparison adjustment by false discovery rate.