Identification of the intestinal microbes associated with muscle strength

doi:10.21203/rs.3.rs-2649903/v1

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Research Article

Identification of the intestinal microbes associated with muscle strength

https://doi.org/10.21203/rs.3.rs-2649903/v1

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Background

Considering the effect of the gut microbiome on human physiology and aging, the gut microbiome may affect muscle strength in the same way as the host's own genes. Previous research has shown that the gut microbiome can have both positive and negative effects on muscle strength, suggesting the presence of two different types of gut microbiome. In this study, we demonstrate that the gut microbiome can contribute differently to muscle strength.

Results

We remodelled the original gut microbiome of mice through fecal microbiome transplant (FMT) using human feces, and compared the changes in muscle strength of the same mice before and three months after FMT. We found that FMT affected muscle strength in three different ways: positive, none, and negative. Analysis of the phylogenesis, α-diversities, and β-diversities on the gut microbiome in the three groups showed that a more diverse group of intestinal microbes was established after FMT in each of the three groups, indicating that the human gut microbiome is more diverse than that of mice. The remodelled gut microbiome by FMT in each group was also different from each other. Fold change and linear correlation analyses identified Phocaeicola barnesiae, Eisenbergiella massiliensis, and Anaeroplasma abactoclasticum in the gut microbiome as positive contributors to muscle strength, while Ileibacterium valens and Ethanoligenens harbinense were found to have negative effects.

Conclusions

This study not only confirms the presence of gut microbiomes that contribute differently to muscle strength, but also explains the mixed results in previous research on the association between the gut microbiome and muscle strength.

muscle strength

the gut microbiome

fecal microbiome transplant

FMT

Phocaeicola barnesiae

Eisenbergiella massiliensis

Anaeroplasma abactoclasticum

Ileibacterium valens

Ethanoligenens harbinense

A typical human carries a complex and massive microbial community consisting of 100 trillion microbes in the intestine, known as the gut microbiome [1]. Recent research more and more evidently shows that the gut microbiome play a significant role in overall health, including digestion, immunity, and even mental health [2–6]. Because of its critical roles, dysbiosis of the gut microbiome can cause various diseases such as inflammatory bowel disease, gastrointestinal tract malignancies, cancer, cholelithiasis, autism, sarcopenia, cachexia, hepatic encephalopathy, allergies, obesity, diabetes, atherosclerosis, metabolic syndrome, Alzheimer’s disease, Parkinson’s disease, etc [7–16]. On the other hand, it has been shown that maintaining a healthy gut microbiome have many health benefits [17–22]. Although current research obviously showed that the gut microbiome affects the digestion, immunity, and mental health of its host [23], the effect of the gut microbiome on physiology has not been thoroughly investigated.

Considering the results of current research on the function of the gut microbiome, there is a strong possibility that the gut microbiome may affect muscle strength of its host as much as the host's own genes do [24]. However, the role of the gut microbiome in muscle strength showed mixed results. Initial studies did not showed a positive role of the gut microbiome on the maintenance of whole body lean mass [23]. Whole body lean mass was decreased by 7–9% in young germ-free mice after transplantation of fecal samples from conventionally-raised mice [23]. In contrary to the initial research, subsequent research showed that the gut microbiome increased skeletal muscle mass, reduced muscle atrophy markers, improved oxidative metabolic capacity of the muscle, and elevated expression of the neuromuscular junction assembly genes Rapsyn and Lrp4 [25–27]. Furthermore, transplantation of the gut microbiome from young mice to old mice has been shown to increase muscle fiber thickness and grip strength [28].

Although these research suggest a possible association between the gut microbiome and muscle strength, there is not yet any direct evidence linking the gut microbiome to muscle strength. Mammalian phenotypes such as muscle strength are influenced by both genes and the gut microbiome [18, 19, 29–32]. The genetic variation among individuals means that evaluating the effect of the gut microbiome on muscle strength would be greatly influenced by the genetic background of the host. Especially, human studies, where individual genetic backgrounds cannot be controlled, have limitations in accurately evaluating the effect of the gut microbiome. On the other hand, mouse studies, where genetic factors can be controlled, allow for a more accurate evaluation of the effect of the gut microbiome by eliminating the influence of genetic factors [33].

In order to overcome the difficulty of separating the effects of genetic factors from the gut microbiome in the evaluation of the gut microbiome's impact on muscle strength, a new gut microbiome analysis method was developed in this work. This method eliminated the individual genetic backgrounds, allowing for a more precise evaluation of the effect of the gut microbiome on muscle strength. The method compared the muscle strengths of mice with the same individual mice after remodelling their gut microbiome through fecal microbiome transplant (FMT), thereby accurately identifying the intestinal microbes associated with muscle strength. By comparing the muscle strength within the same mouse after remodelling their gut microbiome under the same environmental conditions, the effects of the gut microbiome to muscle strength were more accurately determined.

A random colonization of human gut microbiome into conventional mice through FMT had a different impact on muscle strength.

The ongoing debate about the regulation of muscle strength by intestinal bacteria prompted us to explore a new approach to identify the specific bacteria responsible. Utilizing the concept of subgroup analysis, which helps uncover the cause of complex issues in large data sets, we performed a novel in vivo experiment. The aim was to identify the intestinal bacteria that regulate muscle strength through randomized subgroup analysis. We achieved this by feeding a fresh fecal sample to conventional mice (C57BL/6) whose gut microbiome had been depleted through a mixture of 3 broad-spectrum antibiotics and nystatin (as shown in Fig. 1a).

The effect of the subset FMT with the human gut microbiome in conventional mice was vastly different on muscle strength, as shown in Fig. 1b,c. In order to exclude genetic factors and evaluate the effect of the gut microbiome on muscle strength change alone, the rotarod records were monitored in the same mice after replacing their original gut microbiome with human gut microbiome through the FMT. The changes in the muscle strength of the mice over the three-month experimental period can be grouped into three categories: the group with increased muscle strength, in which the holding time on the rotarod increased by 23.5 ± 5.7 seconds (SMS); the group with unchanged muscle strength, in which the holding time on the rotarod remained within the range of 1.6 ± 0.8 seconds (MMS); and the group with decreased muscle strength, in which the holding time on the rotarod decreased by -16.1 ± 3.2 seconds (WMS) (Fig. 1b,c).The changes of muscle strength in each group were confirmed by the histological examination, which showed a high degree of muscle fiber accumulation in SMS, medium accumulation in MMS, and the lowest accumulation in WMS (Fig. 1d). The levels of blood glucose and lipid profiles did not change significantly before or after the gut microbiome replacement in the mice (Figure S1). These results suggest that a different subset of the human gut microbiome randomly replaced the original gut microbiome in each mouse, resulting in different effects on muscle strength.

Different types of gut microbiome were established in each of the experimental mice after the FMT with human feces.

The differential effects on muscle strength after gut microbiome replacement prompted us to compare the compositional changes of the gut microbiome before and after FMT with human feces in the mice. We sequenced the V3-V4 regions of the 16S rRNA genes of the gut microbiome of each mouse before and after replacement using the MiSeq platform (Illumina). We filtered out the sequence reads that potentially contained incorrect primer or barcode sequences, sequences with more than one ambiguous base, low-quality sequences, or chimeras, which comprised about 0.001% of the total reads. The filtered 16S rRNA sequences were used to identify individual microbes by matching them to the SILVA reference database (region V3-V4) (https://www.arb-silva.de/). The identified 16S rRNA sequences, represented as operational taxonomic units (OTUs), were classified into nine different phyla: Bacteroidetes (48.471%), Firmicutes (37.456%), Verrucomicrobia (10.197%), Proteobacteria (1.924%), Patescibacteria (0.337%), Actinobacteria (0.907%), Tenericutes (0.48%), Cyanobacteria (0.228%), and Lentisphaerae (0.002%) (Figure S2 and Table S2). The OTUs were further classified to the species level.

The comparison of the OTUs showed a clear difference before and after the FMT in all of the mice, indicating that the replacement of their original gut microbiome with those of the humans was successful as shown in Figure S2. The differences were most notable at the genus and species levels (Figure S2c). In accordance with the individual variation in muscle strength after the FMT, the composition of the replaced gut microbiome was very different from one mouse to another (as shown in Fig. 2, Figure S3, and Table S2).

The differences in muscle strength were correlated with the shifts in the composition of the gut microbiome.

After confirming the individual differences in the composition of the replaced gut microbiome and its correlation with muscle strength, the gut microbiome of each mouse was analysed. The α-diversity metrics, which measure both richness and evenness, showed that different subsets of the human gut microbiome were replaced in each group of mice (as shown in Fig. 3 and Table S3). Although the α-diversity indices considering both richness and evenness (Shannon, Simpson, and InvSimpson) showed slight differences between the three groups, separate measurements of richness and evenness visualized the structural differences in the ecological community. The evenness indices before and after the FMT were similar, but the richness indices were increased after the FMT, suggesting that the human gut microbiome is more diverse, although the compositional characteristics of the human and mouse gut microbiomes are similar. Furthermore, the richness and evenness diversity indices of the mice with stronger muscle strength (SMS) were higher than in the other two groups, indicating a more diverse gut microbiome in SMS.

Other than α-diversity analyses, β-diversity analyses have also confirmed that the replaced gut microbiome is much more diverse than its original gut microbiome in each mouse. As shown in Fig. 4a,b, both β-diversity metrics, as measured by NMDS and MDS plots, show that the features are more diverse after the gut microbiome replacement.

To examine the impact of gut microbiome on muscle strength, we compared the gut microbiome composition before the FMT (T0) and three months after the FMT (T3). The comparison using unsupervised hierarchical clustering of the most abundant Operational Taxonomic Units (OTUs) based on the Bray Curtis distance revealed that the replacement of the gut microbiome affected the three groups of mice differently. The unsupervised hierarchical cluster analysis at T0 showed that each group of mice was completely distinct from each other (as seen in Fig. 4c, left). However, when considering muscle strength differences, individual mice from the three groups clustered together (as seen in Fig. 4c, right). This study only took into account muscle strength changes within individual mice, so the gut microbiome at the starting point did not show any group differences because the starting point was not related. However, the gut microbiome's impact on muscle strength is evident in the unsupervised hierarchical cluster analysis at T3, which shows the gut microbiome composition grouping individual mice according to differences in muscle strength.

Different microbial communities were established in each of the three groups of mice following the FMT.

The differences between the gut microbiomes of the three groups of mice (SMS, MMS, and WMS) were analysed by constructing phylogenetic trees. A maximum-likelihood phylogeny of each microbiome was built based on the 16S rDNA sequence (Fig. 5). The diversity of the gut microbiomes of all three groups expanded after the replacement of the gut microbiome: 66.2% in SMS, 46% in MMS, and 62% in WMS at the species level (Fig. 5). In addition to the increased diversity, the composition of the intestinal bacteria in the gut microbiome changed dramatically after the replacement.

Before the FMT, the main bacteria that constituted the original gut microbiomes at the phylum level were Bacteroidetes (56.093% in SMS, 53.922% in MMS, and 63.633% in WMS), Firmicutes (40.116% in SMS, 42.569% in MMS, and 33.938% in WMS), and Patescibacteria (3.24% in SMS, 2.791% in MMS, and 2.03% in WMS); at the class level, Bacteroidia (56.093% in SMS, 53.922% in MMS, and 63.633% in WMS), Clostridia (36.684% in SMS, 38.674% in MMS, and 29.873% in WMS), and Bacilli (2.489% in SMS, 2.499% in MMS, and 3.37% in WMS); at the order level, Bacteroidales (56.093% in SMS, 53.92% in MMS, and 63.628% in WMS), Clostridiales (36.684% in SMS, 38.674% in MMS, and 29.873% in WMS), and Lactobacillales (2.489% in SMS, 2.499% in MMS, and 3.37% in WMS); and at the family level, Muribaculaceae (54.598% in SMS, 50.66% in MMS, and 61.67% in WMS), Lachnospiraceae (24.471% in SMS, 24.611% in MMS, and 19.667% in WMS), and Ruminococcaceae (9.72% in SMS, 10.737% in MMS, and 7.399% in WMS). However, the composition of bacteria changed three months after the FMT: at the phylum level, Bacteroidetes (52.068% in SMS, 43.928% in MMS, and 49.416% in WMS), Firmicutes (36.244% in SMS, 37.628% in MMS, and 38.496% in WMS), and Verrucomicrobia (7.552% in SMS, 14.915% in MMS, and 8.123% in WMS); at the class level, Bacteroidia (52.068% in SMS, 43.928% in MMS, and 49.416% in WMS), Clostridia (30.437% in SMS, 32.948% in MMS, and 32. 293% in WMS), and Verrucomicrobiae (7.552% in SMS, 14.915% in MMS, and 8.123% in WMS); at the order level, Bacteroidales (56.093% in SMS, 53.92% in MMS, and 63.628% in WMS), Clostridiales (36.684% in SMS, 38.674% in MMS, and 29.873% in WMS), and Lactobacillales (2.489% in SMS, 2.499% in MMS, and 3.37% in WMS); at a family level, Muribaculaceae (40.613% in SMS, 33.303% in MMS, and 40.784% in WMS), Lachnospiraceae (16.383% in SMS, 18.101% in MMS, and 19.08% in WMS), and Ruminococcaceae (12.561% in SMS, 13.346% in MMS, and 11.583% in WMS) (Table S2). These results indicate that the human gut microbiome is not only more diverse, but also its composition significantly differs from that of mice.

The phylogenetic analysis showed that the replaced gut microbiomes of SMS, MMS, and WMS were different, despite the fact that their original gut microbiomes were not different from each other (Fig. 5). Consistent with the phylogenetic analysis, the co-occurrence network analysis also demonstrated that the microbial communities became more diverse after the gut microbiome was replaced in all three classified groups (Fig. 6 and Table S4). The 21 communities in SMS before the gut microbiome replacement expanded to 74, while the 28 communities in MMS became 60 and the 31 communities in WMS became 58. There was no significant difference in the number of nodes and edges within the microbial communities, which suggests that bacteria interact with each other in a similar manner. The higher number of microbial communities in SMS compared to MMS and WMS indicates that muscle strength is associated with a more diverse gut microbiome.

Intestinal microbes that affect muscle strength were identified at the species level.

The above gross gut microbiome analyses revealed a clear correlation between gut microbiome composition and muscle strength. However, the group analyses did not visualize a specific group of bacteria responsible for either promoting or reducing muscle strength (Figs. 2–6). Due to the limitations of group analysis in identifying intestinal microbes at the species level, we utilized the concept of fold change at the log2 scale and a linear correlation to analyse the abundance of intestinal microbes in relation to muscle strength (Fig. 7). Among the bacterial species, Phocaeicola barnesiae, Eisenbergiella massiliensis, and Anaeroplasma abactoclasticum were most abundant in SMS, indicating a positive correlation with muscle strength. On the other hand, Ileibacterium valens and Ethanoligenens harbinense were most abundant in WMS, indicating a negative correlation with muscle strength. The bacteria associated with strong muscle strength were classified under the phylum Bacteroidetes, Firmicutes, and Tenericutes, while those associated with weak muscle strength belonged to the phylum Firmicutes.

It has been well established that the gut microbiome plays a crucial role in the energy extraction of its host from food through fermentation processes and an increase in villous vascularization [34, 35]. However, the role of the gut microbiome is not limited to just energy extraction. Recent research has shown that the gut microbiome has a significant impact on a wide range of physiological processes, including metabolism, digestion, immunity, and brain function [2–6]. Additionally, the gut microbiome serves as a crucial epigenetic factor that modulates the expression of our own genes [36–40].

Considering the significant role of the gut microbiome, one would expect it to play a critical role in determining its host's muscle strength. However, previous research has not shown a clear association between the gut microbiome and muscle strength. In contrast to the initial study that found a negative association between the gut microbiome and muscle strength [23], later studies have shown that the gut microbiome can actually enhance muscle strength [25–27]. The gut microbiome is a complex ecosystem composed of tens of trillions of bacteria and fungi, with hundreds of different species and thousands of strains [41]. The number of microorganisms in the gut microbiome can vary greatly between individuals. Given the diversity of an individual's gut microbiome and the vast number of intestinal microbes, the composition of the gut microbiome could consist of a variety of combinations of intestinal microbes, meaning that some gut microbiomes could negatively impact muscle strength while others could positively impact it [42, 43]. As a result, it is natural to see a mix of evidence in the association between muscle strength and the gut microbiome in previous research.

The comparison of the gut microbiome between control and experimental groups through metagenomics has some limitations in its analysis. To address these limitations, a new method for gut microbiome analysis has been developed in this study. This method involves transferring human fecal microbiomes to mice and observing changes in phenotype in the same individual mice. The use of subset analysis within a large dataset is very useful for uncovering differences that might otherwise go unnoticed. Therefore, the transfer of the human gut microbiome (large dataset) to mice and subsequent analysis of the transplanted microbiome in mice (subset data) offers a valuable approach for analysing the human gut microbiome. Additionally, by comparing changes in phenotype, such as changes in muscle strength, within the same individual mouse, genetic factors affecting muscle strength can be controlled for, thus revealing the sole impact of the gut microbiome on phenotypes [33].

Our experimental approach has revealed that the gut microbiome has a dual impact on muscle strength: some gut microbiomes positively affect muscle strength, while others have a negative effect (Fig. 1). Alpha and beta diversity analyses confirmed that these gut microbiomes differ in terms of the composition and diversity of their constituent intestinal microbes (as shown in Figs. 2–5). These findings were further supported by the co-occurrence network analysis, which indicated that the gut microbiome affecting muscle strength differed from others (Fig. 6). Our findings demonstrate that the gut microbiome contributes positively or negatively to muscle strength, which clearly explains the mixed results in previous research on the association between muscle strength and the gut microbiome [25–28].

Our FMT-based analysis of the gut microbiome subset showed not only the potential presence of a positive or negative relationship between the gut microbiome and muscle strength but also identified the specific gut microbes that are positively or negatively related to muscle strength (Fig. 7). As depicted in Fig. 7, P. barnesiae, E. massiliensis, and A. abactoclasticum had a positive effect on muscle strength, while I. valens and E. harbinense had a negative impact. P. barnesiae (Bacteroides barnesiae) is a strict anaerobic chemoorganotroph capable of fermenting various sugars, including glucose, lactose, and sucrose, among others [44, 45], while A. abactoclasticum is an obligate anaerobic bacterium [46]. Although P. barnesiae and A. abactoclasticum have been isolated from healthy chicken and cattle, they have not yet been found in humans. E. massiliensis is a Gram-negative, obligate anaerobic bacterium that was isolated from the stool of an overweight person [47]. Notably, the two bacteria that had a negative effect on muscle strength, I. valens and E. harbinense, were found in lean individuals [48, 49]. This finding of bacteria positively associated with muscle strength in overweight individuals and vice versa in lean individuals seems to align well with human physiology.

Finally, this study has successfully demonstrated that the in vivo subset analysis of the human gut microbiome using an animal model is a valuable approach to study this complex and heterogeneous population of trillions of microbes. We believe that this concept could be applied to identify the gut microbes associated with other human phenotypes or diseases.

Study design and animal experiment

Thirty C57BL/6 mice purchased from the Korea Basic Science Institute (Gwangju, Republic of Korea) were used in this study. The mice were housed individually in a sterile environment and had access to sterilized food and water. They were maintained under a specific light/dark cycle and temperature. After a week of acclimation, the mice were weighed and their feces and blood were collected. To deplete their endogenous microbiota, the mice were given water containing antibiotics (1 g/L ampicillin, 0.5 g/L kanamycin, and 0.5 g/L cefoxitin; Sigma-Aldrich, St. Louis, MO, USA) and an antifungal (0.5 g/L nystatin) for one week.

The fecal samples from 10 healthy volunteers were collected and mixed in the optimized medium to make mixed feces containing various human gut microbes. The fecal medium was created on the scheduled day for oral gavage and stored at room temperature in anaerobic containers. The mixed human feces medium was used to perform a FMT in mice. The FMT was performed twice a week for a total of three months by administering 20 ul of the mixture to the mice via oral gavage. All of the mice were weighed after finishing feeding of the human fecal sample, and the feces and blood of the mice were individually collected.

After completing FMT with the new human microbiome, the mice were kept in a controlled laboratory environment for an additional three months in the above normal mouse facility for 3 months. The mice were weighed again on the final day of the experiment, and the feces and blood of the mice were individually collected.

Rotarod test

The rotarod apparatus (B.S Technolab INC, Korea) was used to evaluate the motor coordination, strength, and balance of the mice [50]. The apparatus consisted of a base platform and a rotating rod with a diameter of 3.5 cm and a non-slippery surface. The rod was placed at a height of 30 cm from the base. The mice was placed on the rotating rod, which gradually increases in speed, and measuring the time it takes for the mouse to fall off. The mice received three trials per day, at 30 rpm for three consecutive days. On the fourth day, mice underwent testing. During testing, each animal received three trials at 30 rpm fixed speed. The mean latency to fall off the rotarod was recorded and used in the subsequent analysis.

Analyses of biochemical parameters

The serum levels of total cholesterol (TCHO), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-CHO) were determined by enzymatic methods using commercial assay kits (Asan Pharmaceutical Co., Seoul, Korea) as described previously [51]. In brief, the low-density lipoprotein cholesterol (LDL-CHO) levels were calculated using Friedewald’s equation [(LDL-CHO) = (TCHO) − ((HDL-CHO) − (TG)/5)].

The blood glucose level was determined using glucose test strips and a handheld blood glucose meter (Accu-Chek Active; Roche Diagnostic GmbH, Mannheim, Germany).

Histological analysis

The histological analysis was determined by a method as described previously [52]. Briefly, muscle tissue were prepared with mice at 3 month of the experimental intervention and were used for microscopic analysis after being stained with H&E. Immediately after isolation, muscle tissue sections were fixed at 10% neutral buffered formalin and were paraffin-embedded. Serial 6 µm thick sections were randomly selected. Paraffin was removed from the tissue sections by hot water. The tissue sections were placed on microscopic slides, and the slides were air-dried and baked overnight at 65 ℃. Finally, the tissue sections were stained with hematoxylin and eosin (Vector laboratories Inc., CA, USA) according to standard laboratory procedure. The H&E-stained tissue sections were observed under a light microscope (AmScope, T690C-PL), and images were captured with a microscopic digital camera (AmScope, MU-1803).

DNA extraction and 16S rRNA gene sequencing

Total bacterial genomic DNA from each sample was extracted using the phenol-chloroform isoamyl alcohol extraction method as described previously [53, 54]. Briefly, samples suspended in lysis buffer (200 mM NaCl, 200 mM Tris-HCl (pH 8.0), 20 mM EDTA) were processed by bead-beating. Genomic DNA was recovered from the aqueous phase by Phenol:Chloroform:Isoamylalcohol. DNA was precipitated with the addition of 3 M sodium acetate followed by isopropanol. After rinsing with 70% ethanol and drying, the DNA pellet was dissolved in TE buffer (10 mM Tris-HCl (pH 8.0), 1 mM EDTA). The concentration and purity of the extracted DNA were measured using a BioSpec-nano spectrophotometer (Shimadzu Biotech, Kyoto, Japan), and the integrity was evaluated on a 1% (w/v) agarose gel.

Metagenome sequencing analyses of the gut microbiome DNA samples were processed and sequenced by a commercial company, ebiogen, Inc. in South Korea. Briefly, each sequenced sample was prepared according to the Illumina 16S Metagenomic Sequencing Library protocols, and the genes were amplified using 16S V3-V4 primers; 16S Amplicon PCR Forward Primer with a sequence of 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3’ and 16S Amplicon PCR Reverse Primer with a sequence of 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3’. Input genomic DNA was amplified with 16S V3-V4 primers. A subsequent limited-cycle amplification step was performed to add multiplexing indices and Illumina sequencing adapters. The final products were normalized and pooled using the PicoGreen, and the size of the libraries was verified using the Agilent TapeStation DNA ScreenTape D1000 system (Agilent Technologies, Santa Clara, CA, USA). Finally, the pooled libraries were sequenced (2 × 300) using the MiSeq platform (Illumina, San Diego, CA, USA). The amplicon error was modelled from merged fastq using DADA2 (ver.1.10.1); noise sequence was filtered out, errors in marginal sequences were corrected, chimeric sequences and singletons were removed, and sequences were de-replicated [55]. The raw data were deposited in the repository at figshare (https://doi.org/10.6084/m9.figshare.21838947).

Data and statistical analyses

All Data and statistical analyses were determined as described previously [53, 54]. In brief, the Q2-Feature classifier is a Naive Bayes classifier trained based on the SILVA reference (region V3-V4) database (https://www.arb-silva.de/) to classify bacterial species. We used the program to classify our datasets after setting De-noise-single function as the default parameter. The q2-diversity under the option of “sampling-depth” was used for the diversity calculation and statistical tests. We employed a sequencing quality score threshold of at least 20 and rarefaction depth at 11,510. After confirming the quality of sequencing results, the sequencing results in “table.qzv” files were filtered by using the threshold values in QIIME 2. The metagenomic data OTU and taxonomic classification tables were imported into phyloseq (1.28.0) package in R version 3.6.1 for visualization of alpha and beta diversity. Statistical analysis was done using Kruskal-Wallis rank sum test for alpha diversity. To detect statistical differences in beta diversity metrics between groups, we used permutational multivariate analysis of variance (PERMANOVA) in the vegan package in R. ADONIS was used with 999 permutations in the vegan package in R to quantify the effect size of variables explaining Bray-Curtis distance. All P-value was corrected by Benjamini and Hochberg's adjustment, and significance was declared at P < 0.05.

The α-diversity analysis for relative abundance evaluation of material and microbiome

The α-diversity Analysis for Relative Abundance were determined as described previously [53, 54]. We used the phyloseq (1.28.0) and metagenomeSeq (1.16.0) packages to identify the central taxa present in each group. The metadata, OTUs, and taxonomic classification tables were imported into the phyloseq package, and the data were processed as instructed [56, 57]. The phyloseq class object was converted to metagenomeseq objects and was normalized by cumulative-sum-scaling (CSS), which was specially built for metagenome data in the bioConductor package metagenomeSeq (1.16.0). Normalized data were converted to phyloseq class objects in R for further analysis and visualization.

Normalized OTU data were used for abundance calculation, and each taxonomic level was glommed for plotting. For clear visualization of abundance data, taxa were collected into “other” if they had relative abundances below 5% except at the phylum and class levels.

The β-analysis for relative abundance evaluation of material and microbiome

The β-diversity was computed for non-metric multidimensional scaling (NMDS) from log-transformed OTU data using Bray-Curtis dissimilarity in the vegan package as described previously [53, 54]. Using the metaMDS function in the ‘vegan’ package, NMDS was performed on the Bray-Curtis dissimilarity matrix, which reduced dimensionality while retaining as much information as possible about relationships among samples.

Construction of heatmap and phylogenetic tree

A heatmap and cluster analysis were generated using the relative abundances of genera from all OTU values or core abundant OTU values in the Heatplus (2.30.0) package from Bioconductor and the vegan package in R as described previously [33, 58]. Average linkage hierarchical clustering and Bray–Curtis distance metrics were used for cluster analysis and heatmap generation, respectively. Unsupervised prevalence filtering was performed with a 5% threshold in total samples to collect the most abundant taxa for heatmap generation.

Phylogenetic trees for each sampling site were constructed from row sequences without any filtering to show direct visualization of sample richness with relation to taxonomic classification as described previously [33, 58]. Briefly, taxa that could not be classified down to the species level were reclassified based on the NCBI accession number using the taxonomizr (0.5.3) package in R. Then, 16S rRNA sequences from each sampling site were aligned in ClustalW with a default parameter, and the resulting alignments were used to construct the maximum-likelihood phylogenetic trees in MEGAX with 500 bootstrap replicates. All phylogenetic trees were visualized in iTOL.

Co-occurrence network construction

Co-abundance networks were created by a permutation-renormalization-bootstrap network construction strategy as described previously to observe the microbial co-occurrence relationships through the mice’s rotarod record change [33, 58]. Briefly, non-normalized abundance data was uploaded to CoNet, a Java Cytoscape plug-in. All networks were in-dependently constructed by splitting the OTU abundance matrix into High, Medium, and Low groups. The microbial networks and links or edges were obtained from OTU occurrence data. Multiple ensemble correlation methods in CoNet were used to identify significant co-presences across the samples while OTUs that occurred in less than three samples were discarded (“row_minocc” = 3). Five similarity measures, including Spearman and Pearson correlation coefficients, the Mutual information Score, and the Bray–Curtis and Kullback–Leibler Dissimilarity, were calculated by CoNet for the creation of an ensemble network, and the P-value was merged by Brown’s method. The P-value was corrected by the Benjamini–Hochberg correction method (adjusted P-value < 0.05). If at least two of the five metrics suggested significant co-abundance between the two OTUs, the relationship was kept in the final network to be represented as an edge. The final co-occurrence network model was displayed by the igraph package in R by using an implementation of the Louvain algorithm to identify communities within each network so that the modularity score of each OTU was maximized within a given network.

Differential abundance

DESeq2 (version 1.24.0) was used to estimate the fold-change of taxa in gut microbiome according to the muscle strength record change groups [59]. Taxa that were not observed in at least 0.5% of samples were excluded from the DESeq2 analyses.

Quantification and statistical analysis

All statistical analyses are reported as the mean ± SEM, and the differences in the relative abundance of bacterial populations containing feces were analysed using the Mann–Whitney sum rank tests in R software. Significance was declared at P < 0.05 with Benjamini and Hochberg's adjustment. All graphs were prepared with R software.

Acknowledgements

Not applicable.

Authors’ contributions.

Conceptualization, J.-S.A. and H.-J.C.; methodology, J.-S.A. and B.-C. K.; software, J.-S.A. and H.-S. K.; validation, J.-S.A., S.-J. L. and W.-W.J.; formal analysis, J.-S.A. and Y.-J. C.; investigation, J.-S.A. and B.-C. K.; resources, J.-S.A.; data curation, J.-S.A.; writing—original draft preparation, H.-J.C. and S.-T.H.; writing—review and editing, H.-J.C. and S.-T.H; visualization, J.-S.A.; supervision, H.-J.C. and S.-T.H.; project administration, H.-J.C. and S.-T.H.; funding acquisition, H.-J.C. and S.-T.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Korea Basic Science Institute (KBSI) grants C380300, C320000 and C330340 and funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HV22C0171).

Availability of data and materials

The raw data were deposited in the repository at figshare (https://doi.org/10.6084/m9.figshare.21838947).

Ethics approval and consent to participate

All the experimental procedures complied with the ARRIVE guidelines and Institutional Animal Care and Use Committee of the Korea Basic Science Institute approved the animal protocols (KBSI-IACUC-23-12).

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Supplementary Information

The online version contains supplementary material available at http://doi.org/xxxxxxxxxx.

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No competing interests reported.

Additionalfile1.jpg
Additional file 1: Figure S1. The effects of FMT on changes in blood glucose, blood pressure, and lipid levels. The average values of (a) Weight, (b) Blood glucose, (c) Total glycerol (TG), (d) Total cholesterol (TCHO), (e) HDL-cholesterol (HDL-CHO), and (f) LDL-cholesterol (LDL-CHO) for each group are shown. The values are represented as mean ± SEM. 0m represents before the FMT, and 3m represents 3 months after the FMT. *p < 0.05; **p < 0.01; ***p < 0.001; n.s: not significant (p > 0.05).
Additionalfile2.jpg
Additional file 2: Figure S2. Changes in the gut microbiome before and after FMT. (a) The α-diversity, (b) β-diversity, and (c) abundance bar plot analyses revealed that the composition of the gut microbiome in the mice was significantly altered following FMT. 0m represents before the FMT, and 3m represents 3 months after the FMT.
Additionalfile3.jpg
Additional file 3: Figure S3. Changes in the composition of the gut microbiome of each mouse before and after FMT. The compositional changes of the gut microbiome at (a) phylum level, (b) class level, (c) order level, (d) family level, and (e) genus level are shown. 0m represents before the FMT, and 3m represents 3 months after the FMT. S, M, and W represent the SMS group, MMS group, and WMS group respectively.
Additionalfile4.xlsx
Additional file 4: Table S1. The valid reads of 16S rRNA amplicon sequence. All values are the mean ± SEM. 0m represents before the FMT, and 3m represents 3 months after the FMT. S, M, and W represent the SMS group, MMS group, and WMS group respectively.
Additionalfile5.xlsx
Additional file 5: Table S2. Comparison of taxonomy abundance at the phylum, class, order, family, genus and species level. 0m represents before the FMT, and 3m represents 3 months after the FMT. S, M, and W represent the SMS group, MMS group, and WMS group respectively.
Additionalfile6.xlsx
Additional file 6: Table S3. The α-diversity indexes for each sample and group. 0m represents before the FMT, and 3m represents 3 months after the FMT. S, M, and W represent the SMS group, MMS group, and WMS group respectively.
Additionalfile7.xlsx
Additional file 7: Table S4. Co-occurrence network indices. 0m represents before the FMT, and 3m represents 3 months after the FMT. S, M, and W represent the SMS group, MMS group, and WMS group respectively.

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Identification of the intestinal microbes associated with muscle strength

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Abstract

Background

Results

Conclusions

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Introduction

Results And Discussion

Conclusion

Materials And Methods

Study design and animal experiment

Rotarod test

Analyses of biochemical parameters

Histological analysis

DNA extraction and 16S rRNA gene sequencing

Data and statistical analyses

The α-diversity analysis for relative abundance evaluation of material and microbiome

The β-analysis for relative abundance evaluation of material and microbiome

Construction of heatmap and phylogenetic tree

Co-occurrence network construction

Differential abundance

Quantification and statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

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