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Case Study. Nutrient Partitioning in Cows. Talk With An Expert About Your Project. Signals from the gut to the brain are important in regulating metabolism and energy balance and have been linked with food reward and preference in metabolically healthy individuals with normal body mass index.
In particular, post-ingestive signaling related to glucose metabolism has been linked with food reward and preference. However, not much is known about how these gut and brain signals interact to influence eating behaviors in states of obesity or altered metabolic health.
In addition, evidence in rodent models and human studies indicates obesity is associated with a blunted brain response to foods compared with normal body weight. However, whether altered nutrient utilization, termed metabolic inflexibility, influences the relationship between obesity and food reward has yet to be studied.
The overall objective of this proof-of-concept pilot study is to assess the feasibility of measuring reward response following a flavor-nutrient conditioning paradigm across the normal to obese body mass index BMI range and in states of altered metabolic health.
Data sourced from clinicaltrials. Notes about this trial. The Role of Altered Nutrient Partitioning in Food Reward. Status Completed. Overweight and Obesity. Other: Conditioned Stimulus - CS- : Flavored beverage solution with sweetness-matched sucralose. Other: High-Fat Test Meal Inside a Metabolic Chamber.
Other: High-Carbohydrate Test Meal Inside a Metabolic Chamber. Study type. Microbiota-depleted bees were obtained from colonies of Apis mellifera carnica located at the University of Lausanne following the procedure described in Kešnerová et al.
Five days post-colonization, 10 rectums were dissected and homogenized in 1xPBS. An aliquot of each homogenized gut was used for CFU plating to enumerate the total bacterial load and for amplicon sequencing to obtain the relative abundance of each community member see below.
This was repeated for a total of six serial passages. Food was provided ad libitum. Each of the four strains was cultured in liquid medium overnight for about 16 hr as described above. Detailed information about pollen extract preparation can be found in the Supporting methods section of Kešnerová et al.
Pollen grain solutions were prepared by adding 1. The plates were incubated for 24 hr at 34°C under anaerobic conditions without shaking rpm. After 24 hr of incubation, an aliquot of each sample was subjected to CFU plating to enumerate the total bacterial load.
These transfers were repeated 10, respectively, 20 times for the two independent experiments. CFUs were counted after 24 hr and at the final transfer.
The relative abundance of the four strains across all transfer experiments was obtained using amplicon sequencing of a bp long fragment of a housekeeping gene encoding a DNA formamidopyrimidine-glycosylase which allows to discriminate the four strains from each other Ellegaard et al.
For the in vitro transfer experiments, the PCR fragment was amplified from crude cell lysates. They were generated by mixing 5 μL of culture with 50 μL of lysis solution, containing 45 μL of lysis buffer 10 mM Tris- HCl, 1 mM EDTA, 0. The samples were incubated for 10 min at 37°C, for 20 min at 55 °C, and for 10 min at 95 °C, followed by a short spin before preparing the PCR 1 min, rpm.
For the in vivo transfer experiment, DNA was isolated from the homogenized gut samples using the hot phenol protocol used in Kešnerová et al. To amplify the gene fragment and to add the Illumina barcodes and adapters, the two-step PCR strategy published in Ellegaard et al.
For the first PCR, 5 μL of DNA or 5 μL of cell lysate were mixed with The PCR I was performed as follows: initial denaturation 95°C — 3 min , 30 times denaturation-annealing-extension 95°C — 30 s, 64°C — 30 s, 72°C — 30 s , final extension 72 °C — 5 min.
and incubated for 30 min at 37°C and for 15 min at 80°C. For the second PCR reaction, 5 μL of purified PCR products were mixed with the same reagents as before.
The PCR program was the same as above with the exception that the annealing temperature was set to 60°C and the denaturation-annealing-extension steps were repeated for only eight times.
The barcoded primers are listed in Supplementary file 1. The amplicons of the second PCR were purified using the Exo-SAP program as described above. To prepare the sequencing of the amplicons, DNA concentrations were measured using Quant-iT PicoGreen for dsDNA Invitrogen.
Each sample was adjusted to a DNA concentration of 0. The pooled sample was loaded on a 0. Illumina reads were demultiplexed by retrieving the unique barcodes of the different samples and quality-filtered using Trimmomatic Trimmomatic Each forward and reverse read pair was assembled using PEAR -m n j 4 -q 26 v 10 -b 33 Zhang et al.
See details in Supplementary material. To obtain absolute abundance data for each strain, we combined the relative abundance data from the amplicon sequencing with CFU counts obtained from plating homogenized bee guts in the case of the in vivo experiments see above or by carrying out qPCR with Lactobacillus -specific primers as described in Kešnerová et al.
For the in vitro transfer, the stability of the four-species community over time was calculated using the codyn R package Hallett et al. For the in vivo RNA sequencing, microbiota-depleted bees were colonized with the four species community as described above and fed with either sugar water and pollen grains or with sugar water only.
After 5 days of colonization, the rectums of five bees per treatment all kept in the same cage were dissected and snap-frozen in liquid nitrogen in separate tubes containing glass beads 0.
Tubes were then thawed on ice and a previously developed hot phenol RNA extraction protocol was followed Sharma et al.
After 16 hr of growth, μL of STOP solution was added to 1 mL of culture followed by the same steps as described above. After the precipitation step, samples were treated with DNaseI NEB to degrade DNA.
RNA concentration and quality were assessed using Nanodrop ThermoFisher Scientific , Qubit ThermoFisher Scientific, RNA — High Sensitivity reagents and settings and Bioanalyzer Agilent.
High-quality RNA samples were selected to prepare RNA libraries. For the in vivo RNA sequencing, libraries were prepared using the Zymo-Seq RiboFree Total RNA Library kit Zymo Research. The libraries were sequenced by the GTF facility of the University of Lausanne using HiSeq SR sequencing bp reads Illumina.
For the in vitro RNA sequencing, libraries were prepared following the protocol developed by Avraham et al. Libraries were then prepared for sequencing following the Illumina MiniSeq System guide for denaturate and dilute libraries.
Libraries were sequenced using the Illumina MiniSeq technology using High Output Reagent Cartridges bp reads and MiniSeq flow cells. For the in vitro samples, raw reads were demultiplexed using a script provided by Dr. Jelle Slager Personal communication For the in vivo samples, the reads were already demultiplexed by the sequencing facility.
For both experiments, the reads were trimmed with Trimmomatic Trimmomatic For the in vivo samples, trimmed reads were sorted with sortmerna Reads were mapped onto the genomes of the selected strains Ellegaard and Engel, Lapi, Lhel, Lmel, and Lkul using Bowtie bowtie Quality filtered reads were then quantified using HTseq Version 0.
Differential gene expression between samples cultured in pollen extract and samples cultured in glucose, and between mono-cultures and co-cultures, was calculated using the R package EdgeR Robinson et al.
Counts per million were calculated and only genes with at least one count per million were used for the analysis. EdgeR fits negative binomial models to the data.
The counts were normalized for RNA composition by adjusting the log 2 FC according to the library size, and the quantile-adjusted conditional maximum likelihood qCML method was used to estimate the common dispersion and the tag-wise dispersion.
Transcripts per million TPM were visualized using the Integrated Genome Browser software Freese et al. After collection of all samples, they were prepared for metabolomics analysis.
The samples were thawed on ice and centrifuged again 20, g, 4°C, 5 min , then diluted 10 times with ddH 2 O. For metabolomics analysis, 25 μL of each diluted sample was sent in a well plate on dry ice to the laboratory of Prof.
Uwe Sauer for analysis ETH Zürich, Switzerland. Three replicates of a pollen-extract dilution series 10 serial 2x dilutions as well as undiluted pollen-extracts and water used for performing the dilution series were included in the metabolomics analysis.
Tubes were vortexed thoroughly and incubated for 5 min 4°C, shaking 14, rpm. For untargeted metabolomics analysis, each sample was injected twice technical replicate into an Agilent time-of-flight mass spectrometer ESI-iFunnel Q-TOF, Agilent Technologies as detailed in Kešnerová et al.
Alternative annotation can be found in Supplementary file When available, metabolites categories were assigned to ions based on KEGG ontology.
Metabolomics data analysis was carried out using R version 3. Variation of raw ion intensities obtained from untargeted metabolomics analysis for the two technical replicates was determined by assessing the correlation between ion intensities of the respective technical replicates.
Then, mean ion intensities of technical replicates were calculated. log 2 FC values between the two time-points were calculated with respect to the mean intensity in the T0 time point.
To identify pollen-derived ions, and distinguish them from background originating from culture medium and experimental noise, the ion intensities of the pollen dilution series were plotted for each ion and the R 2 of the obtained linear fit was extracted.
In addition, we calculated the log 2 FC difference between undiluted pollen and water. All ions were included for downstream analysis e. PCA and then they were discriminated between pollen-derived and non-pollen-derived. For each liquid culture sample, μL was collected and centrifuged 15, g, 4°C, 15 min.
Once that all the samples were collected, soluble metabolites were extracted. To extract soluble metabolites, tubes were thawed on ice, and 75 μL of sample was combined with 5 μL of 20 mM internal standard norleucine and norvaline, Sigma-Aldrich and U- 13 C 6 glucose [Cambridge Isotope laboratories].
A volume of μL of cold methanol:water:chloroform solution was added to the sample and vortexed for 30 s. Tubes were centrifuged for 5 min at 10, g at 4°C. The supernatant was removed and extraction was repeated using μL of ice cold chloroform:methanol , tubes were vortexed and left on ice for 30 min.
Tubes were centrifuged 5 min at rpm at 4°C and the liquid phase was transferred to the previous extracted aqueous phase. A total of μL of water was added and tubes were centrifuged 5 min at rpm.
The aqueous phase was transferred to a 2 mL microcentrifuge tube. The aqueous extract was dried using a vacuum concentrator at ambient temperature overnight Univapo ECH vacuum concentrator centrifuge. The samples were injected in split mode with an inlet temperature of °C.
The VF-5ms 30 m x μm x 0. Compounds are noted as either confirmed with our own standards, or the best match and associated matching factor against the NIST library are reported Supplementary file Peaks were picked and integrated using the Agilent MassHunter Quantitative Analysis software.
Peak areas were normalized to the internal standards. The data were processed using R version 3. The amplicon sequencing data and the RNA sequencing data are available under the NCBI Bioproject PRJNA and the GEO record GSE All differential expression analysis results of this study are included in Supplementary file The amplicon sequencing data and the RNA sequencing data are available under the NCBI Bioproject PRJNA and the GEO record GSE respectively.
All data generated or analysed during this study are included in the manuscript and supporting files. Bacterial abundance data CFUs are included into Supplementary file 3, amplicon sequencing processed data are included into Supplementary file 4, RNA sequencing processed data, statistical analysis results enrichment tests and transcript per million data are included into Supplementary file 5—9, metabolomics analysis data are included into Supplementary file Our editorial process produces two outputs: i public reviews designed to be posted alongside the preprint for the benefit of readers; ii feedback on the manuscript for the authors, including requests for revisions, shown below.
We also include an acceptance summary that explains what the editors found interesting or important about the work. To address this question, the authors use an elegant model, relying on honeybees colonized with a defined bacterial community.
They provide compelling empirical evidence that a nutritionally complex diet, together with microbial metabolic diversity, play a key role, enabling species partitioned resource utilization and co-existence of closely related honeybee gut microbiota.
Thank you for submitting your article "Niche partitioning facilitates coexistence of closely related gut bacteria" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Karina Xavier as the Reviewing Editor and Reviewer 1, and the evaluation has been overseen by Christian Rutz as Senior Editor.
The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission. In general, all three reviewers found the work exciting and solid, and thus suitable for publication in eLife.
Below is a list of essential revisions to help improve the clarity and presentation of the work. Additionally, you can find at the end of this letter, for context, the separate "recommendations for the authors" written by the individual reviewers.
We hope you will use this information to improve your manuscript, but you do not need to reply to the comments raised by the reviewers -- you will only need to reply to the following essential points summarized here:. Since we believe it is unlikely to affect the major findings of the paper where comparisons of simple sugars and more complex diets are made, we are not asking for additional experiments.
But we think it is important that you explain your rationale, and discuss if and how these choices may impact your results. We acknowledge that a comprehensive understanding of the mechanisms involved in the simple diet are beyond the scope of the present paper, given that here you provide a clear proposal of the more interesting problem, that is the co-existence and persistence in the complex diet.
The current study does not explore what happens in the simplified diet both in vitro and in the Bee gut. The authors propose in the discussion that in the presence of few and simple carbon sources sugars there is competition for nutrients and competitive exclusion is driving extinction of most species and domination of a single species.
However, there is not much experimental support for this conclusion. Can this be explained by competitive exclusion? Is there transcriptomics or genomic data in this study that supports this possibility? If the data cannot provide strong hypothesis for competitive exclusion by nutrition competition in the simplified environment the authors should discuss other possible mechanisms that could explain such phenotypes in the simplified diets.
One point that it is not address is the number of calories provided by the different diets. Could it be that the simplified diet is also lower in calories and thus more a limiting and that differences in calories as opposed to the different number of nutrient sources could contribute to the results observed?
Regarding experimental design it is not clear why a sucrose solution SW was used for the in vivo studies and Glucose was used for the in vitro studies. Additionally, in vitro the results with pollen grains were better in promoting persistence than the Pollen extracts, but the follow up studies were performed with pollen extract that enables persistence but not as well.
The authors should explain the rationale for these choices and discuss if these choices may or may not have impact on the results obtained. My ability to make specific comments was hindered by the lack of line numbers and page numbers in the merged document.
Perhaps these fell out of the compiled version, but if not, I urge the authors to include page numbers and line numbers in their future submissions. I think that this may impact some statements that the authors are making regarding the comparison between in vitro and in vivo.
I've noted specific cases in my comments below but wanted to reiterate this overarching concern here. You are covering a lot of ground in the introduction- one thing that got lost for me until the second read through was that I think you specifically choose the Firm5 subset of the bee gut microbiota because of close taxonomic relatedness.
Consider motivating the paragraph beginning "One of the most prevalent phylotypes…" so that this is clearer. I think that you have very well thought out reasons for choosing the species and strains for this study, and I think that it would strengthen the manuscript to spend a little more time on this in the introduction.
Some of this you mentioned in the discussion- I think it would strengthen the manuscript to present these details sooner. Specific questions that I think need to be addressed in the introduction are: Why were Lactobacilli chosen specifically, over other major phylotypes?
What fraction of the total bee gut diversity does this phylotype represent? Is there generally only one strain of a species present in the bee gut? How were strains chosen as representatives?
Section: The coexistence of four related Lactobacillus species in the honey bee gut depends on the host diet. I think it would help the reader to unpack the idea in your summary paragraph.
This is the first place where niche partitioning is mentioned partitioning is referred to in the abstract , and you don't define the term.
I think the link between niche partitioning and your results is that pollen is a complex resource, and sugar water is not. From consumer-resource models, one would expect that there would be only one dominant competitor on one resource.
If multiple resources are available, then one would expect that the community would be as diverse as the of resources.
I am not suggesting more experiments here, but have you considered seeing whether you could support the 4-member community on sugars found in pollen and detected as consumed by the metabolomics? Showing that this could work in a defined environment with these strains would be a nice although not surprising extension of the resource partitioning idea.
Section: in vitro co-culture experiments recapitulate the diet-dependent coexistence of the four Lactobacillus species.
In culture, you observe higher yield for all four strains on glucose than on pollen or pollen extract, but you see the opposite in the bee gut. Have you followed up on why this might be? I ask because you are using yield as a proxy for 'niche space', and I think a fundamental assumption there is that resources that define the niches are limiting.
If in culture a different resource is limiting let's say resource B , then all of your strains might reach the same yield and stop growing due to resource B limitation before they have consumed all of the glucose or pollen. I think that establishing that pollen or glucose is truly growth limiting in these experiments is critical if you want to make the statement that "portioning of pollen-derived nutrients is sufficient for enabling coexistence".
But the experiments in this section use either pollen grains, pollen extract, or sugar. Without comparing the same conditions, I don't think that you can conclude from this section that "In summary, these findings show that the dietary-dependent coexistence of the four Lactobacillus species observed in vivo can be recapitulated in vitro in a simple co- culture experiment, suggesting that the partitioning of pollen-derived nutrients is sufficient for enabling coexistence.
Section: The four Lactobacillus species upregulate divergent carbohydrate transport and metabolism functions during gut colonization in the presence of pollen. Pollen is supplying dietary amino acids, whereas SW alone does not.
Sometimes transcription becomes elevated when substrates are scarce, is there evidence in the literature that this could be the case?
I'm surprised that you didn't encounter problems with reads mapping to multiple reference genomes with HTSeq. I'd expect there to be some frequency of mers sharing 1 SNP, which I understand from the results was the threshold for mapping reads to the genomes.
I don't see any discussion of this in the text. Could you comment on what controls were done to explore the frequency of cross-mapping of reads? Figure 4. My understanding of GH families is that they can be quite broad. For example, the GH16 family encompasses 15 different types of enzyme, not just the one cited in the figure.
Could the authors provide more detailed information about why specific activities were attributed the GH families highlighted in the figure panels? Section: Transcriptional responses to pollen are concordant in vivo and in vitro.
Echoing my comments in the section about in vitro co-culturing, I'm wondering why the comparison was not glucose vs. In the host, when there is pollen available glucose is also available.
Because of this, I don't agree with the interpretation of the data that the transcriptional responses are concordant, and I think that the interpretation of differential expression to suggest that "genes are specifically expressed in vivo" is overly strong. I think that these data can be used to make the overall point that the authors are presenting: the four strains do express different sugar-catabolism related genes when grown on pollen.
Rather than comparing directly to the RNA-Seq from the host, I'd encourage the authors to focus instead on what their data show about the regulation of COG-G category genes in response to nutrient source. How does gene expression change between mono- and co-culture on the same carbon source?
If the strains differentially expressing genes when they are co-cultured, is a more diverse profile of COG-genes expressed, or do the strains tailor their expression? I think there is only one direct comparison: co-culture communities on glucose.
Are the COG genes differentially expressed the co-culture glucose community in vitro also the ones that are differentially expressed in the context of the in vivo community grown on glucose? Supplementary Figure 3 indicates that some host samples profiled by RNASeq did not cluster by mds because of low read mapping.
Please clarify if these samples were included in other analyses involving RNASeq data or if they were removed. If they were included, please comment on the rational for inclusion given low reads and lack of clustering.
Section: Metabolomics analysis reveals differences in flavonoid and sugar metabolism across the four Lactobacillus species. In studies of non-host associated microbial communities, an observation is that there is strain-level heterogeneity with respect to the profile of carbon sources consumed.
From the metabolomics data, it looks as though some strains don't consume some carbon sources. Do you know whether all 4 strains can grow on the different carbon sources that you find by metabolomics in pollen, in particular the carbon sources that are most abundant in pollen?
I don't think that the comparison to hydra is fair- hydra microbiota assemble on the host epithelium, not a gut lumen. You note that the primary site for colonization of Lactobacillus in the bee gut the rectum is known, and that other microbial taxa of the bee gut microbiota display different tissue tropism.
I think that this is a really interesting point. Would we expect the dynamics of bee gut taxa with a closer host association to be different? Was the choice of strains motivated by the tissue tropism? I'm surprised that you don't see evidence of cross-feeding on the complex resource.
Do you think that this is due to the taxonomic similarity of the isolates do you think crossfeeding is restricted to other phylotypes , or the small used in this study, or that the community doesn't become limited for the resources in pollen?
Can you differentiate among these possibilities with your data? It is true that the in vitro and in vivo nutrient conditions were not identical, and retrospectively it would have been more consistent to compare pollen versus glucose both in vitro and in vivo.
However, we need to keep in mind that whatever we would have fed to the bees, would have been pre-digested and partially absorbed by the host tissue in the midgut before it reaches the bacteria in the hindgut.
Editorial on the Research Topic Partitionung Integration: Gut Health and Physiological Functions Roasted sunflower seeds Animals. Among Gut health and nutrient partitioning gut health paftitioning, the gut Youth athlete supplements has strong metabolic activities and plays pqrtitioning important roles in animals and poultry. It nurrient Natural body detox regulation of nutrient utilization and physiological functions of the host, including the digestion and Natural body detox nutrieent nutrients, fermentation of complex macronutrients, and nutrient and vitamin production, contributing to the construction of the intestinal epithelial barrier, the development and function of the host immune system, competing with pathogenic bacteria to prevent their harmful propagation, and physiological metabolism in distal organs or tissues 2 — 4. In addition to the composition of the gut microbiota, small molecule metabolites derived from gut microbiota can enter into the systemic circulation and play regulatory roles signaling molecules or toxins, affecting the performance and health of animals 1. In this context, maintaining a healthy gut microbiota has become a prominent strategy to improve animal and poultry's health and production performance.Gut health and nutrient partitioning -
The challenges in replicating the native environment such that it is possible to study relevant interactions of host-associated microbes in vitro are formidable.
These were highlighted in a recent study on the microbial community associated with the freshwater polyp hydra that could not recapitulate the coexistence of the dominant microbiota members in vitro Deines et al. Here, we aimed to approximate the nutritional conditions in the honey bee gut by culturing the bacteria in pollen infused media, that is the natural diet of bees.
In both the in vivo and in vitro transfer experiment, we assessed the effect of pollen on the dynamics of the community by comparing it to a simple sugar treatment. Although not identical, the nutritional conditions in vitro were sufficiently similar as to recapitulate the overall community dynamics observed in vivo: pollen nutrients supported the stable coexistence of the four species, while the simple sugars led to the dominance of a single species.
As the bee and members of the bee gut microbiota pre-digest pollen and sugars upstream of the rectum, it is difficult to exactly replicate the metabolic environment of the rectum. For example, sucrose is largely absorbed via the midgut epithelium and cleaved into glucose and fructose by host enzymes, while fermentative bacteria such as Gilliamella apicola degrade and modify a diverse range of carbohydrates in the ileum Kešnerová et al.
These metabolic alterations may explain some of the differences observed between the in vivo and in vitro experiments, such as the dominance of different species in the simple sugar conditions sucrose and glucose, respectively.
We therefore suspect that different species would dominate in vitro or in vivo with an alternative simple sugar composition. Our findings are consistent with the consumer-resource model, which predicts that the number of species that can coexist depends on the number of available resources Tilman, Correspondingly, in the presence of a single substrate, such as in the case of glucose in vitro, competition for the same nutrient results in the competitive exclusion of all but one species.
However, depending on the nutrient availability, the dietary transit time, the crosstalk with the host, or the spatial structure of the gut, the ecological processes governing bacterial coexistence may differ across host-associated microbiomes. For example, the Lactobacillus species of the honey bee gut microbiota primarily colonize the luminal space of the rectum, where partially digested pollen accumulates.
In contrast, some of the Proteobacteria of the bee gut microbiota adhere to the epithelial surface of the ileum Zheng et al. We expect that in the latter case interactions with the host play a more important role for microbial coexistence than in the case of the Lactobacilli in the rectum.
Although ecological interactions in bacterial communities have been investigated across a wide range of experimental systems, few studies have tackled the molecular mechanisms underlying coexistence.
In some cases, cross-feeding of metabolic by-products facilitates the maintenance of diversity in bacterial communities, such as after passaging leaf and soil samples in single carbon sources Goldford et al.
However, cross-feeding does not seem to play an important role in maintaining coexistence of the four Lactobacillus species in this study. Unlike the above example, feeding a single carbon source led to the extinction of all but one species.
Our metabolomics analysis also did not reveal any major metabolites that could potentially be cross-fed, that is were produced by one species and utilized by another. Finally, we identified no transcriptional changes that would suggest cross-feeding activities when comparing mono-cultures and co-cultures of the four Lactobacillus species.
Instead, our combined transcriptomics and metabolomics analyses suggest that coexistence is facilitated by specialization toward distinct pollen-derived nutrients. We found that all four species upregulated carbohydrate transport and metabolism functions dedicated to the utilization of different carbon sources in the presence of pollen when colonizing the bee gut, and these changes were reproducible in vitro.
Our metabolomics analysis identified a number of pollen-derived glycosides that were utilized in a species-specific manner. In particular, Lmel specialized in the utilization of flavonoids at the expense of simple sugars, which may explain why this species rapidly went extinct in presence of only simple sugars during the transfer experiments.
While the importance of pollen-derived flavonoids in niche partitioning needs to be validated, the species-specific deglycosylation of these secondary plant compounds illustrates that the four species have different hydrolytic capabilities that may also be involved in the cleavage of other carbohydrates.
The metabolic specialization on plant glycans may be a common phenomenon in animal gut communities, as similar transcriptional changes have been described in other gut symbionts when the host diet was supplemented with specific plant glycans Sonnenburg et al.
In contrast to the species specific metabolism of glycoside, we observed few differences in the utilization of simple saccharides among the four species in our time-resolved GC-MS analysis.
While this may seem surprising, theoretical work has established that resource preference for at least one substrate is sufficient to explain coexistence Meszéna et al.
Moreover, it is plausible that the four species utilize the same sugars, but extract them from different pollen-derived glycans, such as starch, hemicellulose, flavonoids, or other glycosylated secondary plant metabolites. While this work focused on niche partitioning based on degradation of complex carbohydrates, it is noteworthy that all four Lactobacillus species engaged to a variable extent in co-fermentation of the carboxylic acids citrate and malate present in pollen.
The two species, Lkul and Lhel, that upregulated citrate fermentation pathways in the presence of pollen also consumed citrate at the fastest rate. Citrate co-fermentation has been linked to competitive advantages in lactic acid bacteria, though whether the varying levels of co-fermentation contribute to colonization stability in this system remains an outstanding question Laëtitia et al.
Previous work suggested that the large diversity of carbohydrate transport and metabolism functions in the accessory gene pool of Lactobacillus Firm5 is an adaptation to the pollen-based diet of the host and a consequence of the nutrient competition with closely related species Ellegaard and Engel, ; Ellegaard et al.
Our findings support this hypothesis and provide the first experimental evidence for a link between the coexistence of the four Lactobacillus species, the large diversity of carbohydrate metabolism functions in their genomes, and the pollen diet of the host.
Moreover, these results suggest that dietary differences between host species or natural variation in pollen diversity influence the diversity of Lactobacillus Firm5 and could, for example explain why the Asian honey bee, Apis cerana, harbors only one species of this phylotype in its gut Ellegaard et al.
However, we have only tested a single strain of each of the four species. Therefore, given the extensive genomic diversity within these species Ellegaard and Engel, , more work is needed to determine if the identified patterns of coexistence reflect stable ecological niches occupied by the four species or are rather the result of the specific strains selected for our experiments.
In a recent study on pitcher plant microbiomes, it was shown that even strains that differ by only a few base pairs can have different ecological trajectories in communities and coexist over extended period of time Bittleston et al. Expanding our approach to strains within species presents an exciting next step to understand at which level discrete ecological niches are defined in the bee gut and how diversity can be maintained in such ecosystems.
We used the following four bacterial strains of Lhel, Lmel, Lapi, and Lkul for our experiments: ESL, ESL, ESL, and ESL Kešnerová et al. MRSA plates were incubated for three days in anaerobic conditions at 34°C to obtain single colonies.
Microbiota-depleted bees were obtained from colonies of Apis mellifera carnica located at the University of Lausanne following the procedure described in Kešnerová et al.
Five days post-colonization, 10 rectums were dissected and homogenized in 1xPBS. An aliquot of each homogenized gut was used for CFU plating to enumerate the total bacterial load and for amplicon sequencing to obtain the relative abundance of each community member see below.
This was repeated for a total of six serial passages. Food was provided ad libitum. Each of the four strains was cultured in liquid medium overnight for about 16 hr as described above.
Detailed information about pollen extract preparation can be found in the Supporting methods section of Kešnerová et al. Pollen grain solutions were prepared by adding 1. The plates were incubated for 24 hr at 34°C under anaerobic conditions without shaking rpm.
After 24 hr of incubation, an aliquot of each sample was subjected to CFU plating to enumerate the total bacterial load. These transfers were repeated 10, respectively, 20 times for the two independent experiments. CFUs were counted after 24 hr and at the final transfer.
The relative abundance of the four strains across all transfer experiments was obtained using amplicon sequencing of a bp long fragment of a housekeeping gene encoding a DNA formamidopyrimidine-glycosylase which allows to discriminate the four strains from each other Ellegaard et al.
For the in vitro transfer experiments, the PCR fragment was amplified from crude cell lysates. They were generated by mixing 5 μL of culture with 50 μL of lysis solution, containing 45 μL of lysis buffer 10 mM Tris- HCl, 1 mM EDTA, 0. The samples were incubated for 10 min at 37°C, for 20 min at 55 °C, and for 10 min at 95 °C, followed by a short spin before preparing the PCR 1 min, rpm.
For the in vivo transfer experiment, DNA was isolated from the homogenized gut samples using the hot phenol protocol used in Kešnerová et al.
To amplify the gene fragment and to add the Illumina barcodes and adapters, the two-step PCR strategy published in Ellegaard et al. For the first PCR, 5 μL of DNA or 5 μL of cell lysate were mixed with The PCR I was performed as follows: initial denaturation 95°C — 3 min , 30 times denaturation-annealing-extension 95°C — 30 s, 64°C — 30 s, 72°C — 30 s , final extension 72 °C — 5 min.
and incubated for 30 min at 37°C and for 15 min at 80°C. For the second PCR reaction, 5 μL of purified PCR products were mixed with the same reagents as before.
The PCR program was the same as above with the exception that the annealing temperature was set to 60°C and the denaturation-annealing-extension steps were repeated for only eight times. The barcoded primers are listed in Supplementary file 1.
The amplicons of the second PCR were purified using the Exo-SAP program as described above. To prepare the sequencing of the amplicons, DNA concentrations were measured using Quant-iT PicoGreen for dsDNA Invitrogen. Each sample was adjusted to a DNA concentration of 0. The pooled sample was loaded on a 0.
Illumina reads were demultiplexed by retrieving the unique barcodes of the different samples and quality-filtered using Trimmomatic Trimmomatic Each forward and reverse read pair was assembled using PEAR -m n j 4 -q 26 v 10 -b 33 Zhang et al.
See details in Supplementary material. To obtain absolute abundance data for each strain, we combined the relative abundance data from the amplicon sequencing with CFU counts obtained from plating homogenized bee guts in the case of the in vivo experiments see above or by carrying out qPCR with Lactobacillus -specific primers as described in Kešnerová et al.
For the in vitro transfer, the stability of the four-species community over time was calculated using the codyn R package Hallett et al. For the in vivo RNA sequencing, microbiota-depleted bees were colonized with the four species community as described above and fed with either sugar water and pollen grains or with sugar water only.
After 5 days of colonization, the rectums of five bees per treatment all kept in the same cage were dissected and snap-frozen in liquid nitrogen in separate tubes containing glass beads 0.
Tubes were then thawed on ice and a previously developed hot phenol RNA extraction protocol was followed Sharma et al. After 16 hr of growth, μL of STOP solution was added to 1 mL of culture followed by the same steps as described above.
After the precipitation step, samples were treated with DNaseI NEB to degrade DNA. RNA concentration and quality were assessed using Nanodrop ThermoFisher Scientific , Qubit ThermoFisher Scientific, RNA — High Sensitivity reagents and settings and Bioanalyzer Agilent.
High-quality RNA samples were selected to prepare RNA libraries. For the in vivo RNA sequencing, libraries were prepared using the Zymo-Seq RiboFree Total RNA Library kit Zymo Research.
The libraries were sequenced by the GTF facility of the University of Lausanne using HiSeq SR sequencing bp reads Illumina. For the in vitro RNA sequencing, libraries were prepared following the protocol developed by Avraham et al. Libraries were then prepared for sequencing following the Illumina MiniSeq System guide for denaturate and dilute libraries.
Libraries were sequenced using the Illumina MiniSeq technology using High Output Reagent Cartridges bp reads and MiniSeq flow cells. For the in vitro samples, raw reads were demultiplexed using a script provided by Dr. Jelle Slager Personal communication For the in vivo samples, the reads were already demultiplexed by the sequencing facility.
For both experiments, the reads were trimmed with Trimmomatic Trimmomatic For the in vivo samples, trimmed reads were sorted with sortmerna Reads were mapped onto the genomes of the selected strains Ellegaard and Engel, Lapi, Lhel, Lmel, and Lkul using Bowtie bowtie Quality filtered reads were then quantified using HTseq Version 0.
Differential gene expression between samples cultured in pollen extract and samples cultured in glucose, and between mono-cultures and co-cultures, was calculated using the R package EdgeR Robinson et al.
Counts per million were calculated and only genes with at least one count per million were used for the analysis. EdgeR fits negative binomial models to the data. The counts were normalized for RNA composition by adjusting the log 2 FC according to the library size, and the quantile-adjusted conditional maximum likelihood qCML method was used to estimate the common dispersion and the tag-wise dispersion.
Transcripts per million TPM were visualized using the Integrated Genome Browser software Freese et al. After collection of all samples, they were prepared for metabolomics analysis.
The samples were thawed on ice and centrifuged again 20, g, 4°C, 5 min , then diluted 10 times with ddH 2 O. For metabolomics analysis, 25 μL of each diluted sample was sent in a well plate on dry ice to the laboratory of Prof.
Uwe Sauer for analysis ETH Zürich, Switzerland. Three replicates of a pollen-extract dilution series 10 serial 2x dilutions as well as undiluted pollen-extracts and water used for performing the dilution series were included in the metabolomics analysis.
Tubes were vortexed thoroughly and incubated for 5 min 4°C, shaking 14, rpm. For untargeted metabolomics analysis, each sample was injected twice technical replicate into an Agilent time-of-flight mass spectrometer ESI-iFunnel Q-TOF, Agilent Technologies as detailed in Kešnerová et al.
Alternative annotation can be found in Supplementary file When available, metabolites categories were assigned to ions based on KEGG ontology. Metabolomics data analysis was carried out using R version 3.
Variation of raw ion intensities obtained from untargeted metabolomics analysis for the two technical replicates was determined by assessing the correlation between ion intensities of the respective technical replicates. Then, mean ion intensities of technical replicates were calculated.
log 2 FC values between the two time-points were calculated with respect to the mean intensity in the T0 time point. To identify pollen-derived ions, and distinguish them from background originating from culture medium and experimental noise, the ion intensities of the pollen dilution series were plotted for each ion and the R 2 of the obtained linear fit was extracted.
In addition, we calculated the log 2 FC difference between undiluted pollen and water. All ions were included for downstream analysis e. PCA and then they were discriminated between pollen-derived and non-pollen-derived.
For each liquid culture sample, μL was collected and centrifuged 15, g, 4°C, 15 min. Once that all the samples were collected, soluble metabolites were extracted. To extract soluble metabolites, tubes were thawed on ice, and 75 μL of sample was combined with 5 μL of 20 mM internal standard norleucine and norvaline, Sigma-Aldrich and U- 13 C 6 glucose [Cambridge Isotope laboratories].
A volume of μL of cold methanol:water:chloroform solution was added to the sample and vortexed for 30 s. Tubes were centrifuged for 5 min at 10, g at 4°C. The supernatant was removed and extraction was repeated using μL of ice cold chloroform:methanol , tubes were vortexed and left on ice for 30 min.
Tubes were centrifuged 5 min at rpm at 4°C and the liquid phase was transferred to the previous extracted aqueous phase.
A total of μL of water was added and tubes were centrifuged 5 min at rpm. The aqueous phase was transferred to a 2 mL microcentrifuge tube. The aqueous extract was dried using a vacuum concentrator at ambient temperature overnight Univapo ECH vacuum concentrator centrifuge.
The samples were injected in split mode with an inlet temperature of °C. The VF-5ms 30 m x μm x 0. Compounds are noted as either confirmed with our own standards, or the best match and associated matching factor against the NIST library are reported Supplementary file Peaks were picked and integrated using the Agilent MassHunter Quantitative Analysis software.
Peak areas were normalized to the internal standards. The data were processed using R version 3. The amplicon sequencing data and the RNA sequencing data are available under the NCBI Bioproject PRJNA and the GEO record GSE All differential expression analysis results of this study are included in Supplementary file The amplicon sequencing data and the RNA sequencing data are available under the NCBI Bioproject PRJNA and the GEO record GSE respectively.
All data generated or analysed during this study are included in the manuscript and supporting files. Bacterial abundance data CFUs are included into Supplementary file 3, amplicon sequencing processed data are included into Supplementary file 4, RNA sequencing processed data, statistical analysis results enrichment tests and transcript per million data are included into Supplementary file 5—9, metabolomics analysis data are included into Supplementary file Our editorial process produces two outputs: i public reviews designed to be posted alongside the preprint for the benefit of readers; ii feedback on the manuscript for the authors, including requests for revisions, shown below.
We also include an acceptance summary that explains what the editors found interesting or important about the work. To address this question, the authors use an elegant model, relying on honeybees colonized with a defined bacterial community. They provide compelling empirical evidence that a nutritionally complex diet, together with microbial metabolic diversity, play a key role, enabling species partitioned resource utilization and co-existence of closely related honeybee gut microbiota.
Thank you for submitting your article "Niche partitioning facilitates coexistence of closely related gut bacteria" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Karina Xavier as the Reviewing Editor and Reviewer 1, and the evaluation has been overseen by Christian Rutz as Senior Editor.
The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission. In general, all three reviewers found the work exciting and solid, and thus suitable for publication in eLife.
Below is a list of essential revisions to help improve the clarity and presentation of the work. Additionally, you can find at the end of this letter, for context, the separate "recommendations for the authors" written by the individual reviewers. We hope you will use this information to improve your manuscript, but you do not need to reply to the comments raised by the reviewers -- you will only need to reply to the following essential points summarized here:.
Since we believe it is unlikely to affect the major findings of the paper where comparisons of simple sugars and more complex diets are made, we are not asking for additional experiments.
But we think it is important that you explain your rationale, and discuss if and how these choices may impact your results. We acknowledge that a comprehensive understanding of the mechanisms involved in the simple diet are beyond the scope of the present paper, given that here you provide a clear proposal of the more interesting problem, that is the co-existence and persistence in the complex diet.
The current study does not explore what happens in the simplified diet both in vitro and in the Bee gut. The authors propose in the discussion that in the presence of few and simple carbon sources sugars there is competition for nutrients and competitive exclusion is driving extinction of most species and domination of a single species.
However, there is not much experimental support for this conclusion. Can this be explained by competitive exclusion? Is there transcriptomics or genomic data in this study that supports this possibility? If the data cannot provide strong hypothesis for competitive exclusion by nutrition competition in the simplified environment the authors should discuss other possible mechanisms that could explain such phenotypes in the simplified diets.
One point that it is not address is the number of calories provided by the different diets. Could it be that the simplified diet is also lower in calories and thus more a limiting and that differences in calories as opposed to the different number of nutrient sources could contribute to the results observed?
Regarding experimental design it is not clear why a sucrose solution SW was used for the in vivo studies and Glucose was used for the in vitro studies. Additionally, in vitro the results with pollen grains were better in promoting persistence than the Pollen extracts, but the follow up studies were performed with pollen extract that enables persistence but not as well.
The authors should explain the rationale for these choices and discuss if these choices may or may not have impact on the results obtained. My ability to make specific comments was hindered by the lack of line numbers and page numbers in the merged document.
Perhaps these fell out of the compiled version, but if not, I urge the authors to include page numbers and line numbers in their future submissions.
I think that this may impact some statements that the authors are making regarding the comparison between in vitro and in vivo. I've noted specific cases in my comments below but wanted to reiterate this overarching concern here.
You are covering a lot of ground in the introduction- one thing that got lost for me until the second read through was that I think you specifically choose the Firm5 subset of the bee gut microbiota because of close taxonomic relatedness. Consider motivating the paragraph beginning "One of the most prevalent phylotypes…" so that this is clearer.
I think that you have very well thought out reasons for choosing the species and strains for this study, and I think that it would strengthen the manuscript to spend a little more time on this in the introduction. Some of this you mentioned in the discussion- I think it would strengthen the manuscript to present these details sooner.
Specific questions that I think need to be addressed in the introduction are: Why were Lactobacilli chosen specifically, over other major phylotypes? What fraction of the total bee gut diversity does this phylotype represent? Is there generally only one strain of a species present in the bee gut?
How were strains chosen as representatives? Section: The coexistence of four related Lactobacillus species in the honey bee gut depends on the host diet. I think it would help the reader to unpack the idea in your summary paragraph. This is the first place where niche partitioning is mentioned partitioning is referred to in the abstract , and you don't define the term.
I think the link between niche partitioning and your results is that pollen is a complex resource, and sugar water is not. From consumer-resource models, one would expect that there would be only one dominant competitor on one resource. If multiple resources are available, then one would expect that the community would be as diverse as the of resources.
I am not suggesting more experiments here, but have you considered seeing whether you could support the 4-member community on sugars found in pollen and detected as consumed by the metabolomics?
Showing that this could work in a defined environment with these strains would be a nice although not surprising extension of the resource partitioning idea. Section: in vitro co-culture experiments recapitulate the diet-dependent coexistence of the four Lactobacillus species.
In culture, you observe higher yield for all four strains on glucose than on pollen or pollen extract, but you see the opposite in the bee gut. Have you followed up on why this might be?
I ask because you are using yield as a proxy for 'niche space', and I think a fundamental assumption there is that resources that define the niches are limiting.
Such an integrated approach should help in the design of microbiome-based solutions for health or performance. Moderate endurance exercise reduces inflammation, improves body composition and leads to positive effects on gut microbial diversity and composition and its metabolic contribution to human health.
Endurance exercise exhibits positive effects on human health and on the gut microbial ecosystem, provided that the exercise intensity is controlled. Elite athletes seem to have a higher gut microbial diversity and a shift toward bacterial species involved in specific pathways such as the production of short-chain fatty acids butyrate, propionate.
Confounding factors such as diet, body composition, study design, and analytical methods limit the conclusions of the existing studies. This review will focus on the interconnection between gut microbiota and exercise.
Confounding factors such as diet can impact this interconnection. These factors will also be discussed in this review. Athlete cohorts, diseased populations and overweight populations will be used to expand on the effects and mechanisms of this interconnection.
Specific animal models will also be highlighted to provide details on the mechanisms not yet clarified in humans.
In endurance exercise, a common definition of performance is the time to complete a certain distance. Therefore, athletes try to maximize their average speed during the defined distance to complete, but performance is always constrained by human body limits.
In endurance exercises, researchers have been trying, for many years, to pinpoint the factors limiting performance from a physiological perspective and ways to overcome them. First, during endurance aerobic exercise, muscles rely mainly on the breakdown of stored glycogen-glucose for energy production.
However, as glycogen stores are limited, the existence of other energy sources is essential 1. These energy sources can rely on endogenous and exogenous substrates. Therefore, the intake of carbohydrates during exercise has been a widespread strategy to improve performance. Carbohydrates are absorbed in the blood flow due to transporters in the intestine.
This step is crucial and often limiting in terms of performance 2 , and training the gut to absorb exogenous energy substrates during exercise can improve endurance performance as well as provide a better experience for athletes 3.
Second, performance in endurance exercises is limited by the cardiovascular capacity, often measured using VO 2max maximum oxygen uptake - the maximum rate of oxygen consumption that the body can use during exercise. When a person trains at progressively higher intensities, oxygen uptake increases linearly to meet the demand of active skeletal muscles, until maximum oxygen uptake is reached 4.
The principal limitation of the cardiovascular capacity is cardiac output. This increase in blood flow can have major consequences for the digestive system including ischemia in the gut due to blood flow redistribution. This can lead to lower gastrointestinal GI disorders abdominal pain or discomfort, bloating, diarrhea, constipation as well as upper gastrointestinal disorders stomach pain, nausea, vomiting 5.
The alteration of gut transit time is also detrimental to the microbiome balance. Unsurprisingly, this is one of the main reasons why ultrarunners do not finish an ultramarathon 8. In view of these elements, the proper functioning of the digestive tract and the associated microbiota need to be considered in order to perform well in endurance sports.
The main focus of this review will therefore be the relationship between exercise and the gut microbiota in endurance sports. The human body is inhabited by a large number of bacteria, viruses, archaea and unicellular eukaryotes 9 called the microbiota After a first estimate that the human microbiota contains up to 10 13 14 bacterial cells, 10 times more than cells in the human body 11 , a recent update established a ratio between the bacterial cells and the human body cells Microorganisms are also widespread on the surface of the human body, colonizing the skin as well as the genitourinary, gastrointestinal, and respiratory tracts 10 , The gastrointestinal tract is an organ system that has many functions: it takes in food, digests it to extract and absorb energy and nutrients, and expels then the remaining waste as feces.
It consists of the upper gastrointestinal tract formed by the esophagus and stomach and the lower gastrointestinal tract composed of the small intestine duodenum, jejunum, and ileum and large intestine cecum, colon, rectum, and anal canal.
The intestine has a large exchange surface area of ~80 m 2 15 , due to the villi in the epithelium layer. The gut microbiota is located in the intestinal lumen, next to but also within the first outer layer of the mucus bilayer 16 — At the level of bacterial strains, as seen in classical microbiology, the gut microbiota demonstrates tremendous diversity and variation between individuals 19 , The human gut microbiota consists of four main phyla: Firmicutes and Bacteroidetes, quantitatively the most abundant, as well as Actinobacteria and Proteobacteria The microbial populations can be stratified into 3 enterotypes and these bacterial gene correlation networks were shown to be driven by the following genera: Prevotella, Bacteroides , and Ruminococcus Their relative prevalence has been shown to be largely driven by dietary habits 21 , The need to stratify into enterotypes is particularly relevant in clinical settings: for ranging from direct disease associations to prospective study stratification or even personalized dietary interventions or other gut modulation treatments The gut microbiota has coevolved with the host over thousands of years to form an intricate and mutually beneficial relationship The microbiota offers many benefits to the host through a range of physiological functions affecting host nutrition, metabolic function, and maturation of the immune system 25 , The gut microbiome contributes to digestion and promotes food absorption for host energy production Microbiome fermentation leads to metabolites that are very relevant to athletes, such as short-chain fatty acids SCFAs , lactate and branched-chain fatty acids.
The most abundant SCFAs are found at proportions of for acetic acid C 2 , propionic acid C 3 and butyric acid C 4 SCFAs have distinct physiological effects: they can be used as energy sources by host cells and the intestinal microbiota, but they can also contribute to shaping the gut environment, influencing the physiology of the colon, and participating in different host-signaling mechanisms 27 , as well as possessing some anti-inflammatory effects.
SCFAs appear to be of paramount importance as a marker of changes in intestinal ecology 28 and highlight the close link between diet, the gut microbiota and metabolic function. Secondary bile acids, produced in the colon by the microbiota, also exert effects on the metabolic function of the host, particularly on the metabolism of triglycerides and glucose 28 , Indeed, after being produced in the colon, they can be transported in the blood and reach a variety of organs, including the liver and kidneys.
The gut microbiota is highly linked to the host immune system 30 , 31 : protection from pathogens with the mucosal firewall, induction of effector T and B cell responses against pathogens, competition for nutrients with pathogens, production of antimicrobial molecules and metabolites that affect the survival and virulence of these pathogens, and reinforcement of tight junctions.
It also helps in the stimulation and maturation of epithelial cells Another aspect of gut health is the interrelation among the gut microbiota, intestinal permeability and inflammation.
For a recent review discussing the definition of a healthy microbiome see Shanahan et al. Transepithelial or transcellular permeability consists of the specific transport of solutes, thanks to specialized transporters, across epithelial cells. Paracellular permeability depends on transport through the spaces that exist between epithelial cells.
It is mediated by the intestinal epithelium and regulated by intercellular tight junctions. This is the main route of the passive flow of water and solutes across the intestinal epithelium. Normally, permeability allows the maintenance of a balance between nutrients passing through the gut while keeping potentially harmful substances, such as antigens, from migrating to other body parts or fluid bodies A disruption in gut mucus thickness 35 , an imbalance in the gut microbiota composition or a decrease in gastrointestinal blood flow 34 , caused by intense exercise, can lead to impairments in these fluxes.
Therefore, harmful substances such as endotoxins from the outer membrane of Gram-negative bacterial strains, namely, lipopolysaccharides LPS , can then pass through the barrier Often, the LPS blood concentration increases together with inflammatory cytokines.
Hence, chronic inflammatory responses can be established in the body with major consequences on host health. Moreover, alterations in gut microbiota have been linked to functional and inflammatory disorders It is key to understand their strengths and limits to understand the data they provide and how to interpret them.
In an increasing number of studies, different methods are being combined to obtain a better picture of the physiological impact of the microbiota, instead of only inferring functions from the bacterial composition. Table 1. Analytical methods to study the microbiome [adapted from Lepage et al.
Non-targeted metabolomics approaches using nuclear magnetic resonance NMR have been performed on gut samples and body fluids from humans and animals. In endurance sports, both an acute bout of exercise and a long training period can have an effect on microbiota and health.
Acute bouts of exercise can be separated into moderate and intense exercise. This review will include data on a wide range of participants: from overweight or diabetic subjects to elite athletes. This wide range of participants will make it possible to compare the different responses observed and to discuss the presence or absence of a continuum between all these populations Figure 1 Figure 1.
Beneficial effects of exercise and gut microbiota modifications in inactive subjects. Exercise induces beneficial molecular adaptations allowing the enhancement of cardiorespiratory fitness. Bacterial diversity increases, including SCFA- producing species. Conversely, pathobionts such as E.
coli or E. faecalis , potentially disease-causing species which, under normal circumstances, are found as a non-harming symbiont, decrease. Longitudinal studies monitoring exercise intensity and modality, diet, subjects' characteristics and gut microbiota are still lacking.
Modified from Aya et al. Some of these beneficial effects of moderate exercise on the host might be mediated by decreased intestinal permeability 41 , which prevents pathogens from crossing the intestinal barrier and then reduces systemic inflammation.
In parallel, an acute session of exercise at moderate intensity leads to several effects on the microbiota. The effect on the microbiota can be assessed by measuring the diversity or functions. α-Diversity represents the overall diversity of samples, while β-diversity compares how different bacterial species are distributed among different samples An investigation of the gut microbiota response to a half-marathon in amateur runners showed that the abundance of 7 taxa decreased, while the abundance of 20 bacterial clades increased At the genus level, the top 4 biomarkers increased after the race were Pseudobutyrivibrio, Coprococcus 2, Collinsella , and Mitsuokella while Bacteroides coprophilus was the most decreased bacterial clade.
Regrettably, no dietary questionnaire and no Bristol score that would indicate any gastrointestinal discomfort or bowel transit time difference were performed during this study.
When omics methods were used, such as shotgun metagenomics and metabolomics, modest changes in gut microbial gene composition and functions were reported following increased physical activity These data from two studies indicate that exercise can modify the gut microbial composition and production of SCFAs and thus fecal metabolites produced in the gut environment.
Based on the available studies, these sessions, compared to moderate exercise, seem to cause more significant disturbances than moderate exercise on the human body's homeostasis.
Elite athletes have been shown to experience high levels of inflammation following an acute bout of exercise 45 , 46 but also after intense exercise as attested by an increase in blood and urine markers of inflammation However, elite rugby players have a lower inflammatory status compared to that of controls [higher interleukin IL and IL-8; lower IL-6, tumor necrosis factor alpha TNF-α , and IL-1β] Endurance athletes are particularly concerned with gastrointestinal symptoms.
A study conducted during a long-distance triathlon concluded that LPS do enter the circulation after ultraendurance exercise. LPS may thus, with muscle damage, be responsible for the increased cytokine response and hence gastrointestinal complaints in these athletes In parallel, a fold increase in IL-6 production was observed immediately after the race.
Even if there was no significant correlation between LPS and IL-6 concentrations, these results indicate that increased intestinal permeability could occur simultaneously with an increased cytokine response and thus could contribute to an increased inflammatory response after exercise.
Similarly, in a multiple-stressor military training environment, regardless of the diet group, both intestinal permeability and inflammation increased Small intestine permeability was also increased during exertional heat stress However, this increase was smaller in the glucose- or energy-matched whey protein hydrolysate groups than in the water-consuming control group.
These changes, although negatively impacting host health, are only temporary and the benefits of such a high exercise load outweigh the temporary drawbacks. Interestingly, the abundance of less dominant taxa increased at the expense of the dominant Bacteroides.
Furthermore, in a study focusing on four well-trained male athletes performing a high-intensity unsupported day, 5,km transoceanic rowing race, changes in microbial diversity, abundance and metabolic capacity measured using 16S rDNA, metagenomics and metaproteomics, respectively were recorded 52 ; microbial diversity increased throughout the ultraendurance event together with an increased abundance of butyrate-producing species as well as others associated with improved metabolic health and insulin sensitivity.
The microbial genes involved in specific amino and fatty acid biosynthesis were also overrepresented. Notably, many of these adaptations in microbial community structure and function persisted at the 3-month follow-up. Microbial diversity thus increased even during intense exercise.
Beyond the effect of exercise load, the fitness status also impacts the microbiome. Regarding the relative importance of these two stimuli, the current consensus is that it is fitness that matters. The microbiome of fit individuals, in good physical shape, has been shown to display increased butyrate production due to the increased abundances of key butyrate-producing bacterial taxa belonging to the Firmicutes phylum Clostridiales, Roseburia, Lachnospiraceae , and Erysipelotrichaceae However, none of the fitness, nutritional intake, or anthropometric variables correlated with the broad Firmicutes to Bacteroidetes ratio.
In a 6-week intervention of endurance exercise in lean adults, exercise induced alterations in the gut microbiota composition and increased fecal concentrations of SCFAs in participants. Cardiorespiratory fitness seems to be related to the relative composition of the gut microbiota in humans.
When healthy elderly women were allocated to two groups receiving exercise interventions, either trunk muscle training or aerobic exercise training including brisk walking 55 , the relative abundance of intestinal Bacteroides significantly increased in the aerobic exercise training group only.
Interestingly, after stopping of exercise training, exercise-induced changes in the microbiota were largely reversed The former exhibited a higher abundance of the health-promoting bacterial species Faecalibacterium prausnitzii, Roseburia hominis , and Akkermansia muciniphila In another 6-week endurance exercise study without dietary changes, metagenomic analysis 16S rRNA gene sequencing and Illumina metagenomic analyses revealed taxonomic shifts, including an increase in Akkermansia and a decrease in Proteobacteria Importantly, these changes were independent of age, weight, and fat percentage as well as energy and fiber intake.
Similarly in male subjects with insulin resistance, both sprint intervals and moderate-intensity continuous trainings reduced systematic and intestinal inflammatory markers and increased Bacteroidetes phylum proportions The links between adaptations to endurance exercise and the gut microbiota are summarized in Figure 2.
These conclusions need to be confirmed by longitudinal studies, but very few are currently available. One of them follows two initially unfit volunteers during 6 months while undertaking progressive exercise training During this training period, fitness and body composition improved.
In parallel, α-diversity increased as well as the concentration of some physiologically-relevant metabolites. Figure 2. Ecosystem level adaptation of gut microbiota in athletes. Recent research indicates that unique gut microbiota may be present in elite athletes, and special and unique species can positively impact the host, providing metabolites from the fermentation of dietary fiber.
Ecosystem level syntrophy: gut bacterial species can hydrolyze fibers and subsequently ferment the sugar monomers into SCFA, while other fermentative species depend upon the hydrolytic ones. Such a syntrophy have been described between Bacteroides and Bifidobacterium strains.
Elite athletes can also be used as a paradigm of the limit of the trained human body. After several years of intense training, elite athletes have special features in terms of athletic performance but also in terms of morphology and metabolic adaptations.
A human study among elite rugby players vs. controls provided evidence of a beneficial impact of exercise on gut microbiota diversity: athletes had a higher diversity, representing 22 distinct phyla However, the results indicated that these differences between the elite and control groups were associated with dietary extremes that could represent confounding factors.
In terms of the proportions of different bacterial populations and their inherent metabolic activities, a study conducted on elite rugby players demonstrated that athletes had relative increases in specific pathways e.
These pathways were associated with enhanced muscle turnover and overall health when compared with the control groups. Differences in fecal microbiota between athletes and sedentary controls showed larger differences at the metagenomic and metabolomic levels than at the compositional levels and provided added insight into the diet-exercise-gut microbiota paradigm.
Another study in international level rugby players showed differences in the composition and functional capacity of the gut microbiome, as well as in microbial and human derived metabolites The use of food frequency questionnaires reinforced the validity of these results.
Focusing on cycling, another study compared professional and amateur athletes At baseline, it was possible to split the gut microbiomes of the 33 cyclists into three taxonomic clusters: one with high Prevotella , one with high Bacteroides or one with a large set of genera including Bacteroides, Prevotella, Eubacterium, Ruminococcus , and Akkermansia.
However, based on these taxonomic clusters, it was not possible to distinguish between professional or amateur cyclists.
Methanobrevibacter smithii transcripts abundance was also increased among a number of professional cyclists compared to amateur cyclists. A study in elite race walkers also reported that at baseline, the microbiota could be separated into the same distinct enterotypes with either a Prevotella- or Bacteroides -dominated enterotype Rodent studies can be used to assess certain conditions that are difficult to test in human studies, particularly without use of overly invasive methods.
Living conditions and diet are also easier to control in such studies. Rodent studies can help distinguish the effects of each of these factors distinctly.
Rodents are also good models for imitating human physiology. Indeed, in rodent studies, both the diversity and specific taxa of the gut microbiota have been shown to be impacted by exercise. Nonetheless, some bacteria generally appear to respond to exercise, including increased Lactobacillus, Bifidobacterium , and Akkermansia and decreased Proteobacteria.
Finally, butyrate-producing taxa as well as SCFA production have been consistently shown to increase in response to exercise 61 , 73 , while the majority of studies also showed increased α-diversity following exercise. Interestingly, some studies have investigated the effect of the gut microbiome on performance.
The effect of the presence of the microbiome has been addressed by comparing germ-free GF to specific pathogen-free SPF mice and showing a higher exercise capacity in SPF mice Moreover, exercise capacity improved in mice colonized with individual bacterial taxa compared to their GF counterparts.
However, differences were observed between bacteria in the degree of impact This suggests that if the gut microbiome may have a global positive impact on performance, its effect may depend on its composition.
Interestingly, regardless of the bacterial species used to monocolonize GF mice, SPF mice always showed the greatest performance in a test of endurance swimming, suggesting that a more diverse microbiome may be necessary to exert beneficial effects.
Recent studies have also shown that gut microbiota may be critical for optimal muscle function. Indeed, depletion of the microbiota using antibiotics led to a reduction in running capacity and in muscle contractile function 75 , Interestingly, similar results were obtained using a low-microbiota accessible carbohydrate diet that lowered SCFA production.
Finally, restoration of the microbiota 75 or infusion of acetate 76 reversed the loss of endurance capacity and muscle contractile function.
An interesting aspect of animal studies is the possibility of performing fecal microbiota transplants FMT. A few studies established that the beneficial health effect of exercise may be mediated through gut microbiome changes. Indeed, high-fat diet-fed mice receiving FMT from exercised donors not only showed markedly reduced food efficacy but also improved metabolic profiles The transmissible beneficial effects of FMT were associated with the bacterial genera Helicobacter and Odoribacter , as well as an overrepresentation of oxidative phosphorylation and glycolysis genes in the metagenome.
Similarly, it has been shown recently that the gut microbiome determines the efficacy of exercise for diabetes prevention. Exercise was first shown to improve glucose homeostasis only in a fraction of pre-diabetic individuals responders. The microbiome of responders exhibited an enhanced capacity for the biosynthesis of SCFAs and catabolism of branched-chain amino acids.
Moreover, the baseline microbiome signature could predict individual exercise responses. Remarkably, following FMT, gut microbiota from responders conferred the metabolic benefits of exercise to recipient mice Rodent studies have recently produced interesting new results, indicating that each exercise modality causes its own alterations of the gut microbiome First, both voluntary wheel running and forced treadmill running altered many individual bacterial taxa, including Turicibacter spp.
In mice fed a high-fat diet, exercise was proven to increase the Bacteroidetes phylum, while it decreased Firmicutes proportionately to the distance the mice ran The high-fat diet component in this study is an important parameter to consider as it has been shown to cause modifications in mouse gut microbiota at nearly the same magnitude as exercise alone As in animal models, exercise and diet may together impact the composition of the human gut microbiota.
For example, a study investigating the gut microbial response in amateur half-marathon runners observed some changes in 40 fecal metabolites and some shifts in specific gut bacterial populations.
However, the authors concluded that these observed differences might have been the shared outcome of running and diet As reviewed by Mitchell et al.
In particular, the amount of fiber consumed should be taken into account before drawing any conclusions when comparing the results of different studies.
Their bulking effect on transit time, stool frequency, and gut health 84 comes from the fact that some fibers are not absorbed in the small intestine and are thus fermented in the large intestine. Consequently, differences in fiber consumption impact the type and amount of SCFAs produced by the microbiota For example, the gut microbiota of children from Burkina Faso, whose diet contains a large amount of fibers compared to European children, was significantly enriched in Bacteroidetes and depleted in Firmicutes Furthermore, significantly more SCFAs were found in Burkina Faso children's feces compared to in European children's feces.
Species from the Bacteroidetes phylum mainly produce acetate and propionate, whereas butyrate-producing bacteria are found within the Firmicutes phylum The increasing fiber consumption resulted in higher microbiota stability associated with higher microbiota richness.
Table 2. The different types of dietary fiber [modified from 83 ]. Fiber intake is often low in the diet of athletes. Several studies, involving female artistic gymnastics, rhythmic gymnastics and ballet dance athletes 88 , or competitive American adolescent swimmers 89 reported that athletes' fiber consumption was often below the nutritional guidelines of 25 g per day based on a 2,calorie diet Only a few studies reported fiber consumption above the nutritional guidelines, and one of the few examples is female and male Dutch ultramarathon runners Athletes may be reluctant to adopt such dietary habits because of higher satiety sensation or digestion and gastrointestinal discomfort issues In parallel, to avoid gastrointestinal symptoms associated with exercise, some athletes turn to a low FODMAP Fermentable Oligo-, Di-, Mono-saccharides And Polyols diet to limit the presence of highly fermentable carbohydrates in their digestive tract Indeed, undigested carbohydrates may increase the osmotic load in the small intestine and contribute to increased osmotic water translocation, volume, and physiological issues such as loose stool or diarrhea 94 , Particular attention must also be paid when comparing elite athletes with sedentary controls.
Indeed, dietary protein intake differs largely in elite athletes and sedentary controls diets A recent study dealt with the effects of protein supplementation on the gut microbial composition Protein supplementation increased the abundance of the Bacteroidetes phylum and decreased the presence of health-related taxa, including Roseburia, Blautia , and Bifidobacterium longum.
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Virginia Bealth Institute and State Pzrtitioning. Obesity remains a public health epidemic despite substantial advances in uealth strategies Cellulite reduction therapies in the last decade. Effective strategies to support maintenance untrient Gut health and nutrient partitioning partitiojing health and Herbal Anti-Inflammatory Natural body detox weight partitioming still needed. Signals from the gut to the brain are important in regulating metabolism and energy balance and have been linked with food reward and preference in metabolically healthy individuals with normal body mass index. In particular, post-ingestive signaling related to glucose metabolism has been linked with food reward and preference. However, not much is known about how these gut and brain signals interact to influence eating behaviors in states of obesity or altered metabolic health. Ecological Gut health and nutrient partitioning underlying bacterial coexistence in the gut are not partitionlng understood. Here, Restorative skincare solutions disentangled the nutgient of the host partitionint the Astaxanthin health benefits on ntrient coexistence of four closely related Natural body detox species colonizing partitioninh honey bee gut. We serially passaged the four species through gnotobiotic bees and in liquid cultures in the presence of either pollen bee diet or simple sugars. Although the four species engaged in negative interactions, they were able to stably coexist, both in vivo and in vitro. However, coexistence was only possible in the presence of pollen, and not in simple sugars, independent of the environment.
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