The microbiome interacts with the circadian clock and dietary composition to regulate metabolite cycling in the gut

Reviewer #1 (Public Review):

The authors build on their previous study that showed the midgut microbiome does not oscillate in Drosophila. Here, they focus on metabolites and find that these rhythms are in fact microbiome-dependent. Tests of time-restricted feeding, a clock gene mutant, and diet reveal additional regulatory roles for factors that dictate the timing and rhythmicity of metabolites. The study is well-written and straightforward, adding to a growing body of literature that shows the time of food consumption affects microbial metabolism which in turn could affect the host.

Some additional questions and considerations remain:

(1) The main finding that the microbiome promotes metabolite rhythms is very interesting. Which microbiota are likely to be responsible for these effects? The author's previous work in this area may shed light on this question. Are specific microbiota linked to some of the metabolic pathways investigated in Figure 5?

(2) TF increases the number of rhythmic metabolites in both microbiome-containing and abiotic flies in Figure 1. This is somewhat surprising given that flies typically eat during the daytime rather than at night, very similar to TF conditions. I would have assumed that in a clock-functioning animal, the effect of restricting food availability should not make a huge difference in the time of food consumption, and thus downstream impacts on metabolism and microbiome. Can the authors measure food intake directly to compare the ad-lib vs TF flies to see if there are changes in food intake? Would restricting feeding to other times of day shift the timing of metabolites accordingly?

(3) In Figure 2, Per loss of function reveals a change in the phase of rhythmic metabolites. In addition, the effect of the microbiome on these is very different = The per mutants show increased numbers of rhythmic metabolites when the microbiome is absent, unlike the controls. Is it possible that these changes are due to altered daily feeding rhythms in per mutants? Testing the time and amount of food consumed by the per mutant flies would address this question. Would TF in the per mutants rescue their metabolite rhythms and make them resemble clock-functioning controls?

(4) The calorie content of each diet - normal vs high protein vs high-sugar are different. The possibility of a calorie effect rather than a difference in nutrition (protein/carbohydrate) should be discussed. Another issue worth considering is the effect of high protein/sugar on the microbiome itself. While the microbiome doesn't seem to affect rhythms in the high-protein diet, the high-sugar diet seems highly microbiome-dependent in Supplementary Fig 8C vs D. Does the diet impact the microbiome and thus metabolite rhythmicity downstream?

(5) It would be good if a supplementary table was provided outlining the specific metabolites that are shown in the radial plots. It is not clear if the rhythms shown in the figures refer to the same metabolites peaking at the same time, or rather the overall abundance of completely different metabolites. This information would be useful for future research in this area.

Reviewer #2 (Public Review):

Summary:

The paper addresses several factors that influence daily changes in concentration of metabolites in the Drosophila melanogaster gut. The authors describe metabolomes extracted from fly guts at four time-points during a 24-hour period, comparing profiles of primary metabolites, lipids, and biogenic amines. The study elucidates that the percentage of metabolites that exhibit a circadian cycle, peak phases of their increased appearance, and the cycling amplitude depends on the combination of factors (microbiome status, composition or timing of the diet, circadian clock genotype). Multiple general conclusions based on the data obtained with modern metabolomics techniques are provided in each part of the article. Descriptive analysis of the data supports the finding that microbiome increases the number of metabolites for which concentration oscillates during the day period. Results of the experiments show that timed feeding significantly enhanced metabolite cycling and changed its phase regardless of the presence of a microbiome. The authors suggest that the host circadian rhythm modifies both metabolite cycling period and the number of such metabolites.

Strengths:

The obvious strength of the study is the data on circadian cycling of the detected 843, 4510, and 4330 total primary metabolites, lipids, and biogenic amines respectively in iso31 flies and 623, 2245, and 2791 respective metabolites in per01 mutants. The comparison of the abundance of these metabolites, their cycling phase, and the ratio of cycling/non-cycling metabolites is well described and illustrated. The conditions tested represent significant experimental interest for contemporary chronobiology: with/without microbiota, wild-type/mutant period gene, ad libitum/time-restricted feeding, and high-sugar/high-protein diet. The authors conclude that the complex interaction between these factors exists, and some metabolic implications of combinations of these factors can be perceived as reminiscent of metabolic implications of another combination ("...the microbiome and time-restricted feeding paradigms can compensate for each other, suggesting that different strategies can be leveraged to serve organismal health"). Enrichment analysis of cycling metabolites leads to an interesting suggestion that oscillation of metabolites related to amino acids is promoted by the absence of microbiota, alteration of circadian clock, and time-restricted feeding. In contrast, association with microbiota induces oscillation of alpha-linolenic acid-related metabolites. These results provide the initial step for hypothesising about functional explanations of the uncovered observations.

Weaknesses:

Among the weaknesses of the study, one might point out too generalist interpretations of the results, which propose hypothetical conclusions without their mechanistic proof. The quantitative indices analysed are obviously of particular interest, however are not self-explaining and exhaustive. More specific biological examples would add valuable insights into the results of this study, making conclusions clearer. More specific comments on the weaknesses are listed below:

(1) The criterion of the percentage of cycling metabolites used for comparisons has its own limitations. It is not clear, whether the cycling metabolites are the same in the guts with/without microbiota, or whether there are totally different groups of metabolites that cycle in each condition. GO enrichment analysis gives only a partial assessment, but is still not quantitative enough.

(2) The period of cycling data is based on only 4 time points during 24 hours in 4 replicates (>200 guts per replicate) on the fifth day of the experiment (10-12-day-old adults). It does not convincingly prove that these metabolites cycle the following days or more finely within the day. Moreover, it is not clear how peaks in polar histogram plots were detected in between the timepoints of ZT0, ZT6, ZT12, and ZT18.

(3) Average expression and amplitude are analysed for groups of many metabolites, whereas the data on distinct metabolites is hidden behind these general comparisons. This kind of loss of information can be misleading, making interpretation of the mentioned parameters quite complicated for authors and their readers. Probably more particular datasets can be extracted to be discussed more thoroughly, rather than those general descriptions.

(4) The metabolites' preservation is crucial for this type of analysis, and both proper sampling plus normalisation require more attention. More details about measures taken to avoid different degradation rates, different sizes of intestines, and different amounts of microbes inside them will be beneficial for data interpretation.

(5) The data in the article describes formal phenomena, not directly connected with organism physiology. The parameters discussed obviously depend on the behaviour of flies. Food consumption, sleep, and locomotor activity could be additionally taken into account.

(6) Division of metabolites into three classes limits functional discussion of found differences. Since the enrichment analysis provided insights into groups of metabolites of particular interest (for example, amino acid metabolism), a comparison of their cycling characteristics can be shown separately and discussed.

Reviewer #3 (Public Review):

Summary:

The authors. sought to quantify the influence of the gut microbiome on metabolite cycling in a Drosophila model with extensive metabolomic profiling over a 24-hour period. The major strength of the work is the production of a large dataset of metabolites that can be the basis for hypothesis generation for more specific experiments. There are several weaknesses that make the conclusions difficult to evaluate. Additional experiments to quantify food intake over time will be required to determine the direct role of the microbiome in metabolite cycling.

Strengths:

An extensive metabolomic dataset was collected with treatments designed to determine the influence of the gut microbiome on metabolite circadian cycling.

Weaknesses:

(1) The major strength of this study is the extensive metabolomic data, but as far as I can tell, the raw data is not made publicly available to the community. The presentation of highly processed data in the figures further underscores the need to provide the raw data (see comment 3).

(2) Feeding times heavily influence the metabolome. The authors use timed feeding to constrain when flies can eat, but there is no measurement of how much they ate and when. That needs to be addressed.

Since food is the major source of metabolites, the timing of feeding needs to be measured for each of the treatment groups. In the previous paper (Zhang et al 2023 PNAS), the feeding activity of groups of 4 male flies was measured for the wildtype flies. That is not sufficient to determine to what extent feeding controls the metabolic profile of the flies. Additionally, timed feeding opportunities do not equate to the precise time of feeding. They may also result in dietary restriction, leading to the loss of stress resistance in the TF flies. The authors need to measure food consumption over time in the exact conditions under which transcriptomic and metabolomic cycling are measured. I suggest using the EX-Q assay as it is much less effort than the CAFE assay and can be more easily adapted to the rearing conditions of the experiments.

(3) The data on the cycling of metabolites is presented in a heavily analyzed form, making it difficult to evaluate the validity of the findings, particularly when a lack of cycling is detected. The normalization to calculate the change in cycling due to particular treatments is particularly unclear and makes me question whether it is affecting the conclusions. More presentation of the raw data to show when cycling is occurring versus not would help address this concern, as would a more thorough explanation of how the normalization is calculated - the brief description in the methods is not sufficient.

For instance, the authors state that "timed feeding had less effect on flies containing a microbiome relative to sterile flies." One alternative interpretation of that result is that both treatments are cycling but that the normalization of one treatment to the other removes the apparent effect. This concern should be straightforward to address by showing the raw data for individual metabolites rather than the group.