Time of sample collection is critical for the replicability of microbiome analyses | Nature Metabolism

Nature Metabolism volume 6, pages 1282–1293 (2024)Cite this article

4416 Accesses

2 Citations

198 Altmetric

Metrics details

As the microbiome field moves from descriptive and associative research to mechanistic and interventional studies, being able to account for all confounding variables in the experimental design, which includes the maternal effect1, cage effect2, facility differences3, as well as laboratory and sample handling protocols4, is critical for interpretability of results. Despite significant procedural and bioinformatic improvements, unexplained variability and lack of replicability still occur. One underexplored factor is that the microbiome is dynamic and exhibits diurnal oscillations that can change microbiome composition5,6,7. In this retrospective analysis of 16S amplicon sequencing studies in male mice, we show that sample collection time affects the conclusions drawn from microbiome studies and its effect size is larger than those of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups is unique to each experiment. Sample collection times as short as only 4 hours apart can lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. The impact of diurnal rhythms on the outcomes and study design of other fields is unknown but likely significant.

This is a preview of subscription content, access via your institution

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

$29.99 / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

$119.00 per year

only $9.92 per issue

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Literature review data are at https://github.com/knightlab-analyses/dynamics/data/. Figure 1, mock data are at https://github.com/knightlab-analyses/dynamics/data/MockData. Figure 2 (Allaband/Zarrinpar 2021) data are under EBI accession ERP110592. Figure 3 data (longitudinal IHC) are under EBI accession ERP110592 and (longitudinal circadian TRF) EBI accession ERP123226. Figure 4 data (Zarrinpar/Panda 2014) are in the Supplementary Excel file attached to the source paper13; (Leone/Chang 2015) figshare for the 16S amplicon sequence data are at https://doi.org/10.6084/m9.figshare.882928 (ref. 63). Extended Data Fig. 2 data (Caporaso/Knight 2011) are at MG-RAST project mgp93 (IDs mgm4457768.3 and mgm4459735.3). Extended Data Fig. 3 data (Wu/Chen 2018) are under ENA accession PRJEB22049. Extended Data Fig. 4 data (Tuganbaev/Elinav 2021) are under ENA accession PRJEB38869.

All relevant code notebooks are on GitHub at https://github.com/knightlab-analyses/dynamics/notebooks.

Schloss, P. D. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9, e00525–18 (2018).

Article PubMed PubMed Central Google Scholar

Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

Article CAS PubMed PubMed Central Google Scholar

Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).

Article CAS PubMed Google Scholar

Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).

Article CAS PubMed PubMed Central Google Scholar

Deloris Alexander, A. et al. Quantitative PCR assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by the microenvironment. Mamm. Genome 17, 1093–1104 (2006).

Article CAS PubMed Google Scholar

Friswell, M. K. et al. Site and strain-specific variation in gut microbiota profiles and metabolism in experimental mice. PLoS ONE 5, e8584 (2010).

Article PubMed PubMed Central Google Scholar

Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017).

Article CAS PubMed PubMed Central Google Scholar

Alvarez, Y., Glotfelty, L. G., Blank, N., Dohnalová, L. & Thaiss, C. A. The microbiome as a circadian coordinator of metabolism. Endocrinology 161, bqaa059 (2020).

Article PubMed PubMed Central Google Scholar

Frazier, K. & Chang, E. B. Intersection of the gut microbiome and circadian rhythms in metabolism. Trends Endocrinol. Metab. 31, 25–36 (2020).

Article CAS PubMed Google Scholar

Heddes, M. et al. The intestinal clock drives the microbiome to maintain gastrointestinal homeostasis. Nat. Commun. 13, 6068 (2022).

Article CAS PubMed PubMed Central Google Scholar

Leone, V. et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe 17, 681–689 (2015).

Article CAS PubMed PubMed Central Google Scholar

Thaiss, C. A. et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529 (2014).

Article CAS PubMed Google Scholar

Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).

Article CAS PubMed PubMed Central Google Scholar

Liang, X., Bushman, F. D. & FitzGerald, G. A. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc. Natl Acad. Sci. USA 112, 10479–10484 (2015).

Article CAS PubMed PubMed Central Google Scholar

Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510 (2016).

Article CAS PubMed Google Scholar

Yu, F. et al. Deficiency of intestinal Bmal1 prevents obesity induced by high-fat feeding. Nat. Commun. 12, 5323 (2021).

Article CAS PubMed PubMed Central Google Scholar

Leone, V. A. et al. Atypical behavioral and thermoregulatory circadian rhythms in mice lacking a microbiome. Sci. Rep. 12, 14491 (2022).

Article CAS PubMed PubMed Central Google Scholar

Thaiss, C. A., Zeevi, D., Levy, M., Segal, E. & Elinav, E. A day in the life of the meta-organism: diurnal rhythms of the intestinal microbiome and its host. Gut Microbes 6, 137–142 (2015).

Article CAS PubMed PubMed Central Google Scholar

Mukherji, A., Kobiita, A., Ye, T. & Chambon, P. Homeostasis in intestinal epithelium is orchestrated by the circadian clock and microbiota cues transduced by TLRs. Cell 153, 812–827 (2013).

Article CAS PubMed Google Scholar

Weger, B. D. et al. The mouse microbiome is required for sex-specific diurnal rhythms of gene expression and metabolism. Cell Metab. 29, 362–382 (2019).

Article CAS PubMed PubMed Central Google Scholar

Kaczmarek, J. L., Musaad, S. M. & Holscher, H. D. Time of day and eating behaviors are associated with the composition and function of the human gastrointestinal microbiota. Am. J. Clin. Nutr. 106, 1220–1231 (2017).

Article CAS PubMed Google Scholar

Skarke, C. et al. A pilot characterization of the human chronobiome. Sci. Rep. 7, 17141 (2017).

Article PubMed PubMed Central Google Scholar

Jones, J., Reinke, S. N., Ali, A., Palmer, D. J. & Christophersen, C. T. Fecal sample collection methods and time of day impact microbiome composition and short chain fatty acid concentrations. Sci. Rep. 11, 13964 (2021).

Article CAS PubMed PubMed Central Google Scholar

Collado, M. C. et al. Timing of food intake impacts daily rhythms of human salivary microbiota: a randomized, crossover study. FASEB J. 32, 2060–2072 (2018).

Article CAS PubMed PubMed Central Google Scholar

Kohn, J. N. et al. Differing salivary microbiome diversity, community and diurnal rhythmicity in association with affective state and peripheral inflammation in adults. Brain. Behav. Immun. 87, 591–602 (2020).

Article CAS PubMed PubMed Central Google Scholar

Takayasu, L. et al. Circadian oscillations of microbial and functional composition in the human salivary microbiome. DNA Res. 24, 261–270 (2017).

Article CAS PubMed PubMed Central Google Scholar

Reitmeier, S. et al. Arrhythmic gut microbiome signatures predict risk of type 2 diabetes. Cell Host Microbe 28, 258–272 (2020).

Article CAS PubMed Google Scholar

Allaband, C. et al. Intermittent hypoxia and hypercapnia alter diurnal rhythms of luminal gut microbiome and metabolome. mSystems https://doi.org/10.1128/mSystems.00116-21 (2021).

Tuganbaev, T. et al. Diet diurnally regulates small intestinal microbiome-epithelial-immune homeostasis and enteritis. Cell 182, 1441–1459 (2020).

Article CAS PubMed Google Scholar

Wu, G. et al. Light exposure influences the diurnal oscillation of gut microbiota in mice. Biochem. Biophys. Res. Commun. 501, 16–23 (2018).

Article CAS PubMed Google Scholar

Nelson, R. J. et al. Time of day as a critical variable in biology. BMC Biol. 20, 142 (2022).

Article PubMed PubMed Central Google Scholar

Dantas Machado, A. C. et al. Diet and feeding pattern modulate diurnal dynamics of the ileal microbiome and transcriptome. Cell Rep. 40, 111008 (2022).

Article CAS PubMed Google Scholar

Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).

Article PubMed PubMed Central Google Scholar

Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

Article PubMed PubMed Central Google Scholar

Bisanz, J. E., Upadhyay, V., Turnbaugh, J. A., Ly, K. & Turnbaugh, P. J. Meta-analysis reveals reproducible gut microbiome alterations in response to a high-fat diet. Cell Host Microbe 26, 265–272.e4 (2019).

Article CAS PubMed PubMed Central Google Scholar

Kohsaka, A. et al. High-fat diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab. 6, 414–421 (2007).

Article CAS PubMed Google Scholar

Hatori, M. et al. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 15, 848–860 (2012).

Article CAS PubMed PubMed Central Google Scholar

Baker, F. Normal rumen microflora and microfauna of cattle. Nature 149, 220 (1942).

Article Google Scholar

Zhang, L., Wu, W., Lee, Y.-K., Xie, J. & Zhang, H. Spatial heterogeneity and co-occurrence of mucosal and luminal microbiome across swine intestinal tract. Front. Microbiol. 9, 48 (2018).

Article PubMed PubMed Central Google Scholar

Klymiuk, I. et al. Characterization of the luminal and mucosa-associated microbiome along the gastrointestinal tract: results from surgically treated preterm infants and a murine model. Nutrients 13, 1030 (2021).

Article CAS PubMed PubMed Central Google Scholar

Kim, D. et al. Comparison of sampling methods in assessing the microbiome from patients with ulcerative colitis. BMC Gastroenterol. 21, 396 (2021).

Article CAS PubMed PubMed Central Google Scholar

Tripathi, A. et al. Intermittent hypoxia and hypercapnia reproducibly change the gut microbiome and metabolome across rodent model systems. mSystems 4, e00058–19 (2019).

Article CAS PubMed PubMed Central Google Scholar

Uhr, G. T., Dohnalová, L. & Thaiss, C. A. The Dimension of Time in Host-Microbiome Interactions. mSystems 4, e00216–e00218 (2019).

Article PubMed PubMed Central Google Scholar

Voigt, R. M. et al. Circadian disorganization alters intestinal microbiota. PLoS ONE 9, e97500 (2014).

Article PubMed PubMed Central Google Scholar

McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).

Article CAS PubMed PubMed Central Google Scholar

Borodulin, K. et al. Cohort profile: the National FINRISK Study. Int. J. Epidemiol. 47, 696–696i (2018).

Article PubMed Google Scholar

Ren, B. et al. Methionine restriction improves gut barrier function by reshaping diurnal rhythms of inflammation-related microbes in aged mice. Front. Nutr. 8, 746592 (2021).

Article PubMed PubMed Central Google Scholar

Beli, E., Prabakaran, S., Krishnan, P., Evans-Molina, C. & Grant, M. B. Loss of diurnal oscillatory rhythms in gut microbiota correlates with changes in circulating metabolites in type 2 diabetic db/db mice. Nutrients 11, E2310 (2019).

Article Google Scholar

Wang, L. et al. Methionine restriction regulates cognitive function in high-fat diet-fed mice: roles of diurnal rhythms of SCFAs producing- and inflammation-related microbes. Mol. Nutr. Food Res. 64, e2000190 (2020).

Article PubMed Google Scholar

Guo, T. et al. Oolong tea polyphenols ameliorate circadian rhythm of intestinal microbiome and liver clock genes in mouse model. J. Agric. Food Chem. 67, 11969–11976 (2019).

Article CAS PubMed Google Scholar

Mistry, P. et al. Circadian influence on the microbiome improves heart failure outcomes. J. Mol. Cell. Cardiol. 149, 54–72 (2020).

Article CAS PubMed PubMed Central Google Scholar

Shao, Y. et al. Effects of sleeve gastrectomy on the composition and diurnal oscillation of gut microbiota related to the metabolic improvements. Surg. Obes. Relat. Dis. 14, 731–739 (2018).

Article PubMed Google Scholar

Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

Article CAS PubMed PubMed Central Google Scholar

Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191–16 (2017).

Article PubMed PubMed Central Google Scholar

Mirarab, S., Nguyen, N. & Warnow, T. in Biocomputing 2012, 247–258 (World Scientific, 2011).

Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).

Article PubMed Google Scholar

Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. & Fierer, N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples: Influence of short-term storage conditions on microbiota. FEMS Microbiol. Lett. 307, 80–86 (2010).

Article CAS PubMed Google Scholar

Marotz, C. et al. Evaluation of the effect of storage methods on fecal, saliva, and skin microbiome composition. mSystems 6, e01329–20 (2021).

Article CAS PubMed PubMed Central Google Scholar

Song, S. J. et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, e00021–16 (2016).

Article PubMed PubMed Central Google Scholar

Wu, G. D. et al. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 10, 206 (2010).

Article PubMed PubMed Central Google Scholar

Piedrahita, J. A., Zhang, S. H., Hagaman, J. R., Oliver, P. M. & Maeda, N. Generation of mice carrying a mutant apolipoprotein E gene inactivated by gene targeting in embryonic stem cells. Proc. Natl Acad. Sci. USA 89, 4471–4475 (1992).

Article CAS PubMed PubMed Central Google Scholar

Chaix, A., Zarrinpar, A., Miu, P. & Panda, S. Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Cell Metab. 20, 991–1005 (2014).

Article CAS PubMed PubMed Central Google Scholar

Gibbons, S. Diel Mouse Gut Study (HF/LF diet). figshare https://doi.org/10.6084/m9.figshare.882928 (2015).

Download references

C.A. was supported by NIH T32 OD017863. S.F.R. is supported by the Soros Foundation. A.L. is supported by the AHA Postdoctoral Fellowship grant. T.K. is supported by NIH T32 GM719876. A.C.D.M. is supported by R01 HL148801-02S1. G.G.H. and A.Z. are supported by NIH R01 HL157445. A.Z. is further supported by the VA Merit BLR&D Award I01 BX005707 and NIH grants R01 AI163483, R01 HL148801, R01 EB030134 and U01 CA265719. All authors receive institutional support from NIH P30 DK120515, P30 DK063491, P30 CA014195, P50 AA011999 and UL1 TR001442.

Division of Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA

Celeste Allaband & Stephany Flores Ramos

Division of Gastroenterology, University of California, San Diego, La Jolla, CA, USA

Celeste Allaband, Amulya Lingaraju, Stephany Flores Ramos, Haniyeh Javaheri, Maria D. Tiu, Ana Carolina Dantas Machado, R. Alexander Richter & Amir Zarrinpar

Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

Celeste Allaband, Stephany Flores Ramos, Gabriel G. Haddad, Pieter C. Dorrestein & Rob Knight

Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA

Tanya Kumar

Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

Emmanuel Elijah & Pieter C. Dorrestein

Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA

Emmanuel Elijah, Pieter C. Dorrestein, Rob Knight & Amir Zarrinpar

Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA

Gabriel G. Haddad

Rady Children’s Hospital, San Diego, CA, USA

Gabriel G. Haddad

Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA

Vanessa A. Leone

Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, CA, USA

Pieter C. Dorrestein

Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

Rob Knight

Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA

Rob Knight

Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

Rob Knight & Amir Zarrinpar

Division of Gastroenterology, Jennifer Moreno Department of Veterans Affairs Medical Center, La Jolla, CA, USA

Amir Zarrinpar

Institute of Diabetes and Metabolic Health, University of California, San Diego, La Jolla, CA, USA

Amir Zarrinpar

You can also search for this author in PubMed Google Scholar

C.A. and A.Z. conceptualized the work. C.A., E.E., P.C.D., R.K. and A.Z. determined the methodology. C.A., A.L., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. were involved in data investigation. C.A., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. created visualizations. A.Z. acquired funding and was the project administrator. R.K. and A.Z. supervised the work. G.G.H. and V.A.L. provided resources. C.A., A.L., S.F.R., T.K., H.J., M.D.T. and A.Z. wrote the first draft. All authors contributed to the review and editing of the manuscript.

Correspondence to Amir Zarrinpar.

A.Z. is a co-founder and a chief medical officer, and holds equity in Endure Biotherapeutics. P.C.D. is an advisor to Cybele and co-founder and advisor to Ometa and Enveda with previous approval from the University of California, San Diego. All other authors declare no competing interests.

Nature Metabolism thanks Robin Voigt-Zuwala, Jacqueline M. Kimmey, John R. Kirby and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A) 2019 Literature Review Summary. Of the 586 articles containing microbiome (16 S or metagenomic) data, found as described in the methods section, the percentage of microbiome articles from each of the publication groups. B) The percentage of microbiome articles belonging to each individual journal in 2019. Because the numerous individual journals from Science represented low percentages individually, they were grouped together. C) The percentage articles where collection time was explicitly stated (yes: 8 AM, ZT4, etc.), implicitly stated (relative: ‘before surgery’, ‘in the morning’, etc.), or unstated (not provided: ‘daily’, ‘once a week’, etc.). D) Meta-Analysis Inclusion Criteria Flow Chart. Literature review resulting in the five previously published datasets for meta-analysis11,13,28,29,30.

A) Weighted UniFrac PCoA Plot - modified example from Moving Pictures Qiime2 tutorial data [https://docs.qiime2.org/2022.11/tutorials/moving-pictures/]. Each point is a sample. Points were coloured by body site of origin. There are 8 gut, 8 left palm, 9 right palm, and 9 tongue samples. B) Within-Condition Distances (WCD) boxplot/stripplot for each body site (n = 8–9 mouse per group per time point). C) Between Condition Distances (BCD) boxplot/stripplot for each unique body site comparison (n = 8–9 mouse per group per time point). D) All pairwise grouping comparisons, both WCD and BCD, are shown in the boxplots/stripplots (n = 8–9 mouse per group per time point). Only WCD to BCD statistical differences are shown. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: ns (not significant) = p > 0.05, * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

A) Weighted UniFrac PCoA stacked view (same as Fig. 2b but different orientation). Good for assessing overall similarity not broken down by time point. Significance determined by PERMANOVA (p = 0.005). B) Weighted UniFrac PCoA of only axis 1 over time. C) Boxplot/scatterplot of within-group weighted UniFrac distance values for the control group (Air, n = 3–4 samples per time point). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. D) Boxplot/scatterplot of within-group weighted UniFrac distance values for the experimental group (IHC, n = 3–4 samples per time point)). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. E) Boxplot/scatterplot of within-group weighted UniFrac distance values for both control (Air) and experimental (IHC) groups [n = 3–4 samples per group per time point]. Mann-Whitney-Wilcoxon test with Bonferroni correction used to determine significant differences between groups. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Notation: ns = not significant, p > 0.05; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.

A) Experimental design. Balb/c mice were fed NCD ad libitum under 0:24 L:D (24 hr darkness, DD) experimental conditions and compared to 12:12 L:D (LD) control conditions. After 2 weeks, mice from each group were euthanized every 4 hours for 24 hours (N = 4–5 mice/condition) and samples were collected from the proximal small intestine (‘jejunum’) and distal small intestine (‘ileum’) contents. B) BCD for luminal contents of proximal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction; notation: **** = p < 0.00001. C) BCD for luminal contents of distal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values.

A) Experimental design and sample collection for a local site study. Small intestinal samples were collected every 4 hours for 24 hours (N = 4–5 mice/condition, skipping ZT8). Mice were fed ad libitum on the same diet (NCD) for 4 weeks before samples were taken. B) BCD for luminal vs mucosal conditions (N = 4–5 mice/condition). The dotted line is the average of all shown weighted UniFrac distances. Significance is determined using the Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. C) Heatmap of mean BCD distances comparing luminal and mucosal by time point (N = 4–5 mice/condition). Highest value highlighted in navy, lowest value highlighted in gold. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001. D) Experimentally relevant log ratio, highlighting the changes seen at ZT20 (N = 4–5 mice/condition). Boxplot center line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

Reprints and permissions

Allaband, C., Lingaraju, A., Flores Ramos, S. et al. Time of sample collection is critical for the replicability of microbiome analyses. Nat Metab 6, 1282–1293 (2024). https://doi.org/10.1038/s42255-024-01064-1

Download citation

Received: 27 October 2022

Accepted: 08 May 2024

Published: 01 July 2024

Issue Date: July 2024

DOI: https://doi.org/10.1038/s42255-024-01064-1

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative