Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs
Abstract
:1. Introduction
2. Results and Discussion
2.1. Pilot Study
2.2. Validation of the Pipeline with Third Party Data
3. Materials and Methods
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Karp, N.A.; Fry, D. What Is the Optimum Design for My Animal Experiment? BMJ Open Sci. 2021, 5, e100126. [Google Scholar] [CrossRef] [PubMed]
- Prescott, M.J.; Lidster, K. Improving Quality of Science through Better Animal Welfare: The NC3Rs Strategy. Lab Anim. 2017, 46, 152–156. [Google Scholar] [CrossRef]
- Tannenbaum, J.; Bennett, B.T. Russell and Burch’s 3Rs Then and Now: The Need for Clarity in Definition and Purpose. J. Am. Assoc. Lab. Anim. Sci. 2015, 54, 120–132. [Google Scholar] [PubMed]
- Nosek, B.A.; Alter, G.; Banks, G.C.; Borsboom, D.; Bowman, S.D.; Breckler, S.J.; Buck, S.; Chambers, C.D.; Chin, G.; Christensen, G.; et al. Promoting an Open Research Culture. Science 2015, 348, 1422–1425. [Google Scholar] [CrossRef]
- Kilkenny, C.; Browne, W.; Cuthill, I.C.; Emerson, M.; Altman, D.G. NC3Rs Reporting Guidelines Working Group Animal Research: Reporting in Vivo Experiments: The ARRIVE Guidelines. Br. J. Pharmacol. 2010, 160, 1577–1579. [Google Scholar] [CrossRef] [PubMed]
- Percie du Sert, N.; Hurst, V.; Ahluwalia, A.; Alam, S.; Avey, M.T.; Baker, M.; Browne, W.J.; Clark, A.; Cuthill, I.C.; Dirnagl, U.; et al. The ARRIVE Guidelines 2.0: Updated Guidelines for Reporting Animal Research. PLoS Biol. 2020, 18, e3000410. [Google Scholar] [CrossRef]
- du Sert, N.P.; Bamsey, I.; Bate, S.T.; Berdoy, M.; Clark, R.A.; Cuthill, I.; Fry, D.; Karp, N.A.; Macleod, M.; Moon, L.; et al. The Experimental Design Assistant. PLOS Biol. 2017, 15, e2003779. [Google Scholar] [CrossRef] [PubMed]
- Smith, A.J.; Clutton, R.E.; Lilley, E.; Hansen, K.E.A.; Brattelid, T. PREPARE: Guidelines for Planning Animal Research and Testing. Lab. Anim. 2018, 52, 135–141. [Google Scholar] [CrossRef] [PubMed]
- von Kortzfleisch, V.T.; Karp, N.A.; Palme, R.; Kaiser, S.; Sachser, N.; Richter, S.H. Improving Reproducibility in Animal Research by Splitting the Study Population into Several ‘Mini-Experiments. Sci. Rep. 2020, 10, 16579. [Google Scholar] [CrossRef]
- Laukens, D.; Brinkman, B.M.; Raes, J.; De Vos, M.; Vandenabeele, P. Heterogeneity of the Gut Microbiome in Mice: Guidelines for Optimizing Experimental Design. FEMS Microbiol. Rev. 2016, 40, 117–132. [Google Scholar] [CrossRef]
- Alegre, M.-L. Mouse Microbiomes: Overlooked Culprits of Experimental Variability. Genome Biol. 2019, 20, 108. [Google Scholar] [CrossRef]
- Debelius, J.; Song, S.J.; Vazquez-Baeza, Y.; Xu, Z.Z.; Gonzalez, A.; Knight, R. Tiny Microbes, Enormous Impacts: What Matters in Gut Microbiome Studies? Genome Biol. 2016, 17, 217. [Google Scholar] [CrossRef]
- Friswell, M.K.; Gika, H.; Stratford, I.J.; Theodoridis, G.; Telfer, B.; Wilson, I.D.; McBain, A.J. Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice. PLoS ONE 2010, 5, e8584. [Google Scholar] [CrossRef] [PubMed]
- Rowland, I.; Gibson, G.; Heinken, A.; Scott, K.; Swann, J.; Thiele, I.; Tuohy, K. Gut Microbiota Functions: Metabolism of Nutrients and Other Food Components. Eur. J. Nutr. 2018, 57, 1–24. [Google Scholar] [CrossRef]
- Org, E.; Lusis, A.J. Using the Natural Variation of Mouse Populations to Understand Host-Gut Microbiome Interactions. Drug Discov. Today Dis. Models 2018, 28, 61–71. [Google Scholar] [CrossRef]
- Neff, E.P. Littermate Wanted: Standardizing Mouse Gut Microbiota Requires More than Cohousing. Lab Anim. 2019, 48, 197. [Google Scholar] [CrossRef]
- Witjes, V.M.; Boleij, A.; Halffman, W. Reducing versus Embracing Variation as Strategies for Reproducibility: The Microbiome of Laboratory Mice. Animals 2020, 10, 2415. [Google Scholar] [CrossRef] [PubMed]
- Darnaud, M.; De Vadder, F.; Bogeat, P.; Boucinha, L.; Bulteau, A.-L.; Bunescu, A.; Couturier, C.; Delgado, A.; Dugua, H.; Elie, C.; et al. A Standardized Gnotobiotic Mouse Model Harboring a Minimal 15-Member Mouse Gut Microbiota Recapitulates SOPF/SPF Phenotypes. Nat. Commun. 2021, 12, 6686. [Google Scholar] [CrossRef] [PubMed]
- Robertson, S.J.; Lemire, P.; Maughan, H.; Goethel, A.; Turpin, W.; Bedrani, L.; Guttman, D.S.; Croitoru, K.; Girardin, S.E.; Philpott, D.J. Comparison of Co-Housing and Littermate Methods for Microbiota Standardization in Mouse Models. Cell Rep. 2019, 27, 1910–1919.e2. [Google Scholar] [CrossRef] [PubMed]
- Neuman, H.; Mor, H.; Bashi, T.; Givol, O.; Watad, A.; Shemer, A.; Volkov, A.; Barshack, I.; Fridkin, M.; Blank, M.; et al. Helminth-Based Product and the Microbiome of Mice with Lupus. mSystems 2019, 4, e00160-18. [Google Scholar] [CrossRef]
- Wood, J. RcppAlgos: High Performance Tools for Combinatorics and Computational Mathematics. R Package Version 2020, 2, 540. [Google Scholar]
- Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef]
- McMurdie, P.J.; Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef]
- Chen, J.; Bittinger, K.; Charlson, E.S.; Hoffmann, C.; Lewis, J.; Wu, G.D.; Collman, R.G.; Bushman, F.D.; Li, H. Associating Microbiome Composition with Environmental Covariates Using Generalized UniFrac Distances. Bioinformatics 2012, 28, 2106–2113. [Google Scholar] [CrossRef] [PubMed]
- Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-Resolution Sample Inference from Illumina Amplicon Data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [PubMed]
- Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2--Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef] [PubMed]
- Katoh, K.; Standley, D.M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 2013, 30, 772–780. [Google Scholar] [CrossRef] [PubMed]
- Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
- Alberdi, A.; Gilbert, M.T.P. Hilldiv: An R Package for the Integral Analysis of Diversity Based on Hill Numbers. Biorxiv 2019, 545665. [Google Scholar]
- Simpson, G.L.; Oksanen, J. Analogue: Analogue and Weighted Averaging Methods for Palaeoecology. R Package Version 0.17-5. Available online: https://cran.r-project.org/web/packages/analogue/analogue.pdf (accessed on 3 February 2023).
- Papenberg, M.; Klau, G.W. Using Anticlustering to Partition Data Sets into Equivalent Parts. Psychol. Methods 2021, 26, 161–174. [Google Scholar] [CrossRef] [PubMed]
- Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016; ISBN 978-3-319-24277-4. [Google Scholar]
- Oksanen, J.; Blanchet, G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package. R Package Version 2013, 2, 321–326. [Google Scholar]
- Lin, H.; Peddada, S.D. Analysis of Compositions of Microbiomes with Bias Correction. Nat. Commun. 2020, 11, 3514. [Google Scholar] [CrossRef] [PubMed]
n Animals | n Groups | n Possible Designs | CPU Time (s) | CPU Time (Year) |
---|---|---|---|---|
10 | 2 | 1.26 × 102 | 4.50 | 1.43 × 10−7 |
12 | 2 | 4.62 × 102 | 1.66 × 101 | 5.26 × 10−7 |
12 | 3 | 5.78 × 103 | 2.08 × 102 | 6.58 × 10−6 |
20 | 2 | 9.24 × 104 | 3.32 × 103 | 1.05 × 10−4 |
20 | 4 | 4.89 × 108 | 1.06 × 109 | 3.35 × 101 |
24 | 2 | 1.35 × 106 | 2.92 × 106 | 9.25 × 10−2 |
24 | 3 | 1.58 × 109 | 3.41 × 109 | 1.08 × 102 |
27 | 3 | 3.80 × 1010 | 8.20 × 1010 | 2.60 × 103 |
28 | 4 | 1.97 × 1013 | 4.25 × 1013 | 1.35 × 106 |
30 | 2 | 7.76 × 107 | 1.67 × 108 | 5.30 |
30 | 3 | 9.25 × 1011 | 2.00 × 1012 | 6.33 × 104 |
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Even, G.; Mouray, A.; Vandenabeele, N.; Martel, S.; Merlin, S.; Lebrun-Ruer, S.; Chabé, M.; Audebert, C. Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs. Int. J. Mol. Sci. 2023, 24, 7912. https://doi.org/10.3390/ijms24097912
Even G, Mouray A, Vandenabeele N, Martel S, Merlin S, Lebrun-Ruer S, Chabé M, Audebert C. Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs. International Journal of Molecular Sciences. 2023; 24(9):7912. https://doi.org/10.3390/ijms24097912
Chicago/Turabian StyleEven, Gaël, Anthony Mouray, Nicolas Vandenabeele, Sophie Martel, Sophie Merlin, Ségolène Lebrun-Ruer, Magali Chabé, and Christophe Audebert. 2023. "Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs" International Journal of Molecular Sciences 24, no. 9: 7912. https://doi.org/10.3390/ijms24097912