Gut microbiota promoting propionic acid production accompanies caloric restriction-induced intentional weight loss in cats.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 May 2024
24 May 2024
Historique:
received:
17
08
2023
accepted:
15
05
2024
medline:
25
5
2024
pubmed:
25
5
2024
entrez:
24
5
2024
Statut:
epublish
Résumé
Rodent models and human clinical studies have shown gut microbiota-derived short-chain fatty acids (SCFAs) play roles in obesity and insulin resistance. These roles have been minimally explored in cats, where in the USA an estimated 60% of cats are overweight or obese. Overweight/obese research cats (n = 7) were transitioned from a maintenance diet to a reduced calorie diet fed ad libitum for 7 days, then calories were restricted to achieve 1-2% weight loss per week for an additional 77 days. Cats then received their original maintenance diet again for 14 days. Significant intentional weight loss was noted after calorie restriction (adjusted p < 0.0001). 16S rRNA gene amplicon sequencing and targeted SCFA metabolomics were performed on fecal samples. Fecal microbial community structure significantly differed between the four study phases (PERMANOVA p = 0.011). Fecal propionic acid was significantly higher during caloric restriction-induced weight loss (adjusted p < 0.05). Repeated measures correlation revealed the relative abundances of Prevotella 9 copri (correlation coefficient = 0.532, 95% CI (0.275, 0.717), p = 0.0002) significantly correlated with propionic acid composition. Like humans, obese cats experienced an altered microbial community structure and function, favoring propionic acid production, during caloric restriction-induced weight loss.
Identifiants
pubmed: 38789518
doi: 10.1038/s41598-024-62243-4
pii: 10.1038/s41598-024-62243-4
doi:
Substances chimiques
propionic acid
JHU490RVYR
Propionates
0
RNA, Ribosomal, 16S
0
Fatty Acids, Volatile
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11901Subventions
Organisme : Nestle Purina Resident Research Grant
ID : 53743
Informations de copyright
© 2024. The Author(s).
Références
Sonnenburg, J. L. & Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
pubmed: 27383980
pmcid: 5991619
doi: 10.1038/nature18846
Kim, K. N., Yao, Y. & Ju, S. Y. Short chain fatty acids and fecal microbiota abundance in humans with obesity: A systematic review and meta-analysis. Nutrients 11, 2512 (2019).
pubmed: 31635264
pmcid: 6835694
doi: 10.3390/nu11102512
Psichas, A. et al. The short chain fatty acid propionate stimulates GLP-1 and PYY secretion via free fatty acid receptor 2 in rodents. Int. J. Obes. 39, 424–429 (2015).
doi: 10.1038/ijo.2014.153
Murugesan, S. et al. Gut microbiome production of short-chain fatty acids and obesity in children. Eur. J. Clin. Microbiol. Infect. Dis. 37, 621–625 (2018).
pubmed: 29196878
doi: 10.1007/s10096-017-3143-0
Priyadarshini, M., Wicksteed, B., Schiltz, G. E., Gilchrist, A. & Layden, B. T. SCFA receptors in pancreatic β cells: Novel diabetes targets?. Trends Endocrinol. Metab. 27, 653–664 (2016).
pubmed: 27091493
pmcid: 4992600
doi: 10.1016/j.tem.2016.03.011
van der Hee, B. & Wells, J. M. Microbial regulation of host physiology by short-chain fatty acids. Trends Microbiol. 29, 700–712 (2021).
pubmed: 33674141
doi: 10.1016/j.tim.2021.02.001
Cave, N. J., Allan, F. J., Schokkenbroek, S. L., Metekohy, C. A. M. & Pfeiffer, D. U. A cross-sectional study to compare changes in the prevalence and risk factors for feline obesity between 1993 and 2007 in New Zealand. Prev. Vet. Med. 107, 121–133 (2012).
pubmed: 22703979
doi: 10.1016/j.prevetmed.2012.05.006
Chiang, C.-F., Villaverde, C., Chang, W.-C., Fascetti, A. J. & Larsen, J. A. Prevalence, risk factors, and disease associations of overweight and obesity in cats that visited the Veterinary Medical Teaching Hospital at the University of California, Davis from January 2006 to December 2015. Top. Companion Anim. Med. 47, 100620 (2022).
pubmed: 34936906
doi: 10.1016/j.tcam.2021.100620
Hoenig, M., Thomaseth, K., Waldron, M. & Ferguson, D. C. Insulin sensitivity, fat distribution, and adipocytokine response to different diets in lean and obese cats before and after weight loss. Am. J. Physiol. Regul. Integr. Comp. Physiol. 292, R227–R234 (2007).
pubmed: 16902186
doi: 10.1152/ajpregu.00313.2006
Pilla, R. & Suchodolski, J. S. The gut microbiome of dogs and cats, and the influence of diet. Vet. Clin. Small Anim. Pract. 51, 605–621 (2021).
doi: 10.1016/j.cvsm.2021.01.002
Hesta, M., Janssens, G. P. J., Debraekeleer, J. & De Wilde, R. The effect of oligofructose and inulin on faecal characteristics and nutrient digestibility in healthy cats. J. Anim. Physiol. Anim. Nutr. 85, 135–141 (2001).
doi: 10.1046/j.1439-0396.2001.00308.x
Barry, K. A. et al. Dietary cellulose, fructooligosaccharides, and pectin modify fecal protein catabolites and microbial populations in adult cats. J. Anim. Sci. 88, 2978–2987 (2010).
pubmed: 20495116
doi: 10.2527/jas.2009-2464
Kanakupt, K., Vester Boler, B. M., Dunsford, B. R. & Fahey, G. C. Jr. Effects of short-chain fructooligosaccharides and galactooligosaccharides, individually and in combination, on nutrient digestibility, fecal fermentative metabolite concentrations, and large bowel microbial ecology of healthy adults cats. J. Anim. Sci. 89, 1376–1384 (2011).
pubmed: 21216981
doi: 10.2527/jas.2010-3201
Deb-Choudhury, S. et al. The effects of a wool hydrolysate on short-chain fatty acid production and fecal microbial composition in the domestic cat (Felis catus). Food Funct. 9, 4107–4121 (2018).
pubmed: 30039140
doi: 10.1039/C7FO02004J
Jackson, M. I., Waldy, C. & Jewell, D. E. Dietary resistant starch preserved through mild extrusion of grain alters fecal microbiome metabolism of dietary macronutrients while increasing immunoglobulin A in the cat. PLoS One 15, e0241037 (2020).
pubmed: 33141838
pmcid: 7608938
doi: 10.1371/journal.pone.0241037
Summers, S. et al. Preliminary evaluation of fecal fatty acid concentrations in cats with chronic kidney disease and correlation with indoxyl sulfate and p-cresol sulfate. J. Vet. Intern. Med. 34, 206–215 (2020).
pubmed: 31693251
doi: 10.1111/jvim.15634
du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020).
doi: 10.1371/journal.pbio.3000411
Brooks, D. et al. 2014 AAHA weight management guidelines for dogs and cats. J. Am. Anim. Hosp. Assoc. 50, 1–11 (2014).
pubmed: 24216501
doi: 10.5326/JAAHA-MS-6331
Cline, M. G. et al. 2021 AAHA nutrition and weight management guidelines for dogs and cats. J. Am. Anim. Hosp. Assoc. 57, 153–178 (2021).
pubmed: 34228790
doi: 10.5326/JAAHA-MS-7232
McCool, K. E., Rudinsky, A. J., Parker, V. J., Herbert, C. O. & Gilor, C. The effect of diet, adiposity, and weight loss on the secretion of incretin hormones in cats. Domest. Anim. Endocrinol. 62, 67–75 (2018).
pubmed: 29128557
doi: 10.1016/j.domaniend.2017.10.004
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108(Suppl 1), 4516–4522 (2011).
pubmed: 20534432
doi: 10.1073/pnas.1000080107
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).
pubmed: 23793624
pmcid: 3753973
doi: 10.1128/AEM.01043-13
Nealon, N. J. et al. Fecal identification markers impact the feline fecal microbiota. Front. Vet. Sci. 10, 1039931 (2023).
pubmed: 36846255
pmcid: 9946173
doi: 10.3389/fvets.2023.1039931
Computing, R. R: A Language and Environment for Statistical Computing (R Core Team, 2013).
RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
pubmed: 23193283
doi: 10.1093/nar/gks1219
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).
pubmed: 23630581
pmcid: 3632530
doi: 10.1371/journal.pone.0061217
Oksanen, J. et al. vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan (2022).
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
doi: 10.1111/j.1442-9993.1993.tb00438.x
Tangerman, A. & Nagengast, F. M. A gas chromatographic analysis of fecal short-chain fatty acids, using the direct injection method. Anal. Biochem. 236, 1–8 (1996).
pubmed: 8619472
doi: 10.1006/abio.1996.0123
Zentek, J. et al. Dietary protein source and manufacturing processes affect macronutrient digestibility, fecal consistency, and presence of fecal Clostridium perfringens in adult dogs. J. Nutr. 134, 2158S-2161S (2004).
pubmed: 15284426
doi: 10.1093/jn/134.8.2158S
Benjamini, Y., Krieger, A. M. & Yekutieli, D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507 (2006).
doi: 10.1093/biomet/93.3.491
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol 12, R60 (2011).
pubmed: 21702898
pmcid: 3218848
doi: 10.1186/gb-2011-12-6-r60
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Minamoto, Y. et al. Fecal short-chain fatty acid concentrations and dysbiosis in dogs with chronic enteropathy. J. Vet. Intern. Med. 33, 1608–1618 (2019).
pubmed: 31099928
pmcid: 6639498
doi: 10.1111/jvim.15520
Bridgman, S. L. et al. Fecal short-chain fatty acid variations by breastfeeding status in infants at 4 months: Differences in relative versus absolute concentrations. Front. Nutr. 4, 11 (2017).
pubmed: 28443284
pmcid: 5385454
doi: 10.3389/fnut.2017.00011
Heath, A.-L.M. et al. Association between the faecal short-chain fatty acid propionate and infant sleep. Eur. J. Clin. Nutr. 74, 1362–1365 (2020).
pubmed: 31969698
doi: 10.1038/s41430-019-0556-0
Bakdash, J. Z. & Marusich, L. R. Repeated measures correlation. Front. Psychol. 8, 456 (2017).
pubmed: 28439244
pmcid: 5383908
doi: 10.3389/fpsyg.2017.00456
Marusich, L. R. & Bakdash, J. Z. rmcorrShiny: A web and standalone application for repeated measures correlation. F1000Res 10, 697 (2021).
pubmed: 34621514
pmcid: 8456376
doi: 10.12688/f1000research.55027.1
Boshuizen, H. C. & te Beest, D. E. Pitfalls in the statistical analysis of microbiome amplicon sequencing data. Mol. Ecol. Resour. 23, 539–548 (2023).
pubmed: 36330663
doi: 10.1111/1755-0998.13730
Meijerink, J. The intestinal fatty acid-enteroendocrine interplay, emerging roles for olfactory signaling and serotonin conjugates. Molecules 26, 1416 (2021).
pubmed: 33807994
pmcid: 7961910
doi: 10.3390/molecules26051416
Singh, R. K. et al. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 15, 73 (2017).
pubmed: 28388917
pmcid: 5385025
doi: 10.1186/s12967-017-1175-y
Stoddart, L. A., Smith, N. J. & Milligan, G. International Union of Pharmacology. LXXI. Free fatty acid receptors FFA1, -2, and -3: Pharmacology and pathophysiological functions. Pharmacol. Rev. 60, 405–417 (2008).
pubmed: 19047536
doi: 10.1124/pr.108.00802
Brown, A. J. et al. The Orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J. Biol. Chem. 278, 11312–11319 (2003).
pubmed: 12496283
doi: 10.1074/jbc.M211609200
Tolhurst, G. et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes 61, 364–371 (2012).
pubmed: 22190648
pmcid: 3266401
doi: 10.2337/db11-1019
Jiao, A. et al. Sodium acetate, propionate, and butyrate reduce fat accumulation in mice via modulating appetite and relevant genes. Nutrition 87–88, 111198 (2021).
pubmed: 33761444
doi: 10.1016/j.nut.2021.111198
Lu, Y. et al. Short chain fatty acids prevent high-fat-diet-induced obesity in mice by regulating G protein-coupled receptors and gut microbiota. Sci. Rep. 6, 37589 (2016).
pubmed: 27892486
pmcid: 5124860
doi: 10.1038/srep37589
Lin, H. V. et al. Butyrate and propionate protect against diet-induced obesity and regulate gut hormones via free fatty acid receptor 3-independent mechanisms. PLoS One 7, e35240 (2012).
pubmed: 22506074
pmcid: 3323649
doi: 10.1371/journal.pone.0035240
Scuderi, M. A. et al. Safety and efficacy assessment of a GLP-1 mimetic: Insulin glargine combination for treatment of feline diabetes mellitus. Domest. Anim. Endocrinol. 65, 80–89 (2018).
pubmed: 30015124
doi: 10.1016/j.domaniend.2018.04.003
Tang, C. et al. Loss of FFA2 and FFA3 increases insulin secretion and improves glucose tolerance in type 2 diabetes. Nat. Med. 21, 173–177 (2015).
pubmed: 25581519
doi: 10.1038/nm.3779
Verbrugghe, A. et al. Propionate absorbed from the colon acts as gluconeogenic substrate in a strict carnivore, the domestic cat (Felis catus). J. Anim. Physiol. Anim. Nutr. 96, 1054–1064 (2012).
doi: 10.1111/j.1439-0396.2011.01220.x
Choi, B.S.-Y. et al. Feeding diversified protein sources exacerbates hepatic insulin resistance via increased gut microbial branched-chain fatty acids and mTORC1 signaling in obese mice. Nat. Commun. 12, 3377 (2021).
pubmed: 34099716
pmcid: 8184893
doi: 10.1038/s41467-021-23782-w
Shi, C. et al. Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication. Acta Diabetol. https://doi.org/10.1007/s00592-023-02094-z (2023).
doi: 10.1007/s00592-023-02094-z
pubmed: 37184672
pmcid: 10359369
Ganz, H. H. et al. The Kitty Microbiome Project: Defining the healthy fecal “core microbiome” in pet domestic cats. Vet. Sci. 9, 635 (2022).
pubmed: 36423084
pmcid: 9698023
doi: 10.3390/vetsci9110635
Butowski, C. F. et al. Addition of plant dietary fibre to a raw red meat high protein, high fat diet, alters the faecal bacteriome and organic acid profiles of the domestic cat (Felis catus). PLoS One 14, e0216072 (2019).
pubmed: 31042730
pmcid: 6493751
doi: 10.1371/journal.pone.0216072
Franke, T. & Deppenmeier, U. Physiology and central carbon metabolism of the gut bacterium Prevotella copri. Mol. Microbiol. 109, 528–540 (2018).
pubmed: 29995973
doi: 10.1111/mmi.14058
Liu, X. et al. Blautia—A new functional genus with potential probiotic properties?. Gut Microbes 13, 1875796 (2021).
pubmed: 33525961
pmcid: 7872077
doi: 10.1080/19490976.2021.1875796
Hosomi, K. et al. Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota. Nat. Commun. 13, 4477 (2022).
pubmed: 35982037
pmcid: 9388534
doi: 10.1038/s41467-022-32015-7
Lagkouvardos, I. et al. The Mouse Intestinal Bacterial Collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat. Microbiol. 1, 1–15 (2016).
Rudinsky, A. J., Rowe, J. C. & Parker, V. J. Nutritional management of chronic enteropathies in dogs and cats. J. Am. Vet. Med. Assoc. 253, 570–578 (2018).
pubmed: 30110216
doi: 10.2460/javma.253.5.570
Moreno, A. A., Parker, V. J., Winston, J. A. & Rudinsky, A. J. Dietary fiber aids in the management of canine and feline gastrointestinal disease. J. Am. Vet. Med. Assoc. 260, S33–S45 (2022).
pubmed: 36288203
doi: 10.2460/javma.22.08.0351
Wang, X. et al. Interleukin-22 alleviates metabolic disorders and restores mucosal immunity in diabetes. Nature 514, 237–241 (2014).
pubmed: 25119041
doi: 10.1038/nature13564
Boulangé, C. L., Neves, A. L., Chilloux, J., Nicholson, J. K. & Dumas, M.-E. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med. 8, 42 (2016).
pubmed: 27098727
pmcid: 4839080
doi: 10.1186/s13073-016-0303-2
Nelson, R. W. & Reusch, C. E. Animal models of disease: Classification and etiology of diabetes in dogs and cats. J. Endocrinol. 222, T1-9 (2014).
pubmed: 24982466
doi: 10.1530/JOE-14-0202
Dandona, P., Aljada, A. & Bandyopadhyay, A. Inflammation: The link between insulin resistance, obesity and diabetes. Trends Immunol. 25, 4–7 (2004).
pubmed: 14698276
doi: 10.1016/j.it.2003.10.013
Shoelson, S. E., Herrero, L. & Naaz, A. Obesity, inflammation, and insulin resistance. Gastroenterology 132, 2169–2180 (2007).
pubmed: 17498510
doi: 10.1053/j.gastro.2007.03.059
Biourge, V. et al. Effect of weight gain and subsequent weight loss on glucose tolerance and insulin response in healthy cats. J. Vet. Intern. Med. 11, 86–91 (1997).
pubmed: 9127295
doi: 10.1111/j.1939-1676.1997.tb00078.x
Hoenig, M., McGoldrick, J. B., deBeer, M., Demacker, P. N. M. & Ferguson, D. C. Activity and tissue-specific expression of lipases and tumor-necrosis factor alpha in lean and obese cats. Domest. Anim. Endocrinol. 30, 333–344 (2006).
pubmed: 16219442
doi: 10.1016/j.domaniend.2005.09.001
Fung, T. C. et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat. Microbiol. 4, 2064–2073 (2019).
pubmed: 31477894
pmcid: 6879823
doi: 10.1038/s41564-019-0540-4
Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).
pubmed: 25860609
pmcid: 4393509
doi: 10.1016/j.cell.2015.02.047
Hoffman, J. M. & Margolis, K. G. Building community in the gut: A role for mucosal serotonin. Nat. Rev. Gastroenterol. Hepatol. 17, 6–8 (2020).
pubmed: 31624372
pmcid: 6930332
doi: 10.1038/s41575-019-0227-6
Chung, E. et al. Metabolic benefits of annatto-extracted tocotrienol on glucose homeostasis, inflammation, and gut microbiome. Nutr. Res. 77, 97–107 (2020).
pubmed: 32438021
doi: 10.1016/j.nutres.2020.04.001
Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).
pubmed: 32582143
pmcid: 7296073
doi: 10.3389/fimmu.2020.00906