Literature DB >> 29790949

Fecal microbiota composition changes after a BW loss diet in Beagle dogs.

Anna Salas-Mani1, Isabelle Jeusette1, Inmaculada Castillo2, Carmen L Manuelian3, Clement Lionnet4, Neus Iraculis1, Nuria Sanchez1, Sonia Fernández5, Lluís Vilaseca1, Celina Torre1.   

Abstract

In developed countries, dogs and cats frequently suffer from obesity. Recently, gut microbiota composition in humans has been related to obesity and metabolic diseases. This study aimed to evaluate changes in body composition, and gut microbiota composition in obese Beagle dogs after a 17-wk BW loss program. A total of six neutered adult Beagle dogs with an average initial BW of 16.34 ± 1.52 kg and BCS of 7.8 ± 0.1 points (9-point scale) were restrictedly fed with a hypocaloric, low-fat and high-fiber dry-type diet. Body composition was assessed with dual-energy X-ray absorptiometry scan, before (T0) and after (T1) BW loss program. Individual stool samples were collected at T0 and T1 for the 16S rRNA analyses of gut microbiota. Taxonomic analysis was done with amplicon-based metagenomic results, and functional analysis of the metabolic potential of the microbial community was done with shotgun metagenomic results. All dogs reached their ideal BW at T1, with an average weekly proportion of BW loss of -1.07 ± 0.03% of starting BW. Body fat (T0, 7.02 ± 0.76 kg) was reduced by half (P < 0.001), while bone (T0, 0.56 ± 0.06 kg) and muscle mass (T0, 8.89 ± 0.80 kg) remained stable (P > 0.05). The most abundant identified phylum was Firmicutes (T0, 74.27 ± 0.08%; T1, 69.38 ± 0.07%), followed by Bacteroidetes (T0, 12.68 ± 0.08%; T1, 16.68 ± 0.05%), Fusobacteria (T0, 7.45 ± 0.02%; T1, 10.18 ± 0.03%), Actinobacteria (T0, 4.53 ± 0.02%; T1, 3.34 ± 0.01%), and Proteobacteria (T0, 1.06 ± 0.01%; T1, 1.40 ± 0.00%). At genus level, the presence of Clostridium, Lactobacillus, and Dorea, at T1 decreased (P = 0.028), while Allobaculum increased (P = 0.046). Although the microbiota communities at T0 and T1 showed a low separation level when compared (Anosim's R value = 0.39), they were significantly biodiverse (P = 0.01). Those differences on microbiota composition could be explained by 13 genus (α = 0.05, linear discriminant analysis (LDA) score > 2.0). Additionally, differences between both communities could also be explained by the expression of 18 enzymes and 27 pathways (α = 0.05, LDA score > 2.0). In conclusion, restricted feeding of a low-fat and high-fiber dry-type diet successfully modifies gut microbiota in obese dogs, increasing biodiversity with a different representation of microbial genus and metabolic pathways.

Entities:  

Mesh:

Substances:

Year:  2018        PMID: 29790949      PMCID: PMC6095273          DOI: 10.1093/jas/sky193

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  29 in total

1.  Weight loss in obese dogs: evaluation of a high-protein, low-carbohydrate diet.

Authors:  Marianne Diez; Patrick Nguyen; Isabelle Jeusette; Claire Devois; Louis Istasse; Vincent Biourge
Journal:  J Nutr       Date:  2002-06       Impact factor: 4.798

2.  Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

Authors:  T Z DeSantis; P Hugenholtz; N Larsen; M Rojas; E L Brodie; K Keller; T Huber; D Dalevi; P Hu; G L Andersen
Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

3.  Assessment of microbial diversity along the feline intestinal tract using 16S rRNA gene analysis.

Authors:  Lauren E Ritchie; Jörg M Steiner; Jan S Suchodolski
Journal:  FEMS Microbiol Ecol       Date:  2008-12       Impact factor: 4.194

4.  An obesity-associated gut microbiome with increased capacity for energy harvest.

Authors:  Peter J Turnbaugh; Ruth E Ley; Michael A Mahowald; Vincent Magrini; Elaine R Mardis; Jeffrey I Gordon
Journal:  Nature       Date:  2006-12-21       Impact factor: 49.962

5.  High-fat diet determines the composition of the murine gut microbiome independently of obesity.

Authors:  Marie A Hildebrandt; Christian Hoffmann; Scott A Sherrill-Mix; Sue A Keilbaugh; Micah Hamady; Ying-Yu Chen; Rob Knight; Rexford S Ahima; Frederic Bushman; Gary D Wu
Journal:  Gastroenterology       Date:  2009-08-23       Impact factor: 22.682

6.  Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome.

Authors:  Peter J Turnbaugh; Fredrik Bäckhed; Lucinda Fulton; Jeffrey I Gordon
Journal:  Cell Host Microbe       Date:  2008-04-17       Impact factor: 21.023

7.  Analysis of bacterial diversity in the canine duodenum, jejunum, ileum, and colon by comparative 16S rRNA gene analysis.

Authors:  Jan S Suchodolski; Jennifer Camacho; Jörg M Steiner
Journal:  FEMS Microbiol Ecol       Date:  2008-06-16       Impact factor: 4.194

8.  HTQC: a fast quality control toolkit for Illumina sequencing data.

Authors:  Xi Yang; Di Liu; Fei Liu; Jun Wu; Jing Zou; Xue Xiao; Fangqing Zhao; Baoli Zhu
Journal:  BMC Bioinformatics       Date:  2013-01-31       Impact factor: 3.169

9.  Association of obesity with serum leptin, adiponectin, and serotonin and gut microflora in beagle dogs.

Authors:  H-J Park; S-E Lee; H-B Kim; R E Isaacson; K-W Seo; K-H Song
Journal:  J Vet Intern Med       Date:  2014-11-19       Impact factor: 3.333

10.  Advancing our understanding of the human microbiome using QIIME.

Authors:  José A Navas-Molina; Juan M Peralta-Sánchez; Antonio González; Paul J McMurdie; Yoshiki Vázquez-Baeza; Zhenjiang Xu; Luke K Ursell; Christian Lauber; Hongwei Zhou; Se Jin Song; James Huntley; Gail L Ackermann; Donna Berg-Lyons; Susan Holmes; J Gregory Caporaso; Rob Knight
Journal:  Methods Enzymol       Date:  2013       Impact factor: 1.600

View more
  18 in total

1.  The canine gastrointestinal microbiota: early studies and research frontiers.

Authors:  Zongyu Huang; Zhiyuan Pan; Ruifu Yang; Yujing Bi; Xiaohui Xiong
Journal:  Gut Microbes       Date:  2020-01-28

2.  Weight loss and high-protein, high-fiber diet consumption impact blood metabolite profiles, body composition, voluntary physical activity, fecal microbiota, and fecal metabolites of adult dogs.

Authors:  Thunyaporn Phungviwatnikul; Anne H Lee; Sara E Belchik; Jan S Suchodolski; Kelly S Swanson
Journal:  J Anim Sci       Date:  2022-02-01       Impact factor: 3.159

3.  Characterization of intestinal microbiota in normal weight and overweight Border Collie and Labrador Retriever dogs.

Authors:  Giada Morelli; Ilaria Patuzzi; Carmen Losasso; Antonia Ricci; Barbara Contiero; Igino Andrighetto; Rebecca Ricci
Journal:  Sci Rep       Date:  2022-06-02       Impact factor: 4.996

4.  The microbiota of healthy dogs demonstrates individualized responses to synbiotic supplementation in a randomized controlled trial.

Authors:  Jirayu Tanprasertsuk; Aashish R Jha; Justin Shmalberg; Roshonda B Jones; LeeAnn M Perry; Heather Maughan; Ryan W Honaker
Journal:  Anim Microbiome       Date:  2021-05-10

5.  Functional properties of miscanthus fiber and prebiotic blends in extruded canine diets.

Authors:  Shannon Finet; Fei He; Lindsay V Clark; Maria Regina Cattai de Godoy
Journal:  J Anim Sci       Date:  2022-04-01       Impact factor: 3.338

Review 6.  Akkermansia and Microbial Degradation of Mucus in Cats and Dogs: Implications to the Growing Worldwide Epidemic of Pet Obesity.

Authors:  Jose F Garcia-Mazcorro; Yasushi Minamoto; Jorge R Kawas; Jan S Suchodolski; Willem M de Vos
Journal:  Vet Sci       Date:  2020-04-15

7.  Fecal microbiota changes associated with dehorning and castration stress primarily affects light-weight dairy calves.

Authors:  Raies A Mir; Michael D Kleinhenz; Johann F Coetzee; Heather K Allen; Indira T Kudva
Journal:  PLoS One       Date:  2019-01-23       Impact factor: 3.240

Review 8.  Health impact of the Anthropocene: the complex relationship between gut microbiota, epigenetics, and human health, using obesity as an example.

Authors:  Cecilie Torp Austvoll; Valentina Gallo; Doreen Montag
Journal:  Glob Health Epidemiol Genom       Date:  2020-04-20

9.  Heterogeneity of gut microbial responses in healthy household dogs transitioning from an extruded to a mildly cooked diet.

Authors:  Jirayu Tanprasertsuk; Justin Shmalberg; Heather Maughan; Devon E Tate; LeeAnn M Perry; Aashish R Jha; Ryan W Honaker
Journal:  PeerJ       Date:  2021-06-30       Impact factor: 2.984

10.  Untargeted fecal metabolome analysis in obese dogs after weight loss achieved by feeding a high-fiber-high-protein diet.

Authors:  Sandra Bermudez Sanchez; Rachel Pilla; Benjamin Sarawichitr; Alessandro Gramenzi; Fulvio Marsilio; Joerg M Steiner; Jonathan A Lidbury; Georgiana R T Woods; Jan S Suchodolski; Alexander J German
Journal:  Metabolomics       Date:  2021-07-06       Impact factor: 4.290

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.