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Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment

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Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment

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Martinez Alvaro, M.; Zubiri-Gaitán, A.; Hernández, P.; Greenacre, M.; Ferrer, A.; Blasco Mateu, A. (2021). Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment. Communications Biology. 4:1-10. https://doi.org/10.1038/s42003-021-02784-w

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Title: Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment
Author: MARTINEZ ALVARO, MARINA Zubiri-Gaitán, Agostina Hernández, Pilar Greenacre, Michael Ferrer, Alberto Blasco Mateu, Agustín
UPV Unit: Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
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[EN] Our study provides an exhaustive comparison of the microbiome core functionalities (captured by 3,936 microbial gene abundances) between hosts with divergent genotypes for intramuscular lipid deposition. After 10 ...[+]
Copyrigths: Reserva de todos los derechos
Communications Biology. (eissn: 2399-3642 )
DOI: 10.1038/s42003-021-02784-w
Springer Nature
Publisher version: https://doi.org/10.1038/s42003-021-02784-w
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-86083-C2-1-P/ES/ESTUDIO MULTIOMICO SOBRE SENSIBILIDAD AMBIENTAL, LONGEVIDAD Y DEPOSICION GRASA EN LINEAS SELECCIONADAS DE CONEJO/
We acknowledge Prof. J.J. Egozcue and V. Pawlosky for their advice in the analysis of compositional data. This work was supported by project AGL2017-86083-C2-1-P from the Spanish National research plan. M. Martinez-alvaro ...[+]
Type: Artículo


Di Cesare, M. et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 387, 1377–1396 (2016).

Bray, G. A. Medical consequences of obesity. J. Clin. Endocrinol. Metab. 89, 2583–2589 (2004).

Chu, D. T. et al. An update on physical health and economic consequences of overweight and obesity. Diabetes Metab. Syndr. Clin. Res. Rev. 12, 1095–1100 (2018). [+]
Di Cesare, M. et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 387, 1377–1396 (2016).

Bray, G. A. Medical consequences of obesity. J. Clin. Endocrinol. Metab. 89, 2583–2589 (2004).

Chu, D. T. et al. An update on physical health and economic consequences of overweight and obesity. Diabetes Metab. Syndr. Clin. Res. Rev. 12, 1095–1100 (2018).

Chu, D. T. et al. An update on obesity: mental consequences and psychological interventions. Diabetes Metab. Syndr. Clin. Res. Rev. 13, 155–160 (2019).

Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

Herrera, B. M. & Lindgren, C. M. The genetics of obesity. Curr. Diab. Rep. 10, 498–505 (2010).

Martínez-Álvaro, M., Hernández, P. & Blasco, A. Divergent selection on intramuscular fat in rabbits: responses to selection and genetic parameters. J. Anim. Sci. 94, 4993–5003 (2016).

Schwab, C. R., Baas, T. J. & Stalder, K. J. Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. II. Genet. Parameters Trends J. Anim. Sci. 88, 69–79 (2010).

Goodarzi, M. O. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. 6, 223–236 (2018).

Snyder, E. E. et al. The human obesity gene map: the 2003 update. Obes. Res. 12, 369–439 (2004).

Horvat, S. et al. Mapping of obesity QTLs in a cross between mouse lines divergently selected on fat content. Mamm. Genome 11, 2–7 (2000).

Schertzer, J. D. et al. NOD1 activators link innate inmmunity to insulin resistance. Diabetes 60, 2206 (2011).

Doddapattar, P. et al. Fibronectin splicing variants containing extra domain a promote atherosclerosis in mice through toll-like receptor 4. Arterioscler. Thromb. Vasc. Biol. 35, 2391–2400 (2015).

Michelsen, K. S. et al. Lack of toll-like receptor 4 or myeloid differentiation factor 88 reduces atherosclerosis and alters plaque phenotype in mice deficient in apolipoprotein E. Proc. Natl Acad. Sci. USA 101, 10679–10684 (2004).

Knuefermann, P. et al. CD14-deficient mice are protected against lipopolysaccharide-induced cardiac inflammation and left ventricular dysfunction. Circulation 106, 2608–2615 (2002).

Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).

Lin, H. et al. Butyrate and propionate protect agains diet-induced obesity and regulate gut hormones. PLoS ONE 7, e35240 (2012).

Lim, Y. Y., Lee, Y. S. & Ooi, D. S. Q. Engineering the gut microbiome for treatment of obesity: a review of current understanding and progress. Biotechnol. J. 15, 1–10 (2020).

Xiong, Y. et al. Short-chain fatty acids stimulate leptin production in adipocytes through the G protein-coupled receptor GPR41. Proc. Natl Acad. Sci. USA 101, 1045–1050 (2004).

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).

Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0433-9 (2020).

Krajmalnik-Brown, R., Ilhan, Z.-E. E., Kang, D.-W. W. & DiBaise, J. K. Effects of gut microbes on nutrient absorption and energy regulation. Nutr. Clin. Pract. 27, 201–214 (2012).

Cox, L. M. & Blaser, M. J. Pathways in microbe-induced obesity. Cell Metab. 17, 883–894 (2013).

Ringseis, R., Gessner, D. K. & Eder, K. The gut-liver axis in the control of energy metabolism and food intake in animals. Annu. Rev. Anim. Biosci. 8, 295–319 (2020).

Hotamisligil, G. S. Inflammation and metabolic disorders. Insight Rev. - Nat. 444, 860–867 (2006).

Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 386–389 (2007).

Dehghan, M. et al. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 6736, 1–13 (2017).

Tamrakar, A. K. et al. NOD2 activation induces muscle cell-autonomous innate immune responses and insulin resistance. Endocrinology 151, 5624–5637 (2010).

Chi, W. et al. Bacterial peptidoglycan stimulates adipocytes lipolysis. PLoS ONE 9, e97675 (2014).

Zhao, L., Hu, P., Zhou, Y., Purohit, J. & Hwang, D. NOD1 activation induces proinflammatory gene expression and insulin resistance in 3T3-L1 adipocytes. Am. J. Physiol. Endocrinol. Metab. 301, 587–598 (2011).

Tabrett, A. & Horton, M. W. The influence of host genetics on the microbiome. F1000 Res. 9, 1–9 (2020).

Martínez-Álvaro, M. et al. Bovine host genome acts on specific metabolism, communication and genetic processes of rumen microbes host-genomically linked to methane emissions. Res. Sq. https://doi.org/10.21203/rs.3.rs-290150/v1 (2021).

Kurilshikov, A. et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat. Genet. 53, 156–165 (2021).

Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016).

Poole, A. C. et al. Human salivary amylase gene copy number impacts oral and gut microbiomes. Cell Host Microbe 25, 553–564.e7 (2019).

Qin, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population. medRxiv https://doi.org/10.1101/2020.09.12.20193045 (2020).

Hughes, D. A. et al. Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat. Microbiol. 5, 1079–1087 (2020).

Combes, S., Fortun-Lamothe, L., Cauquil, L. & Gidenne, T. Engineering the rabbit digestive ecosystem to improve digestive health and efficacy. Animal 7, 1429–1439 (2013).

Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Human gut asociated with obesity. Nature 444, 234–270 (2018).

Liu, Y. et al. Gut microbiome alterations in high-fat-diet-fed mice are associated with antibiotic tolerance. Nat. Microbiol. https://doi.org/10.1038/s41564-021-00912-0 (2021).

Cani, P. D., Bibiloni, R., Knauf, C., Neyrinck, A. M. & Delzenne, N. M. Changes in gut microbiota control metabolic diet–induced obesity and diabetes in mice. Diabetes 57, 1470–1481 (2008).

Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1131 (2006).

Goodpaster, B. H., Theriault, R., Watkins, S. C. & Kelley, D. E. Intramuscular lipid content is increased in obesity and decreased by weight loss. Metabolism 49, 467–472 (2000).

Maltecca, C. et al. Predicting growth and carcass traits in swine using metagenomic data and machine learning algorithms. Sci. Rep. 9, 6574 (2019).

Fang, S., Xiong, X., Su, Y., Huang, L. & Chen, C. 16S rRNA gene-based association study identified microbial taxa associated with pork intramuscular fat content in feces and cecum lumen. BMC Microbiol. 17, 1–9 (2017).

Wang, H. et al. Rumen fermentation, intramuscular fat fatty acid profiles and related rumen bacterial populations of Holstein bulls fed diets with different energy levels. Appl. Microbiol. Biotechnol. 103, 4931–4942 (2019).

Fan, J. & Watanabe, T. Transgenic rabbits as therapeutic protein bioreactors and human disease models. Pharmacol. Ther. 99, 261–282 (2003).

Kawai, T. et al. Hereditary postprandial hypertriglyceridemic rabbit exhibits insulin resistance and central obesity: a novel model of metabolic syndrome. Arterioscler. Thromb. Vasc. Biol. 26, 2752–2757 (2006).

Zhao, S. et al. Diet-induced central obesity and insulin resistance in rabbits. J. Anim. Physiol. Anim. Nutr. (Berl.). 92, 105–111 (2008).

Martínez-Álvaro, M., Hernández, P., Agha, S. & Blasco, A. Correlated responses to selection for intramuscular fat in several muscles in rabbits. Meat Sci. 139, 187–191 (2018).

Martínez-Álvaro, M., Agha, S., Blasco, A. & Hernández, P. Muscle lipid metabolism in two rabbit lines divergently selected for intramuscular fat. J. Anim. Sci. https://doi.org/10.2527/jas2017.1371 (2017).

Martínez-Álvaro, M., Paucar, Y., Satué, K., Blasco, A. & Hernández, P. Liver metabolism traits in two rabbit lines divergently selected for intramuscular fat. J. Anim. Sci. 95, 2576–2584 (2017).

Martínez-Álvaro, M., Blasco, A. & Hernandez, P. Effect of selection for intramuscular fat on the fatty acid composition of rabbit meat. Animal 12, 2002–2008 (2018).

Zeng, B. et al. The bacterial communities associated with fecal types and body weight of rex rabbits. Sci. Rep. 5, 1–8 (2015).

Feng, Y., Duan, C., Pang, H. & Mo, X. Cloning and identification of novel cellulase genes from uncultured microorganisms in rabbit cecum and characterization of the expressed cellulases. Appl. Microbiol. Biotechnol. 75, 319–328, https://doi.org/10.1007/s00253-006-0820-9 (2007).

Tatusov, R. L. et al. The COG database: an updated vesion includes eukaryotes. BMC Bioinforma. 4, 1–14 (2003).

Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).

Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

Greenacre, M., Grunsky, E. & Bacon-Shone, J. A comparison of isometric and amalgamation logratio balances in compositional data analysis. Comput. Geosci. 148, 104621 (2021).

Greenacre, M. Variable selection in compositional data analysis using pairwise logratios. Math. Geosci. 51, 649–682 (2018).

Greenacre, M. Compositional Data Analysis in Practise (CRC Press, 2019).

Greenacre, M., Martínez-Álvaro, M. & Blasco, A. Compositional data analysis of microbiome and any-omics datasets: a revalidation of the additive logratio transformation. Front. Microbiol. https://doi.org/10.1101/2021.05.15.444300 (2021).

Barker, M. & Rayens, W. Partial least squares for discrimination. J. Chemom. 17, 166–173 (2003).

Geladi, P. & Kowalski, B. R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986).

Blasco, A. Bayesian Data Analysis for Animal Scientists: The Basics (Springer, 2017).

Blasco, A. The Bayesian controversy in animal breeding. J. Anim. Sci. 79, 2023–2046 (2001).

Amar, J. et al. Intestinal mucosal adherence and translocation of commensal bacteria at the early onset of type 2 diabetes: molecular mechanisms and probiotic treatment. EMBO Mol. Med. 3, 559–572 (2011).

Brun, P. et al. Increased intestinal permeability in obese mice: new evidence in the pathogenesis of nonalcoholic steatohepatitis. Am. J. Physiol. Gastrointest. Liver Physiol. 292, 518–525 (2007).

Cani, P. & Delzenne, N. The role of the gut microbiota in energy metabolism and metabolic disease. Curr. Pharm. Des. 15, 1546–1558 (2009).

Sutcliffe, I. C., Harrington, D. J. & Hutchings, M. I. A phylum level analysis reveals lipoprotein biosynthesis to be a fundamental property of bacteria. Protein Cell 3, 163–170 (2012).

Nakayama, H., Kurokawa, K. & Lee, B. L. Lipoproteins in bacteria: Structures and biosynthetic pathways. FEBS J. 279, 4247–4268 (2012).

Iwasaki, A. & Modzhitov, R. Regulation of adaptative immunity by the innate immune system. Science 321, 291–295 (2010).

Rogero, M. M. & Calder, P. C. Obesity, inflammation, toll-like receptor 4 and fatty acids. Nutrients 10, 1–19 (2018).

Jialal, I., Kaur, H. & Devaraj, S. Toll-like receptor status in obesity and metabolic syndrome: a translation perspective. J. Endocrinol. Metab. 99, 39–48 (2014).

Severi, E. et al. Sialic acid mutarotation is catalyzed by the Escherichia coli β-propeller protein YjhT. J. Biol. Chem. 283, 4841–4849 (2008).

Juge, N., Tailford, L. & Owen, C. D. Sialidases from gut bacteria: a mini-review. Biochem. Soc. Trans. 44, 166–175 (2016).

Tailford, L. E. et al. Discovery of intramolecular trans-sialidases in human gut microbiota suggests novel mechanisms of mucosal adaptation. Nat. Commun. 6, 7624 (2015).

Fraser, A. G. Neuraminidase production by Clostridia. J. Med. Microbiol. 11, 269–280 (1978).

Derrien, M., Vaughan, E. E., Plugge, C. M. & de Vos, W. M. Akkermansia municiphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 54, 1469–1476 (2004).

Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066–9071 (2013).

Anhê, F. F., Schertzer, J. D., & Marette, A. Bacteria to alleviate metabolic syndrome. Nat. Med. 25, 1030–1031 (2019).

Depommier, C. et al. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat. Med. 25, 1096–1103 (2019).

Kerscher, S., Dröse, S., Zickermann, V. & Brandt, U. The three families of respiratory NADH dehydrogenases. in Results and Problems in Cell Differentiation 45, 185–222 (Springer, 2008).

Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

Million, M. et al. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Int. J. Obes. 36, 817–825 (2012).

Schwiertz, A. et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 18, 190–195 (2010).

Luo, Y. H. et al. Lean breed landrace pigs harbor fecal Methanogens at higher diversity and density than obese breed Erhualian pigs. Archaea 2012, 605289 (2012).

Marty, J. F., Vernay, M. Y. & Abravanel, G. M. Acetate absorption and metabolism in the rabbit hindgut. Gut 26, 562–569 (1985).

Mika, A. et al. A comprehensive study of serum odd- and branched-chain fatty acids in patients with excess weight. Obesity 24, 1669–1676 (2016).

Su, X. et al. Adipose tissue monomethyl branched-chain fatty acids and insulin sensitivity: effects of obesity and weight loss. Obesity 23, 329–334 (2015).

Wahle, K. W. J. & Hare, R. The effect of dietary methyl branched-chain fatty acids on aspects of hepatic lipid metabolism in the rat. Br. J. Nutr. 47, 61 (1982).

Nishina, P. M. & Freedland, R. A. Effects of propionate on lipid biosynthesis in isolated rat hepatocytes. J. Nutr. 120, 668–673 (1990).

Sosa-Madrid, S., Martínez-Álvaro, M., Paucar, Y., Hernández, P. & Blasco, A. Efecto de la selección divergente por grasa intramuscular en caracteres de eficiencia alimentaria. in XVII Jornadas sobre Producción Animal 480–482 (AIDA, ITEA, 2017).

Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

Baik, M. et al. Triennial growth and development symposium: Molecular mechanisms related to bovine intramuscular fat deposition in the longissimus muscle. J. Anim. Sci. 95, 2284–2303 (2017).

Veaudor, T., Cassier-Chauvat, C. & Chauvat, F. Genomics of urea transport and catabolism in cyanobacteria: biotechnological implications. Front. Microbiol. 10, 2052 (2019).

He, M. et al. Evaluating the contribution of gut microbiota to the variation of porcine fatness with the cecum and fecal samples. Front. Microbiol. 7, 1–13 (2016).

Sachs, G., Kraut, J. A., Wen, Y., Feng, J. & Scott, D. R. Urea transport in bacteria: acid acclimation by gastric Helicobacter spp. J. Membr. Biol. 212, 71–82 (2006).

Del Fiol, F. S., Balcao, V., Barberate-Fillho, S., Lopes, L. C. & Bergamaschi, C. Obesity: a new adverse effect of antibiotics. Front. Pharmacol. 9, 1408 (2018).

Safari, Z. et al. Murine genetic background overcomes gut microbiota changes to explain metabolic response to high-fat diet. Nutrients 12, 287 (2020).

Fujisaka, S. et al. Antibiotic effects on gut microbiota and metabolism are host dependent. J. Clin. Invest. 126, 4430–4443 (2016).

Thomas, C. et al. TGR5-mediated bile acid sensing controls glucose homeostasis. Cell Metab. 10, 167–177 (2009).

Prawitt, J. et al. Farnesoid X receptor deficiency improves glucose homeostasis in mouse models of obesity. Diabetes 60, 1861–1871 (2011).

Tobin, C. Removal and replacement of ribosomal proteins (Uppsala University, 2011).

Greenblum, S., Turnbaugh, P. J. & Borenstein, E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc. Natl Acad. Sci. USA 109, 594–599 (2012).

Chen, L. et al. Gut microbial co-abundance networks show specificity in inflammatory bowel disease and obesity. Nat. Commun. 11, 1–12 (2020).

Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

Blasco, A. & Ouhayoun, J. Harmonization of criteria and terminology in rabbit meat research. Revised proposal. World Rabbit Sci. 4, 93–99 (1996).

Zomeño, C., Juste, V. & Hernández, P. Application of NIRS for predicting fatty acids in intramuscular fat of rabbit. Meat Sci. 91, 155–159 (2012).

Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2014).

Tenenbaum, D. & Maintainer, B. Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG). Bioconductor https://doi.org/10.18129/B9.bioc.KEGGREST (2020).

Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B Methodol. 44, 139–177 (1982).

Greenacre, M. Compositional data analysis. Annu. Rev. Stat. Appl. 8, 271–299 (2021).

Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. Gigascience 8, 1–14 (2019).

Le Cao, K.-A. et al. mixOmics: Omics Data Integration Project. R package version 6.1.1 (mixOmics, 2016).




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