1932

Abstract

Pet owners have increasing concerns about the nutrition of their pets, and they desire foods and treats that are safe, traceable, and of high nutritive value. To meet these high expectations, detailed chemical composition characterization of ingredients well beyond that provided by proximate analysis will be required, as will information about host physiology and metabolism. Use of faster and more precise analytical methodology and novel technologies that have the potential to improve pet food safety and quality will be implemented. In vitro and in vivo assays will continue to be used as screening tools to evaluate nutrient quality and adequacy in novel ingredients prior to their use in animal diets. The use of molecular and high-throughput technologies allows implementation of noninvasive studies in dogs and cats to investigate the impact of dietary interventions by using systems biology approaches. These approaches may further improve the health and longevity of pets.

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2016-02-15
2024-04-13
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Literature Cited

  1. 1. Am. Pet Prod. Assoc 2013. New survey reveals pet ownership at all-time high. Press Release, Feb. 21. http://media.americanpetproducts.org/press.php?include=144262
  2. Faber TA, Fahey GC Jr. 2.  2011. Animal, in vitro, and cell culture models to study the role of dietary fibers in the gastrointestinal tract of humans. Nondigestible Carbohydrates and Digestive Health TM Paeschke, WR Aimutis 97–123 Ames, IA: Blackwell/Inst. Food Technol. [Google Scholar]
  3. Henneberg W, Stohmann F. 3.  1859. Ueber das erhaltungsfutter volljaehrigen rindviehs. J. Landwirtsch. 3:485–551 [Google Scholar]
  4. Keen CL, Jue T, Tran CD, Vogel J, Downing RG. 4.  et al. 2003. Analytical methods: improvements, advancements and new horizons. J. Nutr. 133:1574S–78S [Google Scholar]
  5. Noel RJ, Hambleton LG. 5.  1976. Collaborative study of a semiautomated method for the determination of crude protein in animal feeds. J. Assoc. Off. Anal. Chem. 59:134–40 [Google Scholar]
  6. Gilani GS, Moughan PJ. 6.  2008. Accurate methodology for amino acids and bioactive peptides in functional foods and dietary supplements for assessing protein adequacy and health effects. J. AOAC Int. 91:892–93 [Google Scholar]
  7. Palmquist DL, Jenkins TC. 7.  2003. Challenges with fats and fatty acid methods. J. Anim. Sci. 81:3250–54 [Google Scholar]
  8. Kienzle E. 8.  1993. Carbohydrate-metabolism of the cat. 2. Digestion of starch. J. Anim. Physiol. Anim. Nutr. 69:102–14 [Google Scholar]
  9. Englyst KN, Liu S, Englyst HN. 9.  2007. Nutritional characterization and measurement of dietary carbohydrates. Eur. J. Clin. Nutr. 61:S19–S39 [Google Scholar]
  10. Kienzle E, Biourge V, Schonmeier A. 10.  2006. Prediction of energy digestibility in complete dry foods for dogs and cats by total dietary fiber. J. Nutr. 136:2041S–44S [Google Scholar]
  11. 11. Assoc. Am. Feed Control Off 2009. 2009 Official Publication. Champaign, IL: Assoc. Am. Feed Control Off.
  12. Castrillo C, Vicente F, Guada JA. 12.  2001. The effect of crude fibre on apparent digestibility and digestible energy content of extruded dog foods. J. Anim. Physiol. Anim. Nutr. 85:231–36 [Google Scholar]
  13. Banta CA, Clemens ET, Krinsky MM, Sheffy BE. 13.  1979. Sites of organic-acid production and patterns of digesta movement in the gastro-intestinal tract of dogs. J. Nutr. 109:1592–600 [Google Scholar]
  14. Englyst HN, Cummings JH. 14.  1988. Improved method for measurement of dietary fiber as non-starch polysaccharides in plant foods. J. Assoc. Off. Anal. Chem. 71:808–14 [Google Scholar]
  15. Van Soest PJ, Robertson JB, Lewis BA. 15.  1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–97 [Google Scholar]
  16. Prosky L, Asp NG, Furda I, Devries JW, Schweizer TF, Harland BF. 16.  1984. Determination of total dietary fiber in foods, food-products, and total diets—interlaboratory study. J. Assoc. Off. Anal. Chem. 67:1044–52 [Google Scholar]
  17. Englyst HN, Kingman SM, Cummings JH. 17.  1992. Classification and measurement of nutritionally important starch fractions. Eur. J. Clin. Nutr. 46:S33–S50 [Google Scholar]
  18. Asp NG. 18.  1987. Definition and analysis of dietary fibre. Scand. J. Gastroenterol. Suppl. 129:16–20 [Google Scholar]
  19. Jeraci JL, Van Soest PJ. 19.  1990. Improved methods for analysis and biological characterization of fiber. Adv. Exp. Med. Biol. 270:245–63 [Google Scholar]
  20. Theander O, Aman P, Westerlund E, Graham H. 20.  1994. Enzymatic/chemical analysis of dietary fiber. J. AOAC Int. 77:703–9 [Google Scholar]
  21. Marlett JA, Chester JG. 21.  1985. Measuring dietary fiber in human foods. J. Food Sci. 50:410–14 [Google Scholar]
  22. Fahey GC, Merchen NR, Corbin JE, Hamilton AK, Serbe KA, Hirakawa DA. 22.  1990. Dietary fiber for dogs. 2. Iso-total dietary fiber (TDF) additions of divergent fiber sources to dog diets and their effects on nutrient intake, digestibility, metabolizable energy and digesta mean retention time. J. Anim. Sci. 68:4229–35 [Google Scholar]
  23. 23. Assoc. Off. Anal. Chem 2012. Official Methods of Analysis. Gaithersburg, MD: Assoc. Off. Anal. Chem., 18th ed..
  24. 24. Natl. Res. Counc. 1985. Nutrient Requirements of Dogs. Washington, DC: Natl. Acad. Press
  25. 25. Assoc. Am. Feed Control Off 2015. 2015 Official Publication. Champaign, IL: Assoc. Am. Feed Control Off.
  26. Kienzle E, Opitz B, Earle KE, Smith PM, Maskell IE. 26.  1998. The influence of dietary fibre components on the apparent digestibility of organic matter and energy in prepared dog and cat foods. J. Anim. Physiol. Anim. Nutr. 79:46–56 [Google Scholar]
  27. Laflamme DP. 27.  2001. Determining metabolizable energy content in commercial pet foods. J. Anim. Physiol. Anim. Nutr. 85:222–30 [Google Scholar]
  28. Kienzle E, Opitz B, Earle KE, Smith PM, Maskell IE, Iben C. 28.  1998. The development of an improved method of predicting the energy content in prepared dog and cat food. J. Anim. Physiol. Anim. Nutr. 79:69–79 [Google Scholar]
  29. Norris KH, Barnes RF, Moore JE, Shenk JS. 29.  1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43:889–97 [Google Scholar]
  30. Leeson S, Valdes EV, de Lange CFM. 30.  2000. Near infrared reflectance spectroscopy and related technologies for the analysis of feed ingredients. Feed Evaluation: Principles and Practice PJ Moughan, MW Verstegen, MI Visser-Reyneveld 93–104 Wageningen, Neth: Wageningen Acad. Publ. [Google Scholar]
  31. Biston R, Clamot G. 31.  1982. Use of near-infrared reflectance spectroscopy and dye-binding techniques for estimating protein in oat groats. Cereal Chem. 59:333–35 [Google Scholar]
  32. Campbell MR, Brumm TJ, Glover DV. 32.  1997. Whole grain amylose analysis in maize using near-infrared transmittance spectroscopy. Cereal Chem. 74:300–3 [Google Scholar]
  33. Kays SE, Barton FE. 33.  2002. Rapid prediction of gross energy and utilizable energy in cereal food products using near-infrared reflectance spectroscopy. J. Agric. Food Chem. 50:1284–89 [Google Scholar]
  34. Osborne BG, Fearn T, Randall PG. 34.  1983. Measurement of fat and sucrose in dry cake mixes by near-infrared reflectance spectroscopy. J. Food Technol. 18:651–56 [Google Scholar]
  35. Starr C, Suttle J, Morgan AG, Smith DB. 35.  1985. A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near-infrared spectroscopy. J. Agric. Sci. 104:317–23 [Google Scholar]
  36. Norris KH, Williams PC. 36.  1984. Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat. 1. Influence of particle size. Cereal Chem. 61:158–65 [Google Scholar]
  37. Williams PC, Norris KH, Sobering DC. 37.  1985. Determination of protein and moisture in wheat and barley by near-infrared transmission. J. Agric. Food Chem. 33:239–44 [Google Scholar]
  38. Wrigley CW. 38.  1999. Potential methodologies and strategies for the rapid assessment of feed-grain quality. Aust. J. Agric. Res. 50:789–805 [Google Scholar]
  39. Nielsen JP, Bertrand D, Micklander E, Courcoux P, Munck L. 39.  2001. Study of NIR spectra, particle size distributions and chemical parameters of wheat flours: a multi-way approach. J. Near Infrared Spectr. 9:275–85 [Google Scholar]
  40. Valdes EV, Leeson S. 40.  1992. Near-infrared reflectance analysis as a method to measure metabolizable energy in complete poultry feeds. Poult. Sci. 71:1179–87 [Google Scholar]
  41. Valdes EV, Leeson S. 41.  1994. Measurement of metabolizable energy, gross energy, and moisture in feed grade fats by near-infrared reflectance spectroscopy. Poult. Sci. 73:163–71 [Google Scholar]
  42. Aufrere J, Graviou D, Demarquilly C, Perez JM, Andrieu J. 42.  1996. Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Anim. Feed Sci. Technol. 62:77–90 [Google Scholar]
  43. van Barneveld RJ, Nuttall JD, Flinn PC, Osborne BG. 43.  1999. Near infrared reflectance measurement of the digestible energy content of cereals for growing pigs. J. Near Infrared Spectr. 7:1–7 [Google Scholar]
  44. Van Kempen T, Bodin JC. 44.  1998. Near-infrared reflectance spectroscopy (NIRS) appears to be superior to nitrogen-based regression as a rapid tool in predicting the poultry digestible amino acid content of commonly used feedstuffs. Anim. Feed Sci. Technol. 76:139–47 [Google Scholar]
  45. Van Leeuwen P, Verstegen MWA, van Lonkhuijsen HJ, van Kempen GJM. 45.  1991. Near-infrared reflectance spectroscopy to estimate the apparent ileal digestibility of protein in feedstuffs. Digestive Physiology in Pigs MWA Verstegen, J Huisman, LA de Hartog 260–65 Wageningen, Neth: PuDoc [Google Scholar]
  46. Givens DI, De Boever JL, Deaville ER. 46.  1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutr. Res. Rev. 10:83–114 [Google Scholar]
  47. Castrillo C, Baucells M, Vicente F, Muñoz F, Andueza D. 47.  2005. Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy. J. Anim. Physiol. Anim. Nutr. 89:194–98 [Google Scholar]
  48. Alomar D, Hodgkinson S, Abarzúa D, Fuchslocher R, Alvarado C, Rosales E. 48.  2006. Nutritional evaluation of commercial dry dog foods by near infrared reflectance spectroscopy. J. Anim. Physiol. Anim. Nutr. 90:223–29 [Google Scholar]
  49. Hervera M, Castrillo C, Albanell E, Baucells MD. 49.  2012. Use of near-infrared spectroscopy to predict energy content of commercial dog food. J. Anim. Sci. 90:4401–7 [Google Scholar]
  50. 50. Natl. Res. Counc 2006. Nutrient Requirements of Dogs and Cats. Washington, DC: Natl. Acad. Press
  51. Maynard AD, Aitken RJ, Butz T, Colvin V, Donaldson K. 51.  et al. 2006. Safe handling of nanotechnology. Nature 444:267–69 [Google Scholar]
  52. Grobe A, Rissanen ME. 52.  2012. Nanotechnologies in agriculture and food—an overview of different fields of application, risk assessment and public perception. Recent Pat. Food Nutr. Agric. 4:176–86 [Google Scholar]
  53. Sonkaria S, Ahn SH, Khare V. 53.  2012. Nanotechnology and its impact on food and nutrition: a review. Recent Pat. Food Nutr. Agric. 4:8–18 [Google Scholar]
  54. Chaudhry Q, Scotter M, Blackburn J, Ross B, Boxall A. 54.  et al. 2008. Applications and implications of nanotechnologies for the food sector. Food Addit. Contam. A 25:241–58 [Google Scholar]
  55. Bouwmeester H, Dekkers S, Noordam MY, Hagens WI, Bulder AS. 55.  et al. 2009. Review of health safety aspects of nanotechnologies in food production. Regul. Toxicol. Pharmacol. 53:52–62 [Google Scholar]
  56. 56. Eur. Food Saf. Auth 2011. Scientific opinion. Guidance on the risk assessment of the application of nanoscience and nanotechnologies in the food and feed chain. EFSA J. 9:2140–76 [Google Scholar]
  57. 57. US Dep. Health Hum. Serv 2012. Guidance for industry: assessing the effects of significant manufacturing process changes, including emerging technologies, on the safety and regulatory status of food ingredients and food contact substances, including food ingredients that are color additives. Draft Guid., Food Drug Adm., Rockville, MD
  58. Tilley JMA, Terry RA. 58.  1963. A two-stage technique for the in vitro digestion of forage crops. J. Br. Grassl. Soc. 18:104–11 [Google Scholar]
  59. Boisen S. 59.  2000. In vitro digestibility methods: history and specific approaches. Feed Evaluation: Principles and Practices PJ Moughan, MWA Verstegen, MI Visser-Reyneveld 153–68 Wageningen, Neth: Wageningen Acad. Publ. [Google Scholar]
  60. Longland AC. 60.  1991. Digestive enzyme activities in pigs and poultry. In Vitro Digestion for Pigs and Poultry MF Fuller 3–18 Wallingford, UK: CAB Intl. [Google Scholar]
  61. Beames RM, Helm JH, Eggum BO, Boisen S, Bach Knudsen KE, Swift ML. 61.  1996. A comparison of methods for measuring the nutritive value for pigs of a range of hulled and hulless barley cultivars. Anim. Feed Sci. Technol. 62:189–201 [Google Scholar]
  62. Boisen S, Fernandez JA. 62.  1991. In vitro digestibility of energy and amino acids in pig feeds. Digestive Physiology in Pigs MWA Verstegen, J Huisman, LA Hartog 231–36 Wageningen, Neth: PuDoc [Google Scholar]
  63. Hervera M, Baucells MD, González G, Pérez E, Castrillo C. 63.  2009. Prediction of digestible protein content of dry extruded dog foods: comparison of methods. J. Anim. Physiol. Anim. Nutr. 93:366–72 [Google Scholar]
  64. Minekus M, Marteau P, Havenaar R, Huis in't Veld JHJ. 64.  1995. A multicompartmental dynamic computer-controlled model simulating the stomach and small intestine. Alt. Lab. Anim. 23:197–209 [Google Scholar]
  65. Smeets-Peeters MJE. 65.  2000. Feeding FIDO: development, validation and application of a dynamic in vitro model of the gastrointestinal tract of the dog. PhD Thesis, Wageningen Univ. Neth.
  66. Minekus M, Smeets-Peeters M, Bernalier A, Marol-Bonnin S, Havenaar R. 66.  et al. 1999. A computer-controlled system to simulate conditions of the large intestine with peristaltic mixing, water absorption and absorption of fermentation products. Appl. Microbiol. Biotechnol. 53:108–14 [Google Scholar]
  67. Molly K, Vande Woestyne M, Verstraete W. 67.  1993. Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem. Appl. Microbiol. Biotechnol. 39:254–58 [Google Scholar]
  68. Macfarlane GT, Macfarlane S, Gibson GR. 68.  1998. Validation of a three-stage compound continuous culture system for investigating the effect of retention time on the ecology and metabolism of bacteria in the human colon. Microbiol. Ecol. 35:180–87 [Google Scholar]
  69. Makivuokko H, Nurmi J, Nurminen P, Stowell J, Rautonen N. 69.  2005. In vitro effects on polydextrose by colonic bacteria and caco-2 cell cyclooxygenase gene expression. Nutr. Cancer 52:94–104 [Google Scholar]
  70. de Lange CFM. 70.  2000. Overview of determinants of the nutritional value of feed ingredients. Feed Evaluation Principles and Practice PJ Moughan, MWA Verstegen, MI Visser-Reyneveld 17–32 Wageningen, Neth: Wageningen Press [Google Scholar]
  71. Nyachoti CM, de Lange CFM, McBride BW, Schulze H. 71.  1997. Significance of endogenous nitrogen losses in the nutrition of growing pigs—a review. Can. J. Anim. Sci. 77:149–63 [Google Scholar]
  72. Muir HES, Murray M, Fahey GC Jr, Merchen NR, Reinhart GA. 72.  1996. Nutrient digestion by ileal cannulated dogs as affected by dietary fibers with various fermentation characteristics. J. Anim. Sci. 74:1641–48 [Google Scholar]
  73. Zuo Y, Fahey GC Jr, Merchen NR, Bajjalieh NL. 73.  1996. Digestion responses to low oligosaccharide soybean meal by ileally cannulated dogs. J. Anim. Sci. 74:2441–49 [Google Scholar]
  74. Murray SM, Patil AR, Fahey GC Jr, Merchen NR, Hughes DM. 74.  1997. Raw and rendered animal by-products as ingredients in dog diets. J. Anim. Sci. 75:2497–505 [Google Scholar]
  75. Johnson ML, Parsons CM, Fahey GC Jr, Merchen NR, Aldrich CG. 75.  1998. Effects of species raw material source, ash content, and processing temperature on amino acid digestibility of animal by-product meals by cecectomized roosters and ileally cannulated dogs. J. Anim. Sci. 76:1112–22 [Google Scholar]
  76. Murray SM, Patil AR, Fahey GC Jr, Merchen NR, Wolf W. 76.  et al. 1998. Apparent digestibility of a debranched amylopectin-lipid complex and resistant starch incorporated into enteral formulas fed to ileal-cannulated dogs. J. Nutr. 128:2032–35 [Google Scholar]
  77. Murray SM, Patil AR, Fahey GC Jr, Merchen NR, Wolf W. 77.  et al. 1999. Apparent digestibility and glycaemic responses to an experimental induced viscosity dietary fibre incorporated into an enteral formula fed to dogs cannulated in the ileum. Food Chem. Toxicol. 37:47–56 [Google Scholar]
  78. Faber TA, Bechtel PJ, Hernot DC, Parsons CM, Swanson KS. 78.  et al. 2010. Protein digestibility evaluation of meat and fish substrates using laboratory, avian, and ileally cannulated dog assays. J. Anim. Sci. 88:1421–32 [Google Scholar]
  79. Sauer WC, Ozimek L. 79.  1986. Digestibility of amino acids in swine: results and their practical applications. A review. Livest. Prod. Sci. 15:367–88 [Google Scholar]
  80. Scott TW, Silversides FG, Classen HL, Swift ML, Bedford MR, Hall JW. 80.  1998. A broiler chick bioassay for measuring the feeding value of wheat and barley in complete diets. Poult. Sci. 77:449–55 [Google Scholar]
  81. Shields RG. 81.  1993. Digestibility and metabolizable energy measurement in dogs and cats. Proc. Petfood Forum 199321–35 Mt. Morris, IL: Watt [Google Scholar]
  82. Howe EE, Jansen GR, Gilfillan EW. 82.  1965. Amino acid supplementation of cereal grains as related to world food supply. Am. J. Clin. Nutr. 16:315–20 [Google Scholar]
  83. Escalona RR, Pesti GM, Vaughters PD. 83.  1986. Nutritive value of poultry by-product meal. 2. Comparisons of methods of determining protein quality. Poult. Sci. 65:2268–80 [Google Scholar]
  84. Parsons CM, Baker DH, Harter JM. 84.  1983. Distillers dried grains with solubles as a protein source for the chick. Poult. Sci. 62:2445–51 [Google Scholar]
  85. Johnson ML, Parsons CM. 85.  1997. Effects of raw material source, ash content, and assay length on protein efficiency ratio and net protein ratio values for animal protein meals. Poult. Sci. 76:1722–27 [Google Scholar]
  86. Willis GM, Baker DH. 86.  1980. Evaluation of turfgrass clippings as a dietary ingredient for the growing chick. Poult. Sci. 59:404–11 [Google Scholar]
  87. Steinke FH. 87.  1977. Protein efficiency ratio pitfalls and causes of variability: a review. Cereal Chem 54:949–57 [Google Scholar]
  88. Dust JM, Grieshop CM, Parsons CM, Karr-Lilienthal LK, Schasteen CS. 88.  et al. 2005. Chemical composition, protein quality, palatability, and digestibility of alternative protein sources for dogs. J. Anim. Sci. 83:2414–22 [Google Scholar]
  89. Folador JF, Karr-Lilienthal LK, Parsons CM, Bauer LL, Utterback PL. 89.  et al. 2006. Fish meals, fish components, and fish protein hydrolysates as potential ingredients in pet foods. J. Anim. Sci. 84:2752–65 [Google Scholar]
  90. de Godoy MRC, Bauer LL, Parsons CM, Fahey GC Jr. 90.  2009. Select corn coproducts from the ethanol industry and their potential as ingredients in pet foods. J. Anim. Sci. 87:189–99 [Google Scholar]
  91. Parsons CM. 91.  1985. Influence of caecectomy on digestibility of amino acids by roosters fed distiller's dried grains with solubles. J. Agric. Sci. 104:469–72 [Google Scholar]
  92. Sibbald IR. 92.  1979. A bioassay for available amino acids and true metabolizable energy in feedstuffs. Poult. Sci. 58:668–73 [Google Scholar]
  93. Parsons CM, Potter LM, Bliss BA. 93.  1982. True metabolizable energy corrected to nitrogen equilibrium. Poult. Sci. 61:2241–46 [Google Scholar]
  94. Knapp BK, Parsons CM, Swanson KS, Fahey GC Jr. 94.  2008. Physiological responses to novel carbohydrates as assessed using canine and avian models. J. Agric. Food Chem. 56:7999–8006 [Google Scholar]
  95. Knapp BK, Parsons CM, Bauer LL, Swanson KS, Fahey GC Jr. 95.  2010. Soluble fiber dextrins and pullulans vary in extent of hydrolytic digestion in vitro and in energy value and attenuate glycemic and insulinemic responses in dogs. J. Agric. Food Chem. 58:11355–63 [Google Scholar]
  96. de Godoy MRC, Knapp BK, Parsons CM, Swanson KS, Fahey GC Jr. 96.  2014. In vitro hydrolytic digestion, glycemic response in dogs, and true metabolizable energy content of soluble corn fibers. J. Anim. Sci. 92:2447–57 [Google Scholar]
  97. 97. Banfield Pet Hosp 2013. State of Pet Health 2013 Report Portland, OR: Banfield Pet Hosp Accessed Oct. 1, 2015. http://www.stateofpethealth.com/content/pdf/Banfield-State-of-Pet-Health-Report_2013.pdf
  98. Laflamme DP, Abood SK, Fascetti AJ, Fleeman LM, Freeman LM. 98.  et al. 2008. Pet feeding practices of dog and cat owners in the United States and Australia. J. Am. Vet. Med. Assoc. 232:687–94 [Google Scholar]
  99. Deng P, Swanson KS. 99.  2015. Future aspects and perceptions of companion animal nutrition and sustainability. J. Anim. Sci. 93:823–34 [Google Scholar]
  100. Afman L, Muller M. 100.  2006. Nutrigenomics: from molecular nutrition to prevention of disease. J. Am. Diet. Assoc. 106:569–76 [Google Scholar]
  101. Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SPA. 101.  1994. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. PNAS 91:5022–26 [Google Scholar]
  102. Schena M, Shalon D, Davis RW, Brown PO. 102.  1994. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–70 [Google Scholar]
  103. Middelbos IS, Vester BM, Karr-Lilienthal LK, Schook LB, Swanson KS. 103.  2009. Age and diet affect gene expression profile in canine skeletal muscle. PLOS ONE 4:e4481 [Google Scholar]
  104. Swanson KS, Belsito KR, Vester BM, Schook LB. 104.  2009. Adipose tissue gene expression profiles of healthy young adult and geriatric dogs. Arch. Anim. Nutr. 63:160–71 [Google Scholar]
  105. Swanson KS, Vester BM, Apanavicius CJ, Kirby NA, Schook LB. 105.  2009. Implications of age and diet on canine cerebral cortex gene transcription. Neurobiol. Aging 30:1314–26 [Google Scholar]
  106. Kil DY, Vester Boler BM, Apanavicius CJ, Schook LB, Swanson KS. 106.  2010. Gene expression profiles of colonic mucosa in healthy young adult and senior dogs. PLOS ONE 5:e12882 [Google Scholar]
  107. Kil DY, Vester Boler BM, Apanavicius CJ, Schook LB, Swanson KS. 107.  2010. Age and diet affect gene expression profiles in canine liver tissue. PLOS ONE 5:e13319 [Google Scholar]
  108. Grant RW, Vester Boler BM, Ridge TK, Graves TK, Swanson KS. 108.  2011. Adipose tissue transcriptome changes during obesity development in female dogs. Physiol. Genomics 43:295–307 [Google Scholar]
  109. Grant RW, Vester Boler BM, Ridge TK, Graves TK, Swanson KS. 109.  2013. Subcutaneous and gonadal adipose tissue transcriptome differences in lean and obese female dogs. Anim. Genet. 44:728–35 [Google Scholar]
  110. Grant RW, Vester Boler BM, Ridge TK, Graves TK, Swanson KS. 110.  2013. Skeletal muscle tissue transcriptome differences in lean and obese female beagle dogs. Anim. Genet. 44:560–68 [Google Scholar]
  111. Collison KS, Zaidi MZ, Saleh SM, Makhoul NJ, Inglis A. 111.  et al. 2012. Nutrigenomics of hepatic steatosis in a feline model: effect of monosodium glutamate, fructose, and trans-fat feeding. Genes Nutr. 7:265–80 [Google Scholar]
  112. Mori A, Kappen KL, Dilger AC, Swanson KS. 112.  2014. Effect of photoperiod on the feline adipose transcriptome as assessed by RNA sequencing (RNA-seq). BMC Vet. Res. 10:146 [Google Scholar]
  113. Fels L, Distl O. 113.  2014. Identification and validation of quantitative trait loci (QTL) for canine hip dysplasia (CHD) in German Shepherd dogs. PLOS ONE 9:e96618 [Google Scholar]
  114. Drögemüller M, Jagannathan V, Howard J, Bruggmann R, Drögemüller C. 114.  et al. 2014. A frameshift mutation in the cubilin gene (CUBN) in Beagles with Imerslund-Gräsbeck syndrome (selective cobalamin malabsorption). Anim. Genet. 45:148–50 [Google Scholar]
  115. Rigottier-Gois L, Le Bourhis AG, Gramet G, Rochet V, Doré J. 115.  2003. Fluorescent hybridization combined with flow cytometry and hybridization of total RNA to analyse the composition of microbial communities in human fecal samples using 16S rRNA probes. FEMS Microbiol. Ecol. 43:237–45 [Google Scholar]
  116. Richards JD, Gong J, de Lange CFM. 116.  2005. The gastrointestinal microbiota and its role in monogastric nutrition and health with an emphasis on pigs: current understanding, possible modulations and new technologies for ecological studies. Can. J. Anim. Sci. 85:421–35 [Google Scholar]
  117. Pinkel D, Straume T, Gray JW. 117.  1986. Cytogenetic analysis using quantitative, high-sensitivity, fluorescence hybridization. PNAS 83:2934–38 [Google Scholar]
  118. Liu WT, Marsh TL, Cheng H, Forney LJ. 118.  1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol. 63:4516–22 [Google Scholar]
  119. Schütte UME, Abdo Z, Bent SJ, Shyu C, Williams CJ. 119.  et al. 2008. Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities. Appl. Microbiol. Biotechnol. 80:365–80 [Google Scholar]
  120. Muyzer G, de Waal EC, Uitterlinden AG. 120.  1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes encoding for 16S rRNA. Appl. Environ. Microbiol. 59:695–700 [Google Scholar]
  121. Zoetendal EG, Akkermans ADL, de Vos WM. 121.  1998. Temperature gradient gel electrophoresis analysis from human fecal samples reveals stable and host-specific communities of active bacteria. Appl. Environ. Microbiol. 64:3854–59 [Google Scholar]
  122. Petrosino JF, Highlander S, Luna RA, Gibbs RA, Versalovic J. 122.  2009. Metagenomic pyrosequencing and microbial identification. Clin. Chem. 55:856–66 [Google Scholar]
  123. Weinstock GM. 123.  2012. Genomic approaches to studying the human microbiota. Nature 489:250–56 [Google Scholar]
  124. Suchodolski JS, Ruaux CG, Steine JM, Fetz K, Williams DA. 124.  2005. Assessment of the qualitative variation in bacterial microflora among compartments of the intestinal tract of dogs by use of a molecular fingerprinting technique. Am. J. Vet. Res. 66:1556–62 [Google Scholar]
  125. Simpson KW, Dogan B, Rishniw M, Goldstein RE, Klaessig S. 125.  et al. 2006. Adherent and invasive Escherichia coli is associated with granulomatous colitis in boxer dogs. Infect. Immun. 74:4778–92 [Google Scholar]
  126. Jergens AE, Pressel M, Crandell J, Morrison JA, Sorden SD. 126.  et al. 2009. Fluorescence in situ hybridization confirms clearance of visible Helicobacter spp. associated with gastritis in dogs and cats. J. Vet. Intern. Med. 23:16–23 [Google Scholar]
  127. Lubbs DC, Vester BM, Fastinger ND, Swanson KS. 127.  2009. Dietary protein concentration affects intestinal microbiota of adult cats: a study using DGGE and qPCR to evaluate differences in microbial populations in the feline gastrointestinal tract. J. Anim. Phys. Anim. Nutr. 93:113–21 [Google Scholar]
  128. Vester BM, Dalsing BL, Middelbos IS, Apanavicius CJ, Lubbs DC, Swanson KS. 128.  2009. Faecal microbial populations of growing kittens fed high- or moderate-protein diets. Arch. Anim. Nutr. 63:254–65 [Google Scholar]
  129. Jia J, Frantz N, Khoo C, Gibson GR, Rastall RA, McCartney AL. 129.  2011. Investigation of the faecal microbiota of kittens: monitoring bacterial succession and effect of diet. FEMS Microbiol. Ecol. 78:395–404 [Google Scholar]
  130. Biagi G, Cipollini I, Bonaldo A, Grandi M, Pompei A. 130.  et al. 2013. Effect of feeding a selected combination of galacto-oligosaccharides and a strain of Bifidobacterium pseudocatenulatum on the intestinal microbiota of cats. Am. J. Vet. Res 74:90–95 [Google Scholar]
  131. Xenoulis PG, Palculict B, Allenspach K, Steiner JM, Van House AM, Suchodolski JS. 131.  2008. Molecular-phylogenetic characterization of microbial communities imbalances in the small intestine of dogs with inflammatory bowel disease. FEMS Microbiol. Ecol. 66:579–89 [Google Scholar]
  132. Ritchie LE, Steiner JM, Suchodolski JS. 132.  2008. Assessment of microbial diversity along the feline intestinal tract using 16S rRNA gene analysis. FEMS Microbiol. Ecol. 66:590–98 [Google Scholar]
  133. Middelbos IS, Vester Boler BM, Qu A, White BA, Swanson KS, Fahey GC Jr. 133.  2010. Phylogenetic characterization of fecal microbial communities of dogs fed diets with or without supplemental dietary fiber using 454 pyrosequencing. PLOS ONE 5:e9768 [Google Scholar]
  134. Handl S, Dowd S, Garcia-Mazcorro JF, Steiner JM, Suchodolski JS. 134.  2011. Massive parallel 16S rRNA gene pyrosequencing reveals highly diverse fecal bacterial and fungal communities in healthy dogs and cats. FEMS Microbiol. Ecol. 76:301–10 [Google Scholar]
  135. Swanson KS, Dowd SE, Suchodolski JS, Middelbos IS, Vester BM. 135.  et al. 2011. Phylogenetic and gene-centric metagenomics of the canine intestinal microbiome reveals similarities with humans and mice. ISME J. 5:639–49 [Google Scholar]
  136. Barry KA, Middelbos IS, Vester Boler BM, Dowd SE, Suchodolski JS. 136.  et al. 2012. Effects of dietary fiber on the feline gastrointestinal metagenome. J. Proteome Res. 11:5924–33 [Google Scholar]
  137. Tun HM, Brar MS, Khin N, Jun L, Hui RK. 137.  et al. 2012. Gene-centric metagenomics analysis of feline intestinal microbiome using 454 junior pyrosequencing. J. Microbiol. Methods 88:369–76 [Google Scholar]
  138. Deusch O, O'Flynn C, Colyer A, Morris P, Allaway D. 138.  et al. 2014. Deep Illumina-based shotgun sequencing reveals dietary effects on the structure and function of the fecal microbiome of growing kittens. PLOS ONE 9:e101021 [Google Scholar]
  139. Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G. 139.  et al. 2012. Host-gut microbiota metabolic interactions. Science 336:1262–67 [Google Scholar]
  140. Oresic M. 140.  2009. Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction. Nutr. Metab. Cardiovasc. Dis. 19:816–24 [Google Scholar]
  141. Lei Z, Huhman DV, Summer LW. 141.  2011. Mass spectrometry strategies in metabolomics. J. Biol. Chem. 286:25435–42 [Google Scholar]
  142. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B. 142.  2005. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 82:497–503 [Google Scholar]
  143. Kind T, Scholz M, Fiehn O. 143.  2009. How large is the metabolome? A critical analysis of data exchange practices in chemistry. PLOS ONE 4:e5440 [Google Scholar]
  144. Wenk MR. 144.  2005. The emerging field of lipidomics. Nat. Rev. Drug Discov. 4:594–610 [Google Scholar]
  145. Hedrick VE, Dietrich AM, Estabrooks PA, Savla J, Serrano E, Davy BM. 145.  2012. Dietary biomarkers: advances, limitations, and future directions. Nutr. J. 11:109–22 [Google Scholar]
  146. Moco S, Bino RJ, De Vos RCH, Vervoont J. 146.  2007. Metabolomics technologies and metabolite identification. Trends Anal. Chem. 26:855–66 [Google Scholar]
  147. Pauling L, Robinson AB, Teranishi R, Cary P. 147.  1971. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. PNAS 68:2374–76 [Google Scholar]
  148. 148. Natl. Inst. Health 2003. The NIH Roadmap. Bethesda, MD: Natl. Inst. Health http://nihroadmap.nih.gov
  149. Zhang AH, Sun H, Han Y, Yan GL, Yuan Y. 149.  et al. 2013. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets. Anal. Chem. 85:7606–12 [Google Scholar]
  150. de Laeter JR, Böhlke JK, de Bièvre P, Hidaka H, Peiser HS. 150.  et al. 2003. Atomic weights of the elements: review 2000 (IUPAC Technical Report). Pure Appl. Chem. 75:683–800 [Google Scholar]
  151. Moco S, Tseng LH, Spraul M, Chen Z. 151.  2006. Building-up a comprehensive database of flavonoids based on nuclear magnetic resonance data. Chromatographia 64:503–8 [Google Scholar]
  152. Exarchou V, Godejohan M, van Beek TA, Gerothanassis IP, Vervoort J. 152.  2003. LC-UV-solid-phase and its application to the identification of compounds present in Greek oregano. Anal. Chem. 75:6288–94 [Google Scholar]
  153. Allaway D, Kamlage B, Gilham MS, Hewson-Hughes AK, Wiemer JC. 153.  et al. 2013. Effects of dietary glucose supplementation on the fasted plasma metabolome in cats and dogs. Metabology 9:1096–108 [Google Scholar]
  154. Deng P, Jones JC, Swanson KS. 154.  2014. Effects of dietary macronutrient composition on the fasted plasma metabolome of healthy adult cats. Metabology 10:638–50 [Google Scholar]
  155. de Godoy MRC, Pappan KL, Grant RW, Swanson KS. 155.  2015. Plasma metabolite profiling and search for biomarkers of metabolic dysfunction in dogs undergoing rapid weight gain. Curr. Metab. 3:1–19 [Google Scholar]
  156. Colyer A, Gilham MS, Kamlage B, Rein D, Allaway D. 156.  2011. Identification of intra- and inter-individual metabolite variation in plasma metabolite profiles of cats and dogs. Br. J. Nutr. 106:S146–S49 [Google Scholar]
  157. Beckmann M, Enot DP, Overy DP, Scott IM, Jones PG. 157.  et al. 2010. Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs. Br. J. Nutr. 103:1127–38 [Google Scholar]
  158. Zhang H, Patrone L, Kozlosky J, Tomlinson L, Cosma G, Horvath J. 158.  2010. Pooled sample strategy in conjunction with high-resolution liquid chromatography-mass spectrometry-based background subtraction to identify toxicological markers in dogs treated with ibipinabant. Anal. Chem. 82:3834–39 [Google Scholar]
  159. Musteata M, Nicolescu A, Solcan G, Deleanu C. 159.  2013. The 1H NMR profile of healthy dog cerebrospinal fluid. PLOS ONE 8:e81192 [Google Scholar]
  160. Hasegawa T, Sumita M, Horitani Y, Tamai R, Tanaka K. 160.  et al. 2014. Gas chromatography-mass spectrometry-based metabolic profiling of cerebrospinal fluid from epileptic dogs. J. Vet. Med. Sci. 76:517–22 [Google Scholar]
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