Determining nutrients for animals through gene expression

• US 20100151062A1
• Filed: 12/16/2008
• Published: 06/17/2010
• Est. Priority Date: 12/16/2008
• Status: Abandoned Application

• Current Assignee: Bruno Stefanon
• Original Assignee: Bruno Stefanon
• Inventors: Stefanon, Bruno
• Application Number:
  US12/316,877
• Publication Number:
  US 20100151062A1
• Time in Patent Office: Days
• US Class Current: 424/737
• CPC Class Codes: A61K 31/12 Ketones
• CPC Class Codes: A61K 31/341 not condensed with another ring, e.g. ranitidine, furosemide, bufetolol, muscarine
• CPC Class Codes: G01N 33/48 Biological material, e.g. blood, urine G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence; Haemocytometers counting blood corpuscules distributed over a surface by scanning the surface G06M11/02

Abstract

Genetic information of DNA polymorphism, the functional genomic profile and the different response of an animal to a biologically active nutrient are used to identify the biologically active nutrient composition of the diet for an animal. The response to a nutrient exposure is dynamic since it depends upon the polymorphism of nutritionally inducible genes (as SNPs) that can lead to a different effect of the same nutrient in animals having different genotypes. The assessment of the biologically active nutrient composition of the diet arises from using reference data relating to normal healthy animals with different genotypes, target data relating to unhealthy animals with different genotypes, and nutritional data relating to a different effect of biologically active nutritional compounds in animals with different genotypes. Analysis is affected by relating gene, protein or metabolite expression.

Claims

1. A method of identifying a biologically active nutrient for an individual animal having a genotype, comprising:

(a) using a “

reference”

dataset containing functional genomic profiles of biological samples of the genotypes of different animals of the species, the different animals being healthy animals;

(b) selecting a “

target”

dataset containing the functional genomic profile of biological samples of the genotypes of different animals, the different animals being unhealthy animals;

(c) using a “

biologically active nutrient”

dataset comprising different effects of biologically active nutritional components on functional genomic profiles of the different animals of different genotypes from those of the target group (b), the different genotypes being differently responsive to the same biologically active nutritional components;

(d) having the reference dataset or target dataset include an individual animal for which the biolologically active nutrient is to be identified; and

(e) relating at least one of the “

reference”

or “

target group”

datasets with the “

biologically active nutrient”

dataset to identify a biologically active nutrient for the selected animal genotype to prevent, treat, control, or modulate a state of physiological homeostasis or pathophysiological condition of the individual animal in the reference dataset or target group.

2. A method as claimed in claim 1 wherein the identification is based on the molecular dietary signature being the expression of a gene or a set of genes which may differ for the genotypes of different animals of the same species, and the nutrient identification includes the molecular dietary signature that the biologically active nutrient induces in the individual animal.

3. A method as claimed in claim 1 wherein the animal is either a canine or a feline;

the canine or feline is from the group consisting of one or more of breed type, specific breed, chronological age, physiological age, activity level, healthy, and unhealthy.

4. A method as claimed in claim 2 wherein the animal is either a canine or a feline;

the canine or feline is from the group consisting of one or more of breed type, specific breed, chronological age, physiological age, activity level, healthy, and unhealthy.

5. A method as claimed in claim 1 wherein the unhealthy animal includes a condition of at least one of autoimmunity, anxiety, arthritis, depression, variable body condition score, immune suppression, or inflammation.

6. A method as claimed in claim 2 wherein the unhealthy animal includes a condition of at least one of autoimmunity, anxiety, arthritis, depression, variable body condition score, immune suppression, or inflammation.

7. A method as claimed in claim 1, wherein the data of the animal is one or more data items related to genotype, selected from the group consisting of breed, breed(s) of parents, pedigree, sex, coat type, and evident hereditary conditions and disorders and a physiological condition is selected from the group consisting of age, weight, veterinary medical history, reproductive history, present health or unhealthy state, appetite, physical activity level, mental acuity, behavioural abnormalities and disposition.

8. A method as claimed in claim 2, wherein the data of the animal is one or more data items related to genotype, selected from the group consisting of breed, breed(s) of parents, pedigree, sex, coat type, and evident hereditary conditions and disorders and a physiological condition is selected from the group consisting of age, weight, veterinary medical history, reproductive history, present health or unhealthy state, appetite, physical activity level, mental acuity, behavioural abnormalities and disposition.

9. A method as claimed in claim 1, wherein the reference dataset includes data selected from group of animals with different genotypes in physiological homeostasis and include DNA, RNA, proteins, metabolites and biomarkers.

10. A method as claimed in claim 2, wherein the reference dataset includes data selected from group of animals with different genotypes in physiological homeostasis and include DNA, RNA, proteins, metabolites and biomarkers.

11. A method as claimed in claim 1, wherein the target dataset includes data selected from groups of animals with different genotypes in non physiological homeostasis and include DNA, RNA, proteins, metabolites and biomarkers.

12. A method as claimed in claim 2, wherein the target dataset includes data selected from groups of animals with different genotypes in non physiological homeostasis and include DNA, RNA, proteins, metabolites and biomarkers.

13. A method as claimed in claim 1, wherein the biologically active nutrient dataset includes data selected from groups of animals with different genotypes, the different genotypes being responsive differently to the same biologically active nutritional components, and include DNA, RNA, proteins, metabolites and biomarkers.

14. A method as claimed in claim 2, wherein the biologically active nutrient dataset includes data selected from groups of animals with different genotypes, the different genotypes being responsive differently to the same biologically active nutritional components, and include DNA, RNA, proteins, metabolites and biomarkers.

15. A method as claimed in claim 1, wherein the identified biologically active nutrient is a food, a supplement, a nutraceutical selected to promote wellness by enhancing an aspect of health of one or more animals and wherein wellness is promoted by preventing, attenuating or eliminating at least one unhealthy state in one or more animals.

16. A method as claimed in claim 2, wherein the identified biologically active nutrient is a food, a supplement, a nutraceutical selected to promote wellness by enhancing an aspect of health of one or more animals and wherein wellness is promoted by preventing, attenuating or eliminating at least one unhealthy state in one or more animals.

18. A biolologically active nutrient prepared as a result of the identified biologically active nutrient, achieved by the method of claim 1.

19. A biolologically active nutrient prepared as results of the identified biologically active nutrient, achieved by the method of claim 2.

17. A method of identifying a biologically active nutrient for animals, comprising:

(a) using a “

reference”

dataset containing functional genomic profiles of biological samples of the genotypes of different animals of the species, the different animals being healthy animals;

(b) selecting a “

target group”

dataset containing the functional genomic profile of biological samples of the genotypes of different animals, the animals being unhealthy animals;

(c) using a “

biologically active nutrient”

dataset comprising different effects of biologically active nutritional components on functional genomic profiles of the different animals of different genotypes from those of the target group (b), the different genotypes being differently responsive to the same biologically active nutritional components;

(d) having the reference group or target group include the animals; and

(e) relating at least one of the “

reference”

or “

target group”

datasets with the “

biologically active nutrient”

dataset to identify a biologically active nutrient for the selected animal genotypes to prevent, treat, control, or modulate a state of physiological homeostasis or patho-physiological condition of the animal in the reference dataset or target group, and analyzing the gene or protein expression or the metabolite expression in the biological samples of the target dataset.

20. A biolologically active nutrient prepared as results of the identified biologically active nutrient, achieved by the method of claim 17.