METHOD FOR DETERMINING THE STATE OF THE INTESTINAL MICROBIOME

Claims

1. A method for creating a trained regression model for determining the state of the gut microbiome in a subject, comprising: (a) obtaining gut metagenome data from a population of healthy subjects; and (b) training a regression model on the gut metagenome data, wherein the age of healthy subjects at the time of data collection is regressed on one or more carbohydrate-active enzyme (CAZyme) abundance values obtained from the gut metagenomic data.

2. The method according to claim 1, wherein the CAZymes are selected from glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE) and their associated carbohydrate-binding modules (CBM), wherein preferably the CAZymes are selected from one or more of: GH1, GH2, GH3, GH4, GH5, GH6, GH7, GH8, GH9, GH10, GH11, GH12, GH13, GH14, GH15, GH16, GH17, GH18, GH19, GH20, GH21, GH22, GH23, GH24, GH25, GH26, GH27, GH28, GH29, GH30, GH31, GH32, GH33, GH34, GH35, GH36, GH37, GH38, GH39, GH40, GH41, GH42, GH43, GH44, GH45, GH46, GH47, GH48, GH49, GH50, GH51, GH52, GH53, GH54, GH55, GH56, GH57, GH58, GH59, GH60, GH61, GH62, GH63, GH64, GH65, GH66, GH67, GH68, GH69, GH70, GH71, GH72, GH73, GH74, GH75, GH76, GH77, GH78, GH79, GH80, GH81, GH82, GH83, GH84, GH85, GH86, GH87, GH88, GH89, GH90, GH91, GH92, GH93, GH94, GH95, GH96, GH97, GH98, GH99, GH100, GH101, GH102, GH103, GH104, GH105, GH106, GH107, GH108, GH109, GH110, GH111, GH112, GH113, GH114, GH115, GH116, GH117, GH118, GH119, GH120, GH121,GH122, GH123, GH124, GH125, GH126, GH127, GH128, GH129, GH130, GH131, GH132, GH133, GH134, GH135, GH136, GH137, GH138, GH139, GH140, GH141, GH142, GH143, GH144, GH145, GH146, GH147, GH148, GH149, GH150, GH151, GH152, GH153, GH154, GH155, GH156, GH157, GH158, GH159, GH160, GH161, GH162, GH163, GH164, GH165, GH166, GH167, GH168, GH169, GH170, GH171, GH172, GT1, GT2, GT3, GT4, GT5, GT6, GT7, GT8, GT9, GT10, GT11, GT12, GT13, GT14, GT15, GT16, GT17, GT18, GT19, GT20, GT21, GT22, GT23, GT24, GT25, GT26, GT27, GT28, GT29, GT30, GT31, GT32, GT33, GT34, GT35, GT36, GT37, GT38, GT39, GT40, GT41, GT42, GT43, GT44, GT45, GT46, GT47, GT48, GT49, GT50, GT51, GT52, GT53, GT54, GT55, GT56, GT57, GT58, GT59, GT60, GT61, GT62, GT63, GT64, GT65, GT66, GT67, GT68, GT69, GT70, GT71, GT72, GT73, GT74, GT75, GT76, GT77, GT78, GT79, GT80, GT81, GT82, GT83, GT84, GT85, GT86, GT87, GT88, GT89, GT90, GT91, GT92, GT93, GT94, GT95, GT96, GT97, GT98, GT99, GT100, GT101, GT102, GT103, GT104, GT105, GT106, GT107,GT108, GT109, GT110, GT111, GT112, GT113, GT114, PL1, PL2, PL3, PL4, PL5, PL6, PL7, PL8, PL9, PL10, PL11, PL12, PL13, PL14, PL15, PL16, PL17, PL18, PL19, PL20, PL21, PL22, PL23, PL24, PL25, PL26, PL27, PL28, PL29, PL30, PL31, PL32, PL33, PL34, PL35, PL36, PL37, PL38, PL39, PL40, PL41, PL42, CE1, CE2, CE3, CE4, CE5, CE6, CE7, CE8, CE9, CE10, CE11, CE12, CE13, CE14, CE15, CE16, CE17, CE18, CE19, CBM1, CBM2, CBM3, CBM4, CBM5, CBM6, CBM7, CBM8, CBM9, CBM10, CBM11, CBM12, CBM13, CBM14, CBM15, CBM16, CBM17, CBM18, CBM19, CBM20, CBM21, CBM22, CBM23, CBM24, CBM25, CBM26, CBM27, CBM28, CBM29, CBM30, CBM31, CBM32, CBM33, CBM34, CBM35, CBM36, CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46, CBM47, CBM48, CBM49, CBM50, CBM51, CBM52, CBM53, CBM54, CBM55, CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, CBM65, CBM66, CBM67, CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86,CBM87 and CBM88., 3. The method according to claim 1 or 2, further comprising obtaining gut metagenome data from a population of healthy subjects, wherein preferably the gut metagenome data is or can be obtained from stool samples and / or by next generation sequencing.

4. The method according to any preceding claim, wherein the healthy subjects are infants and / or children, wherein the healthy subjects are preferably 0-5 years old, or 0-3 years old, or 0-2 years old, and more preferably the healthy subjects are 0-24 months old.

5. The method according to any preceding claim, wherein the regression model is a tree regression model, preferably a random forest regression model.

6. A trained regression model that is or can be obtained using the method in any of the preceding paragraphs.

7. A trained regression model to determine the state of the gut microbiome in a subject given one or more CAZyme abundance values derived from the subject's gut metagenome data.

8. A method for determining the state of the intestinal microbiome in a subject, comprising: (a) obtaining a trained regression model using the method of any one of claims 1 to 5 or a trained regression model according to claim 6 or 7; (b) obtaining gut metagenome data from the subject; and (c) determining whether a subject is an outlier or not in the trained regression model; wherein the state of the gut microbiome of the subject is healthy if the subject is not an outlier in the trained regression model, and / or wherein the state of the gut microbiome of the subject is not healthy if the subject is an outlier in the trained regression model.

9. A method for maintaining or improving the intestinal microbiome in a subject, comprising: (a) determining the state of the gut microbiome in a subject using the method of claim 8; and (b) adjusting the diet, nutrient intake, and / or lifestyle of the subject to maintain or improve the gut microbiome status of the subject.

10. The method of claim 9, wherein the subject is administered food and / or supplements to increase the prevalence of favorable CAZymes and / or decrease the prevalence of unfavorable CAZymes, wherein preferably the food and / or supplements comprise one or more carbohydrates, wherein more preferably the carbohydrates are selected from one or more of: 4-methylumbelliferyl-6-azido-6-deoxy-beta-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycan, O-antigen, O-glycan, acarbose, acetylated glucuronoxylan, agar, alduronate, alginate, alpha-galactoside, alpha-glucan, alpha-mannan, alpha-mannoside, arabinan, arabinoxylooligosaccharide, arabinofuranoligosaccharide, arabinofuranose, arabinogalactan, arabinose, arabinoxylan, beta-galactooligosaccharide, beta-galactoside, beta-glucan, beta-glucoside, beta-mannan, capsular polysaccharide, carboxymethylcellulose, carrageenan, cellobiose, cellodextrin, cellooligosaccharide,cellotriose, cellulose, chitin, chitobiose, chitooligosaccharide, chondroitin disaccharide, chondroitin sulfate, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran, emulsan, exopolysaccharide, fructan, fructooligosaccharide, fucose, fucosyllactose, galactan, galactomannan, galactooligosaccharide, galactose, galacturonic acid, gentiobiose, glucan, glucomannan, glucosamine, glucose, glycogen, glycosaminoglycan, hemicellulose, heparosan, homogalacturonan, host glycan, human breast milk oligosaccharide, inulin, isomaltose, isomaltotriose, kestose, lacto-n-triose, lactose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharide, maltodextrin, maltooligosaccharide, maltose, maltotriose, mannan, mannooligosaccharide, mannose, melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer central capsular polysaccharide, panose, pectin, plant polysaccharide,polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharide, raffinose, rhamnogalacturonan, rhamnose, ribose, sialic acid, sialoglycoconjugate, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronate disaccharide, xanthan, xylan, xylobiose, xylodextrin, xyloglucan, xylooligosaccharide, xylose and xylotriose., 11. A data processing system comprising a means for implementing the method according to any one of paragraphs 1–5 or 8.

12. A computer program containing instructions that, when executed by a computer, cause the computer to perform the method according to any of paragraphs 1–5 or 8.

13. Using one or more CAZyme abundance values obtained from the subject's gut metagenome data to predict the subject's age or determine the state of the subject's gut microbiome.

14. Using one or more CAZyme abundance values obtained from gut metagenome data from a population of healthy subjects to train a regression model.

15. Using the trained regression model of paragraph 6 or 7 to predict the age of a subject or determine the state of the gut microbiome of a subject.