WO2022079320A1 - Treatment or prevention of hepatic metabolic conditions associated with diabetes - Google Patents

Abstract

A whole (1→3)-β-D-glucan particle for treating or preventing a hepatic metabolic condition associated with obesity or diabetes in a subject is provided. The method comprises administering an effective amount of a whole (1→3)-β-D-glucan particle to said subject. The glucan is β-1,6 branched β-1,3 glucan.

Description

Title of the invention

Treatment or prevention of hepatic metabolic conditions associated with diabetes.

Field of the invention

The current invention relates to treatment or prevention of a metabolic condition comprising administering p-glucan, particularly p-(1— >3)-glucan, to a subject. In particular, the invention relates to treatment or prevention of a metabolic condition. The common metabolic condition is ‘fatty liver’ or non-alcoholic fatty liver disease (NAFLD), associated with obesity and/or diabetes.

Background of the invention

Nonalcoholic fatty liver disease (NAFLD) currently impacts 25% of adults, typically they seem healthy, apart from carrying extra central body weight. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology 67, 123-133, doi:10.1002/hep.29466 (2018).). NAFLD often precipitates, type 2 diabetes (T2D), which accounts for 90-95% of diagnosed diabetes cases which affected more than 500 million people worldwide in 2018 (Centers for Disease Control and Prevention, 2017; Kaiser et al., 2018). NAFLD is one of the leading causes for liver transplantation and hepatocellular carcinoma worldwide (Estes, C., Razavi, H., Loomba, R., Younossi, Z. & Sanyal, A. J). Diabetes and NALFD are both highly prevalent due to poor diet (high-fat and sugar diets), inactive lifestyles and obesity. There is a lot of heterogeneity (variability) with respect to the impact of obesity on NALFD and T2D risk, which is ascribed to differences between individuals. Some of this variation is likely due to the gut microbiome, which has emerged in the last decade as an additional environmental risk factor for T2D.

There is no effective drug to treat NAFLD. Diet-induced weight loss prevents and resolves NAFLD, however compliance and long-term maintenance of weight loss is very difficult. Metformin is the standard first-line drug for the treatment of T2D (that co-exists with NAFLD), an effective therapeutic, lowering blood glucose concentrations, without weight gain, low risk for triggering hypoglycemia, and potential cardiovascular benefit. However up to 25% of patients experience gastrointestinal intolerance with approximately 5% of patients discontinuing the therapy (Dujic et al., 2015). The use of oral anti-diabetic alternative drugs is limited due to common side effects such as weight gain and hypoglycemia. Thus, the search for new therapeutic targets is still ongoing, aiming to not only address blood glucose levels, but also to reduce T2D-associated complications especially obesity.

An increasing important alternative to drug therapy is a dietary intervention. Both NAFLD and T2D respond well to diet therapy that attains weight loss, however compliance and therefore efficacy can be variable - thus functional foods that can enhance efficacy is a key innovation. High-fat and high-sugar diets, particularly saturated fatty acids (SFA), which are highly abundant in processed foods, specifically drive metabolic disease, inflammation and insulin resistance - key elements common to NAFLD and T2D (Ralston et al., 2017). Modifying dietary fatty acids composition greatly impacts metabolic inflammation. The replacement of SFA with monounsaturated fatty acids (MU FA) reduces inflammation and improves metabolism by improving insulin resistance (Finucane et al., 2015). Long chain n-3 polyunsaturated fatty acids (PUFA) has proven to decrease hepatic steatosis (fat accumulation) a key characteristic of NAFLD (Rosqvist et al., 2014).

The human gut microbiota interacts with host metabolism in conditions including obesity, insulin resistance, NAFLD and/or T2D through several effector metabolites derived from the microbiome including lipopolysaccharide, short-chain fatty acids, bile acids and branched chain amino acids. Peripheral insulin sensitivity in subjects with metabolic syndrome can be ameliorated by fecal microbiota transplant from a healthy donor (Vrieze A, et al., Gastroenterology. 2012 Oct;143(4):913-6. 2012 Jun 20), and incipient T2D is characterized by loss of the diurnal rhythmicity of the gut microbiome that occurs in metabolically healthy subjects (Sandra Reitmeier et al., Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes Cell Host Microbe, 2020 Aug 12;28(2):258-272. 020 Jul 2). This altered gut microbiome is associated with different metabolic capacity, suggesting that altering gut derived microbiome metabolites is a potential therapeutic target for the treatment of NAFLD and diabetes. The key microbiome differences in T2D appear to be functional changes rather than compositional changes (Forslund et al., “Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota”, Nature. 2015 Dec 10;528(7581):262-266 2015 Dec 2).

P-glucans are naturally occurring polysaccharides found in the cell walls of cereals, bacteria, and fungi. Typically, p-glucans form a linear backbone with 1-3 p-glycosidic bonds but vary with respect to molecular mass, solubility viscosity, branching structure and gelation properties. The most common forms of p-glucans are those comprising D-glucose units with P-1 ,3 links. P(1 ,3)D-glucan for supplement use is derived from the cell wall of baker's yeast (Saccharomyces cerevisiae). p-glucans found in the cell walls of yeast contain a 1 ,3 carbon backbone with elongated 1 ,6 carbon branches. Yeast derived (1^3)-p-Dglucan functionality are specifically focused on here.

Cereal fibers are a rich source of p-glucan. Their structure and function are very different to yeast. They lack the metabolic - inflammatory co-regulation capability (De Marco Castro, (Nutr Food Res. 2021 Jan;65(1): 2021). Oat derived p-glucan fibers have antidiabetic effect, reduce blood glucose, lower cholesterol levels, and improve hepatic lipid/cholesterol metabolism (Aoki et al., 2015; Kusmiati and Dhewantara, 2016; Silva Vde et al., 2015). Consumption of barley p-glucan by patients at high risk for metabolic syndrome lowered plasma cholesterol and elevated abundance of Bifidobacterium spp. and Akkermansia municiphila suggesting that the gut microbiota modulated a diet-induced metabolic response (Velikonja et al., 2019). Cereal p-glucans are linear (no branches) with 1 ,3 and/or 1 ,4 glycosidic bonds.

Other p-glucans sourced from fungus and yeasts have novel potential health effects - albeit less understood. Aoki et al., discloses the use of Aureobasidium pullulans (fungus)-derived p- glucan (AP-PG) consisting of a p-(1 ,3)-linked glucose main chain and p-(1 ,6)-linked glucose branches. The AP-PG glucan described has a branching structure and linkage pattern different from glucans derived from Saccharomyces cerevisiae (Tada et al, 2008 Glycoconjugate Journal 25:851-861). Mushroom/fungus derived p 3/1 ,6 glucans are in an entirely soluble form (not a WGP) and of low molecular weight. The beta 1-,6 branches of mushroom beta glucans are quite short (1-2 glucose molecules) and can be either 1 ,6 or 1 ,3 linked. The beta glucans also have a high degree of branching (5-12%). Therefore, experts in the field would expect beta glucan from A. pullulans to have different biological activity than beta glucan from Saccharomyces cerevisiae.

Kusmiati and Dhewantara (2016) disclose S. cerevisiae (Brewer’s yeast) (1^3)-p-D-glucan and Silva Vde et al., discuss p-glucans isolated from Saccharomyces cerevisiae (Brewer’s yeast) (1^3)-p-D-glucan.

Feeding yeast p-glucan promoted glycogen synthesis, reduce inflammation, and increased the proportion of Akkermansia in high-fat diet induced obese /T2D mouse model (Cao et al., Mol Nutr Food Res, 2016, 60, 2678-26902016). Importantly, this group used very high doses of a small low (25kDa) molecular weight, i.e. linear, p-glucan, at which induced concomitant body weight loss with supplementation, which would also explain the observed health effects ascribed to the p-glucan. As obesity was attenuated due to excessive fibre/glucan supplementation., theweight loss would resolve the phenotype. Hence the results could not be solely ascribed to the 25kDa p-glucan.

Guilarducci J.S., et al., (Diabetology and Metabolic Syndrome, 2020, vol. 2) discloses a p- glucan isolated from S. cerevisiae and its use on inflammatory and metabolic parameters of rats induced to diabetes by streptozotocin. The authors state that the glucan reduced blood glucose as well as serum total cholesterol. A reduction in hepatic enzymes ALT and AST was seen as well as an indication that the immune response was modulated in view of a reduction in TNF-a. In contrast, the current invention uses a humanized model of microbiome and high- fat diet induced NAFLD. Mitchelson K.A.J. et al., discloses feeding of (1-3) p-D-glucan specifically resolved human obese diabetic microbiome and high-fat diet (HFD) induced hepatic steatosis in obese/type 2 diabetic mice. The authors investigated the different metabolic properties of human gut microbiome transplantation, derived from obese healthy microbiome (OBH) and obese diabetic microbiome (OBD) subjects on hepatic health and metabolism. The authors concluded that the microbiome source specifically alters glucose, insulin, hepatic fatty acid and cholesterol metabolism following human OBD microbiome transplantation plus high-fat diet, which p-glucan supplementation specifically alleviated these adverse health effects, despite concomitant obesity. The authors speculate that this may provide specific protection from high-fat diet and/or microbiome induced hepatosteotosis (fatty liver) (K.A.J. Mitchelson, et al., (Moderated Oral Presentation at the 2020 UCD Conway Festival of Research and Innovation in Dublin, Ireland, October 2020), K. A. J. Mitchelson, et al., (Poster Presentation at the European and International Congress on Obesity in Dublin, Ireland, September 2020), K.A.J. Mitchelson, et al., (Poster Presentation at U21 Health Sciences Group Annual Meeting in Dublin, Ireland, August 2020), K.A.J. Mitchelson, et al., (Poster Presentation al 13^th European Nutrition Conference in Dublin, Ireland, October 2019) and K.A.J. Mitchelson, et al..(Published Abstract. Proceedings of the Nutrition Society (2020)).

Cao et al. , (Argricultural and food chemistry, 2017, 65, 9665-9674) discloses the effect of orally administered baker’s yeast glucan (BYG) on glucose and lipid homeostasis in the livers of mice. The authors state that BYG decreased blood glucose and hepatic glucose. The authors conclude that BYG could be beneficial for regulating glucose and lipid homeostasis in diabetic mice. As stated by the authors, the glucan is a linear glucan with an average molecular weight of ~25KDa and so it is not a whole glucan particle.

It is an object of the invention to overcome at least one of the above-referenced problems and provide a method for treatment and prevention of obesity and diabetes related metabolic conditions in a subject.

Summary of the invention

The current inventors demonstrated that Wellmune supplementation improved glucose and insulin metabolism, hepatic lipid metabolism and inflammation, induced as a result of feeding high-fat diets (HFD) in the presence of a human obese diabetic microbiome without requiring weight loss, thus improving risk of NAFLD and T2D, despite obesity. The inventors have identified novel mechanisms, via proteomic and GeneSeq approaches, that underpin these health effects which are mediated by altering metabolic-inflammation in the liver.

The current inventors have surprisingly discovered that p-glucan, in particular (1— >3)-p-D- glucan, alleviated NAFLD and diabetic-like perturbations, such as improved glucose, insulin, hepatic fatty acid and cholesterol metabolism, in a mouse model of the human obese diabetic microbiome. The inventors discovered that the use of p-glucan administration is surprisingly effective against NAFLD in particular. This effect and the molecular mechanisms underpinning this effect have not been previously disclosed in the prior art.

Despite equal total body weight gain in response to high-fat diet (HFD), the diabetic microbiome induced a much more severe phenotype than a human obese healthy (OBH) microbiome, p-glucan supplementation resolved the obese diabetic (OBD) microbiome phenotype, providing protection from HFD induced hepatic steatosis in a humanized T2D setting. Hepatic metabolic-inflammation was greatly impacted by the obese diabetic microbiome, but this adverse high-fat diet induced phenotype was specifically reconfigured by supplementation. This functionality of p-glucan was not evident following OBH microbiome transplantation- showing different efficacy based on different human sub-groups of obesity, with/without diabetes. It is a clear example of precision nutrition I precision medicine.

The glucan of the current invention was found to reduce fasting insulin levels, reduce insulin resistance, reduced hepatic triacylglycerol (TAG) and cholesterol accumulation and reduced hepatic metabolic dysfunction. It may be used for one or more of these effects. Notably, p- glucan supplementation elicited health effects, and specifically for NAFLD, without associated body weight loss. All work on the prior art is associated with concomitant weight loss and does not work in a humanized microbiome induced context.

Importantly the novel mechanisms underpinning diet I microbiome induced changes in metabolism and hepatic metabolic health were revealed by performing hepatic MS-based proteomics. Specifically, the most significant pathways that were upregulated following p- glucan supplementation were EIF2 signaling and tRNA charging while sirtuin signaling pathway was downregulated, suggesting that oxidative phosphorylation and fatty acid metabolism were altered with the addition of p-glucan to the HFD.

Additionally, GeneSeq analysis demonstrated that p -glucan supplementation modulated key transcription factors in the liver that regulated lipid metabolism and inflammation, namely PPARa, LXR/RXR, chREBP in concert with STAT3, via TGF signalling pathway modulation. The latter three targets being completely new and not reported thus far in terms of novel mechanisms.

Prior to the current invention, no groups determined the dual impact of obese diabetic microbiome plus high fat feeding in the presence of p glucan supplementation on hepatic lipid and insulin metabolism and microbiome composition, in a weight stable but obese context. This is very important as most weight loss regimes fail - therefore the current inventors provide an alternative wherein metabolic-inflammation can be improved/remediated despite high-fat diet indued obesity. Thus, this approach attenuates the impact of the most important instigator of NAFLD and T2D.

The current invention provides a method for treating or preventing a metabolic condition associated with obesity or diabetes in a subject, said method comprising administering an effective amount of (1— >3)-p-D-glucan particle (herein referred to as “glucan of the invention”) to said subject.

In an embodiment, the metabolic condition is a hepatic condition. The metabolic condition is NAFLD. Typically, diabetes is type 2 diabetes (T2D).

In an embodiment, the (1— >3)-p-D-glucan is a p-1 ,6 branched p-1 ,3 glucan (or “P-(1 ,3/1 ,6)) or a p-1 ,4 branched p-1 ,3 glucan.

Preferably, the (1^3)-p-D-glucan glucan is a p-1 ,6 branched p-1 ,3 glucan and has the following structure: p (1-3)-linked Branch

OH OH OH OH OH

” - p (1-3)-linked Backbone - -

In an embodiment, the 1 ,6 linked side chains of the glucan of the invention are in the range of 3-6 glucose molecules, e.g. 4-5 glucose molecules.

The glucan of the invention may have a degree of branching of from 3 to 5%, e.g. 4%.

In an embodiment, the glucan of the invention is a whole glucan particle (WGP) In an embodiment, the WGP has a size of from about 1 to about 6 microns (or pm), or from about 2 to about 5 microns, or from about 3 to about 4 microns. Typically, the WGP is insoluble.

In an embodiment, the glucan is from yeast, preferably from baker’s yeast (Saccharomyces cerevisiae). Typically, the glucan is one derived or obtained from the cell wall of baker’s yeast.

In an embodiment, the metabolic condition is a hepatic metabolic condition or dysfunction. The hepatic condition may be hepatic metabolic inflammation or hepatic steatosis.

The metabolic condition may be NAFLD. The NAFLD may be non-alcoholic steatohepatitis (NASH).

In an embodiment, the metabolic condition is hepatic insulin resistance.

In an embodiment, the subject may be an overweight or obese individual, a diabetic, typically a type 2 diabetic, or may be an obese diabetic (OBD) individual or overweight diabetic individual. Typically, said individual comprises an obese diabetic microbiome. Typically, said individual is on or ingesting a high fat diet. Typically, an overweight individual has BMI over >25 to 29 and an obese individual has a BMI of greater than or equal to 30.

In an embodiment of the invention, the subject is one that is not overweight or obese. The subject is one with a BMI of <25. The subject may be one without diabetes.

In the method of the invention, the glucan of the invention is administered in combination with a high fat diet. This reflects the habitual diet of most Western populations with a prevalence of obesity, meaning that it has applicability to the vast majority of obese people susceptible to NAFLD and T2D.

In an embodiment, the glucan of the invention is provided as or formulated as a dietary or food supplement comprising the glucan of the invention. It may be a food supplement enriched with the glucan of the invention. The supplement may be selected from the group comprising, but not limited to, tablets, capsules, gummies, and powders, beverages/drinks and energy bars.

In an embodiment, the food supplement comprises > 75% beta 1 ,3/1 ,6 glucan on a dry weight basis.

In an embodiment, the supplement is Wellmune supplement.

Typically, the supplement comprises > 75% beta 1 ,3/1 ,6 glucan on a dry weight basis, <3.5% protein, <10% fat, <3% ash, <8% moisture, <0.1mg/kg mercury, <0.5mg/kg lead, <1.0 mg/kg arsenic, and <1.0 mg/kg cadmium. The current invention provides a method for altering the human microbiome of a subject, said method comprising administering an effective amount of (1— >3)-p-D-glucan of the invention to said subject.

In an embodiment, the human microbiome is altered to a microbiome functionally equivalent to a non-diabetic individual microbiome, i.e. , an obese healthy phenotype.

The functionality of (1— >3)-p-D-glucan is dependent upon the nature of the obese human microbiome, e.g., the presence of obese diabetic microbiome (OBD).

In one embodiment, the method comprises a first step of identifying a subject suitable for the treatment. This step may involve identifying a subject with an obese diabetic microbiome.

In an aspect, the current invention provides a method for treating or preventing NALFD and/or hepatic insulin resistance I inflammation in a subject, said method comprising administering an effective amount of (1— >3)-p-D-glucan of the invention to said subject.

An aspect of the current invention provides a method for treating or preventing NAFLD and diabetes, in particular T2D, in a subject, said method comprising administering an effective amount of (1— >3)-p-D-glucan of the invention to said subject. Preferably, the subject is obese.

The current invention provides a method for treating obesity in a subject, said method comprising administering an effective amount of (1— >3)-p-D-glucan of the invention to said subject.

In an aspect, the current invention provides the glucan of the invention for use in a method for treating or preventing a metabolic condition in a subject. The metabolic condition is one associated with diabetes or obesity. The metabolic condition may be a hepatic metabolic condition as disclosed herein.

In an aspect, the current invention provides the glucan of the invention for use in a method for treating or preventing NALFD in a subject.

In an aspect, the current invention provides the glucan of the invention for use in a method for treating or preventing diabetes, in particular T2D, in a subject. Preferably, the subject is obese.

In an aspect, the current invention provides the glucan of the invention for use in a method for treating or preventing obesity in a subject.

The glucan of the invention for use in the methods may be that disclosed herein in relation to the method(s) of the invention. The subject may be that disclosed herein in relation to the method(s) of the invention. The embodiments or features disclosed in relation to the methods of the invention may also apply to the medical use of the invention.

Definitions and general preferences

Where used herein and unless specifically indicated otherwise, the following terms are intended to have the following meanings in addition to any broader (or narrower) meanings the terms might enjoy in the art:

Unless otherwise required by context, the use herein of the singular is to be read to include the plural and vice versa. The term "a" or "an" used in relation to an entity is to be read to refer to one or more of that entity. As such, the terms "a" (or "an"), "one or more," and "at least one" are used interchangeably herein.

As used herein, the term "comprise," or variations thereof such as "comprises" or "comprising," are to be read to indicate the inclusion of any recited integer (e.g. a feature, element, characteristic, property, method/process step or limitation) or group of integers (e.g. features, element, characteristics, properties, method/process steps or limitations) but not the exclusion of any other integer or group of integers. Thus, as used herein the term "comprising" is inclusive or open-ended and does not exclude additional, unrecited integers or method/process steps.

As used herein, the term “disease” or “condition” is used to define any abnormal condition that impairs physiological function and is associated with specific symptoms. The term is used broadly to encompass any disorder, illness, abnormality, pathology, sickness, condition or syndrome in which physiological function is impaired irrespective of the nature of the aetiology (or indeed whether the aetiological basis for the disease is established). It therefore encompasses conditions arising from infection, trauma, injury, surgery, radiological ablation, poisoning or nutritional deficiencies.

In this context the “condition” to be treated or prevented is a metabolic condition or disease associated with obesity and diabetes.

As used herein, the term "treatment" or "treating" refer to an intervention (e.g. the administration of an agent to a subject) which cures, ameliorates or lessens the symptoms of a condition or disease or removes (or lessens the impact of) its cause(s). In this case, the term is used synonymously with the term “therapy”. It can be manifested by a permanent or temporary improvement in the subject's condition. In this context it includes limiting and/or reversing disease progression. As used herein the terms "prevention" or "preventing" refer to an intervention (e.g. the administration of an agent to a subject), which prevents or delays the onset or progression of a condition, or the severity of a condition in a subject, or reduces (or eradicates) its incidence within a treated population.

When used herein, the term “composition” should be understood to mean something made by the hand of man, and not including naturally occurring compositions. Compositions may be formulated in unit dosage form, i.e. , in the form of discrete portions containing a unit dose, or a multiple or sub-unit of a unit dose.

The term "symptom" is defined as an indication of disease, illness, injury, or that something is not right in the body.

As used herein, the term “effective amount or a therapeutically effective amount” as applied to the glucan of the invention defines an amount that can be administered to a subject without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit/risk ratio, but one that is sufficient to provide the desired effect. The amount will vary from subject to subject, depending on the age and general condition of the individual, mode of administration and other factors. Thus, while it is not possible to specify an exact effective amount, those skilled in the art will be able to determine an appropriate "effective" amount in any individual case using routine experimentation and background general knowledge. A therapeutic result need not be a complete cure. A therapeutic result may be a permanent or temporary improvement in the subject’s condition.

The term “subject” means a human or animal, more typically a mammal. In one aspect, the subject is a human.

The term “microbiome” refers to the community of microorganisms, including bacteria, fungi and viruses, that inhabit a particular environment, such as living in or on the human body. One example is the human gut microbiome.

The term “metabolic syndrome” is the combination of obesity, NAFLD, diabetes and hypertension.

“B-glucans” are polysaccharides found inside the cell wall of bacteria and fungus. They are glucose (D-glucose) polymers linked together by a 1^3 liner p-glycosidic chain core and differ from each other by their length and branching structures. The branches derived from the glycosidic chain core are variable and the two main groups of branching are 1^4 or 1 -^6 glycosidic chains. When used herein the term “(1— >3)-p-D-glucan” is a glucan comprising D-glucose units with P-1 ,3 links.

“P-1 ,6 branched p-1 ,3 glucan (or “P-(1 ,3/1 ,6)) is composed of a backbone of glucose molecules linked via unique chemical links pi ,3 chains to which are attached to glucose chains via p 1 ,6 links.

When used herein the term “obese healthy” (OBH) individual refers to an individual, typically a human, having a BMI>30 with a fasting glucose level of <6mmol/l and HbA1C of <42 mmol/mol. Fasting glucose levels and HbA1C are calculated using methods known in the art.

When used herein the term “obese diabetic” (OBD) individual refers to an individual, typically a human, having a BMI>30 with a fasting glucose >7 mmol/l fasting HbA1C of >48 mmol/mol. Fasting glucose levels and HbA1C are calculated using methods known in the art.

When used herein the term “obese diabetic human microbiome” refers to a microbiome of an individual classified as OBD. Typically, the subject has type 2 diabetes. An example of a method to determine an obese diabetic microbiome is provided in Thingholm et al., 2019 (Cell Host & Microbe, 26, 252-264).

When used herein the term “obese healthy human microbiome” refers to a microbiome of an individual classified as OBH. Typically, the subject is obese but not type 2 diabetic, i.e. , obese but metabolically healthy.

Brief description of the Figures

The invention will be described with reference to the following Figures in which;

Figure 1 : The taxonomic composition and the functional pathways of the human gut microbiome associate with diabetic and metformin treatment. (A) Principle coordinates analysis plots based on unweighted and weighted UniFrac distances grouped by T2D and metformin treatment. The significant differences between groups were calculated by permutational multivariate analysis of variance (PERMANOVA) tests. (B) The gut microbiota composition of obese adults with T2D versus healthy were characterized by 16S rRNA gene amplicon sequencing and shotgun metagenomics. (C) Significantly differentially abundant bacterial taxa and functional pathways of shotgun data were identified by Linear Discriminant Analysis Effect Size (LEfSe) analysis (p < 0.05, Mann-Whitney U test; and LDA score > 2.0).

Figure 2: Mice that received obese diabetic microbiome displayed altered metabolic phenotype and increased hepatic lipid accumulation (A) Experimental design for murine intervention study. Conventional mice (N=8-9) were inoculated with human gut microbiota from either an obese healthy(OBH) or an obese T2D (OBD) subject at week 0 for three days (HGM). Arrows indicate the week on which the faecal samples were collected. AFG, antifungal gavage; GTT, glucose tolerance testing; ITT, insulin tolerance testing. (B) Response to glucose, insulin and glucose stimulated insulin secretion following HFD. (C) HOMA-IR (D) Hepatic TAG and cholesterol (E) Markers of hepatic lipid metabolism were assessed by RT- PCR with 18S as the appropriate housekeeping gene. (F) Hepatic stress was measured by ALT enzymatic levels. Citrate and lactate levels are shown in (G) and (H) respectively, *p<0.05,**p<0.01 , ***p<0.001 W.R.T. OBD vs OBH, #p<0.05 WRT OBD vs OBD+pG, $p<0.05 WRT OBD vs OBH+pG, + p<0.05, +++p<0.001WRT OBH vs OBH+pG, ¥¥¥ p<0.001 WRT OBH vs OBD+pG

Figure 3: Body weight and food uptake. All groups displayed equal body weight and food intake (A) Final body weight and (B) dietary intake

Figure 4: Significantly differentially abundant taxa at family level and genus level in obese T2D microbiota inoculated mice or obese healthy inoculated mice associated with p-glucan consumption at each time point. (A) Mean relative abundance of the represented microbial taxa. Only taxa having a mean relative abundance of >1% are shown. (B) The significant differences in taxa between two diet groups were determined by Mann-Whitney II adjusted using Benjamini-Hochberg correction.

Figure 5: Significant differences in a- and p- diversity between time points in obese healthy and T2D inoculated mice with/without p-glucan. (A) Significant changes in bacterial a-diversity between time points were calculated using Wilcoxon rank sum test adjusted using Benjamini- Hochberg (BH) correction. (B) Principle coordinates analysis plots based on Bray-Curtis distances visualizing the dissimilarity gut microbiota composition over time. The significant differences among groups were calculated by permutational multivariate analysis of variance (PERMANOVA) tests. (C) Boxplots represents the distribution of Bray-Curtis distances of the faecal microbiota profiles between HFD-diet weeks (W5, W10 and W12) and LFD-diet week (W3) in non p-glucan consumption (BG-) versus p-glucan consumption (BG+). Mann-Whitney II tests were performed between two diet groups. *p < 0.05, **p < 0.01 .

Figure 6: Dietary supplementation with p-glucan drives gut microbiota diversity changes in obese healthy and obese T2D inoculated mice. (A) Difference in a-diversity between BG- and BG+ were determined by Mann-Whitney II test adjusted using Benjamini-Hochberg correction, **adjusted p < 0.01. (B) Principle coordinates analysis plots based on Bray-Curtis distances. The significant differences between groups were calculated by permutational multivariate analysis of variance (PERMANOVA) tests. Figure 7: The p-glucan responsive taxa at species level differed between obese T2D inoculated mice and obese healthy inoculated mice. (A) Mean relative abundance of the represented microbial taxa at species level. Only taxa having a mean relative abundance of >1 % are shown. (B) Significantly differentially abundant taxa between two diet groups were determined by Mann-Whitney II test adjusted using Benjamini-Hochberg correction. (C) Box plot of the relative abundance distribution of selected species associted with p-glucan consumption from week 3 to week 5. Time points in bold indicates nominal p-value < 0.05.

Figure 8: Microbiome and p-glucan alters hepatic protein signatures (A) OBH and OBD hepatic proteomic signature heat map (B) OBD and OBD +p-glucan hepatic proteomic signature heatmap. Red and blue bars indicate proteins significantly up- or down-regulated respectively (p<0.05) Canonical pathway analysis of differentially expressed hepatic protein using I PA (C) Top 20 Canonical pathways as per p-value and as per z-score in OBD with respect to OBH (D) Top 20 Canonical pathways as per p-value and as per z-score in OBD+p- glucan with respect to OBD.

Figure 9: p-glucan alters hepatic glucose protein signatures A.) OBH and OBH + -glucan hepatic proteomic signature heat map. Red and blue bars indicate proteins significantly up- or down-regulated respectively (p<0.05)

Figure 10: Method of Example 2.

Figure 11 : Microbiome source and diet influences insulin resistance HOMA-IR measurements calculated from fasting glucose and insulin secretion levels.

Figure 12: Obese Diabetic microbiome transplant increases glucose tolerance, insulin resistance and insulin secretion (A) Glucose tolerance test (GTT) (B) insulin tolerance test (ITT) and (C) insulin secretion ELISA from OBH and OBD mice with or without p-glucan in their diet *p,0.05, **p<0.01 w.r.t OBH vs OBD mice, #p<0.05 w.r.t OBD vs OBD+ PG mice.

Figure 13: OBD microbiome alters fatty acid metabolism in liver following HFD (A) triacylglycerol (TAG) levels in hepatic tissue (B) fatty acid oxidation (CPT1a) expression increases in OBD microbiome mice. *p,0.05, **p<0.01 w.r.t OBH mice.

Figure 14: Cholesterol excretion impacted by microbiome source (A) Cholesterol levels in hepatic tissue (B) Cholesterol excretion (ABCG8) expression decreases in OBD microbiome mice. ***p<0.001 w.r.t OBH mice.

Figure 15: Microbiome and p-glucan alters hepatic protein signatures. (A) OBH versus OBD hepatic proteomic signature heatmap (B) OBD versus OBD +p-glucan hepatic proteomic signature heatmap. Red and blue bars indicate proteins significantly up- and down-regulated respectively (p<0.05).

Figure 16: Diabetic microbiome disrupts hepatic citric acid cycle functionality. (A) pyruvate (B) lactate and (C) citrate levels in hepatic tissue (D) the citric acid cycle is altered in OBD hepatic tissue with increased citrate synthase expression but decreased expression of all other enzymatic proteins. *p,0.05, **p<0.01 w.r.t OBH mice.

Detailed description of the invention

All publications, patents, patent applications and other references mentioned herein are hereby incorporated by reference in their entireties for all purposes as if each individual publication, patent or patent application were specifically and individually indicated to be incorporated by reference and the content thereof recited in full.

To address the combined effects of diet-microbiome-metabolism interaction, the current inventors investigated the differential impact of feeding a high fat diet (HFD) in combination with obese (but healthy (OBH)) versus obese diabetic (OBD) human microbiome. The inventors accomplished this by harvesting and cryoprotecting the gut microbiome of obese healthy (OBH) and obese type 2 diabetic subjects and engrafting it into mice that had been treated with antibiotics to eradicate their endogenous murine microbiota.

Via this mouse model, the current inventors demonstrated that the combination of feeding a HFD and the OBD human microbiome greatly accelerated development of pathology-related insulin levels and hepatic lipid metabolism. Strikingly, this adverse metabolic disease phenotype was prevented when animals (mice) were fed the HFD in conjunction with yeast p- glucan supplementation, presented as a WGP (whole glucan particle) (Wellmune). Metabolic health status of the human microbiome donor significantly impacted hepatic metabolic within increased triacylglycerides (TAG) and cholesterol accumulation, altered expression of markers of fatty acid metabolism and modified hepatic proteomic signatures. Gut microbiota composition altered, and diversity responded positively to the consumption of p-glucans accompanied by a trend to reduce liver TAG and cholesterol levels.

Wellmune is a dietary food, beverage and supplement ingredient. It is an insoluble, large molecular weight whole glucan particle (WGP) derived from the cell wall of baker’s yeast (Saccharomyces cerevisiae) wherein the p-1 ,3/1 ,6-glucan is composed of a backbone of glucose molecules linked via unique chemical links pi ,3 chains to which are attached to glucose chains via p 1 ,6 links. The Wellmune glucan may be as disclosed in US4810646, US4992540, US5037972, US5082936, US5028703, US5250436, and US5506124, each of which is incorporated herein by reference. The combination of HFD and p-glucan supplementation specifically improved the adverse health phenotype associated with OBD microbiome. The inventors found that readouts including fasting plasma insulin concentrations, HOMA-index of insulin resistance, hepatic triacylglycerol and cholesterol concentrations were markedly improved, despite equivalent obesity. No other work to date has shown beneficial effects in a weight matched context. All other work has been associated weight loos which undoubtedly partly explain associated health benefits. The metabolic remodeling was associated with altered hepatic lipid metabolism, hepatic proteomic signature, and gut microbiome. This modulation of the metabolic phenotype by Wellmune was accompanied by marked alteration of the OBD microbiome namely increased alpha diversity, moving of the obese-diabetic microbiome closer in composition to that of the obese healthy microbiota type, increased abundance of 7 microbial taxa and reduced abundance of 6 taxa.

In an aspect, the current invention provides a method for treating or preventing a metabolic condition associated with obesity and diabetes, in particular, with type 2 diabetes (T2D), in a subject, said method comprising administering an effective amount of a whole (1— >3)-p-D- glucan particle to said subject.

Notably, the glucan of the invention is a whole glucan particle (WGP). A whole glucan particle is one isolated from glucan containing cell walls and substantially retaining the in vivo glucan morphology. WGP is not soluble. Methods of preparing a WGP are known in the art. Exemplary methods are disclosed in the publications disclosing Wellmune above. For example, Saccharomyces cerevisiae is selectively grown to obtain a pure culture and expanded in stainless steel fermentation vessels. Following fermentation, the cells are lysed by holding them at 45-55°C for approximately 24 hours. After autolysis the cell wall is separated from soluble yeast extract using a continuous centrifugal separator. The collected yeast cell wall is further processed through a series of alkali and hot water washes (70-90°C) to remove cell wall mannosylated proteins and any residual cellular lipids. In a subsequent acidification step the cell wall chitin is removed and the remaining purified beta-1 ,3/1 ,6 glucan slurry is washed in hot water, concentrated and pH adjusted as required. The resulting product is flash pasteurized and spray dried. This method results in a glucan that has retained its yeast cell like macro structure.

It is not a linear soluble beta glucan (small molecular weight) such as those disclosed by the prior art.

In an embodiment, the particle has a particle size, i.e. , diameter, of from about 1 micro to about 5 microns (or pm), from about 2 microns to about 4.5 microns, from about 2.5 microns to about 4 microns, or from about 3 to about 3.5 microns. This may be the average diameter. At least 80% to 99%, or >85% or 90%, of the particles may have a diameter in this range. Particle size is measured using a laser differentiation particle size analyser and such methods are known in the art.

Preferably, the glucan is from or isolated from the cell wall of baker’s yeast (Saccharomyces cerevisiae).

The metabolic condition is one associated with obesity and diabetes. It may be one or more of the conditions selected from the group comprising a hepatic metabolic condition and insulin resistance. It may be metabolic syndrome.

The hepatic condition may be hepatic metabolic inflammation or hepatic steatosis.

In an embodiment, the subject is an obese diabetic (OBD) individual with a microbiome typical of an obese diabetic individual.

The glucan of the invention may be a dietary or food supplement. The supplement may be selected from the group comprising a beverage, including a “shot” or small drink portion, a bakery product, a meat product, a vegetable product, a fish product, a grain, nut or seed product, a protein product, a dairy product, a snack product, powder product, powdered milk, confectionary product, yoghurt, breakfast cereal, a bread product, nutritional supplement, a sports nutritional supplement or any suitable comestible product. The supplement may be a powder supplement incorporated into a food or beverage product.

The glucan of the invention may be a preparation comprising a plurality of WGPs. The WGP in the preparation have an average size, or size distribution in which the average value is from 1 to 6 microns, preferably 2 to 4 microns.

The glucan may be formulated in a capsule or tablet form.

In one embodiment, the method comprises a first step of identifying a subject suitable for the treatment. This step may involve identifying a subject with an obese diabetic microbiome and such a person would be classified as suitable for treatment with the particle of the invention.

In the current invention results were achieved using a physiological dose that can be well tolerated by consumers. All previous work in the art worked with supra-physiological doses. The high doses prevented food absorption, hence the weight loss, which would resolve the adverse phenotypes observed and it is arguable incorrect to presume it was due to b-glucan. It will be appreciated that a person skilled in the art would be capable of determining an appropriate dose of the glucan or supplement comprising the glucan of the invention to administer without undue experimentation. It may depend on the particular condition, disease or disorder to be treated or cared for and the age, body weight and/or health of the person. It will depend on a variety of factors including the age, body weight, general health, sex, diet, mode and time of administration, the severity of the particular condition, and the individual undergoing therapy. The amount and the frequency is as best suited to the purpose. The frequency of application or administration can vary greatly, depending on the needs of each subject, with a recommendation of an application or administration range from once a month to ten times a day, preferably from once a week to four times a day, more preferably from three times a week to three times a day, even more preferably once or twice a day.

Metabolic health of the microbiome source significantly impacted hepatic metabolic within increased triacylglycerides (TAG) and cholesterol accumulation, altered expression of markers of fatty acid metabolism and modified hepatic proteomic signatures. Gut microbiota composition and diversity responded positively to the consumption of p-glucans accompanied by a trend to reduce liver TAG and cholesterol levels, but not immune markers in this humanized murine model.

A composition comprising the glucan of the invention is also provided. It may further comprise at least pharmaceutically acceptable excipient, or excipient suitable for comesitble use. Acceptable excipient are well known in the art and any known excipient, may be used. Preferably any excipient included is present in trace amounts. The amount of excipient included will depend on numerous factors, including the type of excipient used, the nature of the excipient, and/or the intended use of the composition. The nature and amount of any excipient should not unacceptably alter the benefits of the glucan of this invention.

A food or dietary supplement comprising the glucan of the invention is also provided.

It will be understood that the features of the embodiments of the invention may be combined in any combination. The invention will now be described with reference to specific Examples. These are merely exemplary and for illustrative purposes only: they are not intended to be limiting in any way to the scope of the monopoly claimed or to the invention described. These examples constitute the best mode currently contemplated for practicing the invention.

EXAMPLES

EXAMPLE 1

METHODOLOGY

Participant Recruitment and Faecal Sample Collection

Subjects were recruited from the Croi Heart and Stroke Centre, Galway, Ireland which delivers structured lifestyle modification programmes for at-risk individuals. Ethical approval was granted from the local Clinical Research Ethics Committee. This study adheres to the guidelines dictated by the Declaration of Helsinki and those of the Research Ethics Committee, Ireland. Information about the study was provided to obese (BMI > 30) individuals attending the centre, participation sought and written informed consent obtained. Participants were invited to the clinic for one morning visit (fasted) where they answered a proforma of health and diet related questionnaires. Bloods were obtained by vacutainer and biochemically assessed for typical markers (including glucose and lipid profiles). Two groups were targeted based on glucose tolerance, obese-normoglycemic and obese diabetic groups. Glucose tolerance was determined based on HbA1c and glucose from fasting bloods; with normoglycemia indicated by a fasting glucose levels of <6mmol/l and HbA1C of <42 mmol/mol and the diabetic group defined by a fasting glucose >7mmol/l, fasting HbA1C of >48 mmol/mol. A freshly voided (same day) faecal sample was provided for determining faecal microbiota. Faecal samples were maintained in an anaerobic environment from time of collection in the morning until same day delivery to the microbiology laboratory for further processing.

Mice and Study Design

Male conventional C57BL/6J mice were obtained Envigo, United Kingdom. A schematic overview of the experimental study design is presented in Figure 1A. At 5 weeks of age, mice were given an antibiotic cocktail comprised of ampicillin, metronidazole, vancomycin, imipenem and ciprofloxacin HCI for 6 weeks in their drinking water to deplete the murine gut microbiota and were fed sterile standard chow after two weeks of acclimation. The cocktail was book-ended with 6 treatments in total (1 each on the first 3 and last 3 days of antibiotic treatment) with an anti-fungal agent (Amphotericin B), administered by oral gavage. Following antibiotic treatment, after one day of wash out, mice were inoculated with human microbiota derived from either an obese healthy subject or an obese diabetic subject not receiving metformin. A total of 300pl of fecal material was given in two doses of 50pl per day for 3 consecutive days. From 9 weeks of age, mice were fed a low-fat diet (LFD; 10% kcal from fat) for 2 weeks. Half of the cohort was subsequently change to a LFD with p-glucan. From 15 weeks of age mice were switched to a high-fat diet (HFD; 45% kcal from fat) with half the cohort continued with p-glucan added to the diet. All diets were purchased from Research Diets (New Brunswick, NJ, USA) with p-glucan being supplied by Kerry (Wellmune Details). Mice were housed in metabolic cages in a 12: 12-h light-dark cycle and allowed to feed ad libitum from 8 g fresh feed and water provided daily. Body weight and food intake were measured weekly. Faecal samples were collected from each animal at several time points: before the antibiotic treatment (week -6) and between 1 and 12 weeks after inoculation (week 1 , week 3, week 5, week 10 and week 12). One to two faecal pellets were collected at each time point and were immediately transferred to -80 °C. For some animals, and a minority of timepoints, faecal samples could not be collected. After sacrifice, spleen, mesenteric lymph nodes and liver were isolated immediately for analysis of the immune and metabolic responses. All experiments were approved by the Animal Ethics Experimentation Committee of University College Cork (AE19130/P072).

Genomic DNA Extraction and microbiota profiling

Total DNA was extracted from human faecal samples and murine faecal pellets using the Qlamp Fast DNA Stool (Qiagen, Manchester, U.K.) kit. The samples were weighted and placed into sterile tubes containing 0.1 , 0.5, and 1.0 mm zirconia/glass beads (Thistle Scientific, U.K.). InhibitEX buffer (750 pL) was added to the samples and then homogenized using a Mini-Beadbeader (Biospec Products, USA). Specifically, samples were homogenized in two intervals (1 min and 40 s) with intermediate step when the samples were placed on ice for 1 min. The samples were then placed in a 95 °C heat-block for 5 min. The subsequent steps of the DNA extraction were carried out as described in the Qiagen protocol using 15 pL of proteinase K with 200 pL of AL buffer, 200 pL of lysate, and 200 pL of ethanol at the relevant steps. The obtained genomic DNA was used as a template in library preparation for 16S rRNA gene amplicon sequencing.

The V3-V4 variable region of the 16S rRNA gene was amplified using the universal 16S rRNA gene primer pair S-D-Bact-0341-b-S-17 (5’-CCTACGGGNGGCWGCAG-3’)/S-D- Bact-0785- a-A-21 (5’-GACTACHVGGGTATCTAATCC-3’) (Klindworth et al., 2013). Amplification was performed in 30 pL containing 2 pL of template DNA (final concentration 15 ng/pL), 15 pL of 2x Phusion High-Fidelity PCR Master Mix (ThermoFisher Scientific, Waltham, MA, USA), 1.2 pL of each 16S primer (final concentration 0.2 pM) and 10.6 pL of nuclease-free H2O. The cycling conditions were as follows: initial denaturation at 98 °C for 30 s; 25 cycles at 98 °C for 10 s, at 55 °C for 15 s and at 72 °C for 20 s; final extension at 72 °C for 5 min, followed by a hold at 4 °C. The PCR products were purified using the Agencourt AMPure XP-PCR Purification system (Beckman Coulter, Inc., California, USA), and the subsequent library preparation steps were performed according to the Illumina MiSeq system protocol. Dualindex barcodes were attached to the amplicon (Nextera XT V.2 Index Kits sets A and D, Illumina) and then purified as before. The indexed amplicons were quantified using the Qubit dsDNA HS Assay Kit (Thermo Fischer Scientific, MA, USA). Purified amplicons were pooled in equal volumes. Sequencing (2 x 250 bp) of the pooled library was performed using the Illumina MiSeq instrument (Illumina, Inc., San Diego, CA, USA) in the Eurofins GATC Biotech GmbH (Constance, Germany).

Library preparation and shotgun metagenomic sequencing

Shotgun libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina) following the manufacturer's instructions. First, total genomic DNA was quantified using Qubit™ dsDNA HS Assay Kit (Thermo Fisher). One ng genomic DNA of each sample was fragmented by adding 5 pl of Amplicon Tagment Mix with 10 pl of Tagment DNA Buffer. The tagmented DNA were amplified by PCR with 5 pl of each Illumina Nextera index adapters (i5 and i7) and 15 pl of Nextera PCR Master Mix (Nextera XT Index Kit v2 Set B, Illumina). The cycling conditions were as follows: initial denaturation at 95 °C for 30 s; 12 cycles at 95 °C for 30 s, at 55 °C for 30 s and at 72 °C for 30 s; final extension at 72 °C for 5 min, followed by a hold at 10 °C. The PCR products were purified using the Agencourt AMPure XP-PCR Purification system (Beckman Coutler, Inc., California, USA). The shotgun libraries were sequenced using Illumina NextSeq® 500/550 High Output v2 (300 cycles) at the National Irish Sequencing Centre (Teagasc Food Research Centre, Ireland) to generate 150 bp paired-end read libraries according to the manufacturer’s instructions.

Glucose and Insulin Tolerance Tests

Mice were fasted for 12h then injected intraperitoneally with 25% (wt/vol) glucose (1.5g/kg; check glucose brand) for glucose tolerance testing (GTT). Mice were fasted for 6 hours then injected intraperitoneally with insulin (0.5U/kg: Company Location TBC) for insulin tolerance test (ITT). Glucose levels were measured at baseline, 15, 60, 90, and 120 minutes post glucose/insulin challenge by an accu-check glucometer (Roche, Dublin, Ireland). Tail-vein bleeds were sampled at baseline as well as 15 and 60 minutes post glucose challenge to measure the insulin secretory response. ELISA (Crystal Chem, Inc., IL, USA) was used to quantify insulin levels post glucose challenge.

Hepatic Triacylglycerol, Cholesterol, Alanine Aminotransferase Activity, Citrate and Lactate Quantification

Flash frozen liver tissue (50mg) was homogenized in a Tissue lyser (Qiagen). For triacylglycerol and cholesterol assays, methanol was added to the homogenate followed by chloroform. The chloroform phase was collected and dried under nitrogen gas. Toluene was added to the dried substrate, followed by chloroform and then 1 % Triton X 100 in chloroform. The homogenate was dried before each subsequent solvent was added. The dried sample was re-suspended in water. Hepatic TAG (Wako LabAssay™ Triglyceride kit, FuggerstraBe, Neuss, Germany) and hepatic cholesterol (Wako LabAssay™ Cholesterol kit) was measured as per manufacturer’s instructions. Hepatic ALT (Sigma-Aldrich ALT Activity Assay), citrate (Sigma-Aldrich Citrate Assay Kit) and lactate (Sigma-Aldrich Lactate Assay Kit) was measured as per manufacturer’s instruction. Gene Expression Analyses

RNA was extracted from flash frozen hepatic tissue with trizol. Chloroform was added, mixed by inversion and the colourless aqueous phase was transferred to a fresh tube. Isopropanol was added to precipitate the RNA overnight. RNA was quantified using a NanoDrop 8000 UV- Vis spectrophotometer. Equal amounts of cDNA were synthesized using the Applied Biosystems High Capacity cDNA kit (Applied Biosystems by Thermo Fisher Scientific). Fasn, Dgatl, Cptla, HMGCoA, ABCG5, ABC8, GPR109A primers and TaqMan Universal Mastermix were obtained from Applied Biosystems by Thermofisher Scientific. mRNA expression was measured by real-time PCR on Applied Biosystems™ QuantStudio™ 7 Flex Real-Time PCR System. Housekeeping gene 18S was used for hepatic gene expression. Comparison of 2-(AACt) determined fold change as previously described (McGillicuddy et al., 2011).

Flow Cytometry Data Analysis

Mesenteric lymph nodes (MLNs) and spleens were dissected and kept on ice in 15 mL falcon tubes with 10 mL of PBS + 1 % fetal bovine serum (FBS) until cell isolation. MLNs were forced through a 100 pm cell strainer (Greiner Bio-One, Frickenhausen Germany) positioned on top of a 50 mL falcon tube using a plunger of a 1mL syringe and the strainer was washed with 10 mL PBS + 1% FBS. The cell suspension was centrifuged at 1200 rpm for 5 minutes. The same technique was used for cell isolation from spleen. In spleen, the pellets were re-suspended in 5 mL of eBioscience™ 1X RBC Lysis Buffer (Carlsbad, CA) and incubated for 7 minutes at room temperature to lyse the red blood cells. After incubation, 25 mL of PBS + 1 % FBS was added and the cell suspension was centrifuged at 1200 rpm for 5 minutes. Cells (1 x10⁶) were incubated with Mouse BD Fc Block (clone 2.4G2, BD Biosciences) for 10 min on ice, and then were stained with the appropriate antibodies to surface markers at 4°C for 30 min in the dark or were fixed overnight using True-Nuclear™ Transcription Factor Buffer Set (BioLegend®, San Diego, CA) according to the manufacturer’s protocol and stained with intracellular antibody at room temperature for 30 minutes. Antibodies used were: CD4- FITC (Clone RM4-5), CD25-PE/Cy-7 (Clone 3C7), CD45-APC (Clone 30-F11), CD11b-PE (Clone M1/70), F4/80-PerCP (Clone BM8), MHC II (l-AZI-E)-FITC (Clone Clone M5/114.15.2), FoxP3-BC421 (Clone MF-14) from BioLegend® and CD8-AF700 (Clone 53-6.7), CD11c- PE/Cy-7 (Clone N418) from eBioscience™ (Carlsbad, CA). The stained populations were analyzed using BD FACSCelesta™ flow cytometer (BD, Franklin Lakes, NJ) and FlowJo software (BD, Version 10). Bioinformatic analysis of of 16S amplicon sequencing data

The inventors applied a similar 16S rRNA gene amplicon pipeline as previously described (Tran et al., 2019). The Illumina MiSeq paired-end sequencing reads of V3-V4 16S rRNA regions were first were merged using Fast Length Adjustment of Short Reads (FLASH) v.1.2.8 (Magoc and Salzberg, 2011) and trimmed the forward primers using cutadapt v1.8.3 (Martin, 2011). The reads were filtered with a minimum quality score of 19 using split_libraries_fastq.py and then removed the reversed primers using truncate_reverse_primer.py from QIIME v1.9.1 (Caporaso et al., 2010). The quality-filtered sequences were dereplicated, filtered by length of 373-473nt, removed singleton and were then clustered into Operational Taxonomic Units (OTUs) at a threshold similarity of 97% with the USEARCH v6.1 (Edgar, 2010). The chimeric sequences were discarded using UCHIME against the GOLD reference database (Haas et al., 2011) and all post-QIIME quality-filtered reads were mapped back to the representative OTU sequences to generate the OTU table using the -usearch_global option from USEARCH. To ensure an even sampling depth, the OTU table was rarefied using QIIME with single_rarefaction.py at a depth of 14,335 reads per sample as the lowest read count in the dataset and used for calculating a- and p- diversity of the gut microbial community with alpha_diversity.py and beta_diversity.py scripts. Taxonomic assignment for each representative OTU sequences were performed using mothur v1.36.1 against the RDP database version 11.5 for genus classification and SPINGO v1.3 against the species reference database (RDP version 11.2) for species classification (Allard et al., 2015; Cole et al., 2014; Schloss et al., 2009). For both classifications, sequences with a confidence score below 80% were considered as unclassified.

Hepatic Proteomic Analysis

Protein isolation: Protein was isolated with the addition of trichloroacetic acid (20%). After centrifugation at 14,000rpm for 10mins and aspiration, cell pellets were twice washed in ice- cold acetone with centrifugation repeated. Protein pellets were resuspended 8M Urea in. Protein concentration was determined using the Bradford Assay.

In-solution digestion: Cysteine residues were reduced using dithiothreitol followed by alkylation with iodoacetamide. Dithiothreitol, iodoacetamide and urea concentrations were diluted using 50mM NH4HCO3 before trypsin singles TM proteomic grade (Sigma) was added, ensuring a urea concentration of less than 2M. Digestion was carried out overnight at 37°C. After drying in vacuum centrifuge, peptides were acidified by acetic acid (AA), desalted with c18 STAGE tips (Rappsilber et al., 2007), and resuspended in 2.5% acetonitrile (ACN), 0.5% AA. Mass Spectrometry: Peptide fractions were analyzed on a quadrupole Orbitrap (Q-Exactive, Thermo Scientific) mass spectrometer equipped with a reversed-phase NanoLC UltiMate 3000 HPLC system (Dionex LC Packings, now Thermo Scientific). Peptide samples were loaded onto C18 reversed phase columns (10 cm length, 75 pm inner diameter) and eluted with a linear gradient from 1 to 27% buffer B containing 0.5% AA 97% ACN in 118 min at a flow rate of 250 nL/min. The injection volume was 5 pl. The mass spectrometer was operated in data dependent mode, automatically switching between MS and MS2 acquisition. Survey full scan MS spectra (m/z 300 - 1200) were acquired in the Orbitrap with a resolution of 70,000. MS2 spectra had a resolution of 17,500. The twelve most intense ions were sequentially isolated and fragmented by higher-energy C-trap dissociation.

Protein identification: Raw data from the Orbitrap Q-Exactive was processed using MaxQuant version 1.6.3.4 (Cox and Mann, 2008), incorporating the Andromeda search engine (Cox et al., 2011). To identify peptides and proteins, MS/MS spectra were matched to the Uniprot mus mus musculus database (2018_09) containing 53,780 entries. All searches were performed with tryptic specificity allowing two missed cleavages. The database searches were performed with carbamidomethyl (C) as fixed modification and acetylation (protein N terminus) and oxidation (M) as variable modifications. Mass spectra were searched using the default setting of MaxQuant namely a false discovery rate of 1% on the peptide and protein level. For the generation of label free quantitative (LFQ) ion intensities for protein profiles, signals of corresponding peptides in different nano-HPLC MS/MS runs were matched by MaxQuant in a maximum time window of 1 min (Cox et al., 2014).

Proteomic data analysis: The Perseus computational platform (version 1.6.2.3) was used to process MaxQuant results (Tyanova et al., 2016). Data was log transformed. T-test comparisons (at p-value 0.05) were carried out between x y liver proteomes specifying that a protein needed to be observed in six samples of samples in at least one group. For visualization of data using heat maps, missing values were imputed with values from a normal distribution and the dataset was normalized by z-score. Pathway enrichment analysis was performed using the ClueGo (v2.5.2) (Bindea et al., 2009) and Cluepedia (v1.5.2) (Bindea et al., 2013) plugins in Cytoscape (v3.6.1) (Shannon et al., 2003) with the mus musculus (10090) marker set. The Gene ontology (GO) biological process and reactome pathway databases, consisting of 21 ,259 and 9,292 genes, were used (Ashburner et al., 2000). GO tree levels (min = 3; max = 8) and GO term restriction (min#genes = 3, min% = 1 %) were set and terms were grouped using a Kappa Score Threshold of 0.4. The classification was performed by the right-sided enrichment hypergeometric statistic test, and its probability value was corrected by the Bonferroni method (Adjusted % Term p-value <0.05) (Bindea et al., 2009). Bioinformatic Pathway Analysis

Bioinformatic analysis was performed to analyze differentiable expressed hepatic protein between OBH and OBD groups and OBD and OBD + p-glucan groups. Briefly, outcomes from proteomic analysis were uploaded into Qiagen’s Ingenuity Pathway Analysis system for core analysis and overlaid with the Ingenuity pathway knowledge base. I PA was performed to identify canonical pathways, and putative upstream regulators that are the most significant global molecular networks. These results were ranked based on their p value (P<0.05) or activation score of pathway activated/inhibition.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis

Statistical analysis was carried out using R v.3.5.5 software packages (R Core Team, 2016). The significant differences in beta diversity were detected using permutational multivariate ANOVA (PERMANOVA) (vegan R package (Oksanen et al., 2019)). Differences of alpha diversity and different taxonomic levels were identified with Mann-Whitney U test for the comparison of two diet groups according p-glucan consumption (unpaired data) and Wilcoxon signed rank test for the comparison between time points (paired data). P-values were adjusted for multiple comparisons using Benjamini-Hochberg correction. The relationships between the metabolite’s measurements and gut microbiota composition at OTU level were done using envfit (vegan R package) and co-inertia analysis (CIA) (ade4 R packages (Dray and Dufour, 2007)). Both analyses were constructed using OTUs that were present in at least 50% of the samples belonging to any of diet groups. The association between two datasets were measured by RV-coefficient with Their significance assessed using the Monte-Carlo permutation test (Moonseong and Ruben Gabriel, 1998).

Metabolic phenotype data are expressed as mean ± standard error of the mean (S.E.M). For GTT, ITT and insulin secretion a two-way repeated measures Analysis of Variance (ANOVA) was completed followed by a post hoc Bonferroni test (if the ANOVA reached statistical significance). For in between group comparisons, a one-way ANOVA followed by post-hoc Bonferroni test was performed. Analyses was completed using GraphPad Prism 5.0 software (GraphPad Prism Software, Inc, La Jolla, CA, USA)

DATA AVAILABILITY

Raw 16S rRNA gene sequencing data is available at the NCBI Sequence Read Archive (SRA) under BioProject SUB8324205. RESULTS

Microbiome profiles of obese human subjects and donor selection

Clinical characteristics of the obese T2D (n = 10) versus obese non-T2D (n = 15) participants are shown in Table 1.

Data are expressed as mean ± SD.

P -value was calculated by Kruskal-Wallis test () and Fisher's exact test (^t).

Table 1 : Clinical characteristics of participants, grouped by diabetic group

The diabetic versus non-diabetic subjects did not differ significantly with respect to age, BMI, or gender; but displayed the expected elevated fasting glucose and HbA1c values, and metformin treatment status. There was no significant separation by p-diversity analysis of overall gut microbiota composition between obese healthy individuals and obese T2D subjects, with/without metformin treatment (unweighted and weighted UniFrac measures: p > 0.05; Figure 1A). Although 16S rRNA amplicon sequencing was biased towards detect higher proportions of Lachnospiraceae compared with shotgun metagenomic sequencing, the ratio of taxonomic composition between subjects was largely consistent by both amplicon sequencing and metagenomics (Figure 1 B). Obese T2D (OBD) subjects treated with metformin displayed significant loss of the Clostridiales order (unclassified Lachnospiraceae genus and Eubacterium eligens) and higher abundance of Streptococcus spp. and Lactobacillus spp. compared to obese healthy (OBH) subjects, and altered microbial metabolic pathway abundances (Figure 1 C). The increase in the relative abundance of Lactobacillus with metformin is established (Bauer et al., 2018), as well as other re-configurations of the human gut microbiota. Therefore, the inventors selected an obese T2D (OBD) non-metformin treated subject and an obese healthy (OBH) subject as donors for the subsequent feeding intervention to understand the impact of the humanized diabetic versus non-diabetic microbiome on the diet-induced gut microbiome interaction. P-glucan improves hepatic glucose, fatty acid and cholesterol metabolism

The inventors humanized mice with the gut microbiome from the selected obese non-diabetic (OBH) or obese type 2 diabetic human (OBD) subjects, following the experimental design shown in Figure 2A. Following two-week acclimatization, mice received 6 weeks’ treatment with a cocktail of broad spectrum antibiotics which virtually eliminates their microbiome, previously validated in our lab (Tran et al., 2019). Mice that were inoculated with the obese T2D (OBD) microbiome type became more glucose intolerant, insulin resistant and hyperinsulinemic following a high-fat dietary challenge compared to HFD challenged OBH inoculated mice. Interestingly, the addition of p-glucan (PG) to the high-fat diet partly corrected the OBD/HFD-induced adverse metabolic phenotype, wherein fasting insulin concentrations were significantly lower, compared to the OBD/HFD microbiome-induced group (Figure 2B). HOMA-IR, a measure of insulin resistance, deteriorated in the OBD group following HFD, but supplementation of dietary PG attenuated the insulin resistant state associated with the OBD microbiome after HFD (Figure 2C). Important potential confounders, such as total weight gain (Figure 3A) were not different according to microbiome type or dietary fat. Cumulative food intake was similar between all groups (Figure 3B).

Hepatic TAG and cholesterol concentrations were significantly higher in OBD HFD mice, compared to the OBH HFD group (Figure 2D). Liver weights were similar irrespective of the microbiome source or HFD and PG supplementation (data not shown). Hepatic fatty acid composition showed a trend for increased storage of saturated, monounsaturated and polyunsaturated fatty acids in OBD mice while PG supplementation trended to reduce storage and reduce total fatty acid levels (Table 2).

OBH OBH > PG OBD OBD+pG

Table 2: Hepatic fatty acid composition

In terms of defining whether enhanced lipogenesis I lipid storage or reduced oxidation, contributed to higher hepatic TAG and cholesterol levels, we investigated a number of markers of lipid and cholesterol homeostasis. Counterintuitively, hepatic Cptla mRNA, an indicator of mitochondrial fatty acid oxidation, was increased in OBD HFD mice, compared to OBH HFD. The increase Cptla was greatest in the presence of PG supplementation. In contrast, markers of fatty acid synthesis (Fasn) and TAG assembly (Atgl) were not significantly different. In terms of cholesterol homeostasis, OBD mice also displayed lower expression of the cholesterol transporters ABCG8 (Figure 2E). Basic metabolite profiling illustrated interesting differences between groups (Figure 2F-H). Hepatic alanine aminotransferase (ALT) levels were markedly increased in the OBH group following HFD+pG supplementation (Figure 2F). Hepatic citrate (Figure 2G) and lactate (Figure 2H) levels were increased in OBH-HFD mice, but not when supplemented with G.

The current inventors next investigated if p-glucan modulated immune responses, p-glucan supplementation was not associated with any significant differences in mice humanized with either microbiota/metabolic phenotype, in regulatory T cells (Tregs), dendritic cells, or macrophages within CD45⁺ cell populations from the mesenteric lymph nodes and spleen (Figure 4). p-glucan supplementation did not impact on immune responses of regulatory T cells dendritic cells and macrophages. 1 The diabetes-associated microbiota composition moves towards a health-associated composition upon p-glucan supplementation

Faecal samples were collected from individual animals at key time points and were subjected to microbiome profiling by 16S rRNA gene amplicon sequencing (Figure 2A). The a-diversity (diversity within samples) measured by phylogenetic diversity and observed species was consistently lower at week 1 than that at subsequent weeks, and remained stable or increased slightly from week 3 to week 12 in both OBH and OBD (Figure 5). The net increase in diversity during subsequent weeks was expected because the murine gut microbiota diversity had been lowered sharply by the administration of antibiotics and the engraftment of the human gut microbiota into antibiotic-treated mice was successful. The a-diversity of the gut microbiota in the OBD was lower than that of OBH (Figure 6A). Supplementation of the diet with p-glucan increased the a-diversity of the gut microbiota in OBD at week 3 (phylogenetic diversity: p = 0.006, observed species: p = 0.01), but no statistically significant differences in diversity were associated with p-glucan consumption from week 5 to week 12 when mice were receiving the HFD. However, the OBH- animals receiving p-glucan showed lower phylogenetic diversity compared to those without p-glucan at week 3 (p = 0.001 , Figure 6A). This result indicates that supplementation with p-glucan while on a LFD temporarily lowers microbiota diversity in a normal microbiota, but increases bacterial diversity in low-diversity communities.

We next investigated the relatedness of the microbiota community structures (P-diversity) between OBH and OBD with or without p-glucan supplementation (Figure 6B). Principal coordinates analysis (PcoA) based on a Bray-Curtis distance (which measures the dissimilarity of gut microbiota composition between different samples) at week 1 showed that the main separation was between the human donor metabolic phenotype (PERMANOVA: R² = 0.31 , p = 0.001). Supplementation with p-glucan at this early time-point had a marginal effect in OBD (PERMANOVA: R² = 0.18, p = 0.047) but did not impact on OBH (PERMANOVA: R² = 0.05, p = 0.529). By week 3, the p-glucan effect on the microbiota was significant for both donor metabolic types, but more strikingly, p-glucan supplementation moved the OBD microbiota closer to that of the OBH microbial type especially the non-p-glucan diet group (Figure 6B). This effect was retained at week 5, but the p-glucan supplementation effect on the microbiota was weakened within microbiota metabolic types. By week 10, there was general convergence, although p-glucan consumption still had a significant effect within the OBD microbiota (PERMANOVA: R² = 0.18, p = 0.027). These data indicate that p-glucan supplementation altered gut microbiota composition on both LFD and HFD, but its effect on the OBD microbiota type was greater on LFD than HFD. On a larger scale where all time points were mapped, the murine microbiota datasets were closer to the respective human donor microbiota than the pre-treatment conventional mouse microbiota as week 6 (Figure 5B). Dietary p-glucan supplementation significantly separated mice in the PCoA, but the distances between two diet groups were reduced with time on the HFD especially within the OBD group. Furthermore, the change of gut microbiota (measured with Bray-Curtis distances) between HFD-diet week (W5, W10 or W12) versus LFD-diet week (W3) from p-glucan consumption was significantly lower than that from the non p-glucan group. This suggests that P-glucan might reduce the effect of HFD on gut microbiota in both OBD and OBH (Figure 5C). Thus, the magnitude of microbiota composition change (as measured by Bray-Curtis distance) between the last LFD time point and the first after HFD switch was far smaller if receiving p- glucan supplementation. This suggests that p-glucan reduces the effect of HFD on gut microbiota in both OBD and OBH (Figure 5C). Besides, we observed a weak, but significant correlation between gut microbiota composition and hepatic triacylglycerol and cholesterol levels at week 5 (envfit r2 < 0.3, p < 0.05), suggesting that the difference in the metabolic phenotype between humanized mice treated with and without p-glucan might due to an accumulation of bacterial taxa and their metabolites over time.

P-glucan responsive taxa differ between obese T2D inoculated mice and obese healthy inoculated mice

Analysis of differentially abundant taxa linked to p-glucan supplementation identified five significantly different families detected at week 3 between with and without p-glucan consumption were no longer significant when the diet was switched to HFD except Clostridiaceae 1 and Erysipelotrichaceae in the OBD group, whereas increased relative abundance of Verrucomicrobiaceae, Ruminococcaceae, Desulfovibrionaceae, Porphyromonadaceae and a decrease in Streptococcaceae, Bacteroidaceae, Rhodospirillaceae, Enterobacteriaceae were consistently associated with p-glucan consumption from week 3 to week 10. At the genus level, 27 genera were associated with p- glucan consumption of which 9 were genera in the OBH group and 8 genera in OBD group which were consistently more or less abundant in mice receiving p-glucan compared to those without p-glucan for at least two time points. Flavonifractor, Akkermansia, Roseburia, Parabacteroides in OBH group and Clostridium XlVa, Odoribacter, Anaerostipes in OBD group were found in significantly higher proportions after supplementation with p-glucan. The Escherichia/Shigella abundance was consistently lower at all time points in the OBH group receiving p-glucan (Figure 4).

At the species level, 69 species accounted for 73.2±19% (mean±SD) of the microbial composition abundance across all samples. Most samples were dominated by Akkermansia muciniphila, and Bacteroides vulgatus, although samples in the OBH group were also dominated by Parabacteroides distasonis and Bacteroides xylanisolvens. Bacteroides uniformis was dominant in the OBD group, particularly in non p-glucan diet (Figure 7A). Akkermansia muciniphila was initially more abundant in p-glucan-treated mice, but this difference in OBD group was not maintained after moving to the HFD diet. Four species including Bacteroides vulgatus, Alistipes massiliensis, Blautia hydrogenotrophica, Odoribacter splanchnicus were significantly more abundant in at least two consecutive time points in the OBD group receiving p-glucan, while the opposite applied for Clostridium paraputrificum, Clostridium ramosum, Bacteroides uniformis. Unlike the OBD, higher abundance of Parabacteroides distasonis, Akkermansia muciniphila and lower abundance of Bacteroides vulgatus, Bacteroides thetaiotaomicron, Bacteroides acidifaciens at week 10 were associated with p-glucan consumption in the OBH group, differences which were consistent with the observations at week 3 and week 5 (Figure 7B and Figure 7C). This suggests that the effect of p-glucan on certain specific taxa was more stable in OBH than in OBD, even after 6 weeks following HFD feeding.

Hepatic Proteomic Expression is Altered by Both Microbiome Health and p-Glucan Ingestion

The liver is a key hub, integrating signals from the gut and integral to metabolism. In order to identify potential mechanisms underpinning diet / microbiome induced changes in metabolism, hepatic MS-based proteomics was completed. The hepatic samples were analyzed from animals in the OBD transfer group, who displayed the most adverse metabolic phenotype using OBH as the control or relative comparator (ANOVA p<0.05; q<0.02) A total of 130 proteins were differentially expressed between OBD and OBH livers, 29 of which were increased and 101 proteins were decreased (Figure 8A). In conjunction with the improved phenotype in OBD following PG supplement, 130 proteins were differentially produced between OBD and OBD + PG livers, of which 79 had production increased and 51 with production decreased (Figure 8B). Hepatic proteomic comparison between OBH+ PG versus OBH showed that 132 proteins increased, and 161 protein decreased (Figure 9). The differentially produced proteins were involved in fatty acid metabolism, mitochondrial dysfunction and inflammation.

Ingenuity pathway analysis (I PA) was used to identify key pathways of interest. There were many differentially expressed pathways when comparing OBD versus OBH (Figure 8C) and OBD + p-Glucan with respect to OBD (Figure 8D) hepatic proteomes. Interestingly, this proteomics approach showed that the pathways that were modulated by OBD versus OBH microbiome, were different in magnitude of modulation to those with the provision of p-G supplementation in OBD mice wherein hepatic steatosis and insulin resistance was partly resolved. Specifically, mitochondrial dysfunction, EIF2 signaling, oxidative phosphorylation, sirtuin signaling pathway and regulation of EIF4 and p7056K signaling were significantly altered in the OBD group, compared to OBH (Table 3).

Table S3 OBH vs OBD Canonical Pathways Top five ranked either by -log (p value) or z score

Table 3 OBH vs OBD canonical pathways top five ranked either by -log(p value) or z score

The most significant pathways in the OBD group following p-Glucan supplementation include EIF2 signaling, regulation of EIF4 and p7056K signaling, mTOR signaling mitochondrial dysfunction and tRNA charging (Table 4).

Table 4: OBH vs OBD upstream regulators

Investigations were focused between these comparisons due to the hepatic metabolic phenotype displayed which was not as evident with the OBH in relation to OBH + p-Glucan livers. The top 5 canonical pathways differentially up- or down-regulated in OBD versus OBH livers are listed in Table 3. Key pathways inhibited in OBD livers include tRNA charging, EIF2 signaling, Citrulline Metabolism, Citrulline Biosynthesis and Unfolded protein response. In contrast, galactose degradation, RhoGDI signaling, SPINK1 Pancreatic Cancer Pathway, NRF2-Mediated Oxidative Stress Response and gluconeogenesis were up-regulated in OBD livers (Table 3).

I PA was used to identify putative upstream regulators of changes in the proteomic dataset. MYCN, MYC, Glucagon, ACOXIand CLPP were identified as potential upstream regulators that were down-regulated in OBD versus OBH mice (Table 4). Upstream regulators which were upregulated in OBD were PPARA PNPLA2, ACSS2, PCGEM1 and FOXA2 (Table 5).

Table 5 OBD vs OBD+BG upstream regulators

Importantly, several proteins, predominantly transcription factors, including CEBPA, CEBPB, SREBF1 , FOX01 RXRA and SIRT1 were shared between key pathways indicating important coincident dysregulated regulation of hepatic mitochondrial, fatty acid, insulin, and glucose metabolism following inoculation with the diabetic microbiome and high-fat feeding in OBD liver, compared to the OBH hepatic proteome.

Surprisingly, far fewer canonical pathways were enriched or unenriched when comparing OBD to OBD +PG (Table 6).

Table 6 OBD vs OBD+BG canonical pathways top five ranked either by -log (p value) or z score

The pathways include EIF2 signaling, tRNA charging and sirtuin signaling pathway, suggesting that oxidative phosphorylation and fatty acid metabolism were altered with the addition of PG to the HFD. Putative upstream regulators within these pathways were RICTOR, INS1 , BDNF and activated MYC, MYN, SYVN1 , XBP1 and NFE2I2 (Table 5). Of note here is the MYC family was inhibited in OBD livers but activated in OBD + PG. This could be a mechanism through which G is able to increase insulin sensitivity and decrease fatty acid accumulation within the liver.

P-glucan responsive taxa differ between obese T2D inoculated mice and obese healthy inoculated mice at family and genus level

Analysis of differentially abundant taxa linked to p-glucan supplementation identified 15 microbial families among which members of the Verrucomicrobiaceae and Ruminococcaceae families (representing 23.1 % of total abundance on average) were negatively associated, and Peptostreptococcaceae (occupying 3.4% of total abundance on average) was negatively associated in both obese healthy and T2D groups (Figure 8). Five significantly differential families detected at week 3 were lost when the diet was switched to HFD except Clostridiaceae 1 and Erysipelotrichaceae in the obese T2D-humanized group. Increased relative abundance of Verrucomicrobiaceae, Ruminococcaceae, Desulfovibrionaceae, Porphyromonadaceae and a decrease in Streptococcaceae, Bacteroidaceae, Rhodospirillaceae, Enterobacteriaceae were consistently associated with p-glucan consumption from week 3 to week 10. The only exception among the Enterobacteriaceae was the genus Escherichia/Shigella which was present at all time points in the obese healthy- humanized group (Figure 8B). At the genus level, Parabacteroides was the most predominant taxon in the obese healthy inoculated mice, accounting for more than 60% in total abundance, while Lactococcus and Bacteroides were present in higher proportions (of more than 90% relative abundance in the obese T2D) inoculated mice at week 1. These relative abundances decreased in the subsequent weeks, but Bacteroides was one of the most abundant genera in both obese healthy and T2D-humanized groups at week 1 and remained abundant over time in non-p-glucan diet and obese T2D groups, whereas they were less abundant with an average of less than 25% total abundance until week 10 in obese healthy inoculated mice receiving p-glucan (Figure 8A). These differences of Bacteroides in healthy obese group upon P-glucan supplementation were statistically significant (Mann-Whitney II test, BH-adjusted p < 0.05, Figure 8B). Moreover, 27 genera were associated with p-glucan consumption of which 9 were genera in the obese healthy humanized group and 8 genera in obese T2D group which were consistently more or less abundant in mice receiving p-glucan compared to those without P-glucan for at least two time points. Flavonifractor, Akkermansia, Roseburia, Parabacteroides in obese healthy group and Clostridium XlVa, Odoribacter, Anaerostipes in obese T2D group were found in significantly higher proportions in the supplementation with p-glucan. DISCUSSION

The current inventors investigated the potential of p-glucan for the treatment of T2D and obesity in a humanized mouse model. Our findings identify features of the human gut microbiota response associated with metabolic shifts in response to p-glucan supplementation and thus identify potential therapeutic targets relevant for diabetes and weight management.

The abundance of Akkermansia muciniphila, a well characterized mucin-degrading bacterium, significantly increased following p-glucan supplementation. Although the exact mechanism for this remains unknown, this trend was originally reported in earlier studies of p-glucans (Cao et al., 2016; Velikonja et al., 2019) and Akkermansia muciniphila has also been reported as a marker for healthier metabolic status (Dao et al., 2016) and negatively correlated with T2D (Ghosh et al., 2020; Zhang et al., 2013). However, these observations seem to conflict with a study by Desai et. al., as the abundance of Akkermansia muciniphila increased in the absence of fiber (Desai et al., 2016). A recent study reported that direct supplementation with Akkermansia muciniphila for three months improved insulin sensitivity and reduced plasma insulin and total cholesterol levels in overweight/obese subjects (Depommier et al., 2019). Similarly, the inventors detected a strong association between Parabacteroides distasonis abundance and p-glucan supplementation in mice harbouring the obese healthy gut microbiota type. The oral administration of Parabacteroides distasonis significantly reduced body weight gain, hyperglycemia, and hepatic steatosis via transforming bile acids and enhancing the level of succinate in the murine gut (Wang et al., 2019). The increase abundance detected by the inventors of these bacteria in obese healthy inoculated mice was offset by the proportional decrease in Bacteroides species, which was reported to be associated with long-term diet of protein and animal fat (Wu et al., 2011). Additionally, the abundance of Bacteroides vulgatus positively correlated with the biosynthesis of branched- chain amino acids and insulin resistance in 277 non-diabetic individuals (Pedersen et al., 2016), and with fat mass and glucose homeostasis in obese women (Dewulf et al., 2013). However, Bacteroides, particularly Bacteroides vulgatus and Bacteroides dorei, are dominant components of the human gut microbiota, which can actively refine the gut environment and increase gut barrier function (Wexler and Goodman, 2017; Yoshida et al., 2018) Here, the inventors found that obese T2D inoculated mice receiving p-glucan displayed increased abundance of Bacteroides vulgatus, Alistipes massiliensis, Blautia hydrogenotrophica, Odoribacter splanchnicus and decreased abundance of Clostridium paraputrificum, Clostridium ramosum, Bacteroides uniformis. Odoribacter splanchnicu, well-characterized butyrate-producing bacteria. Several Alistipes species (but not Alistipes massiliensis) have been reported to be negatively associated with T2D, whereas Clostridium ramosum which promoted diet-induced obesity (Woting et al., 2014) is positively associated with T2D. Blautia hydrogenotrophica, which can produce acetate (Rey et al., 2010), was less abundant in prediabetic subjects (Yang et al., 2015).

The alterations displayed between healthy and diabetic microbiome composition from obese subjects greatly impacted metabolic phenotype. Given OBD mice were hyperinsulinemic following HFD compared to OBH mice although body weight and metabolic organ weight, specifically adipose and liver were comparable. This suggests that the driver of insulin resistance is the microbiome received. Following supplementation with p-glucan reduced circulating insulin levels in OBD.

Given the liver is a first pass organ from the gut, focus on hepatic metabolic health was identified wherein OBD mice displayed greater TAG and cholesterol content, despite equal hepatic tissue weight. This indicates severe hepatic steatosis is present within OBD mice. This is displayed by impacted lipid handling capabilities in increased lipogenic gene expression (Fasn, Dgatl) content. Additionally, the p-glucan supplement showed to improve lipid handling through increased fatty oxidation (Cptla). In addition to altered lipid handling capabilities, OBD recipients also displayed altered cholesterol metabolism. Despite similar level cholesterol synthesis, OBD mice had reduced expression of cholesterol transporter (ABCG8) which is responsible for cholesterol excretion from the liver. Impairment of cholesterol excretion displayed in OBD recipients may account for the increased cholesterol levels. Previous reports of yeast p-glucan impact on microbiome (Cao et al., 2016) and hepatic metabolic health (Cao et al., 2017) showed altered total body weight gain, an effect the current inventors did not observe. Cao et al uses linear p-glucan and not a whole glucan particle.

Energy metabolism in the liver is tightly controlled by insulin which stimulates glycolysis. The altered insulin circulating levels prompted investigation into hepatic metabolic stress. OBD mice displayed increased citrate and lactate levels but equal pyruvate synthesis. This suggests the TCA cycle is disrupted and the citrate is instead shuttles off into Acyl CoA fatty acid synthesis and when that is overloaded the remaining pyruvate is shuttled to the lactic acid pathway.

The metabolic state of the microbiome transplanted into mice greatly impacted their hepatic proteomic signatures. Firstly, OBD recipients displayed altered activated and inhibited pathways compared to OBH recipients. Pathways which were inhibited include the tRNA charging pathway which is responsible for attaching amino acids to tRNA to during protein synthesis. The EIF2 signaling pathway regulates mRNA translation, both specifically and globally, within the cell. The unfolded protein response pathway is activated in response to Endoplasmic Reticulum stress resulting from unfolded or misfolding proteins. This suggest that protein synthesis is inhibited in OBD livers. Previous studies have shown that protein synthesis is reduced in T2D (Jefferson et al., 1983) and specifically insulin impact protein synthesis rates in hepatic tissue(Ahlman et al., 2001). The citrulline metabolism and citrulline biosynthesis pathways were also inhibited in OBD livers. Citrulline is downstream of Acyl-CoA and glutamate in the urea cycle within hepatic mitochondria. This indicates that the urea cycle is inhibited in OBD livers. Pathways which were activated include the galactose degradation pathway which is responsible for the degradation of D-galactose so that it can enter the glycolysis pathway. Gluconeogenesis is also activated which is the generation of glucose from non-carbohydrate precursors. The activation of both of these pathways indicates an alteration in glucose metabolism in the OBD liver. RhoGDI Signaling is a chaperone which prevents Rho protein degradation and alters cellular growth and regeneration patterns. NRF2-Mediated Oxidative stress response is a regulator of cellular resistance to oxidants within the liver. This indicates that there is both altered cellular growth patterns and increased oxidative stress within the OBD liver. Pathway analysis also indicated putative upstream regulators that changed in OBD recipients in comparison to OBH recipients. MYC downregulation indicates reduce gene transcription and supports the inhibition of protein synthesis present in OBD. Additional downregulated genes suggest an increase in glycolysis (glucagon), fatty acid accumulation (ACOX1) and alterations in mitochondria protein biogenesis, trafficking and degradation (CLPP). Activated upstream regulators in OBD hepatic tissue display altered gene transcriptions patterns specific to fatty acid beta-oxidation (PPARA, PNPLA2) and fatty acid storage (ACSS2). These putative activated upstream regulators within OBD in comparison to OBH indicate that gene transcription patterns and fatty acid metabolism/storage is altered in OBD livers.

The differential effect of the obese T2D microbiome on insulin resistance and hepatic metabolic functionality compared to an obese healthy microbiome shows that that obesity alone may not be sufficient to impair metabolic health or interact negatively with microbiome composition.

EXAMPLE 2

This study investigated yeast (1->3)-p-D-glucans interaction with either an obese healthy versus obese diabetic gut microbiome and the impact on hepatic metabolic health and immuno-metabolism in a high-fat feeding challenge.

METHODOLOGY

Male C57BL/6J mice received an antibiotic cocktail in their drinking water of Ampicillin, Metronidazole, Vancomycin, Imipenem and Ciprofloxacin HCI for 6 weeks to diminish the endogenous gut microbiota. Mice were inoculated with microbiota from obese healthy (OBH) or diabetic (OBD) humans twice daily for 3 days by oral dosing. Mice were fed a low-fat diet (LFD) (10% kcal) for 4 weeks followed by a high-fat diet (HFD) (45% kcal) for 9 weeks. Both LFD and HFD were with/without baker’s yeast (1->3)-p-D glucan.

Following HFD challenge, glucose tolerance (1.5g/kg), insulin tolerance (0.5U/kg) and gut microbial compositions were assessed. Hepatic triacylglycerol (TAG), cholesterol, and citric acid cycle (TCA) intermediates including pyruvate, lactate and citrate levels were determined. Hepatic metabolic markers were assessed using real-time PCR. Hepatic transcriptomic and proteomic analysis was completed to determine the altered immuno-metabolic pathways to compliment the phenotypic data.

Statistical analysis included 1-way or 2-way ANOVA, where appropriate, with Bonferroni post- hoc comparisons.

The method is illustrated by Figure 10.

RESULTS

Microbiome source significantly impacted hepatic metabolic health and inflammation. OBD mice fed HFD were more glucose intolerant, insulin insensitive and displayed hepatic lipotoxicity, compared to weight matched HFD-OBH mice. This adverse metabolic phenotype in OBD mice was resolved by -glucan supplementation. Yeast -glucan supplementation increased the abundance of health-related bacterial taxa. Detailed hepatic proteomic and transcriptomic signatures indicated clear modulation of metabolism and inflammatory coregulation. Derivatives of the TCA cycle were also affected in the livers from OBD mice, with higher pyruvate generation capabilities, lactate and citrate levels which may indicate a break in the citric acid cycle. OBD livers also displayed higher inflammation (NF- K B) in addition to an increase in metabolic stressors. Hepatic proteomic analysis is ongoing in order to ascertain the interaction between the OBD versus OBH dysbiosis with/without yeast -glucan supplementation with specific attention on immune-metabolism (Figures 11 to 16).

DISCUSSION

The nature of the human microbiome transplantation altered glucose, insulin, hepatic fatty acid and cholesterol metabolism in response to feeding HFD. The human obese diabetic microbiome induced hepatic steatosis and insulin resistance. The human obese healthy microbiome did not. A novel yeast -glucan supplement partly alleviated these diabetic-like perturbations. Thus despite equal total body weight gain in response to high-fat feeding (data not shown), the diabetic microbiome induced a much more severe phenotype. However, yeast -glucan supplementation resolved the obese diabetic microbiome phenotype, providing protection from HFD induced hepatic steatosis. Further microbiome and proteomic analysis is ongoing to elucidate the mechanism of hepatic dysfunction.

Hepatic metabolic-inflammation was greatly impacted by an obese diabetic microbiome, but this adverse high-fat diet induced phenotype could be re-configured by dietary supplementation with yeast -glucan. Novel dietary interventions are an alternative approach to manage hepatic metabolic-inflammation indicative of diabetes.

EXAMPLE 3

METHODOLOGY

Hepatic mRNA Seq analysis was conducted using the TruSeq Stranded mRNA assay for RNA-seq library preparation and the Illumina Nova Seq 6000 SP100 (2X50bp @>25M reads/ sample) platform, following RNA extraction from flash frozen hepatic tissue using the RNeasy mini kit (Qiagen, Hilden, Germany). In terms of bioinformatics differential expression analysis was conducted using the Deseq2 package (doi: 10.1186/s13059-014-0550-8). Genes that displayed a +/- 20% fold change along with a <0.1 adjusted p-value were taken forward for further investigation. Pathway enrichment analysis was conducted using EnrichR (DOI: 10.1186/1471-2105-14-128), an interactive online tool that returns enriched terms from a wide variety of pathway and expression libraries, ranked by a z-score adjusted q-value . In conjunction, transcription factor association analysis was conducted with Chea3 (doi:10.1093/nar/gkz446), mining various CHIPseq and co-expression studies for overlap with the input gene set.

To investigate the effects of p-glucan on liver health of the obese mouse model +/- obese and/or diabetic microbiome the gene expression of the obese healthy (OBH) mouse liver was compared to both the (obese diabetic) OBD and OBD + p-glucan supplementation. The overall trend showed a higher number of differentially expressed genes between OBH vs OBD (+285/- 363), which were then resolved with the addition of p-glucan supplementation in both OBH and OBD groups.

RESULTS

To this end, the completed GeneSeq analysis demonstrated specific mechanisms targeting liver health, wherein feeding p-glucan targets key transcription factors that regulated lipid metabolism and inflammation, namely PPARa, LXR/RXR, chREBP in concert with STAT3, via TGFp signalling pathway modulation. The latter three targets being completely new and not reported thus far in terms of novel mechanisms. CONCLUSION

Importantly all the above were observed in the presence of equal obesity in the presence of human microbiome transplantation. In all previous work, p-glucan supplementation was so high, concomitant weight loss was induced, which would have confounded results wherein it would have been impossible to directly attribute efficacy to p-glucan supplementation alone, as weight loss improves hepatic lipid metabolism.

The interaction with the human microbiome induced phenotypes relating to liver health has not been previously disclosed. This represents a humanised model of obesity induced fatty liver disease that is preventable by feeding a high fat diet in conjunction p-glucan supplementation.

Equivalents

The foregoing description details presently preferred embodiments of the present invention. Numerous modifications and variations in practice thereof are expected to occur to those skilled in the art upon consideration of these descriptions. Those modifications and variations are intended to be encompassed within the claims appended hereto.

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Claims

Claims

1. An effective amount of a whole (1— >3)-p-D-glucan particle for use in a method for treating or preventing non-alcoholic fatty liver disease (NAFLD) in a subject.

2. The particle for use of Claim 1 , wherein the particle has an average particle size of from 1 pm to 6 pm.

3. The particle for use of Claim 1 or 2, wherein the NAFLD is non-alcoholic steatohepatitis (NASH).

4. The particle of use of any one of the preceding claims, wherein the subject is an obese individual.

5. The particle for use of any one of the preceding claims, wherein the subject is an obese diabetic (OBD) individual.

6. The particle for use of any one of the preceding claims, wherein the subject has an obese diabetic microbiome.

7. The particle for use of any one of the preceding claims, wherein the glucan is p-1,6 branched p-1 ,3 glucan with the following structure: p (1-3)-linked Branch

OH OH OH OH OH p (1-3)-linked Backbone

8. The particle for use of any one of the preceding claims, wherein the glucan is from yeast.

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9. The particle for use of any one of the preceding claims, wherein the yeast is baker’s yeast (Saccharomyces cerevisiae).

10. The particle for use of any one of the preceding claims, wherein said subject has a high fat diet.

11 . The particle for use of any one of the preceding claims, wherein the glucan is provided as a dietary or food supplement comprising said glucan.

12. The particle for use of any one of the preceding claims, wherein the supplement is Wellmune®.

13. The particle for use of any one of the preceding claims, wherein there is no associated body weight loss.

14. The particle for use of any one of the preceding claims, wherein the method comprises a first step of identifying a subject suitable for treatment or prevention.

15. The particle for use of Claim 16, wherein said step comprises identifying a subject with an obese diabetic microbiome.

16. An effective amount of a whole (1— >3)-p-D-glucan particle for use in a method for altering the human microbiome of a subject.

17. The particle for use of Claim 16, wherein said subject has an obese diabetic microbiome and said method is for altering said microbiome to an obese healthy microbiome.

18. A method for treating or preventing NAFLD in a subject, said method comprising administering an effective amount whole (1— >3)-p-D-glucan particle to said subject.

19. The method of Claim 18, wherein the subject is an obese diabetic (OBD) individual.

20. The method of Claim 18 or 19, wherein the subject has an obese diabetic microbiome.

21 . A method for altering the human microbiome of a subject comprising administering an effective amount of a whole (1— >3)-p-D-glucan particle to said subject.

22. The method of Claim 21 , for altering an obese diabetic microbiome in a subject to an obese healthy microbiome.

23. The method of Claim 21 or 22 wherein the subject is an obese diabetic (OBD) individual.

24. An effective amount of a whole (1— >3)-p-D-glucan particle for use in a method for treating or preventing diabetes, in particular T2D, in a subject.

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25. The particle of use of Claim 24, wherein said subject is obese.

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