Method for diagnosing microbiota disorders by detecting glycoprotein 2 (gp2) in faeces

Abstract

The present invention relates to an in vitro method for diagnosing microbiota disorders in a subject by detecting glycoprotein 2 (GP2). In an embodiment, the invention relates to an in vitro method comprising providing a sample of feces of a subject, providing a GP2 binding agent, contacting the sample with the binding agent, thereby forming a GP2-comprising complex from the sample and the GP2 binding agent, and determining a level of GP2 in the sample, where the level of GP2 is indicative of a microbiota disorder in the subject. The invention also relates to a kit and its use for diagnosing a microbiota disorder in a subject by detecting Glycoprotein 2 (GP2) in faeces.

Description

Technical Field

[0001] This invention relates to the fields of molecular biology, diagnostics, and microbiology, particularly the use of biomarkers to identify microbial dysbiosis.

[0002] This invention relates to an in vitro method for diagnosing microbiome dysbiosis in a subject by detecting glycoprotein 2 (GP2). Specifically, the in vitro method includes providing a fecal sample from the subject, providing a GP2 binding reagent, contacting the sample with the binding reagent to form a GP2-containing complex, and determining the level of GP2 in the sample, wherein the level of GP2 indicates microbiome dysbiosis in the subject.

[0003] The present invention also relates to the use of GP2 binding reagents in an in vitro method for diagnosing microbiome dysbiosis in subjects for detecting glycoprotein 2 (GP2) in feces.

[0004] The present invention also relates to a kit for diagnosing microbiome dysbiosis in a subject by detecting glycoprotein 2 (GP2) in feces. Specifically, the kit comprises a GP2 binding reagent, preferably having a solid surface for immobilizing the reagent, or preferably a GP2 binding reagent immobilized to a solid surface; a second affinity reagent labeled for GP2, preferably a tool for detecting signals emitted from the label; and reference data corresponding to GP2 levels indicative of microbiome dysbiosis, preferably also indicating that the subject has and / or is developing systemic inflammation. The reference data may be stored on a computer-readable medium and / or used in the form of computer-executable code configured to compare determined GP2 levels with the reference data; and optionally, a sample collection device adapted to obtain a fecal sample. Background Technology

[0005] Gut bacteria are not passive bystanders, but perform important metabolic functions, and their interactions with the antimicrobial components of the host immune system are crucial for maintaining health. Pathological changes in the gut microbiome (so-called dysbiosis) have been involved in the development and progression of a variety of diseases [1]. To date, many determinants of the composition of the gut microbiome have been described, but many more variables influencing changes in the gut microbiome remain unknown [2, 3].

[0006] Recently, the exocrine pancreas has been identified as one of the most important host factors influencing the composition of the gut microbiome in the general population [4, 5]. Preserved exocrine pancreatic function not only supports a healthy gut microbiome composition but also predicts increased future microbiome stability [6].

[0007] Nevertheless, the relevance of these observations is not fully understood. On the one hand, loss of exocrine pancreatic function can alter the nutritional substrate composition in the large intestine and select specific bacteria based on their primary energy source [4, 7]. On the other hand, secretory proteins from pancreatic acinar cells, such as microbiome-sensing pancreatic glycoprotein 2 (GP2), have been described as playing important roles in innate immune responses against different commensal bacteria or opportunistic pathogens such as Escherichia coli (E. coli) and in the associated potential loss of tolerance [8-10].

[0008] Furthermore, GP2 expression has been detected at the apical plasma membrane of small intestinal M cells and possibly colonic enteroendocrine L cells [8, 11]. In inflammatory bowel disease and autoimmune liver disease, GP2 appears to be a potential autoantigen target associated with severity and tumorigenesis [12, 13]. Loss of tolerance to GP2 in the form of an autoantibody appears to be a stratifying factor in Crohn's disease and appears to be associated with changes in gut microbiota composition [14–16]. Patients with IBD show higher fecal GP2 levels, while GP2-deficient colitis mice exhibit a phenotype characterized by increased intestinal inflammation and E. coli epithelial attachment

[19] . Moreover, in primary sclerosing cholangitis (an autoimmune liver disease associated with specific changes in gut microbiota), the presence of GP2 IgA is a predictor of disease severity and cholangiocarcinoma development [17, 18].

[0009] However, the role and importance of GP2 in the general population without obvious gastrointestinal diseases is unclear. Furthermore, clinicians are seeking new methods to determine the presence or absence of microbiome dysfunction or dysbiosis without directly analyzing comorbidities or other bacterial complications in the human microbiome, which is often complex and requires time-intensive laboratory techniques.

[0010] US2019 / 216861A1 discloses a method for treating Clostridium difficile infection, which involves administering a composition containing at least three specific bacterial strains that are typically reduced in cases of gut microbiota dysbiosis. Additionally, US2019 / 216861A1 describes the use of various biomarkers (including free amino acids and short-chain fatty acids) extracted from fecal samples to diagnose microbial dysbiosis.

[0011] Debyser Griet et al. published fecal metaproteomics for analyzing the composition of host and microbial proteins in the gastrointestinal tract. They identified differences in microbial diversity and protein abundance by examining fecal samples from children with cystic fibrosis and their unaffected siblings. While GP2 was briefly mentioned, it was not considered a decisive factor in the analysis. Furthermore, GP2 was undetectable in patients affected by dysbiosis, and precise measurements of GP2 concentration levels were not performed.

[0012] EP3299818A discloses an in vitro method for diagnosing acute pancreatitis (AP) by detecting protein glycoprotein 2 isoform α (GP2a).

[0013] Kurashima Yosuke et al. investigated the biological functions of GP2 in the intestine and demonstrated that luminal GP2, derived from pancreatic acinar cells and TNF-induced during colitis, binds to symbiotic Escherichia coli, thereby preventing its adhesion to and penetration of the intestinal epithelium. Furthermore, Kurashima Yosuke et al. revealed that the absence and presence of GP2 in the colon are associated with variations in different bacterial species populations. However, while the role of GP2 in regulating bacterial interactions in the gut has been explored, this does not suggest the use of GP2 as a biomarker for diagnosing or treating dysbiosis.

[0014] Therefore, none of the existing technologies offer a solution for determining the presence or absence of microbiome dysfunction or dysregulation without directly analyzing the human microbiome itself. While existing technology disclosures address aspects of microbiome composition and function, they do not propose methods or biomarkers, such as GP2, for assessing microbiome dysregulation. Summary of the Invention

[0015] Given the prior art, a potential technical problem of the present invention is to provide alternative and / or improved means for diagnosing and / or assessing microbiome dysbiosis. Another potential problem of the present invention is to provide alternative and / or improved means for diagnosing and / or assessing systemic inflammation. Yet another potential problem of the present invention includes providing means for determining the presence or absence of microbiome dysfunction or dysbiosis without direct analysis of the human microbiome.

[0016] These problems are addressed by the features of the independent claims. Preferred embodiments of the invention are provided by the dependent claims.

[0017] Therefore, the present invention relates to an in vitro method for diagnosing microbiome dysbiosis in a subject by detecting glycoprotein 2 (GP2). In an embodiment, the method includes: providing a fecal sample from a subject; providing a GP2 binding reagent; contacting the sample with the binding reagent to form a GP2-containing complex; and determining the level of GP2 in the sample, wherein the level of GP2 indicates microbiome dysbiosis in the subject.

[0018] The present invention also relates to a kit for diagnosing microbiome dysbiosis in a subject by detecting glycoprotein 2 (GP2) in feces. In one embodiment, the kit comprises: a GP2 binding reagent, preferably having a solid surface for immobilizing the reagent, or preferably a GP2 binding reagent immobilized to a solid surface; a second affinity reagent for a GP2-labeled substance, preferably a tool for detecting a signal emitted from the label; and reference data corresponding to GP2 levels indicative of microbiome dysbiosis. In another embodiment, the reference data further includes reference data indicating that the subject has and / or is developing systemic inflammation, wherein the reference data is stored on a computer-readable medium and / or used in the form of computer-executable code configured to compare determined GP2 levels with the reference data, and optionally, the kit includes a sample collection device suitable for obtaining or retaining a fecal sample.

[0019] All aspects of this invention are unified by, benefit from, based on, and / or related to the common and surprising finding of the correlation between fecal glycoprotein 2 and gut microbiota dysbiosis. The relationship between GP2 and the human gut microbiota can represent an important factor in clinical practice regarding gut microbiota dysbiosis and gastrointestinal inflammation. To the best of the inventors' knowledge, there is no inspiration in the art regarding the diagnosis of microbiota dysbiosis and systemic inflammation by detecting fecal GP2.

[0020] In one aspect, the present invention relates to an in vitro method for diagnosing microbiome dysbiosis in a subject by detecting glycoprotein 2 (GP2), comprising:

[0021] - Provide stool samples from the subjects.

[0022] - Provide GP2 binding reagents,

[0023] - Contact the sample with the binding agent to form a GP2-containing complex between the sample and the GP2 binding agent, and

[0024] - Determine the level of GP2 in the sample, wherein the level of GP2 indicates dysbiosis in the subject.

[0025] The primary source of fecal GP2 is thought to be secreted by zymogen granules in pancreatic acinar cells

[31] . Another source has also been reported in the follicle-associated epithelium of the Peyer's plate in the small intestine and L cells in the colon [8, 11]. Elevated expression of GP2 has been found in exocrine pancreas and intestinal mucosa, and it is associated with inflammatory processes in patients with Crohn's disease or Crohn's-like inflammation [9, 11, 19]. Unbound by theory, this could be due to tumor necrosis factor (TNF)-triggered GP2 synthesis and increased secretion from the exocrine pancreas

[19] . Alternatively, it could indicate the secretion of intestinal-derived GP2 into the intestinal lumen. Fecal GP2 levels are measured lower in individuals with preserved pancreatic exocrine function. Thus, GP2 is an established marker of inflammatory processes in the gut, such as in various forms of IBD. However, it has been surprising to find that fecal GP2 levels are associated with changes in the composition of the gut microbiome, particularly higher fecal GP2 levels, which are associated with gut microbiome dysbiosis. As shown in more detail in the following examples, changes in fecal GP2 levels are associated with microbial variability. In linear regression and binary regression models, 33 and 41 bacterial taxa, respectively, were detected that were significantly associated with fecal GP2. The use of GP2 assays (detection, measurement, quantification) provides diagnostic and / or prognostic information about an individual's microbiome composition and health status. GP2 levels are associated with microbiome dysbiosis, and the detection of one or more protein molecules in fecal samples allows GP2 detection to be used in reliable and direct molecular methods to provide information about the overall health and status of the human microbiome.

[0026] In implementation, positive correlations were found, for example, with Clostridium XIVa, Collinsella, or opportunistic pathogens Haemophilus and Streptococcus. In recent publications, the presence of Clostridium XIVa in the gut microbiome of (still) healthy individuals has been associated with fatty liver disease or diabetes for more than 5 years, and a causal relationship may exist [6]. Detection of Collinsella has been associated with diabetes, obesity, and prior to the development of fatty liver disease [6, 33, 34]. Therefore, microbiome assessments achieved by GP2 have subsequent relevance and thus prognostic potential in a variety of diseases associated with microbiome dysbiosis.

[0027] In this implementation, a reduction in the presence of opportunistic Gram-negative pathogens Escherichia / Shigella and Citrobacter was found in individuals with elevated fecal GP2 levels.

[0028] In this implementation, elevated GP2 levels are negatively correlated with the presence of gut microbiota pathways required for short-chain fatty acid (SCFA) biosynthesis, such as Ruminococcus, Butyrivibrio, Faecalibacterium, Lachnospiraceae, or Roseburia.

[0029] In this embodiment, GP2 levels are positively correlated with one or more bacteria selected from the following: *Turicibacter*, *Erysipelotrichaceae*, *Phascolarctobacterium*, *Mitsuokella*, *Allisonella*, *Sutterella*, *Parasutterella*, *Haemophilus*, *Collinsella*, *Slackia*, *Prevotella*, *Streptococcus*, *Clostridium sensu stricto*, *Roseburia*, and *Clostridium XIVa*. XIVa), Fusicatenibacter, Dorea, Lachnospiraceae, Romboutsia, Faecalibacterium and / or Ruminococcus.

[0030] like Figure 2 As shown, the presence of these bacteria is positively correlated with GP2 levels, although there are different correlation coefficients indicating the strength of the relationship between the GP2 levels and the bacteria.

[0031] In implementations, the correlation coefficient can be greater than 0, such as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. In one implementation, GP2 levels are positively correlated with *Pseudomonas* spp., with a correlation coefficient exceeding 0.3. Therefore, excessive proliferation of *Pseudomonas* spp. can be identified by elevated fecal GP2 levels, thus indicating microbiome dysbiosis.

[0032] In this embodiment, GP2 levels are positively correlated with one or more bacteria selected from the following: Haemophilus spp., Collins spp., Streptococcus spp., and / or Clostridium spp. XIVa.

[0033] The presence of Clostridium XIVa in the gut microbiota of (still) healthy individuals is associated with fatty liver disease or diabetes for more than 5 years, suggesting a possible causal relationship. Collins spp. were detected and associated with diabetes, obesity, and prior to the development of fatty liver disease. Advantageously, based on such associations, GP2 can be used to predict the likelihood or risk of developing fatty liver disease or diabetes in subjects.

[0034] In this embodiment, GP2 levels are negatively correlated with one or more of the following bacteria: Clostridium IV, Osillibacter, Anaerobic bacteria, Pseudoflavonifractor, Ruminococcus, Victivallis, Oxalobacter, Akkermansia, Odoribacter, Butyricimonas, Alistipes, and / or Catabacter.

[0035] like Figure 2 As shown, these bacteria are negatively correlated with GP2 levels, with different correlation coefficients indicating the strength of the relationship.

[0036] In this implementation, the GP2 level indicates an increased presence (above a threshold or average population level) of one or more of the following bacteria: Haemophilus spp., Actinomyces spp., Gordonibacter spp., Streptococcus spp., Clostridium spp., Dornier spp., Corynebacterium spp., Rombutz spp., Bacillus spp., Clostridium spp. XVIII and / or Veillonella spp.

[0037] In this implementation, the GP2 level indicates a reduction in the presence of one or more bacteria selected from the following (below a threshold or average population level): *Ceratophyllum demersum*, *Acidobacterium*, *Sartella*, *Desulfovibrio*, *Bilophila*, *Desulfovibrionaceae*, *Escherichia* / *Shigella*, *Citrobacter*, *Coraliomargarita*, *Ackermania*, *Olsenella*, *Coriobacteriaceae*, *Barnesiella*, *Coprobacter*, *Butymonas*, and *Porphyromonas*. The family includes *Nadaceae*, *Prevotella*, *Paraprevotella*, *Alloprevotella*, Rikenellaceae, *Calactobacillus*, *Anaerovorax*, *Mogibacterium*, *Butyrivibrio*, *Eisenbergiella*, *Peptococcus*, *Ethanoligenens*, *Sporobacter*, *Anaerofilum*, and / or *Coprobacillus*.

[0038] In this implementation, GP2 levels are negatively correlated with the presence and / or amount of Escherichia coli / Shigella and / or Citrobacter.

[0039] Escherichia coli / Shigella and Citrobacter are opportunistic Gram-negative pathogens. One reason for the negative correlation, without being bound by theory, is that GP2 is a specific intestinal M-cell endocytic transport receptor for type I fimbriae, involved in the mucosal immune response to these bacteria. This finding allows for the assessment of a subject's susceptibility to infection by these opportunistic Gram-negative pathogens.

[0040] In one embodiment, elevated GP2 levels indicate a reduced amount of bacteria involved in short-chain fatty acid (SCFA) synthesis. In other embodiments, elevated GP2 levels indicate reduced SCFA synthesis. In this embodiment, reduced SCFA synthesis may be harmful to human health.

[0041] Advantageously, elevated GP2 levels (above the threshold or average population level) can indicate impaired and / or pathogenic biosynthesis of short-chain fatty acids and / or lactate, which are associated with the prevention of systemic inflammation.

[0042] In one implementation, elevated GP2 levels (above a threshold or average population level, such as above the average GP2 level in healthy individuals) can indicate microbial dysbiosis and systemic inflammation.

[0043] In the implementation, GP2 levels were negatively correlated with the microbial pathway of pyruvate fermentation into acetone.

[0044] In the implementation, GP2 levels were negatively correlated with the microbial pathway of heterologous lactic acid fermentation.

[0045] In the implementation, GP2 levels were negatively correlated with the Bifidobacterium bypass microbial pathway.

[0046] In the implementation, GP2 levels were negatively correlated with the microbial pathway of succinic acid fermentation into butyric acid.

[0047] In this implementation, GP2 levels were negatively correlated with the microbial pathway of acetyl-CoA fermentation into butyrate II.

[0048] In the implementation, GP2 levels were negatively correlated with the microbial pathway of pyruvate fermentation into acetic acid and lactate II.

[0049] In the implementation, GP2 levels were negatively correlated with the microbial pathway of homolactic fermentation.

[0050] In the implementation, GP2 levels were negatively correlated with the microbial pathway of 4-aminobutyric acid (GABA) degradation of V.

[0051] In the implementation, GP2 levels were negatively correlated with the microbial pathway of pyruvate fermentation into isobutanol.

[0052] In the implementation, GP2 levels were negatively correlated with the microbial pathway of acetylene degradation.

[0053] In the implementation, GP2 levels were negatively correlated with the microbial pathway of L-1,2-propanediol degradation.

[0054] In implementation, elevated GP2 levels (e.g., above the average of healthy subjects or the population) indicate the absence of one or more microbial taxa.

[0055] In this implementation, elevated GP2 levels (e.g., above the average of healthy subjects or the population) indicate reduced gut microbiota α diversity.

[0056] In the implementation, elevated GP2 levels (e.g., above the average of healthy subjects or the population) are negatively correlated with microdiversity scores such as the Simpson Diversity Index (N2), Shannon Diversity Index (H), Chao1 estimate, and species richness (N0)

[0057] In another aspect, the present invention relates to an in vitro method for systemic inflammatory risk stratification, wherein:

[0058] - GP2 levels below the threshold indicate a low or no risk of having and / or developing systemic inflammation.

[0059] - GP2 levels equal to or above the threshold level indicate a high risk of having and / or developing systemic inflammation.

[0060] Surprisingly, high fecal GP2 levels were associated with increased systemic inflammation, as indicated by high hs-CRP (C-reactive protein) levels (Table 1). Unbound by theory, the inflammatory process leads to elevated TNF levels, which can trigger increased pancreatic secretion of GP2. Furthermore, high fecal GP2 levels were associated with a significant reduction in a broad range of SCFA-producing bacteria. Therefore, this invention enables the identification or diagnosis of unhealthy metabolic phenotypes with increased systemic inflammation.

[0061] In an implementation, if the GP2 level indicates microbiome dysbiosis, the method may additionally include instructing for and / or administering said treatment, said treatment being selected from one or more of the following: administration of probiotics, administration of prebiotics comprising fructooligosaccharides (FOS), galactooligosaccharides (GOS), and trans-galactooligosaccharides (TOS), dietary modifications, fecal microbiota transplantation (FMT), or antibiotic treatment.

[0062] In an implementation, if a subject has or is suspected of having a gut microbiota dysbiosis, the method may further include instructing appropriate treatment and / or administering treatment selected from one or more of the following: administration of probiotics, prebiotics containing fructooligosaccharides (FOS), galactooligosaccharides (GOS), and trans-galactooligosaccharides (TOS), dietary modifications, fecal microbiota transplantation (FMT), or antibiotic treatment.

[0063] In an embodiment, if the GP2 level indicates microbiome dysbiosis, the method further includes indicative administration and / or administration of one or more bacteria selected from the group consisting of: Clostridium IV, Aerobicobacterium, Anaerobicobacterium, Pseudomonas, Ruminococci, Cerebrolysium, Oxalis, Akkermansia, Osmobacterium, Butymonas, Paroxysmaltobacter and / or Catalbacter, Cerebrolysium, Oxalis, Sartorius, Desulfovibrio, Bilitrophus, Desulfovibrioceae, Escherichia / Shigella *Citrobacter*, *Candida*, *Ackermania*, *Oersensella*, *Rhodotorulata*, *Barnes*, *Factobacillus*, *Butymonas*, *Porphyromonas*, *Prevotella*, *Parprevotella*, *Isprevotella*, *Riken Bacteriaceae*, *Catylobacterium*, *Gastrophagia*, *Diplostomum*, *Vibrio butyricum*, *Eisenbergia*, *Peptococcus*, *Ethanologenic Bacteria*, *Bacillus*, *Anaerobes* and / or *Bacillus*, *Escherichia* / *Shigella* and / or *Citrobacter*.

[0064] In an implementation, if the level of GP2 indicates microbial dysbiosis, the method further includes instructing administration and / or administering one or more microbial groups selected from the group consisting of: Lactococcus, Lactobacillus, Bifidobacterium, Yeast, or non-pathogenic Escherichia coli or Enterococcus.

[0065] In an embodiment, if a subject has or is suspected of having a microbiome dysbiosis, the method further includes instructing the administration and / or application of one or more microbial groups selected from the group consisting of: Clostridium IV, Aerobicobacterium, Anaerobicobacterium, Pseudomonas, Ruminococci, Cerebrolysium, Oxalis, Akkermansia, Osmobacterium, Butymonas, Paroxysmaltobacter and / or Catalbacter, Cerebrolysium, Oxalis, Sartorius, Desulfovibrio, Bilitrophus, Desulfovibrioceae, Escherichia coli / Shigella. *Rheumatoides*, *Citrobacter*, *Candida*, *Ackermania*, *Oersenes*, *Rhodotorula*, *Barnes*, *Factobacillus juvenileus*, *Butymonas*, *Porphyromonas*, *Prevotella*, *Parprevotella*, *Isprevotella*, *Rikenaceae*, *Catobacillus peroxidase*, *Gastrophagia*, *Diplostomum*, *Vibrio butyricum*, *Eisenbergia*, *Peptococcus*, *Ethanologenic Bacteria*, *Bacillus*, *Anaerobes* and / or *Bacillus faecalis*, *Escherichia* / *Shigella* and / or *Citrobacter*.

[0066] In an implementation, if a subject has or is suspected of having a microbiome dysbiosis, the method further includes instructing the administration and / or application of one or more microbial groups selected from the group consisting of: Lactococcus, Lactobacillus, Bifidobacterium, Yeast, or non-pathogenic Escherichia coli or Enterococcus.

[0067] Regarding the aspects and implementations described herein, one or more cutoff or threshold levels may be employed to determine whether any given GP2 level indicates any given indication and / or corresponding treatment.

[0068] In implementations, the threshold levels are 200 ± 20%, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3200, 3400, 3600, 3800, or 4000 ± 20% ng / g. The threshold levels may also fall within the range defined by any two given values ​​provided above. In embodiments of the invention, protection is also claimed for deviations from these possible cutoff values, such as ±30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or ±1%, which apply to any given threshold disclosed herein.

[0069] The cutoff values ​​disclosed herein preferably refer to the protein levels of GP2 or fragments thereof in a fecal sample or a sample prepared from a fecal sample measured by the detection methods described in the following examples. For example, to determine the GP2 level in feces, a natural fecal sample was analyzed using the Pancreatitis GP2 ELISA (GA Generic Assays, Berlin, Germany) according to the manufacturer's protocol. This assay detects the larger isotype of GP2

[22] . OD measurements were performed using a spectrophotometer (e.g., a SpectraMax 190 from MolecularDevices), and GP2 values ​​were calculated as ng GP2 / mg feces or ng GP2 / g feces based on an internal GP2 standard curve. Therefore, the values ​​disclosed herein may vary to some extent depending on the detection / measurement method employed, and the specific values ​​disclosed herein are intended to be consistent with corresponding values ​​determined by other methods.

[0070] As shown in the examples below, the study population was randomly selected from the general population in northeastern Germany to study the prevalence and characteristics of common diseases in the region and their risk factors. The median GP2 level was 1118.7 ng / g feces, with 215.4 being the first quartile and 3659.1 being the third quartile.

[0071] In this implementation, the threshold level is equal to or higher than 215 ± 20% ng / g. This threshold identifies subjects with GP2 levels at the second to fourth quartiles.

[0072] In this implementation, the threshold level is equal to or higher than 1118 ± 20% ng / g. This threshold identifies subjects in healthy individuals and / or the general population whose GP2 levels are higher than the median level, without requiring pre-selection for specific medical conditions.

[0073] In this implementation, the threshold level is equal to or higher than 3659 ± 20% ng / g. This threshold identifies subjects with GP2 levels at the fourth quartile, representing one-quarter of the population with relatively high GP2 levels. In this implementation, similar but divergent threshold levels may be used, such as 3500, 3550, 3600, 3650, 3700, 3750, or 3800 ng / g, or any value within a range between any two of these values.

[0074] In other embodiments, the lowest 20% of the population had fecal GP2 levels below 124.4 ng / g ± 20%.

[0075] In other embodiments, the fecal GP2 level of the second 20% of the population ranged from 124.4 ng / g ± 20% to 686.8 ng / g ± 20%.

[0076] In other embodiments, the fecal GP2 level of the third 20% of the population ranged from 686.9 ng / g ± 20% to 1853.7 ng / g ± 20%.

[0077] In other embodiments, the fecal GP2 level of the fourth 20% of the population ranged from 1853.8 ± 20% ng / g to 4605.6 ± 20% ng / g.

[0078] In other embodiments, the highest 20% of the population had fecal GP2 levels higher than 4605.7 ± 20% ng / g.

[0079] In embodiments of the invention, the cutoff value for GP2 can define a transition from low risk to high risk and can be any value disclosed herein, or any value within a range defined by the values ​​disclosed herein. Any disclosed ±20% value can also be defined by any other variation disclosed herein, such as ±30% to ±1%, as described above.

[0080] In implementations, the methods used herein also include diagnosing, prognosticating, and / or risk stratifying subjects with and / or developing systemic inflammation (e.g., systemic inflammation associated with gastrointestinal disorders) by detecting glycoprotein 2 (GP2), wherein the level of GP2 indicates that the subject has and / or is developing systemic inflammation. Because GP2 levels are associated with certain bacterial strains or taxa, elevated or decreased GP2 levels compared to the population mean can be used to indicate the presence or absence of microbiome dysbiosis. Considering the studies described in the embodiments, using a randomized population, GP2 values ​​can be measured, and an appropriate cutoff value can be selected based on the distribution of GP2 levels in the population. Therefore, the correlation between GP2 and microbiome dysbiosis or other conditions (e.g., systemic inflammation) can be used to identify said medical conditions without having to fix a precise cutoff level. However, those skilled in the art can determine an appropriate cutoff value using methods established in the art.

[0081] For example, ROC-based analysis can be used to determine statistically significant differences between two clinical patient groups. The Receiver Operating Characteristic (ROC) curve measures the ranking efficiency of the model fit probability relative to the response levels. ROC curves can also help set benchmarks in diagnostic tests. The higher the curve from the diagonal, the better the fit. If the logistic fit has more than two response levels, it produces a generalized ROC curve. In such a graph, there is a curve for each response level, which is the ROC curve for that level relative to all other levels. Software capable of performing this analysis to establish appropriate reference levels and cutoffs is available, such as JMP 12, JMP 13, and 20 StatisticalDiscovery from SAS.

[0082] In the implementation method:

[0083] - GP2 levels below the threshold indicate the absence or non-severity of microbiome dysbiosis, and / or the absence or low risk of having and / or developing systemic inflammation.

[0084] - GP2 levels equal to or above the threshold level indicate the presence or severity of microbiome dysbiosis, and / or a high risk of having and / or developing systemic inflammation.

[0085] - Wherein, the threshold level is 3659.1 ± 20% ng / g.

[0086] According to the present invention, in the context of “indicating microbiome dysbiosis” or other medical conditions or states, the term “indicating” is intended as a measure of risk and / or probability. Preferably, the presence or absence of an “indicating” (e.g., microbiome dysbiosis) is intended as a risk assessment and should not generally be interpreted in a restrictive manner as explicitly pointing to the absolute presence or absence of said state. However, in consideration of the foregoing, determining the level of GP2 is highly reliable in determining the presence or absence of microbiome dysbiosis, enabling risk assessment to facilitate appropriate action by healthcare professionals.

[0087] In this implementation, the detected GP2 is glycoprotein 2 isotype α (GP2a).

[0088] In this implementation, the detected GP2 is glycoprotein 2 isotype β (GP2b).

[0089] In this implementation, GP2 is detected by detecting isotype α (GP2a) protein, wherein glycoprotein 2 isotype α (GP2a) comprises or is composed of the following proteins:

[0090] a) Has the amino acid sequence according to SEQ ID NO 1 or 2,

[0091] b) A truncated amino acid sequence according to SEQ ID NO 1 or 2, wherein no more than 50 amino acids are missing from the N-terminus and / or C-terminus of the sequence, or

[0092] c) An amino acid sequence having greater than 80%, greater than 85%, greater than 90%, or more preferably greater than 95% sequence identity with a) or b).

[0093] Isotypes of GP2 also include those amino acid sequences that are substantially the same as those explicitly listed. This refers to one or more amino acid sequences that are similar to but different from the amino acid sequences explicitly provided herein.

[0094] In another embodiment of the invention as described herein, the affinity reagent specifically binds to GP2a.

[0095] - Wherein, GP2a preferably contains or is composed of proteins.

[0096] a) Has the amino acid sequence according to SEQ ID NO 1 or 2,

[0097] b) A truncated amino acid sequence according to SEQ ID NO 1 or 2, wherein no more than 50 amino acids are missing from the N-terminus and / or C-terminus of the sequence, or

[0098] c) An amino acid sequence having greater than 80%, greater than 85%, greater than 90%, or more preferably greater than 95% sequence identity with a) or b).

[0099] In this implementation, GP2 is detected by detecting isotype β (GP2b) protein, wherein glycoprotein 2 isotype β (GP2b) comprises or is composed of the following proteins:

[0100] a) Has the amino acid sequence according to SEQ ID NO 3 or 4,

[0101] b) A truncated amino acid sequence according to SEQ ID NO 3 or 4, wherein no more than 50 amino acids are missing from the N-terminus and / or C-terminus of the sequence, or

[0102] c) An amino acid sequence having greater than 80%, greater than 85%, greater than 90%, or more preferably greater than 95% sequence identity with a) or b).

[0103] In other embodiments, the affinity reagent specifically binds to glycoprotein 2 isoform β (GP2b).

[0104] - Wherein, GP2b preferably contains or is composed of proteins.

[0105] a) Has the amino acid sequence according to SEQ ID NO 3 or 4,

[0106] b) A truncated amino acid sequence according to SEQ ID NO 3 or 4, wherein no more than 50 amino acids are missing from the N-terminus and / or C-terminus of the sequence, or

[0107] c) An amino acid sequence having greater than 80%, greater than 85%, greater than 90%, or more preferably greater than 95% sequence identity with a) or b).

[0108] In other embodiments, the affinity reagent binds to both GP2a and GP2b. In other embodiments, the affinity reagent specifically binds to GP2a, and preferably does not bind to or binds negligibly to glycoprotein 2 isoform β (GP2b) according to SEQ ID NO 3 or 4. In other embodiments, the affinity reagent specifically binds to GP2b, and preferably does not bind to or binds negligibly to glycoprotein 2 isoform α (GP2a) according to SEQ ID NO 1 or 2.

[0109] This invention also covers variations in the amino acid sequence and the length of the encoding nucleic acid as described herein. Those skilled in the art can detect amino acid sequence variants that are longer or shorter than specific sequences of SEQ ID NO 1 to 4, which will still exhibit sufficient similarity to the native form to provide the diagnostic results described herein. For example, shorter variants or fragments of a longer isotype (SEQ ID NO 1 or 2) containing 10, 20, 30, 40, or 50 fewer amino acids than the full-length form can also achieve effective diagnostic results, as described herein. Similarly, longer variants of a shorter isotype (SEQ ID NO 3 or 4) containing 10, 20, 30, 40, or 50 more amino acids than the native length form can also achieve effective diagnostic results, as described herein.

[0110] In another embodiment, the method described herein is performed as an enzyme-linked immunosorbent assay (ELISA), wherein the affinity reagent is immobilized on a solid surface prior to contact with the sample. A significant advantage of the method according to the invention is that it can be performed as an ELISA, a common and routine laboratory technique that can be performed in virtually every diagnostic laboratory. Because the method can be performed as an ELISA, complex diagnostic procedures such as endoscopy or biopsy analysis can be avoided, thereby enabling a wider target population to undergo the method according to the invention.

[0111] In one embodiment, the method of the present invention is characterized in that the determination of GP2 concentration is performed by:

[0112] a) GP2 is captured from the sample via a GP2 affinity reagent immobilized on a solid surface.

[0113] b) Treat the captured GP2 with a second affinity reagent labeled for GP2.

[0114] c) Detect the signal emitted from the second affinity reagent against the GP2 label, and

[0115] d) The signal obtained from the second affinity reagent of the label is compared with the signal from one or more control samples of a predetermined GP2 concentration.

[0116] In one embodiment, the method of the present invention is characterized in that the GP2a concentration is determined by the following:

[0117] a) GP2a is captured from the sample via a GP2a affinity reagent immobilized on a solid surface.

[0118] b) Treat the captured GP2a with a second affinity reagent labeled for GP2.

[0119] c) Detect the signal emitted from the second affinity reagent against the GP2 label, and

[0120] d) The signal obtained from the second affinity reagent of the label is compared with the signal from one or more control samples at a predetermined GP2a concentration.

[0121] In one embodiment, the method of the present invention is characterized in that the determination of GP2b concentration is performed by:

[0122] a) GP2b is captured from the sample via a GP2b affinity reagent immobilized on a solid surface.

[0123] b) Treat the captured GP2b with a second affinity reagent labeled for GP2.

[0124] c) Detect the signal emitted from the second affinity reagent against the GP2 label, and

[0125] d) The signal obtained from the second affinity reagent of the label is compared with the signal from one or more control samples at a predetermined GP2b concentration.

[0126] A significant advantage of this invention is that the concentration of GP2 in a sample can be determined by comparing a signal emitted from a second affinity reagent labeled with GP2 captured in the sample with a signal emitted from a second affinity reagent from one or more control samples with predetermined GP2 concentrations. Such control samples can be readily generated during this method using recombinant GP2. This allows for the determination of GP2 concentrations using routine laboratory procedures requiring only standard laboratory equipment.

[0127] The fact that a GP2-specific affinity reagent is immobilized allows for the removal of all other sample components besides GP2 from the surface coupled with the GP2 affinity reagent by washing the solid surface after capturing GP2 molecules present in the sample. In the case of isotype-specific detection, since no other GP2 besides the isolated isotype is present on the solid surface after washing away all other sample components, a second affinity reagent that is not specific to the GP2 isotype but only requires the ability to recognize any labeled GP2 affinity reagent can be used. Such affinity reagents are known in the art and readily available, which is highly advantageous for making the present invention widely usable.

[0128] In a preferred embodiment of the invention, the signal is preferably derived from horseradish peroxidase conjugated with a second affinity reagent. Horseradish peroxidase (HRP) is used in biochemical applications primarily because of its ability to amplify weak signals and increase the detectability of target molecules. By making its presence visible through the use of a substrate, a characteristic change detectable by spectrophotometric methods is produced when HRP is oxidized using hydrogen peroxide as an oxidant. Many substrates for horseradish peroxidase have been described and commercialized to utilize the desired characteristics of HRP. Horseradish peroxidase is also commonly used in techniques such as ELISA and immunohistochemistry due to its monomeric nature and ease of producing colored products. Compared to other commonly used alternatives, horseradish peroxidase is ideal for these applications in many respects because it is smaller, more stable, and less expensive. It also has a high conversion rate that allows for the generation of strong signals over a relatively short time span.

[0129] In one embodiment, the invention further includes informing the patient of the results of the diagnostic methods described herein.

[0130] In another aspect, the present invention relates to the use of GP2 binding reagents in an in vitro method for diagnosing microbiome dysbiosis in subjects for detecting glycoprotein 2 (GP2) in feces.

[0131] On the other hand, the present invention relates to a kit for diagnosing microbiome dysbiosis in subjects by detecting glycoprotein 2 (GP2) in feces, comprising:

[0132] - A GP2 binding agent, preferably having a solid surface for immobilizing the agent, or preferably a GP2 binding agent immobilized to a solid surface.

[0133] - A second affinity reagent for the GP2 tag, preferably used in tools for detecting signals emitted from the tag, and

[0134] - Reference data corresponding to GP2 levels indicating microbiota dysbiosis, preferably also including reference data indicating that the subject has and / or is developing systemic inflammation, wherein the reference data is stored on a computer-readable medium and / or used in the form of computer-executable code configured to compare determined GP2 levels with the reference data, and

[0135] - Optionally, a sample collection device suitable for obtaining fecal samples.

[0136] In one implementation, the reference data may be a list of bacteria that are overexpressed and / or underexpressed in relation to fecal GP2 levels. In another implementation, the reference data may be an index of dysregulation regarding the correlation between fecal GP2 levels and the bacterial population. In yet another implementation, the reference data may be a threshold level of fecal GP2 indicating systemic inflammation.

[0137] The kit according to the invention, combined with a computer system suitable for automated analysis of one or more samples, was inspired solely by the novel and unexpected discoveries of the invention. Therefore, the combination of the kit and the computer system components is considered an unexpected development in the art. No suggestion in the relevant literature suggests that a system including said components should have been developed.

[0138] A significant advantage of computer systems for automated analysis of one or more samples is that they include components available in most laboratories performing in vitro diagnostic methods. Those skilled in the art will understand different implementations of computer processing devices, microplate readers, and camera devices for detecting the signal of a second affinity reagent labeled against GP2.

[0139] The embodiments and features described herein for diagnosing microbiome dysbiosis by detecting GP2 in fecal samples of subjects are considered to be disclosed with respect to various and each other aspect of this disclosure, such that features characterizing the methods as used herein can be used to characterize the use of fecal GP2 for diagnosing microbiome dysbiosis and the kits as described herein, and vice versa.

[0140] All aspects of the present invention are unified, benefited from, based on, and / or associated with the common and surprising finding of the correlation between fecal glycoprotein 2 and gut microbiota dysbiosis. Detailed Implementation

[0141] All cited references in patent and non-patent literature are incorporated into this paper in their entirety through citation.

[0142] The term "microbiome dysbiosis" refers to alterations in the composition and function of the gut microbiota. In a preferred embodiment, dysbiosis is an alteration of the gut microbiota that is detrimental to the health of the subject. The most typical characteristics of dysbiosis are a reduction in microbiome diversity, loss of beneficial microbiota, or overgrowth of harmful microbiota. The term "gut microbiota" includes all microorganisms, not only bacteria, but also fungi, protozoa, archaea, and viruses residing in the gastrointestinal tract. All of these can be potential triggers for gut microbiome dysbiosis.

[0143] Gut microbiota dysbiosis is associated with a number of adverse conditions, such as Clostridium difficile (CDI) infection, metabolic syndrome, inflammatory bowel disease (IBD), colorectal cancer, chronic hepatitis, common variant immunodeficiency, and even schizophrenia. Dysbiosis has also been observed in non-gut microbiota, such as the gingival, oral mucosa and saliva microbiota, as well as the scalp and forehead microbiota. The application of the term "dysbiosis" can range from alterations in single species to disturbances in the entire microbiota.

[0144] In implementation, the dysregulation can be caused by host-specific factors such as genetic background, health status (infection, inflammation), lifestyle habits, or environmental factors such as diet (high sugar, low fiber), exogenous substances (antibiotics, drugs, food additives) and hygiene.

[0145] As used herein, the term "microbiome" encompasses bacteria, archaea, protozoa, fungi, and viruses—ecological communities of symbiotic, commensal, and pathogenic microorganisms found both inside and outside all multicellular organisms, from plants to animals. The microbiome is crucial for the immunity, hormones, and metabolic homeostasis of its host. Different microbiomes live in different parts of the body, prefer different foods, and perform different functions. Examples include the oral microbiome, the skin microbiome with its many subcategories (armpit, nose, feet, etc.), and the gut microbiome.

[0146] The term "microbiome" describes the collection of genomes of microorganisms inhabiting an environmental niche, or the microorganisms themselves, including microbial structural elements such as proteins / peptides, lipids, polysaccharides, nucleic acids, mobile genetic elements, or microbial metabolites such as signaling molecules, toxins, and (a) organic molecule. The terms "microbiota" and "microbiome" are used interchangeably herein.

[0147] An "imbalance index" is a measure that characterizes disease and adverse conditions or predicts treatment outcomes. Most imbalance indices are based on comparisons with a set of individuals or samples used as a reference. In implementations, imbalance indices can include multiple categories based on one or more methodologies used to assess the microbiota, for example, including five categories including large-scale bacterial marker profiles, related taxonomic methods, neighborhood classification, random forest prediction, and combined α-β diversity (as shown in Shangdong Wei et al., 2021, Determining Gut Microbial Dysbiosis: a Review of Applied Indexes for Assessment of Intestinal Microbiota Imbalances). Imbalance indices can be used, for example, as reference data for diagnosis. A technician can create their own imbalance index based on the correlation between fecal GP2 levels and bacteria.

[0148] The term "fecal sample" refers to any sample containing fecal matter, such as that used for fecal testing, which involves the collection and analysis of fecal matter in medical diagnostic techniques. Tests performed on fecal samples include microbial analysis (culture), microscopy, and chemical testing. In the context of this invention, fecal GP2 level refers to the GP2 level detected in a fecal sample.

[0149] In this embodiment, the fecal sample may be processed prior to analysis. In this embodiment, the fecal sample contacted with the GP2 binding reagent has been processed and is suitable for immunoassay. Standard procedures known to those skilled in the art can be employed. In this embodiment, the sample is derived from a fecal sample. In this embodiment, the fecal sample is obtained prior to this method and used in this method, which is a completely ex vivo or in vitro method. Therefore, in this embodiment, sample collection may be performed prior to this method.

[0150] As a non-limiting example, participants were provided with stool collection tubes. Stool samples were collected at home by study participants and brought in person to the research center or by mail. To determine GP2 levels in stool, natural stool samples were analyzed using the Pancreatitis GP2 ELISA (GA Generic Assays, Berlin, Germany) according to the manufacturer’s protocol. This assay detects the larger isotype of GP2

[22] . Briefly, 25 mg of stool was homogenized in 1.25 ml of extraction buffer and centrifuged at 3,000 g for 10 min. Subsequently, 20 μl of the supernatant was analyzed in a 96-well plate containing GP2 standards as well as positive and negative controls. The final OD values ​​were measured on a spectrophotometer (e.g., a SpectraMax 190 from Molecular Devices) and GP2 values ​​were calculated as [ng GP2 / mg stool] or [ng GP2 / g stool] based on an internal GP2 standard curve.

[0151] The term "probiotics" should refer to live microorganisms that, when consumed, typically provide health benefits by improving or restoring the gut microbiota. Non-limiting examples include Lactococcus, Lactobacillus, Bifidobacterium, yeast, or non-pathogenic Escherichia coli or Enterococcus.

[0152] The term "prebiotic" refers to compounds in food that promote the growth or activity of beneficial microorganisms such as bacteria and fungi. The most common setting considered is the gastrointestinal tract, where prebiotics can alter the composition of organisms in the gut microbiome.

[0153] The term "taxa" (singular taxa) refers to a group of one or more populations of one or more organisms observed by taxonomists to form a unit.

[0154] In the context of this invention, the term "diagnosis" refers to the identification and (early) detection of a subject's clinical condition associated with microbiome dysbiosis and / or systemic inflammation. Furthermore, the term may encompass the assessment of the severity of the condition.

[0155] The term "prognosis" refers to the prediction of a subject's outcome or specific risk based on microbiome dysbiosis. This may also include an estimate of the subject's chance of recovery or the likelihood of an adverse outcome.

[0156] In this invention, the terms "risk assessment" and "risk stratification" refer to grouping subjects into different risk groups based on their further prognosis. Risk assessment also involves stratification for the application of preventive and / or therapeutic measures. Examples of risk stratification are the low, intermediate, and high risk levels disclosed herein.

[0157] The term "correlation" refers to a relationship between two or more variables. This relationship does not necessarily imply cause and effect. When two variables are correlated, it means that as one variable changes, the other also changes. As an example, correlation can be measured by calculating a statistic called the correlation coefficient. The correlation coefficient can be a number from -1 to +1, indicating the strength and direction of the relationship between the variables (e.g., as shown in the original text). Figure 2 and Figure 3 (As shown), usually represented by the letter r. This invention is based on the unexpected discovery of a correlation between fecal GP2 and microbial diversity (especially microbial dysbiosis). This correlation reveals the relationship between fecal GP2 and microbial diversity, and enables a novel diagnostic method for microbial dysbiosis by detecting fecal GP2.

[0158] The term "correlation coefficient" is a measure of the strength of a relationship. The numerical part of the correlation coefficient indicates the strength of the relationship. The closer the number is to 1 (whether negative or positive), the stronger the correlation between the variables, and the more predictable the effect of a change in one variable on the other. The closer the number is to zero, the weaker the relationship, and the less predictable the relationship between the variables. For example, a correlation coefficient of 0.9 indicates a much stronger relationship than a correlation coefficient of 0.3. If the variables are not correlated at all, the correlation coefficient is 0. In this invention, a correlation coefficient greater than 0 indicates a relationship between fecal GP2 levels and bacteria. The closer the number is to 1, the stronger the relationship, and the more predictable the relationship between fecal GP2 and bacteria.

[0159] The term "positive correlation" means that the variables change in the same direction. In other words, it means that as one variable increases, the other also increases, and conversely, when one variable decreases, the other also decreases.

[0160] In this embodiment, fecal GP2 levels are positively correlated with one or more bacteria selected from the following: *Zurichella* spp., *Erysipelothrix* spp., *Koala* spp., *Osmia* spp., *Alison* spp., *Sartreus* spp., *Parasartreus* spp., *Haemophilus* spp., *Collins* spp., *Shrekris* spp., *Prevotella* spp., *Streptococcus* spp., *Clostridium* spp., *Lactobacillus* spp., *Clostridium* spp. XIVa, *Clostridium* spp., *Dolberella* spp., *Trichophyton* spp., *Rombutz* spp., *Femtobacter* spp. and / or *Ruminococcus* spp., *Haemophilus* spp., *Collins* spp., *Streptococcus* spp. and / or *Clostridium* spp. XIVa, *Haemophilus* spp., *Actinomyces* spp., *Goldenbacter* spp., *Streptococcus* spp., *Clostridium* spp., *Dolberella* spp., *Corynebacterium* spp., *Rombutz* spp., *Zurichella* spp., *Clostridium* spp. XVIII and / or *Veillonella* spp. Each positive correlation has a different correlation coefficient, which indicates not only the existing relationship between fecal GP2, but also the strength of the relationship.

[0161] The term "inverse correlation" (also known as "negative correlation") should indicate that variables change in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other, and vice versa.

[0162] In this embodiment, fecal GP2 levels are negatively correlated with one or more bacteria selected from the following: Clostridium IV, Aerobicobacterium, Anaerobic Bacteria, Pseudomonas flavonoids, Ruminococci, Cerebrolysium, Oxalis, Akkermansia, Osmobacterium, Butymonas, Alternaria and / or Catalobacterium, Cerebrolysium, Oxalis, Sartorius, Desulfovibrio, Biliophilia, Desulfovibrioceae, Escherichia / Shigella, Citrobacter, and Candida albicans. Genus *Akkermansia*, *Oersensiella*, Rhodotorulaceae, *Barnes*, *Femtobacter*, *Butymonas*, *Porphyromonas*, *Prevotella*, *Parprevotella*, *Isprevotella*, Rikenaceae, *Catobacillus*, *Vibrio*, *Butyrica*, *Essenbergia*, *Peptococcus*, *Ethanologenic Bacteria*, *Bacillus*, *Anaerobes* and / or *Bacillus*, *Escherichia* / *Shigella* and / or *Citrobacter*. The correlation coefficient for each inverse correlation is different, indicating not only the existing relationship between fecal GP2s but also the strength of the relationship.

[0163] The term “reduction in presence” should be understood to mean a reduction in bacteria in the microbiome diversity compared to the population average of healthy subjects.

[0164] The term "gut microbial alpha diversity" refers to a measure of microbiome diversity applicable to a single sample. Many indices exist, each reflecting a different aspect of community heterogeneity. In implementation, alpha diversity measures can be viewed as a summary statistic of a single population (within sample diversity). The term "gut microbial beta diversity" refers to a measure of the similarity or dissimilarity between two communities. It is a fundamental measure in many popular statistical methods in ecology, such as rule-based methods, and is used to study the association between environmental variables and microbial composition.

[0165] As used herein, the term "therapy" refers to the application of certain therapeutic or medical interventions based on the values ​​of one or more biomarkers and / or clinical parameters and / or clinical scores.

[0166] The term "reference data" should not be limited to a threshold level of fecal GP2, but can also refer to the overexpression and / or underexpression of bacteria associated with fecal GP2 levels. In embodiments, reference data can be a threshold level of fecal GP2 indicating systemic inflammation. In embodiments, reference data can be an index of dysregulation regarding the correlation between fecal GP2 levels and bacterial populations.

[0167] The terms "short-chain fatty acids" (SCFAs) and / or "lactic acid" refer to primary metabolites that can influence the composition and function of the human microbiome. Many bacteria that produce SCFAs and lactic acid have been identified in the gut microbiome.

[0168] The term "systemic inflammation" refers to the result of the release of pro-inflammatory cytokines from immune-related cells and the activation of the innate immune system. In this embodiment, systemic inflammation is chronic inflammation. It can contribute to the development or progression of certain conditions, such as cardiovascular disease, cancer, diabetes, chronic kidney disease, non-alcoholic fatty liver disease, autoimmune and neurodegenerative diseases, and coronary heart disease. Certain microbial species produce specific enzymes that can ferment nutrients into absorbable forms, including the fermentation of indigestible carbohydrates into short-chain fatty acids (SCFAs). These SCFAs can have anti-inflammatory and immunomodulatory effects. In this invention, fecal GP2 levels are negatively correlated with the gut microbial pathways required for the biosynthesis of SCFAs, which are indicators of systemic inflammation.

[0169] Gut bacteria possess the ability to metabolize complex carbohydrates that cannot be digested by the host into SCFAs. SCFAs play a crucial role in the interaction between diet, gut microbiota, and the activation or inhibition of downstream inflammatory cascades. Furthermore, they contribute to the homeostatic control of energy and appetite regulation through their effects on metabolic pathways. Their effects on inflammation vary depending on the type and concentration of SCFAs, and SCFA levels may differ between obese and lean phenotypes. In numerous animal studies, the SCFA butyrate has been associated with multiple roles in combating the onset of metabolic disorders. Through epigenetic interactions, butyrate promotes lipolysis and mitochondrial function in adipocytes, leading to greater energy expenditure and preventing the onset or maintenance of obesity. Butyrate is a known anti-inflammatory metabolite that inhibits pathways leading to the production of pro-inflammatory cytokines. In a clinical study of 13 patients with Crohn's disease, oral administration of butyrate was found to reduce inflammation in 9 patients. Additionally, butyrate minimizes the risk of developing insulin resistance by improving insulin signaling. Butyrate has also been shown to minimize LPS translocation in the gut, thereby reducing LPS-related effects. *Faecalibacterium prausnitzii* has been identified as a butyrate-producing bacterium negatively correlated with various pro-inflammatory markers. *Faecalibacterium prausnitzii* abundance is reduced in obese individuals compared to those with a healthy weight.

[0170] Acetic acid (another SCFA) is an important molecule in the processes of lipogenesis and glucose production. Acetic acid can serve as a substrate for cholesterol synthesis, thus contributing to increased serum cholesterol levels. In rat studies, unlike butyrate, acetic acid was found to be associated with greater insulin resistance and increased ghrelin secretion due to its activation of the parasympathetic nervous system. Since ghrelin is an appetite-stimulating hormone associated with increased food intake, acetic acid may be related to weight gain.

[0171] Therefore, the present invention also relates to methods for diagnosing, prognosing, or determining the risk or likelihood of a subject having altered SCFA metabolism and related medical conditions using the GP2 detection and identification methods described herein.

[0172] As used herein, "patient" or "subject" can refer to a vertebrate. In the context of this invention, the term "subject" includes humans and animals, particularly mammals and other organisms. The terms "individual," "subject," or "patient" generally refer to humans, but also to other animals, including, for example, other primates, rodents, canines, felines, equines, sheep, pigs, etc.

[0173] The term "in vitro method" refers to methods used on samples (e.g., but not limited to fecal samples, tissues, or body fluids) outside their normal biological environment.

[0174] As used herein, the term "substantially identical amino acid sequence" includes amino acid sequences that are similar to, but not identical to, naturally occurring amino acid sequences. For example, an amino acid sequence, i.e., a polypeptide, having a substantially identical amino acid sequence to the GP2 isotypes in SEQ ID NO 1 to 4, and may have one or more modifications, such as the addition, deletion, or substitution of amino acids relative to the amino acid sequence of the GP2 isotype, if the modified polypeptide substantially retains at least one biological activity of GP2, such as immunoreactivity.

[0175] As used herein, the term "GP2 isotype" includes proteins having at least about 70% amino acid identity with one or more SEQ ID Nos. 1 to 4. As a non-limiting example, the GP2 isotype of the present invention may have at least about 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with one or more SEQ ID Nos. 1 to 4.

[0176] Therefore, the present invention can use affinity reagents, such as antibodies, against GP2 isotypes or GP2 proteins having substantially the same amino acid sequence to detect GP2.

[0177] The analysis described herein, which measures GP2 concentration by means of antibodies binding to one or more GP2 isotypes, is the preferred method of the present invention. Alternatively, or in combination, as already obtained from prior analytical tests, control values ​​or standards providing samples with GP2 isotypes or representative amounts thereof can be used. Control values ​​can be generated by testing cohorts or other large numbers of subjects suffering from any given disease or control group. Appropriate statistical methods for analyzing and comparing these datasets are known to those skilled in the art. Control samples used for positive controls (e.g., patients with disease) or negative controls (from healthy subjects) can be used as reference values ​​for simultaneous or non-simultaneous comparisons.

[0178] In the context of this invention, the term "affinity reagent" refers to an antibody, peptide, nucleic acid, small molecule, or any other molecule that specifically binds to a target molecule to recognize, track, capture, or influence its activity. The term "capture" refers to the binding of a target molecule by an affinity reagent.

[0179] The term "second affinity reagent" refers to any affinity reagent as defined above, which is used to bind an antigen that has already been bound by another affinity reagent.

[0180] As used herein, the term "antibody" includes a group of immunoglobulin molecules, which may be polyclonal or monoclonal and of any isotype, or immunoglobulin molecules of an immunoglobulin molecule that are immunoactively bound. Such immunoactive fragments contain variable regions of heavy and light chains that constitute the antibody molecule portion that specifically binds to antigens. For example, immunoactive fragments of immunoglobulin molecules known in the art as Fab, Fab', or F(ab')2 are included within the meaning of the term antibody.

[0181] The term "monoclonal antibody" refers to an antibody prepared from the same immune cell, which is a unique clone of the parent cell, as opposed to polyclonal antibodies prepared from multiple different immune cells. Because monoclonal antibodies bind to the same epitope (the part of the antigen the antibody recognizes), they can have monovalent affinity. Engineered bispecific monoclonal antibodies also exist, where each "arm" of the antibody is specific to a different epitope. Given that almost any substance can produce monoclonal antibodies that specifically bind to it, these can be used to detect or purify that substance.

[0182] In another advantageous embodiment, an immunoassay is used for the detection of GP2, presumably with a GP2-specific antibody binding to the ends of a solid phase. After the addition of a sample solution, the patient's GP2 contained therein binds to the GP2 antibody. For GP2 obtained, for example, from the patient's stool and bound to the GP2 antibody, it is subsequently detected using a label or labeled reagent and optionally quantified.

[0183] Therefore, according to the present invention, the detection of GP2 in this method is achieved using a labeling reagent based on the well-known ELISA (Enzyme-Linked Immunosorbent Assay) technology. Thus, the label according to the present invention comprises an enzyme that catalyzes a chemical reaction, which can be determined by optical means, particularly by chromogenic substrates, chemiluminescence methods, or fluorescent dyes. In another preferred embodiment, GP2 is detected by labeling with a weakly radioactive substance in a radioimmunoassay (RIA), wherein the resulting radioactivity is measured.

[0184] As an example of the means used to detect markers in the methods of the present invention, a variety of immunoassay techniques, including competitive and non-competitive immunoassays, can be used to determine the presence or level of one or more markers in a sample (see, for example, Self et al., Curr. Opin. Biotechnol., 7:60-65 (1996)). The term immunoassay includes, but is not limited to, the following techniques: enzyme immunoassay (EIA), such as enzyme amplified immunoassay (EMIT), enzyme-linked immunosorbent assay (ELISA), antigen capture ELISA, sandwich ELISA, IgM antibody capture ELISA (MAC ELISA), and microparticle enzyme immunoassay (MEIA); capillary electrophoresis immunoassay (CEIA); radioimmunoassay (RIA); immunoradioassay (IRMA); fluorescence polarization immunoassay (FPIA); lateral flow assay; and chemiluminescence assay (CL).

[0185] In another preferred embodiment of the method according to the invention, GP2 is detected in a lateral flow assay, also known as an immunochromatographic assay or lateral flow immunochromatographic assay. The lateral flow assay is preferably based on a series of capillary beds, such as porous paper, microstructured polymers, or sintered polymers. Each of these elements is capable of spontaneously transporting fluid. The first element (sample pad) acts as a sponge and holds excess sample solution. After soaking, the fluid migrates to a second element (conjugation pad), in which the manufacturer stores a so-called conjugate, a dried form of bioactive particle in a salt-sugar matrix (see below), containing all the substances necessary to ensure an optimized chemical reaction between the target molecule (e.g., antigen) and its chemical counterpart (e.g., antibody) immobilized on the particle surface. The particles are also dissolved as the sample fluid dissolves the salt-sugar matrix, and the sample is dissolved in a combined transport action, as well as the conjugation mixture is dissolved upon flow through the porous structure. In this way, the analyte binds to the particles upon further migration through a third capillary bed. This material has one or more regions (commonly referred to as strips) where a third molecule has been immobilized by the manufacturer. When the sample-conjugate mixture reaches these strips, the analyte is already bound to the particles, and a third "capture" molecule binds to the complex. Over time, as more fluid passes through the strips, the particles accumulate and the strip region changes color. Typically, at least two strips are present: the first (control) captures any particles, demonstrating that the reaction conditions and techniques are working correctly, and the second contains specific capture molecules and captures only those particles to which the analyte molecules are already immobilized. After passing through these reaction zones, the fluid flows into the final porous material, which serves only as a waste container. Sideflow testing can be performed as a competitive assay or a sandwich assay and, in principle, can be done using any colored particles. However, latex (blue) or nano-sized gold (red) particles are commonly used. Gold particles appear red due to localized surface plasmon resonance. Fluorescently or magnetically labeled particles can also be used, but these require an electronic reader to evaluate the test results.

[0186] If desired, such immunoassays can be automated. Immunoassays can also be used in conjunction with laser-induced fluorescence (see, for example, Schmalzing et al., *Electrophoresis*, 18:2184-2193 (1997); Bao, J. Chromatogr. B. Biomed. Sci., 699:463-480 (1997)). Liposome immunoassays, such as flow injection liposome immunoassays and liposome immunosensors, are also suitable for this invention (see, for example, Rongen et al., *J. Immunol. Methods*, 204:105-133 (1997)). Furthermore, turbidity assays are suitable for this invention, wherein the formation of the protein / antibody complex leads to increased light scattering, which is converted into a peak rate signal as a function of the marker concentration. Turbidity determination is commercially available from Beckman Coulter (Brea, Calif.; Kit #449430) and can be performed using a Behring turbidity analyzer (Fink et al., J. Clin. Chem. Clin. Biol. Chem., 27:261-276(1989)).

[0187] In another preferred embodiment of the method according to the invention, GP2 is detected in an immunoassay, preferably by directly or indirectly coupling a reactant to a labeled substance. This allows the method to be flexibly adapted to the potential and requirements of different laboratories and their laboratory diagnostic equipment. In an advantageous embodiment, GP2 is detected in an immunoassay in a form dissolved in a liquid phase, preferably in a conventional buffer solution well known to those skilled in the art or diluted in undiluted body fluids. According to the invention, fecal samples can also be used for detection.

[0188] In another preferred embodiment of the invention, a soluble or solid-bound antibody is used to bind a specific isotype of GP2. In the second reaction step, a second anti-GP2 affinity reagent, preferably a second anti-GP2 antibody, is used. This antibody is a detectable labeled conjugate of two components that can be conjugated to any conventional labeling enzyme, especially chromogenic and / or chemiluminescent substrates, preferably horseradish peroxidase or alkaline phosphatase. The advantage of this embodiment is the use of ELISA technology commonly available in laboratory facilities, thus allowing for cost-effective detection according to the invention. In another preferred embodiment of the invention, the second anti-GP2 affinity reagent is detectably conjugated to fluorescein isothiocyanate (FITC). Very similar to the ELISA described above, FITC technology represents a system that can be used in many places, thus allowing for the smooth and low-cost establishment of the detection of the invention in routine laboratory settings.

[0189] The term "denaturing sample conditions" refers to the conditions that induce denaturation of molecules contained in a sample. Denaturation is the process by which proteins or nucleic acids lose their quaternary, tertiary, and secondary structures in their native state through the application of external stress, compounds such as strong acids or bases, concentrated inorganic salts, organic solvents (such as alcohol or chloroform), radiation, or heat.

[0190] "Diagnostic kits" include all the necessary analyte-specific reagents required to perform diagnostic tests. The kit may also include instructions on how to use the provided reagents to perform the tests.

[0191] The specific immune binding of affinity reagents such as antibodies to target biomarkers can be detected directly or indirectly by the emitting signal of the label. Any given means for detecting these labels can be considered a means for detecting the labels according to the method of the present invention. Direct labeling includes fluorescent or luminescent tags attached to antibodies, metals, dyes, radionuclides, etc. Iodine-125 (125I) labeled antibodies can be used to determine the level of one or more biomarkers in a sample. Chemiluminescence assays using biomarker-specific chemiluminescent antibodies are suitable for sensitive, non-radioactive detection of biomarker levels. Antibodies labeled with fluorescent dyes are also suitable for determining the level of one or more biomarkers in a sample. Examples of fluorescent dyes include, but are not limited to, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red, and lissamine. Secondary antibodies linked to fluorescent dyes are commercially available, for example, goat F(ab')2 anti-human IgG-FITC is available from Tago Immunologicals (Burlingame, California).

[0192] Indirect labeling includes various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), β-galactosidase, urease, etc. The horseradish peroxidase detection system can be used, for example, with a chromogenic substrate such as tetramethylbenzidine (TMB), which produces a soluble product in the presence of hydrogen peroxide that is detectable at 450 nm. The alkaline phosphatase detection system can be used with a chromogenic substrate such as p-nitrophenyl phosphate, which produces a soluble product readily detectable at 405 nm. Similarly, the β-galactosidase detection system can be used with a chromogenic substrate such as o-nitrophenyl-β-D-galactopyranoside (ONPG), which produces a soluble product detectable at 410 nm.

[0193] Methods known to those skilled in the art for determining the concentration of a specific target molecule in a sample. For example, the concentration of a target molecule (e.g., a specific isotype of GP2) in a sample is determined by comparing a signal generated by a second affinity reagent according to the invention that captures the target molecule in the sample with a signal generated by a second affinity reagent that captures the target molecule in a control sample, wherein the concentration of the target molecule in the control sample is known.

[0194] The methods described herein can also be described based on the determination of the amount of GP2, as an alternative or supplementary description for determining the concentration of GP2.

[0195] A plate reader, also known as an ELISA reader or microplate spectrophotometer, is an instrument used to detect biological, chemical, or physical events in samples within a microtiter plate. They are widely used in research, drug discovery, bioassay validation, quality control, and manufacturing processes in the pharmaceutical and biotechnology industries and academic organizations. For example, but not limited to, they can measure sample reactions in microtiter plates with wells ranging from 6 to 1536. Common detection modes used for microplate assays include, but are not limited to, absorbance, fluorescence intensity, luminescence, time-resolved fluorescence, and fluorescence polarization.

[0196] In the context of this invention, a "camera device" is an apparatus suitable for detecting the signal of a second affinity reagent for a GP2-tagged label. The camera device may be included in a flatbed reader or provided separately. Those skilled in the art are familiar with such apparatuses for selection based on a second affinity reagent-based label.

[0197] Signals from direct or indirect labeling can be analyzed, for example, by detecting color from a chromogenic substrate using a spectrophotometer; detecting radiation using a radiation counter, such as a gamma counter to detect 125I; or detecting fluorescence in the presence of light of a specific wavelength using a fluorometer. To detect enzyme-linked antibodies, the amount of marker level can be quantitatively analyzed using a spectrophotometer such as an EMAX microplate reader (Molecular Devices, Menlo Park, California) according to the manufacturer's instructions. If desired, the assays of this invention can be performed automatically or robotically, and signals from multiple samples can be detected simultaneously.

[0198] The present invention also relates to protein and nucleic acid molecules corresponding to the sequences described herein, such as protein or nucleic acid molecules comprising or composed of the sequences described herein.

[0199] As used herein, the terms “GP2 isotype,” “GP2,” “GP2-antigen,” “GP2-molecule,” “GP2-protein,” “GP2-peptide,” or “GP2-autoantigen,” or other GP2 reference phrases, refer to GP2 isotypes of sequences disclosed herein or sequences functionally similar to those of isotypes 1, 2, 3, and 4, preferably those of isotypes 1, 2, 3, and 4. In a preferred embodiment of the method according to the invention, the GP2 isotype is of human, animal, recombinant, or synthetic origin. GP2 represents a highly conserved peptide, making GP2 from any source advantageous for detection, provided that the sequence is functionally similar to the sequence according to the invention.

[0200] In another preferred embodiment of the invention, an anti-GP2 antibody against GP2 binds to a solid phase according to one or more sequences disclosed herein. Binding of the anti-GP2 antibody against the solid phase according to one or more sequences disclosed herein can be achieved via a spacer region. All compounds having suitable structural and functional prerequisites for the function of the spacer region can be used as spacers, provided they do not alter the binding behavior in a way that adversely affects the binding of the anti-GP2 antibody against GP2 according to one or more sequences disclosed herein.

[0201] In another preferred embodiment of the invention, the affinity reagent according to this application is immobilized. More specifically, an anti-GP2 antibody, preferably bound to an organic, inorganic, synthetic, and / or mixed polymer, according to one or more sequences disclosed herein, is immobilized against the GP2 molecule. The polymer is preferably agarose, cellulose, silica gel, polyamide, and / or polyvinyl alcohol. In the context of this invention, immobilization is understood to involve various methods and techniques for immobilizing peptides on a specific carrier, for example, according to WO 99 / 56126 or WO 02 / 26292. For example, immobilization can be used to stabilize peptides so that their activity is not diminished or adversely altered by biological, chemical, or physical exposure, especially during storage or single-batch use. Immobilization of peptides allows for reuse under technical or clinical routine conditions; furthermore, samples, preferably blood components, can be reacted with at least one peptide according to the invention in a continuous manner. In particular, this can be achieved by various immobilization techniques in which the binding of the peptide to other peptides or molecules or carriers is carried out in such a way that the three-dimensional structure of the corresponding molecule, especially the three-dimensional structure of the peptide, is not altered, particularly the three-dimensional structure of the active site mediating the interaction with the binding partner. Advantageously, due to this fixation, there is no loss of specificity for the GP2a antibody. In the context of this invention, three basic methods can be used for fixation:

[0202] (i) Cross-linking: In cross-linking, peptides are fixed together without adversely affecting their activity. Advantageously, they become insoluble due to this cross-linking.

[0203] (ii) Binding to a carrier: For example, binding to a carrier can occur via adsorption, ionization, or covalent binding. This binding can also occur within microbial cells, liposomes, or other membrane-like, closed, or open structures. Advantageously, the peptide is not adversely affected by this method of immobilization. For example, carrier-bound peptides can be advantageously used multiple or consecutively in clinical diagnostics or treatment.

[0204] (iii) Composition: In the sense of this invention, composition is particularly carried out in the form of a gel, fibrils, or fibers within a semipermeable membrane. Advantageously, the encapsulated peptide can be separated from the surrounding sample solution through the semipermeable membrane in a manner that still allows interaction with the binding partner or fragment thereof. Various methods can be used for immobilization on inert or charged inorganic or organic supports, such as adsorption. For example, such supports can be porous gels, alumina, bentonite, agarose, starch, nylon, or polyacrylamide. Immobilization by physical binding forces typically involves hydrophobic interactions and ionic binding. Advantageously, these methods are easy to handle and have little effect on the conformation of the peptide. Advantageously, binding can be improved due to the electrostatic binding forces between the charged groups of the peptide and the support, for example, by using ion exchangers, particularly dextran gels.

[0205] Another approach is covalent bonding with a carrier material. Furthermore, the carrier can have reactive groups that form homopolar bonds with the amino acid side chains. Suitable groups in peptides are carboxyl, hydroxyl, and sulfide groups, especially the terminal amino group of lysine. Aromatic groups enable diazo coupling. The surface of microporous glass particles can be activated by treatment with silanes and then reacted with peptides. For example, the hydroxyl groups of natural polymers can be activated with bromocyanide and subsequently coupled with peptides. Advantageously, large quantities of peptides can be directly covalently bonded to polyacrylamide resins. In three-dimensional networks, this involves containing peptides in ion-transfer gels or other structures known to those skilled in the art. More specifically, the pores of the matrix are inherently peptide-retaining, allowing interaction with target molecules. In crosslinking, peptides are converted into polymer aggregates by crosslinking with bifunctional reagents. This structure is gel-like, easily deformable, and particularly suitable for various reactors. Mechanical and binding properties can be advantageously improved by adding other inactive components such as gelatin during crosslinking. In microencapsulation, the reaction volume of the peptide is limited by the membrane. For example, microencapsulation can be carried out in the form of interfacial polymerization. Because of immobilization during microencapsulation, the peptides become insoluble and therefore reusable. In the context of this invention, immobilized peptides refer to all peptides that are in a condition that allows for their reuse. Limiting the migration and solubility of antibodies through chemical, biological, or physical means advantageously results in lower process costs.

[0206] The present invention also relates to diagnostic kits. The diagnostic kits optionally include instructions regarding the contents of the combination kit for detecting AP and differentiating it from other diseases. For example, these instructions may be in the form of an instruction booklet or other media, providing the user with information about the type of method in which the substances to be used will be employed. Obviously, the information does not necessarily have to be in the form of an instruction leaflet; for example, the information may also be transmitted via the Internet.

[0207] As used herein, the terms “comprising” and “including” or their grammatical variations should be considered as intended to describe the stated feature, integer, step, or component, but do not preclude the addition of one or more additional features, integers, steps, components, or combinations thereof. This term encompasses the terms “consisting of” and “substantially consisting of”. Therefore, the terms “comprising” / “including” / “having” mean that any other component (or similar feature, integer, step, etc.) may / may be present. The term “consisting of” means that no other component (or similar feature, integer, step, etc.) exists.

[0208] According to the sequence list of the present invention:

[0209]

[0210] Attached Figure Description

[0211] The following figures are provided to illustrate specific embodiments of the invention, but do not limit the scope of the invention.

[0212] Brief description of the attached diagram:

[0213] Figure 1 The contribution of GP2 levels and other important host factors to gut microbiota diversity.

[0214] Figure 2 The association between GP2 levels and gut microbiota.

[0215] Figure 3 Association of GP2 levels with gut microbiota presence-absence patterns.

[0216] Figure 4 The association between GP2 levels and reduced microbial α diversity.

[0217] Figure 5 The association between GP2 levels and reduced SCFA and lactate biosynthetic capacity.

[0218] Detailed description of the attached diagram:

[0219] Figure 1Contributions of GP2 levels and other key host factors to gut microbiota diversity. Two main PCo1 and PCo2 are shown, each point representing an independent gut microbiome sample, colored according to the amount of fecal GP2. The length of the blue arrows indicates the magnitude of the effect on specific variable contributions to gut microbiota β diversity. Changes in fecal GP2 and pancreatic elastase levels have the highest impact on microbiota variation. BMI: Body Mass Index. FFS: Food Frequency Score (healthy diet).

[0220] Figure 2 Association between GP2 levels and gut microbiota. An evolutionary clade is shown, depicting microbial genera or families (continuous data) and their association with fecal GP2 levels. Significance results (q < 0.05) are depicted with red (positive correlation) or blue (negative correlation) dots. Dot diameters correspond to the magnitude of regression effect estimates. Different phyla are color-coded. The analysis revealed significant changes in the gut microbiota associated with changes in fecal GP2 levels. G: Genus. F: Family.

[0221] Figure 3 Association of GP2 levels with gut microbiota presence-absence patterns. An evolutionary clade is shown, depicting microbial genera or families (presence-absence data) and their association with fecal glycoprotein 2 (GP2) levels. Significance results (q < 0.05) are depicted by red (positive correlation) or blue (negative correlation) dots. Dot diameters correspond to the magnitude of regression effect estimates. Different phyla are color-coded. Analysis shows the loss of different microbial taxa in individuals with higher fecal GP2 levels. G: Genus. F: Family.

[0222] Figure 4 The association between GP2 levels and decreased microbial α diversity. The bar plot shows the negative correlation between fecal GP2 levels and microbial diversity scores (Simpson diversity index (N2), Shannon diversity index (H), Chao1 estimate, and species abundance (N0)). : Indicates a significant result (p < 0.05).

[0223] Figure 5 The association between GP2 levels and reduced SCFA and lactate biosynthetic capacity. Bar graphs show negative (blue) or positive (red) correlations between fecal GP2 levels and predicted microbial pathways for SCFA or lactate biosynthesis. Analysis showed that individuals with higher fecal GP2 levels had significantly reduced microbial capacity for SCFA or lactate biosynthesis. : Indicates a significant result (q<0.05).

[0224] Example

[0225] Example 1: Association Analysis between GP2 and Phenotypic Factors

[0226] The complete cohort included 2,812 individuals from whom fecal GP2 levels were available. The median GP2 level was 1,118.7 ng / g feces (215.4–3,659.1, first–third quartiles). Phenotypic characteristics of the cohort are given in Table 1. Simple linear regression analysis showed that fecal GP2 levels were significantly positively correlated with smoking (p<0.001), BMI (p<0.001), alcohol intake (p=0.001), hs-CRP (p=0.002), and fatty liver disease (p<0.005), while negatively correlated with higher FFS values ​​(p<0.001), female sex (p<0.001), fecal elastase (p<0.001), age (p<0.001), and chronic kidney disease (p=0.047). No association was found between it and hypothyroidism or other cardiometabolic disorders (e.g., diabetes, dyslipidemia, hypertension) or atherosclerotic disease. After including all the above significantly associated variables in a multiple regression model, fecal elastase (p<0.001), age (p=0.001), female sex (p=0.019), and FFS (p=0.023) remained negatively correlated with fecal GP2 levels, while BMI (p<0.001), smoking (p=0.007), and hs-CRP (p=0.042) were confirmed to be positively correlated with fecal GP2 levels.

[0227] Table 1. Phenotypic characteristics and regression analysis

[0228]

[0229] Example 2: Contribution of GP2 to gut microbial β-diversity

[0230] Dissimilarity of the β-diversity measure “Bray-Curtis” was calculated, followed by PCoA to identify microbial variations attributable to different phenotypic factors. We analyzed only those factors that showed a significant correlation with GP2 levels in the multiple regression model. Figure 1 The study showed that the factor with the highest impact on microbial variation in this dataset could be attributed to fecal GP2 (r 2 =13.5%, p<0.001) or pancreatic elastase (r 2 =9.4%, p<0.001). A significant effect also came from age (r 2 =5.8%, p<0.001), BMI(r 2 =4.4%, p<0.001), gender (r 2 =3.4%, p<0.001), smoking (r 2 =1.8%, p<0.001) or FFS(r 2=1.4%, p<0.001). Microbiome changes attributable to higher GP2 levels were positively correlated with smoking or high BMI, while a negative correlation pattern (along the PCo2 axis) was observed for high pancreatic elastase levels, female sex, FFS (healthy diet), or age.

[0231] Example 3: Association Pattern between GP2 and Gut Microbiota

[0232] A linear regression model was used to analyze the association between fecal GP2 levels and individual gut microbiota taxa. The model found that 21 taxa were significantly associated with increased GP2 levels, while 12 taxa showed a negative correlation. Figure 2 Logistic regression was used to analyze how fecal GP levels affect the presence-absence pattern of the gut microbiota, revealing 11 positive correlations and 30 negative correlations. Figure 3 ).

[0233] Example 4: Association Pattern between GP2 and Microbial α Diversity

[0234] Higher GP2 levels were largely associated with the absence, rather than the presence, of different microbial taxa. To investigate the consequences of this finding on “in-sample” microbial diversity (α-diversity), a linear regression model was used to calculate the association between GP2 levels and four different microbial diversity scores (Simpson diversity index (N2), Shannon diversity index (H), Chao1 estimate, and species abundance (N0)). This analysis revealed a strong negative correlation between higher GP2 levels and N2 (p=0.002), H (p<0.001), Chao1 (p<0.001), and N0 (p<0.001). Figure 4 ).

[0235] Example 5: Intestinal SCFA and lactate biosynthesis capacity and their relationship with GP2

[0236] Diverse variations were found to be associated with high or low fecal GP2 levels, including microbial taxa known to be involved in SCFA biosynthesis, such as Ruminococci, Faecalibacterium, Trichophyceae, or Rhesus. To investigate whether fecal GP2 levels were associated with lower or higher SCFA or lactate biosynthetic capacity, we analyzed predicted metagenomic pathways (PICRUSt) for the corresponding metabolites (see Methods for details). Figure 5 The study showed that in individuals with higher fecal GP2 levels, 12 out of 15 analyzed predicted microbial pathways for SCFA or lactate biosynthesis were depleted.

[0237] Discussion of Examples 1-5:

[0238] In this study, we analyzed how fecal GP2 levels in a cohort of 2,812 individuals correlated with a range of different host factors, disease phenotypes, and gut microbiome composition. The primary source of fecal GP2 is thought to be secreted by zymogen granules in pancreatic acinar cells

[31] . Another source has also been reported, expressed in the follicle-associated epithelium of the Peyer's plate in the small intestine and L cells in the colon [8, 11]. Elevated GP2 expression has been found in the exocrine pancreas and intestinal mucosa associated with inflammatory processes in patients with Crohn's disease or Crohn's-like inflammation [9, 11, 19].

[0239] Alternatively, it can indicate the secretion of gut-derived GP2 into the intestinal lumen. We measured lower fecal GP2 levels in individuals with preserved exocrine pancreatic function and also found a negative correlation between fecal GP2 and a balanced diet (FFS) and female sex. On the other hand, high fecal GP2 levels were associated with smoking, higher BMI, and increased systemic inflammation (as indicated by high hs-CRP levels). The latter would support the hypothesis that inflammatory processes leading to elevated TNF levels can trigger increased pancreatic GP2 secretion. No significant association was found with cardiovascular disease or diabetes. Regression analysis revealed a significant association with fatty liver disease; however, this association disappeared after including BMI as a stronger independent variable in the multivariate regression analysis. Given the predictive power of mucosal tolerance to larger GP2 isoforms in the severity of primary sclerosing cholangitis

[13] , the predominance of males in dysregulated autoimmune liver disease

[18] , and the association between males and higher fecal GP2 levels in this study, this could suggest a presumptive susceptibility to hepatic autoimmunity in men with high fecal GP2 levels. Furthermore, in another study, PSC patients showed a significant reduction in a broad range of SCFA-producing bacteria, similar to the predicted reduction in SCFA-producing microbial pathways we observed associated with higher GP2 levels

[32] . In conclusion, high fecal GP2 levels clearly indicate an unhealthy metabolic phenotype with increased systemic inflammation.

[0240] The underlying mechanism leading to this observation suggests that higher fecal GP2 levels are associated with changes in the gut microbiome, and that altered fecal GP2 levels explain most of the microbial changes in the PCoA. In linear and binary regression models, we detected 33 and 41 taxa that were significantly associated with fecal GP2, respectively. Positive correlations were found with Clostridium XIVa, Collins, or opportunistic pathogens Haemophilus and Streptococcus. Recent publications suggest that the presence of Clostridium XIVa in the gut microbiome of (still) healthy individuals is associated with the development of fatty liver disease or diabetes over 5 years, possibly causally [6]. Detection of Collins is associated with diabetes, obesity, and prior to the development of fatty liver disease [6, 33, 34]. Interestingly, we observed a reduction in the presence of opportunistic Gram-negative pathogens Escherichia / Shigella and Citrobacter in individuals with higher fecal GP2 levels. This observation can be explained by the fact that GP2 is a endocytic transport receptor for intestinal M cells specific to type I fimbriae and is involved in the mucosal immune response to these bacteria[8].

[0241] In individuals with higher GP2 levels, several taxa known to be involved in SCFA biosynthesis, such as Ruminococci, Vibrio butyricum, Faecalibacterium, Trichophyceae, or Rhodospirillum, showed altered abundance or presence. Analysis of predicted metagenomic microbial pathways revealed that the vast majority of these SCFA pathways, including those essential for lactate biosynthesis, were depleted. SCFAs such as acetic acid, propionic acid, or butyric acid play important roles in gut physiology. They represent important energy sources for the colonic epithelium, have anti-inflammatory properties, and promote intestinal barrier function [35, 36]. Similarly, lactate-producing bacteria have beneficial effects on intestinal barrier function

[37] , and depletion of these microbial pathways can lead to increased intestinal permeability, bacterial translocation, and increased (subclinical) systemic inflammation, as indicated by elevated serum hs-CRP levels. Even though our cross-sectional study did not detect an association between cardiovascular disease or obvious metabolic disorders and higher fecal GP2 levels, in the long term, elevated systemic inflammation may still increase the risk of cardiovascular disease or have a negative impact on metabolic conditions such as fatty liver disease or diabetes [38, 39].

[0242] Another detrimental aspect of the gut microbiome in individuals with high GP2 levels is a significant reduction in microbial α diversity. Low microbial diversity is often associated with obesity

[40] , gastrointestinal conditions such as pancreatitis

[41] , or after cholecystectomy

[24] . The result of low microbial diversity is increased gut microbiome instability, which can promote the loss of potentially beneficial bacteria and the accumulation of pathogens over time[6].

[0243] In summary, the data suggest that the intestinal mucosa may be a more significant source of fecal GP2 than pancreatic acinar cells, provided that fecal GP2 is not increased in a TNF-dependent manner in the latter. GP2 is a biomarker of gut microbiota dysbiosis and is associated with increased systemic inflammation. The potential clinical value of GP2 as a biomarker of gut microbiota dysbiosis and intestinal inflammation needs to be clarified in future studies, including disease-specific patient cohorts.

[0244] Materials and methods:

[0245] research group

[0246] All individuals were participants in the Pomeranian Health Study (SHIP), a population-based longitudinal cohort study comprising two separate cohorts, SHIP-TREND and SHIP-START

[20] . Participants in both cohorts were randomly selected from a general population in northeastern Germany, as SHIP aims to investigate the prevalence and characteristics of common diseases in the region and their risk factors

[21] . Fecal samples were collected during the initial recruitment period of SHIP-TREND (n=4,420, 2008–2012) and during the second follow-up period of the SHIP-START cohort (n=2,333, 2008–2012, initial recruitment 1997–2001). GP2 measurements were available for a total of 2,919 individuals. Of these, 107 participants with GP2 levels outside the mean + / - 3 standard deviations (SD) or a history of pancreatic disease were removed, leaving 2,812 datasets for analysis. Based on 16S rRNA gene sequencing, corresponding gut microbiome data for 2,671 individuals were available. All participants provided written informed consent, and the study was approved by the local ethics committee of Medicine Greifswald University (BB 39 / 08 and BB 122 / 13).

[0247] Collection of fecal samples

[0248] Participants provided two stool collection tubes, one with and one without stable DNA, containing EDTA buffer (0.5 M Tris, 10 mM NaCl, 100 mM EDTA, pH 7.0). As previously stated, stool samples were collected by study participants at home [4] and delivered in person to the research center or by mail.

[0249] Measurement of fecal glycoprotein 2 (GP2) levels

[0250] To determine the level of GP2 in feces, natural fecal samples were analyzed using the Pancreatitis GP2 ELISA (GA Generic Assays, Berlin, Germany) according to the manufacturer’s protocol. This assay detects the larger isotype of GP2

[22] . Briefly, 25 mg of feces was homogenized in 1.25 ml of extraction buffer and centrifuged at 3,000 g for 10 min. Subsequently, 20 μl of the supernatant was analyzed in 96-well plates containing GP2 standards as well as positive and negative controls. The final OD values ​​were measured by Molecular Devices on a spectrophotometer (SpectraMax 190), and the GP2 values ​​were calculated from the internal GP2 standard curve as [ng GP2 / mg feces].

[0251] 16S rRNA gene sequencing of fecal samples

[0252] Gut microbiota profiling was performed using 16S rRNA gene sequencing, as described in detail above [4]. Briefly, DNA was extracted from fecal samples stored in EDTA buffer containing stable DNA using the PSP Spin Stool DNA kit (Stratec Biomedical AG, Birkenfeld, Germany) according to the manufacturer’s instructions. The isolates were stored at -20°C until sequencing of the V1 / V2 regions of the bacterial 16S rRNA gene on the MiSeq platform (Illumina, San Diego, USA) using primers 27F and 338R.

[0253] Classification and metagenomic prediction

[0254] MiSeq FastQ files were created using CASAVA 1.8.2 (https: / / support.illumina.com / sequencing / sequencing_software / casava). The amplicon data were then processed using the open-source software DADA2

[23] following the recommended large dataset processing workflow (https: / / benjjneb.github.io / dada2 / bigdata.html), which was adapted for the V1 / V2 16S rRNA gene region as previously described

[24] . For taxonomic assignments, a Bayesian classifier and the Ribosome Database Project (RDP) version 16 training set were used. All data were diluted to 10,000 reads per sample prior to analysis. Metagenomic microbial functions were predicted based on amplicon sequence variants (ASVs) derived from the DADA2 workflow, using the PICRUSt2 package

[25] and the standardized workflow described at https: / / github.com / picrust / picrust2 / wiki / Workflow.

[0255] Other laboratory and phenotypic data

[0256] Fecal pancreatic elastase levels were measured using a single-specific pancreatic elastase ELISA assay (BIOSERV Diagnostics GmbH, Germany) according to the manufacturer’s protocol, as described in detail above [4]. Laboratory parameters (alanine aminotransferase (ALT), creatinine, high-density lipoprotein (HDL), high-sensitivity CRP (hs-CRP), low-density lipoprotein (LDL), and thyroid-stimulating hormone (TSH)) were measured on a Dimension VISTA platform (Siemens Healthcare Diagnostics, Eschborn, Germany). High-performance liquid chromatography (Bio-Rad Diamat, Munich, Germany) was performed to determine glycated hemoglobin concentration (HbA1c). The CKD-EPI equation was used to estimate glomerular filtration rate (eGFR)

[26] . Individuals with an eGFR value below 60 ml / min were classified as having chronic kidney disease. Body mass index (BMI) was calculated as kg / body height (m) squared. Patients with a history of diabetes and a positive result in combination with current treatment (diet, oral, and / or insulin injections), or with an HbA1c measurement ≥6.5% or a random blood glucose level ≥11.1 mmol / L, were assigned to have diabetes. Patients on thyroid hormone replacement therapy or with TSH ≥4 mU / L were assigned to have hypothyroidism. Patients with an LDL / HDL ratio >3.5 or >3 in men or women, or currently on anti-lipid medications, were considered to have dyslipidemia. To estimate daily alcohol intake (in grams of alcohol per day), all alcoholic beverages consumed over the past 30 days were counted and their average alcohol content was calculated. Participants who were current smokers were assigned to have smokers. Fatty liver disease was diagnosed in patients with hyperechoic liver tissue detected by high-resolution ultrasound in B-mode (Vivid i, GE Healthcare, Chicago, Illinois, USA) and serum alanine aminotransferase (ALT) levels within the top 25% of the studied population. To assess diet quality, a food frequency score (FFS) based on consumption data of 15 food categories (meat, sausage, fish, boiled potatoes, pasta, rice, raw vegetables, boiled vegetables, fruit, whole grain / dark / crispy bread, oatmeal / cornflakes, eggs, cakes / cookies, sweets, and savory snacks) was calculated as described by other methods [24, 27, 28], where higher values ​​indicated a healthier diet. Hypertension was assigned in the presence of antihypertensive medication, or systolic or diastolic blood pressure ≥140 mmHg or ≥90 mmHg, respectively. Overt atherosclerotic disease was assigned in the presence of a history of myocardial infarction or stroke.

[0257] Data Analysis

[0258] All statistical analyses were performed using R (v.3.6.3, https: / / www.R-project.org / )

[29] . All plots were created using the ggplot2 or ggraph package

[30] . Square root transformation of continuous phenotypic variables was performed before all association analyses. To analyze the association patterns between fecal GP2 levels and other phenotypic variables, a two-step approach was used: i) simple linear regression analysis was performed with GP2 levels as the result and the corresponding phenotypic variables as explanatory variables (functions lm, stats package). ii) all variables that were significantly associated with GP2 levels in the simple linear regression were combined as predictors in a multiple linear regression model to validate the robustness of the associations. Microbial β diversity was calculated using Bray-Curtis dissimilarity based on gut microbiota counts (functions vegdist, vegan package). Then, principal coordinate analysis (PCoA) was performed (using the function "cmdscale", "vegan" package), and the contribution of different phenotypic variables to the permutation results was analyzed using the "envfit" function ("vegan" package). Statistical significance was assessed through 1,000 permutations.

[0259] To analyze the association between individual gut microbiota taxa (outcome) and fecal GP2 levels (explanatory variable), two different models were computed, including potential confounding factors: pancreatic elastase, age, sex, BMI, FFS, smoking, cohort, and sequencing batch: i) A linear regression model was constructed using log-transformed continuous gut microbiota abundance data. Only taxa present in at least 10% of all samples were considered. To avoid spurious results due to zero inflation, zero values ​​were treated as NA. Outliers far from the mean ± 3 SD were removed within each taxa. All taxa were normalized to achieve comparable effect estimates. ii) A logistic regression model was constructed using binary gut microbiota data (absence vs. presence). Only taxa present in at least 10% but no more than 80% of samples were considered. The "vegan" package was used to calculate the "Chao1 estimate" or "Shannon Diversity Index" (H) and "Simpson Diversity Index" (N2) for the α-diversity score (Chao1 estimate: "estimate R" of the function; H and N2: "diversity" of the function). To analyze the association between microbial α-diversity (outcome) and fecal GP2 levels (explanatory variable), a linear regression model was used, which included the same potential confounding factors as in taxonomic-trait association analysis.

[0260] To analyze the association between predicted microbial pathways for short-chain fatty acid (SCFA) or lactate biosynthesis (outcomes) and fecal GP2 levels (explanatory variables), pathway gene abundance data were removed from outliers (±3 SD), square-root transformed, and normalized. Similarly, the same potential confounding factors as in taxonomic-trait association analyses were used. All p-values ​​derived from taxonomic-trait or pathway-trait associations were corrected for multiple testing using the Benjamini & Hochberg method and then termed q-values. P-values ​​or q-values ​​<0.05 were considered statistically significant.

[0261] References

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Claims

1. An in vitro method for diagnosing microbiome dysbiosis in subjects by detecting glycoprotein 2 (GP2), comprising: - Provide stool samples from the subjects. - Provide GP2 binding reagents, - Contact the sample with the binding agent to form a GP2-containing complex between the sample and the GP2 binding agent, and - Measure the GP2 level in the sample, wherein the GP2 level indicates microbiome dysbiosis in the subject.

2. The method according to the preceding claim, wherein, The GP2 level was positively correlated with one or more of the following bacteria: *Turicibacter*, *Erysipelotrichaceae*, *Phascolarctobacterium*, *Mitsuokella*, *Allisonella*, *Sutterella*, *Parasutterella*, *Haemophilus*, *Collinsella*, *Slackia*, *Prevotella*, *Streptococcus*, *Clostridium sensu stricto*, *Roseburia*, and *Clostridium XIVa*. XIVa), Fusicatenibacter, Dorea, Lachnospiraceae, Romboutsia, Faecalibacterium and / or Ruminococcus.

3. The method according to any one of the preceding claims, wherein, The GP2 level is positively correlated with one or more bacteria selected from the following: Haemophilus spp., Collins spp., Streptococcus spp. and / or Clostridium spp. XIVa.

4. The method according to any one of the preceding claims, wherein, The GP2 level was negatively correlated with one or more of the following bacteria: Clostridium IV, Osillibacter, Anaerobic bacteria, Pseudoflavonifractor, Ruminococcus, Victivallis, Oxalobacter, Akkermansia, Odoribacter, Butyricimonas, Alistipes, and / or Catabacter.

5. The method according to any one of the preceding claims, wherein, The GP2 level indicates an increase in the amount and / or presence (above the threshold or average population level) of one or more of the following bacteria: Haemophilus spp., Actinomyces spp., Gordonibacter spp., Streptococcus spp., Clostridium spp., Dornier spp., Anaerostipes spp., Rombutz spp., Zurich bacillus spp., Clostridium spp. XVIII and / or Veillonella spp.

6. The method according to any one of the preceding claims, wherein, The GP2 level indicates a reduction in and / or presence (below the threshold or average population level) of one or more of the following bacteria: *Ceratophyllum demersum*, *Acidobacterium*, *Sartella*, *Desulfovibrio*, *Bilophila*, *Desulfovibrionaceae*, *Escherichia / Shigella*, *Citrobacter*, *Coraliomargarita*, *Ackermania*, *Olsenella*, *Coriobacteriaceae*, *Barnesiella*, *Coprobacter*, and *Butymonas*. Genus, Porphyromonadaceae, *Prevotella*, *Paraprevotella*, *Alloprevotella*, Rikenellaceae, *Calactobacillus*, *Anaerovorax*, *Mogibacterium*, *Butyrivibrio*, *Eisenbergiella*, *Peptococcus*, *Ethanoligenens*, *Sporobacter*, *Anaerofilum*, and / or *Coprobacillus*.

7. The method according to any one of the preceding claims, wherein, The GP2 level was negatively correlated with the presence and / or amount of Escherichia coli / Shigella and / or Citrobacter.

8. The method according to any one of the preceding claims, wherein, Elevated GP2 levels (above the threshold or average population level) indicate impaired and / or pathogenic biosynthesis of short-chain fatty acids and / or lactate.

9. The method according to any one of the preceding claims, wherein, Elevated GP2 levels (above the threshold or average population level) indicate reduced gut microbiota α diversity.

10. The method according to any one of the preceding claims, wherein, The method also includes diagnosing, prognosticating, and / or risk stratifying subjects who have and / or are developing systemic inflammation (e.g., systemic inflammation associated with gastrointestinal disorders) by detecting glycoprotein 2 (GP2), wherein the GP2 level indicates that the subject has and / or is developing systemic inflammation.

11. The method according to any one of the preceding claims, wherein, - GP2 levels below the threshold indicate the absence or non-severity of microbiome dysbiosis, and / or the absence or low risk of having and / or developing systemic inflammation. - GP2 levels equal to or above the threshold indicate the presence or severity of microbiome dysbiosis, and / or a high risk of having and / or developing systemic inflammation. - Wherein, the threshold level is 3659.1 ± 20% ng / g.

12. The method according to any one of the preceding claims, wherein, If the GP2 level indicates microbiome dysbiosis, the method further includes instructing appropriate treatment selected from one or more of the following: administration of probiotics, administration of prebiotics containing fructooligosaccharides (FOS), galactooligosaccharides (GOS), and trans-galactooligosaccharides (TOS), dietary modification, fecal microbiota transplantation (FMT), and antibiotic treatment.

13. The method according to any one of the preceding claims, wherein, If the GP2 level indicates microbiome dysbiosis, the method further includes instructing the subject to administer one or more microbiomes according to any one of claims 4, 6, and 7.

14. Use of GP2 binding reagents in in vitro methods for diagnosing microbiome dysbiosis in subjects for the detection of glycoprotein 2 (GP2) in feces.

15. A kit for diagnosing microbiome dysbiosis in subjects by detecting glycoprotein 2 (GP2) in feces, comprising: - A GP2 binding agent, preferably having a solid surface for immobilizing the agent, or preferably a GP2 binding agent immobilized to a solid surface. - A second affinity reagent for the GP2 tag, preferably a tool for detecting signals emitted from the tag, and - Reference data corresponding to GP2 levels indicating microbiome dysbiosis, preferably also reference data indicating that the subject has and / or is developing systemic inflammation, wherein, The reference data is stored on a computer-readable medium and / or used in the form of computer-executable code configured to compare the determined GP2 level with the reference data. - Optionally, a sample collection device suitable for obtaining fecal samples.

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