Biomarkers of clostridium perfringens infection necrotic enteritis and uses thereof

Abstract

Disclosed herein is a method of screening for, diagnosing or detecting infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: measuring a level butyric acid in a sample from the one or more subject, and comparing the level of butyric acid with a control; wherein an increased level butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

Description

TITLE: BIOMARKERS OF CLOSTRIDIUM PERFRINGENS INFECTION NECROTIC ENTERITIS AND USES THEREOFFIELD

[0001] The present disclosure relates to detection of Clostridium perfringens infection and / or necrotic enteritis in a subject, and in particular biomarkers for use in the detection in a subject.CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Application No. 63 / 674,176, filed July 22, 2024, the contents of which is incorporated herein by reference in its entirety.BACKGROUND

[0003] Necrotic enteritis (NE) is accountable for significant economic losses in the poultry industry.The causative agent of NE is Clostridium perfringens (CP), a gram-positive, anaerobic, rod-shaped, sporeforming bacterium that thrives in various environments like soil, litter, and dust. Reduction of in-feed prophylactic antimicrobials is leading to an increased incidence of NE in recent years. CP is an opportunistic pathogen which induces NE by excessive proliferation of bacteria and producing toxins including alpha (cpa), beta (cpb), epsilon (etx), iota ( / fx), enterotoxin (cpe), necrotic enteritis B like toxin (neffi) and large clostridial toxins (TpeL)

[0001] , However, several predisposing factors such as infections with coccidia, immunosuppressive viruses, high protein diets and abrupt change in protein contents of the diet have been identified as contributing factors for the adhesion and colonization of CP in the intestinal mucosa. It has recently been demonstrated that an association of CP isolates with netB and TpeL toxin genes induces severe pathological lesions of NE and associated dysbiosis of the microbiome of the intestine in broiler chickens [2], NE primarily affects broiler chickens 2-6 weeks of age, causing subclinical and clinical forms of NE. A sudden increase of mortality up to 50%, poor performance and condemnations of carcasses at processing accounts for huge economic losses to the broiler industry worldwide [3], According to the United States Center for Diseases Control and Prevention, NE is the number one health concern for the broiler chicken industry. Hence, CP was estimated to be the second most common cause of the foodborne illness due to consumption of contaminated poultry meat in the USA with about one million people affected by CP contaminated poultry products annually [4],

[0004] Besides antibiotic treatment, there are no effective control strategies against NE and the management of the disease in broiler chickens. Host-pathogen interactions associated with the pathogenesis of NE, particularly changes of the intestinal microbiome at the onset of NE, is poorly understood [5],

[0005] End products of various cellular regulatory processes are known as metabolites while a set of metabolites are known as a metabolome

[0010] , Metabolites might act as biomarkers at the onset of NE and exploration of theses metabolites and metabolic pathways of the host will assist in exploration of the disease process of NE

[0011] , Host-pathogen interactions and how metabolic products of microbes in theintestine regulate metabolites of host are complex and dynamic

[0012] , Furthermore, production of metabolites during active infection further affects host pathogen interactions. Pathogens are extremely malleable to alter gene expression of the pathogen to acquire nutrients from the host [13,14], CP lacks amino acid synthesis machinery, hence CP utilizes the resources from the host to produce enzymes such as sialidases, collagenases, hyaluronidases, membrane damaging and pore-forming exo-toxins [1 ,15], All these activities are responsible for severe diffuse necrosis of the intestinal mucosa which is the characteristic of clinical disease of NE in broiler chickens. In severe infections of NE, biosynthesis of tryptophan, tyrosine, phenylalanine (aromatic amines) and valine, leucine, isoleucine (branched amino acids) have been predicted to be increased in broiler chickens

[0016] , In birds with subclinical NE, azelaic acid, indole, oleic acid, L-glutamic acid, hydro-cinnamic acid, valeric acid, hypoxanthine, D-glucose, 3-hydroxybenzoic acid, pantothenic acid, and nicotinic acid were found to be upregulated compared to the healthy birds

[0017] , Various compounds have been used to prevent intestinal cell death and inflammation associated with NE. A secondary bile acid, deoxycholic acid, has been demonstrated to prevent NE induced body weight loss and to mitigate inflammatory cyclooxygenase (COX) responses [5], A mixture of short-chain fatty acids (SCFAs) and medium-chain fatty acids have been demonstrated to reduce NE in broiler chickens

[0018] ,

[0006] In poultry, butyric acid has been utilized as a feed supplement

[0044] , NE infection disrupts the intestinal barrier function by downregulating the claudin-4, ZO-1 , occludin, LEAP-2 and mucin-2 gene expression in the jejunum however, supplementation with dietary microencapsulated sodium butyrate stimulated the tight junction proteins and antimicrobial peptide LEAP-2 gene expression

[0037] , Also, butyric acid has the capability to reduce the expressions of cytokine genes, tumor necrosis factor a (TNFa), IL1 p, IL2, IL6, IL8, and IL12 by inhibiting prototypical proinflammatory signaling nuclear factor NF-KB pathway

[0045] ,

[0007] In one trial, butyric acid when combined with medium chain fatty -acids and essential oils, reduced with the amount of gross NE lesions , in a second trial, higher concentration combination was ineffective against NE

[0049] ,

[0008] Histamine is a biogenic amine and acts as a mediator of allergic as well as non-allergic inflammatory processes in the gut mucosa. Histamine plays a protective or negative role against bacterial infections

[0050] ,

[0009] The early detection of pathogenic infections is critical in controlling pathogens and thus, imminent disease outbreaks and antimicrobial use (AMU). The chicken industry is currently mostly relying on serological blood testing to measure antibodies against pathogens to detect pathogenic infection. However, serological tests detect diseases only 10-14 days after pathogenic exposure. Besides, PCR (pathogen’s DNA detection) and bacterial culture-based diagnosis methods are primarily contingent on the types of tissue and pathogen’s predilection site. There is no diagnostic test available in the poultry industry. The Canadian chicken industry lacks the ability to detect pathogens within 1 -2 days post-infection.SUMMARY

[0010] It is demonstrated herein that butyric acid and / or histamine levels in jejunum, serum, fecal samples can be used to discriminate birds with Necrotic enteritis (NE) from birds without, regardless of the Clostridium perfringens (CP) toxin gene profile. Both butyric acid and histamine show consistent upregulation across various tissues in affected birds. These biomarkers as shown herein serve as valuable tools for early disease monitoring.

[0011] Accordingly, an aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: measuring a level of histamine and / or butyric acid thereof in one or more sample from the one or more subject, and comparing the level of histamine and / or butyric acid with a control; wherein an increased level of histamine and / or butyric acid compared to the control is indicative that the one or more subject has been or is at an increased likelihood of being infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0012] Another aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from the one or more subject, and comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0013] Another aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: collecting one or more sample from the one or more subject; sending the one or more sample to be analyzed for its level of butyric acid; and receiving an indication that the one or more subject has been infected or is likely to have been infected with Clostridium perfringens and / or necrotic enteritis where the one or more sample has an increased level of butyric acid as comapred to a control.

[0014] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising:measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0015] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0016] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0017] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0018] Another aspect of the disclosure includes a method of treating one or more subject with Clostridium perfringens (CP) and / or necrotic enteritis in a subject, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, optionally seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens, and treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment.

[0019] Another aspect of the disclosure includes a method of monitoring disease progression in a subject or population, the method comprising: measuring a level of butyric acid, and optionally histamine, in a first sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis,measuring a level of butyric acid, and optionally histamine, in one or more additional sample from the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling, and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample, optionally the first sample, and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0020] Another aspect of the disclosure includes a method of monitoring disease progression in a subject or population, the method comprising: measuring a level of butyric acid, and optionally histamine, in a first sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, measuring a level of butyric acid, and optionally histamine, in one or more additional sample from the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling, and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample, optionally the first sample, and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0021] A further aspect is a device for testing a liquid sample for the concentration of butyric acid and / or histamine, the device comprising: a porous solid phase material carrying in a first zone a labelled reagent which is retained in the first zone while the porous material is in the dry state but is free to migrate through the porous material when the porous material is moistened, for example by the application of an aqueous liquid sample suspected of containing butyric acid and / or histamine, the porous material carrying in a second zone, which is spatially distinct from the first zone,an unlabelled specific binding reagent having specificity for / capacity to react with butyric acid and / or histamine, and which is capable of binding with the labelled reagent to form a complex or reacting with the labelled reagent, the unlabelled specific binding reagent being firmly immobilised on the porous material such that it is not free to migrate when the porous material is in the moist state, said complex or rection being observable through a test result observation aperture, thereby to indicate the presence of said analyte in said liquid biological sample.

[0022] In some embodiments, the liquid sample is a blood sample, optionally a serum sample. In some embodiments, the liquid sample is a fecal sample, optionally wherein the fecal sample has been diluted, and optionally filtered.

[0023] Another aspect of the disclosure includes a kit for detecting butyric acid, and optionally histamine, optionally for use in any method described herein, the kit comprising one or more reagents or standards for measuring a level of histamine and / or butyric acid, and optionally instructions for use.

[0024] Encompassed herein are also uses based on the methods described herein.

[0025] The preceding section is provided by way of example only and is not intended to be limiting on the scope of the present disclosure and appended claims. Additional objects and advantages associated with the compositions and methods of the present disclosure will be appreciated by one of ordinary skill in the art in light of the instant claims, description, and examples. For example, the various aspects and embodiments of the disclosure may be utilized in numerous combinations, all of which are expressly contemplated by the present description. These additional advantages objects and embodiments are expressly included within the scope of the present disclosure. The publications and other materials used herein to illuminate the background of the disclosure, and in particular cases, to provide additional details respecting the practice, are incorporated by reference, and for convenience are listed in the appended reference section.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Further objects, features and advantages of the disclosure will become apparent from the following detailed description taken in conjunction with the accompanying figures showing illustrative embodiments of the disclosure, in which:

[0027] Fig. 1 - Integrated Metabolomic Workflow for Evaluating the Impact of Clostridium perfringens Challenge on Broiler Chicken Metabolism: This figure outlines the timeline from chick placement to feed changes, pathogen challenge, and necropsy sampling. The workflow illustrates the metabolite extraction, LC-MS analysis, and subsequent data processing steps leading to the identification of significant metabolites associated with necrotic enteritis (NE) pathogenesis.

[0028] Figs. 2A-E - Gross and histopathological lesions of NE in broiler chickens: A. Distended intestine with visible gas pockets and necrotic patches through the serosa, indicative of NE. B. Openedmucosal surface with diffuse necrosis. C. Histopathological evidence of diffuse coagulative necrosis of the intestinal villi. D. The incidence of NE lesions in Clostridium perfringens (CP)-challenged versus control groups, demonstrating a significant increase in lesion severity with CP challenge. E. The bar graph shows the percentage of birds with microscopic NE lesions with different toxin gene combinations of CP challenge.

[0029] Figs. 3A-D - Metabolite expression in CP challenged broiler chickens: Volcano Plots (A-D) illustrate the statistical significance versus fold change of jejunal metabolites across clinical, subclinical, challenged-no lesions and combined NE groups compared to controls, showcasing distinct metabolic profiles associated with NE severity post CP challenge. Identified metabolites shown with a higher value than zero on X-axis are upregulated, while identified metabolites with a value lower than zero on the X-axis are downregulated.

[0030] Figs. 4A-C - Overview of Shared Metabolic Profiles and Metabolic Profile Alterations inCP-Exposed Broiler Chickens: A. Venn diagram identifying 34 significant metabolites shared among NE (clinical, subclinical, and CP challenged but no lesions), and control group birds. B. Violin plots depict the over expression of Butyric acid in CP-challenged NE birds (HE-healthy, SC-Subclinical, CL-Clinical and CH-Challenged) relative to healthy controls ( P < 0.01 for all comparisons). C. PCA plots showing distinct metabolite profile between NE versus healthy control groups.

[0031] Fig. 5- Metabolic Pathway Analysis and Metabolite-Protein Interaction Networks in CP-Exposed Broiler Chickens. Panel A depicts bubble plots of 34 jejunal metabolites within key metabolic pathways, with a focus on the significant role of butyric acid metabolism (KEGG pathway: gga00330) in broiler chickens (p < 0.04). The size of each bubble corresponds to the significance of the pathway, while its horizontal axis placement reflects the impact score.

[0032] Figs. 6A-D - Metabolite expression in broiler chickens challenged with CP with different toxin genes: Volcano Plots (A-D) illustrate the statistical significance versus fold change of jejunal metabolites across CP isolates with 2T, 3T, 4T and combined toxin gene groups in NE birds compared to controls, showcasing distinct metabolic profiles associated with NE severity post CP challenge. Identified metabolites shown with a higher value than zero on X-axis are upregulated, while identified metabolites with a value lower than zero on the X-axis are downregulated.

[0033] Figs. 7A-E - Overview of shared metabolic profiles, their expression patterns, multivariate analysis of metabolites in broiler chickens challenged with CP and its different toxin combinations: A. Venn diagram identifying 21 metabolites consistently altered across all NE categories. B. Heatmap showing upregulation of butyric acid, methylmalonic acid and histamine, in clinical, subclinical, and challenged group birds compared to controls. C. Violin plots depict the over expression of butyric acid in NE birds challenged with different toxin gene numbers (2T, 3T and 4T) relative to healthy controls (P < 0.01 for all comparisons). D. PCA plots showing Variances across NE groups with 21 metabolites; PC1 (50%) and PC2 (50%). Challenged group shows a prominent cluster. E. Healthy group metabolites are distinctly separated from those of CP infected NE birds, indicating subtle metabolic changes due to infection.

[0034] Figs. 8A-B - Metabolic pathway analysis and metabolite-protein interaction networks in broiler chickens challenged with CP and its different toxin combinations: Panel A depicts bubble plots of 21 jejunal metabolites within key metabolic pathways, with a focus on the Arginine-Proline metabolism (KEGG pathway: gga00330) in broiler chickens. The size of each bubble corresponds to the significance of the pathway, while its horizontal axis placement reflects the impact score. Arrow mark shows the enrichment of butyric acid pathway. Panel E3 shows the metabolite-protein interaction network under CP (different toxin gene combinations) exposure, using ellipses for metabolites and circles for proteins, illustrating the web of interactions between them. The radial layout highlights Arginine and Proline metabolite interactor proteins (GATM, ALDH18A1 , NOS1 and NOS2) with their first neighbor metabolites (Gin, Pro and Arg).

[0035] Fig. 9 - Integrated Metabolic Pathway Network Analysis in NE-Affected Jejunal Tissues.This schematic represents the intricate metabolite-protein networks highlight the involvement of metabolites (Glutamate, Proline, Butanoate (Butyric Acid), and Citrate and proteins (GATM and ALDH18A1 and PC, PCK, CS, ACO and IDH). Glutamate acts as a precursor to arginine, proline and butanoate metabolites. Butanoate is further connected to Acetyl Co-A, indicating metabolic adaptations in pathophysiology of CP induced NE in broiler chickens.

[0036] Figs. 10A-C - Distribution of Butyric Acid and Histamine Levels in Jejunum (J), Serum (S), and Rectum (R) (feces) of Broiler Chickens with Necrotic Enteritis (NE) and Controls (C). For each substance and location, the plots provide a visual representation of the data distribution, where the width of the plot indicates the frequency of data at different concentration levels. The white dot represents the median of the dataset, and the thick bar inside the plots shows the interquartile range. Statistically significant differences between the NE and control groups are marked with asterisks: * indicates p < 0.05, ** indicates p < 0.01 , and *** indicates p < 0.001 , as determined by t-tests.

[0037] Figs. 11 A-B - Boxplot of Butyric acid and Histamine across control and infected samples which shows upregulation in infected samples across all tissues.

[0038] Fig. 12 depicts workflow for metabolomics analysis of different biological samples of NEBirds. The diagram highlights the timeline and dietary manipulation, sample collection (serum, jejunal, and rectal contents), sample processing (supernatant extraction and derivatization), targeted quantitative metabolomics using LC-MS (TMIC-Mega assay), statistical analysis, and pathway identification for biomarker discovery.

[0039] Figs. 13A-D depict the histopathological NE lesions score in pre-CP and post-CP infection in broiler chickens. (A) represents the normal intestinal mucosa (Score 0), (B) showing mild, focal (arrow), acute necrosis of intestinal villi (Score 1), (C) showing moderate (notched arrow), multifocal to coalescing, moderate, acute necrosis of intestinal villi (Score 2) and (D). representing severe, diffuse (stars), acute necrosis and loss of intestinal villi (Score 3).

[0040] Figs. 14A-B depict results from metabolomics analysis of serum samples of NE birds. (A)Volcano plot showing 196 upregulated and 114 downregulated metabolites in NE birds versus control serum samples. (E3) PCA plot showing multivariate analysis of serum metabolite data. Although there is partial overlap between NE and control samples, NE samples demonstrated greater metabolic variability. Principal components 1 and 2 (PC1 and PC2, respectively) together explain 50% of the total variance, highlighting clear metabolic differences between NE and control groups.

[0041] Figs. 15A-E3 depict results from metabolomics analysis of jejunal samples in NEBirds. (A) Volcano plot displaying 58 metabolites (55.56%) significantly upregulated and 67 metabolites (48.44%) significantly downregulated in NE birds compared to control jejunal samples. (B) PCA plot illustrating multivariate analysis of jejunal metabolite data. Although some overlap exists, NE samples exhibit greater metabolic variability. Principal components 1 and 2 (PC1 and PC2, respectively) together explain 50% of the variance, differentiating NE affected birds from control groups based on their metabolic profiles.

[0042] Figs. 16A-B depict results from metabolomics analysis of fecal samples of NEBird. (A) Volcano plot displaying 122 metabolites (67.03%) significantly upregulated and 60 metabolites (32.96%) significantly downregulated in NE birds compared to control fecal samples. (B) PCA plot of fecal metabolite profiles. Principal Component 1 (PC1) partially separates NE-affected from control samples; Principal Component 2 (PC2) shows considerable overlap, reflecting shared metabolic features.

[0043] Figs. 17A-C depict results from comprehensive analysis of common metabolites across serum, jejunal contents and feces in NE birds. (A) Venn diagram shows the distribution of differentially expressed metabolites across jejunum, feces, and serum, with 19 common metabolites. (B) Heatmap displays clustering of upregulated (red) and downregulated (blue) metabolites across NE and control samples. (C) Dot plot highlighting 19 key metabolites, including histamine and butyric acid, across jejunal, serum, and fecal samples. Bubble size corresponds to expression ratios, and color intensity indicates the LogFC values.

[0044] Figs. 18A-B depict expression status of histamine and butyric acid metabolites across serum, fecal and jejunal samples in NE birds. Violin plots illustrate the distribution and central tendency differences in fold changes (Iog2 scale) of (A) histamine and (B) butyric acid across serum, fecal, and jejunal samples between NE and control groups (P< 0.05). Positive values on the y-axis indicate upregulation, while negative values represent downregulation.

[0045] Figs. 19A-B depict results from pathway analysis of common metabolites across serum, jejunal contents, and feces in ne-affected birds. (A) Pathway impact analysis of differentially expressed metabolites common to serum, feces and jejunal contents, showing pathway impact (x-axis) and significance (y-axis). Circle size and color represent the number of metabolite hits per pathway, emphasizing critical disruptions in the TCA cycle, glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, histidine metabolism, and butanoate metabolism. (B) GO Chord diagram linking keymetabolites to their respective metabolic pathways, illustrating the involvement of citrate, butyric acid, histamine, spermidine, and spermine in pathways like the citrate cycle, arginine-proline metabolism, butanoate metabolism, and histidine metabolism.

[0046] Fig. 20 depicts a heatmap of metabolites from Serum samples in NE Birds: Heatmap showing the top 20 differentially expressed metabolites between NE and control (CN) serum samples.

[0047] Fig. 21 depicts a heatmap analysis of Jejunal Samples in NE Birds: (B) Heatmap displaying the top 20 differentially expressed metabolites between NE-affected and control jejunal samples. The color gradient indicates the relative expression levels of metabolites.

[0048] Fig. 22 depicts a heatmap analysis of rectal Samples in NE Birds: Heatmap displaying the top 20 differentially expressed metabolites between NE-affected and control jejunal samples. The color gradient indicates the relative expression levels of metabolites.

[0049] Figs. 23A-C depict results from metabolite correlation analysis of common metabolites across serum, jejunal contents and feces in NE Birds: Correlation Plot showing Pairwise correlations among key metabolites across jejunum (A), serum (S), and fecal contents (C)Darker shades indicate stronger relationships.DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

[0050] The following is a detailed description provided to aid those skilled in the art in practicing the present disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the disclosure. All publications, patent applications, patents, figures and other references mentioned herein are expressly incorporated by reference in their entirety.

[0051] The following non-limiting examples are illustrative of the present application:Definitions

[0052] As used herein, the following terms may have meanings ascribed to them below, unless specified otherwise. However, it should be understood that other meanings that are known or understood by those having ordinary skill in the art are also possible, and within the scope of the present disclosure. In the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0053] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the description. Ranges from any lower limit to any upper limit are contemplated. The upper and lower limits of these smaller ranges which may independently be included in the smaller ranges is also encompassedwithin the description, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either both of those included limits are also included in the description.

[0054] The term “about” as used herein may be used to take into account experimental error and variations that would be expected by a person having ordinary skill in the art. For example, “about” may mean plus or minus 10%, or plus or minus 5%, of the indicated value to which reference is being made.

[0055] As used herein the singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise.

[0056] The phrase "and / or," as used herein in the specification and in the claims, should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be construed in the same fashion, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and / or" clause, whether related or unrelated to those elements specifically identified.

[0057] As used herein in the specification and in the claims, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as "only one of or "exactly one of or, when used in the claims, "consisting of will refer to the inclusion of exactly one element of a number or list of elements. In general, the term "or" as used herein shall only be interpreted as indicating exclusive alternatives (i.e., "one or the other but not both") when preceded by terms of exclusivity, such as "either," "one of," "only one of," or "exactly one of."

[0058] As used herein, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "composed of," and the like are to be understood to be open- ended, i.e., to mean including but not limited to. Only the transitional phrases "consisting of and "consisting essentially of shall be closed or semi-closed transitional phrases, respectively.

[0059] As used herein in the specification and in the claims, the phrase "at least one," in reference to a list of one or more elements, should be understood to mean at least one element selected from anyone or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.

[0060] It should also be understood that, in certain methods described herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited unless the context indicates otherwise.

[0061] The term “C5OH” as used herein refers to the lipid metabolite hydroxyvalerylcarnitine. The term “C6” as used herein refers to the lipid metabolite hexanoylcarnitine. The term “C8” as used herein refers to the lipid metabolite octanoylcarnitine. The term “C10” as used herein refers to the lipid metabolite decanoylcarnitine. The term “C14” as used herein refers to the lipid metabolite tetradecanoylcarnitine. The term “C16:2” as used herein refers to the lipid metabolite hexadecadienylcarnitine acid.

[0062] The term “CP” or “Clostridium Perfringens" as used herein refers to the Gram-positive bacillus bacterium of the species Clostridium Perfringens and includes isolates of Clostridium Perfringens with different combinations of toxin genes (e.g., cpa, cpb2, netB, TpeL cpa, cpb2, netB; or cpa, cpb2).

[0063] The term “LCFA” and “LCFAs” as used herein refer to long-chain fatty acids, either saturated or unsaturated, containing 13 to 21 carbon atoms. The term “LYSOC14:0” as used herein refers to the lipid metabolite lysophosphatidylcholine with a saturated 14-carbon acyl chain.

[0064] The term “NE” as used herein refers to the pathological condition necrotic enteritis, such as the condition caused in the digestive tract of poultry by Clostridium Perfringens. Necrotic enteritis includes, early stage necrotic enteritis as well as clinical and subclinical necrotic enteritis.

[0065] The term “early stage necrotic enteritis” refers to a subject that is infected with CP without overt clinical signs or any clinical signs (e.g. subclinical), where there may be no lesions present in the small intestine (e.g., there may be ultrastructural or biochemical changes present but no gross lesions) and / or prior to subjects displaying signs of NE (e.g., before showing any overt sign such as diarrhea, lesions or increased flock mortality), . As demonstrated herein, before the appearance of macroscopic NE lesions and even microscopic lesions, there are biochemical changes which occur at the cellular or tissue levels that allow for early NE detection. Signs of subclinical necrotic enteritis include reduction in water or feed intake, increased consumption of food with low weight gain, reduced growth, and / or slow movement.

[0066] The term “early stage sample” refers to a sample collected from a subject prior to the subject displaying overt signs of infection with CP and / or necrotic enteritis, such as diarrhea, depression, or increased flock mortality.

[0067] The term “SCFA” and “SCFAs” as used herein refer to short-chain fatty acids, containing 6 carbon atoms or less.

[0068] The term “tRNA” as used herein refers to transfer ribonucleic acid, which is an adaptor between messenger ribonucleic acid and the amino acid sequence of proteins wherein tRNA acts as a temporary carrier of amino acids during protein synthesis. For example, aminoacyl-tRNA delivers a specific amino acid to a ribosome during translation.

[0069] An aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: measuring a level of histamine and / or butyric acid thereof in one or more sample from the one or more subject, and comparing the level of histamine and / or butyric acid with a control; wherein an increased level of histamine and / or butyric acid compared to the control is indicative that the one or more subject has been or is at an increased likelihood of being infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0070] Another aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from the one or more subject, and comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0071] In some embodiments, the method further comprises measuring a level of histamine in one or more sample from the one or more subject, and comparing the level of histamine with a control, wherein an increased level of butyric acid and histamine compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0072] Various assays and platforms can be used to detect and / or quantify the biomarkers described herein. Colorimetric platforms for biomarker detection offer a versatile, affordable and accessible approach to disease diagnosis in the food-animal industry, particularly in resource-limited settings. They utilize color changes to indicate the presence and concentration of specific biomarkers, enabling rapid, cost-effective, and portable diagnostics. Colorimetric platforms can detect biomarkers even at low concentrations, allowing for early disease diagnosis, which is crucial for effective treatment and improved disease management in the poultry industry. Colorimetric platforms would be advantageous under field conditions in the poultry industry using a blood sample as a rapid diagnostic material. In some embodiments, the measuring of the level of butyric acid and / or histamine comprises measuring a pH change and / or ionization characteristics.

[0073] In some embodiments, the measuring of the level of butyric acid comprises using a colorimetric assay. In some embodiments, the measuring of the level of butyric acid and histamine comprises using a colorimetric assay. In some embodiments, the measuring of the level of histamine comprises using a colorimetric assay. In some embodiments, the measuring of the level of butyric acidand / or histamine comprises measuring high-performance liquid chromatography (HPLC) or an enzyme immunoassay (EIA) optionally ELISA.

[0074] Examples of measuring butyric acid using a colorimetric assay are known in the art e.g.,Allgeier, R J et al. “A COLORIMETRIC METHOD FOR THE DETERMINATION OF BUTYRIC ACID.” Journal of bacteriology vol. 17,2 (1929): 79-87. doi:10.1128 / jb.17.2.79-87.1929, which is incorporated by reference herein.

[0075] Examples of measuring histamine levels using a colorimetric assay are known in the art, e.g., commercially available kits for such measurement are available (e.g., Abcam’s Histamine Assay Kit (Colorimetric) (ab235630) and Millipore Sigma’s Histamine Quantification Assay Kit (MAK432-1 KT)). Examples of ELISA kits for measuring butyric acid are known in the art (e.g., Abbexa’s Butyric Acid ELISA Kit (abx258338)). Examples of colorimetric assays for detection of histamine are known in the art and commercially available e.g., PerkinElmer’s HistaStrip™, (FOOD-1100-01). Examples of colorimetric assays for detection of histamine are known in the art and commercially available e.g., PerkinElmer’s HistaStrip™, (FOOD-1100-01), Romer Lab’s RapidChek® Histamine (10007183), Kikkoman’s Histamine Check Swab (60448), BioAssay Systems’ QuantiQuik™ Histamine Quick Test Strips (QQHIST10). Examples of ELISA based tests for histamine are known in the art and commercially available e.g., Romer Lab’s AgraQuant® Histamine Rapid (10002018). One of these tests or similar tests can be used in the methods described herein. The devices described herein can use similar components to the aforementioned products / kits.

[0076] Measuring the level of butyric acid can include measuring butyric acid and its isomers (e.g., isobutyric acid), and / or derivatives (e.g., butyrate or salts such as sodium, potassium, or calcium butyrate).

[0077] The biomarkers described herein can be measured using the same platform or different platforms. They can be measured in a same sample or in different samples.

[0078] In some embodiments, the method is for controlling or reducing contamination with or spread of Clostridium perfringens and / or necrotic enteritis in a population and further comprises: selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population and / or treatment; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0079] As shown herein, levels of the biomarkers described herein can be an early sentinel of NE. Accordingly, detection of increased levels of the biomarkers such as butyric acid and / or histamine, can allow producers to isolate or separate subjects suspected of infection prior to overt symptoms of NE.

[0080] In some embodiments, the method is for controlling or reducing contamination with, or spread of, Clostridium perfringens and / or necrotic enteritis in a population and further comprises:selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population and / or treatment; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0081] In some embodiments, the method is for monitoring disease progression in a subject or population, wherein the method further comprises: measuring a level of butyric acid, and optionally histamine, in the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0082] In some embodiments, the method is for monitoring disease progression in a subject or population, wherein the method further comprises: measuring a level of butyric acid, and optionally histamine, in the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0083] Another aspect of the disclosure includes a method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, the method comprising: collecting one or more sample from the one or more subject; sending the one or more sample to be analyzed for its level of butyric acid; and

[0084] receiving an indication that the one or more subject has been infected or is likely to have been infected with Clostridium perfringens and / or necrotic enteritis where the one or more sample has anincreased level of butyric acid as comapred to a control. Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0085] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0086] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / orisolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0087] Another aspect of the disclosure includes a method of controlling spread of Clostridium perfringens and / or necrotic enteritis in a population, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0088] Another aspect of the disclosure includes a method of treating one or more subject with Clostridium perfringens (CP) and / or necrotic enteritis in a subject, the method comprising: measuring a level of butyric acid, and optionally histamine, in one or more sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, optionally seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens, and treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment.

[0089] Another aspect of the disclosure includes a method of monitoring disease progression in a subject or population, the method comprising: measuring a level of butyric acid, and optionally histamine, in a first sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis,measuring a level of butyric acid, and optionally histamine, in one or more additional sample from the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling, and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample, optionally the first sample, and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

[0090] Another aspect of the disclosure includes a method of monitoring disease progression in a subject or population, the method comprising: measuring a level of butyric acid, and optionally histamine, in a first sample from one or more subject, comparing the level of butyric acid with a control; wherein an increased level of butyric acid compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, measuring a level of butyric acid, and optionally histamine, in one or more additional sample from the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; and selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling, and / or treatment when the level of butyric acid, and optionally histamine, in the one or more additional sample is increased as compared to a reference value or previous sample, optionally the first sample, and / or isolating and / or seeking a prescription for treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis, and optionally treating the one or more subject.

[0091] In some embodiments, the method is for early detection of infection with CP and / or necrotic enteritis in one or more subject. For example, feed and water intake are in many establishments and farms monitored, such that when a disruption (e.g., decrease) in food or water intake occurs in a subject or a population of poultry, the methods described herein may be used to detect infection with CP and / or affliction with NE in one or more subject in the population. The one or more subject identified as being infection with CP and / or afflicted with NE can then be isolated from the population and / or culled, a prescription fortreating the population may be sought and / or the whole population may be treated. The detection may occur e.g., before signs of clinical NE begin to appear in the population and therefore allow for early treatment (e.g.,priorto signs of clinical NE appearing in the population). In some embodiments, the method is for increasing weight gain in the one more subject. In some embodiments, the method is for reducing mortality related to CP infection and / or NE in one or more subject.

[0092] In some embodiments, the population comprises one or more subject described herein. In some embodiments, the population comprises a plurality of subjects described herein. In some embodiments, the population is a population of birds. In some embodiments, the population is a population of poultry. In some embodiments, the poultry is chicken, turkey, or duck. In some embodiments, the poultry is a chicken. In some embodiments, the poultry is a broiler chicken.

[0093] In some embodiments, the method further comprises sending the one or more sample to be analyzed for its level of histamine, and receiving an indication that the one or more subject has been infected or is likely to have been infected with Clostridium perfringens and / or necrotic enteritis where the one or more sample has an increased level of butyric acid and histamine as compared to a control.

[0094] In some embodiments, the control is an earlier sample taken from the one or more subject.In some embodiments, the control is baseline sample, optionally from the one or more subject. In some embodiments, the baseline sample is a sample taken at diagnosis or detection of infection with CP by the one or more subject.

[0095] In some embodiments, at least 2 subjects are tested, for example prior to taking further action, for example isolation, culling and / or treatment (e.g. with vitamins and / or antibiotics) or seeking a medical prescription. In other embodiments at least 5 subjects are tested, at least 10 subjects, or at least 1 / 100 subjects or 1 / 1000 subjects. The average of the multiple subjects can for example be compared to the control.

[0096] In some embodiments, the control is a level associated with a subject or a plurality of subjects that is / are not afflicted with NE or infected with CP (e.g. the control is a level associated with normal floral levels). In some embodiments, the control is a reference value, reference range or series of values or ranges, for example calculated from one or more samples from one or more subjects which are not afflicted with NE or infected with CP. The control can also be a sample from such a subject or a plurality of subjects.

[0097] In some embodiments, the one or more subject is afflicted with early stage, subclinical or clinical necrotic enteritis. A detailed scoring system can be found for example in., Gautam H, Ayalew LE, Shaik NA, Subhasinghe I, Popowich S, Chow-Lockerbie B, Dixon A, Ahmed KA, Tikoo SK, Gomis S. Exploring the predictive power of jejunal microbiome composition in clinical and subclinical necrotic enteritis caused by Clostridium perfringens: insights from a broiler chicken model. Journal of Translational Medicine. 2024 Jan 19;22(1 ):80, incorporated herein by reference. In some embodiments, the one or more subject is afflicted with early stage NE. Signs of subclinical necrotic enteritis include reduction in water or feed intake,increased consumption of food with low weight gain, reduced growth, slow movement. Clinical necrotic enteritis is characterized by visible signs of illness including depression, diarrhea, and high mortality rates.

[0098] In some embodiments, the one or more subject tested is displaying one or more sign of necrotic enteritis (e.g., reduction in water or feed intake, increased consumption of food with low weight gain, reduced growth, slow movement, depression, diarrhea, and / or high mortality rates). In some embodiments, the one or more subject is not displaying one or more sign of necrotic enteritis. In some embodiments, the one or more subject has been in contact or in the presence of one or more other subject(s) known or suspected of being infected with CP or afflicted with necrotic enteritis. In some embodiments, the one or more subject is pre-disposed to being infected with CP or afflicted with necrotic enteritis (e.g., the one or more subject was previously infected or suspected of being infected with a coccidia infection, and / or immunosuppressive virus).

[0099] In some embodiments, the one or more sample is a fecal sample (e.g., fecal swab or sample taken from rectum or cloaca or fresh droppings) or a blood sample. For example, fresh droppings may be collected without knowing from which subject they originated and / or wherein the sample of fresh droppings originated from more than one subject. Fecal samples may be processed e.g., by mixing fecal sample with a diluent (e.g., saline) and filtering the sample to remove debris. In some embodiments, the blood sample is a serum sample. By testing the sample e.g. feces or blood, birds that have been infected with CP and / or afflicted with necrotic enteritis can be identified, in early stages of infection with CP which will give ample time to take appropriate actions in order to prevent the economic losses (e.g., by farmers). Increased levels of sub-clinical CP in the intestines of poultry are responsible for poor gut health, increased feed intake and less weight gain. These as well as increased mortality seen in clinical NE are the main reason of the severe economic losses to the poultry industry.

[0100] In some embodiments, the one or more subject(s) is is / are birds. In some embodiments, the one or more subject(s) is is / are poultry. In some embodiments, the one or more subject(s) is is / are poultry and the method is for improving food safety. In some embodiments, the poultry is chicken, turkey, or duck. In some embodiments, the poultry is a chicken. In some embodiments, the poultry is a broiler chicken. In some embodiments, the one or more subject is a population. In some embodiments, the one or more subject is a flock.

[0101] The one or more subject may be any age. Poultry might get a CP infection due to a sudden diet change or being infected with a viral infection etc. However, broiler chickens are most susceptible between 2-6 weeks of age due to the reduction in maternal antibodies by 2 weeks and their rapidly developing immune system. The peak time of NE susceptibility in broiler chickens is 3 -4 weeks.

[0102] In some embodiments, the one or more subject is less than 14 days old. In some embodiments, the one or more subject is at least about 14 days old. In some embodiments, the one or more subject is less than 21 days old. In some embodiments, the one or more subject is at least about 21 days old. In some embodiments, the one or more subject is less than 28 days old. In some embodiments,the one or more subject is less than 84 days old. In some embodiments, the one or more subject is less than 180 days old. In some embodiments, the one or more subject is between about 14 days old and about 180 days old. In some embodiments, the one or more subject is between about 14 days old and about 84 days old. In some embodiments, the one or more subject is between about 28 days old and about 180 days old. In some embodiments, the one or more subject is between about 28 days old and about 84 days old.

[0103] In some embodiments, the one or more subject is between 14 days old and 42 days old. In some embodiments, the one or more subject is about 23 days old.

[0104] In some embodiments, the sample is obtained from one or more subject 1-2 days after the one or more subject has been or is suspected to have been exposed to CP.

[0105] In some embodiments, the one or more sample is an early sample.

[0106] In some embodiments, the one or more sample is or was collected from one or more subject that is less than 14 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is at least about 14 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is less than 21 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is at least about 21 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is less than 28 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is less than 84 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is less than 180 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is between about 14 days old and about 180 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is between about 14 days old and about 84 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is between about 28 days old and about 180 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is between about 28 days old and about 84 days old.

[0107] In some embodiments, the one or more sample is or was collected from one or more subject that is between 14 days old and 42 days old. In some embodiments, the one or more sample is or was collected from one or more subject that is about 23 days old.

[0108] In some embodiments, the increased level of butyric acid, is an increase of at least about 1-fold, at least about 1.5-fold, at least about 2-fold, or at least about 2.5-fold. In some embodiments, the increased level of butyric acid, is an increase of about 2-fold. In some embodiments, the increased level of butyric acid, is an increase of about 1.89-fold.

[0109] In some embodiments, the increased level of histamine, is an increase of about 0.5-fold, at least about 1 -fold, at least about 2-fold, or at least about 3-fold. In some embodiments, the increased level of histamine, is an increase of about 0.5-fold.

[0110] In some embodiments, the CP comprises at least one of the cpa, netB, cpb2 , and TpeL genes. In some embodiments, the CP comprises at least two of the cpa, netB, cpb2 , and TpeL genes. In some embodiments, the CP comprises at least three of the cpa, netB, cpb2 , and TpeL genes. In some embodiments, the CP comprises cpa, netB, cpb2 , and TpeL genes. In some embodiments, wherein the CP comprises cpa, cpb2, and netB genes. In some embodiments, the CP comprises cpa and cpb2 genes.

[0111] In some embodiments, the method further comprises a step of obtaining or collecting the one or more sample.

[0112] In some embodiments, the analyzing for an increased level butyric acid comprises measuring a pH change and / or ionization characteristics. In some embodiments, the analyzing for an increased level butyric acid comprises using a colorimetric assay.

[0113] In some embodiments, the one or more subject is a flock. In some embodiments, the population is a flock.

[0114] Treatments for CP infection and / or NE are known in the art and include antibiotics. In some embodiments, the one or more subject is treated with an antibiotic. In some embodiments, the population is treated with an antibiotic. In some embodiments, the antibiotic is selected from bacitracin and penicillin. In some embodiments, the treatment is administered in the feed. In some embodiments, the antibiotic is bacitracin and is administered in the feed. In some embodiments, the antibiotic is penicillin and is administered in the water. In some embodiments, the treatment is administered in the water. In some embodiments, the treatment comprises vitamins or other supplements. In some embodiments, the treatment comprises vitamin D.

[0115] In some embodiments, the method comprises measuring a level of butyric acid and a level of histamine.

[0116] In some embodiments, the method further comprises the step of seeking a prescription for treatment prior to treating.

[0117] In some embodiments, the method further comprises treating a population wherein the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis was present in the population. In some embodiments, the population is a flock.

[0118] A further aspect is a device for testing a liquid sample for the concentration of butyric acid and / or histamine, the device comprising: a porous solid phase material carrying in a first zone a labelled reagent which is retained in the first zone while the porous material is in the dry state but is free to migrate through the porous material when the porous material is moistened, for example by the application of an aqueous liquid sample suspected of containing butyric acid and / or histamine, the porous material carrying in a second zone, which is spatially distinct from the first zone,an unlabelled specific binding reagent having specificity for / capacity to react with butyric acid and / or histamine, and which is capable of binding with the labelled reagent to form a complex or reacting with the labelled reagent, the unlabelled specific binding reagent being firmly immobilised on the porous material such that it is not free to migrate when the porous material is in the moist state, said complex or rection being observable through a test result observation aperture, thereby to indicate the presence of said analyte in said liquid biological sample.

[0119] In some embodiments, the liquid sample is a blood sample, optionally a serum sample. In some embodiments, the liquid sample is a fecal sample, optionally wherein the fecal sample has been diluted, and optionally filtered.

[0120]

[0121] Another aspect of the disclosure includes a kit for detecting butyric acid, and optionally histamine, optionally for use in any method described herein, the kit comprising one or more reagents or standards for measuring a level of histamine and / or butyric acid, and optionally instructions for use. The one or more standards can for example be histamine or butyric acid that can for example be used as a standard in HPLC or in an EIA optionally ELISA. The one or more standards can for example be a known amount, for example that can be used for example for preparing a standard curve. In some embodiments, the kit further comprises at least one control, optionally a positive control comprising C perfringens or a negative control lacking C perfringens. In some embodiments, the kit is, or the one or more reagents or standards are for use in, a colorimetric assay or an ELISA assay. The kit may comprise collection vessels for collecting blood e.g., serum samples. In some embodiments, the kit comprises one or more collection vessels for fecal samples. In some embodiments, the kit comprises one or more collection instrument, for example, a swab e.g., for collecting a fecal and / or rectal sample. In some embodiments, one or more components (e.g., reagents, collection vessels or swabs) of the kit is sterile. In some embodiments, the kit comprises one or more sterile rectal swabs. In some embodiments, the kit comprises a diluent (e.g., for diluting fecal samples) e.g., saline. In some embodiments, the kit comprises a filter e.g., for filtering debris from a fecal sample e.g., mixed with a diluent. In some embodiments, the kit a companion diagnostic. In some embodiments, the kit comprises one or more test strips. In some embodiments, the kit comprises one or more of plate, optionally a pre-coated 96-well microplate, a standard, one or more buffers (e.g., a standard diluent buffer or wash buffer), one or more detection reagents, one or more diluent, one or more substrate, a stop solution, and a plate sealer. In some embodiments, the kit comprises a device described herein. Encompassed herein are also uses based on the methods described herein.

[0122] Further, the definitions and embodiments described in particular sections are intended to be applicable to other embodiments herein described for which they are suitable as would be understood by a person skilled in the art. For example, in the following passages, different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unlessclearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous.EXAMPLESExample 1Study 1

[0123] Necrotic enteritis (NE) is an economically important disease of broiler chickens caused by Clostridium perfringens (CP). The pathogenesis, or disease process, of NE is still not clear. This data aimed to identify the alterations of metabolites and metabolic pathways associated with subclinical or clinical NE in CP infected birds and to investigate the possible variations in the metabolic profile of birds infected with different isolates of CP.

[0124] Using an established NE model, the protein content of feed was changed abruptly before exposing birds to CP isolates with different toxin gene combinations (cpa, cpb2, netB, tpeL cpa, cpb2, netB; or cpa, cpb2). Metabolomics analysis of jejunal contents was performed by a targeted, fully quantitative liquid chromatography tandem mass spectrometry (LC-MS / MS) based assay.

[0125] This data detected statistically significant differential expression of 34 metabolites including organic acids, amino acids, fatty acids, and biogenic amines, including elevation of butyric acid at onset of NE in broiler chickens. Subsequent analysis of broilers infected with CP isolates with different toxin gene combinations confirmed an elevation of butyric acid consistently among 21 differentially expressed metabolites including organic acids, amino acids, and biogenic amines, underscoring its potential role during the development of NE. Furthermore, protein-metabolite network analysis revealed significant alterations in butyric acid and arginine-proline metabolisms.

[0126] This data indicates a significant metabolic difference between CP-infected and noninfected broiler chickens. Among all the metabolites, butyric acid increased significantly in CP-infected birds compared to non-infected healthy broilers. Logistic regression analysis revealed a positive association between butyric acid (coefficient: 1.23, P < 0.01) and CP infection, while showing a negative association with amino acid metabolism. These findings suggest that butyric acid could be a metabolite linked to the occurrence of NE in broiler chickens and may serve as an early indicator of the disease at the farm level..

[0127] The investigation into the metabolic disruptions caused by necrotic enteritis (NE) in broiler chickens has highlighted significant variations in butyric acid and histamine levels across three different tissues: the jejunum, serum, and rectum (feces). These alterations not only signal the disease's presence but also its systemic influence, presenting a complex interaction between NE and the host's physiological state.

[0128] In the jejunum, NE birds displayed a marked increase in butyric acid levels, suggesting an enhanced local production possibly due to a modified gut microbiota or increased fermentation within the diseased intestine.

[0129] The serum levels of butyric acid were also significantly elevated in NE birds, implying that the alterations in butyric acid are not merely local but rather have systemic implications that could influence metabolic processes throughout the body.

[0130] The increase in butyric acid extended to the rectum (feces), consistent with the jejunum findings, indicating that the effects of NE may involve the entire gut, potentially due to a widespread change in microbial composition or a generalized inflammatory response.

[0131] The jejunum of NE birds showed significantly heightened levels of histamine, potentially pointing to an active local immune response, or a shift in the metabolism of histamine in this affected region of the gut.

[0132] In the serum, histamine concentrations were elevated in NE birds compared to controls, reflective of a systemic inflammatory response, where histamine may be released in larger quantities due to intestinal inflammation and absorbed into the bloodstream.

[0133] Similarly, in the rectum(feces), NE birds exhibited increased histamine levels, supporting the presence of a pervasive inflammatory response throughout the gut, or a disease-specific effect on histamine metabolism or the immune system in this region as well.

[0134] When assessing the tissue-based responses to NE, each tissue demonstrated a distinct pattern. Butyric acid levels consistently increased across all examined sites, suggesting a significant metabolic shift possibly linked to gut microbial changes and systemic effects of the disease. Histamine also showed a consistent rise, aligning with the known roles of histamine in immune responses and inflammation.

[0135] The findings underscore both butyric acid and histamine as biomarkers for NE, given their consistent upregulation across various tissues in affected birds. These biomarkers serve as valuable tools for disease monitoring and particularly early disease monitoring.

[0136] This application establishes these metabolites as reliable biomarkers for the early detection, diagnosis, and management of necrotic enteritis in subjects such as poultry.

[0137] This disclosure includes identifying metabolites responsible for the progression of subclinical and clinical NE in an animal model of broiler chickens. The present disclosure aims to (1) explore the metabolomic profile in the jejunum and metabolic pathways leading to subclinical and clinical NE in broiler chickens; and (2) to characterize the metabolomic profile in the jejunum and metabolic pathways associated with the pathogenesis of NE with CP isolates carrying different combinations of toxin genes.

[0138] Necrotic enteritis (NE) is an emerging disease in the broiler chicken industry caused by Clostridium perfringens (CP). The pathogenesis, or disease process, of NE is still not clear. This study aims to explore the associated metabolite profile and metabolic pathways of subclinical and clinical NE, and the role of various combinations of toxin genes of CP on the pathogenesis of the disease.

[0139] Using an established NE model, the protein content of feed was changed abruptly before exposing birds to CP isolates with different combinations of toxin genes (cpa, cpb2, netB, TpeL cpa, cpb2, netB; or cpa, cpb2). Metabolomics analysis of jejunal contents was performed by liquid chromatographytandem mass spectrometry (LC-MS / MS) based TMIC PRIME Assay. Raw data underwent logarithmic transformation and differential expression analysis. Visualization included heatmaps, volcano, box plots, and Venn diagrams. Univariate, multivariate, and logistic regression identified key metabolite patterns and disease links. Pathway analysis employed MetaboAnalyst V5 and STITCH, with Cytoscape for network visualization. Significance was set at p < 0.05.

[0140] This study detected differential expressions of 34 metabolites including organic acids, amino acids, fatty acids and biogenic amines, including elevation of butyric acid at onset of NE in broiler chickens. Subsequent analysis of broilers infected with CP isolates with different toxin gene combinations confirmed elevation of butyric acid consistently among 12 differentially expressed metabolites including amino acids, organic acids, and biogenic amines, underscoring its role in the development of NE. Furthermore, protein-metabolite network analysis revealed significant alterations in butanoate (butyric acid), arginine-proline and TCA cycle metabolisms. These findings suggest that butyrate tries to repair jejunal tissue damage and restore gut health by acting as a compensatory energy source amidst CP infection.Methods

[0141] The overall workflow of this study and different steps involved is represented in Figure 1 .Housing of birds and animal model of NE

[0142] All animal experiments conducted in this study were approved by the Animal Research Ethics Board at the University of Saskatchewan. The experimental procedures strictly adhered to the guidelines set by the Canadian Council on Animal Care. Birds were raised at the Animal Care Unit (ACU), Western College of Veterinary Medicine (WCVM), University of Saskatchewan, on soft wood shavings of depth 3-5 cm. At placement until 3 days of age, chicks received 23 hours of light and 1 hour of darkness at 40 lux. The darkness period increased to 8 hours and intensity increased during the light period. The temperature was set at 30-32 °C and decreased by 0.5 °C until 21 °C was maintained. As described earlier, an abrupt increase in protein content in the feed was used to predispose broiler chickens to CP infection and development of NE. Briefly, day-old broiler chickens were fed a 20% protein raised without antibiotics (RWA) poultry starter (Farm Choice™ RWA, MasterFeeds, Canada) until 18 days of age. Later, feed was withdrawn at 19 days of age. Thereafter, broilers were fed 28% protein by mixing 25% RWAturkey / gamebird starter crumble (MasterFeeds, Canada) with 38% layer / grower supplement (MasterFeeds, Canada) at a 10:3 ratio. Broilers were fed the 28% protein ration with added Fluid Thioglycollate media- grown CP culture in 1 :1 (v / w) ratio for 3 consecutive days (20 to 22 days of age) (Table 1). Based on the severity of necrosis, lesions were scored from 0 to 3, wherein 0 represents the normal intestinal mucosa; 1 shows mild, focal, acute necrosis of intestinal villi; 2 shows moderate, multifocal to coalescing, acute necrosis of intestinal villi; and 3 represents severe, diffuse, acute necrosis of intestinal villi. Birds with NE had severe, diffuse necrosis of the intestine predominantly in the jejunum (Figures 2B and 2C). Histopathological scoring of NE lesions was conducted as described above.

[0143] Table 1 : Composition of Feed UsedExperimental designExploration of the metabolomic profile in the jejunum of broiler chickens with subclinical NE, clinical NE and birds with no macroscopic or microscopic pathology following CP challenge

[0144] Day old broiler chickens from a local hatchery in Saskatchewan, Canada (Prairie Pride Chick Sales, Grandora, SK. Canada.) were placed at the ACU, WCVM, University of Saskatchewan, Canada. Birds were randomly allocated to 2 groups: (1) no CP challenge and; (2) CP challenge with isolate with cpa, netB, cpb2 and TpeL genes.

[0145] Birds with NE had severe, diffuse necrosis of the intestine predominantly in the jejunum (Figures 2A, 2B and 2C). No mortality was observed in groups 1 or 2 following CP challenge. No macroscopic (gross) or microscopic (histopathological) lesions of NE were detected in birds not challenged with CP (group 1). In contrast, birds challenged with CP had 57% of birds with NE lesions. 24% of birds challenged with CP had lesions of score 1 and 2 (subclinical NE), 33 % of birds challenged with CP had lesions of score 3 (clinical NE) and the remaining 43% of birds had a score of 0 (no NE lesions following CP challenge) (Figure 2D).

[0146] Following CP challenge and at the terminination of the animal experiment at 23 days of age, jejunal contents were collected from individual birds. Based on gross and histopathological lesions of NE, jejunal contents were divided into three groups for metabolomics analysis: (a) subclinical NE, characterized by histopathological lesions; (b) clinical NE characterized by both gross and histopathological lesions of NE; and (c) birds challenged with CP but no gross and histopathological lesions of NE . Volcano plots (Figures 3A-D) distinguished these three groups, showing statistically significant metabolites as dots.Characterization of the metabolomic profile in the jejunum of broiler chickens with NE following challenge with CP isolates carrying different combinations of toxin genes

[0147] To understand the significance of CP isolates carrying different combinations of toxin genes and associated virulence, the metabolic profile in broiler chickens was quantified following challenge with different CP isolates carrying different combinations of toxin genes. The NE challenge experiment was conducted using the NE infection model as described above. A total of 80, day-old broiler chicks (Prairie Pride Chick Sales, Grandora, SK, Canada) were randomly divided into four groups (n=20 / group) and challenged with CP accordingly: (1) no CP challenge; (2) CP isolate containing cpa, netB, cpb2 and TpeL genes; (3) CP isolate containing cpa, cpb2 and netB genes; and (4) CP isolate containing cpa and cpb2 genes. Broilers were monitored for mortality and jejunal lesions were scored at the end of the trial at 23 days of age as described above.

[0148] No gross or histopathological lesions were found in the group not challenged with CP (group 1). Group 2 (cpa, netB, cpb2 and tpeL) had 75% (n=15) with subclinical NE, 15% (n=3) with clinical NE and 10% (n=2) with no histopathology following CP challenge. Group 3 (cpa, cpb2 and netB) had 15% (n=3) with subclinical NE, 5% (n=1) with clinical NE and 80% (n=16) with no histopathology following CP challenge. Group 4 (cpa and cpb2) had 0% (n=0) with subclinical NE, 10% (n=2) with clinical NE and 90% (n=18) with no histopathology following CP challenge (Figure 2E).

[0149] Based on gross and histopathological lesions of NE, jejunal contents were divided into four groups for metabolomics analysis: (1) no CP challenge (n=20); (2) subclinical NE, characterized by histopathological lesions (n=18); (3) clinical NE characterized by both gross and histopathological lesions of NE (n=6); (4) birds challenged with CP but no gross and histopathological lesions of NE (n=36).Collection of samples for metabolomics analysis

[0150] At the termination of the experiment at 23 days of age, the entire length of the intestine was examined for gross lesions of NE. Sections of the jejunum were collected for histopathology to confirm NE. A section of unopened jejunum was carefully removed and intestinal contents collected into sterile 5mL Eppendorf tubes and placed on ice. Intestinal contents were then centrifuged for 5 min at 4000 revolutions per minute (rpm) to separate intestinal debris. Eppendorf tubes containing fluid were flash frozen using dry ice and ethanol. Flash frozen samples were stored at -80°C until metabolomics analysis at The Metabolomics Innovation Centre (TMIC), University of Alberta, AB, Canada.TMIC Prime Assay DI / LC-MS / MS method

[0151] A targeted quantitative metabolomics approach was employed, utilizing a synergy of direct injection mass spectrometry and a reverse-phase LC-MS / MS custom assay. This custom assay, paired with an ABSciex 4000 QTRAP® (Applied Biosystems / MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of up to 150 different endogenous metabolites including amino acids, acylcarnitines, biogenic amines and derivatives, uremic toxins, glycerophospholipids, sphingolipids and sugars [19,20], The method combines the derivatization and extraction of analytes, and the selective mass-spectrometric detection using multiple reaction monitoring (MRM) pairs. For precise quantification of metabolites, isotope-labeled internal standards, among other internal standards, were utilized. The custom assay setup included a 96 deep-well plate with an attached filter plate, sealed with tape, and the necessary reagents and solvents for plate preparation. The first 14 wells were allocated for a blank, three "zero" samples, seven standards, and three quality control samples. For all metabolites, excluding organic acids, samples were thawed on ice, vortexed, and centrifuged at 13,000x g. Then, 10 pL of each sample was applied to the center of the filter on the upper 96-well plate and dried under a nitrogen stream. Subsequently, phenyl-isothiocyanate was added for derivatization. The metabolites were then extracted by adding 300 pL of extraction solvent. This was followed by centrifugation into the lower 96-deep well plate, and a final dilution step using the MS running solvent, preparing the samples for subsequent analysis.

[0152] For organic acid analysis, 150 pL of ice-cold methanol and 10 pL of isotope-labeled internal standard mixture was added to 50 pL of sample for overnight protein precipitation. Then it was centrifuged at 13000x g for 20 min. 50 pL of supernatant was loaded into the center of wells of a 96-deep well plate, followed by the addition of 3-nitrophenylhydrazine (NPH) reagent. After incubation for 2 hours, BHT stabilizer and water were added before LC-MS injection.

[0153] Mass spectrometric analysis was performed on an ABSciex 4000 QTRAP® tandem mass spectrometry instrument (Applied Biosystems / MDS Analytical Technologies, Foster City, CA) equipped with an Agilent 1260 series UHPLC system (Agilent Technologies, Palo Alto, CA). The samples were delivered to the mass spectrometer by an LC method followed by a direct injection (DI) method. Data acquisition, processing, and analysis were performed using Analyst 1 .6.2.Catecholamine assay

[0154] Samples were initially thawed on ice and kept in the dark prior to analysis. A 10 pL of the ISTD mixture solution and 25 pL of samples (PBS (phosphate buffered saline) for “zero” samples, calibration curve standards, and samples) were pipetted directly onto the center of each spot in a 96-well filter plate. The whole plate was evaporated under nitrogen flow to dryness for 45 min. Following this, 50 pL of PITC (phenylisothiocyanate) derivatization solution, which targets free amines, was added to each well. The plate was left at room temperature for 20 min to complete the derivatization reaction. Postreaction, the samples were dried again under nitrogen flow for 90 min to eliminate any residual liquid, particularly the excess PITC solution. Subsequently, 75 pL of methanol containing 5 mM ammonium acetate was added to each well. The 96 well plate was then covered and shaken at 450 rpm for 30 min at room temperature, and then spun in a centrifuge for 15 min at 1200 rpm. To each well of the collection plate, 75 pL of water was added and mixed thoroughly, and then 40 pL was injected into an HPLC-equipped 4000 QTRAP® mass spectrometer for LC-MS / MS analysis.Metabolite data processing, differential metabolite expression, data visualization and statistical analysis

[0155] Initially, metabolite data underwent logarithmic transformation using R software to standardize the data distribution. Missing values and values below the limit of detection (LCD) were replaced with zeros to ensure consistency and integrity in the dataset, thus minimizing potential biases in the analysis. Differential expression analysis of metabolites was conducted using the limma algorithm

[0022] focusing on comparisons such as combined [clinical-*- subclinical + challenged] vs. control, clinical vs. control, subclinical vs. control, and challenged vs. control. Univariate T-test analysis was used to scrutinize individual metabolite variations between groups. A significant cutoff for p-values was set at <0.05. Box plots (ggpubr R package) illustrated metabolite concentration differences across groups, while Volcano plots (Enhanced Volcano R package) highlighted differentially expressed metabolites, emphasizing those with log fold changes and p-values below the 0.05 significance threshold

[0023] , Venn diagrams (via an R package) and heatmaps (pheatmap R package) were used to visualize overlapping metabolites and individual sample expression patterns, respectively

[0024] , The unsupervised Principal Component Analysis (PCA) with ggplot2 was employed for data dimensionality reduction, identifying key metabolite patterns among groups [25,26], Logistic Regression analysis (rms R package) characterized relationships between metabolite levels and disease state

[0027] ,Pathway analysis of differentially expressed metabolites.

[0156] The pathway analysis of the differentially expressed jejunal metabolites was performed using MetaboAnalyst V5, with Gallus gallus as the reference dataset. For all statistical comparisons, p values with <0.05 were statistically significant. Pathway significance was depicted via a bubble plot, showing the ratio of metabolite hits to total pathway metabolites. Additionally, STITCH analysis was conducted to explore metabolite-pathway interactions, with Gallus gallus selected as the organism ofinterest and a medium confidence level set at a C-score of 0.40. To visualize the resulting interaction networks, we utilized Cytoscape™, a widely recognized software tool for network analysis and visualization

[0028] ,RESULTSQuantification of the metabolomic profile in the jejunum of broiler chickens with no clinical signs, subclinical and clinical NE following CP challengeJejunal metabolic composition

[0157] The jejunal metabolomic analysis in NE affected birds revealed distinct metabolic profiles across clinical, subclinical, and challenged but no microscopic lesions of NE groups. Volcano plots (Figures 3A-D) distinguished these groups, showing statistically significant metabolites as dots. Broiler chickens with clinical NE had 9 upregulated and 37 downregulated metabolites, and birds with subclinical disease had 6 upregulated and 38 downregulated, while the birds that were challenged with CP but having no microscopic lesions of NE, showed upregulations in 7 and 47 downregulated. Overall, in all CP infected birds, there were 7 upregulated and 44 downregulated metabolites, with other comparisons to controls being nonsignificant.

[0158] Further investigation through a Venn diagram (Figure 4A) identified 34 metabolites consistently altered across all NE categories (Table 2). Heatmap analysis and dendrogram on the heatmap revealed a pattern of sample clustering, indicating a degree of homogeneity of the metabolites within each NE group.

[0159] Among the 34 metabolites analyzed, butyric acid, a SCFA, demonstrated robust upregulation (logarithmic scale of the fold change (logFC)) 1 .89, p<0.001) across all CP challenged broilers, with its expression intensifying with disease severity. Histamine and C5OH, a biogenic amine and lipid metabolite respectively, also showed a significant increase in expression, particularly in birds with clinical NE birds (histamine logFC 2.00, p<0.001 ; C5OH logFC 0.03, p<0.001).

[0160] A significant decrease in long-chain fatty acids (LCFAs) in NE-affected birds was noted in contrast to controls. Minor metabolites C9 and C8 were significantly reduced (logFC -0.02 and -0.03, respectively; p<0.001), in addition to C4:1 , C14:2, and C14 (logFCs -0.04 to -0.07; p values ranging from 0.00006 to 0.0001). Further, a significant decrease in C10, C3:1 , and C12:1 , with logFCs reaching -0.20 to -0.27 (p<0.0001), in addition to a significant most drop in C12 (logFC -0.31 , p<0.0001).

[0161] Amino acids, including isoleucine (logFC -0.35, p=0.0001), valine (logFC -1.23, p=0.00007), and tryptophan (logFC -1.37, p=0.00008), exhibited significant reductions, particularly in birds challenged with CP but no lesions. Further, alanine (logFC -0.73, p<0.0005), glycine (logFC -1.30, p<0.0005), histidine, tyrosine, methionine (logFC ranges from -0.76 to -1 .42; p<0.0005), proline, glutamate, aspartate (logFC up to -1 .75; p<0.0005), threonine, aspartate, and lysine (logFC -1 .65 to -1 .94; p<0.0005)showed significant reductions across all NE affected birds. In addition, downregulation of glutamine and asparagine (logFC -2.26 and -2.59; p<0.0005) occurred across disease severities.

[0162] Energy metabolism metabolites were notably altered with down regulation of glucose (logFC -2.62, p=0.003) and citric acid (logFC -4.39, p=0.00001) levels. In addition, choline was significantly reduced (logFC -1.48, p=0.000003), particularly in challenged but no lesion birds. Compounds integral to cellular health such as spermidine (logFC -1.04, p=0.01), methionine sulfoxide (Met-SO) (logFC -1.34, p=0.000004), and LYSOC14:0 (logFC -0.52, p=0.0008) were also decreased across all NE severities.

[0163] A logistic regression model targeted to histamine, butyric acid, citric acid, tyramine, ornithine revealed butyric acid as the most significant (p=0.004) metabolite with a highly significant association with NE infected birds compared to the controls. Figure 4B shows the box plots butyric acid levels are higher in broiler chickens with subclinical and clinical NE birds compared to healthy birds.

[0164] PCA plots elucidate the metabolic distinctions among various groups of chickens. PCA plot analysis suggests that metabolic profiling can distinguish between healthy and CP-challenged broiler chickens, as well as between different severities of NE. In a PCA plot of 34 common metabolites, PC1 explains 27.22% of the variance in the dataset, and PC2 explains 16.26%. Together, they provide a combined explanatory power of 43.48% for the dataset variance. The PCA plot demonstrates that the challenged, but no lesions group exhibits a distinct and consistent metabolic profile separate from healthy control birds, while the overlap between the clinical and subclinical groups indicates shared metabolic processes in NE progression, although with individual variability likely influenced by infection severity individual bird resilience, or other environmental factors. In Figure 4C, the PCA plot illustrates that PC1 and PC2 together capture distinct variations in the metabolic profiles between healthy chickens and those with NE infected group indicating specific metabolic shifts along these principal components were captured.Jejunal metabolite analysis identifies potential metabolic pathway alterations

[0165] The pathway analysis conducted on 34 jejunal metabolites revealed their significant involvement in multiple metabolic pathways, as evidenced by the ratio of metabolite hits to the total number of metabolites involved in each specific pathway (Figure 5). Notably, butyric acid metabolism emerged as one of the significant pathways affected by the disease (p = <0.04). Amino acid metabolism pathways, including aminoacyl-tRNA biosynthesis (p=<0.001), arginine biosynthesis (p= <0.001), and glycine, serine, and threonine metabolism (p=<0.001), were also dysregulated. Energy metabolism pathways such as glyoxylate and dicarboxylate metabolism (p=<0.001), glycolysis / gluconeogenesis (p=<0.02), and the citrate cycle (p=<0.01) were impacted. Additionally, pathways related to the metabolism of key metabolites like glutathione (p=< 0.003), nitrogen (p=<0.007), and tryptophan (p=<0.01) were also significantly connected to the metabolites altered in NE birds (Table 2).

[0166] Table 2: 34 differentially expressed metabolites across different NE categories of birds versus healthy control birds.Deciphering jejunal metabolic pathway networks in cellular processes based on metabolite-protein- analysis.

[0167] The network analysis of metabolites and proteins uncovers their complex interplay within metabolic pathways providing insights into disease mechanisms at the cellular level. Of the 34 differentially expressed metabolites a network encompassing 30 nodes was constructed: 21 metabolites (represented as ellipses) and 9 proteins (circles), linked to the central glycine node by 196 interactions (edges) in STITCH database. The specific metabolites involved include valine (Vai), glutamine (Gin), glucose, proline (Pro), glutamate (Glu), tryptophan (Trp), C8, glycine (Gly), spermidine, kynurenine, threonine (Thr), aspartate (Asp), C10, C14, lysine (Lys), tyrosine (Tyr), citric acid, histamine, choline, alanine (Ala), and methionine (Met). The identified proteins include NADSYN1 , GALM, ALDH18A1 , PAICS, GART, GRIN1 , ACLY, HNMT, and GATM. All these interactions had a minimum confidence score of 0.4 as per the data evidence from biological experiments, functional biology databases, and various genomic associations. The enrichment analysis of this network revealed the significance of butanoate (butyric acid), arginine and proline metabolisms (KEGG pathway: gga00330), with a p-value of < 0.0185, as evidenced by the ratio of proteinmetabolite hits to the total number of protein-metabolite hits involved in each specific pathway. Of the 9 protein interactors, ALDH18A1 and GATM have shown strong functional enrichment with metabolic pathways of arginine, proline and glutamate, which further acts as precursors to butaonate synthesis via ornithine (Table 3).

[0168] Table 3: Logistic Regression Analysis of Key Metabolites Identified in Clostridium Perfringens infected birds.Characterization of the metabolomic profile in the jejunum of broiler chickens with NE following challenge with CP isolates carrying different combinations of toxin genesJejunal metabolite composition

[0169] Jejunal metabolomic analysis of NE birds revealed a profound reprogramming of metabolic profiles, characterized by a consistent downregulation of several metabolites across CP isolates with 2T, 3T, 4T and combined NE categories compared with healthy controls. This trend was evident in all pairwise comparisons shown in volcano plots, with birds exposed to 2T CP isolate exhibiting 6 upregulated and 27 downregulated metabolites. On the other hand, birds exposed to 3T isolate manifested 6 upregulated and 30 downregulated metabolites, and birds exposed to 4T isolate demonstrated 3 upregulated and 36 downregulated metabolites. Notably, the combined infected group also displayed a similar pattern, with 7 upregulated and 32 downregulated metabolites (Figures 6A-D). The venn diagram (Figure 7A) provides valuable insights into the alteration of 21 metabolites across all CP isolates (2T, 3T, 4T and combined) (Table 4). Among the expression patterns of these 21 metabolites, amines and organic acids exhibit upregulation. Short-chain fatty acids like butyric acid (logFC range: 1.39 - 3.08), exhibited a marked upregulation across all groups. Interestingly, butyric acid shows its highest expression in response to the 3T challenge. Similarly, histamine (imidazole alkaloid; logFC range: 1 .36 - 2.26) and tyramine (amine; logFC range: 1 .60 - 3.89) display consistent upregulation across all challenged groups.

[0170] Table 4: Comparative Analysis of 21 Metabolites in Broiler Chickens Infected with CP isolates with different gene combinations (2T, 3T, 4T and combined)

[0171] All amino acids demonstrated downregulation across the 2T, 3T, and 4T toxin gene counts in NE-affected birds, with significant P < 0.05 and log fold changes (logFC) below -0.5. Asparagine, glutamine, and lysine displayed an interesting trend between toxin gene count and expression level. These metabolites showed the most substantial downregulation at the 3T challenge group (asparagine logFC - 0.94, glutamine logFC -1.14, lysine logFC -1 .23), with minimal recovery observed at 4T. In contrast, serine, threonine, and tyrosine exhibited a weaker correlation between toxin gene count and expression level. Their logFC values showed some variation across the challenge groups (serine: -0.49 to -0.57, threonine: -0.56 to -0.65, tyrosine: -0.61 to -0.59), but without a clear trend of increasing or decreasing expression in response to increasing toxin gene burden.

[0172] Putrescine and spermidine, essential polyamines, exhibited differential responses.Putrescine showed a strong downregulation (logFC -1.17) at the 3T challenge group. Spermidine's response might differ (logFC data not provided), possibly exhibiting a weaker correlation or a different downregulation pattern compared to Putrescine. Additionally, citric Acid, a key player in energy metabolism,displayed substantial downregulation (logFC around -1 .76) at 3T, suggesting a possible decrease in energy production in response to the toxin burden. Finally, acetylornithine, a vital intermediate in arginine synthesis, also exhibited downregulation across the challenge groups. The logFC values for acetylornithine likely range from (logFC -0.68 to -0.80) across the 2T, 3T, and 4T groups. Lastly, glutamic acid and methionine show a consistent degree of downregulation across all gene counts (glutamic acid logFC around -0.8, methionine logFC -0.91 at 2T to -0.76 at 4T), indicating a possible threshold effect.

[0173] The heatmap (Figure 7B) illustrates distinct variations in the jejunal metabolomes of NE-affected birds, revealing complex responses to CP isolates with various combinations of toxin genes (cpa, cpb2, netB, and TpeL). Metabolites such as butyric acid, histamine, and tyramine are overexpressed across all toxin-exposed groups compared to the control.

[0174] Logistic regression analysis employing butyric acid, lysine, tyramine, citric acid, acetylornithine, putrescine, spermidine metabolites has identified the statistically significant positive association with butyric acid, with a coefficient of 1 .23 and a P < 0.01 highlighting it as contributor to the NE pathology. Although tyramine exhibited a positive coefficient as well, it’s association did not reach statistical significance. Lysine, citric acid, acetyl-ornithine, putrescine, and spermidine were associated negatively, suggesting an inverse relationship with the condition, but these findings were not statistically significant (Table 5). The violin plot in Figure 7C reveals a significant increase in butyric acid levels in the birds challenged with CP isolates of different toxin genes 2T (P < 0.001), 3T (P < 0.001), and 4T (P < 0.001) compared to the control group birds.

[0175] Table 5: Logistic Regression Analysis of Key Metabolites Identified in Clostridium Perfringens infected birds.= p value is <0.001 , ** = p value is <0.01 ,

[0176] Figure 7D illustrates the PCA analysis for metabolomic profiling of broiler chickens based on varying CP toxin gene dosages (2T, 3T, and 4T) and control groups. The clustering shows the variation in metabolomic data, with PC1 explaining 50% and PC2 explaining another 50% of the variance. Clusters for each group (2T, 3T, and 4T) seem to partially overlap with each other but are generally distinct from the control group, suggesting a differentiated metabolomic impact due to NE which may not increase linearlywith the toxin gene count. The PCA plot in Figure 7E effectively differentiates the metabolomic profiles of chickens across the treated (combined toxin genes) and control groups, as evidenced by the distinct clustering pattern. Notably, both PC1 and PC2 capture a significant portion of the variance (50% each), indicating a robust representation of the metabolic alterations. This clear separation between the control and CP-treated groups suggests that the presence of CP leads to a distinct metabolic signature regardless of the type of toxin gene combinations.Metabolic pathway alterations in NE-affected broilers induced by different CP isolates.

[0177] Notable metabolic shifts were evident in the jejunum of broiler chickens afflicted with NE caused by different CP isolates. Glyoxylate and dicarboxylate metabolism (P < 0.001), arginine biosynthesis (P < 0.001), and Arginine and proline metabolism (P < 0.001) were among the significant pathways (Figure 8A) (Table 6).

[0178] Table 6: Pathway analysis of 21 jenunal metabolites identified in NE birds treated with CP isolates with toxin gene combinationsNetwork analysis based on metabolite-protein-analysis.

[0179] A radial network map visualized interactions between 21 jejunal metabolites (ellipses) and 14 associated proteins (circles) (Figure 8B). Lines depict functional relationships, with prominent connections observed for histamine, butyrate, lysine, putrescine, and tyramine. The network's complexity is evident with 81 edges, suggesting a tightly interwoven web of interactions between these molecules. Notably, a minimum confidence score of 0.4 ensures a degree of reliability for the depicted interactions. This network map provides a valuable window into the jejunal metabolic landscape, offering insights into the intricate interplay between metabolites and proteins that govern intestinal function. This radial network analysis highlighted the enrichment in arginine and proline metabolism (KEGG pathway: gga00330) as evidenced by P < 0.01. This network also highlighted the interaction between GATM, ALDH18A1 , NOS1 and NOS2 proteins and the metabolites like glutamine, proline, and arginine (Table 7).

[0180] Table 7: Metabolite-Protein Interaction Network Analysis of 21 jejunal metabolites exposed to CP infection with different toxin gene combinations

[0181] One objective was to explore jejunal metabolome and metabolic pathways associated with subclinical and clinical NE following CP challenge with a CP isolate containing cpa, netB, cpb2 and TpeL genes. Another objective was to characterize jejunal metabolome and metabolic pathways associated with subclinical and clinical NE following CP challenge with three different CP isolates containing cpa, cpb2, netB genes; cpa, cpb2 genes and cpa, netB, cpb2 and TpeL genes. This experiment was performed to determine if different CP strains could induce the production of different metabolites. This approach was to assist in the elucidation of whether the metabolomics landscape of NE depends on CP isolates with different combinations toxin genes or other virulence factors of CP. Based on the metabolomics data, itappears that butyric acid increased in the jejunum of birds with either subclinical (microscopic lesions) or clinical (macroscopic lesions) NE irrespective of the severity of NE. Similarly, butyric acid was elevated in the jejunum of birds with NE lesions caused by CP isolates with any combination of toxin genes (cpa, cpb2, netB genes; cpa, cpb2 genes or cpa, netB, cpb2 TpeL genes). This suggests the role of virulent factors beyond cpa, cpb2, netB toxins. Furthermore, it is to be noted that butyric acid is elevated in birds with no macroscopic or microscopic NE lesions but following challenge with CP isolates with any combination of toxin genes. Based on this observation, it appears that butyric acid elevation in broiler chickens indicates a vital adaptive mechanism against CP-induced tissue damage in NE, suggesting it as an early marker for the disease onset.

[0182] In the first experiment, upregulation of butyric acid, histamine, tyramine, and citric acid in CP challenged birds suggested their role in the NE lesion development. Among which butyric acid and histamine demonstrated a significant up regulation in all NE infected bird’s regardless of the categories clinical, subclinical, and challenged but no microscopic lesions of NE. Based on the findings in this present application, it can also be hypothesized that butyric acid stimulates the intestinal mucin secretion, which further utilized by bacteria like CP to adhere mucosal epithelium and proliferate

[0041] , CP has the ability to use mucous as an energy source by secreting enzyme sialidases Nani (77 kDa), NanJ (129 kDa) and NanH (43 kDa)

[0042] ,

[0183] Similarly, it can be hypothesized from this present application that CP proliferation and associated microbiome imbalance potentially are the sources of histamine in the jejunum of NE affected birds.

[0184] Amino acids such as isoleucine and valine were significantly (p<0.0001) reduced in birds with no lesion after CP challenge which indicates early metabolic alterations before the appearance of microscopic disease. These are branched-chain amino acids and supports the proliferation of pathogenic bacteria through protein synthesis. CP lacks amino acid synthesis machinery hence proliferation and colonization requires amino acids availability

[0053] . In NE infected birds, both amino acid and fatty acid metabolism were downregulated are mainly related to the utilization of nutrients and requirements of CP to colonize in the gut. Doubling time of CP is less than 10 minutes, hence nutrient demand is elevated. Also, amino acids and fatty acids are integral part of gram-positive cell membranes and cell wall respectively

[0054] , Choline, a biomarker of cell membrane turnover and nucleotide metabolism, was significantly reduced particularly in birds challenged with CP but had no lesions, suggesting potential membrane integrity and signaling pathway dysregulation

[0055] ,

[0185] Energy metabolism metabolites were notably reduced, with glucose (p=0.003) and citric acid (p=0.00001) indicating energy deficits and impaired citric acid cycle function. In contrast, glycogen- synthesis-pathway and acetyl-CoA pathways were shown to be elevated in cecal transcriptome analysis of CP coinfected with Eimeria oocysts model of NE

[0056] , Conversion of butyric acid to acetyl-CoA via p- oxidation in the mitochondrial matrix could compensate partial energy requirements

[0046] within the gut ofCP-infected birds. Elevated levels of butyric acid were observed in CP-infected chickens, correlating with the severity of NE.

[0186] In the second experiment, despite the different isolates with toxin gene combinations upon NE development, the metabolic profile change was similar to the first experiment. Notably, some changes, like butyric acid and citric acid, were consistent across disease severities. These findings signify disruptions in fundamental processes such as protein metabolism, amino acid metabolism, and antioxidant defenses in NE-affected birds. Additionally, the enrichment of arginine biosynthesis highlights its importance in nitrogen metabolism and immune function indicating perturbations in essential cellular pathways and immune responses amidst NE development.

[0187] Protein-metabolite network analysis improves the identification of disease-associated metabolic pathways beyond metabolite-centric approaches. Network analysis of metabolites in CP induced NE chickens has highlighted interactions between ALDH18A1 and GATM proteins, enriched in arginineproline and glutamate metabolism, potentially providing precursors to butyrate synthesis by butanoate metabolism Figure 9

[0058] , This is significant because proline, derived from glutamate, fuels protein synthesis, polyamine production, and the nitric oxide pathway, all crucial for intestinal health and inflammatory responses

[0059] , Interestingly, enrichment of butyrate metabolism suggests a potential compensatory mechanism for disrupted energy homeostasis. Butyric acid, a bacterial fermentation product, serves as an alternative energy source for enterocytes, modulates inflammation, and acidifies the gut lumen, hindering pathogen colonization [60,61], The observed increase in butyrate could be a consequence of either direct CP-mediated fermentation or a shift in gut microbiota favoring butyrate producers, potentially aimed at restoring gut health and repairing tissue damage

[0062] , In contrast, jejunal tissues from chickens infected with different toxin genes showed enrichment of the citric acid (TCA) cycle with CS, ACO, IDH, PC, and PCK protein interactors, despite a decrease in citrate levels. The dysfunctional TCA cycle, a key pathway for cellular energy production, utilizes acetyl-CoA derived from various sources. This suggests potential CP toxin effects or cellular damage limiting substrate availability

[0063] , Furthermore, CP infection might promote a hypoxic environment favoring C. perfringens while stressing oxygen-dependent gut bacteria. Reduced citrate could indicate a shift away from aerobic energy metabolism, potentially due to tissue damage or inflammation

[0064] . Overall, protein-metabolite network analysis reveals a metabolic adaptation of jejunal tissues in CP infection, disrupted TCA cycle function, and a potential compensatory mechanism involving increased butyrate production to maintain energy homeostasis in compromised jejunal tissues.

[0188] This study indicates that jejunal butyric acid levels rise significantly in broilers with NE, suggesting it is a sensitive predictor of disease occurrence at the farm level. This elevation occurs irrespective of NE severity or CP toxin gene profiles.

[0189] The first objective of this application revealed a multifaceted interaction involving butyric acid, and CP infection in birds with microscopic and / or macroscopic NE lesions. Broiler chickens with nomicroscopic lesions of NE but challenged with CP also revealed a significant butyric acid metabolite along with changes in protein, LCFAs and energy metabolism compared to healthy birds. In the second objective, it was concluded that despite broilers challenged with different CP isolates (toxin gene combinations), butyric acid was significantly present in the jejunum. The metabolic profile and metabolic pathways were consistent in both the experiments. Butyric acid metabolite levels in blood or in fecal swabs or fecal samples (e.g., feces from the rectum / cloaca or fresh droppings) may act as a diagnostic tool to predict NE in broiler chickens.Example 2Study 2

[0190] Serum and feces samples were tested as described in Example 1. Very similar profiles were seen in both serum and feces.

[0191] Figures 10A-C and Table 8 illustrate the distribution of butyric acid and histamine levels in the jejunum, serum, and feces from the rectum of broiler chickens affected by necrotic enteritis (NE) compared to control chickens. NE birds have significantly higher butyric acid levels compared to controls in all three locations (jejunum, serum, and rectum (feces)). NE birds show significantly higher histamine levels compared to controls across all three locations (jejunum, serum, and rectum (feces)). These findings suggest potential alterations in gut microbiota and inflammatory responses in NE birds.

[0192] Table 8: T-test two groups comparison / Wilcox test (two groups comparison) - results from Figs. 10A-C).

[0193] Figures 1 1A-B present boxplots of butyric acid and histamine across control and infected samples, which show upregulation in infected samples across serum, jejunum, and fecal samples.Example 3

[0194] Methodology: The protein content of the feed was abruptly altered from 20% to 28% using a well-established NE model before challenging the birds with CP. Then, we performed a targeted, fully quantitative liquid chromatography-tandem mass spectrometry (LC-MS / MS) -based assay for analyzing the metabolomics profile of serum, feces, and jejunal contents in NE birds. The data were analyzed to understand the trend of metabolite distribution, relationships between metabolites and pathway impacts.

[0195] Results: Birds with NE showed metabolic variations including lipids, amino acids, and organic acids, across all the biological samples analyzed. This variation was higher in serum samples (310 / 597 metabolites, 51.92%), compared to fecal (182 / 608 metabolites, 29.93%), and jejunal samples (125 / 607 metabolites, 20.59%). A robust statistical analysis of these metabolites identified 19 common metabolites, including butyric acid and histamine. Pathway analysis identified that six of them were enriched in key pathways, like tricarboxylic acid cycle (TCA cycle) (citric acid and cis-aconitic acid), glyoxylate and dicarboxylate metabolism (citric acid and cis-aconitic acid), arginine-proline metabolism (spermine and creatinine), butanoate metabolism (butyric acid), and histidine metabolism (histamine). These pathways were related to energy synthesis, nitrogen metabolism, and immune response in NE birds.

[0196] This highlights metabolic differences in birds with NE and underscores butyric acid and histamine as early biomarkers for NE diagnosis. The upregulation of these metabolites across serum, jejunal and fecal samples reflects their local and systemic impacts on the disease.

[0197] Example 1 discusses the role of butyric acid in the jejunum of broiler chickens during the progression of NE (Gautam et al., 2025). Herein, metabolic alterations in serum and feces are explored using an advanced liquid chromatography-tandem mass spectrometry (LC-MS) based on The Metabolomics Innovation Centre (TMIC) mega targeted metabolomics approach. Early detection of NE will benefit both poultry and human health by mitigating pathogenic proliferation, optimizing therapeutic efficacy, safeguarding production sustainability, and promoting food security and safety. Current diagnostic procedures regarding NE require pathological examination followed by bacterial culture and histopathology of infected tissues, which takes 48-72 h to obtain results. Biomarker-based diagnostics procedures can be conducted in real time. By identifying key metabolite changes and associated pathways, it is aimed to uncover potential biomarkers that could facilitate early and non-invasive detection of NE, ultimately improving health management and NE control in the broiler chicken industry.Material and methods

[0198] The overall workflow for this study is presented in Figure 12.Animal care

[0199] All animal experiments conducted for this study were granted by the Animal Research Ethics Board at the University of Saskatchewan and Canadian Council on Animal Care guidelines followed. Animal work was conducted at the Animal Care Unit (ACU), Western College of Veterinary Medicine (WCVM), University of Saskatchewan. Birds are raised on soft wood shavings of depth 3-5 cm. Fromplacement until 3 days of age, chicks received 23 h of light and 1 h of darkness at 40 lux. After 3 days of age, the darkness period increased to 8 h and the light intensity decreased to 30 lux during the 16 h light period. The initial temperature was set at 30-32°C for first 3 days and decreased by 0.5°C per day until a temperature of 21 °C was maintained.Animal model of CP in broiler chickens

[0200] A well-established animal model of NE in broiler chickens was used to study the metabolic profile and pathways for this study (Gautam et al., 2025). Briefly, broiler chickens were fed a commercial raised without antibiotics (RWA) broiler starter ration containing 20% protein (Farm Choice™ RWA, Masterfeeds, Canada) until 18 days. Feed was withdrawn at 19 days. At 20 days of age, a new feed ration was introduced containing 28% protein. The 28% RWA feed was prepared by mixing a commercially available 25% RWA turkey starter (MasterFeeds, Canada) with 38% poultry supplement (MasterFeeds, Canada) at a 10:3 ratio. In this model, a sudden increase of protein in the feed, from 20% to 28% immediately before the CP challenge, was used as a predisposing factor. In order to minimize variables, an abrupt increase in protein content was conducted in both control and CP challenge groups. A CP isolate (CP 21) containing cpa, netB, cpb2, and tpeL toxin genes was grown in fluid thioglycollate (FTG) media (Sigma-Aldrich, Oakville, ON, Canada). The culture was added to the ration (1 :1 v / w) and fed twice daily for 3 consecutive days (20-22 days of age) as previously described in Example 1 . Birds were observed for clinical signs and mortality three times per day until the termination of the experiment at 23 days of age. Mortality, gross and histopathological scoring of the intestine were conducted as previously described in Example 1 . Briefly, histopathological intestinal lesions were scored as: 0 = no lesions / healthy mucosa; 1 = focal necrosis of intestinal villi, acute; 2 = necrosis of intestinal villi, multifocal to coalescing, acute; and 3 = diffuse, necrosis of intestinal villi, acute, severe in all birds (Figures 13A-D).Experiment A

[0201] The metabolic profile and pathways associated with NE development in commercial broiler chickens was investigated. On the day of hatch, a total of 47 broiler chicks were randomly divided into two groups: (1) no CP challenge (n = 23); (2) CP challenge (n = 24). At the termination of the experiment at 23 days of age, sections of intestines were collected for histopathology from all the birds. Microscopic lesions of NE were scored as described above. In order to study the metabolic derangements, the contents of the jejunum and rectum (fecal contents) of the broiler chickens were collected along with blood samples from individual birds (Figure 13C). To study the metabolic derangements, intestinal contents from jejunum, rectum / fecal samples and blood samples were collected from broiler chickens.Experiment B

[0202] (1) the consistency and repeatability of the metabolites and metabolic pathways identified during the development of NE; (2) to increase the statistical power in bioinformatics analysis were investigated. On the day of hatch, a total of 60-broiler chicks were randomly assigned into two groups (n =30 / group) (1) no CP challenge; (2) CP challenge. At the end of the experiment at 23 days of age, sections of the jejunum were collected to confirm the NE by histopathology as described above in experiment A. To study the metabolic profile, intestinal contents from jejunum, rectum / fecal samples along with the blood samples were collected from broiler chickens.Metabolomics sampling and processing

[0203] At 23 days of age, three types of samples were collected from each bird for metabolomics analysis: (1) blood / serum samples, (2) rectal / fecal samples, and (3) jejunal samples. Before euthanasia, blood samples were collected from the brachial vein of each bird. Further, blood samples were centrifuged for 5 min, and serum was collected and then flash-frozen immediately using dry ice and ethanol. Also, rectum / fecal and jejunal samples were collected in 1 mL Eppendorf tubes and immediately placed on ice. Gross lesions of NE in the intestine were recorded. Intestinal sections were collected for histopathology to score NE lesions. Sections of jejunum are carefully removed, and intestinal contents are collected gently into sterile 5 mL eppendorf tubes and placed on ice. Rectal / fecal and jejunal contents were centrifuged for 5 min at 1 ,968 g. The flash frozen samples were stored in -80°C until shipped to TMIC, University of Alberta, Canada on dry ice for analysis.Metabolomic analysis

[0204] The TMIC Mega Metabolomics assay is employed to identify and quantify up to 900 targeted endogenous metabolites using a combination of direct injection (DI) mass spectrometry and reverse-phase LC-tandem mass spectrometry (LC-MS / MS) (Applied Biosystems / MDS Analytical Technologies). This assay can detect metabolites encompassing a broad range of biochemical classes, including amino acids, derivatives, sugars, biogenic amines, organic acids, nucleobases, vitamins, cofactors, amine oxides, short-chain fatty acids, acylcarnitines, and various lipid types (sphingomyelins, triglycerides, glucosylceramides, ceramides, cholesterol esters, and diglycerides). A detailed description of the TMIC Mega Metabolomics assay is provided in Example 1 .

[0205] In brief, isotope-labeled internal standards (ISTDs) with concentrations ranging from 1 to 10 pM and chemical derivatization reagents such as PITC for amino acids and 3-NPH for organic acids were added to enhance ionization and separation during mass spectrometry analysis. Stock solutions with concentrations from 0.01 to 1 mM for each analyte were prepared by dissolving accurately weighed chemicals in appropriate solvents. Seven calibration curve standards (Call to Cal7, with concentrations ranging from 0.01 to 100 pM) and three quality control (QC) standards (low, medium, and high concentrations: 0.05, 0.5, and 5 pM, respectively) were prepared by mixing and diluting stock solutions with appropriate solvents. For amino acids, amino acid derivatives, biogenic amines, and nucleotide / nucleosides, PITC derivatization was performed by drying the samples under a nitrogen stream, adding a 5% PITC derivatization solution, and extracting the targeted analytes with methanol containing 5 mM ammonium acetate. LC-MS / MS analysis was then conducted by transferring 50 pL of extracts to a new 96-deep-well plate and diluting with 450 pL of LC / MS-grade waterto quantify these metabolites. For organicacids, 3-NPH derivatization was utilized by adding a derivatization reagent consisting of 250 mM 3-NPH in 50% aqueous methanol, shaking the mixture at room temperature for two hours, adding LC / MS water and 2 mg / mL butylated hydroxytoluene (BHT) dissolved in methanol, and further diluting the mixture. DI-MS / MS analysis was conducted by transferring 10 pL of the remaining extracts to another 96-deep-well plate and diluting with 490 pL of direct flow injection (DFI) buffer to quantify lipids, acylcarnitine’s, and glucose / hexose.

[0206] To ensure accuracy and precision, three QC samples at low, medium, and high concentrations were included, and each sample was analyzed in triplicate for reproducibility. Internal standards were used to normalize the data, and limits of detection (LOD) and quantification (LOQ) were determined for each metabolite. Blank samples were included to monitor potential contamination and carryover effects. The robustness and reproducibility of the method were validated through these rigorous quality control measures, as outlined in previous publications (De Prefer and Verbeke, 2013).Metabolomics data analysis

[0207] Metabolomic data, quantifying metabolite concentrations in micromolar (pM) units, underwent rigorous preprocessing to ensure data reliability and comparability. To address missing values below the LOD, imputation with half the minimum positive value was employed for each metabolite. To stabilize variance and normalize data distribution, logarithmic transformation was applied.

[0208] Differential metabolite analysis was conducted using the limma package in R to identify metabolites significantly altered between serum and tissue groups (Ritchie et al., 2015). To account for multiple comparisons, p-values were adjusted using the false discovery rate (FDR) method, with a significance threshold of FDR < 0.05. To visualize overall metabolic profiles and identify potential outliers, unsupervised principal component analysis (PCA) was performed. For pairwise group comparisons, volcano plots were generated to highlight significantly altered metabolites based on fold change and adjusted p-values were employed to display expression patterns across different sample types for individual samples.

[0209] Subsequently, differential metabolite analysis was conducted using the limma package in R to identify metabolites that were significantly altered between serum and tissues. Univariate analysis was performed to assess the distribution, normality, and homogeneity of variance of each metabolite. To mitigate the risk of false positives due to multiple comparisons, the FDR method was applied, setting a significance threshold of FDR < 0.05. To visualize overall metabolic trends and identify potential outliers, unsupervised PCA was performed. To identify metabolites with significant differential abundance between experimental groups, pairwise volcano plots were generated. These plots display both the magnitude of fold change and statistical significance (adjusted p-value) for each metabolite.

[0210] To further explore metabolite relationships and patterns, heatmaps were created using MetaboAnalyst to visualize the top metabolites, and correlation matrices were generated to assess the strength of associations among metabolites. Additionally, regression analysis was conducted to investigatethe relationship between metabolite levels and relevant outcomes. To compare metabolite levels between control and NE infected groups, violin plots accompanied by Wilcoxon test (two-group comparison, nonparametric) were utilized employed to compare levels of metabolites of interest between control and NE groups.

[0211] To gain biological insights into the identified differentially expressed metabolites, pathway enrichment analysis was performed using MetaboAnalyst with Gallus gallus as the reference organism. Significantly enriched pathways were determined based on a p-value cutoff of < 0.05. Visualizations such as GO chord plots were generated using Cytoscape.

[0212] All the statistical analyses and visualizations are primarily performed using R statistical software, with the aid of packages including limma (Ritchie et al., 2015), ggplot2 (Wickham, 2016), enhanced volcano, 1 heatmap (Gu et al., 2016), pathway analysis and network visualization were conducted using XCMS (Smith et al., 2006), MetaboAnalyst (Chong et al., 2018) and Cytoscape (Shannon et al., 2003), respectively.ResultsExperiment A

[0213] There was no mortality in group 1 birds (no CP challenge), in contrast, group 2 birds (CP challenge) had 20% mortality (CP challenge). No gross or microscopic NE lesions were observed in group 1 (no CP challenge). In contrast, 100% of birds in group 2 (CP challenge) exhibited NE lesions, with 85% showing a score of 3 and 15% a score of 2.Experiment B

[0214] There was no mortality in group 1 (no CP challenge) in contrast, group 2 (CP challenge) had 20% mortality. No gross or microscopic NE lesions in group 1 (no CP challenge) in contrast, 100% of birds in group 2 (CP challenge) had NE lesions (score 3 in 90% birds and score 2 lesions in 10% birds) (p < 0.0001) (Figures 13A-D).Metabolomics analysis of samples of NE and control birdsSerum samples

[0215] Serum samples underwent variance and abundance filtering to enhance the quality of metabolomics data between NE and control groups. Density plots before normalization highlight shifts in metabolite concentrations due to normalization, while scatter and box plots after normalization demonstrate clearer distinctions between NE-affected and control samples, enhancing statistical analysis. Univariate analysis identified significant alterations in 310 out of 597 metabolites (51.92%) in NE birds compared to controls (P < 0.05) . These 310 included 202 lipids and derivatives (65.16%), 62 amino acids and derivatives (20%), 33 organic acids (10.65%), 12 nucleotides and amine derivatives (3.87%), and 1 small molecule (0.32%). Of these, 196 metabolites (63.22%) were upregulated, with a log fold change (LogFC) range of0.07 to 2.38, while 114 metabolites (36.77%) were down regulated, with a LogFC range of -1.35 to -0.09 (Figure 14A). The heatmap illustrates the expression patterns of the top 20 differentially expressed metabolites in NE-affected serum samples (Figure 20).

[0216] Multivariate analysis using PCA showed that while there is some overlap between NE- affected and control samples, NE samples exhibited greater metabolic variability. Both Principal Component (PC) 1 and PC2 accounted for 50% of the variance in the serum metabolite data, indicating distinct metabolic differences between the groups (Figure 14B). Correlation analysis further identified significant associations among specific metabolites in serum. For example, among the triglycerides, TG (16:0-34:1) and TG (18:2-34:2) exhibited a very high positive correlation (r = 0.98), suggesting their coregulation in lipid metabolic processes. Similarly, isoleucine and leucine demonstrated a strong positive correlation (r = 0.98), highlighting their linked roles in protein synthesis and energy production, which are critical responses to NE-induced stress. Additionally, alpha-ketoglutarate and 2-oxoisocaproate showed a strong positive correlation (r = 0.98), indicating their interconnected roles in the TCA cycle and amino acid metabolism. For histamine, the top positively correlated metabolites were creatine (r = 0.7) and spermine (r = 0.6), indicating coordinated changes in energy storage and cellular metabolism, while norepinephrine (r = -0.52) exhibited the strongest negative correlation. In the case of butyric acid and isobutyric acid, N- acetyl-glutamic acid (r = 0.75) and 2-hydroxy-3-methylvaleric acid (r = 0.7) showed strong positive correlations, whereas TG (16:0-34:1) (r = -0.56) was the most negatively correlated.

[0217] Regression analysis reinforced these findings, with citrulline exhibiting a strong positive correlation with NE, as evidenced by a regression coefficient of 2.28 and a highly significant p-value of 3.43E-06. Other metabolites, such as TG (20:2-34:1) and LysoPC a C20:3, also showed significant associations, implicating their roles in altered lipid metabolism and membrane signaling. Finally, pathway enrichment analysis revealed significant disruptions in arginine and proline metabolism, glycine, serine, and threonine metabolism, the citrate cycle, and butanoate metabolism, suggesting alterations in protein synthesis, amino acid metabolism, energy production, and other essential processes in serum samples of NE birds (P < 0.05) (Table 9).

[0218] Table 9: Pathway analysis of serum metabolites in NE birds.Jejunal samples

[0219] In the jejunal samples, data normalization was done to ensure the reliability and consistency of the metabolomics data, thereby improving the comparability between NE-affected and control groups. Density plots before normalization highlight shifts in metabolite concentrations due to normalization, while scatter and box plots after normalization demonstrate clearer distinctions between NE affected and control samples, enhancing statistical analysis. Univariate analysis identified significantalterations in 125 out of 607 metabolites (20.59%) in NE-affected jejunal contents. The distribution of these 125 differentially expressed metabolites included 52 lipids and derivatives (44.44%), 37 amino acids and derivatives (31 .62%), 22 organic acids (18.80%), 9 nucleotides and amine derivatives (7.69%), and 5 other organic small molecules (4.27%). Of these, 58 metabolites (55.56%) were up-regulated, with a LogFC range of 0.19 to 3.16, while 67 metabolites (48.44%) were downregulated, with a LogFC range of -0.33 to -3.57 (Figure 15A). The heatmap depicts the expression patterns of the top 20 differentially expressed metabolites, showcasing distinct upregulation and downregulation patterns between NE-affected and control jejunal tissues (Figure 21).

[0220] PCA of the jejunal content data showed a substantial overlap between NE-affected and control samples. However, NE samples tended to cluster more toward the positive side of PC1 , indicating some degree of metabolic differentiation. The considerable overlap along PC2, however, suggested that the metabolic profiles of NE-affected tissues shared similarities with those of controls (15B). Correlation analysis fur-ther highlighted strong associations among N-acetylated amino acids in the jejunal contents . The strongest correlation was observed between N-acetyl-proline and N-acetyl-glycine (r = 0.97), suggesting a combined role in collagen synthesis and cellular detoxification. Additionally, N-acetyl-tyrosine and N-acetyl-proline exhibited a notable correlation (r = 0.97), pointing toward their involvement in protein syn-thesis and stress modulation. Histamine displayed a positive correlation with N-acetyl-methionine (r = 0.92), while choline showed a negative correlation (r = -0.49). Butyric acid and iso-butyric acid demonstrated a positive correlation with alpha-amino-isobutyric Acid (r = 0.81) and a negative correlation with quinoline-4 carboxylic acid (r = -0.55). Regression analysis further showed of histamine with a regression coefficient of 0.1 165 and a significant p-value of 0.003. Propionic acid and betaine also demonstrated a strong positive association, hinting at their role in the NE-affected metabolic pathways. Finally, jejunal pathway enrichment analysis has shown considerable disruptions in arginine and proline metabolism, betaalanine metabolism, and glutathione metabolism (p-values < 0.05). These findings suggest disrup-tions in protein synthesis, energy metabolism, and oxidative stress response, all of which are critical in the pathology of NE (Table 10).

[0221] Table 10: Pathway analysis of jejunal metabolites in NE birds.Fecal samples

[0222] Fecal samples underwent variance and abundance filtering to improve data quality. Density plots in be-fore normalization highlight shifts in metabolite concentrations due to normalization, while scatter and box plots after normalization demonstrate clearer distinctions between NE affected and control samples, enhancing statistical analysis.

[0223] Univariate analysis revealed significant alterations in 182 out of 608 metabolites (29.93%), including 70 lipids and derivatives (44.87%), 50 amino acids and derivatives (32.05%), 18 organic acids (11.54%), 3 nucleotides and amine derivatives (1.92%), and 41 other small organic molecules (6.75%). In total, 122 metabolites (67.03%) were upregulated, with LogFC values ranging from 0.05 to 4.70, while 60 metabo-lites (32.96%) were downregulated, with LogFC values ranging from -2.54 to -0.11 (Figure 16A).The heatmap of the top 20 differentially expressed metabolites illustrates the expression patterns between NE-affected and control fecal samples (Figure 22). PCA analysis showed some separation between NE- affected and control samples, with a lot of overlaps, indicating similar metabolic features (Figure 16B).

[0224] Correlation analysis of fecal samples showed many strong links between important metabolites. For example, there was a very strong positive link (r = 0.98) between leucine and isoleucine, indicating coordinated regulation in response to NE. Similarly, PC as C34:2 and PC as C36:2 showed a strong positive correlation (R = 0.95), as did LysoPC a C16:0 and LysoPC a C18:1 (r = 0.94), suggesting interconnected roles in metabolic pathways. Conversely, asparagine exhibited strong negative correlations with Cer (d18:1 / 24:1) (r = -0.68), and TG (16:1-34:3) had strong negative correlations with PC aa C32:1 (r = -0.68) and PC aa C36:1 (r = -0.69). Additionally, TG (16:0-37:3) demonstrated a strong negative correlation with PC ae C34:1 (r = -0.68), and TG (20:3-32:2) exhibited the strongest negative correlation (r = -0.65). N-Acetyl putrescine showed the strongest positive correlation with butyric acid and iso-butyric acid (r = 0.70), while C6:1 had a strong positive correlation with histamine (r = 0.65). Orotic acid (coefficient: 0.48, P = 0.01) and SM(OH) C22:2 (coefficient: 1.14, P < 0.02) also demonstrated significant associations, implicating disruptions in nucleotide synthesis and membrane integrity. Finally, pathway enrichment analysis in fecal samples highlighted significant disruptions in several metabolic pathways, including glutathione metabolism, alanine, aspartate, and glutamate metabolism, glyoxylate and dicarboxylate metabolism, and arginine biosynthesis (p < 0.05). These disruptions may collectively affect detoxification, amino acid metabolism, energy production, and protein synthesis, providing further insights into the metabolic alterations occurring in fecal samples in NE birds (Tablel 1).

[0225] Table 11 : Pathway analysis of rectal metabolites of NE birdsComprehensive analysis of differentially expressed metabolites across serum, jejunal contents and fecal samples in NE birds

[0226] The significant metabolites across serum, jejunal contents and fecal samples of the rectum in NE birds were investigated to find the common metabolites. The Venn diagram in Figure 17A illustrates the distribution of significant metabolites across serum, rectum (feces), and jejunal samples in NE birds. Serum had the highest number of unique metabolites, with 172 metabolites (38.6%), followed by fecal samples from the rectum with 71 metabolites (15.9%), and jejunal contents with 51 metabolites (11.4%). Particularly, 19 metabolites (4.3%) were found common among all the 3 sample types, suggesting them as universal biomarkers for NE. Moreover, 41 (9.2%) metabolites were common between serum and jejunal contents, 14 (3.1 %) metabolites between rectal and jejunal contents, and 78 (17.5%) metabolites between serum and feces. This distinct metabolite distribution highlights both localized and systemic metabolicinfluences of the NE disease. The heatmap illustrated distinct metabolite expression pattern between NE and control samples, confirming the NE-induced metabolic shifts in chicken .

[0227] The results illustrated in Figure 17B show differential expressions of 19 metabolites in serum, fecal, and fecal contents of chickens with NE . The bubble plot shows the relationship expression levels of selected metabolites, where bubble size corresponds to the expression ratios and color intensity, indicating the degree of up and down-regulation. This analysis points to the metabolites, which are upregulated among all samples, which position them as potential NE biomarkers. Of these metabolites, histamine is seen to be the most prominent upregulated metabolite with LogFC of 3.16 in Jejunum, 2.72 in the rectum (feces), and 0.69 in serum, all with high significance p-values (1 .43E-09, 6.01 E-14, and 0.0013). Similarly, butyric acid and iso-butyric acid have also shown up-regulation (LogFC: 2.46 in the jejunal contents, 0.96 in the feces, and 1 .49 in serum), with respective p-values like 1 .32E-06, 0.0027, and 2.37E- 11. Similarly, valeric acid and isovaleric acid have also shown this metabolic trend (LogFC: 2.10 in the jejunal contents, 2.05 in the feces, and 1.60 in serum) with significant p-values (0.0006, 0.0002, and 0.0010). The consistent upregulation of these metabolites across the jejunal, feces, and serum samples not only underscore their importance in disease pathogenesis but also confirms them as NE biomarkers.

[0228] The PCA plot in Figure 17C shows clear metabolic separation across the jejunal, feces, and serum samples between NE and control group chicken. PC1 and PC2 each explain 50% of the variance, capturing the major differences in the dataset. The jejunal contents, feces, and serum samples (circles, crosses, stars) in the NE cluster are distinct from control samples (squares, triangles, rectangles) in all samples, indicating significant metabolic shifts due to NE. The tight clustering of control samples suggests minimal variation in healthy chickens, while the more dispersed NE clusters reflect the disease’s impact. This separation confirms that NE causes significant and systemic metabolic changes.The correlation analysis across the jejunal contents, feces, and serum of NE-affected chickens reveal notable patterns, with both commonalities and distinct relationships across these tissue fluids as shown in Figure 23A-C. In the Jejunum, the primary site affected in NE, strong correlations between key metabolites, such as HexCer (d18:1 / 26:1) and LysoPC a C26:1 (r = 0.88), creatine and spermine (r = 0.91), indicate a tightly regulated response in lipid metabolism and energy / cellular repair processes (Figure 23A). In the serum, the correlation between HexCer (d18:1 / 26:1) and LysoPC a C26:1 decreases to r = 0.78, indicating systemic but less intense metabolism regulation, while the correlation between creatine and spermine remains moderate (r = 0.71). The negative correlation between butyric acid and iso and N-acetyl-histidine is weaker in serum (r = -0.20664), suggesting that the disruptions in energy metabolism are more localized to the jejunal contents and feces. Histamine shows strong correlations in the serum with creatine (r = 0.65) and spermine (r = 0.61), reflecting its role in systemic immune regulation and energy metabolism (Figure 23E3). Negative correlations, such as butyric acid and iso and N-acetyl-histidine (r = -0.46) and 2-hydroxy- 3-methyl and spermine (r = -0.54), further highlight disruptions in energy metabolism and immune response. Notably, histamine shows a moderate positive correlation with spermine (r = 0.45) in the jejunum,linking immune activation with tissue repair. In feces, similar lipid metabolism patterns are observed, with HexCer (d18:1 / 26:1) and LysoPC a C26:1 showing a nearly identical correlation (r = 0.88), though creatine and spermine are less strongly correlated (r = 0.44), and the impact on 2-hydroxy-3-methyl and spermine is minimal (r = -0.05). Histamine also shows a weaker correlation with spermine (r = 0.23), suggesting a less pronounced immune-metabolic interaction in feces (Figure 23C). Pathway impact analysis of 19 metabolites common across serum, feces, and jejunal contents reveal key metabolic disruptions in NE-affected birds

[0229] The pathway impact analysis of 19 common metabolites across serum, jejunal contents, and feces in NE-affected birds highlights significant metabolic disruptions, with 6 metabolites (31.6%) enriched in key pathways including the TCA cycle (citrate cycle), glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, histidine metabolism, and butanoate metabolism. Their expression levels of these metabolites across the serum, jejunum and fecal samples of NE and control birds are shown in Figures 18A, B. These pathways point to critical disruptions in energy production, nitrogen metabolism, fatty acid utilization, and immune regulation. The pathway enrichment scatter plot (Figure 19A) visually demonstrates the significance and impact of these pathways, with the TCA cycle and histidine metabolism emerging as highly impacted pathways (Table 12). The TCA cycle had two hits (citric acid and cis-aconitic acid), with a pathway impact score of 0.14 and a highly significant p-value of 0.005, indicating substantial alterations in energy production and oxidative phosphorylation. Glyoxylate and dicarboxylate metabolism, which involved citric acid and butyric acid, had an impact score of 0.05 and a p-value of 0.01 , suggesting disruptions in gluconeogenesis.

[0230] Table 12: Pathway analysis of common metabolites across serum, jejunal contents, and feces in ne-affected birds

[0231] In the arginine and proline metabolism pathway, spermine and creatinine is involved, showing an impact score of 0.02 and a p-value of 0.01 , reflecting disruptions in polyamine synthesis and nitrogen metabolism, critical for cellular repair processes. Butanoate metabolism featured butyric acid, which had a p-value of 0.08 but no significant pathway impact, indicating changes in gut energy metabolism and fatty acid utilization. Histamine, involved in the histidine metabolism pathway, demonstrated a relatively high pathway impact of 0.18. .

[0232] The gene ontology (GO) chord plots (Figure 19B) provide further visualization of the metabolite-pathway correlations, illustrating how specific metabolites (e.g., histamine, citrate, spermine) are connected to the key metabolic pathways like TCA cycle, histidine metabolism, and arginine and proline metabolism. These relationships emphasize the significant metabolic disruptions occurring across these pathways in NE-affected birds. The remaining 13 metabolites (68.4%), including N-acetyl-histidine, N1- acetyl-lysine, valeric acid and isovaleric acid, 2-hydroxy-3-methylvaleric acid, p-cresol sulfate, C16, HexCer (d18:1 / 26:1), C16 2OH, 2-hydroxybutyric acid, LysoPC a C26:1 , N-acetyl-glycine, N-alpha-aminobutyric acid, and N-acetyl-glutamic acid, were not mapped to any KEGG pathways, indicating a lack of identified metabolic roles in the current pathway databases.

[0233] One of the strategic priorities of the broiler chicken industry is enhancing disease control by managing pathogens at the onset of the disease to reduce economic losses, improve profitability of the sector, and improve food safety. The early detection of pathogenic infections is critical in controlling pathogens and, thus, imminent disease outbreaks and antimicrobial use. The chicken industry is currently relying on serological blood testing to measure antibodies against pathogens to detect pathogenic infection. However, serological tests detect diseases only 10-14 days after pathogenic exposure. Besides, PCR (pathogen DNA detection) and bacterial culture-based diagnosis methods are primarily contingent on the types of tissue and the pathogen’s predilection site. The broiler chicken industry cannot detect pathogens within 1-2 days of post-infection. The metabolomics approach will provide novel rapid disease diagnostic tools to detect microbial infections before the onset of clinical signs to implement disease control strategies and provide tools to monitor poultry performance, which will improve competitiveness, food safety, and profitability of the broiler chicken industry.

[0234] The serum and fecal metabolome, and the metabolic pathways associated with pathological lesions of NE in the jejunum following CP challenge containing toxin genes cpa, netB, cpb2, and tpeL was explored. This approach was designed to help elucidate whether the metabolomic landscape of NE assists with the diagnosis of NE early in the onset of development of NE. In Example 1 , a strong correlation of elevated butyric acid in the jejunum of broiler chickens with NE following CP challenge irrespective of toxin gene combinations of CP was demonstrated. Furthermore, in this NE animal model, some birds had no microscopic or macroscopic NE lesions, some had only microscopic NE lesions, andsome birds had both macroscopic and microscopic NE lesions following CP challenge. It was found that fold changes of butyric acid increased according to severity of NE lesions, lowest levels of butyric acid in birds with no microscopic or macroscopic NE lesions, then birds with only microscopic NE lesions, then the highest levels of butyric acid in birds with both macroscopic and microscopic NE lesions. Although butyric acid content increased according to the severity of NE, butyric acid content was significantly higher in all three variables compared to the control group with no exposure to CP

[0019] , Based on the metabolomics data, it is clear that butyric acid and histamine increased in the jejunum, serum, and feces in birds that developed NE irrespective of the severity of NE. Moreover, these findings were consistently replicated across two independent experiments, highlighting the reliability and robustness of our results.

[0235] CP has a wide variety of virulence factors including extracellular toxins such as pore forming or membrane damaging toxins. Also, CP produces enzymes including collagenases, hyaluronidases, adhesion molecules, quorum sensing molecules, iron acquisition systems, and regulatory proteins (VirRA / irS) that influence pathogenicity (Camargo et al., 2024). Studies support the role of these factors in toxin production and host responses. Understanding these virulence factors and their impact on host metabolism is crucial for developing effective NE control strategies and development of diagnostic tools to detect onset of NE in acute stages of the disease. In the present work, metabolic variability evident across the across serum, jejunal and fecal samples of NE birds reflect the systemic and localized impacts of CP infection. The pathway analysis of metabolites common among all these samples identified butyric acid (butanoate metabolism), histamine (histidine metabolism), citric acid and cis-aconitic acid (TCA cycle and glyoxylate and dicarboxylate metabolism), spermine and creatinine (arginine-proline metabolism) as potential candidates for NE. However, butyric acid and histamine are selected due to their specific roles in gut barrier disruption, microbial dysbiosis, tissue and immune system activation, and systemic inflammation key features of NE progression (Liu et al., 2024; Recharla et al., 2023). On the other hand, citric acid and cis-aconitic acid, though involved in metabolic pathways, are less directly implicated in NE pathogenesis (Sharifuzzaman et al., 2025). Spermine and creatinine, though important in cellular processes and in muscle metabolism, respectively, have little relevance to NE or intestinal inflammation. Furthermore, in Example 1 , both butyric acid and histamine were elevated in CP-challenged (with any combination of CP virulence genes) birds with no macroscopic or microscopic NE lesions.

[0236] Our pathway analysis shows that butyric acid is enriched in butanoate metabolism, which supports epithelial tissue repair in normal conditions but in CP-induced NE condition.

[0237] Butyric acid exhibited the greatest elevation in the jejunum (logFC = 2.46), indicating localized metabolic disruption. Rectal levels (logFC = 0.96) were moderately elevated, supporting its use in non-invasive diagnostics, while serum (logFC = 1 .49) reflects systemic metabolic shifts linked to NE. It also modulates inflammatory cytokines such as TNF-a and IL-1 p through the NF-KB pathway. Its early elevation, even in CP-challenged birds without NE lesions, suggests it as a preclinical biomarker. Moreover, butyricacid detection in fecal samples offers practical advantages over serum analysis, particularly for farm-level diagnostics of NE.

[0238] Similar to butyric acid, histamine (C5H9N3, 111.15 g / mol), a biogenic amine derived from histidine, was significantly elevated in birds affected with NE. The Jejunum, the primary site of infection, exhibited the highest upregulation (LogFC = 3.16), reflecting localized inflammation. Rectal samples showed a notable increase (LogFC = 2.76), suggesting systemic inflammation and microbial dysbiosis. Serum levels, while elevated (LogFC = 0.69), indicated a milder systemic response. The pathway enrichment analysis showed the disturbances in histidine metabolism, highlighting histidine, a precursor metabolite involved in histamine overproduction during NE. Regression analysis showed histamine as a strong predictor of NE-associated disruption in metabolism, demonstrating strong links to metabolites such as N-acetyl-histidine and butyric acid, which are highly relevant in immune modulation and energy metabolism.

[0239] The elevated histamine levels observed in this study points out the intricate connection between microbial dysbiosis and CP proliferation, both locally in the jejunum and systemically, as evidenced by its presence in serum and fecal contents. Similarto butyric acid in the gut, histamine can play a protective or negative role against bacterial infections (Metz et al., 2011).

[0240] The detectability of elevated levels of butyric acid and histamine in serum, feces, and jejunum across both experiments suggests they are consistent diagnostic markers for early NE detection. Fecal detection of metabolites is a feasible non-invasive industry technique that does not require any personnel training compared to blood sample collection, hence has the advantage of developing disease diagnostic tool at the farm level. For field applications, developing point-of-care metabolite detection devices like portable GC-MS, biosensors, and colorimetric or enzymatic tests for histamine and butyric acid detection may be useful.

[0241] It is shown herein that butyric acid and histamine could act as biomarkers for the early, non-invasive diagnosis of NE in broilers. They are in increased concentrations in serum, feces, and jejunum. Real-time detection of NE in a flock, before the development of any clinical signs or pathological lesions of NE in the intestine, can be developed utilizing metabolomics technology as a rapid diagnostic kit. This approach will benefit poultry producers to improve poultry health and welfare by implementation of timely mitigating strategies. The observed metabolic changes, including disruptions in histidine metabolism and the TCA cycle, underscore the complex interplay between microbial dysbiosis, host metabolism, and inflammation during NE.

[0242] While the present application has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the application is not limited to the disclosed examples. To the contrary, the application is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[0243] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Specifically, the sequences associated with each accession numbers provided herein including for example accession numbers and / or biomarker sequences (e.g. protein and / or nucleic acid) provided in the Tables or elsewhere, are incorporated by reference in its entirely.

[0244] The scope of the claims should not be limited by the preferred embodiments and examples but should be given the broadest interpretation consistent with the description as a whole.REFERENCES1. Navarro MA, McClane BA, Uzal FA %J T. Mechanisms of action and cell death associated with Clostridium perfringens toxins. 2018;10:212.2. Gautam H, Ayalew LE, Shaik NA, Subhasinghe I, Popowich S, Chow-Lockerbie B, et al. Ex-ploring the predictive power of jejunal microbiome composition in clinical and subclinical necrotic enteritis caused by Clostridium perfringens: insights from a broiler chicken model. Journal of Translational Medicine. 2024;22:1-18.3. Wade B, Keyburn A. The true cost of necrotic enteritis. World Poult. 2015 ;31 :16-7.4. Grass JE, Gould LH, Mahon BE. Epidemiology of foodborne disease outbreaks caused by Clostridium perfringens, United States, 1998-2010. Foodborne pathogens and disease. 2013;10:131-6.5. Wang H, Latorre JD, Bansal M, Abraha M, Al-Rubaye B, Tellez-lsaias G, et al. Microbial me-tabolite deoxycholic acid controls Clostridium perfringens-induced chicken necrotic enteritis through attenuating inflammatory cyclooxygenase signaling. Scientific reports. 2019;9:1-12.6. Wu S-B, Stanley D, Rodgers N, Swick RA, Moore RJ. Two necrotic enteritis predisposing factors, dietary fishmeal and Eimeria infection, induce large changes in the caecal microbiota of broiler chickens. Veterinary microbiology. 2014;169:188-97.7. Meng X, Zhang G, Cao H, Yu D, Fang X, de Vos WM, et al. Gut dysbacteriosis and intestinal disease: mechanism and treatment. Journal of applied microbiology. 2020;129:787-805.8. De Maesschalck C, Eeckhaut V, Maertens L, De Lange L, Marchal L, Nezer C, et al. Effects of xylooligosaccharides on broiler chicken performance and microbiota. Applied and Environ-mental Microbiology. 2015;81 :5880-8.9. Fasina YO, Lillehoj HS. Characterization of intestinal immune response to Clostridium perfringens infection in broiler chickens. Poultry science. 2019;98:188-98.10. Fiehn O. Metabolomics-the link between genotypes and phenotypes. Plant molecular biolo-gy. 2002;48:155-71.11 . Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, et al. Small molecule metabolites: discovery of bi-omarkers and therapeutic targets. Signal Transduction and Targeted Therapy. 2023;8:132.12. Lamichhane S, Sen P, Dickens AM, Oresic M, Bertram HC. Gut metabolome meets microbi-ome: A methodological perspective to understand the relationship between host and microbe. Methods. 2018;149:3-12.13. Lung TWF, Charytonowicz D, Beaumont KG, Shah SS, Sridhar SH, Gorrie CL, et al. Klebsiella pneumoniae induces host metabolic stress that promotes tolerance to pulmonary in-fection. Cell Metabolism. 2022;34:761-74.14. Urso A, Prince A. Anti-inflammatory metabolites in the pathogenesis of bacterial infection. Frontiers in Cellular and Infection Microbiology. 2022;12:925746.15. Shimizu T, Ohtani K, Hirakawa H, Ohshima K, Yamashita A, Shiba T, et al. Complete ge-nome sequence of Clostridium perfringens, an anaerobic flesh-eater. 2002;99:996-1001 .16. Yang Q, Liu J, Wang X, Robinson K, Whitmore MA, Stewart SN, et al. Identification of an in-testinal microbiota signature associated with the severity of necrotic enteritis. Frontiers in Micro-biology. 2021 ; 12.17. Wang Y, Xu Y, Xu S, Yang J, Wang K, Zhan X. Bacillus subtilis DSM29784 alleviates nega-tive effects on growth performance in broilers by improving the intestinal health under necrotic enteritis challenge. Frontiers in Microbiology. 2021 ;12:723187.18. Kumar A, Toghyani M, Kheravii SK, Pineda L, Han Y, Swick RA, et al. Organic acid blends improve intestinal integrity, modulate short-chain fatty acids profiles and alter microbiota of broil-ers under necrotic enteritis challenge. Animal Nutrition. 2022;8:82-90.19. Foroutan A, Guo AC, Vazquez-Fresno R, Lipfert M, Zhang L, Zheng J, et al. Chemical com-position of commercial cow’s milk. Journal of agricultural and food chemistry. 2019;67:4897-914.20. Yasuhara S, Maekawa M, Bamba S, Kurihara M, Nakanishi N, Yamamoto T, et al. Energy metabolism and nutritional status in hospitalized patients with chronic heart failure. Annals of Nu-trition and Metabolism. 2020;76:129-39.21. Zheng J, Mandal R, Wishart DS. A sensitive, high-throughput LC-MS / MS method for measur-ing catecholamines in low volume serum. Analytica chimica acta. 2018;1037:159-67.22. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expres-sion analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43:e47-e47.23. Blighe K, Rana S, Lewis M. EnhancedVolcano: Publication-ready volcano plots with en-hanced colouring and labeling, doi: 10.18129 / B9. bioc. EnhancedVolcano, R package version 1.20. 0. 2023;24. Ivanisevic J, Benton HP, Rinehart D, Epstein A, Kurczy ME, Boska MD, et al. An interactive cluster heat map to visualize and explore multidimensional metabolomic data. Metabolomics. 2015;11 :1029-34.25. Valero-Mora PM. ggplot2: elegant graphics for data analysis. Journal of Statistical Software. 2010;35:1 - 3.26. Yao F, Coquery J, Le Cao K-A. Independent principal component analysis for biologically meaningful dimension reduction of large biological data sets. BMC bioinformatics. 2012;13:1 -15.27. Xi B, Gu H, Baniasadi H, Raftery D. Statistical analysis and modeling of mass spectrometry-based metabolomics data. Mass spectrometry in metabolomics: methods and protocols. 2014;333-53.28. Utriainen M, Morris JH. clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape. BMC bioinformatics. 2023;24:134.29. Kiu R, Hall LJ. An update on the human and animal enteric pathogen Clostridium perfringens. Emerging microbes & infections. 2018;7:1-15.30. Cheung JK, Rood JI. The VirR response regulator from Clostridium perfringens binds inde-pendently to two imperfect direct repeats located upstream of the pfoA promoter. Journal of Bac-teriology. 2000;182:57-66.31. Adachi K, Ohtani K, Kawano M, Singh RP, Yousuf E3, Sonomoto K, et al. Metabolic dependent and independent pH-drop shuts down VirSR quorum sensing in Clostridium perfringens. Journal of bioscience and bioengineering. 2018;125:525-31.32. Mehdizadeh Gohari I, A. Navarro M, Li J, Shrestha A, Uzal F, A. McClane E3. Pathogenicity and virulence of Clostridium perfringens. Virulence. 2021 ;12:723-53.33. Cassir N, Simeoni U, La Scola E3. Gut microbiota and the pathogenesis of necrotizing enter-ocolitis in preterm neonates. Future microbiology. 2016;11 :273-92.34. Hosny M, Cassir N, La Scola E3. Updating on gut microbiota and its relationship with the oc-currence of necrotizing enterocolitis. Human Microbiome Journal. 2017;4:14-9.35. Kurita-Ochiai T, Fukushima K, Ochiai K. Butyric acid-induced apoptosis of murine thymo-cytes, splenic T cells, and human Jurkat T cells. Infection and immunity. 1997;65:35-41.36. Nankova BB, Agarwal R, MacFabe DF, La Gamma EF. Enteric bacterial metabolites propion-ic and butyric acid modulate gene expression, including CREB-dependent catecholaminergic neurotransmission, in PC12 cells-possible relevance to autism spectrum disorders. PLoS One. 2014;9:e103740.37. Song B, Li H, Wu Y, Zhen W, Wang Z, Xia Z, et al. Effect of microencapsulated sodium bu-tyrate dietary supplementation on growth performance and intestinal barrier function of broiler chickens infected with necrotic enteritis. Animal Feed Science and Technology. 2017;232:6-15.38. Eeckhaut V, Van Immerseel F, Croubels S, De Baere S, Haesebrouck F, Ducatelle R, et al. Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum. Microbial biotechnology. 2011 ;4:503-12.39. Pryde SE, Duncan SH, Hold GL, Stewart OS, Flint HJ. The microbiology of butyrate for-mation in the human colon. FEMS microbiology letters. 2002;217:133-9.40. Eeckhaut V, Van Immerseel F, Teirlynck E, Pasmans F, Fievez V, Snauwaert C, et al. Butyr-icicoccus pullicaecorum gen. nov., sp. nov., an anaerobic, butyrate-producing bacterium isolated from the caecal content of a broiler chicken. International Journal of Systematic and Evolutionary Microbiology. 2008;58:2799-802.41 . Jung A, Rautenschlein S. Comprehensive report of an Enterococcus cecorum infection in a broiler flock in Northern Germany. BMC veterinary research. 2014;10:311 .42. Li J, McClane BA. The sialidases of Clostridium perfringens type D strain CN3718 differ in their properties and sensitivities to inhibitors. Applied and environmental microbiology. 2014;80:1701-9.43. McClane BA, Robertson SL, Li J. Clostridium perfringens. Food microbiology: fundamentals and frontiers. 2012;465-89.44. Wang H-B, Wang P-Y, Wang X, Wan Y-L, Liu Y-C. Butyrate enhances intestinal epithelial barrier function via up-regulation of tight junction protein Claudin-1 transcription. Digestive dis-eases and sciences. 2012;57:3126-35.45. Sossai P. Butyric acid: what is the future for this old substance? Swiss medical weekly. 2012;142:w13596-w13596.46. Abdelli N, Perez JF, Vilarrasa E, Cabeza Luna I, Melo-Duran D, D’Angelo M, et al. Targeted-release organic acids and essential oils improve performance and digestive function in broilers under a necrotic enteritis challenge. Animals. 2020;10:259.47. Lin J, Nafday SM, Chauvin SN, Magid MS, Pabbatireddy S, Holzman IR, et al. Variable ef-fects of short chain fatty acids and lactic acid in inducing intestinal mucosal injury in newborn rats. Journal of pediatric gastroenterology and nutrition. 2002;35:545-50.48. Li H, Myeroff L, Smiraglia D, Romero MF, Pretlow TP, Kasturi L, et al. SLC5A8, a sodium transporter, is a tumor suppressor gene silenced by methylation in human colon aberrant crypt foci and cancers. Proceedings of the National Academy of Sciences. 2003;100:8412-7.49. Timbermont L, Lanckriet A, Dewulf J, Nollet N, Schwarzer K, Haesebrouck F, et al. Control of Clostridium perfringens-induced necrotic enteritis in broilers by target-released butyric acid, fatty acids and essential oils. Avian Pathology. 2010;39:117-21.50. Metz M, Doyle E, Bindslev-Jensen C, Watanabe T, Zuberbier T, Maurer M. Effects of antihis-tamines on innate immune responses to severe bacterial infection in mice. International archives of allergy and immunology. 2011 ;155:355-60.51. Smolinska S, Winiarska E, Globinska A, Jutel M. Histamine: A Mediator of Intestinal Disor-ders — A Review. Metabolites. 2022; 12:895.52. Sanchez-Perez S, Comas-Baste O, Duelo A, Veciana-Nogues MT, Berlanga M, Latorre-Moratalla ML, et al. Intestinal dysbiosis in patients with histamine intolerance. Nutrients. 2022; 14: 1774.53. Uzal FA, Songer JG, Prescott JF, Popoff MR. Clostridial diseases of animals. John Wiley & Sons; 2016.54. Kaiser JC, Heinrichs DE. Branching out: Alterations in bacterial physiology and virulence due to branched-chain amino acid deprivation. MBio. 2018;9:e01188-18.55. Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Critical reviews in biochemistry and molecular biology. 2013;48:20-38.56. Akerele G, Al Hakeem WG, Lourenco J, Selvaraj RK. The effect of necrotic enteritis chal-lenge on production performance, cecal microbiome, and cecal tonsil transcriptome in broilers. Pathogens. 2022;11 :839.57. Peng L, Li Z-R, Green RS, Holzmanr IR, Lin J. Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. The Journal of nutrition. 2009;139:1619-25.58. by Analyzing SP. Revealing the Bacterial Butyrate. 2014;59. Reeds PJ, Burrin DG, Stoll B, Jahoor F. Intestinal glutamate metabolism. The Journal of nu-trition. 2000;130:978S-982S.60. Salvi PS, Cowles RA. Butyrate and the intestinal epithelium: modulation of proliferation and inflammation in homeostasis and disease. Cells. 2021 ;10:1775.61. Abdulkhakov S, Markelova M, Safina D, Siniagina M, Khusnutdinova D, Abdulkhakov R, et al. Butyric Acid Supplementation Reduces Changes in the Taxonomic and Functional Composition of Gut Microbio-ta Caused by H. pylori Eradication Therapy. Microorganisms. 2024;12:319.62. Banasiewicz T, Domagalska D, Borycka-Kiciak K, Rydzewska G. Determination of butyric acid dosage based on clinical and experimental studies-a literature review. Gastroenterology Review / Przeglqd Gas- troenterologiczny. 2020;15:119-25.63. Anand S, Kaur H, Mande SS. Comparative in silico analysis of butyrate production pathways in gut commensals and pathogens. Frontiers in microbiology. 2016;7:231022.64. Chaukimath P, Frankel G, Visweswariah SS. The metabolic impact of bacterial infection in the gut. The FEBS Journal. 2023;290:3928-45.

Claims

CLAIMS1. A method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, wherein the one or more subject is poultry, the method comprising: a. measuring a level of butyric acid, and histamine in a sample from the one or more subject, and b. comparing the level of butyric acid and histamine, with a control; wherein an increased level butyric acid and histamine, compared to the control is indicative that the one or more subject has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

2. The method of claim 1 , wherein the control is a sample from a subject not infected with CP or afflicted with necrotic enteritis.

3. The method of claim 1 or 2, wherein the one or more subject is displaying one or more symptoms of necrotic enteritis.

4. The method of claim 1 or 2, wherein the one or more subject is not displaying one or more symptoms of necrotic enteritis, optionally wherein the subject has early stage necrotic enteritis.

5. The method of any one of claims 1 to 4, wherein the one or more subject has been in contact with another subject known or suspected of being infected with CP or having necrotic enteritis.

6. The method of any one of claims 1 to 5, wherein the sample is a fecal sample or a blood sample.

7. The method of claim 6, wherein the blood sample is a serum sample.

8. The method of any one of claims 1 to 7, wherein the poultry is chicken, turkey, or duck.

9. The method of any one of claims 1 to 8, wherein the poultry is a chicken.

10. The method of any one of claims 1 to 9, wherein the poultry is a broiler chicken.11 . The method of any one of claims 1 to 9, wherein the one or more subject is a plurality of subjects.

12. The method of any one of claims 1 to 11 , wherein the one or more subject is at least about 14 days old.

13. The method of any one of claims 1 to 12, wherein the one or more subject is between about 14 days old and about 180 days old.

14. The method of any one of claims 1 to 13, wherein the one or more subject is between about 14 days old and about 84 days old.

15. The method of any one of claims 1 to 14, wherein the one or more subject is between 14 days old and 42 days old.

16. The method of any one of claims 1 to 14, wherein the one or more subject is about 23 days old.

17. The method of any one of claims 1 to 16, wherein the sample is obtained from the one or more subject 1-2 days after the one or more subject has been or is suspected to have been exposed to CP.

18. The method of any one of claims 1 to 17, wherein the increased level of butyric acid, is an increase of at least about 1-fold, at least about 1 .5-fold, at least about 2-fold, or at least about 2.5-fold.

19. The method of any one of claims 1 to 18, wherein the increased level of butyric acid, is an increase of about 2-fold.

20. The method of any one of claims 1 to 19, wherein the increased level of histamine, is an increase of about 0.5-fold, at least about 1-fold, at least about 2-fold, or at least about 3-fold.21 . The method of any one of claims 1 to 20, wherein the CP comprises cpa, netB, cpb2 , and TpeL genes.

22. The method of any one of claims 1 to 20, wherein the CP comprises cpa, cpb2, and netB genes.

23. The method of any one of claims 1 to 20, wherein the CP comprises cpa and cpb2 genes.

24. The method of any one of claims 1 to 23, wherein the method is for controlling or reducing contamination with or spread of Clostridium perfringens and / or necrotic enteritis in a population and further comprises: selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation from the population, culling and / or treatment; and / or isolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

25. The method of any one of claims 1 to 23, wherein the method is for monitoring disease progression in a subject or population, wherein the method further comprises: measuring a level of butyric acid, and optionally histamine in an additional sample from the one or more subject, in the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis; selecting the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis for isolation, culling from the population and / or treatment when the level of butyric acid, and optionally histamine, in the additional sample is increased as compared to a reference value or previous sample; and / orisolating and / or treating the one or more subject that has been infected with Clostridium perfringens and / or is afflicted with necrotic enteritis.

26. A kit for detecting butyric acid, and optionally histamine, for use in a method of any one of claims 1 to 25 comprising one or more reagents or standards for measuring the level of butyric acid, and optionally histamine.

27. The kit of claim 26, wherein the kit further comprises at least one control.

28. A method of screening for or detecting infection or likelihood of infection with Clostridium perfringens (CP) and / or necrotic enteritis in one or more subject, wherein the one or more subject is poultry, the method comprising: a. collecting a sample from the one or more subject; b. sending the sample to be analyzed for its level of butyric acid and histamine; and c. receiving an indication that the one or more subject has been infected or is likely to have been infected with Clostridium perfringens and / or afflicted or likely to be afflicted with necrotic enteritis where the sample has an increased level of butyric acid as compared to a control.

29. The method of claim 28, wherein the one or more subject is displaying one or more symptoms of necrotic enteritis.

30. The method of claim 28, wherein the one or more subject is not displaying one or more symptoms of necrotic enteritis, optionally wherein the one or more subject has early stage necrotic enteritis.31 . The method of any one of claims 28 to 30, wherein the one or more subject has been in contact with another subject known or suspected of being infected with CP or afflicted with necrotic enteritis.

32. The method of any one of claims 28 to 29, wherein the sample is a fecal sample or a blood sample.

33. The method of claim 32, wherein the blood sample is a serum sample.

34. The method of any one of claims 28 to 33, wherein the one or more subject is a plurality of subjects.

35. The method of any one of claims 28 to 34, wherein the poultry is chicken, turkey, or duck.

36. The method of any one of claims 28 to 35, wherein the poultry is a chicken.

37. The method of any one of claims 28 to 36, wherein the poultry is a broiler chicken.

38. The method of any one of claims 28 to 37, wherein the one or more subject is at least about 14 days old.

39. The method of any one of claims 28 to 38, wherein the one or more subject is between about 14 days old and about 180 days old.

40. The method of any one of claims 28 to 39, wherein the one or more subject is between about 14 days old and about 84 days.41 . The method of any one of claims 28 to 40, wherein the one or more subject is between 14 days old and 42 days old.

42. The method of any one of claims 28 to 41 , wherein the control is a baseline sample from the one or more subject.

43. The method of claim 42, wherein the baseline sample is a sample taken at diagnosis or detection of infection with CP by the one or more subject.

44. The method of any one of claims 28 to 41 , wherein the control is a reference value.

45. The method of any one of claims 28 to 44, wherein the CP comprises cpa, netB, cpb2 , and TpeL genes.

46. The method of any one of claims 28 to 44, wherein the CP comprises cpa, cpb2, and netB genes.

47. The method of any one of claims 28 to 44, wherein the CP comprises cpa and cpb2 genes.

48. The method of claim 24 or 25, wherein the one or more subject is treated with one or more antibiotic, optionally penicillin or bacitracin.

49. The method of any one of claims 1 to 25 or 28 to 48, wherein the sample is an early stage sample.

50. A device fortesting a liquid sample for the concentration of butyric acid and / or histamine, the device comprising: a porous solid phase material carrying in a first zone a labelled reagent which is retained in the first zone while the porous material is in the dry state but is free to migrate through the porous material when the porous material is moistened, for example by the application of an aqueous liquid sample suspected of containing butyric acid and / or histamine, the porous material carrying in a second zone, which is spatially distinct from the first zone, an unlabelled specific binding reagent having specificity for / capacity to react with butyric acid and / or histamine, and which is capable of binding with the labelled reagent to form a complex or reacting with the labelled reagent, the unlabelled specific binding reagent being firmly immobilised on the porous material such that it is not free to migrate when the porous material is in the moist state, said complex or rection being observable through a test result observation aperture, thereby to indicate the presence of said analyte in said liquid biological sample.

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