Index for risk assessment of antibiotic resistance of a microbial community

Abstract

The invention relates methods for assessing a risk of antibiotic resistance of a microbial community, comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, and the use of the methods for the assessment of animal welfare, animal health, animal performance and / or feed conversion rate (FCR). The present invention also relates to electromagnetic signal, data processing systems, computer programs, and / or apparatuses for carrying out the methods of the invention.

Description

New International patent applicationApplicant: DSM IP Assets B.V.Our ref.: ERB17589PCTINDEX FOR RISK ASSESSMENT OF ANTIBIOTIC RESISTANCE OF A MICROBIAL COMMUNITYFIELD OF THE INVENTION

[0001] The invention relates methods for assessing a risk of antibiotic resistance of a microbial community, comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, and the use of the methods for the assessment of animal welfare, animal health, animal performance and / or feed conversion rate (FCR). The present invention also relates to electromagnetic signal, data processing systems, computer programs, and / or apparatuses for carrying out the methods of the invention.BACKGROUND

[0002] Antibiotic resistance is a major topic because of the threat to human and animal health and welfare, as well as its economic consequences. Many studies, based on metagenomic sequencing techniques, have determined the presence of putative antibiotic resistance genes in different ecosystems like livestock gut, human gut, soil and water. In particular, shotgun metagenomics approaches represent the state of art for evaluating the antibiotic resistome in different biomes. In these approaches, the microbial DNA is extracted from the samples and subjected to DNA sequencing. The obtained DNA sequences are then mapped against a specific database of antibiotic resistance genes (ARGs) and ARG abundance is estimated based on the number of times each ARG is detected in the samples, which is then used for the comparison between the samples. At the community level, indices such as “observed” (the number of unique ARGs per sample / group) and “diversity” are widely used, which do not account for the differences in nature of ARGs, like mobile or housekeeping. Thus, when working with state-of- the-art approaches, good knowledge on ARGs is required for interpreting any obtained results.

[0003] However, problems usually arise as regards how such studies are interpreted and understood at least in three aspects. First, the way in which antibiotic resistance genes are usually bioinformatically evaluated after metagenomic sequencing gives an overall count and identification of which genes were detected and how frequent they are in a bacterial community - however, there is in most cases no way to determine if these genes are carried by a potentialpathogen, if they are intrinsic or acquired or if they are in any way a direct result of exposure to antibiotic in recent times. Second, many studies have shown that most classes of putative ARGs are intrinsic to bacterial genomes and can be considered housekeeping genes - however, these genes are usually not a concern until they become “decontextualized’” by mobilization; this is why the authors of these studies frequently state that finding potential resistance genes in a specific ecosystem poses a risk for human health because these genes “could” be acquired by bacterial pathogens. And third, not only the broader public but also scientists of related fields, like medical doctors, veterinarians, and nutritionists, understand the term “antibiotic resistance” mostly in a straightforward phenotypical sense, i.e. that a bacterial infection is treated with an antibiotic which has no effect as the causative bacteria are not inhibited by the used antibiotic - however, the presence of resistance genes does seldom correlate to “resistance” in this narrow sense.

[0004] Hence, there is a disconnect in understanding, for example in view of the observation that a gut sample of healthy livestock might contain a lot of antibiotic resistance genes without giving any indication if those genes are a potential risk or if a future outbreak could or could not be treated with those antibiotics. Thus, while the broader public and even veterinarians or nutritionist will usually have a sense that antibiotic resistance is centered on the treatability and prevention of infectious diseases, whenever a bacterial population is sequenced many antibiotic resistant genes are detected that do not relate to antibiotic resistance in this narrow sense but are intrinsic to bacterial genomes and can be considered housekeeping genes. This causes sequencing data to continuously give the impression that “resistance genes are everywhere” regardless of the actual risk and thus creates the danger to simultaneously over- and underestimate the risk caused by antibiotic resistance genes. Moreover, if there is no clear way of differentiating a “risky” bacterial community from a harmless one, strategies for intervention become unworkable.

[0005] Different attempts have been made so far to gain further insights on the topic. For example, Slizovskiy et al. (2020, Front. Microbiol. 11 : 1376) disclosed an evaluation of different bioinformatics and statistical approaches for performing resistome-mobilome analysis on shotgun metagenomic data using two publicly available datasets and several common statistical techniques. Nielsen et al. (2021, bioRxiv 2021.01.10.426126) investigated the mobility of antibiotic resistance genes differing in their resistance mechanism based on ARG predictions and categorization of associations between ARGs and mobile genetic elements (MGEs). However, all these approaches were decontextualized from, as ARGs were obtained by annotating adatabase and thus, the ARGs were also decoupled from any informative insight on the risk of microbial antibiotic resistance.

[0006] Hence, there is still an unmet need to need to provide methods and means for assessing a risk of antibiotic resistance in a real word microbial community.

[0007] The present application addresses the need for by providing the embodiments as recited in the claims.SUMMARY OF THE INVENTION

[0008] The invention relates to a method for assessing a risk of antibiotic resistance of a microbial community, the method comprising: screening, preferably by metagenomic sequencing, genetic material of the microbial community extracted from one or more samples thereof, to obtain biological sequences from the respective samples; identifying a plurality of putative antibiotic resistance genes (ARGs) by aligning the biological sequences from the one or more samples against a predetermined database of ARGs, and estimating the abundance of each ARG in a respective sample of the one or more samples, wherein a plurality of ARGs identified for a sample of the one or more samples is said to form a resistome of said sample; obtaining, for each ARG g, identified in the database, a mobility score ARG-MOB of said ARG, indicating how much the ARG is mobilized; determining for each ARG g identified for a sample i of the one or more samples a weighted relative abundance Wg,i asWg,i = ng,i * ARG-MOBg wherein ng,i is the relative abundance of ARG g in sample i; and determining, for each sample i of the one or more samples, i = 1 to n, an antibiotic Resistome Risk Index, RRIi asso as to quantify a risk of antibiotic resistance of respective resistomes of the one or more samples of genetic material.

[0009] The present invention also relates to a method for comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, the method comprising: determining for a sample of the first microbial community and for a sample of the second microbial community, respectively, antibiotic Resistome Risk Indices by performing the method as defined in any one of the preceding claims; and optionally comparing the antibiotic RRI of both samples.

[0010] The present invention also relates to a method for improving health and / or welfare of a subject comprising: determining the antibiotic RRI as described herein in a sample obtained from the subject, and optionally applying to the subject having a high antibiotic RRI a treatment that reduces antibiotic RRI, if the subject has a high antibiotic resistome risk index, thereby increasing the welfare / health status.

[0011] The present invention also relates to a method for stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, comprising determining the antibiotic RRI as described herein in a sample obtained from the subject, stratifying the subject for a treatment that reduces antibiotic RRI, if the subject or the sample has a high antibiotic resistome risk index.

[0012] The present invention also relates to a method of assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, comprising determining the antibiotic RRI in a sample from the subject or group of subjects before receiving the treatment, determining the antibiotic RRI in a sample after or during receiving the treatment, wherein the antibiotic RRI is determined by performing the method as defined in any one of claims XX, wherein a decrease of the antibiotic resistome risk index is preferably indicative that the treatment is effective.

[0013] The present invention also relates to a of method diagnosing and preventing, ameliorating or treating a risk of a subject or a group of subjects for developing antibiotic resistance of a microbial community comprised in the gut of said subject or group of subjects, comprising (a) determining the antibiotic resistome risk index in a sample from the subject or group of subjects determined by performing the method as defined in any one of claims 1-5, and (b) administering to the subject a treatment that reduces antibiotic resistome risk index, if the subject has a high antibiotic resistome risk index.

[0014] The present invention also relates to a treatment of the disclosure for use in a method of the invention.

[0015] The present invention also relates to an electromagnetic signal carrying computer- readable instructions for performing a method of the disclosure.

[0016] The present invention also relates to a data processing system comprising a processor configured to perform a method of the disclosure.

[0017] The present invention also relates to a computer program comprising instructions to cause the data processing system disclosed herein to carry out a method of the disclosure.

[0018] The present invention also relates to a computer program product adapted to carry out a method of the disclosure.

[0019] The present invention also relates to an apparatus adapted to carry out a method of the disclosure or comprising the data processing system of the disclosure.

[0020] The present invention also relates to a use of the antibiotic resistome risk index as described herein as a biomarker for animal welfare assessment, animal health assessment, animal performance assessment and / or feed conversion rate (FCR) assessment.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1: Richness of ARG across treatment groups (CONTROL: control group; HIGH: 50 mg / kg / day enrofloxacin administration; OPT: 12.5 mg / kg / day enrofloxacin administration; OPT PS: 12.5 mg / kg / day enrofloxacin administration with synbiotic supplementation) and time. The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end. Kruskal-Wallis test FDR corrected, *P < 0.05, **P < 0.01, respectively

[0022] Figure 2: Diversity (Shannon) of ARG across treatment groups (CONTROL: control group; HIGH: 50 mg / kg / day enrofloxacin administration; OPT: 12.5 mg / kg / day enrofloxacin administration; OPT PS: 12.5 mg / kg / day enrofloxacin administration with probiotic supplementation) and time. The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end. Kruskal-Wallis test FDR corrected, *P < 0.05, **P < 0.01, respectively.

[0023] Figure 3: Antibiotic resistome risk index of the metagenome sample across treatments and time. CONTROL: control group; HIGH: 50 mg / kg / day enrofloxacin administration; OPT: 12.5 mg / kg / day enrofloxacin administration; OPT_PS: 12.5 mg / kg / day enrofloxacin administration with synbiotic supplementation). The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end. Kruskal-Wallis test FDR corrected, *P < 0.05, **P < 0.01, (*) P = 0.06, respectively.

[0024] Figure 4: Significant (q < 0.10) log-fold changes in the abundances of ARG hits in a) samples from OPT PS taken at day 20, b) samples from High group taken at day 24, c) samples from OPT group taken at day 24, and d) samples from OPT PS group taken at day 24.

[0025] Figure 5: Impact of synbiotic and / or enrofloxacin application on differential abundances of ARGs (BH-corrected P value < 0.10). The bar plot shows the number of significantly affected ARGs (either increased or decreased) based on differential abundance analysis in broiler chicken cecum resistomes.

[0026] Figure 6: Final body weight distribution of the broiler chickens in different groups at 37 days of age. (CONTROL: control group; HIGH: 50 mg / kg bw / day enrofloxacin; OPT: 12.5 mg / kg bw / day enrofloxacin; OPT_PS: 12.5 mg / kg bw / day enrofloxacin administration with synbiotic supplementation). FDR-corrected p-value from the Mann- Whitney statistical analysis is shown.

[0027] Figure 7: Program used for Poultry Star Sol application during customer collaboration in Thailand.

[0028] Figure 8: Richness (A) and Diversity (Shannon, B) of ARG across gut sections and treatment groups (CONTROL: control group; PS: 20g PoultryStarsol / 1000 birds via drinking water over 14 different pulses (see Figure 7 for the program). The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end.

[0029] Figure 9: Antibiotic resistome risk index of the metagenome sample across gut sections and treatments. (CONTROL: control group; PS: 20g PoultryStarsol / 1000 birds via drinking water over 14 different pulses (Fsee Figure xxx for the program). The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated asboxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end.

[0030] Figure 10: Scatterplots showing a mid to high significant Spearman's rho correlation coefficient (R) between the ARG resistome load and the abundance of the average bodyweight in broiler chicken.

[0031] Figure 11: Alpha diversity analysis (richness A, and Shannon B) of the ARG resistome in pig fecal microbiome. The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end. Kruskal-Wallis test FDR corrected, *P < 0.05, **P < 0.01, (*) P < 0.09, respectively.

[0032] Figure 12: Antibiotic resistome risk index of the ARG resistome in pig fecal microbiome. The median values are indicated as central black horizontal lines and the 25th and 75th percentiles are indicated as boxes. The whiskers extending from each end of the box to the most extreme values within 1.5 times the interquartile range from the respective end. Kruskal- Wallis test FDR corrected, *P < 0.05, **P < 0.01, (*) P < 0.09, respectively.

[0033] Figure 13: Scatterplots showing a significant Spearman's rho correlation coefficient between the relative abundance of the antibiotic resistance genes with a HIGH mobility score and the final body weight of the pigs at day 80 of ageDETAILED DESCRIPTION

[0034] The inventors of the present application have surprisingly found, that by calculating the inventive antibiotic resistome risk index (RRI) in a microbial community the ARGs remain genetically contextualized concerning the underlying microbial community. Thus, an informative risk factor for antibiotic resistance can be determined for a given microbial community based on the predicted risks associated with the identified resistance genes and their respective abundance in a given microbiome. The antibiotic RRI is can be used as a standardized and comparable index for assessing the risk of antibiotitic resistance, without the necessity for for deep understanding of the role of each ARGs in the antibiotic resistome pool of the samples. Additionally, the risk as calculated by the method according to the present invention more closely resembles the widely understood notion of antibiotic resistance as phenotypic resistance is relevant for treating infections. As also illustrated by the Examples, the antibiotic RRI is advantageous for applications in research and industry, like the evaluation of research trials andproduct developments, and can be easily integrated into existing microbiome analysis sequencing pipelines.

[0035] The inventors of the present application have surprisingly found that there are two key parameters play a role in the determination of the risk of developing a phenotypic antibiotic resistance of a microbial community. One key parameter is the relative abundance of a given AGR in the microbial community, the second parameter is how much the ARG is mobilized.

[0036] Accordingly, the invention relates to a method for assessing a risk of antibiotic resistance of a microbial community, the method comprising: screening, preferably by metagenomic sequencing, genetic material of the microbial community from one or more samples thereof, to obtain biological sequences from the respective samples; identifying a plurality of putative antibiotic resistance genes (ARGs) by aligning the biological sequences from the one or more samples against a predetermined database of ARGs, and estimating the abundance of each ARG in a respective sample of the one or more samples, wherein a plurality of ARGs identified for a sample of the one or more samples is said to form a resistome of said sample; obtaining, for each ARG g, identified in the database, a mobility score ARG-MOB of said ARG, indicating how much the ARG is mobilized; determining for each ARG g identified for a sample i of the one or more samples a weighted relative abundance Wg,i asWg,i = ng,i * ARG-MOBg wherein ng,i is the relative abundance of ARG g in sample i; and determining, for each sample i of the one or more samples, i = 1 to n, an antibiotic Resistome Risk Index, RRIi asso as to quantify a risk of antibiotic resistance of respective resistomes of the one or more samples of genetic material.

[0037] In the methods of the present disclosure, it is understood that the risk of antibiotic resistance of a microbial community can be caused by a plurality of antibiotic resistance genes, ARGs.ARG databases

[0038] For the identification of putative antibiotic resistance genes (ARGs), one can make use of a predetermined database of ARGs. Non-limiting examples for such databases are CARD (McArthur et al, 2013, Antimicrob. Agents Chemother., doi: 10.1128 / AAC.00419-13), ARDB (Liu et al., 2009, Nucleic Acids Res., doi: 10.1093 / nar / gkn656), MEGARes (Lakin et al., 2017, Nucleic Acids Res., doi: 10.1093 / nar / gkwl009), ResFinder (Zankari et al., 2012, J. Antimicrob. 661 Chemother., doi: 10.1093 / jac / dks261), SARG (Yin et al., 2018, Bioinformatics, doi: 10.1093 / bioinformatics / bty053), ARG-ANNOT (Gupta et al., 2014, Antimicrob. Agents Chemother, doi: 10.1128 / AAC.01310-13), DeepARG-DB (Arango-Argoty et al., 2018, Microbiome, doi: 10.1186 / s40168-018-670 0401-z), ARG-miner (Argoty et al., 2018, bioRxiv, doi: 10.1101 / 274282), FARME (Wallace et al., 2017, Database, doi: 10.1093 / database / bawl65), and others databases that are know to the skilled person. A preferred database is CARD, such as version 3.2.4 of 2022-07-27.Obtaining bacterial genome(s)

[0039] For the calculation of the ARG-MOB score, at least one bacterial genome(s) is preferably obtained. Preferably, at least one bacterial genome is obtained, preferably at least one bacterial reference genome. This can be done for example by downloading at least one, preferably complete, bacterial genome(s) from at least one data source like a publicly available data source and / or webpage. A preferred publicly available data source is RefSeq database, such as release 213 of July 15, 2022. Complete bacterial genomes can be downloaded from RefSeq using the ncbi-genome-download tool (https: / / github.com / kblin / ncbi-genome-download). However, the method may be applied additionally or alternatively to incomplete bacterial genomes.Annotating obtained bacterial reference genome(s)

[0040] For the calculation of the ARG-MOB score, obtained bacterial reference genome(s) may be annotated. The obtained bacterial genome(s) may be annotated or may not be annotated. Thus, genes within the bacterial genome(s) are preferably predicted using a suitable tool. For example, Prodigal (Hyatt et al., 2010, BMC Bioinformatics, doi: 10.1186 / 1471-2105-11-119) can be used to predict genes from nucleotide sequences and to write corresponding amino acid sequences from RefSeq genomes. Since Prodigal first trains itself based on the input sequence, gene prediction can be performed on subsets of each genus present in RefSeq genomes. For example, per genus, two rounds of Prodigal can be performed with the -meta flag enabled in the second run to predict genes that were missed in the single genome mode and vice versa. Results fromthe “single” and “meta” gene predictions can be combined and redundant annotations found with both methods can be merged.Identifying ARGs

[0041] ARGs can identified in the at least one annotated bacterial genome using at least one ARG database. For identification of ARGs, a similarity-based approach can be used. For example, DIAMOND blastp (Buchfink et al., 2014, Nature Methods, doi: 10.1038 / nmeth.3176) can be used to identify putative ARGs in the obtained RefSeq genomes. For blastp against any antibiotic resistance gene database, both query and subject coverages are preferably set to a minimum of 80%, while E-value cutoffs are preferably set to le-10, to limit the rate of spurious hits. Preferably, for each query protein from all RefSeq genomes, only the single best ARG match is kept.Identifying IS

[0042] Any identified ARG is further analyzed as regards an insertion sequence (IS) in close proximity. Herein, for all identified ARG hits, e.g. with blastp as described herein above,, up to 12,170 bp both up- and downstream of the hit are preferably extracted from the respective RefSeq replicon. This can e.g. done using the faidx command from Samtools (Li et al., 2009, Bioinformatics, doi: 10.1093 / bioinformatics / btp352). IS in ARG loci can then predicted, e.g., using DIAMOND blastp against the ISfinder database implemented in Prokka (https: / / github.com / tseemann / prokka), the E-value cutoff is preferably set to le-30 and the minimum query coverage is preferably set to 90%. Preferably, only the top IS hit for each query protein is kept. Preferably, ARGs not within 12.17 kbp of an IS are not considered when calculating the mean ARG-IS distances.Clustering of ARGs

[0043] Extracted loci with identified ARGs are preferably grouped to remove redundancy. Thus, the false positive rate of ARG hits can be reduced and accuracy can be increased. For example, extracted loci can be clustered with USEARCH. Per each ARG, sequence loci can be clustered using the “-cluster fast” command in usearch, preferably with the criteria that sequences in a cluster were at least 99% similar over at least 90% of the length (both target- and query coverage) and only the single best hit is preferably allowed per sequence. Optionally, the “-sort length” flag can be enabled to sort loci by length before clustering. For each cluster, the centroid sequence can be used as representative sequence for downstream analyses (i.e., “-centroids” flag can be used).Identifying integrons

[0044] Integrons can be identified using for example an integron database. Herein, integrons and cassette arrays can be predicted using IntegronFinder (https: / / github.com / gem- pasteur / Integron Finder ) using the centroid sequences as input. IntegronFinder can predict complete integrons including gene cassettes, InO elements where only integrase is present, and CALINs (Cluster of attC sites Lacking Integrase nearby). All three classes of integrons are preferably included in the analyses.Calculating mobility metrics and the ARG-MOB scale

[0045] For calculating an ARG-MOB score, four mobility metrics are preferably obtained, preferably calculated. Said mobility metrics preferably comprise IS ratio, Replicon ratio, Integron ratio, and / or Diversity index, preferably all four of them.

[0046] For the caluclaiton of the IS ratio, for each ARG, the number of centroid sequences with and without identified IS is counted and the IS ratio can be derived, which indicates the a ratio of centroid sequences in an ARG had IS in proximity. Accordingly, an IS ratio of 1 may indicate that all centroid sequence belonging to a given ARG have an IS within 12,170 bp either up- or downstream of the ARG. Vice versa, an IS ratio of 0 may indicate that none of the centroid sequences in an ARG had IS in proximity.

[0047] Similarly, the Replicon ratio can be calculated per ARG based on the centroid sequences’ location on either plasmids or chromosomes. For example, a Replicon ratio of 1 indicated that all centroid sequences in a given ARG were of plasmid origin and of 0 that all centroid sequences in a given ARG were from chromosomes.

[0048] The Integron ratio indicated how many centroid sequences were inserted in integrons per ARG. For example, an Integron ratio of 1 may indicate that all centroid sequences in a given ARG were inserted in an integron and of 0 that none of the centroid sequences in a given ARG were inserted in an integron.

[0049] For measuring the taxonomic distribution of each ARG category, the Simpson diversity index (range 0 to 1) can be calculated per ARG using unclustered sequences and the genera they were identified in.

[0050] The ARG-MOB scale (0-1) preferably represents the mean of the four mobility metrics described herein. It can serve as a ranking scheme to evaluate how the degree to which members of an ARGs have been mobilized. Based on the smoothed kernel density estimates of all ARG-MOB scores, groupings can be made to categorize ARGs by their ARG-MOB score. An ARGMOB score of 0 indicates that ARGs of the given ARG are not once found to be mobilized in the RefSeq genomes and a score of 0 is thus categorized as “very low”. Valleys in the density distribution of ARG-MOB scores can be used to computationally pinpoint thresholds between ARG-MOB categories. For example, for the CARD database used herein, the “low” group ARGMOB scores ranged from 0.0 to 0.182, the “medium” group from 0.182 to 0.378, “high” from 0.378 to 0.685, and “very high” from 0.685 to 1.0.

[0051] It is further envisioned that the ARG-MOB scale may be extended, preferably by incorporating Integrative and conjugative element (ICE) elements. For identification of ICE elements, prediction tools may be applied as described in the context of the other mobility metrics and / or information obtained from a respective database (e.g. ICEberg database, such as version 2.0, updated September 2018, M. Liu, X. Li, Y. Xie, D. Bi, J. Sun, J. Li, C. Tai, Z. Deng, H.Y. Ou (2019) ICEberg 2.0: an updated database of bacterial integrative and conjugative elements. Nucleic Acids Research, 47(D1): D660-D665, https: / / bioinfo- mml. sjtu.edu. cn / ICEberg2 / index.php). Information as regards ICE elements may thus be incorporated into an ARG-MOB scale as ICE ratio described herein in case of plasmid and integron analyses. This means that the ICE ratio indicates the ratio of ARGs that are associated with an ICE.

[0052] Accordingly, the ARG-MOB score is preferably calculated (preferably as arithmetic mean) using the metris of IS ratio, Replicon ratio, Integron ratio, and Diversity index described herein. Alternatively, but less preferred, the ARG-MOB score can be calculated (preferably as arithmetic mean) using the metris of IS ratio, Replicon ratio, Integron ratio, Diversity index, and ICE ratio, described herein.

[0053] Once having information on the mobility likelihood of different antibiotic resistance genes at hand, mobility scores can be used to quantify the antibiotic resistome risk for any microbiome sample.

[0054] The ARG-MOB score can indicate how much the ARG is mobilized on a scale of 0 to 1 The score is preferably calculated as described herein and / or as described by NIELSEN, et al., Mobilization of antibiotic resistance genes differ by resistance mechanism. https: / / doi.org / 10.1101 / 2021.01.10.426126, posted January 11, 2021, which is incorporated herewith by reference in its entirety.

[0055] In view of this, Antibiotic Resistome Risk Index (RRI) can be calculated by further taking into account the relative abundance of the ARGs in a microbial collection. For the calculation, the following mathematical formula was developed:

[0056] For antibiotic resistance gene of g in sample i a “weighed abundance” Wg, i is defined as:where Wg, i is the weighted abundance of antibiotic resistance gene of g in sample i, ng, i is the relative abundance of genes g in sample i, and ARG — M0Bgis the mobility score of antibiotic resistance gene of g. For each antibiotic resistance gene in the same sample i, their “weighted abundance” Wg, i is defined analogously.

[0057] Based thereon, the “Antibiotic Resistome Risk Index (RRI)” for sample i is defined as:2) RRlt = Wg, i achieving a value between 0 to 1 with 1 representing very high mobilization, and therefore high risk.

[0058] The risk of antibiotic resistance referred to herein is preferably a phenotypic risk of antibiotic resistance.

[0059] According to the methods of the present disclosure, the antibiotic RRI can be calculated for any microbial community of interest. For example, the risk of antibiotic resistance can be assessed for any microbial community. Preferably the microbial community is comprised in a gut or a gut sample of a subject or a group of subjects. Preferably, the microbial community is comprised in digesta collected from a subject or a group of subjects.

[0060] A “sample” as used herein can be any sample comprising biological material derived from a microbial community. Preferably, a sample is derived from a subject. A sample can be a gut sample, a fecal sample, or a cloacal swab sample. Preferably, the sample comprises gut digesta or is a gut digesta sample.

[0061] A “subject” as used herein refers to a vertebrate, preferably a mammal, an avian, or a fish, preferably a mammal or an avian. The term “mammal” as used herein refers to any animal classified as a mammal, including, without limitation, humans, pigs, cows, horses, dogs, or primates such as cynomolgus monkeys, to name only a few illustrative examples. A subject canbe a domestic animal, a livestock animal, a zoo animal, a sport animal, and / or a pet animal, with a livestock animal being preferred. Preferred subjects include a pig, a human, a cow, a horse, a dog, a chicken, a fish, in particular puffer, and / or a catfish. A preferred subject is of the genus Sus, preferably of the species Sus scrofa. Another preferred subject is a chicken.

[0062] The present invention also relates to a method for comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, the method comprising: determining for a sample of the first microbial community and for a sample of the second microbial community, respectively, antibiotic Resistome Risk Indices by performing the method as defined in any one of the preceding claims; and optionally comparing the antibiotic RRI of both samples.

[0063] When comparing the antibiotic RRI of two microbial communities or identified in two samples, a higher antibiotic RRI in one sample can indicate a higher risk of antibiotic resistance as compared to the other sample, or a comparable or equal antibiotic RRI in both samples can indicate comparable or equal risks of antibiotic resistance in both samples.

[0064] In the methods of the present disclosure, the first microbial community and the second microbial community are preferably collected from a livestock species. Preferably, the first microbial community and the second microbial community both comprise gut digesta.

[0065] The first microbial community and the second microbial community can be obtained from a subject or a group of subjects that have been subjected to different treatments.

[0066] The first microbial community and the second microbial community can be obtained from the same subject or the same group of subjects at different timepoints. For example, the first microbial community can be obtained before the subject received a treatment, while the second microbial community is obtained after and / or during the subject has received and / or is receiving a treatment.

[0067] A method for comparing a risk of antibiotic resistance disclosed herein may comprise the determination of antibiotic RRI in multiple samples of the first and / or second microbial community. Accordingly, the method may comprise determining for each sample in a first group of samples of the first microbial community and for each sample in a second group of samples of the second microbial community, respectively, antibiotic Resistome Risk Indices for each of the first group and the second group of samples; and optionally statistically assessing the ResistomeRisk Indices of the first group of samples and the second group of samples, respectively, to compare their risk of antibiotic resistance.

[0068] To compare two or more groups of samples receiving different treatment and / or intervention strategies, the mean value per each group can be estimated and reported with plus / minus standard deviation. The higher the value, the higher the abundance of risky ARGs defined with high decontextualization likelihood and a wide phylogenetic dispersal across different microbial genera.

[0069] The present invention also relates to a method for improving health and / or welfare of a subject comprising: determining the antibiotic RRI as described herein in a sample obtained from the subject, and optionally applying to the subject having a high antibiotic RRI an treatment that reduces antibiotic RRI, preferably if the subject has a high antibiotic resistome risk index, thereby increasing the welfare / health status.

[0070] The present invention also relates to a method for stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, comprising determining the antibiotic RRI as described herein in a sample obtained from the subject, stratifying the subject for a treatment that reduces antibiotic RRI, preferably if the subject or the sample has a high antibiotic resistome risk index.

[0071] The present invention also relates to a method of assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, comprising determining the antibiotic RRI in a sample from the subject or group of subjects before receiving the treatment, determining the antibiotic RRI in a sample after or during receiving the treatment, wherein the antibiotic RRI is determined by performing the method as defined herein, wherein a decrease of the antibiotic resistome risk index is preferably indicative that the treatment is effective.

[0072] The present invention also relates to a of method diagnosing and preventing, ameliorating or treating a risk of a subject or group of subjects for developing antibiotic resistance of a microbial community comprised in the gut of said subject or group of subjects, comprising (a) determining the antibiotic resistome risk index in a sample from the subject or group of subjects determined by performing the method as defined in any one of claims 1-5, and (b) administering to the subject a treatment that reduces antibiotic resistome risk index, preferably if the subject has a high antibiotic resistome risk index.

[0073] The present invention also relates to a method of reducing a risk of developing antibiotic resistance of a microbial community in a gut of a subject, comprising administering to thesubject a treatment disclosed herein. The treatment may be one or more probiotic microbial strains selected from the group consisting of Enterococcus fciecium. Pediococcus acidilaclici. Bifidobacterium animalis. Lactobacillus salivarius. and / or Lactobacillus reuteri.

[0074] The present invention also relates to a treatment disclosed herein for use in a method of the invention.

[0075] The present invention also relates to a treatment disclosed herein for the manufacture of a composition, which is preferably for use in a method disclosed herein.

[0076] The treatment is preferably applied if the subject has a high antibiotic RRI. By applying the treatment, the health and / or status is preferably increased.

[0077] It is understood that increasing the health / welfare status preferably comprises increasing feed conversion rate (FCR) of the subject.

[0078] A high antibiotic RRI is preferably defined by a value that is higher than a reference value, wherein the reference value is preferably determined in a control group of healthy subjects.

[0079] A “treatment” as used herein may comprise administering to the subject a composition that is preferably suitable of reducing antibiotic RRI of a subject. The composition may be a feed or a feed additive. The feed or feed additive may comprise one or more probiotic microorganisms and / or one or more phytogenic substances.

[0080] The composition may comprise one or more probiotics. “Probiotics” as used herein are microorganisms that are believed to provide health benefits when consumed. Probiotics have to be alive when administered. In principle any probiotic can be used. The person skilled in the art knows probiotics suitable for use in the present invention.

[0081] The composition may thus comprise one or more probiotic microorganism(s). In principle, any suitable microorganism may be added to the composition. Exemplary microorganisms include Bacteroides fragilis, B. vulgatus, Listeria monocytogenes and species of Lactobacillus and Bifidobacterium such as Bifidobacterium bifidum or its biologically functional equivalent, Clostridium such as Clostridium perfringens and Eubacteria, or D SMI 1798 also referred to as BBSH 797 herein.

[0082] The composition or feed or feed additive may also comprise one, two, three, four, five, or more probiotic microorganism(s). For example, the composition or feed or feed additive may comprise one, two, three, four, or five probiotic microorganim(s) selected from the groupconsisting of Enterococcus faecium, Pediococcus acidilaclici, Bifidobacterium animalis. Lactobacillus salivarius. and / or Lactobacillus reuteri. Accordingly, a treatment may comprise one, two, three, four, or five probiotic microorganim(s) selected from the group consisting of Enterococcus faecium, Pediococcus acidilaclici, Bifidobacterium animalis, Lactobacillus salivarius, and / or Lactobacillus reuteri.

[0083] The composition may additionally or alternatively comprise one or more prebiotics. “Prebiotics” as used herein are food ingredients that induce the growth or activity of beneficial microorganisms (e.g., bacteria and fungi). More precisely a prebiotic may be a selectively fermented ingredient that allows specific changes, both in the composition and / or activity in the gastrointestinal microflora, that confer benefits. In principle any prebiotic can be used. The person skilled in the art knows prebiotics suitable for use in the present invention.

[0084] Prebiotics can be non-digestible fiber compounds that pass undigested through the upper part of the gastrointestinal tract and stimulate the growth or activity of advantageous bacteria that colonize the large bowel by acting as substrate for them. Foods that comprise prebiotics and that can be added to the composition include Gum Arabic, Raw, Dry Chicory Root Raw, Dry Jerusalem Artichoke Raw, Dry Dandelion Greens Raw, Dry Garlic Raw, Dry Leek Raw, Dry Onion Raw Asparagus Raw Wheat bran Whole Wheat flour, and Cooked Raw Banana. The prebiotic may also comprise a fiber as described in Slavin (2013) “Fiber and Prebiotics: Mechanisms and Health Benefits” Nutrients. 5(4): 1417-1435. The prebiotic may also be a galactooligosaccharid.

[0085] Therefore, the composition may additionally or alternatively comprise one or more sources of galactooligosaccharides. For example, the composition may further comprise one or more of liquid milk, dried milk powder such as whole milk powder, skimmed milk powder, fat filled milk powders, whey powders, fermented dairy products, beverages, cereals, bread, food and feed supplements, dietary supplements, animal feeds, poultry feeds or indeed any other food or beverage. Further galactooligosaccharides and how galactooligosaccharides can be obtained is for example described in Torres et al. (2010) “Galacto-Oligosaccharides: Production, Properties, Applications, and Significance as Prebiotics” Comprehensive Reviews in Food Science and Food Safety, Volume 9, Issue 5, p. 438-454.

[0086] The composition may comprise one or more phytogenic substances, also referred to as phytogenic feed additive (PF A). The phytogenic substances may be one or more essential oils, such as one or more extracts or oils from oregano, caraway, black cumin, rosemary, cinnamon,fenugreek, anise, clove bud, clove oil, savory, peppermint, catnip, tea leave, laurel, sage, myrtle, fennes, citrus peel, garlic, limonene, thymol, carvacrol, p-cymene, y-terpinene, menthol, caryophyllene, cadinene, humulene, germacrene, zingiberene. Phytogenic substances are potential natural alternative to antibiotic to improve animal health and performance as e.g. shown in Koorakula et al. 2022, Frontiers in Microbiology, Volume 1, Article 833790, which is incorporated herewith by reference in its entirety. Accordingly, a treatment may comprise one or more phytogenic substances disclosed herein.

[0087] The composition may additionally or alternatively comprise at least one component selected from the group of vitamins, minerals, enzymes and components for detoxifying mycotoxins. The enzyme may be selected from the group of proteases, amylases, cellulases or glucanases, hydrolases, lipolytic enzymes, mannosidases, oxidases, oxidoreductases, phytases and xylanases and / or combinations thereof. Mycotoxin detoxifying components may be selected from the mycotoxin detoxifying enzymes such as aflatoxin oxidase, ergotamine hydrolases, ergotamine amidases, ochratoxin amidases, fumonisin carboxylesterases, fumonisin aminotransferases, aminopolyol aminoxidases, deoxynivalenol epoxide hydrolases, zearalenone hydrolases; or mycotoxin-detoxifying microorganisms; or mycotoxin-binding components such as microbial cell walls or inorganic materials such as bentonite. It is also envisioned that the composition may comprise bentonite and / or a fumonisin aminotransferase e.g. EC 3.1.1.87.

[0088] Any one of the methods disclosed herein can be a computer-implemented method or comprise a computer-implemented method or one or more computer-implemented method steps.

[0089] The present invention also relates to an electromagnetic signal carrying computer- readable instructions for performing a method of the disclosure.

[0090] The present invention also relates to a data processing system comprising a processor configured to perform a method of the disclosure.

[0091] The present invention also relates to a computer program comprising instructions to cause the data processing system disclosed herein to carry out a method of the disclosure.

[0092] The present invention also relates to a computer program product adapted to carry out a method of the disclosure.

[0093] The present invention also relates to an apparatus adapted to carry out a method of the disclosure or comprising a data processing system of the disclosure.

[0094] The present invention also relates to a use of the antibiotic resistome risk index as described herein as a biomarker for animal welfare assessment, animal health assessment, animal performance assessment and / or feed conversion rate (FCR) assessment. The antibiotic resistome risk index can be determined by a method of the disclosure.

[0095] The present invention also relates to a use of an electromagnetic signal disclosed herein, a data processing system disclosed herein, a computer program disclosed herein, a computer program product disclosed herein, and / or an apparatus disclosed herein, for animal welfare assessment, animal health assessment, animal performance assessment and / or feed conversion rate (FCR) assessment.

[0096] It must be noted that as used herein, the singular forms "a", "an" and "the" include plural references and vice versa unless the context clearly indicates otherwise.

[0097] Unless otherwise indicated, the term "at least" preceding a series of elements is to be understood to refer to every element in the series.

[0098] Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the present invention.

[0099] The term "and / or" wherever used herein includes the meaning of "and", "or" and "all or any other combination of the elements connected by said term".

[0100] The term "about" or "approximately" as used herein means within 20%, preferably within 10%, and more preferably within 5% of a given value or range. It includes, however, also the concrete number, e.g., about 20 includes 20.

[0101] Throughout this specification and the claims, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integer or step. When used herein the term “comprising” can be substituted with the term “containing” or “including” or sometimes when used herein with the term “having”.

[0102] When used herein “consisting of' excludes any element, step, or ingredient not specified in the claim element. When used herein, "consisting essentially of' does not exclude materials or steps that do not materially affect the basic and novel characteristics of the claim.

[0103] In each instance herein any of the terms "comprising", "consisting essentially of' and "consisting of' may be replaced with either of the other two terms. E.g., the term "comprising" is meant to provide explicit support also for "consisting essentially of' and "consisting of, the term "consisting essentially of' is meant to provide explicit support also for "comprising" and "consisting of, the term "consisting of is meant to provide explicit support also for "consisting essentially of and "comprising". The possibility to replace terms with each other is not to be understood that these terms are synonymous.

[0104] The invention is further characterized by the following items.

[0105] Item 1. A method for assessing a risk of antibiotic resistance of a microbial community, the method comprising: screening genetic material of the microbial community from one or more samples thereof, to obtain biological sequences from the respective one or more samples; identifying a plurality of putative antibiotic resistance genes (ARGs) by aligning the biological sequences from the one or more samples against a predetermined database of ARGs, and estimating the abundance of each ARG in a respective sample of the one or more samples, wherein a plurality of ARGs identified for a sample of the one or more samples is said to form a resistome of said sample; obtaining, for each ARG g, identified in the database, a mobility score ARG-MOB of said ARG, indicating how much the ARG is mobilized; determining for each ARG g identified for a sample i of the one or more samples a weighted relative abundance Wg,i asWg,i = ng,i * ARG-MOBg wherein ng,i is the relative abundance of ARG g in sample i; and determining, for each sample i of the one or more samples, i = 1 to n, an antibiotic Resistome Risk Index, RRIi asso as to quantify a risk of antibiotic resistance of respective resistomes of the one or more samples of genetic material.

[0106] Item 2. The method of item 1, wherein the risk of antibiotic resistance of a microbial community is caused by a plurality of antibiotic resistance genes, ARGs.

[0107] Item 3. The method of item 1 or 2, wherein the microbial community is comprised in gut digesta collected from a livestock species.

[0108] Item 4. The method of the preceding item, wherein the livestock species is a mammalian species, such as pig, and / or an avian species, such as chicken.

[0109] Item 5. The method of any one of the preceding items, wherein the mobility score ARGMOB of an ARG, indicating how much the ARG is mobilized on a scale of 0 to 1, is determined according to: NIELSEN, et al., Mobilization of antibiotic resistance genes differ by resistance mechanism, https: / / doi.org / 10.1101 / 2021.01.10.426126, posted January 11, 2021.

[0110] Item 6. A method for comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, the method comprising: determining for a sample of the first microbial community and for a sample of the second microbial community, antibiotic Resistome Risk Indices by performing the method as defined in any one of the preceding items; and optionally comparing the RRI of both samples.[OHl] Item 7. The method of item 6, wherein a higher RRI in one sample indicates a higher risk of antibiotic resistance as compared to the other sample, or wherein a comparable or equal RRI in both samples indicate comparable or equal risks of antibiotic resistance.

[0112] Item 8. The method of item 6 or 7, wherein the first microbial community and the second microbial community are collected from a livestock species.

[0113] Item 9. The method of any one of items 6-8, wherein the first microbial community and the second microbial community both comprise gut digesta.

[0114] Item 10. The method of any one of items 6-9, wherein first microbial community and the second microbial community are obtained from a subject or a group of subjects that have been subjected to different treatments.

[0115] Item 11. The method of any one of items 6-10, wherein first microbial community and the second microbial community are obtained from the same subject or the same group of subjects at different timepoints.

[0116] Item 12. The method of any one of items 6-11, comprising determining for each sample in a first group of samples of the first microbial community and for each sample in a secondgroup of samples of the second microbial community antibiotic Resistome Risk Indices for each of the first group and the second group of samples; and optionally statistically assessing the Resistome Risk Indices of the first group of samples and the second group of samples to compare their risk of antibiotic resistance.

[0117] Item 13. The method of the item 12, wherein statistically assessing the antibiotic Resistome Risk Indices of the first group of samples and the second group of samples, comprises comparing their respective mean value and standard deviation, wherein a higher mean value of one group of samples or the other points to a higher abundance of ARGs causing antibiotic resistance in the respective group.

[0118] Item 14. A method for improving health and / or welfare of a subject comprising: determining the antibiotic resistome risk index according to any one of items 1-5 in a sample obtained from the subject, and optionally applying to the subject a treatment that reduces antibiotic resistome risk index, preferably if the subject has a high antibiotic resistome risk index.

[0119] Item 15. A method for stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, comprising: determining the antibiotic resistome risk index according to any one of items 1-5 in a sample obtained from the subject, stratifying the subject for a treatment that reduces antibiotic resistome risk index, preferably if the subject or the sample has a high antibiotic resistome risk index.

[0120] Item 16. A method for assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, comprising determining the antibiotic resistome risk index in a sample from the subject or group of subjects before receiving the treatment, determining the antibiotic resistome risk index in a sample from the subject or group of subjects after or during receiving the treatment, wherein the antibiotic resistome risk index is determined by performing the method as defined in any one of items 1-5, wherein a decrease of the antibiotic resistome risk index is indicative that the treatment is effective.

[0121] Item 17. A method of diagnosing and preventing, ameliorating or treating a risk of a subject or group of subjects for developing antibiotic resistance of a microbial community comprised in the gut of said subject or group of subjects, comprising (a) determining the antibiotic resistome risk index in a sample from the subject or group of subjects determined by performing the method as defined in any one of items 1-5, and (b) administering to the subject atreatment that reduces antibiotic resistome risk index, preferably if the subject has a high antibiotic resistome risk index.

[0122] Item 18. The method of any one of items 14-17, wherein the treatment comprises the application of a feed or feed additive.

[0123] Item 19. The method of item 18, wherein the feed or feed additive comprises a probiotic and / or a prebiotic.

[0124] Item 20. The method of item 18 or 19, wherein the feed or feed additive comprises one or more probiotic microorganisms and / or one or more phytogenic substances.

[0125] Item 21. The method of item 20, wherein the feed or feed additive comprises one, two, three, four, five, or more probiotic microorganism(s).

[0126] Item 22. The method of item 20 or 21, wherein the one or more probiotic microorganisms is selected from Enterococcus faecium, Pediococcus acidilactici, Bifidobacterium animalis, Lactobacillus salivarius, and / or Lactobacillus reuteri.

[0127] Item 23. The method of item 20, wherein the one or more phytogenic substances is one or more essential oils, such as one or more extracts or oils from oregano, caraway, black cumin, rosemary, cinnamon, fenugreek, anise, clove bud, clove oil, savory, peppermint, catnip, tea leave, laurel, sage, myrtle, fennes, citrus peel, garlic, limonene, thymol, carvacrol, p-cymene, y- terpinene, menthol, caryophyllene, cadinene, humulene, germacrene, zingiberene.

[0128] Item 24. The method of any one of items 14-23, wherein a high resistome risk index is defined by a value that is higher than a reference value, wherein the reference value is preferably determined in a control group of healthy subjects.

[0129] Item 25. The method of any one of items 1-24, wherein the method is or comprises a computer-implemented method.

[0130] Item 26. An electromagnetic signal carrying computer-readable instructions for performing the method of any one of items 1-25.

[0131] Item 27. A data processing system comprising a processor configured to carry out the method of any one of items 1-25.

[0132] Item 28. A computer program comprising instructions to cause the data processing system of item 27 to carry out a method of any one of items 1-25.

[0133] Item 29. A computer program product adapted to carry out the method of any one of items 1-25.

[0134] Item 30. An apparatus adapted to carry out the method as itemed in any one of items 1-25 or comprising the data processing system of item 27.

[0135] Item 31. Use of the antibiotic resistome risk index as determined in a method of any one of items 1-5 as a biomarker for animal welfare assessment, animal health assessment, animal performance assessment and / or feed conversion rate (FCR) assessment.

[0136] Item 32. One or more probiotic microbial strains selected from the group consisting of Enterococcus fciecium. Pediococcus acidilaclici. Bifidobacterium animalis. Lactobacillus salivarius. or Lactobacillus reuteri, for use in reducing a risk of developing antibiotic resistance of a microbial community in a gut of a subject.EXAMPLES

[0137] Methods and materials are described herein for use in the present disclosure; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting.

[0138] Briefly, biological material was obtained, mainly gut digesta from a selected number of livestock species, including pigs and chickens. The genetic materials of microbial community were extracted from the samples and sequenced by applying a shotgun metagenomics approach using a nanopore-based sequencer or Illumina sequencers. By applying the bioinformatics analysis illustrated herein, the obtained microbial DNA sequences were aligned against a database of antibiotic resistance genes and their (relative) abundance estimated. Thus, metagenome sequencing data could be evaluated in the examples to assess how risky a whole community of bacteria was in terms of antibiotic resistance. Moreover, the antibiotic RRI index disclosed herein captured even better the difference between the applications of a risk factor such as AGP application of antibiotics, versus natural antibiotic alternatives such as prebiotics, probiotics, phytogenic.Example 1:

[0139] Fluoroquinolone agents are considered critically important for human medicine by the World Health Organization (WHO). Yet, they are often used for the treatment of avian colibacillosis in poultry production, creating a considerable concern on the potential spread of fluoroquinolone resistance genes from commensals to pathogens. By applying a shotgunmetagenomics approach, the time-course change in the cecal microbiome and resistome of broiler chickens receiving enrofloxacin via drinking water during the growing period was investigated. State-of-art resistome diversity index and Resistome Risk index were used to evaluate the effect of antibiotic application on the pool of antibiotic resistance genes in the cecum microbiome.Experimental design

[0140] Eighty-four healthy-looking one-day old male broiler chicks (Ross 308), with similar body weight of around 55g, were randomly allocated to one of four treatments (n = 7 replicates / treatment) :1) standard commercial feed (Table 1, Farm mash 1&2, Versele-Laga, Deinze, Belgium) for 37 days (control group),2) antibiotic-treated group 1 (OPT, 12.5 mg / kg bw / day Baytril® 10% oral solution (Bayer, Diegem, Belgium) consecutively via the drinking water from day 21 to day 23 of age),3) antibiotic-treated group 2 (HIGH, 50 mg / kg bw / day Baytril® 10% oral solution consecutively via the drinking water from day 21 to day 23 of age), and4) synbiotic supplemented group (OPT PS), in which chickens supplemented with a synbiotic product (PoultryStar®, DSM Austria GmbH) during their lifetime, in conjunction with the receiving the enrofloxacin at the dose of 12.5 mg / kg bw / day via drinking water from day 21 to day 23 of age.

[0141] The synbiotic supplement contained five probiotic strains (Enterococcus faecium, Pediococcus acidilactici, Bifidobacterium animalis, Lactobacillus salivarius, and Lactobacillus reuteri) and prebiotic inulin. The amount of mL enrofloxacin that needed to be added in the drinking water was calculated using the following formula (Equation 3)Cecal sampling, DNA extraction, library preparation and sequencing

[0142] Immediately before (day 20), after enrofloxacin treatment (day 24), and two weeks after antibiotic withdrawal (day 37), one bird per replicate was sacrificed and digesta samples from cecum were collected in Eppendorf cups, immediately snap frozen (liquid N2) and stored at - 20°C. The animals were euthanized with pentobarbital IV (Sodium pentobarbital 20%, Kela, Hoogstraten, Belgium), dosed approximately 100 mg / kg. DNA extraction was performed using the QIAamp PowerFecal Pro DNA Kit (Qiagen, Antwerp, Belgium). Assessment of the purity of the DNA was performed by evaluating the 260 / 280 and 260 / 230 absorbances using Nanodrop ® (ND- 1000, Thermo Fisher Scientific, Merelbeke, Belgium), while Quantus ® (Promega, Leiden,The Netherlands) was used for the estimation of the DNA concentration. Finally, the integrity of the DNA was visually validated with gel electrophoresis. The DNA were sent to LGC Genomics GmbH, Berlin, Germany, where paired-end sequencing libraries were built using the Illumina Nextera XT Library Preparation Kit (Illumina Inc., San Diego, CA) followed by sequencing on the Illumina NextSeq 500 platform using high-output chemistry (2* 150bp) according to the manufacturer’s instructions.Sequenced data processing and statistical analysis

[0143] Once demultiplexed and the adaptors were trimmed, quantifying the abundances of ARGs was performed by mapping the reads against the hand-curated antimicrobial resistance database MEGARes v2.0 by using USEARCH (vlO). In MEGARes 2.0, the nodes of the acyclic hierarchical ontology included four antimicrobial compound types, 57 classes, 220 mechanisms of resistance, and 1,345 gene groups that classified the 7,868 accessions. High confidence matches to the sequence in MEGARes database were obtained by considering the entire coverage of the query reads against ARGs genes with an identity threshold of 90% (parameters were set as “-usearch-global -id 0.9, maxaccepts 1, threads 50”). The obtained count data, where the abundance of detected ARGs was reported for each of the sample, was used for evaluating the effect of antibiotic treatments on the cecal antibiotic resistome diversity (state-of-art approach). Resistome Risk Index (RRI) per each samples was calculated based on formula 1 and 2. A Mann- Whitney test was used for statistical comparisons between the two groups in terms of the resistome diversity and RRI per each sampling time points.Change in cecal resistome of broiler chickens treated with enrofloxacin

[0144] Alpha diversity metrics summarize the structure of an ecological community with respect to its richness (number of taxonomic / gene groups), evenness (distribution of abundances of the groups), or both. Because many perturbations to a community affect the alpha diversity of a community, summarizing and comparing community structure via alpha diversity is an ubiquitous approach to analyzing community surveys. In resistome studies, analyzing the alpha diversity of data is a common approach to assessing differences between experimental groups in terms of the resistome. An important measure of alpha diversity is richness, defined as the number of different species / genes present in an environment (R obs). Another commonly used indicator of alpha diversity is evenness, which measures the homogeneity in abundance of the different species / genes in a sample, defined as (Equation 4):where ng, i represents the relative abundances of the gene g in sample i.

[0145] Alpha diversity analysis suggested that fluoroquinolone antimicrobial treatment significantly decreased the richness of the cecal resistome across the three antimicrobial treatment groups (Figure 1). Two weeks after the discontinuance of the antimicrobial treatment, the group receiving the high dose of enrofloxacin still displayed a decreased, but variable richness when compared with the other treatment groups. However, this difference was no longer significant.

[0146] In alignment with the cecal resistome richness, the diversity (Shannon; Figure 2) of resistome was significantly decreased during antimicrobial treatment. The effect was most conspicuous for the group receiving the 12.5 mg / kg / day enrofloxacin with the probiotic. Two weeks after the withdrawal of antibiotic, the diversity of the cecal resistome had largely recovered (no longer significant differences with the control group).

[0147] Many classes of presumptive ARG elements are intrinsic to bacterial genomes and can therefore be considered as housekeeping genes. These genes do not confer antimicrobial resistance and posing a risk until become decontextualized via mobilization. To overcome this, in a further assessment of the enrofloxacin effects on the antibiotic resistome pool, the ARG resistome risk index (Equations 1 and 2) was implemented in the analysis. Fluoroquinolone antimicrobial treatments significantly increased the overall risk index of the caecal resistome from day 21 to day 23, with a more decided increase for the group receiving the 12.5 mg / kg / day dose (0.32± 0.01 to 0.34 ± 0.01, P= 0.022), followed by the group receiving the high dose of enrofloxacin (0.32 ± 0.01 to 0.33 ± 0.009, P= 0.043). A less outspoken and non-significant increase was observed for the 12.5 mg / kg / day dose plus synbiotic supplementation group (0.32 ± 0.02 to 0.33 ± 0.002, P= 0.12). Two weeks after cessation of treatment, there were no significant difference in the resistome mobility index among the groups, except for the group receiving the synbiotic that showed (significantly, P=0.06) lower value compared to the control and OPT group, and similar to day 20 of sampling, the caecum resistome of the animals in this treatment showed the lowest mean value compared to all the treatments (Figure 3).

[0148] In line with the observation from the resistome risk index that enrofloxacin application significantly increased the abundance of transferable ARGs in the cecal metagenome of broiler chickens, differential abundance (DA) analysis revealed a significant increase in the abundance of ARGs belonging to different classes of antibiotics (mainly MLS, aminoglycosides and tetracyclines) in these two groups from day 20 to day 24, with the optimized dosage applicationresulting in a twofold higher number of DA ARG compared to high dose application (32 DA ARGs vs 17 DA ARGs, respectively, Figures 4 and 5, Table 1-4). Examples of the increased ARGs included CAT and CATA (chloramphenicol acetyltransferases), ERMB (23 S methyltransferases) and MLS23S (Macrolide-resistant 23 S rRNA mutation) (Figure 4, Table 1-4). While several of those enriched ARGs in enrofloxacin treated groups were based on resistance mutations of a chromosomal gene (e.g. A16S, MLS23S, TET16S), the majority of enriched genes (ERMB, CAT, CATA, ILES, SAT, TETW / O / 32, ANT6, APH2-DPRIME, DFRF) are known to be associated with mobile genetic elements, explaining the significant increase in the RRI.

[0149] Withdrawal of antibiotic administration seemed to result in a recovery of ARG resistome to the same level as the control group as there was almost no DA ARGs in the caecum metagenomes of broiler chicken between the high / optimized dosage and those in the control group (Figure 5). For the samples taken from the OPT PS group at day 20, where only synbiotic product was applied, the abundance of 13 ARGs were significantly lower compared to control group with only three ARGs showed a significantly higher abundance in the chicken caecum metagenomes (Figures 4 and 5, Table 1-4). At day 24, for the animals receiving the synbiotic product while additionally receiving the optimized dosage of antibiotic (OPT PS), an alleviating effect of synbiotic application on the ARG burden was observed (Figures 4 and 5, Table 1-4). Fifty-three ARGs (vs 32 ARGs in OPT) were significantly lower in abundance in this group compared to control group and only 13 genes (vs 32 ARGs in OPT), mainly belonging to tetracycline resistance, showed significantly higher abundance compared to the control group (Figure 4, Table 1-4). No DA ARGs were found for the animals in this group at day 37 compared to control group. The beneficial effect can be explained through ecological effects of the synbiotic supplement on the microbiome, such as its pronounced effect against strains that likely carry the expanded ARGs in the cecal microbiota. This hypothesis was supported by the observation that already by day 20 of sampling, where all animals but the ones in the synbiotic group received the similar diet, a significant (BH-corrected P value < 0.10) inhibition of several important pathobionts belonging to Proteobacteria such as E. coli and Campylobacter spp. was observed in the cecal microbiota of the chickens in the synbiotic group. Further, some other important pathobionts that carried different ARGs against P-lactams and aminoglycoside antibiotics and had potential negative impact the chicken gut integrity, such as Enterococcus cecorum, Enterococcus hirae and Enterococcus gallinarum, were significantly less abundant in the cecal microbiota of the animals synbiotic group when they were receiving the synbioticproduct (i.e. day 21- day 23) on the top of the optimized dosage of enrofloxacin. Such antagonistic effect was previously observed and reported for the synbiotic product used in the study. Without wishing to be bound to theory, this effect might explain at least partly the significantly higher body weight gain of the animals in this group compared to control group(Figure 6).

[0150] Thus, results suggested novel insights on the dose-dependent effect of enrofloxacin application on the expansion of the broiler gut (risk) resistome, which was mitigated by a synbiotic application.Table 1. Data shown in Figure 4 A:Table 2. Data shown in Figure 4B:Table 3. Data shown in Figure 4C:Table 4. Data shown in Figure 4D:Example 2: PoultryStar® sol application resulted in lower abundance of transferrable antibiotic resistance genes in broiler

[0151] The aim was to investigate the effect of PoultryStar® sol (PS Sol) application on the performance of broiler chickens, on reducing microbial pathogens and outbreaks, as well as the impact on reducing the load of antibiotic resistance genes (AB resistome) in the gut broiler chickens in a commercial production condition.Experimental Design

[0152] Three commercial farms of a chicken producer in Thailand (consisting of 11 - 14 houses / farm were used for the field evaluation over 4-6 production cycles (crop). For each crop, all the chickens received at the hatchery PoultryStar® Hatchery via a gel applicator, before being transferred to the respective farms. Afterwards, houses were seeded with 60,000 - 100,000 day- old (male and female) Cobb broiler chicken. Depending on the farm, 6-7 houses were allocated to either the control group which received standard broiler diet according to common practices in the respective farm or the PoultryStar group which received on top of the standard diet, PoultryStar sol at the concentration of 20g / 1000 birds via drinking water in different pulses (Figure 7).Cecal sampling, DNA extraction, library preparation and sequencing

[0153] From up to 120 birds per house cloacal swab samples were taken from the birds at the age between 21-28. Depending on the available type of the swabs (COPAN FLOQSwabs 501CS01 Minitip Flocked Swab with 80mm Breakpoint, PURITAN HydraFlock 6” Sterile Small Flock Swab w / Polysterene Handle, 80mm Breakpoint) stored in pairs of 2 or bunches of 5 in 2 ml Eppendorf Safe-Lock tube containing 1 ml NAP. Prior to inserting the swab in the cloaca, the tip was moisturized with 0.9 % Saline solution, inserted in the cloaca, turned left and right 5 times and put into sampling buffer. To fit the tip into the tube it needs to be cut off. Supported by the responsible veterinarian of the farm, the gastrointestinal tract of a subset of the chicken was obtained and the intestinal content was squeezed into a petri dish, homogenized using a sterile spatula and approx. 250 mg were put into the labeled 2 ml Eppendorf Safe-Lock tube containing 1 ml of Nucleic Acid Preservation Buffer (NAP). DNA extraction procedure was done using the procedure explained in Example 1. Shotgun metagenomics approach was performed by using the MinlON sequencing device. For the size-selected ONT library, 600ng of gDNA was used and quality controlled using Agilent TapeStation. The DNA was sheared using Covaris g-Tubes to generate >7-8 kb fragments (Covaris, Inc., Woburn, Ma, USA). After clean-up, DNA was repaired and end-prepared using the NEBNext FFPE DNA Repair kit (New England BioLabs, Ipswich, MA, USA). AMPure XP beads were added to the repaired DNA and incubated at RT for 30minutes on a Hula mixer, followed by two washes with 70% EtOH. Beads were then resuspended with 61 pl of nuclease-free (NF) water and incubated at RT for 30 minutes on a Hula mixer; 61 pl of the eluate was then transferred into a clean 1.5 ml Eppendorf tube. The resulting DNA was quantified using the Qubit HS DNA kit. Adapter ligation and clean-up was performed using the Ligation Sequencing Kit SQK-LSK109 (Oxford Nanopore Technologies, Oxford, United Kingdom) with a slightly changed protocol (the modified version attached to the Protocols section): Ligation buffer, NEBNext Quick T4 DNA ligase, and adapter mix were added to the repaired DNA and incubated at RT for 10 minutes and then overnight at 4°C. The ligated sample was purified using lOOpl of AMPure XP beads during a 30minute incubation at RT on the Hula mixer, two bead washing steps using the kit-provided wash buffer and resuspension of the beads in 40pl of elution buffer at RT for 30minutes on the Hula mixer; 40pl of the eluate was then transferred into a clean 1.5ml tube. The library was then sequenced on a MK1C device using R10 flow cell sequencing chemistry and data were processed using the MinlON MklC software version 20.03.Sequenced data processing and statistical analysis

[0154] Data analysis was conducted using as described in Example 1.Result

[0155] State-of-art resistome diversity analysis (Figure 8) did not show an effect of the PS Sol administration on the gut resistome of the broiler chickens. As explained in Example 1, such analysis does not provide the full picture on how certain treatments affected the abundance of the transferable or non-transferable ARGs, which is regarded an important criteria on the risk of ARGs.

[0156] A further assessment on the effect of the PS Sol on the antibiotic resistome pool was done using ARG resistome risk index which was calculated based on Equations 1 and 2 (Figure 9). The analysis revealed that regardless of the gut section and type of the samples investigated for the resistome analysis, PS Sol application resulted in a lower ARI compared to control group. This suggested that PS Sol application resulted in lower abundance of the transferable ARGs in the gut microbiome of broiler chickens. Notably, when feeding either one of the probiotic strains Enterococcus faecium, Pediococcus acidilactici, Bifidobacterium animalis, Lactobacillus salivarius, or Lactobacillus reuteri alone, antibiotic risk index reduction could also be achieved, essentially analogous to providing combinations thereof.

[0157] Performance parameters were recorded for the birds in each of the production cycle based on the average body weight of all birds (60,000- 100,000) at the end of cycle and FCR. Spearman correlation analysis revealed an negative correlation between the relative abundance of the antibiotic resistance genes with a HIGH mobility index (0.378 < ARG-MOB < 0.681), and therefore high risk, and the final body weight for both the male and female birds as it can be seen in Figure 10.Example 3: Resistome Risk Index captures the association of antibiotic treatment with expansion of antibiotic resistance genes in swine

[0158] The aim was to investigate the effect of an antibiotic compound (amoxicillin) as well as a phytogenic feed additive as antibiotic alternative on the diversity and dynamics of weaned piglets over 55 days, by applying a shotgun metagenomics approach.Experimental Design

[0159] 180 weaned piglets at about 25 of age were selected for this experiment. Pigs were then blocked by sex, within block, randomly assigned to one of three treatments (n= 12 pens / treatments, 5 pigs per pen); 1) Standard mash feed diet for 55 days (Control); 2) Antibioticsupplemented diet (20mg amoxycillin / kg body weight twice a day) for 5 days followed by 50 days standard diet (Antibiotic); 3) Phytogenic-supplemented diet (Digestarom DC XCel 150g / t) for 55 days. The duration of the study was 55 days. Pigs were housed in identical pens in similar climatically controlled rooms and were allowed ad libitum access to water and feed. Pigs showing signs of ill health were treated as appropriate and all veterinary treatments were recorded.Fecal sampling, DNA extraction, library preparation and sequencing

[0160] Fecal samples were collected in sterile containers by rectal stimulation from 6 pigs / treatment on days 0, 6, and 55, placed in sterile plastic containers containing the NAP buffer and was shipped to LGC sequencing company for DNA extraction and sequencing, as described in Example 1.Sequenced data processing and statistical analysis

[0161] Data analysis was conducted as described in Examples 1 and 2.Result

[0162] The overall size of the resistome (i.e., the number of unique ARGs) was not changed significantly over the course of AB application or DC product. Diversity analysis based on the Shannon index showed a significant reduction of ARGs diversity in the fecal resistome of pigs in control group and of the pigs receiving Digestarom DC over the 55 days of experiment (Figure 11). In AB treatments, especially from day 0 to day 6, when the antibiotic was applied, a small increase in the diversity of ARGs was observed. As the diversity analysis does not include information as regards the biological context s explained above, a conclusion can not be made if a higher or lower diversity of ARGs were to be regarded as a positive or negative aspect for antibiotic of the feed additive.

[0163] Unlike the diversity analysis, RRI calculation takes into account not only the number and abundance of ARGs, but also their biological relevance to the risk of their transmission in the gut and later on to the environment. The analysis based on ARI (Figure 12) suggested that the antibiotic application over the fist 5 days of the experiment increased the risk index by 2%, while over the same time period the administration of Digestarom DC resulted in 2% reduction in the antibiotic risk index, with even a more reduction (3%) by the end of experiment.

[0164] Spearman correlation analysis revealed a negative correlation between the relative abundance of the antibiotic resistance genes with a HIGH mobility score (0.378 < ARG-MOB <0.681) and the final body weight of the pigs at day 55 of sampling (80 days of age) as can be seen in Figure 13.

[0165] Embodiments illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present embodiments have been specifically disclosed by preferred embodiments and optional features, modification and variations thereof may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention. Each of the narrower species and subgeneric groupings falling within the generic disclosure also forms part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein. In addition, where features are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0166] Equivalents: Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

[0167] It should be understood that this invention is not limited to the particular methodology, protocols, material, reagents, and substances, etc., described herein and as such can vary. The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention, which is defined solely by the claims.

[0168] All publications cited throughout the text of this specification (including all patents, patent applications, scientific publications, manufacturer’s specifications, instructions, etc.) are hereby incorporated by reference in their entirety. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention. To the extent the material incorporated by reference contradicts or is inconsistent with this specification, the specification will supersede any such material.Further embodiments will become apparent from the following claims.

Claims

CLAIMS1. A method for assessing a risk of antibiotic resistance of a microbial community, the method comprising: screening genetic material of the microbial community from one or more samples thereof, to obtain biological sequences from the respective one or more samples; identifying a plurality of putative antibiotic resistance genes (ARGs) by aligning the biological sequences from the one or more samples against a predetermined database of ARGs, and estimating the abundance of each ARG in a respective sample of the one or more samples, wherein a plurality of ARGs identified for a sample of the one or more samples is said to form a resistome of said sample; obtaining, for each ARG g, identified in the database, a mobility score ARG-MOB of said ARG, indicating how much the ARG is mobilized; determining for each ARG g identified for a sample i of the one or more samples a weighted relative abundance Wg,i asWg,i = ng,i * ARG-MOBg wherein ng,i is the relative abundance of ARG g in sample i; and determining, for each sample i of the one or more samples, i = 1 to n, an antibiotic Resistome Risk Index, RRIi asso as to quantify a risk of antibiotic resistance of respective resistomes of the one or more samples of genetic material.

2. The method of claim 1, wherein the risk of antibiotic resistance of a microbial community is caused by a plurality of antibiotic resistance genes, ARGs.

3. The method of claim 1 or 2, wherein the microbial community is comprised in gut digesta collected from a livestock species.

4. The method of the preceding claim, wherein the livestock species is a mammalian species, such as pig, and / or an avian species, such as chicken.

5. A method for comparing a risk of antibiotic resistance caused by a plurality of antibiotic resistance genes of a first microbial community and a plurality of antibiotic resistance genes of a second microbial community, the method comprising: determining for a sample of the first microbial community and for a sample of the second microbial community, antibiotic Resistome Risk Indices by performing the method as defined in any one of the preceding claims; and optionally comparing the RRI of both samples.

6. The method of claim 5, wherein a higher RRI in one sample indicates a higher risk of antibiotic resistance as compared to the other sample, or wherein a comparable or equal RRI in both samples indicates comparable or equal risks of antibiotic resistance.

7. The method of claim 5 or 6, wherein the first microbial community and the second microbial community are collected from a livestock species.

8. The method of any one of claims 5-7, wherein the first microbial community and the second microbial community both comprise gut digesta.

9. The method of any one of claims 5-8, wherein first microbial community and the second microbial community are obtained from a subject or a group of subjects that have been subjected to different treatments.

10. The method of any one of claims 5-9, wherein first microbial community and the second microbial community are obtained from the same subject or the same group of subjects at different timepoints.

11. The method of any one of claims 5-10, comprising determining for each sample in a first group of samples of the first microbial community and for each sample in a second group of samples of the second microbial community antibiotic Resistome Risk Indices for each of the first group and the second group of samples; and optionally statistically assessing the Resistome Risk Indices of the first group of samples and the second group of samples to compare their risk of antibiotic resistance.3912. The method of claim 11, wherein statistically assessing the antibiotic Resistome Risk Indices of the first group of samples and the second group of samples, comprises comparing their respective mean value and standard deviation, wherein a higher mean value of one group of samples or the other points to a higher abundance of ARGs causing antibiotic resistance in the respective group.

13. A method for improving health and / or welfare of a subject comprising: determining the antibiotic resistome risk index according to any one of claims 1-12 in a sample obtained from the subject, and optionally applying to the subject a treatment that reduces antibiotic resistome risk index, if the subject has a high antibiotic resistome risk index.

14. A method for stratifying subjects for the treatment with an agent that increases welfare and / or health status of the subject, comprising: determining the antibiotic resistome risk index according to any one of claims 1-12 in a sample obtained from the subject, stratifying the subject for a treatment that reduces antibiotic resistome risk index, if the subject or the sample has a high antibiotic resistome risk index.

15. A method for assessing the effect of a treatment on the antibiotic resistome of a subject or a group of subjects, comprising determining the antibiotic resistome risk index in a sample from the subject or group of subjects before receiving the treatment, determining the antibiotic resistome risk index in a sample from the subject or group of subjects after or during receiving the treatment, wherein the antibiotic resistome risk index is determined by performing the method as defined in any one of claims 1-12, wherein a decrease of the antibiotic resistome risk index is indicative that the treatment is effective.

16. A treatment for use in a method of diagnosing and preventing, ameliorating or treating a risk of a subject or group of subjects for developing antibiotic resistance of a microbial community comprised in the gut of said subject or group of subjects, comprising a. determining the antibiotic resistome risk index in a sample from the subject or group of subjects determined by performing the method as defined in any one of claims 1- 12, and40b. administering to the subject a treatment that reduces antibiotic resistome risk index, if the subject has a high antibiotic resistome risk index.

17. The method of any one of claims 13-15 or the treatment for the use of claim 16, wherein the treatment comprises the application of a feed or feed additive.

18. The method or the treatment for the use of claim 17, wherein the feed or feed additive comprises a probiotic and / or a prebiotic.

19. The method or the treatment for the use of claim 17 or 18, wherein the feed or feed additive comprises one or more probiotic microorganisms and / or one or more phytogenic substances.

20. The method or the treatment for the use of claim 19, wherein the feed or feed additive comprises one, two, three, four, five, or more probiotic microorganism(s).

21. The method or the treatment for the use of claim 19 or 20, wherein the one or more probiotic microorganisms is selected from Enterococcus faecium, Pediococcus acidilactici, Bifidobacterium animalis, Lactobacillus salivarius, and / or Lactobacillus reuteri.

22. The method or the treatment for the use of claim 19, wherein the one or more phytogenic substances is one or more essential oils, such as one or more extracts or oils from oregano, caraway, black cumin, rosemary, cinnamon, fenugreek, anise, clove bud, clove oil, savory, peppermint, catnip, tea leave, laurel, sage, myrtle, fennes, citrus peel, garlic, limonene, thymol, carvacrol, p-cymene, y-terpinene, menthol, caryophyllene, cadinene, humulene, germacrene, zingiberene.

23. The method or the treatment for the use of any one of claims 13-22, wherein a high resistome risk index is defined by a value that is higher than a reference value, wherein the reference value is preferably determined in a control group of healthy subjects.

24. The method of any one of claims 1-23, wherein the method is or comprises a computer- implemented method.

25. An electromagnetic signal carrying computer-readable instructions for performing the method of any one of claims 1-24.

26. A data processing system comprising a processor configured to carry out the method of any one of claims 1-24 or computer program comprising instructions to cause said data processing system to carry out the method of any one of claims 1-24, or a computer program product adapted to carry out the method of any one of claims 1-24.

27. A computer program comprising instructions to cause the data processing system of claim 26 to carry out a method of any one of claims 1-24.

28. A computer program product adapted to carry out the method of any one of claims 1-24.

29. An apparatus adapted to carry out the method as claimed in any one of claims 1-24 or comprising the data processing system of claim 26.

30. Use of the antibiotic resistome risk index as determined in a method of any one of claims 1-12 as a biomarker for animal welfare assessment, animal health assessment, animal performance assessment and / or feed conversion rate (FCR) assessment.

31. One or more probiotic microbial strains selected from the group consisting of Enterococcus fciecium. Pediococcus acidilaclici. Bifidobacterium animalis. Lactobacillus salivarius. or Lactobacillus reuteri, for use in reducing a risk of developing antibiotic resistance of a microbial community in a gut of a subject.

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