Faecal microbiota composition based random forest model predicts Mycobacterium Avium subsp. Paratuberculosis (MAP) shedding severity in cattle

Paratuberculosis is a devastating infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). The development of the paratuberculosis clinical symptoms in cattle could take up to a few years and vastly differs between individuals in severity of the symptoms and shedding of the pathogen in the environment. Identification of high shedding animals that significantly increase the burden of the pathogen in a farm environment is essential for paratuberculosis control and minimization of economic losses. Widely used methods for detection and quantification of MAP, such as culturing and PCR based techniques rely on direct presence of the pathogen in a sample and have little to no predictive value for the disease development. In the current study we investigated possibility of prediction of the shedding severity through the life of a cow based on fecal microbiota composition. Twenty calves were experimentally infected with MAP and fecal samples were collected biweekly up to four years of age. All collected samples were subjected to culturing on the selective media to obtain data about shedding severity. Faecal microbiota were profiled in a subset of samples that reflects important time points in cattle husbandry. Using faecal microbiota composition and shedding intensity data we build a random forest classifier for prediction of the animals shedding status. We found that machine learning approaches applied to microbial composition can be used to classify cows that are severely shedding MAP into the environment. However, classification accuracy strongly correlates with age of the animals and use of samples from older individuals results higher precision of classification. Classification model based on samples from the first 12 month of life showed AUC between 0.78 and 0.79, where is model based on samples from animals older than 24 month showed AUC between 0.91 and 0.92 (95% CI). . We also showed that only a relatively small number of microbial taxa are important for classification and could be considered as biomarkers. The study provides evidence for the link between microbiota composition and severity of MAP infection and shedding, as well as lays ground for development of predictive diagnostic tools based on the microbiota composition

Faecal microbiota composition based random forest model predicts Mycobacterium Avium subsp. Paratuberculosis (MAP) shedding severity in cattle

Paratuberculosis is a devastating infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). The development of the paratuberculosis clinical symptoms in cattle could take up to a few years and vastly differs between individuals in severity of the symptoms and shedding of the pathogen in the environment. Identification of high shedding animals that significantly increase the burden of the pathogen in a farm environment is essential for paratuberculosis control and minimization of economic losses. Widely used methods for detection and quantification of MAP, such as culturing and PCR based techniques rely on direct presence of the pathogen in a sample and have little to no predictive value for the disease development. In the current study we investigated possibility of prediction of the shedding severity through the life of a cow based on fecal microbiota composition. Twenty calves were experimentally infected with MAP and fecal samples were collected biweekly up to four years of age. All collected samples were subjected to culturing on the selective media to obtain data about shedding severity. Faecal microbiota were profiled in a subset of samples that reflects important time points in cattle husbandry. Using faecal microbiota composition and shedding intensity data we build a random forest classifier for prediction of the animals shedding status. We found that machine learning approaches applied to microbial composition can be used to classify cows that are severely shedding MAP into the environment. However, classification accuracy strongly correlates with age of the animals and use of samples from older individuals results higher precision of classification. Classification model based on samples from the first 12 month of life showed AUC between 0.78 and 0.79, where is model based on samples from animals older than 24 month showed AUC between 0.91 and 0.92 (95% CI). . We also showed that only a relatively small number of microbial taxa are important for classification and could be considered as biomarkers. The study provides evidence for the link between microbiota composition and severity of MAP infection and shedding, as well as lays ground for development of predictive diagnostic tools based on the microbiota composition

Enrichment of in vivo transcription data from dietary intervention studies with in vitro data provides improved insight into gene regulation mechanisms in the intestinal mucosa

BackgroundGene expression profiles of intestinal mucosa of chickens and pigs fed over long-term periods (days/weeks) with a diet rich in rye and a diet supplemented with zinc, respectively, or of chickens after a one-day amoxicillin treatment of chickens, were recorded recently. Such dietary interventions are frequently used to modulate animal performance or therapeutically for monogastric livestock. In this study, changes in gene expression induced by these three interventions in cultured “Intestinal Porcine Epithelial Cells” (IPEC-J2) recorded after a short-term period of 2 and 6 hours, were compared to the in vivo gene expression profiles in order to evaluate the capability of this in vitro bioassay in predicting in vivo responses.MethodsLists of response genes were analysed with bioinformatics programs to identify common biological pathways induced in vivo as well as in vitro. Furthermore, overlapping genes and pathways were evaluated for possible involvement in the biological processes induced in vivo by datamining and consulting literature.ResultsFor all three interventions, only a limited number of identical genes and a few common biological processes/pathways were found to be affected by the respective interventions. However, several enterocyte-specific regulatory and secreted effector proteins that responded in vitro could be related to processes regulated in vivo, i.e. processes related to mineral absorption, (epithelial) cell adherence and tight junction formation for zinc, microtubule and cytoskeleton integrity for amoxicillin, and cell-cycle progression and mucus production for rye.ConclusionsShort-term gene expression responses to dietary interventions as measured in the in vitro bioassay have a low predictability for long-term responses as measured in the intestinal mucosa in vivo. The short-term responses of a set regulatory and effector genes, as measured in this bioassay, however, provided additional insight into how specific processes in piglets and broilers may be modulated by “early” signalling molecules produced by enterocytes. The relevance of this set of regulatory/effector genes and cognate biological processes for zinc deficiency and supplementation, gluten allergy (rye), and amoxicillin administration in humans is discussed.<br/

Significant enriched DAVID clusters (ES > 1.3) comparing T3 vs. T1 at day 55.

<p>Significant enriched DAVID clusters (ES > 1.3) comparing T3 vs. T1 at day 55.</p

Genus-level phylogenetic groups changed in T2 and/or T3 animals.

<p>¹ ARC: average relative contribution [%] of a microbial group. Values represent means ± SD. The microbial groups with a relative abundance lower than 0.1% in all three treatments are not shown.</p><p>² “↑” or “↓” indicates whether the average relative contribution of the microbial group was increased or decreased in the particular comparison.</p><p>³q is the corrected p-value (Benjamini Hochberg)</p><p>Genus-level phylogenetic groups changed in T2 and/or T3 animals.</p

Schematic representation of results.

<p>Overview of the time-line, birth (day 0), administration of the treatments (day 4), measurements days 8, 55, and 176, as well as the hypothetical interpretation of all results from the whole experiment, results from the previous paper about day 8 are included too [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116523#pone.0116523.ref016" target="_blank">16</a>]. On the left we categorized the gut system in three different ‘blocks’, immune programming which occurs in early life, followed by an instable period which includes weaning and later in life a stable period (homeostasis). Note that the treatments, antibiotic and/or stress, were at day 4 during the immune programming period. Next to this the significant findings of microbiota or gene expression data between treatments per time-point are depicted by “+”, and no differences between treatments with a “-”. On the right a metaphorical landscape of the gut system in time is depicted, where the top is day 0 (birth) and bottom is day 176 (slaughter). Spheres depict the current state of the system for day 8, 55, and 176, and colours correspond to the different treatments (T1; red, T2; blue, and T3; green).</p

Principal Component Analysis of jejunal and ileal tissue gene expression for three different treatments at day 55 and 176.

<p>Each symbol represents all expressed genes (approximately 44k probes) of a particular sample. A) top-panel represents day 55 and B) bottom-panel represents day 176. Three different treatments are depicted, T1 (red), T2 (blue) and T3 (green) and two different tissues, jejunum (JEJ, triangles) and ileum (IL, squares).</p

Triplot for RDA analysis of jejunal microbiota composition on day 55 and day 176.

<p>Nominal environmental variables T1, T2 and T3 are represented by red triangles (▲). Samples are grouped by treatment: T1 (red; ○), T2 (blue; □) and T3 (green; ◇), each symbol represents a pool of four pigs, and numbers represent pool identifiers. A) Top-panel shows the RDA analysis of jejunal microbiota composition on day 55. Microbial groups contributing at least 40% to the explanatory axes are represented as vectors. Both axes together explain 15% of the total variance in the dataset. B) Bottom-panel shows the RDA analysis of jejunal microbiota composition on day 55. Microbial groups contributing at least 52% to the explanatory axes are represented as vectors. Both axes together explain 27.8% of the total variance in the dataset.</p

Significantly enriched DAVID clusters (ES > 1.3) comparing T2 vs. T1 at day 55.

<p>Significantly enriched DAVID clusters (ES > 1.3) comparing T2 vs. T1 at day 55.</p