Sanitary Conditions on the Farm Alters Fecal Metabolite Profile in Growing Pigs

The aim of this study was to use fecal metabolite profiling to evaluate the effects of contrasting sanitary conditions and the associated subclinical health status of pigs. We analyzed fecal metabolite profiles by nuclear magnetic resonance (1 H NMR) from pigs aged 14 and 22 weeks. Pigs kept under low and high sanitary conditions differed in fecal metabolites related to the degradation of dietary starch, metabolism of the gut microbiome, and degradation of components of animal (host) origin. The metabolites that differed significantly (FDR < 0.1) were from metabolic processes involved in either maintaining nutrient digestive capacity, including purine metabolism, energy metabolism, bile acid breakdown and recycling, or immune system metabolism. The results show that the fecal metabolite profiles reflect the sanitary conditions under which the pigs are kept. The fecal metabolite profiles closely resembled the profiles of metabolites found in the colon of pigs. Fecal valerate and kynurenic acid could potentially be used as “non-invasive” biomarkers of immune or inflammatory status that could form the basis for monitoring subclinical health status in pigs

Sanitary Conditions Affect the Colonic Microbiome and the Colonic and Systemic Metabolome of Female Pigs

Differences in sanitary conditions, as model to induce differences in subclinical immune stimulation, affect the growth performance and nutrient metabolism in pigs. The objective of the present study was to evaluate the colonic microbiota and the colonic and systemic metabolome of female pigs differing in health status induced by sanitary conditions. We analyzed blood and colon digesta metabolite profiles using Nuclear Magnetic Resonance (1H NMR) and Triple quadrupole mass spectrometry, as well as colonic microbiota profiles. 1H NMR is a quantitative metabolomics technique applicable to biological samples. Weaned piglets of 4 weeks of age were kept under high or low sanitary conditions for the first 9 weeks of life. The microbiota diversity in colon digesta was higher in pigs subjected to low sanitary conditions (n = 18 per treatment group). The abundance of 34 bacterial genera was higher in colon digesta of low sanitary condition pigs, while colon digesta of high sanitary status pigs showed a higher abundance for four bacterial groups including the Megasphaera genus (p < 0.003) involved in lactate fermentation. Metabolite profiles (n = 18 per treatment group) in blood were different between both groups of pigs. These different profiles suggested changes in general nutrient metabolism, and more specifically in amino acid metabolism. Moreover, differences in compounds related to the immune system and responses to stress were observed. Microbiome-specific metabolites in blood were also affected by sanitary status of the pigs. We conclude that the microbiome composition in colon and the systemic metabolite profiles are affected by sanitary conditions and related to suboptimal health. These data are useful for exploring further relationships between health, metabolic status and performance and for the identification of biomarkers related to health (indices) and performance

Exploring Human Milk Dynamics : Interindividual Variation in Milk Proteome, Peptidome, and Metabolome

Human milk is a dynamic biofluid, and its detailed composition receives increasing attention. While most studies focus on changes over time or differences between maternal characteristics, interindividual variation receives little attention. Nevertheless, a comprehensive insight into this can help interpret human milk studies and help human milk banks provide targeted milk for recipients. This study aimed to map interindividual variation in the human milk proteome, peptidome, and metabolome and to investigate possible explanations for this variation. A set of 286 milk samples was collected from 29 mothers in the third month postpartum. Samples were pooled per mother, and proteins, peptides, and metabolites were analyzed. A substantial coefficient of variation (>100%) was observed for 4.6% and 36.2% of the proteins and peptides, respectively. In addition, using weighted correlation network analysis (WGCNA), 5 protein and 11 peptide clusters were obtained, showing distinct characteristics. With this, several associations were found between the different data sets and with specific sample characteristics. This study provides insight into the dynamics of human milk protein, peptide, and metabolite composition. In addition, it will support future studies that evaluate the effect size of a parameter of interest by enabling a comparison with natural variability

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.</p

Streptococcus salivarius MS-oral-D6 promotes gingival re-epithelialization in vitro through a secreted serine protease

Gingival re-epithelialization represents an essential phase of oral wound healing in which epithelial integrity is re-establish. We developed an automated high-throughput re-epithelialization kinetic model, using the gingival epithelial cell line Ca9-22. The model was employed to screen 39 lactic acid bacteria, predominantly including oral isolates, for their capacity to accelerate gingival re-epithelialization. This screen identified several strains of Streptococcus salivarius that stimulated re-epithelialization. Further analysis revealed that S. salivarius strain MS-oral-D6 significantly promoted re-epithelialization through a secreted proteinaceous compound and subsequent experiments identified a secreted serine protease as the most likely candidate to be involved in re-epithelialization stimulation. The identification of bacteria or their products that stimulate gingival wound repair may inspire novel strategies for the maintenance of oral health.</p

Impact of nanoparticle surface functionalization on the protein corona and cellular adhesion, uptake and transport

Background: Upon ingestion, nanoparticles can interact with the intestinal epithelial barrier potentially resulting in systemic uptake of nanoparticles. Nanoparticle properties have been described to influence the protein corona formation and subsequent cellular adhesion, uptake and transport. Here, we aimed to study the effects of nanoparticle size and surface chemistry on the protein corona formation and subsequent cellular adhesion, uptake and transport. Caco-2 intestinal cells, were exposed to negatively charged polystyrene nanoparticles (PSNPs) (50 and 200 nm), functionalized with sulfone or carboxyl groups, at nine nominal concentrations (15-250 μg/ml) for 10 up to 120 min. The protein coronas were analysed by LC-MS/MS. Results: Subtle differences in the protein composition of the two PSNPs with different surface chemistry were noted. High-content imaging analysis demonstrated that sulfone PSNPs were associated with the cells to a significantly higher extent than the other PSNPs. The apparent cellular adhesion and uptake of 200 nm PSNPs was not significantly increased compared to 50 nm PSNPs with the same surface charge and chemistry. Surface chemistry outweighs the impact of size on the observed PSNP cellular associations. Also transport of the sulfone PSNPs through the monolayer of cells was significantly higher than that of carboxyl PSNPs. Conclusions: The results suggest that the composition of the protein corona and the PSNP surface chemistry influences cellular adhesion, uptake and monolayer transport, which might be predictive of the intestinal transport potency of NPs.</p

An in vitro model for microbial fructoselysine degradation shows substantial interindividual differences in metabolic capacities of human fecal slurries

Fructoselysine is formed upon heating during processing of food products, and being a key intermediate in advanced glycation end product formation considered to be potentially hazardous to human health. Human gut microbes can degrade fructoselysine to yield the short chain fatty acid butyrate. However, quantitative information on these biochemical reactions is lacking, and interindividual differences therein are not well established. Anaerobic incubations with pooled and individual human fecal slurries were optimized and applied to derive quantitative kinetic information for these biochemical reactions. Of 16 individuals tested, 11 were fructoselysine metabolizers, with Vmax, Km and kcat-values varying up to 14.6-fold, 9.5-fold, and 4.4-fold, respectively. Following fructoselysine exposure, 10 of these 11 metabolizers produced significantly increased butyrate concentrations, varying up to 8.6-fold. Bacterial taxonomic profiling of the fecal samples revealed differential abundant taxa for these reactions (e.g. families Ruminococcaceae, Christenellaceae), and Ruminococcus_1 showed the strongest correlation with fructoselysine degradation and butyrate production (ρ ≥ 0.8). This study highlights substantial interindividual differences in gut microbial degradation of fructoselysine. The presented method allows for quantification of gut microbial degradation kinetics for foodborne xenobiotics, and interindividual differences therein, which can be used to refine prediction of internal exposure.</p

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.</p

Short prokaryotic Argonaute systems trigger cell death upon detection of invading DNA

Argonaute proteins use single-stranded RNA or DNA guides to target complementary nucleic acids. This allows eukaryotic Argonaute proteins to mediate RNA interference and long prokaryotic Argonaute proteins to interfere with invading nucleic acids. The function and mechanisms of the phylogenetically distinct short prokaryotic Argonaute proteins remain poorly understood. We demonstrate that short prokaryotic Argonaute and the associated TIR-APAZ (SPARTA) proteins form heterodimeric complexes. Upon guide RNA-mediated target DNA binding, four SPARTA heterodimers form oligomers in which TIR domain-mediated NAD(P)ase activity is unleashed. When expressed in Escherichia coli, SPARTA is activated in the presence of highly transcribed multicopy plasmid DNA, which causes cell death through NAD(P)+ depletion. This results in the removal of plasmid-invaded cells from bacterial cultures. Furthermore, we show that SPARTA can be repurposed for the programmable detection of DNA sequences. In conclusion, our work identifies SPARTA as a prokaryotic immune system that reduces cell viability upon RNA-guided detection of invading DNA.BN/Stan Brouns La