Diet modulates cecum bacterial diversity and physiological phenotypes across the BXD mouse genetic reference population.

The BXD family has become one of the preeminent genetic reference populations to understand the genetic and environmental control of phenotypic variation. Here we evaluate the responses to different levels of fat in the diet using both chow diet (CD, 13-18% fat) and a high-fat diet (HFD, 45-60% fat). We studied cohorts of BXD strains, both inbred parents C57BL/6J and DBA/2J (commonly known as B6 and D2, respectively), as well as B6D2 and D2B6 reciprocal F1 hybrids. The comparative impact of genetic and dietary factors was analyzed by profiling a range of phenotypes, most prominently their cecum bacterial composition. The parents of the BXDs and F1 hybrids express limited differences in terms of weight and body fat gain on CD. In contrast, the strain differences on HFD are substantial for percent body fat, with DBA/2J accumulating 12.5% more fat than C57BL/6J (P < 0.0001). The F1 hybrids born to DBA/2J dams (D2B6F1) have 10.6% more body fat (P < 0.001) than those born to C57BL/6J dams. Sequence analysis of the cecum microbiota reveals important differences in bacterial composition among BXD family members with a substantial shift in composition caused by HFD. Relative to CD, the HFD induces a decline in diversity at the phylum level with a substantial increase in Firmicutes (+13.8%) and a reduction in Actinobacteria (-7.9%). In the majority of BXD strains, the HFD also increases cecal sIgA (P < 0.0001)-an important component of the adaptive immunity response against microbial pathogens. Host genetics modulates variation in cecum bacterial composition at the genus level in CD, with significant quantitative trait loci (QTLs) for Oscillibacter mapped to Chr 3 (18.7-19.2 Mb, LRS = 21.4) and for Bifidobacterium mapped to Chr 6 (89.21-89.37 Mb, LRS = 19.4). Introduction of HFD served as an environmental suppressor of these QTLs due to a reduction in the contribution of both genera (P < 0.001). Relations among liver metabolites and cecum bacterial composition were predominant in CD cohort, but these correlations do not persist following the shift to HFD. Overall, these findings demonstrate the important impact of environmental/dietary manipulation on the relationships between host genetics, gastrointestinal bacterial composition, immunological parameters, and metabolites-knowledge that will help in the understanding of the causal sources of metabolic disorders

Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children

Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children

Murine Gut Microbiota Is Defined by Host Genetics and Modulates Variation of Metabolic Traits

The gastrointestinal tract harbors a complex and diverse microbiota that has an important role in host metabolism. Microbial diversity is influenced by a combination of environmental and host genetic factors and is associated with several polygenic diseases. In this study we combined next-generation sequencing, genetic mapping, and a set of physiological traits of the BXD mouse population to explore genetic factors that explain differences in gut microbiota and its impact on metabolic traits. Molecular profiling of the gut microbiota revealed important quantitative differences in microbial composition among BXD strains. These differences in gut microbial composition are influenced by host-genetics, which is complex and involves many loci. Linkage analysis defined Quantitative Trait Loci (QTLs) restricted to a particular taxon, branch or that influenced the variation of taxa across phyla. Gene expression within the gastrointestinal tract and sequence analysis of the parental genomes in the QTL regions uncovered candidate genes with potential to alter gut immunological profiles and impact the balance between gut microbial communities. A QTL region on Chr 4 that overlaps several interferon genes modulates the population of Bacteroides, and potentially Bacteroidetes and Firmicutes–the predominant BXD gut phyla. Irak4, a signaling molecule in the Toll-like receptor pathways is a candidate for the QTL on Chr15 that modulates Rikenellaceae, whereas Tgfb3, a cytokine modulating the barrier function of the intestine and tolerance to commensal bacteria, overlaps a QTL on Chr 12 that influence Prevotellaceae. Relationships between gut microflora, morphological and metabolic traits were uncovered, some potentially a result of common genetic sources of variation

Murine Gut Microbiota Is Defined by Host Genetics and Modulates Variation of Metabolic Traits

The gastrointestinal tract harbors a complex and diverse microbiota that has an important role in host metabolism. Microbial diversity is influenced by a combination of environmental and host genetic factors and is associated with several polygenic diseases. In this study we combined next-generation sequencing, genetic mapping, and a set of physiological traits of the BXD mouse population to explore genetic factors that explain differences in gut microbiota and its impact on metabolic traits. Molecular profiling of the gut microbiota revealed important quantitative differences in microbial composition among BXD strains. These differences in gut microbial composition are influenced by host-genetics, which is complex and involves many loci. Linkage analysis defined Quantitative Trait Loci (QTLs) restricted to a particular taxon, branch or that influenced the variation of taxa across phyla. Gene expression within the gastrointestinal tract and sequence analysis of the parental genomes in the QTL regions uncovered candidate genes with potential to alter gut immunological profiles and impact the balance between gut microbial communities. A QTL region on Chr 4 that overlaps several interferon genes modulates the population of Bacteroides, and potentially Bacteroidetes and Firmicutes–the predominant BXD gut phyla. Irak4, a signaling molecule in the Toll-like receptor pathways is a candidate for the QTL on Chr15 that modulates Rikenellaceae, whereas Tgfb3, a cytokine modulating the barrier function of the intestine and tolerance to commensal bacteria, overlaps a QTL on Chr 12 that influence Prevotellaceae. Relationships between gut microflora, morphological and metabolic traits were uncovered, some potentially a result of common genetic sources of variation

Meropenem extraction by

Background: Meropenem is a broad-spectrum carbapenem-type antibiotic commonly used to treat critically ill patients infected with extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae. As many of these patients require extracorporeal membrane oxygenation (ECMO) and/or continuous renal replacement therapy (CRRT), it is important to understand how these extracorporeal life support circuits impact meropenem pharmacokinetics. Based on the physicochemical properties of meropenem, it is expected that ECMO circuits will minimally extract meropenem, while CRRT circuits will rapidly clear meropenem. The present study seeks to determine the extraction of meropenem from ex vivo ECMO and CRRT circuits and elucidate the contribution of different ECMO circuit components to extraction. Methods: Standard doses of meropenem were administered to three different configurations (n = 3 per configuration) of blood-primed ex vivo ECMO circuits and serial sampling was conducted over 24 h. Similarly, standard doses of meropenem were administered to CRRT circuits (n = 4) and serial sampling was conducted over 4 h. Meropenem was administered to separate tubes primed with circuit blood to serve as controls to account for drug degradation. Meropenem concentrations were quantified, and percent recovery was calculated for each sample. Results: Meropenem was cleared at a similar rate in ECMO circuits of different configurations (n = 3) and controls (n = 6), with mean (standard deviation) recovery at 24 h of 15.6% (12.9) in Complete circuits, 37.9% (8.3) in Oxygenator circuits, 47.1% (8.2) in Pump circuits, and 20.6% (20.6) in controls. In CRRT circuits (n = 4) meropenem was cleared rapidly compared with controls (n = 6) with a mean recovery at 2 h of 2.36% (1.44) in circuits and 93.0% (7.1) in controls. Conclusion: Meropenem is rapidly cleared by hemodiafiltration during CRRT. There is minimal adsorption of meropenem to ECMO circuit components; however, meropenem undergoes significant degradation and/or plasma metabolism at physiological conditions. These ex vivo findings will advise pharmacists and physicians on the appropriate dosing of meropenem

Meropenem extraction by ex vivo extracorporeal life support circuits

Background: Meropenem is a broad-spectrum carbapenem-type antibiotic commonly used to treat critically ill patients infected with extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae. As many of these patients require extracorporeal membrane oxygenation (ECMO) and/or continuous renal replacement therapy (CRRT), it is important to understand how these extracorporeal life support circuits impact meropenem pharmacokinetics. Based on the physicochemical properties of meropenem, it is expected that ECMO circuits will minimally extract meropenem, while CRRT circuits will rapidly clear meropenem. The present study seeks to determine the extraction of meropenem from ex vivo ECMO and CRRT circuits and elucidate the contribution of different ECMO circuit components to extraction. Methods: Standard doses of meropenem were administered to three different configurations (n = 3 per configuration) of blood-primed ex vivo ECMO circuits and serial sampling was conducted over 24 h. Similarly, standard doses of meropenem were administered to CRRT circuits (n = 4) and serial sampling was conducted over 4 h. Meropenem was administered to separate tubes primed with circuit blood to serve as controls to account for drug degradation. Meropenem concentrations were quantified, and percent recovery was calculated for each sample. Results: Meropenem was cleared at a similar rate in ECMO circuits of different configurations (n = 3) and controls (n = 6), with mean (standard deviation) recovery at 24 h of 15.6% (12.9) in Complete circuits, 37.9% (8.3) in Oxygenator circuits, 47.1% (8.2) in Pump circuits, and 20.6% (20.6) in controls. In CRRT circuits (n = 4) meropenem was cleared rapidly compared with controls (n = 6) with a mean recovery at 2 h of 2.36% (1.44) in circuits and 93.0% (7.1) in controls. Conclusion: Meropenem is rapidly cleared by hemodiafiltration during CRRT. There is minimal adsorption of meropenem to ECMO circuit components; however, meropenem undergoes significant degradation and/or plasma metabolism at physiological conditions. These ex vivo findings will advise pharmacists and physicians on the appropriate dosing of meropenem

Diet modulates cecum bacterial diversity and physiological phenotypes across the BXD mouse genetic reference population

The BXD family has become one of the preeminent genetic reference populations to understand the genetic and environmental control of phenotypic variation. Here we evaluate the responses to different levels of fat in the diet using both chow diet (CD, 13-18% fat) and a high-fat diet (HFD, 45-60% fat). We studied cohorts of BXD strains, both inbred parents C57BL/6J and DBA/2J (commonly known as B6 and D2, respectively), as well as B6D2 and D2B6 reciprocal F1 hybrids. The comparative impact of genetic and dietary factors was analyzed by profiling a range of phenotypes, most prominently their cecum bacterial composition. The parents of the BXDs and F1 hybrids express limited differences in terms of weight and body fat gain on CD. In contrast, the strain differences on HFD are substantial for percent body fat, with DBA/2J accumulating 12.5% more fat than C57BL/6J (P < 0.0001). The F1 hybrids born to DBA/2J dams (D2B6F1) have 10.6% more body fat (P < 0.001) than those born to C57BL/6J dams. Sequence analysis of the cecum microbiota reveals important differences in bacterial composition among BXD family members with a substantial shift in composition caused by HFD. Relative to CD, the HFD induces a decline in diversity at the phylum level with a substantial increase in Firmicutes (+13.8%) and a reduction in Actinobacteria (-7.9%). In the majority of BXD strains, the HFD also increases cecal sIgA (P < 0.0001)-an important component of the adaptive immunity response against microbial pathogens. Host genetics modulates variation in cecum bacterial composition at the genus level in CD, with significant quantitative trait loci (QTLs) for Oscillibacter mapped to Chr 3 (18.7-19.2 Mb, LRS = 21.4) and for Bifidobacterium mapped to Chr 6 (89.21-89.37 Mb, LRS = 19.4). Introduction of HFD served as an environmental suppressor of these QTLs due to a reduction in the contribution of both genera (P < 0.001). Relations among liver metabolites and cecum bacterial composition were predominant in CD cohort, but these correlations do not persist following the shift to HFD. Overall, these findings demonstrate the important impact of environmental/dietary manipulation on the relationships between host genetics, gastrointestinal bacterial composition, immunological parameters, and metabolites-knowledge that will help in the understanding of the causal sources of metabolic disorders

Variation in time and magnitude of immune response and viremia in experimental challenges with Porcine circovirus 2b

Background: Porcine circovirus 2 is the primary agent responsible for inducing a group of associated diseases known as Porcine Circovirus Associated Diseases (PCVAD), which can have detrimental effects on production efficiency as well as causing significant mortality. The objective of this study was to evaluate variation in viral replication, immune response and growth across pigs (n = 974) from different crossbred lines. The approach used in this study was experimental infection with a PCV2b strain of pigs at an average of 43 days of age. Results: The sequence of the PCV2b isolate used in the challenge was similar with a cluster of PCV2b isolates known to induce PCVAD and increased mortality rates. The swine leukocyte antigen class II (SLAII) profile of the population was diverse, with nine DQB1 haplotypes being present. Individual viremia and antibody profiles during challenge demonstrate variation in magnitude and time of viral surge and immune response. The correlations between PCV2 specific antibodies and average daily gain (ADG) were relatively low and varied between - 0.14 to 0.08 for IgM and −0.02 and 0.11 for IgG. In contrast, PCV2 viremia was an important driver of ADG decline following infection; a moderate negative correlation was observed between viral load and overall ADG (r = − 0.35, P \u3c 0.001). The pigs with the lowest 10% level of viral load maintained a steady increase in weekly ADG (P \u3c 0.0001) compared to the pigs that had the 10% greatest viral load (P \u3c 0.55). In addition, the highly viremic group expressed higher IgM and IgG starting with d 14 and d 21 respectively, and higher tumor necrosis factor – alpha (TNF-α) at d 21 (P \u3c 0.005), compared to low viremic group. Conclusions: Molecular sources of the observed differences in viremia and immune response could provide a better understanding of the host factors that influence the development of PCVAD and lead to improved knowledge of swine immunity

Mean and measures of variability of phyla, genera and Operational Taxonomic Units (OTU) of the BXD strains, their parental lines C57BL/6J and DBA/2J and F1 hybrids (D2B6F1) used in QTL mapping.

<p>Mean and measures of variability of phyla, genera and Operational Taxonomic Units (OTU) of the BXD strains, their parental lines C57BL/6J and DBA/2J and F1 hybrids (D2B6F1) used in QTL mapping.</p