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.

Bacterial proteome adaptation during fermentation in dairy environments.

The enzymatic repertoire of starter cultures belonging to the Lactococcus genus determines various important characteristics of fermented dairy products but might change in response to the substantial environmental changes in the manufacturing process. Assessing bacterial proteome adaptation in dairy and other food environments is challenging due to the high matrix-protein concentration and is even further complicated in particularly cheese by the high fat concentrations, the semi-solid state of that matrix, and the non-growing state of the bacteria. Here, we present bacterial harvesting and processing procedures that enable reproducible, high-resolution proteome determination in lactococcal cultures harvested from laboratory media, milk, and miniature Gouda cheese. Comparative proteome analysis of Lactococcus cremoris NCDO712 grown in laboratory medium and milk revealed proteome adaptations that predominantly reflect the differential (micro-)nutrient availability in these two environments. Additionally, the drastic environmental changes during cheese manufacturing only elicited subtle changes in the L. cremoris NCDO712 proteome, including modified expression levels of enzymes involved in flavour formation. The technical advances we describe offer novel opportunities to evaluate bacterial proteomes in relation to their performance in complex, protein- and/or fat-rich food matrices and highlight the potential of steering starter culture performance by preculture condition adjustments.

Associations between breast milk intake volume, macronutrient intake and infant growth in a longitudinal birth cohort: the Cambridge Baby Growth and Breastfeeding Study (CBGS-BF).

Growth patterns of breastfed infants show substantial inter-individual differences, partly influenced by breast milk (BM) nutritional composition. However, BM nutritional composition does not accurately indicate BM nutrient intakes. This study aimed to examine the associations between both BM intake volumes and macronutrient intakes with infant growth. Mother-infant dyads (n 94) were recruited into the Cambridge Baby Growth and Breastfeeding Study (CBGS-BF) from a single maternity hospital at birth; all infants received exclusive breast-feeding (EBF) for at least 6 weeks. Infant weight, length and skinfolds thicknesses (adiposity) were repeatedly measured from birth to 12 months. Post-feed BM samples were collected at 6 weeks to measure TAG (fat), lactose (carbohydrate) (both by 1H-NMR) and protein concentrations (Dumas method). BM intake volume was estimated from seventy infants between 4 and 6 weeks using dose-to-the-mother deuterium oxide (2H2O) turnover. In the full cohort and among sixty infants who received EBF for 3+ months, higher BM intake at 6 weeks was associated with initial faster growth between 0 and 6 weeks (ß + se 3·58 + 0·47 for weight and 4·53 + 0·6 for adiposity gains, both P < 0·0001) but subsequent slower growth between 3 and 12 months (ß + se - 2·27 + 0·7 for weight and -2·65 + 0·69 for adiposity gains, both P < 0·005). BM carbohydrate and protein intakes at 4-6 weeks were positively associated with early (0-6 weeks) but tended to be negatively related with later (3-12 months) adiposity gains, while BM fat intake showed no association, suggesting that carbohydrate and protein intakes may have more functional relevance to later infant growth and adiposity.

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.

Metabolomics of Milk Reflects a Negative Energy Balance in Cows.

Dairy cows can experience a negative energy balance (NEB) in early lactation when feed intake is too low to meet the energy requirements for body maintenance and milk production. Metabolic changes occur in mammary gland cells of animals experiencing a negative energy balance. We studied these metabolic changes in milk samples from dairy cows in relation to energy balance status using liquid chromatography-mass spectrometry (QQQ-LC-MS) and nuclear magnetic resonance (1H NMR). NMR and LC-MS techniques are complementary techniques that enabled a comprehensive overview of milk metabolites in our study. Energy balance and milk samples were obtained from 87 dairy cows. A total of 55 milk metabolites were reliably detected, of which 15 metabolites were positively correlated to energy balance and 20 were negatively correlated to energy balance. Cows in NEB produced more milk with increased milk fat yield and higher concentrations of citrate, cis-aconitate, creatinine, glycine, phosphocreatine, galactose-1-phosphate, glucose-1-phosphate, UDP-N-acetyl-galactosamine, UDP-N-acetyl-glucosamine, and phosphocholine but lower concentrations of choline, ethanolamine, fucose, N-acetyl-neuraminic acid, N-acetyl-glucosamine, and N-acetyl-galactosamine. During NEB, we observed an increased leakage of cellular content, increased synthesis of nucleic acids and cell membrane phospholipids, an increase in one-carbon metabolic processes, and an increase in lipid-triglyceride anabolism. Overall, both apoptosis combined with cellular renewal is paramount in the mammary gland in cows in NEB.

Molecular Dynamics and In Vitro Quantification of Safrole DNA Adducts Reveal DNA Adduct Persistence Due to Limited DNA Distortion Resulting in Inefficient Repair.

The formation and repair of N2-(trans-isosafrol-3'-yl)-2'-deoxyguanosine (S-3'-N2-dG) DNA adduct derived from the spice and herbal alkenylbenzene constituent safrole were investigated. DNA adduct formation and repair were studied in vitro and using molecular dynamics (MD) simulations. DNA adduct formation was quantified using liquid chromatography-mass spectrometry (LCMS) in wild type and NER (nucleotide excision repair) deficient CHO cells and also in HepaRG cells and primary rat hepatocytes after different periods of repair following exposure to safrole or 1'-hydroxysafrole (1'-OH safrole). The slower repair of the DNA adducts found in NER deficient cells compared to that in CHO wild type cells indicates a role for NER in repair of S-3'-N2-dG DNA adducts. However, DNA repair in liver cell models appeared to be limited, with over 90% of the adducts remaining even after 24 or 48 h recovery. In our further studies, MD simulations indicated that S-3'-N2-dG adduct formation causes only subtle changes in the DNA structure, potentially explaining inefficient activation of NER. Inefficiency of NER mediated repair of S-3'-N2-dG adducts points at persistence and potential bioaccumulation of safrole DNA adducts upon daily dietary exposure.

Cellular levels and molecular dynamics simulations of estragole DNA adducts point at inefficient repair resulting from limited distortion of the double-stranded DNA helix.

Estragole, naturally occurring in a variety of herbs and spices, can form DNA adducts after bioactivation. Estragole DNA adduct formation and repair was studied in in vitro liver cell models, and a molecular dynamics simulation was used to investigate the conformation dependent (in)efficiency of N2-(trans-isoestragol-3'-yl)-2'-deoxyguanosine (E-3'-N2-dG) DNA adduct repair. HepG2, HepaRG cells, primary rat hepatocytes and CHO cells (including CHO wild-type and three NER-deficient mutants) were exposed to 50 µM estragole or 1'-hydroxyestragole and DNA adduct formation was quantified by LC-MS immediately following exposure and after a period of repair. Results obtained from CHO cell lines indicated that NER plays a role in repair of E-3'-N2-dG adducts, however, with limited efficiency since in the CHO wt cells 80% DNA adducts remained upon 24 h repair. Inefficiency of DNA repair was also found in HepaRG cells and primary rat hepatocytes. Changes in DNA structure resulting from E-3'-N2-dG adduct formation were investigated by molecular dynamics simulations. Results from molecular dynamics simulations revealed that conformational changes in double-stranded DNA by E-3'-N2-dG adduct formation are small, providing a possible explanation for the restrained repair, which may require larger distortions in the DNA structure. NER-mediated enzymatic repair of E-3'-N2-dG DNA adducts upon exposure to estragole will be limited, providing opportunities for accumulation of damage upon repeated daily exposure. The inability of this enzymatic repair is likely due to a limited distortion of the DNA double-stranded helix resulting in inefficient activation of nucleotide excision repair.

Short communication: Prediction of hyperketonemia in dairy cows in early lactation using on-farm cow data and net energy intake by partial least square discriminant analysis.

The objectives of this study were (1) to evaluate if hyperketonemia in dairy cows (defined as plasma ß-hydroxybutyrate ≥1.0 mmol/L) can be predicted using on-farm cow data either in current or previous lactation week, and (2) to study if adding individual net energy intake (NEI) can improve the predictive ability of the model. Plasma ß-hydroxybutyrate concentration, on-farm cow data (milk yield, percentage of fat, protein and lactose, fat- and protein-corrected milk yield, body weight, body weight change, dry period length, parity, and somatic cell count), and NEI of 424 individual cows were available weekly through lactation wk 1 to 5 postpartum. To predict hyperketonemia in dairy cows, models were first trained by partial least square discriminant analysis, using on-farm cow data in the same or previous lactation week. Second, NEI was included in models to evaluate the improvement of the predictability of the models. Through leave-one trial-out cross-validation, models were evaluated by accuracy (the ratio of the sum of true positive and true negative), sensitivity (68.2% to 84.9%), specificity (61.5% to 98.7%), positive predictive value (57.7% to 98.7%), and negative predictive value (66.2% to 86.1%) to predict hyperketonemia of dairy cows. Through lactation wk 1 to 5, the accuracy to predict hyperketonemia using data in the same week was 64.4% to 85.5% (on-farm cow data only), 66.1% to 87.0% (model including NEI), and using data in the previous week was 58.5% to 82.0% (on-farm cow data only), 59.7% to 85.1% (model including NEI). An improvement of the accuracy of the model due to including NEI ranged among lactation weeks from 1.0% to 4.4% when using data in the same lactation week and 0.2% to 6.6% when using data in the previous lactation week. In conclusion, trained models via partial least square discriminant analysis have potential to predict hyperketonemia in dairy cows not only using data in the current lactation week, but also using data in the previous lactation week. Net energy intake can improve the accuracy of the model, but only to a limited extent. Besides NEI, body weight, body weight change, milk fat, and protein content were important variables to predict hyperketonemia, but their rank of importance differed across lactation weeks.

Relationship between energy balance and metabolic profiles in plasma and milk of dairy cows in early lactation.

Negative energy balance in dairy cows in early lactation is related to alteration of metabolic status. However, the relationships among energy balance, metabolic profile in plasma, and metabolic profile in milk have not been reported. In this study our aims were: (1) to reveal the metabolic profiles of plasma and milk by integrating results from nuclear magnetic resonance (NMR) with data from liquid chromatography triple quadrupole mass spectrometry (LC-MS); and (2) to investigate the relationship between energy balance and the metabolic profiles of plasma and milk. For this study 24 individual dairy cows (parity 2.5 ± 0.5; mean ± standard deviation) were studied in lactation wk 2. Body weight (mean ± standard deviation; 627.4 ± 56.4 kg) and milk yield (28.1 ± 6.7 kg/d; mean ± standard deviation) were monitored daily. Milk composition (fat, protein, and lactose) and net energy balance were calculated. Plasma and milk samples were collected and analyzed using LC-MS and NMR. From all plasma metabolites measured, 27 were correlated with energy balance. These plasma metabolites were related to body reserve mobilization from body fat, muscle, and bone; increased blood flow; and gluconeogenesis. From all milk metabolites measured, 30 were correlated with energy balance. These milk metabolites were related to cell apoptosis and cell proliferation. Nine metabolites detected in both plasma and milk were correlated with each other and with energy balance. These metabolites were mainly related to hyperketonemia; ß-oxidation of fatty acids; and one-carbon metabolism. The metabolic profiles of plasma and milk provide an in-depth insight into the physiological pathways of dairy cows in negative energy balance in early lactation. In addition to the classical indicators for energy balance (e.g., ß-hydroxybutyrate, acetone, and glucose), the current study presents some new metabolites (e.g., glycine in plasma and milk; kynurenine, panthothenate, or arginine in plasma) in lactating dairy cows that are related to energy balance and may be of interest as new indicators for energy balance.

Human Milk Short-Chain Fatty Acid Composition is Associated with Adiposity Outcomes in Infants.

BACKGROUND: Presumed benefits of human milk (HM) in avoiding rapid infancy weight gain and later obesity could relate to its nutrient composition. However, data on breast milk composition and its relation with growth are sparse. OBJECTIVE: We investigated whether short-chain fatty acids (SCFAs), known to be present in HM and linked to energy metabolism, are associated with infancy anthropometrics. METHODS: In a prospective birth cohort, HM hindmilk samples were collected from 619 lactating mothers at 4-8 wk postnatally [median (IQR) age: 33.9 (31.3-36.5) y, body mass index (BMI) (kg/m2): 22.8 (20.9-25.2)]. Their offspring, born at 40.1 (39.1-41.0) wk gestation with weight 3.56 (3.22-3.87) kg and 51% male, were assessed with measurement of weight, length, and skinfold thickness at ages 3, 12, and 24 mo, and transformed to age- and sex-adjusted z scores. HM SCFAs were measured by 1H-nuclear magnetic resonance spectroscopy (NMR) and GC-MS. Multivariable linear regression models were conducted to analyze the relations between NMR HM SCFAs and infancy growth parameters with adjustment for potential confounders. RESULTS: NMR peaks for HM butyrate, acetate, and formic acid, but not propionate, were detected. Butyrate peaks were 17.8% higher in HM from exclusively breastfeeding mothers than mixed-feeding mothers (P = 0.003). HM butyrate peak values were negatively associated with changes in infant weight (standardized B  = -0.10, P = 0.019) and BMI (B = -0.10, P = 0.018) between 3 and 12 mo, and negatively associated with BMI (B = -0.10, P = 0.018) and mean skinfold thickness (B = -0.10, P = 0.049) at age 12 mo. HM formic acid peak values showed a consistent negative association with infant BMI at all time points (B < = -0.10, P < = 0.014), whereas HM acetate was negatively associated with skinfold thickness at 3 mo (B = -0.10, P = 0.028) and 24 mo (B = -0.10, P = 0.036). CONCLUSIONS: These results suggest that HM SCFAs play a beneficial role in weight gain and adiposity during infancy. Further knowledge of HM SCFA function may inform future strategies to support healthy growth.

Risk assessment of herbal supplements containing ingredients that are genotoxic and carcinogenic.

Botanicals and botanical preparations including plant food supplements as well as medicinal herbal supplements can contain possible beneficial health compounds, but also ingredients of concern. Compounds that are both genotoxic and carcinogenic have been found in herbal supplements and may raise a safety concern. Genotoxic carcinogens that can be present in botanicals and botanical preprations include especially pyrrolizidine alkaloids (PAs), aristolochic acids (AAs) and alkenylbenzenes (ABs). The present manuscript provides an overview of the levels of these compounds reported to date to be present in herbal supplements with an associated risk assessment. Exposure was estimated based on levels of PAs, AAs and ABs in individual supplements and their proposed uses. In addition a probabilistic exposure assessment was performed based on the distribution of the level of the compounds of concern in the food supplements and of the recommended uses, resulting in 5th to 95th percentile consumer exposure values. To evaluate the risk of these exposures, the margin of exposure (MOE) approach for lifetime exposure was used. To correct exposure estimates for shorter than lifetime exposure, Haber's rule as a first tier approach was applied. It is concluded that the proposed uses and use levels as well as the presence of AAs, ABs and PAs in food supplements should be carefully monitored to manage potential consumer risks. More information on estimated daily intake resulting from supplement use, as well as consequences of concomitant exposure will further improve the risk evaluation of products containing these compounds of concern.

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 ß-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.

Modification of chrysanthemum odour and taste with chrysanthemol synthase induces strong dual resistance against cotton aphids.

Aphids are pests of chrysanthemum that employ plant volatiles to select host plants and ingest cell contents to probe host quality before engaging in prolonged feeding and reproduction. Changes in volatile and nonvolatile metabolite profiles can disrupt aphid-plant interactions and provide new methods of pest control. Chrysanthemol synthase (CHS) from Tanacetum cinerariifolium represents the first committed step in the biosynthesis of pyrethrin ester insecticides, but no biological role for the chrysanthemol product alone has yet been documented. In this study, the TcCHS gene was over-expressed in Chrysanthemum morifolium and resulted in both the emission of volatile chrysanthemol (ca. 47 pmol/h/gFW) and accumulation of a chrysanthemol glycoside derivative, identified by NMR as chrysanthemyl-6-O-malonyl-ß-D-glucopyranoside (ca. 1.1 mM), with no detrimental phenotypic effects. Dual-choice assays separately assaying these compounds in pure form and as part of the headspace and extract demonstrated independent bioactivity of both components against the cotton aphid (Aphis gossypii). Performance assays showed that the TcCHS plants significantly reduced aphid reproduction, consistent with disturbance of aphid probing activities on these plants as revealed by electropenetrogram (EPG) studies. In open-field trials, aphid population development was very strongly impaired demonstrating the robustness and high impact of the trait. The results suggest that expression of the TcCHS gene induces a dual defence system, with both repellence by chrysanthemol odour and deterrence by its nonvolatile glycoside, introducing a promising new option for engineering aphid control into plants.

Identifying anti-HSV compounds from unrelated plants using NMR and LC-MS metabolomic analysis.

INTRODUCTION: Plants have been used to treat various ailments and diseases, including viral infections. Often activity is reported after screening plants traditionally used, without identifying the active principles. OBJECTIVES: This study investigated the use of metabolomics to identify common compound groups or compounds from unrelated plants, but with similar reported biological activity. Plants with anti-viral activities against Herpes Simplex Virus (HSV), Cytomegalovirus (CMV) and Human Immunodeficiency Virus (HIV) were collected and analysed. A few non-active plants, with no reported anti-viral activity were included as control samples. METHODS: 1H-NMR and LC-MS metabolomic analysis were conducted, to determine the chemical similarity between plants with similar activity using SIMCA and XCMS online. RESULTS: Plants with anti-HSV, anti-HIV and anti-CMV activity, presented specific clusters, with the non-active samples separating from the active samples. The anti-HSV group presented a clear contribution plot and chlorogenic acid was identified by NMR. LC-MS metabolomic analysis confirmed the NMR results and furthermore identified several chlorogenic acid isomers including the main substructures of chlorogenic acids. CONCLUSION: Metabolomic analysis on unrelated plants with similar activity can be used to identify the active compound groups or compounds, thereby eliminating the need for screening of plants to determine biological activity, additionally providing information on possible active principles. The two analytical methods identified chlorogenic acids and its building blocks as common and important compounds within plants with anti-HSV activity. Intensified research on plants containing chlorogenic acids should be the focus of future research for development of accessible anti-HSV treatments.

Type 2 diabetes-related proteins derived from an in vitro model of inflamed fat tissue.

Currently, there is a worldwide increase of patients with type 2 diabetes (T2D). During the progression of healthy obese to T2D status, there is an influx of immune cells, in particular macrophages, into visceral adipose tissue, accompanied by an increase of inflammatory cytokines, such as, IL6, TNFα and Hp. To get a better insight in the underlying mechanisms, we performed a quantitative LCMS analysis on a modified in vitro assay, combining 3T3L1 adipocytes and activated RAW264.7 macrophages, thus mimicking inflamed adipose tissue. Clinically known proteins, e.g. IL6, TNFα, AdipoQ, complement factor C3, B and D were identified, thus confirming the assay. In addition, we found 54 new proteins that can potentially be used for research into the mechanism of T2D. Comparison of our results to a study on human visceral fat of obese non-diabetic and obese diabetic subjects, indicated that AUH, NAGK, pCYT2, NNMT, STK39 and CSNK2A2 might indeed be linked to insulin resistance in humans. Moreover, the expression of some of these genes was also altered in human blood samples at early or later stages of insulin desensitization. Overall, we conclude that the direct contact co-culture of 3T3L1 adipocytes with activated macrophages could be a mechanistically relevant and partially translational model of inflamed visceral adipose tissue.

Use of proteomics to detect sex-related differences in effects of toxicants: implications for using proteomics in toxicology.

This review provides an overview of results obtained when using proteome analysis for detecting sex-based differences in response to toxicants. It reveals implications to be taken into account when considering the use of proteomics in toxicological studies. It appears that results may differ when studying the same chemical in the same species in different target tissues. Another result of interest is the limited dose-response behavior of differential abundance patterns observed in studies where more than one dose level is tested. It is concluded that use of proteomics to study differences in modes of action of toxic compounds is an active area of research. The examples from use of proteomics to study sex-dependent differences also reveal that further studies are needed to provide reliable insight in modes of action, novel biomarkers or even novel therapies. To eventually reach this aim for this and other toxicological endpoints, it is essential to consider background variability, consequences of timing of toxicant administration, dose-response behavior, relevant species and target organ, species and organ variability and the presence of proteoforms.

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.

Modulation of the gut microbiota impacts nonalcoholic fatty liver disease: a potential role for bile acids.

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, yet the pathogenesis of NAFLD is only partially understood. Here, we investigated the role of the gut bacteria in NAFLD by stimulating the gut bacteria via feeding mice the fermentable dietary fiber, guar gum (GG), and suppressing the gut bacteria via chronic oral administration of antibiotics. GG feeding profoundly altered the gut microbiota composition, in parallel with reduced diet-induced obesity and improved glucose tolerance. Strikingly, despite reducing adipose tissue mass and inflammation, GG enhanced hepatic inflammation and fibrosis, concurrent with markedly elevated plasma and hepatic bile acid levels. Consistent with a role of elevated bile acids in the liver phenotype, treatment of mice with taurocholic acid stimulated hepatic inflammation and fibrosis. In contrast to GG, chronic oral administration of antibiotics effectively suppressed the gut bacteria, decreased portal secondary bile acid levels, and attenuated hepatic inflammation and fibrosis. Neither GG nor antibiotics influenced plasma lipopolysaccharide levels. In conclusion, our data indicate a causal link between changes in gut microbiota and hepatic inflammation and fibrosis in a mouse model of NAFLD, possibly via alterations in bile acids.

Transcriptional Analysis of serk1 and serk3 Coreceptor Mutants.

Somatic embryogenesis receptor kinases (SERKs) are ligand-binding coreceptors that are able to combine with different ligand-perceiving receptors such as BRASSINOSTEROID INSENSITIVE1 (BRI1) and FLAGELLIN-SENSITIVE2. Phenotypical analysis of serk single mutants is not straightforward because multiple pathways can be affected, while redundancy is observed for a single phenotype. For example, serk1serk3 double mutant roots are insensitive toward brassinosteroids but have a phenotype different from bri1 mutant roots. To decipher these effects, 4-d-old Arabidopsis (Arabidopsis thaliana) roots were studied using microarray analysis. A total of 698 genes, involved in multiple biological processes, were found to be differentially regulated in serk1-3serk3-2 double mutants. About half of these are related to brassinosteroid signaling. The remainder appear to be unlinked to brassinosteroids and related to primary and secondary metabolism. In addition, methionine-derived glucosinolate biosynthesis genes are up-regulated, which was verified by metabolite profiling. The results also show that the gene expression pattern in serk3-2 mutant roots is similar to that of the serk1-3serk3-2 double mutant roots. This confirms the existence of partial redundancy between SERK3 and SERK1 as well as the promoting or repressive activity of a single coreceptor in multiple simultaneously active pathways.

Physiologically based kinetic modeling of the bioactivation of myristicin.

The present study describes physiologically based kinetic (PBK) models for the alkenylbenzene myristicin that were developed by extension of the PBK models for the structurally related alkenylbenzene safrole in rat and human. The newly developed myristicin models revealed that the formation of the proximate carcinogenic metabolite 1'-hydroxymyristicin in liver is at most 1.8 fold higher in rat than in human and limited for the ultimate carcinogenic metabolite 1'-sulfoxymyristicin to (2.8-4.0)-fold higher in human. In addition, a comparison was made between the relative importance of bioactivation for myristicin and safrole. Model predictions indicate that for these related compounds, the formation of the 1'-sulfoxy metabolites in rat and human liver is comparable with a difference of <2.2-fold over a wide dose range. The results from this PBK analysis support that risk assessment of myristicin may be based on the BMDL10 derived for safrole of 1.9-5.1 mg/kg bw per day. Using an estimated daily intake of myristicin of 0.0019 mg/kg bw per day resulting from the use of herbs and spices, this results in MOE values for myristicin that amount to 1000-2700, indicating a priority for risk management. The results obtained illustrate that PBK modeling provides insight into possible species differences in the metabolic activation of myristicin. Moreover, they provide an example of how PBK modeling can facilitate a read-across in risk assessment from a compound for which in vivo toxicity studies are available to a related compound for which tumor data are not reported, thus contributing to alternatives in animal testing.