Benchmark of Pep2Path on 18 recently discovered NRPS BGCs.

<p>For each tag size, all possible search tags of that size in the test set of peptides (<b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003822#pcbi.1003822.s003" target="_blank">Table S1</a></b>) were used as queries. For each BGC search space size, 50 search spaces were generated from randomly selected BGCs from the same (sub)phylum that the NRP originates from. The resulting percentages represent the average number of cases in which the correct BGC ended up as the (shared) best hit across all possible sequence tags and across all possible search space permutations. Shared best hits were included because of the frequent presence of orthologous BGCs encoding the same molecule in related genomes. The <i>n</i> in the left column signifies the number of test peptides large enough to be included in the analysis for this tag size; from each of these test peptides, all possible subtags were used in cases where the length of the tag is shorter than the length of the peptide.</p><p>Benchmark of Pep2Path on 18 recently discovered NRPS BGCs.</p

Outline of the NRP2Path matching process.

<p>The input for NRP2Path consists of mass shift sequences (or amino acid search tags) on the one hand, and genome sequences on the other hand. The latter are processed into databases by <i>makedb</i>, using antiSMASH and NRPSPredictor2. When a database is queried with a mass shift sequence or amino acid search tag, Pep2Path scores all possible matches between search tags and all possible assembly line configurations of each of the NRPS BGCs in the database.</p

Novel matches of NORINE-derived NRPs to BGCs detected in genome sequences.

<p>Candidate BGCs for trichotoxin, ferintoic acid, plusbacin and amphibactin B were discovered by searching within the taxonomic range of the species in which the molecules were found. The candidate BGC for tripropeptin A was discovered by searching the entire Pep2Path database.</p><p>Novel matches of NORINE-derived NRPs to BGCs detected in genome sequences.</p

Quality of NRP2Path predictions with varying sequence tag lengths and NRPSPredictor2 prediction qualities.

<p>The heat map shows the average number of correct BGC predictions for Pep2Path searches with the stendomycin sequence tag V-V-T(S)-T(S)-A-I(L)-V-G across the <i>Streptomyces hygroscopicus</i> ATCC 53653 genome (20 NRPS BGCs) or across all <i>Streptomyces</i> nucleotide entries (342 NRPS BGCs). The searches were done for all possible search subtags of 2–8 amino acids long, and for all combinations of 0–8 simulated mispredictions for the corresponding NRPS modules. Mispredictions are simulated with zero scores given by Pep2Path for sequence tags matching to these domains.</p

Matching of mass sequence tags to RiPP gene clusters using RiPP2Path.

<p>Seven out of the nine search tags resulted in unique matches in their corresponding <i>Streptomyces</i> genomes.</p><p>Matching of mass sequence tags to RiPP gene clusters using RiPP2Path.</p

Metabolic Syndrome development is associated with hepatic steatosis in both dyslipidemic and non-dyslipidemic phenotypes.

<p>The mean trajectories of the liver lipid profiles (calculated from the top 10% best trajectories from n = 1,000) are depicted for the hepatic triglyceride pool (a), hepatic free cholesterol pool (b) and the hepatic cholesteryl ester pool (c). Experimental data was obtained at the end of the study and is depicted by the black error bars representing mean ± standard deviation for each of the groups. The data from the LFD cohort is used as initial value, assuming no hepatic lipid accumulation to have occurred in this control group. Differences between groups were determined using one-way ANOVA test. When significant differences were found, Fisher’s LSD test was used as a post hoc test to determine the differences between two independent groups: * P<0.05; ** P<0.01; *** P<0.001 as compared to LFD ^# P<0.05; ^## P<0.01; ^### P<0.001 as compared to HFD ^$ P<0.05; ^$$ P<0.01; ^$$$ P<0.001 as compared to HFD+C (MetS_non-DLP).</p

<i>In vivo</i> development of the Metabolic Syndrome results in different phenotypes.

<p>Experimentally observed metabolic parameters upon dietary induction in male E3L.CETP mice over the time course of three months is displayed in two ways: in the left panels the data are expressed as mean ± standard deviation (error bars) for the low-fat diet (LFD; n = 8; light blue), high-fat diet (HFD; n = 12 (pooled from two groups n = 7 for the full time period, n = 5 until 2 months of dietary induction; dark blue) and high-fat diet with 0.25% cholesterol (HFD+C; n = 8; green) groups, whereas in the right panels the data of the animals on HFD+C are depicted for each animal individually. Individuals in this cohort were subdivided into two groups based on the plasma triglyceride (TG) and plasma total cholesterol (TC) levels. The dyslipidemic Metabolic Syndrome phenotypes are depicted in red (MetS_DLP; mice with high plasma TG and simultaneous high plasma TC at t = 3 months) and the non-dyslipidemic Metabolic Syndrome phenotypes in gray (MetS_non-DLP; mice with low plasma TG and simultaneous low plasma TC at t = 3 months). Differences between groups were determined using one-way ANOVA test. When significant differences were found, Fisher’s LSD test was used as a post hoc test to determine the differences between two independent groups: * P<0.05; ** P<0.01; *** P<0.001 HFD as compared to LFD ^# P<0.05; ^## P<0.01; ^### P<0.001 HFD+C as compared to HFD.</p

Schematic representation of the computational model MINGLeD.

<p>MINGLeD describes the metabolic pathways of glucose and lipids to describe the development of MetS. This multi-compartment framework encompasses pathways in dietary absorption, hepatic, peripheral and intestinal lipid metabolism, hepatic and plasma lipoprotein metabolism and plasma, hepatic and peripheral carbohydrate metabolism. The metabolite pools in the different tissue compartments are displayed in the frames; the corresponding metabolic fluxes are represented using the arrows. The dashed arrows represent the dietary inflow in terms of the different macronutrients derived from the experimental data. AA, amino acid; ACAT, Acyl-coenzyme A:cholesterol acyltransferase; ACoA, Acetyl CoA; BA, bile acid; C, cholesterol; CE, cholesteryl ester; CEH, cholesterol ester hydrolase; CETP, cholesteryl ester transfer protein; CM, chylomicron; DNL, <i>de novo</i> lipogenesis; (F)C, (free) cholesterol; (F)FA, (free) fatty acid; G, glucose; G6P, glucose-6-phosphate; GNG, gluconeogenesis; HDL, high density lipoprotein; TG, triglyceride; TICE, transintestinal cholesterol absorption; (V)LDL, (very) low density lipoprotein.</p

Metabolic flux trajectory analysis depicts differences among phenotypes and dyslipidemia development.

<p>Trajectory analysis reveals decreased dietary cholesterol absorption from the intestinal lumen in the non-dyslipidemic Metabolic Syndrome phenotype (a) and increased hepatic activity in the dyslipidemia Metabolic Syndrome phenotype (b-f). The median metabolic flux trajectories (calculated from the top 10% best trajectories from n = 1,000) are depicted with a solid line for the hepatic dietary cholesterol absorption from the intestinal lumen (a), hepatic (V)LDL-TG uptake from the plasma (b), hepatic fatty acid uptake from the plasma (c), hepatic bile acid synthesis from cholesterol (d), hepatic <i>de novo</i> lipogenesis (e), and hepatic β-oxidation (f). The shaded area depicts the 10% range of trajectories around the median. The low-fat diet cohort is depicted in light blue; the high-fat cohort in dark blue; the non-dyslipidemic Metabolic Syndrome phenotype in gray and the dyslipidemic Metabolic Syndrome phenotype in red. The experimental hepatic <i>de novo</i> lipogenesis (e) data are shown as black error bars that represent mean ± standard deviation.</p

MINGLeD describes metabolic phenotypes of male E3L.CETP mice upon different diets and time points.

<p>The metabolic phenotypes are depicted for three different diets (with HFD+C composed of two subgroups that emerged after two months of dietary induction) at four different time points. Model fits (colored error bars: mean ± standard deviation) of MINGLeD calibrated to the phenotype snapshots (raw, individual mouse data shown in gray) separately. Only acceptable model simulations were included, which was classified as having a weighted sum of squared errors (see Eq 1 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006145#pcbi.1006145.s004" target="_blank">S3 Note</a>) below 100.</p