Metabolomic study of obesity and its treatment with palmitoylated

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Metabolomic study of obesity and its treatment with palmitoylated prolactin-releasing peptide analog in spontaneously hypertensive and normotensive rats Martina Cermakova, Helena Pelantová, Barbora Neprasova, Blanka Sediva, Lenka Maletinska, Jaroslav Kunes, Petra Tomasova, Blanka Zelezna, and Marek Kuzma J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00964 • Publication Date (Web): 27 Feb 2019 Downloaded from http://pubs.acs.org on March 1, 2019

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Journal of Proteome Research

Metabolomic study of obesity and its treatment with palmitoylated prolactin-releasing peptide analog in spontaneously hypertensive and normotensive rats Martina Čermáková1,2, Helena Pelantová1, Barbora Neprašová3,4, Blanka Šedivá1,5, Lenka Maletínská3, Jaroslav Kuneš3,4, Petra Tomášová1,6, Blanka Železná3, and Marek Kuzma1,*

1

Institute of Microbiology, Czech Academy of Sciences, Vídeňská 1083, 142 20, Prague 4, Czech Republic

2

Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague 6, Czech Republic 3

Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo nám. 2, 166 10, Prague 6, Czech Republic

4

Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, 142 20, Prague 4, Czech Republic

5

Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14, Plzeň, Czech Republic

ACS Paragon Plus Environment

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Fourth Medical Department, First Faculty of Medicine, Charles University in Prague and General University Hospital, U nemocnice 1, 128 08 Praha 2, Czech Republic

* corresponding author's e-mail: [email protected]

ABSTRACT

In this study, the combination of metabolomics and standard biochemical and biometric parameters was used to describe the metabolic effects of diet-induced obesity and its treatment with the novel anti-obesity

compound

palm11-PrRP31

(palmitoylated

prolactin-releasing

peptide)

in

spontaneously hypertensive rats (SHR) and normotensive Wistar Kyoto rats (WKY). The results showed that SHR on a high-fat (HF) diet were normoglycemic with obesity and hypertension, while WKY on the HF diet were normotensive and obese with prediabetes. NMR-based metabolomics revealed mainly several microbial cometabolites altered by the HF diet, particularly in urine. The HF diet induced similar changes in both models. However, two groups of few genotype-specific metabolites were defined: metabolites specific to the genotype at baseline (e.g. 1-methylnicotinamide, phenylacetylglycine, taurine, methylamine) and metabolites reacting specifically to the HF diet in individual genotypes (2-oxoglutarate, dimethylamine, Nbutyrylglycine, p-cresyl sulfate). The palm11-PrRP31 lowered body weight and improved biochemical and biometric parameters in both strains, and it improved glucose tolerance in WKY rats on the HF diet. In urine, the therapy induced significant decrease of formate and 1methylnicotinamide in SHR and alanine, allantoin, dimethylamine and N-butyrylglycine in WKY. Altogether, our study confirms the effectiveness of palm11-PrRP31 for anti-obesity treatment.

Keywords: NMR, metabolomics, obesity, hypertension, high-fat diet, anti-obesity therapy


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INTRODUCTION Obesity is the most prevalent metabolic problem worldwide and is a prerequisite for metabolic syndrome (MetS), a cluster of risk factors such as insulin resistance, dyslipidemia, and hypertension that together culminate in an increased risk of type 2 diabetes mellitus (TDM2) and cardiovascular diseases1–4. Unfortunately, despite tremendous efforts, there is still no efficient noninvasive therapy for obesity5–7. Several anti-obesity drugs were withdrawn from the market because of their severe psychiatric or cardiovascular side effects (for reviews, see8–10). One promising tool to treat obesity and its complications are anorexigenic peptides, which can regulate the energy balance to decrease food intake and body weight. In experimental models, these peptides showed minimal side effects during a long-term anti-obesity treatment11–15. Glucagonlike peptide-1 (GLP-1) receptor agonists are used for glycemic control in patients with TDM2 and have been studied for their weight loss effects in patients with and without diabetes. The longlasting GLP-1 receptor agonists exendin-4 and liraglutide decrease food intake and body weight in humans and animal models5,16. Moreover, liraglutide has been approved for anti-obesity treatment in the USA and Europe (Saxenda). Other peptides evaluated as potential drugs for obesity treatment are pancreatic polypeptide and peptide tyrosine tyrosine (PYY). These peptides, called gut-brain peptides, are released and affect the gastrointestinal tract then they are released into circulation and have central anorexigenic effects17. We have previously shown that prolactin-releasing peptide (PrRP) is a good candidate for antiobesity drug development13,18,19. According to the results of our recent studies, peripheral administration of PrRP analogs modified by palmitoylation or myristoylation showed a central anorexigenic effect in various animal models18,20–22. Furthermore, their long-lasting and sustained anorexigenic properties had a beneficial effect on obesity-related metabolic disturbances, and they


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exhibited prolonged stability in blood. In our previous study, a decrease in food intake and body weight (BW), as well as improved glucose tolerance, was also shown in rats with diet-induced obesity (DIO) after a two-week treatment with PrRP31 palmitoylated at the N-terminus. The identical treatment decreased only food intake and did not significantly affect BW or glucose tolerance in Zucker diabetic fatty rats21, probably because of severe leptin resistance due to a nonfunctional leptin receptor23. Another analog of PrRP31 palmitoylated at position 11 (palm11PrRP31) was tested in spontaneously hypertensive obese (SHROB) rats, a model of MetS with impaired leptin receptor signaling, and in their spontaneously hypertensive rat (SHR) controls. BW-lowering effect after the three-week treatment was observed only in SHR control group, probably because of impaired leptin receptor signaling in SHROB rats. Surprisingly, a significant improvement of glucose tolerance in both SHROB rats and SHR was observed14. One of the major risks for the development of cardiovascular and metabolic dysfunction, including obesity, prediabetes, and hypertension, is a high intake of dietary fat. Hypercaloric diets rich in lipids are widely used in experimental studies to mimic metabolic disorders that are commonly found in humans24–27. Most rodents tend to become obese and develop pathologies of MetS on specific diets28–30. One of the studied models is the SHR model, in which the animals are fed a high fat (HF) diet. SHR is a well-known experimental model of genetic hypertension, which is thought to be similar in many respects to hypertension in humans31,32. The SHR model has been proposed as a model of insulin resistance, which is susceptible to nutritional disturbances33. Oliveira et al. observed that the HF diet increased cardiac remodeling and precipitated the emergence of ventricular diastolic dysfunction in SHR26. Another study demonstrated that SHR on the HF diet developed pathologies of MetS such as increased body weight, total cholesterol, and blood pressure, as well as enhanced cardiac abnormalities34. Additionally, Dolinsky et al.


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showed that calorie restriction prevented an increase in blood pressure and cardiac hypertrophy in SHR35. Yanagihara et al. studied the effect of telmisartan, an angiotensin II receptor blocker in SHR fed the HF diet. The HF diet decreased plasma adiponectin and caused insulin resistance, hypertriglyceridemia and renal damage, which were improved by telmisartan36. Moreover, a recent study showed that the HF diet aggravated cardiac atrial, ventricular dilation and hypertrophy in SHR and caused significant atrial dilatation in both normotensive control Wistar Kyoto (WKY) rats and SHR37. In WKY rats, consumption of the HF diet for 10 weeks caused mild obesity and hypertension38. Metabolomics is an established method for the study of metabolism through the analysis of small molecules in various biofluids. Since it attempts to capture the changes on a molecular level, which could provide an explanation for the processes responsible for ongoing pathologies as well as comprehensive mechanisms of action of their therapies, metabolomics has become very popular in overall disease management39,40. Due to its robustness, ease of sample preparation and direct quantitative measurements, nuclear magnetic resonance (NMR) spectroscopy is, along with MS, the most frequently used analytical platform in metabolomics. Previously, we have shown that NMR-based metabolomics is a useful tool for monitoring antidiabetic and anti-obesity therapies effects in mouse models of DIO18,41,42. In the present study, we had two main goals: 1. to characterize the model of diet-induced obesity in SHR using a combination of biochemical and metabolomic approaches for the very first time and subsequently compare the metabolic effect of HF diet feeding between SHR and their normotensive WKY controls; 2. to explore the effect of treatment with the palm 11-PrRP31 on the metabolic status of both genotypes.


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To the best of our knowledge, such a comprehensive metabolomic characterization across these rat models and their comparison, especially the effect of HF diet feeding in SHR rats, has not been performed to date. The results from NMR-based metabolomics of urine and serum were combined with biometric, biochemical, and hormonal parameters to provide aggregate view of the effects of both the HF diet and subsequent treatment. Finally, the potential connection between discovered metabolic features of obesity with prediabetes and measured biochemical and hormonal parameters was investigated through cross-correlations.

MATERIALS AND METHODS Synthesis of the prolactin-releasing peptide analog The synthesis and purification of human palm11-PrRP31 analog (SRTHRHSMEI K(N-γ-E (Npalm))

TPDINPAWYASRGIRPVGRF-NH2)

are

described

in

our

previous

study14.

Palmitoylation at position 11 was performed as shown previously using a fully protected peptide on resin as the last step18. The purity and identity of the peptide were determined by highperformance liquid chromatography and using a Q-TOF micro MS technique (Waters, Milford, MA, USA).

Animals and diets All animal experiments followed the ethical guidelines for work with animals by the Act of the Czech Republic Nr. 246/1992 and were approved by the Committee for Experiments with Laboratory Animals of the Czech Academy of Sciences. Spontaneously hypertensive and Wistar Kyoto male rats, 4 weeks old, were obtained from Charles River (Sulzfeld, Germany). The animals were acclimatized for 1 month prior to initiation


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of research diet feeding. Rats were fed either the HF diet D12492 (60% fat kcal, 20% carbohydrate kcal, 20% protein kcal) (n=16 for each genotype) or the low-fat (LF) diet D12450B (10% fat kcal, 70% carbohydrate kcal, 20% protein kcal) (n=8 for each genotype) (Research Diets Inc., USA) and supplied with water ad libitum. The rats were housed under controlled conditions with a constant temperature of 22 ± 2 °C, relative humidity of 45-65% and a fixed daylight cycle (6 am 6 pm).

Experimental design and drug administration The experimental design with a sample collection schedule is summarized in Figure 1. Throughout the experiment, samples of biofluids (urine and blood plasma and serum) were collected twice: 1 – after 14 weeks on the HF diet before the start of the treatment (at 23 weeks of age); 2 – after 3 weeks of treatment (at 26 weeks of age at the end of the experiment). Blood serum for NMR analysis was collected at the end of the experiment only, i.e., after the treatment.

Figure 1. Study design. BW-body weight.


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Before the start of the treatment, BW was monitored once a week. At the age of 23 weeks, the urine for NMR analysis and blood plasma from the tail vessels were obtained for basic biochemical analysis. To collect the urine samples, rats were housed overnight (from 4 pm to 8 am) in individual metabolic cages (Tecniplast, Buguggiate, Italy). Animals had free access to water and no access to food while staying in the metabolic cages. A solution of NaN3 was added to each container for urine collection to prevent any bacterial contamination. All collected samples were stored at −80 °C until analyses. Next, the following six experimental groups were randomly established (n = 8): (A) SHR HF vehicle, (B) SHR HF palm11-PrRP31, (C) SHR LF vehicle, (D) WKY HF vehicle, (E) WKY HF palm11-PrRP31, and (F) WKY LF vehicle, and three-week therapy by palm11-PrRP31 was initiated. Palm11-PrRP31 was dissolved in 50 mM phosphate-buffered saline, pH = 6 (PBS) (vehicle) for intraperitoneal (IP) administration and applied at a dose of 5 mg/kg once a day (at 15:00 h) in a dosing volume of 1.0 ml/kg IP for 21 days. At the end of the experiment, the rats were fasted overnight. At first, urine samples for NMR metabolomics analysis and blood samples were collected. The ethylenediaminetetraacetic acid (EDTA)-treated blood samples for biochemical analyses were collected from the tail vessels and centrifuged (10,000 x g, 5 min, 4 °C) to prepare the plasma for measurements of biochemical parameters. Blood plasma samples were stored at −20 °C until analysis. Then, the oral glucose tolerance test (OGTT) was performed followed by blood pressure measurement. Blood pressure was measured by direct puncture of the carotid artery under light ether anesthesia. The animals were connected to the PowerLab system (ADInstruments Ltd, Bella Vista, Australia) to record the blood pressure and heart rate. Serum for NMR analysis was obtained from the aorta. The animals were then sacrificed, and tissue samples were collected. The liver, heart, kidney, white adipose tissue (WAT) and brain were dissected and


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weighed. The subcutaneous adipose tissue (SCAT) and liver samples were stored at −70 °C for mRNA analysis. The urine-based model characterization was performed using samples collected before the treatment; the effect of therapy was determined from urine collected at the end of the experiment. The serum-based NMR metabolomics had only terminal samples available, which served for both serum-based model characterization and for evaluation of the effect of treatment in particular SHR and WKY rat models.

Biometric, biochemical and hormonal parameter measurement Oral glucose tolerance test The OGTT was performed after overnight fasting at the end of the experiment. At time point 0 (8 am), blood was collected from the tail vessels, and the animals were loaded with glucose at a dose of 2 g/kg perorally (PO). Blood samples were subsequently collected from the tail vessels at 15, 30, 60, 90 and 180 min thereafter. The blood glucose concentrations were determined using a Glucocard glucometer (Arkray, Kyoto, Japan).

Determination of biochemical parameters – plasma profile Free fatty acid (FFA) levels were determined by a colorimetric assay (Roche, Mannheim, Germany). Insulin was determined using commercial ultrasensitive rat insulin by the RIA assay (Millipore, St. Charles, MI, USA). The plasma triglyceride (TAG) levels were measured by enzymatic photometric determination (Erba Lachema). Leptin concentrations were determined by ELISA assay (Millipore, St. Charles, MI, USA).


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Determination of mRNA expression SCAT and liver were processed as described previously20. Determination of the mRNA expression of Acaca (acetyl CoA carboxylase), Fasn (fatty acid synthase), Srebf (sterol regulatory element-binding protein-1), Fabp4 (fatty acid binding protein 4), Lpl (lipoprotein lipase), Lipe (hormone-sensitive lipase), and Irs1,2 (insulin receptor substrate 1,2) in SCAT and Acaca, Fasn, G6pc (glucose-6-phosphatase) and Pck1 (phosphoenolpyruvate carboxykinase 1) in liver was performed using an ABI PRISM 7500 instrument (Applied Biosystems). The expression of B2m (beta-2- microglobulin) was used to compensate for variations in input RNA amounts and the efficiency of reverse transcription.

Statistical analysis The biometric, biochemical and hormonal parameters were statistically evaluated using GraphPad Prism 5 software (Graph-Pad Software, San Diego, CA, USA). The resulting data are presented as the mean ± SEM. Based on the data normality testing, an unpaired Mann-Whitney test was applied to determine the statistical significance of differences between the treated and control groups. Repeated-measures ANOVA with Bonferroni's post hoc test was used to analyze the OGTT results.

NMR-based metabolomics of urine and serum Sample preparation and NMR experiments The samples of urine and blood serum were thawed at room temperature, and the urine samples were then centrifuged at 13,684 g for 5 min. Subsequent sample preparation was run in automatic mode using the Gilson Liquid Handler 2015 (Gilson, Inc., Middleton, WI, USA). A 200-μl aliquot


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of urine supernatant was mixed with 340 μl H2O and 60 μl phosphate buffer (1.5 M KH2PO4 in D2O containing 2 mM NaN3 and 0.1% (w/v) trimethylsilyl propionic acid (TSP), pH 7.4) to reach the final H2O:D2O ratio of 9:1. A 300-μl aliquot of blood serum was mixed with the same volume of another phosphate buffer (0.142 M Na2HPO4 in H2O:D2O (4:1) containing NaN3 (0.04%) and 0.08% (w/v) TSP, pH 7.4). The NMR spectra were recorded on a 600 MHz Bruker Avance III spectrometer (Bruker BioSpin, Rheinstetten, Germany) equipped with a 5-mm TCI cryogenic probe head. The experiments were performed at 300 K and 310 K for urine and serum, respectively. Tuning, matching, shimming and adjusting of the 90° pulse length were carried out for each sample automatically. The proton spectra of both urine and serum samples were acquired using a Carr-PurcellMeiboom-Gill (CPMG) pulse program (cpmgpr1d) with presaturation during relaxation delay (4 s) using a 25-Hz saturation pulse centered on the water resonance; number of scans (NS)=64 for serum, (NS)=48 for urine; number of data points (TD)=64k; spectral width (SW)=30 ppm for serum and (SW)=20 ppm for urine; echo time=0.3 ms; and loop for T2 filter=126. CPMG was used to overcome the problem of broad signals of protein naturally occurring not only in serum but also in rodent urine43. Additionally, a short J-resolved experiment with presaturation (jresgpprqf, NS=1, SW=16 ppm, TD=8k, number of increments=40, SW=78.125 Hz in the indirect dimension, and relaxation delay=2 s) was recorded for each sample to facilitate metabolite identification, especially in crowded regions of the spectra. The structural data obtained by 1D experiments was confirmed and additionally enriched by the information from the 2D experiments 1

H-1H correlation spectroscopy (COSY, Bruker pulse sequence cosygpprqf) and

1

H-13C

heteronuclear single quantum coherence (HSQC) experiment (Bruker pulse sequence


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hsqcedetgpsisp2.4), which were applied to selected urine and serum samples. The detailed information about the sample preparation for the 2D experiments and the settings for these experiments is provided in Supplementary Information S1.

Data preprocessing The raw spectral data were processed using TopSpin 3.5 software (Bruker BioSpin, Rheinstetten, Germany). The processing was initialized by multiplication of free induction decays (FIDs) by an exponential window function (LB=0.3 Hz) before the Fourier transformation. Next, the spectra were automatically phased and referenced to TSP (δ = 0.000 ppm) in the urine or anomeric proton of α-glucose (left peak of doublet at δ = 5.236 ppm) in serum samples. The spectral baselines were adjusted according to the following two steps: to estimate the baseline within multiple shifted windows of width 200 separation units and to calculate regressions of the varying baseline to the window points using a spline approximation. The spectra were normalized by probabilistic quotient normalization (PQN)44 with the LF diet-fed group as a reference. For multivariate analysis, the normalized data within 0.2 to 10.0 ppm range were binned into 0.01 ppm intervals in MATLAB software45; regions with water (4.61 - 4.90 ppm in urine and 4.35 - 4.85 ppm in serum samples) and urea resonance (5.61-6.00 ppm in urine samples) were excluded.

Statistical analysis of the NMR data Statistical analysis of the spectra was carried out using MetaboAnalyst 3.6 software46,47 and MATLAB. Multivariate analysis: The partial least square-discriminant analysis (PLS-DA) was applied to binned spectra to determine differences in metabolite relationships between the particular groups.


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The model validation was performed using leave-one-out cross-validation and the permutation test. Univariate statistical analysis: In the next step, information extracted from 1D and 2D spectra served for metabolite identification using the Chenomx NMR Suite 7.7 database (Chenomx Inc., Edmonton, AB, Canada), the Human Metabolome Database (HMDB)48–51 and previously published metabolomic studies52–56. However, univariate statistics was then targeted to all individual signals (multiplets), which could have been reliably quantified, regardless of whether they had been identified. Application of the unpaired Mann-Whitney test was based on the result of the Lilliefors test for normality; the p value cutoff was 0.05. Only significant changes in selected metabolites were considered crucial for a description of differences between the investigated groups. The potential association between levels of particular metabolites and biochemical, biometric and hormonal parameters was evaluated using Spearman's partial correlation. RESULTS AND DISCUSSION In our work, we used NMR-based metabolomics of urine and serum along with standard biochemical and biometric parameters to follow the specific metabolic features of the SHR genotype in response to HF diet feeding with a subsequent description of the metabolic effect of novel anti-obesity therapy by a palm11-PrRP31.

Characterization of the SHR model with diet-induced obesity Biochemical aspects of HF diet feeding in SHR and WKY rats Table 1 summarizes parameters obtained after 3.5 months on the HF diet. HF diet feeding significantly increased body weight and leptin in both genotypes when compared to the respective


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control animals on the LF diet, which agrees with the literature34,38 including our previous study of DIO rats21. However, this HF diet feeding significantly increased glucose level only in WKY rats. Table 1 also shows that both fasting glucose and leptin were significantly higher in WKYHF in comparison to SHR-HF, as also described by Oliveira et al.26. A significant difference in glucose level between SHR and WKY rats was also found in groups on the LF diet, where higher glucose levels in the WKY-LF group could indicate a greater natural susceptibility of the WKY model to develop glucose intolerance. Literature data comparisons of blood glucose and insulin levels in WKY and SHR rats are contradictory. Hyperglycemia and insulin resistance were found in some studies of SHR33,57, whereas other authors did not find differences between genotypes58,59. The SHR genotype has a higher genetic predisposition to hyperglycemia and insulin resistance, which arises especially with maturation33,59. The young adult SHR (23 weeks) in this study compared to 40 weeks in the study by Natalucci et al.59 could explain why hyperglycemia was not observed in SHR in our study. Additional studies are needed to clarify these findings. Table 1. Body weight, glucose and leptin levels in WKY and SHR rats fed LF or HF diet prior the therapy. SHR-LF

SHR-HF

WKY-LF

WKY-HF

Body weight [g]

343.4 ± 11.5

401.4 ± 6.6**

341.3 ± 2.4

406.9 ± 5.7***

Glucose [mmol/l]

4.43 ± 0.08$$

4.44 ± 0.06###

5.06 ± 0.14

5.51 ± 0.13*

Leptin [ng/ml]

2.03 ± 0.27

4.89 ± 0.71**#

1.66 ± 0.14

8.12 ± 0.86***

Statistical analysis was performed using unpaired Mann-Whitney test, n=8 animals/group; the values are expressed as mean ± SEM; statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001 vs. LF diet-fed group in each genotype; #p < 0.05, # # p < 0.01, # # # p < 0.001 vs. WKY-HF; $p < 0.05, $$p < 0.01, $$$p < 0.001 vs. WKY-LF.

Urine-based metabolomic characterization of diet-induced obesity in SHR and WKY rats


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To obtain an overall image of the effects of the HF diet on the urine metabolome depending on the genotype, we applied a multivariate statistical approach across both rat models. The PLS-DA analysis of urine spectra showed substantial differences between particular groups of samples (Figure 2). The model tended to be determined in the first component by diet (48.2%) and in the second component by genotype (18.1%). The resulting model suggested that both factors - diet and genotype - could be described by a unique combination of metabolites.

Figure 2. Scores plot of PLS-DA model of urine samples from SHR and WKY rats on HF and LF diet (n=8 animals/group). The model cross validation results: number of components: 2; accuracy: 0.875; R2 = 0.837; Q2 = 0.770; p value of permutation test (2000 repetitions) < 0.001). SHR-HF group is marked in red, SHR-LF in green, WKY-HF in orange, WKY-LF in blue. The representative spectrum of urine with the respective signal assignment is shown in Figure S1 and Table S1 in Supplementary Information (SI)). Taken together, we were able to assign 38 metabolites. Next, the univariate analysis revealed the individual changes triggered by the HF diet. First, we wanted to highlight the changes induced in the SHR genotype, which have not been described thus


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far. Table 2 reports all the metabolites that were significantly affected by the HF diet in SHR. Additionally, the effects of the HF diet in WKY have been extensively reported. The magnitude of the HF diet effect is presented as a percentage decrease or increase in relation to the LF controls; only changes higher than ± 15% were taken into account. Table 2. High fat diet-induced changes in urine metabolome of SHR and WKY rats.

Metabolite 1-Methylnicotinamide 2-Deoxyuridine 2-Hydroxyisobutyrate 2-Oxoglutarate 3-Hydroxybutyrate 3-Indoxylsulfate Allantoin Citrate Creatinine Dimethylamine Dimethylglycine Formate Fumarate Glucose Hippurate Lactate Mannose Methylamine N-butyrylglycine p-Cresyl glucuronide p-Cresyl sulfate Phenylacetylglycine Tartrate Taurine Trigonelline Unknown doublet (1.05 ppm) Unknown doublet (4.13 ppm) Unknown doublet (6.87 ppm) Unknown singlet (7.68 ppm)

SHR (HF-LF)/LF [%] 19 70 -34 -45 3 -33 18 -49 23 2 -31 -16 -75 55 -84 -21 66 -45 -33 -55 -14 -62 ↑ 142 -79 -48 46 -45 18

p value 0.005 0.007 0.007 0.002 n.s. 0.005 0.001 0.021 0.038 n.s. 0.007 n.s. < 0.001 < 0.001 < 0.001 0.038 0.007 < 0.001 < 0.001 0.005 n.s. < 0.001 0.005 < 0.001 < 0.001 0.003 < 0.001 < 0.001 < 0.001

WKY (HF-LF)/LF [%] ↑ 96 20 -3 27 -48 26 -35 46 37 -18 -38 -49 65 -81 21 48 -34 -12 -67 -26 -67 ↑ 295 -84 -38 50 -46 26

p value 0.010 < 0.001 n.s. n.s. 0.002 < 0.001 < 0.001 0.015 < 0.001 0.001 0.028 0.005 0.005 < 0.001 < 0.001 0.007 < 0.001 < 0.001 n.s. < 0.001 0.021 < 0.001 < 0.001 < 0.001 < 0.001 0.001 < 0.001 < 0.001 < 0.001

The significance was calculated using unpaired Mann-Whitney test; n.s., nonsignificant. The values are expressed as the change of mean. ↑ significant increase was not quantified, because the value in lean group was very close to detection limit.


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The most remarkable changes in urinary metabolic levels, evaluated simultaneously in both SHR and WKY DIO models, are presented using box-plots in Figure 3.


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Figure 3. The summary of the most remarkable metabolites changes induced by HF diet in SHR and normotensive WKY controls. The significance was calculated using unpaired Mann-Whitney test. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001. The comparison of SHR and WKY DIO models revealed the analogous metabolic response to the HF diet feeding in both genotypes. Although the direction of the changes remained the same, their significance and intensity as well as the initial concentration of particular metabolites depended on the individual genotype. However, it is important to realize that unlike the comprehensive PLS-DA models (Figure 2), the univariate analysis described the SHR and WKY DIO models using only a limited set of identified metabolites. To verify that the univariate approach with selected metabolites was capable of capturing the particular group separation like the multivariate statistics and without omitting any significantly different features, we carried out PLS-DA using only this set of metabolites. The resulting PLS-DA model of urine is shown in Figure S2 (SI). Similarly, to the bin-based model of urine (Figure 2), the particular groups were almost completely separated, and the cross-validation confirmed the quality of this model obtained from the selected metabolites only. In urine, several substantial differences between genotypes and diets could be identified even by simple visual inspection of the spectra, as shown in Figure 4. The selected regions of the average spectra demonstrated that the level of 1-methylnicotinamide (1-MNA) was mainly genotypespecific, while trigonelline, hippurate and tartrate levels were evidently associated with the diet.


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Figure 4. Considerable differences in urinary metabolites between particular experimental groups visible by simple visual inspection of the spectra.

As previously indicated by the raw spectra (Figure 4), the nicotinamide metabolites 1-MNA and trigonelline seemed to be highly sensitive to both different diets as well as different genotypes. While the trigonelline levels were predominantly driven by diet in both genotypes, the 1-MNA levels differed primarily between the two genotypes, although the effect of diet in particular genotypes was significant. The HF diet-induced decrease in trigonelline in both genotypes agrees with our previous study using monosodium glutamate (MSG)-induced obesity in NMRI mice, in which we also observed a decline in trigonelline in obese subjects41. Trigonelline is synthetized by the gut microbiota through the methionine cycle and is involved in lipid and carbohydrate metabolism60. The significant increase in the 1-MNA level, which was observed in both the SHR and WKY obese models, has also been reported in MSG obese mice41 as well as diet-induced obese C57BL/6J mice61. Salek et al. highlighted 1-MNA as a possible biomarker of TDM2 progression62. However, the differences between genotypes were much larger than those induced by the HF diet, which might predict the higher genetic predisposition of SHR to hyperglycemia and insulin resistance in older animals33,59.


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In our study, the decrease in several microbial cometabolites, such as the p-cresol conjugates pcresol sulfate (pCS), p-cresol glucuronide (pCG) and 3-indoxylsulfate, hippurate and phenylacetylglycine (PAG), resulted from HF diet feeding, which seems to be in compliance with the previously mentioned theory of a metabolic dysfunction-induced reduction of microbial diversity63. This result is not surprising, as the absorption and transport of dietary phenolic compounds, which are the common precursors of the above-mentioned microbial cometabolites, are reduced in TDM2 and obesity in both humans and rodents64. Hippurate was reduced by HF diet uniformly in both genotypes, while no differences were observed between either SHR-LF and WKY-LF or SHR-HF and WKY-HF. Akira et al. observed a decrease in hippurate in SHR compared with WKY in rats between 7 and 14 weeks of age, but not in 20-week-old rats54,65, which is consistent with the absence of significant intergenotype differences in hippurate levels in our study. Moreover, several other researchers have reported decreased levels of urine hippurate in obese Zucker (fa/fa) rats62,66, HF diet-fed C57BL/6J mice67 and obese insulin-resistant humans68. Taken together, the decreased hippurate in obese phenotypes is in concordance with the given references and indicates the strong involvement of the microbiota in metabolism modified by the HF diet. PAG originates from the conjugation of glycine to phenylacetic acid, which is predominantly produced by microbes69. Similarly to hippurate, the PAG concentration significantly decreased in response to HF diet feeding in both genotypes, but the PAG levels already differed in the SHR and WKY groups on the LF diet. Previously, obese Zucker rats have been reported to have significantly lower levels of bifidobacteria, which was then strongly correlated to the concentrations of PAG and hippurate66. Akira et al. showed significant differences in PAG levels between SHR and WKY genotypes only in younger animals54,65. Except for age, the particular diet composition plays a


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crucial role and may be responsible for the discrepancies between the results of Akira´s group and our findings. Levels of 3-indoxylsulfate and pCG, other microbial cometabolites, showed patterns that were identical to PAG. Moreover, the levels of 3-indoxylsulfate and pCG were strongly positively correlated with the PAG concentration (data not shown), which may reflect similar microbial metabolic activities in the particular groups under study. Both metabolites were decreased in response to the HF diet in both genotypes, with a significant difference between genotypes at baseline (i.e., between SHR and WKY on the LF diet). Tyrosine is the primary precursor of p-cresol, which is formed in the presence of certain microbe strains and eliminated via the urine as either glucuronide or sulfate conjugate70. In our study, we detected and quantified both conjugates and found that pCG was produced in larger amounts than pCS in all groups except SHR-HF (data not shown). The concentration of urine pCG was reduced after HF diet feeding in both genotypes. However, the baseline levels differed between genotypes, with a higher level in the WKY model. An obesity-induced decrease in urine p-cresol was also found in obese ob/ob mice compared with the lean controls71. An increase in pCG has been reported previously in SHR rats in comparison to WKY rats on a standard chow diet in 5 and 14, but not at 20 weeks of age54. Accordingly, pCG production seems to change continuously, depending on age or diet. In contrast to pCG, the production of pCS was decreased in the WKYHF only but not changed in SHR-HF, when comparing to relevant genotype on LF diet. pCS levels in SHR were not affected by the HF diet, suggesting an unchanged transformation of p-cresol to its sulfate conjugate in SHR regardless of obesity development. Dimethylamine (DMA) and dimethylglycine (DMG) are formed predominantly from dietary choline. Anaerobic intestinal gut microbes catalyze the transformation of choline to


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trimethylamine, which is then converted to DMA60. In contrast, mitochondrial pathways are involved in the formation of DMG from choline72. The HF diet induced a genotype-specific increase of DMA in WKY rats only. DMA levels in SHR showed no alterations in response to the HF diet. The levels of methylamine were significantly decreased after HF diet feeding in both genotypes. The same increasing patterns have been observed in MSG-induced obese mice after 2, 5 and even 9 months after MSG administration41. Figure 3 shows that the levels of methylamine varied between SHR and WKY at baseline. The levels of DMG were decreased after HF diet administration in both genotypes, while the decrease in SHR was slightly more dramatic. This finding complies with our earlier study in C57BL mice with diet-induced obesity61. DMG levels were also reported to be negatively correlated with the AUC of OGTT. The same relationship was also observed herein (Figure 5). Kim et al. reported an association of DMG levels with diet; higher levels of DMG were found in wild-type mice on a standard chow diet compared with those on the HF diet and with HF dietresistant AHNAK-/- mice73. Taken together, the changes in dimethylamine were considered to be associated with microbial changes74. Thus, the preferential formation of dimethylamine instead of dimethylglycine may be specific for an early stage of TDM2 in WKY75. As shown in Figure 3, tartrate was detected only in the urine of HF diet-fed rats of both genotypes. Interestingly, a similar situation has been observed in the case of HF diet-fed AHNAK/-

mice and their wild-type controls73, in which tartrate was only found in HF diet-fed animals. The

primary source of tartrate is the diet. In our study, both diets contained choline bitartrate in the same amounts, and moreover, mice on the LF diet had higher food intakes than those on the HF diet (data not shown). Thus, the observed differences between HF and LF-fed mice cannot be


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explained by different food intakes. Tartaric acid might also be produced in Saccharomyces. Therefore, its presence in urine is considered to originate from intestinal yeast overgrowth76,77. Thus, the different urine tartaric acid excretion levels between LF and HF diet animals may have been caused by microbial changes accompanying the dietary switch, which only suggests the previously described HF diet-induced metabolic modifications. As one would expect, high calorie intake also significantly affected the metabolites of the tricarboxylic acid cycle (TCA cycle). The HF diet decreased the levels of citrate and fumarate in both genotypes, while the levels of 2-OG were specifically reduced only in SHR. Interestingly, succinate levels were associated with genotype only; no effect of diet was observed. However, the literature provides divergent results regarding TCA cycle changes induced by obesity. For example, Salek et al. have shown inconsistencies in particular TCA cycle metabolite levels in rats and humans62, and Akira et al. have reported decreased levels of the citrate cycle intermediates 2OG, succinate, and citrate in 7-week-old SHR in comparison to WKY65. In a subsequent study, they described either a decrease or no difference in TCA metabolite levels between SHR and WKY over the 20-week period, both on a standard chow diet54. We suspect that more comprehensive mechanisms are involved in this process, which depend on the particular diet, length of feeding, and genotype, among other factors. As expected, the glucose level excreted in urine was increased in animals on the HF compared with the LF diet in both genotypes, which agrees with our previous study in DIO mice61. The levels of the antioxidant agent taurine were increased by the HF diet in both rat genotypes. However, the baseline levels differed between the two genotypes with higher taurine levels in SHR. Akira et al. reported increased taurine levels in SHR compared with WKY at 20 weeks of age, but not in younger animals54. Moreover, taurine tended to be elevated in a stroke-prone SHR


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model in comparison to WKY in 26-week-old rats78. Therefore, the author speculated that the increasing taurine excretion was associated with an older subject age. Increased urinary taurine has been suggested to be a marker of liver damage79,80. In our study, taurine was significantly positively correlated with liver weight. Since the liver is the most metabolically active organ in the body, it would not be surprising that the altered metabolism in obese WKY and SHR negatively affected this organ and its metabolic functions. Obesity might also be characterized by permanently elevated oxidative stress. Increased allantoin has been found in oxidative stress-associated disorders such as diabetes81. Therefore, increased allantoin levels in both SHR and WKY models after HF diet feeding may indicate a reaction of the organism to diet-induced oxidative stress. Interestingly, no difference was observed in the reaction of particular genotypes to oxidative stress induced by the HF diet. Acylglycines such as N-butyrylglycine are formed by the conjugation of acyl-CoA esters with glycine and are usually used as diagnostic markers of errors in fatty acid or amino acid metabolism. N-butyrylglycine but not glycine decreased after HF diet feeding only in SHR. One would therefore suspect changes in fatty acid metabolism. Mutual correlations between biochemical parameters and data obtained by NMR metabolomics were used to highlight the interconnection of these two methods. Figure 5 shows the significant correlations of relevant biochemical parameters (HF and LF diet-fed SHR and WKY at the end of the experiment) with metabolites in urine (HF and LF diet-fed SHR and WKY at the end of the experiment) for groups only on saline. We reported only those correlations, which were significant both before and after the treatment as the model characterization was made from the collection before the intervention and we wanted to ensure the metabolite levels were not affected by saline administration. Except for glucose, the correlation of particular biochemical parameters with the


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set of metabolites displayed the same patterns. In comparison, the correlations of glucose with the same set of metabolites was always reversed. The significant correlations found between substantially changed urine metabolites and relevant biochemical and biometric parameters validate the association of the observed alterations with the metabolic disturbances induced by the HF diet.

Figure 5. The significant correlations (p < 0.05) of urine metabolites levels with biochemical and biometric parameters. Color code: blue – negative correlations, red – positive correlations. The numbers express correlation coefficients. The strength of correlation is visualized by color intensity. BW – body weight, AUC – area under the curve, ½ WAT – the half of white adipose tissue.


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Serum-based metabolomic characterization of diet-induced obesity in WKY and SHR rats The serum sample distribution visualized by PLS-DA in Figure 6 did not resemble the distribution of urinary data. The SHR rats on both diets and WKY controls on the HF diet seemed to have a large number of common features, which differentiated them from the group of WKY on the LF diet. The very similar serum metabolic signature of HF diet feeding in both genotypes, as well as SHR on the LF diet, suggested that HF-diet induced similar metabolic changes in serum as hypertension, resulting in much more associated serum metabolic fingerprints of these three groups in comparison to the urine ones.

Figure 6. PLS-DA model of serum samples from SHR and WKY rats on HF and LF diet (n=8 animals/group). The model cross validation results: number of components: 3; accuracy: 0.625; R2 = 0.951; Q2 = 0.902; p value of permutation test (2000 repetitions) < 0.001). SHR-HF group is marked in red, SHR-LF in green, WKY-HF in orange, WKY-LF in blue.

The sample spectrum of serum with identified signals (in total 24 metabolites identified) is shown in Figure S3 (SI) and Table S2 (SI).


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Unlike a large number of altered metabolites in urine, the univariate analysis of particular metabolites identified in serum revealed only several alterations after HF diet feeding in SHR and WKY (Table S3). A significant decrease of approximately 20% along with changes in signal shapes was observed in the intensity of the broad resonances approximately 0.78-0.90, 1.21-1.31, 2.7-2.83, and 5.25-5.36 ppm. These resonances are usually assigned to serum lipids and lipoproteins82. However, their proper interpretation and assignment require further precise analysis, which is beyond the scope of the present work. The level of 3-hydroxybutyrate increased with HF diet feeding only in SHR, but not WKY rats. The accumulation of this ketone body could lead to diabetic ketoacidosis, a severe complication of TDM2. Salek et al. also described elevated 3-hydroxybutyrate in diabetic humans and rodents62. The correlations of serum metabolites with standard biochemical and biometric parameters are shown in Figure S4 in SI. Based on the results of the univariate analysis, it is obvious that no striking changes could have been captured in serum, as previously indicated by the PLS-DA model.

The effect of novel anti-obesity therapy on both SHR and WKY rats Biochemical effects of the therapy The novel anti-obesity agent palm11-PrRP31 had significant effects on basic metabolic parameters. Among them, the most significant was weight loss induced by the 3-week treatment in both genotypes (Figure 7). In addition to body weight loss, the palm11-PrRP31 analog induced changes in several other obesity and diabetes-related biochemical and biometric parameters, as reviewed in Table 3. The remaining biometric and biochemical results, which were not directly associated with obesity and diabetes, are summarized in Table S4 in SI.


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Body weight development during the experiment, including significant weight reduction as a consequence of the therapy, is shown in Figure 7A and B for SHR and WKY rats, respectively. Additionally, Figure 7 also shows the weight difference between the group on the LF and HF diets. In WKY, significant differences were obvious beginning at 13 weeks of age, and in SHR beginning at 14 weeks of age.

Figure 7. The development of body weight in the course of the whole experiment in SHR (A) and WKY (B) rats. Palm11-PrPR31 was administered intraperitoneally at a dose of 5 mg/kg once a day for 21 days. Body weight was monitored once a week before treatment and every 2 days during drug application. Data are presented as means ± S.E.M. Statistical analysis was performed by repeated measures ANOVA with Bonferroni post hoc test, significance is *p < 0.05, **p < 0.01, ***p < 0.001 vs. the HF diet vehicle-treated group (n=8).

Food intake was reduced in both WKY and SHR (data not shown) after the therapy. The significant loss of body weight in both genotypes was accompanied by a significant decrease in leptin levels in SHR and a nonsignificant decrease in leptin levels in WKY rats. The amount of WAT was significantly decreased in WKY after the treatment; in SHR, the decrease did not reach significance. Furthermore, a significant decrease in liver weight in SHR was observed after the


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treatment (Table 3). These results agree with previously published data on DIO mice18,22,83 or rat models of obesity and its complications14,21. Our previous study has shown that two weeks of subcutaneous administration of palmitoylated-PrRP31 and myristoylated-PrRP20 decrease food intake and body weight, improve metabolic parameters, and attenuate lipogenesis in mice with DIO20. In our other study, two linkers at position 11 or/and the N-terminus were applied for the palmitoylation of PrRP31. Administration of the two analogs decreased the body weight of the DIO mice in two weeks. The significant lowering of body fat was confirmed by the decrease in subcutaneous and perirenal fat pad weights and the lowering of leptin levels, as well as by the decrease in liver weight18. Moreover, Holubová et al. demonstrated that palm11-PrRP31 positively affected feeding, body weight, and leptin-related hypothalamic signaling, not only after 28 days of treatment, but even 14 days after the termination of a 14-day-long treatment, without the yo-yo effect observed in DIO mice83. In DIO but not in ZDF rats with severe leptin resistance due to a nonfunctional leptin receptor, the decrease in BW was observed after a two-week treatment with PrRP31 that was palmitoylated at the N-terminus21. Furthermore, the BW-lowering effect after the three-week treatment with palm11-PrRP31 was observed in SHR but not in SHROB, probably due to the impaired leptin receptor signaling in SHROB rats14. WKY rats fed the LF diet in this study were normotensive, and the mean arterial pressure (MAP) in SHR fed the LF diet was significantly increased compared with WKY-LF. Neither the HF diet nor the treatment significantly changed MAP in both genotypes. Insulin plasma levels were significantly increased in the WKY-HF model compared with the LF controls. The treatment lowered insulin levels nonsignificantly, probably because of the short duration of the treatment. In SHR, we did not detect changes in insulin levels. This result is supported by some studies in which SHR did not show significant differences in insulin sensitivity with respect to normotensive WKY rats58,59. The plasma level of FFA were


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increased after the treatment in SHR rats similarly to the FFA levels in our previous study of SHR and SHROB rats14. Plasma levels of TAG were not changed after the treatment in either model.

Table 3. Biochemical and biometric parameters measured in fasted SHR and WKY rats at the end of the experiment.

SHR-LF

SHR-HF

SHR- palm11WKY-LF PrRP31

WKY-HF

WKY- palm11PrRP31

BW [g]

339.8 ± 11.8

427.5 ± 11.2**

361.1 ± 4.8###

339.6 ± 2.9

413.6 ± 5.9***

381.9 ± 5.9##

Glucose [mmol/l]

4.51 ± 0.19

4.19 ± 0.15

4.05 ± 0.07

5.06 ± 0.08

5.10 ± 0.23

5.61 ± 0.18

Insulin [ng/ml]

0.88 ± 0.17

0.64 ± 0.10

0.61 ± 0.07

0.98 ± 0.13

2.63 ± 0.25***

1.97 ± 0.27

FFA [mmol/l]

1.36 ± 0.11

1.18 ± 0.06

1.45 ± 0.07#

0.87 ± 0.04

0.62 ± 0.04**

0.75 ± 0.05

leptin [ng/ml]

2.45 ± 0.30

5.73 ± 0.79***

2.12 ± 0.39###

1.48 ± 0.06

6.39 ± 0.64***

4.68 ± 0.48

TAG [mmol/l]

0.13 ± 0.03

0.10 ± 0.03

0.08 ± 0.01

0.85 ± 0.04

0.76 ± 0.04

0.72 ± 0.04

½ WAT [g]

1.84 ± 0.17

4.02 ± 0.34***

3.30 ± 0.18

1.99 ± 0.07

4.81 ± 0.22***

4.01 ± 0.20#

liver [g]

9.25 ± 0.30

10.04 ± 0.18

8.39 ± 0.13###

7.65 ± 0.14

7.76 ± 0.18

7.47 ± 0.19

OGTT–ΔAUC [mmol/l*min]

467.9 ± 32.50

594.0 ± 33.57*

544.4 ± 31.4

186.1 ± 20.1

453.6 ± 51.5**

197.6 ± 14.05##

MAP [mmHg]

150.5 ± 9.2

132.3 ± 4.2

137.6 ± 9.3

97.9 ± 5.2

91.5 ± 4.9

100.3 ± 9.5

The significance was calculated using unpaired Mann-Whitney test. The values are expressed as mean ± SEM. Significance: *p < 0.05, **p < 0.01, ***p < 0.001 vs. LF diet-fed group in each genotype, # vs. treatment in HF diet-fed group in each genotype, n=8 (per group). Abbreviations: MAP - mean arterial pressure, BW – body weight, FFA – free fatty acids, TAG – triglycerides, WAT – white adipose tissue, OGTT – oral glucose tolerance test, AUC – area under the curve, MAP – mean arterial pressure.

The rats of both genotypes were normoglycemic at the end of the experiment, and the glucose levels did not differ within either genotype. However, the AUC of OGTT was significantly different depending on the diet and therapy (Table 4). In WKY rats, HF diet feeding significantly


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increased the OGTT area under the curve (AUC) and significantly lowered the OGTT-AUC in the WKY-treated group compared with WKY-HF group. In contrast, we did not detect any differences in OGTT-AUC in SHR-LF compared with SHR-HF, and only OGTT-ΔAUC in the SHR-HF group was significantly increased in comparison to the SHR-LF group. The therapy had no significant effect on glucose level development after OGTT, although it tended to be lower than in SHR on the HF diet. Figure 8A, B show the glucose curve development during OGTT in both genotypes. Evidently, glucose in both LF diet-fed WKY rats as well as treated rats was utilized much more quickly in comparison to the HF diet-fed group. In contrast, there were no substitutional changes in the OGTT curve of SHR. In our previous study, a significant glucose-lowering effect of palm11PrRP31was found in DIO rats after the OGTT test 21.

Figure 8. Oral glucose tolerance test (OGTT) at the end of the experiment. Chronic treatment with palm11-PrRP31 on oral glucose tolerance test (OGTT) response in SHR (A) and WKY (B) rats. Palm11-PrPR31 was administered intraperitoneally at a dose of 5 mg/kg once a day for 21 days. Results are shown as glucose profile. Data are presented as means ± S.E.M. Statistical analysis was performed by repeated measures ANOVA with Bonferroni post hoc test, significance is *p < 0.05, **p < 0.01, ***p < 0.001 vs. the HF diet group (n=8). Glc – glycemia.


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The effect of the therapy on mRNA expression Since the previous data indicated the higher sensitivity of WKY to the HF diet and, consequently, also to the treatment, the mRNA expression of relevant enzymes was determined only in the WKY genotype. Table 4 summarizes the significant changes induced by either the HF diet or treatment.

Table 4. The expression of mRNA of genes associated with metabolic disturbances. Gene

SCAT

Acaca/B2m

-

↓**

Fasn/B2m

↑*

↓***

Srebf1/B2m

↑*

↓***

Fabp4/B2m

-

↑**

Lpl/B2m

-

↑*

Lipe/B2m

-

↑*

Irs-1/B2m

↓***

↑***

Irs-2/B2m

↓***

↑**

Acaca/B2m

↑***

↓*

Fasn/B2m

↑***

-

G6pc/B2m

-

↓***

Pck1/B2m

-

↓**

liver

WKY-HF vs. WKY-LF

WKY-palm11-PrRP31 vs. WKY-HF

Tissue

Statistical analysis was performed by unpaired Mann-Whitney test. Significance is *p < 0.05, **p < 0.01, ***p < 0.001 vs. the respective vehicle-treated control group (n = 8). The expression of particular genes was normalized to beta-2-microglobulin (B2m). ↓ decrease, ↑ increase. SCAT - subcutaneous adipose tissue, Acaca - acetyl-CoA carboxylase 1, Lpl - lipoprotein lipase, Lipe – hormone-sensitive lipase, G6pc – glucose-6-phosphatase, Irs1,2 - insulin receptor substrate 1,2, Srebf1 - sterol regulatory element-binding protein 1, Fabp4 – fatty acid binding protein 4, Fasn fatty acid synthase, Pck1 – phosphoenolpyruvate carboxykinase 1.


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Regarding SCAT in WKY rats with DIO, HF diet feeding induced mRNA expression of Fasn and Srebf1 – a Fasn transcription factor - suggesting an increase in lipogenesis. In contrast, mRNA transcription of genes involved in lipolysis - Fabp4, Lpl, and Lipe - were not affected by HF feeding in SCAT. A very significant decrease in both SCAT Irs1 and Irs2 mRNAs by HF diet feeding suggested improper insulin signaling that could be linked to an increase in plasma insulin. Palm11-PrRP31 treatment resulted in dramatically decreased expression not only of Fasn and Srebf1 but also of Acaca, and thereby inhibited lipogenesis in SCAT of DIO WKY rats. In contrast, SCAT mRNA transcription of the genes involved in lipolysis - Fabp4, Lpl, and Lipe - were increased, as were SCAT Irs-1 and Irs-2 mRNA expression after palm11-PrRP31 treatment in WKY rats. However, the drop in insulin plasma levels after the treatment did not reach significance. In the liver of WKY rats, the HF diet also resulted in a very significant increase in lipogenesis through increased Fasn and Acaca mRNA expression, and palm11-PrRP31 treatment decreased only Acaca mRNA expression. Although the HF diet did not affect gluconeogenesis through G6pc and Pck1 mRNA, palm11-PrRP31 treatment led to a very significant attenuation of the mRNA expression of both gluconeogenesis genes mentioned. Similarly, the treatment improved glucose tolerance, which was impaired in the HF saline group. This study with DIO WKY rats revealed a similar impact of palm11-PrRP31 on lipid metabolism as in our previous studies with DIO mice, in which palmitoylated PrRP31 attenuated liver lipogenesis through decreased mRNA expressions of Acaca and Fasn20 and Srepf118,20, and transcription factors for both enzymes. In contrast, palm11PrRP31 treatment upregulated lipolysis through an increase in Lpl mRNA expression in SCAT and WAT18, again similarly to the results of the present study.


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In our previous study with DIO Sprague-Dawley rats, the mRNA of Acaca, a rate-limiting step of de novo lipogenesis, was attenuated, as were the mRNA expressions of Fasn and Srebp1 mRNA after the treatment with palm11-PrRP3121, again similarly to the present results in WKY rats. A new finding of this study is that gluconeogenesis was dramatically attenuated through the transcription of regulatory genes in response to palmitoylated PrRP31 in DIO WKY rats. We demonstrated herein again that palmitoylated PrRP31 clearly decreased de novo lipogenesis in the liver and adipose tissue in rodents with DIO caused by HF feeding and that in addition to decreased lipogenesis, an increase in lipolysis contributed to the body weight loss. An attenuation of liver gluconeogenesis contributed to the beneficial effects of palm11-PrRP31 on DIO.

Metabolomic effects of the therapy The metabolic changes induced by the HF diet were substantial; however, the effects of the therapy on the urine and serum metabolome were not remarkable. Figure 9A and B show the PLSDA models of urine and serum, respectively. A) Urine

B) serum


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Figure 9. Scores plot of PLS-DA model of urine (A) and serum (B) samples after the treatment (n=8 animals/group). The model cross validation results: urine - number of components: 3; accuracy: 0.719; R2 = 0.887; Q2 = 0.643; p value of permutation test (2000 repetitions) < 0.0005); serum - number of components: 5; accuracy: 0.656; R2 = 0.874; Q2 = 0.743; p value of permutation test (2000 repetitions) < 0.0005). SHR-HF group is marked in red, SHR-palm11-PrRP31 in light blue, WKY-HF in orange, WKY-palm11-PrRP31 in grey. Although PLS-DA did not show differences between the treated and obese groups of animals, the univariate analysis was able to capture several changes, which were specific for one of the genotypes. The changes induced by the therapy are reviewed in Table 5 and Table S5 for urine and serum, respectively.

Urine In SHR, the anti-obesity therapy induced a decrease in formate and 1-MNA levels. In WKY, the therapy decreased the levels of alanine and N-butyrylglycine. Moreover, it also decreased the levels of allantoin, DMA and an unassigned singlet (at 7.68 ppm).

Table 5. The significant effects of the palm11-PrRP31 analog on the urine metabolome.

genotype

palm11PrRP31/HF [%]

p value

Formate

SHR

- 41

0.024

1-Methylnicotinamide

SHR

- 20

0.032

Alanine

WKY

- 27

0.009

Allantoin

WKY

- 16

0.010

Dimethylamine

WKY

- 22

0.001


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N-butyrylglycine

WKY

- 24

0.043

Unknown singlet (7.68 ppm)

WKY

- 18

0.006

The significance was calculated using unpaired Mann-Whitney test. The values are expressed as the change of mean.

Serum In the serum of SHR, the therapy induced an elevation of the acetyl group signal (2.07 ppm), which is usually related to glycoproteins. Similarly, two unknown doublets (at 7.81 and 7.24 ppm), as well as an unknown singlet (6.99 ppm), were increased by the therapy. In contrast, allantoin was decreased in comparison to the HF diet-fed group. In WKY, 2-deoxyuridine and phenylalanine were significantly decreased after the therapy.

In general, while the HF diet induced almost identical changes in both genotypes, the therapy effect was specific for particular genotypes. Moreover, only few metabolites changes were observed, even though the therapy significantly decreased body weight in both genotypes. We guess that it could be caused by the fact that palm11-PrRP31 exhibits primarily effect in CNS, and the induction of metabolic changes is postponed, so their manifestation would require prolonged treatment.

CONCLUSIONS In the current study, we aimed to comprehensively describe the metabolic differences induced by the HF diet in both SHR and WKY and, consequently, to capture the metabolic changes induced by palm11-PrRP31 treatment.


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Based on our results, we can claim that SHR on the HF diet represents a normoglycemic model with obesity and hypertension. In contrast, WKY on the HF diet is a normotensive model of obesity and prediabetes. NMR-based metabolomic analysis of urine was able to distinguish the effect of the HF diet from the effect of genotype. Diet-induced obesity triggered analogous changes in both genotypes, and the majority of the metabolites seemed to be connected to the microbiome, thus supporting the idea of a close relationship of microbial metabolism to obesity and impaired glucose tolerance. Moreover, many of the metabolites that were changed by HF diet feeding were significantly correlated to lipid and glucose metabolism parameters. In addition, genotype-specific metabolites were defined at two levels – 11 metabolites were specific at baseline (1methylnicotinamide,

phenylacetylglycine,

taurine,

methylamine,

trigonelline,

p-cresyl

glucuronide, 3-indoxyl sulfate, 2-oxoglutarate, succinate, glucose, tartrate), and metabolites that specifically reacted to the HF diet, (dimethylamine, 2-oxoglutarate, N-butyrylglycine, p-cresyl sulfate). The palm11-PrRP31 treatment reduced BW and improved metabolic parameters in SHR and WKY rats. Moreover, an improvement in glucose tolerance in WKY rats after the treatment was found. In urine metabolome, the therapy introduced genotype-specific alterations. The significant decrease of formate and 1-methylnicotinamide was found in SHR, while significant decrease of alanine, allantoin, dimethylamine and N-butyrylglycine was observed in WKY. Altogether, the therapy by palm11-PrRP31 seems to be very promising, but other studies are needed to prove its anti-obesity effect on a long-term basis as well as potential benefits arising from therapy elongation.

SUPPORTING INFORMATION


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S1, Urine and serum sample preparation procedure for 2D experiments. S2, 2D experiments parameters. S3, 2D spectra preprocessing. Figure S1, The representative spectrum of urine with identified metabolites. Table S1, The list of identified metabolites in urine (with corresponding 1H and 13C chemical shifts). Figure S2, The PLS-DA model of urine of SHR and WKY on both HF and LF diets made of only selected set of metabolites. Figure S3, The representative spectrum of serum with identified metabolites. Table S2, The list of identified metabolites in serum (with corresponding 1H and

13

C chemical shifts). Table S3, High fat diet-induced changes in serum

metabolome of SHR and WKY rats. Figure S4, The significant correlations of serum metabolites with biochemical and biometric parameters. Table S4, Biochemical and biometric parameters measured in SHR and WKY rats at the end of the experiment. Table S5, The significant effects of the palm11-PrRP31 analog on the serum metabolome.

*corresponding author: Marek Kuzma, Laboratory of Molecular Structure Characterization, Institute of Microbiology Czech Academy of Sciences Videnska 1083, Prague 142 20, Czech republic e-mail: [email protected] telephone: +420 241 062 645

AUTHOR CONTRIBUTIONS LM, JK, MK and HP established the study design of rat experiments. LM, BN, and JK were responsible for rats handling, measurement and evaluation of final biochemical and biometric analyses. MC and HP performed all NMR experiments and were responsible for metabolomic data


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interpretation. BS carried out the statistical analysis of metabolomic data. MC, HP, BN, JK, LM, MK, PT, BZ drafted the manuscript. Finally, all authors reviewed and approved the final version of manuscript.

DECLARATION OF INTEREST The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This research was financially supported by institutional supports RVO: 61388963, 67985823, and 61388971. The authors acknowledge the project LO1509 of the Ministry of Education, Youth and Sports of the Czech Republic and the grant agency of the Czech Republic No. 18-10591S. We would like to thank Z. Lacinová (IKEM) for mRNA analysis. ABBREVIATIONS 1-MNA, 1-methylnicotinamide; 2-OG, 2-oxoglutarate, Acaca, acetyl CoA carboxylase; AUC, area under the curve, B2m, beta-2- macroglobulin; BW, body weight; COSY, 1H-1H correlation spectroscopy; CPMG, Carr-Purcell-Meiboom-Gill sequnce; DIO, diet-induced obesity; DMA, dimethylamine; DMG, dimethylglycine; EDTA, ethylenediaminetetraacetic acid; Fabp4, fatty acid binding protein 4; Fasn, fatty acid synthase; FFA, free fatty acids; FID, free induction decay; G6pc, glucose-6-phosphatase; glc, glycemia; GLP-1, glucagon-like peptide; HF, high-fat; HMDB, human metabolome database; HSQC, 1H-13C heteronuclear single quantum coherence sequence; IP, intraperitoneal; Irs1,2, insulin receptor substrate 1,2; LF, low-fat; Lipe, hormone-sensitive lipase, Lpl, lipoprotein lipase; MAP, mean arterial pressure; MetS, metabolic syndrome; MS, mass


39


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spectrometry; MSG, monosodium glutamate, NMR, nuclear magnetic resonance; OGTT, oral glucose

tolerance

test;

pCS,

p-cresol

sulfate;

pCG,

p-cresol

glucuronide;

PAG,

phenylacetylglycine; PBS, phosphate buffered saline; Pck1, phosphoenolpyruvate carboxykinase 1; PLS-DA, partial least square-discriminant analysis; PO, perorally; palm11-PrRP31, palmitoylated prolactin-releasing peptide; PrRP, prolactin-releasing peptide; PQN, probabilistic quotient normalization; PYY, peptide YY; SCAT, subcutaneous adipose tissue; SHR, spontaneously hypertensive rats; SHROB, spontaneously hypertensive obese rats; Srebf, sterol regulatory element-binding protein-1; TAG, triglycerides; TCA, tricarboxylic acid cycle; TDM2, type 2 diabetes mellitus; TSP, trimethylsilyl propionic acid, WAT, white adipose tissue; WKY, Wistar Kyoto rats.

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