Imbalance of plasma amino acids, metabolites and lipids in patients with lysinuric protein intolerance (LPI)

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Metabolism www.metabolismjournal.com

Imbalance of plasma amino acids, metabolites and lipids in patients with lysinuric protein intolerance (LPI) Johanna Kurko a,⁎, Maaria Tringham a , Laura Tanner a, b , Kirsti Näntö-Salonen c , Mari Vähä-Mäkilä c , Heli Nygren d , Päivi Pöhö e , Niina Lietzen f , Ismo Mattila g , Anu Olkku h , Tuulia Hyötyläinen g , Matej Orešič g , Olli Simell c , Harri Niinikoski c , Juha Mykkänen c, i a

Department of Medical Biochemistry and Genetics, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland Department of Clinical Genetics, Turku University Hospital, Kiinamyllynkatu 4–8, PL 52, 20521 Turku, Finland c Department of Pediatrics, Turku University Hospital and University of Turku, Kiinamyllynkatu 4–8, PL 52, 20521 Turku, Finland d VTT Technical Research Centre of Finland, Tietotie 2, P.O. Boxs 1000, Espoo 02044 VTT, Finland e Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, P.O. Boxs 56, Helsinki 00014, Finland f Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland g Steno Diabetes Center A/S, Niels Steensens Vej 2, 2820 Gentofte, Denmark h Eastern Finland Laboratory Centre, Puijonlaaksontie 2, 70210 Kuopio, Finland i Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland b

A R T I C LE I N FO Article history: Received 7 December 2015 Accepted 20 May 2016

AB S T R A C T Background. Lysinuric protein intolerance (LPI [MIM 222700]) is an aminoaciduria with defective transport of cationic amino acids in epithelial cells in the small intestine and proximal kidney tubules due to mutations in the SLC7A7 gene. LPI is characterized by protein malnutrition, failure to thrive and hyperammonemia. Many patients also suffer from combined hyperlipidemia and chronic kidney disease (CKD) with an unknown etiology.

Keywords:

Methods. Here, we studied the plasma metabolomes of the Finnish LPI patients (n = 26)

Lysinuric protein intolerance

and healthy control individuals (n = 19) using a targeted platform for analysis of amino

Chronic kidney disease

acids as well as two analytical platforms with comprehensive coverage of molecular lipids

Combined hyperlipidemia

and polar metabolites.

Metabolomics Lipidomics

Results. Our results demonstrated that LPI patients have a dichotomy of amino acid profiles, with both decreased essential and increased non-essential amino acids. Altered levels of metabolites participating in pathways such as sugar, energy, amino acid and lipid

Abbreviations: 4F2hc, surface antigen 4F2 heavy chain; α-KG, alpha-ketoglutaric acid; BAIBA, beta-aminoisobutyric acid; CAA, cationic amino acid; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FDCA, 2,5-furandicarboxylic acid; G3P, glycerol-3phosphate; HDL, high-density lipoprotein; HPA, 4-hydroxyphenylacetic acid; IAA, indole-3-acetic acid; LC, lipid cluster; LDL, low-density lipoprotein; LPI, lysinuric protein intolerance; MN, membranous nephropathy; NO, nitric oxide; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PPARα, peroxisome proliferator-activated receptor alpha; PXR, pregnane X receptor; SM, sphingomyelin; TG, triacylglycerol; VLDL, very low-density lipoprotein; y+ LAT1, system y+ L amino acid transporter, member 1. ⁎ Corresponding author at: Department of Medical Biochemistry and Genetics, University of Turku, Kiinamyllynkatu 10, 20520, Turku, Finland. Tel.: +358 2 3337456. E-mail addresses: [email protected] (J. Kurko), [email protected] (M. Tringham), [email protected] (L. Tanner), [email protected] (K. Näntö-Salonen), [email protected] (M. Vähä-Mäkilä), [email protected] (H. Nygren), [email protected] (P. Pöhö), [email protected] (N. Lietzen), [email protected] (I. Mattila), [email protected] (A. Olkku), [email protected] (T. Hyötyläinen), [email protected] (M. Orešič), [email protected] (O. Simell), [email protected] (H. Niinikoski), [email protected] (J. Mykkänen). http://dx.doi.org/10.1016/j.metabol.2016.05.012 0026-0495/© 2016 Elsevier Inc. All rights reserved.

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metabolism were observed. Furthermore, of these metabolites, myo-inositol, threonic acid, 2,5-furandicarboxylic acid, galactaric acid, 4-hydroxyphenylacetic acid, indole-3-acetic acid and beta-aminoisobutyric acid associated significantly (P < 0.001) with the CKD status. Lipid analysis showed reduced levels of phosphatidylcholines and elevated levels of triacylglycerols, of which long-chain triacylglycerols associated (P < 0.01) with CKD. Conclusions. This study revealed an amino acid imbalance affecting the basic cellular metabolism, disturbances in plasma lipid composition suggesting hepatic steatosis and fibrosis and novel metabolites correlating with CKD in LPI. In addition, the CKD-associated metabolite profile along with increased nitrite plasma levels suggests that LPI may be characterized by increased oxidative stress and apoptosis, altered microbial metabolism in the intestine and uremic toxicity. © 2016 Elsevier Inc. All rights reserved.

1.

Introduction

Lysinuric protein intolerance (LPI [MIM 222700]) is an autosomal recessive aminoaciduria in which the transport of cationic amino acids (CAAs) lysine, arginine and ornithine is defective in the small intestine and proximal kidney tubules [1]. The transport defect leads to reduced absorption of CAAs into the blood and extensive excretion of these amino acids into the urine. LPI is caused by a mutation in the SLC7A7 (solute carrier family 7, member 7) gene encoding y+ LAT1 (system y+ L amino acid transporter, member 1) [2], which, together with 4F2hc (surface antigen 4F2 heavy chain), exports CAAs in exchange of neutral amino acids and sodium in the basolateral membrane of the epithelial cells in the small intestine and kidney [3]. Despite the wide diversity of mutations in SLC7A7, all the Finnish LPI patients share the same LPIFin splice-site mutation IVS6AS, A-T, −2, c.895-2A>T [2]. The symptoms of LPI include strong protein aversion after weaning, failure to thrive and an impaired nutritional status affecting the patients' growth: the patients have a short stature, defects in the bone development and muscle weakness [1]. Due to protein malnutrition, the patients also suffer from hypocarnitinemia [4]. Hyperammonemia following a nitrogen load from protein-rich food is caused by an impaired urea cycle function due to the low plasma concentrations of its intermediates, arginine and ornithine [1]. Hyperammonemia can be prevented and protein tolerance improved with oral supplementation of citrulline, another intermediate in the urea cycle. Hepatosplenomegaly is continuously observed in patients [1], and, furthermore, hepatic cirrhosis and extensive cholestasis in hepatocytes have been detected in a few LPI cases [5–7]. However, as the patients suffer from hemorrhagic diathesis, liver biopsies are not routinely collected and, thus, hepatic abnormalities in LPI are difficult to study. Over half of the Finnish LPI patients suffer from chronic kidney disease (CKD), the pathogenesis of which remains poorly understood [8]. Signs of glomerulonephritis, glomerular amyloidosis, mesangial sclerosis, atrophy of the renal tubules and interstitial fibrosis have been observed in some Finnish cases [8,9]. It has been suggested that arginine accumulation and the resulting nitric oxide (NO) turnover inside the proximal kidney tubule cells may cause cellular damage, resulting in inflammation and apoptosis in both the tubular and mesangial cells in the kidney thus leading to tubulopathy and glomerular dysfunction, respectively [10,11]. Our recent study demonstrated that the LPI

patients have systemic inflammation with high plasma chemokine levels associating with the patients' CKD stages [12]. In addition to the renal and hepatic complications, almost all Finnish LPI patients suffer from combined hyperlipidemia with high serum triacylglycerol (TG) and total and low-density lipoprotein (LDL) cholesterol levels, and slightly subnormal high-density lipoprotein (HDL) cholesterol levels [13]. Hyperlipidemia is even more prominent in the patients with renal dysfunction and cannot be explained by mere dietary fat consumption [13]. Since LPI patients are on a permanent low-protein diet, one might expect that in addition to the deficiency of CAAs, the patients may also suffer from the scarcity of other amino acids. In addition to being the building blocks of proteins, amino acids are intermediates of various metabolites in multiple biochemical pathways. This led us to hypothesize that there may be hitherto uncharacterized systemic metabolic and lipid alterations in LPI, which may help elucidate the etiology of the defects in the hepatic and renal functions, and explain the frequent occurrence of hyperlipidemia. In order to unravel the systemic metabolic changes in LPI, we herein carried out an amino acid profiling analysis and global profiling of polar metabolites (metabolomics) and molecular lipids (lipidomics) in the LPI patients' plasma samples.

2.

Materials and Methods

2.1.

Study Subjects

A total of 26 Finnish LPI patients (17 females and 9 males) with ages ranging from 12 to 65 years (mean age 38.0 years) were included in this study (Supplementary Table 1). The patients represent a clinically heterogeneous population; therefore, their treatment and medications are also varied. The patients were on a protein-restricted diet and they received a low dose of oral citrulline supplementation (up to 100 mg/kg/d), either alone or in combination with sodium benzoate or sodium phenylbutyrate as nitrogen scavengers to reduce the blood ammonia level. All but three patients were administered an oral lysine supplement (up to 20–30 mg/kg/d), and five patients received carnitine supplementation for hypocarnitinemia. Multivitamins were given as supplements to 22 patients, and additional calcium and vitamin D supplements were received by 25 and eight patients, respectively. Two

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patients suffered from hypothyreosis for which they received levothyroxine, and another two patients with epilepsy received levetiracetam for seizure prophylaxis. Twenty patients had combined hyperlipidemia with high plasma TGs, and either high total or LDL cholesterol or low HDL cholesterol (the mean concentrations are shown in Supplementary Table 2). Fifteen patients were treated with 3-hydroxy-3-methylglutarylcoenzyme A reductase inhibitors, either simvastatin or atorvastatin. Eight patients received antihypertensive medication. One patient suffered from active pulmonary alveolar proteinosis and another was in remission. In total, 16 patients suffered from proteinuria, and 11 and 13 patients had elevated creatinine and cystatin C levels, respectively, suggesting that these patients suffered from chronic renal insufficiency. One of these patients had undergone a kidney transplant. Those patients with CKD were on the same diet and medication as the other patients. All the patient samples were collected during the clinical follow-up visits at the Department of Pediatrics, Turku University Hospital and University of Turku. Written informed consent was obtained from all the patients or their parents. The investigation corresponds to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Hospital District of Southwest Finland. Nineteen healthy sex- and agematched volunteers (12 females and 7 males), with ages ranging from 12 to 65 years (mean age 40.1 years), were recruited as controls for this study.

2.2.

samples compared to the control [median 18.81 (IQR 17.35– 19.80) μmol/L] samples (Supplementary Fig. 1). This shows that, in LPI, circulating NO levels are elevated, possibly contributing to the defects observed both at the cellular and systemic levels.

3.3.

An Altered Amino Acid Profile in LPI

Although the levels of several clinically relevant amino acids are regularly monitored in the Finnish LPI patients, no large-scale amino acid analyses in a case-control setting have been made in LPI prior to this work. The amino acid profiling revealed that the levels of ornithine, arginine, lysine, tryptophan, tyrosine, leucine, methionine, valine and phenylalanine were significantly (P < 0.0015) decreased in the LPI patient plasma samples compared to those of the controls (Table 1). In contrast, the levels of homocitrulline, citrulline, beta-aminoisobutyric acid (BAIBA), glutamic acid, glycine, aspartic acid, proline and serine were significantly increased in the patients. Accordingly, among the proteinogenic amino acids, the levels of the essential ones are decreased while the levels of the non-essential ones are increased in LPI. However, the level of non-essential tyrosine remained low since its biosynthesis is dependent on phenylalanine, and the levels of essential lysine, non-essential arginine and non-proteinogenic ornithine were decreased due to the CAA transport defect.

Methods

The clinical laboratory analyses of the serum/plasma, the patient estimated glomerular filtration rate (eGFR) value calculations, plasma sample collections, NO/nitrite measurements, the amino acid profiling, global metabolomics and lipidomics analyses and statistical analyses are described in Supplementary Methods.

3.

Results

3.1.

CKD Stages in LPI Patients

In order to assess the kidney function in our patient cohort, the patients were divided into five CKD stages based on the calculated eGFR values (Supplementary Table 1). One patient had an end-stage kidney failure (CKD5). Three patients were classified as stage 4 CKD patients suffering from severely reduced kidney function, and four patients had moderately reduced kidney function (CKD3). Six patients were classified as stage 2 CKD patients suffering from mildly reduced kidney function. Of these, patient 17 had experienced kidney transplants. Normal kidney function (CKD1) was observed in 12 patients.

3.2.

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Plasma Nitrite Concentration is Elevated in LPI

Since it is hypothesized that LPI patients may have elevated NO production in cells with defective y+ LAT1/4F2hc transporter, we wished to scrutinize plasma NO levels in our patient cohort. We used the Griess reaction to measure the total plasma nitrite as a surrogate for NO. The nitrite concentration levels were slightly but significantly (P = 0.02) increased in the patient [median 19.75 (IQR 18.98–20.56) μmol/L]

3.4. Significant Intercorrelations of Altered Amino Acids and eGFR Pairwise correlations between the above-mentioned altered amino acids, NO/nitrite, clinical laboratory variables, statin medication, the supplementations and eGFR were performed (Fig. 1, Supplementary Table 3). We observed that the branchedchain (leucine and valine) and aromatic (phenylalanine, tryptophan and tyrosine) amino acids had strong positive correlations between one another. Positive correlations were also detected between the CAAs (lysine, arginine and ornithine). Urea cyclerelated amino acids showed positive correlations as aspartic acid correlated with glutamic acid and arginine. eGFR correlated negatively with several amino acids: the most significant (P < 0.001) being with BAIBA (r = −0.771) and homocitrulline (r = −0.696) but also with phenylalanine, methionine, glycine, lysine, arginine and ornithine. In addition, eGFR correlated inversely with NO/nitrite.

3.5. Hierarchical Clustering Groups the Patients According to their CKD Stages As we wished to investigate the global metabolite composition in LPI, we performed a comprehensive plasma metabolite analysis. Significantly (q < 0.05) changed levels were detected in a total of 146 polar plasma metabolites (data not shown), of which full identity was obtained for 58 (Table 2). Of these, 36 had increased and 22 decreased levels in the LPI patients compared to the controls. Based on the biochemical pathway analysis, these metabolites participate in pathways relating to sugar, energy, amino acid, fatty acid and lipid metabolism, for example (Table 2).

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Table 1 – The results from the amino acid analysis of the LPI patient (n = 26) and control (n = 19) plasma samples. Amino acid

LPI median (IQR) (μM)

Control median (IQR) (μM)

Pa

Fold change b

Cysteine Homocitrulline Citrulline Beta-aminoisobutyric acid Glutamic acid Gamma-aminobutyric acid Alpha-aminoadipic acid Glycine Aspartic acid Proline Serine Alanine Hydroxyproline Glutamine Sarcosine 1-Methylhistidine 3-Methylhistidine Asparagine Taurine Histidine Ethanolamine Threonine Phenylalanine Beta-alanine Isoleucine Alpha-aminobutyric acid Valine Methionine Leucine Tyrosine Tryptophan Lysine Arginine Ornithine

0.0 (0.0–5.6) 1.8 (0.9–2.2) 123 (75.6–209) 4.3 (2.6–5.9) 236 (186–334) 0.0 (0.0–0.0) 2.1 (1.2–3.3) 672 (486–805) 11.9 (7.8–15.7) 376 (283–533) 199 (174–246) 647 (495–924) 16.1 (11.0–22.7) 667 (473–795) 2.6 (1.9–3.8) 5.9 (3.2–8.4) 4.4 (2.7–5.7) 74.8 (61.2–90.7) 46.8 (40.4–66.0) 83.5 (70.6–91.5) 9.4 (8.4–11.7) 156 (127–183) 51.1 (44.9–57.3) 34.0 (26.8–40.0) 63.4 (57.2–82.9) 14.5 (10.7–17.5) 208 (159–242) 21.2 (17.1–22.8) 108 (88.2–130) 42.5 (36.5–46.6) 36.3 (24.0–41.1) 116 (89.4–136) 45.2 (33.2–63.4) 32.3 (22.4–53.9)

0.0 (0.0–0.0) 0.0 (0.0–0.0) 33.6 (23.7–37.2) 1.2 (0.9–1.8) 76.7 (50.2–112) 0.0 (0.0–0.2) 1.1 (0.7–1.2) 308 (232–359) 6.5 (4.6–8.8) 235 (185–265) 145 (109–167) 452 (417–574) 14.9 (8.4–21.3) 531 (422–576) 2.0 (1.7–2.9) 4.8 (1.5–8.4) 4.1 (3.2–4.5) 68.8 (56.7–79.1) 62.2 (55.3–66.7) 77.7 (75.3–91.3) 9.8 (9.2–10.3) 169 (137–190) 61.7 (55.0–70.7) 42.7 (36.2–47.3) 82.4 (74.0–98.2) 20.5 (17.9–26.5) 292 (239–317) 28.6 (23.0–36.0) 151 (127–192) 74.6 (62.1–89.4) 58.7 (54.6–62.4) 197 (184–216) 101 (72.5–112) 74.3 (52.7–81.3)

0.0021 7.79 × 10−7 1.11 × 10−7 4.74 × 10−7 2.06 × 10−7 0.5537 0.0017 6.66 × 10−6 1.43 × 10−4 5.78 × 10−5 2.13 × 10−5 0.0051 0.5971 0.0123 0.0908 0.4904 0.4478 0.1864 0.0243 0.9542 0.4619 0.4346 3.84 × 10−4 0.0082 0.0049 0.0056 4.81 × 10−6 9.80 × 10−5 3.71 × 10−5 6.14 × 10−7 1.61 × 10−7 1.38 × 10−8 5.65 × 10−6 4.30 × 10−6

30.14 23.55 4.73 4.17 2.93 2.14 2.10 2.04 1.81 1.78 1.52 1.42 1.31 1.29 1.27 1.23 1.18 1.13 1.02 −1.01 −1.01 −1.05 −1.24 −1.24 −1.27 −1.40 −1.40 −1.43 −1.45 −1.68 −1.74 −1.82 −1.95 −1.98

IQR, interquartile range. a The changes in the amino acid levels between the patients and controls were tested using the Mann–Whitney U test. The P value limit < 0.05 was Bonferroni-corrected to P < 0.0015 according to the 34 measurable amino acids in the data. The metabolites with the P < 0.0015 are bolded. b Fold change equals to the ratio of mean concentration between the patients and controls.

In order to hierarchically cluster the LPI patients and controls by the levels of those 58 significantly changed metabolites, a heatmap with dendrograms was computed showing that the control and patient samples formed two separate clusters (Fig. 2). The patients were further divided into subclusters, grouping the patients with severe or moderate CKD and mildly reduced or normal kidney function together. Clustering the metabolites (Fig. 2) revealed two subclusters with decreased metabolites in the patients including sugar derivatives and amino acids. The cluster of increased metabolites was divided into three subclusters. The first consisted of myo-inositol, 2,5furandicarboxylic acid (FDCA), 4-hydroxyphenylacetic acid (HPA), threonic acid, 2,4-dihydroxybutanoic acid, 3,4-dihydroxybutanoic acid, galactaric acid, fucose, galacturonic acid, glucopyranose derivative 1 and 2-deoxy-erythro-pentonic acid, of which levels were highest in the patients with the most strongly reduced kidney function (highlighted with a red rectangle in Fig. 2). The second subcluster included amino acids and the third fatty acids, both saturated (palmitic acid,

stearic acid, lauric acid and myristic acid) and unsaturated (oleic acid, linoleic acid, linolenic acid, 11-eicosenoic acid and 9-tetradecenoic acid). Our results demonstrated that the LPI patients with CKD, especially those with an end-stage kidney failure and moderate to severe CKD, share a unique metabolite profile with high levels of certain metabolites.

3.6. in LPI

Six Metabolites Correlate Significantly with eGFR

Since over half of the LPI patients (14/26) included in this study suffer from CKD, we wanted to scrutinize the possible correlation of the metabolites with the eGFR values, but also with NO/nitrite and clinical variables. In total, our results revealed 250 positive and 84 negative significant pairwise correlations with the P value limit < 0.05 (Fig. 3, Supplementary Table 4). A large positively intercorrelating cluster consisting of 14 metabolites, albumin and urine proteins

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Fig. 1 – Pairwise correlations of amino acids and clinical variables in LPI. The heatmap with dendrograms was computed based on the Spearman correlation coefficients calculated for each pair of variables including 17 amino acids with significantly changed levels, nitric oxide (NO)/nitrite, estimated glomerular filtration rate (eGFR), clinical variables, statin medication and supplementations in the LPI patients. Hierarchical clustering shows amino acids and other variables with the most similar pairwise-correlation patterns. The categorical variable of the patients' statin medication was annotated as following: no medication = 0, medication = 1. ALP, alkaline phosphatase; ALT, alanine transaminase; NH4, ammonium; LDL, low-density lipoprotein; HDL, high-density lipoprotein. The P values were Benjamini–Hochberg-corrected. *P < 0.05, **P < 0.01, ***P < 0.001.

was detected, which, in addition, had a strong negative correlation with eGFR. Metabolites with the most significant correlations (P < 0.001) with eGFR were myo-inositol (r = − 0.928), galactaric acid (r = −0.668), threonic acid (r = −0.669), HPA (r = −0.800), FDCA (r = −0.717) and indole-3-acetic acid (IAA) (r = −0.729). Positive significant correlations were observed, such as those between the following metabolites: the TCA cycle metabolites malic acid and alpha-ketoglutaric acid (α-KG); valine, leucine and isoleucine biosynthesis/degradation pathway-included metabolites; phenylalanine and its catabolite HPA; and sugar metabolites glucopyranose

derivative 2, two fructose derivatives and α-KG. The urea cycle-related metabolites also revealed associations as ammonium correlated positively with glutamic acid and negatively with 4-methyl-2-oxovaleric acid. Glutathione synthesis precursor 5-oxoproline correlated positively with S-methylcysteine. Fatty acids and glycerolipid/ glycerophospholipid metabolites correlated significantly as positive correlations were observed within the cluster of fatty acids, which also correlated positively with glycerol, and negatively with glycerol-3-phosphate (G3P). G3P correlated positively with the total cholesterol and LDL.

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Table 2 – The significantly changed (q < 0.05) metabolites in the LPI patient plasma samples compared to the controls. Metabolite Cystine

LPI median (IQR)

Control median (IQR)

q value a

FC b

Biochemical pathway

−2

Cysteine and methionine metabolism Furfural degradation Fatty acid beta-oxidation Pentose and glucuronate interconversions, ascorbate and aldarate metabolism Glycolysis, gluconeogenesis, galactose/fructose and mannose/ starch and sucrose metabolism Ascorbate and aldarate metabolism Tyrosine/phenylalanine metabolism Pyrimidine metabolism – – TCA cycle, ascorbate and aldarate/ alanine, aspartate and glutamate/ glyoxylate and dicarboxylate metabolism Biosynthesis of unsaturated fatty acids Purine/glutathione/glycine, serine and threonine/glyoxylate and dicarboxylate metabolism Alpha linolenic acid metabolism Fructose and mannose degradation Alanine, aspartate and glutamate/ glutathione/glyoxylate and dicarboxylate metabolism – – Galactose/ascorbate and aldarate/ inositol phosphate metabolism Arginine and proline metabolism Galactose/glycerolipid metabolism Fatty acid biosynthesis TCA cycle, pyruvate/glyoxylate and dicarboxylate metabolism Alanine, aspartate and glutamate metabolism – Ascorbate and aldarate metabolism Glutathione metabolism Glycine, serine and threonine/ glyoxylate and dicarboxylate metabolism Fatty acid biosynthesis Caprolactam degradation Fatty acid biosynthesis Linoleic acid metabolism Alanine, aspartate and glutamate metabolism Biosynthesis of unsaturated fatty acids Fatty acid biosynthesis Fatty acid biosynthesis Propanoate metabolism Valine, leucine and isoleucine biosynthesis/degradation Phenylalanine, tyrosine and tryptophan biosynthesis Phosphonate and phosphinate/ glycerophospholipid metabolism

0.0 (0.0–18.7)

0.0 (0.0–0.0)

1.24 × 10

33.95

2,5-Furandicarboxylic acid (FDCA) 9-Tetradecenoic acid Galacturonic acid

18.6 (0.0–50.1) 0.0 (0.0–24.8) 6.9 (0.0–39.6)

0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.0 (0.0–0.0)

1.12 × 10−3 3.66 × 10−3 1.05 × 10−2

33.07 17.94 10.51

Glucopyranose der1

350 (90.9–624)

76.6 (25.6–149)

4.68 × 10−3

4.91

Galactaric acid 4-Hydroxyphenylacetic acid (HPA)

0.0 (0.0–27.5) 12.5 (0.0–19.4)

0.0 (0.0–0.0) 0.0 (0.0–13.4)

2.91 × 10−2 4.61 × 10−2

3.99 3.40

Uridine 2-Deoxy-erythro-pentonic acid S-methylcysteine Alpha-ketoglutaric acid

25.1 247 42.4 78.3

(20.8–36.8) (149–394) (30.6–59.8) (38.6–122)

10.0 (0.0–16.0) 120 (41.9–166) 16.0 (0.0–21.5) 30.6 (25.3–55.1)

2.47 1.29 2.46 2.74

10−4 10−3 10−4 10−3

3.06 2.93 2.69 2.63

11-Eicosenoic acid

56.8 (36.4–81.9)

22.7 (15.3–29.9)

1.07 × 10−4

2.52

Glycine*

463 (286–623)

187 (118–277)

1.49 × 10−4

2.43

Linolenic acid Fucose Glutamic acid*

287 (159–576) 14.2 (0.0–17.2) 241 (160–396)

160 (91.2–222) 6.5 (0.0–10.7) 119 (70.7–186)

1.46 × 10−3 1.35 × 10−2 1.53 × 10−3

2.33 2.10 2.09

3,4-Dihydroxybutanoic acid* Myo-inositol-2-phosphate Myo-inositol

8.3 (6.8–11.2) 137 (115–174) 3839 (2369–5075)

4.4 (3.6–5.0) 107 (92.3–112) 2105 (1967–2608)

8.36 × 10−6 1.45 × 10−3 1.46 × 10−3

1.98 1.98 1.93

Proline* Glycerol Oleic acid* Malic acid

545 5204 1116 109

355 3029 611 60.8

1.38 1.49 2.64 4.40

10−3 10−4 10−4 10−4

1.85 1.83 1.81 1.79

2.20 × 10−3

1.75

Alanine*

(400–880) (3606–7793) (804–1541) (79.7–133)

730 (536–1325)

(278–433) (2524–3525) (552–741) (56.7–72.5)

486 (405–625)

× × × ×

× × × ×

2,4-Dihydroxybutanoic acid* Threonic acid 5-Oxoproline Serine*

7.1 351 7206 314

(5.8–13.3) (253–511) (5759–7991) (235–381)

5.0 (4.3–6.9) 279 (225–355) 4695 (3775–5201) 210 (156–232)

1.44 2.54 8.36 5.87

× × × ×

10−2 10−2 10−6 10−4

1.64 1.64 1.61 1.60

Lauric acid Adipic acid Myristic acid Linoleic acid* Aspartic acid*

101 22.0 339 656 7.0

(72.0–135) (16.9–31.1) (206–421) (519–796) (6.0–9.0)

71.5 (55.9–103) 18.7 (14.4–22.6) 220 (193–267) 475 (406–581) 5.4 (4.4–7.8)

6.56 4.76 1.80 4.00 2.79

× × × × ×

10−3 10−2 10−2 10−3 10−2

1.53 1.48 1.44 1.40 1.26

Eicosanoic acid

18.9 (16.7–21.5)

15.1 (13.6–18.7)

3.04 × 10−3

1.24

Palmitic acid* Stearic acid* 2-Hydroxybutyric acid* Isoleucine*

869 432 29.9 99.5

687 (634–796) 381 (367–406) 44.5 (27.0–56.9) 134 (125–187)

5.97 2.33 4.23 4.20

10−3 10−2 10−2 10−3

1.23 1.14 −1.27 −1.32

Phenylalanine*

120 (101–134)

152 (140–179)

1.86 × 10−4

−1.34

Ethanolamine

77.1 (50.3–114)

109 (96.9–132)

1.23 × 10−2

−1.39

Methionine*

41.3 (36.3–55.8)

56.0 (48.9–73.1)

3.63 × 10−3

−1.39

(742–998) (392–486) (18.5–44.3) (88.6–135)

× × × ×

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Table 2 (continued) Metabolite

LPI median (IQR)

Control median (IQR)

q value a

FC b

21.3 (16.4–32.4) 294 (251–360)

39.5 (27.4–50.2) 395 (364–506)

5.32 × 10−3 2.47 × 10−4

−1.40 −1.40

955 (701–1121) 6343 (0.0–13,121)

1159 (836–1719) 13,936 (1962–14,728)

2.54 × 10−2 4.76 × 10−2

−1.43 −1.50

Glycerol-3-phosphate

780 (671–1098)

1045 (770–1694)

2.29 × 10−2

−1.51

Ornithine

408 (269–578)

686 (462–864)

1.41 × 10−2

−1.51

2678 (2168–3537) 180 (149–240)

3879 (2448–5200) 278 (252–401)

3.09 × 10−2 4.95 × 10−5

−1.52 −1.64

Glyceric acid*

12.5 (9.0–18.0)

25.4 (21.8–29.7)

1.49 × 10−4

−1.72

Oxalic acid

7879 (4056–18,401)

14,164 (7824–22,113)

4.23 × 10−2

−1.73

Indole-3-acetic acid (IAA) Valine* D-Fructose der2 Glucopyranose der2

D-Fructose der1 Leucine*

0.0 (0.0–4074) 44.6 (0.0–83.6)

3799 (0.0–8673) 74.9 (40.1–145)

4.61 × 10−2 2.57 × 10−2

−1.88 −1.97

4-Methyl-2-oxovaleric acid

257 (175–306)

496 (410–606)

1.02 × 10−4

−1.98

Uric acid Tyrosine*

879 (491–1347) 120 (71.2–140)

1976 (1456–2740) 349 (185–468)

5.50 × 10−4 1.92 × 10−5

−2.03 −2.50

Tryptophan

109 (58.7–343)

541 (329–683)

4.40 × 10−4

−2.52

Tartronic acid

21.2 (0.0–52.4)

111.5 (58.1–132)

1.08 × 10−4

−3.50

D-Galactose 2-Oxoisovaleric acid

Biochemical pathway Cysteine and methionine metabolism Tryptophan metabolism Valine, leucine and isoleucine biosynthesis/degradation Starch and sucrose metabolism Glycolysis, gluconeogenesis, galactose/fructose and mannose/ starch and sucrose metabolism Glycerolipid/glycerophospholipid metabolism Arginine and proline/D-arginine and D-ornithine metabolism Starch and sucrose metabolism Valine, leucine and isoleucine biosynthesis/degradation Glycerolipid/glyoxylate and dicarboxylate/glycine, serine and threonine metabolism Glyoxylate and dicarboxylate metabolism Galactose metabolism Valine, leucine and isoleucine biosynthesis/degradation Valine, leucine and isoleucine biosynthesis/degradation Purine metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Phenylalanine, tyrosine and tryptophan biosynthesis, glycine, serine and threonine metabolism –

The metabolites marked with the asterisks were measured quantitatively (ng/15 μL) while the other metabolites were measured semiquantitatively from the normalized peak areas. IQR, interquartile range. a The q values were calculated based on the Mann–Whitney U test-derived P values. b Fold change equals to the ratio of mean concentration or normalized peak area between the patients and controls.

3.7.

Post-Hoc Analysis of the eGFR-Correlated Metabolites

We wanted to study whether metabolites associating with the reduced kidney function have significant concentration level differences between the patients with and without CKD. Hence, we performed a post-hoc analysis of the metabolites correlating with eGFR with the P value limit < 0.001 between the controls and patients without and with CKD (Table 3). Our results showed that the levels of myo-inositol, galactaric acid, threonic acid, HPA, FDCA, IAA and BAIBA were significantly (P < 0.0063) increased in the LPI patients with CKD compared to the patients without CKD and the controls, indicating that these metabolites associate with CKD in LPI. In contrast, the difference in the homocitrulline level between the patients with and without CKD was not significant.

3.8. The Levels of Phosphatidylcholines are Reduced and eGFRCorrelated Long-Chain Triacylglycerols Elevated in the LPI Plasma In order to acquire a global lipid profile of LPI, molecular lipids of the patients' and controls' plasma were analyzed. We found a

total of 447 different lipids with significantly (q < 0.05) changed levels in the LPI patients, and, of these, 244 were identified. Of the identified lipids, 198 had increased and 46 had decreased concentrations in the patients compared to the controls. The identified lipids were further analyzed by clustering the lipidomics data into eight lipid clusters (LC1-LC8) (Table 4, Supplementary Table 5). As expected, the cluster division mainly followed the functional and structural lipid groups. LC1 consisted of ceramides, phosphatidylcholines (PCs), lysoPCs, phosphatidylethanolamines (PEs) and sphingomyelins (SMs). LC3 included mainly PCs with polyunsaturated fatty acids. LC2 and LC4LC8 consisted of TGs containing monounsaturated, polyunsaturated and saturated fatty acids. LC6 and LC8 contained mainly long-chain TGs and LC7 consisted of short-chain TGs. All the lipid clusters were significantly increased in the LPI patients except for LC3, which was decreased. Furthermore, lipid clusters, NO/nitrite, clinical variables and eGFR were pairwise correlated (Fig. 4, Supplementary Table 6). The routine laboratory TGs correlated positively with LC1 and all lipid clusters containing TGs, except for LC5. As expected, LC1 correlated positively with LC3. We observed

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Fig. 2 – LPI patients are hierarchically clustered according to their CKD stages. The heatmap with dendrograms was computed by the log2-transferred concentration values of 58 metabolites with q < 0.05 in order to hierarchically cluster LPI patients (P1P26) and controls (C1-C19), and also metabolites. The LPI patients with the CKD stages 2–5 are indicated by both ID markings and red lines in the dendrogram. The red rectangular indicates both the cluster of metabolites correlating significantly with estimated glomerular filtration rate (eGFR) (see Fig. 3) and the cluster of the LPI patients with the most severe CKD.

that eGFR correlated inversely with LC6 and LC8, and that statin medication correlated negatively with eGFR and positively with LC8. Citrulline supplementation correlated negatively with LC1, LC4 and LC8.

4.

Discussion

We performed the first global metabolomics and lipidomics study on LPI, which is characterized by a growth failure, hyperlipidemia and dysfunction of the urea cycle and kidneys. Our results demonstrate that the defective y+ LAT1/ 4F2hc transporter along with protein malnutrition lead to the decreased concentrations of CAAs and essential amino acids in the plasma, and an increased de novo synthesis of nonessential ones. Since amino acids participate in the biosynthesis and catabolism of various metabolites, their imbalance in LPI results in altered metabolic and lipid profiles affecting several

biochemical pathways. A deficiency of essential amino acids, particularly of leucine, may lead to an inactivation of the mTOR system for normal cell growth and an induction of autophagy to sustain intracellular energy supply [14]. However, autophagy may be inhibited in LPI by an increased uptake of essential amino acids, as shown in our previous whole-blood transcriptomics study [15] in which an upregulation of the genes encoding the LAT1 and ASCT2 amino acid transporters, necessary for mTOR activation [14], were detected. Energy supply in LPI may also be secured by the accelerated synthesis of indispensable amino acids, which can provide their carbon skeletons for the production of the TCA cycle intermediates. Glutamic acid in particular is a central actor in metabolism. It supplies the nascent amino acids with amino groups and feeds α-KG into the TCA cycle, further inducing gluconeogenesis [16]. As the levels of glutamic acid, α-KG and malic acid were highly elevated in the patients, it indicates that the synthesis of non-essential amino acids is increased and, moreover, suggests anaplerosis in LPI.

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Fig. 3 – A group of metabolites has a high inverse correlation with kidney function in LPI. The heatmap with dendrograms was computed based on the Spearman correlation coefficients calculated for each pair of variables including 58 metabolites with significantly altered levels, nitric oxide (NO)/nitrite, estimated glomerular filtration rate (eGFR), clinical variables, statin medication and supplementations in the LPI patients. Hierarchical clustering shows metabolites and other variables with the most similar pairwise-correlation patterns. The categorical variable of the patients' statin medication was annotated as following: no medication = 0, medication = 1. ALP, alkaline phosphatase; ALT, alanine transaminase; NH4, ammonium; LDL, low-density lipoprotein; HDL, high-density lipoprotein. The P values were Benjamini–Hochberg-corrected. *P < 0.05, **P < 0.01, ***P < 0.001.

Interestingly, the above-mentioned metabolites and aspartate are also parts of the malate–aspartate shuttle, which may be affected by increases in their levels. Our data also revealed

other metabolites closely related to energy metabolism. The increased concentration of glycerol and the decreased levels of its oxidized products, highly correlating glyceric and

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Table 3 – The post-hoc analysis of the eGFR-correlated metabolites (P < 0.001) between the healthy controls, LPI patients without chronic kidney disease (LPI non-CKD) and LPI patients suffering from chronic kidney disease (LPI CKD). Metabolite

Control median (IQR)

LPI non-CKD median (IQR)

LPI CKD median (IQR)

LPI non-CKD vs. control Pa

FDCA 0.0 (0.0–0.0) 0.0 (0.0–12.5) 47.9 (18.2–152) Galactaric acid 0.0 (0.0–0.0) 0.0 (0.0–0.0) 24.0 (6.6–33.2) HPA 0.0 (0.0–13.4) 0.0 (0.0–0.0) 19.0 (13.7–40.0) Myo-inositol 2105 (1967–2608) 2365 (1970–3328) 4976 (4152–8212) Threonic acid 279 (225–355) 279 (207–344) 478 (352–907) IAA 39.5 (27.4–50.2) 17.5 (12.6–21.5) 28.3 (19.6–47.1) BAIBA* 1.2 (0.9–1.8) 2.7 (1.9–3.9) 5.3 (4.3–11.3) Homocitrulline* 0.0 (0.0–0.0) 1.0 (0.2–1.8) 2.1 (1.8–2.5)

LPI CKD vs. control

Fold Pa change b

0.056 4.60 0.626 −1.47 0.292 −2.32 0.491 1.10 0.746 1.02 9.89 × 10−5 −2.33 2.15 4.77 × 10−4 9.32 × 10−5 15.96

2.76 × 4.98 × 2.55 × 1.83 × 1.31 × 0.259 1.23 × 7.78 ×

LPI CKD vs. LPI non-CKD

Fold Pa change b 10−5 10−4 10−4 10−6 10−4 10−6 10−7

57.47 6.83 5.95 2.64 2.17 −1.04 5.90 30.07

Fold change b

0.00461 12.49 0.00156 10.08 1.18 × 10−4 13.82 1.56 × 10−5 2.39 5.69 × 10−4 2.14 0.00397 2.23 9.82 × 10−4 2.75 0.00851 1.88

IQR, interquartile range; FDCA, 2,5-furandicarboxylic acid; HPA, 4-hydroxyphenylacetic acid; IAA, indole-3-acetic acid; BAIBA, betaaminoisobutyric acid. a The metabolite level changes between the three groups were tested using the Kruskall–Wallis test. The significant P values (P < 0.0063) after Bonferroni-correction according to the eight tested metabolites and amino acids are in bold. b Fold change equals to the ratio of mean normalized peak area or concentration* (μM) between the patients and controls.

tartronic acids [17], indicate that the oxidation of glycerol through the TCA cycle [18] could be reduced in LPI. Our data further suggest that glycolysis may be increased in LPI as the reduced levels of three sugar compounds, glucopyranose (der2) and two fructose derivatives, indicate increased sugar consumption in the patients. Interestingly, the levels of several ammonia-related metabolites were altered in the LPI patients despite the fact that their plasma ammonium levels were within the normal range. In addition to the TCA cycle, glutamate, aspartate, α-KG and malate are also linked to the urea cycle, the function of which is impaired in LPI. High glutamic acid and slightly elevated glutamine levels in the patients suggest increased ammonia detoxification. Furthermore, the increased levels of uridine, the pyrimidine pathway product, and its inverse correlation with the citrulline supplementation in the patients are consistent with hyperammonemia-associated orotic aciduria in LPI [19]. In LPI, one of the conundrums has been the pathogenesis of CKD which many Finnish patients suffer from. CKD is linked to protein malnutrition, both of which associate with systemic inflammation and oxidative stress [20–23]. An increase in the oxidized forms of antioxidants in the plasma

is a good indirect marker of oxidative stress [22]. Remarkably, this was seen in the LPI patients with CKD as a high level of threonic acid, an oxidized product of ascorbic acid [24]. A decreased plasma ascorbic acid level has been observed in CKD, possible due to its excessive excretion into the urine concurrently with proteinuria [25]. Based on the food diary data, the LPI patients receive a sufficient amount of ascorbic acid [26] which, however, is not routinely measured in the patients, allowing us to only speculate on whether its level is decreased in LPI by increased oxidation and excretion into the urine. Also, the production of glutathione may be accelerated in LPI since the levels of 5-oxoproline, glutamate, cysteine and glycine, all the metabolites of the γ-glutamyl cycle for the glutathione synthesis [27], were elevated in the patients. In addition, 5-oxoproline correlated with elevated Smethylcysteine, known to have antioxidant effects [28] and to increase the glutathione peroxidase activity in the plasma [29]. These results indicate that oxidative stress may be increased in LPI, especially in patients with CKD. Interestingly, dietary modifications can induce changes in the microbial environment of the intestine and result in altered metabolite composition in the plasma and urine

Table 4 – The description of the identified lipid clusters (LC) dysregulated in the LPI patients' global lipidome. LC name

n

General LC description

Pa

LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8

67 20 46 30 10 16 13 42

Ceramides, LysoPCs, PCs, PEs and SMs DGs and TGs containing PUFAs PCs containing PUFAs TGs containing SFAs, MUFAs and PUFAs TGs containing SFAs and MUFAs Long-chain TGs containing SFAs, MUFAs and PUFAs Short-chain TGs containing SFAs, MUFAs and PUFAs Long-chain TGs containing PUFAs

1.25 1.39 2.06 3.09 2.58 2.35 7.17 5.36

Fold change b × × × × × × × ×

10−7 10−6 10−7 10−6 10−4 10−5 10−3 10−6

1.88 2.33 −1.83 2.24 1.83 1.73 1.46 2.01

PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; DG, diacylglycerol; TG, triacylglycerol; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; MUFA, monounsaturated fatty acid. n = number of specific lipids in the lipid cluster (see Supplementary Table 6 for the specific lipids). a The changes in the lipid levels in the clusters between the patients and controls were tested using the Mann–Whitney U test. b Fold change equals to the ratio of mean concentration between the patients and controls.

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Fig. 4 – Pairwise correlations of the lipid clusters (LC1-LC8) and clinical variables in LPI. The heatmap with dendrograms was computed based on the Spearman correlation coefficients calculated for each pair of variables including eight lipid clusters with significantly changes levels, nitric oxide (NO)/nitrite, estimated glomerular filtration rate (eGFR), clinical variables, statin medication and supplementations in the LPI patients. Hierarchical clustering shows lipid clusters and other variables with the most similar pairwise-correlation patterns. The categorical variable of the patients' statin medication was annotated as following: no medication = 0, medication = 1. ALP, alkaline phosphatase; ALT, alanine transaminase; NH4, ammonium; LDL, low-density lipoprotein; HDL, high-density lipoprotein. The P values were Benjamini–Hochberg-corrected. *P < 0.05, **P < 0.01, ***P < 0.001.

[30,31]. There is evidence of intestinal barrier dysfunction and altered gut microbe composition in advanced CKD, leading to the formation of pro-oxidant and proinflammatory byproducts that contribute to uremic toxicity, inflammation, malnutrition and cardiovascular complications [31]. In the light of these studies, it is likely that in LPI, in which protein malnutrition is typical, the microbial environment of the intestine is disturbed. To support this, our data showed eGFR correlating with HPA and IAA, metabolites processed from dietary amino acids by the intestinal bacteria. Highly increased levels of HPA, the catabolite of phenylalanine and tyrosine [32,33], were found in the LPI patients with CKD. Elevated levels of HPA have also been found in the urine of patients with the bacterial overgrowth syndrome [33], gastrointestinal disorders [32] and membranous nephropathy (MN) [34]. IAA, the breakdown product of tryptophan [35], is known to be a uremic solute [36] inducing inflammation and

endothelial dysfunction in CKD [37]. Surprisingly, compared to the controls, the levels of IAA were decreased in the patients without CKD, while in the patients with CKD the levels were almost at the control level, indicating that generally in LPI, the production of IAA is reduced due to low tryptophan levels. The CKD-associated increased levels of HPA and IAA in LPI derive most probably from their impaired renal clearance but also, possibly, from the altered activity of the intestinal bacteria. FDCA, increased in the LPI patients with CKD, is a normal constituent of the human urine [38] and plasma [39], although barely detectable in the controls of this study. FDCA is likely metabolized by the human intestinal bacteria from the glucuronidated furan derivative 5-hydroxymethylfurfural [40] which is produced from sugars by strong heating during food preparation [38]. Galacturonic acid, another less significantly eGFR-correlated metabolite in our study, is the main

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component of pectin, a constituent of many fruits. Pectin in humans is fermented by the gut bacteria to produce galacturonic acid, and further galactaric acid and FDCA [41]. Galactaric acid has been associated with renal disease before as Hirayama and others [42] discovered that its level was significantly lowered in diabetic nephropathy patients, which, however, is quite the opposite of our finding. Increased levels of FDCA, galactaric acid and galacturonic acid in CDK in LPI may, again, indicate the dysfunction of glomerular filtration and alterations in microbial sugar processing. To our knowledge, this is the first time that increased FDCA and galactaric acid plasma levels have been associated with CKD, making them novel biomarker candidates for CKD. Myo-inositol and BAIBA levels were also increased in the LPI patients with CKD. Association of myo-inositol with renal dysfunctions such as uremia [36], MN [34] and CKD [43] is well known. Since the kidney is the most important site of myoinositol catabolism [43], its impairment, along with reduced GFR, may cause the elevated plasma levels of myo-inositol. The BAIBA levels are markedly increased in the serum in uremia, which may influence the development of uremic toxemia [44]. BAIBA induces white fat browning, and its plasma level increases with exercise, whereas the low plasma concentration associates with the metabolic risk factors such as high total cholesterol, TGs and body mass index [45]. In LPI, especially in the patients with CKD, its increased level may be an attempt to protect the body from the metabolic perturbations. Our results show obvious alterations in the LPI lipid metabolism despite statin medication and indicate that the patients in this study may suffer from hepatic steatosis [46]. Increased TG, fatty acid and glycerol plasma levels in LPI may indicate that the TG synthesis and excretion from the liver are increased as a consequence of intensified lipolysis and release of free fatty acids from the adipose tissue and their subsequent conversion to TGs in the liver. The uptake of fatty acids into the liver and lipogenesis is induced by the PXR (pregnane X receptor) transcription factor [47], activated by genes with differential, mainly upregulated, expression pattern in LPI, as revealed by our transcriptome study [15]. The transcriptome data also showed mainly downregulation of genes activating fatty acid-regulated PPARα (peroxisome proliferator-activated receptor alpha), which induces enzymes needed in fatty acid β-oxidation [48]. This indicates that β-oxidation of free fatty acids and, therefore, energy combustion is defective in LPI, which may further increase the TG synthesis and induce hepatic steatosis, ultimately leading to the death of hepatocytes [49]. Apoptotic hepatocytes release TGs, which, along with the unmetabolized fatty acids, leads to lipotoxicity and steatohepatitis. An injured liver is susceptible to secondary assaults by reactive oxygen species, gut-derived endotoxins and cytokines resulting in oxidative stress and inflammation, further leading to the activation of stellate cells and hepatic fibrosis. Consistently, our gene expression study supports also the occurrence of hepatic fibrosis in LPI [15]. Further evidence of lipid overload in the LPI liver is indicated by the increased plasma levels of ceramides, lysoPCs and SMs, all known to associate with hepatic

steatosis [50–52]. In contrast, PCs, the phospholipid components of lipoproteins, were mainly downregulated in the LPI patients. The decreased synthesis of PCs in the liver [51] and the deficiency of dietary methionine [53], the PC synthesis intermediate [30] reduced in LPI, have been detected in hepatic steatosis patients. As it has been demonstrated that the plasma PC levels may directly reflect the rate of hepatic PC synthesis [54], reduced levels of certain PCs in the LPI plasma may be markers of hepatic steatosis. To sum up the above, our data suggest that the lipid metabolism in the liver, normally balanced between fatty acid and TG synthesis by lipogenesis (energy intake) and degradation by lipolysis and further fatty acid β-oxidation (energy combustion), is distorted in LPI, demonstrated by the changed expression pattern of lipid-regulating genes, altered levels of metabolites directly related to the synthesis and catabolism of lipids, and hepatosplenomegaly often observed in hepatic steatosis. However, exogenous citrulline may have a beneficial role in inducing lipolysis and glycerol oxidation in LPI as citrulline supplementation was seen to correlate negatively with the levels of G3P, routine laboratory TGs and lipid clusters containing TGs, and positively with glycerol, glyceric acid, tartronic acid and ethanolamine, necessary for PE synthesis. Cardiovascular disease and endothelial dysfunction also go hand in hand with CKD [20,21]. It is well established that hypertriglyceridemia with high VLDL and LDL, and low HDL cholesterols [55] as well as intracellular lipid overload [56] is common in CKD. However, in the current patient cohort, the routine laboratory lipid values did not differ significantly between the patients with and without CKD. In contrast, eGFR correlated negatively with long-chain TGs, seen also in acute renal failure, in which the elimination of long-chain TGs was significantly decreased [57]. The suggested explanation for the lipid abnormalities in CKD includes oxidation of lipoproteins and their impaired catabolism by reduced lipase activities [55]. Further, it has been proposed that the uptake of TG-rich VLDLs by glomerular cells is increased in CKD, and that the increased accumulation of fatty acids in podocytes leads to apoptosis and glomerulosclerosis [56]. Apoptosis, possibly induced by oxidative stress, promotes the loss of renal epithelial cells [58]. This is in accordance with our transcriptome study showing that genes related to the death of renal cells had an altered expression pattern in the LPI patients [15]. In addition to the liver, nuclear receptors PXR and PPARα are known to be expressed in the kidney; thus, in LPI, their altered activation may affect renal cholesterol clearance and detoxification, oxidative stress, inflammation, fatty acid β-oxidation and lipotoxicity [59]. It has been suggested that, in LPI, the export defect of CAAs may lead to an elevated level of arginine in the tubular kidney cells, further enhanced by increased arginine production due to the citrulline supply, encouraging NO production and causing impaired kidney function [10,11]. Contrary to this, our results did not support the participation of exogenous citrulline in NO production and CKD, explained possibly by generally decreased arginine production as a result of a reduced citrulline uptake by injured kidneys [60]. However, we found that the increased total plasma levels of nitrite (a surrogate for NO) associate with CKD, thus suggesting increased renal production of NO. The role of NO in the kidney is controversial, but it

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has been proven toxic as increased NO reacting with superoxide produces reactive nitrogen species causing post-ischemic renal failure and immune-mediated kidney disorders such as glomerulonephritis [61]. Our study provides the first comprehensive metabolomics and lipidomics analysis of LPI conducted on one of the largest and best-characterized patient cohorts in the world. The weakness of our study is that the patients are genetically homogenous and, therefore, applying the results to nonFinnish patients should be done with caution. However, as genotype–phenotype correlation has not been observed in LPI, we believe that our results may also be applicable to nonFinnish patients. Another limitation is that we were unable to perform a follow-up study on metabolites pre and post CKD and verify those metabolites independently of LPI. Nevertheless, our study represents novel aspects on the pathophysiology of LPI and amino acid metabolism in general which may be considered in the treatment of the LPI patients. Our study also provides novel biomarker candidates for CKD in patients with or without LPI. In conclusion, we discovered that the amino acid imbalance in LPI may affect the TCA cycle and thus energy production. In addition, our data indicate disturbances in lipogenesis and lipolysis/β-oxidation that may induce hepatic steatosis and fibrosis. We also suggest that, in addition to the lipid overload, CKD in LPI may be caused by a complex chain of consecutive events, including oxidative stress, apoptosis of renal cells, uremic toxicity and a changed microbial environment in the intestine.

Author Contributions Conception and design of the study: JK, TH, MO, OS, and JM. Sample collections: JK, MT, and MV-M. Acquisition of the clinical data: LT, KN-S, and HNi. Acquisition and analysis of the amino acid data: AO, metabolomics data: NL and IM and lipidomics data: HNy and PP. NO measurements: JK. Statistics: JK. Interpretation of the data: JK and JM. Drafting the manuscript: JK, HNy, AO, HNi, and JM. All authors have revised and approved the submitted version of the manuscript.

Funding This work was supported by the Turku University Foundation, the Finnish Cultural Foundation, the Finnish Concordia Fund, the Maud Kuistila Memorial Foundation, the Päivikki and Sakari Sohlberg Foundation, the Foundation for Pediatric Research, The Tyks Foundation, the Magnus Ehrnrooth Foundation and the Turku University Hospital ERVA Fund. The funding sources were not involved with the preparation of the article.

Acknowledgments We warmly thank the Finnish LPI patients for taking part in this study. We also thank Miina Laine at the Tyks Microbiology and Genetics in Turku University Hospital and the staffs of the DIPP clinic and the Department of Pediatrics in Turku University

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Hospital. We acknowledge Henriette Undeutsch at the Department of Physiology in University of Turku for helping with NO measurements, Maiju Saarinen at the Department of Public Health in University of Turku for assisting with statistical analyses, Anna Linko-Parvinen at the Department of Clinical Chemistry in University of Turku for helping in interpreting the metabolomics data and Damon Tringham for kindly revising the language.

Conflicts of Interest None.

Appendix A. Supplementary Data Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.metabol.2016.05.012.

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