Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI)

Molecular Genetics and Metabolism 105 (2012) 408–415

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Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI) Maaria Tringham a,⁎, Johanna Kurko a, Laura Tanner b, Johannes Tuikkala c, Olli S. Nevalainen c, Harri Niinikoski b, Kirsti Näntö-Salonen b, Marja Hietala a, d, Olli Simell b, Juha Mykkänen b a

Department of Medical Biochemistry and Genetics, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland Department of Pediatrics, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland Department of Information Technology and TUCS, University of Turku, 20014, Finland d The Clinical Genetics Unit, Turku University Hospital, Kiinamyllynkatu 4-8, 20520 Turku, Finland b c

a r t i c l e

i n f o

Article history: Received 9 December 2011 Accepted 9 December 2011 Available online 16 December 2011 Keywords: Lysinuric protein intolerance LPI SLC7A7 Transcriptome Expression analysis Amino acid transport

a b s t r a c t Lysinuric protein intolerance (LPI) is an autosomal recessive disorder caused by mutations in cationic amino acid transporter gene SLC7A7. Although all Finnish patients share the same homozygous mutation, their clinical manifestations vary greatly. The symptoms range from failure to thrive, protein aversion, anemia and hyperammonaemia, to immunological abnormalities, nephropathy and pulmonary alveolar proteinosis. To unravel the molecular mechanisms behind those symptoms not explained directly by the primary mutation, gene expression profiles of LPI patients were studied using genome-wide microarray technology. As a result, we discovered 926 differentially-expressed genes, including cationic and neutral amino acid transporters. The functional annotation analysis revealed a significant accumulation of such biological processes as inflammatory response, immune system processes and apoptosis. We conclude that changes in the expression of genes other than SLC7A7 may be linked to the various symptoms of LPI, indicating a complex interplay between amino acid transporters and various cellular processes. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Lysinuric protein intolerance (LPI [MIM 222700]) is a rare aminoaciduria with an autosomal recessive inheritance. It is a classic monogenic disease, caused by mutations in cationic amino acid (CAA) transporter gene SLC7A7 [1,2]. Its gene product, y +LAT1, heteromerizes with SLC3A2-encoded 4F2hc [3–5] to form an exchanger for cationic and neutral amino acids, thereby providing the main export route for CAAs from both epithelial and non-polar cells [6,7]. This transporter is involved in the absorption of CAAs in the small intestine and the proximal kidney tubules. Hence, LPI mutations impair both the intestinal and renal transport of CAAs [8]. Their effects in non-polar cells (for example lymphocytes) are currently less wellknown [9,10]. To date, 51 different mutations have been discovered in SLC7A7 world-wide, including insertions, deletions, point mutations and frame shift mutations [11–15]. The Finnish LPI patients, however, all share the same point mutation, LPIFin, a substitution of T for A at cDNA position 1181–2 [1,2] (IV5-2A-T [12], c.895-2A > T [14]), that obliterates a splicing site, which in turn results in a frame shift and consequent putative shortening of the polypeptide. This genetic

⁎ Corresponding author. Fax: + 358 2 3337300. E-mail address: maaria.tringham@utu.fi (M. Tringham). 1096-7192/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ymgme.2011.12.007

uniformity of the Finnish patients offers an excellent basis for studying the impact of other genes and factors on the variable symptoms of LPI. An additional advantage is that the patients are clinically well characterized, and their follow-up is centralized at our clinic. The symptoms of LPI are extremely variable even among patients sharing the same mutation. Therefore, no correlation between the genotype and the clinical manifestations has been detected. The connection between the CAA transport defect and variable multisystem symptoms remains poorly characterized, but it has been suggested that the accumulation of arginine in the cells with the defect and the subsequent increase of nitric oxide (NO) may be of essence [10]. The symptoms include failure to thrive, protein aversion, osteoporosis, hepatosplenomegaly and immunological abnormalities (e.g. deficient B cell functions: low concentrations of IgG subclasses and poor vaccination response), but some patients also develop severe lifethreatening complications, such as pulmonary alveolar proteinosis and nephropathy [8,16–19]. Due to the transport defect, without treatment, the patients' plasma concentrations of lysine, arginine and ornithine are from one third to a half of normal values, and those of serine, alanine, glycine, proline, citrulline and glutamine are slightly raised [20]. Most of the Finnish patients suffer from slight thrombocytopenia and unexplained normochromic normocytic anemia, which is particularly severe and persistent in those who have undergone kidney transplantation (unpublished data).

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The aim of this study was to define the genetic factors and molecular mechanisms behind the clinical variability in the Finnish patients by examining their transcriptome by means of microarray technology. The exact cause of the variable symptoms still remains unclear but, based on our results, changes in the expression of genes other than SLC7A7 may be linked to the LPI symptoms and their variability. More generally, our study demonstrates the complexity of the genetic network contributing to the clinical outcome of this monogenic disease. 2. Materials and methods 2.1. Study subjects For the microarray study, thirteen Finnish LPI patients were chosen from a cohort of 38 patients in follow-up at the Department of Pediatrics, University of Turku and the Turku University Hospital. Patients with severe complications such as end-stage renal disease and active alveolar proteinosis were excluded from the study since our aim was to examine the patients with moderate symptoms representing the ‘classic’ clinical characteristics of LPI. Of the 13 patients included, seven were female. Their mean age was 29.9 years (range from 7 to 47 years). The control samples were collected from 10 healthy volunteers (five female) who were age and sex-matched to the patients. The age of the controls varied from 9 to 48 years (mean 30 years). The diets of the control individuals were not controlled, but they can be expected to differ markedly from those of the patients, the patients being on a permanent protein-restricted diet. In addition to health reasons, the patients' diet is maintained by their strong natural protein aversion. Therefore, recruiting healthy volunteers on a similar diet was unfortunately impossible. The patients' mean age at the time of the LPI diagnosis was 7.3 years (range from 0.1 to 31 years). Three patients suffered from chronic nephropathy with mild proteinuria, haematuria and a moderately elevated serum creatinine concentration. Two patients had experienced one or more episodes of alveolar proteinosis, but were currently in remission. One patient had been diagnosed with type 2 diabetes. All 13 patients suffered from moderate to severe osteopaenia, and six of them had a history of multiple fractures in childhood. Skeletal maturation was delayed in the children. Blood hemoglobin

409

values were normal in all the patients, but two had microcytosis (Table 1, Supplementary Table 1A). The amino acid supplementation of the patients had been adjusted according to the plasma amino acid concentrations. The treatment consisted of a protein-restricted diet, calcium and vitamin D supplements, and oral supplementation with citrulline, either alone or in combination with sodium benzoate or sodium phenylbutyrate if nitrogen scavengers were needed to control hyperammonaemia. In addition, 7 patients received low-dose oral lysine supplementation at meal times. Twelve of the 13 patients had combined hyperlipidemia and responded poorly to dietary therapy, and 5 were treated with statins. Six patients were on antihypertensive medication, and one patient received carnitine supplementation for hypocarnitinemia. The laboratory findings and amino acid supplementation of the 13 patients included in the microarray study are presented in more detail in Table 1. The second part of the study consisted of quantitative real-time PCR verification of the genes of interest uncovered in the microarray study. For this part, we collected samples from an additional 22 LPI patients raising the number of study subjects to 35. The medication and symptoms of these patients are presented in Supplementary Table 1A and B, respectively [20]. 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. 2.2. Methods 2.2.1. Blood collection and RNA extraction Whole blood from the patients and control subjects was collected into PAXgene Blood collection tubes (PreAnalytiX, Hombrechtikon, Switzerland). The total RNA was subsequently extracted from the peripheral blood cells using the PAXgene Blood RNA Kit (PreAnalytiX) according to the manufacturer's instructions, apart from the elution step; instead of the elution buffer provided in the kit, we used sterile, pyrogen and RNAase-free water (Promega Corporation, Madison, WI, USA). 2.2.2. RNA amplification 5.0 μg of the total RNA of each patient (apart from one patient, from whom the yield of total RNA was only 4.5 μg) and of the pooled

Table 1 Laboratory findings and relevant medication of the patients in the microarray analysis (P1-P13). P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

P13

F 1.0 6.9

F 4.9 10.7

F 3.5 14.8

F 3.0 18.8

M 20.6 20.6

M 5.4 31.8

F 3.0 36.4

F 0.2 37.1

M 7.0 39.3

M 0.3 39.6

M 2.2 41.6

F 12.0 44.8

M 30.6 46.6

Unit

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

P13

Mean

P-arginine P-citrullinea P-glutaminea P-lysinea S-cholesterolb Creatinine Haemoglobin Ferritine Thrombocytes Leukocytes

μmol/l μmol/l μmol/l μmol/l mmol/l μmol/l g/l μg/l E9/l E9/l

38 84 1717 130 5.0 51 118 551 144 3.9

42 69 1557 132 5.7 60 131 512 208 5.6

39 92 1056 119 7.3 104 154 916 123 5.8

44 116 1921 138 5.6 75 123 753 174 3.8

24 60 2761 43 5.4 67 111 1057 107 3.4

40 58 1638 116 7.2 105 151 3932 112 4.2

25 105 703 105 6.4 75 146 953 84 3.4

20 54 1112 91 6.8 73 132 652 225 5.9

65 110 1007 22 6.3 146 137 1698 204 4.5

29 94 740 94 5.3 108 133 3645 154 6.4

30 86 663 108 8.3 60 155 537 162 5.2

20 81 821 157 4.7 80 139 922 146 6.3

50 91 744 165 5.3 156 132 860 184 4.9

36 86 1211 109 6.1 95 136 1501 155 4.8

Medication

Dose

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

P13

Mean

Statine Citrulline suppl. Lysine suppl. Na benzoate

mg/kg/d mg/kg/d mg/kg/d

179 122

116 -

86 13 -

128 28 -

80 15 -

91 22 86

yes 80 25

86 -

yes 70 21 -

yes 159 10 -

50 -

yes 94 34 -

yes 90 -

93 22 -

Gender Age at dg Age at study Laboratory findings a

a b

P = Plasma concentration. S = Serum concentration.

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control samples (500 ng of each) was amplified using the RiboAmp® OA1 Round RNA Amplification Kit (Arcturus, Sunnyvale, CA, USA) according to the manufacturer's instructions. cDNAs were in vitrotranscribed into cRNA in an over-night reaction (14 h) using the Illumina RNA Amplification Kit (Ambion, Huntingdon, UK). During the reaction, the cRNA was labeled with biotine 11 dUTP (PerkinElmer, Wellesley, MA, USA). The concentration of the RNA and cRNA samples was measured using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) both before and after amplification. The RNA/cRNA quality was checked using the Experion electrophoresis system (Bio-Rad Laboratories, Hercules, CA, USA). 2.2.3. Hybridization 850 ng of each of the labeled samples was hybridized on a Sentrix® HumanRef-8 Expression BeadChip Array (Illumina, San Diego, CA, USA) at 55 °C overnight (18 h) according to the instructions for Illumina® BeadStation 500X Revision D. Hybridization was detected using 1 μg/ml of cyanine3-streptavidine (GE Healthcare Europe, Munich, Germany). The arrays were scanned with the Illumina BeadArray Reader (PMT = 655), and the results were converted into numerical data using the Bead Studio v1.5.1.34 Data Analysis Software (Illumina). 2.2.4. Statistical analysis of the microarray results The scanned raw data had 24 254 probes. First, the data were normalized using the quantile normalization tool of the Inforsense Knowledge Discovery Environment (KDE, Inforsense, London, UK). Second, signal log ratios (SLR) were computed using the signals obtained from the LPI samples and the average control signal. Those genes whose average SLR was less than 0.75 or whose average signal or average control was less than or equal to 300 were filtered out. A threshold value of 300 was chosen since it exceeded the average background signal (220). After filtering, 9 920 probes were preserved. The genes were further filtered using a log 2 fold change limit of 0.8, and a p-value limit of 0.05 for the results of the t-test. 2.2.5. Functional analysis of the results The functional distribution of the resulting genes with altered expression was studied by counting and plotting the frequencies of their gene ontology annotations (GOA). In addition, the ratio of the GOA frequency of the genes with altered expression to those of the genes with unaltered expression was plotted for each GOA category in order to pursue the change of gene functions observed in LPI. The functional annotations were studied, and a clustering of functional annotations was performed using the free online Functional Annotation Tool of the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [21–22]. Further functional and canonical pathway analyses of differentially expressed genes were carried out using Ingenuity Pathways Analysis (IPA) software version 9.0 (Ingenuity Systems, Redwood City, CA, USA). A hierarchical clustering was performed on the differentially expressed genes using the R/Bioconductor software [23]. The microarray data were deposited in the EMBL-EBI microarray database (accession number E-TABM-572). 2.2.6. Verification of the expression level alterations of selected genes with quantitative real-time PCR The expression levels of four SLC7 family genes (SLC7A1, SLC7A5, SLC7A6 and SLC7A7) and two other selected amino acid transporters (SLC3A2 and SLC1A5) were measured for each patient and control sample using quantitative real-time PCR (iCycler® IQ™5, Bio-Rad, Hercules, CA, USA). First, 100 ng of mRNA was reverse-transcribed into cDNA (iScript™ cDNA synthesis Kit, Bio-Rad) under the following conditions: 25 °C for 5 min, 42 °C for 30 min and 85 °C for 5 min. Amplification of the template cDNA was performed using the SYBR Green method (IQ™ SYBR® Green Supermix, Bio-Rad) under the following conditions: 95 °C for 5 min, 95 °C for 10 s, 57.8 °C (or 60.0 °C for

SLC7A1) for 30 s, for a total of 45 cycles. The primers (Biomers, Ulm, Germany) were designed with the Beacon designer 4 program (Supplementary Table 2) (PREMIER Biosoft International, Palo Alto, CA, USA). All measurements were performed in duplicate in two separate runs using samples derived from all the LPI patients included in the study (n = 35), and the control samples (n = 10) for the microarray study. The relative quantifications (ΔΔCT values) were calculated using GNB2L1 (guanine nucleotide binding protein, beta-peptide 2-like 1) and TRAP1 (TNF receptor-associated protein 1) as internal reference genes. The statistical significance of the expression changes was tested using the t-test. The expression changes of different genes of interest were tested for correlation (Pearson's correlation) using the SPSS 11.0.1 software (IBM, Armonk, NY, USA). 2.2.7. Flow cytometric assay of the patients' lymphocyte subpopulations The LPI patients' lymphocyte subpopulations were analyzed by staining their blood samples with the Simultest™ IMK Plus immuno-staining kit for flow cytometry (BD Becton Dickinson UK, Oxford, UK) according to the manufacturer's instructions. The stained, prepared samples were incubated overnight at + 4 °C and run with the BD FACScan flow cytometry analyzer. The percentages of the lymphocyte subpopulations were compared to the reference values in use in two Finnish university hospitals (Helsinki and Turku). 3. Results 3.1. The microarray results The microarray study revealed that the selected 13 Finnish LPI patients shared 935 transcripts whose expression pattern was altered compared to the control population and which exceeded the set p-value limit of b0.5 and log 2 fold change limit of ±0.8 (2 ± 0.8, equalling more than 1.74 times or less than 0.57 times the expression of the control population) (Supplementary Table 3). The 935 transcripts represent 926 individual genes, of which 487 were up-regulated and 439 down-regulated. The genes with an altered expression affected a variety of basic functions of the cell, including cell cycle control, metabolism, cell signaling, amino acid and ion transport, and apoptosis. Based on the biological processes in which they were involved, the genes could be divided into different gene ontology categories (GOs). 19% of the genes over-expressed in the LPI patients were related to immunological processes, 8% to signal transduction, 7% to inflammatory response and 6% to intracellular protein transport. Among the under-expressed genes, the most affected biological processes were development (10%), regulation of transcription (8%), homophilic cell adhesion (7%) and synaptic transmission (7%). The hierarchical clustering (Supplementary Fig. 1) performed on the differentially expressed transcripts did not reveal any distinct connection between the severity of symptoms and gene expression changes. However, the functional annotation analysis with the DAVID bioinformatics tool revealed several biological processes affected in LPI, including immune response, inflammatory response and processes related to cell death and apoptosis (Table 2). The IPA pathway analysis revealed significant changes (p b 0.01) in canonical pathways related to antiviral immune response, cellular growth and differentiation and hepatic/ liver function. Interestingly, the analysis also uncovered the significant involvement of the differentially expressed genes with one toxicological function associated with renal necrosis (Table 3). In the microarray results, we discovered 16 up-regulated genes related to cellular transport, 10 of which were genes of different solute carrier family (SLC) members. Seven transporter genes, including six SLC genes, were down-regulated. Of the SLC7 amino acid transporter family, there were three members with altered expression (Supplementary Table 3). SLC7A7 was the most down-regulated gene, with

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Table 2 The results of the DAVID gene ontology analysis of the differentially expressed transcripts. Category

GO ID

BP/CC/MFa

Gene Count

FEb

p-value

Corrected p-valuec

Immune response Response to wounding Chemotaxis Taxis Negative regulation of apoptosis Inflammatory response Negative regulation of programmed cell death Negative regulation of cell death Apoptosis Death Erythrocyte differentiation Programmed cell death Regulation of apoptosis Locomotory behavior Cell death Lysosome Lytic vacuole Endoplasmic reticulum Growth factor binding

0006955 0009611 0006935 0042330 0043066 0006954 0043069 0060548 0006915 0016265 0030218 0012501 0042981 0007626 0008219 0005764 0000323 0005783 0019838

BP BP BP BP BP BP BP BP BP BP BP BP BP BP BP CC CC CC MF

59 47 21 21 33 31 33 33 47 54 9 47 58 26 53 22 22 65 14

1.8 1.9 2.8 2.8 2.0 2.0 2.0 2.0 1.7 1.6 4.5 1.6 1.5 2.0 1.6 2.3 2.3 1.5 2.9

8.86E-6 3.58E-5 5.51E-5 5.51E-5 2.67E-4 2.89E-4 3.42E-4 3.60E-4 6.59E-4 7.32E-4 7.30E-4 8.98E-4 1.00E-3 1.06E-3 1.08E-3 5.46E-4 5.46E-4 9.05E-4 1.00E-3

0.025 0.050 0.051 0.051 0.172 0.151 0.149 0.136 0.208 0.187 0.205 0.225 0.228 0.222 0.210 0.216 0.216 0.182 0.577

a b c

BP = Biological process, CC = cellular component, MF = molecular function. FE = Fold Enrichment. EASE b 0.05, FDR b 2%.

a log 2 fold change of −2.37 (19% of the normal expression). The expression of SLC7A6 encoding y +LAT2, the sister y +L transporter to y +LAT1, was, however, not altered. Two SLC7 members were upregulated: SLC7A1 (CAT1) by a fold change of 1.05 and SLC7A5 (LAT1) by 0.88. In addition, the neutral amino acid transporter SLC1A5 (ASCT2) was also considerably up-regulated (fold change 1.10). The expression changes of SLC7A1, SLC7A5, SLC7A7, and SLC1A5 were validated using quantitative real-time PCR. The expression of SLC3A2 and SLC7A6 was discovered unaltered in the microarray study. Nonetheless, these genes were still included in the qRTPCR validation step as they were of special interest to us. 3.2. Quantitative real-time PCR The expression levels of the transporter genes, SLC7A1 (CAT1), SLC7A5 (LAT1), SLC7A6 (y +LAT2), SLC7A7 (y +LAT1), SLC1A5 (ASCT2) and SLC3A2 (4F2hc) were verified using quantitative realTable 3 The LPI-associated canonical pathways or toxicological functions uncovered in the Ingenuity Pathway Analysis (IPA). Canonical pathway or toxicological function

pvalue

Interferon signaling

0.001

Hepatic fibrosis/hepatic stellate cell activation

0.002 16

Hepatic cholestasis

0.005 15

PXR/RXR activation

0.005

ERK/MAPK signaling

0.006 18

PPARα/RXRα activation

Genes Associated genes (n) 7

9

0.009 16

Renal necrosis/cell death 0.001 18 (apoptosis of kidney cell lines)

IFIT3, IFIT1, OAS1, IFNGR2, IFITM1, IFNGR1, BAX IL8, IL18RAP, IFNGR2, IFNGR1, MYH11, BAX, PDGFB, PGF, IL1R2, LY96, TGFB3, SMAD4, IL1B, MYL4, IL1RAP, AGTR1 PRKACB, IL8, IL18RAP, SLCO1A2, CETP, IL1R2, LY96, NFKBIA, PRKAR2B, INS, IL1B, ABCC3, IL1RAP, SLCO1B3, PRKAR1A PRKACB, CYP1A2, PRKAR2B, INS, G6PC, IGFBP1, ABCC3, SLCO1B3, PRKAR1A RAP1B, PRKACB, ITGB1, ETS1, PPP1CB, KRAS, RAP1A, ELF1, YWHAQ, FOS, ELF2, PPP1R3D, LAMTOR3, PRKAR2B, DUSP1, PPP2R2B, RPS6KA5, PRKAR1A PRKACB, IL18RAP, TGFBR3, GNAQ, KRAS, GK, ADIPOR1, IL1R2, NFKBIA, PRKAR2B, INS, TGFB3, SMAD4, IL1B, IL1RAP, PRKAR1A ATP2B2, BAX, BCL2L1, BCL6, BNIP3L, CASP3, CHMP5, HSPA1A/HSPA1B, ITGB1, KRAS, LGALS12, MAP3K8, NF2, RAD21, TMX1, TP53INP1, TP73, YWHAQ

time PCR (Table 4). A statistical analysis of the qRT-PCR results of the differentially-expressed amino acid transporters demonstrated a statistically significant expression change in all the patients (n = 35) vs. controls for all the transporters measured but SLC3A2. The results for the 13 patients were largely similar to those obtained using the microarray, and those for all the 35 patients were similar to those of the smaller cohort. Some differences, however, were observed, the most notable being the expression of SLC7A5 and SLC7A6. While there was no compensatory SLC7A6 function in the peripheral blood cells for the 13 patients when averaged, its expression varied considerably between the individual patients. However, no association between any increase or decrease of SLC7A6 expression and clinical symptoms could be detected, nor any correlation to the expression changes of SLC7A7 (Table 5). In contrast to this, significant correlations were detected (Pearson's correlation) between the expression changes of several other transporters: SLC3A2 correlated significantly with SLC7A7, SLC7A5 and SLC1A5, SLC1A5 with SLC7A5 and SLC7A1, and SLC7A5 with SLC7A6 (Table 5). 3.3. Flow cytometric assay of lymphocyte subpopulations The patients' lymphocyte subpopulations, including T lymphocytes (CD3 +), B lymphocytes (CD19 +), helper/inducer T cells (CD4 +), suppressor/cytotoxic T cells (CD8 +), NK cells (CD16/65 +) and activated T cells, were measured, and the CD4 +/CD8 + ratio was then calculated. With the exception of activated T cells, the

Table 4 The qRT-PCR verification of the selected amino acid transporters. Gene ID

SLC7A7 SLC3A2 SLC7A1 SLC7A5 SLC7A6 SLC1A5 a b c

Microarraya

qRT-PCR of array patientsa

qRT-PCR of all patientsb

Log 2 FCc

Log 2 FC

P

Log 2 FC

P

− 2.37 NA 1.05 0.88 NA 1.10

− 2.66 0.11 0.46 2.34 − 0.06 1.87

b0.001 NS b0.001 b0.001 NS b0.001

− 3.05 0.04 0.73 1.79 − 0.79 1.86

b 0.001 NS b 0.001 b 0.001 b 0.05 b 0.001

n = 13. n = 35. FC = Fold Change, NS = not significant, NA = not available.

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Table 5 Correlations (Pearson) of the amino acid transporter genes studied.

SLC3A2 SLC7A7 SLC7A6 SLC7A5 SLC7A1 SLC1A5

SLC3A2

SLC7A7

SLC7A6

SLC7A5

SLC7A1

SLC1A5

1 0.501⁎⁎ − 0.090 0.470⁎⁎ 0.279 0.439⁎⁎

0.501⁎⁎ 1 0.166 0.320 0.194 0.231

− 0.090 0.166 1 0.394⁎ − 0.231 0.101

0.470⁎⁎ 0.320 0.394⁎ 1 0.230 0.702⁎⁎⁎

0.279 0.194 − 0.231 0.230 1 0.577⁎⁎

0.439⁎⁎ 0.231 0.101 0.702⁎⁎⁎ 0.577⁎⁎ 1

⁎ P b 0.05. ⁎⁎ P b 0.01. ⁎⁎⁎ P b 0.001.

proportions of the lymphocyte subpopulations did not differ greatly from the reference values. The T lymphocyte values varied from slightly subnormal to normal, as did the CD4 + T cell population, whereas the B cell percentages varied from slightly decreased to slightly elevated. The NK cell populations were normal, as were the CD8 + T cell populations, apart from one patient with a slightly increased CD8 + cell population which yielded a decreased ratio of CD4 +/CD8 + cells. On the whole, the proportions of the lymphocyte subpopulation were fairly low. Apart from two exceptions, they were either fractionally subnormal or just within the normal limits. Contrary to this, the proportions of activated, CD3 and HLA DRpositive T cells differed considerably from the reference values in all patients but three. The individual FACS results are illustrated in detail in Table 6. 4. Discussion We carried out a whole-blood microarray study in order to discover the gene expression changes related to the variable symptoms of the Finnish LPI patients. Although the known target tissues of LPI are mainly the epithelial cells of the small intestine and kidney tubules [8], the disease manifests itself multisystem as SLC7A7 is also expressed in other tissues [24], for instance monocytes/macrophages and lymphocytes to a high degree [9,25–26]. Therefore, peripheral blood was an optimal source material for our study. In addition, the patients have a haemorrhagic diathesis [27–28] and all invasive sample collection techniques are thus to be avoided. There were, however a number of limitations to this study. As our sample material was whole blood which was lysed immediately upon collection, it was not possible to determine the proportions of peripheral blood cell populations the resulting RNA is derived from. An additional difficulty is provided by the patients' diet and medication — we cannot rule out the effect of these factors on the patients' gene expression; on the contrary, they are relatively likely to have caused some changes. However, since the diet and medication are both essential for the

Table 6 The flow-cytometric results of the LPI patients' lymphocyte subpopulations. Patient ID

CD3 (%)

CD19 CD4 (%) (%)

CD8 (%)

CD4/CD8 CD16/ CD3 + HLA ratio 56 (%) DR + (%)

Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Patient 11 Patient 12 Patient 13 Reference values

57 71 59 80 78 56 70 58 61 80 63 61 65 60–85

23 15 28 11 6 18 13 4 6 7 14 13 14 7–23

25 43 24 29 46 31 40 70 35 39 31 33 25 19–48

0.7 0.7 1.6 1.8 0.8 1.1 1.0 0.2 1.2 1.2 1.2 1.2 1.9 0.6–2.8

19 30 39 54 39 34 39 13 41 47 38 41 47 29–59

10 8 8 6 8 16 10 7 26 6 9 17 13 6–29

3 5 4 3 2 5 4 8 2 7 4 16 2 8–17

patients' well-being as they are designed to minimize the symptoms, their effects are part of the disease outcome as well. The results of our study revealed that the etiology of lysinuric protein intolerance involves more elements than was previously thought. It appears that a mutation in the SLC7A7 gene not only influences the transcription of this particular gene but is connected, directly or indirectly, to the expression of approximately 1 000 other genes. Indications of this multi-genic nature of LPI were already observed in the gene expression analysis of the Slc7a7 nullizygous mice by Sperandeo and others [29], as they, too, discovered hundreds of genes with altered expression profiles in the liver and small intestinal samples of the Slc7a7 −/− mice. However, while these two sets of LPI microarray results have some similarities, they also have major differences, as can only be expected with the different organisms and study target tissues. The genes of an individual are constantly influenced by a multitude of epigenetic factors, nutritional status being among the key environmental stimuli. The permanent low-protein diet which is an essential part of the LPI patients' treatment can therefore be expected to have an effect on their gene expression. The large number of differentially expressed genes regulating basic cellular functions seen in our results is thus more likely to be an indication of their altered nutritional homeostasis rather than directly related to their symptoms. It has also been previously documented that the expression of certain amino acid transporters can be modulated by environmental stimuli; nutritional limitation, particularly depletion of arginine in a cell is reported to lead to increased expression of LAT1 and translation of the CAT1 mRNA [5, 30–31]. The up-regulation of these transporters may therefore be the patients' cellular response to the impaired transport of cationic amino acids; an attempt to recreate a new, more satisfying amino acid balance in the compromised cells. Nevertheless, despite these potentially compensatory systemic changes, the LPI patients following a tight nutritional and medicinal regime [32–33] provide an exceptionally uniform and homogeneous target population for gene expression studies as they are likely to share the alterations in gene expression related to the re-regulated cellular functions in a nutritionally altered environment. The impact of nutrition on the individuals' gene expression is particularly striking during embryonal development [34] but also has lasting effects during later growth and development. The LPI patients have normal pre-natal development as their mothers have normal amino acid profiles and the embryos are not subjected to amino acid starvation during gestation. However, the effects of the protein limitation and amino acid starvation on development will take place post-natally in LPI, seen as the poor growth and delayed bone age of the pediatric patients [8] and the changed gene expression pattern shared by the patients. Both the microarray results and the subsequent qRT-PCR results confirmed that the most down-regulated gene in the LPI patients was SLC7A7. The reason for its down-regulation is yet unclear. Potentially, the mutation altering the carboxy terminus of the nascent protein subjects the mRNA to nonsense-mediated RNA decay or other cellular quality control mechanisms eliminating the defective product and reducing the quantities of its mRNA. As the silencing is not complete but the gene is still expressed at an approximate rate of 20%, the resulting aberrant proteins may have deleterious effects on cell viability seen as the up-regulated apoptosis pathways (Table 2). In the expression systems of previous studies, we have seen the defective trafficking of the y +LAT1 proteins harboring the LPIFin mutation [35] and their accumulation into the endoplasmic reticulum potentially due to misfolding caused by the premature stop codon introduced by the mutation [1,2]. This is likely to cause ER stress and activate the unfolded protein response (UPR) in the compromised cells, promoting apoptosis [36–38]. The exact mechanism of the inactive CAA transporter to cause the symptoms of LPI is yet unknown. Recently, one of the causes has been suggested to be the accumulation of arginine in cells where y +LAT1/

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4F2hc normally provides the only efflux route for CAAs [10]. In LPI, as the influx of CAAs functions normally despite the impaired efflux, the imported arginine accumulates in these cells in increasing quantities. In addition to hindering normal cellular function, the excess arginine would also be converted into raised levels of NO by an up-regulated iNOS, causing cellular distress and damage, and increasing apoptosis. In this study, we see up-regulation of cellular responses to damage and inflammation, and increased function of apoptotic pathways (Table 2). However, no differential expression of the NOS signaling genes or pathways was observed in the microarray data analyses. However, two closely NOS-related interferon signaling pathway genes, IFNGR1 and IFNGR2, were discovered slightly down-regulated (Table 3). Based on previous publications, there seems to be a certain level of ambiguity concerning the patients' NO and arginine levels, and their connection to the up-regulation of iNOS. An Italian study revealed raised NO3− levels in patients without a corresponding upregulation of iNOS but with normal plasma arginine levels [39], whereas a Japanese one reported patients with low plasma NO levels due to an arginine deficiency [11]. The current plasma arginine levels of the patients included in our study are artificially maintained at a low normal level as the patients receive a citrulline supplement to boost these levels (personal communication, L. Tanner). The reduced efflux of CAAs may also have a pathomechanism of another type, as the retention of arginine inside cells reduces its extra-cellular availability. The observed up-regulation of the system y + transporter, CAT1, may relate to this as it functions as a CAA importer [40–41]. It has been suggested previously that the regulation of arginine efflux from monocytes serves as a finely tuned control mechanism for dividing arginine between the intra- and extracellular niches [26,42]. In LPI, this mechanism would be obliterated, leading to increased intra-cellular and depleted extra-cellular arginine reserves. This is likely to have deleterious effects on processes dependent on extra-cellular arginine, for example the differentiation and activation of T cells [43]. This is supported by our flow cytometry findings demonstrating that the patients had relatively low levels of lymphocyte subpopulations, particularly of activated T cells, in the peripheral blood (Table 6). Similarly, the DAVID gene ontology analysis of the microarray results revealed alteration of immunological pathways. In accordance with this, the LPI patients have immunological abnormalities [18] and are particularly susceptible to certain viral infections, such as varicella [44] (Lukkarinen et al. 1998). This immunological involvement is further corroborated by the clustering of the differentially expressed genes into the pathway related to antiviral immune response (‘interferon signaling’) in the IPA pathway analysis. The IPA pathway analysis also revealed other interesting aspects of LPI through the changed canonical pathways in which the differentially expressed genes were involved in. So far, the hepatic clinical findings in LPI patients have been associated with the dysfunction of the urea cycle. However, based on the genes involved in the pathways related to ‘hepatic fibrosis/hepatic stellate cell activation’, ‘hepatic cholestasis’, ‘PXR/RXR activation’ and ‘PPARα/RXRα activation’ uncovered by our IPA analysis (the genes involved are shown in Table 3), it appears that at least to a certain extent, the hepatic alterations may also have an immunological background. We were also pleased to discover that by investigating gene expression changes in the blood one can, indeed, follow changes in other tissues as well. We discovered differential expression of genes relating to ‘renal necrosis/cell death’ (Table 3) which can be directly linked to the renal complications typical of LPI. As the SLC7A7-encoded y +LAT1 has a sister transporter, y +LAT2 (encoded by SLC7A6), sharing the same y +L transport profile, we wished to explore the potential compensatory function of this sister transporter to see whether it has any connection to the vast variability of the LPI patients' clinical symptoms. We expected to see some interaction between y +LAT1 and y +LAT2 as two reports on it have been published: on the one hand, increased expression of SLC7A6 was

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detected in lymphoblasts extracted from LPI patients [25], suggesting a compensatory interaction, whereas another study documented negative interaction in the form of interference by mutated y +LAT1 molecules on the transport function of y +LAT2 in a test setting of Xenopus oocytes [45]. Contrary to both the above, we observed no change in the expression of y +LAT2-encoding SLC7A6 in our microarray data. Similarly, the quantitative real-time PCR results for the entire LPI patient population level revealed no significant uniform change in its expression. However, we saw alterations in its expression in individual patients (Table 5), which suggests that there might, after all, be some degree of interplay between these transporters, potentially forming a compensatory mechanism for the impaired transport of CAAs. However, we discovered no correlation between the expression changes of SLC7A7 and SLC7A6, nor between the differential expression of SLC7A6 and the severity of clinical findings, which indicates a more subtle effect for these expression changes on the system. Nonetheless, we discovered a significant correlation between the expression changes of a number of other amino acid transporters. This is particularly interesting as it has been proposed that there is a specific functional connection between two of these: the neutral amino acid transporters ASCT2 and LAT1. It has been suggested that, by importing glutamine, ASCT2 facilitates the import of leucine by LAT1 as LAT1 exports glutamine with high affinity [46]. This amino acid transport “shuttle” has been linked to the activation of the mTOR pathways by essential amino acids, including leucine [47]. The suppression of autophagy orchestrated by activated mTOR may provide one potential mechanism behind LPI patients' immunological abnormalities as autophagy is one of the ways for a cell to disable and dispose of invading pathogens, and to remove potentially harmful structures and damaged organelles. It is also designed to supply the cell with nutrients during nutritionally challenging times, such as fasting, by degrading the cell's own structures in order to maintain vital functions. The activation of mTOR, which blocks this pathway, occurs naturally in the presence of ample nutrition [48]. In the disturbed nutritional situation of LPI patients' cells, however, blocking off the autophagy option for obtaining compensatory nutrients can have radical effects on the cellular homeostasis. Autophagy has also been shown to be involved in programmed cell death [49], and its suppression may therefore also be illustrated by death-related altered pathways in the DAVID gene ontology analysis, as seen in Table 2. Despite the strong down-regulation of y +LAT1, the microarray experiment detected no significant change in the expression of SLC3A2. The qRT-PCR validation, however, revealed small scale expression changes which correlated significantly (P b 0.01) with those of SLC7A7 (and those of SLC7A5 and SLC1A5), indicating a clear connection between the expression of these transporters (Table 5). The small scale of the over-all change in the expression of SLC3A2 is not a big surprise as, in addition to amino acid transport, its product, 4F2hc, participates in various other crucial cellular functions, including the differentiation, attachment and fusion of activated or growing cells [3] and the maintenance of cell polarity [50–51]. It may also be that it is through these functions that the wider effect of the mutated y +LAT1 is mediated to the systemic level. Previous studies have also revealed that y +LAT1 and the other amino acid transporter light chains cannot reach the plasma membrane in the absence of 4F2hc [52]. Furthermore, when y +LAT1 carrying the LPIFin mutation heteromerizes with 4F2hc, the resulting holotransporter does not reach the plasma membrane [53]. As the retention of 4F2hc in the cytoplasm is not compensated for by increasing its transcription, this may create a shortage of 4F2hc in the plasma membrane and tamper with those cellular functions requiring its presence, thereby affecting the balance of the system and potentially exacerbating the clinical symptoms. Our gene expression study on peripheral blood cells of a uniform patient cohort is the first of its kind conducted on LPI patients. The results showed changes in immunological pathways, amino acid

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transport and in genes related to basic cellular functions, such as cellular development, differentiation and apoptosis. These changes are the sum of various factors; the differential expression of the amino acid transporters is likely to alter the nutritional status in the cell, the accumulation of arginine inside cells may cause cellular distress in some cells and deprive others of their vital component, the potential deficiency of 4F2hc at the plasma membrane can tamper with the cell's surface structures and potentially its signaling, and the activation of mTOR suppresses autophagy and thereby strips the cell of one of its methods of not only pathogen defense but also waste disposal, thereby hampering its plasticity. However, elucidation of the exact connections between the defective amino acid transport, unsettled cellular homeostasis and the multiplicity of symptoms of lysinuric protein intolerance remains a task for the future. It is to be hoped that the present study will initiate considerable further investigation. Supplementary materials related to this article can be found online at doi:10.1016/j.ymgme.2011.12.007.

Disclosure statement The authors declare no conflict of interest.

Acknowledgements We are grateful to the Finnish LPI patients for taking part in this study. We also want to thank the following funds and organizations for financial support: the Sigrid Jusélius Foundation, the Turku University Foundation, the Signe and Ane Gyllenberg Foundation, the Finnish Cultural Foundation, Finnish Concordia Fund, the European Commission (grant EUGINDAT LSHM-CT-2003-502852 to O.S.) and the Foundation for Paediatric Research, Finland. The funding sources were not involved in the study design, the collection, analysis or interpretation of data, the writing of the report or the decision to submit the paper for publication.

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