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Published in final edited form as: Mol Genet Metab. 2020 Aug 5;131(1-2):147–154. doi: 10.1016/j.ymgme.2020.07.013

Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway

Brian J Shayota 1, Taraka R Donti 1,*, Jing Xiao 2, Charul Gijavanekar 2, Adam D Kennedy 3,^, Leroy Hubert 1,#, Lance Rodan 4, Christina Vanderpluym 5, Catherine Nowak 4, Hans T Bjornsson 6,7,8, Rebecca Ganetzky 9, Gerard T Berry 4, Kirk L Pappan 3,^, V Reid Sutton 1,2, Qin Sun 1,2, Sarah H Elsea 1,2
PMCID: PMC8630378  NIHMSID: NIHMS1622025  PMID: 32828637

Abstract

Inborn errors of metabolism (IEM) involving the non-oxidative pentose phosphate pathway (PPP) include the two relatively rare conditions, transketolase deficiency and transaldolase deficiency, both of which can be difficult to diagnosis given their non-specific clinical presentations. Current biochemical testing approaches require an index of suspicion to consider targeted urine polyol testing. To determine whether a broad-spectrum biochemical test could accurately identify a specific metabolic pattern defining IEMs of the non-oxidative PPP, we employed the use of clinical metabolomic profiling as an unbiased novel approach to diagnosis.

Subjects with molecularly confirmed IEMs of the PPP were included in this study. Targeted quantitative analysis of polyols in urine and plasma samples was accomplished with chromatography and mass spectrometry. Semi-quantitative unbiased metabolomic analysis of urine and plasma samples was achieved by assessing small molecules via liquid chromatography and high-resolution mass spectrometry. Results from untargeted and targeted analyses were then compared and analyzed for diagnostic acuity.

Two siblings with transketolase (TKT) deficiency and three unrelated individuals with transaldolase (TALDO) deficiency were identified for inclusion in the study. For both IEMs, targeted polyol testing and untargeted metabolomic testing on urine and/or plasma samples identified typical perturbations of the respective disorder. Additionally, untargeted metabolomic testing revealed elevations in other PPP metabolites not typically measured with targeted polyol testing, including ribonate, ribose, and erythronate for TKT deficiency and ribonate, erythronate, and sedoheptulose 7-phosphate in TALDO deficiency. Non-PPP alternations were also noted involving tryptophan, purine, and pyrimidine metabolism for both TKT and TALDO deficient patients.

Targeted polyol testing and untargeted metabolomic testing methods were both able to identify specific biochemical patterns indicative of TKT and TALDO deficiency in both plasma and urine samples. In addition, untargeted metabolomics was able to identify novel biomarkers, thereby expanding the current knowledge of both conditions and providing further insight into potential underlying pathophysiological mechanisms. Furthermore, untargeted metabolomic testing offers the advantage of having a single effective biochemical screening test for identification of rare IEMs, like TKT and TALDO deficiencies, that may otherwise go undiagnosed due to their generally non-specific clinical presentations.

Keywords: pentose phosphate pathway, inborn error of metabolism, transaldolase deficiency, transketolase deficiency, metabolome, developmental delay

1. Introduction

The pentose phosphate pathway (PPP) comprises a conserved set of metabolic reactions that can be subdivided into the oxidative and non-oxidative pathways. The oxidative pathway serves as the first sequence of reactions with the goal of converting glucose 6-phosphate into ribulose 5-phosphate while producing two NADPH in the process. The NADPH formed by these reactions provides reducing power in several anabolic pathways in addition to protecting the cell from reactive oxygen intermediates (ROI) via the glutathione and thioredoxin/peroxiredoxin systems (Grant, 2008; Pollak, Dolle, & Ziegler, 2007).

The non-oxidation branch of the pathway utilizes ribulose 5-phosphate to form either ribose 5-phosphate via ribose 5-phosphate isomerase (RPIA) or xylulose 5-phosphate via ribulose 5-phosphate epimerase (RPE). The ribose 5-phosphate is then used for the synthesis of nucleotides, DNA and RNA, while some carbons from xylulose 5-phosphate are shunted to glycolysis via an erythrose 4-phosphate intermediate (Stincone et al., 2015). Depending on the demands of the cell, the metabolites of the non-oxidative portion can reversibly be directed to form the glycolytic intermediates, glyceraldehyde 3-phosphate and fructose 6-phosphate, catalyzed by the enzymes transketolase (TKT) and transaldolase (TALDO).

The clinical importance of the PPP has until recently centered around glucose 6-phosphate dehydrogenase (G6PD) deficiency (OMIM: 305900), a condition that affects hundreds of millions worldwide with potentially life-threatening hemolysis secondary to NADPH depletion (Cappellini & Fiorelli, 2008; Ruwende et al., 1995; Vulliamy, Mason, & Luzzatto, 1992). Enzymatic deficiencies in the remainder of the PPP, particularly those involved in the non-oxidative branch, are far less common and less well understood. There are four known inborn errors of metabolism (IEM) caused by deficiencies of enzymes involved in the non-oxidative PPP: RPIA deficiency (OMIM: 608611), sedoheptulokinase (SHPK) deficiency (OMIM: 617213), TKT deficiency (OMIM: 617044), and TALDO deficiency (OMIM 606003). RPIA deficiency is the rarest, with only four confirmed cases in the current literature presenting clinically with leukoencephalopathy, severe intellectual disability, and peripheral neuropathy (Brooks, Anderson, Bhise, & Botti, 2018; Kaur, Wamelink, van der Knaap, Girisha, & Shukla, 2019; Naik, Shah, Wamelink, van der Knaap, & Hingwala, 2017; van der Knaap et al., 1999). SHPK deficiency is also exceptionally rare with only five reported cases in the current literature. Wamelink et al. described two cases, characterized by neonatal cholestasis, hypoglycemia, and anemia in one patient and congenital arthrogryposis multiplex and dysmorphic facies in the second [Wamelink, 2015]. Three others were found to be homozygous for the common 57-kb cystinosis causing deletion, which is a contiguous deletion that includes the sedoheptulose encoding gene, SHPK (Wamelink et al., 2008). Interestingly, all three were also found to have elevations in sedoheptulose and erythritol, which was not true for patients with cystinosis caused by variants not involving SHPK.

TKT is one of two non-oxidative PPP enzymes that provide a connection to the glycolytic pathway via conversion of ribose 5-phosphate and xylulose 5-phosphate into glyceraldehyde 3-phosphate, and erythrose 4-phosphate into fructose 6-phosphate. TKT deficiency is clinically characterized by developmental delay, short stature, and congenital heart defects, and biochemically by elevated erythritol, arabitol, and ribitol and to a lesser degree elevated ribose (Boyle et al., 2016).

TALDO provides the second connection between the non-oxidative PPP and glycolytic pathway and is clinically characterized with a wide range of multisystem features. More than 30 patients have been described, typically presenting antenatally with intra-uterine growth restriction, oligohydramnios, and hydrops fetalis (Valayannopoulos et al., 2006). Others may present neonatally with liver failure manifested by hepatosplenomegaly, elevated liver enzymes, cholestatic jaundice, bleeding diathesis, and cirrhosis. Additional features include congenital heart defects, hemolytic anemia, dysmorphic features, and various endocrine and renal anomalies (Loeffen et al., 2012). Depletion of NADPH has been suggested as the primary pathological mechanism of disease, potentially caused by the conversion of five-carbon sugar phosphates to five-carbon polyols by aldose reductase (Perl, Hanczko, Telarico, Oaks, & Landas, 2011). The biochemical profile has been characterized most prominently by increased urinary excretion of erythritol, arabitol, and sedoheptulose, and less commonly elevated ribitol, erythronate, sedoheptitol, perseitol, mannoheptulose, and sedoheptulose-7-phosphate (Engelke et al., 2010; Williams et al., 2019).

Nonspecific findings like developmental delay, intellectual disability, congenital heart defects, anemia, and dysmorphic features often may not prompt an extensive evaluation for IEMs. PPP IEMs have traditionally been diagnosed by targeted urine polyol testing and confirmed by enzymatic or molecular analysis, as common biochemical testing like plasma amino acids, acylcarnitine analyses, and urine organic acids would not be able to identify these conditions. Despite these established methods of detection, diagnosis may be delayed due to nonspecific clinical presentations and the general rarity of the conditions, which can result in unfocused investigational efforts.

Untargeted metabolomic profiling is a semi-quantitative screening test capable of detecting over 900 small molecules ranging in size from 75–1000 Da in a single plasma, urine, or CSF sample to provide a more complete view of a patient’s metabolic status, as opposed to performing several individual metabolic studies (Kennedy et al., 2017; Kennedy et al., 2018; Miller et al., 2015). In this study, we report results from untargeted small molecule metabolomic profiling on plasma and urine samples from two TKT and three TALDO deficient patients and demonstrate the ability of untargeted metabolomic profiling to screen for a wide range of metabolites from a single plasma or urine sample.

2. Materials and Methods

2.1. Subjects

This study was conducted at Baylor College of Medicine, Houston, TX with approval from the Institutional Review Board (IRB H32701). Subjects were ascertained based on an internal search at Baylor Genetics Laboratories (Houston, TX) for patients with metabolomic studies performed on plasma and/or urine, molecularly confirmed cases of TKT deficiency and TALDO deficiency, and/or those with suggestive targeted polyol testing who then had molecular confirmation.

2.2. Targeted Polyol Analysis

Targeted polyol analyses were performed by Baylor Genetics Laboratories (Houston, TX) using standard methods. A polyol mix was prepared containing 0.5 mM of the compounds meso-erythritol (Sigma-Aldrich), D-(+)-arabitol (Sigma-Aldrich), ribitol (Sigma-Aldrich), xylitol (Sigma-Aldrich), sedoheptitol (Sigma-Aldrich), sedoheptulose (Sigma-Aldrich), and galactitol (Sigma-Aldrich). A calibration curve ranging from 25 μM to 1000 μM polyol mix was constructed with HPLC grade water (VWR) supplemented with the polyol mix and 10 μl of a 5 mM internal standard, myo-inositol-1,2,3,4,5,6-D6 (CDN Isotopes Inc). Plasma or urine (100 ul) was mixed with 10 μl of 5 mM internal standard and 500 μl of 100% methanol (VWR) then incubated at room temperature for 5 minutes and centrifuged for 5 minutes at ~10,000 rpm. The sample supernatant and standard solutions were evaporated to dryness under a stream of nitrogen. All specimens were derivatized using pyridine and acetic anhydride and evaporated under a stream of nitrogen, and the dried residue was extracted using a hexane:ethylacetate (4:1) mixture. The organic phase was evaporated under nitrogen and reconstituted in ethyl acetate. All specimens were assessed by gas chromatography–mass spectrometry (GC-MS) using the Agilent 6890 Gas Chromatograph/5973 Mass Spectrometer to determine the concentration of 5 different polyols and a monosaccharide (arabitol, erythritol, ribitol, xylitol, galactitol, and sedoheptulose).

2.3. Global Metabolomic Profiling

Metabolomic profiling of plasma and urine was performed by Baylor Genetics Laboratories (Houston, TX) and Metabolon, Inc. (Durham, NC) as described previously (Evans, DeHaven, Barrett, Mitchell, & Milgram, 2009; Ford L et al., 2020; Kennedy et al., 2016; Miller et al., 2015). Small molecules were extracted from 100 ul of sample in an 80% methanol solution, and samples were run on four independent platforms, each able to measure a different set of metabolites: (1) positive ionization with Waters BEH C18 chromatographic separation of hydrophilic compounds (LC/MS/MS Pos Polar), (2) positive ionization with Waters BEH C18 chromatographic separation of hydrophobic compounds (LC/MS/MS Pos Lipid), (3) negative ionization with Waters BEH C18 optimized conditions (LC/MS/MS Neg), and (4) negative ionization with Waters BEH Amide (HILIC) chromatography (LC/MS/MS Polar). All chromatography was performed using a Waters Acquity UPLC (Waters, Milford, MA) held at 40–50°C. Metabolites were identified with known chemical structure by matching the ion chromatographic retention index, accurate mass, and mass spectral fragmentation signatures with reference library entries created from authentic standard metabolites under the identical analytical procedure as the experimental samples (DeHaven, Evans, Dai, & Lawton, 2010). Currently, the reference library contains entries for approximately >4000 molecules, including 2500 unique human metabolites.

Z-score analysis was conducted by comparing patient samples to a healthy reference population connected through a set of invariant anchor specimens included in each batch. Raw spectral intensity values were normalized to the anchor samples then log transformed and compared to a normalized, log transformed reference population to generate z-score values. The reference population was comprised of primarily pediatric subjects without known inherited metabolic disorders. Rare compounds were defined as analytes detected in the patient specimen but only rarely seen in the reference population (<10% of all patients tested). Median raw intensity values were calculated for all analytes identified in ≥2/3 of the anchor specimen and these median values were then used to normalize corresponding analyte raw intensity values in patient specimen. Analytes not identified in 2/3 or more of the anchor specimens were excluded from z-score analysis. Data collected from urine samples were additionally normalized to creatinine. Z-scores were calculated using the mean and standard deviation of the entire median-scaled log-transformed reference population dataset.

2.4. Statistical Analysis

In the single-metabolite analysis, we assessed whether subject metabolite levels were altered compared to controls. Using GraphPad Prism (v8.0.1), whisker plots were generated with measured metabolites for TKT and TALDO deficient subjects, in addition to controls. The mean is represented by the center line and top and bottom ends of whiskers to represent the 75th percentile plus the 1.5*Inter-Quartile Range (IQR) and 25th percentile minus the 1.5*IQR respectively; dots outside the whiskers represent outlier values. Controls included data points from all other targeted polyol tests performed within the database at Baylor Genetics. The only exception is that polyol testing from patients known to have galactosemia were removed from the statistical analysis since urine polyols are commonly ordered to monitor for galactitol levels.

In the untargeted metabolomic analysis, similar plots were included as described above. Since values are reported as Z-scores, control data points are not included. Because sedoheptulose is a rare metabolite that is not well detected in the control reference population, a Z-score could not appropriately be calculated. Therefore, a fold change method was used in which the mean was calculated for all controls in which sedoheptulose could be detected, and plotted data points represent the multiples above this calculated mean.

3. Results

Demographic, genotype, and basic clinical findings are listed in Table 1. In summary, the clinical presentations were consistent with prior reports with both TKT deficient patients presenting with short stature, developmental delay, and congenital heart defects. Additionally, the TALDO deficient patients had characteristic multisystem involvement including hepatic dysfunction, developmental delay, and dysmorphic features. The suspicion of a PPP IEM for three of the four families arose only after exome sequencing (ES) identified variants in the corresponding genes and were subsequently followed up with targeted biochemical testing.

Table 1.

Summary of the TALDO deficiency and TKT deficiency patient demographics and presenting phenotypes

Pt #1 Pt #2 Pt #3 Pt #4 Pt #5
Age (years) 22 12 3 2 0.58
Sex F M M M M
Deficient enzyme Transketolase Transketolase Transaldolase Transaldolase Transaldolase
Gene TKT TKT TALDO1 TALDO1 TALDO1
HGVS NM_001135055.2 NM_001135055.2 NM_006755.2 NM_006755.2 NM_006755.2
Nucleotide change c.769_770insCTA CCTCCTTATCT TCTG (homozygous) c.769_770insCTA CCTCCTTATCT TCTG (homozygous) c.574C>T (homozygous) c.512_514del c.931G>T c.512C>T (homozygous)
Amino acid change p.Trp257delinsSe rThrSerLeuSerSer Gly p.Trp257delinsSe rThrSerLeuSerSer Gly p.Arg192Cys p.Ser171del p.Gly311Trp p.Ser171Phe
Age at presentation Birth Birth Birth Birth Birth
Dysmorphic features + + + + −
Developmental delay + + + + −
Failure to thrive + + + + +
Hepatic dysfunction − − + + +
Congenital heart defect VSD, PDA 2 VSDs − − PDA
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+, patient positive for phenotype; −, patient negative for phenotype; VSD, ventricular septal defect; PDA, patent ductus arteriosus

3.1. Clinical Presentation

Patient 1 is a 22 year old female of Ashkenazi Jewish ancestry who presented in infancy with bilateral congenital cataracts, ventricular septal defect, patent foramen ovale, and patent ductus arteriosus. She went on to develop chronic blepharoconjunctivitis, microcephaly, failure to thrive, developmental delay, and seizures. She has also developed behavioral problems including self-injurious behaviors, anxiety, and obsessive-compulsive tendencies. Her adult height, weight, and head circumference were all well below the first percentile despite a normal endocrine evaluation. Dysmorphic features include posteriorly rotated ears with flattened superior helices, broad and full eyebrows with synophrys, mildly flattened nasal bridge, and slight underbite. Brain MRI revealed bilateral septations traversing the anterior portions of the lateral ventricles bilaterally. She was diagnosed with transketolase deficiency after ES identified a homozygous pathogenic variant in TKT, c.769_770ins18 (Table 1). This patient has since been included in a prior publication (Boyle et al., 2016).

Patient 2 is a 12 year old male and the full sibling of Patient 1. He also presented in infancy with bilateral congenital cataracts, microcephaly, two ventricular septal defects, hypotonia, severe eczema, and a foot deformity requiring ankle-foot orthotics. He went on to develop short stature, chronic blepharoconjunctivitis, significant developmental delays, and self-injurious behaviors. His most recent height, weight, and head circumference were well below the first percentile. Dysmorphic features include a flattened, broad, and slightly upturned nose, flattened philtrum, and thin upper lip. A diagnosis of transketolase deficiency had been made based on the older sister’s diagnosis, and he was confirmed to also be homozygous for the same TKT pathogenic variant, c.769_770ins18 (Table 1). This patient was also included in a prior publication (Boyle et al., 2016).

Patient 3 is a 3 year old male of Emirati ancestry who presented in infancy with feeding difficulties, hepatomegaly, elevated transaminases, abnormal clotting profile, and thrombocytopenia. At 9 months of age, his height and weight were both less than the first percentile, while his head circumference was at the 35th percentile. Dysmorphic features included a triangular face, reduced subcutaneous fat, arachnodactyly, two small capillary hemangiomas, and loose, wrinkled skin. Developmental and neurological evaluations were normal at his most recent visit at 3 years of age. Molecular testing confirmed the diagnosis of transaldolase deficiency with a homozygous pathogenic variant c.574C>T (p.Arg192Cys) in TALDO1 (Table 1). This patient was included in a prior publication (Rodan & Berry, 2017).

Patient 4 is a 2 year old male who was noted prenatally to have intrauterine growth restriction identified at 30 weeks gestation. At birth, he was small for gestational age and had decreased subcutaneous fat, wrinkled peeling skin, split sagittal suture, and enlarged anterior fontanelle. Neonatally, he developed hypothermia, erratic blood glucose levels, neutropenia, and thrombocytopenia. At 15 months of age, he was noted to have developmental delays and failure to thrive. He also had an aged, bronze appearance to his skin, minimal subcutaneous fat, prominent visible veins on his scalp, and hepatosplenomegaly. A liver MRI showed numerous T1 hyperintense nodules throughout the liver, the largest measuring 1.1 cm. ES revealed compound heterozygous variants c.512_514delCCT (p.Ser171del) and c.931G>T (p.Gly311Trp) in TALDO1 (Table 1). This patient was included in a prior publication (Lee-Barber et al., 2019).

Patient 5 is a 7 month old male who prenatally had concerns for fetal cardiomegaly with high output at 35 weeks gestation. At birth, he was noted to have hydrops, liver dysfunction, thrombocytopenia, coagulopathy, hemolytic anemia, and bilateral inguinal hernias. The liver dysfunction began to improve, lowering the suspicion for TALDO deficiency, until ES identified an apparently homozygous variant c.512C>T (p.Ser171Phe) in TALDO1. The patient has since had clinical improvement with no residual hydrops, no cutis laxa, and no significant hematologic abnormalities.

3.2. Targeted Urine Polyol Testing

All five patients were subsequently found to have a specific pattern of elevated polyols based on targeted biochemical testing (Figure 1). For the TKT deficiency patients (Patients 1, 2), this included significant elevations in arabitol, erythritol, ribitol, xylitol, and sedoheptulose identified on targeted plasma polyol testing (Figure 1A). For the TALDO deficiency patients (Patients 3, 4, 5), this included significant elevations in arabitol, erythritol, ribitol, xylitol, and sedoheptulose in plasma (Figure 1A) and arabitol and sedoheptulose identified in urine (Figure 1B) with targeted polyol testing. Variable levels of erythritol in patients and controls was observed, likely due to consumption of the metabolite which is a food additive commonly used as a low-calorie sweetener.

Figure 1. Targeted polyol analysis in TK and TALDO deficiency.

Figure 1.

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Targeted polyol results in the (A) plasma and (B) urine of TKT deficient (red triangles) and TALDO deficient (blue squares) patients compared to unaffected individuals (gray circles). The mean is represented by the center line and top and bottom ends of whiskers represent the 75th percentile plus the 1.5*Inter-Quartile Range (IQR) and 25th percentile minus the 1.5*IQR.

3.3. Metabolomic profiling

Plasma and urine metabolomic profiling of all specimens revealed several elevated intermediates of the PPP. For the TKT deficient patients (Patient 1, 2), seven plasma and four urine samples taken at different encounters were obtained for metabolomic studies. Similar to targeted polyol testing, plasma and urine metabolomic testing was also able to identify significant elevations of arabitol/xylitol, ribitol, and erythritol (Figure 2A, 2B). Given the similar structure and molecular weight of the compounds, arabitol could not be distinguished from xylitol or arabonate from xylonate with the metabolomic methods utilized. In addition to the metabolites measured with targeted polyol testing, untargeted metabolomic testing was also able to identify elevated ribose levels, as well as related PPP metabolites not previously been reported in TKT deficiency including elevated levels of ribonate and erythronate in plasma (Figure 2A) and ribulose/xylulose in urine (Figure 2B). Metabolic disturbances of non-PPP metabolites were found as well, including elevations of multiple metabolites involved in tryptophan metabolism including kynurenine, xanthurenate, quinolinate, and indolelactate in plasma (Figure 2A) and kynurenine and 3-hydroxykynurenine in urine (Figure 2B). Alterations involving purine metabolism were also found with elevations of inosine in plasma (Figure 2A) and xanthosine and guanosine in urine (Figure 2B).

Figure 2. Untargeted metabolomics in TKT and TALDO deficiencies identifies abnormalities in multiple metabolic pathways.

Figure 2.

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Abnormal metabolite Z-scores in biofluids from TKT deficiency patients in (A) plasma and (B) urine. Abnormal metabolite scores in biofluids from TALDO deficiency patients in (C) plasma and (D) urine. Metabolites from different metabolic pathways are noted by color: PPP (black), tryptophan metabolism (green), purine metabolism (brown), pyrimidine metabolism (orange), and citric acid cycle (purple). The mean is represented by the center line and top and bottom ends of whiskers represent the 75th percentile plus the 1.5*Inter-Quartile Range (IQR) and 25th percentile minus the 1.5*IQR.

For the TALDO deficient patients (Patients 3, 4, 5), two plasma and two urine samples taken at different encounters were submitted for metabolomic studies. Similar to targeted polyol testing, plasma and urine metabolomic analyses were also able to identify significant elevations of arabitol/xylitol, ribitol, and sedoheptulose (Figure 2C, 2D). Erythritol was also found to be elevated but only in the plasma samples. Again, this testing method was unable to differentiate arabitol from xylitol or arabonate from xylonate. In addition to those measured with targeted polyol testing, untargeted metabolomic testing was able to identify additional metabolite perturbations including elevations of erythronate, as well as ribonate which had not previously been reported in TALDO deficiency. Non-PPP perturbations observed include a lesser degree of tryptophan metabolism dysregulation, with mild elevations of kynurenate and xanthurenate in plasma (Figure 2C) and quinolinate and xanthurenate in urine (Figure 2D). Abnormal levels of Kreb cycle intermediates were also found with elevated levels of alpha-ketoglutarate in plasma (Figure 2C) and succinate, fumarate, and malate in urine (Figure 2D). One of the TALDO deficient patients also had multiple mild elevations of metabolites involved in bile acid metabolism including tauro-beta-muricholate, taurochenodeoxycholate, and taurocholate, which may be related to the overall liver dysfunction of the patient at the time.

Fold-change analysis relative to the mean of unaffected individuals in which the specified rare metabolite was detected was performed for sedoheptulose, sedoheptulose 7-phosphate, and ribulose/xylulose. Metabolomic profiling revealed a mild sedoheptulose elevation in 7/7 plasma and 4/4 urine TKT deficient patient samples and a significant elevation of sedoheptulose in 2/2 plasma and 2/2 urine samples of TALDO deficient patients (Figure 3). Sedoheptulose 7-phosphate was not detected in the plasma of either group but was present in the urine of both samples obtained in the TALDO deficient patients, although not at significantly higher levels than the very few controls in which it was also detected. Sedoheptulose 7-phosphate was not present in the urine of the TKT deficiency patients. Ribulose/xylulose, two similar metabolites which could not be differentiated by these testing methods, were altogether absent in the plasma but present in the urine of both groups.

Figure 3. Rare metabolites identified in untargeted metabolomics in TKT deficiency and TALDO deficiency.

Figure 3.

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(A) plasma and (B) urine of TKT and TALDO deficient patients as denoted by fold-change compared to the mean of unaffected individuals in which the rare molecule was measurable. The mean is represented by the center line and top and bottom ends of whiskers represent the 75th percentile plus the 1.5*Inter-Quartile Range (IQR) and 25th percentile minus the 1.5*IQR.

Notably, some unaffected individuals had isolated sedoheptulose elevations within the range of our TKT deficient patients for plasma and urine. Additional medical information including clinical history and/or molecular testing was not available to determine the cause of these elevations. However, the other metabolites commonly elevated in both TKT and TALDO deficiency were not observed in any of these samples. Other IEMs of the non-oxidative PPP, like SHKP deficiency, are also less likely since erythritol was within the normal range for these samples, as well. Complete PPP metabolite disturbances for TKT and TALDO deficiencies are shown in Figures 4A and 4B, respectively.

Figure 4. Pentose phosphate metabolism is altered in TKT deficiency and TALDO deficiency.

Figure 4.

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PPP metabolites in (A) TKT deficiency and (B) TALDO deficiency are shown. Elevations of metabolites as determined by untargeted metabolomic testing of plasma (blue circles) and urine (yellow circles) samples are shown. The size of each circle is representative of the Z-score for that metabolite, with the exception of sedoheptulose, which is representative of a fold-change. Arabitol and xylitol are isomers and could not be differentiated by this method and, therefore, share a circle representative of their shared Z-score. Metabolites outlined in orange are included on routine targeted polyol testing.

4. Discussion

In this study, we sought to examine the utility of untargeted metabolomics in diagnosing IEMs of the non-oxidative PPP. In all five cases studied, untargeted metabolomics was able to detect patterns of polyol alterations traditionally found with targeted urine polyol testing. This includes the typical pattern of elevated arabitol, erythritol, ribitol, and xylitol found in both TKT and TALDO deficiency and elevated sedoheptulose more specific to TALDO deficiency. Moreover, several novel disease-associated biomarkers were discovered, including significant elevations of other PPP metabolites like ribonate and erythronate in TKT deficiency and ribonate in TALDO deficiency.

Several non-PPP metabolite alterations were also observed in both plasma and urine. Notably, TKT deficient patients were found to have a pattern of abnormalities in tryptophan, purine, and pyrimidine metabolism. The PPP is directly connected to purine and pyrimidine biosynthesis via ribose 5-phosphate; therefore, it is not surprising that a block in TKT activity would result in a proximal build up of metabolites like inosine, xanthosine, guanosine, and N-carbamoylaspartate. Similarly, TALDO deficient patients were also found to have alterations in metabolites involved in tryptophan, purines, and pyrimidines metabolism, although to a lesser degree than the TKT deficient patients. Increased Kreb cycle intermediates were more specific to the TALDO deficient patients and may reflect a shunting of glyceraldehyde 3-phosphate into glycolysis and the Krebs cycle. However, whether or not these altered profiles of metabolism have a role in the pathogenesis of either TKT or TALDO deficiency remains unclear at this time. Interestingly, nicotinamide levels were relatively low but still within the normal range for both sets of patients, which goes against the hypothesis that a deficiency of NADPH produced from the oxidative PPP may be part of the underlying pathophysiological mechanism. Additionally, metabolites involved in glutathione metabolism appeared to be undisturbed.

In this study, we describe a novel diagnostic approach to the diagnosis of PPP-associated IEMs, which typically present with non-specific findings and can go undiagnosed for years. At present, a routine diagnostic evaluation of individuals with non-specific findings, including developmental delay, rarely includes polyol assessment. For example, one patient in this study was diagnosed only after 16 months of evaluations that were both time-consuming and costly. These findings are analogous to other studies that demonstrate the diagnostic efficiency of untargeted metabolomic profiling for screening of inborn errors of metabolism in patients that may have subtle or ambiguous clinical features (Alaimo et al., 2020; Burrage et al., 2019; Duval et al., 2013; Miller et al., 2015; Wangler et al., 2018).

Current practice offers two approaches to solving this dilemma. Either investigate with targeted biochemical studies or untargeted molecular testing such as ES. The relatively common IEMs are more readily identified on the newborn screen and/or with common targeted metabolic studies on fatty acids, amino acids, and organic acids. However, IEMs with non-specific phenotypes, such as PPP defects, may be missed by this approach. ES can identify some of these rare IEMs and shorten the diagnostic odyssey; however, additional testing is still needed with biochemical studies to confirm the diagnostic pattern of metabolic derangement, particularly when variants of uncertain clinical significance are identified. Untargeted metabolomic testing is a novel approach that carries the benefits of both methods by casting a wide diagnostic net to capture rare IEMs while also being able to support a diagnosis by directly identifying the specific metabolic pattern that defines the disorder. In addition, follow-up sequencing of these difficult-to-diagnose patients will identify novel genetic variants, further improving the interpretation of variants of unknown significance (Alaimo et al., 2020).

Theoretically, this approach may also be able to identify the other particularly rare IEMs of the non-oxidative PPP that could not be included in this study, including RPIA and SHPK deficiency. Reports of RPIA deficiency specifically have described elevations of arabitol and ribitol in plasma, urine, and CSF, (Kaur et al., 2019; Naik et al., 2017; van der Knaap et al., 1999), and SHPK deficiency with elevations of sedoheptulose and erythritol (Wamelink et al., 2015; Wamelink et al., 2008), all of which are measurable by this platform. However, specific studies to validate this approach would still be necessary and other novel biomarkers may exist, as well.

5. Conclusion

Targeted polyol testing and untargeted metabolomic testing methods were both able to identify a biochemical pattern indicative of IEMs of the non-oxidative PPP. However, compared to traditional targeted testing which may be time consuming and costly, the ability of a single broad-spectrum test to identify both common and rare IEMs, highlights the utility of untargeted metabolomic profiling. This approach may be particularly useful for IEMs of the PPP, which can present with non-specific clinical features and may not be at the top of the differential diagnoses for many providers.

Highlights.

  • Metabolomic profiling can identify metabolic disorders of the pentose phosphate pathway

  • Novel biomarkers exist that are not identified with targeted polyol testing

  • Alterations in tryptophan, purine, and pyrimidine metabolism occurs as well

Acknowledgments

Financial support:

Brian Shayota and Leroy Hubert received funding through the NIH T32 (GM07526-41) Medical Genetics Trainee Grant. Brian Shayota also received funding from the Takeda Next Generation Medical Biochemical Subspecialty Fellowship Grant.

Footnotes

Conflicts of Interests:

BJS, VRS, QS, JX, and SHE are employees of Baylor College of Medicine. JX and CG are employees of Baylor Genetics. Baylor Genetics generates genetic testing revenue in a partnership with Baylor College of Medicine. ADK and KLP are employees of Metabolon, Inc. and, as such, have affiliations with or financial involvement with Metabolon, Inc. HTB is a consultant for Millennium Therapeutics. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the article apart from those disclosed.

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