Targeted metabolic profiling of the Tg197 mouse model reveals itaconic

acid as a marker of Rheumatoid Arthritis.

Filippos Michopoulos1,2,, Niki Karagianni3, Nichola Whalley1, Mike Firth8, Christoforos Nikolaou5,6, ,

Ian D Wilson4,. Susan E Critchlow1, George Kollias5,7* Georgios Theodoridis2*

1Bioscience, Oncology iMED, AstraZeneca, Alderley Park, Macclesfield, Cheshire, UK

2Department of Chemistry, Aristotle University of Thessaloniki, 541 24 Greece.

3Biomedcode Hellas SA, 34 Fleming Str., 16672 Vari, Greece

4Department of Surgery and Cancer, Imperial College, London UK.

5Biomedical Sience Research Center “Alexander Fleming”, 34 Fleming Str., 16672 Vari, Greece

6 Department of Biology, University of Crete, Heraklion, Greece

7Department of Physiology, Faculty of Medicine, National and Kapodistrian University of Athens,

Greece

8Discovery Science, Innovative Medicine, AstraZeneca, Cambridge, UK

*Author for correspondence

Email:

Georgios Theodoridis: gtheodor@chem.auth.gr,

George Kollias: kollias@fleming.gr

Tel:+302310997718 +302109656507

Fax: :+302310997719 +302109656563

1

Abstract

Rheumatoid arthritis is a progressive, highly debilitating disease where early diagnosis, enabling

rapid clinical intervention, would provide obvious benefits to patients, healthcare systems and

society. Novel biomarkers that enable non-invasive early diagnosis of the onset and progression of

the disease provide one route to achieving this goal. Here a metabolic profiling method has been

applied to investigate disease development in the Tg197 arthritis mouse model. Hind limb extract

profiling demonstrated clear differences in metabolic phenotypes between control (wild type), and

Tg197 transgenic mice and highlighted raised concentrations of itaconic acid as a potential marker

of the disease. These changes in itaconic acid concentrations were moderated or indeed reversed,

when the Tg197 mice were treated with the anti-hTNF biologic infliximab (10mg/kg twice weekly for

6 weeks). Further in vitro studies on synovial fibroblasts obtained from healthy wild-type, arthritic

Tg197 and infliximab-treated Tg197 transgenic mice, confirmed the association of itaconic acid

with rheumatoid arthritis and disease moderating drug effects. Preliminary indications of the

potential value of itaconic acid as a translational biomarker were obtained when studies on K4IM

human fibroblasts treated with hTNF showed an increase in the concentrations of this metabolite.

Keywords: rheumatoid arthritis, metabolomics, intracellular metabolites, targeted analysis,

biomarker, mass spectrometry

Introduction

2

Rheumatoid arthritis (RA) is a debilitating, progressive disease that places a considerable burden

on healthcare systems. Novel biomarkers that enable non-invasive early diagnosis of the onset

and progression of the disease could provide significant benefits to both patients and society by

enabling early clinical intervention. They may also allow optimization of individual patient

treatments especially if undertaken prior to permanent damage of the osteo-cartilage. One

potential source of novel biomarkers is metabolic phenotyping (or metabotyping) of the sort

practiced in metabolomics/metabonomics studies1-4. Whilst metabotyping has been employed in

the investigation of the pathophysiology of RA for some time there have been relatively few

publications as a result. However, such studies as have been reported demonstrate the value of

metabolic phenotyping with most of this work having involved the analysis of human clinical

samples such as synovial fluid5-8, plasma9-11 and urine/plasma10. For example Naughton et al,6

using 1H-NMR spectroscopy, examined the metabolic profile of synovial fluid samples and

compared these profiles to those of matched serum samples and showed increased lactate

concentrations and strong glucose depletion in synovial fluid compared to serum. In addition

ketone bodies (3-hydroxybutyrate, acetone and acetoacetate) were enriched in the synovial fluid

with significant reduction in chylomicron and VLDL-associated triacylglycerols (which also

appeared to have a reduced mean chain length). This metabolic perturbation was consistent with

the hypoxic conditions of the inflamed joint, restricting the diffusion of glucose and large molecules

from the blood into the synovium. These effects resulted in increased lipid degradation concomitant

to ketone body enrichment within the synovium to serve the energy demands of the joint tissues.

Subsequently, the same group compared the metabolic phenotype of normal synovial fluid with

that of RA patients7. Once again, the synovial fluid from RA patients was associated with

comparatively high lactate concentrations and lower quantities of lipoprotein-associated fatty acids

and glycoproteins. Williamson et al.,5 reported changes in triglycerides, glycoproteins and

creatinine after the analysis of serial synovial fluid samples collected from two RA patients and the

results were found to be correlated with the disease activity. Meshitsuka et al,8 reported that the

lactate to alanine ratio in the synovial fluid could be used as a marker to discriminate RA from

osteoarthritis12. Recent work by Giera et al.,13 used liquid chromatography coupled to mass

3

spectrometry (LC-MS) for the analysis of synovial fluid and the characterisation of many lipid

classes and lipid mediators (resolving D5, lipoxin A4, maresin 1) in RA patients.

1H NMR spectroscopic analysis of plasma from patients with RA9 revealed elevated

concentrations of lactate, cholesterol, acetylated glycoprotein and unsaturated lipids and reduced

amounts of high density lipoprotein (HDL). These findings were found to correlate with disease

severity and support the positive association between RA and coronary artery disease14. Gas

chromatography mass spectrometric (GC-MS) analysis of human serum samples11,15 reported high

concentrations of L-asparagine, L-alanine, 2-oxy-butanoic acid, palmitic acid and heptanoic acid in

RA patients while 2-butenoic acid, undecanoic acid, glucuronic acid and stearic acid were found in

relatively larger amounts in the healthy control group. In subsequent work from the same group 10

LC-MS analysis of urine samples revealed significant changes in the acylcarnitine profile. Analysis

of plasma showed increased concentrations of tryptophan, α-ketoisovaleric acid, 3-methyl-2-

oxovaleric acid, cholesterol sulfate, uric acid, indoxyl sulfate, 4-methyl-2-oxovaleric acid and

dehydroepiandrosterone sulfate in the heat joint sample group compared to the controls.

Smolenska et al.,16 reported significant depletion in the circulating blood concentrations of

hypoxanthine, uridine and uric acid in RA patients following methotrexate treatment. These

investigators proposed that the observed perturbation of purine/pyrimidine metabolism affects the

availability of biogenic amines for DNA/RNA synthesis which subsequently may affect immune cell

proliferation and the cytokine expression profile. In addition, changes in amino acid, hypoxanthine,

uric acid, lactate, uracil, trimethylamine-N-oxide and α-ketoglutaric acid concentrations have

recently been reported as markers of methotrexate treatment in RA patients with early disease

onset17. Clearly, based on these studies, RA is associated with numerous perturbations of the

metabolome.

4

Animal models have been (and remain) instrumental in the study of complex diseases such

as RA, eliminating much of the inherent variability of human samples, providing better control and

a more direct framework for the elucidation of underlying biological mechanisms driving the

pathology. However, despite their undoubted value in the study of RA, to date only a limited

number of metabolic profiling studies based on validated animal models have been reported. In

one of these the analysis of serum samples was obtained from K/B×N transgenic mice, a model of

severe inflammatory arthritis, by 1H NMR spectroscopy. This study reported a range of changes in

the metabolic profiles of these animals compared to healthy control mice alterations in pathways

associated with nucleic acid metabolism (xanthine, hypoxanthine, uridine, uracil, and

trimethylamine–N-oxide), amino acid metabolism (glutamate, serine, phenylalanine, glycine,

methionine, asparagine) lipid/fatty acid metabolism (glycerol, choline, 2-hydroxybutyrate,

acetylcarnitine) and oxidative stress (taurine, methionine, glycine, xanthine, hypoxanthine,

trimethylamine-N-oxide)18.

The human TNF transgenic (Tg197) mouse is a well-established animal model of human

inflammatory polyarthritis that recapitulates human disease. In these mice, spontaneous

development of pathology is due to deregulated human TNF expression produced by a 3’-UTR

modified human TNF transgene19. Similarly to human disease, synovial fibroblasts play a central

role in pathogenesis including TNF production and gradual activation, acquisition of a, hyperplastic

phenotype and release of a variety of proinflammatory cytokines, matrix metalloproteinases and

others factors promoting different features of the pathology 20,21. The Tg197 arthritis model was

used in the present study to obtain concise arthritis metabolic profiles of blood serum and urine

collected at different stages of disease development, including samples from mice treated with

anti-TNF disease-modifying drug. We applied the same methodology to analyze aqueous extracts

of hind limb tissue and synovial fibroblasts to allow the comparison of modulation of metabolic

pathways at organism and cellular level. More importantly, we implemented a systems biology

approach that integrates the obtained metabolic profiles with gene expression data, which allows

us to gain further insight on the overall modulation of metabolic pathways.

Experimental procedures5

Materials

Water (18.2 MΩ) was obtained from a Purelab Ultra System from Elga (Bucks, UK). Methanol,

acetonitrile and isopropanol used for sample extraction and analysis were of HPLC grade (Sigma-

Aldrich, Gillingham, UK). Tributylamine (TBA), acetic acid and all analytical standards, of the

highest purity available, were purchased from Sigma-Aldrich.

In vivo sample collection

Tg197 human TNF transgenic (TG) mice were bred and maintained on CBA-C57BL/6J background

in Biomedcode Hellas SA animal facility, fed a normal diet and water ad libitum. Animal study

protocols were approved by the directorate of Agricultural and Veterinary Policy of the Attica

Region for compliance with regulations. For sample collection a therapeutic protocol was applied,

using 6 week old male and female Tg197 mice, with established disease pathology. Groups of 6 or

7 mice TG (depending upon the study) were allocated and received (TR) or not i.p. injections of the

commercial anti-hTNF biologic infliximab at 10mg/kg twice weekly and for a time period of 6

weeks. A third group of 6 or 7 wild type (WT) littermates served as a control. Body weight and

clinical arthritis score, based on a 3-point scale were recorded and urine sample collections were

made at a standard time of the day to avoid the effects of circadian rhythm fluctuations. At the end

of the study period mice were sacrificed and serum as well as hind limb tissue samples were

collected. Additional serum samples were collected from groups of wild-type and transgenic mice

sacrificed at 6 weeks of age, before the initiation of the treatment. Four studies (1,3 and 4), of

similar study design but with modifications to the samples collected and their frequency, were

performed in order to investigate particular aspects of the evolving metabolic phenotypes of TG

and TR mice . All samples were stored frozen (-80ºC) on collection until analysis. Further

information about group size and gender composition sampling time points for the in vivo sample

collection is provided in Table S1.

Mouse synovial fibroblast culture

6

Synovial fibroblasts from healthy WT, TG and TR animals at 9 weeks of age were isolated and

cultured as described previously22. Synovial fibroblasts isolated from TR animals treated in vivo

with anti-hTNF were cultured in the presence of 1 μg/ml infliximab to maintain treatment during the

in vitro phase of the experiment (study 2, Table S1).

K4IM human synovial fibroblast cell line culture

K4IM cells were cultured in Dulbecco’s Modified Eagle's Medium supplemented with 2 mM

glutamine and 10% fetal calf serum. Cells were grown under normoxic conditions (5% CO2) at

37ºC and stimulated for 24 hours with 10 ng/ml human tumour necrosis factor (Sigma Aldrich, UK).

Sample Preparation

In the present paper a number of different specimens were analysed by LC-MS/MS (including cell

lysates, hind limb extract and serum and urine). The sample preparation procedure aimed to

prepare the biological sample by extracting the primary metabolites of interest, ensuring analytical

stability and sensitivity whilst maintaining the integrity of the analytical system. The procedure

applied was optimized for each specimen as outlined below.

Hind limb joints

Hind limb joints were ground to powder in liquid nitrogen using pestle and mortar. Powder was

placed in a 2 mL Fast-Prep tubes (lysis matrix A) and extracted with 0.82 ml/100mg tissue

ACN/H2O 50/50 v/v on a Fast Prep 24 module (MP Biomedicals, USA) in a sequence of two

cycles (30 seconds each) of shaking at 5M/s with a 20 sec pause between cycles. The clear

supernatant obtained after centrifugation at 20800 g was transferred to 2 mL glass vials and stored

at -80oC. Prior to analysis 50 µL of each sample was mixed with 100 µL cold methanol (MeOH) and

centrifuged at 20800 g. The clear supernatant was transferred to a 0.3 ml polypropylene HPLC

microvial and was dried at ambient temperature in SpeedVac. Samples were resuspended in 50 µL

of ultrapure water and the resulting aqueous suspensions were centrifuged at 3270 g before

analysis.7

Urine-Serum

Urine and serum samples (10 µl) were subjected to protein removal by the addition of 40 µl of cold

(-20ºC) mixture of MeOH/ACN 50:50 v/v and centrifugation at 20800g. Then 40 µl of the clear

supernatant were transferred to 0.3 mL polypropylene HPLC microvials and dried at ambient

temperature in a Savant SPD2010 SpeedVac (Thermo Fisher Loughborough, UK). Urine extracts

were resuspended in 200 μl and serum extracts in 40 μl of ultra pure water. Resuspended samples

were centrifuged at 3270 g for 10 min at 4ºC before analysis.

Mouse Synovial fibroblasts

Cell pellets were extracted with 1ml ACN/H2O 50:50 v/v. The extracts were centrifuged at 20800 g

and supernatants were transferred to clean tubes and stored at -20ºC. Prior to analysis, 100 µL of

the synovial fibroblast extracts were treated with 100 µL cold MeOH (-20oC) to precipitate

remaining soluble proteins and were centrifuged at 20800 g. The clear supernatant was transferred

to a 0.3 ml polypropylene HPLC microvial and dried at ambient temperature in SpeedVac for 60

min. Samples were resuspended in 50 µL of ultrapure water and aqueous suspensions were

centrifuged 3270 g for 10 min at 4ºC before analysis.

K4IM human synovial fibroblast cell line

At the end of the incubation period the culture media was removed and cell metabolism quenched

by the addition of 400 μl of 40/40/20 v/v/v MeOH:ACN:H2O (-20ºC) to each of the wells. The cells

were stored at -20ºC with the extraction solvent for 20 min to extract intracellular metabolites

followed by cell adhesion disruption with a cell scraper (BD, Oxford, UK). The contents of each well

were transferred to 1.5 ml Eppendorf tubes and centrifuged at 16,000 g (Model 5415D, Eppendorf)

for 5 min to precipitate proteins and cell debris. The supernatants were transferred to clear 1.5 mL

Eppendorf tubes and stored at -20ºC until sample analysis. For LC-MS analysis, aliquots of 100 μl

of each extract were transferred to polypropylene HPLC microvials (VWR Ltd, UK) and dried at

ambient temperature in a Savant SPD2010 SpeedVac for approximately 1 h. Metabolite extracts

were then re-suspended in 50 μl of ultra pure water and centrifuged at 3270 g for 10 min at 4ºC,

before analysis.

Quality control8

For all matrices a biological quality control (QC) sample was prepared by mixing equal volumes

from each of the test samples. The QC sample was treated as a test sample and was analysed

periodically within each batch. To confirm metabolite retention times, a test mixture containing all of

the measured metabolites at a concentration of 5 µM was analysed at the beginning and the end of

the analytical batch in addition to a QC sample spiked with the test mix at a final concentration of 5

µM. Prior to the start of each analytical run ten injections (5 µl) of the QC sample were performed

to ensure adequate system conditioning. Moreover, one QC sample was analysed every five to ten

samples depending on the size of the analytical batch. Samples forming the test set were run in a

random order.

Sample-Data analysis.

Liquid Chromatography–tandem mass spectrometry (LC-MS)

Prepared samples were analysed by LC-MS on a system consisting of an Ultimate 3000RS

chromatographic system (Thermo, UK) in combination with AB4000 Q-Trap (ABSCIEX, UK) mass

spectrometer operating in negative ion mode. Metabolites were resolved using gradient elution on

a binary solvent system consisted of buffer A (H2O, 10 mM tributylammonium, 15 mM acetic acid)

and buffer B (MeOH/Isopropanol 80/20). A full description of the methodology is given in

Michopoulos et al23. To address analytical variability across sample batch a pooled sample (QC)

was analysed in regular intervals24. The raw spectrometric data was analysed and peaks were

integrated with MultiQuan 2.0.2 (Applied Biosystems/ MDS Sciex) and the results were exported to

Excel for normalization and univariate statistical analysis by f-test and t-test. Acceptable analytical

reproducibility for each peak detected was based on the determination of the coefficient of

variation (CV) for each metabolite peak present in the QC samples as being lower than 30% 24,25.

T-test results that gave p-values of less than 0.05 and an increase or decrease of the average

metabolite peak area between groups of greater than 30% were set as the criteria for significant

metabolite perturbation.

Results and Discussion

In vivo evaluation9

In this mouse model signs of clinical pathology become evident by week 3 of age, while animals

fail to properly gain weight, possibly due to the catabolic effect of circulating human TNF. Upon

treatment with anti-hTNF antibody treatment, starting either at early disease stages (week 3 of

age) or at established disease (week 6) pathology, as performed here, signs disappear or

significantly ameliorate depending on treatment efficacy and dosing. The model has been

instrumental in providing the proof of concept of the pathogenic role of TNF in rheumatoid arthritis

and the application of anti-TNF treatment in arthritis pathology. The model has been widely used

for the exploration of molecular mechanisms contributing to the development of arthritis

pathology26,27 as well as for the validation of the therapeutic potential of arthritis therapeutics with a

focus in anti-human TNF28,29.

Arthritis scoring, which evaluates grip strength, joint swelling and distortion, reflecting disease

progress showed that in the transgenic group (TG) an increase in the arthritis score from 1.1 to

1.75 was observed over between weeks 6 and 11 whilst for the Infliximab treated group (TR) the

arthritis score was reduced from 0.8 to 0.4. Significant differences were seen in weight gain

between the groups ( Figure S1)

Bioanalyisis

A total of 109 metabolites ( Table S2) were profiled in all biospecimens collected in this study using

a targeted ion pair LC-MS method23. Based on analysis criterion of 30 % maximum CV in QC

data24,25 a number of metabolites were excluded from the dataset and the statistical analysis was

based on 53 metabolites present in the hind limb extracts and 76 metabolites in the synovial

fibroblast extracts.

Analysis of Hind Limbs

10

Data obtained for the hind limb tissue was first processed in a multivariate manner using principal

component analysis (Simca P14+). Underlying non-correlated variability to group metabolic

phenotype was removed with O2PLS discriminant analysis resulting a clear discrimination between

WT, TG and TR phenotype (Figure 1A). Model validation was performed with a CV-Anova test

resulting a p-value<0.05. Further analysis using univariate statistics showed that 30 of the 53

metabolites CVs were differentially modulated between WT, TG and TR animals (p-value< 0.05)

(Table 1). Of these metabolites significant depletion was observed for some of the glycolytic

intermediates in the hind limb extracts obtained from TG mice (e.g. for galactose 1 phosphate,

glucose 6 phosphate, mannose 6 phosphate) compared to the WT animals. In addition α-

ketoglutaric acid was only detected in samples obtained from WT mice, whilst itaconic acid

(methylene succinic acid) was only found in extracts from the TG group (Table 1). As these two

metabolites were detected only in one group, the univariate statistical data was manually corrected

for log2 Fold Changes to -5 and 5 respectively and p-values (0.001) to enable metabolite

visualisation and pathway annotation. The same correction was applied to any metabolite that

appeared in one group of the binary comparisons across all the studies described in this

manuscript. Metabolites such N-acetylglucosamine, riboflavin, uracil and glucuronic acid were

found to be enriched in the TG groups compared to WT and TR animals. It should be noted that

upon infliximab treatment the metabolic phenotype of TR and WT animals appeared to be similar

with the main difference being a modest enrichment for α-ketoglutaric acid and riboflavin for the TR

animals compared to the WT (Figure 2A).

11

A software interface, developed at AstraZeneca, allowed automatic metabolite data

annotation onto the KEGG (Kyoto Encyclopaedia of Genes and Genomes) mammalian metabolic

pathways to enable assessment of the underlying metabolic pathway perturbations. Using this

software and based on the analysis of the hind limb extracts glycolysis, pentose and glucuronate

inter-conversions, aminoacyl-tRNA biosynthesis, amino sugar and nucleotide sugar metabolism,

starch and sucrose metabolism were amongst the metabolic pathways that appeared to be the

most perturbed in the hind limbs of the diseased TG mice. These changes are summarised in

Figure 3A which shows the relative pathway perturbations between TG and WT or TR and WT

animals as defined by the number of significant modulated metabolites (|log2 Fold change|>0.5, QC

CV<30 and p-value<=0.05) per metabolic pathway. Closer examination of the pathway modulation

(Figure 3A) revealed that metabolic intermediates associated with fundamental cellular

mechanisms of energy production, such as glycolysis and the pentose phosphate pathway

(glucose 6 phosphate, fructose 6 phosphate, pyruvic acid and lactic acid), were significantly higher

in the WT control animals compared to the transgenic mice. On the other hand UDP glucose and

glucuronic acid (starch/sucrose, pentose/glucuronate interconversion and amino sugar/nucleotide

sugar metabolic intermediates) were strongly enriched in the TG animals. This metabolic signature

may be indicative of greater biomass production in the WT animals, which subsequently activates

anabolic mechanisms through the starch/sucrose, pentose/glucuronate interconversion and amino

sugar/nucleotide sugar pathways, or alternatively might be an indication of significant catabolic

activation for energy production through these pathways in the TG animals as result of the

acquired cachexia following the disease progression. As indicated above the body weight of each

animal was monitored between weeks 6 to 11 of the study (Figure S1A) showing very little weight

gain (11%) for the TG group while TR and WT littermates had gained 37% and 31% respectively

compared to their weight at the beginning of the study. These in vivo measures in the TG animals,

combined with the apparent aforementioned pathway modulation, provide further support for the

idea of increased catabolic activation as a result of the disease progression. Furthermore the

glycolytic and TCA cycle perturbation (pyruvate and α-ketoglutaric acid) seen for the hind limb

tissue data may be in concordance with the hypoxic conditions in the synovium found in clinical

data7. Hypoxia restricts the diffusion of glucose into the inflamed joint and subsequently activates 12

beta-oxidation catabolic reaction leading to an increase of ketone bodies that serve the energy

demands of the joint tissue. Under these conditions it may also activate starch metabolism,

providing an anaplerotic mechanism to serve energy demands in diseased animals. In the case of

the TG to TR comparison the overall metabolic perturbation was very similar that of the TG to WT

group (Figure 2A) indicating the efficacy of the anti-TNFa (infliximab) intervention by restoring the

TR phenotype to that of the WT. Primarily the differences in metabolic profile between TG/WT and

TG/TR animals were driven by corresponding differential abundance of metabolites such as,

pyruvic acid, lactic acid, orotic acid, tyrosine, tryptophan and UDP-glucose that strongly altered the

pathway deregulation of aminoacyl t-RNA biosynthesis glyconeogenesis and starch/sucrose,

pentose phosphate and pyrimidine metabolism. However, the metabolic phenotype of TR

compared to WT animals was almost identical, with only α-ketoglutaric acid and riboflavin found to

be significantly differently modulated in the TR mice, showing the direct benefits of the infliximab

treatment.

Synovial Fibroblasts

Clearly the finding of disease-related differences in the extracts of hind limbs between TG

and WT mice, and between TG and TR animals is of considerable interest. However, for further

investigations into the metabolic changes seen here it would be advantageous to be able to use

higher throughput in vitro-based systems. For this reason we explored the metabolic phenotypes of

synovial fibroblasts cultured from these animals, a key player in the arthritis pathogenesis30.

13

Multivariate statistical analysis using O2PLS-DA modelling on the synovial fibroblast data

showed clear differentiation between the phenotype of WT, TG and TR groups (Figure 1B).

Univariate statistical analysis revealed the most outstanding metabolic changes were a very

significant enrichment of cis-aconitic acid, citric acid and isocitric acid in the TG group-derived

synovial fibroblasts, whilst malic acid, maleic acid and asparagine were relatively higher in cells

derived from the WT animals (Figure 2B). More interestingly, and in concordance with the hind limb

extract data, itaconic acid, was only detected in extracts of samples from synovial fibroblasts

derived from TG mice. Two other metabolites, glyoxylic acid and fructose 1,6-bisphosphate (data is

superimposed with itaconic acid in (Figure 2B) were also only seen in TG cell-derived extracts.

Overall, the above metabolic perturbations seem to have resulted from a significant deregulation of

the TCA cycle and glyoxylate/dicarboxylate metabolism, followed by milder changes in purine,

pyrimidine, glycine/serine/threonine metabolism and pentose phosphate pathway and aminoacyl t-

RNA biosynthesis (Figure 3B).

When comparing synovial fibroblast data from TR and WT mice a greater degree of similarity

was observed. However, significant alterations were found: glyoxylic acid and fructose 1,6-

bisphosphate were only detected in the TR group (data is superimposed with itaconic acid in

Figure 2B, while asparagine was only found in the WT-derived samples. Similarly, cis-aconitic and

citric acids were found to be higher whilst serine was found lower in the TR-derived synovial

fibroblast extracts (Figure 2B).

Finally when comparing the metabolic profiles from TG and TR synovial fibroblasts a number

of differences were found. Thus ribose 5-phosphate, mannose 6-phosphate, fructose 1,6-

bisphosphate, succinic acid, itaconic acid and isocitric acid were found in relatively higher

concentrations in the TG group while the amounts of maleic acid, glutathione disulphide, proline

and tryptophan were greater in the TR group (Figure 1B). These changes in metabolite

concentrations imply modulation of the TCA cycle, the pentose phosphate pathway,

purine/pyrimidine metabolism, fructose/mannose metabolism and glyoxylate/dicarboxylate

metabolism (Figure 2B).

14

In an attempt to assess metabolic phenotype similarities between TG, TR and WT groups,

we performed hierarchical clustering of the metabolite profiles, concentrating on the existence or

not of a certain metabolite, coupled with its enrichment over the background, WT levels. In the

column dendrogram shown in Figure 3B, TG/WT and TR/WT cluster together in the binary group

comparison whilst the TG/TR cluster separately.

Other specimens.

These results suggest that there is potential for the study of itaconic acid as a RA biomarker. The

clinical utility of a biomarker is greatly enhanced if this is measured in non-invasive or minimally

invasive samples such as urine and plasma, which represent the key bio-specimens for clinical

chemistry analyses. In this study urine and blood were also collected from independent in vivo

studies, allowing both biomarker discovery and validation studies to be performed in these

relatively accessible specimens. These data clearly demonstrated that serum/urine itaconic acid

concentrations were elevated in TG mice compared to WT or TR mice. Furthermore, by week 11,

infliximab treatment restored itaconic acid levels to WT levels (Figure S2). Itaconic acid

concentrations at TG animals were not only differentiated from those of WT animals at week 11 but

also at week 6 showing potential early diagnostic value as a disease marker for the TG197 animal

model.

To further evaluate itaconic acid as a biomarker and assess the relevance of these animal

model-derived results to patients, human fibroblast cells (K4IM cell line) were assayed with the

same method in a proof of concept study. Itaconic acid was indeed detected only in the hTNFa

treated cells grown in DMEM culture medium and not under any of the other experimental

conditions.

Role of Itaconic acid

15

Amongst the many “enriched” metabolites mentioned above, itaconic acid was only ever detected

in the hind limb extracts derived from the TG group, showing a direct association with the arthritic

“diseased” metabolic phenotype. In contrast α-ketoglutaric acid was not detected at all in the TG

group. This observation was investigated further in order to understand the biological importance of

itaconic acid in living organisms. Interestingly the KEGG database only refers to itaconic acid as a

bacterial metabolite, which is perhaps surprising as recently a number of metabolic phenotyping

studies have highlighted this compound as a perturbed metabolic intermediate in mammals 31-34 .

Using canonical metabolic pathways itaconic acid is part of C5 branched dibasic acid metabolism

as shown in a representation of the metabolic flux in Figure 4A in relation to central carbon

metabolism. Surprisingly the relative amounts of cis-aconitic acid, a precursor of itaconic acid,

were not enriched in the hind limb extracts of any of the sample groups. Instead concentrations of

some more distant metabolic intermediates (pyruvic acid, lactic acid, α-ketoglutaric acid) were

decreased and glutamic acid was increased. These metabolic changes may be associated with

increased itaconic acid biosynthesis as part of a broader metabolic network comprising the whole

area of metabolism that tries to compensate for the underlying biological and metabolic

perturbation. In the case of synovial fibroblasts all the precursors of itaconic acid biosynthesis,

such as cis-aconitic acid, citric acid and isocitric acid, were enriched in the TG group-derived cells.

A representation of itaconic acid biosynthesis, prepared using KEGG canonical metabolic

pathways, is given in Figure 4B. Itaconic acid has been associated with the antimicrobial activity

because of its high potency in the inhibition of isocitrate lyase 35,36 a key enzyme in the glyoxylate

cycle. This biological mechanism enables biomass production from carbon sources other than

glucose and glutamine such as fatty acids. Even though the glyoxylate cycle is inactive in

mammalian cells, pathogens express strong pathway activity in order to utilise the fatty acids that

are the main carbon source in macrophages 37 to enable pathogen survival38. Immuno responsive

gene 1 (Irg 1) has been reported 39 to regulate the decarboxylation of cis-aconitic acid to form

itaconic acid, as well as suppressing expression of the pro-inflammatory cytokine tumour necrosis

factor TNF-a, IL-6, and INF-β in LPS treated macrophages. It has also been found to increase

expression of reactive oxygen species (ROS) that induce expression of TNFAIP340 a key player in

NF-kB regulation associated with several immunopathologies41. Irg1 gene expression was found to 16

be positively regulated by interferon regulatory factor 1 (IRF1) in human and murine

macrophages42. However, recently Irg1 induced itaconate biosynthesis was shown to promote a

pro-inflammatory response by inhibiting succinate dehydrogenase (SDH) leading to succinic acid

accumulation in stimulated macrophages in vivo and in vitro43,44.Increased of succinate

concentrations intracellulary were found to be associated with M1 from M2 macrophage phenotype

differentiation 45 via induction of IL-1β and stabilisation of the HIF-1α transcription factor 46.

Gene expression analysis of the hind limb tissue confirmed that, in TG animals, there was a higher

expression of the Irg1 gene in comparison to WT (Figure 5). It was also found that proteins such as

aconitase1 and 2 (Aco1,2) and isocitrate dehydrogenase 1,2 (Idh 1,2) that regulate cytoplasmic

and mitochondrian citrate conversion to cis-aconitic acid and α-ketoglutaric acid (Figure S3) were

not differentially expressed in TG animals compared to WT. In synovial fibroblasts a similar pattern

of gene expression was observed but in case of Idh2, the gene that regulates isocitrate conversion

to α-ketoglutaric acid in mitochondria, was shown to have differentially lower expression in TG

synovial fibroblasts compared to WT. The Irg1 gene observations provide a clear mechanist insight

into itaconic acid cytoplasmic biosynthesis in synovial fibroblasts. Undoubtedly RA mediates high

abundance of activated synovial fibroblasts 21,30 and macrophages47-49 that contribute significantly to

the disease pathophysiology. Thus, the detected itaconic acid upregulation in the TG group, as we

clearly show above is produced by activated synovial fibroblasts with a possible contribution from

activated macrophages39 infiltrating the arthritic joint. HIF1α and SDH enzyme expresion in both

hind limb tissue and SFs was not found to be significantly altered between the TG and WT groups

(data not shown) indicating that the biological mechanism of itaconic acid action may be more

pleotropic than that recently reported in literature43,44.

Recently itaconic acid has been found to inhibit phosphofructokinase 2 50, an enzyme that

catalyses the phosphorylation of fructose-6-phosphate to fructose – 2,6 – bisphosphate. Incubation

of itaconic acid with liver cells was shown to deregulate glycolysis by reducing fructose – 2,6 –

bisphosphate and this translated into a decrease of fatty acid synthesis and a further reduction in

visceral fat. This may be associated with our earlier observed metabolic perturbations that

consistently found glycolytic intermediates to be enriched (Table 1) in the WT compared to the TG

17

group. Theoretically, inhibition of fructose – 2,6 – bisphosphate should result in an increase in the

upper glycolysis part of the pathway (Glucose 6 phosphate, fructose 6 phosphate) and a decrease

in levels of intermediates such as glycerate 1,3 bisphosphate, phosphoenolpyruvate and pyruvate.

The discrepancy between the experimental data reported here and the theoretical prediction

indicates that the expected perturbation represents a very simplified approach to explain

metabolism by considering only a single metabolic pathway. In reality metabolism is a complex

network of many metabolic pathways that are interlinked and communicate closely to compensate

and adapt to a given metabolic perturbation. For example in our theoretical predictions we did not

take into account the potential flux of the upper glycolytic intermediates through the pentose

phosphate pathway in conditions where itaconic acid inhibits phosphofructokinase 2.

In the preliminary data obtained from examining normal human fibroblast cells (K4IM cell

line) treated with hTNFa for activation, itaconic acid was detected only in the hTNFa treated cells

(data not shown). Whilst itaconic acid may turn out to be a rather general marker for an

inflammatory response this result may provide a first step in the translation of the current mouse-

derived data into a clinically useful marker but clearly much more work is needed to demonstrate

the value of itaconic acid in human disease.

18

Conclusions

In the present work we profiled the metabolic content of samples from in vivo animal RA model and

in vitro cell culture derived from Tg197 mouse model. A multi-method targeted on metabolites

associated with the major energy producing pathways, aminoacid and purine/pyrimidine

metabolism, detected a specific RA disease phenotype in TG compared to control WT samples.

Treatment with infliximab normalized the metabolic profile closely to that of WT animals exhibiting

possibility of drug efficacy monitoring. Itaconic acid was found to show a strong association with

the disease phenotype in all investigated specimens (synovial fibroblast extracts, serum plasma

and hind limb from TG animals). Preliminary in vitro work using human fibroblasts also showed that

itaconic acid was produced in response to exposure to TNFa suggesting the potential for the

translation of these animal model-derived findings to humans for clinical evaluation of the potential

diagnostic value of itaconic acid in RA and related diseases. The implementation of a systems

biology integrating the results from metabolic profiling with genome wide expression data enabled

insights to be obtained on the overall modulation of metabolic pathways highlighting itaconic that

has only recently been reported to be involved in inflammatory settings34,51 as a possible marker for

RA. The studies reported here provide a mechanistic link between the enrichment in itaconatic acid

and the over-expression of a recently discovered cis-aconitase39 for the first time in an RA setting.

Overall the combination of metabolic profiling and genome wide expression data provides

complementary information in regards to itaconic acid deregulation in inflammation and its possible

use as a marker for RA.

Acknowledgement

This research has been partially co-financed by the European Union [European Social Fund – ESF,

FP7-HEALTH project MASTERSWITCH (Grant 223404) and IMI project BTCure (115142-2)] and

Greek national funds through the Operational Program "Education and Lifelong Learning" of the

National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II.

Investing in knowledge society through the European Social Fund.

19

Supporting information available

Table S1: List of animals per group, gender and time used to collect hind limb tissue, urine serum

and SF samples

Table S2: Metabolite list profiled at all biospecimens with the targeted LC-MS platform

Figure S1: (A) Mouse body weight trajectory and (B) arthritic score across 6 weeks of in vivo

study. Healthy control (WT), diseased (TG) and treated with 10mg/Kg Infliximab animals (TR).

Figure S2: Itaconic acid median peak area distribution at serum and urine samples collected at

week 6 and 11 of age.

Figure S3: Detailed representation of citrate to a-ketoglutarate metabolism with associated

enzyme located in the cytoplasm and mitochondrion.

20

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24

Table 1: List of modulated metabolites (p-value<0.05) at aqueous hind limb and synovial fibroblast extracts. All metabolites showed QC

CV<30%. N.D: non detected metabolite. Metabolites highlighted in red and blue were found significantly up-regulated or down regulated at the

first group of the binary comparison respectively. For explanation of acronyms please refer to Table S2.

25

Hind limb tissue Synovial fibroblasts

TG to WT TG to TR TR to WT TG to WT TG to TR TR to WT

Metabolite Log2 Fold

Change

p-value Log2 Fold

Change

p-value Log2 Fold

Change

p-value Metabolite Log2 Fold

Change

p-value Log2 Fold

Change

p-value Log2 Fold

Change

p-value

α-Ketoglutaric

acidN.D at TG N.D at TG 0.54 1.55E-02 Adenine 1.2 3.70E-02 0.4 2.68E-01 0.8 3.71E-02

Aspartic acid 0.39 4.34E-03 -0.09 8.35E-01 0.09 3.14E-01 AsparagineOnly at

WTN.D Only at WT

Galactose 1

phosphate-3.04 8.90E-04 -2.4 5.71E-03 -0.63 2.56E-01 cAMP -0.2 4.63E-01 0.4 7.24E-02 -0.6 2.62E-02

Glucose 6

phosphate-2.91 3.50E-04 -2.3 1.63E-02 -0.62 3.33E-01 cis aconitic acid 0.8 1.59E-03 0.2 1.29E-01 0.6 5.97E-03

Glucuronic

acid0.95 9.90E-05 0.8 2.57E-04 0.1 6.20E-01 Citric acid 2 9.63E-03 0.5 1.56E-01 1.4 2.12E-02

Glutamate 0.18 1.28E-03 0.27 3.09E-03 -0.12 1.24E-01 Citrulline -1.9 2.21E-02 -0.6 2.28E-01 -1.3 6.88E-02

GMP -1.81 4.12E-03 -1.3 3.34E-02 -0.52 2.45E-01 dCMP 1.1 3.21E-02 0.5 4.17E-02 0.6 1.67E-01

Glutathione

oxidized0.58 0.058 0.63 0.0002 0.08 0.738 FBP N.D at WT 1.6 7.96E-04 N.D at WT

Glutathione

reduced-0.44 0.064 -0.44 0.038 -0.001 0.993 Glucuronic acid -2.3 2.88E-02 -0.5 5.47E-01 -1.8 8.60E-02

Guanosine 0.35 4.67E-04 0.4 2.34E-02 -0.08 5.87E-01 Glutathione ox -1.1 2.67E-01 -3.8 3.49E-03 2.6 2.59E-02

26

Inosine -0.3 9.33E-03 -0.09 4.58E-01 -0.24 5.54E-02 Glyoxylic acid N.D at WT 0.2 5.29E-01 N.D at WT

Itaconic acid N.D at WT N.D at TR N.D at TR and WT Inosine 0.2 7.72E-01 1.4 7.82E-03 -1.2 1.50E-01

Lactic acid -0.5 9.40E-04 -0.12 3.34E-01 -0.41 1.42E-02 Isocitratic acid 1.5 1.65E-02 0.6 4.26E-02 0.9 5.20E-02

Mannose 6

phosphate-3.51 7.80E-05 -1.7 1.25E-06 -1.1 2.30E-01 Itaconic acid N.D at WT N.D at WT N.D at TR and WT

NAD -1.51 1.51E-05 -2.4 4.07E-02 0.2 3.28E-01 Malate -0.5 1.94E-03 -0.5 5.72E-04 0.05 6.44E-01

NAG 0.97 9.85E-04 0.8 2.17E-02 0.11 7.30E-01 Maleic acid -0.5 3.36E-03 -0.6 3.38E-03 0.01 9.43E-01

Pyruvic acid -0.51 1.63E-02 -0.4 5.78E-02 -0.15 4.80E-01 Mannose 6 P 0.8 1.19E-01 0.7 4.88E-05 0.1 8.35E-01

Riboflavin 0.88 3.35E-04 0.35 5.95E-02 0.5 3.32E-02 orotic acid 0.3 3.06E-01 -0.4 9.17E-03 0.8 3.39E-02

Tryptophan 0.51 2.32E-03 0.39 2.36E-02 0.09 5.11E-01 Pantothenic acid -0.5 7.43E-02 -0.5 3.92E-02 0.1 6.40E-01

Tyrosine 0.5 3.09E-04 0.39 6.26E-03 0.08 4.94E-01 Proline 0.01 9.63E-01 -0.5 1.23E-02 0.5 1.12E-02

UDP glucose 0.64 1.77E-02 0.4 1.26E-01 0.21 2.37E-01 Pyruvic acid -0.8 1.66E-01 0.4 3.64E-01 -1.3 2.23E-02

UMP -1.3 1.08E-02 -1.4 1.13E-02 0.06 8.78E-01 Ribose 5 P 1.8 4.91E-02 1 1.32E-05 0.9 2.69E-01

Uracil 1.67 6.20E-06 1.7 1.48E-04 -0.04 8.92E-01 Serine -0.8 1.57E-02 -0.2 2.32E-01 -0.6 2.30E-02

Uridine 0.27 9.96E-03 0.34 4.70E-02 -0.1 5.01E-01 Succinic acid 0.2 3.11E-01 0.6 6.58E-03 -0.5 2.11E-02

Orotic acid 0.59 1.14E-01 0.8 3.82E-02 -0.29 6.75E-03 Tryptophan -0.4 1.48E-01 -0.6 3.73E-02 0.2 4.35E-01

Creatine -0.76 3.27E-03 -0.5 2.63E-02 -0.23 9.88E-02 Uracil 1.3 3.30E-02 0.8 1.37E-01 0.5 2.55E-01

Histidine -0.57 1.36E-05 -0.6 9.93E-07 0.08 1.92E-01

Isoleucine 0.25 2.47E-02 0.17 7.69E-02 0.08 2.41E-01

27

Leucine 0.18 1.44E-02 0.05 4.15E-01 0.13 1.51E-02

Threonine 0.26 4.19E-02 0.45 1.63E-03 -0.19 9.41E-03

28

Figure 1: Scores plots of O2PLS discriminant analysis of (A) hind limb tissue and (B) synovial fibroblast data. Group separation represents metabolic phenotype differentiation between healthy controls (WT), diseased (TG) and animals treated with 10mg/Kg Infliximab (TR).

29

A

B

TRTGWT

Figure 2: Volcano plots with metabolites measured in week 11 (A) aqueous hind limb extracts and (B) synovial fibroblast extracts on the targeted metabolite platform. Metabolites that have QC CV<30%, log2|(Fold Change)|>0.5 and p-value<0.05, Metabolites that have p-value<0.05 but do not meet one or both of the other validation criteria (QC CV<30%, log2|(Fold Change)|>0.5),

Metabolites with p-value>0.05. Along the x-axis log2 scaled values of the fold change for the binary group comparison described, indicate metabolite enrichment on either “test” or “control” group. On the y-axis p-values resulting from a uni-variate t-test describe the statistical significance of a given metabolite in the binary group differentiation.

30

Figure 3: Heat map listing the number of validated metabolites seen to be significantly modulated (|log2 Fold change|>0.5, QC CV<30 and p-value<30%) per metabolic pathway on a binary group comparison at (A) aqueous hind limb joint, (B) synovial fibroblasts extracts. Red colour represents most impacted metabolic pathways while blue represents minimal pathway perturbation. Dendrograms on the left and top of the figure represent pathway and group comparison clustering.

31

A

B

Figure 4: Schematic representation of itaconic acid metabolism based on KEGG canonical metabolic pathways (A) Hind limb and (B) Synovial fibroblast TG/WT data. Metabolites in i) grey were not measured in the targeted analysis, ii) blue were enriched at WT animals iii) red were enriched at TG animals and iv) black were detected but not found to be enriched in either TG or WT.

32

B

A

Figure 5: Gene expression in (A) hind limb (B) synovial fibroblasts from diseased (TG) against normal (Wt) mice for genes related with the production of itaconic acid. Bars represent the mean of a minimum of three independent measurements. Irg1: Immunoresponsive gene 1, Aco1,2: Aconitase 1 or 2. Idh1,2: Isocitrate dehydrogenase 1 or 2. *** p-value<0.005, ** p-value<0.05

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A

B

Gene Expression

Gene

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