J.!Barr!(1),!A.!Dominguez"Diez(2),!C.!Fernandez"Escalante(2),!M.!Gomez"Fleita, g , ,Caballeria(3) A Martin"Duce(4) O Lo Iacono(5) C Alonso(1) R Suero(1) A Galan(Caballeria( ),!A.!Martin"Duce( ),!O.!Lo!Iacono( ),!C.!Alonso( ),!R.!Suero( ),!A.!Galan(

Martinez Arrieta(7) JA Pajares Diaz(8) Y Le Marchand Brustel(6) SC LuMartinez"Arrieta(7),!JA.!Pajares Diaz(8),!Y.!Le!Marchand"Brustel(6),!SC.!Lu(1) OWL Genomics, Technological Park ( ) g

(2) Hospital Universitario Marques de Valdecilla. Universidad de Cantabria. (3) Liver Unit, Hospital ClinSpain. (5) Hospital del Tajo, Aranjuez, Madrid, Spain. (6) Institut National de la Sante et de la Rec

Madrid, Spain. (9) Division of Gastrointestinal and liver diseases, USC, Los An

NAFLD and metabolomics 1 Experimental procedureNAFLD and metabolomics 1. Experimental procedure

Th id tifi ti f l bi k S l ll iThe identification of novel serum biomarkers Sample collection:differentiating between steatosis and NASH is of fundamental S ll t d f 244differentiating between steatosis and NASH is of fundamentalimportance for effective NAFLD diagnosis and treatment

Sera was collected from 244importance for effective NAFLD diagnosis and treatment

h fi ld f b l i h hpatients (See Table), each with a

assessment. The emergent field of metabolomics has thepat e ts (See ab e), eac w t a1 3 NASH grade 1) establishg

potential to reveal such biomarkers Recent technological1-3, NASH grade 1) establishbi l i h bpotential to reveal such biomarkers. Recent technological

b kth h h id d h ith th it tbiopsy samples, in the absence o

breakthroughs have provided researchers with the capacity top y p

drug-induced) causes of NAFLDmeasure hundreds or even thousands of small-molecule

drug induced) causes of NAFLD

metabolites in as little as a few minutes per sample pavingmetabolites in as little as a few minutes per sample, pavingh f h h i i di id ll i dthe way for hypothesis generation studies ideally suited to

complex disease scenarios. The approach is particularlycomplex disease scenarios. The approach is particularlyapplicable to liver injury assessment where the most Clinical deapplicable to liver injury assessment, where the most Clinical decommonly available sample for laboratory tests is serum ory p yurine – ideal for metabolomics analysisurine ideal for metabolomics analysis. Healt

SteatosSteatos

Animal models SteatosAnimal modelsS

h i l i bili h b l i hSteatos

The potential suitability of the metabolomics approach for Np y ppthe study of NAFLD has been evaluated in our laboratory

Nthe study of NAFLD has been evaluated in our laboratory

i t diff t i l d l B th i k t b tiusing two different animal models. Both invoke perturbations Metabolic Profilingof the methionine cycle, long recognised as being of key

gy , g g g y

importance in liver disease A global metabolite profiling Uimportance in liver disease. g p gthe samples characterised by mthe samples, characterised by m

d d t t d tMethionine Cycle: procedure used to study metMethionine Cycle:(1) corresponding to putative bioma

PPi + PiATP(1) p g p

PPi +!PiATP

Methionine SAMeMATI/III 2 Sample profiling and putDi th l l i

SAMeMATI/III 2. Sample profiling and putGlycineDimethylglycine

BHMT GNMTMS (2)BHMT GNMTMS (2)

N"MethylglycineBetaine

SAHHomocysteine SAHSAH!HydrolaseHydrolase

S-adenosylhomocysteine (SAMe) is the principal biological methyl donorS-adenosylhomocysteine (SAMe) is the principal biological methyl donorand participates in multiple cellular reactions Both low and high SAMeand participates in multiple cellular reactions. Both low and high SAMel l b l d li di h ilevels may be related to liver disease pathogenesis.

(1) MAT1A Knockout mice(1). MAT1A Knockout miceThe deletion of methionine adenosyltransferase (MATI/III) leads to aThe deletion of methionine adenosyltransferase (MATI/III) leads to amarked reduction of liver SAMe content MAT1A knockout mice aremarked reduction of liver SAMe content. MAT1A knockout mice arepredisposed to spontaneo s de elopment of NAFLD (steatosispredisposed to spontaneous development of NAFLD (steatosis,

h i i ) d h isteatohepatitis) and hepatocarcinoma.

The Hematoxylin/eosin staining revealsWT MAT1A-KO 8 MMAT1A-KO 3 M

The Hematoxylin/eosin staining revealsmacrovesicular steatosis in 3months old PCA scores plot (components t[1], t[2macrovesicular steatosis in 3months oldMAT1A-KO mice, that progresses to

PCA scores plot (components t[1], t[2serum metabolic profiling data obtainedMAT1A KO mice, that progresses to

NASH at 8 months old, as shown by theserum metabolic profiling data obtainedpatients from Hospital Universitario M, yappearance of focal areas of patients from Hospital Universitario M

d 1 3 d h NASH bl kinflammation. grades 1-3, red spheres; NASH, blackcluster in the upper-right quadrantsphere.

Scores: Component 1/Component 2p

1

0,6PCA scores plot of the UPLC®-MS ,pserum metabolic profiling data obtained

0,2

p gfrom WT and MAT1A-KO mice Clear

1 0 5 0 0 5 1

t![2]from WT and MAT1A KO mice. Clear

differentiation between the WT and "0,2"1 "0,5 0 0,5 1differentiation between the WT andMAT1A KO i i b d i th fi t Plasmalogen PC(O"18:1"0,6MAT1A!WTMAT1A-KO mice is observed in the first g (

l l (

0,6MAT"KO!2,5M

MAT KO 5Mprincipal component.

Plasmalogen PC(O"16:1"1t [1]

MAT"KO!5M

MAT"KO!8M

Plasmalogen PC(O"16:1/t![1]

(2). GNMT Knockout mice Plasmalogen PC(O"16:1/( ). G oc ou ceSphingomyelin SM(d18:2/

The deletion of glycine-N-methyltransferase (GNMT) results in anp g y (

St id C j t S lf l lithincrease of methionine and SAMe content. Steroid!Conjugate Sulfoglycolith

WT 3M GNMT KO 3 M Steroid Conjugate Hyodeoxycholate"6WT 3M GNMT-KO 3 M Steroid!Conjugate Hyodeoxycholate 6

Bile!Acid TrihydroxycoproD l ti f GNMT l d t t t i d fib i At 3

&H

ND NDDelection of GNMT leads to steatosis and fibrosis. At 3months of age macro and microvesicular steatosis couldE ND NDmonths of age macro and microvesicular steatosis couldbe seen through the hepatic lobule in GNMT-KO mice

ND NDbe seen through the hepatic lobule in GNMT-KO micecompared with wild type animals. Collagen deposits ND NDcompared with wild type animals. Collagen deposits(sirius red staining) indicate moderate liver fibrosis.

SR

ND ND( g)

S

ND NDND ND

Scores: Component 1/Component 2

M b li li d f hi h h1

Scores: Component 1/Component 2

Metabolites are listed for which therePCA scores plot of the UPLC®-MSNASH and steatosis samples [Wilco

PCA scores plot of the UPLC MSserum metabolic profiling data obtained

0,6 p [p-values < 0.05] from Hospital Clinic.

serum metabolic profiling data obtainedfrom WT and GNMT KO mice Clear 0 22] p values 0.05] from Hospital Clinic.

class [putative metabolite identificatiofrom WT and GNMT-KO mice. Cleardiff ti ti b t th WT d

0,2

t[2

class [putative metabolite identificatiodatabase searching and fragment ion an

differentiation between the WT and "0,2"1 "0,5 0 0,5 1 database searching and fragment ion anf t t l i t d

GNMT-KO mice is observed in the firstof structural assignment proposedprincipal component. "0,6GNMT!WTInitiative: “Based upon characteristic ph

p p p"1

GNMT"KO!4M

GNMT KO 6 5M

class of compounds, or by spectral1t[1]

GNMT"KO!6,5M

p , y pchemical class ”]; Common name li

On the basis of the promise shown in the animal studies thischemical class. ]; Common name, liMAPS convention (www lipidmaps orgOn the basis of the promise shown in the animal studies this

k l th t b li fil f h NAFLDMAPS convention (www.lipidmaps.orgth h t hi kwork explores the serum metabolic profiles of human NAFLD the average chromatographic peak are

h hi kpatients, the aim being to identify metabolite biomarkers able to average chromatographic peak areapatients, the aim being to identify metabolite biomarkers able todistinguish between steatosis and NASH determined.distinguish between steatosis and NASH.

as(2),!M.!Garcia"Unzueta(2),!L.!Campo"Alegria(2),!M.!Mayorga"Fernandez(2),!J.!, , p g , y g ,1) M Vazquez"Chantada(1) R Bataller(2) S Delgado(2) A Tran(6) J Tordjman(6) F),!M.!Vazquez"Chantada( ),!R.!Bataller( ),!S.!Delgado( ),!A.!Tran( ),!J.!Tordjman( ),!F.!u(9) ML Martinez Chantar(10) A Castro (1) P Gual(6) and J M Mato (10)u(9),!ML.!Martinez"Chantar(10) ,!A.!Castro!(1),!P.!Gual(6) and!!J.!M.!Mato (10)

of Vizcaya, Ed. 502, 48160, Derio, Spainy pic, Ciberehd. Barcelona, Spain. (4) Departamento de Enfermeria, Alcala de Henares University, Madrid. cherche Medicale (INSERM). (7) Hospital Puerta de Hierro Madrid, Spain. (8) HGU Gregorio Marañon, ngeles, USA. (10) CICbioGUNE, Unidad de Metabolomica. Ciberehd. Derio, Spain.

esesSample preparation:Sample preparation:

b (BMI > 30 k / 2) Proteins were precipitated from the defrosted serum/plasma samplesobese (BMI > 30 kg/m2) Proteins were precipitated from the defrosted serum/plasma samples(100 L) b ddi f l f h l Af b i f ia diagnosis (steatosis grade (100 !L) by adding four volumes of methanol. After brief vortexinga d ag os s (steatos s g ade

hed histologically in liver the samples were incubated overnight at -20 C Supernatants werehed histologically in liverf h ( i l l h l

the samples were incubated overnight at 20 C. Supernatants werecollected after centrifugation at 13 000 rpm for 10 minutes dried andof other (viral-, alcohol-, or collected after centrifugation at 13,000 rpm for 10 minutes, dried, and(re-suspended in 120 !L of 80% methanol. The resulting extracted. p ! gsamples were then transferred to vials for UPLC®-MS analysissamples were then transferred to vials for UPLC -MS analysis.

etails of the patients included in the study. Values are given as mean ± standard error of the mean.etails of the patients included in the study. Values are given as mean standard error of the mean.

thy Liver 24 4 34.4!± 1.8 46.8!± 1.7 29.5!± 4.8 6.0!± 0.5

sis Grade 1 64 12 40 4 ± 1 5 46 2 ± 0 9 27 7 ± 1 7 5 7 ± 0 2sis Grade!1 64 12 40.4 ± 1.5 46.2!± 0.9 27.7!± 1.7 5.7!± 0.2

sis Grade!2 46 17 41.7!+ 1.2 47.3!+ 0.8 37.4!+ 2.3 6.5!+ 0.4

i G d 3 38 12 39 7 1 7 48 4 1 2 40 4 2 9 6 7 0 3sis Grade!3 38 12 39.7!+ 1.7 48.4!+ 1.2 40.4!+ 2.9 6.7!+ 0.3

NASH 20 7 45 7 + 1 9 48 7 + 2 2 39 9 + 3 7 7 2 + 0 7NASH 20 7 45.7!+ 1.9 48.7!+ 2.2 39.9!+ 3.7 7.2!+ 0.7

®UPLC®-MS methodology was employed where all endogenous metabolite related features detected ingy p y gmass-to-charge ratio m/z and retention time Rt are included in a subsequent multivariate analysismass to charge ratio m/z and retention time Rt, are included in a subsequent multivariate analysist b li diff b t th diff t f l Wh ibl Rt / f ttabolic differences between the different groups of samples. Where possible, Rt-m/z featuresarkers were later identified.

tative biomarkers 3 Validation studiestative biomarkers 3. Validation studies

2020

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Steatosis NASH

S l t d t b lit bi k ( t t t t di ) h i i ilSelected metabolite biomarker (structure patent pending) showing similarsteatosis–NASH discrimination in Spanish (Hospital Clinic) (n = 41) andFrench (INSERM Paris and INSERM Nice) (n = 32) patient cohorts.( ) ( ) p

To assess the diagnostic value of the NASH metabolic biomarkers local PCATo assess the diagnostic value of the NASH metabolic biomarkers, local PCAd l t d f th t t i d NASH l dmodels were computed for the steatosis and NASH samples and

classification lists generated for an external blind set of 10 serum samples.The metabolomics diagnosis was determined by the highest membership

2] and t[4]) of the UPLC®-MSg y g p

probability of belonging to either the steatosis or NASH models; membership2] and t[4]) of the UPLC MSd from different groups of obese

probability of belonging to either the steatosis or NASH models; membershipprobability values > 0 05 (95% confidence interval) are shown in bold typed from different groups of obese

Marqués de Valdecilla: steatosisprobability values > 0.05 (95% confidence interval) are shown in bold type,

hil t ll fid i t l b biliti h i it liMarqués de Valdecilla: steatosish NASH l t d t

whilst smaller confidence interval probabilities are shown in italics.spheres. NASH samples tend toof hotelling’s 95% confidence

1 Steatosis 1 0 451 0 912 Steatosis1 Steatosis 1 0.451 0.912 Steatosis2 S i 1 0 043 0 137 S i2 Steatosis 1 0.043 0.137 Steatosis3 Steatosis 2 0.726 0.115 NASH4 Steatosis 3 0.989 0.997 Steatosis5 Steatosis 3 0 093 0 441 Steatosis5 Steatosis 3 0.093 0.441 Steatosis6 Steatosis 3 0 054 0 912 Steatosis

1/0:0) 1.22 0.0136 Steatosis!3 0.054 0.912 Steatosis

/ )

/ )7 NASH 0.775 0.288 NASH

1/0:0) 1.15 0.026 8 NASH 0.113 1.61!! 10"5 NASH

/20:4) 1 29 0 012 9 NASH 0.717 0.466 NASH/20:4) 1.29 0.012 9 NASH 0.717 0.466 NASH10 NASH 0 727 0 018 NASH

/15:0) 0.69 0.00310 NASH 0.727 0.018 NASH

)

h h l t 1 32 0 039hocholate 1.32 0.039

"O"glucuronide 1 75 0 003Conclusion

O glucuronide 1.75 0.003Conclusionstanoic!acid 1.11 0.046

This work indicates that a classifier, generated using1 33 0 052 This work indicates that a classifier, generated usingserum metabolic information alone may be able

1.33 0.052serum metabolic information alone, may be able

1.33 0.005differentiate between steatosis and NASH patients. The

1.33 0.005p

NASH classifier described in this work together with other1.41 0.015 NASH classifier described in this work, together with othert b d ti li i l d l b t1 33 0 043 score systems based on routine clinical and laboratory1.33 0.043

measurements (such as FibroTest, NAFLD fibrosis score,( , ,SteatoTest NashTest and ELF test) may deem liver biopsyi i ifi di i i i b SteatoTest, NashTest and ELF test), may deem liver biopsy

i i I ddi i hi l fe is significant discrimination between

unnecessary in many instances. In addition, this panel ofoxon rank sum (Mann Whitney) testserum metabolic biomarkers may provide clinicians with

( y)Metabolite Class, metabolite chemical serum metabolic biomarkers may provide clinicians with

valuable information for making management decisionsMetabolite Class, metabolite chemical

on was performed by accurate mass valuable information for making management decisions.on was performed by accurate massnalysis corresponding to the third levelnalysis, corresponding to the third level

ithi th M t b l i St d dwithin the Metabolomics StandardsAcknowledgementshysiochemical properties of a chemical Acknowledgements

similarity to known compounds of aThis work is supported by grants from SAF 2008-04800 and

y pipid nomenclature follows the LIPID

ETORTEK-2008, HEPADIP-EULSHM-CT-205, Ciberehd, NIH AT-1576,ipid nomenclature follows the LIPIDg); Fold change obtained by dividing INTEK 06-20, 07-29, FIT-06-101, FIS PI060085, Interface Grant from CHU ofg); Fold change, obtained by dividing

f d i th NASH l b th Nice, and charities ALFEDIAM and AFEF/Schering-Plough.a found in the NASH samples by thei h i l The contribution to this work from the technicians Ziortza Ispizua, Jessicain the steatosis samples. ND, not p

Arribas, Mónica Martínez and Stephanie Bounnafous is gratefully, p g yacknowledged.g