# CHKA, un " marqueur-cible " potentiel pour le CHC-MASLD à un stade précoce de fibrose : analyse par approche des " omiques "

Jihan Sun, Fatima Dahboul, Estelle Pujos-Guillot, Stéphanie Durand, Mélanie Pétéra, Delphine Centeno, Benoit Colsch, Guillaume Zoulim, Delphine Weil, Vincent Di Martino, et al.

► To cite this version:

Jihan Sun, Fatima Dahboul, Estelle Pujos-Guillot, Stéphanie Durand, Mélanie Pétéra, et al. CHKA, un " marqueur-cible " potentiel pour le CHC-MASLD à un stade précoce de fibrose: analyse par approche des " omiques ". JFHOD 2024 - Journées Francophones d'Hépato-gastroentérologie et d'Oncologie Digestive, Mar 2024, Paris, France. hal-04645222

HAL Id: hal-04645222

https://hal.science/hal-04645222v1

Submitted on 11 Jul 2024

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# CHKA, un « marqueur-cible » potentiel pour le CHC-MASLD à un stade précoce de fibrose : analyse par approche des « omiques »

**Jihan Sun**<fnref n="1" />, Fatima Dahboul<fnref n="1" />, Estelle Pujos-Guillot<fnref n="2" />, Stéphanie Durand<fnref n="2" />, Mélanie Petera<fnref n="2" />, Delphine Centeno<fnref n="2" />, Benoit Colsch<fnref n="2" />, Zoulim Guillaume<fnref n="1" />, Delphine Weil<fnref n="4" />, Vincent Di Martino<fnref n="4" />, Aicha Demidem<fnref n="1" />, Armando Abergel<fnref n="3" />

<footnote n="1">¹ Team ECREIN, UMR 1019, INRAE, Clermont-Auvergne Université, Clermont-Ferrand, France</footnote>

<footnote n="2">² Plateforme exploration du Métabolisme, METABOHUB, INRAE, Theix, France</footnote>

<footnote n="3">³ Hepato-Gastroenterology Unit, Université Hospital Estaing, Clermont-Ferrand, France</footnote>

<footnote n="4">⁴ Service d'Hépatologie et de Soins Intensifs Digestifs, Hôpital Jean Minjoz, Besançon, France</footnote>
# Liens d'intérêts

- L'orateur a déclaré sur le site des JFHOD ne pas avoir de liens d'intérêts.
# Metabolic dysfunction-associated steatotic liver disease (MASLD)

* **Old name:** non-alcoholic fatty liver disease (NAFLD) or metabolic dysfunction-associated fatty liver disease (MAFLD), changed in **June, 2023** (Rinella et al., 2023)

* **Diagnostic criteria:** Hepatic steatosis is identified by imaging or biopsy with ≥ 1 of 5 cardiometabolic risk factor

* **MASLD** affects nearly **25% of the global** population, with the prevalence rates showing an **upward trend**
# MASLD - HCC

* Obesity
* Insulin resistance
* Unhealthy lifestyle
* Genetic polymorphism: *PNPLA3*, *MBOAT7*...

<figure><img src="image_3.png" /></figure>
# New pathway of MASLD-HCC

<figure><img src="image_4.png" /></figure>
# Fibrosis - METAVIR classification

* The METAVIR classification is currently the reference for assessing histological fibrosis level from absence of fibrosis (F0) to cirrhosis (F4)

## 5 degree of fibrosis :

F0 = absence

F1 = mild

F2 = moderate

F3 = severe

F4 = cirrhosis

Without or with mild fibrosis

MASLD-HCC-F0F1

<figure><img src="image_5.png" /></figure>

Cirrhosis

MASLD-HCC-F3F4

<figure><img src="image_6.png" /></figure>
# Purpose

* Our previous study revealed two metabolomics phenotypes of MASLD-HCC according to fibrosis level (Buchard et al., 2021)

* Use combined **metabolomics and transcriptomics** analyses on **human liver tissues**, to explore **lipid metabolic pathways** and to identify potential **biomarkers** for **MASLD-HCC** according to **fibrosis level**
# Methods - Cohort design (CRB. Foie)

<figure><img src="image_7.png" /></figure>

TT : tumoral tissue

NTT : non-tumoral tissue
# Metabolomics

<figure><img src="image_8.png" /><figcaption>## Workflow of Untargeted Lipidomics</figcaption></figure>

**LC-HRMS:** Liquid chromatography–high resolution mass spectrometry
# Transcriptomics

## Workflow of RT-qPCR

<figure><img src="image_9.png" /></figure>

Results of LC-HRMS

<figure><img src="image_10.png" /></figure>

Related pathways and major enzymes

<figure><img src="image_11.png" /></figure>

Genes

<figure><img src="image_12.png" /></figure>

RT-qPCR

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>
# Results - Metabolomics

<figure><img src="image_15.png" /></figure>

1744 ions detected

138 ions (lipids) identified

36 discriminant lipids identified

4 families:

- Cholesterol ester

- Glycerolipid

- Sphingolipid

- Glycerophospholipid

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>
# Results - Metabolomics

Intrahepatic lipids differentiating TT from NTT in the two patient groups with MASLD-HCC

## NTT vs. TT

<table><thead><tr><th>Families / lipid compound</th><th>F0F1 NTT vs. TT</th><th>F3F4 NTT vs. TT</th></tr></thead><tbody><tr><td><strong>Cholesterol esters</strong></td><td></td><td></td></tr><tr><td>CE (18:2)</td><td>*</td><td></td></tr><tr><td><strong>Glycerolipid</strong></td><td></td><td></td></tr><tr><td>TAG (16:0_18:1_18:2)</td><td></td><td>*</td></tr><tr><td>TAG (16:0_18:2_18:2)</td><td>*</td><td>*</td></tr><tr><td>TAG (16:1_18:2_18:2)</td><td>**</td><td>*</td></tr><tr><td>TAG (18:1_18:2_18:2)</td><td>*</td><td></td></tr><tr><td>TAG (18:1_18:2_18:3)</td><td>*</td><td>*</td></tr><tr><td><strong>Sphingolipid</strong></td><td></td><td></td></tr><tr><td>Cer (d18:1/22:0)</td><td>*</td><td></td></tr><tr><td>Cer (d18:1/24:0)</td><td></td><td>*</td></tr><tr><td>SM (35:1)</td><td>*</td><td></td></tr><tr><td>SM (38:1)</td><td></td><td>*</td></tr><tr><td>SM (39:1)</td><td></td><td>*</td></tr><tr><td>SM (40:2)</td><td>**</td><td></td></tr><tr><td>SM (41:1)</td><td>*</td><td>**</td></tr><tr><td>SM (42:3)</td><td>**</td><td></td></tr><tr><td><strong>Glycerophospholipid</strong></td><td></td><td></td></tr><tr><td>PC (16:0_14:0)</td><td>*</td><td></td></tr><tr><td>PC (16:0/16:0)</td><td>*</td><td></td></tr><tr><td>PC (16:0_20:4)</td><td></td><td>*</td></tr><tr><td>PC (16:0_22:6)</td><td></td><td>*</td></tr><tr><td>PC (18:0_18:1)</td><td>**</td><td>**</td></tr><tr><td>PC (18:0_20:3)</td><td></td><td>*</td></tr><tr><td>PC (40:6)</td><td></td><td>*</td></tr><tr><td>PC (40:8)</td><td></td><td></td></tr><tr><td>PE (16:0_22:6)</td><td></td><td>**</td></tr><tr><td>PE (18:0_18:1)</td><td></td><td>**</td></tr><tr><td>PE (18:0_22:6)</td><td></td><td>*</td></tr><tr><td>PE (O-16:1_20:4) or PE (P-16:0_20:4)</td><td>*</td><td></td></tr><tr><td>PI (16:0_18:1)</td><td></td><td>*</td></tr><tr><td>PI (18:0_20:4)</td><td></td><td>**</td></tr><tr><td>LPC (18:1)</td><td>*</td><td></td></tr><tr><td>LPE (18:1)</td><td>*</td><td></td></tr></tbody></table>

Increase

Decrease

Unchanged

1. Cholesterol ester (CE)

2. Glycerolipid:

TAG: triacylglycerol

3. Sphingolipid:

Cer: ceramide

SM: sphingomyelin

4. Glycerophospholipid:

PC: phosphatidylcholine

PE: phosphatidylethanolamine

PI: phosphatidylinositol

LPC: lysophosphatidylcholine

LPE: lysophosphatidylethanolamine
## F0F1: Increased metabolic contents

<table><tr><td>-</td><td>CE(18:2)</td></tr><tr><td>-</td><td>TAG(18:1_18:2_18:2)</td></tr><tr><td>SM (35:1)<br>SM (40:2)<br>SM (42:3)</td><td>Cer (d18:1/22:0)</td></tr><tr><td>PC(16:0_14:0)<br>PC(16:0/16:0)<br>PC(40:8)<br>PE(18:0_18:1)<br>LPC(18:1)<br>LPE(18:1)</td><td>-</td></tr></table>

<figure><img src="image_21.png" /></figure>

SM (41:1)
PE(O-16:1_20:4)
TAG(16:0_18:2_18:2)
TAG(16:1_18:2_18:2)
TAG(18:1_18:2_18:3)

## F3F4: Decreased metabolic contents

<table><tr><td>-</td><td>-</td></tr><tr><td>-</td><td>TAG(16:0_18:1_18:2)</td></tr><tr><td>-</td><td>Cer (d18:1/24:0)<br>SM (38:1)<br>SM (39:1)</td></tr><tr><td>PC(18:0_20:3)<br>PI(16:0_18:1)</td><td>PC(16:0_20:4)<br>PC(16:0_22:6)<br>PC(40:6)<br>PE(16:0_22:6)<br>PE(18:0_22:6)<br>PI(18:0_20:4)</td></tr></table>

Onco-metabolite
(Yang et al., 2018)

## Amplified anabolism

LC-HRMS analysis confirmed the existence of **two metabolomics**

**phenotypes** of MASLD-HCC according to fibrosis level
# Results - Transcriptomics

4 lipid families identified

9 related pathways

12 major enzymes

16 coding genes

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

Housekeeping gene:
18s

5 healthy liver tissues
as control
Family 1 - Cholesterol ester

1.ACAT2

Cholesterol esterification

2.AADACL1

Cholesterol ester hydrolysis

Family 2 - Glycerolipid

3. DGAT1

DAG → TAG

4. DGAT2

5. SPTLC1

Ceramide *De Novo* synthesis pathway

6. SPTLC2

7. SGMS1

Ceramide → Sphingomyelin

8. SGMS2

9. SMPD1

Sphingomyelin → Ceramide

10. SMPD3

Family 3 - Sphingolipid

11. MBOAT7

Remodeling of PI

12. ACOX1

Synthesis of Docosahexaenoic acid (C22:6) (DHA)

13.PEMT

14. CHPT1

15. PCYT1A

16. CHKA

Kennedy pathway (PE,PC synthesis)

Family 4 - Glycerophospholipid
# Family 1 - Cholesterol ester
## AADACL1 and ACAT2

<figure><img src="image_25.png" /></figure>

ACAT2: Acetyl-CoA Acetyltransferase 2

AADACL1: ArylAcetamide DeACetyLase 1

The expression of the gene coding for **ACAT2** is
**exclusively up-regulated in MASLD-HCC-F0F1**
# Family 2 - Glycerolipid
## DGAT1 and DGAT2

<figure><img src="image_26.png" /></figure>

The expression of the two isoforms of gene coding for **DGAT1 and 2** is **exclusively up-regulated** in MASLD-HCC-F0F1

<caption>DGAT1/2: Diacylglycerol Acyltransferase 1/2</caption>
# Family 3 – Sphingolipid

<figure><img src="image_27.png" /></figure>

<table><thead><tr><th rowspan="2">Gene</th><th colspan="2">Control: healthy liver tissues</th></tr><tr><th>F0F1 NTT vs. TT</th><th>F3F4 NTT vs. TT</th></tr></thead><tbody><tr><td>SGMS1</td><td>↑</td><td>↑</td></tr><tr><td>SGMS2</td><td>↑</td><td>↑</td></tr><tr><td>SMPD1</td><td>↑</td><td>↑</td></tr><tr><td>SMPD3</td><td>ns</td><td>ns</td></tr><tr><td>SPTLC1</td><td>↑</td><td>↑</td></tr><tr><td>SPTLC2</td><td>↑</td><td>↑</td></tr></tbody></table>

The expression of 6 genes coding for **SPTLC1/2**, **SGMS1/2** and **SMPD1/3** shows **no difference** between the two groups of MASLD-HCC
# Family 4 - Glycerophospholipid

<table><tr><td>11. MBOAT7</td><td>Remodeling of PI</td></tr><tr><td>12. ACOX1</td><td>Synthesis of Docosahexaenoic acid (C22:6) (DHA)</td></tr><tr><td>13. PEMT</td><td rowspan="4">Kennedy pathway (PE,PC synthesis)</td></tr><tr><td>14. CHPT1</td></tr><tr><td>15. PCYT1A</td></tr><tr><td>16. CHKA</td></tr></table>

MBOAT7: Membrane Bound O-AcylTransferase 7

De novo synthesised PI

<figure><img src="image_28.png" /></figure>

PLA2

Endoplasmic Reticulum

LPI(18:0) + C20:4

<figure><img src="image_29.png" /></figure>

PLA2

<figure><img src="image_30.png" /></figure>

MBOAT7

PI (18:0_20:4)

The expression of gene coding for **MBOAT7** is **up-regulated** in both groups of MASLD-HCC
# Family 4 - Glycerophospholipid

<table><tr><td>11. MBOAT7</td><td>Remodeling of PI</td></tr><tr><td>12. ACOX1</td><td>Synthesis of Docosahexaenoic acid (C22:6) (DHA)</td></tr><tr><td>13.PEMT</td><td rowspan="4">Kennedy pathway (PE,PC synthesis)</td></tr><tr><td>14. CHPT1</td></tr><tr><td>15. PCYT1A</td></tr><tr><td>16. CHKA</td></tr></table>

<figure><img src="image_31.png" /></figure>

ALA: alpha-linolenic acid

<figure><img src="image_32.png" /></figure>

The expression of gene coding for ACOX1 is **exclusively up-regulated** in MASLD-HCC-F0F1
# Family 4 - Glycerophospholipid

<table><tr><td>11. MBOAT7</td><td>Remodeling of PI</td></tr><tr><td>12. ACOX1</td><td>Synthesis of Docosahexaenoic acid (C22:6) (DHA)</td></tr><tr><td>13. PEMT</td><td rowspan="4">Kennedy pathway (PE,PC synthesis)</td></tr><tr><td>14. CHPT1</td></tr><tr><td>15. PCYT1A</td></tr><tr><td>16. CHKA</td></tr></table>

PEMT: PhosphatidylEthanolamine N-MethylTransferase

CHPT1: Choline PhosphoTransferase 1

PCYT1A: Phosphocholine CytidylylTransferase

CHKA: Choline Kinase Alpha

<table><thead><tr><th rowspan="2">Family</th><th rowspan="2">Gene</th><th colspan="2">Control: healthy liver tissues</th></tr><tr><th>F0F1 NTT vs. TT</th><th>F3F4 NTT vs. TT</th></tr></thead><tbody><tr><td rowspan="3">Glycerophospholipid</td><td>PEMT</td><td>ns</td><td>ns</td></tr><tr><td>CHPT1</td><td>ns</td><td>ns</td></tr><tr><td>PCYT1A</td><td>ns</td><td>ns</td></tr></tbody></table>

<figure><img src="image_33.png" /></figure>

The expression of 3 genes coding for **PEMT, CHPT1 and PCYT1A** shows **no difference** between the two groups of MASLD-HCC
Family 4 - Glycerophospholipid

<table><tr><td>11. MBOAT7</td><td>Remodeling of PI</td></tr><tr><td>12. ACOX1</td><td>Synthesis of Docosahexaenoic acid (C22:6) (DHA)</td></tr><tr><td>13.PEMT</td><td rowspan="4">Kennedy pathway (PE,PC synthesis)</td></tr><tr><td>14. CHPT1</td></tr><tr><td>15. PCYT1A</td></tr><tr><td>16. CHKA</td></tr></table>

<figure><img src="image_34.png" /></figure>

The expression of gene coding for **CHKA** is **exclusively up-regulated** in MASLD-HCC-F0F1
<table><thead><tr><th rowspan="2">Family</th><th rowspan="2">Gene</th><th colspan="2">Control: healthy liver tissues</th></tr><tr><th>F0F1 NTT vs. TT</th><th>F3F4 NTT vs. TT</th></tr></thead><tbody><tr><td rowspan="2">Cholesterol ester</td><td>ACAT2</td><td>ns</td><td></td></tr><tr><td>AADACL1</td><td></td><td></td></tr><tr><td rowspan="2">Glycerolipid</td><td>DGAT1</td><td>ns</td><td></td></tr><tr><td>DGAT2</td><td>ns</td><td></td></tr><tr><td rowspan="7">Sphingolipid</td><td>SGMS1</td><td></td><td></td></tr><tr><td>SGMS2</td><td></td><td></td></tr><tr><td>SMPD1</td><td></td><td></td></tr><tr><td>SMPD3</td><td>ns</td><td>ns</td></tr><tr><td>SPTLC1</td><td></td><td></td></tr><tr><td>SPTLC2</td><td></td><td></td></tr><tr><td>ACOX1</td><td>ns</td><td></td></tr><tr><td rowspan="5">Glycerophospholipid</td><td>MBOAT7</td><td></td><td></td></tr><tr><td>PEMT</td><td>ns</td><td>ns</td></tr><tr><td>CHPT1</td><td></td><td></td></tr><tr><td>PCYT1A</td><td></td><td></td></tr><tr><td>CHKA</td><td>ns</td><td></td></tr></tbody></table>

<figure><img src="image_35.png" /></figure>

Transcriptomics analysis of **16 genes** revealed:

* The upregulation of 9 genes is common in the two groups

* MASLD-HCC-F0F1: five genes are specifically **up-regulated**

* MASLD-HCC-F3F4: none of the gene is changed
NTT vs. TT

Increase

Unchange

Specific to F0F1

MASLD-F0F1

Choline Transporter

Kennedy pathway

De novo ceramide synthesis

C24:6

β-oxidation
DHA

ACOX1

Peroxisome

Ethanolamine

Choline

CHKA

PCYT1A

CHPT1

PEMT

PE

PC

LPE

LPC

PA

DAG

MBOAT7

PI(18:0_20:4)

PI

TAG

Remodelling of PI

Serine + Palmitoyl-CoA

SPTLC1

SPTLC2

Cer

SGMS1

SMPD1

SGMS2

SMPD3

SM

CE

Cholesterol + C(18:2)

MASLD-F3F4

Choline Transporter

Kennedy pathway

De novo ceramide synthesis

C24:6

β-oxidation
DHA

ACOX1

Peroxisome

Ethanolamine

Choline

CHKA

PCYT1A

CHPT1

PEMT

PE

PC

LPE

LPC

PA

DAG

MBOAT7

PI(18:0_20:4)

PI

TAG

Remodelling of PI

Serine + Palmitoyl-CoA

SPTLC1

SPTLC2

Cer

SGMS1

SMPD1

SGMS2

SMPD3

SM

CE

ACAT2

AADACL1

Cholesterol + C(18:2)
# Conclusions

Combined metabolomics and transcriptomics analyses allow us to discriminate MASLD-HCC based on fibrosis level

* **Metabolomics** analysis revealed that in **MASLD-HCC-F0F1**, sphingolipid and glycerophospholipid contents **increase**; while these metabolites contents **decrease** in **MASLD-HCC-F3F4**

* **Transcriptomics** analysis revealed that the expression of 5 genes coding for: **ACAT2**, **DGAT1**, **DGAT2**, **ACOX1** and **CHKA** is **up-regulated** exclusively in **MASLD-HCC-F0F1**
# CHKA - Clinical application

Labeled choline may be favored as a more effective tracer for PET scanning in HCC patients with minimal fibrosis

Due to:

* MASLD-HCC-F0F1 mainly overexpressed choline derivatives contents(Buchard et al., 2021)

* CHKA is up-regulated exclusively in MASLD-HCC-F0F1
Thank you for your attention

Thanks again for Dr. Aicha Demidem, Dr. Fatima Dahboul, and Pr. Armand Abergel

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

CLERMONT-FERRAND

CENTRE HOSPITALIER UNIVERSITAIRE

The study was approved by the Ethic Committee Sud-Est VI Clermont-Ferrand (Agreement number AU887, 04/03/2011)

Fare 2022