Alterations of the peptidomic composition of peripheral plasma after portal hypertension correction by transjugular intrahepatic portosystemic shunt

doi:10.21203/rs.3.rs-3965182/v1

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Alterations of the peptidomic composition of peripheral plasma after portal hypertension correction by transjugular intrahepatic portosystemic shunt

https://doi.org/10.21203/rs.3.rs-3965182/v1

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published 10 Jun, 2024

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Portal-hypertension develops in patients with advanced chronic liver diseases(CLD), especially cirrhosis and is associated with complications, such as gastrointestinal bleeding and ascites resulting in high mortality. The transjugular intrahepatic portosystemic shunt(TIPS) is a treatment option for portal-hypertension, aiming to decrease portal venous pressure by establishing an artificial passage for blood from the gastrointestinal tract to the liver vein. This study focuses on the differences in the molecular composition of plasma samples from patients with portal-hypertension before and after TIPS intervention to identify and characterise mediators influencing gut-liver cross-talk.

The plasma of 23 patients suffering from advanced CLD with portal-hypertension was collected from peripheral veins before and after TIPS treatment and analysed using a well-established non-targeted chromatography-mass spectrometric(LC-MS) approach.

Sialomucin core protein 24(CD164)(160–180), meckelin(99–118), Histone-lysine N-methyltransferase(MLL3)(3019–3045) and transient receptor potential cation channel subfamily V member 5(TRPV5)(614–630) were identified to be downregulated after the TIPS treatment. In addition, the metabolites 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid(CMPF), uric acid, Dopamine, homoarginine, leucylproline and 5-methyluridine were significantly decreased after TIPS, whereas one yet unidentified low molecular-weight metabolite showed an increase after the medical procedure.

In conclusion, these substances are novel biomarkers for portal-hypertension in patients with CLD, with mechanistic clues of involvement in regulating pathological gut-liver cross-talk.

Biological sciences/Biochemistry/Hormones

Biological sciences/Biochemistry

Health sciences/Diseases

Health sciences/Gastroenterology

Portal-hypertension

chronic liver disease (CLD)

gut-liver axis

mass-spectrometry

peptidomics

metabolomics

Portal hypertension is a frequent complication of liver cirrhosis¹, defined by a more than 6 mmHg pressure gradient between the portal vein and the vena cava². Portal hypertension causes complications like (a) gastroesophageal varices, associated with a high risk of variceal bleeding^3,4, (b) ascites, resulting in abdominal distension⁴ and (c) high risk for bacterial infections, resulting in bacterial peritonitis and/or pneumonia⁴. Therefore, advanced chronic liver diseases (CLD) and their complications are increasing health burdens and causing high mortality⁵.

Portal hypertension impacts the bidirectional, biomolecule-mediated gut and liver relationship. Portal hypertension, accompanied by dysbiosis and CLD, increases intestinal barrier permeability and translocates bacterial components and biomolecules, potentially inducing systemic and low-grade inflammation⁶. This triggers an enhanced liver exposure to ‘pathogen-associated molecular patterns’ (PAMPs)^7,8. PAMPS interact with the hepatic immune system cells leading to activation of the immune response and release of pro-inflammatory cytokines causing liver injuries^9–11. Gut-derived mediators, such as tryptophan and indole derivates, access the liver through the portal vein blood and affect the hepatic status¹². In addition, systemic circulation influences the interaction between the intestine and the liver by transferring metabolites from the liver to the intestine via capillary diffusion¹³.

Management of portal hypertension complications involves pharmacological treatment with β-blockers like carvedilol¹⁴. However, the most effective and established clinical procedure for portal hypertension management is a transjugular intrahepatic portosystemic shunt (TIPS) between the hepatic and portal vein^15,16, thereby decreasing the portal pressure and avoiding portal hypertension-associated complications¹⁷.

Since methods like transient elastography or magnetic resonance which cannot be employed on certain patients^18,19 where they are unable to measure hepatic venous pressure gradient²⁰, there is a strong need for alternative non-invasive, broadly applicable diagnostic biomarkers for portal hypertension.

Plasma biomolecule concentrations are promissing diagnostic markers for early detection of portal hypertension. Peptidomics is a proteomics-derived omics technique based on e. g. liquid chromatography-mass-spectrometry (LC-MS) as an efficient method for both targeted and non-targeted peptide identification in diseases^21–23.

Therefore, this study used a non-targeted peptidomics approach to identify low molecular weight biomolecules dysregulated in the plasma of patients affected by CLD and portal hypertension as biomarkers and potential mediators of pathological gut-liver cross-talk. The cohort considered in this study was selected to investigate the portal hypertension changes after TIPS and the plasma samples were collected within 7–14 days after surgery, representing the typical recovery window for TIPS intervention²⁴.

Patients' characteristics

A total of 23 patients (65% males), who suffered from portal hypertension and underwent TIPS procedures, were included in this study. Patients' ages ranged from 34 and 81 years old, with an average value of 55.7 (± 12.5). The mean body mass index of the patients was 26.3 (± 4.6), and the mean heart rate was 76.1 bpm (± 12.5). Biochemistry evaluation of blood collected on the first visit was performed as shown in Table 1 and represents the clinical image of advanced CLD with liver dysfunction based on Child-Pugh scores, inflammation based on C reactive protein and portal hypertension based on the portal and systemic hemodynamic parameters. Demographic data like age, gender, and BMI was registered. The patients were diagnosed with i) alcoholism-induced liver cirrhosis (n = 9), ii) hepatitis C virus (n = 1) and iii) other aetiologies such as sinusoidal obstruction syndrome, autoimmune disorders, non-alcoholic steatohepatitis (NASH syndrome and portal hypertension without liver cirrhosis (n = 13). Child-Pugh score before intervention was 7.5 (± 1.9), indicating moderately impaired hepatic function²⁵, and the score significantly decreased after TIPS to 6.1 (± 1.1). Nine patients were classified in class A of Child-Pugh scores with 5–6 points signifying a good hepatic function, nine patients in class B of Child-Pugh scores with 7–9 points signifying moderately impaired hepatic function and one patient in class C of Child-Pugh scores with 10–15 points signifying advanced hepatic dysfunction before TIPS and improved to 14 patients in class A and six patients in B class after TIPS treatment.

Table 1

Characteristics of the 23 patients before and after TIPS placement. * Indicates markers outside of the reference values.
Variable	Before TIPS	After TIPS	p-value
	(n = 23)
Age	55.7 ± 12.8
Male	13 (59.1%)
BMI	25.1 ± 4.3	26.2 ± 4.4	0.086
Liver cirrhosis	20 (90.9%)
Etiology
Alcohol	9 (42.9%)
HCV	1 (4.8%)
Other	13 (61.9%)
TIPS indication
Variceal bleeding	6 (31.6%)
Ascites	10 (52.6%)
Both indications	3 (15.8%)
TIPS stent type: Viatorr (covered)	23 (100%)
Year of TIPS: 2014/2015/2016/2017	2(9.1%)/3(13.6%)/10(45.5%)/7(31.8%)
Scores
Child-Pugh	7.5 ± 1.9	6.1 ± 1.1	0.001
Child class (A/B/C)	9(47.4%)/9(47.4%)/1(5.3%)	14(70.0%)/6(30.0%)/0(0.0%)
MELD	9.9 ± 4.6	9.1 ± 4.4	0.267
MELD-Na	11.7 ± 5.5	10.5 ± 5.5	0.143
Portal and systemic hemodynamic parameters
PHPG (mmHg)	17.6 ± 6.9	8.8 ± 3.7	< 0.0001
Portal pressure	22.2 ± 6.9	16 ± 5.1	< 0.0001
Systolic arterial pressure (mmHg)	114.4 ± 12.3	112.4 ± 17.4	0.562
Diastolic arterial pressure (mmHg)	69.6 ± 10.8	65.2 ± 10.1	0.510
Laboratory
Leucocytes (g/L)	7.9 ± 3.9	6.2 ± 2.2	0.047
Hb (g/dL)	9.8 ± 1.9	12.1 ± 2.3	< 0.0001
Platelets (g/L)	146.7 ± 78.1	169.2 ± 99.9	0.378
INR	1.1 ± 0.2	1.3 ± 0.4	0.273
Bilirubin (mg/dL)*	1.6 ± 1.8	3.9 ± 11.5	0.289
ALT (Units/L)	38.8 ± 29.2	33.1 ± 11.7	0.382
AST (Units/L)*	54.3 ± 48.1	49 ± 22.8	0.629
y-GT ( Units/L)	216.6 ± 219	167.7 ± 142.8	0.276
AP (Units/L)*	194.2 ± 152.5	180 ± 95.1	0.842
Albumin (g/L)	32.2 ± 8	37.1 ± 8.3	0.012
Serum creatinine (mg/dL)	1.2 ± 1.1	1 ± 0.5	0.289
Sodium (mmol/L)	139.3 ± 5.3	139.9 ± 2.7	0.602
Urea (mg/dL)*	47.5 ± 27.9	35.4 ± 18.7	0.046
CRP (ml/L)*	19.7 ± 21.6	7.4 ± 9.4	0.069
Treatment
Beta blocker	9 (42.9%)	10 (47.6%)	0.761
Diuretics	20 (90.9%)	19 (86.4%)	0.635
BMI, body mass index; MELD, model for end-stage liver disease; MELD-Na, model for end-stage liver disease; PHPG, portal hepatic venous pressure gradient; Hb, haemoglobin; INR, internal normalised ratio; ALT, alanine aminotransferase; AST, aspartate transaminase; γ-GT, serum glutamyltranspeptidase; A.P., alkaline phosphatase; CRP, C-reactive protein.

MELD score went from 9.9 (± 4.6) before to 9.1 (± 4.1) after TIPS treatment. The MELD-Na score ranged from 11.7 (± 5.5) before to 10.5 (± 5.5) after the TIPS intervention. The mean portal hepatic venous pressure gradient (PHPG) before TIPS treatment was 17.6 mmHg (± 6.9) and 8.8 mmHg (± 3.7) after the intervention. Portal pressure (mmHg) decreased from 22.2 (± 6.9) to 16 (± 5.1) after the intervention. The systolic and diastolic blood pressure before and after surgery was not significantly different, with a change from114.4 ± 12.3 to 112.4 ± 17.4 and from 69.6 ± 10.8 to 65.2 ± 10.1 respectively. Patients also showed lower levels of bilirubin, alanine aminotransferase²⁶, aspartate transaminase (AST), gamma-glutamyl transferase (γ-GT), alkaline phosphatase (A.P.), inflammatory marker C-reactive protein (CRP) and urea after the TIPS treatment as summarized in Table 1. Before the TIPS procedure patients with or without cirrhosis had similar Child-Pugh scores, MELD scores, and portal and systemic hemodynamic parameters but different MELD-Na scores, among other characteristics, as summarised in Supplemental material Table 1.s.

Peptidomic analyses of plasma samples

A characteristic reversed-phase chromatogram of an EDTA plasma sample isolated from peripheral veins is shown in Fig. 1C, and the corresponding 'total ion chromatogram' of the mass spectrometric analysis of plasma from a patient before the TIPS treatment is shown in Fig. 1D. The corresponding 'total ion chromatogram' from the plasma of the same patient after TIPS is shown in Fig. 1E.

Identification of differentially expressed molecular features by limma statistics

By bucketing, unique molecular features within the plasma samples were identified, and mass-signal intensities were collected in buckets defined by R.T. and m/z with a retention time tolerance of 0.3 min and mass-spectrometric tolerance of 0.4 Da. The final analysis included 79 normal distributed molecular features. The differentially expressed molecular features were ranked by their adjusted p-values and B values before and after TIPS treatment.

Eleven significant differentially occurring molecular features in the peripheral vein before vs. after TIPS were identified (adjusted p-value ≤ 0.05). These molecular features, their logFC, and adjusted p-values are given in Table 2. The molecular features measured after TIPS treatment showed a logFC 5-fold lower than their respective logFC before TIPS treatment, except the molecular feature showed a positive logFC indicating an increase after TIPS treatment.

Table 2

**List of molecular features identified to be differentially expressed in peripheral vein plasma samples before vs. after TIPS.** Shown are the molecular features with relative logFC and adjusted p-values. Significant differences were identified through the test limma. Negative values indicate a decrease in the peripheral vein after vs. before TIPS; positive values indicate an increase in the peripheral vein after vs. before TIPS. *LogFC: log2-fold-change.*
Molecular feature	LogFC	Adjusted p-value
1.85 min: 153.0 m/z	-11.85	0.001
3.35 min: 154.2 m/z	7.88	0.041
1.25 min: 169.0 m/z	-7.79	0.012
4.55 min: 187.8 m/z	-12.67	0.003
1.25 min: 229.0 m/z	-8.59	0.012
1.55 min: 241.0 m/z	-5.60	0.041
1.25 min: 259.0 m/z	-8.12	0.012
9.05 min: 863.4 m/z	-8.22	0.041
2.15 min: 943.0 m/z	-9.13	0.012
10.85 min: 951.4 m/z	-7.16	0.026
1.85 min: 1045.8 m/z	-5.52	0.044

The mass signal intensities of the molecular features at 863.4 m/z, 943.0 m/z, 951.4 m/z and 1045.8 m/z within the peripheral vein plasma significantly decreased after TIPS intervention (Fig. 2). The identical effect was observed for the biomolecules with mass signal intensities at 153.0 m/z, 169.0 m/z, 241.0 m/z, 187.8 m/z, 229.0 m/z and 259.0 m/z (Fig. 3). The molecular feature at 154.2 m/z showed an increased mass-signal intensity after the TIPS treatment, but was not identified based on the low molecular weight(supplemental material Fig. 1.s). The Supplemental material Table 2.s reports the percentages of zero intensity values in the cohorts.

Correlation analyses

The baseline molecular feature at 154.2 m/z positively correlated with the PHPG after TIPS treatment (Fig. 4A). The baseline of the molecular feature at 187.8 m/z negatively correlated with leucocytes after the TIPS treatment (Fig. 4A). Interestingly, the baseline value before TIPS insertion at 951.4 m/z negatively correlated with PHPG after treatment (Fig. 4A), while the increase after TIPS insertion (∆) showed a strong significant positive correlation (Fig. 4B). Additionally, 943.0 m/z at baseline correlated positively with serum creatinine (Fig. 4A), while the changes correlated negatively (Fig. 4B). Moreover, the feature at 863.4 m/z showed a negative correlation with a marker for liver damage alanine aminotransferase (ALT) after TIPS treatment (Fig. 4A).

Sequencing mediators

The underlying amino acid sequences of the molecular features were identified by MALDI-TOF/TOF mass-spectrometry (Ultraflex-III TOF/TOF, Bruker-Daltonic, Germany), Orbitrap mass-spectrometry (Thermo-Fisher, Germany) as well as LC-ESI mass-spectrometry (HCT, Bruker-Daltonics, Germany). Figure 5A shows a characteristic HCT mass-spectrum of the molecular feature at 169.0 m/z. The underlying mediator was fragmented by MS/MS, resulting in the identification of uric acid, as shown in Fig. 5B.

The peptide with the m/z ratio 863.4 originates from the protein ´Histone-lysine N-methyltransferase´ (MLL3) (3019–3045). The peptide with m/z ratio of 943 was found to be Meckelin peptide (99–118) and the peptide with m/z ratio of 951.4 is cleaved from the ´Transient receptor potential cation channel subfamily V member 5´ (TRPV5). The m/z ratio of 1045.8 corresponded to amino acids 160–180 of sialomucin core protein 24 (CD164) (Table 3). The Table 3 lists metabolites identified by LC-ESI-MS/MS and literature mining, which led to the structure identification of Dopamine (m/z ratio 153), uric acid (m/z ratio 169), homoarginine (m/z ratio 187.8), leucylproline (m/z ratio 229), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) (m/z ratio 241) and 5-methyluridine (m/z ratio 259).

Table 3

List of peptides and metabolites identified by MALDI-TOF/TOF mass-spectrometry and LC-ESI-MS/MS and list of metabolites identified by LC-ESI-MS/MS to be differentially expressed in peripheral blood before vs. after TIPS. Shown are the molecular features in terms of R.T. and m/z, followed by the sequence, name of the protein of origin and amino acid position; or followed by the molecular formula and the name of the metabolite. *R.T.: retention time.*
R.T. min – m/z	Sequence	Parent protein and amino acid position
9.05 min: 863.4 m/z	HSLGTGKPATQTGPQTSQSGTSSMSG	Histone-lysine N-methyltransferase MLL3 (3019–3045)
2.15 min: 943.0 m/z	KCPENMKGVTEDGWNCISCPSDLTAE	Meckelin (99–118)
10.85 min: 951.4 m/z	PRSGICGCEFGLGDRWF	Transient receptor potential cation channel subfamily V (614–630)
1.85 min: 1045.8 m/z	TFDAASFIGGIVLVLGVQAVI	Sialomucin core protein 24 (160–180)
	Chemical formula	Metabolite name
1.85 min: 153.0 m/z	C₈H₁₁NO₂	Dopamine
1.25 min: 169.0 m/z	C₅H₄N₄O₃	Uric acid
4.55 min: 187.8 m/z	C₇H₁₆N₄O₂	Homoarginine
1.25 min: 229.0 m/z	C₁₁H₂₀N₂O₃	Leucylproline
1.55 min: 241.0 m/z	C₁₂H₁₆O₅	3-carboxy-4-methyl-5-propyl-2-furanpropionic acid
1.25 min: 259.0 m/z	C₁₀H₁₄N₂O₆	5-Methyluridine

Portal hypertension impacts the physiology and pathophysiology of the gut and the liver through the gut-liver axis, leading to venous congestion in the intestine and disrupting the intestinal barrier. Since the knowledge of gut-liver cross-talk is limited, an increasing number of studies are focusing on analysing the role of gut-liver cross-talk in chronic inflammatory diseases²⁷. This includes the investigation of gut dysbiosis in liver diseases like NAFLD, alcoholic liver disease, liver cirrhosis and hepatic carcinogenesis²⁸, as well as the development of novel therapeutic strategies targeting the gut microbiota in liver diseases¹¹. By analysing the plasma of patients suffering from portal hypertension, biomarkers were identified as diagnostic tools and therapeutic mediators contributing to understanding the gut and liver cross-talk.

Using an untargeted peptidomics approach, we identified novel mediators differentially occurring in the blood of patients who underwent TIPS treatment. We developed a for peptidomics data analyses to discover and determine new biomarkers^21,22 and identify novel peptides to be used in prevention and prediction models²⁹. The peptidomics approach focused on mediators with a significantly different expression before vs after TIPS treatment. The peptidomics data were analysed by 'limma' statistics^30,31 and t-statistics, identifying ten molecular features that decreased after TIPS treatment and increased intensity of one molecular feature. The ∆s of four molecular markers (187.8, 863.4, 943.0, 951.4 each m/z) correlated with leucocytes amount, ALT, serum creatinine and PHPG, demonstrating the potential of the novel biomolecules in the contest of the portal hypertension diagnosis and treatment.

Four of these ten biomolecules were identified and sequenced as peptides (Table 3). The literature points to the potential mechanisms by which the parent proteins of these peptides impact portal hypertension or liver regeneration (summarised Fig. 6A):

The origin protein of peptide MLL3 (3019–3045) regulates metabolic processes and the hepatic circadian control of bile acid homeostasis³². MLL3 is involved in fatty liver development³³ and the non-alcoholic fatty liver disease (NAFLD) pathology by methylation of histone 3 forming H3K4me3, which interacts with the promoters of PPARγ resulting in increased de novo lipogenesis in hepatocytes leading to NAFLD³⁴.

The parent protein of Meckelin (99–118) negatively regulates the canonical Wnt signaling, and activates the non-canonical cascade stimulated by Wnt5A³⁵, a regulator of β-catenin signaling and hepatocyte proliferation³⁶. The Wnt5a/Frizzled-2 axis suppresses β-catenin signalling in hepatocytes contributing to the conclusion of the liver regeneration process. Therefore, the reduction in Meckelin after the TIPS procedure might reduce the stimulation of WNT5A, promoting liver regeneration and overcoming the injury of portal hypertension.

The peptide TRPV5 (614–630) was cleaved from a protein, regulating Ca²⁺ reabsorption in the kidney and intestine^37–39. Deranged hepatocyte intracellular Ca²⁺ homeostasis promotes the progression of NAFLD^40,41, but the impact on portal hypertension or liver distress has not yet been identified until now.

The parent protein of the peptide CD164 (160–180) is involved in haematopoiesis⁴², with high expression in the pathological context of portal hypertension before TIPS treatment and is increasingly expressed in liver tissue in pro-inflammatory, high-cholesterol conditions⁴³. In addition, CD164 is a downstream functional effector of ´Insulin-Like Growth Factor 2 mRNA-Binding Protein 3´ (IGF2BP3)⁴⁴, which is overexpressed in acute liver injury⁴⁵. CD164 activates the CXCR4 axis, promoting inflammation and migration of cells⁴⁴. Therefore, the downregulation of CD164 after the TIPS procedure is concurrent with the reduction of pathological stress on the liver cells caused due to portal hypertension.

Our findings show the downregulation of MLL3 (3019–3045), Meckelin (99–118), TRPV5 (614–630) and CD164 (160–180) are therefore the first step towards establishing these peptides as biomarkers of portal hypertension.

The remaining mediators are derived from gut and liver metabolism and/or previously related to liver (patho)physiology (Table 3). Since the concentration of these mediators decreased after TIPS, they are correlated to portal hypertension and might act as biomarkers for early, non-invasive diagnostics. The mechanisms through which these metabolites influence portal hypertension or liver regeneration are shown in Fig. 6B.

Approximately 50% of Dopamine is produced in the gastrointestinal tract⁴⁶ and is involved in cell immunity regulation as well as in suppressing autoimmune hepatitis⁴⁶. Dopamine has therapeutic efficacy against acute liver failure by inhibiting NLRP3 inflammasome activation and enhancing hepatic regeneration⁴⁷. In addition, agonism of dopamine receptor D1 hinders the nuclear localisation of YAP/TAZ, which is causal for acute liver injury. Therefore, binding Dopamine to its receptors is an essential step in liver regeneration and accounts for the lower levels of dopamine post-TIPS treatment.

The liver produces uric acid and induces hepatic steatosis through mitochondrial oxidative stress production hereby⁴⁸. Higher serum levels of uric acid are associated with a higher probability of developing NAFLD⁴⁹. Uric acid leads to monosodium urate crystals (MUC) forming, which activate the NLRP3 inflammasome promoting acute liver injury. The underlying mechanism involved is activating the Toll-like receptor 2/4-Myd88 pathway and increased transcriptional levels of pro-IL-1β through the NF-κB pathway⁵⁰. Therefore, a decrease in uric acid coincides with an expected reduction in liver distress after the TIPS treatment.

Homoarginine is synthesised bythe liver^51,52 and is an inhibitor of alkaline phosphatases^53,54. Abnormal liver biomarkers such as Alanine aminotransferase, Aspartate-amino transferase, fibrosis (Fib-4) score, liver fat content, and cholinesterase are related to greater circulating hArg concentrations in the general population⁵¹, supporting our finding that reduction in portal hypertension leads to a reduction of circulating homoarginine levels.

As shown in the present study, L-Leucine and Leucylproline concentrations are higher in plasma from patients affected by NAFLD-cirrhosis and decreased after TIPS treatment. Under cirrhotic conditions, there is an increase in oxidised leucine, decreasing protein synthesis⁵⁵. Pathophysiological leucine levels are antagonistic to insulin, promoting glucose uptake via the myostatin-AMP-activated protein kinase pathway leading to hepatic cell triglyceride accumulation⁵⁶. Therefore, even though supplementation of L-leucine promotes protein turnover, and stimulates protein synthesis⁵⁷, the effects of endogenous leucine need to be clarified based on its metabolism⁵⁶.

CMPF is suggested as a NAFLD prediction marker⁵⁸ since high plasma levels of CMPF negatively correlate with lipid metabolism. CMPF promotes the production of reactive oxygen species, lipid peroxidation, and intracellular iron accumulation⁵⁹. In this context, detecting a reduced CMPF concentration by the TIPS treatment is highly relevant for patients suffering from liver diseases. Last but not least, the metabolite 5-methyluridine was deteted in humans⁶⁰ and E. Coli⁶¹ but has not been so far linked to liver cirrhosis, NAFLD, NASH or portal hypertension. The results of the present studies provide interesting approaches for investigating these interactions.

In summary, eleven mediators were differentially expressed in the plasma of peripheral veins after TIPS treatment and require further in vivo validation based on the solid evidence from published data linking these mediators to liver regeneration, NAFLD and liver cirrhosis, among other pathophysiological pathways. The current study clearly demonstrates that these mediators are undoubtedly relevant for understanding the pathophysiology of the gut-liver axis organ cross-talk.

Description of the patient cohort

A total of 23 patients from the observational NEPTUN (‘Non-invasive Evaluation Program for TIPS and Follow-Up Network’; clinicaltrials.gov identifier: NCT03628807) with a pressure gradient between the portal vein and vena cava above 6 mmHg, and undergoing a TIPS treatment using VIATORR® Controlled Expansion (VCX) type were included in the study (Fig. 1A, Table 1). The study was approved by the local ethics committee of the University of Bonn (029/13), and all patients signed informed written consent following the Declaration of Helsinki.

200 µL EDTA plasma was collected from peripheral veins before and after TIPS treatment (7–14 days after intervention), and plasma was subsequently stored at -80°C until analysis. Scores and classes to predict the outcome of portal hypertension in patients, such as ‘Child-Pugh score’, ‘Child class and Model for End-Stage Liver Disease’ (MELD) score, were collected, as well as the ‘Model for End-Stage Liver Disease-Na’ (MELD-Na).

Plasma sample preparation for chromatographic analysis

The plasma samples were defrosted and centrifuged at 956 g for 10 min at 4°C. A total of 1 µg of Sar-Thr-angiotensin II (Sigma-Aldrich, Germany) was added as an internal standard. Plasma proteins were denatured by adding perchloric acid 70% (PCA) with a ratio of 65 µL per mL of plasma. After centrifugation at 956 g for 10 min at 4°C, the supernatant was transferred to a fresh tube and the pH was adjusted to a value ≥ 9.0 by adding potassium hydroxide (5 mol/L). Plasma samples were frozen at -20°C for 24 h, thawed and centrifuged at 956 g for 10 min at 4°C. The supernatant was collected, and the pH was adjusted to 7.0 ± 0.1 by adding PCA before proceeding with high-performance liquid chromatography.

High-performance liquid chromatography (HPLC)

The deproteinated plasma samples were diluted by adding 0.1% trifluoroacetic acid (TFA) and were fractionated by high-performance liquid chromatography (HPLC) by using a C¹⁸-chromolith™ reverse-phase chromatographic column (100 mm × 4.6 mm; Merck, Germany). TFA in water (0.1% v/v) was used as the polar solvent A, and hydrophobic solvent B was constituted by ethanol (80% v/v) in water, maintaining a flow rate of 1 mL/min for 46 min. The fractionation was performed by a gradient as follows: 0–10 min: 0% B, 10–20 min: 20% B, 20–30 min: 40% B, 30–38 min: 100% B, 38–46 min: 100% A and 0% B. The fractions were lyophilised with a freeze-drying technique by employing a Savant Speed Vac (SPD101B, Thermo Fisher Scientific, USA) and ‘Refrigerated Vapor Trap’ (Thermo Fisher Scientific, USA) after the chromatographic separation.

Liquid chromatography coupled with electrospray ionisation-mass-spectrometry (LC-ESI-MS) for peptide identification

The lyophilised samples were re-suspended in water and formic acid (0.1% v/v) in a total volume of 50 µL. The samples underwent fractionation by capillary-HPLC (Agilent 1200, Germany), employing a “C¹⁸ SB Zorbax” column (150x0.5 mm, 5 µm, Agilent Technologies, Germany). Eluent A was formic acid (0.1% v/v), and eluent B was acetonitrile (99.9% v/v) (Thermo Fisher Scientific, Germany) with formic acid (0.1% v/v). The flow rate of the solvents was 60 µL/min for 22 min, and the temperature was stable at 50°C. The re-suspended samples were fractioned by 0–2 min: 0% eluent B, 2–10 min: 0–30% B, 30–100% B, 13.5–15.5 min: 100% B, 15.5–16.0 min: 0-100% A and 100-0% B, and 16–22 min: 100% A and 0% B. The eluate obtained after the chromatographic separation was analysed with a high-capacity ion-trap (HCT) mass spectrometer (Bruker-Daltonics, Germany).

The mass analyser employed was used in the positive ion mode with a source temperature of 300°C to detect the eluted mediator’s molecular masses (m/z). The nebuliser gas was kept at 20 psi, and the dry gas flow was accustomed to 9 L/min. For the detection of compounds, the mass spectrometric accumulation time of the ion trap was set to 200 ms. The mass spectrometer was tuned in the wide-mode option on m/z 800 and operated in enhanced mode scanning for m/z between 100 and 1,500. Data were acquired and processed with the software HyStar 3.2 (Bruker-Daltonics, Germany).

Data processing

Mass-spectrometry raw data were processed, and the molecular features were visualised through the mass-spectrometric software ‘DataAnalysis 4.0’ (Bruker-Daltonics, Germany). Specific molecular features were selected with the “FindMolecularFeatures” (FMF) algorithm and were characterised by the retention time (R.T.), the m/z, and the intensity of the peak. The FMF algorithm was based on (i) signal-to-noise ratio (S/N), used for discriminating between noise and compound (used value: 2)⁶² (ii) correlation coefficient threshold, which defines the minimal time correlation to combine peak clusters (used value: 0.79); (iii) minimum compound length, which specifies the number of spectra to determine a peak (used value: 10); (iv) smoothing width, to smooth out the chromatogram with the value of 2; and (v) background subtraction. The resulting data were transposed into a bucket table, characterising the features by their R.T., m/z and mass intensity. Similar compounds were bundled together with an R.T. tolerance of 0.3 min and m/z tolerance of 0.4, aiming at the reduction of size and complexity of the dataset, easing out the following statistical analyses. Features that had more than 80% of zero values and were not unique regarding R.T. and m/z, were excluded from further analyses.

Statistical analyses

The bucketed data were statistically analysed using the RStudio open source software (RStudio Team, 2016. M.A., USA) and GraphPad Prism version (9.1.1 for Windows, GraphPad Software, CA, USA). The intensities of the molecular features were normalised by the internal standard, converted to log₂ and tested for normal distribution. The ‘limma’⁶³ was implemented in the RStudio workflow to fit the data to a linear model, with the function ‘lmfit’, followed by ‘makeContrasts’ and ‘contrasts.fit’⁶³ to estimate the coefficients and standard errors for every contrast declared in the original model. The method’ eBayes'⁶³ was used to identify differential abundances of the molecular features. A false discovery rate adjustment was performed, obtaining adjusted p-values for multiple t-tests using the "toptable" function and extracting the best-ranked molecular features. The statistical approach analysed differences in log₂ intensities of the molecular features in samples before vs. after TIPS intervention.

Two sets of correlation analyses were performed to investigate whether the molecular features correlated with the CLD clinical markers. In the first set of correlations, the intensities of the molecular feature before TIPS were compared to the clinical markers, while for the second set of correlations, a delta was calculated by subtracting the mass-signal intensity after treatment from the intensity before treatment (∆). The data was adjusted, and Pearson correlation analyses were performed using RStudio.

Identification of amino acid sequences of the molecular features

To investigate the amino acid sequences of the identified molecular features, 1 µL of the re-suspended samples and a matrix (α-4-hydroxycinnamic acid, 2.5 mg/mL) were applied on a Rapiflex plate support and analysed by employing a BrukerUltraflex-III TOF/TOF instrument (Bruker-Daltonic, Germany). MS/MS fragmentation was performed to fragment the molecular features, reconstruct the sequences using ions a, b and y, and align the results to the ´Mascot database´ (Matrix Science Inc, Boston (M.A.), U.S.). Identification of low-weight molecular weight features was performed by MS/MS employing the LC-ESI mass-spectrometry (Bruker-Daltonics, Germany) followed by literature mining with open sources databases ´human metabolome database´ (HMDB)⁶² and ´PubChem´²³. The workflow steps of sample preparation, data processing and analysis is shown in Fig. 1B.

In conclusion, the changes molecular composition of plasma before and after TIPS treatment of patients with portal hypertension, reveal novel mediators of portal hypertension and pathological gut-liver cross-talk. The results of the current study provide novel insights into the cross-talk between gut and liver on a molecular level.

AUTHOR CONTRIBUTIONS

J.J. and S.B. designed research studies, G.I.B., F.C. and S.B. conducted experiments, G.I.B., W.G., V.J., J.S., E.B., C.K., O.T. and S.B. acquired data, G.I.B., O.T. C.K. and S.B. analyzed data, R.S., H.N. and J.T. provided reagents, G.I.B., J.J. and S.B. wrote the manuscript.

ACKNOWLEDGEMENTS

The authors are supported by grants from the German Research Foundation (DFG) (SFB/TRR 219 project-ID: 322900939 and SFB 1382 Project-ID 403224013) as well as by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 764474 (CaReSyAn) and No 860329 (Strategy-CKD). This study was supported by the German Federal Ministry of Education and Research (BMBF) for the DEEP-HCC project, and by the Hessian Ministry of Higher Education, Research and the Arts (HMWK) for the ACLF-I cluster project. The MICROB-PREDICT (project ID 825694), DECISION (project ID 847949), GALAXY (project ID 668031), LIVERHOPE (project ID 731875), and IHMCSA (project ID 964590) projects have received funding from the European Union's Horizon 2020 research and innovation program.

Conflict of interest:

The authors have declared that no conflict of interest exists.

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Alterations of the peptidomic composition of peripheral plasma after portal hypertension correction by transjugular intrahepatic portosystemic shunt

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Abstract

Figures

INTRODUCTION

RESULTS

Patients' characteristics

Peptidomic analyses of plasma samples

Identification of differentially expressed molecular features by limma statistics

Correlation analyses

Sequencing mediators

DISCUSSION

MATERIAL AND METHODS

Description of the patient cohort

Plasma sample preparation for chromatographic analysis

High-performance liquid chromatography (HPLC)

Liquid chromatography coupled with electrospray ionisation-mass-spectrometry (LC-ESI-MS) for peptide identification

Data processing

Statistical analyses

Identification of amino acid sequences of the molecular features

CONCLUSIONS

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

Version 1