Treatment withβ-adrenoceptor agonist isoproterenol reduces non- parenchymal cell responses in LPS/D-Galn-induced liver injury

doi:10.21203/rs.3.rs-3341449/v1

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Research Article

Treatment withβ-adrenoceptor agonist isoproterenol reduces non- parenchymal cell responses in LPS/D-Galn-induced liver injury

https://doi.org/10.21203/rs.3.rs-3341449/v1

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Journal Publication

published 21 Dec, 2023

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Background and Aims

There is growing evidence suggesting the involvement of sympathetic nervous system(SNS) in the development of liver disease. In order to gain a better understanding of the unknown mechanism by which the SNS reduces inflammatory harm through non-parenchymal liver cells(NPCs) during acute liver failure (ALF), we utilized isoproterenol (ISO), a beta-adrenoceptor agonist to mimic SNS signaling.

Methods

C57BL/6J mice were administered ISO in an ALF model established using LPS/D-GalN. Tandem mass tag(TMT) labeling proteomic analysis was employed to identify screen for differentially expressed proteins (DEPs).

Results

Pretreatment with ISO resulted in a reduction of LPS-stimulated inflammation signaling markers, speciallyMapk14 and NF-kB in human THP-1 cells. Additionally, ISO administration led to a decreasein serum levels of proinflammatory cytokines TNF-α, IL-1β, and IL-6 in ALF mice, thereby mitigating liver damage. Through TMT analysis, a total of 1587 differentially expressed proteins (DEPs) were identified in isolated NPCs. Notably, over 60% of the DEPs observed in the ISO vs. ALF comparison were also found in the Con vs. ALF comparison. Enrichment analysis revealed that the DEPs influenced by ISO treatment were associated with heme and fatty acid metabolism, interferon gamma response, TNFA signaling pathway and mitochondrial oxidation function. PPI network analysis indicated Mapk14 and Caspase3 signaling may serve as potentially valuable indicators of ISO intervention. Specifically, the markers on activate macrophages were identified downregulated in ISO initiation, such as Mapk14, Casp1, Casp8, and Mrc1. ISO treatment increased the abundance of anti-inflammatory markers in macrophages, as evidenced by the immunohistochemistry(IHC) slides showing an increase in Arg+ staining, and a reduction in iNOS+ cell infiltration.

Conclusion

Prior treatment with ISO could potentially modulate the biological functions of NPCs and may serve as an innovative pharmacotherapy for the purpose of delaying the pathogenesis and progression of ALF.

Proteomic

Hepatic Non-Parenchymal Cells

Isoproterenol

Acute liver failure

Acute liver failure (ALF) is a potentially fatal condition characterized by extensive hepatocyte damage, rapid deterioration of hepatic function, and uncontrolled intrahepatic inflammation. The severity and diverse nature of this clinical syndrome pose challenges in terms of finding effective treatments, leading to elevated short-term mortality rates in intensive care units [1, 2].

The intricate composition of the liver, complex structure of the liver, consisting of diverse cellular components, enables it to carry out essential metabolic and immunological functions. Prior researches have indicated that non-parenchymal hepatic cells, such as Kupffer cells, hepatic stellate cells, liver sinusoidal endothelial cells, and various immune cell types, significantly contribute to the development of acute liver failure. They do so by producing a range of inflammatory mediators, cytokines, oxidative stress products, microRNA, and lipid metabolites, or by directly interacting with parenchymal hepatocytes in hepatic sinusoids. Therefore, acquiring a thorough comprehension of the functions of hepatic non-parenchymal cells in acute liver failure (ALF) could unveil fresh possibilities for treatment[3, 4].

Currently, researchers have been focusing on the autonomic nervous system, encompassing both the sympathetic and parasympathetic branches, due to its significant contribution in upholding the homeostasis of liver tissue. [5-7]. Surprisingly, the hepatic sympathetic nervous system (SNS) plays a role in controlling different liver conditions [7-9], including cirrhosis and nonalcoholic fatty liver disease. α- and β-adrenergic receptor agents, which play a role in neurotransmission and control the sympathetic nervous system, have shown an impact on models of liver injury. For example, prazosin, an antagonist of α-adrenergic receptors, alleviates liver injury, while propranolol, an antagonist of β-adrenergic receptors, exacerbates it [10]. In particular, substances that stimulate the adrenergic system and receptors have the potential to enhance the growth of precursor cells and liver cells, as well as provide defense against sudden liver damage [10-12]. The expansion of precursor cells only constitutes a minor fraction of liver cells, whereas other majority of non-parenchymal hepatic cells, such as Kupffer cells, also play a significant role in regulating the advancement of inflammation. However, the detailed effect of adrenergic receptors on hepatic non-parenchymal cells in regulating the inflammatory process of ALF remains to be clarified. To this end, we used the β-adrenoceptor agonist, isoproterenol (ISO), in an animal model of ALF to investigate the biological effect of β-adrenoceptors on non-parenchymal cells in liver.

Animal Studies

Female C57BL/6J mice, age 6-8weeks and weighing 20±2g, were obtained from Xi'an Jiaotong University's Animal Experimentation Centre. All mice were kept in a controlled environment free from pathogens, with a temperature range of 23 ± 2°C, humidity between 50% and 70%, 12-hour cycles of light and darkness, and unrestricted access to food and water. The animal care and experimental procedures underwent approval from the Xi'an Jiaotong University Animal Experiment Ethics Committee and Authority, as evidenced by Approval No. 2022-390.

ALF model was built by injecting intraperitoneally with LPS (30 mg/kg, Sigma, USA) and D-galactosamine (600mg/kg, Sigma, USA) in C57BL/6J mice, and was monitored up to 12h in a controlled environment. To establish the ISO+ALF group , ISO (10mg/kg) was administrated intraperitoneally one hour prior to LPS/D-Galn injection. To further investigate the results, mice were sacrificed and serum samples and liver tissue were obtained.

Cell culture and treatment

THP-1, a cell line of human macrophages, was acquired from the National Collection of Authenticated Cell Cultures of China. The cells were cultivated in 1640 medium supplemented with 1% penicillin/streptomycin and 10% fetal bovine serum. The incubator was maintained at 37°C with a 5% CO2 atmosphere. In order to trigger inflammation, THP-1 cells were subjected to LPS (20 ng/ml; Sigma) for a duration of 2 hours. ISO (at a concentration of 50 μM; MCE) was dissolved in DMSO and given 2 hours prior to LPS stimulation. The process of extracting cell lysis and gathering mRNA was carried out, and the collected samples were then stored at a temperature of -80 °C until they were ready for use.

Protein Extraction, Digestion and for Proteomics analysis

After isolating the sample, liquid nitrogen was used to grind the cells into powder, and the powder was then transferred to a 5-mL centrifuge tube. Following this, four volumes of lysis buffer (8 M urea, 1% protease inhibitor cocktail) were added to the cell powder and subjected to three rounds of ultrasonication on ice using a high intensity ultrasonic processor (Scientz). In the case of PTM experiments, inhibitors were additionally included in the lysis buffer, for example. For acetylation, a concentration of 3 μM TSA and 50 mM NAM was used, while for phosphorylation, 1% phosphatase inhibitor was added. Centrifugation at 12,000 g at 4 °C for 10 minutes effectively eliminated the remaining debris. Afterward, the liquid above the sediment was gathered and the amount of protein was measured using the BCA kit as per the instructions provided by the manufacturer.

For the process of digestion, the protein solution underwent reduction using a 5 mM dithiothreitol solution for a duration of 30 minutes at a temperature of 56 °C. Subsequently, the solution was alkylated with an 11 mM iodoacetamide solution for a duration of 15 minutes at room temperature in a dark environment. To decrease the concentration of urea in the protein sample to less than 2 M, it was diluted by the addition of a 100 mM TEAB solution. Following this, trypsin was introduced at a trypsin-to-protein mass ratio of 1:50 for the initial overnight digestion, and at a ratio of 1:100 for a subsequent 4-hour digestion. Finally, the peptides were purified through the use of a C18 solid-phase extraction column.

TMT Labelling and HPLC Fractionation

In the process of TMT labelling, the tryptic peptides were initially dissolved in a solution of 0.5 M TEAB. Subsequently, each individual channel of peptide was labelled using the corresponding TMT reagent (ThermoFisher Scientific) according to the guidelines provided by the manufacturer, and then incubated at room temperature for a duration of 2 hours. To assess the efficiency of labeling, the samples were combined, purified, and subjected to MS analysis, using a volume of five microliters for each sample.Following the assessment of labeling effectiveness, the samples were quenched by the addition of 5% hydroxylamine.Next, the combined samples underwent desalting using a Strata X C18 SPE column from Phenomenex, followed by drying through vacuum centrifugation.

The sample was subjected to HPLC grading, wherein it was fractionated into distinct fractions using a high pH reverse-phase HPLC method employing an Agilent 300 Extend C18 column (consisting of 5 μm particles, with an inner diameter of 4.6 mm and a length of 250 mm). In summary, peptides were separated by employing a gradient of acetonitrile ranging from 2% to 60% in a 10 mM ammonium bicarbonate solution at pH 10, over a duration of 80 minutes, resulting in the generation of 80 fractions. Subsequently, the peptides were consolidated into 9 fractions and subsequently dried through vacuum centrifugation.

LC-MS/MS analysis

The tryptic peptides were dissolved in solvent A, which consisted of 0.1% formic acid and 2% acetonitrile in water. Subsequently, they were loaded onto a reversed-phase analytical column that was 25 centimeters in length and had an inner diameter of 75 micrometers, which was prepared at home. The peptides were isolated using a gradual change in solvent B (0.1% formic acid in 90% acetonitrile) over a period of 60 minutes, followed by a transition from 25% to 35% in 22 minutes and a rapid increase to 80% in 4 minutes. The concentration was maintained at 80% for the final 4 minutes, while the flowrate remained constant at 450 nL/min on an EASY-nLC 1200 UPLC system(ThermoFisher Scientific).The peptides that had been separated were examined using a nano-electrospray ion source in QExactiveTM HF-X(ThermoFisher Scientific). A voltage of 2.0 kV was applied for electrospray.For a scan range of 350–1600 m/z, the complete MS scan resolution was adjusted to 60,000. 30s dynamic exclusion was used to select the top 20 most prevalent precursors for subsequent MS/MS analyses. The fragmentation of HCD was carried out with a collision energy (NCE) of 28%.Fragments were identified in the Orbitrap with a resolution of 30,000. The initial mass was fixed at 100 m/z.The AGC target for automatic gain control was established at 1E5, with a threshold of 3.3E4 for intensity and a maximum injection time of 60 ms.

Identification and quantitation of proteins

MaxQuant search engine (v.1.6.15.0) was utilized to process the above LC-MS/MS data. The human SwissProt database (20422 entries) was used to match tandem mass spectra. In the initial search, the tolerance for precursor ions was set at 20 ppm, while it reduced to 5 ppm in the primary search. Additionally, the tolerance for fragment ions was defined as 0.02 Da. Carbamidomethyl on Cys was referred to fixed modification, while acetylation on protein N-terminal and oxidation on Met was referred to variable modifications. The adjustment of FDR was made to less than 0.001.

Gene set enrichment analysis

The potential pathway of two gene lists was analyzed using gene set enrichment analysis (GSEA), annotated with the gene set mh.all.v2022.1. Mm.symbols.gmt as the references. Input protein sets that have a p value < 0.05, a normalized enrichment score (NES) > 1 or < -1, and a False Discovery Rate (FDR) < 0.25 were categorized as significantly enriched sets. Data visualization was drawn in R(version 4.2.1) package ggplot2 and clusterProfiler.

Bioinformatic Analysis

The protein list was analyzed by using DEqMS package in R to determine differentially

expressed proteins (DEPs). DEPs were defined as expressing with a p value < 0.05, fold change > 1.5 or < 0.75 and false discovery rate (FDR) < 0.01 between each sample group. GO (https://www.geneontology.org) and KEGG (https://www.genome.jp/kegg/) enrichment analysis was conducted to analyze the biological functions of DEPs, [13, 14]. GO enrichment analysis was performed for three ontologies, including biological process, molecular function, and cellular component. A significant GO term or pathway was identified with a threshold of protein number more than 2 and p-value less than 0.05. Protein-protein interaction (PPI) analysis was constructed by using the String (https://string.embl.de/) database and Cytoscape (https://www.cytoscape.org/) software. Furthermore, we utilized ClusterViz (V. 1.0.3, Bochum, Germany) in Cytoscape to visualize the clustering analysis based on the molecular complex detection(MCODE) algorithm. We set the criteria as a degree cut-off of 2 and a node score cut-off as 0.2, for the MCODE algorithm, with a max depth as 100 k-score as 2, and high confidence as no less than 0.7. Additionally, rank abundance curves, clustering heatmaps, bubble plots, and Venn diagrams were carried out by using the R package in version 4.2.1.

Specially, k-means hierarchical cluster for heatmap analysis of DEPs were taken [15].

RNA Extraction and qRT-PCR Analysis

RNA extraction and quantitative real-time PCR (qRT-PCR) were conducted following previously established protocols. Briefly, total RNA was extracted from frozen liver or cultured cells using TRIzol reagent (Thermo, Life Technologies, Carlsbad, CA, USA). The concentration and quality of the RNA were assessed, and mRNA was subsequently reverse transcribed into cDNA using the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Rockford, AL, USA). Subsequently, the relative levels of mRNA transcripts were determined through qRT-PCR analysis using the SYBR Premix ExTaq™II kit (TaKaRa, Dalian, China) on an iQTM multicolor real-time PCR detection system (Bio-Rad, Hercules, CA, USA). The 2−ΔΔCT method was applied to calculate the relative expression of each gene using either actin or Glyceraldehyde-3-phosphate dehydrogenase as internal standards. Three technical replicates and three biological replicates were used for each PCR reaction.

Western Blotting

RIPA lysis buffer supplemented with protease inhibitor cocktail tablets (Roche) and phosSTOP phosphatase inhibitor cocktails (Roche) were prepared to extracts total protein from cells or liver tissues. After quantified using BCA assay, equal amounts of protein (30-50μg) were loaded onto SDS/PAGE gels, separated and transferred onto PVDF membranes. Then the membranes were blocked in a blocking buffer with 5% non-fat milk at room temperature for 1 h. The blocked membranes were incubated with specific primary antibodies overnight at 4℃, followed by incubation with HRP conjugated secondary antibodies. The blots were visualized using ImageQuant LAS-4000 (Fujifilm) on a standard exposure setting and quantified using ImageJ (rsb.info.nih.gov/ij).

Histological Examination

Liver tissue samples from each group were promptly preserved in paraformaldehyde and then encased in paraffin. Tissues were sectioned into 4–5 μm with microtome and mounted on glass microscope slides. After routine staining with Hemotoxilin-Eosin (H&E), two independent researchers analyzed the slides using light microscopy (DIALUX 20 EB) at 40×.

Flow Cytometry

Flow cytometry was used to analyze the surface marker on THP-1 cells in both resting (M0) and M1 polarized states. Following the 24-hour stimulation, the cells were freshed thrice with PBS, then softly scraped, and moved into FACS tubes. Afterwards, surface CD14 (Pacific blue, Biolegend) and CD40 (FITC, Biolegend) fluorochrome-labeled monoclonal antibodies were added to identify the M0 phenotype, and M1 phenotype, respectively. After being incubated in 4°C for 4 hours, the cells were washed three times in PBS and examined using a FacsCanto II flow cytometer (BD Biosciences), with a minimum of 20,000 events recorded for every sample. All groups were performed at least in triplicate and was figured by utilizing the FacsDiva software.

Statistical Analysis

Statistical data is presented as means ± standard deviation. Statistical differences among various treatment groups were conducted using one-way analysis of variance (ANOVA) with Tukey's test in SPSS 13.0 (Chicago, IL, USA) or GraphPad Prism 5. Statistical significance was determined by considering P values as two-tailed, with a threshold of < 0.05.

1.1 ISO alleviated Pathological Liver Injury Induced by LPS/D-GalN-Treated in Mice

Acute liver failure was induced in mice by intraperitoneally co-administering LPS (30 mg/kg, Sigma, USA) and D-galactosamine (600mg/kg, Sigma, USA), while maintaining control with an equal amount of PBS. As revealed by gross morphology, the massive hepatic injury was apparent after 4~6 h; and there are a lot of visible necrosis areas, inflammatory cell infiltration and congestion in the liver tissue with hematoxylin and eosin (H&E) staining (Figure 1A). Interestingly, after pretreating the ALF mice with ISO (10mg/kg) for 1 hour, we observed significant enhancements in the liver's pathology, characterized by reduced hyperemia and swelling, as well as decreased hepatocellular necrosis (Figure 1B). Likewise, pre-treated ISO can reduce the elevation of the serum concentrations of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) caused by LPS/D-GalN in ALF mice (Figure 1C). The aforementioned findings suggest that ISO demonstrates a defensive effect against mortality and liver damage induced by LPS/D-GalN. After injecting LPS/D-GalN, we proceeded to compare the survival rates of mice that received ISO treatment with those that did not receive ISO treatment. As anticipated, the injection of LPS/D-GalN led to complete mortality within 12 hours, however, prior administration of ISO can reduce the mortality rate in ALF mice (Figure 1D). Additionally, we endeavored to investigate the impact of ISO on the production of inflammatory cytokines induced by LPS/D-GalN. The expressions of hepatic TNF-α mRNA were significantly higher in mice stimulated with LPS/D-GalN compared to control mice. In contrast, the levels of TNF-α, IL-6, and IL1-β mRNAs decreased noticeably after ISO treatment (Figures 1E, p<0.05). In general, the findings indicate that the ALF mouse model created in this research using LPS/D-GalN was beneficial, and ISO demonstrates a protective effect against the liver damage caused by LPS/D-GalN.

1.2 Validations of Filtering and Quality Control for Proteomic Data in the Liver Samples

Our subsequent attention was directed towards investigating the safeguarding mechanism of ISO against liver injury induced by LPS/D-GalN. According to a previous investigation, the results indicated that the usual receptors of ISO, including both α- and β-adrenergic receptors, were predominantly present in liver cells that are not parenchymal[5]. We next mainly investigated whether ISO plays a role among the stromal cells. Proteins extracted from non-parenchymal cells of mouse liver tissues were isolated in the control group, as well as in the LPS/D-GalN and ISO+ LPS/D-GalN groups. TMT labelling proteomics approach was conducted to identify the protein expression pattern.

In order to achieve accurate analysis outcomes, the spectrogram, peptide, and protein identification were set with an accuracy FDR of 1% to obtain high-quality results (Figure 2). A grand total of 320,737 spectrums were generated through the process of mass spectrometry analysis. There was a total of 56,779 spectrums that aligned with the protein, and the spectrograms were utilized at a rate of 17.7%. Spectrogram analysis successfully identified a grand total of 18,297 peptides, with a remarkable count of 17,696 being distinct peptide segments. In the end, a total of 3,914 proteins were identified, with 3,840 being measurable (Figure S1A). Figure S1B–F illustrates the verification of data quality control, including the examination of protein coverage distribution, distribution of peptide length, distribution of peptide number, and distribution of protein molecular weight. The majority of proteins have a coverage of less than 30% (Figure S1B), while the mainly peptides are found within the range of 6 to 20 amino acids (Figure S1C). According to the illustration in Figure S1E, themajority of proteins are represented by two or more peptides. When quantifying, it is advantageous for enhancing the precision and reliability of quantitative outcomes to get a protein that corresponds to multiple specific peptides (or multiple spectrograms). Overall, this data confirms that the outcomes of filtering the data in the serum samples adhere to the quality control standards.

Principal component analysis (PCA) result shows that the data within the group were spatially clustered (Fig. S1F). It displays clear differences of protein expression pattern between the treated and control groups. The PCA results could annotated 80% components in each sample. The analysis of Pearson correlation coefficients showed a strong correlation in protein expression profiles within the same group (Con group: 0.79–0.80, ALF group: 0.93–0.94, ISO group: 0.82-0.83), but a weak correlation was observed among samples from different groups (–0.76–0.51; Fig. 2H). Overall, he findings indicated that the quantitative reproducibility of both isolated liver total protein samples is satisfactory.

1.3 Identification of differentially expressed proteins between the ISO treated or non-treated ALF and control mice

A 1.2-fold change cut-off with an adjust p-value < 0.01 was set for volcano plot to filter DEPs in each comparison (Figure 2A). Significant expression among different comparisons was observed in a total of 1587 proteins. As compared to the control group, there were 34 upregulated and 17 downregulated differential proteins displayed in LPS/D-GalN treated mice. Similar number of of DEPs (375 upregulated and 268 down-regulated) was observed when compared the LPS/D-GalN injected group to ISO+ LPS/D-GalN treated group. However, a lower number of DEPs (375 upregulated and 268 down-regulated) was obtained in the comparison of ISO+ LPS/D-GalN with control group (Figure 2A and 2B). Further analyzing the characteristics of DEPs, venn diagram showed 64.1% DEPs were common between LPS/D-GalN vs. Con and LPS/D-GalN vs.ISO+ LPS/D-GalN comparisons (Figure 2C). Afterwards, the distinct proteins were organized into a hierarchical cluster and displayed in an expression heatmap (Fig. 2D). The clustering results showed a closer relationship of ISO+LPS/D-GalN to control group, indicating that the protein expression patterns in ISO+LPS/D-GalN and control group were similar. In contrast, the clustering heatmap exhibited a clear distinction of ISO+LPS/D-GalN from LPS/D-GalN group, denoting that the expression patterns of differential proteins in drug used and healthy groups were quite different from those in the disease group (Fig. 2D). Furthermore, we ranked proteins according to the fold changes and found top 20 upregulated and downregulated proteins were similarly expressed between the comparisons of AFL vs. Con (Figure 2E) and ALF vs. ISO (Figure 2F), including upregulated Zg16, Dmtn, Mrpl24, Pglyrp2, Cpa1, Amy2 and Serpini2 proteins, and downregulated Nmral1, Atp5f1d, H2bc3, Sf3a2, Abhd11, Nedd8, U2af1, Nfu1 proteins. Among them, Amy2, Pglyrp2, and Cpa1 were particularly ranked top 20 in downregulated proteins between the comparison of ISO and Con group (Figure 2G).

1.4 Functional enrichment analysis of the screened DEPs

For each comparison, GSEA, GO, and KEGG pathway enrichment analysis were conducted to classify the annotated proteins and ascertain their functions. The GSEA results presented in Figure 3A showed the proteins were positively enriched in heme metabolism (Figure 3B), interferon gamma response (Figure 3C), TNFA signaling pathway, and fatty acid metabolism pathway in the comparisons of ALF vs. Control group. Heme metabolism, interferon gamma response, fatty acid metabolism (Figure 3E) and adipogenesis (Figure 3F) pathway were also enriched in the comparisons of ALF vs. ISO group (Figure 3D). Coagulation (Figure 3H), complement, and MTORC1 signaling pathway were enriched in in the comparisons of ISO vs. Con group (Figure 3G).

To visualize the effect of ISO on anti-ALF, hierarchical cluster analysis was performed with the 3095 DEPs. We computed five hierarchical clusters the detect the changes of DEPs, three of which were exhibited closed characteristics to the healthy status (Figure 3D, Figure 4 A-E). The clustered DEPs were subjected to GO enrichment analysis in order to examine their functions. Cluster 1 majorly contained the upregulated proteins in ALF group, of which were enriched in proteolysis, cytoplasm, proteasome complex and protein binding GO terms (Figure 4F). According to the KEGG pathway analysis, it was found that the cluster 1 proteins were enriched in coronavirus disease, prion disease and phagosome terms (Figure 5A). Cluster 4 proteins were involved in the GO terms of lipid metabolism, fatty acid metabolism, mitochondrion, endoplasmic reticulum, and oxidoreductase activity, and KEGG terms of metabolic pathways (Figure 4G and 5B). Cluster 5 proteins mainly participated in the lipid metabolic process, membrane component, oxidoreductase activity, and PPAR signaling pathway (Figure 4H and 5C).

A global perspective of the hub DEPs in various comparisons, particularly those related to response mechanisms, was obtained by constructing a PPI network. MODE was used to modularly and deeply observe the important DEPs from each clustered proteins of heatmap in the Cytoscape software. Within the most enriched model in the Cluster 1 of DEPs, the top 10 hub proteins were presented in Figure 5D. The upregulation of Mapk14 and Caspase 3 were playing essential roles in the Clustered 1 PPI network. Subunits of respiratory complex, including Uqcrc1, Uqcrc2, Uqcrh, Ndufs7, and Ndufs3 proteins were verified as the top 10 hub DEPs in the downregulated proteins of Clustered 4 in ALF groups (Figure 5E). Besides, the lower protein abundances of immune-inflammatory proteins, including B2m, Fas, Thy1 and Fas in Cytoplasm were detected as the top 10 in the Cluster 5 DEPs in ALF mice (Figure 5F), indicating that a large amount of pro-inflammatory components were depleted during inflammation. Nevertheless, the network requires verification, but it narrowed the pool of protein–protein interactions that potentially contributing to the collaboration and synchronization of their roles in non-parenchymal liver cells during the response to LPS/D-Galn induced ALF.

1.5 Confirmation of DEPs for Macrophage Markers in Mice Liver

Since macrophages were the key nonparenchymal cells contributing to inflammatory progress of ALF, we strove to figure out the characteristics of macrophages in the isolated nonparenchymal cells. Firstly, 29 macrophage related markers were selected and presented in the Figure 6A. According to the proteomic results, the expression levels of Nup85, St6gal1, Myo9b, Mapk14, Marco, Mtus1, Thbs1, Sbno2, Casp1, C5, Ddt, Csf1r, Cd44, Aif1, Casp8, Pde2a, Syk, Mrc1, Itgb2, Mif, Itgb3 and Myo18a proteins were upregulated in LPS/D-GalN treated liver tissues compared to healthy group, while the rest proteins of Fcgr2, Slc7a2, C5ar1, Apob, Cd74, Hmgb1 and Ptprj were significantly upregulated in ISO intervention group compared to the non-ISO ALF group. qRT-PCR was confirmed to verify the reliability of the above 29 DEPs for markers of macrophage (Figure S2). Consistently, the transcript levels of Mapk14, Marco, Mtus1, Thbs1, Sbno2, Casp1, C5, Ddt, Csf1r, Cd44, Aif1, Casp8, Pde2a, Syk, and Mrc1were upregulated under LPS/D-GalN treatment compared with the group injected with ISO. Aligning with the findings from the proteomic analysis, the expression level of Apob, Cd74, Hmgb1 and Ptprj coding genes were differently downregulated following with the LPS/D-GalN treatment compared with the ISO group. Nevertheless, the expression levels of the remaining genes contradicted the findings of the proteomic analysis, probably attributed to the influence of post-translational modification procedures. Next, we imputed these macrophage related markers into STRING database and found they were belonged to macrophage migration inhibitory factor receptor complex, caspase complex and HGB1 complex in the cell compartment category, macrophage cell line in the tissue category, and inflammatory signal pathways (Figure 6B). Therefore, we conducted the IHC assay to identify the classic protein markers of M1 and M2 phenotype of macrophage in liver tissues. According to the staining results, the markers of M1 macrophage, iNOS (Figure 6C) was presented more significant in the nonparenchymal cells of ALF liver tissues, while the markers of M2 macrophage, ARG-1 was visualized more significantly in the nonparenchymal cells of ALF liver tissues under the treatment of ISO (Figure 6D). Furthermore, we conducted Western-Blotting assay to investigate the protein expression level of the several hub DEPs (Caspase 8, Caspase3, and Caspase1) in NPCs, and found these proteins were reduced in ISO treated ALF mice (Figure 6F).

1.6 ISO Suppressed inflammatory cytokines in THP-1 cell, probably via the Inhibition of p38 MAPK Signaling in THP-1 cell.

To further verify the proteomic results regarding whether ISO could affect inflammation mechanism through macrophage, we conducted Western-Blotting assay to investigate the protein expression level of the several hub genes (NF-kB, and Mapk14) in vitro THP-1 cell line. Although LPS induction markedly upregulated TNF-α, and IL-6 mRNA (P<0.05) excluding IL-10, after 1h in THP-1 cells, the expression of these cytokine expressions had an available diminish after ISO treatment (P< 0.05) (Figures 7A–C). The activation of THP-1 cells was detected using flow cytometry, which showed a significant increase in the population of CD14+CD40+ macrophages (representing M1 phenotype) in the LPS treated group, but was significantly reduced by ISO (Figure 7D-G). Furthermore, we explored whether the MAPK signaling pathway enriched in our results of KEGG enrichment participated in this process. We found that Mapk14 (p38 MAPK), and NF-kB (p65) was significantly activated after LPS administration (Figure 7D, 7E), but was inhibited by ISO. Collectively, the administration of ISO effectively reduced the elevations of inflammatory cytokines, likely by activating the p38 MAPK and NF-kB pathways.

The administration of LPS/D-GALN is a common approach for generating a mouse model of ALF [16, 17]. Our ongoing study shows that the liver tissue of ALF mice exhibited more pronounced inflammation and hyperemia than in healthy mice, which was associated with poor survival and liver dysfunction. These results coincide with previous studies. To identify proteins involved in ALF progression, we isolated liver non-parenchymal tissue for TMT analysis.

We have demonstrated that the prior administration of SNS agonist, ISO, has been shown effectively prevented against LPS/D-GALN induced hepatotoxicity and mortality. ISO reduced AILI, as indicated by decreased levels of ALT and reduced necrosis. With its safety and convenience, ISO may hold as a promising treatment option for acute LPS toxicity. The mechanism underlying the protective effects of ISO appears to be due to shield non-parenchymal hepatocytes from LPS/D-Galn-induced toxicity and inflammation. Additional research is needed to investigate whether ISO is still effective when there was a delay in administration.

ALF is a severe liver injury involving acute inflammation, and many efforts have been taken to develop therapies for its cure. Evidence suggests that the liver is innervated by the sympathetic nervous system (SNS) to regulate innate immunity and liver fibrosis. Beta-adrenergic receptors, activated by neurotransmitters, are known to contribute to the regulation of SNS on normal and diseased liver function. Studies have demonstrated that several adrenergic receptors are involved in the pathogenesis of liver disease. ISO, a non-selective beta-adrenergic receptor agonist, has been revealed to induce the proliferation of progenitor stem liver cells and alleviate the progression of APAP-induced liver failure[12]. Furthermore, beta-adrenergic receptors have been shown to accelerate the proliferation of hepatocytes to mitigate the development of liver failure. During the pathogenesis of acute liver failure, inflammatory mechanisms are key events in inducing liver damage. Non-parenchymal liver tissues, such as Kuffer cells, hepatic stellate cells, and endothelial cells, are important components of the liver contributing to inflammation. However, no studies have reported the dynamic changes of protein level in non-parenchymal liver cells during ISO treatment of ALF.[11]. In our present study, ISO was pre-injected into mice before LPS/GalN and found to protect the mice from acute liver damage, as evident by the morphology of microscopical observations. ISO also effectively reduced the inflammatory invasion and mortality of ALF mice, which is consistent with other studies. To further investigate the mechanism of ISO in regulating ALF inflammation, we isolated non-parenchymal liver cells for TME proteomic analysis to make a thorough understanding of the protein functions. Quality control of the proteomic data indicated successful isolation of liver cells. Interestingly, the amount of protein in the ISO-ALF group comparison was comparable to that of the ISO-control group comparison. Moreover, over 50% of the DEPs in the ISO vs. ALF group comparison were shared with the ISO vs. control group comparison, and their fold changes had similar trends. Altogether, these observations confirmed the effect of ISO treatment in alleviating the development of ALF.

Lipopolysaccharide (LPS) is a common pathogen-associated molecular pattern released from Gram-negative bacteria that can initiate potent innate immune responses against further infection[18, 19]. Recognition of lipopolysaccharide pattern by Toll-like receptor (TLR) contributes to activation of multiple signaling components, including NF-κB and MAPK, and the subsequent production of pro-inflammatory cytokines. The intraperitoneal application of lipopolysaccharide (LPS) combing with other hepatotoxins is an general experimental approach to achieve acute liver injury model[19]. After LPS/D-Galn administration, we respectively screened out the top 10 up-expressed and down-expressed proteins in the comparison with ISO pretreated mouse model, and validated their ranking list in the comparison with control mice. Human zymogen granule protein 16 (ZG16), mitochondrial ribosomal protein large 24 (MRPL24), peptidoglycan recognition protein 2 (PGLYRP2), carboxypeptidase A1 (CPA1), pancreatic alpha-amylase (AMY2), and serpin I2 (SERPINI2) were also top higher expressed in LPS/D-Galn mice when compared to PBS treated group. ZG16 is highly expressed in mucus-secreting cells and is involved in the recognition of pathogens through its binding to glycosaminoglycans with putative functions in the promotes local immunity of T cells[20]. Mitochondrial ribosomal protein has been demonstrated to play a role not only in deficiencies of the primary mitochondrial respiratory chain activity, but also in various other inflammatory disorders. Mutations in MRPL24 genes could cause cerebellar atrophy, intellectual disability[21]. PGLYRP2, a highly conserved proteins of host defense in mammals that detect bacterial cell, is mainly expressed in the live r [22]. In bone marrow-derived macrophages, gene expression of PGLYRP2 was greatly increased[22]. Carboxypeptidases, identified as a large group of enzymes that cleave one or two amino acids from the carboxy terminus of proteins or peptides, may participate in antigen processing and be associated with the production or regulation of several proinflammatory mediators[23, 24]. Serpins, also known as serine protease inhibitors, function through a distinct and irreversible mechanism divergent from that of typical protease inhibitors, correlated with inflammatory disease, such as COPD and chronic lung diseases, and are actually increased in expression[25]. Therefore, we inferred that ISO somehow possessed the ability of suppressing the recognition of pathogens and reducing proinflammatory progress.

NMRA-like protein NMRAL1, was defined as a redox-sensitive transcriptional regulator among the screened downregulated proteins in the comparison of ISO vs. ALF. Reduced NMRAL1 enhanced nitric oxide (NO) production and diminished cell viability. As far, NMRAL1 has been demonstrated as a potential mechanism of controlling inflammatory signaling in LPS-treated cells[26]. SF3A subunits including SF3A1 and SF3A2, were required for mRNA splicing, and the SF3A mRNA splicing complex was suspended to inhibit by LPS induced MyD88[27]. αβ-hydrolase domain-containing 11 (ABHD11), a mitochondrial enzyme that controls OGDHc activity, is the master inhibitor of HIF-1α that plays a crucial role in activating inflammatory macrophage [28, 29]. The mutant U2 small nuclear RNA auxiliary factor 1(U2AF1) leaded to elevated basal mRNA levels of IL-6 and IL-8, but not TNFα, when compared to the U2AF1-wild macrophage[30]. NFU1 Iron-sulfur cluster scaffold protein (NFU1) assembles [4Fe-4S] clusters and delivers them to target proteins. Iron–sulfur (Fe–S) clusters are required for numerous biological processes but its biogenesis machinery could be actively suppressed by TLR activation[31, 32]. Therefore, we can suspend ISO certainly increased the antiinflammation mechanism in LPS/D-Galn damaged hepatic non-parenchymal tissues.

From the above discussion, isoproterenol seemed to played different role in acute liver injury from that in ISO inducing myocardial injury[33]. We consequently identified whether isoproterenol had different function in regulating macrophage activity in acute liver damage from that in cardiac macrophage[34].

Since macrophages are the key cells involved in the systemic inflammation of ALF, we conducted an analysis of the 29 DEPs (differentially expressed proteins) associated with macrophage functions. Results from the PPI analysis using the STRING database showed that these DEPs were mostly enriched in macrophages. The proteins related to M1 macrophages were significantly decreased in the ISO-treated group, while the DEPs related to M2 macrophages were statistically increased. RT-qPCR confirmed the mRNA levels in the animal models. We further studied the effects of ISO treatment on inflammation regulation in a human macrophage cell line. Previous studies have shown that the B-adrenergic receptor agonist, isoproterenol, synergistically amplifies the anti-inflammatory role of cAMP pathway inducers, resulting in reduced expression of the key pro-inflammatory cytokine TNFα, and increased expression of the key anti-inflammatory cytokine IL-10 in macrophage cell lines[35, 36]. Our results were consistent with this, showing that ISO could effectively reduce inflammation in ALF mice.

Heme metabolism, fatty acid metabolism and mitochondrial oxidation signaling pathways all play essential roles in the anti-inflammatory polarization of macrophages [37-39]. ISO may affect the progression of ALF mainly through the regulation of fatty acid metabolism and mitochondrial oxidation in monocytes/macrophages, and by upregulating anti-inflammatory molecules to reduce ALF severity. However, several studies have demonstrated that ISO significantly induces inflammation, which is contrary to our current results. Thus, further research is needed to determine the specific role of ISO in decreasing ALF inflammation, and whether it is due to a biological impact from other liver cells, instead of a direct effect on macrophages.

Currently, one of the most probable explanations for the clear evident of hepatoprotective effect of ISO is that the expansion of hepatic precursor cells consequently release Wnt ligands to protect hepatocytes from damage [11]. However, due to the pleiotropic role of adrenergic agonists, it is possible that ISO might mitigate AILI via other mechanisms. Studies have shown that the SNS can influence the immune response and related inflammation[40-42]. Therefore, it is not possible to rule out the possibility that ISO treatment provides partial liver protection by modulating the inflammatory process. Additionally, it has been previously suggested that the hepatic SNS plays a vital role in protecting the liver against Fas-induced hepatic injury[27], which may be attributed to antiapoptotic proteins and IL-6[44]. The results of our models, further supported by which significant difference in IL-6 expression in prior ISO treatment.

However, we cannot ignore several limitations in our study. Although the proteomic data suggested a global effect of ISO on non-parenchymal cells, the specific impact of ISO on a particular type of non-parenchymal cells still requires further analysis. Moreover, how ISO participates in regulating the monotype of macrophages in Kuffer cells also needs more specific study.

In conclusion, our data provide the first comprehensive quantitative proteomic analysis of human liver tissue and freshly isolated non-parenchymal hepatocytes to explore the effect of SNS stimulation on AILI. The role of non-parenchymal hepatocytes protected liver against acute injury, which may be carried out through multiple pathways. Our results emphasize that ISO, due to its efficacy when administered earlier, as well as its safety and ready availability, may be a potentially favorable option for treating LPS/D-Galn-induced hepatotoxicity.

CRediT authorship contribution statement

Yuchao Wu, Tianzhi Ni, Mengmeng Zhang, Yingli He, Yuan Yang and Jinfeng Liu, contributed to conception/design of the research; Yuchao Wu, Tianzhi Ni, Mengmeng Zhang, Danfeng Ren, Taotao Yan, and Ze Zhang contributed to acquisition of the experiment; Yuchao Wu, Tianzhi Ni, and Shan Fu contributed to analysis, and interpretation of the data; Yuchao Wu, Tianzhi Ni, and Jinfeng Liu drafted the manuscript. All authors critically revised the manuscript, agree to be fully accountable for ensuring the integrity and accuracy of the work, and read and approved the final manuscript.

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of competing interest

The authors declare no other funding or conflicts of interest to declare, and they do not have a conflict of interest relating to the publication of this manuscript.

Funding

This research was supported by the Nature Science Foundation of Shaanxi (No.2020JM-394), National Natural Science Foundation of China, Grant/Award (No. 81971310), Key R&D Program of Shaanxi (2018ZDXM-SF-037), Natural Science Basic Research Program Youth Project of Shaanxi Provincial Science and Technology Department (No. 2023-JC-QN-0830), the China Postdoctoral Science Foundation (No.2023M730802) and Clinical Medical Research Center for Infectious Diseases of Shaanxi (2020LCZX-01).

Acknowledgements

We thank all our authors listed in this manuscript.

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No competing interests reported.

FigureS1.jpg
Figure S1. Quantitative of screened proteins and data quality control of liver samples. The number of filtered proteins matched in the search library(A), distribution of protein coverage(B), (C) peptide length, (D) protein molecular weight, and (E) peptide number per protein. (F) Principal component analysis (PCA) of proteome data among three groups of samples. (G) Normalization of proteomic data, and (H) Pearson correlation analysis of the samples among the three groups.
Figure2d1.jpg
Figure S2. RT-qPCR analysis for the mRNA expression of 29 differentially expressed protein coding genes related to macrophage activation from isolated liver non-parenchymal tissues. Data are presented as mean ± SEM. Statistical significance was performed by Student’s t-test. *P < 0.05; **P < 0.01; ***P < 0.001.

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Treatment withβ-adrenoceptor agonist isoproterenol reduces non- parenchymal cell responses in LPS/D-Galn-induced liver injury

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