A combined proteomic and metabolomic analyses of the priming phase during rat liver regeneration

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

Baitouweng decoction suppresses growth of esophageal carcinoma cells through miR-495-3p/BUB1/STAT3 axis

BACKGROUND

Esophageal carcinoma (EC) is one of the most prevalent cancers in human populations worldwide. Baitouweng decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer.

AIM

To investigate the role and mechanism of Baitouweng decoction on EC cells.

METHODS

Differentially expressed genes (DEGs) in EC tissues and normal tissues were screened by the cDNA microarray technique and by bioinformatics methods. The target genes of microRNAs were predicted based on the TargetScan database and verified by dual luciferase gene reporter assay. We used Baitouweng decoction to intervene EC cells, and detected the activity of EC9706 and KYSE150 cells by the MTT method. Cell cycle and apoptosis were measured by flow cytometry. The expression of BUB1 mRNA and miR-495-3p was measured by qRT-PCR. The protein levels of BUB1, STAT3, p-STAT3, CCNB1, CDK1, Bax, Caspase3, and Caspase9 were measured by Western blot analysis. The migration and invasion abilities of the cells were measured by wound-healing assay and Transwell invasion assay, respectively.

RESULTS

DEGs identified are involved in biological processes, signaling pathways, and network construction, which are mainly related to mitosis. BUB1 was the key hub gene, and it is also a target gene of miR-495-3p. Baitouweng decoction could upregulate miR-495-3p and inhibit BUB1 expression. In vitro experiments showed that Baitouweng decoction significantly inhibited the migration and invasion of EC cells and induced apoptosis and G2/M phase arrest. After treatment with Baitouweng decoction, the expression of Bax, Caspase 3, and Caspase 9 in EC cells increased significantly, while the expression of BUB1, CCNB1, and CDK1 decreased significantly. Moreover, the STAT3 signaling pathway may play an important role in this process.

CONCLUSION

Baitouweng decoction has a significant inhibitory effect on EC cell growth. BUB1 is a potential therapeutic target for EC. Further analysis showed that Baitouweng decoction may inhibit the growth of EC cells by upregulating miR-495-3p targeting the BUB1-mediated STAT3 signal pathway.

Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration

Objective

Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH).

Methods

The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index.

Results

Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively.

Conclusion

The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

PA200-Mediated Proteasomal Protein Degradation and Regulation of Cellular Senescence

Cellular senescence is closely related to DNA damage, proteasome inactivity, histone loss, epigenetic alterations, and tumorigenesis. The mammalian proteasome activator PA200 (also referred to as PSME4) or its yeast ortholog Blm10 promotes the acetylation-dependent degradation of the core histones during transcription, DNA repair, and spermatogenesis. According to recent studies, PA200 plays an important role in senescence, probably because of its role in promoting the degradation of the core histones. Loss of PA200 or Blm10 is a major cause of the decrease in proteasome activity during senescence. In this paper, recent research progress on the association of PA200 with cellular senescence is summarized, and the potential of PA200 to serve as a therapeutic target in age-related diseases is discussed.

RNA-seq transcriptome profiling of liver regeneration in mice identifies the miR-34b-5p/phosphoinositide-dependent protein kinase 1 axis as a potential target for hepatocyte proliferation

Characterized by compensatory hyperplasia dependent on hepatocyte proliferation, the liver will initiate regeneration after partial hepatectomy (PH) and acute or chronic injuries. A variety of genes and noncoding RNAs play pivotal roles in these cell proliferation and growth processes. However, it is still unclear how competition endogenous RNAs (ceRNAs) modulate cellular activities during each phase of liver regeneration, and the specific mechanisms of posttranscriptional gene expression regulation in hepatocyte proliferation remain to be elucidated. To investigate the mechanism of liver regeneration through RNA-seq profiling and to determine the role of miR-34b-5p/PDK1 on hepatocyte proliferation, we established a 2/3 PH mouse model for whole transcriptome profiling based on high-throughput sequencing techniques. We subsequently constructed a lncRNA-miRNA-mRNA ceRNA regulatory network through integrative analyses of RNA interactions. Finally, plasmid transfection in NCTC 1469 cells, dual luciferase reporter gene assay, quantitative real-time PCR, western blotting, Cell Counting Kit-8, and EdU-DNA synthesis cell proliferation assay were used to demonstrate the role of the miR-34b-5p/PDK1 axis in hepatocyte proliferation in vitro. A total of 1443 mRNAs (962 up, 481 down), 48 miRNAs (35 up, 13 down), and 1955 lncRNAs (986 up, 969 down) were identified as significantly differentially expressed. We then successfully constructed a ceRNA regulatory network consisting of 7 lncRNAs, 15 miRNAs, and 347 mRNAs based on the predicted inverse interactions among ceRNAs. Additionally, miR-34b-5p/PDK1 was predicted to be closely related to hepatocyte proliferation. We further demonstrated that miR-34b-5p could bind specifically to the 3'-untranslated region (3'-UTR) of PDK1 using the dual luciferase reporter assay. Ectopic overexpression of miR-34b-5p significantly reduced the mRNA and protein expression of PDK1, while it markedly inhibited the proliferation of mouse NCTC 1469 cells in vitro. In contrast, knocking down miR-34b-5p exhibited the inverse effects on PDK1 expression and hepatocyte proliferation. Through analyzing the ceRNA network during mouse liver regeneration, this study reveals that miR-34b-5p can inhibit hepatocyte proliferation through negatively regulating PDK1 and may be a potential pharmacological intervention target.

iTRAQ-based quantitative proteomic analysis of the liver regeneration termination phase after partial hepatectomy in mice

Liver regeneration (LR) is an important biological process after liver injury. As the "brake" in the process of LR, the termination phase of LR not only suppresses the continuous increase in liver volume but also effectively promotes the recovery of liver function. However, the mechanisms underlying the termination phase of LR are still not clear. In our study, we used isobaric tags for relative and absolute quantification (iTRAQ)-based quantitative proteomic analysis to determine the protein expression profiles of livers in the termination phase of mouse LR after partial hepatectomy (PH). We found that the expression of 197 proteins increased gradually during LR; in addition, 187 proteins were upregulated and 264 proteins were downregulated specifically in the termination phase of LR. The GO analysis of the proteins revealed the upregulation of "cell-cell adhesion" and "translation" and the downregulation of the "oxidation-reduction process". The KEGG pathway analysis showed that "biosynthesis of antibiotics" and "ribosomes" were significantly upregulated, while "metabolic pathways" were significantly downregulated. These analyses indicated that the termination phase of LR mainly focuses on restoring cellular structure and function. Differentially expressed proteins such as SNX5 were also screened out from biological processes. SIGNIFICANCE: The key regulatory factors in the termination phase of LR were studied by iTRAQ-based proteomics to lay a foundation for further study of the molecular mechanism and biomarkers of the termination phase of LR. This study will guide the clinical perioperative management of patients after hepatectomy.

PSME4 Activates mTOR Signaling and Promotes the Malignant Progression of Hepatocellular Carcinoma

Background

As hepatocellular carcinoma (HCC) having the second-highest mortality rate globally, the early diagnosis and prognosis of HCC have always been the focus of various studies. Although PSME4 has been reported to be closely related to several malignancies, its role in HCC remains unclear.

Materials and Methods

The TCGA-LIHC database and HCC tissues were used to explore the expression of PSME4 in HCC. Gene set enrichment analysis (GSEA) was used to forecast the biological behavior of HCC cells that PSME4 might be involved in regulation. In addition, CCK-8, colony formation and flow cytometry assays were used to explore the effect of PSME4 on HCC cells. Furthermore, the underlying PSME4-related signaling pathways in HCC were further confirmed using GSEA.

Results

We found that the expression of PSME4 in HCC tissues was significantly higher than that in adjacent normal tissues, and patients with high PSME4 expression have a poor prognosis. CCK-8, colony formation and flow cytometry assays shown that knockdown of PSME4 inhibits HCC cell proliferation of HCC cells, promotes cell apoptosis and moves the cell cycle away from the S phase. Mechanistically, PSME4 may promote the development of HCC through mTOR signaling pathway.

Conclusion

The high expression of PSME4 in HCC promotes the proliferation of HCC cells via the mTOR signalling pathway. Therefore, PSME4 is an emerging tumour marker for the early diagnosis and prognosis of HCC.

Transcriptome and metabolome analyses of response of Synechocystis sp. PCC 6803 to methyl viologen

The toxicity of methyl viologen (MV) to organisms is mainly due to the oxidative stress caused by reactive oxygen species produced from cell response. This study mainly investigated the response of Synechocystis sp. PCC 6803 to MV by combining transcriptomic and metabolomic analyses. Through transcriptome sequencing, we found many genes responding to MV stress, and analyzed them by weighted gene co-expression network analysis (WGCNA). Meanwhile, many metabolites were also found by metabolomic analysis to be regulated post MV treatment. Based on the analysis results of Kyoto encyclopedia of genes and genomes (KEGG) of the differentially expressed genes (DEGs) in the transcriptome and the differential metabolites in the metabolome, the dynamic changes of genes and metabolites involved in ten metabolic pathways in response to MV were analyzed. The results indicated that although the oxidative stress caused by MV was the strongest at 6 h, the proportion of the upregulated genes and metabolites involved in these ten metabolic pathways was the highest. Photosynthesis positively regulated the response to MV-induced oxidative stress, and the regulation of environmental information processing was inhibited by MV. Other metabolic pathways played different roles at different times and interacted with each other to respond to MV. This study comprehensively analyzed the response of Synechocystis sp. PCC 6803 to oxidative stress caused by MV from a multi-omics perspective, with providing key data and important information for in-depth analysis of the response of organisms to MV, especially photosynthetic organisms. KEY POINTS: • Methyl viologen (MV) treatment caused regulatory changes in genes and metabolites. • Proportion of upregulated genes and metabolites was the highest at 6-h MV treatment. • Photosynthesis and environmental information processing involved in MV response.