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.

Global analysis of DNA methylation changes during experimented lingual carcinogenesis.

OBJECTIVES: This study aims to assess the role of DNA methylation changes in tongue cancer through a comprehensive analysis of global DNA methylation alterations during experimental lingual carcinogenesis. METHODS: C57BL/6J mice were subjected to 16-week oral administration of 4-nitroquinoline-1-oxide (4NQO, 50 mg/L). Lingual mucosa samples, being representative of normal tissue (week 0) and early (week 12) and advanced (week 28) tumorigenesis, were harvested for microarray and methylated DNA immunoprecipitation sequencing (MeDIP-Seq). The mRNA and promoter methylation of transforming growth factor-beta-signaling protein 1 (SMAD1) were evaluated with real-time quantitative reverse transcription polymerase chain reaction and Massarray in human lingual mucosa and tongue cancer cell lines. RESULTS: The cytosine guanine island (CGI) methylation level observed at 28 weeks surpassed that of both 12 weeks and 0 weeks. The promoter methylation level at 12 weeks exceeded that at 0 weeks. Notably, 208 differentially expressed genes were negatively correlated to differential methylation in promoters among 0, 12, and 28 weeks. The mRNA of SMAD1 was upregulated, concurrent with a decrease in promoter methylation levels in cell lines compared to normal mucosa. CONCLUSIONS: DNA methylation changed during lingual carcinogenesis. Overexpression of SMAD1 was correlated to promoter hypomethylation in tongue cancer cell lines.

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.

Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: A retrospective cohort study.

BACKGROUND: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF. METHODS: A total of 226 patients undergoing hepatectomy at a single center were recruited. The study outcome was clinically significant PHLF. Five pre- and postoperative machine learning (ML) models were developed and compared with four clinical scores, namely, the MELD, FIB-4, ALBI, and APRI scores. The robustness of the developed ML models was internally validated using 5-fold cross-validation by calculating the average of the evaluation metrics and was externally validated on an independent temporal dataset, including the area under the curve (AUC) and the area under the precision‒recall curve (AUPRC). SHapley Additive exPlanations analysis was performed to interpret the best performance model. RESULTS: Clinically significant PHLF was observed in 23 of 226 patients (10.2%). The variables in the preoperative model included creatinine, total bilirubin, and Child‒Pugh grade. In addition to the above factors, the extent of resection was also a key variable for the postoperative model. The pre- and postoperative artificial neural network (ANN) models exhibited excellent performance, with mean AUCs of 0.766 and 0.851, respectively, and mean AUPRC values of 0.441 and 0.645, whereas the MELD, FIB-4, ALBI, and APRI scores reached AUCs of 0.714, 0.498, 0.536 and 0.551, respectively, and AUPRC values of 0.204, 0.111, 0.128 and 0.163, respectively. In addition, the AUCs of the pre- and postoperative ANN models were 0.720 and 0.731, respectively, and the AUPRC values were 0.380 and 0.408, respectively, on the temporal dataset. CONCLUSION: Our online interpretable dynamic ML models outperformed common clinical scores and could function as a clinical decision support tool to identify patients at high risk of PHLF pre- and postoperatively.

Discovery of Sovleplenib, a Selective Inhibitor of Syk in Clinical Development for Autoimmune Diseases and Cancers.

Herein we describe the medicinal chemistry efforts that led to the discovery of the clinical-staged Syk inhibitor sovleplenib (41) via a structure-activity relationship investigation and pharmacokinetics (PK) optimization of a pyrido[3,4-b]pyrazine scaffold. Sovleplenib is a potent and selective Syk inhibitor with favorable preclinical PK profiles and robust anti-inflammation efficacy in a preclinical collagen-induced arthritis model. Sovleplenib is now being developed for treating autoimmune diseases such as immune thrombocytopenic purpura and warm antibody hemolytic anemia as well as hematological malignancies.

Laparoscopic hepatectomy for hepatocellular carcinoma in patients with clinically significant portal hypertension: a systematic review and meta-analysis.

OBJECTIVE: To compare the effects of laparoscopic hepatectomy (LH) on the short-term and long-term outcomes in hepatocellular carcinoma (HCC) patients with and without clinically significant portal hypertension (CSPH). METHODS: A systematic literature search of the PubMed, EMBASE, and Cochrane databases was performed for articles published from inception to March 1, 2023. Meta-analysis of surgical and oncological outcomes was performed using a random effects model. Data were summarized as mean difference and risk ratio with 95% confidence intervals. RESULTS: Five cohort studies with a total of 310 HCC patients were included (CSPH 143; Non-CSPH 167). In terms of surgical outcomes, estimated blood loss and the length of hospital stay were significantly lower in the Non-CSPH group than in the CSPH group. There were no significant differences between the two groups regarding other surgical outcomes, including the operative time, ratio of conversion to open surgery, and overall complication rate. In addition, there were also no significant differences between the two groups regarding the oncological outcomes, such as 1-, 3-, and 5-year overall survival. CONCLUSIONS: HCC patients with and without CSPH who underwent LH had comparable surgical and oncological outcomes. LH is a safe and effective treatment for HCC patients with CSPH under the premise of rational screening of patients.

Preclinical Pharmacology Characterization of Sovleplenib (HMPL-523), an Orally Available Syk Inhibitor.

Spleen tyrosine kinase (Syk) is an intracellular tyrosine kinase involved in the signal transduction in immune cells mainly. Its aberrant regulation is associated with diversified allergic disorders, autoimmune diseases and B cell malignancies. Therefore, inhibition of Syk is considered a reasonable approach to treat autoimmune/inflammatory diseases and B cell malignancies. Here we described the preclinical characterization of sovleplenib, a novel, highly potent and selective, oral Syk inhibitor, in several rodent autoimmune disease models. Sovleplenib potently inhibited Syk activity in a recombinant enzymatic assay and Syk-dependent cellular functions in various immune cell lines and human whole blood in vitro. Furthermore, sovleplenib, by oral administration, demonstrated strong in vivo efficacies in murine models of immune thrombocytopenia (ITP), autoimmune hemolytic anemia (AIHA), and chronic graft-versus-host disease (cGVHD), and a rat model of collagen induced arthritis (CIA) respectively, in a dose-dependent manner. Collectively, these results clearly supported sovleplenib as a therapeutic agent in the treatment of autoimmune diseases. Sovleplenib is being globally developed for ITP (Phase III, NCT05029635, Phase Ib/II, NCT03951623), wAIHA (Phase II/III, NCT05535933) and B-cell lymphoma (Phase I, NCT02857998, NCT03779113). SIGNIFICANCE STATEMENT: Syk is a key mediator of signaling pathways downstream of a wide array of receptors important for immune functions, including the B cell receptor, immunoglobulin receptors bearing Fc receptors. Inhibition of Syk could provide a novel therapeutic approach for autoimmune diseases and hematologic malignancies. The manuscript describes the preclinical pharmacology characterization of sovleplenib, a novel Syk inhibitor, in enzymatic and cellular assays in vitro and several murine autoimmune disease models in vivo.

Advances, opportunities and challenges in developing therapeutic cancer vaccines.

Therapeutic cancer vaccines have shown promising efficacy in helping immunotherapy for cancer patients, but the systematic characterization of the clinical application and the method for improving efficacy is lacking. Here, we mainly summarize the classification of therapeutic cancer vaccines, including protein vaccines, nucleic acid vaccines, cellular vaccines and anti-idiotypic antibody vaccines, and subdivide the above vaccines according to different types and delivery forms. Additionally, we outline the clinical efficacy and safety of vaccines, as well as the combination strategies of therapeutic cancer vaccines with other therapies. This review will provide a detailed overview and rationale for the future clinical application and development of therapeutic cancer vaccines.

Robotic versus laparoscopic liver resection for liver malignancy: a systematic review and meta-analysis of propensity score-matched studies.

OBJECTIVE: How different surgical procedures, including the robotic-assisted liver resection (RLR) and laparoscopic liver resection (LLR), can affect the prognosis of patients with liver malignancies is unclear. Thus, in this study, we compared the effects of RLR and LLR on the surgical and oncological outcomes in patients with liver malignancies through propensity score-matched cohort studies. METHODS: The PubMed, Embase, and Cochrane databases were searched using Medical Subject Headings terms and keywords from inception until May 31, 2023. The quality of the included studies was assessed using the Newcastle-Ottawa quality assessment scale. The mean difference with 95% confidence interval (95% CI) was used for analysis of continuous variables; the risk ratio with 95% CI was used for dichotomous variables; and the hazard ratio with 95% CI was used for survival-related variables. Meta-analysis was performed using a random-effects model. RESULTS: Five high-quality cohort studies with 986 patients were included (370 and 616 cases for RLR and LLR, respectively). In terms of surgical outcomes, there were no significant differences in the operation time, conversion rate to open surgery, overall complication rate, major complication rate, and length of hospital stay between the RLR and LLR groups. In terms of oncological outcomes, there were no significant differences in the 5-year overall survival and disease-free survival between the two groups. CONCLUSION: Surgical and oncological outcomes are comparable between RLR and LLR on patients with liver malignancies. Therefore, the benefits of applying RLR in patients with liver malignancies need to be further explored.

How does TCR-T cell therapy exhibit a superior anti-tumor efficacy.

TCR-engineered T cells have achieved great progress in solid tumor therapy, some of which have been applicated in clinical trials. Deep knowledge about the current progress of TCR-T in tumor therapy would be beneficial to understand the direction. Here, we classify tumor antigens into tumor-associated antigens, tumor-specific antigens, tumor antigens expressed by oncogenic viruses, and tumor antigens caused by abnormal protein modification; Then we detail the TCR-T cell therapy effects targeting those tumor antigens in clinical or preclinical trials, and propose that neoantigen specific TCR-T cell therapy is expected to be a promising approach for solid tumors; Furthermore, we summarize the optimization strategies, such as tumor microenvironment, TCR pairing and affinity, to improve the therapeutic effect of TCR-T. Overall, this review provides inspiration for the antigen selection and therapy strategies of TCR-T in the future.

PRMT1 orchestrates with SAMTOR to govern mTORC1 methionine sensing via Arg-methylation of NPRL2.

Methionine is an essential branch of diverse nutrient inputs that dictate mTORC1 activation. In the absence of methionine, SAMTOR binds to GATOR1 and inhibits mTORC1 signaling. However, how mTORC1 is activated upon methionine stimulation remains largely elusive. Here, we report that PRMT1 senses methionine/SAM by utilizing SAM as a cofactor for an enzymatic activity-based regulation of mTORC1 signaling. Under methionine-sufficient conditions, elevated cytosolic SAM releases SAMTOR from GATOR1, which confers the association of PRMT1 with GATOR1. Subsequently, SAM-loaded PRMT1 methylates NPRL2, the catalytic subunit of GATOR1, thereby suppressing its GAP activity and leading to mTORC1 activation. Notably, genetic or pharmacological inhibition of PRMT1 impedes hepatic methionine sensing by mTORC1 and improves insulin sensitivity in aged mice, establishing the role of PRMT1-mediated methionine sensing at physiological levels. Thus, PRMT1 coordinates with SAMTOR to form the methionine-sensing apparatus of mTORC1 signaling.

Expression profiles of lncRNAs, miRNAs, and mRNAs during the proliferative phase of liver regeneration in mice with liver fibrosis.

The role of lncRNAs in the regeneration of fibrotic liver is unclear. To address this issue, we established a 70% hepatectomy model of liver fibrosis in mice, used high-throughput sequencing technology to obtain the expression profiles of lncRNAs, miRNAs, and mRNAs, and constructed a lncRNA-miRNA-mRNA regulatory network. A total of 1329 lncRNAs, 167 miRNAs, and 6458 mRNAs were differentially expressed. On this basis, a lncRNA-miRNA-mRNA ceRNA regulatory network consisting of 38 DE lncRNAs, 24 DE miRNAs, and 299 DE mRNAs was constructed, and a transcription factor (TF) - mRNA regulatory network composed of 20 TFs and 98 DE mRNAs was built. Through the protein network analysis, a core protein interaction network composed of 20 hub genes was derived. Furthermore, Xist/miR-144-3p/Cdc14b and Snhg3/miR-365-3p/Map3k14 axes in the ceRNA regulatory network were verified by Real-Time quantitative PCR. Therefore, we concluded that these new insights may further our understanding of liver regeneration.

Integrated analysis of lncRNA/circRNA-miRNA-mRNA in the proliferative phase of liver regeneration in mice with liver fibrosis.

BACKGROUND: Non-coding RNAs play important roles in liver regeneration; however, their functions and mechanisms of action in the regeneration of fibrotic liver have not been elucidated. We aimed to clarify the expression patterns and regulatory functions of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration. METHODS: Based on a mouse model of liver fibrosis with 70% hepatectomy, whole-transcriptome profiling was performed using high-throughput sequencing on samples collected at 0, 12, 24, 48, and 72 h after hepatectomy. Hub genes were selected by weighted gene co-expression network analysis and subjected to enrichment analysis. Integrated analysis was performed to reveal the interactions of differentially expressed (DE) lncRNAs, circRNAs, miRNAs, and mRNAs, and to construct lncRNA-mRNA cis- and trans-regulatory networks and lncRNA/circRNA-miRNA-mRNA ceRNA regulatory networks. Real-Time quantitative PCR was used to validate part of the ceRNA network. RESULTS: A total of 1,329 lncRNAs, 48 circRNAs, 167 miRNAs, and 6,458 mRNAs were differentially expressed, including 812 hub genes. Based on these DE RNAs, we examined several mechanisms of ncRNA regulatory networks, including lncRNA cis and trans interactions, circRNA parental genes, and ceRNA pathways. We constructed a cis-regulatory core network consisting of 64 lncRNA-mRNA pairs (53 DE lncRNAs and 58 hub genes), a trans-regulatory core network consisting of 103 lncRNA-mRNA pairs (18 DE lncRNAs and 85 hub genes), a lncRNA-miRNA-mRNA ceRNA core regulatory network (20 DE lncRNAs, 12 DE miRNAs, and 33 mRNAs), and a circRNA-miRNA-mRNA ceRNA core regulatory network (5 DE circRNAs, 5 DE miRNAs, and 39 mRNAs). CONCLUSIONS: These results reveal the expression patterns of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration, as well as core regulatory networks of mRNAs and non-coding RNAs underlying liver regeneration. The findings provide insights into molecular mechanisms that may be useful in developing new therapeutic approaches to ameliorate diseases that are characterized by liver fibrosis, which would be beneficial for the prevention of liver failure and treatment of liver cancer.

Prolonged hypoxia alleviates prolyl hydroxylation-mediated suppression of RIPK1 to promote necroptosis and inflammation.

The prolyl hydroxylation of hypoxia-inducible factor 1α (HIF-1α) mediated by the EGLN-pVHL pathway represents a classic signalling mechanism that mediates cellular adaptation under hypoxia. Here we identify RIPK1, a known regulator of cell death mediated by tumour necrosis factor receptor 1 (TNFR1), as a target of EGLN1-pVHL. Prolyl hydroxylation of RIPK1 mediated by EGLN1 promotes the binding of RIPK1 with pVHL to suppress its activation under normoxic conditions. Prolonged hypoxia promotes the activation of RIPK1 kinase by modulating its proline hydroxylation, independent of the TNFα-TNFR1 pathway. As such, inhibiting proline hydroxylation of RIPK1 promotes RIPK1 activation to trigger cell death and inflammation. Hepatocyte-specific Vhl deficiency promoted RIPK1-dependent apoptosis to mediate liver pathology. Our findings illustrate a key role of the EGLN-pVHL pathway in suppressing RIPK1 activation under normoxic conditions to promote cell survival and a model by which hypoxia promotes RIPK1 activation through modulating its proline hydroxylation to mediate cell death and inflammation in human diseases, independent of TNFR1.

Metabolic orchestration of cell death by AMPK-mediated phosphorylation of RIPK1.

Adenosine monophosphate-activated protein kinase (AMPK) activity is stimulated to promote metabolic adaptation upon energy stress. However, sustained metabolic stress may cause cell death. The mechanisms by which AMPK dictates cell death are not fully understood. We report that metabolic stress promoted receptor-interacting protein kinase 1 (RIPK1) activation mediated by TRAIL receptors, whereas AMPK inhibited RIPK1 by phosphorylation at Ser415 to suppress energy stress-induced cell death. Inhibiting pS415-RIPK1 by Ampk deficiency or RIPK1 S415A mutation promoted RIPK1 activation. Furthermore, genetic inactivation of RIPK1 protected against ischemic injury in myeloid Ampkα1-deficient mice. Our studies reveal that AMPK phosphorylation of RIPK1 represents a crucial metabolic checkpoint, which dictates cell fate response to metabolic stress, and highlight a previously unappreciated role for the AMPK-RIPK1 axis in integrating metabolism, cell death, and inflammation.

Lactate fuels mitosis.

Cell cycle and metabolism are intimately intertwined, but how metabolites directly regulate cell-cycle machinery remains elusive. Liu et al.1 reveal that glycolysis end-product lactate directly binds and inhibits the SUMO protease SENP1 to govern the E3 ligase activity of the anaphase-promoting complex, leading to efficient mitotic exit in proliferative cells.

PRMT1 mediated methylation of cGAS suppresses anti-tumor immunity.

Activation of the cGAS/STING innate immunity pathway is essential and effective for anti-tumor immunotherapy. However, it remains largely elusive how tumor-intrinsic cGAS signaling is suppressed to facilitate tumorigenesis by escaping immune surveillance. Here, we report that the protein arginine methyltransferase, PRMT1, methylates cGAS at the conserved Arg133 residue, which prevents cGAS dimerization and suppresses the cGAS/STING signaling in cancer cells. Notably, genetic or pharmaceutical ablation of PRMT1 leads to activation of cGAS/STING-dependent DNA sensing signaling, and robustly elevates the transcription of type I and II interferon response genes. As such, PRMT1 inhibition elevates tumor-infiltrating lymphocytes in a cGAS-dependent manner, and promotes tumoral PD-L1 expression. Thus, combination therapy of PRMT1 inhibitor with anti-PD-1 antibody augments the anti-tumor therapeutic efficacy in vivo. Our study therefore defines the PRMT1/cGAS/PD-L1 regulatory axis as a critical factor in determining immune surveillance efficacy, which serves as a promising therapeutic target for boosting tumor immunity.