Immunological Tolerance in Liver Transplant Recipients: Putative Involvement of Neuroendocrine-Immune Interactions

Gama, Jaciara Fernanda Gomes; Cardoso, Liana Monteiro da Fonseca; Bisaggio, Rodrigo da Cunha; Lagrota-Candido, Jussara; Henriques-Pons, Andréa; Alves, Luiz Anastácio

doi:10.3390/cells11152327

Cited by 3 publications

(3 citation statements)

References 158 publications

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“…there is a strong biological mechanism for its marked difference in biliary vs. TCMR groups, as serotonin is a potent modulator of T cells and has multiple functions in the liver, including regulation of the biliary tree and cholangiocytes [36][37][38]. Furthermore, employing pathway analysis on metabolites with VIP scores exceeding 1, we identified three significantly altered metabolic pathways, as presented in Table 2.…”

Section: Tcmr Group Comparisonmentioning

confidence: 99%

“…In contrast, a couple of carnitines, specifically acetyl carnitine and pimeloyl carnitine, were more prevalent among patients with biliary complications (Figure 3B). While serotonin has not previously been identified as a biomarker of post-LT complications, there is a strong biological mechanism for its marked difference in biliary vs. TCMR groups, as serotonin is a potent modulator of T cells and has multiple functions in the liver, including regulation of the biliary tree and cholangiocytes [36][37][38].…”

Section: Tcmr Group Comparisonmentioning

confidence: 99%

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Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Baciu,

Ghosh,

Naimimohasses

et al. 2024

Metabolites

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Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model’s efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

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Section: Tcmr Group Comparisonmentioning

confidence: 99%

Section: Tcmr Group Comparisonmentioning

confidence: 99%

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Baciu,

Ghosh,

Naimimohasses

et al. 2024

Metabolites

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show abstract

“…In contrast, a couple of carnitines, specifically acetyl carnitine and pimeloyl carnitine, were more prevalent among patients with biliary complications (Figure 1C, lower panel). While serotonin has not previously been identified as a biomarker of post-LT complications, there is a strong biological mechanism for its marked difference in biliary vs TCMR groups, as serotonin is a potent modulator of T cells and has multiple functions in the liver including regulation of the biliary tree and cholangiocytes [33][34][35].…”

Section: Significant Alterations In Butanoate (Butyrate) Metabolism W...mentioning

confidence: 99%

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Baciu,

Ghosh,

Naimimohasses

et al. 2024

Preprint

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Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

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The role of immunotolerance in immunosuppressive therapy after liver transplantation

Anosova,

Poludkin,

Kazymov

2024

vmirvz

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Relevance. Some achievements related to liver transplantation in the field of infectious complications, rejection treatment and surgical tactics have also contributed to improving patient survival. Nevertheless, it is true that the huge progress made in the field of transplantation is mainly due to the emergence of safe and effective immunosuppressive drugs. But complications from immunosuppressive drugs are still a significant problem and the ability to give up immunosuppressants altogether or significantly reduce the dose will help solve it. Aim: to present a review of the literature and to analyze the main aspects of immunotolerance in immunosuppressive therapy after liver transplantation. Materials and methods: Foreign and Russian literature on the topic of immunotolerance and immunosuppressive therapy was used. The search for literary data was carried out in international databases (PubMed/MedLine, ResearchGate), as well as in the scientific electronic library of Russia (eLIBRARY.RU ) for the period 2019– 2024. Conclusion. Undoubtedly, the rejection of immunosuppressive therapy is a brilliant prospect for recipients not only of the liver, but also of other donor organs, therefore, with the future development of interdisciplinary and multifactorial research, the use of various new experimental methods may provide more opportunities and theoretical guidance to find a way to achieve tolerance in liver transplantation.

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Immunological Tolerance in Liver Transplant Recipients: Putative Involvement of Neuroendocrine-Immune Interactions

Cited by 3 publications

References 158 publications

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

The role of immunotolerance in immunosuppressive therapy after liver transplantation

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