A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

Artificial Intelligence in Pediatric Liver Transplantation: Opportunities and Challenges of a New Era

Historically, pediatric liver transplantation has achieved significant milestones, yet recent innovations have predominantly occurred in adult liver transplantation due to higher caseloads and ethical barriers in pediatric studies. Emerging methods subsumed under the term artificial intelligence offer the potential to revolutionize data analysis in pediatric liver transplantation by handling complex datasets without the need for interventional studies, making them particularly suitable for pediatric research. This review provides an overview of artificial intelligence applications in pediatric liver transplantation. Despite some promising early results, artificial intelligence is still in its infancy in the field of pediatric liver transplantation, and its clinical implementation faces several challenges. These include the need for high-quality, large-scale data and ensuring the interpretability and transparency of machine and deep learning models. Ethical considerations, such as data privacy and the potential for bias, must also be addressed. Future directions for artificial intelligence in pediatric liver transplantation include improving donor-recipient matching, managing long-term complications, and integrating diverse data sources to enhance predictive accuracy. Moreover, multicenter collaborations and prospective studies are essential for validating artificial intelligence models and ensuring their generalizability. If successfully integrated, artificial intelligence could lead to substantial improvements in patient outcomes, bringing pediatric liver transplantation again to the forefront of innovation in the transplantation community.

Defining the T cell transcriptional landscape in pediatric liver transplant rejection at single cell resolution

Acute cellular rejection (ACR) affects >80% of pediatric liver transplant recipients within 5 years, and late ACR is associated with graft failure. Traditional anti-rejection therapy for late ACR is ineffective and has remained unchanged for six decades. Although CD8+ T cells promote late ACR, little has been done to define their specificity and gene expression. Here, we used single-cell sequencing and immune repertoire profiling (10X Genomics) on 30 cryopreserved 16G liver biopsies from 14 patients (5 pre-transplant or with no ACR, 9 with ACR). We identified expanded intragraft CD8+ T cell clonotypes (CD8EXP) and their gene expression profiles in response to anti-rejection treatment. Notably, we found that expanded CD8+ clonotypes (CD8EXP) bore markers of effector and CD56hiCD161- 'NK-like' T cells, retaining their clonotype identity and phenotype in subsequent biopsies from the same patients despite histologic ACR resolution. CD8EXP clonotypes localized to portal infiltrates during active ACR, and persisted in the lobule after histologic ACR resolution. CellPhoneDB analysis revealed differential crosstalk between KC and CD8EXP during late ACR, with activation of the LTB-LTBR pathway and downregulation of TGFß signaling. Therefore, persistently-detected intragraft CD8EXP clones remain active despite ACR treatment and may contribute to long-term allograft fibrosis and failure of operational tolerance.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Improving liver transplant outcomes with transplant-omics and network biology

PURPOSE OF REVIEW

Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the 'transplant-ome' - the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients.

RECENT FINDINGS

In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period.

SUMMARY

The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children

Transcriptional regulation of liver transplant (LT) rejection may reveal novel predictive and therapeutic targets. The purpose of this article is to test the role of differential DNA methylation in children with biopsy-proven acute cellular rejection after LT.