Static storage of donor lungs at 10°C enhances mitochondrial protection and is a clinically feasible option to extend viable lung preservation times.

Introduction: Transplantation of lungs from donation after circulatory death (DCD) in addition to donation after brain death (DBD) became routine worldwide to address the global organ shortage. The development ofex vivolung perfusion (EVLP) for donor lung assessment and repair contributed to the increased use of DCD lungs. We hypothesise that a better understanding of the differences between lungs from DBD and DCD donors, and between EVLP and directly transplanted (non-EVLP) lungs, will lead to the discovery of the injury-specific targets for donor lung repair and reconditioning.Methods: Tissue biopsies from human DBD (n=177) and DCD (n=65) donor lungs, assessed with or without EVLP, were collected at the end of cold ischaemic time. All samples were processed with microarray assays. Gene expression, network and pathway analyses were performed using R, Ingenuity Pathway Analysis and STRING. Results were validated with protein assays, multiple logistic regression and 10-fold cross-validation.Results: Our analyses showed that lungs from DBD donors have upregulation of inflammatory cytokines and pathways. In contrast, DCD lungs display a transcriptome signature of pathways associated with cell death, apoptosis and necrosis. Network centrality revealed specific drug targets to rehabilitate DBD lungs. Moreover, in DBD lungs, tumour necrosis factor receptor-1/2 signalling pathways and macrophage migration inhibitory factor-associated pathways were activated in the EVLP group.A panel of genes that differentiate the EVLP from the non-EVLP group in DBD lungs was identified.Conclusion: The examination of gene expression profiling indicates that DBD and DCD lungs have distinguishable biological transcriptome signatures.

Defining the ontogeny of tumor-associated macrophages (TAM) is important to develop therapeutic targets for mesothelioma. We identified two distinct macrophage populations in mouse peritoneal and pleural cavities, the monocyte-derived, small peritoneal/pleural macrophages (SPM), and the tissue-resident large peritoneal/pleural macrophages (LPM). SPM rapidly increased in tumor microenvironment after tumor challenge and contributed to the vast majority of M2-like TAM. The selective depletion of M2-like TAM by conditional deletion of the Dicer1 gene in myeloid cells (D ^−/− ) promoted tumor rejection. Sorted SPM M2-like TAM initiated tumorigenesis in vivo and in vitro, confirming their capacity to support tumor development. The transcriptomic and single-cell RNA sequencing analysis demonstrated that both SPM and LPM contributed to the tumor microenvironment by promoting the IL-2-STAT5 signaling pathway, inflammation, and epithelial–mesenchymal transition. However, while SPM preferentially activated the KRAS and TNF-α/NFkB signaling pathways, LPM activated the IFN-γ response. The importance of LPM in the immune response was confirmed by depleting LPM with intrapleural clodronate liposomes, which abrogated the antitumoral memory immunity. SPM gene signature could be identified in pleural effusion and tumor from patients with untreated mesothelioma. Five genes, TREM2, STAB1, LAIR1, GPNMB , and MARCO , could potentially be specific therapeutic targets. Accordingly, Trem2 gene deletion led to reduced SPM M2-like TAM with compensatory increase in LPM and slower tumor growth. Overall, these experiments demonstrate that SPM M2-like TAM play a key role in mesothelioma development, while LPM more specifically contribute to the immune response. Therefore, selective targeting of monocyte-derived TAM may enhance antitumor immunity through compensatory expansion of tissue-resident TAM.

Background. Metabolic dysfunction‐associated steatohepatitis (MASH) has become the leading cause of chronic liver disease, but there has been no approved pharmacotherapy to date. Methods. We used a network analysis approach to delineate protein‐protein interactions that contribute to the transition from steatosis to MASH, in order to identify and target this transition as a potential pharmacotherapeutic strategy. Acyl‐CoA thioesterase 1 (ACOT1) was identified as a critical node in the protein‐protein interaction (PPI) network of the transition from steatosis to MASH in patient samples. ACOT1 overexpression and silencing effects were tested in vivo on C57BL/6 mice exposed to high‐fat diet (HFD) and inoculated with an adenoviral system to modulate ACOT1 expression. Transcriptomic and untargeted lipidomic profiles were performed on the mouse livers. Results. ACOT1 expression was 3‐fold higher in MASH as compared to steatosis. In patient samples, ACOT1 was significantly correlated with the severity of MASH as reflected by the nonalcoholic fatty liver disease score. Experimental validation showed that downregulation of ACOT1 resulted in decreased lipid accumulation and prevention of MASH in vivo. Conversely, upregulation of ACOT1 via an adenoviral vector resulted in development of MASH, whereas control mice only developed steatosis. Lipidomic analysis revealed glycerophospholipids to be especially abundant in MASH accelerated by ACOT1 upregulation. Conclusion. These results suggest that ACOT1 contributes to the transition from steatosis to MASH through modulation of glycerophospholipid accumulation and its potential as a novel therapeutic target in MASH. This trial is registered with NCT02148471.

Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality and morbidity worldwide. Machine learning (ML) tools have been developed in recent years to generate diagnostic and prognostic molecular biomarkers for this high‐fatality cancer. To delineate the landscape of ML in HCC, we performed a systematic search of Ovid Medline, Ovid Embase, Cochrane Database of Systematic Reviews (Ovid) and Cochrane CENTRAL (Ovid) to identify studies of HCC molecular biomarkers using ML strategies. In total, 75 studies met our inclusion criteria, 53 of which were pertinent to diagnosis of HCC and 22 of which were pertinent to prognostication of HCC. Genomic, transcriptomic, epigenomic, proteomic and metabolomic signatures were derived using various ML techniques (supervised, unsupervised and deep learning approaches) using serum, urine and tissue samples of HCC. The ML algorithms achieved a sensitivity of up to 95% for the diagnosis of HCC. Through pathway analysis of the signatures derived by ML tools, we identified regulators of epithelial‐mesenchymal transition and the cancer pathway Ras/Raf/MAPK as being particularly prognostic of HCC outcome. The application of ML to molecular data in HCC has thus far resulted in the generation of highly sensitive diagnostic and prognostic signatures. In future, development of ML algorithms that incorporate clinical, laboratory, alongside molecular features will be needed to fulfil the promise of personalized HCC diagnosis and treatment.

Liver regeneration is essential for recovery following injury, but this process can be impaired by factors such as sex, age, metabolic disorders, fibrosis, and immunosuppressive therapies. We aimed to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under these diverse conditions using systems biology and machine learning approaches. Six mouse models, each undergoing 75% hepatectomy, were used to study regeneration across distinct clinical contexts: young males and females, aged mice, stage 2 fibrosis, steatosis, and tacrolimus exposure. A novel contrastive deep learning framework with triplet loss was developed to map regenerative trajectories and identify genes associated with regenerative efficiency. Despite achieving ≥75% liver mass restoration by day 7, regeneration was significantly delayed in aged, steatotic, and fibrotic models, as indicated by reduced Ki-67 staining on day 2 (p <0.0001 for all). Interestingly, fibrotic livers exhibited reduced collagen deposition and partial regression to stage 1 fibrosis post-hepatectomy. Transcriptomic and proteomic analyses revealed consistent downregulation of cell cycle genes in impaired regeneration. The deep learning model integrating clinical and transcriptomic data predicted regenerative outcomes with 87.9% accuracy. SHAP (SHapley Additive exPlanations) highlighted six key predictive genes: Wee1, Rbl1, Gnl3, Mdm2, Cdk2, and Ccne2. Proteomic validation and human SPLiT-seq (split-pool ligation-based transcriptome sequencing) data further supported their relevance across species. This study identifies conserved cell cycle regulators underlying efficient liver regeneration and provides a predictive framework for evaluating regenerative capacity. The integration of deep learning and multi-omics profiling provides a promising approach to better understand liver regeneration and may help guide therapeutic strategies, especially in complex clinical settings. The aim of this study was to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under diverse conditions, using systems biology and machine learning approaches. Key molecular drivers of liver regeneration across diverse clinical conditions were identified using innovative deep learning and multi-omics approaches. By identifying conserved cell cycle genes predictive of regenerative outcomes, this study offers a powerful framework to assess and potentially enhance liver recovery in older patients, those with fibrosis or steatosis, and/or those under immunosuppression.