A Comprehensive Review of Liver Allograft Fibrosis and Steatosis: From Cause to Diagnosis.

Despite advances in posttransplant care, long-term outcomes for liver transplant recipients remain unchanged. Approximately 25% of recipients will advance to graft cirrhosis and require retransplantation. Graft fibrosis progresses in the context of de novo or recurrent disease. Recurrent hepatitis C virus infection was previously the most important cause of graft failure but is now curable in the majority of patients. However, with an increasing prevalence of obesity and diabetes and nonalcoholic fatty liver disease as the most rapidly increasing indication for liver transplantation, metabolic dysfunction-associated liver injury is anticipated to become an important cause of graft fibrosis alongside alloimmune hepatitis and alcoholic liver disease. To better understand the landscape of the graft fibrosis literature, we summarize the associated epidemiology, cause, potential mechanisms, diagnosis, and complications. We additionally highlight the need for better noninvasive methods to ameliorate the management of graft fibrosis. Some examples include leveraging the microbiome, genetic, and machine learning methods to address these limitations. Overall, graft fibrosis is routinely seen by transplant clinicians, but it requires a better understanding of its underlying biology and contributors that can help inform diagnostic and therapeutic practices.

Transcriptomic changes in liver transplant recipients with non-alcoholic steatohepatitis indicate dysregulation of wound healing.

Background: Non-alcoholic steatohepatitis (NASH) has become a leading indication for liver transplantation. However, it often recurs in the graft and can also arise de novo in individuals transplanted for other indications. Post-transplant NASH (PT-NASH) is more aggressive and leads to accelerated fibrosis. The mechanistic basis of PT-NASH has not yet been defined and no specific therapeutic strategies are currently available. Methods: Here, we profiled the transcriptomes of livers with PT-NASH from liver transplant recipients to identify dysregulated genes, pathways, and molecular interaction networks. Results: Transcriptomic changes in the PI3K-Akt pathway were observed in association with metabolic alterations in PT-NASH. Other significant changes in gene expression were associated with DNA replication, cell cycle, extracellular matrix organization, and wound healing. A systematic comparison with non-transplant NASH (NT-NASH) liver transcriptomes indicated an increased activation of wound healing and angiogenesis pathways in the post-transplant condition. Conclusion: Beyond altered lipid metabolism, dysregulation of wound healing and tissue repair mechanisms may contribute to the accelerated development of fibrosis associated with PT-NASH. This presents an attractive therapeutic avenue to explore for PT-NASH to optimize the benefit and survival of the graft.

Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions.

Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.

Progressive dosage compensation during Drosophila embryogenesis is reflected by gene arrangement.

In Drosophila melanogaster males, X-chromosome monosomy is compensated by chromosome-wide transcription activation. We found that complete dosage compensation during embryogenesis takes surprisingly long and is incomplete even after 10 h of development. Although the activating dosage compensation complex (DCC) associates with the X-chromosome and MOF acetylates histone H4 early, many genes are not compensated. Acetylation levels on gene bodies continue to increase for several hours after gastrulation in parallel with progressive compensation. Constitutive genes are compensated earlier than developmental genes. Remarkably, later compensation correlates with longer distances to DCC binding sites. This time-space relationship suggests that DCC action on target genes requires maturation of the active chromosome compartment.

Hypomorphic homozygous mutations in phosphoglucomutase 3 (PGM3) impair immunity and increase serum IgE levels.

BACKGROUND: Recurrent bacterial and fungal infections, eczema, and increased serum IgE levels characterize patients with the hyper-IgE syndrome (HIES). Known genetic causes for HIES are mutations in signal transducer and activator of transcription 3 (STAT3) and dedicator of cytokinesis 8 (DOCK8), which are involved in signal transduction pathways. However, glycosylation defects have not been described in patients with HIES. One crucial enzyme in the glycosylation pathway is phosphoglucomutase 3 (PGM3), which catalyzes a key step in the synthesis of uridine diphosphate N-acetylglucosamine, which is required for the biosynthesis of N-glycans. OBJECTIVE: We sought to elucidate the genetic cause in patients with HIES who do not carry mutations in STAT3 or DOCK8. METHODS: After establishing a linkage interval by means of SNPchip genotyping and homozygosity mapping in 2 families with HIES from Tunisia, mutational analysis was performed with selector-based, high-throughput sequencing. Protein expression was analyzed by means of Western blotting, and glycosylation was profiled by using mass spectrometry. RESULTS: Mutational analysis of candidate genes in an 11.9-Mb linkage region on chromosome 6 shared by 2 multiplex families identified 2 homozygous mutations in PGM3 that segregated with disease status and followed recessive inheritance. The mutations predict amino acid changes in PGM3 (p.Glu340del and p.Leu83Ser). A third homozygous mutation (p.Asp502Tyr) and the p.Leu83Ser variant were identified in 2 other affected families, respectively. These hypomorphic mutations have an effect on the biosynthetic reactions involving uridine diphosphate N-acetylglucosamine. Glycomic analysis revealed an aberrant glycosylation pattern in leukocytes demonstrated by a reduced level of tri-antennary and tetra-antennary N-glycans. T-cell proliferation and differentiation were impaired in patients. Most patients had developmental delay, and many had psychomotor retardation. CONCLUSION: Impairment of PGM3 function leads to a novel primary (inborn) error of development and immunity because biallelic hypomorphic mutations are associated with impaired glycosylation and a hyper-IgE-like phenotype.