Deciphering genetic and nongenetic factors underlying tumour dormancy: insights from multiomics analysis of two syngeneic MRD models of melanoma and leukemia.

BACKGROUND: Tumour dormancy, a resistance mechanism employed by cancer cells, is a significant challenge in cancer treatment, contributing to minimal residual disease (MRD) and potential relapse. Despite its clinical importance, the mechanisms underlying tumour dormancy and MRD remain unclear. In this study, we employed two syngeneic murine models of myeloid leukemia and melanoma to investigate the genetic, epigenetic, transcriptomic and protein signatures associated with tumour dormancy. We used a multiomics approach to elucidate the molecular mechanisms driving MRD and identify potential therapeutic targets. RESULTS: We conducted an in-depth omics analysis encompassing whole-exome sequencing (WES), copy number variation (CNV) analysis, chromatin immunoprecipitation followed by sequencing (ChIP-seq), transcriptome and proteome investigations. WES analysis revealed a modest overlap of gene mutations between melanoma and leukemia dormancy models, with a significant number of mutated genes found exclusively in dormant cells. These exclusive genetic signatures suggest selective pressure during MRD, potentially conferring resistance to the microenvironment or therapies. CNV, histone marks and transcriptomic gene expression signatures combined with Gene Ontology (GO) enrichment analysis highlighted the potential functional roles of the mutated genes, providing insights into the pathways associated with MRD. In addition, we compared "murine MRD genes" profiles to the corresponding human disease through public datasets and highlighted common features according to disease progression. Proteomic analysis combined with multi-omics genetic investigations, revealed a dysregulated proteins signature in dormant cells with minimal genetic mechanism involvement. Pathway enrichment analysis revealed the metabolic, differentiation and cytoskeletal remodeling processes involved in MRD. Finally, we identified 11 common proteins differentially expressed in dormant cells from both pathologies. CONCLUSIONS: Our study underscores the complexity of tumour dormancy, implicating both genetic and nongenetic factors. By comparing genomic, transcriptomic, proteomic, and epigenomic datasets, our study provides a comprehensive understanding of the molecular landscape of minimal residual disease. These results provide a robust foundation for forthcoming investigations and offer potential avenues for the advancement of targeted MRD therapies in leukemia and melanoma patients, emphasizing the importance of considering both genetic and nongenetic factors in treatment strategies.

A cellular disease model toward gene therapy of TGM1-dependent lamellar ichthyosis.

Lamellar ichthyosis (LI) is a chronic disease, mostly caused by mutations in the TGM1 gene, marked by impaired skin barrier formation. No definitive therapies are available, and current treatments aim at symptomatic relief. LI mouse models often fail to faithfully replicate the clinical and histopathological features of human skin conditions. To develop advanced therapeutic approaches, such as combined ex vivo cell and gene therapy, we established a human cellular model of LI by efficient CRISPR-Cas9-mediated gene ablation of the TGM1 gene in human primary clonogenic keratinocytes. Gene-edited cells showed complete absence of transglutaminase 1 (TG1) expression and recapitulated a hyperkeratotic phenotype with most of the molecular hallmarks of LI in vitro. Using a self-inactivating γ-retroviral (SINγ-RV) vector expressing transgenic TGM1 under the control of its own promoter, we tested an ex vivo gene therapy approach and validate the model of LI as a platform for pre-clinical evaluation studies. Gene-corrected TGM1-null keratinocytes displayed proper TG1 expression, enzymatic activity, and cornified envelope formation and, hence, restored proper epidermal architecture. Single-cell multiomics analysis demonstrated proviral integrations in holoclone-forming epidermal stem cells, which are crucial for epidermal regeneration. This study serves as a proof of concept for assessing the potential of this therapeutic approach in treating TGM1-dependent LI.

Investigation of transcriptional and immunological disparities among patient groups with varied prognostic risk factors in cholangiocarcinoma.

BACKGROUND: This study explores molecular features associated with better prognosis in cholangiocarcinoma (CCA). METHODS AND RESULTS: The transcriptomic and whole-exome sequencing data obtained from paired tissues of 70 were analyzed, grouping them based on progression-free survival (PFS), differentiation degree, and lymph node metastasis. Among the 70 patients, the TP53 gene mutation frequency was the highest (53%), while FLG gene mutation occurred exclusively in the long PFS group. In the comparison between long and short survival groups, the short PFS group exhibited higher monocyte infiltration levels (p = 0.0287) and upregulation of genes associated with cancer-related transcriptional misregulation, chemokine signaling, and cytokine-cytokine receptor interactions. Differences in immune cell infiltration and gene expression were significant across differentiation and lymph node metastasis groups. Particularly noteworthy was the marked increase in CD8 T cell and NK cell infiltration (p = 0.0291, 0.0459) in the lymph node metastasis group, significantly influences prognosis. Additionally, genes related to platinum resistance, Th17 cell differentiation, and Th1 and Th2 cell differentiation pathways were overexpressed in this group. In summary, higher monocyte infiltration levels in the short PFS group, along with elevated expression of genes associated with cancer-related pathways, suggest a poorer prognosis. The significant increase in CD8 T cell and NK cell infiltration reflects an enhanced anti-tumor immune response, underscoring the relevance of immune infiltration levels and gene expression in predicting outcomes for CCA patients. CONCLUSIONS: In this study, we elucidated the pertinent molecular mechanisms and pathways that influence the prognosis of CCAs through comprehensive multi-omics analysis.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis.

Background: Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis. Methods: We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes. Results: Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered. Conclusions: Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

Integrative analysis of the transcriptome and metabolome reveals the importance of hepatokine FGF21 in liver aging.

Aging is a contributor to liver disease. Hence, the concept of liver aging has become prominent and has attracted considerable interest, but its underlying mechanism remains poorly understood. In our study, the internal mechanism of liver aging was explored via multi-omics analysis and molecular experiments to support future targeted therapy. An aged rat liver model was established with d-galactose, and two other senescent hepatocyte models were established by treating HepG2 cells with d-galactose and H2O2. We then performed transcriptomic and metabolomic assays of the aged liver model and transcriptome analyses of the senescent hepatocyte models. In livers, genes related to peroxisomes, fatty acid elongation, and fatty acid degradation exhibited down-regulated expression with aging, and the hepatokine Fgf21 expression was positively correlated with the down-regulation of these genes. In senescent hepatocytes, similar to the results found in aged livers, FGF21 expression was also decreased. Moreover, the expressions of cell cycle-related genes were significantly down-regulated, and the down-regulated gene E2F8 was the key cell cycle-regulating transcription factor. We then validated that FGF21 overexpression can protect against liver aging and that FGF21 can attenuate the declines in the antioxidant and regenerative capacities in the aging liver. We successfully validated the results from cellular and animal experiments using human liver and blood samples. Our study indicated that FGF21 is an important target for inhibiting liver aging and suggested that pharmacological prevention of the reduction in FGF21 expression due to aging may be used to treat liver aging-related diseases.

Unmasking Spatial Heterogeneity in Phytotoxicology Mechanisms Induced by Carbamazepine by Mass Spectrometry Imaging and Multiomics Analyses.

Previous studies have highlighted the toxicity of pharmaceuticals and personal care products (PPCPs) in plants, yet understanding their spatial distribution within plant tissues and specific toxic effects remains limited. This study investigates the spatial-specific toxic effects of carbamazepine (CBZ), a prevalent PPCP, in plants. Utilizing desorption electrospray ionization mass spectrometry imaging (DESI-MSI), CBZ and its transformation products were observed predominantly at the leaf edges, with 2.3-fold higher concentrations than inner regions, which was confirmed by LC-MS. Transcriptomic and metabolic analyses revealed significant differences in gene expression and metabolite levels between the inner and outer leaf regions, emphasizing the spatial location's role in CBZ response. Notably, photosynthesis-related genes were markedly downregulated, and photosynthetic efficiency was reduced at leaf edges. Additionally, elevated oxidative stress at leaf edges was indicated by higher antioxidant enzyme activity, cell membrane impairment, and increased free fatty acids. Given the increased oxidative stress at the leaf margins, the study suggests using in situ Raman spectroscopy for early detection of CBZ-induced damage by monitoring reactive oxygen species levels. These findings provide crucial insights into the spatial toxicological mechanisms of CBZ in plants, forming a basis for future spatial toxicology research of PPCPs.

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM.

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets.

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

A Well-Established Gut Microbiota Enhances the Efficiency of Nutrient Metabolism and Improves the Growth Performance of Trachinotus ovatus.

The gut microbiota has become an essential component of the host organism and plays a crucial role in the host immune system, metabolism, and physiology. Nevertheless, our comprehension of how the fish gut microbiota contributes to enhancing nutrient utilization in the diet and improving host growth performance remains unclear. In this study, we employed a comprehensive analysis of the microbiome, metabolome, and transcriptome to analyze intestines of the normal control group and the antibiotic-treated model group of T. ovatus to investigate how the gut microbiota enhances fish growth performance and uncover the underlying mechanisms. First, we found that the growth performance of the control group was significantly higher than that of the antibiotic-treated model under the same feeding conditions. Subsequent multiomics analyses showed that the gut microbiota can improve its own composition by mediating the colonization of some probiotics represented by Lactobacillus in the intestine, improving host metabolic efficiency with proteins and lipids, and also influencing the expression of genes in signaling pathways related to cell proliferation, which together contribute to the improved growth performance of T. ovatus. Our results demonstrated the important contribution of gut microbiota and its underlying molecular mechanisms on the growth performance of T. ovatus.

Multiomics Analysis Revealed Colorectal Cancer Pathogenesis.

Gut microbiota-derived microbial compounds may link to the pathogenesis of colorectal cancer (CRC). However, the role of the host-microbiome in the incidence and progression of CRC remains elusive. We performed 16S rRNA sequencing, metabolomics, and proteomic studies on samples from 85 CRC patients who underwent colonoscopy examination and found two distinct changed patterns of microbiome in CRC patients. The relative abundances of Catabacter and Mogibacterium continuously increased from intramucosal carcinoma to advanced stages, whereas Clostridium, Anaerostipes, Vibrio, Flavonifractor, Holdemanella, and Hungatella were significantly altered only in intermediate lesions. Fecal metabolomics analysis exhibited consistent increases in bile acids, indoles, and urobilin as well as a decrease in heme. Serum metabolomics uncovered the highest levels of bilin, glycerides, and nucleosides together with the lowest levels of bile acids and amino acids in the stage of intermediate lesions. Three fecal and one serum dipeptides were elevated in the intermediate lesions. Proteomics analysis of colorectal tissues showed that oxidation and autophagy through the PI3K/Akt-mTOR signaling pathway contribute to the development of CRC. Diagnostic analysis showed multiomics features have good predictive capability, with AUC greater than 0.85. Our overall findings revealed new candidate biomarkers for CRC, with potentially significant diagnostic and prognostic capabilities.