Additional file 12: Table S1. MACS 1.4.2 output file with annotations.A total of 32731 regions were selected as the overlapping peaks between the two replicates and the overlapping regions were annotated using HOMER.
A growing number of long noncoding RNAs (lncRNAs) have emerged as vital metabolic regulators. However, most human lncRNAs are nonconserved and highly tissue specific, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). In this study, we established a pipeline to identify putative hLMRs that are metabolically sensitive, disease relevant, and population applicable. We first progressively processed multilevel human transcriptome data to select liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a nonalcoholic fatty liver disease (NAFLD) population, and respond to dietary intervention in a small NAFLD cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and NAFLD populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we validated the regulatory role of one nonconserved hLMR in cholesterol metabolism by coordinating with an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. Our work overcame the heterogeneity intrinsic to human data to enable the efficient identification and functional definition of disease-relevant human lncRNAs in metabolic homeostasis.
Abstract A growing number of long non-coding RNAs (lncRNAs) have emerged as vital metabolic regulators in research animals suggesting that lncRNAs could also play an important role in human metabolism. However, most human lncRNAs are non-conserved, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). As the sequence-function relation of lncRNAs has yet to be established, the identification of lncRNA metabolic regulators in animals often relies on their regulations by experimental metabolic conditions. But it is very challenging to apply this strategy to human lncRNAs because well-controlled human data are much limited in scope and often confounded by genetic heterogeneity. In this study, we establish an efficient pipeline to identify putative hLMRs that are metabolically sensitive, disease-relevant, and population applicable. We first progressively processed human transcriptome data to select human liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a metabolic disease population, and response to dietary intervention in a small disease cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and metabolic disease populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we were able to validate the regulatory role of one non-conserved hLMR in cholesterol metabolism. Mechanistically, this hLMR binds to an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. In summary, our study provides a pipeline to overcome the variabilities intrinsic to human data to enable the efficient identification and functional definition of hLMRs. The combination of this bioinformatic framework and humanized murine model will enable broader systematic investigation of the physiological role of disease-relevant human lncRNAs in metabolic homeostasis.
165 Background: Colorectal cancer is one of the leading causes of cancer-related death, and the liver represents the most common site of metastases. Greater depth of tissue invasion and lymph node metastases (more advanced T and N stage) are associated with increased risk of liver metastases, but how co-pathologies of the liver associate with disease progression and survival are not known. Further, prognostic factors associated with overall survival in patients with colorectal liver metastases (CRLM) remain poorly understood. Clinical factors affecting liver morphology and biomechanical properties, such as preexisting fibrosis or steatosis, may impact the pathogenesis of CRLM and thus the clinical prognosis. Current literature is mixed about whether prior steatosis and fibrosis contribute significantly to CRLM pathogenesis and prognosis. The purpose of this study is to evaluate whether liver steatosis and fibrosis impact outcomes in patients with CRLM. Methods: All CLRM cases (n = 197) that underwent resection between 2003 and 2007 from The Cancer Imaging Archive were included in our study cohort. For each clinical covariate, we generated a univariate model for overall survival and those meeting a modest predictive threshold (p < 0.15) were included in a multivariable model. These included: presence of major comorbidity, chemotherapy preceding resection of liver metastases, clinical risk score, presence of extrahepatic disease at time of diagnosis, presence of steatosis on CT imaging, percentage of residual tumor after treatment, and presence of fibrosis on CT. All statistics were performed in R (v.2022.12.0). Results: A multivariable Cox proportional hazards model identified four statistically significant clinical factors predictive of overall survival: clinical risk score (HR = 1.60, p = 0.026), presence of extrahepatic disease (HR = 2.33, p = 0.027), presence of steatosis (HR = 0.51, p = 0.0057), and fibrotic proportion of liver tissue less than 40% (HR = 2.63, p = 0.033). There were also four statistically significant clinical factors predictive of disease-free survival in the liver: chemotherapy preceding resection of liver metastases (HR = 2.28, p = 0.0011), presence of extrahepatic disease (HR = 2.18, p = 0.024), presence of steatosis (HR = 0.49, p = 0.0030), and fibrotic proportion of liver tissue less than 40% (HR = 2.74, p = 0.016). Conclusions: Our findings suggest that increased steatosis and fibrosis, as assessed by CT scans, are paradoxically protective in CRLM, showing longer disease-free survival and overall survival rates. We hypothesize that liver fibrosis and steatosis impact the microenvironment of the neoplastic cells in the liver, impairing tumor progression in this milieu. Further research is needed to assess how non-neoplastic co-pathologies resulting in biophysical changes affect tumor growth and overall survival.
Abstract Background: Extracellular vesicles (EVs) mediate critical intercellular communication within healthy tissues, but are also exploited by tumour cells to promote angiogenesis, metastasis, and host immunosuppression under hypoxic stress. We hypothesize that oxygen starvation in developing tumours induces specific hypoxia-sensitive proteins for packing into small EVs to modulate its microenvironment for cancer progression and enhance malignancy. Methods: We employed a heavy isotope pulse/trace quantitative proteomic approach to study hypoxia-sensitive EVs proteins (HSEPs) in hypoxic A549 lung adenocarcinoma cells derived small EVs (<200 nm). Proteomics data mining and pathway analysis were used to reveal potential roles of the HSEPs in enhancing tumour cell progression and in modulating host immunity. Functional clustering was applied to study enhanced EVs biogenesis and secretion in hypoxic cancer cells. Subsequent biochemical functional assays were performed in A549 and H1299 lung cancer cells to validate the hypoxic cancer-derived EVs in promoting cancer progression. Results: Results revealed that hypoxia stimulated cancer cells to synthesize EVs proteins involved in enhancing tumour cell proliferation (NRSN2, WISP2, SPRX1, LCK), metastasis (GOLM1, STC1, MGAT5B), stemness (STC1, TMEM59), angiogenesis (ANGPTL4), and suppressing host immunity (CD70). In addition, functional clustering analyses revealed that tumour hypoxia was strongly associated with rapid synthesis and EV loading of lysosome-related hydrolases and membrane-trafficking proteins to enhance EVs secretion. Moreover, lung cancer-derived EVs were also enriched in signalling molecules capable of inducing epithelial-mesenchymal transition in recipient cancer cells to promote their migration and invasion. Conclusion: Together, these data indicate that lung cancer-derived EVs can act as paracrine/autocrine mediators of tumorigenesis and metastasis in hypoxic microenvironments. Tumour EVs may therefore offer novel opportunities for useful biomarkers discovery and therapeutic targeting of different cancer types and at different stages according to microenvironmental conditions.
Abstract Pancreatic cancer is a leading cause of mortality worldwide due to difficulty detecting early-stage disease and our poor understanding of the mediators that drive the progression of hypoxic solid tumours. We, therefore, used a heavy isotope ‘pulse/trace’ proteomic approach to determine how hypoxia alters pancreatic tumour expression of proteins that confer treatment resistance, promote metastasis, and suppress host immunity. Using this method, we identified that hypoxia stress stimulates pancreatic cancer cells to rapidly translate proteins that enhance metastasis (NOTCH2, NCS1, CD151, NUSAP1), treatment resistant (ABCB6), immune suppression (NFIL3,WDR4), angiogenesis (ANGPT4, ERO1α, FOS), alter cell metabolic activity (HK2, ENO2), and mediate growth-promoting cytokine responses (CLK3, ANGPTL4). Database mining confirmed that elevated gene expression of these hypoxia-induced mediators is significantly associated with poor patient survival in various stages of pancreatic cancer. Among these proteins, the oxidoreductase enzyme ERO1α was highly sensitive to induction by hypoxia stress across a range of different pancreatic cancer cell lines and was associated with particularly poor prognosis in human patients. Consistent with these data, genetic deletion of ERO1α substantially reduced growth rates and colony formation in pancreatic cancer cells when assessed in a series of functional assays in vitro . Accordingly, when transferred into a mouse xenograft model, ERO1α-deficient tumour cells exhibited severe growth restriction and negligible disease progression in vivo . Together, these data indicate that ERO1α is potential prognostic biomarker and novel drug target for pancreatic cancer therapy.
Additional file 3: Table S2. Chromosome-wise fold enrichment of HILS1 peaks. Excel file represents the fold enrichment values for HILS1 peaks across all chromosomes of the rat genome.
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