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Nonalcoholic liver disease is a component of metabolic syndrome associated with obesity, insulin resistance, and hyperlipidemia. Excessive alcohol consumption may accelerate the progression of steatosis, steatohepatitis, and fibrosis. While simple steatosis is considered a benign condition, nonalcoholic steatohepatitis with inflammation and fibrosis may progress to cirrhosis, liver failure, and hepatocellular cancer. Studies in rodent experimental models and primary cell cultures have demonstrated several common cellular and molecular mechanisms in the pathogenesis and regression of liver fibrosis. Chronic injury and death of hepatocytes cause the recruitment of myeloid cells, secretion of inflammatory and fibrogenic cytokines, and activation of myofibroblasts, resulting in liver fibrosis. In this review, we discuss the role of metabolically injured hepatocytes in the pathogenesis of nonalcoholic steatohepatitis and alcohol-associated liver disease. Specifically, the role of chemokine production and de novo lipogenesis in the development of steatotic hepatocytes and the pathways of steatosis regulation are discussed.

KEGG Pathway Enrichment Analysis of Differential Expressed Genes Between Thymus and T-ALL

KEGG Pathway Enrichment Analysis of Differential Expressed Genes Between CD4 T-Cells and T-ALL

Splicing alterations are common in diseases such as cancer, where mutations in splicing factor genes are frequently responsible for aberrant splicing. Here we present an alternative mechanism for splicing regulation in T-cell acute lymphoblastic leukemia (T-ALL) that involves posttranslational stabilization of the splicing machinery via deubiquitination. We demonstrate there are extensive exon skipping changes in disease, affecting proteasomal subunits, cell-cycle regulators, and the RNA machinery. We present that the serine/arginine-rich splicing factors (SRSF), controlling exon skipping, are critical for leukemia cell survival. The ubiquitin-specific peptidase 7 (USP7) regulates SRSF6 protein levels via active deubiquitination, and USP7 inhibition alters the exon skipping pattern and blocks T-ALL growth. The splicing inhibitor H3B-8800 affects splicing of proteasomal transcripts and proteasome activity and acts synergistically with proteasome inhibitors in inhibiting T-ALL growth. Our study provides the proof-of-principle for regulation of splicing factors via deubiquitination and suggests new therapeutic modalities in T-ALL.Significance:Our study provides a new proof-of-principle for posttranslational regulation of splicing factors independently of mutations in aggressive T-cell leukemia. It further suggests a new drug combination of splicing and proteasomal inhibitors, a concept that might apply to other diseases with or without mutations affecting the splicing machinery.This article is highlighted in the In This Issue feature, p. 1241

Differential Gene Expression and Enrichment Analysis Between Control and shSRSF6-Treated JURKAT Cells

Supplementary Figures S1-S15 and Legends

Alternative Spliced Genes Between HR and NHR T-ALL Patients and Enrichment Analysis

SRSF Family Proteins Essentiality in Tumor Cell Lines

Motif Analysis of Skip Exon Events in T-ALL Compared to CD3 T Cells

CRISP Screens of RNA Binding Proteins in T-ALL

KEGG Enrichment Analysis of Differential Expressed Genes Between Thymus and T-ALL

The production of noncanonical mRNA transcripts is associated with cell transformation. Driven by our previous findings on the sensitivity of T cell acute lymphoblastic leukemia (T-ALL) cells to SF3B1 inhibitors, we identified that SF3B1 inhibition blocks T-ALL growth in vivo with no notable associated toxicity. We also revealed protein stabilization of the U2 complex component SF3B1 via deubiquitination. Our studies showed that SF3B1 inhibition perturbs exon skipping, leading to nonsense-mediated decay and diminished levels of DNA damage response–related transcripts, such as the serine/threonine kinase CHEK2, and impaired DNA damage response. We also identified that SF3B1 inhibition leads to a general decrease in R-loop formation. We further demonstrate that clinically used SF3B1 inhibitors synergize with CHEK2 inhibitors and chemotherapeutic drugs to block leukemia growth. Our study provides the proof of principle for posttranslational regulation of splicing components and associated roles and therapeutic implications for the U2 complex in T cell leukemia., Description, The splicing factor SF3B1 is regulated at the protein level and is a therapeutic target in T cell leukemia.

Therapy resistance is a major challenge in the treatment of cancer. Here, we performed CRISPR-Cas9 screens across a broad range of therapies used in acute myeloid leukemia to identify genomic determinants of drug response. Our screens uncover a selective dependency on RNA splicing factors whose loss preferentially enhances response to the BCL2 inhibitor venetoclax. Loss of the splicing factor RBM10 augments response to venetoclax in leukemia yet is completely dispensable for normal hematopoiesis. Combined RBM10 and BCL2 inhibition leads to mis-splicing and inactivation of the inhibitor of apoptosis XIAP and downregulation of BCL2A1, an anti-apoptotic protein implicated in venetoclax resistance. Inhibition of splicing kinase families CLKs (CDC-like kinases) and DYRKs (dual-specificity tyrosine-regulated kinases) leads to aberrant splicing of key splicing and apoptotic factors that synergize with venetoclax, and overcomes resistance to BCL2 inhibition. Our findings underscore the importance of splicing in modulating response to therapies and provide a strategy to improve venetoclax-based treatments.

Nonalcoholic fatty liver disease (NAFLD) has become a worldwide epidemic. A large and growing unmet therapeutic need has inspired numerous studies in the field. Integrating the published genomic data available in the Gene Expression Omnibus (GEO) with NAFLD samples from rodents, we discovered that interferon regulatory factor 6 (IRF6) is significantly downregulated in high-fat diet (HFD)-induced fatty liver. In the current study, we identified IRF6 in hepatocytes as a protective factor in liver steatosis (LS). During HFD challenge, hepatic Irf6 was suppressed by promoter hypermethylation. Severity of HFD-induced LS was exacerbated in hepatocyte-specific Irf6 knockout mice, whereas hepatocyte-specific transgenic mice overexpressing Irf6 (IRF6-HTG) exhibited alleviated steatosis and metabolic disorder in response to HFD feeding. Mechanistic studies in vitro demonstrated that hepatocyte IRF6 directly binds to the promoter of the peroxisome proliferator-activated receptor γ (PPARγ) gene and subsequently halts the transcription of Pparγ and its target genes (e.g., genes that regulate lipogenesis and lipid acid uptake) under physiological conditions. Conclusion: Irf6 is downregulated by promoter hypermethylation upon metabolic stimulus exposure, which fail to inhibit Pparγ and its targets, driving abnormalities of lipid metabolism.

Background The three-dimensional genome organization is critical for gene regulation and can malfunction in diseases like cancer. As a key regulator of genome organization, CCCTC-binding factor (CTCF) has been characterized as a DNA-binding protein with important functions in maintaining the topological structure of chromatin and inducing DNA looping. Among the prolific binding sites in the genome, several events with altered CTCF occupancy have been reported as associated with effects in physiology or disease. However, hitherto there is no comprehensive survey of genome-wide CTCF binding patterns across different human cancers. Results To dissect functions of CTCF binding, we systematically analyze over 700 CTCF ChIP-seq profiles across human tissues and cancers and identify cancer-specific CTCF binding patterns in six cancer types. We show that cancer-specific lost and gained CTCF binding events are associated with altered chromatin interactions, partially with DNA methylation changes, and rarely with sequence mutations. While lost bindings primarily occur near gene promoters, most gained CTCF binding events exhibit enhancer activities and are induced by oncogenic transcription factors. We validate these findings in T cell acute lymphoblastic leukemia cell lines and patient samples and show that oncogenic NOTCH1 induces specific CTCF binding and they cooperatively activate expression of target genes, indicating transcriptional condensation phenomena. Conclusions Specific CTCF binding events occur in human cancers. Cancer-specific CTCF binding can be induced by other transcription factors to regulate oncogenic gene expression. Our results substantiate CTCF binding alteration as a functional epigenomic signature of cancer.

BackgroundThe three-dimensional genome organization is critical for gene regulation and can malfunction in diseases like cancer. As a key regulator of genome organization, CCCTC-binding factor (CTCF) has been characterized as a DNA-binding protein with important functions in maintaining the topological structure of chromatin and inducing DNA looping. Among the prolific binding sites in the genome, several events with altered CTCF occupancy have been reported as associated with effects in physiology or disease. However, there is no hitherto a comprehensive survey of genome-wide CTCF binding patterns across different human cancers.ResultsTo dissect functions of CTCF binding, we systematically analyze over 700 CTCF ChIP-seq profiles across human tissues and cancers and identify cancer-specific CTCF binding patterns in six cancer types. We show that cancer-specific lost and gained CTCF binding events are associated with altered chromatin interactions in patient samples, but not always with DNA methylation changes or sequence mutations. While lost bindings primarily occur near gene promoters, most gained CTCF binding events are induced by oncogenic transcription factors and exhibit enhancer activities. We validate these findings in T-cell acute lymphoblastic leukemia and show that oncogenic NOTCH1 induces specific CTCF binding and they cooperatively activate expression of target genes, indicating transcriptional condensation phenomena.ConclusionsCancer-specific CTCF binding events are not always associated with DNA methylation changes or mutations, but can be induced by other transcription factors to regulate oncogenic gene expression. Our results substantiate CTCF binding alteration as a functional epigenomic signature of cancer.

Additional file 7: Table S6. De novo motif analysis results on cancer-specific lost and gained CTCF binding sites in six cancer types, using HOMER and MEME.

Additional file 1: Fig. S1. Identification of cancer-specific CTCF binding sites. Fig. S2. Characterization of cancer-specific lost/gained CTCF binding sites. Fig. S3. Cancer-specific lost/gained CTCF binding sites associate with changed chromatin accessibility in patients. Fig. S4. Cancer-specific lost/gained CTCF binding sites associate with changed local chromatin interactions in different scales. Fig. S5. Lost/gained CTCF binding events associate with chromatin dynamics regardless of CTCF motif. Fig. S6. Histone modification patterns at cancer-specific lost and gained CTCF binding sites. Fig. S7. Histone modification patterns in normal CD4+ T-cell, T-ALL cell lines and T-ALL patients at T-ALLlost and T-ALLgained CTCF binding sites. Fig. S8. Cancer-specific loss/gain of CTCF events correlate with gene expression. Fig. S9. Patterns of differential DNA methylation near cancer-specific lost and gained CTCF sites with and without CTCF motif. Fig. S10. CTCF binding loss/gain events in T-ALL cell lines and T-ALL patients do not associate with DNA sequence mutations. Fig. S11. CTCF binding loss/gain events in 5 cancer types do not associate with DNA sequence mutations observed in ICGC samples. Fig. S12. Mutation rates around lost/gained CTCF binding sites in 6 cancer types. Fig. S13. Sequence motif analysis on cancer-specific lost and gained CTCF binding sites. Fig. S14. Cancer-specific gained CTCF correlate with oncogenic transcription factor. Fig. S15. Hi-C interaction maps in T-ALL cell line Jurkat and CD4+ T-cell. Fig. S16. Cancer-specific gained CTCF binding sites correlate with oncogenic transcriptional activation. Fig. S17. Pathway and Gene Ontology analyses of the genes located in the same chromatin domain with gained/lost CTCF sites for each cancer.

Additional file 9. Review history.

Deregulation of key regulators of histone modification is important in the initiation and progression of human leukemia. Acidic leucine-rich nuclear phosphoprotein-32A (ANP32A) participates in histone acetylation and its role in acute myeloid leukemia remains unclear. Here we observed significant upregulation of ANP32A in primary AML cells, which was essential for AML cell proliferation, survival, and colony formation. Integrative analysis of the genome-wide histone H3 acetylation and gene expression demonstrated that ANP32A deficiency reduced histone H3 acetylation, in accordance with changes in gene expression. Notably, significant histone H3 acetylation enrichment was associated with mRNA changes in lipid-related genes, including APOC1, PCSK9, P2RX1, and LPPR3. Indeed, over-expression of APOC1 partially compensated the proliferation-defect phenotype in ANP32A deficient AML cells while APOC1 knockdown alone mimicked the effect of ANP32A deficiency. Collectively, our data indicate that ANP32A is a novel regulator of histone H3 acetylation and promotes leukemogenesis.

In recent years, more attention has been attracted on the classification of polarimetric SAR (PolSAR) images and a lot of methods have been proposed. With the resolution increasing, the pixel-based classification methods reveal insufficiency, therefore, this paper proposes an improved SLIC superpixel algorithm for PolSAR images classification. Firstly, effective polarization features, such as polarimetric scattering power of typical scattering mechanism based on target decomposition and spatial texture information based on statistical analysis, are extracted from PolSAR images and these features construct a feature vector to obtain a better description of PolSAR images. Then, the Euclidean distance of the feature vector is used to improve the SLIC algorithm to obtain superpixels segmentation and meantime reduce the execution time. In addition, a useful dissimilarity measurement is implemented to maintain the edges of different area in PolSAR image. At last, based on the superpixel segmentation result, a region-based classification using SVM is conducted. The proposed method is validated by EMISAR test PolSAR image and the experimental results confirm the performance and potential of the proposed method in PolSAR image interpretation.

Summary The transcription factors p65 and IRF3 play key roles in the induction of cellular antiviral responses. Phosphorylation of p65 and IRF3 is required for their activity and constitutes a key checkpoint. Here we report that viral infection induced upregulation of INKIT, an inhibitor for NF-κB and IRF3 that restricted innate antiviral responses by blocking phosphorylation of p65 and IRF3. INKIT overexpression inhibited virus-induced phosphorylation of p65 and IRF3 and expression of downstream genes. In contrast, knockdown or knockout of INKIT had the opposite effect: Inkit −/− mice produced elevated levels of type I interferons and proinflammatory cytokines and were more resistant to lethal viral infection compared to wild-type. INKIT interacted with IKKα/β and TBK1/IKKɛ, impairing the recruitment and phosphorylation of p65 and IRF3. Viral infection induced IKK-mediated phosphorylation of INKIT at Ser58, resulting in its dissociation from the IKKs. Our findings thus uncover INKIT as a regulator of innate antiviral responses.

This paper presents a sparse representation (SR)-based selective ensemble learning method for Polarimetric SAR image classification. Sparse representation uses the least dictionary atoms which come from a structured dictionary to represent the data, however, different training samples will lead to different options of the selected atoms and the corresponding coefficients, which will lead different subsequent classification results. Ensemble learning can be adopted to solve the issue, which uses several different learners to acquire a set of results that are integrated to get the final result. But, the result of each learner isn't all good. Therefore, the paper introduces selective ensemble learning which excludes the learners whose weights are smaller than the pre-set threshold. Experiments are conducted on the PolSAR image data of San Francisco test area to verify the performance of the proposed method.