Data enhancement in the age of spatial biology.

Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.

Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

Cell Competition in Carcinogenesis.

The majority of human cancers evolve over time through the stepwise accumulation of somatic mutations followed by clonal selection akin to Darwinian evolution. However, the in-depth mechanisms that govern clonal dynamics and selection remain elusive, particularly during the earliest stages of tissue transformation. Cell competition (CC), often referred to as 'survival of the fittest' at the cellular level, results in the elimination of less fit cells by their more fit neighbors supporting optimal organism health and function. Alternatively, CC may allow an uncontrolled expansion of super-fit cancer cells to outcompete their less fit neighbors thereby fueling tumorigenesis. Recent research discussed herein highlights the various non-cell-autonomous principles, including interclonal competition and cancer microenvironment competition supporting the ability of a tumor to progress from the initial stages to tissue colonization. In addition, we extend current insights from CC-mediated clonal interactions and selection in normal tissues to better comprehend those factors that contribute to cancer development.

Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these 'high-throughput' experiments, however, is no easy task, as the data is often contaminated with meaningless information, or 'background noise', as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named 'compaRe', combines a range of computational tools that automatically 'clean' the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

Cell competition in intratumoral and tumor microenvironment interactions.

Tumors are complex cellular and acellular environments within which cancer clones are under continuous selection pressures. Cancer cells are in a permanent mode of interaction and competition with each other as well as with the immediate microenvironment. In the course of these competitive interactions, cells share information regarding their general state of fitness, with less-fit cells being typically eliminated via apoptosis at the hands of those cells with greater cellular fitness. Competitive interactions involving exchange of cell fitness information have implications for tumor growth, metastasis, and therapy outcomes. Recent research has highlighted sophisticated pathways such as Flower, Hippo, Myc, and p53 signaling, which are employed by cancer cells and the surrounding microenvironment cells to achieve their evolutionary goals by means of cell competition mechanisms. In this review, we discuss these recent findings and explain their importance and role in evolution, growth, and treatment of cancer. We further consider potential physiological conditions, such as hypoxia and chemotherapy, that can function as selective pressures under which cell competition mechanisms may evolve differently or synergistically to confer oncogenic advantages to cancer.

Deciphering the temporal heterogeneity of cancer-associated fibroblast subpopulations in breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) comprise a heterogeneous population of stromal cells within the tumour microenvironment. CAFs exhibit both tumour-promoting and tumour-suppressing functions, making them exciting targets for improving cancer treatments. Careful isolation, identification, and characterisation of CAF heterogeneity is thus necessary for ex vivo validation and future implementation of CAF-targeted strategies in cancer. METHODS: Murine 4T1 (metastatic) and 4T07 (poorly/non-metastatic) orthotopic triple negative breast cancer tumours were collected after 7, 14, or 21 days. The tumours were analysed via flow cytometry for the simultaneous expression of six CAF markers: alpha smooth muscle actin (αSMA), fibroblast activation protein alpha (FAPα), platelet derived growth factor receptor alpha and beta (PDGFRα and PDGFRß), CD26/DPP4 and podoplanin (PDPN). All non-CAFs were excluded from the analysis using a lineage marker cocktail (CD24, CD31, CD45, CD49f, EpCAM, LYVE-1, and TER-119). In total 128 murine tumours and 12 healthy mammary fat pads were analysed. RESULTS: We have developed a multicolour flow cytometry strategy based on exclusion of non-CAFs and successfully employed this to explore the temporal heterogeneity of freshly isolated CAFs in the 4T1 and 4T07 mouse models of triple-negative breast cancer. Analysing 128 murine tumours, we identified 5-6 main CAF populations and numerous minor ones based on the analysis of αSMA, FAPα, PDGFRα, PDGFRß, CD26, and PDPN. All markers showed temporal changes with a distinct switch from primarily PDGFRα+ fibroblasts in healthy mammary tissue to predominantly PDGFRß+ CAFs in tumours. CD26+ CAFs emerged as a large novel subpopulation, only matched by FAPα+ CAFs in abundance. CONCLUSION: We demonstrate that multiple subpopulations of CAFs co-exist in murine triple negative breast cancer, and that the abundance and dynamics for each marker differ depending on tumour type and time. Our results form the foundation needed to isolate and characterise specific CAF populations, and ultimately provide an opportunity to therapeutically target specific CAF subpopulations.

A multi-omics approach to Epstein-Barr virus immortalization of B-cells reveals EBNA1 chromatin pioneering activities targeting nucleotide metabolism.

Epstein-Barr virus (EBV) immortalizes resting B-lymphocytes through a highly orchestrated reprogramming of host chromatin structure, transcription and metabolism. Here, we use a multi-omics-based approach to investigate these underlying mechanisms. ATAC-seq analysis of cellular chromatin showed that EBV alters over a third of accessible chromatin during the infection time course, with many of these sites overlapping transcription factors such as PU.1, Interferon Regulatory Factors (IRFs), and CTCF. Integration of RNA-seq analysis identified a complex transcriptional response and associations with EBV nuclear antigens (EBNAs). Focusing on EBNA1 revealed enhancer-binding activity at gene targets involved in nucleotide metabolism, supported by metabolomic analysis which indicated that adenosine and purine metabolism are significantly altered by EBV immortalization. We further validated that adenosine deaminase (ADA) is a direct and critical target of the EBV-directed immortalization process. These findings reveal that purine metabolism and ADA may be useful therapeutic targets for EBV-driven lymphoid cancers.

Basement membrane stiffness determines metastases formation.

The basement membrane (BM) is a special type of extracellular matrix and presents the major barrier cancer cells have to overcome multiple times to form metastases. Here we show that BM stiffness is a major determinant of metastases formation in several tissues and identify netrin-4 (Net4) as a key regulator of BM stiffness. Mechanistically, our biophysical and functional analyses in combination with mathematical simulations show that Net4 softens the mechanical properties of native BMs by opening laminin node complexes, decreasing cancer cell potential to transmigrate this barrier despite creating bigger pores. Our results therefore reveal that BM stiffness is dominant over pore size, and that the mechanical properties of 'normal' BMs determine metastases formation and patient survival independent of cancer-mediated alterations. Thus, identifying individual Net4 protein levels within native BMs in major metastatic organs may have the potential to define patient survival even before tumour formation. The ratio of Net4 to laminin molecules determines BM stiffness, such that the more Net4, the softer the BM, thereby decreasing cancer cell invasion activity.

Targeted inhibition of cooperative mutation- and therapy-induced AKT activation in AML effectively enhances response to chemotherapy.

Most AML patients exhibit mutational activation of the PI3K/AKT signaling pathway, which promotes downstream effects including growth, survival, DNA repair, and resistance to chemotherapy. Herein we demonstrate that the inv(16)/KITD816Y AML mouse model exhibits constitutive activation of PI3K/AKT signaling, which was enhanced by chemotherapy-induced DNA damage through DNA-PK-dependent AKT phosphorylation. Strikingly, inhibitors of either PI3K or DNA-PK markedly reduced chemotherapy-induced AKT phosphorylation and signaling leading to increased DNA damage and apoptosis of inv(16)/KITD816Y AML cells in response to chemotherapy. Consistently, combinations of chemotherapy and PI3K or DNA-PK inhibitors synergistically inhibited growth and survival of clonogenic AML cells without substantially inhibiting normal clonogenic bone marrow cells. Moreover, treatment of inv(16)/KITD816Y AML mice with combinations of chemotherapy and PI3K or DNA-PK inhibitors significantly prolonged survival compared to untreated/single-treated mice. Mechanistically, our findings implicate that constitutive activation of PI3K/AKT signaling driven by mutant KIT, and potentially other mutational activators such as FLT3 and RAS, cooperates with chemotherapy-induced DNA-PK-dependent activation of AKT to promote survival, DNA repair, and chemotherapy resistance in AML. Hence, our study provides a rationale to select AML patients exhibiting constitutive PI3K/AKT activation for simultaneous treatment with chemotherapy and inhibitors of DNA-PK and PI3K to improve chemotherapy response and clinical outcome.

Pontin arginine methylation by CARM1 is crucial for epigenetic regulation of autophagy.

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy.

Transcriptomic and Quantitative Proteomic Profiling Reveals Signaling Pathways Critical for Pancreatic Islet Maturation.

Pancreatic ß-cell dysfunction and reduced insulin secretion play a key role in the pathogenesis of diabetes. Fetal and neonatal islets are functionally immature and have blunted glucose responsiveness and decreased insulin secretion in response to stimuli and are far more proliferative. However, the mechanisms underlying functional immaturity are not well understood. Pancreatic islets are composed of a mixture of different cell types, and the microenvironment of islets and interactions between these cell types are critical for ß-cell development and maturation. RNA sequencing and quantitative proteomic data from intact islets isolated from fetal (embryonic day 19) and 2-week-old Sprague-Dawley rats were integrated to compare their gene and protein expression profiles. Ingenuity Pathway Analysis (IPA) was also applied to elucidate pathways and upstream regulators modulating functional maturation of islets. By integrating transcriptome and proteomic data, 917 differentially expressed genes/proteins were identified with a false discovery rate of less than 0.05. A total of 411 and 506 of them were upregulated and downregulated in the 2-week-old islets, respectively. IPA revealed novel critical pathways associated with functional maturation of islets, such as AMPK (adenosine monophosphate-activated protein kinase) and aryl hydrocarbon receptor signaling, as well as the importance of lipid homeostasis/signaling and neuronal function. Furthermore, we also identified many proteins enriched either in fetal or 2-week-old islets related to extracellular matrix and cell communication, suggesting that these pathways play critical roles in islet maturation. Our present study identified novel pathways for mature islet function in addition to confirming previously reported mechanisms, and provided new mechanistic insights for future research on diabetes prevention and treatment.

A Critical Role for Estrogen-Related Receptor Signaling in Cardiac Maturation.

RATIONALE: The heart undergoes dramatic developmental changes during the prenatal to postnatal transition, including maturation of cardiac myocyte energy metabolic and contractile machinery. Delineation of the mechanisms involved in cardiac postnatal development could provide new insight into the fetal shifts that occur in the diseased heart and unveil strategies for driving maturation of stem cell-derived cardiac myocytes. OBJECTIVE: To delineate transcriptional drivers of cardiac maturation. METHODS AND RESULTS: We hypothesized that ERR (estrogen-related receptor) α and γ, known transcriptional regulators of postnatal mitochondrial biogenesis and function, serve a role in the broader cardiac maturation program. We devised a strategy to knockdown the expression of ERRα and γ in heart after birth (pn-csERRα/γ [postnatal cardiac-specific ERRα/γ]) in mice. With high levels of knockdown, pn-csERRα/γ knockdown mice exhibited cardiomyopathy with an arrest in mitochondrial maturation. RNA sequence analysis of pn-csERRα/γ knockdown hearts at 5 weeks of age combined with chromatin immunoprecipitation with deep sequencing and functional characterization conducted in human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CM) demonstrated that ERRγ activates transcription of genes involved in virtually all aspects of postnatal developmental maturation, including mitochondrial energy transduction, contractile function, and ion transport. In addition, ERRγ was found to suppress genes involved in fibroblast activation in hearts of pn-csERRα/γ knockdown mice. Disruption of Esrra and Esrrg in mice during fetal development resulted in perinatal lethality associated with structural and genomic evidence of an arrest in cardiac maturation, including persistent expression of early developmental and noncardiac lineage gene markers including cardiac fibroblast signatures. Lastly, targeted deletion of ESRRA and ESRRG in hiPSC-CM derepressed expression of early (transcription factor 21 or TCF21) and mature (periostin, collagen type III) fibroblast gene signatures. CONCLUSIONS: ERRα and γ are critical regulators of cardiac myocyte maturation, serving as transcriptional activators of adult cardiac metabolic and structural genes, an.d suppressors of noncardiac lineages including fibroblast determination.

Repression of hepatocyte nuclear factor 4 alpha by AP-1 underlies dyslipidemia associated with retinoic acid.

All-trans retinoic acid (atRA) is used to treat certain cancers and dermatologic diseases. A common adverse effect of atRA is hypercholesterolemia; cytochrome P450 (CYP) 7A repression is suggested as a driver. However, the underlying molecular mechanisms remain unclear. We investigated CYP7A1 expression in the presence of atRA in human hepatocytes and hepatic cell lines. In HepaRG cells, atRA increased cholesterol levels dose-dependently alongside dramatic decreases in CYP7A1 expression. Lentiviral-mediated CYP7A1 overexpression reversed atRA-induced cholesterol accumulation, suggesting that CYP7A1 repression mediated cholesterol accumulation. In CYP7A1 promoter reporter assays and gene-knockdown studies, altered binding of hepatocyte nuclear factor 4 α (HNF4α) to the proximal promoter was essential for atRA-mediated CYP7A1 repression. Pharmacologic inhibition of c-Jun N-terminal kinase (JNK) and ERK pathways attenuated atRA-mediated CYP7A1 repression and cholesterol accumulation. Overexpression of AP-1 (c-Jun/c-Fos), a downstream target of JNK and ERK, repressed CYP7A1 expression. In DNA pull-down and chromatin immunoprecipitation assays, AP-1 exhibited sequence-specific binding to the proximal CYP7A1 promoter region overlapping the HNF4α binding site, and atRA increased AP-1 but decreased HNF4α recruitment to the promoter. Collectively, these results indicate that atRA activates JNK and ERK pathways and the downstream target AP-1 represses HNF4α transactivation of the CYP7A1 promoter, potentially responsible for hypercholesterolemia.

Single Cell RNA-Sequencing for the Study of Atherosclerosis.

Atherosclerosis is a major cause of coronary artery disease and stroke. A massive and new type of data has finally arrived in the field of atherosclerosis: single cell RNA sequencing (scRNAseq). Recently, scRNAseq has been successfully applied to the study of atherosclerosis to identify previously uncharacterized cell populations. scRNAseq is an effective approach to evaluate heterogeneous cell populations by measuring the transcriptomic profiles at the single cell level. Besides the studies of atherosclerosis, scRNAseq is being employed in various areas of biology, including cancer research and organ development. In order to analyze these new massive datasets, various analytic approaches have been developed. This review aims to enhance the understanding of this new technology by exploring how the single cell transcriptome has been applied to the study of atherosclerosis and further discuss potential analysis of using scRNAseq.

Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue.

Obesity is characterized by an accumulation of macrophages in adipose, some of which form distinct crown-like structures (CLS) around fat cells. While multiple discrete adipose tissue macrophage (ATM) subsets are thought to exist, their respective effects on adipose tissue, and the transcriptional mechanisms that underlie the functional differences between ATM subsets, are not well understood. We report that obese fat tissue of mice and humans contain multiple distinct populations of ATMs with unique tissue distributions, transcriptomes, chromatin landscapes, and functions. Mouse Ly6c ATMs reside outside of CLS and are adipogenic, while CD9 ATMs reside within CLS, are lipid-laden, and are proinflammatory. Adoptive transfer of Ly6c ATMs into lean mice activates gene programs typical of normal adipocyte physiology. By contrast, adoptive transfer of CD9 ATMs drives gene expression that is characteristic of obesity. Importantly, human adipose tissue contains similar ATM populations, including lipid-laden CD9 ATMs that increase with body mass. These results provide a higher resolution of the cellular and functional heterogeneity within ATMs and provide a framework within which to develop new immune-directed therapies for the treatment of obesity and related sequela.

PKCα-LSD1-NF-κB-Signaling Cascade Is Crucial for Epigenetic Control of the Inflammatory Response.

The inflammatory response mediated by nuclear factor κB (NF-κB) signaling is essential for host defense against pathogens. Although the regulatory mechanism of NF-κB signaling has been well studied, the molecular basis for epigenetic regulation of the inflammatory response is poorly understood. Here we identify a new signaling axis of PKCα-LSD1-NF-κB, which is critical for activation and amplification of the inflammatory response. In response to excessive inflammatory stimuli, PKCα translocates to the nucleus and phosphorylates LSD1. LSD1 phosphorylation is required for p65 binding and facilitates p65 demethylation, leading to enhanced stability. In vivo genetic analysis using Lsd1SA/SA mice with ablation of LSD1 phosphorylation and chemical approaches in wild-type mice with inhibition of PKCα or LSD1 activity show attenuated sepsis-induced inflammatory lung injury and mortality. Together, we demonstrate that the PKCα-LSD1-NF-κB signaling cascade is crucial for epigenetic control of the inflammatory response, and targeting this signaling could be a powerful therapeutic strategy for systemic inflammatory diseases, including sepsis.

Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus.

BACKGROUND: Identification of differentially methylated regions (DMRs) is the initial step towards the study of DNA methylation-mediated gene regulation. Previous approaches to call DMRs suffer from false prediction, use extreme resources, and/or require library installation and input conversion. RESULTS: We developed a new approach called Defiant to identify DMRs. Employing Weighted Welch Expansion (WWE), Defiant showed superior performance to other predictors in the series of benchmarking tests on artificial and real data. Defiant was subsequently used to investigate DNA methylation changes in iron-deficient rat hippocampus. Defiant identified DMRs close to genes associated with neuronal development and plasticity, which were not identified by its competitor. Importantly, Defiant runs between 5 to 479 times faster than currently available software packages. Also, Defiant accepts 10 different input formats widely used for DNA methylation data. CONCLUSIONS: Defiant effectively identifies DMRs for whole-genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS), Tet-assisted bisulfite sequencing (TAB-seq), and HpaII tiny fragment enrichment by ligation-mediated PCR-tag (HELP) assays.

DNA hypermethylation induced by Epstein-Barr virus in the development of Epstein-Barr virus-associated gastric carcinoma.

Epstein-Barr virus (EBV)-associated gastric carcinoma (EBVaGC) is a recently recognized disease entity defined by the presence of EBV in gastric carcinoma cells. EBV infection causes major epigenetic alterations in the EBV genome and its cellular host genome, suggesting that EBV acts as a direct epigenetic driver for EBVaGC. One of the major epigenetic events in the viral and cellular genomes to control transcription is DNA hypo- or hyper-methylation. Particularly, local and global hypermethylation have been reported in EBVaGC. It is therefore important to understand the molecular mechanisms of DNA hypermethylation during EBVaGC carcinogenesis. To understand the functional roles of DNA methylation and suggest therapeutic target candidates for EBVaGC, we reviewed recent literature reporting DNA hypermethylation in EBVaGC. We summarized the identified candidate genes that are markedly hypermethylated in EBVaGC, which can potentially be targets for chemotherapies with demethylating agents.

Histone deacetylase 3 prepares brown adipose tissue for acute thermogenic challenge.

Brown adipose tissue is a thermogenic organ that dissipates chemical energy as heat to protect animals against hypothermia and to counteract metabolic disease. However, the transcriptional mechanisms that determine the thermogenic capacity of brown adipose tissue before environmental cold are unknown. Here we show that histone deacetylase 3 (HDAC3) is required to activate brown adipose tissue enhancers to ensure thermogenic aptitude. Mice with brown adipose tissue-specific genetic ablation of HDAC3 become severely hypothermic and succumb to acute cold exposure. Uncoupling protein 1 (UCP1) is nearly absent in brown adipose tissue lacking HDAC3, and there is also marked downregulation of mitochondrial oxidative phosphorylation genes resulting in diminished mitochondrial respiration. Remarkably, although HDAC3 acts canonically as a transcriptional corepressor, it functions as a coactivator of oestrogen-related receptor α (ERRα) in brown adipose tissue. HDAC3 coactivation of ERRα is mediated by deacetylation of PGC-1α and is required for the transcription of Ucp1, Ppargc1a (encoding PGC-1α), and oxidative phosphorylation genes. Importantly, HDAC3 promotes the basal transcription of these genes independently of adrenergic stimulation. Thus, HDAC3 uniquely primes Ucp1 and the thermogenic transcriptional program to maintain a critical capacity for thermogenesis in brown adipose tissue that can be rapidly engaged upon exposure to dangerously cold temperature.

Stability of the cancer target DDIAS is regulated by the CHIP/HSP70 pathway in lung cancer cells.

DNA damage-induced apoptosis suppressor (DDIAS) rescues lung cancer cells from apoptosis in response to DNA damage. DDIAS is transcriptionally activated by NFATc1 and EGF-mediated ERK5/MEF2B, leading to cisplatin resistance and cell invasion. Therefore, DDIAS is suggested as a therapeutic target for lung cancer. Here, we report that DDIAS stability is regulated by E3 U-box ubiquitin ligase carboxyl terminus of HSP70-interacting protein (CHIP)-mediated proteasomal degradation. We first isolated CHIP as an interacting partner of DDIAS by yeast two-hybrid screening. CHIP physically associated with both the N- and C-terminal regions of DDIAS, targeting it for proteasomal degradation and reducing the DDIAS half-life. CHIP overexpression analyses indicated that the tetratrico peptide repeat (TPR) domain and the U-box are required for DDIAS ubiquitination. It is likely that HSP70-bound DDIAS is recruited to the CHIP E3 ligase via the TPR domain, suggesting DDIAS as a client protein of HSP70. In addition, CHIP overexpression in lung cancer cells expressing high DDIAS levels induced significant growth inhibition by enhancing DDIAS degradation. Furthermore, simultaneous CHIP overexpression and DNA damage agent treatment caused a substantial increase in the apoptosis of lung cancer cells. Taken together, these findings indicate that the stability of the DDIAS protein is regulated by CHIP/HSP70-mediated proteasomal degradation and that CHIP overexpression stimulates the apoptosis of lung cancer cells in response to DNA-damaging agents.