The Majority of CD45- Ter119- CD31- Bone Marrow Cell Fraction Is of Hematopoietic Origin and Contains Erythroid and Lymphoid Progenitors.

The non-hematopoietic cell fraction of the bone marrow (BM) is classically identified as CD45- Ter119- CD31- (herein referred to as triple-negative cells or TNCs). Although TNCs are believed to contain heterogeneous stromal cell populations, they remain poorly defined. Here we showed that the vast majority of TNCs (∼85%) have a hematopoietic rather than mesenchymal origin. Single cell RNA-sequencing revealed erythroid and lymphoid progenitor signatures among CD51- TNCs. Ly6D+ CD44+ CD51- TNCs phenotypically and functionally resembled CD45+ pro-B lymphoid cells, whereas Ly6D- CD44+ CD51- TNCs were enriched in previously unappreciated stromal-dependent erythroid progenitors hierarchically situated between preCFU-E and proerythroblasts. Upon adoptive transfer, CD44+ CD51- TNCs contributed to repopulate the B-lymphoid and erythroid compartments. CD44+ CD51- TNCs also expanded during phenylhydrazine-induced acute hemolysis or in a model of sickle cell anemia. These findings thus uncover physiologically relevant new classes of stromal-associated functional CD45- hematopoietic progenitors.

Redox signaling by glutathione peroxidase 2 links vascular modulation to metabolic plasticity of breast cancer.

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression.

Redox signalling regulates breast cancer metastasis via phenotypic and metabolic reprogramming due to p63 activation by HIF1α.

BACKGROUND: Redox signaling caused by knockdown (KD) of Glutathione Peroxidase 2 (GPx2) in the PyMT mammary tumour model promotes metastasis via phenotypic and metabolic reprogramming. However, the tumour cell subpopulations and transcriptional regulators governing these processes remained unknown. METHODS: We used single-cell transcriptomics to decipher the tumour cell subpopulations stimulated by GPx2 KD in the PyMT mammary tumour and paired pulmonary metastases. We analyzed the EMT spectrum across the various tumour cell clusters using pseudotime trajectory analysis and elucidated the transcriptional and metabolic regulation of the hybrid EMT state. RESULTS: Integration of single-cell transcriptomics between the PyMT/GPx2 KD primary tumour and paired lung metastases unraveled a basal/mesenchymal-like cluster and several luminal-like clusters spanning an EMT spectrum. Interestingly, the luminal clusters at the primary tumour gained mesenchymal gene expression, resulting in epithelial/mesenchymal subpopulations fueled by oxidative phosphorylation (OXPHOS) and glycolysis. By contrast, at distant metastasis, the basal/mesenchymal-like cluster gained luminal and mesenchymal gene expression, resulting in a hybrid subpopulation using OXPHOS, supporting adaptive plasticity. Furthermore, p63 was dramatically upregulated in all hybrid clusters, implying a role in regulating partial EMT and MET at primary and distant sites, respectively. Importantly, these effects were reversed by HIF1α loss or GPx2 gain of function, resulting in metastasis suppression. CONCLUSIONS: Collectively, these results underscored a dramatic effect of redox signaling on p63 activation by HIF1α, underlying phenotypic and metabolic plasticity leading to mammary tumour metastasis.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data.

BACKGROUND: In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA). RESULTS: Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the shape of the distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of distributions were permitted, i.e. the shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression distribution. CONCLUSIONS: Our results highlight the value of studying a gene's distribution shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

Identification of a novel subgroup of endometrial cancer patients with loss of thyroid hormone receptor beta expression and improved survival.

BACKGROUND: Endometrial cancer (EC) is the most common gynecologic cancer in women, and the incidence of EC has increased by about 1% per year in the U. S over the last 10 years. Although 5-year survival rates for early-stage EC are around 80%, certain subtypes of EC that lose nuclear hormone receptor (NHR) expression are associated with poor survival rates. For example, estrogen receptor (ER)-negative EC typically harbors a worse prognosis compared to ER-positive EC. The molecular basis for the loss of NHR expression in endometrial tumors and its contribution to poor survival is largely unknown. Furthermore, there are no tools to systematically identify tumors that lose NHR mRNA expression relative to normal tissue. The development of such an approach could identify sets of NHR-based biomarkers for classifying patients into subgroups with poor survival outcomes. METHODS: Here, a new computational method, termed receptLoss, was developed for identifying NHR expression loss in endometrial cancer relative to adjacent normal tissue. When applied to gene expression data from The Cancer Genome Atlas (TCGA), receptLoss identified 6 NHRs that were highly expressed in normal tissue and exhibited expression loss in a subset of endometrial tumors. RESULTS: Three of the six identified NHRs - estrogen, progesterone, and androgen receptors - that are known to lose expression in ECs were correctly identified by receptLoss. Additionally, a novel association was found between thyroid hormone receptor beta (THRB) expression loss, increased expression of miRNA-146a, and increased rates of 5-year survival in the EC TCGA patient cohort. THRB expression loss occurs independently of estrogen and progesterone expression loss, suggesting the discovery of a distinct, clinically-relevant molecular subgroup. CONCLUSION: ReceptLoss is a novel, open-source software tool to systematically identify NHR expression loss in cancer. The application of receptLoss to endometrial cancer gene expression data identified THRB, a previously undescribed biomarker of survival in endometrial cancer. Applying receptLoss to expression data from additional cancer types could lead to the development of biomarkers of disease progression for patients with any other tumor type. ReceptLoss can be applied to expression data from additional cancer types with the goal of identifying biomarkers of differential survival.

Divergent reprogramming routes lead to alternative stem-cell states.

Pluripotency is defined by the ability of a cell to differentiate to the derivatives of all the three embryonic germ layers: ectoderm, mesoderm and endoderm. Pluripotent cells can be captured via the archetypal derivation of embryonic stem cells or via somatic cell reprogramming. Somatic cells are induced to acquire a pluripotent stem cell (iPSC) state through the forced expression of key transcription factors, and in the mouse these cells can fulfil the strictest of all developmental assays for pluripotent cells by generating completely iPSC-derived embryos and mice. However, it is not known whether there are additional classes of pluripotent cells, or what the spectrum of reprogrammed phenotypes encompasses. Here we explore alternative outcomes of somatic reprogramming by fully characterizing reprogrammed cells independent of preconceived definitions of iPSC states. We demonstrate that by maintaining elevated reprogramming factor expression levels, mouse embryonic fibroblasts go through unique epigenetic modifications to arrive at a stable, Nanog-positive, alternative pluripotent state. In doing so, we prove that the pluripotent spectrum can encompass multiple, unique cell states.

Genome-wide characterization of the routes to pluripotency.

Somatic cell reprogramming to a pluripotent state continues to challenge many of our assumptions about cellular specification, and despite major efforts, we lack a complete molecular characterization of the reprograming process. To address this gap in knowledge, we generated extensive transcriptomic, epigenomic and proteomic data sets describing the reprogramming routes leading from mouse embryonic fibroblasts to induced pluripotency. Through integrative analysis, we reveal that cells transition through distinct gene expression and epigenetic signatures and bifurcate towards reprogramming transgene-dependent and -independent stable pluripotent states. Early transcriptional events, driven by high levels of reprogramming transcription factor expression, are associated with widespread loss of histone H3 lysine 27 (H3K27me3) trimethylation, representing a general opening of the chromatin state. Maintenance of high transgene levels leads to re-acquisition of H3K27me3 and a stable pluripotent state that is alternative to the embryonic stem cell (ESC)-like fate. Lowering transgene levels at an intermediate phase, however, guides the process to the acquisition of ESC-like chromatin and DNA methylation signature. Our data provide a comprehensive molecular description of the reprogramming routes and is accessible through the Project Grandiose portal at http://www.stemformatics.org.

Investigating skewness to understand gene expression heterogeneity in large patient cohorts.

BACKGROUND: Skewness is an under-utilized statistical measure that captures the degree of asymmetry in the distribution of any dataset. This study applied a new metric based on skewness to identify regulators or genes that have outlier expression in large patient cohorts. RESULTS: We investigated whether specific patterns of skewed expression were related to the enrichment of biological pathways or genomic properties like DNA methylation status. Our study used publicly available datasets that were generated using both RNA-sequencing and microarray technology platforms. For comparison, the datasets selected for this study also included different samples derived from control donors and cancer patients. When comparing the shift in expression skewness between cancer and control datasets, we observed an enrichment of pathways related to the immune function that reflects an increase towards positive skewness in the cancer relative to control datasets. A significant correlation was also detected between expression skewness and the top 500 genes corresponding to the most significant differential DNA methylation occurring in the promotor regions for four Cancer Genome Atlas cancer cohorts. CONCLUSIONS: Our results indicate that expression skewness can reveal new insights into transcription based on outlier and asymmetrical behaviour in large patient cohorts.

Aneuvis: web-based exploration of numerical chromosomal variation in single cells.

BACKGROUND: Numerical chromosomal variation is a hallmark of populations of malignant cells. Identifying the factors that promote numerical chromosomal variation is important for understanding mechanisms of carcinogenesis. However, the ability to quantify and visualize differences in chromosome number between experimentally-defined groups (e.g. control vs treated) obtained from single-cell experiments is currently limited by the lack of user-friendly software. RESULTS: Aneuvis is a web application that allows users to determine whether numerical chromosomal variation exists between experimental treatment groups. The web interface allows users to upload molecular cytogenetic or processed single cell whole-genome sequencing data in a cell-by-chromosome matrix format and automatically generates visualizations and summary statistics that reflect the degree of numeric chromosomal variability. CONCLUSIONS: Aneuvis is the first user-friendly web application to help researchers identify the genetic and environmental perturbations that promote numerical chromosomal variation. Aneuvis is freely available as a web application at https://dpique.shinyapps.io/aneuvis/ and the source code for the application is available at https://github.com/dpique/aneuvis .

A novel approach to modelling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinoma.

BACKGROUND: Oncogenes promote the development of therapeutic targets against subsets of cancers. Only several hundred oncogenes have been identified, primarily via mutation-based approaches, in the human genome. Transcriptional overexpression is a less-explored mechanism through which oncogenes can arise. METHODS: Here, a new statistical approach, termed oncomix, which captures transcriptional heterogeneity in tumour and adjacent normal (i.e., tumour-free) mRNA expression profiles, was developed to identify oncogene candidates that were overexpressed in a subset of breast tumours. RESULTS: Intronic DNA methylation was strongly associated with the overexpression of chromobox 2 (CBX2), an oncogene candidate that was identified using our method but not through prior analytical approaches. CBX2 overexpression in breast tumours was associated with the upregulation of genes involved in cell cycle progression and with poorer 5-year survival. The predicted function of CBX2 was confirmed in vitro, providing the first experimental evidence that CBX2 promotes breast cancer cell growth. CONCLUSIONS: Oncomix is a novel approach that captures transcriptional heterogeneity between tumour and adjacent normal tissue, and that has the potential to uncover therapeutic targets that benefit subsets of cancer patients. CBX2 is an oncogene candidate that should be further explored as a potential drug target for aggressive types of breast cancer.

Arteriolar niches maintain haematopoietic stem cell quiescence.

Cell cycle quiescence is a critical feature contributing to haematopoietic stem cell (HSC) maintenance. Although various candidate stromal cells have been identified as potential HSC niches, the spatial localization of quiescent HSCs in the bone marrow remains unclear. Here, using a novel approach that combines whole-mount confocal immunofluorescence imaging techniques and computational modelling to analyse significant three-dimensional associations in the mouse bone marrow among vascular structures, stromal cells and HSCs, we show that quiescent HSCs associate specifically with small arterioles that are preferentially found in endosteal bone marrow. These arterioles are ensheathed exclusively by rare NG2 (also known as CSPG4)(+) pericytes, distinct from sinusoid-associated leptin receptor (LEPR)(+) cells. Pharmacological or genetic activation of the HSC cell cycle alters the distribution of HSCs from NG2(+) periarteriolar niches to LEPR(+) perisinusoidal niches. Conditional depletion of NG2(+) cells induces HSC cycling and reduces functional long-term repopulating HSCs in the bone marrow. These results thus indicate that arteriolar niches are indispensable for maintaining HSC quiescence.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

BACKGROUND: A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. RESULTS: Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. CONCLUSIONS: This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers.

Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar, we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development.

An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that HDDC2 plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.

Chromosome instability in diffuse large B cell lymphomas is suppressed by activation of the noncanonical NF-κB pathway.

Diffuse large B cell lymphoma (DLBCL) is the most common form of lymphoma in the United States. DLBCL comprises biologically distinct subtypes including germinal center-like (GCB) and activated-B-cell-like DLBCL (ABC). The most aggressive type, ABC-DLBCL, displays dysregulation of both canonical and noncanonical NF-κB pathway as well as genomic instability. Although, much is known about the tumorigenic roles of the canonical NF-kB pathway, the precise role of the noncanonical NF-kB pathway remains unknown. Here we show that activation of the noncanonical NF-κB pathway regulates chromosome stability, DNA damage response and centrosome duplication in DLBCL. Analysis of 92 DLBCL samples revealed that activation of the noncanonical NF-κB pathway is associated with low levels of DNA damage and centrosome amplification. Inhibiting the noncanonical pathway in lymphoma cells uncovered baseline DNA damage and prevented doxorubicin-induced DNA damage repair. In addition, it triggered centrosome amplification and chromosome instability, indicated by anaphase bridges, multipolar spindles and chromosome missegregation. We determined that the noncanonical NF-κB pathway execute these functions through the regulation of GADD45α and REDD1 in a p53-independent manner, while it collaborates with p53 to regulate cyclin G2 expression. Furthermore, this pathway regulates GADD45α, REDD1 and cyclin G2 through direct binding of NF-κB sites to their promoter region. Overall, these results indicate that the noncanonical NF-κB pathway plays a central role in maintaining genome integrity in DLBCL. Our data suggests that inhibition of the noncanonical NF-kB pathway should be considered as an important component in DLBCL therapeutic approach.

Genome-wide siRNA screen for mediators of NF-κB activation.

Although canonical NFκB is frequently critical for cell proliferation, survival, or differentiation, NFκB hyperactivation can cause malignant, inflammatory, or autoimmune disorders. Despite intensive study, mammalian NFκB pathway loss-of-function RNAi analyses have been limited to specific protein classes. We therefore undertook a human genome-wide siRNA screen for novel NFκB activation pathway components. Using an Epstein Barr virus latent membrane protein (LMP1) mutant, the transcriptional effects of which are canonical NFκB-dependent, we identified 155 proteins significantly and substantially important for NFκB activation in HEK293 cells. These proteins included many kinases, phosphatases, ubiquitin ligases, and deubiquinating enzymes not previously known to be important for NFκB activation. Relevance to other canonical NFκB pathways was extended by finding that 118 of the 155 LMP1 NF-κB activation pathway components were similarly important for IL-1ß-, and 79 for TNFα-mediated NFκB activation in the same cells. MAP3K8, PIM3, and six other enzymes were uniquely relevant to LMP1-mediated NFκB activation. Most novel pathway components functioned upstream of IκB kinase complex (IKK) activation. Robust siRNA knockdown effects were confirmed for all mRNAs or proteins tested. Although multiple ZC3H-family proteins negatively regulate NFκB, ZC3H13 and ZC3H18 were activation pathway components. ZC3H13 was critical for LMP1, TNFα, and IL-1ß NFκB-dependent transcription, but not for IKK activation, whereas ZC3H18 was critical for IKK activation. Down-modulators of LMP1 mediated NFκB activation were also identified. These experiments identify multiple targets to inhibit or stimulate LMP1-, IL-1ß-, or TNFα-mediated canonical NFκB activation.