A genome-wide RNAi screen identifies proteins modulating aberrant FLT3-ITD signaling

KDEL Receptors: Pathophysiological Functions, Therapeutic Options, and Biotechnological Opportunities

KDEL receptors (KDELRs) are ubiquitous seven-transmembrane domain proteins encoded by three mammalian genes. They bind to and retro-transport endoplasmic reticulum (ER)-resident proteins with a C-terminal Lys-Asp-Glu-Leu (KDEL) sequence or variants thereof. In doing this, KDELR participates in the ER quality control of newly synthesized proteins and the unfolded protein response. The binding of KDEL proteins to KDELR initiates signaling cascades involving three alpha subunits of heterotrimeric G proteins, Src family kinases, protein kinases A (PKAs), and mitogen-activated protein kinases (MAPKs). These signaling pathways coordinate membrane trafficking flows between secretory compartments and control the degradation of the extracellular matrix (ECM), an important step in cancer progression. Considering the basic cellular functions performed by KDELRs, their association with various diseases is not surprising. KDELR mutants unable to bind the collagen-specific chaperon heat-shock protein 47 (HSP47) cause the osteogenesis imperfecta. Moreover, the overexpression of KDELRs appears to be linked to neurodegenerative diseases that share pathological ER-stress and activation of the unfolded protein response (UPR). Even immune function requires a functional KDELR1, as its mutants reduce the number of T lymphocytes and impair antiviral immunity. Several studies have also brought to light the exploitation of the shuttle activity of KDELR during the intoxication and maturation/exit of viral particles. Based on the above, KDELRs can be considered potential targets for the development of novel therapeutic strategies for a variety of diseases involving proteostasis disruption, cancer progression, and infectious disease. However, no drugs targeting KDELR functions are available to date; rather, KDELR has been leveraged to deliver drugs efficiently into cells or improve antigen presentation.

Non-canonical H3K79me2-dependent pathways promote the survival of MLL-rearranged leukemia

MLL-rearranged leukemia depends on H3K79 methylation. Depletion of this transcriptionally activating mark by DOT1L deletion or high concentrations of the inhibitor pinometostat downregulates HOXA9 and MEIS1, and consequently reduces leukemia survival. Yet, some MLL-rearranged leukemias are inexplicably susceptible to low-dose pinometostat, far below concentrations that downregulate this canonical proliferation pathway. In this context, we define alternative proliferation pathways that more directly derive from H3K79me2 loss. By ICeChIP-seq, H3K79me2 is markedly depleted at pinometostat-downregulated and MLL-fusion targets, with paradoxical increases of H3K4me3 and loss of H3K27me3. Although downregulation of polycomb components accounts for some of the proliferation defect, transcriptional downregulation of FLT3 is the major pathway. Loss-of-FLT3-function recapitulates the cytotoxicity and gene expression consequences of low-dose pinometostat, whereas overexpression of constitutively active STAT5A, a target of FLT3-ITD-signaling, largely rescues these defects. This pathway also depends on MLL1, indicating combinations of DOT1L, MLL1 and FLT3 inhibitors should be explored for treating FLT3-mutant leukemia.

Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

Background

Marker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles.

Methods

We first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, "mRMR" is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, "RANWAR" to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel.

Results

The novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated).

Conclusion

The identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.