The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Mixed lineage kinase 3 (MLK3) is a mitogen-activated protein kinase kinase kinase (MAPKKK) that activates c-jun N-terminal kinase (JNK) and can induce cell death in neurons. By contrast, the activation of phosphatidylinositol 3-kinase and AKT/protein kinase B (PKB) acts to suppress neuronal apoptosis. Here, we report a functional interaction between MLK3 and AKT1/PKB␣. Endogenous MLK3 and AKT1 interact in HepG2 cells, and this interaction is regulated by insulin. The interaction domain maps to the C-terminal half of MLK3 (amino acids 511-847), and this region also contains a putative AKT phosphorylation consensus sequence. Endogenous JNK, MKK7, and MLK3 kinase activities in HepG2 cells are significantly attenuated by insulin treatment, whereas the phosphatidylinositol 3-kinase inhibitors LY294002 and wortmannin reversed the effect. Finally, MLK3-mediated JNK activation is inhibited by AKT1. AKT phosphorylates MLK3 on serine 674 both in vitro and in vivo. Furthermore, the expression of activated AKT1 inhibits MLK3-mediated cell death in a manner dependent on serine 674 phosphorylation. Thus, these data provide the first direct link between MLK3-mediated cell death and its regulation by a cell survival signaling protein, AKT1.The cellular decision to undergo either cell death or cell survival is determined by the integration of multiple survival and death signals. Mixed lineage kinase 3 (MLK3) 1 is a member of a growing family of mixed lineage kinases (1). Recently, it has been shown that overexpression of MLK3 or NGF withdrawal leads to neuronal cell death, which can be prevented by treatment with a small molecule inhibitor of MLKs, CEP-1347 (2). Similarly, CEP-11004, an analog of CEP-1347, has also been shown to prevent neuronal cell death upon NGF withdrawal (3). These results indicate a significant and direct involvement of MLKs in regulating cell death; however, the detailed mechanism by which MLKs are regulated is still unknown.The c-jun-N-terminal kinase/stress-activated protein kinase (JNK/SAPK) is stimulated by proinflammatory cytokines, oxidative stress, heat shock, UV, ␥-irradiation, and by other cellular stresses (4, 5). The signals in stress-activated JNK pathway are transmitted through three core modules: MAP3Ks such as members of the mixed lineage kinases or MEKK members, a MAP2K such as SEK1/MKK4 or MKK7, and MAPK such as JNK family members (4, 5). The activated MAP3K phosphorylates and activates MKK7 or SEK1, which in turn phosphorylates and activates JNK. JNKs phosphorylate several nuclear transcription factors that include ELK1, c-Jun, and ATF2 (4, 5). In several cell types, the activation of JNKs is directly linked to cell death (6 -8). Therefore, one mechanism of cell survival could be to block JNK pathway induction. The activation of phosphatidylinositol 3-kinase (PI3K) correlates with increased cell survival, and this effect is largely mediated through the activation of a serine/threonine kinase, AKT (also known as PKB). PI3K agonists such as insulin and insulin-like growth factor-1 (IGF-1) ...

Mixed lineage kinases (MLKs) are MAPKKK members that activate JNK and reportedly lead to cell death. However, the agonist(s) that regulate MLK activity remain unknown. Here, we demonstrate ceramide as the activator of Drosophila MLK (dMLK) and identify ceramide and TNF-alpha as agonists of mammalian MLK3. dMLK and MLK3 are activated by a ceramide analog and bacterial sphingomyelinase in vivo, whereas a low nanomolar concentration of natural ceramide activates them in vitro. Specific inhibition of dMLK and MLK3 significantly attenuates activation of JNK by ceramide in vivo without affecting ceramide-induced p38 or ERK activation. In addition, TNF-alpha also activates MLK3 and evidently leads to JNK activation in vivo. Thus, the ceramide serves as a common agonist of dMLK and MLK3, and MLK3 contributes to JNK activation induced by TNF-alpha.