Search

Synaptic vesicle glycoprotein 2A (SV2A) regulates action potential-dependent neurotransmitter release and is commonly known as the primary binding site of an approved anti-epileptic drug, levetiracetam. Although several rodent knockout models have demonstrated the importance of SV2A for functional neurotransmission, its precise physiological function and role in epilepsy pathophysiology remains to be elucidated. Here, we present a novel sv2a knockout model in zebrafish, a vertebrate with complementary advantages to rodents. We demonstrated that 6 days post fertilization homozygous sv2a–/– mutant zebrafish larvae, but not sv2a+/– and sv2a+/+ larvae, displayed locomotor hyperactivity and spontaneous epileptiform discharges, however, no major brain malformations could be observed. A partial rescue of this epileptiform brain activity could be observed after treatment with two commonly used anti-epileptic drugs, valproic acid and, surprisingly, levetiracetam. This observation indicated that additional targets, besides Sv2a, maybe are involved in the protective effects of levetiracetam against epileptic seizures. Furthermore, a transcriptome analysis provided insights into the neuropathological processes underlying the observed epileptic phenotype. While gene expression profiling revealed only one differentially expressed gene (DEG) between wildtype and sv2a+/– larvae, there were 4386 and 3535 DEGs between wildtype and sv2a–/–, and sv2a+/– and sv2a–/– larvae, respectively. Pathway and gene ontology (GO) enrichment analysis between wildtype and sv2a–/– larvae revealed several pathways and GO terms enriched amongst up- and down-regulated genes, including MAPK signaling, synaptic vesicle cycle, and extracellular matrix organization, all known to be involved in epileptogenesis and epilepsy. Importantly, we used the Connectivity map database to identify compounds with opposing gene signatures compared to the one observed in sv2a–/– larvae, to finally rescue the epileptic phenotype. Two out of three selected compounds rescued electrographic discharges in sv2a–/– larvae, while negative controls did not. Taken together, our results demonstrate that sv2a deficiency leads to increased seizure vulnerability and provide valuable insight into the functional importance of sv2a in the brain in general. Furthermore, we provided evidence that the concept of connectivity mapping represents an attractive and powerful approach in the discovery of novel compounds against epilepsy.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with inferior outcome compared with that of B cell ALL. Here, we show that Runt-related transcription factor 2 (RUNX2) was upregulated in high-risk T-ALL with KMT2A rearrangements (KMT2A-R) or an immature immunophenotype. In KMT2A-R cells, we identified RUNX2 as a direct target of the KMT2A chimeras, where it reciprocally bound the KMT2A promoter, establishing a regulatory feed-forward mechanism. Notably, RUNX2 was required for survival of immature and KMT2A-R T-ALL cells in vitro and in vivo. We report direct transcriptional regulation of CXCR4 signaling by RUNX2, thereby promoting chemotaxis, adhesion, and homing to medullary and extramedullary sites. RUNX2 enabled these energy-demanding processes by increasing metabolic activity in T-ALL cells through positive regulation of both glycolysis and oxidative phosphorylation. Concurrently, RUNX2 upregulation increased mitochondrial dynamics and biogenesis in T-ALL cells. Finally, as a proof of concept, we demonstrate that immature and KMT2A-R T-ALL cells were vulnerable to pharmacological targeting of the interaction between RUNX2 and its cofactor CBF beta. In conclusion, we show that RUNX2 acts as a dependency factor in high-risk subtypes of human T-ALL through concomitant regulation of tumor metabolism and leukemic cell migration.

Current therapeutic approaches are ineffective in many patients with established Diabetic Kidney Disease (DKD) disease, an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed demonstrate that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to firstly identify patients at high risk of progression, and secondly to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

The relevance of different in vitro culture models of cancer cells is a hot topic, but few systematic and definitive analyses in this area exist. In this issue of Cell Chemical Biology, Senkowski et al. (2016) address this issue by studying the transcriptomic profiles of drug-treated cancer cells cultured in two-dimensional and three-dimensional cultures. They describe biological findings with potential therapeutic implications and provide a unique data resource to mine. Non

T cell acute lymphoblastic leukemias (T-ALLs) arise from the malignant transformation of hematopoietic progenitors primed toward T cell development, as result of a multistep oncogenic process involving constitutive activation of NOTCH signaling and genetic alterations in transcription factors, signaling oncogenes, and tumor suppressors. Notably, these genetic alterations define distinct molecular groups of T-ALL with specific gene expression signatures and clinicobiological features. This review summarizes recent advances in our understanding of the molecular genetics of T-ALL.

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Accurate assessment of the individual exposure dose based on easily accessible samples (e.g. blood) immediately following a radiological accident is crucial. We aimed at developing a robust transcription-based signature for biodosimetry from human peripheral blood mononuclear cells irradiated with different doses of X-rays (0.1 and 1.0 Gy) at a dose rate of 0.26 Gy/min. Genome-wide radiation-induced changes in mRNA expression were evaluated at both gene and exon level. Using exon-specific qRT-PCR, we confirmed that several biomarker genes are alternatively spliced or transcribed after irradiation and that different exons of these genes exhibit significantly different levels of induction. Moreover, a significant number of radiation-responsive genes were found to be genomic neighbors. Using three different classification models we found that gene and exon signatures performed equally well on dose prediction, as long as more than 10 features are included. Together, our results highlight the necessity of evaluating gene expression at the level of single exons for radiation biodosimetry in particular and transcriptional biomarker research in general. This approach is especially advisable for practical gene expression-based biodosimetry, for which primer- or probe-based techniques would be the method of choice.

Tissue engineering of Schwann cells (SCs) can serve a number of purposes, such as in vitro SC-related disease modeling, treatment of peripheral nerve diseases or peripheral nerve injury, and, potentially, treatment of CNS diseases. SCs can be generated from autologous stem cells in vitro by recapitulating the various stages of in vivo neural crest formation and SC differentiation. In this review, we survey the cellular and molecular mechanisms underlying these in vivo processes. We then focus on the current in vitro strategies for generating SCs from two sources of pluripotent stem cells, namely embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Different methods for SC engineering from ESCs and iPSCs are reviewed and suggestions are proposed for optimizing the existing protocols. Potential safety issues regarding the clinical application of iPSC-derived SCs are discussed as well. Lastly, we will address future aspects of SC engineering.

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease., Author Summary Complex biological interactions and processes can be modelled as networks, for instance metabolic pathways or protein-protein interactions. The growing availability of large high-throughput data in several experimental conditions now permits the full-scale analysis of biological interactions and processes. However, no reliable and computationally efficient methods for simultaneous analysis of multiple large-scale interaction datasets (networks) have been developed to date. To overcome this shortcoming, we have developed a new computational framework that is parameter-free, computationally efficient and highly reliable. We showed how these distinctive properties make it a useful tool for real genomic data exploration and analyses. Indeed, in extensive simulation studies and real-data analyses we have demonstrated that our method outperformed existing approaches in terms of efficiency and, most importantly, reproducibility of the results. Beyond the computational advantages, we illustrated how our method can be effectively applied to leverage the vast stream of genome-scale transcriptional data that has risen exponentially over the last years. In contrast with existing approaches, using our method we were able to identify and replicate multi-tissue gene co-expression networks that were associated with specific functional processes relevant to phenotypic variation and disease in rats and humans.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematopoietic malignancy of thymocytes affecting preferentially children and adolescents. The disease is heterogeneous and characterized by a large set of chromosomal and genetic alterations that deregulate the growth of maturing thymocytes. The identification of activating point mutations in NOTCH1 in more then 50% of all T-ALL cases highlights the NOTCH1 cascade as a central player of T-ALL pathogenesis. In this review, we summarize and update more recent findings on the molecular mechanisms of T-ALL with a particular emphasis on the oncogenic properties of aberrant NOTCH1 signaling.

Brain tumors are one of the leading causes of death in adults with cancer; however, molecular classification of these tumors with in vivo magnetic resonance spectroscopy (MRS) is limited because of the small number of metabolites detected. In vitro MRS provides highly informative biomarker profiles at higher fields, but also consumes the sample so that it is unavailable for subsequent analysis. In contrast, ex vivo high-resolution magic angle spinning (HRMAS) MRS conserves the sample but requires large samples and can pose technical challenges for producing accurate data, depending on the sample testing temperature. We developed a novel approach that combines a two-dimensional (213), solid-state, HRMAS proton ((1)H) NMR method, TOBSY (total through-bond spectroscopy), which maximizes the advantages of HRMAS and a robust classification strategy. We used similar to 2 mg of tissue at -8 degrees C from each of 55 brain biopsies, and reliably detected 16 different biologically relevant molecular species. We compared two classification strategies, the support vector machine (SVM) classifier and a feed-forward neural network using the Levenberg-Marquardt back-propagation algorithm. We used the minimum redundancy/maximum relevance (MRMR) method as a powerful feature-selection scheme along with the SVM classifier. We suggest that molecular characterization of brain tumors based on highly informative 2D MRS should enable us to type and prognose even inoperable patients with high accuracy in vivo. Int J Oncol