Real-time single cell analysis of Smac/DIABLO release during apoptosis

We examined the temporal and causal relationship between Smac/DIABLO release, cytochrome c (cyt-c) release, and caspase activation at the single cell level during apoptosis. Cells treated with the broad-spectrum caspase inhibitor z-VAD-fmk, caspase-3 (Casp-3)–deficient MCF-7 cells, as well as Bax-deficient DU-145 cells released Smac/DIABLO and cyt-c in response to proapoptotic agents. Real-time confocal imaging of MCF-7 cells stably expressing Smac/DIABLO-yellow fluorescent protein (YFP) revealed that the average duration of Smac/DIABLO-YFP release was greater than that of cyt-c-green fluorescent protein (GFP). However, there was no significant difference in the time to the onset of release, and both cyt-c-GFP and Smac/DIABLO-YFP release coincided with mitochondrial membrane potential depolarization. We also observed no significant differences in the Smac/DIABLO-YFP release kinetics when z-VAD-fmk–sensitive caspases were inhibited or Casp-3 was reintroduced. Simultaneous measurement of DEVDase activation and Smac/DIABLO-YFP release demonstrated that DEVDase activation occurred within 10 min of release, even in the absence of Casp-3

TGF-β1 activates two distinct type I receptors in neurons: implications for neuronal NF-κB signaling

Transforming growth factor-βs (TGF-βs) are pleiotropic cytokines involved in development and maintenance of the nervous system. In several neural lesion paradigms, TGF-β1 exerts potent neuroprotective effects. Neurons treated with TGF-β1 activated the canonical TGF-β receptor I/activin-like kinase receptor 5 (ALK5) pathway. The transcription factor nuclear factor-κB (NF-κB) plays a fundamental role in neuroprotection. Treatment with TGF-β1 enhanced NF-κB activity in gelshift and reporter gene analyses. However, ectopic expression of a constitutively active ALK5 failed to mimic these effects. ALK1 has been described as an alternative TGF-β receptor in endothelial cells. Interestingly, we detected significant basal expression of ALK1 and its injury-induced up-regulation in neurons. Treatment with TGF-β1 also induced a pronounced increase in downstream Smad1 phosphorylation. Overexpression of a constitutively active ALK1 mimicked the effect of TGF-β1 on NF-κB activation and neuroprotection. Our data suggest that TGF-β1 simultaneously activates two distinct receptor pathways in neurons and that the ALK1 pathway mediates TGF-β1–induced NF-κB survival signaling

Gene expression during ER stress–induced apoptosis in neurons: induction of the BH3-only protein Bbc3/PUMA and activation of the mitochondrial apoptosis pathway

Endoplasmic reticulum (ER) stress has been implicated in the pathogenesis of ischemic and neurodegenerative disorders. Treatment of human SH-SY5Y neuroblastoma cells with tunicamycin, an inhibitor of protein glycosylation, rapidly induced the expression of target genes of the unfolded protein response. However, prolonged treatment also triggered a delayed, caspase-dependent cell death. Microarray analysis of gene expression changes during tunicamycin-induced apoptosis revealed that the Bcl-2 homology domain 3-only family member, Bcl-2 binding component 3/p53 upregulated modulator of apoptosis (Bbc3/PUMA), was the most strongly induced pro-apoptotic gene. Expression of Bbc3/PUMA correlated with a Bcl-xL–sensitive release of cytochrome c and the activation of caspase-9 and -3. Increased expression of Bbc3/PUMA was also observed in p53-deficient human cells, in response to the ER stressor thapsigargin, and in rat hippocampal neurons after transient forebrain ischemia. Overexpression of Bbc3/PUMA was sufficient to trigger apoptosis in SH-SY5Y neuroblastoma cells, and human cells deficient in Bbc3/PUMA showed dramatically reduced apoptosis in response to ER stress. Our data suggest that the transcriptional induction of Bbc3/PUMA may be sufficient and necessary for ER stress–induced apoptosis

ALISSA: an automated live-cell imaging system for signal transduction analyses

Probe photobleaching and a specimen’s sensitivity to phototoxicity severely limit the number of possible excitation cycles in time-lapse fluorescent microscopy experiments. Consequently, when a study of cellular processes requires measurements over hours or days, temporal resolution is limited, and spontaneous or rapid events may be missed, thus limiting conclusions about transduction events. We have developed ALISSA, a design framework and reference implementation for an automated live-cell imaging system for signal transduction analysis. It allows an adaptation of image modalities and laser resources tailored to the biological process, and thereby extends temporal resolution from minutes to seconds. The system employs online image analysis to detect cellular events that are then used to exercise microscope control. It consists of a reusable image analysis software for cell segmentation, tracking, and time series extraction, and a measurement-specific process control software that can be easily adapted to various biological settings. We have applied the ALISSA framework to the analysis of apoptosis as a demonstration case for slow onset and rapid execution signaling. The demonstration provides a clear proof-of-concept for ALISSA, and offers guidelines for its application in a broad spectrum of signal transduction studies

Colorectal tumour simulation using agent based modelling and high performance computing

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High- End Computing (ICHEC).Peer ReviewedPostprint (author's final draft

A machine learning platform to optimize the translation of personalized network models to the clinic

PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow

Genomic Exploration of Distinct Molecular Phenotypes Steering Temozolomide Resistance Development in Patient-Derived Glioblastoma Cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells’ molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as “adaptive” (ADA) or “non-adaptive” (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor’s ability to survive. Depending on the tumor’s adaptability potential, subpopulations with acquired resistance mechanisms may arise.</p

AMP kinase–mediated activation of the BH3-only protein Bim couples energy depletion to stress-induced apoptosis

Disturbances in cellular ion gradients by excitotoxicity promote apoptosis through activation of the Bcl-2 family member Bim