Robin E. C. Lee

Metacaspase Yca1 is required for clearance of insoluble protein aggregates

In complex organisms, caspase proteases mediate a variety of cell behaviors, including proliferation, differentiation, and programmed cell death/apoptosis. Structural homologs to the caspase family (termed metacaspases) engage apoptosis in single-cell eukaryotes, yet the molecular mechanisms that contribute to nondeath roles are currently undefined. Here, we report an unexpected role for the Saccharomyces cerevisiae metacaspase Yca1 in protein quality control. Quantitative proteomic analysis of Δyca1 cells identified significant alterations to vacuolar catabolism and stress-response proteins in the absence of induced stress. Yca1 protein complexes are enriched for aggregate-remodeling chaperones that colocalize with Yca1-GFP fusions. Finally, deletion and inactivation mutants of Yca1 accrue protein aggregates and autophagic bodies during log-phase growth. Together, our results show that Yca1 contributes to the fitness and adaptability of growing yeast through an aggregate remodeling activity.

A Non-Death Role of the Yeast Metacaspase: Yca1p Alters Cell Cycle Dynamics

Caspase proteases are a conserved protein family predominantly known for engaging and executing apoptotic cell death. Nevertheless, in higher eukaryotes, caspases also influence a variety of cell behaviors including differentiation, proliferation and growth control. S. cerevisiae expresses a primordial caspase, yca1, and exhibits apoptosis-like death under certain stresses; however, the benefit of a dedicated death program to single cell organisms is controversial. In the absence of a clear rationale to justify the evolutionary retention of a death only pathway, we hypothesize that yca1 also influences non-apoptotic events. We report that genetic ablation and/or catalytic inactivation of Yca1p leads to a longer G1/S transition accompanied by slower growth in fermentation conditions. Downregulation of Yca1p proteolytic activity also results in failure to arrest during nocodazole treatment, indicating that Yca1p participates in the G2/M mitotic checkpoint. 20s proteasome activity and ROS staining of the Δyca1 strain is indistinguishable from its isogenic control suggesting that putative regulation of the oxidative stress response by Yca1p does not instigate the cell cycle phenotype. Our results demonstrate multiple non-death roles for yca1 in the cell cycle.

A novel whole-cell lysate kinase assay identifies substrates of the p38 MAPK in differentiating myoblasts

BackgroundThe p38α mitogen-activated protein kinase (MAPK) is a critical mediator of myoblast differentiation, and does so in part through the phosphorylation and regulation of several transcription factors and chromatin remodelling proteins. However, whether p38α is involved in processes other than gene regulation during myogenesis is currently unknown, and why other p38 isoforms cannot compensate for its loss is unclear.MethodsTo further characterise the involvement of p38α during myoblast differentiation, we developed and applied a simple technique for identifying relevant in vivo kinase substrates and their phosphorylation sites. In addition to identifying substrates for one kinase, the technique can be used in vitro to compare multiple kinases in the same experiment, and we made use of this to study the substrate specificities of the p38α and β isoforms.ResultsApplying the technique to p38α resulted in the identification of seven in vivo phosphorylation sites on six proteins, four of which are cytoplasmic, in lysate derived from differentiating myoblasts. An in vitro comparison with p38β revealed that substrate specificity does not discriminate these two isoforms, but rather that their distinguishing characteristic appears to be cellular localisation.ConclusionOur results suggest p38α has a novel cytoplasmic role during myogenesis and that its unique cellular localisation may be why p38β and other isoforms cannot compensate for its absence. The substrate-finding approach presented here also provides a necessary tool for studying the hundreds of protein kinases that exist and for uncovering the deeper mechanisms of phosphorylation-dependent cell signalling.

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward ‘systems pharmacology’, we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction.

Reconstructing the Regulatory Kinase Pathways of Myogenesis from Phosphopeptide Data

Multiple kinase activities are required for skeletal muscle differentiation. However, the mechanisms by which these kinase pathways converge to coordinate the myogenic process are unknown. Using multiple phosphoprotein and phosphopeptide enrichment techniques we obtained phosphopeptides from growing and differentiating C2C12 muscle cells and determined specific peptide sequences using LC-MS/MS. To place these phosphopeptides into a rational context, a bioinformatics approach was used. Phosphorylation sites were matched to known site-specific and to site non-specific kinase-substrate interactions, and then other substrates and upstream regulators of the implicated kinases were incorporated into a model network of protein-protein interactions. The model network implicated several kinases of known relevance to myogenesis including AKT, GSK3, CDK5, p38, DYRK, and MAPKAPK2 kinases. This combination of proteomics and bioinformatics technologies should offer great utility as the volume of protein-protein and kinase-substrate information continues to increase.

The yeast kinome displays scale free topology with functional hub clusters

BackgroundThe availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an emphasis on studying the global properties of biological interactions using network analysis. While this type of analysis offers a wide variety of global insights it has surprisingly not been used to examine more localized interactions based on mechanism. In as such we have particular interest in the role of key topological components in signal transduction cascades as they are vital regulators of healthy and diseased cell states.ResultsWe have used publicly available databases and a novel software tool termed Hubview to model the interactions of a subset of the yeast interactome, specifically protein kinases and their interaction partners. Analysis of the connectivity distribution has inferred a fat-tailed degree distribution with parameters consistent with those found in other biological networks. In addition, Hubview identified a functional clustering of a large group of kinases, distributed between three separate groupings. The complexity and average degree for each of these clusters is indicative of a specialized function (cell cycle propagation, DNA repair and pheromone response) and relative age for each cluster.ConclusionUsing connectivity analysis on a functional subset of proteins we have evidence that reinforces the scale free topology as a model for protein network evolution. We have identified the hub components of the kinase network and observed a tendency for these kinases to cluster together on a functional basis. As such, these results suggest an inherent trend to preserve scale free characteristics at a domain based modular level within large evolvable networks.

NF-κB signalling and cell fate decisions in response to a short pulse of tumour necrosis factor.

In tissues and tumours, cell behaviours are regulated by multiple time-varying signals. While in the laboratory cells are often exposed to a stimulus for the duration of the experiment, in vivo exposures may be much shorter. In this study, we monitored NF-κB and caspase signalling in human cancer cells treated with a short pulse of Tumour Necrosis Factor (TNF). TNF is an inflammatory cytokine that can induce both the pro-survival NF-κB-driven gene transcription pathway and the pro-apoptotic caspase pathway. We find that a few seconds of exposure to TNF is sufficient to activate the NF-κB pathway in HeLa cells and induce apoptotic cell death in both HeLa and Kym-1 cells. Strikingly, a 1-min pulse of TNF can be more effective at killing than a 1-hour pulse, indicating that in addition to TNF concentration, duration of exposure also coordinates cell fate decisions.

Demystifying the cytokine network: Mathematical models point the way

Abstract Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene‐targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge‐based and data‐driven modeling approaches and the recent advance in single‐cell analysis. The use of modeling to optimize cytokine‐based therapies will also be discussed.

Reconstructing Regulatory Kinase Pathways from Phosphopeptide Data: A Bioinformatics Approach

Protein phosphorylation is a widespread cellular process, and simplistic linear pathway models of kinase signaling likely under-represent the complexity of in vivo pathways. The recent massive increase in information available through protein interaction databases now allows construction of in silico models of protein networks that are underpinned by evidence from real biological systems. By combining protein phosphorylation data with current databases of protein-protein and kinase-substrate interactions, sophisticated models of intracellular protein phosphorylation signaling can be constructed for a system of interest. The kinase interaction network can be visualized, analyzed by graph theory, and investigated for hypotheses that are not otherwise obvious.

Shift from stochastic to spatially-ordered expression of serine-glycine synthesis enzymes in 3D microtumors

Cell-to-cell differences in protein expression in normal tissues and tumors are a common phenomenon, but the underlying principles that govern this heterogeneity are largely unknown. Here, we show that in monolayer cancer cell-line cultures, the expression of the five metabolic enzymes of serine-glycine synthesis (SGS), including its rate-limiting enzyme, phosphoglycerate dehydrogenase (PHGDH), displays stochastic cell-to-cell variation. By contrast, in cancer cell line-derived three-dimensional (3D) microtumors PHGDH expression is restricted to the outermost part of the microtumors’ outer proliferative cell layer, while the four other SGS enzymes display near uniform expression throughout the microtumor. A mathematical model suggests that metabolic stress in the microtumor core activates factors that restrict PHGDH expression. Thus, intracellular enzyme expression in growing cell ecosystems can shift to spatially ordered patterns in 3D structured environments due to emergent cell-cell communication, with potential implications for the design of effective anti-metabolic cancer therapies.