Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

NF-κB signaling dynamics is controlled by a dose-sensing autoregulatory loop.

Over the last decade, multiple studies have shown that signaling proteins activated in different temporal patterns, such as oscillatory, transient, and sustained, can result in distinct gene expression patterns or cell fates. However, the molecular events that ensure appropriate stimulus- and dose-dependent dynamics are not often understood and are difficult to investigate. Here, we used single-cell analysis to dissect the mechanisms underlying the stimulus- and dose-encoding patterns in the innate immune signaling network. We found that Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling dynamics relied on a dose-dependent, autoinhibitory loop that rendered cells refractory to further stimulation. Using inducible gene expression and optogenetics to perturb the network at different levels, we identified IL-1R-associated kinase 1 (IRAK1) as the dose-sensing node responsible for limiting signal flow during the innate immune response. Although the kinase activity of IRAK1 was not required for signal propagation, it played a critical role in inhibiting the nucleocytoplasmic oscillations of the transcription factor NF-κB. Thus, protein activities that may be "dispensable" from a topological perspective can nevertheless be essential in shaping the dynamic response to the external environment.

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level.

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Osmostress induces autophosphorylation of Hog1 via a C-terminal regulatory region that is conserved in p38α.

Many protein kinases require phosphorylation at their activation loop for induction of catalysis. Mitogen-activated protein kinases (MAPKs) are activated by a unique mode of phosphorylation, on neighboring Tyrosine and Threonine residues. Whereas many kinases obtain their activation via autophosphorylation, MAPKs are usually phosphorylated by specific, dedicated, MAPK kinases (MAP2Ks). Here we show however, that the yeast MAPK Hog1, known to be activated by the MAP2K Pbs2, is activated in pbs2Δ cells via an autophosphorylation activity that is induced by osmotic pressure. We mapped a novel domain at the Hog1 C-terminal region that inhibits this activity. Removal of this domain provides a Hog1 protein that is partially independent of MAP2K, namely, partially rescues osmostress sensitivity of pbs2Δ cells. We further mapped a short domain (7 amino acid residues long) that is critical for induction of autophosphorylation. Its removal abolishes autophosphorylation, but maintains Pbs2-mediated phosphorylation. This 7 amino acids stretch is conserved in the human p38α. Similar to the case of Hog1, it's removal from p38α abolishes p38α's autophosphorylation capability, but maintains, although reduces, its activation by MKK6. This study joins a few recent reports to suggest that, like many protein kinases, MAPKs are also regulated via induced autoactivation.

The yeast MAPK Hog1 is not essential for immediate survival under osmostress.

The yeast HOG pathway is activated in response to increased osmolarity and affects many cellular activities. As cells lacking Hog1 are osmo-sensitive it is believed that Hog1 is essential for survival under osmostress. We show, however, that hog1Delta cells survive and even proliferate to some degree under high osmostress for many hours. If forced to enter G1/G0 prior the exposure to osmostress, hog1Delta cells survive for at least 6days. We suggest that the primary role of Hog1 is not to preserve viability upon exposure to stress. We discuss the possibility that Hog1 is needed for proliferation under osmostress.

When expressed in yeast, mammalian mitogen-activated protein kinases lose proper regulation and become spontaneously phosphorylated.

MAPKs (mitogen-activated protein kinases) are key components in cell signalling pathways. Under optimal growth conditions, their activity is kept off, but in response to stimulation it is dramatically evoked. Because of the high degree of evolutionary conservation at the levels of sequence and mode of activation, MAPKs are believed to share similar regulatory mechanisms in all eukaryotes and to be functionally substitutable between them. To assess the reliability of this notion, we systematically analysed the activity, regulation and phenotypic effects of mammalian MAPKs in yeast. Unexpectedly, all mammalian MAPKs tested were spontaneously phosphorylated in yeast. JNKs (c-Jun N-terminal kinases) lost their phosphorylation in pbs2Delta cells, but p38s and ERKs (extracellular-signal-regulated kinases) maintained their spontaneous phosphorylation even in pbs2Deltaste7Deltamkk1Deltamkk2Delta cells. Kinase-dead variants of ERKs and p38s were phosphorylated in strains lacking a single MEK (MAPK/ERK kinase), but not in pbs2Deltaste7Deltamkk1Deltamkk2Delta cells. Thus, in yeast, p38 and ERKs are phosphorylated via a combined mechanism of autophosphorylation and MEK-mediated phosphorylation (any MEK). We further addressed the mechanism allowing mammalian MAPKs to exploit yeast MEKs in the absence of any activating signal. We suggest that mammalian MAPKs lost during evolution a C-terminal region that exists in some yeast MAPKs. Indeed, removal of this region from Hog1 and Mpk1 rendered them spontaneously and highly phosphorylated. It implies that MAPKs possess an efficient inherent autoposphorylation capability that is suppressed in yeast MAPKs via a C-terminal domain and in mammalian MAPKs via as yet unknown means.