How can yeast cells decide between three activated MAP kinase pathways? A model approach

Near-gapless genome and transcriptome analyses provide insights into fruiting body development in Lentinula edodes

Fruiting body development in macrofungi is an intensive research subject. In this study, high-quality genomes were assembled for two sexually compatible monokaryons from a heterokaryotic Lentinula edodes strain WX1, and variations in L. edodes genomes were analyzed. Specifically, differential gene expression and allele-specific expression (ASE) were analyzed using the two monokaryotic genomes and transcriptome data from four different stages of fruiting body development in WX1. Results revealed that after aeration, mycelia sensed cell wall stress, pheromones, and a decrease in CO2 concentration, leading to up-regulated expression in genes related to cell adhesion, cell wall remodeling, proteolysis, and lipid metabolism, which may promote primordium differentiation. Aquaporin genes and those related to proteolysis, mitosis, lipid, and carbohydrate metabolism may play important roles in primordium development, while genes related to tissue differentiation and sexual reproduction were active in fruiting body. Several essential genes for fruiting body development were allele-specifically expressed and the two nuclear types could synergistically regulate fruiting body development by dominantly expressing genes with different functions. ASE was probably induced by long terminal repeat-retrotransposons. Findings here contribute to the further understanding of the mechanism of fruiting body development in macrofungi.

Aberrant expression of JNK-associated leucine-zipper protein, JLP, promotes accelerated growth of ovarian cancer

Ovarian cancer is the most fatal gynecologic cancer with poor prognosis. Etiological factors underlying ovarian cancer genesis and progression are poorly understood. Previously, we have shown that JNK-associated Leucine zipper Protein (JLP), promotes oncogenic signaling. Investigating the role of JLP in ovarian cancer, our present study indicates that JLP is overexpressed in ovarian cancer tissue and ovarian cancer cells. Transient overexpression of JLP promotes proliferation and invasive migration of ovarian cancer cells. In addition, ectopic expression of JLP confers long-term survival and clonogenic potential to normal fallopian tube-derived epithelial cells. Coimmunoprecipitation and colocalization analyses demonstrate the in vivo interaction of JLP and JNK, which is stimulated by lysophosphatidic acid (LPA), an oncogenic lipid growth factor in ovarian cancer. We also show that LPA stimulates the translocation of JLP-JNK complex to the perinuclear region of SKOV3-ip cells. JLP-knockdown using shRNA abrogates LPA-stimulated activation of JNK as well as LPA-stimulated proliferation and invasive migration of SKOV3-ip cells. Studies using ovarian cancer xenograft mouse model indicate that the mice bearing JLP-silenced xenografts exhibits reduced tumor volume. Analysis of the xenograft tumor tissues indicate a reduction in the levels of JLP, JNK, phosphorylated-JNK, c-Jun and phosphorylated-c-Jun in JLP-silenced xenografts, thereby correlating the attenuated JLP-JNK signaling node with suppressed tumor growth. Thus, our results identify a critical role for JLP-signaling axis in ovarian cancer and provide evidence that targeting this signaling node could provide a new avenue for therapy.

Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates “small-world” networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein’s position within a module and to the module’s relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

We discovered a large protein network that regulates fat storage in budding yeast. This network contains 94 proteins, almost all of which bind to other proteins in the network. To understand the functions of large protein collections such as these, it will be necessary to move away from one-by-one analysis of individual proteins and create computational models of entire networks. This will allow classification of networks into categories and permit researchers to identify key network proteins on theoretical grounds. We show here that the fat regulation network fits a Watts-Strogatz small-world model. This model was devised to explain the clustering phenomena often observed in real networks, but has not been previously applied to signaling networks within cells. The short path length and high clustering coefficients characteristic of the Watts-Strogatz topology allow for rapid communication between distant nodes and for division of the network into modules that perform different functions. The fat regulation network has modules, and it is divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We experimentally examined communication between nodes within the network using a combination of genetics and pharmacology, and showed that the communication patterns are consistent with the Watts-Strogatz topology.

Multiplexing oscillatory biochemical signals

In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals.

Response to hyperosmotic stress

An appropriate response and adaptation to hyperosmolarity, i.e., an external osmolarity that is higher than the physiological range, can be a matter of life or death for all cells. It is especially important for free-living organisms such as the yeast Saccharomyces cerevisiae. When exposed to hyperosmotic stress, the yeast initiates a complex adaptive program that includes temporary arrest of cell-cycle progression, adjustment of transcription and translation patterns, and the synthesis and retention of the compatible osmolyte glycerol. These adaptive responses are mostly governed by the high osmolarity glycerol (HOG) pathway, which is composed of membrane-associated osmosensors, an intracellular signaling pathway whose core is the Hog1 MAP kinase (MAPK) cascade, and cytoplasmic and nuclear effector functions. The entire pathway is conserved in diverse fungal species, while the Hog1 MAPK cascade is conserved even in higher eukaryotes including humans. This conservation is illustrated by the fact that the mammalian stress-responsive p38 MAPK can rescue the osmosensitivity of hog1Δ mutations in response to hyperosmotic challenge. As the HOG pathway is one of the best-understood eukaryotic signal transduction pathways, it is useful not only as a model for analysis of osmostress responses, but also as a model for mathematical analysis of signal transduction pathways. In this review, we have summarized the current understanding of both the upstream signaling mechanism and the downstream adaptive responses to hyperosmotic stress in yeast.

Multiplexing biochemical signals

In this Letter we show that living cells can multiplex biochemical signals, i.e., transmit multiple signals through the same signaling pathway simultaneously, and yet respond to them very specifically. We demonstrate how two binary input signals can be encoded in the concentration of a common signaling protein, which is then decoded such that each of the two output signals provides reliable information about one corresponding input. Under biologically relevant conditions the network can reach the maximum amount of information that can be transmitted, which is 2 bits.

The Dynamical Systems Properties of the HOG Signaling Cascade

The High Osmolarity Glycerol (HOG) MAP kinase pathway in the budding yeast Saccharomyces cerevisiae is one of the best characterized model signaling pathways. The pathway processes external signals of increased osmolarity into appropriate physiological responses within the yeast cell. Recent advances in microfluidic technology coupled with quantitative modeling, and techniques from reverse systems engineering have allowed yet further insight into this already well-understood pathway. These new techniques are essential for understanding the dynamical processes at play when cells process external stimuli into biological responses. They are widely applicable to other signaling pathways of interest. Here, we review the recent advances brought by these approaches in the context of understanding the dynamics of the HOG pathway signaling.