Transcriptional Profiling of ubp10 Null Mutant Reveals Altered Subtelomeric Gene Expression and Insurgence of Oxidative Stress Response

UBP10 codes for a deubiquitinating enzyme of Saccharomyces cerevisiae whose loss of function determines slow growth rate and partial impairment of silencing at telomeres and HM loci. A genome-wide analysis performed on a ubp10 disruptant revealed alterations in expression of subtelomeric genes together with a broad change in the whole transcriptional profile, closely parallel to that induced by oxidative stress. This response was accompanied by intracellular accumulation of reactive oxygen species as well as by DNA fragmentation and phosphatidylserine externalization, two markers of apoptosis. SIR4 inactivation mitigated the wide transcriptome remodeling of the ubp10 null mutant affecting particularly the stress transcriptional profile. Moreover, the ubp10sir4 disruptant did not display apoptotic markers. These results argue in favor of an involvement of deubiquitination in transcriptional control and suggest a linkage between oxidative stress and apoptotic pathway in budding yeast

cAMP promotes the synthesis in early G1 of gp115, a yeast glycoprotein containing glycosyl-phosphatidylinositol.

The glycoprotein gp115 (Mr = 115,000, pI 4.8-5) is localized in the plasma membrane of Saccharomyces cerevisiae cells and maximally expressed during G1 phase. To gain insight on the mechanism regulating its synthesis, we have examined various conditions of cell proliferation arrest. We used pulse-labeling experiments with [35S]methionine and two-dimensional gel electrophoresis analysis, which allow the detection of the well characterized 100-kDa precursor of gp115 (p100). In the cAMP-requiring mutant cyr1, p100 synthesis is active during exponential growth, shut off by cAMP removal, and induced when growth is restored by cAMP readdition. The inhibition of p100 synthesis also occurs in TS1 mutant cells (ras1ras2-ts1) shifted from 24 to 37 degrees C. During nitrogen starvation of rca1 cells, a mutant permeable to cAMP, p100 synthesis is also inhibited. cAMP complements the effect of ammonium deprivation, promoting p100 synthesis, even when added to cells which have already entered G0. Experiments with the bcy1 and cyr1bcy1 mutants have indicated the involvement of the cAMP-dependent protein kinases in the control of p100 synthesis. Moreover, the synthesis of p100 was unaffected in A364A cells, terminally arrested at START B by alpha-factor. These results indicate that the switch operating on p100 synthesis is localized in early G1 (START A) and is one of the multiple events controlled by the cAMP pathway

Isolation and deduced amino acid sequence of the gene encoding gp115, a yeast glycophospholipid-anchored protein containing a serine-rich region.

Abstract gp115 is a N- and O-glycosylated protein of Saccharomyces cerevisiae. It is also modified by addition of glycosylphosphatidylinositol, which anchors the protein to the plasma membrane. The gene encoding gp115 (GGP1) has been cloned by a two-step procedure. By an immunoscreening of a yeast genomic DNA library in the expression vector lambda gt11, a 3'-terminal 0.9-kilobase portion of the gene has been isolated and then used as a molecular probe to screen a yeast genomic DNA library in YEp24. In this way, the whole GGP1 gene has been cloned. Its identity with the gp115 gene has been confirmed by gene disruption, which has also indicated that the function of gp115 is not essential for cell viability. The features of the sequence are also entirely consistent with it corresponding to the gp115 gene. The nucleotide sequence of GGP1 predicts a 60-kDa polypeptide, in agreement with the molecular mass of the gp115 precursor detected in sec53 mutant cells at restrictive temperature. Two hydrophobic sequences, one NH2- and the other COOH-terminal were found. The former has the features of the cleavable signal sequence, which allows the entry of proteins in the secretory pathway. The latter could be the signal sequence that has to be removed during the addition of glycosylphosphatidylinositol. The predicted amino acid sequence of gp115 shows 10 sequons for N-glycosylation and a high proportion of serine-threonine residues (22%) that could provide several sites for O-glycosylation. The unusual concentration of 27 serines in the COOH-terminal portion of the protein shares homology with a similar polyserine repeat of the serine repeat antigen (SERA protein) of Plasmodium falciparum. A two-dimensional analysis of the "in vitro" translational product of the GGP1 mRNA has been carried out, allowing the identification of the "in vivo" gp115 precursor in a two-dimensional gel

Systems metabolomics: from metabolomic snapshots to design principles

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O-linked oligosaccharides in yeast glycosyl phosphatidylinositol-anchored protein gp115 are clustered in a serine-rich region not essential for its function

Abstract The protein gp115 is an exocellular yeast glycoprotein modified by O- and N-glycosylation and attached to the plasma membrane through a glycosylphosphatidylinositol. The more remarkable structural feature in gp115 is the presence of a 36-amino acid serine-rich region. Similar sequences have been found in mammalian glycoproteins, such as the low density lipoprotein receptor, the decay-accelerating factor, and the mucins, where they are targets of multiple sites of O-glycosylation. The modification of these regions greatly influences their conformation and gives rise to "rodlike" structures. In this work, we have deleted or duplicated the Ser-rich region of gp115. The analysis of the size and glycosylation state of both mutant proteins indicates that about 52% of the total contribution of the O-glycosylation to the mass of the protein is concentrated in this region. The phenotype of ggp1 null mutant expressing the mutant proteins was also analyzed to understand if this region is important for gp115 function. The defects of slow growth rate and resistance to zymolyase of the ggp1 cells are completely complemented by both mutant proteins, suggesting that this region could be dispensable for gp115 function. A tentative model of gp115 structure is presented on the basis of the obtained data

Neuro-Immune Hemostasis: Homeostasis and Diseases in the Central Nervous System

Coagulation and the immune system interact in several physiological and pathological conditions, including tissue repair, host defense, and homeostatic maintenance. This network plays a key role in diseases of the central nervous system (CNS) by involving several cells (CNS resident cells, platelets, endothelium, and leukocytes) and molecular pathways (protease activity, complement factors, platelet granule content). Endothelial damage prompts platelet activation and the coagulation cascade as the first physiological step to support the rescue of damaged tissues, a flawed rescuing system ultimately producing neuroinflammation. Leukocytes, platelets, and endothelial cells are sensitive to the damage and indeed can release or respond to chemokines and cytokines (platelet factor 4, CXCL4, TNF, interleukins), and growth factors (including platelet-derived growth factor, vascular endothelial growth factor, and brain-derived neurotrophic factor) with platelet activation, change in capillary permeability, migration or differentiation of leukocytes. Thrombin, plasmin, activated complement factors and matrix metalloproteinase-1 (MMP-1), furthermore, activate intracellular transduction through complement or protease-activated receptors. Impairment of the neuro-immune hemostasis network induces acute or chronic CNS pathologies related to the neurovascular unit, either directly or by the systemic activation of its main steps. Neurons, glial cells (astrocytes and microglia) and the extracellular matrix play a crucial function in a “tetrapartite” synaptic model. Taking into account the neurovascular unit, in this review we thoroughly analyzed the influence of neuro-immune hemostasis on these five elements acting as a functional unit (“pentapartite” synapse) in the adaptive and maladaptive plasticity and discuss the relevance of these events in inflammatory, cerebrovascular, Alzheimer, neoplastic and psychiatric diseases. Finally, based on the solid reviewed data, we hypothesize a model of neuro-immune hemostatic network based on protein–protein interactions. In addition, we propose that, to better understand and favor the maintenance of adaptive plasticity, it would be useful to construct predictive molecular models, able to enlighten the regulating logic of the complex molecular network, which belongs to different cellular domains. A modeling approach would help to define how nodes of the network interact with basic cellular functions, such as mitochondrial metabolism, autophagy or apoptosis. It is expected that dynamic systems biology models might help to elucidate the fine structure of molecular events generated by blood coagulation and neuro-immune responses in several CNS diseases, thereby opening the way to more effective treatments

Cell Size at S Phase Initiation: An Emergent Property of the G(1)/S Network

The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G(1), S, G(2), and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclin-dependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G(1) to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, P(S), to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G(1) to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate P(S), the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of P(S) provides a novel relevant conclusion: P(S) is an emergent property of the G(1) to S network that strongly depends on growth rate

A cell sizer network involving Cln3 and Far1 controls entrance into S phase in the mitotic cycle of budding yeast

Saccharomyces cerevisiae must reach a carbon source-modulated critical cell size, protein content per cell at the onset of DNA replication (Ps), in order to enter S phase. Cells grown in glucose are larger than cells grown in ethanol. Here, we show that an increased level of the cyclin-dependent inhibitor Far1 increases cell size, whereas far1Δ cells start bud emergence and DNA replication at a smaller size than wild type. Cln3Δ, far1Δ, and strains overexpressing Far1 do not delay budding during an ethanol glucose shift-up as wild type does. Together, these findings indicate that Cln3 has to overcome Far1 to trigger Cln–Cdc28 activation, which then turns on SBF- and MBF-dependent transcription. We show that a second threshold is required together with the Cln3/Far1 threshold for carbon source modulation of Ps. A new molecular network accounting for the setting of Ps is proposed

Data recovery and integration from public databases uncovers transformation-specific transcriptional downregulation of cAMP-PKA pathway-encoding genes

BACKGROUND: The integration of data from multiple genome-wide assays is essential for understanding dynamic spatio-temporal interactions within cells. Such integration, which leads to a more complete view of cellular processes, offers the opportunity to rationalize better the high amount of "omics" data freely available in several public databases. In particular, integration of microarray-derived transcriptome data with other high-throughput analyses (genomic and mutational analysis, promoter analysis) may allow us to unravel transcriptional regulatory networks under a variety of physio-pathological situations, such as the alteration in the cross-talk between signal transduction pathways in transformed cells. RESULTS: Here we sequentially apply web-based and statistical tools to a case study: the role of oncogenic activation of different signal transduction pathways in the transcriptional regulation of genes encoding proteins involved in the cAMP-PKA pathway. To this end, we first re-analyzed available genome-wide expression data for genes encoding proteins of the downstream branch of the PKA pathway in normal tissues and human tumor cell lines. Then, in order to identify mutation-dependent transcriptional signatures, we classified cancer cells as a function of their mutational state. The results of such procedure were used as a starting point to analyze the structure of PKA pathway-encoding genes promoters, leading to identification of specific combinations of transcription factor binding sites, which are neatly consistent with available experimental data and help to clarify the relation between gene expression, transcriptional factors and oncogenes in our case study. CONCLUSIONS: Genome-wide, large-scale "omics" experimental technologies give different, complementary perspectives on the structure and regulatory properties of complex systems. Even the relatively simple, integrated workflow presented here offers opportunities not only for filtering data noise intrinsic in high throughput data, but also to progressively extract novel information that would have remained hidden otherwise. In fact we have been able to detect a strong transcriptional repression of genes encoding proteins of cAMP/PKA pathway in cancer cells of different genetic origins. The basic workflow presented herein may be easily extended by incorporating other tools and can be applied even by researchers with poor bioinformatics skills

Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure

Background: Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. Results: In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). Conclusions: This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks