Elevated energy costs of biomass production in mitochondrial respiration-deficient Saccharomyces cerevisia.

Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeast Saccharomyces cerevisiae, although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model of S. cerevisiae to compare the optimal metabolic strategies between different conditions. We found that S. cerevisiae with respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants of S. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused by de novo biosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.

An excess of glycolytic enzymes under glucose-limited conditions may enable Saccharomyces cerevisiae to adapt to nutrient availability.

Microorganisms, including the budding yeast Saccharomyces cerevisiae, express glycolytic proteins to a maximal capacity that (largely) exceeds the actual flux through the enzymes, especially at low growth rates. An open question is if this apparent expression level is really an overcapacity, or maintains the (optimal) enzyme capacity needed to carry flux at (very) low substrate availability. Here, we use computational modelling to suggest that yeast maintains a genuine excess of glycolytic enzymes at low specific growth rates. During fast fermentative growth at high glucose levels, the observed expression of the glycolytic enzymes matched the predicted optimal levels. We suggest that the excess glycolytic capacity at low glucose levels is a preparatory strategy in the adaptation to sugar fluctuations in the environment.

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast.

The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models.

Transcription activation is enhanced by multivalent interactions independent of phase separation.

Transcription factors (TFs) consist of a DNA-binding domain and an activation domain (AD) that are frequently considered to be independent and exchangeable modules. However, recent studies report that the physicochemical properties of the AD can control TF assembly at chromatin by driving phase separation into transcriptional condensates. Here, we dissected transcription activation by comparing different synthetic TFs at a reporter gene array with real-time single-cell fluorescence microscopy. In these experiments, binding site occupancy, residence time, and coactivator recruitment in relation to multivalent TF interactions were compared. While phase separation propensity and activation strength of the AD were linked, the actual formation of liquid-like TF droplets had a neutral or inhibitory effect on transcription activation. We conclude that multivalent AD-mediated interactions enhance the transcription activation capacity of a TF by increasing its residence time in the chromatin-bound state and facilitating the recruitment of coactivators independent of phase separation.

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation-known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

Lysosome-targeted photodynamic treatment induces primary keratinocyte differentiation.

Photodynamic therapy is an attractive technique for various skin tumors and non-cancerous skin lesions. However, while the aim of photodynamic therapy is to target and damage only the malignant cells, it unavoidably affects some of the healthy cells surrounding the tumor as well. However, data on the effects of PDT to normal cells are scarce, and the characterization of the pathways activated after the photodamage of normal cells may help to improve clinical photodynamic therapy. In our study, primary human epidermal keratinocytes were used to evaluate photodynamic treatment effects of photosensitizers with different subcellular localization. We compared the response of keratinocytes to lysosomal photodamage induced by phthalocyanines, aluminum phthalocyanine disulfonate (AlPcS2a) or aluminum phthalocyanine tetrasulfonate (AlPcS4), and cellular membrane photodamage by m-tetra(3-hydroxyphenyl)-chlorin (mTHPC). Our data showed that mTHPC-PDT promoted autophagic flux, whereas lysosomal photodamage induced by aluminum phthalocyanines evoked differentiation and apoptosis. Photodamage by AlPcS2a, which is targeted to lysosomal membranes, induced keratinocyte differentiation and apoptosis more efficiently than AlPcS4, which is targeted to lysosomal lumen. Computational analysis of the interplay between these molecular pathways revealed that keratin 10 is the coordinating molecular hub of primary keratinocyte differentiation, apoptosis and autophagy.

Protein cost allocation explains metabolic strategies in Escherichia coli.

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.

miRNA target identification and prediction as a function of time in gene expression data.

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

Epistatic Analysis of the Contribution of Rabs and Kifs to CATCHR Family Dependent Golgi Organization.

Multisubunit members of the CATCHR family: COG and NRZ complexes, mediate intra-Golgi and Golgi to ER vesicle tethering, respectively. We systematically addressed the genetic and functional interrelationships between Rabs, Kifs, and the retrograde CATCHR family proteins: COG3 and ZW10, which are necessary to maintain the organization of the Golgi complex. We scored the ability of siRNAs targeting 19 Golgi-associated Rab proteins and all 44 human Kifs, microtubule-dependent motor proteins, to suppress CATCHR-dependent Golgi fragmentation in an epistatic fluorescent microscopy-based assay. We found that co-depletion of Rab6A, Rab6A', Rab27A, Rab39A and two minus-end Kifs, namely KIFC3 and KIF25, suppressed both COG3- and ZW10-depletion-induced Golgi fragmentation. ZW10-dependent Golgi fragmentation was suppressed selectively by a separate set of Rabs: Rab11A, Rab33B and the little characterized Rab29. 10 Kifs were identified as hits in ZW10-depletion-induced Golgi fragmentation, and, in contrast to the double suppressive Kifs, these were predominantly plus-end motors. No Rabs or Kifs selectively suppressed COG3-depletion-induced Golgi fragmentation. Protein-protein interaction network analysis indicated putative direct and indirect links between suppressive Rabs and tether function. Validation of the suppressive hits by EM confirmed a restored organization of the Golgi cisternal stack. Based on these outcomes, we propose a three-way competitive model of Golgi organization in which Rabs, Kifs and tethers modulate sequentially the balance between Golgi-derived vesicle formation, consumption, and off-Golgi transport.

Exogenous interleukin-1α signaling negatively impacts acquired chemoresistance and alters cell adhesion molecule expression pattern in colorectal carcinoma cells HCT116.

Proinflammatory cytokine and chemokine signaling from the tumor microenvironment is thought to be crucial for developing and sustaining colorectal cancer by regulating a multitude of pathways associated with a variety of cellular mechanisms. Among these pathways there is acquired chemoresistance, which is usually a major obstacle in the way towards successful chemotherapeutic treatment of advanced colorectal cancer cases. Despite of an emerging body of data published on the role of cytokine signaling network in cancer, little is known about the effects of the upstream cytokine interleukin-1α (IL-1α) signaling to the cancer cells. In this study we have shown that the increase in exogenous IL-1α signaling increases chemosensitivity of both chemosensitive and chemoresistant colorectal cancer cell lines, treated with a widely used cytotoxic antimetabolite 5-fluorouracil (5-FU). This was a result of increased cell death but not of the changes in 5-FU-induced cell cycle arrest. Noticeably, combined exogenous IL-1α and 5-FU treatment had significant effects on the expression of cell adhesion molecules, suggesting a decrease in adhesion-dependent chemoresistance and, on the other hand, an increase in metastatic potential of the cells. These results lead to a conclusion that modulation of IL-1 receptor activity could have applications as a part of combination therapy for advanced and highly metastatic colorectal cancers.

The role of interleukin-8 (CXCL8) and CXCR2 in acquired chemoresistance of human colorectal carcinoma cells HCT116.

Colorectal cancer is one of the most common malignant diseases and is a leading cause of cancer mortality in the Western world. Primary or acquired resistance to chemotherapeutic drugs is a common phenomenon which causes a failure in cancer treatment. A diverse range of molecular mechanisms has been implicated in drug resistance: DNA damage repair, alterations in drug metabolism, mutation of drug targets, increased rates of drug efflux, and activation of survival signaling pathways. The aim of this study was to investigate the expression of CXCL8-CXCR1/2 pathway, its impact on cell proliferation and cytokine expression in human colorectal carcinoma HCT116 cells, and their chemotherapy-resistant subline. We found that IL-1 alpha stimulates the production of CXCL8 through IL-1 receptor signaling. Our data indicate that CXCL8 is upregulated in chemoresistant subline of colorectal cancer cells HCT116, and modulation of CXCR2 pathway can be a target for proliferation inhibition of chemoresistant colorectal cancer cells.