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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.

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 yeastSaccharomyces 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 ofS. cerevisiaeto compare the optimal metabolic strategies between different conditions. We found thatS. cerevisiaewith 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 ofS. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused byde novobiosynthesis 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.

In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast Saccharomyces cerevisiae, appear to keep “excess” glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account.Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when S. cerevisiae exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of S. cerevisiae to fluctuations of nutrient availability in the environment.

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.

SummaryTranscription factors (TFs) consist of a DNA binding and an activation domain (AD) that are considered to be independent and exchangeable modules. However, recent studies conclude that also the physico-chemical properties of the AD can control TF assembly at chromatin by driving a 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 readouts. In these experiments, binding site occupancy, residence time and co-activator recruitment in relation to multivalent TF interactions were compared. While phase separation propensity and activation strength of the AD were correlated, the actual formation of liquid-like TF droplets had a neutral or inhibitory effect on transcription activation. Rather, we conclude that multivalent AD mediated interactions increase the transcription activation capacity of a TF by stabilizing chromatin binding and mediating the recruitment of co-activators independent of phase separation.

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.

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.

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.

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.

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.