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Neuronal differentiation is regulated by nerve growth factor (NGF) and other neurotrophins. We explored the impact of NGF on mitochondrial dynamics and metabolism through time‐lapse imaging, metabolomics profiling, and computer modeling studies. We show that NGF may direct differentiation by stimulating fission, thereby causing selective mitochondrial network fragmentation and mitophagy, ultimately leading to increased mitochondrial quality and respiration. Then, we reconstructed the dynamic fusion–fission–mitophagy cycling of mitochondria in a computer model, integrating these processes into a single network mechanism. Both the computational model and the simulations are able to reproduce the proposed mechanism in terms of mitochondrial dynamics, levels of reactive oxygen species (ROS), mitophagy, and mitochondrial quality, thus providing a computational tool for the interpretation of the experimental data and for future studies aiming to detail further the action of NGF on mitochondrial processes. We also show that changes in these mitochondrial processes are intertwined with a metabolic function of NGF in differentiation: NGF directs a profound metabolic rearrangement involving glycolysis, TCA cycle, and the pentose phosphate pathway, altering the redox balance. This metabolic rewiring may ensure: (a) supply of both energy and building blocks for the anabolic processes needed for morphological reorganization, as well as (b) redox homeostasis. [ABSTRACT FROM AUTHOR]

The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs. [ABSTRACT FROM AUTHOR]

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients. Author summary: The study of metabolism and its regulation finds increasing application in various fields, including biotransformations, wellness, and health. Metabolism can be studied using post-genomic technologies. Transcriptomics, proteomics and metabolomics provide snapshots of transcripts, enzyme levels and metabolites in specific physio-pathological conditions. In the health field, the transcriptome and, more recently, the metabolome have been broadly profiled at the pre-clinical and clinical levels, while only more recently proteomic studies profiling metabolic enzymes are becoming available. However, the informative power of single omic technologies is inadequate since metabolism regulation involves a complex interplay of regulatory steps. Gene expression regulates metabolism by setting the upper level of metabolic enzymes, whereas the interaction of metabolites with metabolic enzymes directly auto-regulates metabolism. Therefore, there is a need for methods that integrate multiple data sources. We present INTEGRATE, a computational pipeline that captures dynamic features from the static snapshots provided by transcriptomics and metabolomics data. Through integration in a steady-state metabolic model, the pipeline predicts which reactions are controlled purely by metabolic control rather than by gene expression or a combination of the two. This knowledge is crucial in a clinical setting to develop personalized therapies in patients of multifactorial diseases, such as cancer. Besides cancer, INTEGRATE can be applied to different fields where metabolism plays a driving role. [ABSTRACT FROM AUTHOR]

Histone modifications influence chromatin structure and thus regulate the accessibility of DNA to replication, recombination, repair, and transcription. We show here that the histone deubiquitinating enzyme Ubp10 contributes to the formation/maintenance of silenced chromatin at the rDNA by affecting Sir2p association.

Abstract 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.

In early 2020 the new respiratory syndrome COVID‐19 (caused by the zoonotic SARS‐CoV‐2 virus) spread like a pandemic, starting from Wuhan, China, causing severe economic depression. Despite some advances in drug treatments of medical complications in the later stages of the disease, the pandemic's death toll is tragic, as no vaccine or specific antiviral treatment is currently available. By using a systems approach, we identify the host‐encoded pathway, which provides ribonucleotides to viral RNA synthesis, as a possible target. We show that methotrexate, an FDA‐approved inhibitor of purine biosynthesis, potently inhibits viral RNA replication, viral protein synthesis, and virus release. The effective antiviral methotrexate concentrations are similar to those used for established human therapies using the same drug. Methotrexate should be most effective in patients at the earliest appearance of symptoms to effectively prevent viral replication, diffusion of the infection, and possibly fatal complications. Highlights: The COVID‐19 pandemic caused by the SARS‐CoV‐2 virus is a global public health threat causing over 800000 deaths, millions of infected people, and severe economic depression.No vaccine or specific antiviral treatments are currently available.By inhibiting the host‐encoded pathway, which provides ribonucleotides to viral RNA synthesis, the FDA‐approved drug methotrexate efficiently blocks SARS‐CoV‐2 replication. [ABSTRACT FROM AUTHOR]

Motivation The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy. Results We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments. Availability and implementation The source code of FUMOSO is available under the GPL 2.0 license on GitHub at the following URL: https://github.com/aresio/FUMOSO Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at , as well as the case study datasets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]