Fusion-fission-mitophagy cycling and metabolic reprogramming coordinate nerve growth factor (NGF)-dependent neuronal differentiation.

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.

The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation.

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.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.

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.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Methotrexate inhibits SARS-CoV-2 virus replication "in vitro".

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.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling.

BACKGROUND: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways. METHODS: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity. RESULTS: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism. CONCLUSIONS: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

ROS networks: designs, aging, Parkinson's disease and precision therapies.

How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Nicotinamide, Nicotinamide Riboside and Nicotinic Acid-Emerging Roles in Replicative and Chronological Aging in Yeast.

Nicotinamide, nicotinic acid and nicotinamide riboside are vitamin B3 precursors of NAD+ in the human diet. NAD+ has a fundamental importance for cellular biology, that derives from its essential role as a cofactor of various metabolic redox reactions, as well as an obligate co-substrate for NAD+-consuming enzymes which are involved in many fundamental cellular processes including aging/longevity. During aging, a systemic decrease in NAD+ levels takes place, exposing the organism to the risk of a progressive inefficiency of those processes in which NAD+ is required and, consequently, contributing to the age-associated physiological/functional decline. In this context, dietary supplementation with NAD+ precursors is considered a promising strategy to prevent NAD+ decrease and attenuate in such a way several metabolic defects common to the aging process. The metabolism of NAD+ precursors and its impact on cell longevity have benefited greatly from studies performed in the yeast Saccharomyces cerevisiae, which is one of the most established model systems used to study the aging processes of both proliferating (replicative aging) and non-proliferating cells (chronological aging). In this review we summarize important aspects of the role played by nicotinamide, nicotinic acid and nicotinamide riboside in NAD+ metabolism and how each of these NAD+ precursors contribute to the different aspects that influence both replicative and chronological aging. Taken as a whole, the findings provided by the studies carried out in S. cerevisiae are informative for the understanding of the complex dynamic flexibility of NAD+ metabolism, which is essential for the maintenance of cellular fitness and for the development of dietary supplements based on NAD+ precursors.

Systems metabolomics: from metabolomic snapshots to design principles.

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Neurons, Glia, Extracellular Matrix and Neurovascular Unit: A Systems Biology Approach to the Complexity of Synaptic Plasticity in Health and Disease.

The synaptic cleft has been vastly investigated in the last decades, leading to a novel and fascinating model of the functional and structural modifications linked to synaptic transmission and brain processing. The classic neurocentric model encompassing the neuronal pre- and post-synaptic terminals partly explains the fine-tuned plastic modifications under both pathological and physiological circumstances. Recent experimental evidence has incontrovertibly added oligodendrocytes, astrocytes, and microglia as pivotal elements for synapse formation and remodeling (tripartite synapse) in both the developing and adult brain. Moreover, synaptic plasticity and its pathological counterpart (maladaptive plasticity) have shown a deep connection with other molecular elements of the extracellular matrix (ECM), once considered as a mere extracellular structural scaffold altogether with the cellular glue (i.e., glia). The ECM adds another level of complexity to the modern model of the synapse, particularly, for the long-term plasticity and circuit maintenance. This model, called tetrapartite synapse, can be further implemented by including the neurovascular unit (NVU) and the immune system. Although they were considered so far as tightly separated from the central nervous system (CNS) plasticity, at least in physiological conditions, recent evidence endorsed these elements as structural and paramount actors in synaptic plasticity. This scenario is, as far as speculations and evidence have shown, a consistent model for both adaptive and maladaptive plasticity. However, a comprehensive understanding of brain processes and circuitry complexity is still lacking. Here we propose that a better interpretation of the CNS complexity can be granted by a systems biology approach through the construction of predictive molecular models that enable to enlighten the regulatory logic of the complex molecular networks underlying brain function in health and disease, thus opening the way to more effective treatments.

Single-cell Digital Twins for Cancer Preclinical Investigation.

Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up digital twins essential to fully exploit their potential to face the enormous molecular complexity of human organisms. In particular, due to the close integration of cell metabolism with all other cellular processes, any perturbation in cellular physiology typically reflect on altered cells metabolic profiling. In this regard, changes in metabolism have been shown, also in our laboratory, to drive a causal role in the emergence of cancer disease. Nevertheless, a unique metabolic program does not describe the altered metabolic profile of all tumour cells due to many causes from genetic variability to intratumour heterogeneous dependency on nutrients consumption and metabolism by multiple co-existing subclones. Currently, fluxomics approaches just match with the necessity of characterizing the overall flux distribution of cells within given samples, by disregarding possible heterogeneous behaviors. For the purpose of stratifying cancer heterogeneous subpopulations, quantification of fluxes at the single-cell level is needed. To this aim, we here present a new computational framework called single-cell Flux Balance Analysis (scFBA) that aims to set up digital metabolic twins in the perspective of being better exploited within a framework that makes also use of laboratory patient cell models. In particular, scFBA aims at integrating single-cell RNA-seq data within computational population models in order to depict a snapshot of the corresponding single-cell metabolic phenotypes at a given moment, together with an unsupervised identification of metabolic subpopulations.

Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells.

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.

Differential Modulation of NF-κB in Neurons and Astrocytes Underlies Neuroprotection and Antigliosis Activity of Natural Antioxidant Molecules.

Neuroinflammation, a hallmark of chronic neurodegenerative disorders, is characterized by sustained glial activation and the generation of an inflammatory loop, through the release of cytokines and other neurotoxic mediators that cause oxidative stress and limit functional repair of brain parenchyma. Dietary antioxidants may protect against neurodegenerative diseases by counteracting chronic neuroinflammation and reducing oxidative stress. Here, we describe the effects of a number of natural antioxidants (polyphenols, carotenoids, and thiolic molecules) in rescuing astrocytic function and neuronal viability following glial activation by reducing astrocyte proliferation and restoring astrocytic and neuronal survival and basal levels of reactive oxygen species (ROS). All antioxidant molecules are also effective under conditions of oxidative stress and glutamate toxicity, two maladaptive components of neuroinflammatory processes. Moreover, it is remarkable that their antioxidant and anti-inflammatory activity occurs through differential modulation of NF-κB binding activity in neurons and astrocytes. In fact, we show that inflammatory stimuli promote a significant induction of NF-κB binding activity in astrocytes and its concomitant reduction in neurons. These changes are prevented in astrocytes and neurons pretreated with the antioxidant molecules, suggesting that NF-κB plays a key role in the modulation of survival and anti-inflammatory responses. Finally, we newly demonstrate that effective antigliosis and neuroprotective activity is achieved with a defined cocktail of four natural antioxidants at very low concentrations, suggesting a promising strategy to reduce inflammatory and oxidative damage in neurodegenerative diseases with limited side effects.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.

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 https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

Neural plasticity and adult neurogenesis: the deep biology perspective.

The recognition that neurogenesis does not stop with adolescence has spun off research towards the reduction of brain disorders by enhancing brain regeneration. Adult neurogenesis is one of the tougher problems of developmental biology as it requires the generation of complex intracellular and pericellular anatomies, amidst the danger of neuroinflammation. We here review how a multitude of regulatory pathways optimized for early neurogenesis has to be revamped into a new choreography of time dependencies. Distinct pathways need to be regulated, ranging from neural growth factor induced differentiation to mitochondrial bioenergetics, reactive oxygen metabolism, and apoptosis. Requiring much Gibbs energy consumption, brain depends on aerobic energy metabolism, hence on mitochondrial activity. Mitochondrial fission and fusion, movement and perhaps even mitoptosis, thereby come into play. All these network processes are interlinked and involve a plethora of molecules. We recommend a deep thinking approach to adult neurobiology.

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.

Methionine supplementation stimulates mitochondrial respiration.

Mitochondria play essential metabolic functions in eukaryotes. Although their major role is the generation of energy in the form of ATP, they are also involved in maintenance of cellular redox state, conversion and biosynthesis of metabolites and signal transduction. Most mitochondrial functions are conserved in eukaryotic systems and mitochondrial dysfunctions trigger several human diseases. By using multi-omics approach, we investigate the effect of methionine supplementation on yeast cellular metabolism, considering its role in the regulation of key cellular processes. Methionine supplementation induces an up-regulation of proteins related to mitochondrial functions such as TCA cycle, electron transport chain and respiration, combined with an enhancement of mitochondrial pyruvate uptake and TCA cycle activity. This metabolic signature is more noticeable in cells lacking Snf1/AMPK, the conserved signalling regulator of energy homeostasis. Remarkably, snf1Δ cells strongly depend on mitochondrial respiration and suppression of pyruvate transport is detrimental for this mutant in methionine condition, indicating that respiration mostly relies on pyruvate flux into mitochondrial pathways. These data provide new insights into the regulation of mitochondrial metabolism and extends our understanding on the role of methionine in regulating energy signalling pathways.

Differentiation by nerve growth factor (NGF) involves mechanisms of crosstalk between energy homeostasis and mitochondrial remodeling.

Neuronal differentiation involves extensive modification of biochemical and morphological properties to meet novel functional requirements. Reorganization of the mitochondrial network to match the higher energy demand plays a pivotal role in this process. Mechanisms of neuronal differentiation in response to nerve growth factor (NGF) have been largely characterized in terms of signaling, however, little is known about its impact on mitochondrial remodeling and metabolic function. In this work, we show that NGF-induced differentiation requires the activation of autophagy mediated by Atg9b and Ambra1, as it is disrupted by their genetic knockdown and by autophagy blockers. NGF differentiation involves the induction of P-AMPK and P-CaMK, and is prevented by their pharmacological inhibition. These molecular events correlate with modifications of energy and redox homeostasis, as determined by ATP and NADPH changes, higher oxygen consumption (OCR) and ROS production. Our data indicate that autophagy aims to clear out exhausted mitochondria, as determined by enhanced localization of p62 and Lysotracker-red to mitochondria. In addition, we newly demonstrate that NGF differentiation is accompanied by increased mitochondrial remodeling involving higher levels of fission (P-Drp1) and fusion proteins (Opa1 and Mfn2), as well as induction of Sirt3 and the transcription factors mtTFA and PPARγ, which regulate mitochondria biogenesis and metabolism to sustain increased mitochondrial mass, potential, and bioenergetics. Overall, our data indicate a new NGF-dependent mechanism involving mitophagy and extensive mitochondrial remodeling, which plays a key role in both neurogenesis and nerve regeneration.