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.

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.

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.

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.

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.

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.

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.

Protein Kinase A Activation Promotes Cancer Cell Resistance to Glucose Starvation and Anoikis.

Cancer cells often rely on glycolysis to obtain energy and support anabolic growth. Several studies showed that glycolytic cells are susceptible to cell death when subjected to low glucose availability or to lack of glucose. However, some cancer cells, including glycolytic ones, can efficiently acquire higher tolerance to glucose depletion, leading to their survival and aggressiveness. Although increased resistance to glucose starvation has been shown to be a consequence of signaling pathways and compensatory metabolic routes activation, the full repertoire of the underlying molecular alterations remain elusive. Using omics and computational analyses, we found that cyclic adenosine monophosphate-Protein Kinase A (cAMP-PKA) axis activation is fundamental for cancer cell resistance to glucose starvation and anoikis. Notably, here we show that such a PKA-dependent survival is mediated by parallel activation of autophagy and glutamine utilization that in concert concur to attenuate the endoplasmic reticulum (ER) stress and to sustain cell anabolism. Indeed, the inhibition of PKA-mediated autophagy or glutamine metabolism increased the level of cell death, suggesting that the induction of autophagy and metabolic rewiring by PKA is important for cancer cellular survival under glucose starvation. Importantly, both processes actively participate to cancer cell survival mediated by suspension-activated PKA as well. In addition we identify also a PKA/Src mechanism capable to protect cancer cells from anoikis. Our results reveal for the first time the role of the versatile PKA in cancer cells survival under chronic glucose starvation and anoikis and may be a novel potential target for cancer treatment.

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect.

Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth.

Enhanced amino acid utilization sustains growth of cells lacking Snf1/AMPK.

The metabolism of proliferating cells shows common features even in evolutionary distant organisms such as mammals and yeasts, for example the requirement for anabolic processes under tight control of signaling pathways. Analysis of the rewiring of metabolism, which occurs following the dysregulation of signaling pathways, provides new knowledge about the mechanisms underlying cell proliferation. The key energy regulator in yeast Snf1 and its mammalian ortholog AMPK have earlier been shown to have similar functions at glucose limited conditions and here we show that they also have analogies when grown with glucose excess. We show that loss of Snf1 in cells growing in 2% glucose induces an extensive transcriptional reprogramming, enhances glycolytic activity, fatty acid accumulation and reliance on amino acid utilization for growth. Strikingly, we demonstrate that Snf1/AMPK-deficient cells remodel their metabolism fueling mitochondria and show glucose and amino acids addiction, a typical hallmark of cancer cells.

Astrocytes and Microglia-Mediated Immune Response in Maladaptive Plasticity is Differently Modulated by NGF in the Ventral Horn of the Spinal Cord Following Peripheral Nerve Injury.

Reactive astrocytes and activated microglia are the key players in several pathophysiologic modifications of the central nervous system. We used the spared nerve injury (SNI) of the sciatic nerve to induce glial maladaptive response in the ventral horn of lumbar spinal cord and examine its role in the remodeling of the tripartite synapse plasticity. Imaging the ventral horn revealed that SNI was associated with both an early microglial and astrocytic activation, assessed, respectively, by analysis of Iba1 and GFAP expression. Microglia, in particular, localized peculiarly surrounding the motor neurons somata. Perineuronal astrocytes, which play a key role in maintaining the homeostasis of neuronal circuitry, underwent a substantial phenotypic change following peripheral axotomy, producing reactive gliosis. The gliosis was associated with the reduction of glial aminoacid transporters (GLT1 and GlyT1) and increase of neuronal glutamate transporter EAAC1. Although the expression of GABAergic neuronal marker GAD65/67 showed no change, glutamate increase, as demonstrated by HPLC analysis, shifted the excitatory/inhibitory balance as showed by the net increase of the glutamate/GABA ratio. Moreover, endogenous NGF levels were altered in SNI animals and not restored by the intrathecal NGF administration. This treatment reverted phenotypic changes associated with reactive astrocytosis, but failed to modify microglia activation. These findings on one hand confirm the correlation between gliopathy and maladaptive plasticity of the spinal synaptic circuitry, on the other hand add new data concerning the complex peculiar behavior of different glial cells in neuronal degenerative processes, defining a special role of microglia in sustaining the inflammatory response.

Snf1/AMPK promotes SBF and MBF-dependent transcription in budding yeast.

Snf1, the yeast AMP-activated kinase homolog, regulates the expression of several genes involved in adaptation to glucose limitation and in response to cellular stresses. We previously demonstrated that Snf1 interacts with Swi6, the regulatory subunit of SBF and MBF complexes, and activates CLB5 transcription. Here we report that, in α-factor synchronized cells in 2% glucose, the loss of the Snf1 catalytic subunit impairs the binding of SBF and MBF complexes and the subsequent recruitment of the FACT complex and RNA Polymerase II to promoters of G1-genes. By using an analog-sensitive allele of SNF1, SNF1(as)(I132G), encoding a protein whose catalytic activity is selectively inhibited in vivo by 2-naphthylmethyl pyrazolopyrimidine 1, we show that the inhibition of Snf1 catalytic activity affects the expression of G1-genes causing a delayed entrance into S phase in cells synchronized in G1 phase by α-factor treatment or by elutriation. Moreover, Snf1 is detected in immune complexes of Rpb1, the large subunit of RNA Polymerase II, and is present at both promoters and coding regions of SBF- and MBF-regulated genes 20min after α-factor release, suggesting a direct role for Snf1 in the activation of the G1-regulon transcription.

Protein kinase CK2 holoenzyme promotes start-specific transcription in Saccharomyces cerevisiae.

In Saccharomyces cerevisiae, the entrance into S phase requires the activation of a specific burst of transcription, which depends on SBF (SCB binding factor, Swi4/Swi6) and MBF (MCB binding factor, Mbp1/Swi6) complexes. CK2 is a pleiotropic kinase involved in several cellular processes, including the regulation of the cell cycle. CK2 is composed of two catalytic subunits (α and α') and two regulatory subunits (ß and ß'), both of which are required to form the active holoenzyme. Here we investigate the function of the CK2 holoenzyme in Start-specific transcription. The ckb1Δ ckb2Δ mutant strain, bearing deletions of both genes encoding CK2 regulatory subunits, shows a delay of S-phase entrance due to a severe reduction of the expression of SBF- and MBF-dependent genes. This transcriptional defect is caused by an impaired recruitment of Swi6 and Swi4 to G1 gene promoters. Moreover, CK2 α and ß' subunits interact with RNA polymerase II, whose binding to G1 promoters is positively regulated by the CK2 holoenzyme. Collectively, these findings suggest a novel role for the CK2 holoenzyme in the activation of G1 transcription.

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.

Nutritional limitation sensitizes mammalian cells to GSK-3ß inhibitors and leads to growth impairment.

The serine/threonine kinase GSK-3ß was initially described as a key enzyme involved in glucose metabolism, but it is now known to regulate a wide range of biological processes, including proliferation and apoptosis. We previously reported a transformation-dependent cell death induced by glucose limitation in K-ras-transformed NIH3T3. To address the mechanism of this phenomenon, we analyzed GSK-3ß regulation in these cells in conditions of high versus low glucose availability. We found that glucose depletion caused a marked inhibition of GSK-3ß through posttranslational mechanisms and that this inhibition was much less pronounced in normal cells. Further inhibition of GSK-3ß with lithium chloride, combined with glucose shortage, caused specific activation of AMP-activated protein kinase and significant suppression of proliferation in transformed but not normal cells. The cooperative effect of lithium and low glucose availability on cell growth did not seem to depend exclusively on ras pathway activation because two human cell lines, A549 and MDA-MB-231, both harboring an activated ras gene, showed very different sensitivity to lithium. These findings thus provide a rationale to further analyze the biochemical bases for combined glucose deprivation and GSK-3ß inhibition as a new approach to control transformed cell growth.

Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth.

Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed ¹³C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of ¹5N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.

An acidic loop and cognate phosphorylation sites define a molecular switch that modulates ubiquitin charging activity in Cdc34-like enzymes.

E2 ubiquitin-conjugating enzymes are crucial mediators of protein ubiquitination, which strongly influence the ultimate fate of the target substrates. Recently, it has been shown that the activity of several enzymes of the ubiquitination pathway is finely tuned by phosphorylation, an ubiquitous mechanism for cellular regulation, which modulates protein conformation. In this contribution, we provide the first rationale, at the molecular level, of the regulatory mechanism mediated by casein kinase 2 (CK2) phosphorylation of E2 Cdc34-like enzymes. In particular, we identify two co-evolving signature elements in one of the larger families of E2 enzymes: an acidic insertion in ß4α2 loop in the proximity of the catalytic cysteine and two conserved key serine residues within the catalytic domain, which are phosphorylated by CK2. Our investigations, using yeast Cdc34 as a model, through 2.5 µs molecular dynamics simulations and biochemical assays, define these two elements as an important phosphorylation-controlled switch that modulates opening and closing of the catalytic cleft. The mechanism relies on electrostatic repulsions between a conserved serine phosphorylated by CK2 and the acidic residues of the ß4α2 loop, promoting E2 ubiquitin charging activity. Our investigation identifies a new and unexpected pivotal role for the acidic loop, providing the first evidence that this loop is crucial not only for downstream events related to ubiquitin chain assembly, but is also mandatory for the modulation of an upstream crucial step of the ubiquitin pathway: the ubiquitin charging in the E2 catalytic cleft.

A surface-activated chemical ionization approach allows quantitative phosphorylation analysis of the cyclin-dependent kinase inhibitor Sic1 phosphorylated on Ser201.

RATIONALE: Quantitative phosphoproteomics represents a front line for functional proteomics and hence for systems biology. Here we present a new application of the surface-activated chemical ionization (SACI) technology for quantitative phosphoproteomics analysis. The main advantages of SACI-MS technology are high sensitivity, quantitative accuracy and matrix effect reduction, which allow quantitative estimations. METHODS: A SACI-MS approach was used to investigate the quantitative in vivo phosphorylation of the cyclin-dependent kinase inhibitor Sic1, a low-abundance protein of Saccharomyces cerevisiae, which is phosphorylated on Ser201 by casein kinase 2 (CK2) and compared its phosphorylation status in cells growing in two different carbon sources (glucose or ethanol). RESULTS: Our relative quantification indicated that the Sic1-Ser201 phosphorylation level is about 2-fold higher in ethanol- than in glucose-growing cells, proportional to the Sic1 protein level. This finding is coherent with results of western blot analysis using anti-phospho-Ser201-specific antibody, validating the results obtained with this new SACI approach. CONCLUSIONS: The findings presented in this paper indicate that the innovative LC/SACI-MS method, coupled with immunoprecipitation, is a powerful device to obtain quantitative information on the phosphorylation state of low abundance proteins.

Compaction properties of an intrinsically disordered protein: Sic1 and its kinase-inhibitor domain.

IDPs in their unbound state can transiently acquire secondary and tertiary structure. Describing such intrinsic structure is important to understand the transition between free and bound state, leading to supramolecular complexes with physiological interactors. IDP structure is highly dynamic and, therefore, difficult to study by conventional techniques. This work focuses on conformational analysis of the KID fragment of the Sic1 protein, an IDP with a key regulatory role in the cell-cycle of Saccharomyces cerevisiae. FT-IR spectroscopy, ESI-MS, and IM measurements are used to capture dynamic and short-lived conformational states, probing both secondary and tertiary protein structure. The results indicate that the isolated Sic1 KID retains dynamic helical structure and populates collapsed states of different compactness. A metastable, highly compact species is detected. Comparison between the fragment and the full-length protein suggests that chain length is crucial to the stabilization of compact states of this IDP. The two proteins are compared by a length-independent compaction index.