Inhibition of mitochondrial folate metabolism drives differentiation through mTORC1 mediated purine sensing.

Supporting cell proliferation through nucleotide biosynthesis is an essential requirement for cancer cells. Hence, inhibition of folate-mediated one carbon (1C) metabolism, which is required for nucleotide synthesis, has been successfully exploited in anti-cancer therapy. Here, we reveal that mitochondrial folate metabolism is upregulated in patient-derived leukaemic stem cells (LSCs). We demonstrate that inhibition of mitochondrial 1C metabolism through impairment of de novo purine synthesis has a cytostatic effect on chronic myeloid leukaemia (CML) cells. Consequently, changes in purine nucleotide levels lead to activation of AMPK signalling and suppression of mTORC1 activity. Notably, suppression of mitochondrial 1C metabolism increases expression of erythroid differentiation markers. Moreover, we find that increased differentiation occurs independently of AMPK signalling and can be reversed through reconstitution of purine levels and reactivation of mTORC1. Of clinical relevance, we identify that combination of 1C metabolism inhibition with imatinib, a frontline treatment for CML patients, decreases the number of therapy-resistant CML LSCs in a patient-derived xenograft model. Our results highlight a role for folate metabolism and purine sensing in stem cell fate decisions and leukaemogenesis.

Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs.

Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

HEK293 producing the extracellular domain HER1: Full datasets of continuous fermentation process and metabolites analysis.

The data for provide evidences of the multi steady state of the human cell line HEK 293 was obtained from 2 L bioreactor continuous culture. A HEK 293 cell line transfected to produce soluble HER1 receptor was used. The bioreactor was operated at three different dilution rates in sequential manner. Daily samples of culture broth were collected, a total of 85 samples were processed. Viable cell concentration and culture viability was addressing by trypan blue exclusion method using a hemocytometer. Heterologous HER1 supernatant concentration was quantified by a specific ELISA and the metabolites by mass spectrometry coupled to a liquid chromatography. The primary data were collected in excel files, where it was calculated the kinetic and other variables by using mass balance and mathematical principles. It was compared the steady states behavior each other's to find out the existence of steady states' multiplicity, taking into account the stationary phase with respect to the cell density (which means its coefficient of variation is less than 20 %). From the metabolic measurements by using Liquid Chromatography coupled to mass spectrometry (LC-MS), it was also built the data matrix with the specific rates of the 76 metabolites obtained. The data were processed and analyzed, using multivariate data asssnalysis (MVDA) to reduce the complexity and to find the main patterns present in the data. We describe also the full data of the metabolites not only for steady states but also in the time evolution, which could help others in terms of modeling and deep understanding of HEK293 metabolism, especially under different culture conditions.

Complex hypergraphs.

Providing an abstract representation of natural and human complex structures is a challenging problem. Accounting for the system heterogenous components while allowing for analytical tractability is a difficult balance. Here I introduce complex hypergraphs (chygraphs), bringing together concepts from hypergraphs, multilayer networks, simplicial complexes, and hyperstructures. To illustrate the applicability of this combinatorial structure I calculate the component sizes statistics and identify the transition to a giant component. To this end I introduce a vectorization technique that tackles the multilevel nature of chygraphs. I conclude that chygraphs are a unifying representation of complex systems allowing for analytical insight.

Activity networks determine project performance.

Projects are characterised by activity networks with a critical path, a sequence of activities from start to end, that must be finished on time to complete the project on time. Watching over the critical path is the project manager's strategy to ensure timely project completion. This intense focus on a single path contrasts the broader complex structure of the activity network, and is due to our poor understanding on how that structure influences this critical path. Here, we use a generative model and detailed data from 77 real world projects (+ $10 bn total budget) to demonstrate how this network structure forces us to look beyond the critical path. We introduce a duplication-split model of project schedules that yields (i) identical power-law in- and-out degree distributions and (ii) a vanishing fraction of critical path activities with schedule size. These predictions are corroborated in real projects. We demonstrate that the incidence of delayed activities in real projects is consistent with the expectation from percolation theory in complex networks. We conclude that delay propagation in project schedules is a network property and it is not confined to the critical path.

Tropical approximation to finish time of activity networks.

We breakdown complex projects into activities and their logical dependencies. We estimate the project finish time based on the activity durations and relations. However, adverse events trigger delay cascades shifting the finish time. Here I derive a tropical algebraic equation for the finish time of activity networks, encapsulating the principle of linear superposition of exogenous perturbations in the tropical sense. From the tropical algebraic equation I derive the finish time distribution with explicit reference to the distribution of exogenous delays and the network topology and geometry.

Mitochondria preserve an autarkic one-carbon cycle to confer growth-independent cancer cell migration and metastasis.

Metastasis is the most common cause of death in cancer patients. Canonical drugs target mainly the proliferative capacity of cancer cells, which leaves slow-proliferating, persistent cancer cells unaffected. Metabolic determinants that contribute to growth-independent functions are still poorly understood. Here we show that antifolate treatment results in an uncoupled and autarkic mitochondrial one-carbon (1C) metabolism during cytosolic 1C metabolism impairment. Interestingly, antifolate dependent growth-arrest does not correlate with decreased migration capacity. Therefore, using methotrexate as a tool compound allows us to disentangle proliferation and migration to profile the metabolic phenotype of migrating cells. We observe that increased serine de novo synthesis (SSP) supports mitochondrial serine catabolism and inhibition of SSP using the competitive PHGDH-inhibitor BI-4916 reduces cancer cell migration. Furthermore, we show that sole inhibition of mitochondrial serine catabolism does not affect primary breast tumor growth but strongly inhibits pulmonary metastasis. We conclude that mitochondrial 1C metabolism, despite being dispensable for proliferative capacities, confers an advantage to cancer cells by supporting their motility potential.

Erratum: Tropical approximation to finish time of activity networks [Phys. Rev. E 106, L012301 (2022)].

This corrects the article DOI: 10.1103/PhysRevE.106.L012301.

Metformin Is a Pyridoxal-5'-phosphate (PLP)-Competitive Inhibitor of SHMT2.

The anticancer actions of the biguanide metformin involve the functioning of the serine/glycine one-carbon metabolic network. We report that metformin directly and specifically targets the enzymatic activity of mitochondrial serine hydroxymethyltransferase (SHMT2). In vitro competitive binding assays with human recombinant SHMT1 and SHMT2 isoforms revealed that metformin preferentially inhibits SHMT2 activity by a non-catalytic mechanism. Computational docking coupled with molecular dynamics simulation predicted that metformin could occupy the cofactor pyridoxal-5'-phosphate (PLP) cavity and destabilize the formation of catalytically active SHMT2 oligomers. Differential scanning fluorimetry-based biophysical screening confirmed that metformin diminishes the capacity of PLP to promote the conversion of SHMT2 from an inactive, open state to a highly ordered, catalytically competent closed state. CRISPR/Cas9-based disruption of SHMT2, but not of SHMT1, prevented metformin from inhibiting total SHMT activity in cancer cell lines. Isotope tracing studies in SHMT1 knock-out cells confirmed that metformin decreased the SHMT2-channeled serine-to-formate flux and restricted the formate utilization in thymidylate synthesis upon overexpression of the metformin-unresponsive yeast equivalent of mitochondrial complex I (mCI). While maintaining its capacity to inhibit mitochondrial oxidative phosphorylation, metformin lost its cytotoxic and antiproliferative activity in SHMT2-null cancer cells unable to produce energy-rich NADH or FADH2 molecules from tricarboxylic acid cycle (TCA) metabolites. As currently available SHMT2 inhibitors have not yet reached the clinic, our current data establishing the structural and mechanistic bases of metformin as a small-molecule, PLP-competitive inhibitor of the SHMT2 activating oligomerization should benefit future discovery of biguanide skeleton-based novel SHMT2 inhibitors in cancer prevention and treatment.

Exact solution of infection dynamics with gamma distribution of generation intervals.

Infectious disease outbreaks are expected to grow exponentially in time but their initial dynamics is less known. Here I derive analytical expressions for the infectious disease dynamics with a gamma distribution of generation intervals. Excluding the exponential distribution, the outbreak grows as a power law at short times. At long times, the dynamics is exponential with a growth rate determined by the basic reproductive number and the parameters of the generation interval distribution. These analytical expressions can be deployed to do better estimates of infectious disease parameters.

Multitype branching and graph product theory of infectious disease outbreaks.

The heterogeneity of human populations is a challenge to mathematical descriptions of epidemic outbreaks. Numerical simulations are deployed to account for the many factors influencing the spreading dynamics. Yet, the results from numerical simulations are often as complicated as the reality, leaving us with a sense of confusion about how the different factors account for the simulation results. Here, using a multitype branching together with a graph tensor product approach, I derive a single equation for the effective reproductive number of an infectious disease outbreak. Using this equation I deconvolute the impact of crowd management, targeted testing, contact heterogeneity, stratified vaccination, mask use, and smartphone tracing app use. This equation can be used to gain a basic understanding of infectious disease outbreaks and their simulations.

Immune-regulated IDO1-dependent tryptophan metabolism is source of one-carbon units for pancreatic cancer and stellate cells.

Cancer cells adapt their metabolism to support elevated energetic and anabolic demands of proliferation. Folate-dependent one-carbon metabolism is a critical metabolic process underpinning cellular proliferation supplying carbons for the synthesis of nucleotides incorporated into DNA and RNA. Recent research has focused on the nutrients that supply one-carbons to the folate cycle, particularly serine. Tryptophan is a theoretical source of one-carbon units through metabolism by IDO1, an enzyme intensively investigated in the context of tumor immune evasion. Using in vitro and in vivo pancreatic cancer models, we show that IDO1 expression is highly context dependent, influenced by attachment-independent growth and the canonical activator IFNγ. In IDO1-expressing cancer cells, tryptophan is a bona fide one-carbon donor for purine nucleotide synthesis in vitro and in vivo. Furthermore, we show that cancer cells release tryptophan-derived formate, which can be used by pancreatic stellate cells to support purine nucleotide synthesis.

Transition to multitype mixing in d-dimensional spreading dynamics.

The spreading dynamics of infectious diseases is determined by the interplay between geography and population mixing. There is homogeneous mixing at the local level and human mobility between distant populations. Here I model spatial location as a type and the population mixing by intra- and intertype mixing patterns. Using the theory of multitype branching process, I calculate the expected number of new infections as a function of time. In one dimension the analysis is reduced to the eigenvalue problem of a tridiagonal Teoplitz matrix. In d dimensions I take advantage of the graph cartesian product to construct the eigenvalues and eigenvectors from the eigenvalue problem in 1 one dimension. Using numerical simulations I uncover a transition from linear to multitype mixing exponential growth with increasing the population size. Given that most countries are characterized by a network of cities with more than 100 000 habitants, I conclude that the multitype mixing approximation should be the prevailing scenario.

Zero forcing number of graphs with a power law degree distribution.

The zero forcing number is the minimum number of black vertices that can turn a white graph black following a single neighbor color forcing rule. The zero forcing number provides topological information about linear algebra on graphs, with applications to the controllability of quantum dynamical systems. Here, I investigate the zero forcing number of undirected graphs with a power law degree distribution p_{k}∼k^{-γ} by means of numerical simulations. For graphs generated by the preferential attachment model, with a diameter scaling logarithmically with the graph size, the zero forcing number approaches the graph size when γ→2. In contrast, for graphs generated by the deactivation model, with a diameter scaling linearly with the graph size, the zero forcing number is smaller than the graph size independently of γ. Therefore the scaling of the graph diameter with the graph size is another factor determining the controllability of dynamical systems. These results have implications for the controllability of quantum dynamics on energy landscapes, often characterized by a complex network of couplings between energy basins.

Folate metabolism: a re-emerging therapeutic target in haematological cancers.

Folate-mediated one carbon (1C) metabolism supports a series of processes that are essential for the cell. Through a number of interlinked reactions happening in the cytosol and mitochondria of the cell, folate metabolism contributes to de novo purine and thymidylate synthesis, to the methionine cycle and redox defence. Targeting the folate metabolism gave rise to modern chemotherapy, through the introduction of antifolates to treat paediatric leukaemia. Since then, antifolates, such as methotrexate and pralatrexate have been used to treat a series of blood cancers in clinic. However, traditional antifolates have many deleterious side effects in normal proliferating tissue, highlighting the urgent need for novel strategies to more selectively target 1C metabolism. Notably, mitochondrial 1C enzymes have been shown to be significantly upregulated in various cancers, making them attractive targets for the development of new chemotherapeutic agents. In this article, we present a detailed overview of folate-mediated 1C metabolism, its importance on cellular level and discuss how targeting folate metabolism has been exploited in blood cancers. Additionally, we explore possible therapeutic strategies that could overcome the limitations of traditional antifolates.

Trends in Phase II Trials for Cancer Therapies.

Background: Drug combinations are the standard of care in cancer treatment. Identifying effective cancer drug combinations has become more challenging because of the increasing number of drugs. However, a substantial number of cancer drugs stumble at Phase III clinical trials despite exhibiting favourable efficacy in the earlier Phase. Methods: We analysed recent Phase II cancer trials comprising 2165 response rates to uncover trends in cancer therapies and used a null model of non-interacting agents to infer synergistic and antagonistic drug combinations. We compared our latest efficacy dataset with a previous dataset to assess the progress of cancer therapy. Results: Targeted therapies reach higher response rates when used in combination with cytotoxic drugs. We identify four synergistic and 10 antagonistic combinations based on the observed and expected response rates. We demonstrate that recent targeted agents have not significantly increased the response rates. Conclusions: We conclude that either we are not making progress or response rate measured by tumour shrinkage is not a reliable surrogate endpoint for the targeted agents.

Superspreaders and lockdown timing explain the power-law dynamics of COVID-19.

Infectious disease outbreaks are expected to grow exponentially in time when left unchecked. Containment measures such as lockdown and social distancing can drastically alter the growth dynamics of the outbreak. This is the case for the 2019-2020 COVID-19 outbreak, which is characterized by a power-law growth. Strikingly however, the power-law exponent is different across countries. Here I illustrate the relationship between these two extreme scenarios, exponential and power-law growth, based on the impact of superspreaders and lockdown strategies to contain the outbreak. The theory predicts a relationship between the power- law exponent and the time interval between the first case and lockdown that is validated by the observed COVID-19 data across different countries.

The conversion of formate into purines stimulates mTORC1 leading to CAD-dependent activation of pyrimidine synthesis.

BACKGROUND: Mitochondrial serine catabolism to formate induces a metabolic switch to a hypermetabolic state with high rates of glycolysis, purine synthesis and pyrimidine synthesis. While formate is a purine precursor, it is not clear how formate induces pyrimidine synthesis. METHODS: Here we combine phospho-proteome and metabolic profiling to determine how formate induces pyrimidine synthesis. RESULTS: We discover that formate induces phosphorylation of carbamoyl phosphate synthetase (CAD), which is known to increase CAD enzymatic activity. Mechanistically, formate induces mechanistic target of rapamycin complex 1 (mTORC1) activity as quantified by phosphorylation of its targets S6, 4E-BP1, S6K1 and CAD. Treatment with the allosteric mTORC1 inhibitor rapamycin abrogates CAD phosphorylation and pyrimidine synthesis induced by formate. Furthermore, we show that the formate-dependent induction of mTOR signalling and CAD phosphorylation is dependent on an increase in purine synthesis. CONCLUSIONS: We conclude that formate activates mTORC1 and induces pyrimidine synthesis via the mTORC1-dependent phosphorylation of CAD.

Identification of putative calorie restriction mimetics using mammalian gene expression profiles.

Obesity is a risk factor for cardiovascular diseases, diabetes and cancer. In theory, the obesity problem could be solved by the adherence to a calorie-restricted diet, but that is not generally achieved in practice. An alternative is a pharmacological approach, using compounds that trigger the same metabolic changes associated with calorie restriction. Here, I expand in the pharmacological direction by identifying compounds that induce liver gene signature profiles that mimic those induced by calorie restriction. Using gene expression profiles from mice and rat, I identify corticosteroids, PPAR agonists and some antibacterial/antifungal as candidate compounds mimicking the response to calorie restriction in the liver gene signatures.

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web browser.

BACKGROUND: Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time-consuming. As a consequence of manual data processing, the results are often not analysed in full depth, so potential novel findings might get lost. METHODS: To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose, it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to the number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny application that can be run online in a web browser on https://mpietzke.shinyapps.io/autoplotter. RESULTS: We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis. CONCLUSION: With the help of Metabolite AutoPlotter, it is now possible to simplify data processing and visualisation for a wide audience. High-quality plots from complex data can be generated in a short time by pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further, this eliminates potential copy-and-paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.