Raúl Montañez

Mathematical Modeling of Polyamine Metabolism in Mammals

Polyamines are considered as essential compounds in living cells, since they are involved in cell proliferation, transcription, and translation processes. Furthermore, polyamine homeostasis is necessary to cell survival, and its deregulation is involved in relevant processes, such as cancer and neurodegenerative disorders. Great efforts have been made to elucidate the nature of polyamine homeostasis, giving rise to relevant information concerning the behavior of the different components of polyamine metabolism, and a great amount of information has been generated. However, a complex regulation at transcriptional, translational, and metabolic levels as well as the strong relationship between polyamines and essential cell processes make it difficult to discriminate the role of polyamine regulation itself from the whole cell response when an experimental approach is given in vivo. To overcome this limitation, a bottom-up approach to model mathematically metabolic pathways could allow us to elucidate the systemic behavior from individual kinetic and molecular properties. In this paper, we propose a mathematical model of polyamine metabolism from kinetic constants and both metabolite and enzyme levels extracted from bibliographic sources. This model captures the tendencies observed in transgenic mice for the so-called key enzymes of polyamine metabolism, ornithine decarboxylase, S-adenosylmethionine decarboxylase and spermine spermidine N-acetyl transferase. Furthermore, the model shows a relevant role of S-adenosylmethionine and acetyl-CoA availability in polyamine homeostasis, which are not usually considered in systemic experimental studies.

In silico analysis of arginine catabolism as a source of nitric oxide or polyamines in endothelial cells

Summary.We use a modeling and simulation approach to carry out an in silico analysis of the metabolic pathways involving arginine as a precursor of nitric oxide or polyamines in aorta endothelial cells. Our model predicts conditions of physiological steady state, as well as the response of the system to changes in the control parameter, external arginine concentration. Metabolic flux control analysis allowed us to predict the values of flux control coefficients for all the transporters and enzymes included in the model. This analysis fulfills the flux control coefficient summation theorem and shows that both the low affinity transporter and arginase share the control of the fluxes through these metabolic pathways.

Localization of StarD5 cholesterol binding protein

Human StarD5 belongs to the StarD4 subfamily of START (for steroidogenic acute regulatory lipid transfer) domain proteins. We previously reported that StarD5 is located in the cytosolic fraction of human liver and binds cholesterol and 25-hydroxycholesterol. After overexpression of the gene encoding StarD5 in primary rat hepatocytes, free cholesterol accumulated in intracellular membranes. These findings suggested StarD5 to be a directional cytosolic sterol transporter. The objective of this study was to determine the localization of StarD5 in human liver. Western blot analysis confirmed StarD5s presence in the liver but not in human hepatocytes. Immunohistochemistry studies showed StarD5 localized within sinusoidal lining cells in the human liver and colocalized with CD68, a marker for Kupffer cells. Western blot analyses identified the presence of StarD5 in monocytes and macrophages as well as mast cells, basophils, and promyelocytic cells, but not in human hepatocytes, endothelial cells, fibroblasts, osteocytes, astrocytes, or brain tissue. Cell fractionation and immunocytochemistry studies on THP-1 macrophages localized StarD5 to the cytosol and supported an association with the Golgi. The presence of this cholesterol/25-hydroxycholesterol-binding protein in cells related to inflammatory processes provides new clues to the role of this protein in free sterol transport in the cells and in lipid-mediated atherogenesis.

When metabolism meets topology: Reconciling metabolite and reaction networks

The search for a systems‐level picture of metabolism as a web of molecular interactions provides a paradigmatic example of how the methods used to characterize a system can bias the interpretation of its functional meaning. Metabolic maps have been analyzed using novel techniques from network theory, revealing some non‐trivial, functionally relevant properties. These include a small‐world structure and hierarchical modularity. However, as discussed here, some of these properties might actually result from an inappropriate way of defining network interactions. Starting from the so‐called bipartite organization of metabolism, where the two meaningful subsets (reactions and metabolites) are considered, most current works use only one of the subsets by means of so‐called graph projections. Unfortunately, projected graphs often ignore relevant biological and chemical constraints, thus leading to statistical artifacts. Some of these drawbacks and alternative approaches need to be properly addressed.

MiR-2 family regulates insect metamorphosis by controlling the juvenile hormone signaling pathway

Significance MicroRNAs are short, single-stranded RNAs that bind to target mRNAs and block their translation. Five years ago we observed in the cockroach Blattella germanica that general depletion of microRNAs prevents metamorphosis. This observation led to two key questions: Which microRNAs are involved in this action, and which target do they act on? The results reported herein show that the microRNAs involved are those of an miR-2 family (miR-2, miR-13a, and miR-13b), and the target is the transcription factor Krüppel homolog 1, a master repressor of insect metamorphosis. The data presented indicate that miR-2 microRNAs rapidly clear Krüppel homolog 1 transcripts in the last nymphal instar, a process that is crucial for proper metamorphosis. This reveals the elegant mechanism of an miRNA family leading metamorphosis to its correct conclusion. In 2009 we reported that depletion of Dicer-1, the enzyme that catalyzes the final step of miRNA biosynthesis, prevents metamorphosis in Blattella germanica. However, the precise regulatory roles of miRNAs in the process have remained elusive. In the present work, we have observed that Dicer-1 depletion results in an increase of mRNA levels of Krüppel homolog 1 (Kr-h1), a juvenile hormone-dependent transcription factor that represses metamorphosis, and that depletion of Kr-h1 expression in Dicer-1 knockdown individuals rescues metamorphosis. We have also found that the 3′UTR of Kr-h1 mRNA contains a functional binding site for miR-2 family miRNAs (for miR-2, miR-13a, and miR-13b). These data suggest that metamorphosis impairment caused by Dicer-1 and miRNA depletion is due to a deregulation of Kr-h1 expression and that this deregulation is derived from a deficiency of miR-2 miRNAs. We corroborated this by treating the last nymphal instar of B. germanica with an miR-2 inhibitor, which impaired metamorphosis, and by treating Dicer-1-depleted individuals with an miR-2 mimic to allow nymphal-to-adult metamorphosis to proceed. Taken together, the data indicate that miR-2 miRNAs scavenge Kr-h1 transcripts when the transition from nymph to adult should be taking place, thus crucially contributing to the correct culmination of metamorphosis.

Polyamines: metabolism to systems biology and beyond

Summary.Polyamines and the metabolic and physiopathological processes in which they are involved represent an active field of research that has been continuously growing since the seventies. In the last years, the trends in the focused areas of interest within this field since the 1970s have been confirmed. The impact of “-omics” in polyamine research remains too low in comparison with its deep impact on other biological research areas. These high-throughput approaches, along with systems biology and, in general, more systemic and holistic approaches should contribute to a renewal of this research area in the near future.

Systems biology metabolic modeling assistant

SUMMARY We present Systems Biology Metabolic Modeling Assistant (SBMM Assistant), a tool built using an ontology-based mediator, and designed to facilitate metabolic modeling through the integration of data from repositories that contain valuable metabolic information. This software can be used for the visualization, design and management of metabolic networks; selection, integration and storage of metabolic information; and as an assistant for kinetic modeling. AVAILABILITY SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.

Synthetic biology seeks to envision living cells as a matter of engineering. However, increasing evidence suggests that the genetic load imposed by the incorporation of synthetic devices in a living organism introduces a sort of unpredictability in the design process. As a result, individual part characterization is not enough to predict the behavior of designed circuits and thus, a costly trial-error process is eventually required. In this work, we provide a new theoretical framework for the predictive treatment of the genetic load. We mathematically and experimentally demonstrate that dependences among genes follow a quantitatively predictable behavior. Our theory predicts the observed reduction of the expression of a given synthetic gene when an extra genetic load is introduced in the circuit. The theory also explains that such dependence qualitatively differs when the extra load is added either by transcriptional or translational modifications. We finally show that the limitation of the cellular resources for gene expression leads to a mathematical formulation that converges to an expression analogous to the Ohms law for electric circuits. Similitudes and divergences with this law are outlined. Our work provides a suitable framework with predictive character for the design process of complex genetic devices in synthetic biology.

Synthetic circuit designs for earth terraformation

BackgroundMounting evidence indicates that our planet might experience runaway effects associated to rising temperatures and ecosystem overexploitation, leading to catastrophic shifts on short time scales. Remediation scenarios capable of counterbalancing these effects involve geoengineering, sustainable practices and carbon sequestration, among others. None of these scenarios seems powerful enough to achieve the desired restoration of safe boundaries.Presentation of the hypothesisWe hypothesize that synthetic organisms with the appropriate engineering design could be used to safely prevent declines in some stressed ecosystems and help improving carbon sequestration. Such schemes would include engineering mutualistic dependencies preventing undesired evolutionary processes. We hypothesize that some particular design principles introduce unescapable constraints to the engineered organisms that act as effective firewalls.Testing the hypothesisTesting this designed organisms can be achieved by using controlled bioreactor models, with single and heterogeneous populations, and accurate computational models including different scales (from genetic constructs and metabolic pathways to population dynamics).Implications of the hypothesisOur hypothesis heads towards a future anthropogenic action that should effectively act as Terraforming processes. It also implies a major challenge in the existing biosafety policies, since we suggest release of modified organisms as potentially necessary strategy for success.ReviewersThis article was reviewed by This article was reviewed by Eugene V. Koonin, Tom Ellis (nominated by Purificación Lopez-Garcia) and Eörs Szathmary.

The usefulness of post-genomics tools for characterization of the amine cross-talk in mammalian cells

Evidence is growing in favour of a relationship between cancer and chronic inflammation, and particularly of the role of a polyamine and histamine metabolic interplay involved in these physiopathological problems, which are indeed highly complex biological systems. Decodification of the complex inter- and intra-cellular signalling mechanisms that control these effects is not an easy task, which must be helped by systems biology technologies, including new tools for location and integration of database-stored information and predictive mathematical models, as well as functional genomics and other experimental molecular approaches necessary for hypothesis validation. We review the state of the art and present our latest efforts in this area, focused on the amine metabolism field.