Alvaro Olivera-Nappa

Mathematical modeling of the dynamic storage of iron in ferritin

BackgroundIron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content.ResultsSimulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded.ConclusionsThe results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.

Non‐homogeneous biofilm modeling applied to bioleaching processes

A two‐dimensional non‐homogeneous biofilm model is proposed for the first time to study chemical and biochemical reactions at the microorganism scale applied to biological metal leaching from mineral ores. The spatial and temporal relation between these reactions, microorganism growth and the morphological changes of the biofilm caused by solid inorganic precipitate formation were studied using this model. The model considers diffusion limitations due to accumulation of inorganic particles over the mineral substratum, and allows the study of the effect of discrete phases on chemical and microbiological mineral solubilization. The particle‐based modeling strategy allowed representation of contact reactions between the microorganisms and the insoluble precipitates, such as those required for sulfur attack and solubilization. Time‐dependent simulations of chemical chalcopyrite leaching showed that chalcopyrite passivation occurs only when an impervious solid layer is formed on the mineral surface. This mineral layer hinders the diffusion of one kinetically determinant mineral‐attacking chemical species through a nearly irreversible chemical mechanism. Simulations with iron and sulfur oxidizing microorganisms revealed that chemolithoautotrophic biofilms are able to delay passivation onset by formation of corrosion pits and increase of the solid layer porosity through sulfur dissolution. The model results also show that the observed flat morphology of bioleaching biofilms is favored preferentially at low iron concentrations due to preferential growth at the biofilm edge on the surface of sulfur‐forming minerals. Flat biofilms can also be advantageous for chalcopyrite bioleaching because they tend to favor sulfur dissolution over iron oxidation. The adopted modeling strategy is of great interest for the numerical representation of heterogeneous biofilm systems including abiotic solid particles. Biotechnol. Bioeng. 2010;106: 660–676.

Cold Adaptation, Ca2+ Dependency and Autolytic Stability Are Related Features in a Highly Active Cold-Adapted Trypsin Resistant to Autoproteolysis Engineered for Biotechnological Applications

Pig trypsin is routinely used as a biotechnological tool, due to its high specificity and ability to be stored as an inactive stable zymogen. However, it is not an optimum enzyme for conditions found in wound debriding for medical uses and trypsinization processes for protein analysis and animal cell culturing, where low Ca2+ dependency, high activity in mild conditions and easy inactivation are crucial. We isolated and thermodynamically characterized a highly active cold-adapted trypsin for medical and laboratory use that is four times more active than pig trypsin at 10° C and at least 50% more active than pig trypsin up to 50° C. Contrary to pig trypsin, this enzyme has a broad optimum pH between 7 and 10 and is very insensitive to Ca2+ concentration. The enzyme is only distantly related to previously described cryophilic trypsins. We built and studied molecular structure models of this trypsin and performed molecular dynamic calculations. Key residues and structures associated with calcium dependency and cryophilicity were identified. Experiments indicated that the protein is unstable and susceptible to autoproteolysis. Correlating experimental results and structural predictions, we designed mutations to improve the resistance to autoproteolysis and conserve activity for longer periods after activation. One single mutation provided around 25 times more proteolytic stability. Due to its cryophilic nature, this trypsin is easily inactivated by mild denaturation conditions, which is ideal for controlled proteolysis processes without requiring inhibitors or dilution. We clearly show that cold adaptation, Ca2+ dependency and autolytic stability in trypsins are related phenomena that are linked to shared structural features and evolve in a concerted fashion. Hence, both structurally and evolutionarily they cannot be interpreted and studied separately as previously done.

Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations in proteins

BackgroundFunctionally relevant artificial or natural mutations are difficult to assess or predict if no structure-function information is available for a protein. This is especially important to correctly identify functionally significant non-synonymous single nucleotide polymorphisms (nsSNPs) or to design a site-directed mutagenesis strategy for a target protein. A new and powerful methodology is proposed to guide these two decision strategies, based only on conservation rules of physicochemical properties of amino acids extracted from a multiple alignment of a protein family where the target protein belongs, with no need of explicit structure-function relationships.ResultsA statistical analysis is performed over each amino acid position in the multiple protein alignment, based on different amino acid physical or chemical characteristics, including hydrophobicity, side-chain volume, charge and protein conformational parameters. The variances of each of these properties at each position are combined to obtain a global statistical indicator of the conservation degree of each property. Different types of physicochemical conservation are defined to characterize relevant and irrelevant positions. The differences between statistical variances are taken together as the basis of hypothesis tests at each position to search for functionally significant mutable sites and to identify specific mutagenesis targets. The outcome is used to statistically predict physicochemical consensus sequences based on different properties and to calculate the amino acid propensities at each position in a given protein. Hence, amino acid positions are identified that are putatively responsible for function, specificity, stability or binding interactions in a family of proteins. Once these key functional positions are identified, position-specific statistical distributions are applied to divide the 20 common protein amino acids in each position of the proteins primary sequence into a group of functionally non-disruptive amino acids and a second group of functionally deleterious amino acids.ConclusionsWith this approach, not only conserved amino acid positions in a protein family can be labeled as functionally relevant, but also non-conserved amino acid positions can be identified to have a physicochemically meaningful functional effect. These results become a discriminative tool in the selection and elaboration of rational mutagenesis strategies for the protein. They can also be used to predict if a given nsSNP, identified, for instance, in a genomic-scale analysis, can have a functional implication for a particular protein and which nsSNPs are most likely to be functionally silent for a protein. This analytical tool could be used to rapidly and automatically discard any irrelevant nsSNP and guide the research focus toward functionally significant mutations. Based on preliminary results and applications, this technique shows promising performance as a valuable bioinformatics tool to aid in the development of new protein variants and in the understanding of function-structure relationships in proteins.

Purification of transthyretin as nutritional biomarker of selenium status

Transthyretin has been proposed as nutritional biomarker of selenium intake. Previous transthyretin purification methods used different procedures to isolate transthyretin either from plasma or from pathological urine of humans. In general, the procedure for purification of transthyretin is laborious and expensive, and extensive sample recycling is necessary for purification in appreciable amounts. This work proposes a new, promissory, and cheap two-step process to purify transthyretin from blood plasma, composed by a first aqueous two-phase system fractionation followed by affinity chromatography, using thyroxine-immobilized on epoxy-activated Sepharose CL-6B. The aqueous two-phase system fractionation was demonstrated to perform better than commercial immunoaffinity-based kits for albumin depletion in blood plasma samples and is an effective first step for transthyretin purification. Thyroxine affinity chromatography was designed to bind transthyretin with high affinity, and was demonstrated to be useful to purify transthyretin, but was unable to completely resolve transthyretin from thyroxine-binding globulin and serum albumin, although the relative amount of albumin was lowered in the eluates. This purification process could be used in nutritional diagnosis tools or as a first step in structural and functional studies.

Protein complex prediction via dense subgraphs and false positive analysis

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

An optimized low-cost protocol for standardized production of iron-free apoferritin nanocages with high protein recovery and suitable conformation for nanotechnological applications

Ferritin is a globular protein that consists of 24 subunits forming a hollow nanocage structure that naturally stores iron oxyhydroxides. Elimination of iron atoms to obtain the empty protein called apoferritin is the first step to use this organic shell as a nanoreactor for different nanotechnological applications. Different protocols have been reported for apoferritin formation, but some are time consuming, others are difficult to reproduce and protein recovery yields are seldom reported. Here we tested several protocols and performed a complete material characterization of the apoferritin products using size exclusion chromatography, UV-vis spectroscopy, inductively coupled plasma optical emission spectrometry and dynamic light scattering. Our best method removes more than 99% of the iron from loaded holoferritin, recovering 70-80% of the original protein as monomeric apoferritin nanocages. Our work shows that pH conditions of the reduction step and the presence and nature of chelating agents affect the efficiency of iron removal. Furthermore, process conditions also seem to have an influence on the monomer:aggregate proportion present in the product. We also demonstrate that iron contents markedly increase ferritin absorbance at 280nm. The influence of iron contents on absorbance at 280nm precludes using this simple spectrophotometric measure for protein determination in ferritin‑iron complexes. Apoferritin produced following our protocol only requires readily-available, cheap and biocompatible reagents, which makes this process standardizable, scalable and applicable to be used for in vivo applications of ferritin derivatives as well as nanotechnological and biotechnological uses.