Pharmacokinetic evaluation of a single chain antibody fragment against scorpion toxins in sheep.

A key aspect during the development of antivenoms is the evaluation of the efficiency and security of the therapeutic molecules. In this work, we report the pharmacokinetic analysis of a neutralizing single chain antibody fragment named LR (scFv LR) where three sheep were used as a large animal model. The animals were injected through i.v. route with 2 mg of scFv LR. Blood samples were drawn every minute within the first 15 min, the sampling continues at 20, 25, 30, 45, 60, 90, 120 min, subsequently at 1-h intervals, 3, 4, 5, 6 h, two more samples at 9 and 12 h and, two more samples at 24 and 48 h and finally at one-day intervals during 4 days. scFv LR levels were measured from blood serum and urine samples by an ELISA. The pharmacokinetics of the experimental data was analyzed using the three-exponential kinetics. The value of the fast initial component (τ1=0.409±0.258min) indicated that the scFv is distributed rapidly into the tissues. The mean residence time, MRT, was 45 ± 0.51 min and the clearance (CL), 114.3 ± 14.3 mL/min. From urine samples it was possible to detect significant amounts of scFv LR, which is evidence of renal elimination.

Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts.

Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.

Analysis of the Propionate Metabolism in Bacillus subtilis during 3-Indolacetic Production.

The genera Bacillus belongs to the group of microorganisms that are known as plant growth-promoting bacteria, their metabolism has evolved to produce molecules that benefit the growth of the plant, and the production of 3-indole acetic acid (IAA) is part of its secondary metabolism. In this work, Bacillus subtilis was cultivated in a bioreactor to produce IAA using propionate and glucose as carbon sources in an M9-modified media; in both cases, tryptophan was added as a co-substrate. The yield of IAA using propionate is 17% higher compared to glucose. After 48 h of cultivation, the final concentration was 310 mg IAA/L using propionate and 230 mg IAA/L using glucose, with a concentration of 500 mg Trp/L. To gain more insight into propionate metabolism and its advantages, the genome-scale metabolic model of B. subtilis (iBSU 1147) and computational analysis were used to calculate flux distribution and evaluate the metabolic capabilities to produce IAA using propionate. The metabolic fluxes demonstrate that propionate uptake favors the production of precursors needed for the synthesis of the hormone, and the sensitivity analysis shows that the control of a specific growth rate has a positive impact on the production of IAA.

Iron availability enhances the cellular energetics of aerobic Escherichia coli cultures while upregulating anaerobic respiratory chains.

Aerobic Escherichia coli growth at restricted iron concentrations (≤ 1.75 ± 0.04 µM) is characterized by lower biomass yield, higher acetate accumulation and higher activation of the siderophore iron-acquisition systems. Although iron homeostasis in E. coli has been studied intensively, previous studies focused only on understanding the regulation of the iron import systems and the iron-requiring enzymes. Here, the effect of iron availability on the energy metabolism of E. coli has been investigated. It was established that aerobic cultures growing under limiting iron conditions showed lower ATP yield per glucose, lower growth rate and lower TCA cycle activity and respiration, at the same time as increased glucose consumption, acetate and pyruvate accumulation, practically mimicking microaerobic growth. However, at excess iron, independent of oxygen availability, the cultures showed high cellular energetics (5.8 ATP/mol of glucose) by using pathways requiring iron-rich complex proteins found in the TCA cycle and respiratory chain. In conditions of iron excess, some iron-requiring terminal reductases of the respiratory chain, that were thought to function only under anaerobiosis, were used by the E. coli, when in aerobic conditions, to maintain high respiratory activity. This allowed it to produce more biomass and more reactive oxygen species that were controlled by the higher activity of the antioxidant defenses (SOD, peroxidase and catalase) and the iron-sulfur cluster repair systems.

Propionate as the preferred carbon source to produce 3-indoleacetic acid in B. subtilis: comparative flux analysis using five carbon sources.

3-Indoleacetic acid (IAA) is a phytohormone that promotes plant root growth, improving the use of nutrients and crop yield and it is been reported that bacteria of the genus Bacillus are capable of producing this phytohormone under various growth conditions. Considering this metabolic capability, in this work, Bacillus subtilis was cultivated in five different carbon sources: glucose, acetate, propionate, citrate and glycerol; and l-tryptophan (Trp) was used as an inducer for the IAA production. Based on the experimental results it was observed that the highest growth rate was achieved using glucose as a carbon source (µ = 0.12 h-1) and the lowest value was for citrate (µ = 0.08 h-1). On the other hand, the highest IAA production was obtained using propionate Yp/s = 0.975 (gIAA gTrp-1) and the lowest was when glucose was the substrate Yp/s = 0.803 (gIAA gTrp-1). In order to explore the metabolism and understand these differences, the experimental data was used to calculate the flux distribution using the genomic-scale metabolic model of Bacillus subtilis. Performing a comparative analysis it is observed that the fluxes towards precursors increase when propionate is the carbon source.

Optimization of theoretical maximal quantity of cells to immobilize on solid supports in the rational design of immobilized derivatives strategy.

Current worldwide challenges are to increase the food production and decrease the environmental contamination by industrial emissions. For this, bacteria can produce plant growth promoter phytohormones and mediate the bioremediation of sewage by heavy metals removal. We developed a Rational Design of Immobilized Derivatives (RDID) strategy, applicable for protein, spore and cell immobilization and implemented in the RDID1.0 software. In this work, we propose new algorithms to optimize the theoretical maximal quantity of cells to immobilize (tMQCell) on solid supports, implemented in the RDIDCell software. The main modifications to the preexisting algorithms are related to the sphere packing theory and exclusive immobilization on the support surface. We experimentally validated the new tMQCell parameter by electrostatic immobilization of ten microbial strains on AMBERJET® 4200 Cl- porous solid support. All predicted tMQCell match the practical maximal quantity of cells to immobilize with a 10% confidence. The values predicted by the RDIDCell software are more accurate than the values predicted by the RDID1.0 software. 3-indolacetic acid (IAA) production by one bacterial immobilized derivative was higher (~ 2.6 µg IAA-like indoles/108 cells) than that of the cell suspension (1.5 µg IAA-like indoles/108 cells), and higher than the tryptophan amount added as indole precursor. Another bacterial immobilized derivative was more active (22 µg Cr(III)/108 cells) than the resuspended cells (14.5 µg Cr(III)/108 cells) in bioconversion of Cr(VI) to Cr(III). Optimized RDID strategy can be used to synthesize bacterial immobilized derivatives with useful biotechnological applications.

Heterogeneous oxygen availability affects the titer and topology but not the fidelity of plasmid DNA produced by Escherichia coli.

BACKGROUND: Dissolved oxygen tension (DOT) is hardly constant and homogenously distributed in a bioreactor, which can have a negative impact in the metabolism and product synthesis. However, the effects of DOT on plasmid DNA (pDNA) production and quality have not been thoroughly investigated. In the present study, the effects of aerobic (DOT ≥30% air sat.), microaerobic (constant DOT = 3% air sat.) and oscillatory DOT (from 0 to 100% air sat.) conditions on pDNA production, quality and host performance were characterized. RESULTS: Microaerobic conditions had little effect on pDNA production, supercoiled fraction and sequence fidelity. By contrast, oscillatory DOT caused a 22% decrease in pDNA production compared with aerobic cultures. Although in aerobic cultures the pDNA supercoiled fraction was 98%, it decreased to 80% under heterogeneous DOT conditions. The different oxygen availabilities had no effect on the fidelity of the produced pDNA. The estimated metabolic fluxes indicated substantial differences at the level of the pentose phosphate pathway and TCA cycle under different conditions. Cyclic changes in fermentative pathway fluxes, as well as fast shifts in the fluxes through cytochromes, were also estimated. Model-based genetic modifications that can potentially improve the process performance are suggested. CONCLUSIONS: DOT heterogeneities strongly affected cell performance, pDNA production and topology. This should be considered when operating or scaling-up a bioreactor with deficient mixing. Constant microaerobic conditions affected the bacterial metabolism but not the amount or quality of pDNA. Therefore, pDNA production in microaerobic cultures may be an alternative for bioreactor operation at higher oxygen transfer rates.

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

BACKGROUND: Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore. RESULTS: We reconstructed the metabolic networks of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by improvements in the gluconeogenic pathway the metabolism of the endosymbiont uses more efficiently the carbon source for reduced-carbon production. In addition, our in silico simulations of the evolutionary process leading to the reduced metabolic network of the chromatophore showed that the predicted rate of released reduced-carbon is obtained in less than 5% of the times under a process guided by random gene deletion and genetic drift. We interpret previous findings as evidence that natural selection at holobiont level shaped the rate at which reduced-carbon is exported to the host. Finally, our model also predicts that the ABC phosphate transporter (pstSACB) which is conserved in the genome of the chromatophore of P. chromatophora strain CCAC 0185 is a necessary component to release reduced-carbon molecules to the host. CONCLUSION: Our evolutionary analysis suggests that in the case of Paulinella chromatophora natural selection at the holobiont level played a prominent role in shaping the metabolic specialization of the chromatophore. We propose that natural selection acted as a "metabolic engineer" by favoring metabolic restructurings that led to an increased release of reduced-carbon to the host.

Genome-scale metabolic models of Saccharomyces cerevisiae.

Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature of the underlying cellular processes can be tested and validated with the increasing metabolic knowledge. GSM models are also being exploited in fundamental research studies and industrial applications. In this chapter, the principle concepts for construction, simulation and validation of GSM models, progressive applications of the yeast GSM models, and future perspectives are described. This will support and encourage researchers who are interested in systemic analysis of yeast metabolism and systems biology.

Codon usage variability determines the correlation between proteome and transcriptome fold changes.

BACKGROUND: The availability of high throughput experimental methods has made possible to observe the relationships between proteome and transcriptome. The protein abundances show a positive but weak correlation with the concentrations of their cognate mRNAs. This weak correlation implies that there are other crucial effects involved in the regulation of protein translation, different from the sole availability of mRNA. It is well known that ribosome and tRNA concentrations are sources of variation in protein levels. Thus, by using integrated analysis of omics data, genomic information, transcriptome and proteome, we aim to unravel important variables affecting translation. RESULTS: We identified how much of the variability in the correlation between protein and mRNA concentrations can be attributed to the gene codon frequencies. We propose the hypothesis that the influence of codon frequency is due to the competition of cognate and near-cognate tRNA binding; which in turn is a function of the tRNA concentrations. Transcriptome and proteome data were combined in two analytical steps; first, we used Self-Organizing Maps (SOM) to identify similarities among genes, based on their codon frequencies, grouping them into different clusters; and second, we calculated the variance in the protein mRNA correlation in the sampled genes from each cluster. This procedure is justified within a mathematical framework. CONCLUSIONS: With the proposed method we observed that in all the six studied cases most of the variability in the relation protein-transcript could be explained by the variation in codon composition.

Combining substrate specificity analysis with support vector classifiers reveals feruloyl esterase as a phylogenetically informative protein group.

BACKGROUND: Our understanding of how fungi evolved to develop a variety of ecological niches, is limited but of fundamental biological importance. Specifically, the evolution of enzymes affects how well species can adapt to new environmental conditions. Feruloyl esterases (FAEs) are enzymes able to hydrolyze the ester bonds linking ferulic acid to plant cell wall polysaccharides. The diversity of substrate specificities found in the FAE family shows that this family is old enough to have experienced the emergence and loss of many activities. METHODOLOGY/PRINCIPAL FINDINGS: In this study we evaluate the relative activity of FAEs against a variety of model substrates as a novel predictive tool for Ascomycota taxonomic classification. Our approach consists of two analytical steps; (1) an initial unsupervised analysis to cluster the FAEs substrate specificity data which were generated by cultivation of 34 Ascomycota strains and then an analysis of the produced enzyme cocktail against 10 substituted cinnamate and phenylalkanoate methyl esters, (2) a second, supervised analysis for training a predictor built on these substrate activities. By applying both linear and non-linear models we were able to correctly predict the taxonomic Class (∼86% correct classification), Order (∼88% correct classification) and Family (∼88% correct classification) that the 34 Ascomycota belong to, using the activity profiles of the FAEs. CONCLUSION/SIGNIFICANCE: The good correlation with the FAEs substrate specificities that we have defined via our phylogenetic analysis not only suggests that FAEs are phylogenetically informative proteins but it is also a considerable step towards improved FAEs functional prediction.

Integrative analysis using proteome and transcriptome data from yeast to unravel regulatory patterns at post-transcriptional level.

Exist several studies on the correlation between proteome and transcriptome and these studies have shown that generally there is only a weak positive correlation between these two omes, which means that post-transcriptional events play an important role in determining the protein levels in the cell. In this study we combined proteome and transcriptome data from six different published dataset to identify patterns that can provide new insight into the reasons for these deviations. By using a categorization method and integrating genome-scale information we found that the relation between protein and mRNA is related to the gene function. We could further identify that for genes belonging to amino acid biosynthetic pathways there is no translational regulation, meaning that there is generally a good correlation between mRNA and protein levels. We also found that there is generally translational control for large proteins and there also evidence for a role of conserved motifs in the 3' untranslated regions in the mRNA-protein correlation, probably by controlling the level of mRNA.

BioMet Toolbox: genome-wide analysis of metabolism.

The rapid progress of molecular biology tools for directed genetic modifications, accurate quantitative experimental approaches, high-throughput measurements, together with development of genome sequencing has made the foundation for a new area of metabolic engineering that is driven by metabolic models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/.