Quantification of macromolecular biomass composition for constraint-based metabolic modeling

Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.

Prebiotic Combinations Effects on the Colonization of Staphylococcal Skin Strains

Background: An unbalanced skin microbiota due to an increase in pathogenic vs. commensal bacteria can be efficiently tackled by using prebiotics. The aim of this work was to identify novel prebiotic combinations by exerting species-specific action between S. aureus and S. epidermidis strains. Methods: First, the antimicrobial/antibiofilm effect of Xylitol-XYL and Galacto-OligoSaccharides–GOS combined with each other at different concentrations (1, 2.5, 5%) against S. aureus and S. epidermidis clinical strains was evaluated in time. Second, the most species-specific concentration was used to combine XYL with Fructo-OligoSaccharides–FOS, IsoMalto-Oligosaccharides–IMO, ArabinoGaLactan–LAG, inulin, dextran. Experiments were performed by OD600 detection, biomass quantification and LIVE/DEAD staining. Results: 1% XYL + 1% GOS showed the best species-specific action with an immediate antibacterial/antibiofilm action against S. aureus strains (up to 34.54% ± 5.35/64.68% ± 4.77) without a relevant effect on S. epidermidis. Among the other prebiotic formulations, 1% XYL plus 1% FOS (up to 49.17% ± 21.46/37.59% ± 6.34) or 1% IMO (up to 41.28% ± 4.88/36.70% ± 10.03) or 1% LAG (up to 38.21% ± 5.31/83.06% ± 5.11) showed antimicrobial/antibiofilm effects similar to 1% XYL+1% GOS. For all tested formulations, a prevalent bacteriostatic effect in the planktonic phase and a general reduction of S. aureus biofilm formation without loss of viability were recorded. Conclusion: The combinations of 1% XYL with 1% GOS or 1% FOS or 1% IMO or 1% LAG may help to control the balance of skin microbiota, representing good candidates for topic formulations.

Systematic Review and Study of S Curves for Biomass Quantification in Solid-state Fermentation (SSF) and Digital Image Processing (DIP) Applied to Biomass Measurement in Food Processes

Background: There are several methods for the quantification of biomass in SSF, such as glucosamine measurement, ergosterol content, protein concentration, change in dry weight or evolution of CO2 production. However, all have drawbacks when obtaining accurate data on the progress of the SSF due to the dispersion in cell growth on the solid substrate, and the difficulty encountered in separating the biomass. Studying the disadvantages associated with the process of biomass quantification in SSF, the monitoring of the growth of biomass by a technique known as digital image processing (DIP), consists of obtaining information on the production of different compounds during fermentation, using colorimetric methods based on the pixels that are obtained from photographs. Objective: The purpose of this study was to know about the state of the technology and the advantages of DIP. Methods: The methodology employed four phases; the first describes the search equations for the SSF and the DIP. A search for patents related to SSF and DIP carried out in the Free Patents Online and Patent inspiration databases. Then there is the selection of the most relevant articles in each of the technologies. As a third step, modifications for obtaining the best adjustments were also carried out. Finally, the analysis of the results was done and the inflection years were determined by means of six mathematical models widely studied. Results: For these models, the inflection years were 2018 and 2019 for both the SSF and the DIP. Additionally, the main methods for the measurement of biomass in SSF were found, and are also indicated in the review, as DIP measurement processes have already been carried out using the same technology. Conclusion: In addition, the DIP has shown satisfactory results and could be an interesting alternative for biomass measurement in SSF, due to its ease and versatility.

A Review of Regional and Global Gridded Forest Biomass Datasets

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.