Pentose phosphate and calvin cycles: Similarities and three-dimensional views*

Metabolic plasticity enables lifestyle transitions of Porphyromonas gingivalis

Our understanding of how the oral anaerobe Porphyromonas gingivalis can persist below the gum line, induce ecological changes, and promote polymicrobial infections remains limited. P. gingivalis has long been described as a highly proteolytic and asaccharolytic pathogen that utilizes protein substrates as the main source for energy production and proliferation. Here, we report that P. gingivalis displays a metabolic plasticity that enables the exploitation of non-proteinaceous substrates, specifically the monocarboxylates pyruvate and lactate, as well as human serum components, for colonization and biofilm formation. We show that anabolism of carbohydrates from pyruvate is powered by catabolism of amino acids. Concomitantly, the expression of fimbrial adhesion is upregulated, leading to the enhancement of biofilm formation, stimulation of multispecies biofilm development, and increase of colonization and invasion of the primary gingival epithelial cells by P. gingivalis. These studies provide the first glimpse into the metabolic plasticity of P. gingivalis and its adaptation to the nutritional condition of the host niche. Our findings support the model that in response to specific nutritional parameters, P. gingivalis has the potential to promote host colonization and development of a pathogenic community.

The plastid proteome of the nonphotosynthetic chlorophycean alga Polytomella parva

The unicellular, free-living, nonphotosynthetic chlorophycean alga Polytomella parva, closely related to Chlamydomonas reinhardtii and Volvox carteri, contains colorless, starch-storing plastids. The P. parva plastids lack all light-dependent processes but maintain crucial metabolic pathways. The colorless alga also lacks a plastid genome, meaning no transcription or translation should occur inside the organelle. Here, using an algal fraction enriched in plastids as well as publicly available transcriptome data, we provide a morphological and proteomic characterization of the P. parva plastid, ultimately identifying several plastid proteins, both by mass spectrometry and bioinformatic analyses. Data are available via ProteomeXchange with identifier PXD022051. Altogether these results led us to propose a plastid proteome for P. parva, i.e., a set of proteins that participate in carbohydrate metabolism; in the synthesis and degradation of starch, amino acids and lipids; in the biosynthesis of terpenoids and tetrapyrroles; in solute transport and protein translocation; and in redox homeostasis. This is the first detailed plastid proteome from a unicellular, free-living colorless alga.

An optimization rule for in silico identification of targeted overproduction in metabolic pathways

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.

Modeling the optimal central carbon metabolic pathways under feedback inhibition using flux balance analysis

Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.

Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

Background

In the present article, we propose a method for determining optimal metabolic pathways in terms of the level of concentration of the enzymes catalyzing various reactions in the entire metabolic network. The method, first of all, generates data on reaction fluxes in a pathway based on steady state condition. A set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients.

Results

The effectiveness of the present method is demonstrated on two synthetic systems existing in the literature, two pentose phosphate, two glycolytic pathways, core carbon metabolism and a large network of carotenoid biosynthesis pathway of various organisms belonging to different phylogeny. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. Biological relevance and validation of the results are provided. Finally, the impact of the method on metabolic engineering is explained with a few examples.

Conclusions

The method may be viewed as determining an optimal set of enzymes that is required to get an optimal metabolic pathway. Although it is a simple one, it has been able to identify a carotenoid biosynthesis pathway and the optimal pathway of core carbon metabolic network that is closer to some earlier investigations than that obtained by the extreme pathway analysis. Moreover, the present method has identified correctly optimal pathways for pentose phosphate and glycolytic pathways. It has been mentioned using some examples how the method can suitably be used in the context of metabolic engineering.

Protein Superfamily Evolution and the Last Universal Common Ancestor (LUCA)

By exploiting three-dimensional structure comparison, which is more sensitive than conventional sequence-based methods for detecting remote homology, we have identified a set of 140 ancestral protein domains using very restrictive criteria to minimize the potential error introduced by horizontal gene transfer. These domains are highly likely to have been present in the Last Universal Common Ancestor (LUCA) based on their universality in almost all of 114 completed prokaryotic (Bacteria and Archaea) and eukaryotic genomes. Functional analysis of these ancestral domains reveals a genetically complex LUCA with practically all the essential functional systems present in extant organisms, supporting the theory that life achieved its modern cellular status much before the main kingdom separation (Doolittle 2000). In addition, we have calculated different estimations of the genetic and functional versatility of all the superfamilies and functional groups in the prokaryote subsample. These estimations reveal that some ancestral superfamilies have been more versatile than others during evolution allowing more genetic and functional variation. Furthermore, the differences in genetic versatility between protein families are more attributable to their functional nature rather than the time that they have been evolving. These differences in tolerance to mutation suggest that some protein families have eroded their phylogenetic signal faster than others, hiding in many cases, their ancestral origin and suggesting that the calculation of 140 ancestral domains is probably an underestimate.