In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

Abstract

Background: Bioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment.

Results: We have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation.

Conclusions: This work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes.

Figure 1

P. putida reactions for growth on 1,2,4-TCB. The reaction network used to integrate the degradation products of 1,2,4-TCB into P. putida metabolism and the cellular processes involved in generating the required reducing power (electrons and/or NADH) are shown. Central carbon metabolism includes the Entner-Doudoroff pathway, gluconeogenesis, and the TCA cycle. The acetaldehyde dehydrogenase (acetylating) reaction, involved in toluene metabolism, was also used to generate NADH. The overall reactions for eight different pathways were implemented individually; the reaction network resulting from implementation of overall reaction [K] is given here. Reaction networks for all eight overall reactions studied are shown in Additional File 1. The units for the flux values are mmol/gDW/h. Compound abbreviations are given in the appendix.

Figure 2

Relationship between growth yield and reducing equivalents. Each overall reaction was implemented individually, and MFA was used to predict the maximum growth yield. The number of reducing equivalents is equal to 1/2 the number of electrons required plus the net number of NAD(P)H molecules used to integrate the degradation products into central metabolic pathways. The points are labelled according to the corresponding overall reaction. The best-fit line is drawn to guide the eye.

Figure 3

Flux distribution in central metabolic pathways for growth on 1,2,4-TCB. Flux ranges for the central metabolic pathways are shown where black indicates essential reactions, square brackets denote substitutable reactions, and blocked reactions are marked with an "X". The overall reactions for eight different pathways were implemented individually; the flux distribution resulting from implementation of overall reaction [K] is given here. Flux distribution maps for all eight overall reactions studied are shown in Additional File 2. The units for the flux values are mmol/gDW/h. A negative flux indicates the reaction can proceed in the reverse direction compared to what is shown. Compound abbreviations are given in the appendix. Shaded boxes indicate biomass precursors.

Figure 4

Relationship between biomass production and metabolism of 1,2,4-TCB. Each overall reaction was implemented individually, and MFA was used to predict the (a) maximum 1,2,4-TCB uptake rate and (b) minimum 1,2,4-TCB uptake rate required for a given amount of biomass.