u.s. army combat capabilities development command … · i have addressed most of my research...

UNCLASSIFIED//FOR OFFICIAL USE ONLY//DRAFT//PRE-DECISIONAL


U.S. ARMY COMBAT CAPABILITIES

DEVELOPMENT COMMAND –

ARMY RESEARCH LABORATORY

Leah A. Lewis, [email protected]

Hampton University

DoD HBCU/MI Summer Research Program

06 08 2020

Metabolic Modeling of Pseudomonas putida KT2440 to understand and improve the breakdown of plastic waste

mailto:[email protected]


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• PET plastics from water bottles, packaging: among

largest battlefield waste streams1

– Hard to recycle in remote areas

– Burn pits outlawed in Army2

– Biological process to break down PET attractive• Safe, mild conditions. Low energy input.

• Especially if it could generate useful chem, mat, energy

• PET comes from polymerization of terephthalic acid

(TPA) + ethylene glycol (EG)3

– Hydrolysis of PET returns to these monomers (toxic)

– P. putida: bacterium that can metabolize many org. compounds

– P. putida KT2440 (“KT”) can extract energy from EG

– Whether it can grow on EG as sole C-source unclear

– Most recent analyses indicate yes

BACKGROUND

PET water bottles 1

Army burn pit4

1. Conant, J. M. (2018, March 28). US Army lab finds plastic bottles, other waste products have re-use potential for battlefield. https://www.army.mil/article/202398/us_army_lab_finds_plastic_bottles_other_waste_products_have_re_use_potential_for_battlefield.

2. Preston, B. (2011, May 16). Attack of the Warzone Water Bottles. State of the Planet. https://blogs.ei.columbia.edu/2011/05/16/attack-of-the-warzone-water-bottles/.

3. Salvador, M. et al. (2019). Microbial Genes for a Circular and Sustainable Bio-PET Economy. Genes, 10(5), 373. https://doi.org/10.3390/genes10050373

4. Myers, M. (2019, July 12). Why DoD is still using burn pits, even while now acknowledging their danger. Military Times. https://www.militarytimes.com/news/your-military/2019/07/12/why-dod-is-still-using-burn-pits-even-while-now-acknowledging-their-danger/.

5. iGem. Degradation (2012). Team:TU Darmstadt. http://2012.igem.org/Team:TU_Darmstadt/Project/Degradation.

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We are interested in using whole-genome metabolic models of P. putida

to better-understand its potential to grow on and convert EG & TPA

• Do different methods of constructing and analyzing these models produce different

outputs?

– How do outputs compare to literature as models are updated over time?

• Can flux balance analysis (FBA) predict how well P. putida can grow on EG, TPA, or

both?

– What are the theoretical yields (limits) on what it can generate from a given set of

compounds in the growth medium?

• What do these models say about viability of a bioprocess based on P. putida to convert

EG & TPA into benign biomass or even useful chemicals?

• Can genetic modifications improve the ability of P. putida KT2440 to grow (produce

biomass) on EG and/or TPA as sole carbon sources?

RESEARCH QUESTIONS


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• Whole-Genome Metabolic Model: Reconstruction of all metabolic reactions an organism

can carry out & all compounds involved (“metabolic network”), based on contents of its

genome7

• Flux Balance Analysis (FBA): Calculation of “flow” of compounds through metabolic

network, given set of conditions & constraints. – Constraints represented by “objective functions,” e.g., “maximize biomass formation”8

– Strictly stoichiometric. No kinetics or thermodynamics involved.

• These modeling tools help us predict feasibility & limits of growth potential production

rate of important metabolites.

WHOLE-GENOME METABOLIC MODELS

AND FLUX BALANCE ANALYSIS

7. Nogales, et al. (2008). A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Systems Biology, 2(1), 79. https://doi.org/10.1186/1752-0509-2-79

8. Orth, J. D., Thiele, I., & Palsson, B.Ø. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245–248.

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v: (set of) possible flux vectors through all n reactions

S: stoichiometric matrix.

Sv = 0: conservation of mass

• Z: objective function to maximize

• Solution to this FBA is v that maximizes Z

Based on stoichiometry


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• Web-bases suite of “apps”

• US DOE Systems Biology Knowledgebase

• Metabolic model built for P. putida KT2440 with EG

as sole carbon source in the growth medium

• A flux balance analysis (FBA) was performed,

maximizing the biomass reaction flux

• Several reactions required for growth added

automatically by KBase

• “Gap filling” (not ideal)

• Results indicated biomass formation (growth) is

possible on EG by a plausible route

• Biomass flux of 0.10 h-1obtained on EG, vs.

0.46 h-1on glucose

FBA MODELING TOOL #1:

9. The U.S. Department of Energy Systems Biology Knowledgebase. KBase. http://kbase.us/.

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• COnstraint-Based Reconstruction and Analysis Toolbox for PYthon

• Requires a Python interpreter.

– Installed Anaconda3 Navigator freeware

– Learned required Python scripting with Software Carpentry (https://swcarpentry.github.io/python-novice-inflammation)

• Completed several COBRAPY lessons for building models, importing

growth media, and simulating FBA

• Reactions and metabolites comprising P. putida KT2440 models iJN746

(2008) & iJN1463 (2019) obtained from the BiGG Models database (http://bigg.ucsd.edu/search?query=pseudomonas+putida)

• COBRApy used to perform FBA on both models with EG as sole carbon

source

FBA MODELING TOOL #2:

10. GitHub. cobrapy - constraint-based metabolic modeling in Python. https://opencobra.github.io/cobrapy/.

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• KBase easy to use but slow. Many uploads/downloads, site closures for upgrades

– Models in KBase have little documentation or “quality ratings”

– Gapfilling algorithm not a good substitute for well-curated model

• COBRApy requires learning Python

– Higher-quality models, documented with publications, experimentally-validated

– Faster – Run FBAs without up/downloading each time

– Direct application of specific models: no gapfilling

• Tools not compatible with each other. Different data formats & notation

• Verdict: COBRApy requires more investment to learn, but worth it

KBASE VS. COBRAPY

11. KBase. https://narrative.kbase.us/#dataview/19217/162098/1.

12. The Regents of the University of California. (2019). BiGG Models. BiGG Model iJN1463. http://bigg.ucsd.edu/models/iJN1463.

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• Semi-Automated Metabolic Map Illustrator in

Python (SAMMIpy)13 used to render

COBRApy FBA results into metabolic

pathway diagrams

• SAMMIpy visualizations can be filtered to

show only the compounds and reactions

involved in the metabolism of EG.

• SAMMIpy diagrams useful to visualize

connections between enzymes ( ) &

metabolites ( )

• Upgrades would make tool more useful• Show fluxes on standard metabolic maps (e.g.

TCA cycle)

• More info, display options, & clearer

appearance for figures & presentations

VISUALIZATION OF FBA RESULTS

13. The University of Texas - MD Anderson Cancer Center. Semi-Automated Metabolic Map Illustrator. SAMMI. https://bioinformatics.mdanderson.org/public-software/sammi/.

Visualization of EG catabolism pathway &

fluxes by P. putida KT2440 model iJN1463


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• The BiGG database of metabolic models has 2 entries for P. putida KT2440,

both from the Palsson group at UCSD: – Model iJN746 from 200814 (“746”)

– Model iJN1463 from 201915 (“1463”)

• Model iJN1463 is the newest and most detailed model for P. putida KT2440,

which is can only analyzed through COBRApy.

• iJN1463 has enhancements over earlier models of KT244014,15:– Greater # of C- and N-sources

– More accurate prediction of growth capabilities, growth rates, flux distributions

– More detailed and accurate chemical formula for P. putida biomass

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14. Nogales, et al. (2008). A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Systems Biology, 2(1), 79. https://doi.org/10.1186/1752-0509-2-79

15. Nogales, J., Mueller, J., Gudmundsson, S., Canalejo, F. J., Duque, E., Monk, J., Palsson, B.Ø. (2019). High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities. Environmental Microbiology, 22(1), 255–269.

https://doi.org/10.1111/1462-2920.14843

A TALE OF TWO MODELS


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P. putida KT2440 model iJN1463 was able to grow (produce biomass) on ethylene

glycol, while model iJN746 could not

A TALE OF TWO MODELS (con’t)

• Biomass flux of 0.27 h-1 for iJN1463 on EG compares

favorably to 0.53 h-1 on glucose benchmark

• Analysis of EG (glycol) reaction steps reveal periplasm

active (ATP-consuming) glycolaldehyde (Gcald) transport

step

• Possible genetic engineering opportunity

• Functional expression of a passive transport channel

for EG or Gcald into cytoplasm

• Model predicts this would increase biomass flux to

0.41.

EG

Pa

ssiv

e im

port

Glycol

DHase

Gcald

AB

C tra

nsp

orte

r

ATP

ADP


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EG REACTIONS & FLUXES WITH iJN1463

Biomass flux: 0.269 h-1


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MODELING THE TPA METABOLISM SIDE OF PET

16. The International Genetically Engineered Machine Competition. pNB-Est 13: Esterase PET cleaving enzyme. Registry of Standard Biological Parts. http://parts.igem.org/Part:BBa_K808026.

17. (Pathway Diagram): The International Genetically Engineered Machine Competition. PlastiCure. iGEM. http://2015.igem.org/Team:Pasteur_Paris/Description

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• Neither iJN1463 nor the entire BiGG

database has an entry for TPA!

• Plenty of literature on TPA metabolism

in other microbes, e.g. Rhodococcus3

• The first TPA breakdown product in

iJN1463 is protocatechuate (PCA)

• I have started modeling PCA

metabolism by iJN1463

• If P. putida cannot convert TPA to PCA

another opportunity for genetic

engineering (add genes for enzymes)


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PCA REACTIONS & FLUXES WITH iJN1463

Biomass flux: 0.289 h-1


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iJN1463 WITH EG AND PCA (DUAL C-SOURCES)

P. putida KT2440 model iJN1463 with EG and

PCA as dual C-sources• Same biomass flux of 0.289 as above with PCA

alone!

• Co-utilization not predicted by model to maximize

growth

• Looks like “Diauxic” metabolism

– Commonly seen with sugars, e.g. glucose & lactose

– Controlled by complex genetic regulatory switches

– But, regulation not part of metabolic models

(stoichiometry only)

• Model predicts P. putida will exhaust all PCA

before transitioning to EG

– Only because 0.289 > 0.269 (or weighted average)

& model told to maximize biomass flux

– “Synergy” required to have model use both (e.g.

redox balanced only with both substrates)

• Should be skeptical & test by experiment

0.289 h-10.269 h-1

Together: 0.289 h-1

(only PCA metabolized)

(PCA)

(EG)


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I evaluated 2 major genome-scale metabolic modeling platforms:• KBase, COBRApy

• Learned Python scripting language to run COBRApy

• Recommend COBRApy for quality of models.

• Unfortunately, the tools utilize incompatible data formats

I have addressed most of my research questions:• Different FBA tools & models can produce different answers

– Highest quality & most recent model for P. putida is iJN1463, not on KBase

• Analysis of iJN1463 indicates growth potential of P. putida on EG – About half of potential biomass flux on glucose: 0.27 vs. 0.53 h-1

– Possible improvement to 0.41 h-1 by engineering passive transport into cytoplasm

• Have begun to model TPA metabolism by P. putida– TPA not in BiGG, premier database of metabolic models

– Started with FBA models from PCA (2 steps down) similar biomass flux to EG

– FBA models with EG and PCA as dual C-sources predict PCA used first, not simultaneous consumption

• Additional investigation required about TPAPCA enzymes & transporters in P. putida– Literature, BLAST searches, or experiments in lab

– TPAPCA steps known in other bacteria, could be engineered into P. putida

• Results on PCA + EG allow evaluation of P. putida for both PET monomers– Diauxic consumption of PCA first, then EG not ideal for process

– Model’s prediction should be tested experimentally

– If verified, strategy to consume EG & TPA together: mixed consortium of P. putida + an EG consumer

– EG consumer could be P. putida with knockout of early TPA pathway step(s)

SUMMARY AND NEXT STEPS


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ACKNOWLEDGMENTS

Dr. Alex Tobias Dr. Matthew Perisin

u.s. army combat capabilities development command … · i have addressed most of my research...

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