1. Shewanella oneidensis MR1
Knock Out Experiment
Kim Parker
Rachel Neurath
Savannah Sanchez
Alton Lee
Dimitri Kalenitchenko
Hopkins Microbiology 2013

2. Outline Background Shewanella oneidensis Chemostat versus batch
Objectives and Hypotheses
Experimental Design
Results
Discussion

3. Shewanella oneidensis MR1
Gram-negative γ-proteobacteria
Primarily marine, also found stratified sedimentary systems and soils
Anaerobe, facultative aerobe
Initially recognized for dissimilatory metabolism of manganese and iron oxides
Genome.jgi-psf.org
Biotech-weblog.com
PNNL (2009)
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

4. Shewanella oneidensis: Metabolism
Shewanella oneidensis MR-1 is characterized by a complex and highly versatile metabolism:
ELECTRON DONORS: wide range of organic compounds
ELECTRON ACCEPTORS: O2, Mn-oxides, Fe-oxides, uranium, chromium, plutonium, selenite, etc.
CARBON SOURCE: wide range of organic compounds
Can perform solid-state electron transfer
Contain 42 putative c-type cytochromes
Fredrickson et al. (2008): Nature Reviews Microbiology
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

5. Shewanella oneidensis: Ecological Significance
Well-developed sensing and regulatory systems, along with diverse metabolism and tolerance to extreme conditions, allow for success in wide range of environments
Applications:
Bioremediation and biotechnology
Reference organism for understanding C-metabolic pathways
Park et al. (2011): Journal of Hazardous Materials
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

6. Batch Culture Kinetics : Ln (Xt) = Ln(Xo) + μt X, V, S X, V, S X, V, S
Prescott et al.
X, V, S
X, V, S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

7. Batch Culture Closed System with finite nutrients Kinetics :
Ln (Xt) = Ln(Xo) + μt
Prescott et al.
X, V, S
X, V, S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

8. Serial Batch Culture Closed System with finite nutrients Kinetics :
Accumulation of cells / byproducts
Kinetics :
Ln (Xt) = Ln(Xo) + μt
Prescott et al.
X, V, S
X, V, S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

9. Batch Culture Closed System with finite nutrients Kinetics :
Accumulation of cells / byproducts
No regulation of growth phase
Kinetics :
Ln (Xt) = Ln(Xo) + μt
Prescott et al.
X, V, S
X, V, S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

10. Serial Batch Culture Closed System with finite nutrients Kinetics :
Accumulation of cells / byproducts
No regulation of growth phase
Kinetics :
Ln (Xt) = Ln(Xo) + μt
Prescott et al.
X, V, S
X, V, S
X, V, S
X, V, S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

11. Chemo Continuous Culture
Mass Balance: In + Accumulated + Generated = Out + Consumed
Substrate Mass Balance:
X = Y ( Sin – S)
Bacterial Mass Balance:
μ = F/V = D
FLOW RATE
Sin S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

12. Chemo Continuous Culture
Mass Balance: In + Accumulated + Generated = Out + Consumed
Low continuous concentrations of nutrient
Substrate Mass Balance:
X = Y ( Sin – S)
Bacterial Mass Balance:
μ = F/V = D
FLOW RATE
Sin S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

13. Chemo Continuous Culture
Mass Balance: In + Accumulated + Generated = Out + Consumed
Low continuous concentrations of nutrient
Flux of chemical species
Substrate Mass Balance:
X = Y ( Sin – S)
Bacterial Mass Balance:
μ = F/V = D
FLOW RATE
Sin S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

14. Chemo Continuous Culture
Mass Balance: In + Accumulated + Generated = Out + Consumed
Low continuous concentrations of nutrient
Flux of chemical species
Control “natural” environment to study adaptation/”natural” physiology
Substrate Mass Balance:
X = Y ( Sin – S)
Bacterial Mass Balance:
μ = F/V = D
FLOW RATE
Sin S
X, V, S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

15. BIG PICTURE: Batch vs Chemostat Dynamics
Prescott et al.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

16. Comparing Batch and Chemostat Systems
The batch system goes through twice as many generations as the chemostat  succession progresses at double the rate
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

17. S. oneidensis MR-1 Knock Out Library Experiment
30° C
( OD )
Sample 1mL
Pellet Cells
Extract DNA
PCR Barcode
Sequence
O/N
@ 30° C
3977
Repeat for TF = 72 hrs
3 mL
30° C
( OD )
O/N
@ 30° C
Sample 1mL
Pellet Cells
Extract DNA
PCR Barcode
Sequence
4058
Repeat for TF = 72 hrs

18. Objectives
Compare temporal changes in relative abundance of knock out genes in batch and chemostat systems
Examine ecological and selective pressures exerted by chemostat and batch systems, using Arkin Lab experiment
Link sensitive genes to metabolic and functional pathways
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

19. Hypotheses
Batch and chemostat systems will selectively favor certain gene knock-outs
Arkin Lab experimental conditions that are high in nutrients and relatively stable will more closely resemble the batch system; stress conditions will more closely resemble the chemostat
Declines in relative abundance will be associated with metabolic pathways and functions that are essential
Increases in relative abundance will be associated with metabolic pathways and functions that are either non- essential or even unfavorable
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

20. Results
Hau and Gralnick (2007): Annual Review of Microbiology
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

21. Rate of Change in Relative Abundance
How does the distribution of relative abundance values change over sampling time points in the batch and chemostat systems?
What is the rate of change in relative abundance between time points?
Calculation of rate of change:
What this measure may indicate:
Rapid decline: knock-out was highly detrimental
Rapid increase: knock-out was highly advantageous to the organism (at least relative to other organisms)
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

22. Distribution of Relative Abundance: Batch
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

23. Rate of Change in Relative Abundance: Batch
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

24. Distribution of Relative Abundance: Chemostat
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

25. Rate of Change in Relative Abundance: Chemostat
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

26. “Knocking Out” Diversity
Shannon Index of Diversity:
Where: ρi=n/N
n = species
N = # of individual in species
s = # of species
Simpson Index of Diversity:
Pielou’s Evenness Index:
Richness
Evenness
Purvis and Hector (2000): Nature
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion
Background | Objectives and Hypotheses | Experimental Design | Results | Discussion

27. Temporal Changes in Diversity: Batch versus Chemostat
Batch: Diversity and evenness are constant
Chemostat: Decline in diversity and evenness over time
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

28. Temporal Changes in Diversity: Biofilm
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

29. ß-diversity on our experiment
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

30. Arkin Dataset Anaerobic
Variety of organic and inorganic electron acceptors
Stress
Grown on microplate Heat (42 C) /cold (4 C) exposure
Motility
Isolate cells that can travel from point of innoculation
Carbon Source
Variety of sources of carbon
N/S/P Source
Variety of sources of nitrogen, sulfur, or phosphorous
Temperature and pH
pH ranged from 6-9
Temperature ranged from 15C to 35C
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

31. Workflow for Data ConsolidationUP
DOWN
Consolidate SO (loci) data based on insertion quality
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

32. Workflow for Data ConsolidationUP
DOWN
Consolidate SO (loci) data based on insertion quality
MATCH / MERGE/ REMOVE OUTLIERS
UP/DOWN MERGED
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

33. Up and Down Libraries: Not Exactly Duplicates✕ ✕
✔ ✖ ✕ ✕
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

34. Up and Down Libraries: Not Exactly Duplicates
Evaluate (xUp-xDown)
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

35. Up and Down Libraries: Not Exactly Duplicates
Evaluate (xUp-xDown)
Trim off discrepancies
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

36. PCA with Arkin Dataset is Uninformative
PCA their data
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

37. Multidimensional Scaling
-Visualizes similarity among samples. -Attempts to maintain calculated distances among samples. -Need to define a distance metric.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

38. Arkin Dataset
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

39. Chemostat Data Clusters Further
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

40. Motility Experiments Also Cluster
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

41. Biofilm Experiment
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

42. Workflow for Data ConsolidationUP
DOWN
Consolidate SO (loci) data based on insertion quality
MATCH / MERGE/ REMOVE OUTLIERS
UP/DOWN MERGED
SUBSETS
Positive Chemostat
Negative
Chemostat
Positive Batch
Positive
All
Negative
All
Positive/ Negative
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

43. Batch Vs Chemostat
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

44. MSHA Mannose-sensitive hemagglutinin type 4 pilus
Batch (-)
Motility Assay
Chemostat
Biofilm Formation
Biogenesis Protein
Pilin Protein
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

45. MSHA Mannose-sensitive hemagglutinin type 4 pilus
Batch (-)
Motility Assay
Pili have minimal effect on apparent relative abundance.
(( ))
Chemostat
Biofilm Formation
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

46. MSHA Mannose-sensitive hemagglutinin type 4 pilus
Batch (-)
Motility Assay
Pili prevent cells from reaching sampling location.
Pili have minimal effect on apparent relative abundance.
(( ))
Chemostat
Biofilm Formation
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

47. MSHA Mannose-sensitive hemagglutinin type 4 pilus
Batch (-)
Motility Assay
Pili prevent cells from reaching sampling location.
Pili have minimal effect on apparent relative abundance.
(( ))
Chemostat
Biofilm Formation
Pili allow cells to adhere to surface.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

48. MSHA Mannose-sensitive hemagglutinin type 4 pilus
Batch (-)
Motility Assay
Pili prevent cells from reaching sampling location.
Pili have minimal effect on apparent relative abundance.
(( ))
Chemostat
Biofilm Formation
Shouldn’t pili prevent cells from washing out?
But…
Pili prevent cells from being sampled.
Pili allow cells to adhere to surface. S
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

49. Obligate Offenders NADH:Quinone Oxidoreductase, Na+ α Subunit Nqr B
Ubiquinol Cytochrome C Reductase
FeS Subunit
NADH:Quinone Oxidoreductase, Na+
α Subunit
Nqr B
Nqr D
β Subunit
Effects of mutant results in a gradual decrease in “fitness” within the batch condition
Interestingly, there is always a peak at 48 hrs within the chemostat conditions
The chemostat 24 hr tends to have a larger negative effect on the abundances that even in the 72 hour

50. NADH:Ubiquinone Oxidoreductase Na+ translocating
Out In
Na +
NADH + H+ + Ubiquinone
NAD+ + Ubiquinol
Preferential to Complex I?
Enzymatic inefficiency
[Na] in natural environment
Ion motive force
The associated genes to this complex are found on an operon called the nqr operon
Part of the respiratory chain and is often associated with pathogenic bacteria
Many bacteria us used over complex 1 of electron transport chain ( oxidization of NADH)
Bacterial electron transport chain are typically inducible based on environmental conditions
Verkhovsky & Bogachev, 2009

51. with relative abundance
Pathway overview
Pathways Tool Software
Shewanella Pathway map
with relative abundance
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

52. Chemostat overview
72h
24h
48h

53. Chemostat overview
72h
24h
48h

54. Chemostat overview
72h
24h
48h

55. Fermentation Pathway
Metabolical model knock out pyruvate phosphate
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

56. Fermentation Pathway
Why Knocking out the pyruvate kinase have a positive effect ?
Knocking out the Pyruvate kinase will strongly affect the metabolism
No positive effect ???
Metabolical model knock out pyruvate phosphate
Metabolic Flux Responses to Pyruvate Kinase Knockout in Escherichia coli,
2002, Emmerling et al.

57. Fermentation Pathway
Knocking out a gene always have a bad effect in this part of the pathway !!!
Metabolical model knock out pyruvate phosphate
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

58. Aérobic respiration Pathway
Metabolical model knock out pyruvate phosphate
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

59. Aerobic respiration Pathway
Always a negative effect
Clue for aerobic activity
Why do we see respiration and fermentation at the same time ?
No interest for a cell to ferment if she could respire
Metabolical model knock out pyruvate phosphate
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

60. Possibilities Heterogeneous chemostat
Micro-aerobic condition due to big bubble bubbling
Perfect size will be 300µm
Motarjemi and Jameson, 1978
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

61. And in the batch …
Same pattern than in the chemostat for the respiration versus fermentation.
No huge effect in comparison with the chemostat …
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

62. And in the batch …
Same pattern than in the chemostat for the respiration versus fermentation.
No huge effect in comparison with the chemostat …
But keep in mind that mutants have already been selected in LB batch culture media
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

63. One more thing…
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

64. Relative Abundances of Chemotaxis/ Flagella Proteins Across Conditions
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

65. Flagellar Assembly
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

66. A Pathway View For The Chemostat at 72h
rpoN
flrA
flrB
flrC
RNAP s28
cheW
cheY
cheV
MCP
cheA
cheB
cheR
FlgG
FliI
FlgF
FlgH
FlgB
FlgJ
FlhF
flhA
Flhb
flgT
FlgE
FlgL
FliK
FlgI
motA
motB
FliM
FliC
Pathways from KEGG and Wu et. al (2011) PLoS ONE 6(6): e21479
Flagella-related genes
Chemotaxis-related
Regulatory factor
Regulatory factors
A Pathway View For The Chemostat at 72h
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

67. Batch Reactor vs Chemostat
Essential Functions
Essential Functions
Energy
Substrates
Energy
Substrates
Auxiliary Functions
Auxiliary Functions
Most growth occurs while substrate is in excess.
Most growth occurs while substrate is limited.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

68. Batch Reactor vs Chemostat
Essential Functions
Essential Functions
Energy
Substrates
Energy
Substrates
Auxiliary Functions
Auxiliary Functions
Most growth occurs while substrate is in excess.
Most growth occurs while substrate is limited.
Essential Functions
Essential Functions
Energy
Substrates
Energy
Substrates
Flagella
Flagella
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

69. Returning to Our Hypotheses
Batch and chemostat systems will selectively favor certain gene knock-outs
Arkin Lab experimental conditions that are high in nutrients and relatively stable will more closely resemble the batch system; stress conditions will more closely resemble the chemostat
Declines in relative abundance will be associated with metabolic pathways and functions that are essential
Increases in relative abundance will be associated with metabolic pathways and functions that are either non- essential or even unfavorable
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

70. Returning to Our Hypotheses
Batch and chemostat systems will selectively favor certain gene knock-outs
YES, and the chemostat and batch system selectively favored certain knock-outs. Differences in selection in the chemostat and batch system were most likely driven by substrate availability and growth phase of organisms, driving a rate:yield relationship.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

71. Returning to Our Hypotheses
Arkin Lab experimental conditions that are high in nutrients and relatively stable will more closely resemble the batch system; stress conditions will more closely resemble the chemostat
SOMEWHAT….The most significant finding was that the Arkin conditions selecting for motility were clustered furthest from the our chemostat conditions.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

72. Returning to Our Hypotheses
3. Declines in relative abundance will be associated with metabolic pathways and functions that are essential
4. Increases in relative abundance will be associated with metabolic pathways and functions that are either non- essential or even unfavorable
Probably, it seems that knock-out of genes associated with essential pathways such as NADH dehydrogenase had negative relative abundances. Knock-out of non- essential genes such as flagella had increases in relative abundance.
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion

73. Questions?