Knock Out Experiment Kim Parker Rachel Neurath Savannah Sanchez Alton Lee Dimitri Kalenitchenko Hopkins Microbiology 2013 Outline Background Shewanella oneidensis Chemostat versus batch ID: 783328
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Slide1
Shewanella
oneidensis
MR1
Knock Out Experiment
Kim Parker
Rachel
Neurath
Savannah Sanchez
Alton
Lee
Dimitri
Kalenitchenko
Hopkins Microbiology 2013
Slide2Outline
Background
Shewanella
oneidensisChemostat versus batchObjectives and HypothesesExperimental DesignResultsDiscussion
www.odec.ca
Slide3Shewanella
oneidensis
MR1
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion
PNNL (2009)
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
Slide4Shewanella
oneidensis: Metabolism
Background
| Objectives and Hypotheses | Experimental Design | Results | ConclusionShewanella
oneidensis MR-1 is characterized by a complex and highly versatile metabolism:
ELECTRON DONORS:
wide range of organic compounds
ELECTRON ACCEPTORS:
O
2
, 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
Slide5Shewanella
oneidensis: Ecological Significance
Background
| Objectives and Hypotheses | Experimental Design | Results | ConclusionWell-developed sensing and regulatory systems, along with diverse metabolism and tolerance to extreme conditions, allow for success in wide range of environments
Applications: B
ioremediation and biotechnology
Reference organism for understanding C-metabolic pathways
Park et al. (2011):
Journal of Hazardous Materials
Slide6Batch Culture
Prescott
et al.
X, V, S
X, V, S
X, V, S
Kinetics :
Ln
(
Xt
) =
Ln(Xo
) +
μt
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide7Batch Culture
Prescott
et al.
X, V, S
X, V, S
X, V, S
Kinetics :
Ln
(
Xt
) =
Ln(Xo
) +
μt
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Closed System with finite nutrients
Slide8Serial Batch Culture
Prescott
et al.
X, V, S
X, V, S
X, V, S
Kinetics :
Ln
(
Xt
) =
Ln(Xo
) +
μt
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Closed System with finite nutrients
Accumulation of cells / byproducts
Slide9Batch Culture
Prescott
et al.
X, V, S
X, V, S
X, V, S
Kinetics :
Ln
(
Xt
) =
Ln(Xo
) +
μt
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Closed System with finite nutrients
Accumulation of cells / byproducts
No regulation of growth phase
Slide10Serial Batch Culture
Prescott
et al.
X, V, S
X, V, S
X, V, S
Kinetics :
Ln
(
Xt
) =
Ln(Xo
) +
μt
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Closed System with finite nutrients
Accumulation of cells / byproducts
No regulation of growth phase
X, V, S
X, V, S
Slide11Chemo
Continuous CultureMass Balance: In + Accumulated + Generated = Out + Consumed
Substrate Mass Balance:
X = Y ( Sin – S)Bacterial Mass Balance:μ = F/V = D
S
in
FLOW RATE
X, V, S
S
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide12Chemo
Continuous CultureMass Balance: In + Accumulated + Generated = Out + Consumed
Substrate Mass Balance:
X = Y ( Sin – S)Bacterial Mass Balance:μ = F/V = D
Low continuous concentrations of nutrient
S
in
FLOW RATE
X, V, S
S
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide13Chemo
Continuous CultureMass Balance: In + Accumulated + Generated = Out + Consumed
Substrate Mass Balance:
X = Y ( Sin – S)Bacterial Mass Balance:μ = F/V = D
Low continuous concentrations of nutrient
Flux of chemical species
S
in
FLOW RATE
X, V, S
S
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide14Chemo
Continuous CultureMass Balance: In + Accumulated + Generated = Out + Consumed
Substrate Mass Balance:
X = Y ( Sin – S)Bacterial Mass Balance:μ = F/V = D
Low continuous concentrations of nutrient
Flux of chemical species
Control “natural” environment
to study adaptation/”natural” physiology
S
in
FLOW RATE
X, V, S
S
Background
| Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide15BIG PICTURE: Batch vs
Chemostat DynamicsBackground
| Objectives and Hypotheses | Experimental Design | Results |
ConclusionPrescott et al.
Slide16Comparing 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
Slide17~5hrs @ 30° C
( OD600 2.0)
S.
oneidensis MR-1 Knock Out Library Experiment
4058
3977
~5hrs @ 30° C
( OD600 2.0)
50μL
O/N
@ 30° C
3
mL
O/N
@ 30° C
Sample 1mL
Pellet Cells
Extract DNA
PCR Barcode
Sequence
Sample 1mL
Pellet Cells
Extract DNA
PCR Barcode
Sequence
Repeat for T
F
= 72 hrs
Repeat for T
F
= 72 hrs
Slide18Objectives
Background |
Objectives and Hypotheses
| Experimental Design | Results | ConclusionCompare temporal changes in relative abundance of knock out genes in batch and chemostat systemsExamine ecological and selective pressures exerted by chemostat
and batch systems, using Arkin
Lab experiment
Link sensitive genes to metabolic and functional pathways
Slide19Hypotheses
Background |
Objectives and Hypotheses
| Experimental Design | Results | ConclusionBatch 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
Slide20Results
Background | Objectives and Hypotheses | Experimental Design |
Results
| ConclusionHau and Gralnick (2007): Annual Review of Microbiology
Slide21Rate 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
Slide22Distribution of Relative Abundance: Batch
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide23Rate of Change in Relative Abundance: Batch
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide24Distribution of Relative Abundance:
Chemostat
Background | Objectives and Hypotheses | Experimental Design |
Results | Conclusion
Slide25Rate of Change in Relative Abundance:
Chemostat
Background | Objectives and Hypotheses | Experimental Design |
Results | Conclusion
Slide26Shannon Index of Diversity:
Where:
ρ
i=n/N n = species
N = # of individual in species s = # of species
Simpson Index of Diversity:
Pielou’s
Evenness Index:
Purvis and Hector (2000):
Nature
Richness
Evenness
“Knocking Out” Diversity
Background | Objectives and Hypotheses | Experimental Design |
Results
| Discussion
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide27Temporal Changes in Diversity: Batch versus
Chemostat
Batch:
Diversity and evenness are constantChemostat: Decline in diversity and evenness over time
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide28Temporal Changes in Diversity: Biofilm
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide29ß-diversity on our experiment
Background | Objectives and Hypotheses | Experimental Design | Results
|
Conclusion
Slide30Arkin Dataset
Anaerobic
Variety of organic and inorganic
electron acceptorsStressGrown 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
Slide31Workflow for Data Consolidation
UP
DOWN
Consolidate SO (loci) data based on insertion quality
Consolidate SO (loci) data based on insertion quality
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide32Workflow for Data Consolidation
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
UP
DOWN
Consolidate SO (loci) data based on insertion quality
Consolidate SO (loci) data based on insertion quality
MATCH
/
MERGE/ REMOVE OUTLIERS
UP/DOWN MERGED
Slide33Up and Down Libraries: Not Exactly Duplicates
✔
✖
✕
✕
✕
✕
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide34Up and Down Libraries: Not Exactly Duplicates
Evaluate
(
xUp-x
Down)
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide35Up and Down Libraries: Not Exactly Duplicates
Evaluate
(
x
Up
-x
Down
)
Trim off
discrepancies
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide36PCA with Arkin Dataset is Uninformative
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide37Multidimensional 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
Slide38Arkin Dataset
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide39Chemostat Data Clusters Further
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide40Motility Experiments Also Cluster
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide41Biofilm Experiment
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide42Workflow for Data Consolidation
SUBSETS
Positive
Chemostat
Negative
Chemostat
Positive Batch
Positive
All
Negative
All
Positive/ Negative
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
UP
DOWN
Consolidate SO (loci) data based on insertion quality
Consolidate SO (loci) data based on insertion quality
MATCH
/
MERGE/ REMOVE OUTLIERS
UP/DOWN MERGED
Slide43Batch Vs Chemostat
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide44MSHA
Mannose-sensitive
hemagglutinin
type 4 pilus
Batch (-)
Chemostat
++++
Motility Assay ++
Biofilm
Formation - - -
Biogenesis Protein
Pilin
Protein
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide45MSHA
Mannose-sensitive
hemagglutinin
type 4 pilusBatch (-) Chemostat ++++Motility Assay ++
Biofilm
Formation - - -
(( ))
Pili
have minimal effect on apparent relative abundance.
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide46MSHA
Mannose-sensitive
hemagglutinin
type 4 pilusBatch (-) Chemostat ++++Motility Assay ++
Biofilm
Formation - - -
(( ))
Pili
have minimal effect on apparent relative abundance.
Pili
prevent cells from reaching sampling location.
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide47MSHA
Mannose-sensitive
hemagglutinin
type 4 pilusBatch (-) Chemostat
++++
Motility Assay ++
Biofilm
Formation - - -
(( ))
Pili
have minimal effect on apparent relative abundance.
Pili
prevent cells from reaching sampling location.
Pili
allow cells to adhere to surface.
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide48MSHA
Mannose-sensitive
hemagglutinin
type 4 pilusBatch (-) Chemostat
++++
Motility Assay ++
Biofilm
Formation - - -
S
(( ))
Pili
have minimal effect on apparent relative abundance.
Pili
prevent cells from reaching sampling location.
Pili
allow cells to adhere to surface.
Shouldn’t
pili
prevent cells from washing out?
But…
Pili
prevent cells from being sampled.
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide49Obligate Offenders
α
Subunit
β SubunitNqr B
Nqr
D
Ubiquinol
Cytochrome
C
Reductase
FeS Subunit
NADH:Quinone
Oxidoreductase
, Na+
Slide50NADH:Ubiquinone
Oxidoreductase
Na
+ translocating
Out
In
Na
+
Na
+
NADH + H
+
+
Ubiquinone
NAD+
+
Ubiquinol
Verkhovsky
&
Bogachev
, 2009
Preferential to Complex I?
Enzymatic inefficiency
[Na] in natural environment
Slide51Pathway overview
Pathways Tool Software
Shewanella
Pathway mapwith relative abundance
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide52Chemostat
overview
24h
48h72h
Slide53Chemostat
overview
24h
48h72h
Slide54Chemostat
overview
24h
48h72h
Slide55Fermentation Pathway
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide56Fermentation Pathway
Why Knocking out the pyruvate kinase have a positive effect ?
Knocking out the Pyruvate kinase will strongly affect the metabolism
No positive effect ???
Metabolic
Flux
Responses
to Pyruvate Kinase Knockout in
Escherichia
coli,
2002,
Emmerling
et al.
Slide57Fermentation Pathway
Knocking out a gene always have a bad effect in this part of the pathway !!!
Background | Objectives and Hypotheses | Experimental Design |
Results | Conclusion
Slide58Aérobic respiration Pathway
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide59Aerobic respiration Pathway
Always a negative effectClue for aerobic activity
Why do we see respiration and fermentation at the same time ?
No interest for a cell to ferment if she could respireBackground | Objectives and Hypotheses | Experimental Design | Results | Conclusion
Slide60Possibilities
Micro-aerobic condition due to big bubble bubblingPerfect size will be 300µm
Motarjemi
and Jameson, 1978
Heterogeneous
chemostat
Background | Objectives and Hypotheses | Experimental Design |
Results
|
Conclusion
Slide61And 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
Slide62And 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 mediaBackground | Objectives and Hypotheses | Experimental Design | Results | Conclusion
Slide63One more thing…
Background | Objectives and Hypotheses | Experimental Design |
Results
| Conclusion
Slide64Relative Abundances of
Chemotaxis
/ Flagella Proteins Across Conditions
Background | Objectives and Hypotheses | Experimental Design | Results | Conclusion
Slide65Flagellar
Assembly
Background | Objectives and Hypotheses | Experimental Design |
Results | Conclusion
Slide66r
poN
flrAflrB
f
lrC
RNAP
s
28
c
heW
c
heY
c
heV
MCP
c
heA
c
heB
c
heR
FlgG
FliI
FlgF
FlgH
FlgB
FlgJ
FlhF
f
lhA
Flhb
f
lgT
FlgE
FlgL
FliK
FlgI
m
otA
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
Slide67Batch Reactor
vs
ChemostatMost growth occurs while substrate is in excess.Most growth occurs while substrate is limited.
Energy
Substrates
Essential
Functions
Auxiliary
Functions
Energy
Substrates
Essential
Functions
Auxiliary
Functions
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Slide68Batch Reactor
vs
ChemostatMost growth occurs while substrate is in excess.Most growth occurs while substrate is limited.
Energy
Substrates
Essential
Functions
Auxiliary
Functions
Energy
Substrates
Essential
Functions
Auxiliary
Functions
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Energy
Substrates
Essential
Functions
Flagella
Energy
Substrates
Essential
Functions
Flagella
Slide69Returning to Our Hypotheses
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
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
Slide70Returning to Our Hypotheses
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
Batch and chemostat systems will selectively favor certain gene knock-outsYES
, 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.
Slide71Returning to Our Hypotheses
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
2. 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.
Slide72Returning to Our Hypotheses
Background | Objectives and Hypotheses | Experimental Design | Results |
Conclusion
3. Declines in relative abundance will be associated with metabolic pathways and functions that are essential4. 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.
Slide73Questions?