@ 2020
Krishna Raj Ojha
All Rights Reserved DETERMINATION OF MEMBRANE FLUIDITY AND CORRELATE IT’S EFFECT
IN BULK BACTERIAL CELL RESPIRATION
A Dissertation
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
Krishna Raj Ojha
May, 2020 DETERMINATION OF MEMBRANE FLUIDITY AND CORRELATE IT’S EFFECT
IN BULK BACTERIAL CELL RESPIRATION
Krishna Raj Ojha
Dissertation
Approved: Accepted:
______Advisor Department Chair Dr. Michael C. Konopka Dr. Christopher Ziegler
______Committee Member Dean of the College Dr. David Perry Dr. Linda Subich
______Committee Member Dean of the Graduate School Dr. Adam Smith Dr. Marnie Saunders
______Committee Member Date Dr. Sailaja Paruchuri
______Committee Member Dr. Hazel A Barton
i
ii ABSTRACT
In bacterial membranes, the Electron Transport Chain (ETC) is used to generate energy through the multiple redox reactions during cellular respiration where oxidation of sugar molecules generates ATP via proton pumping across a membrane. My initial goal involved investigating the role membrane fluidity may play in the variation in respiration rates found between individual cells. In particular, determining if it is the direct mobility of a component of the ETC, such as the electron carrier ubiquinone, that is limiting the respiration rates. Alternatively, heterogeneity in respiration rates could be caused by differences in the segregation of oxidative phosphorylation complexes caused by variations in membrane composition. To analyze this phenomenon, membrane fluidity and cell respiration measurements will be performed on the same single cells. Fluorescence
Recovery After Photobleaching (FRAP) technique is used to determine the fluidity of membrane components. Commercial dye BODIPYFL-C12 and lab synthesized dye
HBT/HBO pyridinium cyanines (Py-Cy) are used as an inner membrane lipid probes.
Plasmolysis is done to confirm the inner membrane staining. I look at the dependency of the fluidity of a membrane-based on temperature and the ratio of saturated and unsaturated fatty acids. It is observed that the fluidity of a membrane decrease with the increase in measurement temperature. Moreover, using the fluorescent reporters of the key genes like
iii fabA and fabB in the type II fatty acid biosynthetic pathway, I illustrate that the membrane diffusion is affected by the extent of unsaturated lipids in the membrane. I also utilize alteration of fadR, which is a transcription factor for fatty acid synthesis and regulates the transcription of fabA/fabB genes, which in turn affects the membrane composition. I find that the deletion of fadR effects the expression of fabB, which affects the unsaturated fatty acid synthesis. Bulk cellular respiration measurement in bacteria is carried out by measuring the total oxygen consumption by Escherichia coli with microspheres embedded with Pt-porphyrin is used as an oxygen sensor. Nikon A1 laser scanning confocal microscope is used for imaging and analysis of the bacterial cell respiration. I find that the fluidity of a membrane effect on cellular respiration when compared between wild type and fadR knock out mutants. This concludes that the membrane composition of bacterial cell effects on membrane fluidity, which in turn affects the cellular respiration.
iv ACKNOWLEDGEMENT
The research project would not be completed without a special mention of a few different groups and individuals. My unutterable thanks go to the Department of
Chemistry, The University of Akron, for giving me the opportunity to complete my graduate program. I would like to give sincere gratitude to my research advisor, Dr.
Michael C Konopka, without his guidance, this work would not have been possible.
I am deeply indebted to all the committee members Dr. David Perry, Dr. Adam
Smith and Dr. Sailaja Paruchuri for giving me the suggestion and guidance from the very beginning of my oral exam to final dissertation work. My sincere gratitude goes to Dr.
Hazel Barton for accepting my request to join my dissertation committee. I am thankful to my undergraduate research team, John and Sam, for regular support and work to complete my project. I am also grateful to our research group, Kyle Whiddon, Lucille Ray, and
Ravindra Gudneppanavar for making a cordial environment in the lab.
Finally, I would like to thank my parents for constant inspiration and my wife for love and care at home. You all are always there for me.
v TABLE OF CONTENTS
LIST OF FIGURES ...... xii
LIST OF TABLES ...... xviii
LIST OF ABBREVIATIONS ...... xixx
CHAPTER
I. INTRODUCTION ...... 1
1.1 Purpose ...... 1
1.2 General Introduction ...... 3
1.2.1 Outer Membrane ...... 7
1.2.2 Peptidoglycan Layer/Periplasmic Space ...... 8
1.2.3 Inner Membrane ...... 9
1.2.4 The Fluidity of a Membrane ...... 9
1.2.5 Importance of Ubiquinone Mobility in Bacterial Cell Respiration ..11
1.2.6 Microscopy Techniques to Measure Membrane Fluidity ...... 15
1.2.6.1 Fluorescence recovery after photobleaching (FRAP) ...... 17
1.2.6.2 Fluorescence correlation spectroscopy (FCS) ...... 19
1.2.6.3 Single-particle tracking (SPT) ...... 20
1.2.7 Probes for Membranes ...... 21
1.2.7.1 BODIPYFL-C12 ...... 22
1.2.7.2 FM 1-43 ...... 23
vi
1.2.7.3 Pyridinium cyanine (Py-Cy) ...... 23
1.2.8 Working Principle of Fluorophores ...... 25
1.2.9 Confocal Microscopy ...... 29
1.2.10 Plasmolysis ...... 30
1.2.11 Dependence of Fluidity of a Membrane ...... 33
1.2.11.1 Effect of temperature ...... 33
1.2.11.2 The ratio of saturated/unsaturated fatty acids ...... 34
1.2.12 Bacterial Cell Respiration ...... 40
1.2.12.1 Working principle of phosphorescence lifetime ...... 41
II. MATERIALS AND METHODS ...... 45
2.1 Bacterial Strains and Growth Media ...... 45
2.2 Growth Curve...... 46
2.3 Vector Construction and Gene Cloning ...... 47
2.4 PCR of pUC19 with fabA/fabB Expressed with mCherry ...... 50
2.5 Synthesis of Py-Cyanine ...... 52
2.6 Cell Imaging...... 54
2.6.1 Imaging Under Confocal Microscope ...... 55
2.7 Plasmolysis Protocol ...... 56
2.8 FRAP Technique to Determine Membrane Diffusion ...... 56
2.9 Respiration Rate Measurements ...... 61
vii
III. BACTERIAL PLASMOLYSIS OBSERVED THROUGH CONFOCAL
MICROSCOPE AS AN INDICATOR OF INNER MEMBRANE STAINING .....63
3.1 Introduction ...... 63
3.2 Materials and Methods ...... 65
3.2.1 Plasmolysis Protocol ...... 65
3.2.2 Labeling of Cells with Different Dyes ...... 65
3.2.3 Imaging under Confocal Microscope ...... 66
3.2.4 Defining Sucrose Solution for Plasmolysis ...... 67
3.3 Results and Discussion ...... 67
3.4 Conclusion ...... 77
3.5 Supplementary Information ...... 78
IV. MEASUREMENT OF MEMBRANE FLUIDITY USING PY-CY AND
BODIPYFL-C12 DYES AND LOOK AT THE EFFECT OF TEMPERATURE ON
DIFFUSION IN E. COLI CELLS ...... 85
4.1 Introduction ...... 85
4.2 Materials and Methods ...... 90
4.2.1 Cell Staining...... 90
4.2.2 Preparation of Specimen ...... 90
4.2.3 FRAP Measurements ...... 91
4.3 Results and Discussion ...... 91
4.3.1 Staining with Fluorescent Probes into the Cells ...... 91
viii
4.3.2 FRAP Technique to Look at the Mobility of Lipids ...... 92
4.3.3 Effect of Temperature on Diffusion ...... 93
4.3.4 Photostability of Fluorophores ...... 96
4.4 Conclusion ...... 99
4.5 Supplementary Information ...... 100
V. ROLE OF FABA/FABB GENES ON UNSATURATED FATTY ACID
SYNTHESIS AND EFFECT OF LIPID COMPOSITION ON BACTERIAL CELL
RESPIRATION ...... 103
5.1 Introduction ...... 103
5.1.1 Membrane Fluidity and Bacterial cell respiration ...... 110
5.2 Materials and Methods ...... 112
5.2.1 Isolation of Genomic DNA and Construction for PCR ...... 112
5.2.2 Diffusion Measurements ...... 113
5.2.3 Steps for Deletion of fadR Gene ...... 114
5.2.4 Measurement of Cell Respiration ...... 116
5.3 Results and Discussion ...... 116
5.3.1 Gel Electrophoresis Analysis ...... 116
5.3.2 UFA Production by fabB/fabA Protein...... 117
5.3.3 FadR as a Marker of Fatty Acid Production ...... 120
5.3.4 Oxygen Consumption by Bacterial Cells ...... 122
5.4 Conclusion ...... 124
ix
VI. CONCLUSION...... 127
6.1 Summary of the Work ...... 127
6.2 Future Work ...... 129
REFERENCES ...... 132
x
LIST OF FIGURES
Figure Page
1.0. TD images of E. coli cells under 100X magnification. Scale bar represents 1 µm...... 4
1.01. Schematic diagram of Gram-negative bacterial cell membranes based on the Fluid
Mosaic Model of membranes...... 6
1.02. Structures of major components of different lipids found in the bacterial cell
membrane. PE is a zwitterionic phospholipid, while PG and DPG are anionic
phospholipids...... 7
1.03. E. coli respiration occurring in ETC in aerobic growth ( from EcoCyc E. coli database). ……………………………………………………………………………… 12
1.04. Schematic diagram of FRAP analysis in E. coli. A region of interest (ROI) is
selected, bleached with a laser beam, and fluorescence recovery in ROI is
measured over the time interval...... 18
1.05. Structure of 4,4- Difluoro-5,7- dimethyl-4-bora-3a,4a-diaza-s-indacene-3-
® dodecanoic acid (BODIPY FL-C12, D3822)...... 22
1.06. Structure of FM1-43 [(N-(3 Triethylammoniumpropyl)-4-(4(1 amino) Styryl)
Pyridinium Dibromide)]...... 23
1.07. Structure of HBT Py-Cy in enol and keto form...... 24
xi
1.08. UV-Absorbance and fluorescence emission spectra of compound 1 in DCM...... 25
1.09. Jablonski energy profile diagram showing different electronic transitions after
photoexcitation of a fluorophore (azooptics.com/Article.aspx)...... 26
1.10. Typical laser scanning confocal microscope with light paths where pinholes are
used in front of laser source and photodetector (Paddock, S.W., 2000)...... 30
1.11. Schematic diagram of plasmolysis. When very low (A) or appropriate concentration
(B) of a hypertonic solution has used...... 32
1.12. Synthesis pathway of fatty acids in E. coli involving different enzymes. fabA and
fabB are the two major enzymes that involve in the unsaturation and elongation of
fatty acid chains, respectively...... 35
1.13. fabA and fabB promoters (sequences obtained from www.ncbi.nlm.nih.gov) and
their regions where fadR proteins bind for transcription. ∆ represents the
transcription start point...... 39
1.14. pt-porphyrin chelated with an Oxygen molecule...... 42
1.15. Generalized graph of optimized RLD for measuring lifetime using the ratio of two
integrated areas under the single exponential decay curve (Chan, S. et al., 2001
and Konopka, M., 2017)...... 43
xii
2.0. Plot of doubling time of DGC-102 cell grown in MOPS media at 37o C. Generation
time here is 46.6 min...... 46
2.01. Construction of pUK21 vector with fabA expression vector with sfGFP...... 50
2.02. Gel electrophoresis analysis to observe different bands obtained after amplification
through PCR. fabA/fabB promoters have 500 base pairs (bp), mCherry has 711 bp
and pUC 19 backbone has 2686 bp (left and right sides are DNA ladders)...... 51
2.03. Synthesis of HBT Pyridinium derivative (Cyanine)...... 53
2.04. Synthesis of compound 1 (Pyridinium cyanine coupled with HBO)...... 54
2.05. FRAP analysis in DGC-102 cell labeled with BODIPYFL-C12 (a). Axial intensity
profile diagram of a cell (given by Sochacki et al., 2011) showing before and after
photobleaching (b). Fourier cosine mode 1 amplitude vs time after the bleaching
and least square fit to a single exponential decay profile curve (c)...... 58
2.06. Simple set up of sample placement into a well and sealed with a cover glass
containing pt-porphyrin. The analysis was done under a confocal microscope. (a)
Aluminum plate, (b) Delrin structure to hold the media, (c) 22*22 mm micro
cover glass, (d) Respiratory detection system (RDS) well with pt-porphyrin, (e)
Chip holder and (f) Quartz viewing window...... 62
xiii
3.0. Examples of MG1655 cell labeling using three different dyes; HBT Py-Cy,
BODIPYFL-C12, and FM1-43 from left to the right respectively. Scale bar
indicates 1µm. The certain threshold for intensity was set up so that some of the
cells which show the very low intensity of dyes were discarded in the reading. ..69
3.01. Plasmolysis of DGC-102 cells with 0.6 M sucrose solution at mid-log phase (left)
and stationary phase (right), observed under a confocal microscope under 561 nm
filter (red) and widefield images (colorless). Cells were labeled with HBt py-cy.
Scale bar 1 µm...... 73
3.02. DGC-102 cells labeled with BODIPYFL-C12 and plasmolyzed with 0.6 M sucrose
solution. Scale bar represents 1 µm...... 75
3.03. Staining of DGC-102 cells with FM1-43 and plasmolysis was carried out at
different conditions (a-d). Bacterial cells were labeled with FM1-43 and kept in a
hypertonic solution of 0.6 M sucrose (a), cells were co-stained with FM1-43
(green) and HBT Py-Cy (red) (b), co-stained cells were plasmolyzed (c), and cells
were first treated with cephalexin and the cells were labeled and plasmolyzed(d).
Scale bar represents 1µm...... 77
S3.0. Labeling of MG1655 cells with different dyes in its stationary phase...... 81
S3.1. Plasmolysis of DGC-102 cells with 0.8M sucrose solution. Cells were labeled with
HBT Py-Cy (red) at log phase and FM1-43 (green) at the stationary phase...... 82
xiv
S3.2. Plasmolysis of MG1655 at 0.6M sucrose solution. Cells were labeled with FM1-43
(green) and HBO Py-Cy (red)...... 82
S3.3. plasmolysis of MG1655 at 0.4 M NaCl solution. Cells were labeled with HBO Py-
Cy. Bar, 1 µm...... 83
S3.4. Growth curve of DGC-102 cells under different carbon sources in MOPS media. .84
4.0. Structure of HBT coupled Py-Cy, HBO coupled Py-Cy and commercial BODIPYFL-
C12 (in a box)...... 88
4.02. Staining of DGC-102 cells with BODIPYFL-C12 (green), HBT Py-Cyanine (red),
and under brightfield view. Cells were co-stained and observed under different
filter channels (green at 500-550 nm and red at 663-738nm emission filters).
Scale bar represents 2 µm...... 92
4.03. FRAP analysis of DGC-102 cells using BODIPY FL-C12 (green) and HBT Py-
Cyanine (red). Arrow indicates the region of interest (ROI) for bleaching. Left
and right images are before bleaching and post-recovery, respectively. Bar, 2 µm.
...... 93
4.04. Diffusion coefficient of HBT/HBO Py-Cyanines (red) and BODIPYFL-C12 (green)
in DGC-102 cells at 37o C and 30o C. Growth temperature here is 37o C. [p (t<=t)
one tail = 3.98 E-02 (HBO py-cy), 4.41 E-02 (HBT py-cy) and 3.53 E-08
(BODIPYFL-C12)](p=0.05)...... 95 xv 4.05. Relative intensity of HBT Py-Cy and BODIPYFL-C12 carried out for individual
cells. Cells were scanned for 8 seconds, and images were captured. Images at
different frames were observed for 1 hour (replicates for 3 different samples). ...97
4.06. Staining of cells with HBT/HBO py cyanines separately and looked at the relative
intensity over 1 hour time period...... 98
4.07. Comparision of relative intensities between HBT and HBO Py-Cy...... 99
S4.0. Diffusion measurement of BODIPYFL-C12 and FM1-43 in K-12 cells growing at
37o C and measured at 30o C...... 100
S4.1. Diffusion measurement of DGC-102 cells expressed with GFP grown at 37o C.
Cells were labeled with HBT Py-Cy, and FRAP measurement was done
individually for both HBT Py-Cy and GFP at given temperatures...... 101
S4.2. Co-staining of DGC-102 cells with BODIPYFL- C12 and HBT/HBO Py-Cyanines.
Scale bar represents 2 µm...... 102
5.0. Synthetic pathway of fatty acid production. Acetyl co.A is a starting substrate for
lipid synthesis. fabB which encodes for β-ketoacyl-ACP synthase I play a vital
role in the condensation and elongation of carbon chains while fabA, which
encodes for β- hydroxyacyl-ACP dehydratase has the role in making unsaturation
and isomerization...... 106
xvi 5.01. Schematic diagram of the λ-red recombineering system to knock out and replace of
the gene of interest using antibiotic resistance cassette...... 110
5.02. Gel electrophoresis technique to observe the different DNA bands obtained after
amplification through PCR. pKD3 PCR product is 1.1 kb...... 117
5.03. mCherry tagged fabB (1) and fabA (2) expressed in DGC-102 cells (protocol given
in section 2.4) and labeled with HBT Py-Cy. The effect of relative intensity on the
diffusion of lipid was observed [n = 64 cells (1) and 76 cells (2)]...... 119
5.04. Diffusion measurements of wt MG1655 (A) and ∆fadR MG1655 cells at 37o C and
labeled with HBT Py-Cy. It was observed that the diffusion coefficient of the
mutant decreases significantly compared to the wild type. [P(t<=t) one tail
=1.34E-04] ...... 121
5.05. Plot of oxygen consumption rate by a cell over time. wtMG1655 (black) and ∆fadR
mutant (green). Statistics, including linear fitted values, were given in the box. 123
xvii LIST OF TABLES
Table Page
1.0. Different enzymes involved in biosynthesis of fatty acids...... 36
2.0. Generation time listed of different cells at 37o C...... 46
2.1. Nucleotide sequences for different PCR primers used in cloning...... 48
3.0. Percentage of cell labeling using different dyes in the same MOPS media. Total of
1000 cells was taken for analysis (biological replicates of five different days). Sd
= standard deviation ...... 68
3.1. MG1655 cells grown in two different media and labelled with two different dyes to
compare the ratio of labelling. Table shows the total percentage of labelling of
cells. 1000 cells was taken for (total replicates of five days) ...... 70
3.2. DGC-102 cells grown in different minimal media and labeled with hbt py-cy. total
number of cells count was 1000 (total replicates of five days)...... 72
S3.0. Labeling of cells with different dyes in mops media of a single day at different
growth phase ...... 79
5.0. Oxygen consumption rate of individual cells. negative sign indicates the decrease in
concentration of oxygen over time...... 124
xviii LIST OF ABBREVIATIONS
ACP Acyl carrier protein ATP Adenosine triphosphate CCD Charged coupled device CoA Coenzyme A DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid DPG Diphosphatidyl glycerol E. coli Escherichia coli eGFP Enhanced GFP ESIPT Excited-state intramolecular proton transfer ETC Electron transport chain Ex/Em Excitation/ emission FCS Fluorescence correlation spectroscopy FRAP Fluorescence recovery after photobleaching FRET Fluorescence resonance energy transfer GFP Green fluorescent protein HBO 2-(2’- hydroxyphenyl)-benzoxazole HBT 2-(2’- hydroxyphenyl)-benzothiazole ICT Internal charge transfer ISC Intersystem crossing LB Lysogeny broth LED Light emitting diode LPS Lipopolysaccharide mCherry Monomeric cherry MOPS 3-(N- Morpholino) propane sulphonic acid xix NAG N-acetylglucosamine NAM N-acetylmuramic acid NIR Near infra-red NMR Nuclear magnetic resonance OCR Oxygen consumption rate OD Optical density OMP Outer membrane protein ORLD Optimized rapid lifetime determination OXPHOS Oxidative phosphorylation PBP Penicillin-binding protein PCR Polymerase chain reaction PE Phosphatidyl ethanolamine PG Phosphatidyl glycerol PPM Parts per million Pt-Porphyrin Platinum porphyrin RNA Ribonucleic acid ROI Region of interest Rpm Revolution per minute sfGFP Super folder GFP SMT Single molecule tracking SOB Super optimal broth SOC SOB with catabolite repression TFB I/II Transformation buffer I/II UFA Unsaturated fatty acid
xx CHAPTER I
INTRODUCTION
1.1 Purpose
A long term research goal is to look at the respiration rate of individual bacterial cells in relationship to the fluidity of their membrane. Respiration rate can be measured through oxygen consumption measurements on the specific organism.1 For this, phosphorescent dyes like platinum porphyrin or europium porphyrin can be used as an oxygen sensor because their lifetime on the excited state depends on the oxygen concentration.2 The understanding of electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) is essential here because different enzymes carry out these steps, and oxygen is the terminal electron acceptor. In bacterial membranes, ETC is used to generate energy through the multiple redox reactions during cellular respiration, where oxidation of sugar molecules generates ATP via proton pumping across a membrane.3 The processes involved in the ETC take place in the inner membrane (sometimes referred to as the cytoplasmic membrane), which is largely composed of phospholipids making up the bilayer and some integral proteins.4 The fluidity of the membrane can be affected by the membrane composition, including factors such as lipid tail length and the ratio of saturated vs unsaturated lipids.5 Experimental results have shown wide cell-to-cell variability in membrane fluidity.6-8 I hypothesized that the membrane fluidity might play a role in the variation in respiration rates found between individual cells. Specifically the intention was
1 to look whether or not the direct mobility of a component of the ETC, such as the soluble organic molecule ubiquinone that acts as an electron carrier, could be limiting the respiration rate.9 Ubiquinone is an amphipathic molecule formed by benzoquinone polar head group and six to ten isoprenoid units of lipid tail groups.10 Alternatively, heterogeneity in respiration rates could be caused by differences in the segregation of OXPHOS complexes caused by variations in membrane composition, although some studies rule this out in bacteria.9,11 To analyze these different behaviors inside the cell, bacterial membrane fluidity and cellular respiration measurements would need to be performed on the same single cells, which requires several advances. First, it is necessary to establish an effective fluorescent molecule to use as an inner membrane probe, which presents its own challenges. Different exogenous and endogenous fluorescent markers are used to stain the cells tested by applying a high osmotic shock to plasmolyze the cells and confirm whether there is staining of only the inner membrane. Then, a Fluorescence Recovery After
Photobleaching (FRAP) technique is used to determine the membrane fluidity of bacterial cells.12 Both a controlled experiment carried out between wild type and knock out mutants to adjust the fluidity of the membrane, and an examination of the natural variability in the cell population are used to test the approach on a Nikon A1 confocal microscope. Finally, oxygen consumption measurements of bacterial cells from the bulk sample is carried out in a well by putting the sample into a glass chip with a microliter volume holding well and sealed with cover glass containing Pt-porphyrin as an oxygen sensor.
2 1.2 General Introduction
Escherichia coli is a facultative anaerobic bacterium that can undergo aerobic respiration by utilizing oxygen or follows a fermentation pathway if oxygen is absent. E. coli is a Gram-negative prokaryotic which is a rod-shaped in structure (fig. 1.0).13 Gram- negative bacteria can be differentiated from Gram-positive bacteria by the use of the Gram stain. Gram-positive bacteria have a thicker peptidoglycan layer and inner cytoplasmic membrane, while Gram-negative bacteria have an outer membrane with lipopolysaccharides in addition to a thinner peptidoglycan layer and inner cytoplasmic membrane (fig. 1.01 upper part).4,14 E. coli is a well-studied organism whose manipulation is well understood, which makes it valuable for studying different physiological processes in the laboratory. The fact that it grows easily in various media, has a short cell division time, is available as non-pathogenic strains, and has a fully sequenced genome are among the factors as to why its different strains are used in the lab. In animals, E. coli is commonly found in the gastrointestinal tract and some strains can cause food poisoning.15 Structurally the size is commonly around 2-3 µm with a width of 0.25 µm to 1 µm. Many anatomical and genomic studies have occurred since the discovery of E. coli by German bacteriologist
Theodor Escherich in 1885.16 The transmission image of E. coli viewed through a microscope is shown below (fig. 1.0). Imaging of cells was carried out under the Nikon A1 confocal microscope in my lab with oil immersion on 100X objectives. E. coli can grow in different media (rich, defined, or minimal) containing various carbon sources, such as glucose, glycerol, or succinate.17,18
3
Figure 1.0 TD images of E. coli cells under 100X magnification. Scale bar represents 1 µm.
Bacterial cell membranes are composed of lipids and proteins.19 Lipids are the esters of fatty acids that are insoluble in water. The arrangement of lipids is a two- dimensional bilayer (fig 1.01, lower part), which is described by the Fluid Mosaic Model helps explain the membrane fluidity.20 The bilayer is asymmetric and the lipids are amphipathic, meaning they have a polar head group, which is hydrophilic, and a non-polar hydrophobic core. These hydrophobic core groups make the membrane-impermeable to ions and other polar groups. Gram-negative bacteria have three layers in their morphological structure (fig 1.01). The inner core is the cytoplasm that contains DNA which is then surrounded by the cytoplasmic (inner) membrane which is mostly composed 4
of phosphatidylethanolamine (PE), phosphatidylglycerol (PG) and diphosphatidylglycerol
(DPG).21 The inner membrane is surrounded by a thin peptidoglycan layer which gives the shape of the cell and the outer membrane which also contains phospholipids and lipopolysaccharides.22
5 Figure 1.01 Schematic diagram to differentiate Gram positive and Gram negative bacteria (upper part)[Silhavy, T. et al. 2010] and Gram-negative bacterial cell membranes based on the Fluid Mosaic Model of membranes (lower part).[ The abbreviations WTA, LTA, CAP, IMP, LPS, LP and OMP indicate Wall teichoic acid, Lipoteichoic acid, Covalently attached protein, Integral membrane protein and Lipopolysaccharides, Lipoprotein and Outer membrane protein, respectively].
In terms of lipid composition, the outer membrane of a bacterial cell contains about
80 % of PE, 18 % of PG and 2 % of DPG (fig 1.02) as its major constituents. The inner membrane has the same constituents as the outer membrane, but the proportion of PG and
DPG is higher (increase by 4-6 %) than the outer membrane.21 The cell membrane acts as a barrier between the outer and inner environment of the cell. Sometimes, lipids can form membrane domains which limit the proteins into a small region to provide access for efficient signal transduction shuttles.23
6 Phosphatidylethanolamine (PE) Phosphatidyl glycerol (PG)
Figure 1.02 Structures of major components of different lipids found in the bacterial cell membrane. PE is a zwitterionic phospholipid, while PG and DPG are anionic phospholipids.
1.2.1 Outer Membrane
Structurally, the outer membrane has an asymmetric bilayer composition, which
consists of lipopolysaccharides (LPS) on the outer leaflet and phospholipids in the inner
leaflet. LPS acts as a barrier to the penetration of many hydrophilic and hydrophobic
molecules.14,24 LPS is composed of o-polysaccharides, core-polysaccharides, and lipid-A.25
The proteins present in the outer membrane lack the α- helix protein, however, there is a
7 presence of β-barrel proteins. These β-proteins are also called as outer membrane proteins
(OMP). In addition, the inner leaflet of the outer membrane contains another protein called lipoproteins which mainly help in the transport of molecules, for signal transduction and cell motility.14
1.2.2 Peptidoglycan Layer/Periplasmic Space
In Gram-negative bacteria, the peptidoglycan layer is sandwiched in between the outer and inner membrane.26 It is composed of repeating units of two different glucose derivatives, N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM), the subunits of murein.27 It is a gel-like matrix found in the periplasmic space that helps to maintain the cell shape.19 The periplasmic space is important for the bacteria’s metabolic processes and exchanging ions with the outer membrane. Different enzymes and other secreted materials at some point cross the periplasmic space.28 The peptidoglycan layer can be in a dynamic state as the structure changes during cell growth. A number of different factors can affect the peptidoglycan layer, including the growth media, carbon source, and type of stage of cell growth (exponential vs stationary).29 It has been previously shown that altering the peptidoglycan layer though use on drugs could improve labeling on the inner membrane in bacteria.30 Due to changes in structure, it is possible that the ability of small molecules, such as a fluorescent dye molecule, to pass through the peptidoglycan layer to the inner membrane could differ depending on the growth media used or cell state.
8 1.2.3 Inner Membrane
The inner cytoplasmic membrane is composed of almost equal proportions of phospholipids and integral proteins.31 The majority of phospholipids in E. coli are phosphatidylethanolamine (PE), phosphatidylglycerol (PG) and cardiolipin (a kind of
DPG).32 It is a semi-permeable membrane that facilitates movement in and out of the cytoplasm. Different macromolecules diffuse through this inner membrane bilayer either by passive diffusion, facilitated diffusion, or by active transport.33 The bacterial metabolic process such as ETC and hence the cellular respiration takes place in the inner cytoplasmic membrane.
1.2.4 The Fluidity of a Membrane
One of the goals of my research project is to look over the fluidity of a membrane in bacterial cells since it would affect the movement of macromolecules and small molecules in the membrane. The motions of lipids in a bilayer include trans-gauche transitions, segmental motions (oscillations perpendicular to the membrane plane), rotational motion (perpendicular to the plane), translational motion from one layer to other and lateral diffusion in the membrane.34 Because the proteins are also presented in lipid bilayers in peripheral or integrated positions, those proteins are mobile as well.34 The primary focus of this work is the lateral diffusion of molecules in the membrane, which could be lipid molecules, small molecules or proteins. The mobility of lipids and proteins can help describe many cellular processes like rate of enzymatic reactions, assembly of molecules in the membrane, and signal transduction.35 In sum, one of the important
9
characteristics of the membrane is the lateral diffusion of the lipids in the plane of the membrane. The mobility of lipid membrane can be measured using lipid dyes and expressed as a diffusion coefficient, D.36 The diffusion coefficients of membrane lipids are typically in the range between 0.2-2.5 µm2/s, depending upon the individuality of cells as well as nature of probe used to measure the diffusion.6
There are different parameters that can control the diffusion of lipids. Venable R.M. et al., 1993 studied the relationship between membrane viscosity and the rate of diffusion of lipids through molecular dynamics simulations.37 It was observed that increasing the viscosity slows down diffusion-limited processes.34 Later, Mika, J. et al., 2016 measured the viscosity of the plasma membrane at different temperatures and looked at the diffusion rates within the cells.38 They observed that the bacterial cells maintain their membrane fluidity through “homeoviscous adaptation”, meaning that cells altered the composition of their membranes to facilitate fluidity. This can be accomplished by changing the membrane from liquid to crystalline gel phase. Saffman, P. et al., 1975 studied the protein diffusion within the membrane and considered the lipid as a viscous fluid.39 Among the factors that affect the diffusion of lipid membranes include the membrane composition, the size of the membrane, the effect of various liquids surrounding the membrane and the inertia of the membrane.40,41
10
1.2.5 Importance of Ubiquinone Mobility in Bacterial Cell Respiration
In E. coli, cellular respiration utilizes the electron transport chain, comprised of different dehydrogenases and reductases/oxidases which form complexes. While spatially separated, these complexes are linked by organic soluble components called quinones.42
The different quinones in E. coli are ubiquinones (UQ) in aerobic growth if oxygen is the electron acceptor or menaquinones (MK) in anaerobic growth if fumarate is the acceptor or demethylmenaquinones (DMK) in anaerobic growth if nitrate is the acceptor.3 In aerobic respiration, the mobility of ubiquinones between different complexes carries the electrons from one complex to another. Hence the movement of ubiquinone could be a rate- determining step in the electron transport chain. It is also a redox mediator between the complexes.9 In aerobic respiration, oxygen is the ultimate electron acceptor, and in anaerobic respiration, nitrate or fumarate act as an electron acceptor. The respiratory enzymes have significant variability in E. coli. The first enzyme forms from NADH dehydrogenase I, along with other prosthetic groups FMN and FeS clusters (complex I).43
This complex can operate the proton pumping across the membrane and generate a proton motive force (PMF) through the proton gradient that is created. UQ is used to transport the
44 electron generated in NADH dehydrogenase I complex to cytochrome bo oxidase (bo3).
The proton gradient potential is used to synthesize ATP via membrane-bound ATP synthase in oxidative phosphorylation. A schematic diagram of the aerobic respiratory chain in E. coli given in EcoCyc.org is presented below.
11 Figure 1.03 E. coli respiration occurring in ETC in aerobic growth ( from EcoCyc E. coli database).
NADH cofactors obtained from the TCA cycle are oxidized to NAD+ with the help of NADH dehydrogenase I enzyme (Ndh-1)45 which releases two electrons, as shown in fig 1.03.
NADH NAD+ + H+ +2e-
The amount of primary dehydrogenase complex (I and II) present is dependent on the source of carbon when the cells are growing.46 Another intermediate of the TCA cycle, succinate, can also be used as an electron donor in aerobic respiration.44,47 Succinate dehydrogenase complex II (Sdh) catalyzes the oxidation of succinate into fumarate with the reduction of ubiquinone to ubiquinol. Sdh plays a crucial role by connecting the TCA cycle with the electron transport chain.
12 - + FADH2 FAD + 2e +2H
NADH Dehydrogenase I Complex is composed of prosthetic group flavin mononucleotide (FMN) and enzyme-containing iron-sulfur (Fe-S).43 The iron is alternatively oxidized and reduced and thus carries the electrons which are received by ubiquinone. The protons are pumped to periplasmic space, and high PMF is generated due to the increased proton concentration in the periplasm. Ubiquinone gets reduced to ubiquinol (UQH2) after receiving electrons and diffuses in the membrane because it is lipid- soluble and freely diffuses through the hydrophobic core of the lipid membrane.48
- UQ + 2e UQH2
Finally, UQH2 oxidizes and releases electrons when coupled with cytochrome
3 oxidase, bo3 (CyoABCD). Under higher aeration condition, cytochrome bo3 is more predominant than other cytochrome oxidase enzyme (like CydB). The cytochrome oxidase complex also acts as a proton pump where there is a net movement of H+/e- across the membrane. The free ubiquinone again starts to carry the electrons between the complexes.
The bo3 complex contains heme groups and copper ions. These cytochromes hold the oxygen molecule between iron and copper ions until oxygen is reduced. The reduced oxygen combines with hydrogen ions and electrons to produce water. The expression of
Cyo operon is repressed by Fnr and ArcA/ArcB systems under the anaerobic environment.47
+ - ½ O2 + 2H + 2e H20
13 There is another cytochrome oxidase enzyme, bd-I (CydB), which also catalyzes the oxidation of ubiquinol and releases electrons.49 However, this complex does not act as a proton pump. The expression of cydB is repressed by Fnr anaerobically and by H-NS aerobically. However, it is induced under microaerophilic conditions (low oxygen concentrations) via ArcA/ArcB systems.50
In conclusion, the reduced oxygen molecules which are trapped in cytochrome oxidase bo3 by binding with heme and copper ions are activated only when it accepts electrons to form the water molecule. These electrons are carried from dehydrogenase complexes to cytochrome oxidase (bo3) by ubiquinones. Thus, it is hypothesized that the rate of oxygen consumption is controlled by the mobility of ubiquinone molecules.42,51 In my project, while I am not looking at the rate of direct movement of ubiquinone, the fluidity of a membrane gives an idea about the diffusion of ubiquinone. Diffusion rates of lipophilic fluorescent probes have been found to be similar to the one reported example of a fluorescently derived ubiquinone used to study membrane diffusion.9
During the transfer of electrons between different complexes by the oxidation of
+ NADH and FADH2, there is a generation of the proton gradient (H ) across the inner membrane.52 When these protons spontaneously diffuse through ATP synthase, then a large
- 3 number of ATP is synthesized in the cytoplasm from ADP and HPO4 .
- + ADP + HPO4 + H ATP
14 1.2.6 Microscopy Techniques to Measure Membrane Fluidity
The fluidity of a protein or lipid membranes could be determined by various techniques, depending on whether long-range diffusion (in micrometer scale) or short- range diffusion (in the nanometer scale). Long-range diffusion measurements can be carried out by fluorescence correlation spectroscopy (FCS), single-molecule/particle tracking (SMT or SPT), or fluorescence recovery after photobleaching (FRAP), which are all time-based techniques.38,53 Short-range diffusion measurements are carried out by either nuclear magnetic resonance (NMR) or fluorescence spectroscopy which are frequency- based technique.53
Since there is evidence that the components of the electron transport chain are distributed throughout the cell and not co-localized,9 my project is focused on long-range diffusion measurements. The long-range diffusion (occurs in µm range) is important because the components of membrane OXPHOS are compartmentalized in bacteria like
E. coli. This is because E. coli changes the environment (gel to liquid phase) through the regulation of various electron transport system. It means the supercomplexes in ETC are not fixed and always in dynamic state.9 Llorente-Garcia, I. et al., 2014 found that the different subunits of OXPHOS complexes [NuoF of NADH dehydrogenase I, SdhC of succinate-fumarate oxidoreductase, CydB of cytochrome bd-I complex, CyoA of cytochrome bo3 complex and AtpB of F0F1 ATPase] are not colocalized as a single ETC cluster in the membrane. Rather, they indicate the formation of “segrezone”, meaning segregated zones containing a different complex in the cell membrane isolated from the
15
other components of the ETC. For example, the NADH dehydrogenase I tended to congregate together in a separate location from the cytochrome bd-I complex. Llorente-
Garcia, I. et al., 2014 also explained by Bayesian ranking of diffusion (BARD) analysis that diffusion of OXPHOS complexes showed confined or unconfined (anomalous or
Brownian or directed) diffusion and obtained the mean diffusion coefficient of the complexes as around 0.007 µm2/s. Lenn, T. et al., 2016 estimated the diameter OXPHOS complexes through FWHM to be 100 nm.11 They tagged the two complexes with different green and red fluorescent markers and determined their colocalization pattern. They determined for all combinations tested that no two complexes are colocalized together. It was also determined that the exogenously inserted fluorescent ubiquinone (NBDHA-Q) was not attached to the membrane and more diffused than the OXPHOS complexes. It means they observed that the rate of oxygen consumption was scaled up when the mobility of NBDHA-Q increased.11 Budin, I. et al., 2018 studied the Brownian movement of ubiquinone from dehydrogenase complex I up to terminal oxidoreductase complex (bo3) and determined the ubiquinone diffusion movement in inner membrane vesicles in E. coli.54 From FRAP measurement technique, they found that the nitrobenzoxadiazole conjugated ubiquinone (NBD- UQ) diffusion rate increased from 0.1 to 0.45 µm2/s when the acyl chain unsaturation increased from the level of 10% to 75%. Their study findings on unsaturated fatty acids were low UFA level caused oxidative stress response (OSR) and heat-shocked response (HSR). OSR caused by the inhibition of ETC and HSR from the misfolding of the protein. Also, the increased level of saturated fatty acids makes the
16
membrane more ordered and rigid.54 It should be noted in both of these studies, the fluorescence labeling and subsequent diffusion measurement were done on cells treated to disrupt the peptidoglycan layer to ensure inner membrane labeling and not natural cells.
For my study, I am following one of the techniques of long-range diffusion called fluorescence recovery after photobleaching (FRAP) to measure the diffusion coefficient of the probe in the inner membrane in natural-looking cells.51 This involves determining the diffusion coefficient from the time required to fill the given area after bleaching that spot on a cell by a high-intensity laser beam. I will briefly explain the method, along with other time-related techniques.
1.2.6.1 Fluorescence recovery after photobleaching (FRAP)
FRAP is a quantitative optical technique to determine lateral diffusion of fluorophores (fluorescence materials) either located in specific cell structures or attached to cell components such as lipids or proteins. I have been used to study lipid or protein dynamics and their interaction with other subcellular components.55 It was developed by
Axelrod and coworkers in the 1970s.56 When a high-intensity laser pulse has passed in the region of interest (ROI) occupied by a fluorophore, it photobleaches the fluorophores in that particular region irreversibly. Then recovery takes place by the replacement of bleached fluorophores in the ROI from the diffusion of the surrounding region (fig 1.04).
The recovered fluorescence in ROI is measured as a function of time.57 The intensity of the recovered area over time gives the exponential plot from which I could determine the diffusion coefficient of a dye in the cell membrane. This provides us with information about 17 the fluidity of the membrane. FRAP is easiest for our microscope set-up and is non-invasive and non-destructive, so it can easily be applied to live cells. It is also useful from determining the diffusion of a membrane probe in individual cells and thus shows cell to cell variation in membrane fluidity.58
Figure 1.04 Schematic diagram of FRAP analysis in E. coli. A region of interest (ROI) is selected, bleached with a laser beam, and fluorescence recovery in ROI is measured over the time interval.
In the FRAP technique, after the photobleaching in ROI, the percent recovery measures the fraction of mobile molecules enter into the photobleached region from non- bleached region and the recovery rate constant, which is also a diffusion coefficient, is a measure of speed when the membrane components move inside or outside of the bleached area (ROI).57
One of the major limitations of the FRAP technique is the chance of photoswitching. In photoswitching, the fluorescent molecules regain its fluorescence from the same ROI after photobleaching.59 It means there would be reversible photobleaching.
18 While GFP and some of its derivatives, like the monomeric red fluorescent protein mCherry, show photoswitching behavior,59,60 it is not to a significant degree to affect our results.
1.2.6.2 Fluorescence correlation spectroscopy (FCS)
Fluorescence Correlation Spectroscopy is one of the important microscopic techniques to analyze the molecular diffusion on lipid bilayers, chemical kinetics, and singlet-triplet dynamics.61 The fluctuation in the intensity of different fluorophores in a small volume (normally femtoliter) is mainly measured in FCS. The fluctuation in intensity is considered as a wave and transformed into a correlation measurement to obtain the different information like interaction and diffusion of molecules, molecular motions, binding between ligands and membrane-bound receptors and other conformational change.61,62 Due to its nondestructive nature in situ and precise detection volume, it is widely accepted in biology and microfluidics. It is one of the alternatives to FRAP as it requires low laser power and a lower concentration of the probes.
The major limitations in FCS are the detecting slower diffusion (because of the time required for a fluorophore to enter the laser volume) and obtaining the exact axial position when the laser beam interacts with the membrane.63 If the laser beam does not focus correctly on the membrane, then the spread of the laser beam results in a larger detection area, which scans a larger number of molecules and increases the diffusion time in the
19 detection area. Another potential issue could be the photobleaching of fluorophores on longer exposer of time which affects the correlation curves.62
1.2.6.3 Single-particle tracking (SPT)
SPT is a microscopic technique to observe the motion of individual molecules in two or three-dimensional trajectories. While FRAP takes the average of diffusing molecules, SPT determines the trajectories of individual particles.64 In SPT, we measure the position of the tagged probe as a function of time with high spatial resolution. This allows a mean-squared-displacement to be determined and from that, a diffusion coefficient calculated. The ability to resolve different modes of motion of an individual molecule within the membrane makes SPT more reliable for finding not only Brownian motion but also different modes of non-Brownian type of diffusion like anomalous diffusion (super or subdiffusion).65 The tracking of a particle in 3D space has improved greatly since 1st generation methods, as detail by Liu, C. et al., 2016.66 In brief, the 1st generation methods were based on the frame by frame video analysis in one plane, while
2nd generation method utilized a faster Z-scan to look at the motion of a particle in 3D space throughout the cell. In the 3rd generation method, the Z-position of a single particle in 2D images is observed using multiple cameras. In 4th generation methods, the microscope is designed with a feedback control system which could track the motion of single emitter.66
One major limitation of the SPT method is to analyze the various types of behaviors like anomalous diffusion, confined diffusion, stops due to crowding of macromolecules,
20 acceleration, or deceleration.67 This is because SPT is mainly based on the Brownian motion hypothesis. Some membrane protein or lipid diffusion may have different motion than Brownian diffusion, but the lifetime of the single probe may not be long enough to capture this behavior. The alteration in motion can be caused by binding interactions, crowding, and membrane composition. SPT also requires selective labeling of the membrane so that only a few fluorescent molecules are seen at any one time. Otherwise, too many particles will appear in the field of view and individual particles cannot be tracked.
1.2.7 Probes for Membranes
Fluorescent probes are normally used to stain lipids and proteins in a cell during imaging. Membrane probes are commonly the fluorescent analogs of natural lipids or lipophilic organic dyes containing aromatic groups. They emit light after excitation of the molecules by radiation from their excitation range.68 Hence, they can be markers for the measurement of gene expression, localization of molecules in the live cells, diffusion of membranes and study of protein-protein interactions.69 As described earlier, E. coli cells have different fatty acid compositions in their membrane bilayers. So, it is possible that different lipophilic dyes could be used to stain the outer or inner membrane of bacterial cells depending on the molecule's affinity for each. Conversely, the similarity in affinity for both membranes could make it difficult to differentiate as to which is being labeled.
There is also the peptidoglycan layer, which could affect the ability of membrane probes to enter the cell and reaching the inner membrane.
21 Since I am studying the inner membrane fluidity to look at its effect on cell respiration, a fluorescent lipophilic probe is needed that will stain the inner membrane of live bacterial cells. Also, the desired probe should have absorption and emission wavelengths in the visible range, high extinction coefficient and good quantum yield.68
Here I have discussed different fluorophores linked with fatty acid chains or integrated with cyclic chains that are used for my research projects.
1.2.7.1 BODIPYFL-C12
BODIPYFL-C12 is a derivative of BODIPY linked with a 12-carbon fatty acyl chain.6 This artificial green lipid probe has a relatively sharp emission peak (Ex 503/ Em
512 nm) and is insensitive to the pH and polarity of the environment.70 The structure of
BODIPY FL-C12, as mentioned in molecular probes by Life Technologies, Invitrogen is given in Fig 1.05. The previous study of Nenninger, A. et al., 2014 with BODIPYFL-C12 found that it is particularly useful for labeling the inner membrane of a bacterial cell and measuring its fluidity.6
Figure 1.05 Structure of 4,4- Difluoro-5,7- dimethyl-4-bora-3a,4a-diaza-s- ® indacene-3-dodecanoic acid (BODIPY FL-C12, D3822).
22 1.2.7.2 FM 1-43
FM 1-43 dye is one of a group (FM dyes) of lipophilic styryl dyes used to stain the membrane of a cell. It is nontoxic to live cells and normally non-fluorescent in aqueous media but highly fluorescent when bound to lipid membranes.71 Among the FM dyes, those with longer hydrophobic tails tend to stain more brightly than those with shorter tails.72
Therefore, FM 1-43 has been used more prevalently. It contains a cationic head, which makes it impermeable to a membrane, so it was initially used as a probe to study endocytosis in eukaryotic cells.7374 Initial uses in bacteria reported it as an inner membrane probe, but I will discuss later in more detail why we believe this is incorrect, or at least why that depends on the cell condition. FM 1-43 has a broad peak, which has absorbance and emission of 478 nm and 579 nm, respectively.
Figure 1.06 Structure of FM1-43 [(N-(3 Triethylammoniumpropyl)-4-(4(1 amino) Styryl) Pyridinium Dibromide)].
1.2.7.3 Pyridinium Cyanine (Py-Cy)
Py-Cy(4-(3-(benzothiazol-2-yl)-2-hydroxy-5-methylstyryl)-1-methylpyridin-1- ium cyanine) is a lab synthesized styryl dye from Dr. Pang’s Lab in the Chemistry
Department at The University of Akron. This dye is useful in the co-staining study with
23
GFP as well as mCherry. It has a very large Stokes shift since the absorption and emission of this dye is 405nm and 692 nm, respectively. Thus, there would be no possibility of fluorescence resonance energy transfer (FRET) between the donor and acceptor of two fluorophores.75 As a combined work with Dr. Pang’s group, we published a paper studying the behavior of the 2-(2’-hydroxyphenyl)-benzothiazole (HBT) based Py-Cy in prokaryotic cells (Dahal, D., Ojha K.R. et al., 2018). The structure is described below.
Figure 1.07 Structure of HBT Py-Cy in enol and keto form.
When pyridinium based cyanine is attached to the HBT fragment the dye molecule can undergo excited-state intramolecular proton transfer (ESIPT) to generate the keto tautomer from enol form in its excited state. One of the essential features here is that emission from the HBT keto tautomer has no spectral overlap with its absorption due to the very large Stokes shift. The probe also includes a pyridinium styryl fragment, which is similar to another styryl dye, FM 1-43, for membrane targeting function. However, unlike
FM 1-43 dye which is impermeable to a membrane, the ESIPT based NIR-emitting styryl dye (Py-Cy) is found to exhibit a large fluorescence turn on when it binds to the inner
24
membrane of a bacterial cell. It is unclear as to why there is a difference in the localization to the outer vs inner membrane.
A second cyanine dye in which the HBT linked to Py-Cy is replaced by 2-(2’- hydroxyphenyl)-benzoxazole (HBO) was also used and showed similar characteristics when studied. The structure of Py-Cy (HBO based) is described below.
Figure 1.08 UV-Absorbance and fluorescence emission spectra of compound 1 in DCM.
1.2.8 Working Principle of Fluorophores
When the cells are stained with different fluorophores, the fluorophores absorb and emit radiation when a high-intensity laser beam is passed over them as viewed with a confocal microscope. A Jablonski Diagram is a well-known energy profile diagram to explain the brief mechanism of different fluorescence and phosphorescence processes.76,77
25 It also demonstrates how the fluorophores show different colors based on their absorption and emission bands.
Figure 1.09 Jablonski energy profile diagram showing different electronic transitions after photoexcitation of a fluorophore (azooptics.com/Article.aspx).
Different electronic phenomena occurring after the excitation of a dye are explained briefly in this section.
Absorption
When the high-intensity laser beam is passed over a sample containing a fluorophore, then the ground state electrons from the molecule are excited to a higher energy state. Depending on the energy transferred to the electron, it is populated to different
26 higher energy levels (S1 or S2 and so on) by absorbing light at a wavelength that corresponds to the energy difference between the energy levels. This phenomenon is called absorption which is a very fast transition process and occurs typically in the order of 10-15 sec.78
Vibrational relaxation
It is a non-radiative process usually indicated by the dotted vertical arrows between different vibrational energy levels in the Jablonski diagram (Fig. 1.09). The relaxation occurs between vibrational levels; thus, the electrons do not change from one electronic state to another state. It happens very fast in the range of 10-14 to 10-12 sec.
Internal conversion
When the electrons from the vibrational level of one electronic state jump to the vibrational level of another electronic state (ex: S2 to S1 state in the fig 1.09), then it is called as internal conversion. Internal conversion takes place if the two energy levels are very close or overlap with one another. Internal conversion takes place almost at the same time range of vibrational relaxation and is also a non- radiative process.
Fluorescence
It is one of the radiative processes that occur in fluorophores. Fluorescence is the emission of radiation from a lower excited state to the ground state and occurs typically in
27 the range of 10-9 to 10-7 seconds.79 The energy of fluorescent photons is less than the exciting photons due to loss of energy in a non-radiative manner during vibrational relaxation and internal conversion.76 This shift in energy allows us to use filters specific to the absorption and emission wavelengths of the fluorophore to isolate the emitted photons and see them within the region of interests when doing imaging.
Intersystem Crossing (ISC)
Instead of going from excited state to ground state by a radiative process, sometimes the dissipation of energy occurs where electron changes its spin multiplicity from singlet excited state (S1) to excited triplet state (T1).80 In fig 1.09, it is represented by a straight arrow. This is a slow process and takes place on the order of 10-8 to 10-3 sec. It is a “forbidden” transition that requires spin-orbit coupling. ISC opens another important route called phosphorescence. Alternatively, there would be a possibility of delayed fluorescence where photons return to the first excited singlet state (S1) and emit the radiation to the ground state.
Phosphorescence
The radiative transition from the excited triplet state to the ground state is called as phosphorescence.81 Here, the electron in the excited state has the same spin orientation as the electron in the ground state.79 It is a slow process and occurs in between the range of
10-3 to 10-1 sec. It is also a “forbidden” transition that requires spin-orbit coupling.82
28 Because of the long lifetime in the excited state, the other processes and transition can affect the lifetime of the excited state. For example, platinum porphyrin will show phosphorescence, although when it interacts with reactive oxygen species, it will go to a dark state and there will be no emitted radiation in this case. It is more likely that shorter times in the excited triplet state will phosphoresce while longer-lived excited triplet state molecules will go to a dark state. A higher concentration of oxygen would increase the interaction of reactive oxygen species with the excited triplet state, thus shortening the phosphorescence lifetime. This is why Pt-porphyrin phosphorescence lifetime can be used to make a measurement of oxygen concentration over time to study cell respiration of E. coli cells, as will be explained later in detail.
1.2.9 Confocal Microscopy
The principle of confocal laser scanning microscopy is based on that when the laser beam is passed through a light source aperture, it is focused by the objective lens into a very small defined area on the surface or within a specific depth of the sample.83 The pixel by pixel images is obtained by scanning either the laser or the sample and collecting the emitted photons through a separate pinhole aperture (detector pinhole) and onto a detector.84 For that, we need to insert the fluorophores in the cell sample endogenously or exogenously. The confocal detector pinhole only allows fluorescence signals from in-focus fluorophores to reach the photodetector (such as photomultiplier tube) and block the photons that are out of focus. Here, both light source aperture (source pinhole) and
29 observation aperture (detector pinhole in the fig 1.10) conjugate with the same point in the sample.
Figure 1.10 Typical laser scanning confocal microscope with light paths where pinholes are used in front of laser source and photodetector (Paddock, S.W., 2000).
1.2.10 Plasmolysis
E. coli cells have internal turgor pressure, which is maintained by regulating the internal osmolality continuously. At normal conditions, the concentration of solutes in the cytoplasm is much higher than the outer environment. It creates a pressure on the cell wall
30 and helps to maintain a cell shape as well.85 If I increase the concentration of solute outside the cell, termed as a hyperosmotic shock, then there would be a flow of water from inside the cell to outside the cell. It causes the pressure drop in the semipermeable cytoplasmic membrane due to the water efflux, and there would be a change in cell shape and size. This process is termed as plasmolysis.85,86 However, there are several mechanisms to maintain the turgor pressure if there is a sudden change in the external osmolality. Osmoprotectants, such as potassium ion, betaines, and amino acids, can either be transported into the cytoplasm or synthesize by the cell to change in external osmolality and help the cell to protect from greater osmotic shock .87
Plasmolysis can be used to help determine the staining location of fluorescent probes in the cell membrane, specifically whether it stains the inner or outer cell membrane
(or both). The separation between the inner and outer membranes in bacteria is far too small to differentiate by confocal imaging (limits of resolution is between 200 and 250 nm), even if using two different colored fluorescent probes. However, when cells lose water molecules from the cytoplasm to outside of the cell after being placed into the hypertonic solution, there is shrinkage of the cytoplasmic (inner cell) membrane from the cell wall.88 This is because the outer cell membrane will remain as a rigid structure, while the inner cell membrane will pull away from the outer cell membrane. Thus it creates enough separation between the two membranes and provides good evidence to check whether the fluorophore is localizing to the outer cell membrane, to the inner cell
31 membrane, or to both. A general description of types of plasmolysis, as described by
Schele, P. 1969, is given below.88
Figure 1.11 Schematic diagram of plasmolysis. When very low (A) or appropriate concentration (B) of a hypertonic solution has used.
As shown in fig 1.11, when a low concentration of sucrose (up to 0.2 M) is used for the osmotic upshift, then predominantly, there would be the separation of cytoplasm from the peptidoglycan layer (cell wall) at the end of the poles (A). The effect of the osmotic shock increases if there is an increase in the concentration up to 0.6 M sucrose.89
The separation of the outer and inner membrane is clearly visible here at the sides of the cells (B). I observed in my experiment that at a higher concentration of 0.8 M sucrose solution, there would be extreme shrinkage of the cytoplasm, and the cells cease to be good candidates for analyzing the localization of the fluorophore. There are various factors that affect the extent of plasmolysis, including the attachment of the cell wall, the pore size of the cell wall for the solutes passage, and protoplasmic viscosity play a vital role in the
32 degree of plasmolysis.90 In addition, I could not neglect the possibility of external factors like temperature, use of different hypertonic solutions, and the different media in which the cell grows. Because the different carbon sources in different media could alter the composition of peptidoglycan layers, which directly affect plasmolysis phenomena.18
1.2.11 Dependence of Fluidity of a Membrane
As mentioned earlier (1.2.4), the fluidity of a membrane is affected by various factors. I studied differences in membrane fluidity and membrane composition of E. coli cells in my projects in two ways. First, the cells were grown and imaged at different temperatures to alter the fluidity of the membrane being analyzed. The second method depended on the ratio of saturation and unsaturation of fatty acids in phospholipids when the temperature parameter remains constant. The intention of these known alterations to and characterization of membrane fluidity is to confirm the fluorescent probe would be appropriate for coupled measurements of membrane fluidity with cellular respiration.
1.2.11.1 Effect of temperature
As discussed in earlier papers by Mika, J. et al. 2016 and Nenninger, A. et al. 2014, bacteria maintain their membrane fluidity through homeoviscous adaptation.6,38 When the cells grew at a higher temperature and measured the diffusion at a much lower temperature, there would be a significant decrease in the fluidity of a membrane without alteration of the membrane composition, which indicates a phase transition from the liquid crystalline 33 state to a more viscous crystalline gel state.6,38 Because cells will alter their membrane composition, for example, by altering the tail length though gene regulation, by growing cells at a single temperature and then immediately imaging at a different temperature, it is possible to see membrane fluidity effect before the cell adapts to the new temperature. I grew cells at 37o C and made FRAP measurements under both isothermal and decreasing temperature conditions. Diffusion measurements matched expected results and confirmed that the membranes are more fluid at an imaging higher imaging temperature and their fluidity decrease at a lower imaging temperature.
1.2.11.2 The ratio of saturated/unsaturated fatty acids
The fluidity of a lipid bilayer is largely dependent on the carbon chain length and the ratio of saturated to unsaturated fatty acids.91,92 The extent of saturated vs unsaturated fatty acid synthesis is largely controlled by the fabA and fabB genes.93 The metabolic pathway needs other enzymes (fabD, fabE, fabF, fabG, fabH, fabI, fabZ and acyl carrier protein), but here I will discussing mainly the fabA-fabB enzymes since they determine the extent of saturation and I am looking at the effect of saturation/unsaturation of fatty acids on the fluidity of a membrane. The condensation reaction catalyzed by fabB is the rate- determining step and is the only one irreversible step in the fatty acid synthesis. The enzyme fabA is involved in unsaturated carbon chain synthesis, although it is reversible.94
The detail biosynthetic pathway as explained by Magnuson, K. et al., 1993; Zhang, F. et al., 2012 and Xiao, X. et al., 2013 has explained in the chart below.93,95,96
34 Figure 1.12 Synthesis pathway of fatty acids in E. coli involving different enzymes. fabA and fabB are the two major enzymes that involve in the unsaturation and elongation of fatty acid chains, respectively.
The various enzymes encoded by different genes involved in the biosynthesis of fatty acids are outlined in Table 1.0.95,97
35 TABLE 1.0 DIFFERENT ENZYMES INVOLVED IN BIOSYNTHESIS OF FATTY
ACIDS.
Gene encoded enzyme
fabA β-hydroxydecanoyl ACP dehydratase
fabB β-ketoacyl-ACP synthase I
fabD Malonyl-CoA: ACP transacylase
fabE Acetyl CoA carboxylase
fabF β-ketoacyl- ACP synthase II
fabG β-ketoacyl-ACP reductase
fabH β-ketoacyl-ACP synthase III
fabI enoyl-ACP reductase
fabZ β-hydroxyacyl-ACP dehydratase
The synthesis of fatty acids in E. coli follows the type II synthase system.95 The regulation of fatty acids in E. coli by different enzymes is briefly explained by Fujita et al.,
2007 and Magnuson, K. et al., 1993.95,98 Based on their findings, Acetyl-CoA is an important initial precursor for fatty acid biosynthesis. It acts like a primer for the formation of the acyl chain. The other two cofactors, coenzyme A (CoA) and an acyl carrier protein
(ACP), help to carry over the acyl chain from one enzyme to another. ACP is one of the
36 abundant rod-shaped soluble protein in E. coli. It covalently binds to intermediate products of fatty acids and forms ACP thioesters, which act as a substrate for different enzymes in the overall biosynthetic pathway. Malonyl CoA is used in different elongation steps by converting it to malonyl- ACP (fig 1.12). After the condensation reaction of malonyl-ACP to β-ketoacyl-ACP, the chain elongation reaction begins, which is catalyzed by the enzyme
β-ketoacyl-ACP synthase I/II (encoded by fabB/fabF). Then, the reduction reaction occurs with the enzyme ACP reductase (encoded by fabG) converting it into β-hydroxyacyl-ACP before it is dehydrated to trans-2- unsaturated acyl-ACP by the enzyme ACP dehydratase
(encoded by fabA/fabZ). The final reduction it to acyl-ACP two carbons longer than original acy-ACP by the enzyme β-ketoacyl-ACP synthaseI/II (encoded by fabB/fabF).
The elongation step continues until we obtain C-16 or C-18 longer fatty acids, which is based on the activity of acyl transferase.99,100
Alternatively, the trans 2- unsaturated acyl-ACP can undergo an isomerization reaction catalyzed by fabA to form cis 3- unsaturated acyl-ACP, which is reversible. The cis 3- unsaturated acyl-ACP can be used as a substrate by fabB which results in the chain elongation of unsaturated fatty acids. This is the only irreversible step in chain elongation in fatty acid biosynthesis and traps a fatty acid as being unsaturated. Thus, it is also a rate- determining step. The obtained major fatty acids are palmitoleic and cis-vaccenic acid
(unsaturated fatty acids) as well as palmitic and stearic acid (saturated).95,97,101 The transfer of these fatty acid products into the membrane phospholipids takes place through glycerophosphate acyl transferase system.99
37 Since, as shown in figure 1.12, β-Ketoacyl- ACP synthases are important enzymes for control of chain length and formation of different acids. β-Ketoacyl- ACP synthase III
(encoded by fabH) could not catalyze the condensation reaction with malonyl-ACP because it does not have a fatty acyl-binding site. So, it is not involved in the chain elongation step. The major role of β-Ketoacyl- ACP synthase II (encoded by fabF) is to provide a means for temperature control of the fatty acid composition, and β-Ketoacyl-
ACP synthase I (encoded by fabB) involves in chain elongation, inactivation of which inhibits the production of unsaturated fatty acids. On the other hand, β-hrodoxydecanoyl-
ACP dehydratase (encoded by fabA) helps to add a double bond in the fatty acid chain while β-hydroxyacyl-ACP dehydratase (encoded by fabZ) helps in elongation reactions.
So, my project focuses on the study of the role of fabA and fabB since they control the relative ratio of saturated and unsaturated fatty acids in bacterial cells.102
There is another gene called fadR, which is a transcriptional activator of fabA/ fabB genes.103 The fadR protein has a dual role in fatty acid metabolism. It acts as a repressor in
β-oxidation and as an activator of fabA-fabB gene in unsaturated fatty acid (UFA) biosynthesis.103,104 Through genomic array experiments, Campbell, J. et al., 2001 provided the evidences that fabA-fabB is positively up-regulated by the transcriptional regulator
(fadR).105 According to their findings, fadR binds to acyl-CoA at a very low concentration.
When binding to acyl-CoA, fadR capacity to bind DNA is lowered significantly.
FadR binds to 17 base pairs located around the -40 and -10 region of fabA or fabB promoters.105 This binding site is normally occupied by the activators of σ70 promoters. σ70 38 are the subunits of RNA polymerases that are responsible for gene transcription (RNA synthesis).106 The base pairs at -35 regions are almost similar to that of fadBA and fadL genes (fatty acid degradation genes) where fadR is supposed to act as a repressor.95 This dual role of fadR as an activator for biosynthesis and repressor for the degradation of fatty acids mainly depends on the location of protein binding site from the transcriptional start point.96 When the fadR encoded enzymes are located on downstream of the promoter in between of -30 and +10 region, then fadR acts as repressor whereas when fadR encoded enzymes are positioned at -10 to -40 regions, then fadR acts as an activator. Thus, for the case of fadBA and fadL, the binding site is positioned at +9 and -17 regions downstream of the promoters respectively whereas for fabA and fabB, this site is positioned at -40 and
-10 region upstream of the promotes.95,96,105
Figure 1.13 fabA and fabB promoters (sequences obtained from www.ncbi.nlm.nih.gov) and their regions where fadR proteins bind for transcription. ∆ represents the transcription start point.
39 1.2.12 Bacterial Cell Respiration
The study of bacterial cell respiration is significant in understanding the metabolic activity of the cells.107 One of the final goals of my project was to look at the respiration rate by E. coli cells. Respiration rate could be determined by measuring the oxygen consumption by a cell over a certain period, because in the bacterial cells, oxygen is the terminal electron acceptor in the ETC.108 In eukaryotic cells, respiration takes place in mitochondria, while for prokaryotes, it occurs in the cytoplasmic membrane. We expect that the fluidity of a membrane is somehow related to the bacterial respiration. This is because, during the electron transport chain and oxidative phosphorylation, there is a soluble electron carrier molecule called ubiquinone, whose movement in different protein complexes affects how fast or slow the electron is reached to the oxygen.9 Chazotte, B. et al., 1990 explained that the liquid crystalline phase of bacterial cells has a faster diffusion rate because the ubiquinone is in a highly mobile state unless it undergoes the phase transition.51 Also, the mobility of ubiquinone is somehow affected by the location in the bilayer’s plane. According to Budin, A. et al. 2018, diffusion of ubiquinone control the respiratory flux in the cytoplasmic membrane.54 They simulated the Brownian movement of electron carriers and looked at the rate of electron transfer as a function of diffusion of ubiquinone. If the ubiquinone is located in the bilayer midplane and if it is not intercalated between the phospholipid acyl chains, then it has higher mobility due to a lowering in viscosity in that region of the bilayer. As already explained, the diffusion rate of membrane
40 lipid and proteins could be determined through fluorescence recovery after photobleaching technique (FRAP).
There are multiple methods to measure the dissolved oxygen concentration. Clark electrodes historically have been a gold standard, although they face many limitations when trying to apply it to single cells or even low cell numbers.109,110 First, the size is too large for individual cell measurements. Second, the electrode frequently required calibration.
Likewise, the drift in the signal, low sensitivity, and sometimes the electrode itself consumed oxygen are major problems.109
To minimize these problems, I used a closed system using an optical probe
(platinum porphyrin) as an oxygen sensor to determine the dissolved oxygen concentration.2 Other porphyrins, such as palladium porphyrin, ruthenium complexes, europium porphyrin, which could be used as alternatives of platinum porphyrin (Pt- porphyrin). These probes use phosphorescence lifetime measurement to determine the oxygen concentration.
1.2.12.1 Working principle of phosphorescence lifetime
The oxygen concentration at any time point from the phosphorescent lifetime determination is based on the Stern-Volmer equation.107,111:
Io/I = τo/τ = 1+ KQ. τo [O2] Where,
Io and τo = initial intensity and phosphorescence lifetime at 0% oxygen.
41 I and τ = intensity and phosphorescence lifetime at a particular oxygen concentration (20%).
KQ = Stern-Volmer constant.
[O2] = oxygen concentration (at 20%). While either phosphorescence intensity or lifetime could be used, lifetime measurements have the advantage in that they are independent of probe concentration and photobleaching.111
Figure 1.14 Pt-porphyrin chelated with an Oxygen molecule.
When the Pt-porphyrin sensor absorbs light (ab/em is 540nm/655nm), the electrons move to excited state and quickly to an excited triplet state (mechanism explained in
Jablonski diagram). It interacts with the reactive singlet oxygen (which is more reactive than triplet oxygen), and then there would be quenching of luminescence, and it results in radiationless deactivation while returning to the ground state. As a result, the intensity and lifetime decrease in the presence of oxygen.112 Since the fluorescence lifetime is the average time in which the photons of the sensors remain in the excited state before emitting the radiation. It would be noted that oxygen concentration and phosphorescence lifetime
42 have a reciprocal relation. It means, if there is a higher concentration of oxygen, there would be faster lifetime decay rate.107,112
The oxygen sensor data is collected by an optimized rapid lifetime determination
(ORLD) technique.107,113
Figure 1.15 Generalized graph of optimized RLD for measuring lifetime using the ratio of two integrated areas under the single exponential decay curve (Chan, S. et al., 2001 and Konopka, M., 2017).
As shown in the fig 1.15, the lifetime is measured from a single exponential decay curve by taking the ratio of integrated areas at two different regions (A and B). We have used the gated CCD camera; thus, the two integrated areas are directly obtained from the pixel intensity. The lifetime is then calculated as;113
−∆푡 휏 = 퐵 ln (퐴)
43 The oxygen concentration is then determined by fitting the alternatives of Stern-Volmer equation as;
퐴표 log ( ) 퐵표 = 1 + 𝑐표푛푠푡푎푛푡 ∗ [푂2] 퐴 log (퐵)
Here, the subscript terms show the condition at 0% oxygen. The decay rate constant
(KQ) is obtained through calibration of a lifetime in the presence of oxygen (20%) and in the absence (0%) of oxygen before I run the sample using oxygen sensor. A and B are integrated areas of the unknown sample and [O2] is the oxygen concentration of the unknown sample. When fitting [O2] concentration versus time, the oxygen consumption rate of the given unknown sample is determined. For calibration, only Pt- porphyrin is used at two different controlled oxygen concentration (0% and 20%).
Finally, the sample of particular OD600 (OD600 < 0.1) is placed into the 3.2 µl well to measure the lifetime of the bulk sample over time. Oxygen concentration (obtained in
PPM) vs time (min) gives the total oxygen consumption rate of a bulk sample (total cells in 3.2 µl).107 Later, it is converted to oxygen consumption by individual cells base on the
OD in the well volume.
44 CHAPTER II
MATERIALS AND METHODS
2.1 Bacterial Strains and Growth Media
Different strains of E. coli were used during the study of membrane fluidity and bacterial cell respiration. These were the K-12 MG1655 lab strain, efflux pump knock-out
DGC-102, and DH5α. The cells were grown in different growth media, including carbon- rich LB broth and minimal MOPS media.114 LB contained major ingredients of beef extract, acid hydrolysate of casein, and starch, while MOPS media was made from a 10X
MOPS mixture, K2HPO4, and 0.2% glucose solution. Cells were grown by starting with a loop of freezer stock sample and inoculating in 5 ml of media in a glass test tube. All the cells were grown at 37o C or 30o C, depending on the experimental condition, with constant shaking of 200 rpm in a New Brunswick Scientific Excella E24 Incubator Shaker Series or Thermo Scientific MaxQ 6000. LB media was sterilized by autoclaving at 120o C and
16 Pa, while MOPS media was filter-sterilized to avoid the chance of decomposition of some ingredients used in the MOPS media at the higher temperature and prevent from precipitation.
In order to check the cell population during the growth, a spectrophotometer was
115 used where OD600 was measured. Bacterial incubation and media transfer were done either under the laminar hood with continuous airflow or under the flame on the benchtop to avoid any kind of contamination.
45 2.2 Growth Curve
Different bacterial strains were grown either in LB or MOPS media and their generation time (doubling time) was measured by taking the OD in every 30 min time interval. One of the examples is given in fig 2.0. Then generation time was measured as follows;116
푡 Generation time(G) = 푂퐷푓 3.3 log( ) 푂퐷푖
Where t = time interval in minutes.
ODf = OD after doubling in the log phase.
ODi = OD, where the log phase begins in the growth curve.
2.5 DGC-102 in LB media
2
1.5
OD600 1
0.5
0 0 30 60 90 120 150 180 210 240 270 Time( min)
Figure 2.0 Plot of doubling time of DGC-102 cell grown in MOPS media at 37o C. Generation time here is 46.6 min.
TABLE 2.0 GENERATION TIME LISTED OF DIFFERENT CELLS AT 37O C.
46 Strains Generation time, Generation time, min
min (in LB) (in MOPS)
MG1655 37.55 min 53.26 min
DGC-102 46.60 min 64.17 min
∆fadR MG1655 72.8 min 92.06 min
DGC-102+fabA+mCherry 60.99 min 73.37 min
DGC-102+fabB+mCherry 49.70 min 61.97 min
MG1655+fabA+mCherry 62.23 min 71.78 min
MG1655+fabB+mCherry 43.54 min 57.50 min
2.3 Vector Construction and Gene Cloning
Genomic DNA was isolated using GenEluteTM Bacterial Genomic DNA Kits from
Sigma-Aldrich. The fabA and fabB promoters were selected from 500 bp upstream of fabA
(NCBI reference sequence: NP_415474.1) and fabB genes (NCBI reference sequence:
NP_416826.1). These were inserted into MG1655 and/or DGC-102 genomic cells using pUC19 vectors117 which also expressed mCherry (ex/em 570/610 nm) and/or sfGFP proteins (ex/em 485/510 nm) after the promoter region.118,119 One of the examples of the construction of a pUK21 vector with a fabA expression vector with sfGFP is given below
(fig 2.01). PCR primers were generated according to the NEBuilder assembly web tool
47 (given below Table 2.1) Plasmid assembly was constructed using the NEBuilder HiFi DNA
Assembly cloning Kit.
TABLE 2.1 NUCLEOTIDE SEQUENCES FOR DIFFERENT PCR PRIMERS USED
IN CLONING.
fabA-promoter-pUC19 forward TATCACGAGGCCCTTTCGTCCTCTGCGCCCTGATAAGC reverse TGCTCACCATGTTCTCTGTAAGCCTTATTTTATTGAAG
fabB-promoter-pUC19 forward TATCACGAGGCCCTTTCGTCATGCGCTAAGGCTAAATC reverse TGCTCACCATTCAATACCTCTGTAAGTCG
mCherry-pUC19-fabA forward TACAGAGAACATGGTGAGCAAGGGCGAG reverse GTCATCACCGAAACGCGCGACTACTTGTACAGCTCGTCCATG
mCherry-pUC19-fabB forward GAGGTATTGAATGGTGAGCAAGGGCGAG reverse GTCATCACCGAAACGCGCGACTACTTGTACAGCTCGTCCATG
pUC19-fabA forward TCGCGCGTTTCGGTGATG
48 reverse GACGAAAGGGCCTCGTGATAC
pUC19-fabB forward TCGCGCGTTTCGGTGATG reverse GACGAAAGGGCCTCGTGATAC
pUK21
forward AGCGCCCAATACGCAAAC
reverse CTTCCGCTTCCTCGCTCAC
fabA-promoter
forward AGTGAGCGAGGAAGCGGAAGCTCTGCGCCCTGATAAGC
reverse CTTTACGCATGTTCTCTGTAAGCCCTTATTTTATTGAAG
sfGFP
forward TACAGAGAACATGCGTAAAGGCGAAGAG
reverse CGGTTTGCGTATTGGGCGCTTCATTTGTACAGTTCATCCATAC
49 Figure 2.01 Construction of pUK21 vector with fabA expression vector with sfGFP.
2.4 PCR of pUC19 with fabA/fabB Expressed with mCherry
After getting the required primers, Q5R High-Fidelity PCR Kit was used for PCR.
Genomic DNA (extracted from wild type MG1655) was amplified using High-Fidelity 2X master mix, forward and reverse primers of fabA-promoter-pUC19 and template DNA.
PCR of pUC19 was run using High-fidelity master mix, forward and reverse primers of pUC19 forward and reverse and plasmid DNA (extracted using GenEluteTM HP Plasmid
Miniprep Kit from Sigma-Aldrich). mCherry was amplified using High-Fidelity 2X master mix, mCherry-pUC19-fabA forward and reverse primers and vector DNA. After running
50
the PCR, they were verified using gel electrophoresis (fig 2.02) with respective DNA base pairs.120 The same process followed for fabB promoters as well. After that, DNA assembly was carried out as mentioned in NEBuilder HiFi DNA Assembly Master Mix Assembly
Protocol.
Figure 2.02 Gel electrophoresis analysis to observe different bands obtained after amplification through PCR. fabA/fabB promoters have 500 base pairs (bp), mCherry has 711 bp and pUC 19 backbone has 2686 bp (left and right sides are DNA ladders).
Finally, cells (MG1655 or DGC-102) were made chemically competent by using cold TFB I and TFB II solutions. These competent cells were transformed with the plasmid
51
of our interest (In the above case, it is the DNA assembly product of pUC19) via heat shock as described in chemically competent cells transformation protocols (NEBuilder HiFi DNA
Assembly Master Mix). Alternatively, I could make electrocompetent cells and transform them by electroporation. These strains were first grown in SOC media for 1 hour and later transferred to LB plates for overnight growth. Freezer stock was made in DMSO (1:1 ratio) by selecting a single colony from the plates for future use. They were named as MG1655
+ fabA + mCherry/ MG155 + fabB + mCherry/ DGC-102 + fabA + mCherry/ DGC-102 + fabB + mCherry cell. Different FRAP measurements were carried out from those transformed cells. The same process was followed to transform the competent cells to other different plasmids like pbad24 and pUK21. I used different expressed proteins during the study such as mCherry, eGFP, and sfGFP.
2.5 Synthesis of Pyridinium-Cyanine
548 mg (1 equiv.) of 3-(benzo[d]thiazol-2-yl)-2-hydroxy-5-methyl- benzaldehyde
(compound 1) was added to the solution of 4-methylpicoline (compound 2) (470 mg; 0.98 equiv.) in methanol and 0.5 ml of piperidine was also added in a round-bottomed flask.
The resulting mixture was refluxed overnight, and the solvent was evaporated under reduced pressure after completing the reaction. Then the solid residue was washed with ethyl acetate and filtered. The solid mass was again washed with water and dried under a high vacuum trap to get a pure and dry product with a 65% yield. The entire synthetic route for Py-Cy has been reported in the recently published article by the authors.75
52 Figure 2.03 Synthesis of HBT pyridinium derivative (cyanine).
I also used an HBO based pyridinium cyanine due to its more stable nature than
HBT. The synthesis process (done in Dr. Pang lab) was described below.
In a 50 mL round-bottomed flask, 120 mg (0.51 mmol) of compound 5 was dissolved in methanol, and 0.5 ml of piperidine was added. The mixture was heated to a temperature of 60o C, and 152 mg (0.60 mmol) of compound 4 was added, and the mixture was stirred at 60o C overnight. The solvent was then dried in a rotary evaporator, and the residual solid was washed with 50 ml ethyl acetate. The residue was filtered and dried to get 216 mg (85% yields) of compound 1. The details for the synthesis of compound 4 can be found in our recent publication (Dahal, D; Ojha K.R., et al., 2018). The structure of
Compound 1 was confirmed by NMR (1H and 13C) spectroscopies and mass spectrometry.
The small resonance peak at 11.906 ppm was observed for hydroxyl proton. Further downfield shifting of the proton than pure phenol would be because of the intramolecular hydrogen bond in a compound.
53
Figure 2.04: Synthesis of compound 1 (pyridinium cyanine coupled with HBO) [not published yet].
2.6 Cell Imaging
E. coli cells were grown initially in LB broth from the freezer stock. After that, they were subcultured to other media, such as MOPS, at least two times before imaging to ensure the complete transition to the new media. Cells were always grown to log phase
(OD600 of 0.3-0.7) except for the stationary phase study. Before cell imaging, a 500 µl sample was taken and stained, which final concentrations and staining time depending on the specific fluorophore. FM 1-43 (Thermo/Invitrogen, green, excitation; 479 nm) and FM
4-64 (Thermo/Invitrogen, red, excitation; 506nm) were used at a final concentration of 9
µM. After adding dye, the sample was incubated at required temperatures (30o C or 37o C) 54
for 20 minutes with constant shaking of 200 rpm. BODIPYFL-C12 (Invitrogen, green, excitation 488nm) was used to a final concentration of 1 µM and incubated for at least 2 hours with constant shaking of 200 rpm. BODIPYFL-C12 did not significantly stain cells on a shorter period of time. HBT/ HBO pyridinium cyanine (Py-Cy, red) was used at a final concentration of 2 µM. After adding dye, the sample was incubated for 20 minutes at required temperatures with constant shaking of 200 rpm. Finally, for short term imaging
(about an hour), 5-8 µl of cell sample was placed on a poly-L-lysine (Sigma-Aldrich) coated VWR microscope slides and sealed with poly-L-lysine coated VWR micro cover glass. For long term imaging (overnight) and time analysis imaging was carried out in the
Attofluor cell chamber (Thermo) using poly-L-lysine coated round coverslip as a base. 1-
2 ml of media was used over the sample. All imaging and analysis were carried out in
Nikon A1 laser scanning confocal microscope. Imaging temperature could be adjusted from the Oko Touch microscope cage incubator. For co-staining experiment with FM1-43 and Py-Cy, the dyes were added simultaneously and left in the incubator with shaking for their 20 minutes.
2.6.1 Imaging Under Confocal Microscope
Bacterial cell imaging was carried out on a Nikon A1 confocal microscope with
Nikon elements software packages. The microscope slide was put over a 100X (NA 1.0) oil immersion objective and fluorophores were excited using solid-state lasers (excitations available were 405 nm, 488nm, 561 nm, and 640 nm). The fluorescence emission was
55 separated from the respective lasers using a 425-475 nm filter, 500-550 nm filter, 570-620 nm filter, and 663-738 nm filter.
2.7 Plasmolysis Protocol
Plasmolysis was carried out on cells (MG1655 or DGC-102 cells) labeled with different dyes (BODIPYFL-C12/ HBT and HBO py-cy/ FM1-43). Cells at different phase
(log phase and stationary phase cells) were put under a hypertonic solution of sucrose ranging from a concentration of 0.2 M to 1 M. MG1655 and DGC-102 cells were first grown overnight and sub-cultured in MOPS media. For the log phase study, cells were grown up to OD 0.3-0.6. 500 µl sample was taken in a microcentrifuge tube and the dye was added into the sample and incubated at 37o C for 20 minutes. It was spun down to make a pallet and washed at least three times with potassium and glucose-free MOPS media to ensure no recovery from the osmotic shock. Finally, on a pallet, 250 µl of sucrose solution was added and left to room temperature for 4 minutes. 7 µl sample was put on a microscope slide and imaged as described.
2.8 FRAP Technique to Determine Membrane Diffusion
Fluorescence Recovery After Photobleaching (FRAP) technique was used to study the diffusion in the lipid membranes. For that, the cells were first stained with dyes (see cell imaging section for staining protocol) and FRAP measurement was carried out using
Nikon elements software package. For that, the individual cell was first selected and photobleached in a specific region (ROI). Special attention was made to ensure that the cell 56 was in focus. An image was taken for 252 ms prior to a 252 ms bleaching pulse with a suitable excitation laser (such as 488 nm laser was used for BODIPYFL-C12) at 40-50% of the power. After photobleaching, post-recovery images were taken at every 252 ms time interval in a continuous manner. The total time frame for bleaching and recovery was typically 7-10 sec. The intensity profile over time was quickly checked using the Nikon
Elements software to ensure that the cell was successfully bleached. The diffusion coefficient was then calculated from the decay curve using the MATLAB R2015a software.
57 Figure 2.05 FRAP analysis in DGC-102 cell labeled with BODIPYFL-C12 (a). Axial intensity profile diagram of a cell (given by Sochacki et al., 2011) showing before and after photobleaching (b). Fourier cosine mode 1 amplitude vs time after the bleaching and least square fit to a single exponential decay profile curve (c).
The E. coli cell membrane was considered as a spherocylinder with two hemispheres at both ends. The length of the body of a cell was considered as ‘L’ with a radius of hemisphere ‘R’.121 Konopka, M. et al. 2006, measured the cytoplasmic protein diffusion in E. coli, although subsequent work on the diffusion of periplasmic proteins would be most analogous to using FRAP for determining diffusion in a cell membrane.122
For that, the recovery of fluorescence distribution in the membrane was converted into a
123 one-dimensional profile, I(x,t) where ‘I’ is the intensity at the position x at time t. Fig 2.05
(b) was the intensity vs time plot for the cells of fig 2.05 (a). Prior to photobleaching, the intensity falls at the discontinuous manner at both ends of cells due to the non-tapering of end caps. So, the intensity of two end caps was removed and considered the cell as a
58 cylinder where x is 0 to L (where L is the length of a cylinder, a non-tapering part). It was considered that the concentration of dye at position x and time t was directly proportional to the intensity, I(x,t). I applied the same equations to determine diffusion coefficient because the inner lipid membrane is also considered as spherocylindrical shape. The equation of one dimensional continuous diffusion obtained through the recovery of intensity after photobleaching in that particular cylinder is given by123,124
2 훿퐼( ) 훿 퐼( ) 푥,푡 = 퐷 푥,푡 훿푡 훿푥2
Where D is the membrane diffusion coefficient. The symmetrized model problem gives the information of boundary planes with no end caps under x = -L and L as;
∞
퐼(푥, 푡) = 퐼0 + ∑(퐼푛(푡) cos(q ∙ x) + 퐼푐표푟푟(푥, 푡)) 푛=1
푛휋 Where, 푞 = is the wavenumber and n = 1, 2... is the mode number. For all 퐿 calculations, n = 1 was used. The intensity profile (fig 5.05 (b)) was then transformed to a Fourier amplitude using a cosine function which gives the single exponential decay curve. The Fourier amplitude of each cosine mode at time t is calculated as;
퐿 2 퐼 (푡) = ∫ cos(푞 ∙ 푥)퐼(푥 ∙ 푡) ⅆ푥 퐿 0
The Fourier cosine amplitude, I(t), decay exponentially over time as;
2 퐼(t) = Io exp( − 푞 ∙ 퐷푡) + B
Where B is constant gives the value of non-uniform intensity at t = 0 after the recovery.
Then, axial diffusion coefficient (D) is calculated from the decay rate (k) as; 59 푘퐿2 퐷 = 훱2
Although this calculation was done using just the cylindrical length of the cell without the endcaps, modeling has shown that and effective length, 퐿푒푓푓, can be accurately used to determine the diffusion coefficient. For the periplasmic case (whose image looks like the membrane staining along the outside of the cell, was found to be Leff = L + 2R. R is the radius of the cylinder and calculated from measuring the diameter (2R) of the cylindrical body of the cell from the fluorescent peaks on either side. So, as explained by Sochacki, K et al. 2011 and Konopka, M. et al. 2006, when the cell was considered as a cylinder of length ‘L’ and radius ‘R’ of two hemispherical end caps, then q, for the case where n=1 is 휋 푞 = 퐿푒푓푓
Finally, the corrected diffusion measurement is evaluated as;122,123
2 푘퐿푒푓푓 퐷 = 휋2
Where, k is the exponential decay rate constant of mode 1 amplitude over time (fig 5.02
(c)). Thus, in my study for diffusion measurement through FRAP analysis, these equations are coded in the MATLAB program and when the intensity of distributed membrane dye after photobleaching was placed over time, it gives us the diffusion coefficient of the membrane.
60 2.9 Respiration Rate Measurements
Platinum porphyrin embedded polystyrene beads (used as an oxygen sensor) were taken from the stock solution and washed multiple times with ultrapure water to remove any azide chemical because the sensor beads were stored in sodium azide to inhibit microbial growth.107 5 µl washed sensor was placed a 22x22 mm VWR micro cover glass and dried under VWR hotplate at 120° C for 15 minutes. Meanwhile, the microwell chips
(containing of 3.2 µl well) were cleaned up with 70 % ethanol and ultrapure water and dried on the same hotplate as a cover glass for 15 minutes. A 5 µl sample (with OD of
0.005-0.009) obtained from the serial dilution of a log phase cell culture was placed in the cavity of the microwell chip. The Pt-porphyrin treated cover glass was placed with the sensor facing downwards over the well. Any air bubbles remaining in the sample well was removed by pressing the cover glass. Imaging was then carried out with a 10X air objective and 405 nm LED light source. The gated iStar-CCD camera (Andor) was used as the detector with images taken for lifetime measurements for about 20 - 45 minutes using optimized rapid lifetime determination (ORLD) to get the linear respiration rate.107
Consumption of oxygen by a cell was obtained in terms of PPM and converted it to attomole/ cell*sec.
61 Figure 2.06 Simple set up of sample placement into a well and sealed with a cover glass containing Pt-porphyrin. The analysis was done under a confocal microscope. (a) Aluminum plate, (b) Delrin structure to hold the media, (c) 22*22 mm micro cover glass, (d) Respiratory detection system (RDS) well with Pt-porphyrin, (e) Chip holder and (f) Quartz viewing window.
62 CHAPTER III
BACTERIAL PLASMOLYSIS OBSERVED THROUGH CONFOCAL MICROSCOPE
AS AN INDICATOR OF INNER MEMBRANE STAINING
3.1 Introduction
The reason for doing plasmolysis in my project is to confirm the localization of dye to the inner membrane of the live cells. A long term goal of the project is to look at the fluidity of the inner membrane and relate it to the cellular respiration that takes place there.
The increase in osmolality outside the cells drives water from the cytoplasm to outside the cell. Since the inner membrane is flexible, it will shrink away from the rigid outer membrane and form plasmolysis bays where labeling of the inner versus outer membrane can be distinguished.32 The shape of the cell after plasmolysis depends not only on the magnitude of the shock but also on the nature of solutes. Frequently used solutes for plasmolysis are sodium chloride, sucrose, glutamic acid or lysine hydrochloride, where their kinetics of the permeability would be different.85,125 NaCl can penetrate the outer membrane more quickly than the sucrose solution. To prevent osmoprotectors like potassium ions, betaine, proline, and glutamate from entering the cell or being synthesized, it is necessary to wash the cell culture with potassium/glucose-free media, which maintains the plasmolyzed state of a cell for a longer period.
Cell membrane integrity and cell viability are also important when studying cellular processes like cell elongation, electron transport chain (ETC) activity, and compositional 63 change and fluidity of the membrane. The hyperosmotic response by bacterial cells can be a physical indicator of cell viability.86,126,127 If the cells are dead, the cell membrane does not act as a semipermeable membrane between the cytoplasm and the outer environment and cytoplasmic materials could be leaked out and metabolic processes will have stopped.86
Dead cells will not respond to osmotic stresses. In Gram-negative bacteria like E. coli, there is thin peptidoglycan layer just below the outer membrane. This peptidoglycan layer normally determines the cell shape and prevents cell lysis by balancing with mechanical stress.87,128 Under normal conditions, the concentration of solutes in the cytoplasm is higher than outside the cell. If we provide a hyperosmotic shock, the osmotic pressure will change outside the cell, causing water efflux from the cytoplasm and pressure decreases across the semipermeable membrane. This decrease in pressure results in the detaching of the inner membrane from the cell wall, also called plasmolysis, and its shrinkage of the cell.85,129
In addition to utilizing plasmolysis to determine whether the inner membrane is being labeled, the overall labeling efficiency was investigated. Since the ultimate goal is to measure individual cells, there would be concerns if, for example, only half the cell population was labeled. Would a significant subpopulation potentially be missed from analysis because fluidity measurements could only be done on half the cells? I am searching for suitable fluorescent probes that are bright, stable, label almost all the cells, and that can insert to the inner membrane for staining. Depending on the nature of cells
(wild type/or mutant), variability in the structure of the peptidoglycan layer, and also the nature of dyes, I observed different labeling pattern which is discussed.
64 3.2 Materials and Methods
3.2.1 Plasmolysis Protocol
E. coli cells (DGC-102 or MG1655) were cultured initially in the LB medium from the freezer stock. Then the cells were shifted and grown overnight in MOPS media in starting with a 1:1000 dilution. In the following days, cells were subcultured in the same
MOPS media and grown until the cells grew up to mid-log phase (OD = 0.7-0.8). A 500 µl sample was taken, and different fluorophores designated to be tested (HBT/HBO Py-Cy,
BODIPYFL-C12, FM1-43) were added and left in the incubator as previously described.
To maintain the plasmolyzed state, the cells were centrifuged and washed three times with potassium and glucose-free MOPS buffer. To the final pellets, 250 µl of 0.6 M freshly prepared sucrose solution was added and left at room temperature for 4 minutes. Cells were then imaged under microscope slides coated with poly L-lysine solution to help adherence.
3.2.2 Labeling of Cells with Different Dyes
Several different dyes were used to test the percentage of cell labeling and their specificities to the inner membrane. BODIPYFL-C12 is a green lipid dye with a 12-carbon fatty acyl tail and a previous study showed that BODIPYFL-C12 stains preferentially to the inner membrane when the cells are treated with cephalexin.6 I studied the behavior of
BODIPYFL-C12 here both with and without cephalexin with a 1 µM final concentration of
BODIPYFL-C12. The dye was added to the sample solution and incubated for 120 minutes prior to imaging. The lipophilic styryl dye FM 1-43 was used at a 9 µM final concentration and was incubated for 20 minutes prior to imaging. HBT based Py-Cy was synthesized in 65 Dr. Pang’s lab and tested in the cells at a 2 µM of the final concentration following a 20 minutes incubation. While using the cephalexin, the overnight cell cultures were first diluted with 1:100 and the cephalexin (final concentration of 30 µg ml-1) was added and incubated for 2 hours. Nile red was purchased from Sigma-Aldrich and tested in the cells at a 5 µM of the final concentration and was incubated for 20 minutes for cell imaging. The
Excella E24 incubator shaker was used and maintained a constant temperature of 37o C for all labeling.
3.2.3 Imaging under Confocal Microscope
One of the characteristics of cells put under hypertonic stress with solutions like sucrose is that we do not need any fixatives for plasmolysis as long as they were plasmolyzed under potassium and glucose-free conditions (in this case 10X MOPS buffer).
The 10X buffer contains MOPS along with 0.01M freshly prepared FeSO4, 1.9 M NH4Cl,
0.02 M CaCl2.2H2O, 2.5 M MgCl2, 5M NaCl and other micronutrient stock solutions. After staining, washing, and osmotic shock with sucrose solution, the cells were put on the microscope slide covered with the coverslip and imaged under the Nikon A1 confocal microscope with 100X oil objectives. The slides and coverslips were dipped in a 0.001% poly-L-lysine solution for 10 minutes and dried prior to use. Cells were imaged immediately after putting on the microscope stage to protect from the quick recovery of cells from plasmolysis. The labeled cells were selected based on an imaginary threshold intensity below which the cells were not considered for analysis.
66 3.2.4 Defining Sucrose Solution for Plasmolysis
Determining the right sucrose concentration for best imaging the inner membrane separate from the outer membrane required some testing. The final concentration was selected, such that it should cause plasmolysis but should not completely damage the cells.
Two different strains of E. coli were used in the plasmolysis experiment. One was mutant
DGC-102 that has a deletion in the AcrB efflux pump and the other was the wild type
MG1655 cells. Both the cells were grown in MOPS media before plasmolysis. Cells were grown in all three phases; early log phase (OD ~ 0.1-0.2), mid-log phase (OD ~ 0.7-0.9) and stationary phases (OD > 2.5). Cells were then plasmolyzed with a hypertonic solution of 0.2 M, 0.4 M, 0.6 M, and 0.8 M sucrose solution. The formation of the plasmolyzed bays was monitored under high magnification 100X oil objectives.
3.3 Results and Discussion
Extent of labeling
For the wild type MG1655 and mutant DGC-102 E. coli cells growth media, cell growth stage, and fluorescent probe were all studied. I found that both the nature of the media and strains of E. coli played a role in the proportion of cell labeling. The cells that were grown at minimal media, MOPS media, have a higher percentage of labeling while the cells grow at energy-rich media, LB media, have decreased level of cell labeling
[MOPS: 75 ± 3.55 (HBT Py-Cy)/ 43 ± 3.48 (BODIPYFL-C12) and LB: 20 ± 1.99 (HBT
Py-Cy/ 5 ± 1.77 (BODIPYFL-C12), details on table 3.1]. One possibility deals with the thickness of the peptidoglycan layer. When cells are grown in carbon-rich media, studies 67 have found that the peptidoglycan layer is thicker (compared to growth on minimal media), which could inhibit the entry of dye molecule into the inner membrane. Alternatively, drug efflux pumps are energy-dependent processes that have been shown to pump out some fluorescent molecules.130 In the rich LB the cells could have more energy to devote to exporting dyes from their membrane. The DGC-102 strain, which has a deletion in the
AcrB energy-dependent efflux pump, labels better than the wild type MG1655. Since drug efflux pumps are the protein complexes that are present in the membrane and help to transport the ions or dyes or drugs through its channels.131 E. coli has an RND type efflux pump antiporter, which has AcrA, AcrB, and TolC tripartite complex.132 AcrB is attached to the inner membrane, which has a major role in substrate specificity. TolC is an outer membrane channel and AcrA acts as a linker between AcrB and TolC.133 The detailed cell labeling with different dyes on wild type and AcrB mutant is given in table 3.0.
TABLE 3.0 PERCENTAGE OF CELL LABELING USING DIFFERENT DYES IN THE
SAME MOPS MEDIA. TOTAL OF 1000 CELLS WAS TAKEN FOR ANALYSIS
(BIOLOGICAL REPLICATES OF FIVE DIFFERENT DAYS). SD = STANDARD
DEVIATION
Dyes in MOPS media % of cells % of cells (DGC-
(MG1655) labeled 102) labeled
(± SD) (± SD)
68
HBT Py-Cy 75 ± 3.55 84 ± 1.28
BODIPYFL-C 43 ± 3.48 46 ± 3.56 12
FM1-43 95 ± 2.15 95 ± 2.08
Nile red 45 ± 2.20 65 ± 2.65
Figure 3.0 Examples of MG1655 cell labeling using three different dyes; HBT Py-Cy, BODIPYFL-C12, and FM1-43 from left to the right respectively. Scale bar indicates 1µm. The certain threshold for intensity was set up so that some of the cells which show the very low intensity of dyes were discarded in the reading.
Table 3.0 shows the percentage of cells labeled with different dyes when the experiment was carried out in MOPS minimal media and glucose as the carbon source.
Because I planned to look at the variability of diffusion coefficients across individual cells, it is necessary to find a fluorophore that will readily label most of the cells. For that, I checked the labeling pattern to each fluorophore through the random selection of cells in the microscope for five days. Each day, the cells were grown (subcultured) from their
69
previous culture and grew at the same OD (between 0.3 to 0.6). Cells were labeled with the above-mentioned dyes and observed under the microscope. In the NIS Elements AR computer analysis, cells were scanned (pixel frame was zoomed 4X magnification) so that in each frame, an average of 20 cells were located. By moving of piezo stages, we selected
10 random scanning frames (with constant zooming) so that each day we counted off the average of 200 cells on the exponential phase. After that, the total number of cells that were labeled with the dye and the cells without labeling were counted. The same process was repeated for five different days. From there, I determined the total percentage of labeling of cells. A threshold intensity of dye was set up below which the cells were considered as
“not labeled”, since the fluorescence would be insufficient for performing FRAP experiments. A section of the frame is given in fig 3.0 where non labeled cells are put under the circle.
TABLE 3.1 MG1655 CELLS GROWN IN TWO DIFFERENT MEDIA AND
LABELLED WITH TWO DIFFERENT DYES TO COMPARE THE RATIO OF
LABELLING. TABLE SHOWS THE TOTAL PERCENTAGE OF LABELLING OF
CELLS. 1000 CELLS WAS TAKEN FOR (TOTAL REPLICATES OF FIVE DAYS)
Media HBT Py-Cy BODIPYFL-C 12
% labeling % labeling (± SD) (± SD)
70 MOPS 75 ± 3.55 43 ± 3.48
LB 20 ± 1.99 5 ± 1.77
Table 3.1 shows the growth of cells at two different media and their different behavior on cell labeling when different dyes are used. The inner membrane labeling depends on the structure of the peptidoglycan layer and its thickness because it is interconnected with the outer and inner membrane. The intensity of fluorescently labeled bacterial cell changes by the alteration in structural organization and conformation of the cell envelope.30 So, when using the different media ( carbon-rich LB or minimal MOPS), the polypeptide chain growth rate would be different,18 which causes differences in the percentage of cell labeling. Moreover, large variation in pH effects on cell growth and fluorescence intensity. LB and MOPS have different pH level. A large variation in pH also affects the carbon metabolism, mutation frequency and modification of proteins and nucleic acids (glycation).134
To carry out the experiment (table 3.1), the protocol for cell replicates, labeling and selection of cells on the scanning frame in the microscope were similar to that of previous table 3.0. The protocol was also the same for replicates measurements in table 3.2.
71 TABLE 3.2 DGC-102 CELLS GROWN IN DIFFERENT MINIMAL MEDIA AND
LABELED WITH HBT PY-CY. TOTAL NUMBER OF CELLS COUNT WAS 1000
(TOTAL REPLICATES OF FIVE DAYS).
Carbon Source % of HBT Py-Cy labeled ± SD Glucose 84 ± 1.28 Glycerol 93 ± 2.00 Succinate 95 ± 0.66
Table 3.2 shows the cells grown on MOPS minimal media with different carbon sources. Here glucose (0.2%), glycerol (0.2%), and succinate (20 mM) were added to
MOPS media differently and the growth curve was determined. The % labeling of cells was measured for HBT Py-Cy. When cells were grown on glycerol or succinate as a carbon source, cells were more highly labeled. Labeling on glycerol containing media (93 ± 2.00) or succinate containing media (95 ± 0.66) showed better results than glucose-containing media (84 ± 1.28). However, their cell doubling time was much longer (cells reached to mid-log phase only after 36 hours; growth curve was shown in supplementary section fig
S3.4), so I used glucose added MOPS media in further analysis.
Plasmolysis to confirm localization of dye
Since results showed that FM 1-43 and HBT Py-Cy (Table 3.0) labeled cells the best, MG1655 and DGC-102 cells were cultured in MOPS minimal media with the goal of determining which membrane the dyes labeled. When they were exposed to the hypertonic sucrose solution, it caused the different types of plasmolysis bays to form depending on 72 the sucrose concentration and the stage of cell growth (mid-log or stationary) based on the different invagination pattern found in the cells. Plasmolyzed cells could be normal or severe (fig 3.01). When the cells were put in low osmolality of between 0.2-0.4 M sucrose,
I did not see any invagination. However, when the cells were placed in 0.6 M sucrose solution, there was the separation of cytoplasmic membrane from the cell wall
(peptidoglycan layer) and invagination of cytoplasmic membrane at or around the center has occurred (fig 3.01). Severe plasmolysis was observed with concentrations of 0.8 M or higher sucrose, where I could see more than one invagination site or significantly more shrinkage of cells. It could be due to cytoplasmic collapse or sometimes, due to the cells were dead.88 Some studies used formaldehyde for the fixation of plasmolyzed cells, but I did not find that necessary.
Figure 3.01 Plasmolysis of DGC-102 cells with 0.6 M sucrose solution at mid- log phase (left) and stationary phase (right), observed under a confocal microscope under 561 nm filter (red) and widefield images (colorless). Cells were labeled with HBT Py-Cy. Scale bar 1 µm. 73 Stationary phase cells attended to show more fluorescence intensity at the poles of the cells following plasmolysis (red balls inside cells), which could be indicative of non- homogenous accumulation of the dye or lipids and proteins at stationary phase, although I have no concrete explanation. Plasmolysis analysis indicated that cells in the stationary phase were still labeled primarily at the inner membrane with the HBT based Py-Cy. The same pattern was observed for the MG1655 cells when treated with the HBO based Py-Cy, as shown in the supplementary section (fig S3.2 and S3.3).
Plasmolyzed bays were also different depending on the growth media used. Cells grown on LB media did not show any plasmolysis spaces, however, the cells grown at
MOPS media showed about 45-50% of cells with invaginations ( at 0.6M sucrose solution).
It could be due to the fact that the cells which are grown under LB media condition had a higher prevalence of osmoprotectants than cells grown on minimal media. Alternatively, the turgor pressure is higher in the cells grown in rich media than in minimal media.
During my experiment, bacterial cells in mid-log phase showed normal plasmolysis pattern while the cells in the stationary phase were severely plasmolyzed. The stationary phase cells were more susceptible to the hypertonic solution and the osmotic stress was decreased as well, which drives the cell towards shrinkage of the cytoplasm. This caused the lipids and protein depositions at the poles of the cells (fig. 3.01).
BODIPYFL-C12 was considered for use as an inner membrane probe based on its use in by Nenninger, A. et al. 2014, where they reported no issues with staining the inner
74 membrane with BODIPYFL-C12 in all cells. However, they treated cells with low concentrations of cephalexin which will disrupt the peptidoglycan layer and lead to elongated cells. When I attempted to label normal cells with BODIPYFL-C12 I observed inner membrane staining when applied with a high osmotic shock (fig 3.02), but only 43% were stained with the fluorophore. Treatment with cephalexin (final concentration of 30 µg ml-1) was necessary to replicate their result, further suggesting a role for the peptidoglycan layer in preventing dyes from reaching the inner membrane.
Figure 3.02 DGC-102 cells labeled with BODIPYFL-C12 and plasmolyzed with 0.6 M sucrose solution. Scale bar represents 1 µm.
Many papers on FM 1-43 have still contradicting findings of the localization/specificity of the dye in bacterial cells. Initially, Cochilla, A. et al., 1999, used this probe in eukaryotes for studying endocytosed membranes. Due to the cationic head group, this dye was found to be cell impermeable, making it ideal for studying the dynamics of endocytosed vesicles in eukaryotic cells.72 The initial FM 1-43 staining study in E. coli by Woldringh found that it stained the inner membrane, as determined by plasmolysis.
Future studies showed that FM 1-43 could stain the outer as well as cytoplasmic membranes for some cyanobacteria, another Gram-negative bacteria.135 Due to its high labeled cell percentage and brighter fluorescence, I also studied whether FM 1-43 could be
75 a good probe for my inner membrane fluidity studies. However, I suspected based on my
BODIPYFL-C12 results that it was important to examine the exact growth condition from the Woldringh study to determine whether it acts as an inner membrane stain under normal growth conditions. They used elongated cells as well, therefore I stained the cells with FM
1-43 and plasmolyzed them to check for dye localization. An inner cell membrane probe would only stain the plasmolysis bays, but I found more staining of the outer cell membrane or both the outer cell membrane and inner membrane (fig 3.03a). Co-staining the cells with both FM 1-43 and HBT Py-Cy (fig 3.03b) showed from plasmolysis that FM 1-43 was staining both membranes while HBT Py-Cy stained the inner membrane as expected (fig
3.03c). Finally, I treated the cells with cephalexin prior to staining with FM 1-43. Since cephalexin is a beta-lactam ring containing antibiotics that cross the outer membrane of the bacterial cell and covalently bind to the penicillin-binding proteins (PBP) in the inner cytoplasmic membrane. The PBPs are mainly used for the synthesis of peptidoglycans, which is a basis of the bacterial cell wall.136 Thus, if beta-lactam binds to PBP, there would be inhibition of cell wall formation, and the new cells do not form. After that, the cells were plasmolyzed where I observed the inner staining of the membrane (fig 3.03d) which could be due to the disruption of the peptidoglycan layer and easier access for the dyes to enter to the cytoplasmic membrane.
76 Figure 3.03 Staining of DGC-102 cells with FM1-43 and plasmolysis was carried out at different conditions (a-d). Bacterial cells were labeled with FM1-43 and kept in a hypertonic solution of 0.6 M sucrose (a), cells were co- stained with FM1-43 (green) and HBT Py-Cy (red) (b), co-stained cells were plasmolyzed (c), and cells were first treated with cephalexin and the cells were labeled and plasmolyzed(d). Scale bar represents 1µm.
3.4 Conclusion
Based on the percentage labeling of cells with different fluorophores, I found that
HBT/HBO Py-Cy and FM 1-43 probes are better for cell staining other than BODIPYFL-
C12 and Nile Red. However, I am looking for the best probes that label the inner membrane
77 for fluidity measurements. For that purpose, only HBT Py-Cy or HBO Py-Cy could be used because these dyes preferentially stain the inner membrane and have a good cell labeling percentage. Even though FM1-43 has better brightness, higher stability and higher % cell labeling, it was found that the dye does not have any specificity to the inner membrane.
The specific inner membrane labeling by FM 1-43 is possible only when the cells are treated with cephalexin as shown in fig 3.03 (d), which would not work for doing joint membrane fluidity and cellular respiration measurements on the same cell. Inner vs outer membrane staining was confirmed through plasmolysis, which gives distinct separation of inner membrane from the outer layer. I eliminated the use of Nile Red due to its lower brightness, which could be difficult to carry out fluidity measurement in the future by
FRAP. BODIPYFL-C12 was eliminated because less than half of normal cells were labeled, which leads to concerns about missing a subpopulation of cells in the analysis. I selected MOPS minimal media rather than LB to grow the cells because fluorophores stain the cells at a significantly higher percentage than on LB (table 3.1). This could be due to differences in the structure of the peptidoglycan layer or structural modification of membrane composition of cells on minimum carbon source (0.2% glucose) containing
MOPS media, the dyes stain the cells in a significantly higher ratio.
3.5 Supplementary Information
Percentage labeling of cells (replicates of 5 different days)
DGC-102 labeling (total 1000 cells): MOPS media
78 N= 200 HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red (each day)
Day 1 172 92 190 130 Day 2 167 99 194 123 Day 3 171 83 187 128 Day 4 165 87 197 139 Day 5 169 102 186 133 Mean 168.8 92.6 190.8 130.6 S.D. 2.56 7.12 4.17 5.31
MG1655 labeling (total 1000 cells):MOPS media
N= 200 HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red (each day)
Day 1 150 80 191 92 Day 2 144 78 184 96 Day 3 156 82 197 83 Day 4 140 89 189 87 Day 5 159 97 193 90 Mean 149.8 85.2 190.8 89.6 S.D. 7.11 6.97 4.31 4.41
TABLE S3.0 LABELING OF CELLS WITH DIFFERENT DYES IN MOPS MEDIA
OF A SINGLE DAY AT DIFFERENT GROWTH PHASE
Early log phase: With DGC-102 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 200 200 200 200
79 Labelled cells 160 80 180 66 % of labelling 80 40 90 33
With MG1655 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 200 200 200 200 Labelled cells 140 88 192 80 % of labelling 70 44 96 40
Mid log phase: With DGC-102 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 200 200 200 200 Labelled cells 172 92 190 130 % of labelling 86 46 95 65
With MG1655 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 200 200 200 200 Labelled cells 150 80 191 92 % of labelling 75 40 95 46
Stationary phase: With DGC-102 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 400 400 400 400 Labelled cells 368 280 364 340 % of labelling 92 70 91 85 80 With MG1655 cells
HBT Py-Cy BODIPYFL-C12 FM1-43 Nile red Total cells 400 400 400 400 Labelled cells 360 320 372 220 % of labelling 90 80 93 55
Cell Imaging using different dyes
Figure S3.0 Labeling of MG1655 cells with different dyes in its stationary phase.
Plasmolysis of cells
81 Figure S3.1 Plasmolysis of DGC-102 cells with 0.8M sucrose solution. Cells were labeled with HBT Py-Cy (red) at log phase and FM1-43 (green) at the stationary phase.
Figure S3.2 Plasmolysis of MG1655 at 0.6M sucrose solution. Cells were labeled with FM1-43 (green) and HBO Py-Cy (red).
82 Figure S3.3 plasmolysis of MG1655 at 0.4 M NaCl solution. Cells were labeled with HBO Py-Cy. Bar, 1 µm.
Growth rate of DGC-102 cells with different carbon sources
1.8 DGC-102 with MOPS (succinate) 1.6 1.4 Generation time = 2.92 hour 1.2 1 0.8 OD 0.6 0.4 0.2 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 Time (hour)
83 0.8 0.7 DGC-102 with MOPS (glycerol) 0.6 Generation time = 3.40 hour 0.5 0.4
OD 0.3 0.2 0.1 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Time (hour)
1.4 DGC-102 with MOPS (glucose) 1.2
1 Generation time = 1.06 hour 0.8
OD 0.6
0.4
0.2
0 0 1 2 3 4 5 6 7 Time (hour)
Figure S3.4 Growth curve of DGC-102 cells under different carbon sources in MOPS media.
84 CHAPTER IV
MEASUREMENT OF MEMBRANE FLUIDITY USING PY-CY AND BODIPYFL-C12
DYES AND LOOK AT THE EFFECT OF TEMPERATURE ON DIFFUSION IN E.
COLI CELLS
4.1 Introduction
The diffusion of proteins, lipids, and small molecules in the membrane is related to the fluidity of the membrane, which mainly depends on the lipid composition and the temperature.38 Depending upon the temperature at the time of measurement, lipids may exist in two different forms: a liquid crystalline state at a higher temperature and a viscous gel crystalline state at a lower temperature.6 It means there exists a phase transition between the two states, and fluidity is maintained through homeoviscous adaptation.6,38,137 It is found that when the temperature is lowered below the transition temperature, bacteria will adapt to an increase in the ratio of unsaturated fatty acids and/or shorten chain length. Both will produce a more fluid membrane at lower temperatures.138 This is because the gel crystalline state forms from the alignment of hydrocarbon tails perpendicular to the membrane surface. This “freezes” them in place. The unsaturated (with double bond) lipids help disrupt this structure because the kink in the hydrocarbon tail helps disrupt this rigid tail structure which helps to maintain the fluidity even at lower temperatures. At higher temperatures, lipids adapt more saturated forms and packed together due to
85 dispersion forces between hydrocarbon chains. The larger size and Vander Waals forces between hydrocarbons in lipid balance its fluidity.139
Chazotte, B. et al., 1991 found that the diffusion rate of phospholipids and ubiquinone are the same in a liquid crystalline state in a lipid bilayer membrane since phospholipids are essential solvent for protein and lipoidal molecules.51 In my project, I do not directly measure the rate of ubiquinone movement that involves ETC for electron carriers. However, by measuring the diffusion of a lipid membrane, we could correlate it with the movement of ubiquinone, as explained by Chazotte, B. et al., 1991. Since fluidity of a membrane and cellular respiration is interrelated.54 I expect the increase in the respiration rate to be related to the on increasing the fluidity of a membrane in individual cells. In this chapter, I demonstrate that the Py-Cy fluorescent molecule can track expected changes in membrane fluidity. Since the fluidity of a membrane is dependent on temperature, as well as membrane lipid composition,6,54 one method involves using temperature changes to alter the viscosity of the inner membrane. When I grow the cells at a higher temperature and then measure the diffusion at a lower temperature, the viscosity of the membrane increase, so we would expect a slower diffusion coefficient. The second method involves genetically modifying the cells to alter the membrane composition or give fluorescent reporters of gene expression that helps indicate the membrane composition.
Herein, I illustrate the bacterial membrane fluidity by staining E. coli using lipid probes, like BODIPYFL-C12; a commercial dye and 4-(3-(benzothiazol-2-yl)-2-hydroxy-
5-methylstyryl)-1-methyl pyridin-1-ium cyanine (HBT Py-Cy); a dye our collaborator 86 synthesized. While I previously determined that BODIPYFL-C12 would not be the best probe for possible joint fluidity and respiration rate measurement, it was included in the fluidity measurements because of its previous use as an indicator of membrane fluidity in
E. coli6 and so it could be compared with my preferred fluorophore. The preferred fluorophore HBT Py-Cy has HBT [2-(2’-hydroxyphenyl)Benzothiazole] unit at the hydrophobic end which gives strong ESIPT (Excited State Intramolecular Proton Transfer) emission upon entering the membrane structure. Because of the coupling pyridinium cyanine unit with HBT unit via meta phenylene ring in the structure of Py-Cy, the emission of HBT unit is shifted bathochromically to a near infra-red region (682 nm) with very large Stokes shift ( 287 nm), which is more advantageous than BODIPYFL-C12 with very small Stokes shifts ( 10 nm) for bioimaging, particularly with multiple labels.75,140
The properties of HBO Py-Cy are similar to HBT Py-Cy in all respect but the relative stability of HBO Py-Cy is slightly higher when cells are stained and imaged under the microscope. So in my study, I also use HBO based Py-Cy for those cases where a prolonged experiment was carried out.
87 Figure 4.0 Structure of HBT coupled Py-Cy, HBO coupled Py-Cy and
commercial BODIPYFL-C12 (in a box).
The diffusion is determined using FRAP.141 While the diffusion coefficient of membrane lipids and proteins could be obtained through other microscopic techniques such as fluorescence correlation spectroscopy (FCS) or single-molecule tracking (SMT), FRAP was the easiest on the microscope setup.142 Fluorophores are photobleached with high
88 intensity of laser light and the intensity profile tracked over time. The FRAP experiment was carried out using a confocal laser scanning microscope.143 In the FRAP technique, a region of interest (ROI) of the cell is selected, and a high-intensity pulse laser beam is passed where the fluorophores, BODIPYFL-C12 and HBT/HBO Py-Cy in our study, are photobleached. The entire bacterial cell is imaged over time to look at the recovery of fluorescence which is resulted from the diffusion of dyes from the non-bleached region into the region of interest.144 This process ultimately shows the movement of cellular components from the non-bleached area to the bleached area. Thus, two parameters can be tracked through FRAP; one is the rate of mobility which is related to diffusion time, and the other is the mobile fraction of fluorescent components.55 My focus was on the former.
Since E. coli cells, as Gram-negative bacterial membranes, can be thought of as have three different layers (outer membrane, a peptidoglycan layer, and cytoplasmic membrane), we need to make sure that we are studying diffusion in the inner cytoplasmic membrane where the ETC resides. In ETC, there is the occurrence of an electron carrier component called as ubiquinone, which carries a vital role in cell respiration. To study whether the cells are stained on the plasma membrane, plasmolysis was carried out.6 If
BODIPYFL-C12 and HBT/HBO Py-Cy localize to the plasma membrane, then labeled membrane invaginations are seen after plasmolysis. Here, D-sucrose6,32 and NaCl salt solutions136 are used to increase the osmolality of the surrounding media and cause plasmolysis. In our study, using the lipid probes, it was found that these probes inserted
89 into the inner membrane, which shows the dark plasmolyzed bays6 in the plasmolyzed region (more details on chapter 3).
4.2 Materials and Methods
4.2.1 Cell Staining
The entire experiment was carried out using both E. coli mutant strain DGC-102 and wild type MG1655. The DGC-102 strain has a deletion in the AcrB efflux pump145 that allowed for improved staining of the cytoplasmic membrane. BODIPYFL-C12 is a lipophilic dye obtained from ThermoFisher used to stain the lipids in the inner cytoplasmic membrane. The HBT pyridinium cyanine showed a large Stokes shift, which had absorption and emission wavelengths in nm at 395/682. Likewise, HBO pyridinium cyanine had ab/em wavelengths in nm at 415/675. 10 mM BODIPYFL-C12 was used in the culture solution to make the final concentration of 1 µM, and 1 mM HBT/HBO pyridinium cyanine was used in the cell culture (final concentration 2 µM).
4.2.2 Preparation of Specimen
Bacterial cell cultures were grown overnight at 30o C or 37o C in the incubator using minimal media (MOPS) under constant shaking (200 rpm). 100 µg/ml of the final concentration of kanamycin was used for DGC-102 cell growth. The next day, cells were sub-cultured in the same media in a 1:100 ratio and 0.5 µL BODIPYFL-C12 was put in the
90
culture and kept in the incubator for about 4 hours to get the log phase (OD 0.3-0.6). For the case of HBT and HBO Py-Cy, only after 4 hours of cell growth, the fluorophore was added and kept in an incubator for 20 minutes before imaging. Microscope slides and coverslips were prepared by dipping them into the 0.001% poly-L-lysine solution for 10 minutes and then allowing them to dry to improve cell adhesion. According to the need, the temperature chamber was adjusted to 30o C and 37o C before imaging.
4.2.3 FRAP Measurements
FRAP measurements were done on a Nikon A1 confocal laser scanning microscope as previously described (2.8). For BODIPYFL-C12 labeled cells, a 488 nm solid-state laser was used to excite the fluorophore and emission was detected using a 500-550 bandpass filter. For the pyridinium cyanines, I used a 405 nm laser for excitation and a 663-738 nm bandpass filter for collecting the emission. After FRAP measurement, a one-dimensional fluorescence profile was obtained using a program written in MATLAB which output the diffusion coefficient of the fluorophore in individual cells.
4.3 Results and Discussion
4.3.1 Staining with Fluorescent Probes into the Cells
Staining of E. coli DGC-102 cells with the Pang lab synthesized dye (HBT/HBO
6,38,146 Py-Cy) was compared with commercial lipid dye (BODIPYFL-C12). The staining intensity was similar, but I found that the percentage of cells labeled by both HBT Py-Cy and HBO Py-Cy to be higher than of BODIPYFL-C12. It is easy to see when cells are co- 91
stained with both BODIPYFL-C12, green, and HBT Py-Cy, red (fig 4.02). All ten of the visible cells are stained with HBT Py-Cy, while only three are stained with BODIPYFL-
C12. The localization of the probes to the inner membrane had been previously confirmed by plasmolysis (fig 3.01 and 3.02).
Figure 4.02 Staining of DGC-102 cells with BODIPYFL-C12 (green), HBT Py-Cy (red), and under brightfield view. Cells were co-stained and observed under different filter channels (green at 500-550 nm and red at 663-738nm emission filters). Scale bar represents 2 µm.
4.3.2 FRAP Technique to Look at the Mobility of Lipids
In order to determine the diffusion coefficients for HBT/ or HBO Py-Cy and compare it with BODIPYFL-C12, cells were grown to log phase (OD 0.3-0.6) and labeled with HBT/HBO Py-Cy and BODIPYFL-C12 separately. The active movement of the fluorescent molecules to the bleached region (ROI) gives us information about the membrane components. A more viscous membrane or significant binding events would give slower diffusion coefficients by this method, while with a more fluid membrane, there would be a higher measured diffusion coefficient. Fig 4.03 illustrates an example of the
92 bleached area (indicated by arrow) and subsequent movement of fluorescent molecules from the non-bleached area to the bleached region (ROI) in the post-recovery images.
Figure 4.03 FRAP analysis of DGC-102 cells using BODIPY FL-C12 (green) and HBT Py-Cy (red). Arrow indicates the region of interest (ROI) for bleaching. Left and right images are before bleaching and post-recovery, respectively. Bar, 2 µm.
4.3.3 Effect of Temperature on Diffusion
To determine if both Py-Cy dyes were viable fluidity probes, their diffusion coefficient was compared to that of BODIPYFL-C12 under conditions that would increase the viscosity of the inner membrane. This is similar to the experiment previously done by
Nenninger, A. et al., 2014, with BODIPYFL-C12. All cells were grown and labeled with 93 the respective dye at 37o C, but FRAP measurement was carried out in the microscope
o o chamber at either 37 C or 30 C. HBT/HBO Py-Cy (n = 120) and BODIPYFL-C12 (n =
146) all showed decreased diffusion coefficient when shifted from 37o C to 30o C. The differences in the diffusion at two different temperatures were statistically tested through t-test. HBT Py-Cy (t = 0.0441), HBO Py-Cy (t = 0.0398) and BODIPYFL-C12 (t = 3.53 *
10 -8) clearly showed the variation in fluidity of dyes in the membrane at two different temperatures. All conditions showed a wide distribution in measured diffusion coefficients, indicating wide cell-to-cell variability [HBT Py-Cy; Mean D (±SD) = 0.70 ± 0.39 µm2/s,
2 HBO Py-Cy; Mean D (±SD) = 0.71 ± 0.33 µm /s and BODIPYFL-C12 ; Mean D (±SD) =
0.82 ± 0.43 µm2/s at 37o C]. The fluidity of the membrane should decrease in the cells
o measure at 30 C and both Py-Cy and BODIPYFL-C12 showed a shift in the diffusion coefficient to lower values at the lower imaging temperature compared to those imaged at
37o C [HBT Py-Cy; Mean D (±SD) = 0.53 ± 0.17 µm2/s, HBO Py-Cy; Mean D (±SD) =
2 2 0.52 ± 0.18 µm /s and (±SD) and BODIPYFL-C12 ; Mean D (±SD) = 0.33 ± 0.21 µm /s at
30o C]. This may be due to the shift in the phase transition from liquid crystalline state to viscous gel state.6
94 16% 24% A HBO Py-Cy at 37o C B HBO Py-Cy at 30o C 20% 12% 16%
8% 12%
8%
percentage of cells 4% percentage of cells 4%
0% 0% 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 D (m2/s) D (m2/s)
16% 24% C o HBT Py-Cy at 37 C D HBT Py-Cy at 30o C 20% 12% 16%
8% 12%
8% percentage of cells 4% percentage of cells 4%
0% 0% 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 D (m2/s) D (m2/s)
16% E o F BODIPYFL-C at 30o C BODIPYFL-C12 at 37 C 36% 12 32%
12% 28%
24%
8% 20% 16%
12% percentage of cells 4% percentage of cells 8%
4%
0% 0% 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 D (m2/s) D (m2/s)
Figure 4.04 Diffusion coefficient of HBT/HBO Py-Cy (red) and BODIPYFL- o o C12 (green) in DGC-102 cells at 37 C and 30 C. Growth temperature here is 37o C. [p (t<=t) one tail = 3.98 E-02 (HBO Py-Cy), 4.41 E-02 (HBT Py-Cy) and 3.53 E-08 (BODIPYFL-C12)](p=0.05).
95 4.3.4 Photostability of Fluorophores
The photostability of the small fluorophores depends on how fast the dye molecules react with the quenchers like oxygen, amino acids side chains, dye-dye interaction in the excited states, and the environment of the solvents to cause photobleaching. The whole mechanisms take place in the excited states.147 Qinsi Z. et al., 2017 determined for cyanine dyes that photosensitized singlet oxygen is a primary reagent for photobleaching. Structural modification of the dyes and reducing the conversion of singlet to triplet state through intersystem crossing are the ways to increase the photostability of the dyes. Between HBT
Py-Cy and BODIPYFL-C12 in our study, HBT Py-Cy shows a slightly lower but almost similar range in the stability with the commercial dye in fluorescence imaging through 1- hour time period. However, after 50 minutes, I find that the BODIPYFL-C12 photo bleaches much quickly than HBT Py-Cy which gives the sharp decrease in intensity as shown in the fig 4.05. The stability can be slightly increased on both dyes by reducing the gain and brightness of fluorescence during real-time cell imaging.
HBT Py-Cy 2500 BODIPYFL-C12
2000
1500
1000 Mean Intensity Mean
500
0 5 10 15 20 25 30 35 40 45 50 55 60 65 Time (min)
96 Figure 4.05 Relative intensity of HBT Py-Cy and BODIPYFL-C12 carried out for individual cells. Cells were scanned for 8 seconds, and images were captured. Images at different frames were observed for 1 hour (replicates for 3 different samples).
For the measurement of the stability of dyes, the cells were first labeled with HBT
Py-Cy and BODIPYFL-C12. The temperature of the microscope incubator was adjusted to
37o C (same as growth temperature) and imaging of the cells was carried out. Individual cells were selected through 10X magnification and scanned for 8 sec. Then the intensity of the cell was measured. Another subsequent image of single-cell was taken and scanned for
8 sec and the process was repeated for 1 hour. The intensity vs time provides information about how long a dye could be stable without much photobleaching.
Between the two py-cyanines, I found that HBT has a higher intensity than HBO form. But the HBO Py-Cy has relatively more stable (less photobleached) when imaging under the microscope (fig 4.07) and images in fig 4.06. Thus, for imaging the cell, a prolonged experiment was carried out under HBO Py-Cy staining, while for those cases where we need brighter images, cells were stained with HBT Py-Cy. On diffusion measurement, I didn’t see any significant differences in their values (t-test for HBT Py- Cy
( 0.0441) and HBO Py-Cy ( 0.0398) between the two Py- Cy (fig 4.04).
97 Figure 4.06 Staining of cells with HBT/HBO Py-Cy separately and looked at the relative intensity over 1 hour time period.
HBT Py-Cy HBO Py-Cy 2200 2000 1800 1600 1400 1200 Relative intensity 1000 800 600 400 0 10 20 30 40 50 60 Time (min)
98 Figure 4.07 Comparison of relative intensities between HBT and HBO Py-Cy.
4.4 Conclusion
Inner membrane labeling showed that HBT Py-Cy would be an excellent alternative to BODIPYFL-C12. In many cases (like diffusion measurement and plasmolysis experiments), I found that the labeling of HBT Py-Cy was better than other dyes (FM1-43 or Nile red). We could use this dye not only as a membrane probe but also as a mito-tracker dye in many eukaryotes. Due to its large Stokes shift, there would be less chance of crossover of the dye to different filters. Thus simultaneous FRAP measurements with other expressed proteins like mCherry or GFP would be possible.
Since, based on my experiments, it was observed that HBT Py-Cy is a viable membrane fluidity probe. When the cells were stained with HBT Py-Cy and carried out the
FRAP measurements, it was found that the lower diffusion coefficients at a decreased temperature from the growth temperature when compare between 37o C and 30o C, which showed the significance of this dye as a lipid probe for inner membrane targeting. These
6 results agree with the results of BODIPYFL-C12 calculated by Nenninger, A. et al. 2014.
However, there are still some challenges to measure the diffusion of a membrane accurately. Simultaneous study of both protein and lipids using lipid and protein tagged dyes would give more precise measurements. Because I have not looked at the crowding effects of macromolecules in between lipids and proteins simultaneously during their movement.
99 4.5 Supplementary Information
Diffusion measurement of different cells
Initially, I carried out the FRAP measurement with wild type E. coli K-12 cells and stained with BODIPYFL-C12. However, I could not get the consistent results since only about half of the cells were stained with BODIPYFL-C12. I also measured the diffusion measurement with FM1-43 with healthy cells. However, it showed relatively lower diffusion coefficients (fig S4.0) which could be due to the staining of cells on rigid outer membrane. So, I did not use K-12 cells on my further analysis.
I carried out the simultaneous FRAP measurement with GFP expressed DGC-102 cells stained with HBT Py-Cy. However the diffusion of GFP protein was found to be a much lower than lipid dyes which supports the fact of earlier study done by Nenninger et al., 2014.6
Figure S4.0 Diffusion measurement of BODIPYFL-C12 and FM1-43 in K-12 cells growing at 37o C and measured at 30o C.
100 Figure S4.1 Diffusion measurement of DGC-102 cells expressed with GFP grown at 37o C. Cells were labeled with HBT Py-Cy, and FRAP measurement was done individually for both HBT Py-Cy and GFP at given temperatures.
Co-staining
Co-staining is the spatial overlapping between different fluorophores in cell imaging, which have their own excitation and emission wavelengths.9,148 Co-labeling between HBT Py-Cy and BODIPYFL-C12 is more favorable due to a large Stokes shift of
HBT Py-Cy (395/682 nm) than BODIPYFL-C12 (503/512 nm). A large Stokes shift in HBT
Py-Cy is due to excited-state intramolecular proton transfer (ESIPT) and internal charge transfer (ICT). So, both dyes are very useful to determine whether lipids and proteins are intact in the same subnuclear structures or within the same inner cytoplasmic membrane domains. However, due to the low resolution of the microscope to observe outer vs inner membrane, the colocalization of the dyes (HBT Py-Cy and BODIPYFL-C12) on inner membrane were confirmed through plasmolysis (chapter 3).
101 Figure S4.2 Co-staining of DGC-102 cells with BODIPYFL- C12 and HBT/HBO Py-Cy. Scale bar represents 2 µm.
102 CHAPTER V
ROLE OF FABA/FABB GENES ON UNSATURATED FATTY ACID SYNTHESIS
AND EFFECT OF LIPID COMPOSITION ON BACTERIAL CELL RESPIRATION
5.1 Introduction
The different phospholipids present in the lipid bilayer determines the physical characteristics such as viscosity of the cell membrane in bacteria.54 The ratio of saturated to unsaturated acyl chains determines the fluidity of a membrane when the temperature is kept constant.54,149 E. coli has higher phospholipid unsaturated acyl chains when growing at a lower temperature. In the previous chapter, I discussed membrane fluidity by varying the imaging temperature and how the preferred fluorophores could track those viscosity changes. In this section, I measure the effect of membrane composition on the fluidity of a membrane under constant temperature (at 37o C) and show that HBT Py-Cy tracks expected fluidity from the membrane composition. Two genes that affect saturated and unsaturated fatty acid synthesis (and, therefore, membrane composition) are fabA and fabB.
Fluorescence reporters of gene expression will be utilized to demonstrate that they can indicate the relative levels of saturated vs unsaturated lipids in individual cells. Finally, an initial look at the role of the fluidity of a membrane on bacterial cell respiration is studied through the comparison of cellular respiration rate of wild type MG1655 cells and knock out mutants (∆fadR MG1655).
103 Unsaturated fatty acids play a vital role in bacterial cell growth. The bacterial cell will cease or die if the minimum level of unsaturated fatty acid (about 15-20%) of the total lipid contents is not available during the growth. Primarily, bacteria like E. coli contain palmitoleic acids and cis-vaccenic acids in their membrane phospholipids as a major content of unsaturated fatty acids and palmitic acids as the major content of saturated fatty acids.150 For fatty acid biosynthesis, different enzymes are required in different steps, which are encoded by different genes. Those enzymes involve for unsaturated/saturated fatty acid synthesis include fabA, fabB, fabD, fabF, fabH, fabI, fabG, and fabZ.98 Among all, I am talking here mainly on fabB which involves in condensation reaction and is the rate-determining step of unsaturated fatty acid synthesis and fabA which has a vital role in the addition of double bond and isomerization reaction.97
Role of fabA/fabB
Fatty acids are generated in the cytoplasm of a cell using the glycolytic pathway.
Acetyl-CoA and NADPH are starting precursors for fatty acid biosynthesis.151 Different fatty acid synthases enzymes are involved in this pathway which follows the type II synthase system.101 β-Ketoacyl-ACP synthase I which encodes the gene (fabB) and β- hydroxydecanoyl-ACP dehydratase which encodes the gene (fabA) are important enzymes for the biosynthesis of fatty acid in E. coli MG1655.93,95,101
FabA gene, which encodes for β-hydroxydecanoyl-ACP dehydratase enzyme principally, helps to add the double bond in the synthesized fatty acids (normally produces
104 Palmitoleic acid).152 This enzyme helps to catalyze the dehydration of β-hydroxydecanoyl-
ACP to trans-2- decanoyl- ACP and isomerization to cis- 3-decenoyl-ACP. Similarly,
FabB gene which encodes for a condensing enzyme β-Ketoacyl-ACP synthase I involve in the elongation of fatty acids.153 This condensation step is only one step in the fatty acid biosynthetic pathway that is irreversible where decarboxylation occurs and is also considered as the rate-limiting step. If there is no thermal regulation, at that time, the ratio of unsaturated to saturated fatty acids in bacteria like E. coli depends on the level of β- hydroxydecanoyl-ACP and β-Ketoacyl-ACP synthase I. The overproduction of fabA however, do not involve in increasing the level of unsaturated fatty acids but involves to increase the saturated fatty acids in the membrane phospholipid.95 It means the level of fabA enzyme activity does not limit the unsaturated fatty acid synthesis. However, if there is more β-Ketoacyl-ACP synthase I (of fabB gene), then there would be the overproduction of unsaturated fatty acids. Hence, the rate of unsaturated fatty acid synthesis is limited by elongation of cis -3- decenoyl-ACP, which is produced by the isomerization reaction of trans -2- decanoyl-ACP catalyzed by fabA. This elongation of cis -3- decanoyl-ACP, which is an only irreversible step, is catalyzed by the β-Ketoacyl-ACP synthase I which is produced by fabB to form longer chain unsaturated fatty acids. The brief outline of the unsaturated fatty acid synthesis is outlined in fig 5.0.93,95,96
105 fab I
fabB/fabF
Saturated fatty acid
Figure 5.0 Synthetic pathway of fatty acid production. Acetyl-CoA is a starting substrate for lipid synthesis. fabB which encodes for β-ketoacyl-ACP synthase I play a vital role in the condensation and elongation of carbon chains while fabA, which encodes for β- hydroxyacyl-ACP dehydratase has the role in making unsaturation and isomerization.
106 Role of fadR
FadR is a regulatory protein found in a particular sequence of E. coli. In the past, it was studied that this regulatory protein negatively regulates the expression of fatty acid degradation and positively regulates unsaturated fatty acid synthesis.105 The degradation and synthesis pathways are always in a dynamic state. These pathways frequently turn on or off, depending on the condition of the availability of fatty acids in the membrane of bacterial cells to maintain their membrane lipid homeostasis. The different roles of fadR are mainly dependent on the position of operator site to which the protein binds. As described by Cronan, 1997, when a fadR is a repressor, the binding site in the promoter region is whether overlapped -10 to -35 regions or downstream of -10 regions, but when fadR acts as an activator, the binding site is upstream of -35 region of the promoter (detail data on DNA sequence analysis and the binding region is given by Zhang, F. et al., 2012).
Positively, fadR acts as an activator for the expression of fabA/fabB, which are the two major enzymes for unsaturated fatty acid synthesis. FadR upregulates the fabA/fabB which encodes specific proteins.96 Moreover, acyl-CoAs inhibit the DNA binding activity of fadR and binding of the acyl-CoAs with fadR cause to change the conformation of fadR. It triggers to release of the fadR from the DNA sequences.104 Thus, the overproduction of fadR enhances the production of unsaturated fatty acids and decreases the level of fatty acid degradation, which overall increases the total fatty acid content in the membrane.
Knocking out fadR has been shown to decrease the unsaturated to the saturated fatty acid ratio in E. coli.154
107 In this chapter, I will show through diffusion measurements that HBT Py-Cy is sensitive to fluidity changes caused by membrane composition changes in an fadR knockout strain. For that, I tested both the fadR null mutant (∆fadR MG1655) and wild type E. coli strain, MG1655, which showed that wild type MG1655 has higher diffusion of the lipid membrane than the null type (which is known to have a lower UFA/SFA ratio). It was found from here that expression of fabA/fabB can be used to monitor with fluorescent reporters to give an indication of the fluidity of individual cells.
Knock out of fadR gene by λ-red recombination
To look at the differences in the membrane fluidity caused by the lipid composition, we could knock out a particular gene that affects lipid synthesis and compares the results with the wild type strain. Homologous recombination is an important technique to insert or delete the foreign DNA from a donor cell into the genome of the different recipient cells.155,156 It is more advanced than the classical genetic engineering technique, which uses restriction enzymes and DNA ligase. Also, in classical cloning techniques, large-sized
DNA could be broken down when working with Vivo conditions. For the modern recombination, lambda(λ) red-mediated recombination technique is followed which helps in gene insertion, deletion and point mutations on required chromosomal targets.157 One of the important features of phase mediated recombination is that it can work in the absence of RecA protein in E.coli as well. Since RecA protein binds to the single-stranded DNA
(ssDNA) and looks for homologous sequencing with the other DNA.158 This technique also eliminates the use of restriction enzymes and DNA ligases, which shortens many other
108 time-consuming steps during cloning. From the mechanistic approach, three λ red proteins are required for double-stranded DNA (dsDNA) recombination, which includes Gam, Exo, and Beta.156,159 As described in a paper by Mosberg, J. A. et al., 2010, Gam protects SbcCD and RecBCD nucleases from the digestion of linear dsDNA in E. coli. Exo, also known as lambda exonuclease, degrades dsDNA from 5’ to 3’ leaving the single-stranded DNA whose complementary sequences are degraded. Beta binds to ssDNA which is created by exo and helps in recombination. It also helps to promote annealing to a complementary ssDNA target site into the cell.
In the chromosome of E. coli strains MG1655 using λ-red recombination, the fadR gene which needs to be knocked out is replaced by using an antibiotic resistance gene. It can be generated by using the primers with homology extensions of 35-50 nucleotides, and
PCR is run. After that, the antibiotic resistance gene is also removed using the helper plasmid (pKD46) expressing the FLP recombinase (fig. 5.01) which helps to recognize the FRT (FLP recognition target) sites flanking the resistance gene.160,161 The helper plasmid which is temperature-sensitive, is then removed by growing the cells at 37o C- 42o
C.
109 Figure 5.01 Schematic diagram of the λ-red recombineering system to knock out and replace of the gene of interest using antibiotic resistance cassette.
5.1.1 Membrane Fluidity and Bacterial cell respiration
Bacterial cell respiration generally provides information about how the energy is utilized by the organisms to perform the regular function. Bacteria utilize its energy during glycolysis and produce the energy during ETC and oxidative phosphorylation if oxygen is the ultimate electron acceptor (aerobically) or through fermentation if oxygen is absent
(anaerobically).42,162 The rate of cell respiration could be measured in different ways. These
110 ways include; estimate the amount of oxygen consumed or measure the amount of carbon dioxide produced.163,164 In general, cellular respiration rate could be determined by looking at the amount of oxygen consumption on individual cells. We hypothesized that the increase or decrease in the oxygen consumption rate is affected by the fluidity of inner membrane components.
Cell respiration is usually measured during the log phase. This is because, at the stationary phase, the rate of respiration starts to drop due to limited nutrition. It means the oxygen consumption rate (OCR) declines. In some cases, especially for batch culture of bacteria, cells can survive longer time due to cryptic growth and expression of growth advantage in stationary phase (GASP) phenotype.1 The fluidity of the membrane could play an important role in cellular respiration, and other membrane functions since the electron transport chain reside in the inner membrane of the E. coli cells. A series of redox reactions take place here, where finally, electrons reduce the molecular oxygen to produce water. Ubiquinone, a mobile lipid-soluble electron carrier, transports the electrons between the large immobile macromolecular complexes in the membrane.51 During the diffusion process, oxygen is transported through transmembrane proteins. Oxygen is a key component for Adenosine Triphosphate (ATP) synthesis in electron transport chain (ETC) and oxidative phosphorylation process because it is the terminal electron acceptor in
ETC.9,42,112
111 5.2 Materials and Methods
5.2.1 Isolation of Genomic DNA and Construction for PCR
Genomic DNA was isolated from wild type MG1655 and mutant DGC-102 separately using the standard Sigma-Aldrich GenEluteTM Bacterial Genomic DNA Kits.
Two primers of fabB the promoter were constructed and obtained from InvitrogenTM. The primers were constructed as follows; fabB promoter forward in pUC19 backbone whose sequences are TATCACGAGGCCCTTTCGTCATGCGCTAAGGCTA AATC and fabB promoter reverse in pUC19 backbone whose sequences are TGCTCACCATTCAATA
CCTCTGTAAGTCG.
Construction of pUC19 vector for PCR
pUC19 cloning vector was purchased from Addgene. The template vector DNA was isolated using Sigma-Aldrich genEluteTM HP plasmid miniprep kits. Two primers for pUC19 were constructed and obtained from InvitrogenTM. The primers were constructed as follows; pUC19- forward whose sequences are TCGCGCGTTTCGGTGATG and pUC19-reverse whose sequences are GACGAAAGGGCCTCGTGATAC.
Construction of mCherry cloning vector for PCR
mCherry LIC cloning vector (u-Cherry) was purchased from Addgene and template
DNA was isolated. Two primers were constructed and obtained from InvitrogenTM. The primers were constructed as follows: mCherry forward whose sequences are
112 GAGGTATTGAATGGTGAGCAAGGGGAG and mCherry reverse whose sequences are
GTCATCACCGAAACGCGCGACTACTTGTACAGCTCGTCCATG.
Assembly of DNA
The PCR product obtained after ligation was used for the assembly of DNA. The
PCR products of fabB genomic DNA, pUC19 and mCherry were combined together with calculated concentrations and incubated at 50o C in a thermocycler for assembly. The next day, the assembled DNA reaction sample was transformed into DGC-102 competent cells and grown overnight in the 37o C incubator. The mutant was named as DGC-102 fabB with mCherry at the pUC19 backbone. A similar protocol followed for fabA construction and done successfully. The process was later successfully duplicated with competent MG1655 and inserted the DNA assembly product from both fabA and fabB and successfully obtained the desired ligation. The ligation done from PCR is checked through gel electrophoresis and mCherry expression was seen clearly through imaging under the confocal microscope at suitable emission wavelengths (580-630 nm emitted filters).
5.2.2 Diffusion Measurements
DGC-102 fabB with mCherry at PUC19 cells was grown overnight in MOPS media and subcultured for 4 hours (OD = 0.3-0.4). Externally a lipid dye HBT Py-Cy (final concentration of 2 µm) was added in 500 µl sample and grown for 20 minutes in the incubator shaker with 200 rpm. The cells were then imaged in the A1 Nikon confocal
113 microscope at 37o C. Fluorescence recovery after photobleaching (FRAP) technique was carried out to look at their lateral diffusion of the membrane34, which was quantitatively measured using MATLAB software program.
5.2.3 Steps for Deletion of fadR Gene
1. Formation of competent cells
pKD46 is a vector containing lambda red recombinase. It was a kind gift obtained from Dr. Hazel Barton’s lab, Biology Department at The University of Akron. I transformed the vector into the wild type MG1655 cells following the transformation protocols. The colonies were grown in ampicillin resistance plates at 30o C. A single colony was selected and was grown in SOB media with L- arabinose (final concentration of 1 mM), which is a lambda red inducer.160 At a suitable OD (between 0.4-0.6), the cells were made competent for further analysis.
2. PCR amplification of template plasmid, pKD3, and gene deletion
pKD3 plasmid, which is chloramphenicol (chlm) resistance, was also obtained from
Dr. Barton’s lab. pKD3 forward and pKD3 reverse primers were designed by homologous extension of 50 nucleotides upstream of fadR (form forward primer, H1P1) and 50 nucleotides downstream of fadR (form reverse primer, H2P2) using snap gene viewer.
Here, H1 and H2 are the upstream and downstream sequences of fadR, respectively, and
P1 and P2 are the forward and reverse nucleotide sequences of pKD3, respectively. Thus,
114
upstream and downstream sequences from the gene that needs to be knocked out (fadR) was selected and overhang homologously to forward and reverse primer of pKD3 and sent to Sigma-Aldrich for primer construction. Q5 high fidelity 2x master mix was obtained from New England BioLabs. PCR amplification was carried out in T100TM Thermal
Cycler. The amplified product was verified using gel electrophoresis. The product was then digested using the restriction enzyme, DpnI. The digested product contains the chloramphenicol resistance gene with FRT (FLP recognition target) sites and overhang extensions on both sides (fig 5.01). It was transformed into the competent cells that contain helper plasmid (pKD46), which has a λ-red recombinase. For transformation, the heat shock method was used. Then the cells were grown in SOC media for 1 hour and spread out in the LB plates and plates containing chloramphenicol antibiotics (34 µg/ml) as a control. The λ-red system contains exo, beta and gam proteins. Exo degrades the dsDNA from 5’ to 3’, leaving the ss DNA as it is and beta helps on binding ssDNA through recombination by selecting the FRT sites. The cells on the plates were grown overnight at
37o C at Excella E24 Incubator Shaker Series with 200 rpm. The cells obtained were thus deleted fadR version of MG1655 (∆fadR). Since, pKD46 is a temperature-sensitive replicon. It is removed by growing the cells at 37o C. The deletion was verified by running the PCR of both the DNA of wild type MG1655 and ∆fadR MG1655 and checked the bands through gel electrophoresis.
115
5.2.4 Measurement of Cell Respiration
E. coli cells (wildtype and mutant of MG1655) were grown up in MOPS media. It was subcultured and kept in the New Brunswick Scientific Excella E24 Incubator Shaker
Series at 200 rpm until reaching to log phase (0.3-0.5 OD). The sample was serially diluted until we got 105 cells per ml. 5 µl washed Pt-porphyrin (as an oxygen sensor) was kept in a 22x22 mm VWR micro cover glass and let it stand in 120o C for 15 minutes to dry. A circle will be formed after drying. Acid washed glass chips were put in a well. The chip holds a volume of 3.2 µl. Then 5 µl sample was kept over the chip and sealed with cover glass facing the Pt-porphyrin downward. The set-up apparatus was put in the microscope and viewed under 10X objectives. Andor Solis 64-bit analysis software, MATLAB, oscilloscope and LED was turned on for analysis.
5.3 Results and Discussion
5.3.1 Gel Electrophoresis Analysis
Based on the size of macromolecules like DNA, RNA or proteins, the gel electrophoresis method is used to separate their mixtures.165 Here, PCR product of different
DNA molecules, including pUC19 and mCherry cloning vector, pKD3 were identified using gel electrophoresis, where I used 1% agarose gel in TBE buffer. The molecules move towards the opposite charge when applied to the electrical field. The larger molecules
(higher base pairs) of DNA move slowly, where smaller molecules with lower base pairs move faster. Based on it, I observed different bands when observed in UV
116 spectrophotometer and compared it with the DNA ladder. Different bands of DNA molecules with their base pairs are outlined in the fig 5.02.
Figure 5.02 Gel electrophoresis technique to observe the different DNA bands obtained after amplification through PCR. pKD3 PCR product is 1.1 kb.
5.3.2 UFA Production by fabB/fabA Protein
FabB, which encodes for β- ketoacyl-ACP synthase I catalyze the elongation of cis-
3- dedenoyl-ACP. This step is irreversible as well, which is supposed to be the rate- determining step. The inactivation of fabB results in UFA auxotrophy (inability to synthesize unsaturated fatty acids) while the overexpression is supposed to increase the production of unsaturated fatty acids.102 Fig 5.03(1) shows the relation of the intensity of
117 mCherry fluorescent reporter of FabB expression and diffusion of the lipid membrane probe HBT Py-Cy. In the construction of the backbone vector, pUC19, mCherry protein lies downstream of the fabB promoter. The increase in the intensity of mCherry fluorescent reporter caused by the activation of fabB promoter shows a relationship between the fluidity of the membrane and fabB expression.
Fig 5.03 (2) shows the relationship between the diffusion of the membrane probe vs the intensity of mCherry fluorescent reporter of fabA expression. It does not show any significant correlation between the expression of fabA and membrane fluidity. It means, even though fabA which encodes for β-hydroxydecanoyl-ACP dehydratase is necessary for unsaturated fatty acid production, the overall synthesis of unsaturated fatty acid is controlled by the expression of β-ketoacyl-ACP synthase I (fabB) if the external thermal condition does not fluctuate. In truth, the relationship is likely the ratio of fabB-to-fabA expression which controls the UFA-to-SFA ratio, which is supported by Magnuson, K. et al., 1993.95
118 2.0 1 1.8 1.6 1.4 1.2 1.0 /s), Py-Cy /s), 2 0.8 m 0.6 D ( D 0.4 0.2 0.0 0 500 1000 1500 2000 2500 3000 Relative intensity of mCherry
3.0 2 2.8 2.6 2.4 2.2 2.0 1.8 1.6
/s), Py-Cy /s), 1.4 2 1.2 m 1.0
D ( 0.8 0.6 0.4 0.2 0.0 0 500 1000 1500 2000 2500 3000 Relative intensity of mCherry
Figure 5.03 mCherry expressed fabB (1) and fabA (2) in DGC-102 cells (protocol given in section 2.4) and labeled with HBT Py-Cy. The effect of relative intensity on the diffusion of lipid was observed [n = 64 cells (1) and 76 cells (2)]. 119 5.3.3 FadR as a Marker of Fatty Acid Production
Gram-negative bacteria like E. coli lies under the class of Gammaproteobacterium and its membrane lipid normally comprises of straight chains of fatty acids.166 FadR protein, which is a main transcriptional regulator, plays a vital role in fatty acid synthesis by activating the expression of fabA/fabB proteins.167 To look at the role of fadR on the unsaturated fatty acid synthesis, I carried out the FRAP measurements to observe the membrane diffusion. For that, MG1655 cells were knocked out with fadR and as a control, wild type MG1655 cell was taken. The diffusion coefficients of both wild type and knock out of fadR were given in fig 5.04. I found that when the cell has knocked out fadR, its diffusion coefficient value decreased in a significant manner [∆fadR MG1655; Mean D
(±SD) = 0.45 ± 0.18 while for wtMG1655; Mean D (±SD) = 0.72 ± 0.32). It means the production of unsaturated fatty acid decreases without fadR and the fluidity of the membrane lipid also decreases.
120 16% A
12%
8%
percentage of cells 4%
0% 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 D (m2/s)
20% B
16%
12%
8% percentage of cells 4%
0% 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 D (m2/s)
Figure 5.04 Diffusion measurements of wt MG1655 (A) and ∆fadR MG1655 cells at 37o C and labeled with HBT Py-Cy. It was observed that the diffusion coefficient of the mutant decreases significantly compared to the wild type. [P(t<=t) one tail =1.34E-04](p=0.05).
121 5.3.4 Oxygen Consumption by Bacterial Cells
Using the platinum porphyrin (Pt-porphyrin) as an oxygen sensor, I determined the dissolved oxygen concentration over time in a bulk sample. Because in the bacterial cells, oxygen is the terminal electron acceptor in their metabolic pathway.108 The results were shown in fig 5.05. It was observed that oxygen concentration decreased over time. From the plot, I was able to get how much oxygen was consumed by a bulk sample throughout a certain time frame. Based on its optical density (OD600), then I determined the oxygen consumption per cell per sec. I compared the results with both wtMG1655 and its mutant strains. It was found that wtMG1655 has relatively higher oxygen consumption rate
(average range of 76 attomoles per cell per sec) than mutants (average value of 53 attomoles per cell per sec), which suggests that the more production of unsaturated fatty acids on wildtype and more the fluidity of a membrane which facilitates the faster movement of ubiquinone (an electron carrier).51 For the mutant, fadR that plays an important role in fabA/fabB expression was knocked out and when the cells are deleted with fadR, it slows down the formation of fatty acids in the membrane and hence their diffusion was relatively slower. This slower diffusion could reflect in its respiration rate.
122 8.5 wt MG1655
8 y = -0.0989x + 7.9458 R² = 0.9979 7.5
7 concnetration(PPM)
2 6.5
6
5.5 Normalized O 0 5 10 15 20 25 Time (min)
8.5 횫fadR MG1655 8 7.5 7 y = -0.2023x + 8.149 R² = 0.9972 6.5 6 concentration(PPM)
2 5.5 5 4.5 4
Normalized O 3.5 0 5 10 15 20 25 Time(min)
Intercept Intercept Slope Slope Statistics Value Standard Error Value Standard Error Adj. R-Square knockout fadR 8.14897 0.01979 -0.20231 0.0017 0.99717 wildtype MG1655 7.94578 0.00837 -0.09894 7.20E-04 0.99788
Figure 5.05 Plot of oxygen consumption rate by a cell over time. wtMG1655 (black) and ∆fadR mutant (green). Statistics, including linear fitted values, were given in the box.
123 TABLE 5.0 OXYGEN CONSUMPTION RATE OF INDIVIDUAL CELLS. NEGATIVE
SIGN INDICATES THE DECREASE IN CONCENTRATION OF OXYGEN OVER
TIME.
5.4 Conclusion
Lipid composition, the ratio of saturated and unsaturated fatty acids and temperature are major parameters for the fluidity of a membrane. There are still some challenges to measure the diffusion of a membrane accurately. I examined the role of unsaturated fatty acids on membrane fluidity. For that, two enzymes fabA and fabB that controls fatty acid synthesis were studied. Two promoters of fabA/fabB were expressed with mCherry reporter, and diffusion measurement was carried out. The increase in the intensity of mCherry fluorescent reporter caused by the activation of β-ketoacyl ACP synthase I clearly showed that fabB, which catalyzes the condensation reaction and is a rate-determining step, has the major contribution in unsaturated fatty acid elongation and inactivation of which shows lower membrane diffusion. However, fabA which is supposed 124 to involve in fatty acid unsaturation and isomerization reaction did not show any significant differences on the mCherry expression during my experiment. However, more measurements would be necessary to look at the effect of fabA on mCherry fluorescent reporters. Based on my analysis, the primary role in unsaturated fatty acid synthesis is due to fabB activation. I also studied another transcriptional activator of fabA/fabB genes. This activator, fadR, upregulates the fabA-fabB, deletion of which showed a significant decrease in diffusion of membrane lipids (fig 5.04).
The noteworthy observation I did was the study of dissolved oxygen concentration over time. Bacterial cell respiration could be determined using Andor Solis 64-bit analysis software through the determination of dissolved oxygen in PPM over time. Both wild-type and mutant E. coli cells showed a linear response. The study of a decrease in the concentration of oxygen in a closed sealed well over time revealed that E. coli cells consume oxygen. Depending upon how fast the diffusion occurs, the rate of oxygen consumption changes, which was visible by comparing the results of wildtype and mutant cells (fig 5.05). It was concluded from here that the diffusion of membrane components is related to the bacterial cell respiration.
There are some factors that need to be addressed in the future. I was unable to knock out fabB genes through homologous recombination. The reason might be either the nucleotide sequence is not properly sequenced during PCR or problem during its transformation to competent cells. It is necessary to address these problems in the future.
Gene cloning and transformation is a very challenging job. Also, I tried with different
125 expressed proteins, including eGFP, sfGFP. However, the results were not much satisfactory due to the lower intensity of these proteins when observed under the microscope. It might be due to protein unfolding or not properly sequenced of proteins, or there could be homo-oligomerization.
126 CHAPTER VI
CONCLUSION
6.1 Summary of the Work
Using one of the microscopic technique, FRAP, I am able to measure the membrane fluidity in the bacterial cells. It is characterized the different lipophilic dyes, which show a distinction between the inner and outer membrane. It allows me to differentiate whether the dye is staining the outer membrane or the inner membrane. Previously, the plasmolysis study was focused on the cells that were treated with cephalexin. However, my study on healthy cells without treatment with any cephalexin gives more precise results on diffusion measurements. The bacterial cells are grown in different media, including carbon-rich LB to minimal MOPS. The growth curve from these media helps in the future to grow the cells in appropriate media with the appropriate time. It is found that the wild type cells grow faster than any of the other antibiotic resistance cells or knock out mutants in either of the media.
The cells are grown at 37o C and the fluidity of a membrane is determined by varying the measured temperature from 37o C to 30o C. It is found that the fluidity of the membrane drops when shifted from 37o C to 30o C. It is due to the phase transition of a membrane from a liquid crystalline state to viscous gel state at lower temperatures. I also look at the effect of the saturated and unsaturated fatty acids on the fluidity of a membrane.
For that, it is basically focused on fabA/fabB encoded enzymes, which have a major role in the production of unsaturated fatty acids and on chain elongation steps. It is also 127 observed the effect of fadR protein which is a transcriptional regulator of fabA/fabB gene.
It is successfully knocked out the fadR gene from MG1655 cells using homologous recombination technique and compare the diffusion data between wild type MG1655 and knock out fadR MG1655 cells. I find distinct differences in their fluidity while measuring under similar temperature conditions.
Finally, it is observed the relation of the fluidity of a membrane with the cell respiration. Using Ander Solis 64-bit software analysis, I am able to look at the respiration rate of individual cells. It is found that the wild type cells have a relatively higher oxygen consumption rate than the null mutants. I hypothesize that if the production of unsaturated fatty acids is higher in the membrane, then its lateral diffusion would be higher as well, which is supposed to facilitate the movement of electron carriers (mainly ubiquinone) during electron transport in the cytoplasmic membrane. This movement helps to reach the electrons up to the oxygen, as oxygen is the terminal electron carrier in ETC. If more the oxygen is consumed, it means faster the rate of movement of the molecules in the membrane. Thus the results I obtain support my hypothesis, which is in agreement with
Budin A. et al., 2018, who stated that diffusion of ubiquinone controls the respiratory flux in the cytoplasmic membrane. Llorente-Garcia, I. et al., 2014, studied simultaneously the diffusion of exogenously added fluorescently labeled ubiquinone, NBDHA-Q in E. coli and looked at the oxygen uptake rate using Hansatech Oxylab electrode. The cells were grown at a higher temperature, treated with cephalexin and measured at three different lowered temperatures (15/23/37o C) and carried out the diffusion measurement as well as
128 oxygen consumption measurement for the same cells. They found that the oxygen uptake rate increased with increase in diffusion coefficient [ at 15o C, D = (0.96 ± 0.26 µm2/s) with oxygen uptake rate = (0.590 ± 0.007 µmolml-1h-1) and at 37o C, D = (1.79 ±0.33 µm2/s) with oxygen uptake rate = ( 5.362 ± 0.048 µmolml-1h-1].9 These results would be helpful in the future for comparison when the simultaneous measurement of diffusion of a membrane and oxygen consumption rate is carried out in a single healthy cell without any treatment of cephalexin.
6.2 Future Work
During the experiment, I faced many challenges that need to address in the future.
I use different lipophilic dyes to stain the inner membrane of the cells. However, FM1-43 does not show any particular staining to the inner membrane. FM1-43 has not any specificity and most probably stains both the membrane, which makes our study much difficult. However, on treatment with cephalexin, FM1-43 could stain the inner membrane.
Further study on FM styryl dyes is necessary. Even their labeling pattern is better than others, I do not try on gram-positive bacteria and other eukaryotic cells. Further study will reveal its inner or outer membrane staining. Likewise, the brightness of the commercial dye, Nile red, has much lower than others. So I do not use it for our prolonged study. The structural modifications of this dye could result in better fluorescence intensity. Because we have seen many dyes whose isomers show different quantum yield and better brightness. This is still the subject of interest.
129 During FRAP measurement, adjusting of suitable laser power is always challenging. Even a small increment in laser power could bleach the whole-cell irreversibly. So special attention needs during the fluidity measurements. Besides Nile red and FM1-43, I try with FM4-64 as well. But could not get better results. Since my project targets mainly on the lipid membrane, if we study the simultaneous measurement of both lipids and proteins, we could get better results in the membrane fluidity. Thus, we could focus on its study in the future.
I have done gene editing using a homologous recombination technique. But the success of this technique is still very low. It is successfully knocked out the fadR from wt
MG1655 cells, however, fail to delete fabB genes. In the future, for gene editing, more attention needs from the very beginning of selecting primers, running PCR with proper annealing temperatures and transforming the plasmids to competent cells. While doing a transformation process, I apply both chemically competent cells transformation protocol using heat shock and electrocompetent cells transformation protocol using electroporation cuvette. It is found the mixed success rate in both methods. I have done many plasmid constructions with mCherry or eGFP or sfGFP insertion. These plasmids are transformed into the DGC-102 and MG1655 cells. mCherry expressions are very well relative to eGFP and sfGFP. The intensity of eGFP and sfGFP is very low. So further study is necessary for these proteins. These could be due to improper folding or not properly sequenced into the plasmid.
130
I got the three different strains of E. coli from Professor Dr. Conrad W. Mullineaux,
Queen Mary University of London, UK, which contain different protein complexes of the bacterial respiratory system that are expressed with GFP and mCherry proteins. These are
CyoA-mCherry (CyoA encodes cytochrome bo3 complex), NuoF-GFP: SdhC-mCherry
(Nuof encodes NADH dehydrogenase I and SdhC encodes succinate dehydrogenase complex II), and AtpB-GFP:CydB-mCherry (AtpB encodes F0F1 ATPase complex and
CydB encodes cytochrome bd-I complex). The diffusion measurements using FRAP is carried out in these complexes and look at the expression of GFP or mCherry. However, due to the low intensity of GFP and mCherry, it is difficult to correlate the diffusion of the membrane and GFP/mCherry expression. In the future, this work could be continued to look at the protein level expression with the mobility of ubiquinone. For that, the filters in our microscope should be adjusted to proper emission wavelengths so that we could achieve their highest emission intensity peaks.
I have done the measurement of bacterial cell respiration in the bulk sample.
However, in the future, simultaneous measurements of the fluidity of a membrane and cell respiration rate of an individual cell give accurate information about the diffusion of membrane component and its role on oxygen consumption rate.
131
REFERENCES
1. Riedel, T. E.; Berelson, W. M.; Nealson, K. H.; Finkel, S. E. Appl Environ Microbiol 2013, 79, 4921.
2. Amao, Y.; Asai, K.; Okura, I. J. Porphyrins Phthalocyanines 2000, 4, 292.
3. Haddock, B. A.; Jones, C. W. Bacteriol Rev 1977, 41, 47.
4. Silhavy, T. J.; Kahne, D.; Walker, S. Cold Spring Harbor perspectives in biology 2010, 2, a000414.
5. Yatvin, M. B.; Gipp, J. J.; Dennis, W. H. International Journal of Radiation Biology and Related Studies in Physics, Chemistry and Medicine 1979, 35, 539.
6. Nenninger, A.; Mastroianni, G.; Robson, A.; Lenn, T.; Xue, Q.; Leake, M. C.; Mullineaux, C. W. Mol Microbiol 2014, 92, 1142.
7. Ziemba, B. P.; Falke, J. J. Chemistry and physics of lipids 2013, 172-173, 67.
8. Elowitz, M. B.; Surette, M. G.; Wolf, P. E.; Stock, J. B.; Leibler, S. Journal of bacteriology 1999, 181, 197.
9. Llorente-Garcia, I.; Lenn, T.; Erhardt, H.; Harriman, O. L.; Liu, L. N.; Robson, A.; Chiu, S. W.; Matthews, S.; Willis, N. J.; Bray, C. D.; Lee, S. H.; Shin, J. Y.; Bustamante, C.; Liphardt, J.; Friedrich, T.; Mullineaux, C. W.; Leake, M. C. Biochimica et biophysica acta 2014, 1837, 811.
10. Crane, F. L.; Barr, R. In Methods in Enzymology; Academic Press: 1971; Vol. 18, p 137.
11. Lenn, T.; Leake, M. C. Biochimica et Biophysica Acta (BBA) - Bioenergetics 2016, 1857, 224.
12. Ladha, S.; Mackie, A. R.; Clark, D. C. The Journal of Membrane Biology 1994, 142, 223.
13. Begg, K. J.; Donachie, W. D. Journal of bacteriology 1985, 163, 615. 132 14. Narita, S. I.; Tokuda, H. Biochim Biophys Acta Mol Cell Biol Lipids 2017, 1862, 1414.
15. Lim, J. Y.; Yoon, J.; Hovde, C. J. J Microbiol Biotechnol 2010, 20, 5.
16. Hacker, J.; Blum-Oehler, G. Nature Reviews Microbiology 2007, 5, 902.
17. Deusser, E. Molecular and General Genetics MGG 1972, 119, 249.
18. Forchhammer, J.; Lindahl, L. Journal of Molecular Biology 1971, 55, 563.
19. Costerton, J. W.; Ingram, J. M.; Cheng, K. J. Bacteriol Rev 1974, 38, 87.
20. Singer, S. J.; Nicolson, G. L. Science 1972, 175, 720.
21. Morein, S.; Andersson, A.; Rilfors, L.; Lindblom, G. The Journal of biological chemistry 1996, 271, 6801.
22. Matsuura, M. Frontiers in Immunology 2013, 4.
23. Epand, R. M. In Methods in Membrane Lipids; Owen, D. M., Ed.; Springer New York: New York, NY, 2015, p 1.
24. Alexander, M. K.; Miu, A.; Oh, A.; Reichelt, M.; Ho, H.; Chalouni, C.; Labadie, S.; Wang, L.; Liang, J.; Nickerson, N. N.; Hu, H.; Yu, L.; Du, M.; Yan, D.; Park, S.; Kim, J.; Xu, M.; Sellers, B. D.; Purkey, H. E.; Skelton, N. J.; Koehler, M. F. T.; Payandeh, J.; Verma, V.; Xu, Y.; Koth, C. M.; Nishiyama, M. Antimicrob Agents Chemother 2018, 62.
25. Steimle, A.; Autenrieth, I. B.; Frick, J. S. Int J Med Microbiol 2016, 306, 290.
26. Gumbart, J. C.; Beeby, M.; Jensen, G. J.; Roux, B. PLOS Computational Biology 2014, 10, e1003475.
27. Braun, V.; Rehn, K. European Journal of Biochemistry 1969, 10, 426.
28. Beveridge, T. J. Journal of bacteriology 1999, 181, 4725.
133
29. Scheurwater, E. M.; Burrows, L. L. FEMS Microbiology Letters 2011, 318, 1.
30. Rogers, H. J.; Wright, G. Journal of general microbiology 1987, 133, 2567.
31. Strahl, H.; Errington, J. Annual Review of Microbiology 2017, 71, 519.
32. Fishov, I.; Woldringh, C. L. Mol Microbiol 1999, 32, 1166.
33. Trovato, F.; Tozzini, V. Biophysical journal 2014, 107, 2579.
34. Lenaz, G. Bioscience Reports 1987, 7, 823.
35. Kumar, M.; Mommer, M. S.; Sourjik, V. Biophysical journal 2010, 98, 552.
36. Sankaran, J.; Manna, M.; Guo, L.; Kraut, R.; Wohland, T. Biophysical journal 2009, 97, 2630.
37. Venable, R. M.; Zhang, Y.; Hardy, B. J.; Pastor, R. W. Science 1993, 262, 223.
38. Mika, J. T.; Thompson, A. J.; Dent, M. R.; Brooks, N. J.; Michiels, J.; Hofkens, J.; Kuimova, M. K. Biophysical journal 2016, 111, 1528.
39. Saffman, P. G.; Delbrück, M. Proceedings of the National Academy of Sciences 1975, 72, 3111.
40. Petrov, E. P.; Schwille, P. Biophysical journal 2008, 94, L41.
41. Guigas, G.; Weiss, M. Biophysical journal 2006, 91, 2393.
42. Unden, G.; Bongaerts, J. Biochimica et biophysica acta 1997, 1320, 217.
43. Schneider, D.; Pohl, T.; Walter, J.; Dörner, K.; Kohlstädt, M.; Berger, A.; Spehr, V.; Friedrich, T. Biochimica et Biophysica Acta (BBA) - Bioenergetics 2008, 1777, 735.
44. Anraku, Y. Annual Review of Biochemistry 1988, 57, 101.
134
45. Friedrich, T.; Dekovic, D. K.; Burschel, S. Biochimica et biophysica acta 2016, 1857, 214.
46. Agrawal, S.; Jaswal, K.; Shiver, A. L.; Balecha, H.; Patra, T.; Chaba, R. 2017, 292, 20086.
47. Ingledew, W. J.; Poole, R. K. Microbiol Rev 1984, 48, 222.
48. Melo, A. M.; Teixeira, M. Biochimica et biophysica acta 2016, 1857, 190.
49. Borisov, V. B.; Murali, R.; Verkhovskaya, M. L.; Bloch, D. A.; Han, H.; Gennis, R. B.; Verkhovsky, M. I. Proceedings of the National Academy of Sciences 2011, 108, 17320.
50. Henkel, S. G.; Beek, A. T.; Steinsiek, S.; Stagge, S.; Bettenbrock, K.; de Mattos, M. J. T.; Sauter, T.; Sawodny, O.; Ederer, M. PLOS ONE 2014, 9, e107640.
51. Chazotte, B.; Wu, E. S.; Hackenbrock, C. R. Biochimica et biophysica acta 1991, 1058, 400.
52. Meyrat, A.; von Ballmoos, C. 2019, 9, 3070.
53. Tocanne, J. F.; Dupou-Cezanne, L.; Lopez, A. Prog Lipid Res 1994, 33, 203.
54. Budin, I.; de Rond, T.; Chen, Y.; Chan, L. J. G.; Petzold, C. J.; Keasling, J. D. Science 2018, 362, 1186.
55. Reits, E. A.; Neefjes, J. J. Nat Cell Biol 2001, 3, E145.
56. Ishikawa-Ankerhold, H. C.; Ankerhold, R.; Drummen, G. P. Molecules 2012, 17, 4047.
57. De Los Santos, C.; Chang, C. W.; Mycek, M. A.; Cardullo, R. A. Mol Reprod Dev 2015, 82, 587.
58. SEIFFERT, S.; OPPERMANN, W. Journal of Microscopy 2005, 220, 20.
135 59. Mueller, F.; Morisaki, T.; Mazza, D.; McNally, J. G. Biophysical journal 2012, 102, 1656.
60. Sinnecker, D.; Voigt, P.; Hellwig, N.; Schaefer, M. Biochemistry 2005, 44, 7085.
61. Tian, Y.; Martinez, M. M.; Pappas, D. Appl Spectrosc 2011, 65, 115a.
62. Ries, J.; Schwille, P. Phys Chem Chem Phys 2008, 10, 3487.
63. Levin, M. K.; Carson, J. H. Differentiation 2004, 72, 1.
64. Vrljic, M.; Nishimura, S. Y.; Moerner, W. E. In Lipid Rafts; McIntosh, T. J., Ed.; Humana Press: Totowa, NJ, 2007, p 193.
65. Saxton, M. J.; Jacobson, K. Annu Rev Biophys Biomol Struct 1997, 26, 373.
66. Liu, C.; Liu, Y. L.; Perillo, E. P.; Dunn, A. K.; Yeh, H. C. IEEE J Sel Top Quantum Electron 2016, 22.
67. Alcor, D.; Gouzer, G.; Triller, A. Eur J Neurosci 2009, 30, 987.
68. Loura, L. M. S.; Prates Ramalho, J. P. Biophysical reviews 2009, 1, 141.
69. Zhang, J.; Campbell, R. E.; Ting, A. Y.; Tsien, R. Y. Nat Rev Mol Cell Biol 2002, 3, 906.
70. Rumin, J.; Bonnefond, H.; Saint-Jean, B.; Rouxel, C.; Sciandra, A.; Bernard, O.; Cadoret, J. P.; Bougaran, G. Biotechnol Biofuels 2015, 8, 42.
71. Henkel, A. W.; Lübke, J.; Betz, W. J. Proceedings of the National Academy of Sciences 1996, 93, 1918.
72. Cochilla, A. J.; Angleson, J. K.; Betz, W. J. Annu Rev Neurosci 1999, 22, 1.
73. Amanda J. Cochilla; Joseph K. Angleson, a.; Betz, W. J. Annual Review of Neuroscience 1999, 22, 1.
74. Fishov, I.; Woldringh, C. L. Molecular Microbiology 1999, 32, 1166. 136
75. Dahal, D.; Ojha, K. R.; Alexander, N.; Konopka, M.; Pang, Y. Sens. Actuators, B 2018, 259, 44.
76. Thompson, R. B.; Scarlata, S. In Reviews in Fluorescence 2016; Geddes, C. D., Ed.; Springer International Publishing: Cham, 2017, p 1.
77. Zimmermann, J.; Zeug, A.; Roeder, B. Phys. Chem. Chem. Phys. 2003, 5, 2964.
78. Croney, J. C.; Jameson, D. M.; Learmonth, R. P. Biochemistry and Molecular Biology Education 2001, 29, 60.
79. Lakowicz, J. R. In Principles of Fluorescence Spectroscopy; Springer US: Boston, MA, 1999, p 1.
80. Keller, R. A. Chemical Physics Letters 1969, 3, 27.
81. Valeur, B.; Berberan-Santos, M. N. Journal of Chemical Education 2011, 88, 731.
82. Randall John, T.; Wilkins Maurice Hugh, F.; Oliphant Marcus Laurence, E. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 1945, 184, 365.
83. Hanrahan, O.; Harris, J.; Egan, C. Methods in molecular biology (Clifton, N.J.) 2011, 784, 169.
84. Erie, J. C.; McLaren, J. W.; Patel, S. V. American Journal of Ophthalmology 2009, 148, 639.
85. Pilizota, T.; Shaevitz, J. W. Biophysical journal 2013, 104, 2733.
86. Korber, D. R.; Choi, A.; Wolfaardt, G. M.; Caldwell, D. E. Applied and Environmental Microbiology 1996, 62, 3939.
87. Rojas, E.; Theriot, J. A.; Huang, K. C. Proc Natl Acad Sci U S A 2014, 111, 7807.
88. Scheie, P. O. Journal of bacteriology 1969, 98, 335.
137
89. Olijhoek, A. J.; Van Eden, C. G.; Trueba, F. J.; Pas, E.; Nanninga, N. Journal of bacteriology 1982, 152, 479.
90. Lee-Stadelmann, O. Y.; Stadelmann, E. J. In Methods in Enzymology; Academic Press: 1989; Vol. 174, p 225.
91. Sinensky, M. Proc Natl Acad Sci U S A 1974, 71, 522.
92. Cybulski, L. E.; Albanesi, D.; Mansilla, M. C.; Altabe, S.; Aguilar, P. S.; de Mendoza, D. Mol Microbiol 2002, 45, 1379.
93. Xiao, X.; Yu, X.; Khosla, C. Biochemistry 2013, 52, 8304.
94. Feng, Y.; Cronan, J. E. Mol Microbiol 2011, 80, 195.
95. Magnuson, K.; Jackowski, S.; Rock, C. O.; Cronan, J. E. Microbiological Reviews 1993, 57, 522.
96. Zhang, F.; Ouellet, M.; Batth, T. S.; Adams, P. D.; Petzold, C. J.; Mukhopadhyay, A.; Keasling, J. D. Metab Eng 2012, 14, 653.
97. Feng, Y.; Cronan, J. E. Journal of Biological Chemistry 2009, 284, 29526.
98. Fujita, Y.; Matsuoka, H.; Hirooka, K. Mol Microbiol 2007, 66, 829.
99. Cronan, J. E.; Weisberg, L. J.; Allen, R. G. Journal of Biological Chemistry 1975, 250, 5835.
100. Nunn, W. D.; Giffin, K.; Clark, D.; Cronan, J. E. Journal of bacteriology 1983, 154, 554.
101. Cao, Y.; Yang, J.; Xian, M.; Xu, X.; Liu, W. Applied Microbiology and Biotechnology 2010, 87, 271.
102. Rock, C. O.; Tsay, J. T.; Heath, R.; Jackowski, S. Journal of bacteriology 1996, 178, 5382.
138
103. DiRusso, C. C.; Heimert, T. L.; Metzger, A. K. The Journal of biological chemistry 1992, 267, 8685.
104. Cronan, J. E., Jr. Journal of bacteriology 1997, 179, 1819.
105. Campbell, J. W.; Cronan, J. E., Jr. Journal of bacteriology 2001, 183, 5982.
106. Makino, K.; Amemura, M.; Kim, S. K.; Nakata, A.; Shinagawa, H. Genes Dev 1993, 7, 149.
107. Konopka, M. In Hydrocarbon and Lipid Microbiology Protocols: Activities and Phenotypes; McGenity, T. J., Timmis, K. N., Nogales, B., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2017, p 69.
108. Gnaiger, E.; Steinlechner-Maran, R.; Méndez, G.; Eberl, T.; Margreiter, R. Journal of Bioenergetics and Biomembranes 1995, 27, 583.
109. Pouvreau, L. A. M.; Strampraad, M. J. F.; Berloo, S. V.; Kattenberg, J. H.; de Vries, S. In Methods in Enzymology; Poole, R. K., Ed.; Academic Press: 2008; Vol. 436, p 97.
110. Miniaev, M. V.; Belyakova, M. B.; Kostiuk, N. V.; Leshchenko, D. V.; Fedotova, T. A. Journal of Analytical Methods in Chemistry 2013, 2013, 249752.
111. Mills, A. Platinum Met. Rev. 1997, 41, 115.
112. Dmitriev, R. I.; Papkovsky, D. B. Cell Mol Life Sci 2012, 69, 2025.
113. Chan, S. P.; Fuller, Z. J.; Demas, J. N.; DeGraff, B. A. Analytical Chemistry 2001, 73, 4486.
114. Neidhardt, F. C.; Bloch, P. L.; Smith, D. F. Journal of bacteriology 1974, 119, 736.
115. Begot, C.; Desnier, I.; Daudin, J. D.; Labadie, J. C.; Lebert, A. Journal of Microbiological Methods 1996, 25, 225.
139
116. Pope, C. F.; McHugh, T. D.; Gillespie, S. H. In Antibiotic Resistance Protocols: Second Edition; Gillespie, S. H., McHugh, T. D., Eds.; Humana Press: Totowa, NJ, 2010, p 113.
117. Parke, D. Gene 1990, 93, 135.
118. Fan, J.-Y.; Cui, Z.-Q.; Wei, H.-P.; Zhang, Z.-P.; Zhou, Y.-F.; Wang, Y.-P.; Zhang, X.-E. Biochemical and Biophysical Research Communications 2008, 367, 47.
119. Tanenbaum, Marvin E.; Gilbert, Luke A.; Qi, Lei S.; Weissman, Jonathan S.; Vale, Ronald D. Cell 2014, 159, 635.
120. Voytas, D. Current Protocols in Molecular Biology 2000, 51, 2.5A.1.
121. Sochacki, K. A.; Shkel, I. A.; Record, M. T.; Weisshaar, J. C. Biophysical journal 2011, 100, 22.
122. Sochacki, K. A.; Shkel, I. A.; Record, M. T.; Weisshaar, J. C. Biophysical journal 2011, 100, 22.
123. Konopka, M. C.; Shkel, I. A.; Cayley, S.; Record, M. T.; Weisshaar, J. C. Journal of bacteriology 2006, 188, 6115.
124. Elowitz, M. B.; Surette, M. G.; Wolf, P.-E.; Stock, J. B.; Leibler, S. Journal of Bacteriology 1999, 181, 197.
125. Skjerdal, O. T.; Sletta, H.; Flenstad, S. G.; Josefsen, K. D.; Levine, D. W.; Ellingsen, T. E. Applied Microbiology and Biotechnology 1995, 43, 1099.
126. Wood, J. M. The Journal of General Physiology 2015, 145, 381.
127. Bremer, E.; Krämer, R. Annual Review of Microbiology 2019, 73, null.
128. Rojas, E.; Theriot, J. A.; Huang, K. C. Proceedings of the National Academy of Sciences 2014, 111, 7807.
129. Bayer, M. E. Microbiology 1968, 53, 395.
140
130. Bohnert, J. A.; Karamian, B.; Nikaido, H. Antimicrobial Agents and Chemotherapy 2010, 54, 3770.
131. Venter, H.; Mowla, R.; Ohene-Agyei, T.; Ma, S. Frontiers in microbiology 2015, 6, 377.
132. Hayashi, K.; Nakashima, R.; Sakurai, K.; Kitagawa, K.; Yamasaki, S.; Nishino, K.; Yamaguchi, A. J Bacteriol 2016, 198, 332.
133. Yu, E. W.; Aires, J. R.; Nikaido, H. Journal of Bacteriology 2003, 185, 5657.
134. Kram, K. E.; Finkel, S. E. Applied and environmental microbiology 2015, 81, 4442.
135. Schneider, D.; Fuhrmann, E.; Scholz, I.; Hess, W. R.; Graumann, P. L. BMC Cell Biol 2007, 8, 39.
136. van den Bogaart, G.; Hermans, N.; Krasnikov, V.; Poolman, B. Mol Microbiol 2007, 64, 858.
137. Quinn, P. J. Progress in Biophysics and Molecular Biology 1981, 38, 1.
138. Mansilla, M. C.; Cybulski, L. E.; Albanesi, D.; de Mendoza, D. Journal of bacteriology 2004, 186, 6681.
139. Leekumjorn, S.; Cho, H. J.; Wu, Y.; Wright, N. T.; Sum, A. K.; Chan, C. Biochimica et biophysica acta 2009, 1788, 1508.
140. Wiederschain, G. Y. Biochemistry (Moscow) 2011, 76, 1276.
141. Kang, M.; Day, C. A.; Kenworthy, A. K.; DiBenedetto, E. Traffic 2012, 13, 1589.
142. Mika, J. T.; Poolman, B. Curr. Opin. Biotechnol. 2011, 22, 117.
143. Meyer, P.; Dworkin, J. Res Microbiol 2007, 158, 187.
144. Waharte, F.; Steenkeste, K.; Briandet, R.; Fontaine-Aupart, M. P. Appl Environ Microbiol 2010, 76, 5860.
141
145. Sulavik, M. C.; Houseweart, C.; Cramer, C.; Jiwani, N.; Murgolo, N.; Greene, J.; DiDomenico, B.; Shaw, K. J.; Miller, G. H.; Hare, R.; Shimer, G. Antimicrob Agents Chemother 2001, 45, 1126.
146. Oswald, F.; Varadarajan, A.; Lill, H.; Peterman, E. J.; Bollen, Y. J. Biophysical journal 2016, 110, 1139.
147. Zheng, Q.; Lavis, L. D. Curr Opin Chem Biol 2017, 39, 32.
148. Dunn, K. W.; Kamocka, M. M.; McDonald, J. H. Am J Physiol Cell Physiol 2011, 300, C723.
149. Yatvin, M. B.; Gipp, J. J.; Dennis, W. H. Int J Radiat Biol Relat Stud Phys Chem Med 1979, 35, 539.
150. Nunn, W. D.; Giffin, K.; Clark, D.; Cronan, J. E., Jr. Journal of bacteriology 1983, 154, 554.
151. Numa, S.; Bortz, W. M.; Lynen, F. Advances in Enzyme Regulation 1965, 3, 407.
152. Heath, R. J.; Rock, C. O. Journal of Biological Chemistry 1996, 271, 27795.
153. Handke, P.; Lynch, S. A.; Gill, R. T. Metabolic Engineering 2011, 13, 28.
154. Yu, F.; Liu, X.; Tao, Y.; Zhu, K. FEMS Microbiol Lett 2013, 345, 141.
155. Englaender, J. A.; Jones, J. A.; Cress, B. F. 2017, 6, 710.
156. Donald L. Court; James A. Sawitzke, a.; Thomason, L. C. Annual Review of Genetics 2002, 36, 361.
157. Murphy, K. C. Journal of bacteriology 1998, 180, 2063.
158. Court, D. L.; Sawitzke, J. A.; Thomason, L. C. Annu Rev Genet 2002, 36, 361.
159. Mosberg, J. A.; Lajoie, M. J.; Church, G. M. Genetics 2010, 186, 791.
160. Datsenko, K. A.; Wanner, B. L. Proc Natl Acad Sci U S A 2000, 97, 6640. 142
161. Kuhlman, T. E.; Cox, E. C. Nucleic Acids Research 2010, 38, e92.
162. Henkel, S. G.; Ter Beek, A.; Steinsiek, S.; Stagge, S.; Bettenbrock, K.; de Mattos, M. J.; Sauter, T.; Sawodny, O.; Ederer, M. PLoS One 2014, 9, e107640.
163. Longmuir, I. S. Biochem J 1954, 57, 81.
164. Barron, E. S.; Ardao, M. I.; Hearon, M. J Gen Physiol 1950, 34, 211.
165. Vogelstein, B.; Gillespie, D. Proceedings of the National Academy of Sciences 1979, 76, 615.
166. Gao, B.; Mohan, R.; Gupta, R. S. Int J Syst Evol Microbiol 2009, 59, 234.
167. My, L.; Ghandour Achkar, N.; Viala, J. P.; Bouveret, E. Journal of bacteriology 2015, 197, 1862.
143