Characterizing Bile Acid Association As a Ligand and In

CHARACTERIZING BILE ACID ASSOCIATION AS A LIGAND AND IN

MICELLIZATION.

BRIAN SCOTT WERRY

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Adviser: Dr. Gregory P. Tochtrop

Department of Chemistry

CASE WESTERN RESERVE UNIVERSITY

January, 2014 CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Brian S. Werry

Candidate for the Doctor of Philosophy degree*.

Prof. Blanton S. Tolbert (Chair of the Committee, Department of

Chemistry, CWRU)

Prof. Gregory P. Tochtrop (Department of Chemistry, CWRU)

Prof. Irene Lee (Department of Chemistry, CWRU)

Prof. Paul R. Carey (Department of Chemistry, CWRU)

Prof. Brian A. Cobb (Department of Pathology, CWRU)

Date: November 21st, 2013

* We also certify that written approval has been obtained for any proprietary material

contained therein.

Table of Contents

Table of Contents ...... i

List of Tables ...... iv

List of Figures ...... v

Acknowledgements ...... viii

List of Abbreviations ...... ix

Abstract ...... xiii

Chapter 1. General Introduction ...... 1

1.1. Bile acids ...... 1

1.1.1. Physiologic importance ...... 1

1.1.2. Biosynthesis ...... 3

1.1.3. Secondary modifications ...... 4

1.1.4. Structural basis as detergents ...... 5

1.2. Enterohepatic circulation ...... 7

1.2.1. Molecular biology ...... 7

1.2.2. Fatty acid binding protein 6 (FABP6) ...... 10

1.3. Experimental methods ...... 11

1.3.1. Isothermal titration calorimetry of micellization ...... 11

1.3.2. Isothermal titration calorimetry of protein-ligand binding energetics ...... 12

1.3.3. Two-dimensional NMR of protein-ligand binding selectivity...... 12

1.4. Scope of the projects ...... 13

1.5. References ...... 14

Chapter 2. Site-Selective Recognition of Secondary Bile Acid by FABP6 ...... 17

2.1 Introduction ...... 17

2.1.1 Secondary bile acid – Lithocholic acid ...... 17

2.1.2 FABP6...... 17

2.2. Results and Discussion ...... 19

2.3. Materials and Methods ...... 36

2.4 References ...... 40

Chapter 3. Statistical Determination of a Bile Acid Micellization Model ...... 43

3.1. Introduction ...... 43

3.1.1. Bile acid self-association: Small’s model ...... 43

3.1.2. Micelle polydispersity ...... 47

3.1.3. Isothermal titration calorimetry of demicellization ...... 48

3.2. Proposed study ...... 49

3.3. Results and Discussion ...... 50

3.3.1. Uniform micellar system model...... 52

3.3.2. Non-uniform sequential stepwise model ...... 57

3.3.3. Non-uniform multimer model ...... 61

3.4 Conclusion ...... 67

3.5 Materials and Methods ...... 69

3.6 References ...... 71

Chapter 4. Evolutionary Significance of C24 Bile Acids’ Micellization Characteristics ..75

4.1. Introduction ...... 75

4.1.1. Bile acids as physiological detergents ...... 75

4.1.2. Diversity of bile acid chemical structure ...... 76

4.1.3. Bile acid side chain modification ...... 78

4.1.4. Purposed study ...... 79

4.2. Results and Discussion ...... 80

4.3. Materials and Methods ...... 90

4.3.1. Synthetic procedures ...... 91

4.4 References ...... 98

Appendix I. Origin C fitting functions ...... 99

Appendix II. NMR spectra of synthesized compounds ...... 103

Bibliography ...... 129

iii

List of Tables

Table 1.1.1. Nomenclature and relative abundance in human bile ...... 2

Table 1.1.2. Critical micellar concentrations for physiologically abundant bile acids ...... 7

Table 2.2.1. Stepwise binding parameters for 1:9 bile acid mixtures with hFABP6 ...... 24

Table 3.3.1. Parameter values from the uniform model fitting to ITC experimental data of the demicellization of CA, CDCA, and DCA ...... 56

Table 3.3.2. Parameter values from the non-uniform sequential stepwise model fitting to

ITC experimental data of the demicellization of CA, CDCA, and DCA ...... 60

Table 3.3.3. Parameter values from the non-uniform bi-micelle multimer model fitting to

ITC experimental data of the demicellization of CA, CDCA, and DCA ...... 64

Table 3.3.4. Parameter values from the non-uniform tri-micelle multimer model fitting to ITC experimental data of the demicellization of CA, CDCA, and DCA ...... 66

Table 4.2.1. Thermodynamic parameters for the micellization of CA and CDCA side chain derivatives calculated from the bi-micelle multimer model ...... 83

Table 4.2.2. Interpolated thermodynamic parameters for micellization of CA and CDCA side chain derivatives at 37°C ...... 84

List of Figures

Figure 1.1.1. Chemical structure of cholesterol and various bile acids ...... 2

Figure 1.1.2. Schematic representation of bile acid synthesis...... 4

Figure 1.1.3. Structural diversity of primary and secondary bile acids ...... 5

Figure 1.1.4. Ring conformation of cholic acid ...... 6

Figure 1.2.1. Molecular biology of the enterohepatic circulation ...... 9

Figure 2.2.1. Calorimetric results for LCA binding to human FABP6 ...... 21

Figure 2.2.2. Calorimetric results for LCA and CA binding to human FABP6 ...... 22

Figure 2.2.3. Calorimetric results for 1:9 ratio mixtures of LCA:CA and CDCA:CA

binding to human FABP6 ...... 24

Figure 2.2.4. Illustration representing exposed hydrophobic areas of bile acids ...... 25

Figure 2.2.5. 2D 1H/15N HSQC spectrum contour plot of 15N-GCA alone in buffer ...... 27

Figure 2.2.6. 2D 1H/15N HSQC spectrum contour plot of 3 mM 15N-GCA with 1 mM hFABP6...... 27

Figure 2.2.7. 2D 1H/15N HSQC spectrum contour plot of 1.5 mM 15N-GCA with 1.5 mM unlabeled GCDCA and 1 mM hFABP6...... 28

Figure 2.2.8. 2D 1H/15N HSQC spectrum contour plot of 1.5 mM 15N-GCA with 1.5 mM unlabeled GLCA and 1 mM hFABP6 ...... 28

Figure 2.2.9. A plot of the calculated observed dissociation constants for 15N-GCA with

GLCA and GCDCA ...... 29

Figure 2.2.10. 2D HSQC spectroscopic data depicting 15N-GCDCA switch in site selectivity ...... 31-32

Figure 2.2.11. 2D HSQC spectroscopic data depicting 15N-GCDCA competitive binding assay ...... 32-34

Figure 2.2.12. Plot of percent 15N-GCDCA bound to site 1 in a competitive binding assay against GLCA ...... 35

Figure 2.2.13. 2D HSQC spectroscopic data depicting 15N-GCDCA competitive binding assay against GUDCA ...... 36

Figure 3.1.1. Structural representations of bile acids ...... 44

Figure 3.1.2. A graphical depiction of Small’s self-association mechanism ...... 46

Figure 3.1.3. Aggregate size population ratios from literature ...... 48

Figure 3.3.1. Calorimetric plot of CA demicellization ...... 51

Figure 3.3.2. Enthalpic curve of CA demicellization ...... 51

Figure 3.3.3. Example fitting of the uniform model ...... 55

Figure 3.3.4. Example fitting of non-uniform sequential stepwise model ...... 59

Figure 3.3.5. Example fitting of non-uniform bi-micelle multimer model ...... 63

Figure 4.1.1. Primary bile acids for several species ...... 76

Figure 4.1.2. Phylogenetic tree of vertebrates depicting the evolution of bile acids ...... 77

Figure 4.1.3. The chemical structures of the bile acid side chain length derivatives ...... 80

Figure 4.2.1. The micelle concentration transition ranges for the bile acid side chain

derivatives ...... 81

Figure 4.2.2. Calculated ΔHmic of bile acids as a function of side chain length ...... 85

Figure 4.2.3. Calculated ΔCpmic, of bile acids as a function of side chain length ...... 86

Figure 4.2.4. Calculated micelle aggregate size of bile acids as a function of side chain

length...... 87

Figure 4.2.5. Calculated ΔGmic, of bile acids as a function of side chain length ...... 88

vii

Acknowledgements

First I want to recognize Case Western Reserve University’s chemistry department. In the winter of 2007 CWRU opened a door for me and my career after it seemed all had closed.

A most sincere thanks and gratitude to my advisor and mentor Prof. Gregory P.

Tochtrop for the freedom I desired and needed to grow as a professional scientist, for being there when it was really need most, and for being an inspiration as an admirable scientist who I have tried to model my own growth after.

I would like to thank all of the friends and colleagues I have met during my candidacy. They made a memorable 7 years fly by.

Finally, I would like to thank my family and close friends. Their support and overall caring love towards me gave me the tenacity to never give up. I love you all!

Mom, Dad, Sister-Allison, my beautiful wife Ashley, my beautiful baby girl Valerie

(holding you at the end of a day made everything alright again and renewed my perseverance), my awesome boys Drave and Corbin, Kirby U., Aaron V., Adam O., Jeff

Y., Mike J., and many others who know who they are.

“Let me tell you the secret that has led me to my goal. My strength lies solely in my tenacity.”

– Louis Pasteur.

viii

List of Symbols and Abbreviations

α Alpha

β Beta

°C The degree Celsius

°K The degree Kelvin

2-D Two-dimensional space

AIC Akaike information criterion

AKR1D1 5β-reductase

ASBT Apical sodium dependent bile acid transporter

BSEP Bile salt export pump

CA Cholic acid cal Calorie

CCl4 Tetrachloromethane

CDCA Chenodeoxycholic acid

CDCl3 Deuterated chloroform

CH2Cl2 Dichloromethane

CH2N2 Diazomethane

CH3CN Acetonitrile

CMC Critical micelle concentration

CYP7A1 7α-hydroxylase

ΔCpdemic Heat capacity of demicellization

ΔHdemic Enthalpy of demicellization

ΔHmic Enthalpy of micellization

D2O Deuterium oxide

DCA Deoxycholic acid

EA Ethyl acetate

EHC Enterohepatic circulation

ER Endoplasmic reticulum

EtOH Ethanol

FABP6 Fatty acid binding protein 6

FXR Farnesoid X receptor g Gram

Gly Glycine h Hours

H2 Hydrogen gas

H2O Hydrogen oxide

HCl Hydrogen chloride

Hex Hexane

HPLC High-pressure liquid chromatography

HSQC Heteronuclear single quantum coherence

Hz Hertz

ITC Isothermal titration calorimetry

Kcal Kilocalorie

KCl Potassium chloride kDa Kilodaltons

KN Association constant

KOH Potassium hydroxide

L Liter

LCA Lithocholic acid lit. Literature

M Molar concentration m/z Mass-to-charge ratio

Me Methyl mg Milligram

MHz Megahertz min Minute(s) mL Milliliter mm Millimeter mmol Millimole

Mn Number average molecular weights

Mp Melting point

N Aggregation number

N-mer A micelle made up of N associated bile acids

NaBH4 Sodium borohydride

NaCl Sodium chloride

NaOH Sodium hydroxide

Na2SO4 Sodium sulfate

nm Nanometer

NMR Nuclear magnetic resonance

NTCP Sodium dependent taurocholate cotransporter peptide

OATPs Organic anion transporting polypeptides

OSTα/β Organic solute transporter α & β heterodimer

Pd Palladium pKa Acid dissociation constant

SHP Small heterodimer partner

TFA Trifluoroacetic acid

THF Tetrahydrofuran

TLC Thin-layer chromatography

TRIS Tris(hydroxymethyl)aminomethane

UDCA Ursodeoxycholic acid

UV Ultraviolet

μg Microgram

μL Microliter

μM Micromolar concentration

xii

Characterizing Bile Acid Association as a Ligand and in Micellization

Abstract

BRIAN WERRY

Bile acids are an integral part of digestion and nutritional balance. Their role

within physiology is influenced by their unique amphiphilic structure and their ability to associate into micelles in solution. The work herein presents an experimental analysis of the associative properties of the secondary bile acid, lithocholic acid (LCA), to human

fatty acid binding protein 6 (hFABP6), a bile acid binding protein found exclusively in

the ileum enterocytes. The analysis was conducted through a binding energetics study

using isothermal titration calorimetry (ITC) and a binding-site selective study using a two-dimensional NMR spectroscopic binding site assay method. While LCA exhibited no binding to hFABP6 when alone, it bound with high positive cooperative affinity when in solution with cholic acid (CA). Interestingly, LCA had the highest cooperative binding affinity out of all the bile acid mixtures tested. The self-association of bile acids was also investigated. A statistical comparison of micellization models with differing definitions of micelle size dispersity was conducted using ITC. A bi-micelle multimer model was identified as the best model for fitting calorimetric data from bile acid demicellization studies, and it is suggested as a standard model for all bile acid self- association studies. Application of this standard model was then demonstrated through

xiii its use in fitting calorimetric data and calculation of thermodynamic values for bile acids of varying side chain lengths. The results were used in identifying significant properties for the C24 bile acids, which showed a physiological balance between function as detergents and cytosolic solubility with free energy values of ΔGdimerization = -

-1 -1 559.21 cal*mol and ΔGoctamerization = -2188.62 cal*mol .

xiv

Chapter 1 General Introduction

1.1 Bile acids

1.1.1 Physiologic importance

Medical belief up through the Middle Ages thought the body’s health was dependent on four “humors”, or vital fluids. These were Blood, Phlegm, Yellow Bile, and Black Bile. It was believed that excess of either bile produced aggression and depression in an individual. These theories derived from the belief that the organs of the

body were connected to the soul and one’s emotional state. A healer might attribute

excess anger to liver disorders and the resulting imbalance of humors. Our present day

knowledge of bile has been corrected with detailed scientific accounts. A significant

contribution came from the discovery that the major constituent of bile is a class of

molecules collectively, and conveniently, named bile acids. Since their discovery, an

enormous amount of work has brought us to our current understanding, which includes

their characterization, biosynthesis, physical chemistry, physiologic role, and

pathophysiology. Their biosynthesis from cholesterol, which is the primary means of

cholesterol removal, consists of an assortment of structural modifications that produces a

large diverse class of biomolecules. Structural representations of cholesterol and various

bile acids can be seen in Figure 1.1.1. Bile acids participate as physiological detergents

during the digestion and absorption of dietary lipids and fat-soluble vitamins in the small intestine. At the distal ileum of the small intestine, bile acids are reabsorbed and reused

with remarkable efficiency, which will be discussed in detail below. Table 1.1.1 lists the

most common bile acids along with their relative abundance. During this reabsorption

process, bile acids participate in a number of signaling processes that regulate a network

1 of metabolic pathways. This cycle of synthesis, digestion, reabsorption, and recycling of bile acids is termed the enterohepatic circulation (EHC) and abnormalities in this cycle are attributed to a number of medical diseases, including cholestasis and gallstones.

O 21 23 24

25 27 18 20 22 OH 12 11 17 26 19 13 16 1 9 H 14 2 10 8 15 3 H H 5 7 HO 4 HO OH 6 H Cholesterol Chenodeoxycholic acid

O O

OH NH OH NH O

OH SO3H

HO OH HO H H acid acid Glycocholic Taurodeoxycholic Figure 1.1.1. Chemical structures of cholesterol with carbon numbering and various bile acids.

Relative Bile Acid Abbreviation Hydroxylation Conjugation Abundance Cholic acid CA 3α, 7α, 12α None Trace Glycocholic acid GCA 3α, 7α, 12α Glycine 40% Taurocholic acid TCA 3α, 7α, 12α Taurine 15% Chenodeoxycholic acid CDCA 3α, 7α None Trace Glycochenodeoxycholic acid GCDCA 3α, 7α Glycine 20% Taurochenodeoxycholic acid TCDCA 3α, 7α Taurine 5% Deoxycholic acid DCA 3α, 12α None Trace Glycodeoxycholic acid GDCA 3α, 12α Glycine 7% Taurodeoxycholic acid TDCA 3α, 12α Taurine 3% Lithocholic acid LCA 3α None Trace Glycolithocholic acid GLCA 3α Glycine 3% Taurodeoxycholic acid TLCA 3α Taurine 1%

Table 1.1.1. Nomenclature and relative abundance in human bile.

Scientific interests in bile acids have been focused into three primary categories:

Their metabolic synthesis from cholesterol, detergent properties as emulsifying agents during digestion, and their participation in signaling mechanisms. The projects presented here will address the latter two categories.

1.1.2 Biosynthesis

Bile acid anabolic metabolism from cholesterol is a brilliant example of evolutionary design in frugality and function in that the metabolic pathway not only is the primary means of solubilizing cholesterol for its removal, but it is also utilizing a readily available biomolecule to create a physiologic detergent that is needed in large quantities

(~3-5 grams per meal). A schematic representation of bile acid synthesis can be seen in

Figure 1.1.2. The steroidogenesis of bile acids is unique relative to other steroids. The

initiation of their synthesis involves the hydroxylation of the 7-position carbon by 7α-

hydroxylase (CYP7A1) and then a subsequent cis-AB-ring fusion by 5β-reductase

(AKR1D1), both being structural modifications only seen in bile acids. Furthermore, modification of the side chain through a β-oxidative cleavage produces bile acids with five carbon carboxylic acid side chains. Among steroid biomolecules, this is also a unique modification. A more detailed account of bile acid synthesis with an emphasis on side chain modification will be presented in a Chapter 4. Before digestive excretion, the

substrates are conjugated to either glycine or taurine and under physiological conditions

this conjugation effectively lowers the pKa such that these detergents are fully ionized and soluble. Enzyme expression for the metabolic production of bile acids is tightly

regulated by nuclear hormone receptors and other transcription factors that are sensitive

to the bile acid levels and heterogeneity, and ensures a constant supply of bile acids.

Hepatocyte

(ER) CYP7A1 rate limiting enzyme

HO HO OH Microsomes Cholesterol 7α-hydroxycholesterol

Ring Modifications α - 5 -reduction (AKR1D1) - hydroxylation Mitochondria

β-oxidation

Peroxisomes HO OH HO OH H H 5β-cholestan-3α α-diol Chenodeoxycholic acid ,7

Figure 1.1.2. Schematic representation of bile acid steroidogenesis. CYP7A1: cholesterol 7α hydroxylase; AKR1D1: 5α-reductase; ER: endoplasmic reticulum.

1.1.3 Secondary modifications

A second range of structural modifications to bile acids can take place during digestion. While in the gut, bile acids are subjected to numerous modifications by gut bacteria that include deconjugation, dehydration, oxidation of the hydroxyl groups, and epimerization. Of these modifications, deconjugation and removal of the 7α-hydroxyl

group predominate, which forms deoxycholic acid (DCA; 3 α, 12 α -dihydroxy) and lithocholic acid (LCA; 3 α -monohydroxy) from glycocholic acid and

4 glycochenodeoxycholic acid, respectively. The bile acids resulting from these bacterial modifications are termed secondary bile acids. Compared to primary bile acids, secondary bile acids are not as efficiently reabsorbed. However, their passive absorption in the ileum and colon contributes to the total recycled bile acid pool, albeit constituting a smaller faction of it. The modifications resulting in secondary bile acids impart new physical and physiologic properties that have been the source of interest for numerous investigations into their role within the EHC.1 Figure 1.1.3 depicts the possible structural diversities imparted by secondary gut bacterial modification.

O O O

OH OH OH OH OH OH OH Acids Primary

Bile OH HO OH HO OH HO OH H H OH OH OH Chenodeoxycholic acid Cholic acid All Mammals, Most Vertebrates Humans: 12α-hydroxyl Across species: Variable hydroxylation patterns

Gut Bacterial Modification

Dehydroxylation Epimerization Oxidation trans-cis ring transformation O O O O

OH OH OH OH O OH OH OH Acids Bile Secondary Secondary HO HO OH HO OH HO OH H H H H Deoxycholic acid Ursodeoxycholic acid 12-keto Allocholic chenodeoxycholic acid acid

Figure 1.1.3. Structural diversity that can be found in primary and secondary bile acid modification.

1.1.4 Structural basis as detergents

Bile acids’ primary physiologic role is during digestion, where they aid in emulsification by preforming like detergents. A role their steroidogenesis builds them

for, which effectively imparts an amphiphilic structure into the steroid tetracycle ring

system. This amphipathicity arises from the geometry of all the hydroxyl groups

appended to the α-face, while the β-face is demarcated by the C-18 and C-19 methyl

groups. The facial amphipathicity is illustrated in Figure 1.1.4 with a 3D representation

of bile acids’ structural polar and non-polar regions.

Figure 1.1.4. Ring conformation of cholic acid depicting the aliphatic facial structure.

It is this amphiphilic quality that allows bile acids to self-associate over a range of

concentrations to form micelles. The range at which bile acids undergo self-association

to form micelles is dependent on the modifications made to its steroid ring nucleus, and

side chain. Table 1.1.2 displays this dependency by listing several bile acids with an

average of their reported critical micellar concentration (CMC). The CMC is defined as

the concentration at which a self-associating compound forms micelles and is used in measuring detergency of compounds. Reports on the CMC of bile acids are numerous, as

it is a popular means for describing their detergent properties; however, large

discrepancies exist in the reported values. This has provoked controversies between

reported absolute values.2

Bile Acid CMCa (mM) Cholic acid 9.5

Taurocholic acid 6.9

Deoxycholic acid 4.2

Taurodeoxycholic acid 3.5

Table 1.1.2. Critical micellar concentrations for several physiologically abundant bile

acids. a Values are an average of reported values at 25 °C.2a

Bile acids’ ability to micellize discloses their intended physiological role, which is

to solubilize dietary lipids that would otherwise go through digestion unabsorbed. The

unique nature of these biosynthetic detergents has interested researchers in not only

understanding their collective detergent property during digestion, but also their use in

application in other fields such as protein purification,3 drug delivery,4 and chromatography.5

1.2 Enterohepatic circulation

1.2.1 Molecular biology

Despite the large amounts (~30 g) of bile acids used throughout a day, only a small amount is lost from the body. This efficiency of reabsorption and recycling is in a large part due to the transporting proteins of the EHC system. An illustration of this cycle can be seen in Figure 1.2.1.6 After bile acid synthesis, the enterohepatic circulation

begins with the excretion of bile into the proximal duodenum of the small intestine. After

traversing the intestine and aiding in the absorption of dietary lipids, bile acids are

reabsorbed in the ileum by apical sodium dependent bile acid transporter protein (ASBT)

with high efficiency.7 There bile acids transport and diffuse across the enterocyte to the

7 basolateral membrane, which sets them up for transport across the membrane via organic solute transporter α & β heterodimer (OSTα/β) into the portal blood circulation.8 Finally, their reabsorption into the hepatocytes by Na+-dependent taurocholate cotransporter peptide (NTCP)9 brings bile acids back to the beginning of their circulation cycle. Upon their return, they can be re-modified and re-conjugated before they are transported back into the gallbladder by canalicular bile salt export pump (BSEP) for storage till the next digestion.

Figure 1.2.1. Molecular biology of the enterohepatic circulation. After bile acid synthesis the compounds are exported out of the hepatocyte by bile salt export pump (BSEP) into the gallbladder for storage until digestion. After bile acids have transposed through the small intestine they are reabsorbed at the distal ileum via apical sodium dependent bile acid transporter (ASBT) where within the enterocyte they interact with the binding proteins farnisoid X receptor (FXR) and fatty acid binding protein 6 (FABP6) as signaling molecules in feedback pathways. Their efflux out of the enterocytes is through organic solute transporter α and β (OSTα/β) into the portal venous circulation that transports the reabsorbed bile acids back to the hepatocyte where circulation is completed once Na+-dependent taurocholic cotransporting polypeptide (NTCP) and organic anion transporting polypeptides (OATPs) influx the bile acids back into the hepatocytes for recycling and reuse.

Bile acids participate in signaling mechanisms that control two sides of this

biological system. These are bile acid synthesis and bile acid transport. The nuclear

farnesoid X receptor (FXR) plays a central role in this regulation of bile acid synthesis

and transport.10 Coincidentally, the natural ligands for FXR happen to be bile acids,11 and upon its activation, the expression of small heterodimer partner (SHP), a transcriptional repressor, is induced. The cascade of signaling processes, as a result of

SHP’s inhibition on several transcription factors, results in the following effects:

Lowering bile acid production through inhibition of CYP7A1 and CYP8B1, decreased

reuptake through inhibition of ASBT and NTCP, and increased excretion through

induction of BSEP and OSTα/β. FXR also induces fatty acid binding protein 6, which is an ileum bile acid binding protein. Collectively, FXR activation protects enterocytes and hepatocytes from the cytotoxic effects of high levels of bile acids.12

1.2.2 Fatty acid binding protein 6

Fatty acid binding protein 6 (FABP6) has been shown to be an integral part of the enterohepatic circulation, but still lacks a define description of its full role. It was initially thought to be a hormone, called gastrotropin, which was postulated to participate in initiation of gastric secretions.13 It has since been identified as a member of the fatty

acid binding protein (FABP) family that share similar structures along with small

molecular masses around 14kDa14 and is abundantly expressed in the ileum where it

functions in binding bile acids.15 It is the only member of the FABP family to selectively

bind bile acids over fatty acids. Interestingly, FABP6 has the ability to bind two

molecules of bile acids with weak intrinsic affinities, but with incredible positive

cooperativity.16 More recently it has been reported, FABP6 possesses a remarkable

binding site selectivity that is governed by a single hydroxyl group at the C-12 position

on its bile acid ligands.17 The protein is expressed at high levels in the ileum enterocytes,

which prompts the belief it takes part in bile acids’ trans-cytoplasmic transport and

cytosolic protection from toxic levels of bile acids. FABP6 has the interesting property

that its ligands, bile acids, directly influence its expression through the nuclear receptor

FXR. Work by Nakahara et al. indicates ligand bound FABP6 promotes FXR activity

through a mutual interaction, essentially self-regulating its own expression and acting as a nuclear transporting protein.18 Conceptually, FABP6’s role in the enterohepatic system

is to contribute to a specialized intricate mechanism that is necessary to maintain a

normal bile acid pool to ensure adequate concentration of bile acid micelles in the

intestine. However, its role has yet to be defined completely, and that is a goal of this

work: To gain insights into FABP6’s binding characteristics to structurally diverse bile

acids and apply that insight to understanding FABP6’s role in the EHC.

1.3 Experimental methods

1.3.1 Isothermal titration calorimetry of micellization

Isothermal titration calorimetry (ITC) has proven to be a beneficial tool in

studying detergents.19 It has the advantage of being able to determine both the critical

micelle concentration (CMC) and the enthalpy of demicellization (ΔHdemic) from a single experiment. In addition, the precision of the determined values are an improvement over other experimental techniques, this is due to ITC’s experimentally observed quantity being the direct measurement of ΔHdemic. The derived property values that can be

obtained from this include association equilibrium constants and thermodynamic values.

These physical property values are used in the evaluation and comparison of detergent properties. Therefore, the accuracy of ITC as an experimental technique for monitoring demicellization made it a good choice for the bile acid micellization studies undertook here.

1.3.2 Isothermal titration calorimetry of protein-ligand binding energetics

ITC is a standard method for studying the binding of ligand to proteins. It provides a simple and reliable means for measuring the energetics of the binding process.

Herein, it is used to study the association energies of bile acid binding to FABP6. Fitting calculations on ITC experimental data by a two-step two-binding sites model then provided dissociation constants and thermodynamic values, that were used to describe the mechanism of bile acids binding to FABP6.

1.3.3 Two-dimensional NMR of protein-ligand binding selectivity

Two-dimensional 1H/15N heteronuclear single quantum coherence (HSQC) NMR

was used in conjunction with the ITC analysis to identify secondary bile acid’s binding

site selectivity between the two binding sites on FABP6. This was done using

isotopically enriched glycine conjugated bile acids, cholic acid and chenodeoxycholic

acid. Bound ligands at different binding pockets experience distinct interactions allowing

differentiation of the 15N chemical shifts between unbound, binding site 1, and binding

site 2. A systematic examination of secondary bile acids binding with FABP6 allowed

comparisons of bile acids’ global affinities for FABP6.

1.4 Scope of the projects

Described herein are research projects that develop association profiles for bile

acids to FABP6 and in micellization based on their structural diversity. Chapter 2

addresses binding energetics and site-selective recognition for secondary bile acid lithocholic acid to FABP6 through ITC and two-dimensional 1H/15N (HSQC) NMR

experiments. Chapter 3 describes a problem found within the literature on bile acid micellization where there are numerous discrepancies in reported values. The project takes on a systematic study of bile acid self-association models through least square curve fitting algorithms to ITC experimental data. It is then proposed the best fitting model, being the best representation of bile acid micellization, be used henceforth as the standard in bile acid self-association studies. Chapter 4 reports on the micellization properties of bile acid derivatives with differing side chain lengths using the determined standard model from Chapter 3. The results are used to discern the potential evolutionary significance of the higher evolved species’ C24 bile acids.

1.5 References

1. (a) Staudinger, J. L.; Goodwin, B.; Jones, S. A.; Hawkins-Brown, D.; MacKenzie, K.

I.; Latour, A.; Liu, Y. P.; Klaassen, C. D.; Brown, K. K.; Reinhard, J.; Willson, T. N.; Koller,

B. H.; Kliewer, S. A., The nuclear receptor PXR is a lithocholic acid sensor that protects

against liver toxicity. Proc. Natl. Acad. Sci. U. S. A. 2001, 98 (6), 3369-3374; (b) Trauner,

M.; Graziadei, I. W., Review article: mechanisms of action and therapeutic applications of

ursodeoxycholic acid in chronic liver diseases. Aliment. Pharmacol. Ther. 1999, 13 (8), 979-

995; (c) Bayerdorffer, E.; Mannes, G. A.; Ochsenkuhn, T.; Dirschedl, P.; Wiebecke, B.;

Paumgartner, G., Unconjugated Secondary Bile-Acids in the Serum of Patients with

Colorectal Adenomas. Gut 1995, 36 (2), 268-273.

2. (a) Coello, A.; Meijide, F.; Nunez, E. R.; Tato, J. V., Aggregation behavior of bile

salts in aqueous solution. J. Pharm. Sci. 1996, 85 (1), 9-15; (b) Kratohvil, J. P., Size of bile

salt micelles: Techniques, problems and results. Adv. Colloid Interface Sci. 1986, 26 (0), 131-

154.

3. Hjelmeland, L. M., A Non-Denaturing Zwitterionic Detergent for Membrane

Biochemistry - Design and Synthesis. Proceedings of the National Academy of Sciences of the United States of America-Biological Sciences 1980, 77 (11), 6368-6370.

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Schubert, G.; Urmann, M., Modified bile acids as carriers for peptides and drugs. J.

Controlled Release 1997, 46 (1-2), 17-30.

5. Takeuchi, T.; Chu, J.; Miwa, T., Bile acids as stationary phase in liquid

chromatography. Chromatographia 1998, 47 (3-4), 183-188.

6. Small, D. M.; Dowling, R. H.; Redinger, R. N., The enterohepatic circulation of bile

salts. Arch. Intern. Med. 1972, 130 (4), 552-73.

7. Dawson, P. A.; Oelkers, P., Bile acid transporters. Curr. Opin. Lipidol. 1995, 6 (2),

109-14.

8. Weinberg, S. L.; Burckhardt, G.; Wilson, F. A., Taurocholate transport by rat intestinal basolateral membrane vesicles. Evidence for the presence of an anion exchange transport system. J. Clin. Invest. 1986, 78 (1), 44-50.

9. Hagenbuch, B.; Stieger, B.; Foguet, M.; Lubbert, H.; Meier, P. J., Functional expression cloning and characterization of the hepatocyte Na+/bile acid cotransport system.

Proc. Natl. Acad. Sci. U. S. A. 1991, 88 (23), 10629-33.

10. Chiang, J. Y. L., Regulation of bile acid synthesis: pathways, nuclear receptors, and mechanisms. J. Hepatol. 2004, 40 (3), 539-551.

11. Makishima, M.; Okamoto, A. Y.; Repa, J. J.; Tu, H.; Learned, R. M.; Luk, A.; Hull,

M. V.; Lustig, K. D.; Mangelsdorf, D. J.; Shan, B., Identification of a nuclear receptor for bile acids. Science 1999, 284 (5418), 1362-5.

12. Grober, J.; Zaghini, I.; Fujii, H.; Jones, S. A.; Kliewer, S. A.; Willson, T. M.; Ono,

T.; Besnard, P., Identification of a bile acid-responsive element in the human ileal bile acid- binding protein gene. Involvement of the farnesoid X receptor/9-cis-retinoic acid receptor heterodimer. J. Biol. Chem. 1999, 274 (42), 29749-54.

13. Wider, M. D.; Duhaime, P. M. Q.; Weisman, R. L., Chemical Characterization of

Circulating Porcine Ileal Polypeptide in Plasma from Normal Adult-Pigs. Endocrinology

1986, 118 (4), 1546-1550.

14. Fujita, M.; Fujii, H.; Kanda, T.; Sato, E.; Hatakeyama, K.; Ono, T., Molecular-

Cloning, Expression, and Characterization of a Human Intestinal 15-Kda Protein. Eur. J.

Biochem. 1995, 233 (2), 406-413.

15. (a) Gantz, I.; Nothwehr, S. F.; Lucey, M.; Sacchettini, J. C.; Delvalle, J.; Banaszak,

L. J.; Naud, M.; Gordon, J. I.; Yamada, T., Gastrotropin - Not an Enterooxyntin but a

Member of a Family of Cytoplasmic Hydrophobic Ligand-Binding Proteins. J. Biol. Chem.

1989, 264 (34), 20248-20254; (b) Sacchettini, J. C.; Hauft, S. M.; Vancamp, S. L.; Cistola, D.

P.; Gordon, J. I., Developmental and Structural Studies of an Intracellular Lipid-Binding

Protein Expressed in the Ileal Epithelium. J. Biol. Chem. 1990, 265 (31), 19199-19207; (c)

Miller, K. R.; Cistola, D. P., Titration Calorimetry as a Binding Assay for Lipid-Binding

Proteins. Mol. Cell. Biochem. 1993, 123 (1-2), 29-37.

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Energetics by NMR: Site-specific binding in a positively cooperative system. Proc. Natl.

Acad. Sci. U. S. A. 2002, 99 (4), 1847-1852.

17. Tochtrop, G. P.; DeKoster, G. T.; Covey, D. F.; Cistola, D. P., A Single Hydroxyl

Group Governs Ligand Site Selectivity in Human Ileal Bile Acid Binding Protein. J. Am.

Chem. Soc. 2004, 126 (35), 11024-11029.

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Sensitivity Titration Microcalorimeter. Langmuir 1998, 14 (15), 3986-3990.

Chapter 2 Site-Selective Recognition of Secondary Bile Acid by FABP6

2.1 Introduction

2.1.1 Secondary bile acid – Lithocholic acid

Lithocholic acid (LCA) is the resulting hydrophobic secondary bile acid from gut

bacterial 7α-dehydroxylation of chenodeoxycholic acid. LCA is reported to be at

elevated levels in patients with liver disease and has been linked to intrahepatic

cholestasis. The toxicity that LCA can induce makes it the most potent cytotoxic bile

acid. We have evolved, however, means for protection against LCA’s toxicity. The

nuclear receptor, pregnane X receptor (PXR), acts as a monitor for LCA levels.1 When activated by LCA, PXR up-regulates the expression of cytochrome P450 3A (CYP3A) and sulfotransferase 2A (SULT2), which are metabolic enzymes that modify LCA for its elimination.1 CYP3A catalyzes hydroxylation of the C6-position on reabsorbed LCA to facilitate LCA’s excretion through the feces and urine, thus preventing LCA induced hepatotoxicity.2 Recently, the vitamin D receptor has also been reported to bind LCA and induce CYP3A.3 All these metabolic signaling pathways that involve monitoring

LCA and its induced toxicity prompt the inquiry, “What does the collective binding

mechanisms of LCA wholly entail, and how does something like bile acid binding

proteins impact these signaling mechanisms?”

2.1.2 FABP6

Fatty acid binding proteins (FABP) are a family of proteins that bind fatty acids

and other lipophilic biomolecules that are thought to act as carrier proteins transporting

small molecule ligands between membranes and intracellular receptors. They tend to be

the most abundant soluble proteins in the tissues they are found in, typically accounting

for 2-5% of the total cytosolic protein. The tissue-selective expression of specific FABP isoforms and differing ligand specificity between isoforms suggest specific physiological roles among FABP family members.

FABP6 was first identified by Wider et al.4 from ileum cross sections of pigs and

was initially characterized as a hormone that prompted gastric secretions, and was

consequently termed gastrotropin. Later, contributions from Gantz et al.5 and Sacchettini

et al.6 were able to show FABP6 was not excreted but actually abundantly expressed in

the ileum cytosol and would bind bile acids. Binding activities for this protein were

further defined by Miller et al.7 when they showed FABP6’s ability to bind fatty acids

was greatly reduced in comparison to the rest of the FABP family, and that bile acids

were the preferred ligand. Kramer et al.8 determined the protein to be closely associated

with the sodium dependent bile acid transport across the apical brush border membrane

of the ileum enterocytes. In addition, the identification of bile acid stoichiometric

binding of 2:1 to FABP6 through a cooperative increase in affinity suggests a positive-

feedback regulation mechanism for active uptake of bile acids at the ileum.8-9 Through isothermal titration calorimetry and two-dimensional (2D)-NMR spectroscopy with isotopic enriched bile acids, Tochtrop et al. reported on the macroscopic binding energies and site selectivity of human-FABP6 to several of the major bile acid constituents.10

According to work by Toke et al., this cooperativity and site selectivity are not associated, as the cooperative affinity for bile acids is dependent on both entropic and enthalpic influences, while the selectivity is dependent on localized enthalpic effects.11

These studies have been substantial in the characterization of the EHC and bile

acid reabsorption. However, they have only considered the major bile acids cholic acid,

chenodeoxycholic acid, and deoxycholic acid. A report by Kramer et al.8 did include ursodeoxycholic acid (UDCA) in its differential photoaffinity labeling study on FABP6.

UDCA was shown to have a marked affinity over chenodeoxycholic acid to FABP6.

Besides that one report, no other work has addressed the binding properties of FABP6 to minor secondary bile acids. While they are present in lower concentrations than major bile acids, their physiological role as signaling molecules is significant. In addition,

FABP6’s ability to recognize different bile acids is an important built in mechanism, one not thoroughly investigated yet. Thus, the work presented here undertakes a continuation of preceding works by Tochtrop et al. by using their established ITC and 2D-NMR spectroscopy experimental methods to further investigate the cooperative binding affinities and site selectivity of the secondary bile acid lithocholic acid.

2.2 Results and Discussion

To evaluate FAPB6’s affinity for LCA alone, initial experimentation was done

using ITC. Aliquots of LCA (5.55 mM in “buffer” – 20 mM phosphate buffer, 135 mM

KCl, 10 mM NaCl pH 7.4) were injected into recombinant human-FABP6 (hFABP6)

(0.20 mM protein in buffer) at 37 °C and the heat of binding was measured. Figure

2.2.1.A displays the instrument’s calorimetric data for this experiment along with a similar heat of dilution experiment done with injection of LCA into buffer alone. After subtracting the heat of dilution from the ligand-protein binding results (Figure 2.2.1.B) it

was found that LCA displays no affinity for binding to hFABP6 when in a homogenous

19 bile acid solution. The plot trends along the zero line as a result of no binding. The large point deviations in the plot are a result of LCA’s poor solubility. This preliminary initial result was indicative that there is no affinity between hFABP6 and secondary bile acid

LCA.

Figure 2.2.1. Isothermal titration calorimetry results for lithocholic acid binding to human

FABP6 (hFABP6). A) The calorimetric data collected at 37 °C for 5.55 mM LCA injections into

(●) 0.20 mM hFABP6 in 20 mM phosphate buffer, 135 mM KCl, 10 mM NaCl, pH 7.4, and (X) buffer alone. B) The calorimetric data after subtraction of the heat of dilution.

Guided by the reported asymmetry in hFABP6’s interactions with bile acids,

additional experiments were performed to investigate cooperative effects on LCA’s

affinity when in heterotypic mixtures with primary bile acid CA. When a 1:1 ratio of CA

and LCA (5.55 mM CA, 5.55 mM LCA in buffer) was injected into hFABP6 (0.20 mM

in buffer) at 37 °C a similar calorimetric curve to CA (11.1 mM CA in buffer) binding

hFABP6 (0.20 mM protein in buffer) was observed. Figure 2.2.2 provides overlaying

plots for their comparison. The experimental results indicate that hFABP6 can bind

LCA, but only through the cooperative binding effects of CA.

Figure 2.2.2. Isothermal titration calorimetry results for lithocholic acid and cholic acid binding

to hFABP6. The calorimetric data collected at 37 °C for (●) 5.55 mM LCA and 5.55 mM CA injections into 0.20 mM hFABP6 in 20 mM phosphate buffer, 135 mM KCl, 10 mM NaCl, pH

7.4, and (X) 11.1 mM CA injections into 0.20 mM hFABP6 in buffer.

Further experimental studies were conducted to determine the level of cooperative

binding of LCA with CA, as compared to CDCA with CA, a combination which has

already been shown to possess high positive cooperative binding.9b Again, the low

solubility of LCA caused disorder within the plots, so further mixed ligand samples were

brought down to a 1:9 ratio where LCA’s solubility was much better. Figure 2.2.3

displays the calorimetric binding isotherms for a 1:9 LCA to CA ratio (11.1 mM total bile

acid in buffer), a 1:9 CDCA to CA ratio (11.1 mM total bile acid in buffer), and a

solution of entirely CA (11.1 mM in buffer) into hFABP6 (0.20 mM protein in buffer) at

37 °C. The experimental plots were fit using a two-step binding model that was coded

into a non-linear least squares fitting algorithm with Origin 8.1 software. The average

calculated binding parameters are presented in Table 2.2.1. From the fitting calculations,

obs the observed first step dissociation constant, Kd1 , shows an increase for both mixed

ligand samples, with the constants being of the order CA

obs second ligand shows a decrease in the dissociation constant, Kd2 , for both mixed ligand

samples, with the constants being of the order CA>CDCA>LCA. Calculated Hill

coefficients, a quantitative measure of macroscopic cooperativity, reveal LCA to have the

strongest positive cooperativity, with the values being of the order CA

The CDCA results are not in agreement with the previous report9b of CDCA having a

higher affinity for the first binding step and lower positive cooperativity when compared

to CA. The difference is attributed to this work’s bile acid mixture ratio being much

lower, 1:9 compared to 1:1.

Figure 2.2.3. Isothermal titration calorimetry results for 1:9 ratio mixtures of LCA:CA and

CDCA:CA binding to hFABP6. The calorimetric data collected at 37 °C for: (♦) 11.1 mM CA;

(□) 1.11 mM CDCA and 9.99 mM CA; (▲) 1.11 mM LCA and 9.99 mM CA injections into

0.20 mM hFABP6 in 20 mM phosphate buffer, 135 mM KCl, 10 mM NaCl, pH 7.4.

obs obs obs obs a Bile acid, mixtures are at a 1:9 ratio K d1 (M) K d2 (M) ΔH°1 (cal/mol) ΔH°2 (cal/mol) Hill coeff -4 -5 2 4 Cholic acid 2.01 (±0.05) x 10 6.50 (±0.94) x 10 3.80 (±2.49) x 10 -1.81 (±0.26) x 10 1.28 (±0.08) -4 -5 2 4 Chenodeoxycholic acid + Cholic acid 2.46 (±0.22) x 10 4.60 (±0.44) x 10 7.60 (±3.42) x 10 -1.80 (±0.05) x 10 1.40 (±0.03) -4 -5 2 4 Lithocholic acid + Cholic acid 6.50 (±0.67) x 10 3.28 (±0.70) x 10 13.89 (±0.65) x 10 -2.21 (±0.24) x 10 1.63 (±0.02)

Table 2.2.1. Calculated stepwise binding parameters for bile acid mixtures with hFABP6 at

a obs/ obs 1/2 37 °C. Calculated Hill coefficient at half-saturation using nHill = 2/[1 + (Kd2 Kd1 ) ].

The results here are best described by a two-step dependent binding mechanism to non-identical sites, which is a slight modification to the previously proposed model. This involves the first binding step being of weak intrinsic affinity, with decreasing affinity

obs (increasing Kd1 ) as the hydrophobicity of the bile acid ligand increases.

Hydrophobicity of the tested bile acids go in order of CA

depicts the exposure of each bile acid’s hydrophobic area to emphasize the conceptual

difference in their associative surface interactions. The subsequent binding step at site 2,

being under the cooperative control8-9, 10 of which bile species was bound first, increases

obs in positive cooperativity (decreasing Kd2 ) as the hydrophobicity of already bound bile

acid increases. The binding isotherm parameter values agree with this cooperativity, and

suggest it is affected by the hydrophobic effect of desolvation.12 An example of this

stepwise binding would involve the binding of LCA to hFABP6 at a low affinity, then through a cooperative effect CA binds and encloses LCA into the binding pocket through the driving force of the hydrophobic effect. Interestingly, this mechanism is supported by

obs the more hydrophobic ligand mixtures having larger exothermic ΔH°2 , a

thermodynamic property of removing hydrophobic surfaces from exposure to water.

Figure 2.2.4. Conceptual illustration representing exposed hydrophobic area for cholic acid,

chenodeoxycholic acid, and lithocholic acid.

With evidence of LCA’s cooperativity with CA being greater than the homotypic

complex of CA and the heterotypic complex with CDCA and CA, the question then

arises, “Is the cooperativity significant, especially if you consider LCA only makes up a small fraction of the bile acid pool, which CA and CDCA dominate?” The overall

obs obs affinity, calculated by the products of Kd1 and Kd2 , of the LCA and CA mixture is

the lowest of the three, which does not seem promising. However, this can be misleading

as the binding constants calculated here are only the observed macroscopic binding

constants. For a doubly ligated complex in a mixed ligand sample there are four possible

bound-protein states, so in order to distinguish site selectivity of LCA a type of binding

site assay study using 2D Heteronuclear Single Quantum Coherence (HSQC) NMR spectroscopy was conducted.10

The site selective binding of 15N enriched glycochocholic acid (15N GCA) to

hFABP6 was monitored by 2D HSQC for peak shifts correlating to the following three

ligand states: Unbound, bound to site 1, and bound to site 2. Peak volumes were used as

a correlating value to percentage of their respective ligand state. Experimental

temperatures for all acquisitions were conducted at 10 °C in a solution buffer of 20 mM

phosphate buffer with 135 mM KCl, 10 mM NaCl at pH 7.4 with or without 1.0 mM

recombinant hFABP6 in buffer. Pulse-gradient sensitivity enhanced 1H/15N

heteronuclear sequential quantum correlation (HSQC) spectroscopy of 15N-GCA (3 mM) alone in buffer allowed the determination of the unbound peak, Figure 2.2.5. Then spectroscopy of 15N-GCA (3 mM) with hFABP6 (1 mM) introduced two new peaks to

the spectrum with each being identified as either binding site 1 or 2, Figure 2.2.6. A

binding spectrum of 15N-GCA (1.5 mM), with an equal concentration of unlabeled

glycochenodeoxycholic acid (GCDCA) and 1 mM hFABP6, for a total ligand to protein ratio of 3:1, was only found binding to site 2, Figure 2.2.7. An equivalent experiment using 15N-GCA (1.5 mM), with an equal concentration of unlabeled glycolithocholic acid

(GLCA) and hFABP6 (1 mM), also found selective binding to site 2, Figure 2.2.8. Both

GLCA and GCDCA caused GCA to exhibit selectivity for binding site 2, a selectivity

potentially promoted by their cooperative effects.

Figure 2.2.5. 2D 1H/15N heteronuclear sequential quantum correlation (HSQC) spectrum contour plot of 3 mM 15N-GCA in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C. The peak at 1H: 7.85 ppm and 15N: 119.25 ppm represents unbound ligand.

Figure 2.2.6. 2D 1H/15N HSQC spectrum contour plot of 3 mM 15N-GCA with 1 mM hFABP6 in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C. The peaks represent ligand: unbound at 1H: 7.85 ppm and 15N: 119.25 ppm; bound to site 1 at 1H: 7.35 ppm and 15N: 114.5 ppm; bound to site 2 at 1H: 7.09 ppm and 15N: 117.25 ppm.

Figure 2.2.7. 2D 1H/15N HSQC spectrum contour plot of 1.5 mM 15N-GCA with 1.5 mM unlabeled GCDCA and 1 mM hFABP6 in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C.

Figure 2.2.8. 2D 1H/15N HSQC spectrum contour plot of 1.5 mM 15N-GCA with 1.5 mM unlabeled GLCA and 1 mM hFABP6 in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C.

To compare GLCA’s cooperative binding to that of GCDCA, an experiment

across a range of concentrations for both mixed bile acid samples was conducted using

2D HSQC and 15N-GCA as the affinity probe. Using the peak volumes as a quantitative

measure, concentration of unbound, bound to site 1, and bound to site 2 were calculated.

obs These values were used to calculate dissociation constants for GCA to site 2 (Kd CAsite2)

at different ligand-protein ratios for ligand mixtures of GCDCA or GLCA. A plot of the

inverse values is presented in Figure 2.2.9 to reflect the binding affinity. GCA appears to

have a higher affinity when paired with GCDCA. If the binding mechanism presented

before is followed where the most lipophilic bile acid binds first and then through a

cooperative effect GCA binds at a much higher affinity to site 2, the calculated

dissociation constants here are equivalent to an overall binding affinity. Then the results

from this analysis, the overall binding affinity of GCDCA>>GLCA, are similar to the

ITC results, where the macroscopic binding constants mask any positive microscopic binding cooperativity comparison between GCDCA and GLCA.

Figure 2.2.9. Calculated observed dissociation constants of 15N-GCA with GCDCA (black) and

GLCA (gray) to 1.5, 2, 2.5, and 3 equivalent ratios of ligand to protein.

In a preliminary experiment, Figure 2.2.10, with 15N-GCDCA (1.5 mM), GLCA

(1.5 mM), and hFABP6 (1 mM) it was noticed that GLCA displaces 15N-GCDCA from site 1 and forces this heterotypic complex to exhibit site selectivity of GCDCA to site 2.

This provided experimental means for a competitive binding study of GLCA against

GCDCA. Using 15N-GCDCA as the ligand probe, a true comparative study of hFABP6’s

heterotypic complex affinity between GLCA with GCA and 15N-GCDCA with GCA was conducted. An experimental assay using 15N-GCDCA and an equal ratio of unlabeled

GCA at stoichiometric ratio of 3:1 to hFABP6 was compared to similar experiments with

added stoichiometric ratios of 0.1 eq and 1.0 eq of unlabeled GLCA. Figure 2.2.11 depicts the competitive displacement of 15N-GCDCA from site 1 when unlabeled GLCA

is added. Calculations of percent 15N-GCDCA bound to site 1 for each competitive experiment are plotted in Figure 2.2.12 as a function of equivalence of GLCA added.

The values are well below the mark for an equal competitor, suggesting that GLCA has the higher cooperative affinity.

Figure 2.2.10. 2D HSQC spectroscopic data depicting 15N-GCDCA switch in site selectivity.

2D 1H/15N HSQC spectrum contour plot of: A) 3 mM 15N-GCDCA with 1 mM hFABP6; B)

1.5 mM 15N-GCDCA and 1.5 mM GCA with 1 mM hFABP6; C) 1.5 mM 15N-GCDCA and

1.5 mM GLCA with 1 mM hFABP6 in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C. The peaks represent ligand: unbound at 1H: 7.80 ppm and 15N:

119.75 ppm; bound to site 1 at 1H: 7.65 ppm and 15N: 115.9 ppm; bound to site 2 at 1H: 7.05 ppm and 15N: 118.5 ppm.

Figure 2.2.11. 2D HSQC spectroscopic data depicting 15N-GCDCA competitive binding assay.

2D 1H/15N HSQC spectrum contour plot of: A) 1.5 mM 15N-GCDCA and 1.5 mM GCA with

1 mM hFABP6; B) 1.5 mM 15N-GCDCA, 1.5 mM GCA, and 0.15 mM GLCA with 1 mM hFABP6; C) 1.5 mM 15N-GCDCA, 1.5 mM GCA, and 1.5 mM GLCA with 1 mM hFABP6 in

20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer at 10 °C.

Figure 2.2.12. Plot of percent 15N-GCDCA bound to site 1 in a competitive binding assay against GLCA.

The significance of hFABP6’s diverse affinity for bile acids is an important physiologic development. The mechanism behind its cooperativity and site selectivity seems to be driven by bile acid hydrophobicity, in addition to hydroxylation pattern. The function of being able to recognize different bile acids in relation to their hydrophobic area is a built-in mechanism for binding the necessary bile acids for important physiological mechanisms. These mechanisms could include: The removal of hydrophobic bile acids, like LCA, from the cytosol of enterocytes where they have detrimental influences through their initiation of insoluble formations; a necessary means for transporting signaling molecules to their respective nuclear receptors, like PXR, which control expression of protective enzymes against LCA’s cytotoxicity.

Before further conclusions can really be made, a comprehensive cooperative binding study between all the different heterotypic complex pairs of bile acids, including other untested secondary bile acids like ursodeoxycholic acid and the allo-bile acids must be completed. Such a study would further confirm if the cooperative binding effect is

driven by hydrophobic interactions between the ligands. Figure 2.2.13 presents

preliminary work with glycoursodeoxycholic acid (GUDCA), which shows the most

significant displacement of 15N-GCDCA in a competitive binding study and potentially

has the highest positive cooperative binding effect yet. Obviously, there is more work to

be done in order to fully decipher hFABP6’s full binding characteristics to the

physiologic diversity and levels of bile acids.

Figure 2.2.13. 2D HSQC spectroscopic data depicting 15N-GCDCA competitive binding assay.

2D 1H/15N HSQC spectrum contour plot of 1.5 mM 15N-GCDCA, 1.5 mM GCA, and 1.5 mM

GUDCA with 1 mM hFABP6 in 20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH

7.4 buffer at 10 °C.

2.3 Materials and Methods

hFABP6 Biosynthesis and Purification – Recombinant human-FABP6 was expressed

from an E.coli line harboring the pMON-hIBABP“FABP6” plasmid. Cells were grown

in Terrific Broth media (Fisher) at pH 7.2 in a New Brunswick Scientific Innova 4300

incubator shaker. Cells were grown to a density of 7, as monitored by O.D.600nm, before

5 g/L of sucrose was added, followed by 100 mg/L naladixic acid to induce protein

expression via the recA-promoter. After harvesting the cells by centrifugation in a

Sorvall RC 5C PLUS Superspeed centrifuge at 4 °C, the protein was released from the

cell during a freeze-thaw cell lysis protocol using a 20 mM TRIS pH 7.6 buffer with a

general use protease inhibitor cocktail.13 Collection of the supernatant was loaded onto a

25 x 5 cm column of Q-sepharose Fast Flow for ion exchange separation at 4 °C. The

unbound protein eluate was then loaded onto a 90 x 2.5 cm column of Sephacryl S-100 for gel filtration at 4 °C using a 20 mM potassium phosphate, 135 mM KCl, 10 mM

NaCl, pH 7.4 eluent, and followed by the delipidation of collected 14 kDa protein on a

Lipophilic Sephadex LH-20 14 x 5 cm column at 37 °C. Final protein purity was assessed by overloaded Coomassie stained SDS-PAGE, and confirmed as pure hFABP6.

The final yield of a 4.0 L fermentation was approximately 500 mg. Protein concentrations were calculated through UV-spectroscopy using an established quantitative amino acid calibration value of 1 mg/mL solution of hFABP6 in water corresponding to an O.D.280 value of 0.846.

ITC Sample Preparation – The bile acids, cholic acid and chenodeoxycholic acid, were

obtained from Sigma-Aldrich and used after purification by recrystallization with ethanol

and water. ursodeoxycholic acid (MP Biomedicals) and lithocholic acid (Acros

Organics) were not further purified by recrystallization. Sample purities were confirmed

by thin-layer chromatography to be >98%. Bile acid stock solutions were made by

dissolving the sample into tetrahydrofuran (THF) with accurate concentrations found via

dry weight analysis on a Perkin Elmer AD-6 microgram autobalance. ITC solutions of

definite concentrations were freshly prepared by measuring out a specific volumetric

amount from the THF stock solutions and adding 1.1 equivalents of KOH before freeze-

drying. The sample solutions were prepared fresh before use by dissolving them into

20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4 buffer.

Isothermal Titration Calorimetry – Calorimetric experiments were performed using a

MicroCalTM VP-ITC Microcalorimeter. Bile acid samples (11.1 mM) were injected into a reaction cell containing 1.64 mL hFABP6 (0.18 mM) by aliquot injections consisting of

4 μL for the first 15 injections, and 7 μL for the last 30 injections. Titrations were conducted at 37 °C and in triplicate. The heat of injections for bile acid samples into blank buffer were recorded and subtracted from the ligand-protein titrations. The experimental plots were fit using a two-step binding model that was coded into a nonlinear least squares fitting algorithm with Origin 8.1 software. The stepwise binding model in Origin coding is presented in Appendix I.

NMR Sample Preparation – 15N-enriched conjugated bile acids were synthesized via the peptide coupling of 15N-Glycine to unconjugated bile acid by Tserng et al. published

methodology.14 The isotopic enriched and unenriched conjugated bile acid stock

solutions were made by dissolving the sample into THF with accurate concentrations

found via dry weight analysis on a Perkin Elmer AD-6 microgram autobalance. NMR samples of definite concentrations were freshly prepared by measuring out a specific volumetric amount using Hamilton syringes from the THF stock solutions and adding a

1.1 equivalent of 0.5 M KOH, in addition to 50 μL of NMR sample buffer (20 mM potassium phosphate, 135 mM KCl, 10 mM NaCl, pH 7.4) before freeze-drying overnight under vacuum. 1 equivalent (5.0 x 10-7 moles) of protein solution was then

added to the sample, followed by 50 μL of D2O, and finally brought to a total volume of

500 μL with NMR buffer.

NMR Data Collection – Spectra were recorded on a Bruker 800 MHz and Varian

900 MHz NMR spectrometer with triple channel bio-probes. Two-dimensional (2D) 1H-

15N heteronuclear single-quantum correlation (HSQC) spectra were acquired using an in house pulse sequence at 10 °C. Spectra on the Bruker were acquired with 1H spectral

width of 10,416 Hz (1024 complex points), and 15N spectral width of 973 Hz

(64 hypercomplex points). Spectra on the Varian were acquired with 1H spectral width of

10822 Hz (1024 complex points), and 15N spectral width of 1459 Hz (64 hypercomplex

points). Spectral analyses were performed on NMRPipe

(http://spin.niddk.nih.gov/NMRPipe) using Fedora 15 (http://fedoraproject.org). 2D data

processing functions included poly time-domain correction for solvent subtraction

(POLY) and zero-fill (ZF) to 2048 total complex points. Peak volumes were obtained through NMRPipe’s peak detection software.

2.4 References

1. Staudinger, J. L.; Goodwin, B.; Jones, S. A.; Hawkins-Brown, D.; MacKenzie, K.

I.; Latour, A.; Liu, Y. P.; Klaassen, C. D.; Brown, K. K.; Reinhard, J.; Willson, T. N.;

Koller, B. H.; Kliewer, S. A., The nuclear receptor PXR is a lithocholic acid sensor that

protects against liver toxicity. Proc. Natl. Acad. Sci. U. S. A. 2001, 98 (6), 3369-3374.

2. Araya, Z.; Wikvall, K., 6 alpha-hydroxylation of taurochenodeoxycholic acid and

lithocholic acid by CYP3A4 in human liver microsomes. Biochimica Et Biophysica Acta-

Molecular and Cell Biology of Lipids 1999, 1438 (1), 47-54.

3. Makishima, M.; Lu, T. T.; Xie, W.; Whitfield, G. K.; Domoto, H.; Evans, R. M.;

Haussler, M. R.; Mangelsdorf, D. J., Vitamin D receptor as an intestinal bile acid sensor.

Science 2002, 296 (5571), 1313-1316.

4. (a) Wider, M. D.; Vinik, A. I.; Heldsinger, A., Isolation and Partial

Characterization of an Entero-Oxyntin from Porcine Ileum. Endocrinology 1984, 115 (4),

1484-1491; (b) Borgstrom, A.; Wider, M.; Marks, W.; Lloyd, R.; Herman, G.; Vinik, A.,

Immunohistochemical Localization of a Specific Ileal Peptide in the Pig. Histochemistry

1986, 86 (1), 101-105; (c) Wider, M. D.; Duhaime, P. M. Q.; Weisman, R. L., Chemical

Characterization of Circulating Porcine Ileal Polypeptide in Plasma from Normal Adult-

Pigs. Endocrinology 1986, 118 (4), 1546-1550; (d) Walz, D. A.; Wider, M. D.; Snow, J.

W.; Dass, C.; Desiderio, D. M., The Complete Amino-Acid Sequence of Porcine

Gastrotropin, an Ileal Protein Which Stimulates Gastric-Acid and Pepsinogen Secretion.

J. Biol. Chem. 1988, 263 (28), 14189-14195.

5. Gantz, I.; Nothwehr, S. F.; Lucey, M.; Sacchettini, J. C.; Delvalle, J.; Banaszak,

L. J.; Naud, M.; Gordon, J. I.; Yamada, T., Gastrotropin - Not an Enterooxyntin but a

Member of a Family of Cytoplasmic Hydrophobic Ligand-Binding Proteins. J. Biol.

Chem. 1989, 264 (34), 20248-20254.

6. Sacchettini, J. C.; Hauft, S. M.; Vancamp, S. L.; Cistola, D. P.; Gordon, J. I.,

Developmental and Structural Studies of an Intracellular Lipid-Binding Protein

Expressed in the Ileal Epithelium. J. Biol. Chem. 1990, 265 (31), 19199-19207.

7. Miller, K. R.; Cistola, D. P., Titration Calorimetry as a Binding Assay for Lipid-

Binding Proteins. Mol. Cell. Biochem. 1993, 123 (1-2), 29-37.

8. Kramer, W.; Corsiero, D.; Friedrich, M.; Girbig, F.; Stengelin, S.; Weyland, C.,

Intestinal absorption of bile acids: paradoxical behaviour of the 14 kDa ileal lipid-binding protein in differential photoaffinity labelling. Biochem. J. 1998, 333, 335-341.

9. (a) Tochtrop, G. P.; Richter, K.; Tang, C. G.; Toner, J. J.; Covey, D. F.; Cistola,

D. P., Energetics by NMR: Site-specific binding in a positively cooperative system. Proc.

Natl. Acad. Sci. U. S. A. 2002, 99 (4), 1847-1852; (b) Tochtrop, G. P.; Bruns, J. L.; Tang,

C. G.; Covey, D. F.; Cistola, D. P., Steroid ring hydroxylation patterns govern cooperativity in human bile acid binding protein. Biochemistry (Mosc). 2003, 42 (40),

11561-11567.

10. Tochtrop, G. P.; DeKoster, G. T.; Covey, D. F.; Cistola, D. P., A Single Hydroxyl

Group Governs Ligand Site Selectivity in Human Ileal Bile Acid Binding Protein. J. Am.

Chem. Soc. 2004, 126 (35), 11024-11029.

11. Toke, O.; Monsey, J. D.; DeKoster, G. T.; Tochtrop, G. P.; Tang, C.; Cistola, D.

P., Determinants of Cooperativity and Site Selectivity in Human Ileal Bile Acid Binding

Protein. Biochemistry (Mosc). 2005, 45 (3), 727-737.

12. Tanford, C., The hydrophobic effect: formation of micelles and biological membranes. Wiley: New York,, 1973; p viii, 200 p.

13. Johnson, B. H.; Hecht, M. H., Recombinant Proteins Can Be Isolated from

Escherichia-Coli-Cells by Repeated Cycles of Freezing and Thawing. Bio-Technology

1994, 12 (13), 1357-1360.

14. Tserng, K. Y.; Hachey, D. L.; Klein, P. D., An improved procedure for the synthesis of glycine and taurine conjugates of bile acids. J. Lipid Res. 1977, 18 (3), 404-

Chapter 3 Statistical Determination of a Bile Acid Micellization Model

3.1 Introduction

3.1.1 Bile acid self-association: Small’s model

Bile acids are important physiological detergents that aid in digestion by

effectively solubilizing dietary lipids throughout the small intestine. Their properties as

detergents are unique and have found applications in a number of important areas, such

as drug delivery,1 protein purification,2 and therapeutic treatment of gallstones.3 As a result of their important functions, work towards understanding their properties as detergents, including the process by which they form micelles, is essential.

The self-association of bile acids as detergents is both unique and complex, and as a result, the body of work describing these properties is disparate and enigmatic. Their complex nature is attributed to their unique amphiphilic structure, wherein the molecules are demarcated by a hydrophobic face and an opposing hydrophilic face upon their steroid ring scaffold, as depicted in Figure 3.1.1. The resulting rigid structure stands in contrast to traditional detergents, which typically have polar hydrophilic head groups and unconstrained hydrophobic aliphatic chains. Despite early works reporting bile acid

micellization occurring slowly over broad concentration ranges,4 ensuing work often

treats analysis of their properties by using phase separation models that define critical

micelle concentrations.5 While this is a common method for describing typical

detergents due to its simplicity, its application to bile acids has been generally regarded

as inaccurate.6 More accurate reports have utilized a mass-action model, using multiple

dynamic equilibriums, as being better suited for the analysis of bile acids’ micellar

association.6-7

O R OH

R1 R2 R3 Cholic Acid α-OH α-OH α-OH Chenodeoxycholic acid α-OH α-OH R R Deoxycholic acid α-OH α-OH

OH OH OH

Figure 3.1.1. Structural representations of cholic acid (CA), chenodeoxycholic acid (CDCA),

and deoxycholic acid (DCA) with a depiction of the ring chair confirmation of CA and space filling model depicting shape and amphiphilic regions.

In an effort to discern their micellization from typical detergents Small et al.

proposed a primary and secondary micellization process.4b The first step describes the

association of free monomer into primary micelles highlighted by the hydrophobic β-

faces of the steroids turned inward towards each other, while their hydrophilic hydroxyl

and acidic groups are turned outward interacting with the water solvent. It is this primary

micellization process that is the most often observed experimentally and the process by

which bile acid acts as a detergents. The second step takes place at higher concentrations

and involves the primary micelle’s hydrophilic outer shell undergoing a secondary

association through hydrogen bonding to form larger aggregates. A graphical depiction

can be seen in Figure 3.1.2. While Small’s conceptual model has become a basis for

most bile acid association studies, literature reports defining mass-action association-

44 dissociation equilibrium models are not consistent. The derived thermodynamic values and physical properties are highly dependent on the micelle size dispersity of the model used; unfortunately, the current body of literature has yet to establish a unified model.

A Primary Micellization

Hydrophobic interactions Monomer Primary Micelle

B Secondary Micellization

Hydrogen bonding

Primary Micelles Secondary Micelles

Figure 3.1.2. A graphical depiction of Small’s self-association mechanism. A) Primary micelle association highlighting the hydrophobic interactions. B) Secondary micelle aggregation highlighting the hydrophilic interactions.

3.1.2 Micelle polydispersity

The matter of bile acid micellization being uniform (mono-disperse) or non- uniform (poly-disperse) has been a subject of investigative debate. While the consensus of work supports non-uniformity, the definitions of their micellar make-up differ, and result in dissimilar systems with polydispersity indexes, a measurement of heterogeneity of molecular sizes, varying greatly. Example models used in describing the micellar make-up include: A uniform micellar system where monomer is in association- dissociation equilibrium with a micelle of one aggregate size, a non-uniform sequential stepwise micellar system where monomer is in association-dissociation equilibrium with micelles of growing sizes, and a non-uniform selective multiple micellar “multimer” system where monomer is in association-dissociation with micelles of selective sizes.

The use of these and other differing models within the literature has a consequence on the reported properties, which is they represent entirely different micellar systems. Plots in

Figure 3.1.3 graphically depict these differences. Consequently, the discrepancies from using different models are passed onto the calculated thermodynamic values that are reported. These discrepancies are a problem and need to be resolved, or at least understood when analyzing data. Herein this report plans to address that issue.

FunasaFunasakki eti al. GyimesiGyimeseti al. 1 1 0.9 0.9 0.8 0.8 0.7 0.7 n n o o 0.6 0.6 i i t t io io t t a a l l a a 0.5 0.5 R R

opu 0.4 opu 0.4 P P 0.3 0.3 0.2 0.2 0.1 0.1 0 0 2 3 4 5 6 7 8 2 3 4 5 6 7 8 Aggregate size Aggregate size

BeckeBeckerrditdie teet al. PaulaPauleta al. 1 1 0.9 0.9 0.8 0.8 0.7 0.7 n n o 0.6 o 0.6 i i t t io io t t a a l l a 0.5 a 0.5 R R opu 0.4 opu 0.4 P 0.3 P 0.3 0.2 0.2 0.1 0.1 0 0 2 3 4 5 6 7 8 2 3 4 5 6 7 8 Aggregate size Aggregate size

Figure 3.1.3. Aggregate size population ratios simulated from their respective publications.

(Funasaki,8 taurocholate in 154 mM NaCl at 25 °C; Gyimesi,9 taurocholate in phosphate buffer at

25 °C; Beckerdite,10 CA in 154 mM NaCl at 31 °C and 37 °C; Paula,7 CA in water at

temperatures in the range between 10 °C and 70 °C. These examples show the diversity that

different micellization models can produce.

3.1.3 Isothermal titration calorimetry of demicellization

Isothermal titration calorimetry (ITC) has proven to be a beneficial tool in

studying detergents.11 It has the advantage of being able to determine both the critical micelle concentration (CMC) and the enthalpy of demicellization (ΔHdemic) from a single experiment. In addition, the precision of the determined values are an improvement over

other experimental techniques, this is a result of ΔHdemic being the experimentally

observed quantity. Conversely, other experimental methods indirectly measure the

ΔHdemic by calculating it from a free energy analysis of the CMC in relation to temperature. As a result of bile acids lacking a clear CMC transition, indirect measurements of ΔHdemic lack accuracy. In addition, using the directly measured ΔHdemic

translates to more accurate calculations of other thermodynamic parameters. For

example, of the thermodynamic parameters, the heat capacity (ΔCpdemic), calculated from

the dependence of enthalpy on temperature, gives insight into exposed hydrophobic

surface areas, ergo insight into micelle structure. These derived properties allow for

evaluation and comparison between detergents. Therefore, the accuracy of ITC as an

experimental technique for monitoring demicellization made it a good choice for testing

models of bile acid micellization.

3.2 Proposed study

The lack of a standard model for analyzing experimental results has become a

shortfall in our understanding of bile acids as detergents. It is the aim of this work to test

the above mentioned models by fitting them to calorimetric curves of bile acid

demicellization. Through a systematic statistical testing of the models, the best

representation for bile acid association can be determined, and this model will then be

used to derive thermodynamic values with a higher level of accuracy for a more accurate

means of describing, measuring, and comparing bile acid detergent properties.

3.3 Results and Discussion

An example of an ITC experimental result is displayed in Figure 3.3.1, with the peaks representing the heat evolved from each injection of a micellar solution of CA

(200 mM in 150 mM NaCl, pH 7.5) into 150 mM NaCl (pH 7.5) at 10 °C. Integration of the peaks provides the change in reaction enthalpy as a function of the change in concentration, an example of the resulting enthalpic curve is presented in Figure 3.3.2.

As the total bile acid concentration increases in the monitored reaction cell, approaching

the concentration within the syringe, the heat of reaction gradually decreases, and can be

described through breaking down the plot into three regions. The initial injections at the

low total bile acid concentration region, representing the micelle to monomer phase,

produce heats that are the sum of the heats of dilution for both monomer and micelles,

and the heat of demicellization. As the injections continue the next region of the plot

represents the transition phase where the heat of demicellization gradually decreases.

This phase has the same enthalpic contributions as before, but now the amount of

demicellization gradually becomes less. The final asymptotic region, or micelle

saturation phase, of the plot is where the heat of reaction is its smallest and is only a

product of the micellar solution’s heat of dilution. The heat of demicellization is a

product of the hydrophobic effect, which is an entropic effect driven by the disruption of

highly dynamic hydrogen bonds among water molecules by non-polar regions on an

amphiphilic molecule. It is a process driven by enthalpy at low temperatures and entropy

at higher temperatures, and results in a change of sign for ΔHdemi c around room

temperature. This effect was observed in our calorimetric data, and confirms the process

being monitored is the hydrophobic effect from bile acids’ primary micelles

demicellization.

ucal/sec 10

2 0 50 100 150 200 250 300 350 400 Time (min) Figure 3.3.1. Calorimetric plot (heat flow vs. time) of 200 mM cholic acid in 150 mM NaCl (pH 7.5) into 1.46 mL 150 mM NaCl (pH 7.5) at 10 °C with 2 μL injections.

Figure 3.3.2. Enthalpic curve (reaction enthalpy vs. total bile acid concentration) of 200 mM cholic acid in 150 mM NaCl (pH 7.5) into 1.46 mL 150 mM NaCl (pH 7.5) at 10 °C with 2 μL injections.

Among the micellization models being tested, which include uniform and

variations of non-uniform micelle dispersity, the representing mass-action equation will

be manipulated in order to utilize the Newton-Raphson formula, for finding the

concentration of free monomer, written within our curve fitting algorithm. The mass-

action equations are elucidated in their respective model’s section. Then using statistical

calculations the model that best describes bile acid micellar system will be found.

3.3.1 Uniform micellar system model

The uniform model describes a system in equilibrium between free bile acid, monomer (Mono), and a micelle of N associated bile acids (N-mer), resulting in an uniform micellar solution. The reaction formula then follows

[Mono] х N [N-mer]

This model’s application within literature is almost⇌ entirely found in earlier bile acid self- association reports.4a, 12 However, there have been recent publications treating bile acid

micellization with this model.7, 13 Additional support for the model comes from work by

Liu et al.14 that reports a lack of significant micelle growth with increasing concentration,

and assuming Nagarajan’s proposed theory on aggregate growth concludes uniformity of

micelle size.15 A larger body of work, however, conflicts with this assumption about bile

acid micellization as there are numerous reports of aggregation numbers increasing with

concentration.5c, 8, 16

The mass-action equation for this model is

[BA]t = [Mono] + N*[N-mer]N (Equation 1)

Where [BA]t is the total concentration of bile acid, [Mono] and [N-mer]N are the

concentration of monomer and N-mer, respectively, and N is the number of bile acid

molecules that make up the micelle. The equilibrium is defined by the equation

N KN = [N-mer]N / [Mono] (Equation 2)

Where KN is the association constant. By combining Equations 1 and 2, and then

rearranging the equation to zero a root function of the equation is established.

0 = [Mono] + KN*[Mono]N + [BA]t (Equation 3)

This equation along with its derivative when applied to the Newton-Raphson iteration

method allows for the calculation of [Mono] at any [BA]t when given KN and N. Then

from found parameter values using nonlinear least square analysis (NLS) thermodynamic

values are calculated.

The uniform model was fit to ITC experimental data from demicellization

experiments on CA, CDCA, and DCA in 150 mM NaCl (pH 7.5) at temperatures of

10 °C, 20 °C, 30 °C and 40 °C. Figure 3.3.3 displays an example fitting of 200 mM CA

in 150 mM NaCl (pH 7.5) at 40 °C. Table 3.3.1 reports the fitted values, residual sums,

and R2 values. By this model, the micelle size appears to grow as the temperature

increases from 10 °C to 30 °C, with the exception of DCA that increased from 10 °C to

20 °C. The 30 °C CA and CDCA, and 20 °C DCA experimental data provided curves with little to no measurable ΔHdemic, which resulted in the model producing no fit,

impossible parameter values, or fits with extremely large standard errors. This was

attributed to the low enthalpic contribution from the demicellization at this temperature.

As a result, the fitting calculations for these experimental temperatures could not be

reported. At 40 °C smaller micelle sizes are reported, this is in agreement with previous

literature that report aggregation numbers of bile acids as first slightly growing in size

with increasing temperatures until room temperature where sizes then start decreasing.4a,

13, 16c, d The calculated micelle sizes and association constants were in agreement with

other literature values as well, and followed a similar trend of DCA>CDCA>CA for both

values.

A notable concern from the results is the large standard errors for the association

constants. A contributing factor to this comes from different syringe concentrations used

across experiments that resulted in different N values being found. This result causes

large differences in association constants, KN, that show up as large standard errors in the

fittings. If the system was uniform then concentration would not affect the micelle size.

Therefore, while the uniform model can achieve reasonable fits and thermodynamic

values, the variation in the parameter N as a result of concentration contradicts the

model’s definition of a homogeneous micellar system, and offers proof for multiple

micelle sizes. It is then considered that the value of N in this model represents the mean

micelle sizes, which is concentration dependent. In addition, the residual patterns from

the uniform model fits hint to a significant absence of additional parameters that are needed in the modeling equation. Therefore, it can be concluded that the uniform model is not an accurate representation for the micellization of bile acids.

600

500 1 - 400

300

Q / cal·mol 200

100

0 0 5 10 15 20 25 30 35 40

[BS]t / mM

B 40

20 1 - 10

0 0 5 10 15 20 25 30 35 40 -10 Q / cal·mol

-20

-30

-40 [BS]t / mM

Figure 3.3.3. Example NLS fitting of the uniform model to 200 mM cholic acid in 150 mM

NaCl (pH 7.5) into 1.46 mL 150 mM NaCl (pH 7.5) at 40 °C. A) Shows the calculated curve ( ―

) vs. the experimental curve ( • ); B) displays the resulting residuals

Table 3.3.1. Parameter values for the uniform model fitting to ITC experimental data from the demicellization of CA, CDCA, and DCA.

3.3.2 Non-uniform sequential stepwise model

The sequential stepwise model describes micellization as a stepwise addition of monomer to growing micelles. Beckerdite et al.10 through a similar study on the determination of models for the self-association of bile acids found through fitting vapor pressure osmometry and sedimentation equilibrium data that a sequential equal equilibrium constant model best fit their experimental data. There are other literature examples of stepwise models being used to analyze the association of bile acids, but even among these there are differences in their described associations.8, 17 For example,

Funasaki et al. reported on a sequential independent equilibrium constant model used for fitting gel filtration chromatography data. These different stepwise models were considered and included in the fitting trials.

The sequential stepwise model is described by a series of simultaneous equilibriums.

[ ] [Mono] + [Mono] [Dimer] K2 = [ ] (Equation 4) Dimer 2 Mono ⇌ [ ] [Mono] + [Dimer] [Trimer] K3 = [ ] [ ] (Equation 5) Trimer Mono ∙ Dimer ⇌ [ ] [Mono] + [Trimer] [Tetramer] K4 = [ ] [ ] (Equation 6) Tetramer Mono ∙ Trimer ⇌ [ ] [Mono] + [(N-1) ·mer] [N·mer] Kn = [ ] [( ) ] (Equation 7) N∙mer ⇌ Mono ∙ N−1 ∙mer The mass-action equation for this model is then

[BA]t = [Mono] + 2[Dimer] + 3[Trimer] +…+ N[N-mer]N (Equation 8)

Equations 4-8 can be expressed through the summation

[BA]t = K [Mono] (Equation 9) N 푖 푖 This summation can then be expressed through∑푖=1 푖 the formula

( ) a = (Equation 10) N( ) N+1 N−1 푖 a − N∙a + N−1 ∙a 2 ∑푖=1 푖 1−a Then using equations 8 and 10 in the nonlinear curve fitting algorithm [Mono], KN’s, and thermodynamic values were calculated.

The various stepwise models were fitted to our ITC experimental data. These included a sequential (ki = ki+1) and sequential independent dimer (k2 ≠ ki = ki+1 = k)

where k is related to the association constant by

Ki = (Equation 11) 푖 0 푖 Neither model produced reliable fits. A sample∏ plot푘 shown in Figure 3.3.4 displays the

lack of fit by the model. The fit parameter values are presented in Table 3.3.2. These

results were determined unreliable by the Akaike Information Criterion (AIC), a

statistical measurement of a model’s relative quality using residual sum values. The sequential models were 40 times worse than the uniform model. It was determined that

Beckerdite’s fits lacked data points at lower concentration ranges due to experimental limitations; therefore, producing incomplete fits. The sequential stepwise models produce a more logarithmic curve and do not represent the ITC isotherm sigmoidal curves. Accordingly, the non-uniform sequential stepwise models are highly discouraged against for any type of experimental fittings of bile acids micellization.

600

500 1 - 400

300

Q / cal·mol 200

100

0 0 5 10 15 20 25 30 35 40

[BS]t / mM

B 80 60 40

1 20 - 0 0 5 10 15 20 25 30 35 40 -20

Q / cal·mol -40 -60 -80 -100 [BS]t / mM

Figure 3.3.4. Example NLS fitting of non-uniform sequential stepwise model (k2 ≠ ki = ki+1 = k) to 200 mM cholic acid in 150 mM NaCl (pH 7.5) into 1.46 mL 150 mM NaCl (pH 7.5) at 40 °C.

A shows the calculated curve ( ― ) vs. the experimental curve ( • ); B displays the resulting residuals.

Table 3.3.2. Parameter values from the non-uniform sequential stepwise model fitting to ITC experimental data of the demicellization of CA, CDCA, and DCA.

3.3.3 Non-uniform multimer model

The treatment of bile acid micellar system as selective associations into unique multimers is rare in current literature. While it has been mentioned,6, 12a, 18 few have

utilized it.9, 19 The model describes a system of multiple association-dissociation equilibriums between monomer and multiple micelle species of differing sizes.

[ ] [Mono] · M [M·mer] KM = [ ] (Equation 12) M∙mer M Mono ⇌ [ ] [Mono] · N [N·mer] KN = [ ] (Equation 13) N∙mer N ⇌ Mono Etc…

The mass-action equation for this model is then

[BA]t = [Mono] + M· [M·mer] + N· [N·mer] + Etc (Equation 14)

The first model tested defines a system with two micelle species, and is termed the bi-micelle multimer model. As a result of experimental evidence within literature hinting at bile acid dimer formation,18, 20 the micelle aggregate size for the M-mer was

fixed at 2, while N, KM, KN, and ΔHdemic were parameters left open during experimental

curve fits. ITC experimental data for the bile acids CA, CDCA, and DCA conducted in

the temperature range of 10 °C-40 °C were fit using this model. Table 3.3.3 reports the parameter values, residual sums, and R2 values and Figure 3.3.5 displays an example

curve fit for 200 mM CA in 150 mM NaCl at 40 °C. The bi-micelle multimer model

provided better fits than the uniform model by AIC calculations with fits twice as good.

Reasonably, it is expected for a model with an additional parameter to fit better, so to

further test the model a regression analysis F-test (α=0.01) was conducted to confirm the

bi-micelle multimer model does in fact give a significantly better fit to the data. Worth

noting is, the additional micelle parameter offered better fits across the different

concentration studies, a problem the uniform model displayed. Therefore, the bi-micelle multimer model with dimer species provides a better description of the micellar system for bile acids than the uniform model. The results of the bi-micelle multimer model offer a similar analysis as the uniform model regarding the micelle size dependency on temperature and bile acid species, as well as similar enthalpies of demicellization.

600

500 1 - 400

300

Q / cal·mol 200

100

0 0 5 10 15 20 25 30 35 40

[BS]t / mM

B 30

1 10 -

0 0 5 10 15 20 25 30 35 40

Q / cal·mol -10

-20

-30 [BS]t / mM

Figure 3.3.5. Example NLS fitting of non-uniform bi-micelle multimer model to 200 mM cholic acid in 150 mM NaCl (pH 7.5) into 1.46 mL 150 mM NaCl (pH 7.5) at 40 °C. A shows the calculated curve ( ― ) vs. the experimental curve ( • ); B displays the resulting residuals.

Table 3.3.3. Parameter values from the non-uniform bi-micelle multimer model fitting to ITC experimental data of the demicellization of CA, CDCA, and DCA.

Extending the model to include a third micelle species created the tri-micelle

multimer model. This model further provided a better fit for the self-association of bile acids as confirmed through the AIC and the F-test calculations. However, the improvement was modest being a tenth of an improvement over the bi-micelle model.

The found parameter values from the tri-micelle multimer model fittings are reported in

Table 3.3.4. Further extension to a fourth micelle species, however, resulted in a lower

AIC value and did not pass the F-test. While the tri-micelle multimer model passed a statistical analysis as being an improvement over the bi-micelle multimer model, the additional parameters bring a level of complexity to the calculations and analysis that outweigh the benefits. The efficiency of the bi-micelle model is exceptionally better, and the additional information it offers over the uniform model on the physical state of the micellar solution prove it to be the best fitting model for ITC analysis of bile acid micellization.

Table 3.3.4. Parameter values from the non-uniform tri-micelle multimer model fitting to ITC experimental data of the demicellization of CA, CDCA, and DCA.

3.4 Conclusion.

The statistical analysis of bile acid self-association models’ fitting results provided insight into the mechanism for their micellization, and further provided a model for fitting ITC experimental data. The sequential stepwise model offered the worst description of the micellar system with curve simulations that did not fit. A possible reason for this model’s use in literature is because popular experimental methods for studying aggregation report on number average molecular weights (Mn) of the micelles, which represent an average of the micelle aggregate sizes. This average appears to grow gradually and can be mistaken as the sequential stepwise growth of bile acid micelles.

The uniform model was able to produce fits with the correct sigmoidal shape and R2 values from between 0.989 to 0.999. This model works best for micelle systems of one aggregation size and systems of larger aggregate sizes, like sodium dodecyl sulfate. For this work it offered reasonable fits to ITC enthalpic curves, but according to the patterns found in the residual plots additional parameters are needed in the model. It is postulated the decent fits from the uniform model are a result of the model fitting to the most predominate micelle species. The addition of more parameters to account for additional micelle species, resulting in multimer models, was further able to provide better fits. The addition of a second (bi-micelle) and third (tri-micelle) micelle parameter to the model provided overall better fits, with both passing statistical AIC and F-Test model comparing tests. However, further addition to the model did not pass these tests. The tri- micelle model provided better fits than the bi-micelle model; however, after analysis of the size dispersion the additional micelle species never made up more than 5% of the solution at the highest soluble concentrations. It was concluded the negligible amount of

the additional micelle species did not warrant enough information to support the extra

amount of time and work involved in fitting. This left the bi-micelle model as the best overall multimer model in terms of both efficiency and information. The selection of the bi-micelle multimer model is for the bile acids studied here and may not be the case for other detergent systems, which would require their own model analysis.

The dispersity of bile acids’ micellar make-up has been a topic of debate, and through this work more evidence for the dynamic equilibrium of free bile acid monomer with multiple unique micelle aggregates sizes, including dimer, has been presented.

Along with dimers the predominant micellar sizes for the bile acids were: Cholic acid

forming [7-mers], [8-mers], and [6-mers] at 10 °C, 20 °C, and 40 °C respectively,

chenodeoxycholic acid forming [8-mers] for all temperatures studied, and deoxycholic

acid forming [13-mers], [10-mers], and [9-mers] at 10 °C, 30 °C, and 40 °C respectively.

The micelle sizes showed an increase in size as temperatures approached the temperature

at which ΔHdemic = 0 and then a decrease in size as the temperatures further increases.

The determined association constants followed the same pattern of change based on

temperature and bile acid species with DCA>CDCA>CA. However, enthalpies through

this model are larger in comparison to those previously reported in literature. This is a

result of these fitting models considering the change in free monomer, and not

extrapolating ΔHdemic from ITC enthalpic plots.

The additional micelle species addressed by the multimer model actually provide

an increase in information on the physical state of the micellar system, and considering

its dependency on concentration, the micellar solution’s aggregate size composition can

be known. This information is beneficial to their physical understanding and potential

use in medicine, biology, and industry.

3.5 Materials and Methods

Chemicals and Sample Preparation – Cholic acid, chenodeoxycholic acid, and

deoxycholic acid were obtained from Sigma and used after purification by

recrystallization with ethanol and water. The purity was confirmed by thin-layer

chromatography to be >98%. Bile acid stock solutions were made by dissolving the

sample into tetrahydrofuran (THF) with accurate concentrations found via dry weight

analysis on a microgram balance. ITC solutions of definite concentrations were freshly

prepared by measuring out a specific volumetric amount from the THF stock solutions

and adding 1.1 equivalents of NaOH before freeze drying. The samples were then diluted

to volume with the appropriate amount of 150 mM NaCl filtered stock solution

maintained at a pH of 7.5 to make the experimental bile acid solutions. The pH of the

solutions were monitored with an Orion Micro pH Probe and adjusted when necessary.

Isothermal Titration Calorimetry – Titrations were measured using a MicroCal VP-ITC

microcalorimeter. Micellar solutions, ranging in concentration from 25 mM to 200 mM

depending on bile salt species, were loaded into a 275 uL magnetically stirred syringe.

Injection volumes of 2, 4, or 5 uL over durations of 4, 8, and 10 seconds, respectively,

were injected into a 1.64 mL reaction cell with 150 mM NaCl at a pH of 7.5 with an equilibration time of 300 seconds to ensure the total heat of reaction was measured.

Experiments were repeated for 10 °C, 20 °C, 30 °C, and 40 °C. Experimental data analysis was conducted using the MicroCal Origin software (version 8.5.1).

Titration Curve Fitting – The calorimetric titration curves were fitted using the described model equations after their conversion into appropriate coded algorithms for ORIGIN’s non-linear least squares fitting function that uses the Levenburg-Marquardi algorithm.

The model equations are presented in Origin coding in the Appendix I.

Determination of Enthalpy of Dilutions – A low concentration of 0.1 mM bile salt solutions (low enough to ensure primarily a monomer solution) in 150 mM NaCl with a pH 7.5 at 10 μL aliquots per injection to find the average of the reaction heat for dilution of monomer species. This was taken as the enthalpy of dilution for monomer. Similarly, the average of the micellar saturation phase asymptote (Figure 3.3.2) was taken as the enthalpy of dilution for micellar species.

3.6 References

1. (a) Kramer, W.; Wess, G.; Enhsen, A.; Falk, E.; Hoffmann, A.; Neckermann, G.;

Schubert, G.; Urmann, M., Modified bile acids as carriers for peptides and drugs. J.

Controlled Release 1997, 46 (1-2), 17-30; (b) Tolle-Sander, S.; Lentz, K. A.; Maeda, D. Y.;

Coop, A.; Polli, J. E., Increased Acyclovir Oral Bioavailability via a Bile Acid Conjugate.

Mol. Pharm. 2003, 1 (1), 40-48.

2. Hjelmeland, L. M., A Non-Denaturing Zwitterionic Detergent for Membrane

Biochemistry - Design and Synthesis. Proceedings of the National Academy of Sciences of the United States of America-Biological Sciences 1980, 77 (11), 6368-6370.

3. Bachrach, W. H.; Hofmann, A. F., Ursodeoxycholic acid in the treatment of

cholesterol cholelithiasis. Dig. Dis. Sci. 1982, 27 (8), 737-761.

4. (a) Carey, M. C.; Small, D. M., Micellar Properties of Dihydroxy and Trihydroxy

Bile Salts - Effects of Counterion and Temperature. J. Colloid Interface Sci. 1969, 31 (3),

382-&; (b) Small, D. M., Size and structure of bile salt micelles. Influence of structure,

concentration, counterion concentration, pH, and temperature. Advan. Chem. Ser. 1968, No.

5. (a) Hildebrand, A.; Garidel, P.; Neubert, R.; Blume, A., Thermodynamics of

demicellization of mixed micelles composed of sodium oleate and bile salts. Langmuir 2004,

20 (2), 320-328; (b) Coello, A.; Meijide, F.; Nunez, E. R.; Tato, J. V., Aggregation behavior

of bile salts in aqueous solution. J. Pharm. Sci. 1996, 85 (1), 9-15; (c) Janich, M.; Lange, J.;

Graener, H.; Neubert, R., Extended Light Scattering Investigations on Dihydroxy Bile Salt

Micelles in Low-Salt Aqueous Solutions. The Journal of Physical Chemistry B 1998, 102

(31), 5957-5962.

6. Mukerjee, P.; Cardinal, J. R., Solubilization as a method for studying self-association:

Solubility of naphthalene in the bile salt sodium cholate and the complex pattern of its

aggregation. J. Pharm. Sci. 1976, 65 (6), 882-886.

7. Paula, S.; Sues, W.; Tuchtenhagen, J.; Blume, A., Thermodynamics of Micelle

Formation as a Function of Temperature: A High Sensitivity Titration Calorimetry Study. J.

Reserved.), 11742-51.

8. Funasaki, N.; Ueshiba, R.; Hada, S.; Neya, S., Stepwise Self-Association of Sodium

Taurocholate and Taurodeoxycholate as Revealed by Chromatography. J. Phys. Chem. 1994,

98 (44), 11541-11548.

9. Gyimesi, J.; Szakacs, Z.; Tarnai, M.; Szoko, E., Determination of the aggregation

constants of bile salts by capillary electrophoresis. Chromatographia 2000, 51 (3-4), 235-

238.

10. Beckerdite, J. M.; Adams Jr, E. T., Self-association of sodium cholate in isotonic

saline solutions. Biophys. Chem. 1985, 21 (2), 103-114.

11. Majhi, P. R.; Moulik, S. P., Energetics of Micellization: Reassessment by a High-

Sensitivity Titration Microcalorimeter. Langmuir 1998, 14 (15), 3986-3990.

12. (a) Mazer, N. A.; Carey, M. C.; Kwasnick, R. F.; Benedek, G. B., Quasielastic light

scattering studies of aqueous biliary lipid systems. Size, shape, and thermodynamics of bile

salt micelles. Biochemistry (Mosc). 1979, 18 (14), 3064-75; (b) Chang, Y.; Cardinal, J. R.,

Light-scattering studies on bile acid salts I: Pattern of self-association of sodium cholate,

sodium glycocholate, and sodium taurocholate in aqueous electrolyte solutions. J. Pharm.

Sci. 1978, 67 (2), 174-81.

13. Garidel, P.; Hildebrand, A.; Neubert, R.; Blume, A., Thermodynamic

Characterization of Bile Salt Aggregation as a Function of Temperature and Ionic Strength

14. Liu, C. L., Interactions and Molecular Weights of Simple Micelles and Mixed

Micelles in Taurocholate and Taurocholate−Lecithin Solutions. The Journal of Physical

Chemistry B 1997, 101 (36), 7055-7059.

15. Nagarajan, R., On Interpreting Fluorescence Measurements: What Does

Thermodynamics Have To Say about Change in Micellar Aggregation Number versus

Change in Size Distribution Induced by Increasing Concentration of the Surfactant in

Solution? Langmuir 1994, 10 (6), 2028-2034.

16. (a) Schurtenberger, H.; Vogeli, U.; Pfander, H., Synthesis of Diterpenes as Possible

Biogenetic Precursors of the C-20-Carotenoid Crocetin. Helv. Chim. Acta 1983, 66 (7), 2346-

2357; (b) Ninomiya, R.; Matsuoka, K.; Moroi, Y., Micelle formation of sodium chenodeoxycholate and solubilization into the micelles: comparison with other unconjugated bile salts. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 2003,

1634 (3), 116-125; (c) Matsuoka, K.; Moroi, Y., Micelle formation of sodium deoxycholate and sodium ursodeoxycholate (Part 1). Biochimica et Biophysica Acta (BBA) - Molecular and

Cell Biology of Lipids 2002, 1580 (2–3), 189-199; (d) Sugioka, H.; Matsuoka, K.; Moroi, Y.,

Temperature effect on formation of sodium cholate micelles. J. Colloid Interface Sci. 2003,

259 (1), 156-162; (e) Kratohvil, J. P., Size of bile salt micelles: Techniques, problems and

results. Adv. Colloid Interface Sci. 1986, 26 (0), 131-154.

17. Sugioka, H.; Moroi, Y., Micelle formation of sodium cholate and solubilization into

the micelle. Biochimica et Biophysica Acta (BBA) - Lipids and Lipid Metabolism 1998, 1394

(1), 99-110.

18. Li, G.; McGown, L. B., Model for Bile Salt Micellization and Solubilization from

Studies of a "Polydisperse" Array of Fluorescent Probes and Molecular Modeling. The

Journal of Physical Chemistry 1994, 98 (51), 13711-13719.

19. Funasaki, N.; Fukuba, M.; Hattori, T.; Ishikawa, S.; Okuno, T.; Hirota, S., Micelle formation of bile salts and zwitterionic derivative as studied by two-dimensional NMR

spectroscopy. Chem. Phys. Lipids 2006, 142 (1-2), 43-57.

20. Gyimesi, J.; Barcza, L., Dimerization: First step for micelle preorganization of bile

salts. J. Inclusion Phenom. Mol. Recognit. Chem. 1993, 15 (2), 153-158.

Chapter 4 Evolutionary Significance of C24 Bile Acids’ Micellization Characteristics

4.1 Introduction

4.1.1 Bile acids as physiological detergents

The evolutionary “earliest” bile acids can be linked with the development of the

animal kingdom and the significant introduction of cholesterol as an essential structural component of the Animalia cell membranes. The evolution of bile acids is attributed to the growing importance of cholesterol and a needed means of controlling cholesterol levels. Bile acids provide a metabolic mechanism for cholesterol removal from the biological system, and a tool for efficient absorption of dietary lipids and fat-soluble vitamins by effectively acting as a detergent. This physical property is a result of metabolic structural modifications that give the molecule amphiphilic properties. A characteristic that arises from the nature of their biosynthesis is the hydroxyl groups on the ring system with positioning on the α-face, while the β-face is demarcated by two methyl groups at C-18 and C-19 positions. The facial amphiphilicity is illustrated in

Figure 1.1.4 with a 3D representation of bile acid’s structural polar and non-polar regions. It is this amphiphilic quality that allows bile acids to self-associate over a range of concentration to form micelles. The range at which bile acids undergo self-association to form micelles is dependent on the modifications made to its steroid ring nucleus and side chain, and is a matter of a great deal of research and debate. However, the majority of these studies only consider the three major bile acids found in the bile of most mammals: cholic acid, chenodeoxycholic acid, and deoxycholic acid.

4.1.2 Diversity of bile acid chemical structure

Chapter 1 described how within the class of bile acids there exists a large

structural diversity. However, the examples were narrowed to just the diversity among a

single species, human. When considering the subphylum vertebrates the structural

diversity of bile acids grows substantially, and includes a number of new modifications.

This diversity includes a class of bile alcohols. Bile alcohols like bile acids are the end

products of cholesterol metabolism, but are an earlier evolutionary form.2 At the heart of

this growing number of structurally diverse bile acids and alcohols are evolved variations among species’ biosynthetic pathways that include: functionalization of the side chain; side chain cleavage; A/B ring junction epimerization; and functionalization of the steroid ring system. Figure 4.1.1 displays several of these structural diversities with a reference to which animal species they are found in. A phylogenetic tree of vertebrates and their respective bile acids and alcohols is illustrated in Figure 4.1.2.

C-27-Bile Alcohol C-24-Bile Acid OH O O

OH OH OH OH

HO OH HO OH HO OH H H H OH α α 3α, 7α, 12α-trihydroxycholan-27ol 3α, 4β, 7α-trihydroxycholan-24oic acid 3 , 7 , 23(R)-trihydroxycholan-24oic acid American crocodile Pheasant Duck

C-27-Bile Acid Nor-C-24-Bile Acid COOH O OH OH OH O

HO OH HO O H H HO H 3α, 12α, 16α-trihydroxycholan-27oic acid 3α, 7α-dihydroxy-5β-cholan-23oic acid 3α hydroxyl, 7oxo-cholan-24oic acid Python Adders and Vipers Cavimorphs

Figure 4.1.1. Primary bile acids for several species.

1 Phylogenetic tree of vertebrates depicting the evolution of bile acids. bile of evolution the depicting vertebrates of tree Phylogenetic

. Figure 4.1.2

4.1.3 Bile acid side chain modification

From Figure 4.1.2 it is immediately apparent that the C24 bile acids are the major bile acid form found later in the evolutionary time line. This evolutionary side chain change in modification can be explained through the two main biosynthetic pathways for bile acid synthesis. They are the acidic and neutral pathways. The acidic pathway is initiated by the oxidation of cholesterol’s side chain by sterol-27-hydroxylase

(CYP27A1) to the oxysterol-27-hydroxycholesterol, followed by the hydroxylation of the

7-carbon position by oxysterol-7α-hydroxylase (CYP7B1). Both these microsomal enzymes happen to be expressed in various tissues outside the liver hepatocytes, so any further conversion into bile alcohols or acids requires the delivery of these oxysterol intermediates to the liver where the rest of the required enzymes are located. The neutral pathway takes place entirely in the liver hepatocytes. In this pathway steroid ring modification precedes side chain oxidation with the initial rate limiting step being the hydroxylation of the 7-carbon position by cholesterol 7α-hydroxylase (CYP7A1). Next, the enzyme 3β-hydroxy-Δ5-C27-steroid dehydrogenase/isomerase (HSD3B7) converts

7α-hydroxycholesterol to 7α-hydroxy-4-cholesten-3-one, which is then transported into the cytosol for modification by Δ4-3-oxosteroid-5β-reductase (AKR1D1) and 3α- hydroxysteroid dehydrogenase (AKR1C4) that subsequently reduce the double bond and

3-oxo into a cis-A/B ring fusion and 3α-hydroxyl.

The acidic pathway is the only source able to produce C27 bile alcohols and is

likely the earliest evolutionary pathway. The evolution of the neutral pathway introduced

CYP7A1 and hepatocyte expressed CYP27A1 that completely oxidizes the 27-carbon

into a carboxylic acid, bypassing the C27 alcohol intermediates. Accordingly, C27 bile

alcohols and C27 bile acids are found predominately in the evolutionary early species such as early evolving fish, amphibians, reptiles, and birds. This is a result of these

species not having the enzymes needed for β-oxidation of the side chain. Higher organisms have this developed β-oxidation modification and are able to produce C24 bile

acids. Interestingly, bile acids are the only steroids to have this C24 side chain

modification. Following the Darwin doctrine on evolutionary change, it is then recognized that this distinguishing side chain modification is due to some inherent properties the C24 bile acids have that are evolutionary preferable.

4.1.4. Proposed study

We propose the evolutionary driving force for the biosynthesis of C24 bile acids is

linked to their physical properties as detergents. In order to determine what significant

properties that might be we undertook an analysis of the micellization characteristics, a

quantitative set of thermodynamic properties describing detergency characteristics for

bile acids with varying side chain length. Cholic acid and chenodeoxycholic acid were

used as starting materials for the synthesis of bile acid derivatives with carboxylic acid

side chains at lengths of 3 carbons (C22), 4 carbons (C23), 6 carbons (C25), 7 carbons

(C26), and iso-8 carbons (C27). Their chemical structures are presented in Figure 4.1.3.

Steroid nucleus Abbreviation with hydroxylation composition Side chain total carbon number

OH C22

OH R O C23 O OH C24

HO OH OH

O C25 R: (H) = Chenodeoxycholic acid R: (CH3) = Cholic acid O OH C26

O OH C27

Figure 4.1.3. The chemical structures of the bile acid side chain length derivatives.

ITC was used as the experimental method for monitoring the demicellization of

each derivative. Sample solutions were prepared at concentrations consistent with a

micellar solution. Then using the already described standard model for ITC analysis of bile acid micellization (Chapter 3) the self-association properties of bile acids with varying side chain lengths were determined. These values were then compared to identify any outstanding properties the C24 bile acids might have over the other side chain

derivatives. This analysis provided us with evidence towards an evolutionary physiologic preference for the C24 bile acids that is presented here.

4.2. Results and Discussion

From a simple analysis of the ITC experimental results it is apparent the side chain length does, in fact, have an impact on micelle transition range concentrations. As the side chain length increases, the micelle concentration range becomes lower and the

range at which it takes to reach micellar saturation becomes shorter. Figure 4.2.1 shows

the ranges over which the bile acid side chain derivatives form micelles compared to the

physiological concentrations found at each step of the enterohepatic cycle. With this

initial analysis, it became apparent that the C24 bile acids form micelles over a range that

allows them to be physiologically useful as emulsifiers in the small intestine and

dissociate at concentrations within the enterocytes. The longer side chain length bile

acids form micelles at cytosolic concentrations, thus promoting aggregation and other

cytotoxic interactions that would be potentially deleterious to the cell. The shorter side

chain length bile acids show higher micelle concentration ranges that occur above the

physiological level found in the small intestine, where micellization is needed for

emulsification and absorption of dietary lipids. The C24 bile acids appear to fit the

criterion for a physiological detergent of the enterohepatic circulation. It forms micelles at concentrations consistent with the intestinal lumen, but at cellular cytosol concentrations it dissociates into free monomer form.

Figure 4.2.1. The micelle transition concentration ranges for the bile acid side chain derivatives as found by ITC. The ranges are calculated averages for both CA and CDCA. (♦▬) represents the approximate concentration at which free bile acids begins to associate, (▬●) represents the approximate concentration at which bile acid is predominately in micelle form.

A similar micellization model analysis from Chapter 3 was conducted on the C22 and C27 side chain derivatives to confirm that the best fits are indeed obtained from the

bi-micelle multimer model. This was confirmed. The results also provided insight into

the impact the side chain length has on micellization. Through the relative model

comparing values AIC and F-Test it was found that the C22 bile acids are better fit using

multimer models with more micellar sizes, and alternatively the C27 bile acids did not fit

the bi-micelle multimer model as well as its shorter chained derivatives. These results

indicate that as the side chain length increases the bile acid begins to act more like a typical detergent that are more easily described by the uniform model of micellization.

Conversely, as the side chain length decreases the bile acids act less like detergents and need higher multimer number models to describe its more random solution associations.

Table 4.2.1 reports the thermodynamic values for the bi-micelle multimer model fittings of the CA and CDCA side chain derivatives at 10 °C, 20 °C, 30 °C, and 40 °C in

150 mM NaCl at pH 7.5. From the experimental results, the parameter values at 37 °C were interpolated and are reported in Table 4.2.2. Plots of these values verse bile acid carbon number are presented in Figures 4.2.(2-5). It was immediately apparent that the micellar properties dependency on side chain length did not follow any clear trend.

However, the separate analyses of side chain length’s impact on enthalpy, micelle size, and free energy does provide the information needed to discern the significance of the

C24 bile acids as physiologic detergents.

-1 -(N-1) -1 -1 -1 -1 T (°C) K2 (M ) KN (M ) N ΔGmic (cal·mol ) ΔHmic (cal·mol ) ΔSmic (cal·mol ·K ) C22-CA 10 2.28 1.45E+12 11 -1432.17 1010.33 8.63 20 1.79 3.93E+05 6 -1250.56 720.29 6.72 40 2.51 1.05E+14 13 -1545.13 -157.70 4.43 C22-CDCA 10 2.03 1.77E+14 11 -1678.20 1213.46 10.21 20 9.23 9.88E+13 10 -1863.87 965.45 9.55 40 4.06 1.93E+11 9 -1796.47 -304.15 4.77 C23-CA 10 0.77 5.14E+10 10 -1387.57 1358.08 9.70 20 2.00 1.31E+07 7 -1335.04 593.73 8.48 40 3.55 3.49E+13 12 -1616.87 -656.18 3.07 C23-CDCA 10 3.99 1.39E+12 9 -1747.89 1768.74 12.42 20 6.14 2.70E+14 10 -1935.48 814.53 9.38 40 4.84 7.85E+11 9 -1893.60 -726.16 3.73 C24-CA 10 3.96 1.22E+11 8 -1795.42 1293.11 10.91 20 3.84 5.19E+11 8 -1964.07 606.10 8.77 40 3.49 2.40E+12 9 -1970.79 -767.48 3.84 C24-CDCA 10 9.52 5.63E+17 10 -2299.50 1546.07 13.58 20 14.90 4.32E+18 10 -2499.39 757.15 11.11 40 3.80 1.53E+15 9 -2417.41 -1083.54 4.26 C25-CA 10 11.61 2.48E+18 11 -2166.33 1225.03 11.98 20 23.93 3.79E+21 12 -2411.83 516.99 9.99 40 2.60 5.28E+13 9 -2184.51 -917.43 4.05 C25-CDCA 10 23.32 6.37E+19 10 -2565.60 1864.96 15.65 20 77.67 1.23E+26 12 -2916.02 918.34 13.08 40 3.34 2.57E+12 7 -2539.98 -1656.48 2.82 C26-CA 10 12.12 1.38E+20 11 -2371.90 1504.28 13.69 20 17.80 6.72E+20 11 -2539.48 616.78 10.77 40 2.87 1.11E+15 9 -2395.11 -999.49 4.46 C26-CDCA 10 24.35 2.71E+32 14 -3001.19 1986.50 17.61 20 29.86 1.29E+28 12 -3141.98 845.02 13.60 40 <1 1.06E+16 8 -2869.83 -1586.79 4.10 C27-CA 10 9.54 7.79E+15 9 -2287.46 2072.33 15.40 20 12.02 2.36E+16 9 -2440.00 1158.74 12.28 40 5.89 1.88E+13 8 -2377.39 -680.22 5.42 C27-CDCA 10 21.33 3.41E+29 13 -2942.98 2438.86 18.25 20 28.60 4.86E+35 15 -3190.97 1223.84 14.80 40 7.25 2.46E+21 10 -3064.69 -945.99 6.82

Table 4.2.1. Thermodynamic parameters for micellization of CA and CDCA side chain derivatives calculated from the bi-micelle multimer micellization model.

-1 a -1 -1 -1 -1 K2 (M ) KN N ΔGmic (cal·mol ) ΔHmic (cal·mol ) ΔSmic (cal·mol ·K ) C22-CA 2.28 11.00 12 -1477.75 9.90 4.80 C22-CDCA 2.03 22.45 9 -1917.39 -40.85 6.05 C23-CA 0.77 12.00 11 -1531.37 -483.49 3.38 C23-CDCA 3.99 23.53 9 -1946.34 -505.55 4.65 C24-CA 3.96 25.97 8 -2007.15 -561.40 4.66 C24-CDCA 9.52 46.80 8 -2370.09 -888.90 4.78 C25-CA 11.61 43.05 9 -2318.62 -701.70 5.21 C25-CDCA 23.32 89.60 8 -2770.34 -1250.60 4.90 C26-CA 12.12 56.05 9 -2481.25 -761.59 5.54 C26-CDCA 24.35 130.98 9 -3004.33 -1217.60 5.76 C27-CA 9.54 49.90 10 -2409.62 -404.10 6.47 C27-CDCA 21.33 170.50 12 -3166.84 -528.00 8.51

Table 4.2.2. Interpolated thermodynamic parameters for micellization of CA and CDCA side

chain derivatives at 37 °C. a Association constants have been normalized to per monomer unit for

comparative purposes.

The enthalpy results follow a trend described by the hydrophobic effect that states

as the hydrophobic surface area grows on a molecule the enthalpy of solvation in water

3 increases. This is seen in Figure 4.2.2, which displays a plot of the ΔHmic at 37 °C for all the bile acid side chain length derivatives. The enthalpy is exothermic and as the side chain length grows by a methylene unit the absolute value of enthalpy increases. This pattern excludes the C27CA, C27CDCA, and C26CDCA values, which is likely a result of

a significant change in their association characteristics. In addition, from the temperature

dependence of ΔHmic, heat capacities, ΔCp, were calculated and follow the expected

trend of ~12 cal*mol-1K-1 increase per additional methylene group to the side chain.4 The thermodynamic quantity of heat capacity provides information for elucidating hydrophobic surface area exposure before and after micellization. It is obtained from the slope of a ΔHmic verse temperature plot, and the value of ΔCp is related linearly to the

area of the hydrophobic surface. Figure 4.2.3 shows a plot of the calculated ΔCp for the

bile acid side chain derivatives, which are: Cholic acid (C22C27) 38.9, 72.2, 67.8, 69.4,

-1 -1 81.3, 94.2 cal*mol K ; and chenodeoxycholic acid (C22C27) 50.7, 83.0, 90.7, 114.8,

-1 -1 - 114.7, 102.5 cal*mol K . The apparent deviation of C27 CDCA (ΔΔCp=-12.2 cal*mol

1 -1 -1 -1 -1 -1 K CH2 ) from the 12 cal*mol K CH2 trend is postulated to be due to an intramolecular interaction between the side chain and the hydrophobic facial ring system during free monomer form, essentially lowing the amount of exposed hydrophobic surface.

Figure 4.2.2. Calculated enthalpy of micellization, ΔHmic of bile acids as a function of side chain length (referred to by total carbon count) at 37 °C in 150 mM NaCl pH 7.5.

Figure 4.2.3. Calculated heat capacity of micellization, ΔCpmic, of bile acids as a function of side chain length (referred to by total carbon count) from 10 -40 °C in 150 mM NaCl pH 7.5.

The plot of aggregation size verse side chain length seen in Figure 4.2.4 shows

C24 bile acids as having the smallest micelle size with an aggregation number of eight

bile acids. There appears to be a minima for micelle sizes at the C24 bile acid length with

shortening or extending the side chain resulting in larger micelle sizes. The physiology

of dietary emulsification and absorption could favor smaller micelle sizes. The relation

can be made that smaller sized micelles are a result of individual monomer being able to

more efficiently pack into a hydrophobic interior core. This quality provides a detergent

characteristic that is more favorable for the association with mixed lipid micelles during

digestion.

Figure 4.2.4. Calculated micelle aggregate size of bile acids as a function of side chain length

(referred to by total carbon count) at 37 °C in 150 mM NaCl pH 7.5.

The free energy of micellization (ΔGmic) verse side chain length shown in Figure

4.2.5 has a slight slope increase towards more favorable micellization at the C24 bile

acids with a steady increase in slope as the side chain grows thereafter. This could be

what distinguishes the C24 bile acids as more physiologically favorable. This plot essentially shows the empirical values for the earlier comparison of the ITC experimental

micelle concentration ranges to physiological concentrations. It can be concluded then

-1 that a ΔGmic of ~-2,200 cal*mol is required for a physiological efficient detergent. A

value that favors micellar association at concentrations found in the small intestine, yet

favors free monomer form at concentrations found in the ileocyte and hepatocyte cytosol.

Figure 4.2.5. Calculated free energy of micellization, ΔGmic, of bile acids as a function of side chain length (referred to by total carbon count) at 37 °C in 150 mM NaCl pH 7.5.

Based on this experimental assessment of bile acid side chain length it is concluded the C24 bile acids, with an alkyl chain length of 5 carbons, are the

physiologically preferred metabolite. Their superiority is based on several thermodynamic characteristics shown here. The C24 bile acids form smaller micelle

sizes, which transitions to an ability to form better hydrophobic packing at the exterior

surface of the mixed lipid aggregates that form during digestion. This would equate to

smaller mixed micelle sizes and a greater total surface area for the digestive enzyme

lipase to cleave fatty acids from triglycerides, aiding in their absorption.

These parameter values provided an empirical understanding of the evolutionary

drive for the C24 bile acids. It also provides a better understanding for the role bile acids

take in the enterohepatic circulation. In addition, knowing the quantitative values for the

preferred physiologic detergent properties translates towards design and application in a

number of fields. For example, the pharmacological design of synthetic detergents for

therapeutics, and in understanding drug absorption.

Further work towards a conclusive understanding of the evolutionary significance

of the C24 bile acids would include a similar ITC analysis of bile acid side chain length

derivatives mixed micellization with dietary lipids, and calcium precipitation studies. A

thermodynamic understanding of how these side chain length derivatives associate with

the bulk digestive lipid composition would provide additional evidence for any favorable

association. Calcium binding to bile acids side chain derivatives could be another important physiologic property worth investigating, as precipitation of calcium bile salts within the enterohepatic system are potentially harmful.

4.3 Materials and Methods

Chemicals and Sample Preparation – The bile acids, cholic acid, deoxycholic acid, and

chenodeoxycholic acid, were obtained from Sigma and used after purification by

recrystallization with ethanol and water. The purity was confirmed by thin-layer chromatography. Bile acid stock solutions were made by dissolving the sample into tetrahydrofuran (THF) with accurate concentrations found via dry weight analysis on a microgram balance. ITC solutions of define concentrations were freshly prepared by measuring out a specific volumetric amount from the THF stock solutions and adding 1.1 equivalents of NaOH before freeze thaw drying. The samples were then diluted to volume with the appropriate amount of 150 mM NaCl filtered stock solution maintained at a pH of 7.5 to make the experimental bile acid solutions. The pH of the solutions were monitored with an Orion Micro pH Probe and adjusted when necessary. Synthesis of bile acid side chain derivatives were preformed according to the given literature procedures:

C22 dinorbile acids5; C23 norbile acids6; C25 bile acids7; C26 bile acids8; C27 bile

acids8. 1H and 13C NMR spectra analysis on synthesized products are included in

appendix.

Isothermal Titration Calorimetry – Titrations were measured using a MicroCal VP-ITC

microcalorimeter. Micellar solutions, ranging in concentration from 25 mM to 200 mM

depending on bile salt species, were loaded into a 275 uL magnetically stirred syringe.

Injection volumes of 2, 4, or 5 uL over durations of 4, 8, and 10 seconds, respectively,

were injected into a 1.46 mL reaction cell with 150 mM NaCl at a pH of 7.5 with an equilibration time of 300 seconds to ensure the total heat of reaction was measured.

Experiments were repeated for 10 °C, 20 °C, 30 °C, and 40 °C. Experimental data analysis was conducted using the MicroCal Origin software (version 8.5.1).

Origin fitting codes for these models are presented in Appendix I.

Determination of Enthalpy of Dilutions – A low concentration of 0.1 mM bile salt solutions (low enough to ensure primarily a monomer solution) in 150 mM NaCl with a pH 7.5 at 10 uL aliquots per injection to find the average of the reaction heat for dilution of monomer species. This was taken as the enthalpy of dilution for monomer. Similarly, the average of the micellar saturation phase asymptote was taken as the enthalpy of dilution for micellar species.

4.3.1. Synthetic Procedures

NMR spectra were recorded at room temperature in CD3OD on a 400 MHz varion spectrometer. Final product spectra can be viewed in Appendix II. All reactions are demonstrated on the trihydroxy bile acid substrates, but were also completed on the dihydroxy bile acids.

Synthesis of 3α,7α,12α-Tri(formyloxy)cholic acid (TFCA): Cholic acid (20.6 g;

50.4 mmol) was dissolved into formic acid (80 mL) followed by perchloric acid

(20 drops). This was then heated to 55 ˚C and left for 1.5 hours. The temperature was then lowered to 40 ˚C before acetic anhydride (80 mL) was added drop-wise and left to react for another 1 hour. The reaction was worked up by pouring it into 800 mL of water,

which formed a white precipitate. The precipitate was collected by vacuum filtration and

then recrystallized using boiling ethyle acetate (EA) and hexanes (Hex).

Synthesis of C22-CA (3α,7α,12α-tri(hydroxy)-5β-cholest-22-oic acid):

Formulated nornitrile cholic acid was synthesized from TFCA following the method of

Schteingard et al.6 Under inert atmosphere 5.0 g of TFCA (10.16 mmols) was added to a

cold solution of 20 mL trifluoroacetic acid and 2.33 mL (30.48 mmol) trifluoroacetic

anhydride while stirred at 5 °C until the solid’s complete dissolution. Addition of 0.77 g

sodium nitrite (11.17 mmol), in small portions over one hour, was completed before letting the reaction mixture stir for an additional hour at 5 °C. The reaction mixture was then brought up to a temperature of 40 °C for 1 hour. After cooling to room temperature the reaction mixture was slowly poured into a 350 mL ice solution of 2 M NaOH.

Precipitate was collected by extraction with ethyl ether. The extracted organic layer was washed with 1 M NaOH until no more starting material was seen by TLC, and then washed to neutrality before being dried and evaporated. Obtained crude 3α,7α,12α- triformyloxy-5β-cholane-23-nitrile was added to a 50 mL methanol solution containing

1.95 g of sodium methoxide (36 mmol) for 30 minutes. After cooling, the solution was

poured into 400 mL of water and the product was extracted with EA (50 mL x3). The organic layer was then dried with sodium sulfate and evaporated to give crude 3α,7α,12α- trihydroxy-5β-cholane-23-nitrile. Obtained crude material was dissolved into 50 mL

anhydrous tetrahydrofuran. The solution was cooled to 0°C before 2.5 g potassium t-

butoxide (22.2 mmol) and left to stir for 30 minutes. 2.5 g of 18-crown-6-ether

(9.5 mmol) was added and allowed to come to room temperature while under an oxygen atmosphere for 24 hours. After addition of 250 mL (EA), contents were extracted with

water (4 x 50 mL). Water extract layer was then acidified to precipitate crude 3α,7α,12α-

trihydroxy-5β-cholan-22-oic acid. After decanting water off, 500 mL EA was added to

dissolve crude material which was then washed with water to neutrality. The EA solution

was then dried over sodium sulfate and concentrated. Crystals of 3α,7α,12α-trihydroxy-

5β-cholan-22-oic acid were obtained after refrigerating overnight. Purity and structure

were obtained through TLC and NMR spectral confirmation.

Synthesis of C23-CA (3α,7α,12α-tri(hydroxy)-5β-cholest-23-oic acid):

Formulated nornitrile cholic acid was synthesized from TFCA following the method of

Schteingard et al.6 Under inert atmosphere 5.0 grams of TFCA (10.16 mmols) was added

to a cold solution of 20 mL trifluoroacetic acid and 2.33 mL (30.48 mmol) trifluoroacetic

anhydride while stirred at 5 °C until the solid’s complete dissolution. Addition of 0.77 g

sodium nitrite (11.17 mmol), in small portions over one hour, was completed before

letting the reaction mixture stir for an additional hour at 5 °C. The reaction mixture was

then brought up to a temperature of 40 °C for 1 hour. After cooling to room temperature the reaction mixture was slowly poured into a 350 mL ice solution of 2 M NaOH.

Precipitate was collected by extraction with ethyl ether. The extracted organic layer was

washed with 1 M NaOH until no more starting material was seen by TLC, and then

washed to neutrality before being dried and evaporated. 4.63 g of obtained crude

3α,7α,12α-triformyloxy-5β-cholane-23-nitrile was added to a 50 mL methanol solution containing 1.95 g of sodium methoxide (36 mmol) for 30 minutes. After cooling, the

solution was poured into 400 mL of water and the product was extracted with EA. The organic layer was then dried with sodium sulfate and evaporated to give crude 3α,7α,12α- trihydroxy-5β-cholane-23-nitrile. In a polypropylene bottle fitted with a reflux condenser

1.25 g of crude 3α,7α,12α-Trihydroxy-5β-cholan-23-nitrile was dissolved using 50 mL of

10% KOH in 1:1 ethanol:water. The reaction was refluxed for 96 hrs. The ethanol was then evaporated and the resulting aqueous solution saturated using sodium chloride. The aqueous solution is then washed using ethyl ether before it was acidified using 6 M HCl.

The product was then extracted using EA (50 mL x4). The extractions were combined, washed using 20% sodium chloride, dried, and then evaporated. TLC shows 4 spots the most predominant one being though to be C23 CA. A purification by silica column was

used eluting solvent used was a mix of (66% chloroform, 26% acetone, 8%

methanol). 1H and 13C-NMR on purified product shows it to be 3α,7α,12α-tri(hydroxy)-

5β-cholest-23-oic acid. After purification product yield is 52.6% 0.600 g; (1.58 mmol).

Synthesis of C25-CA (3α,7α,12α-trihydroxy)-5β-cholan-25-oic acid): Into a

round bottom flask previously purged with argon and sealed 1.00 g (2.03 mmol) of TFCA

was weighted. Toluene (14 mL) heated to 60 ˚C was used to dissolve the compound.

The solution was then cooled to 0 ˚C. 1.25 mL (17.2 mmol) of thionyl chloride was then

added, and the solution was heated to 60 ˚C for 4 hours with stirring. The solvent was

then evaporated off and another 10 mL of toluene was added to be evaporated off and

remove excess thionyl chloride (2x). The TFCA-chloride (2.74 g; 5.37 mmol) was

dissolved into diethyl ether (80 mL) then cooled to 0 ˚C before fresh diazomethane

(35 mL; ~12 mmol) was added drop-wise. The temperature was then allowed to come to

room temperature before evaporating off the solvent and excess diazomethane. Without

further purification, the TFCA-diazoketone was then dissolved into 90% Methanol

(135 mL) followed by addition of silver nitrate (1.55 g; 9.14 mmol). The reaction was

then heated to reflux (80 ˚C) for 4 hours. Methanol solvent was then removed by

evaporation, and product mixture extracted with EA (25 mL x3). The combined organic

layers were dried and concentrated. Due to difficulties in removing silver impurities, the crude 3α,7α,12α-trihydroxy-5β-(methyl)cholan-25-oic ester was re-formylated following the same reaction procedure as before. Crude 3α,7α,12α-triformyloxy-5β-

(methyl)cholan-25-oic ester was then purified through silica chromatography using 20:80

EA:Hex as eluent. In a polypropylene bottle fitted with a reflux condenser 0.452 g

(0.868 mmol) of 3α,7α,12α-trihydroxy-5β-(methyl)cholan-25-oic ester was dissolved using 15 mL of 10% KOH(aq) in 40 mL ethanol. The reaction was refluxed for 12 hrs.

The ethanol was then evaporated and the resulting aqueous solution saturated using sodium chloride. The aqueous solution is then washed using ethyl ether before it was acidified using 6 M HCl. The product was then extracted using EA (75 mL x3). The extractions were combined, washed using 20% sodium chloride, dried, and then evaporated. Purity and structure were obtained through TLC and NMR spectral confirmation with a final product yield of 41.6% 0.357 g (0.846 mmol).

Synthesis of C26-CA (3α,7α,12α-trihydroxy)-5β-cholan-26-oic acid) & C27-CA

(3α,7α,12α-trihydroxy)-5β-(26-methyl)-cholan-26-oic acid): TFCA (5.9 g; 11.97 mmol)

was dissolved into tetrahydrofuran (120 mL) followed by ethyl chloroformate (3.6 mL)

then triethyl amine (6 mL) and left to stir at 0 ˚C for 1.5 hours. Then freshly made

sodium borohydride (3 g) in 60 mL of water was slowly added at 0 ˚C and then left to

react for 1 hour. The reaction monitored by TLC showed completion by a lack of starting

material spot. The reaction was then quenched with 1 M HCl. The organics were then

extracted with ethyl acetate (50 mL x3). This ethyl acetate organic layer was then

washed with fresh 1 M HCl then dried with sodium sulfate before removing the solvent

by evaporation. The resulting oil was dissolved into 50:50 EA:Hex and loaded onto a

silica (100 g) column. A solvent of 50:50 EA:Hex was used to elute the compounds.

Purified TFCA-alcohol (Rf 0.21) was obtained at 4.53 g; (9.46 mmol) giving a reaction

step yield of over 80%. The resulting TFCA-alcohol (4.53 g; 9.46 mmol) was dissolved

into dichloromethane (70 mL) followed by addition of pyridinium chlorochromate

(3.107 g; 14.4 mmol) and then left to stir at room temperature. TLC showed a slight spot

for the starting material after 3 hours, so another 0.1 g of pyridinium chlorochromate was

added. After an additional 1 hour TLC showed completion of the reaction by lack of a

starting material spot. Crude product mixture was added on a silica (100 g) column. The

eluting solvent was 20:80 EA:Hex. The purified TFCA-aldehyde collected was (3.36 g;

7.06 mmol) giving a reaction step yield of 69.1%. Sodium hydride (0.33 g; 7.76 mmol)

was added to anhydrous toluene (70 mL) followed by triethyl phosphonoacetate (1.583 g;

7.062 mmol) while at room temperature and kept under an argon atmosphere for

10 minutes. For synthesis of C27 product use triethyl-2-phosphonopropinate in place of triethyl phosphonoacetate. Then TFCA-aldehyde (3.24 g; 7.062 mmol) in toluene

(75 mL) was added to the prepared phosphonate carbanion. After monitoring through

TLC additional phosphonate carbanion was prepared and added to the reaction to further push the reaction to completion. The solvent was then removed and EA was added to dissolve the crude product to load onto a silica (200 g) column. Eluting solvent was

25:75 EA:Hex. The resulting TFCA-C26-α,β unsaturated ethyl ester (2.91 g; 5.32 mmol) was dissolved into freshly distilled ethanol (80 mL) followed by 10% palladium on activated carbon (0.29 g). The reaction vessel was then put under a vacuum till the solvent started to boil. Then the vessel was purged with hydrogen gas from a balloon with

needle. This vacuum/purge cycle was done 5 times before leaving the reaction to stir for

3 hours. The reaction was followed by TLC and determined to be >90% complete. The

crude reaction material was loaded onto a silica (60 g) column and eluted with 25:75

EA:Hex. Saturated TFCA-C26 ethyl ester (3.87 g; 7.06 mmol) was dissolved into ethanol

(80 mL) then 10% KOH (60 mL) was added slowly to try to keep starting material from

falling out. The reaction was then refluxed over night at 105 ˚C. Work up involved acidifying the solution using 0.5 M HCl (~175 mL) to precipitate out the product. The precipitate was collected by vacuum filtration and then recrystallized using boiling ethanol and water. Purity and structure were obtained through TLC and NMR spectral confirmation with a final product yield of 58.6% 3.086 g (7.02 mmol).

4.4 References

1. Hofmann, A. F.; Hagey, L. R.; Krasowski, M. D., Bile salts of vertebrates:

structural variation and possible evolutionary significance. J. Lipid Res. 2010, 51 (2),

226-246.

2. Hofmann, A. F.; Hagey, L. R., Bile acids: Chemistry, pathochemistry, biology,

pathobiology, and therapeutics. Cell. Mol. Life Sci. 2008, 65 (16), 2461-2483.

3. Tanford, C., The hydrophobic effect: formation of micelles and biological

membranes. Wiley: New York,, 1973; p viii, 200 p.

4. Opatowski, E.; Kozlov, M. M.; Pinchuk, I.; Lichtenberg, D., Heat Evolution of

Micelle Formation, Dependence of Enthalpy, and Heat Capacity on the Surfactant Chain

Length and Head Group. J. Colloid Interface Sci. 2002, 246 (2), 380-386.

5. Batta, A. K.; Datta, S. C.; Tint, G. S.; Alberts, D. S.; Earnest, D. L.; Salen, G., A

convenient synthesis of dinorbile acids: Oxidative hydrolysis of norbile acid nitriles.

Steroids 1999, 64 (11), 780-784.

6. Schteingart, C. D.; Hofmann, A. F., Synthesis of 24-nor-5beta-Cholan-23-Oic

Acid-Derivatives - a Convenient and Efficient One-Carbon Degradation of the Side-

Chain of Natural Bile-Acids. J. Lipid Res. 1988, 29 (10), 1387-1395.

7. Dayal, B.; Ertel, N. H.; Padia, J.; Rapole, K. R.; Salen, G., 7β-hydroxy bile alcohols: Facile synthesis and 2D 1H NMR studies of 5β-cholestane-3α,7β,12α,25-tetrol.

Steroids 1997, 62 (5), 409-414.

8. Tochtrop, G. P.; Bruns, J. L.; Tang, C. G.; Covey, D. F.; Cistola, D. P., Steroid ring hydroxylation patterns govern cooperativity in human bile acid binding protein.

Biochemistry (Mosc). 2003, 42 (40), 11561-11567.

Appendix I. Origin C fitting functions

Two-step ligand-protein binding model.

Regression analysis to the binding isotherm calorimetric curves yielded the stepwise dissociation constants (K1, K2) and binding enthalpies (ΔH1, ΔH2). Model algorithms include the concentration of total bile acid (BAt), unbound bile acid ligand

(UL), and total protein (Pt) in a root form of the Adair two-site macroscopic binding equation for fractional saturation.

3 2 0 = UL + UL ·(2·K2+2·Pt-Lt) + UL·(K1K2+[2K2·(Pt-Lt)]) - Lt

This equation used the Newton algorithm for calculating the unbound bile acid concentration, which was then used to calculate ligand bound to site 1 and 2, PL and PLL respectively.

-1 -1 -1 -1 2 PL = Pt · ((K1 ·UL)/(1 + (2·K1 ·UL)+(K1 K2 ·UL ))

-1 -1 2 -1 -1 -1 2 PLL = Pt · ((K1 K2 ·UL )/(1 + (2·K1 ·UL)+(K1 K2 ·UL ))

Ligand state concentrations were then used to calculate the overall binding heat (Q) for each injection. The parameter V represents volume with in the reaction vessel.

Q = V·PL·ΔH1 + V·PLL·(ΔH1+ΔH2)

The calculation performed for the injection before the ith injection was subtracted from the ith injection to give the differential heat for the ith injection.

dQ = Qi – Qi-1

Uniform micelle self-association model.

Regression analysis of the demicellization isotherm calorimetric curves provided

the association constant (KN) and the enthalpy of demicellization (ΔHdemic) for one

solvated bile acid into a hydrophobically packed micelle. Model equation is based off of

the mass-action equation of total bile acid and root form that includes the concentration

of total bile acid (BAt) and dissociated bile acid in monomer form (Mono).

N 0 = Mono + N·KN·Mono - BAt

Using the Newton algorithm the concentration of Mono was calculated for the syringe

and reaction vessel for each injection. The concentration of dissociated (dMono) bile

acid per injection was calculated by subtracting syringe monomer (Monosyr) and the

th Mono from the injection before (Monoi-1) from the i injection monomer (Monoi).

Amounts were calculated based on vessel volume and injection volume, Vves and Vinj

respectively.

dMono = Vves·Monoi – (Vves·Monoi-1 + Vinj·Monosyr)

The heat of binding was then calculated for each injection using the ΔHdemic

dQ = dMono· ΔHdemic

100

Non-uniform sequential stepwise micelle self-association model.

Regression analysis of the demicellization isotherm calorimetric curves provided

the stepwise association constant ki and the enthalpy of demicellization (ΔHdemic) for one solvated bile acid into a hydrophobically packed micelle. Model equation is based off of the mass-action equation of total bile acid and root form that includes the concentration

of total bile acid (BAt) and dissociated bile acid in monomer form (Mono).

-1 0 = (k )· Mono - BAt 푛 푖 푖 ∑푖(=0 푖 ∙ 푘) ∙ ( ) ( ) ( ) ( Mono) = ( ( n )) n+1 푛 푖 푘∙Mono −n∙ 푘∙Mono + n−1 ∙ 푘∙Mono 2 ∑푖=0 푖 ∙ 푘 ∙ 1− 푘∙Mono Then through this equation and the Newton algorithm the concentration of Mono was

calculated for the syringe and reaction vessel for each injection. The concentration of

dissociated (dMono) bile acid per injection was calculated by subtracting syringe

th monomer (Monosyr) and the Mono from the injection before (Monoi-1) from the i

injection monomer (Monoi). Amounts were calculated based on vessel volume and

injection volume, Vves and Vinj respectively.

dMono = Vves·Monoi – (Vves·Monoi-1 + Vinj·Monosyr)

The heat of binding was then calculated for each injection using the ΔHdemic

dQ = dMono· ΔHdemic

101

Non-uniform multimer micelle self-association model. (Bi-micelle example)

Regression analysis of the demicellization isotherm calorimetric curves provided

the association constants (K2 and KN) and the enthalpy of demicellization (ΔHdemic) for

one solvated bile acid into a hydrophobically packed micelle. Model equation is based

off of the mass-action equation of total bile acid and root form that includes the

concentration of total bile acid (BAt) and dissociated bile acid in monomer form (Mono).

2 N 0 = Mono + 2·K2·Mono + N·KN·Mono - BAt

Using the Newton algorithm the concentration of Mono was calculated for the syringe

and reaction vessel for each injection. The concentration of dissociated (dMono) bile

acid per injection was calculated by subtracting syringe monomer (Monosyr) and the

th Mono from the injection before (Monoi-1) from the i injection monomer (Monoi).

Amounts were calculated based on vessel volume and injection volume, Vves and Vinj

respectively.

dMono = Vves·Monoi – (Vves·Monoi-1 + Vinj·Monosyr)

The heat of binding was then calculated for each injection using the ΔHdemic

dQ = dMono· ΔHdemic

102

Appendix II. NMR spectra of synthesized compounds 15 NH O OH O OH OH HO

1 15 Figure AII-1. H NMR spectrum of N-Glyco cholic acid in CD3OD.

103

15 NH O OH O OH HO

1 15 Figure AII-2. H NMR spectrum of N-Glyco chenodeoxycholic acid in CD3OD.

104

NH O OH O HO

1 Figure AII-3. H NMR spectrum of Glyco lithocholic acid in CD3OD.

105

NH O OH O OH OH HO

1 Figure AII-4. H NMR spectrum of Glyco cholic acid in CD3OD.

106

13 15 Figure AII-5. C NMR spectrum of N-Glyco cholic acid in CD3OD.

107

13 NH 13 O OH O OH HO

1 13 Figure AII-6. H NMR spectrum of C1,2 Glyco ursodeoxycholic acid in D2O.

108

OH O OH OH HO

1 Figure AII-7. H NMR spectrum of C22 Cholic acid in CD3OD.

109

OH O OH OH HO

13 Figure AII-8. C NMR spectrum of C22 Cholic acid in CD3OD.

110

OH O OH HO

1 Figure AII-9. H NMR spectrum of C22 Chenodeoxycholic acid in CD3OD.

111

OH O OH

13 Figure AII-10. C NMR spectrum of C22 Chenodeoxycholic acid in CD3OD.

112

OH O OH OH HO

1 Figure AII-11. H NMR spectrum of C23 Cholic acid in CD3OD.

113

OH O OH OH HO

13 Figure AII-12. C NMR spectrum of C23 Cholic acid in CD3OD.

114

OH O OH HO

1 Figure AII-13. H NMR spectrum of C23 Chenodeoxycholic acid in CD3OD.

115

OH O OH HO

13 Figure AII-14. C NMR spectrum of C23 Chenodeoxycholic acid in CD3OD.

116

OH O OH OH HO

1 Figure AII-15. H NMR spectrum of C25 Cholic acid in CD3OD.

117

OH O OH OH HO

13 Figure AII-16. C NMR spectrum of C25 Cholic acid in CD3OD.

118

OH O OH HO

1 Figure AII-17. H NMR spectrum of C25 Chenodeoxycholic acid in CD3OD.

119

OH O OH HO

13 Figure AII-18. C NMR spectrum of C25 Chenodeoxycholic acid in CD3OD.

120

OH O OH OH HO

1 Figure AII-19. H NMR spectrum of C26 Cholic acid in CD3OD.

121

OH O OH OH HO

13 Figure AII-20. C NMR spectrum of C26 Cholic acid in CD3OD.

122

OH O OH HO

1 Figure AII-21. H NMR spectrum of C26 Chenodeoxycholic acid in CD3OD.

123

OH O OH HO

13 Figure AII-22. C NMR spectrum of C26 Chenodeoxycholic acid in CD3OD.

124

OH O OH OH HO

1 Figure AII-23. H NMR spectrum of C27 Cholic acid in CD3OD.

125

OH O OH OH HO

13 Figure AII-24. C NMR spectrum of C27 Cholic acid in CD3OD.

126

OH O OH HO

1 Figure AII-25. H NMR spectrum of C27 Chenodeoxycholic acid in CD3OD.

127

OH O OH HO

13 Figure AII-26. C NMR spectrum of C27 Chenodeoxycholic acid in CD3OD.

128

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