Sweet GLUTs: Quantitative Analysis of Glucose Transporter and

Fructolytic Enzyme Transcript Densities in Ruby-throated Hummingbirds (Archilochus colubris)

by

Alexander Morley Myrka

A thesis submitted in conformity with the requirements for the degree of Master of Science

Cell & Systems Biology University of Toronto

© Copyright by Alexander Morley Myrka 2016

ii

Sweet GLUTs: Quantitative Analysis of Glucose Transporter and

Fructolytic Enzyme Transcript Densities in Ruby-throated

Hummingbirds (Archilochus colubris)

Alexander Morley Myrka

Master of Science

Cell & Systems Biology

University of Toronto

2016

Abstract

Hummingbirds are able to fuel energetically expensive hovering flight entirely with recently

ingested glucose or fructose. In other animals, several steps in the pathway of flux of sugars from

the gut to muscle are rate-limiting, such as transport into muscle and subsequent

phosphorylation. I examined relative expression of facilitative sugar transporters and enzymes of

fructolysis in various ruby-throated hummingbird (Archilochus colubris) tissues via quantitative

polymerase chain reaction. I hypothesized that the expression of these proteins would be

upregulated in hummingbird flight muscle compared to other vertebrates. I found that

hummingbird pectoralis displays the highest expression of certain sugar transporter transcripts

among vertebrates. I also demonstrated that relative transcript densities of fructolytic enzymes

are minimal in hummingbird muscle, suggesting that fructolysis is not a pathway used to rapidly

metabolize fructose in these muscles.

iii

Acknowledgments

I would like to thank my supervisor, Dr. Kenneth Welch Jr., for his guidance, insight and

mentorship in preparing me for the world of research. He has taught me much that cannot be

learned from scientific literature review. I thank my committee members, Dr. Mauricio

Terebiznik and Dr. Amira Klip, for their invaluable viewpoints and suggestions for experimental

design. Many thanks to my fellow members of the Welch lab: Derrick Groom, Lily Hou, and

Brandy Velten, and the undergraduate students, for their lab compatriotism, assistance, and

enjoyable company. Special thanks go to Lily Hou for commiserating in the experience of

writing and defending a thesis and to Derrick Groom for editing and the bouncing of ideas. I

must thank Amina Allalou, Prateek Sehgal, and Chris Chen for doing the initial research that

made this study possible. Thank you to Dr. Aarthi Ashok for her encouragement and assistance

in troubleshooting. I would also like to thank Dr. Robin Marushia for her advice. I am indebted

to Dr. Christopher Guglielmo and his student Morag Dick for sharing with me an RNA

extraction protocol suitable for tiny bird muscles. Finally, thank you to my parents for their

generous support and feedback.

iv

Table of Contents

List of Tables .............................................................................................................................. viii

List of Figures ............................................................................................................................... ix

List of Appendices ....................................................................................................................... xii

Chapter 1: Introduction ................................................................................................................. 1

1 Hummingbird Physiology .......................................................................................................... 1

1.1 A Model for Sugar Flux ...................................................................................................... 1

1.2 Rapid Oxidation of Both Glucose and Fructose ................................................................. 3

2 The Sugar Oxidation Cascade .................................................................................................... 5

2.1 Sugar Absorption from the Intestinal Lumen ..................................................................... 5

2.2 Transport of Ingested Sugar from Circulation to Oxidation by Working Muscle .............. 7

3 Facilitated Sugar Transport ........................................................................................................ 7

4 Glucose and Fructose Phosphorylation .................................................................................... 10

4.1 Hexokinase Pathway (Glycolysis) .................................................................................... 10

4.2 Fructolysis Pathway .......................................................................................................... 11

4.3 Hummingbirds Challenge Models of Fructose Metabolism ............................................. 12

5 Questions and Hypotheses ....................................................................................................... 12

Chapter 2: Materials and Methods .............................................................................................. 15

1 Ethics Statement and Animal Handling ................................................................................... 15

2 Hummingbird DNA Extraction ................................................................................................ 15

3 RNA Isolation and cDNA Synthesis ........................................................................................ 16

3.1 Column Method ................................................................................................................ 16

3.2 Tri Reagent RNA Isolation Method .................................................................................. 17

3.3 DNase I Digestion ............................................................................................................. 18

3.4 cDNA First Strand Synthesis ............................................................................................ 18

v

4 Primer Design........................................................................................................................... 18

4.1 Multiple Sequence Alignment Primer Design .................................................................. 18

4.2 GLUT1 Primer Design using a Calypte anna Sequence .................................................. 19

4.3 GLUT5 and Further GLUT1 Primer Design using Calypte Anna Sequences .................. 19

4.4 KHK and AldoB Primer Design ....................................................................................... 20

4.5 PCR for Primer Testing .................................................................................................... 20

4.6 Efficiency Testing ............................................................................................................. 21

4.7 Primers Chosen for Q-PCR ............................................................................................... 21

5 Sequencing ............................................................................................................................... 21

5.1 Gel Extraction ................................................................................................................... 21

5.2 Sample Preparation for Sanger Sequencing ...................................................................... 22

6 Q-PCR Assays .......................................................................................................................... 22

6.1 Reaction Conditions .......................................................................................................... 22

6.2 Data Analysis .................................................................................................................... 23

7 Protein Isolation ....................................................................................................................... 24

8 Western Blotting ...................................................................................................................... 24

Chapter 3: Results ....................................................................................................................... 27

1 Primer Testing on Hummingbird Genomic DNA .................................................................... 27

2 RNA Extraction Protocol Development................................................................................... 28

3 Primer Testing on Hummingbird cDNA .................................................................................. 29

4 Initial Primer Efficiency Measurements .................................................................................. 30

5 Tests of Calypte Anna-Informed Primers on Hummingbird cDNA ........................................ 31

6 Efficiencies of new GLUT1 and GLUT5 Primers ................................................................... 33

7 GLUT1 and GLUT5 Relative Q-PCR...................................................................................... 34

7.1 GLUT1 Transcript Densities ............................................................................................. 34

7.2 GLUT5 Transcript Densities ............................................................................................. 35

vi

8 KHK and AldoB Primer Testing on Hummingbird cDNA ...................................................... 36

9 KHK and AldoB Primer Efficiencies ....................................................................................... 36

10 KHK and AldoB Relative Q-PCR............................................................................................ 37

10.1 KHK Transcript Densities ................................................................................................. 37

10.2 AldoB Transcript Densities ............................................................................................... 38

11 KHK and Aldo-B Relative Protein Expression ........................................................................ 38

Chapter 4: Discussion ................................................................................................................. 41

1 Impact of the Expression of Reference Genes on Relative Quantification .............................. 41

2 Facilitated Diffusion of Glucose Across the Sarcolemma may be Very Rapid ....................... 43

2.1 GLUT1 Relative Transcript Expression in Hummingbird Muscles may exceed that of

Other Vertebrates Examined ............................................................................................. 43

2.2 GLUT1 may play a Novel Role in Systems Lacking GLUT4 .......................................... 44

3 Capacity for Facilitated Diffusion of Fructose across the Hummingbird Pectoralis

Sarcolemma may be Among the Highest in Vertebrate Muscles ............................................ 46

3.1 Relative Expression of GLUT5 Transcript in Flight Muscle is among the Highest

Observed in Vertebrate Muscles ....................................................................................... 46

3.2 Relation of Findings to Other Nectarivores ...................................................................... 48

4 KHK and AldoB appear Upregulated in Some Hummingbird Tissues Relative to other

Vertebrates, but not in Muscle ................................................................................................. 48

4.1 KHK and AldoB Transcript Levels in Flight Muscle are Surprisingly Low .................... 48

4.2 Fructolysis in Hummingbird Liver is at Least as Abundant as it is in Mammalian

Liver .................................................................................................................................. 50

4.3 Fructose Catabolism Starts at the Intestine ....................................................................... 51

4.4 A High Concentration of Fructose may reach Hummingbird Kidneys ............................ 51

5 Is There a New Role for Hexokinases in Hummingbirds? ...................................................... 52

6 A New Synthetic Model of Sugar Flux in Hummingbirds ...................................................... 53

References .................................................................................................................................... 56

Figures and Tables ...................................................................................................................... 68

vii

Appendices ................................................................................................................................... 97

viii

List of Tables

Table 1: Glucose transporters – distribution and substrates. ....................................................... 69

Table 2: Initial primer sets first tested for Q-PCR. ...................................................................... 71

Table 3: Second batch of GLUT1 primer sets tested for Q-PCR. ................................................ 71

Table 4: GLUT1 and GLUT5 primer sets designed for Q-PCR using Primer-BLAST, Mfold, and

OligoAnalyzer ............................................................................................................................... 71

Table 5: KHK and AldoB primer sets designed for Q-PCR using Primer-BLAST, Mfold, and

OligoAnalyzer ............................................................................................................................... 72

Table 6: Primer sequences used for Q-PCR ................................................................................. 72

Table 7: RNA yields from ruby-throated hummingbirds using a column extraction (Ambion

Purelink RNA Mini Kit) and a phenol/chloroform extraction method (Tri Reagent, Sigma

Aldrich) ......................................................................................................................................... 74

Table 8: Averaged Elf1a/GAPDH Ct values in ruby-throated hummingbird tissues used for Q-

PCR analysis of GLUT1 and GLUT5 ........................................................................................... 82

Table 9: Averaged Elf1a/GAPDH transcript ratios in ruby-throated hummingbird tissues used

for KHK and AldoB Q-PCR analysis ........................................................................................... 88

Table 10: Densitometry results of aldolase B western blot depicted in Figure 11 ....................... 94

ix

List of Figures

Figure 1: Potential rate-limiting steps in mammalian transport of sugar from circulation to the

initiation of oxidation in muscle tissue ......................................................................................... 68

Figure 2: Metabolic pathways of glucose and fructose prior to the exergonic reactions of

glycolysis ...................................................................................................................................... 70

Figure 3: Temperature gradient PCR of Elf1a1 primers .............................................................. 73

Figure 4: Temperature gradient PCR of GAPDH primers .......................................................... 73

Figure 5: Temperature gradient PCR of GLUT1 primers ............................................................ 74

Figure 6: Temperature gradient PCR of GLUT5 primers ............................................................ 74

Figure 7: GLUT5 PCR amplification of cDNA (derived from 0.005 ng/µl of RNA) and RNA

(0.005 ng/µl) from various tissues of a ruby-throated hummingbird ........................................... 75

Figure 8: Partial alignment of an amplicon produced using Elf1a1 primer and Calypte anna

Elf1a1 mRNA (NCBI Reference XM_008490615.1) .................................................................. 75

Figure 9: Partial alignment of an amplicon produced using GAPDH primers and Calypte anna

GAPDH mRNA (NCBI Reference XM_008499176.1). ............................................................. 75

Figure 10: Representative gel of RNA following extraction but prior to DNase I treatment ...... 76

Figure 11: Amplification of a dilution gradient of Elf1a1 template with primer set 1 ................ 77

Figure 12: Amplification of a dilution gradient of GAPDH template with primer set 2 ............. 77

Figure 13: Amplification of a dilution gradient of GLUT5 template with primer set 3 .............. 78

Figure 14: Amplification of a dilution gradient of GLUT1 template with primer set 2 .............. 78

Figure 15: Alignment of Elf1a1 primer set 1 primers with Calypte anna sequence (NCBI

GenBank reference XM_008490615.1). ....................................................................................... 79

x

Figure 16: Alignment of GAPDH primer set 2 primers with Calypte anna sequence (NCBI

GenBank reference XM_008499176.1) ........................................................................................ 79

Figure 17: Alignment of GLUT5 primer set 3 primers with Calypte anna sequence (NCBI

GenBank reference XM_008503671.1) ........................................................................................ 79

Figure 18: PCR of exon-spanning GLUT1 and GLUT5 primers ................................................ 80

Figure 19: Amplification of a dilution gradient of GLUT1 with primer set 10 ........................... 80

Figure 20: Amplification of a dilution gradient of GLUT1 with primer set 11 ........................... 81

Figure 21: Amplification of a dilution gradient of GLUT5 with primer set 4 ............................. 81

Figure 22: Amplification of a dilution gradient of GLUT5 with primer set 7 ............................. 81

Figure 23: GLUT1 mRNA expression in ruby-throated hummingbird tissues normalized to

Elf1a1 (A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C) ........................ 83

Figure 24: GLUT5 mRNA expression in ruby-throated hummingbird tissues normalized to

Elf1a1 (A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C) ........................ 84

Figure 25: PCR with KHK primer sets ........................................................................................ 85

Figure 26: PCR with AldoB primer sets ...................................................................................... 85

Figure 27: Amplification of a dilution gradient of KHK transcript with primer set 2 ................ 86

Figure 28: Amplification of a dilution gradient of KHK transcript with primer set 3 ................ 86

Figure 29: Amplification of a dilution gradient of AldoB transcript with primer set 1 .............. 87

Figure 30: Amplification of a dilution gradient of AldoB transcript with primer set 2 .............. 87

Figure 31: Amplification of a dilution gradient of AldoB transcript with primer set 3 .............. 88

Figure 32: KHK mRNA expression in ruby-throated hummingbird tissues normalized to Elf1a1

(A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C) .................................... 89

xi

Figure 33 AldoB mRNA expression in ruby-throated hummingbird tissues normalized to Elf1a1

(A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C) .................................... 90

Figure 34: BLASTP (NCBI GenBank) alignment of the immunogen used to generate polyclonal

anti-KHK primary antibody (last 50 amino acids of human KHK, GenBank reference

CAA55347.1) and Calypte anna predicted KHK (NCBI GenBank reference XP_008491152.1)

....................................................................................................................................................... 91

Figure 35: BLASTP (NCBI GenBank) alignment of the immunogen used to generate polyclonal

anti-AldoB primary antibody (amino acids 30-60 of human AldoB, GenBank reference

NP_000026.2) and Calypte anna predicted AldoB (NCBI GenBank reference XP_008494663.1)

....................................................................................................................................................... 91

Figure 36: Western blot of ketohexokinase in ruby-throated hummingbird pectoralis and liver

and chicken pectoralis ................................................................................................................... 92

Figure 37: Amido black stain of membrane bound proteins used for western blotting of aldolase

B .................................................................................................................................................... 93

Figure 38: Western blot of aldolase B in ruby-throated hummingbird pectoralis and liver and

chicken pectoralis .......................................................................................................................... 93

Figure 39: Enlarged view of the relationship between pectoralis and liver from Figure 23.

GLUT1 mRNA expression in ruby-throated hummingbird tissues is normalized to Elf1a1 ....... 94

Figure 40: Enlarged view of the relationship between pectoralis and intestine from Figure 24.

GLUT5 mRNA expression in ruby-throated hummingbird tissues is normalized to Elf1a1 ....... 95

Figure 41: Potential rate-limiting steps in mammalian (A) and ruby-throated hummingbird (B)

transport of sugar from circulation to the initiation of oxidation in muscle tissue ....................... 96

xii

List of Appendices

Appendix 1: Aves GAPDH mRNA Coding Strand Multiple Sequence Alignment .................... 97

Appendix 2: Aves Elf1a1 mRNA Coding Strand Multiple Sequence Alignment ..................... 104

Appendix 3: Aves Beta-actin mRNA Coding Strand Multiple Sequence Alignment ............... 113

Appendix 4: Aves GLUT5 mRNA Coding Strand Multiple Sequence Alignment ................... 122

1

Chapter 1 Introduction

1 Hummingbird Physiology

1.1 A Model for Sugar Flux

Energy homeostasis is fundamental to all life. Hummingbirds, small, endothermic vertebrates,

possess metabolic rates that are among the highest known in vertebrate systems (Suarez, 1992),

and face especially great challenges to energy homeostasis.

The accessing of flower nectar by hovering is a niche historically occupied by insect pollinators.

Among the first vertebrates to exploit this niche are the hummingbirds, unique among birds in

their ability to forage from nectar by sustained hovering (Suarez, 1992). This feeding strategy

offers unique advantages as well as challenges. Hummingbirds do not have to perch to feed on

nectar, which grants access to flowers not supported by a strong stem suitable for perching and to

flowers that are not near branches or the ground. Hovering grants access to nectar unavailable to

other nectarivorous birds, such as sunbirds and flowerpeckers, which usually must perch in order

to obtain nectar (Willmer, 1953). Consequently, hummingbirds have access to a food source that

cannot be exploited by other animals apart from insects and some small bats (Suarez, 1992).

Hovering demands a very small body size, a selective pressure that is reinforced by the cost of

this behaviour (Suarez, 1992). Hovering is the most energetically expensive form of aerobic

exercise known to vertebrates, necessitating a low mass to keep the already high cost to a

minimum. Resultantly, the hummingbird taxon includes the smallest of endotherms (Suarez,

1992).

A consequence of constrained body size is high susceptibility to changes in temperature due to

heat loss that accompanies a large surface area to volume ratio (Planinsic and Vollmer, 2008). In

order to conserve energy during cold nights and in the premigratory season, North American

hummingbirds can enter torpor, during which their core temperature drops, approaching ambient

temperature to a minimal range between approximately 12 to 17ºC (Calder, 1994). Metabolic

rate and energetic needs drop dramatically as a result of torpor (Calder, 1994). During torpor

2

hummingbirds rely on fat reserves to sustain themselves until morning (Carpenter and Hixon,

1988).

During the day it is in the hummingbird’s best interest to build up fat reserves to sustain it

through the night, and/or torpor (Carpenter and Hixon, 1988). Hummingbirds have evolved the

ability to power their foraging activity entirely using recently ingested sugar (Chen and Welch,

2014). This spares fat stores, allowing for them to be built up during the day, as ingested sugar

not used to fuel hovering flight can readily be used for lipogenesis in the hummingbird liver

(Suarez et al., 1988). In doing this hummingbirds maintain blood sugar concentrations that cause

tissue damage in other animals (Beuchat and Chong, 1998) and accomplish sugar flux rapid

enough to meet their energetic demands as they feed (Chen and Welch, 2014). Unlike most

foraging birds, hummingbirds spend the majority of their time perching and resting (eg.,

Diamond et al., 1986; Lopez-Calleja and Bozinovic, 2002; Wolf and Hainsworth, 1971), in

between short feeding bouts about once every 15 minutes (Diamond et al., 1986). This helps the

birds minimize calorie expenditure to promote fat gain despite one of the highest metabolic rates

per gram of any vertebrate (Suarez, 1992).

The challenge of simply producing sufficient fat stores for nighttime fasting in spite of

calorically expensive foraging is exacerbated by the added caloric demand of territorial

behaviour. Many North American hummingbird individuals spend time defending territory by

chasing away competing hummingbirds (Sholtis et al., 2015). This behaviour further selects for

the ability to regulate physiology in order to maintain an energy budget (Lopez-Calleja and

Bozinovic, 2002).

Hummingbirds have the ability to fuel hovering flight with newly ingested sugar (Chen and

Welch, 2014), a feat that is facilitated by their nectarivorous habits and maintenance of blood

sugar levels that are higher than most other vertebrates (Beuchat and Chong, 1998). They move

this sugar from their gut to flight muscle at rates sufficient to support the most energetically

expensive form of aerobic exercise seen in vertebrates (Welch and Chen, 2014). These unique

abilities make hummingbirds an ideal organism for the study of sugar flux.

3

1.2 Rapid Oxidation of Both Glucose and Fructose

Intense exercise in mammals is supported by a system of glycogen buffering (Weibel et al.,

1996; Weber et al., 1996; Hoppeler and Weibel, 1998). Humans can fuel approximately 30% of

intense exercise using recently ingested sugar (Jentjens et al., 2004), but capacities for the

transport of sugars to, and uptake and phosphorylation by, exercising muscle are insufficient to

meet demand. Generally, humans and other mammals predominantly rely on lipid oxidation to

sustain muscle function at low to moderate exercise intensities (Brooks and Mercier, 1994;

Weber, 2011). However, as exercise intensity increases, there is a progressive shift towards

reliance on carbohydrate oxidation, with oxidation of glycogen providing most muscle energy

during intense exercise (Brooks and Mercier, 1994; Weber, 2011). During periods of low to no

exercise intensity, glycogen is synthesized in muscle tissue from imported glucose to create a

reserve of complex carbohydrates that can be oxidized during later exercise (Weibel et al., 1996).

Once this glycogen is depleted during exercise, intensity of activity must be decreased to allow

for glycogen resynthesis (Weibel et al., 1996). Buffering of ATP levels by phosphocreatine

stores and lactic acid production via anaerobic glycolysis may delay this need (Robergs et al.,

2004; Schlattner et al., 2006), but ultimately rest will be needed before intense exercise can

resume.

From a flying bird’s point of view, glycogen is an inferior storage medium to fat as wet glycogen

is much heavier per calorie than is fat, due to the volume of water that glycogen polysaccharides

associate with in vivo (Guglielmo, 2010). As they are under strict pressure to minimize weight in

order to maintain hovering flight, hummingbird glycogen stores are minimal (Guglielmo, 2010).

As has been discussed, hummingbirds must build up new fat reserves each day to get through the

night (Carpenter and Hixon, 1988). It follows that, during the day, the rapid metabolism of

recently ingested simple sugars is favourable, as it saves fat reserves for the night and avoids the

accumulation of weighty glycogen (Guglielmo, 2010).

Research has recently shown that hummingbirds can switch rapidly between fueling

approximately 100% of hovering flight with endogenous fat and fueling it instead with recently

ingested sugar (Chen and Welch, 2014). The capacity of some hummingbird individuals to fuel

up to 100% of their activity with recently ingested sugar (Suarez et al., 2011) is unlike mammals,

with only nectar bats approaching this capacity (Welch and Chen, 2014).

4

In most vertebrates, rates of fructose uptake and oxidation in muscle are much lower than they

are for glucose (Zierath et al., 1995). Fructose transport into mammalian muscles provides the

sugar for 10-30% of glycogen formation, with little direct oxidation of imported fructose in

humans (Zierath et al., 1995). The bulk of exogenous fructose is taken up by the liver and is

processed into fat or glucose prior to utilization for energy (Parks et al., 2008). This initial

storage or conversion of most fructose-derived calories postpones their availability to working

muscles (Parks et al., 2008; Sun and Empie, 2012). Glucose, on the other hand, can be

transported readily to working muscles following absorption in the digestive tract, although

overall flux capacity is insufficient to fully support moderate to intense exercise alone (Welch

and Chen, 2014). The “lag” in the availability of ingested fructose limits its utility for sustaining

aerobic activity in most animals.

Recent findings indicate that hummingbirds can utilize newly ingested fructose as an oxidative

fuel in flight muscles just as readily as they can glucose, fueling up to 100% of hovering flight

with exogenous sugar whether it is in the form of glucose, fructose, or sucrose (Chen and Welch,

2014). Most nectar included in the hummingbird diet contains about 25% glucose, 25% fructose,

and 50% sucrose (Baker et al., 1998). As such, following sucrase activity in the gut,

approximately half of the caloric intake of a hummingbird is in the form of fructose (Baker et al.,

1998). Chen and Welch (2014) suggest that hummingbirds are likely to be rapidly oxidizing

fructose directly without first converting it into other forms. This would represent a highly

efficient use of dietary resources to fuel aerobic activity. This rapid oxidation of all forms of

ingested sugar may be necessary to sustain hovering flight on this diet, particularly early in the

day when energy reserves are depleted after overnight fasting.

As previously noted, hummingbirds by and large skip the step of glycogen storage and instead

rely on oxidation of ingested simple sugars during foraging (Guglielmo, 2010). Fructose and

glucose transport and oxidation rates have been estimated in ruby-throated hummingbirds

(Archilochus colubris) using respirometry and stable isotope tracking data (Welch and Chen,

2014). Data suggest that both glucose and fructose uptake rates by flight muscles in

hummingbirds are much higher than uptake rates for glucose in mammals. For example,

calculated uptake rates for both fructose and glucose are over 30 times more rapid in ruby-

throated hummingbirds during hovering than uptake rates of glucose in exercising mice (Welch

and Chen, 2014).

5

2 The Sugar Oxidation Cascade

2.1 Sugar Absorption from the Intestinal Lumen

In order to address the question of how sugar flux through the hummingbird system is

accomplished extremely rapidly relative to other vertebrates, one must first break down the

process into key steps that can be separately considered and addressed. Transport of ingested

sugar to muscle is restricted by a number of potentially rate-limiting steps. The sugar oxidation

cascade is used to describe the possible upregulation of these steps in hummingbirds (Suarez et

al., 2011). The model describes the path of sugar starting at ingestion of nectar from flowers and

ending in the mitochondrial electron transport chain in exercising muscle. For the purposes of

understanding the sugar oxidation cascade model in general and contextualizing it to the

hummingbird system, the model can be adapted to the description of sugar transport in most

mammals.

Sucrose, rather than glucose or fructose, faces an extra first step in the sugar oxidation cascade:

the hydrolysis into glucose and fructose monosaccharides by the digestive enzyme sucrase

(Martinez del Rio, 1990). Next, monosaccharides must pass into the intestinal epithelium via

facilitative transporters (Karasov and Diamond, 1988). In most mammalian intestinal linings,

tight junctions restrict paracellular transport, which is the flow of solutes around and in between

cells, necessitating a tightly-regulated facilitated transport approach (Bazzoni, 2006). Glucose is

cotransported with sodium down the cation’s concentration gradient from the intestinal lumen to

the cytoplasm of the brush border cells. This step occurs through sodium glucose cotransporter 1

(SLGT1) (Drozdowski and Thomson, 2006). Low relative sodium concentration inside the

intestinal epithelium is maintained by sodium/potassium ATPase, which exchanges sodium ions

in the cell with potassium ions in the blood. As such, uptake of glucose into mammalian

intestinal epithelium is an endergonic process (Drozdowski and Thomson, 2006). Fructose, on

the other hand, moves passively down a concentration gradient into the intestinal epithelium via

a facilitative transporter of fructose (GLUT5) (Drozdowski and Thomson, 2006). Passive

absorption of fructose is generally sufficient in mammals as most mammalian diets are richer in

glucose than in fructose. Facilitated transport of both glucose and fructose monosachharides

across the basolateral membrane and into the extracellular space is accomplished by the

transporter GLUT2, a general hexose transporter (Drozdowski and Thomson, 2006). This results

6

in a high concentration of glucose and fructose in connective tissue adjacent to capillaries.

Junctions between endothelial cells are not tightly bound, allowing for rapid flux of nutrients to

and from the circulation (Vock et al., 1996). As such, glucose and fructose diffuse freely from

the extracellular space to the blood (Drozdowski and Thomson, 2006).

Sucrase activity in the intestinal lumen of hummingbirds is calculated to be high enough to

hydrolyze all ingested sucrose (Schondube and Martinez del Rio, 2004). This adaptation ensures

that the ~50% of ingested calories that come in the form of sucrose are not lost and have the

potential to be absorbed by the intestinal epithelium (Baker et al., 1998). Absorption of glucose

is enhanced by the largest capacity for facilitated absorption of ingested glucose observed in

vertebrates (Karasov et al., 1986).

Many small birds have short digestive tracts in comparison to similarly-sized terrestrial animals

(Caviedes-Vidal et al., 2007). A long digestive tract adds weight, and therefore extra energetic

demands, to small birds, especially hummingbirds, in which flight is most expensive. To

minimize weight, many birds, including hummingbirds, have evolved short digestive tracts

(Caviedes-Vidal et al., 2007). As a shorter intestine reduces the available epithelial surface area

across which facilitated transport can occur, such birds have adapted by expressing relatively few

tight junctions between intestinal epithelial cells (Caviedes-Vidal et al., 2007). While reducing

the potential for regulation over what is absorbed from the lumen to the blood, this “leaky”

epithelium allows small ingested nutrients to diffuse freely past the intestinal epithelium via

paracellular transport (Caviedes-Vidal et al., 2007). This happens to be a particularly effective

adaptation for hummingbirds; not only does it enable minimization of the digestive tract, but it

also relieves what would potentially have been a rate-limiting step in sugar absorption and

transport (Bazzoni, 2006). Paracellular transport has been suggested to be great enough across

the intestinal epithelium of hummingbirds to meet the demands of in vivo sugar oxidation in real

time (Diamond et al., 1986; Karasov et al., 1986; Welch and Chen, 2014). The rate of absorption

is therefore not likely to be rate-limiting to sugar flux and oxidation during hovering flight in

hummingbirds.

7

2.2 Transport of Ingested Sugar from Circulation to Oxidation by Working Muscle

The sugar oxidation cascade can be subdivided into three more steps that are potentially rate-

limiting to the delivery of sugar to, and oxidation in, muscle. Step one (Figure 1) is the transport

of sugar from circulation to extracellular space adjacent to a sarcolemma. Step two (Figure 1) is

the uptake of sugar from the extracellular space into muscle fibers. Finally, in step three (Figure

1) both glucose and fructose must be phosphorylated to enter into a catabolic pathway. In

addition to directing the monosaccharides towards glycolysis, this also lowers the intracellular

concentration of these sugars, permitting step two to remain a passive, though facilitated, process

(Stipanuk and Caudill, 2013; Wilson, 2003).

As is the case for uptake of sugar from the extracellular space adjacent to the intestine, glucose

and fructose diffuse freely across the capillary endothelium (Vock et al., 1996). As such, this is

not thought to be a rate-limiting step in most species (Vock et al., 1996). This transport is further

enhanced in hummingbirds by high cardiac output from the heart, high capillary density at the

interface with skeletal muscle, and large surface area of muscle fiber interacting with capillaries

(Bishop, 1997; Mathieu-Costello et al., 1992). Blood glucose levels in hummingbirds are more

than twice that of even nectarivorous mammals, and fructose levels are suspected to be similarly

high but have not yet been measured. This hyperglycemia relative to mammals further increases

the delivery of sugar to working muscles, and contributes to a high concentration gradient for the

diffusion of sugar from circulation toward skeletal muscle (Beuchat and Chong, 1998; Braun and

Sweazea, 2008; Mqokeli and Downs, 2012). Such passive transport is lacking across the

sarcolemma (Uldry and Thorens, 2004), necessitating facilitated transport of glucose and

fructose.

3 Facilitated Sugar Transport

The key to step two in Figure 1 is facilitated transport. Facilitated sugar transport is

accomplished by a family of facilitative hexose transporter proteins known as glucose

transporters (GLUTs) of which there are least 13 highly conserved members (Table 1; Douard

and Ferraris, 2008; Sweazea and Braun 2006). Despite the name, a number of these transporters

are not at all specific to glucose (Deng et al., 2014). Most of these transporters move hexoses

more readily from the exterior of the cell membrane to the interior and have varying levels of

8

affinity for glucose and/or fructose, and in some cases other substrates as well (Thorens and

Mueckler, 2009).

The most widely-expressed GLUT family member is GLUT1 (Uldry and Thorens, 2004).

GLUT1 is the most well-studied glucose transporter, and is specific to glucose. It is especially

abundant in the brain, which is consistent with the organ’s reliance on glucose (Thorens and

Mueckler, 2009). GLUT1 responds to insulin by translocation to the cell membrane in

myocardial cells (Egert et al., 1999; Laybutt et al., 1997). In rat liver epithelial cells, GLUT1

transport capacity is suspected to be increased by AMP-activated protein kinase during osmotic

and metabolic stress, though the mechanism remains elusive as cell surface GLUT1 content did

not change before and after treatment (Barnes et al., 2002). GLUT3 is also specific to glucose.

While widely distributed, it is most strongly associated with the brain, as well as with several

types of leukocytes and with developing embryos. GLUT3 in lymphocytes, monocytes, and

platelets is stored in intracellular vesicles and translocated to the cell membrane when needed

(Thorens and Mueckler, 2009).

In mammals, GLUT1, which is constitutively expressed, works together with insulin-responsive

GLUT4 in muscle and adipose tissue to transport glucose from the bloodstream into these

tissues. Insulin signals the translocation of GLUT4 from intracellular vesicles to the cell

membrane in skeletal muscle, heart and adipose tissue (Shepherd and Kahn, 1999). The role of

GLUT4 in sugar transport is part of a highly conserved set of mechanisms responsible for

regulating blood glucose levels.

Transcripts of both GLUT1 and GLUT3 have been found in all hummingbird tissues tested using

reverse transcription PCR (Welch et al., 2013). GLUT1 protein has also been detected in all of

these tissues by western blots (Welch et al., 2013).

Although insulin-mediated regulation of blood sugar involving the translocation of GLUT4 in

peripheral tissues is a mechanism present in fish, amphibians, reptiles, and mammals (Polakof et

al., 2011), the ubiquity of this regulatory pathway among vertebrates is not certain. Some of

these mechanisms, such as glucagon sensitivity (Hazelwood, 1973), are true of birds, but the

insulin response is not conserved. As with other bird species examined (Carver et al., 2001;

Braun and Sweazea, 2008; Seki et al., 2003; Sweazea and Braun, 2006), GLUT4 mRNA and

protein have not been found in hummingbirds (Welch et al., 2013). The lack of GLUT4 appears

9

broadly true for birds and, accordingly, blood glucose concentration in birds is unresponsive to

insulin (Braun and Sweazea, 2008).

GLUT2 is a transporter with capacity to transport both glucose and fructose (Uldry and Thorens,

2004). It is abundant in the liver, where it is responsible for most uptake of dietary fructose

metabolized in mammals (Parks et al., 2008; Thorens, 2015), as well as in other tissues important

to energy homeostasis, such as the intestine, pancreas, and kidneys. In mammals, GLUT2 serves

a glucose-sensing role, and is required for the regulation of blood glucose through insulin and

glucagon. The high Km of GLUT2 for glucose prevents saturation of the transporter at

physiological glucose concentrations, ensuring that the transporter is always responsive to

changes in glucose concentration (Thorens and Mueckler, 2009). GLUT2 is able to readily

function bidirectionally, whereas most GLUTs transport predominantly in one direction (Dong et

al., 2014; Douard and Ferraris, 2008).

GLUT5 is unique in that it is very specific to fructose (Thorens and Mueckler, 2009). It is found

mostly in the duodenum of mammals and chickens (Douard and Ferraris, 2008) and is also

abundant in the kidneys (Gaster et al., 2000). It is present in peripheral tissues such as muscle,

and functions to transport fructose at low rates (Gaster et al., 2000). The expression of GLUT5 in

mammalian muscle is quite low in comparison to the expression of GLUT1 and GLUT4 (Gaster

et al., 2000).

In the intestine, GLUT5 expression is regulated by a number of factors, including the

anticipation of food. In rats GLUT5 increases in expression a few hours prior to times of

anticipated high food intake and this pattern is modulated by hormonal signals, not the vagus

nerve (Douard and Ferraris, 2008). Expression in the intestine is further regulated by fructose

itself. After exposure to fructose, intestinal GLUT5 protein expression increases in rats;

however, fructose-stimulated increases in GLUT5 transcript density have only been successfully

demonstrated in vitro using cell cultures (Douard and Ferraris, 2008). Intestinal GLUT5 may be

regulated by translocation of existing protein to and from intracellular vesicles, but this remains

to be determined.

Hummingbird GLUT2 mRNA expression follows the same expression pattern as in most

mammals, being abundant in liver, kidney, and intestine, but not found in muscle, heart, or brain

(Welch et al., 2013). Given the absence of GLUT2 transcript in flight muscle, it follows that

10

another GLUT isoform must be responsible for proposed high rates of fructose uptake. Little is

known about GLUT5 in birds and no hummingbird GLUTs have been quantitatively

characterized in expression.

The remaining GLUT isoforms are not as well characterized as those described above and most

are recently discovered. Although expression of several glucose transporters has been

qualitatively characterized for hummingbirds (Welch et al., 2013), capacity for facilitated

transport across the hummingbird sarcolemmal membrane has not been determined.

4 Glucose and Fructose Phosphorylation

The third step in Figure 1 is phosphorylation of glucose and fructose, initiating glycolytic

catabolism. Two major pathways accomplish this step.

4.1 Hexokinase Pathway (Glycolysis)

Hexokinases (HKs) phosphorylate a variety of hexoses, and are thought to play a crucial role in

regulating glycolysis (Enzyme 1, Figure 2; Wilson, 2003). Phosphorylation of glucose by HKs

constitutes the first step of glycolysis and is the second most endergonic step (Berg et al., 2002).

Five isoforms of hexokinase HK are present in most vertebrate genomes including those of

hummingbirds (NCBI GenBank). They are HKA, HKB, HKC, HKD, and hexokinase domain

containing 1 (HKDC1) (Irwin and Tan, 2014). The first four can phosphorylate glucose, 2-

deoxyglucose (2-DG), mannose, and fructose, but with a much higher affinity for glucose than

for other sugars (Cardenas et al., 1998). Like GLUT1, HKA is expressed ubiquitously and is a

key player in the generation of energy throughout the body. Also like GLUT1, it is very highly

expressed in the brain, which depends almost solely on glucose metabolism (Wilson, 2003).

HKB is found primarily in muscle, heart, and adipose tissue, all of which are insulin-responsive

tissues in mammals (Wilson, 2003). HKC is widely distributed but poorly understood (Wilson,

2003). HKD, also known as glucokinase, is primarily an enzyme of the liver and other tissues

involved in glucosensing. Together with GLUT2, HKD is vital to regulation of insulin and

glucagon secretion from the pancreas (Postic et al., 2001). HKD is noteworthy in that it has a

higher Km for glucose than other HKs, preventing enzyme saturation and keeping HKD sensitive

to changes in glucose concentration (Kamata et al., 2004). HKDC1 remains to be thoroughly

characterized (Irwin and Tan, 2013).

11

While glucose is readily phosphorylated by HKs in the first step of glycolysis, this enzyme has a

very low affinity for fructose in vertebrates in which fructose metabolism has been characterized

(Cirillo et al., 2009). The second step in glycolysis is conversion of glucose-6-phosphate to

fructose-6-phosphate by phosphoglucose isomerase (PGI; Enzyme 2, Figure 2). This is the most

endergonic and rate-limiting step to glucose flux though glycolysis (Sun and Empie, 2012).

When fructose is phosphorylated by HK, fructose-6-phosphate is produced (Enzyme 6, Figure

2). Although phosphorylation of fructose by HK results in the skipping of the rate-limiting

reaction of glycolysis (Sun and Empie, 2012), the high Km makes HKs unsuitable for handling

the bulk of fructose metabolism in humans and most other animals (Cirillo et al., 2009).

Suarez et al. (2009a) have measured the maximal activity of hummingbird hexokinases in vitro

and discovered it to be sufficient to match estimated rates of glucose flux through glycolysis

(assuming glucose, and not glycogen, is the sole substrate) during hovering in vivo. HK activity

has not been assayed in hummingbirds using fructose as a substrate.

4.2 Fructolysis Pathway

Vertebrate fructose oxidation is primarily handled by the fructolysis pathway and performed

predominantly in the liver (Figure 2; Patel et al., 2015; Sun, 2012). It also occurs to a lesser

extent in the kidneys, in organisms where distribution of the pathway has been characterized

(Cirillo et al., 2009; Sun and Empie, 2012). Low rates of fructolysis do occur in peripheral

tissues, including muscle, but these make up only a very small percentage of overall fructose

metabolism in the body (Bais et al., 1985; Zierath et al., 1995). That low rates of fructolysis

occur in peripheral tissues is unsurprising given the context that the vertebrate liver expresses

very high levels of GLUT2 and GLUT5 and acts as a “sink” for fructose as circulation of blood

from the digestive tract flows toward the liver via the hepatic portal vein prior to encountering

any other tissue (Sturkie and Abati, 1975; Parks et al., 2008; Thorens, 2015).

The first step in fructolysis is phosphorylation of fructose by ketohexokinase (KHK; a.k.a.

fructokinase) to produce fructose-1-phosphate (F-1-P), rather than fructose-6-phosphate (F-6-P),

as is produced by HKs (Enzymes 7 and 6 respectively, Figure 2). KHK-produced F-1-P cannot

be readily bound by the aldolase isoforms that comprise part of the glycolytic pathway (aldolase

A and aldolase C) (Esposita et al., 2002). Instead, F-1-P produced by ketohexokinase is cleaved

by aldolase B (AldoB) into glyceraldehyde (GA) and dihydroxyacetone phosphate (DHAP)

12

(Enzyme 8, Figure 2; Sun and Empie, 2012). This step is thought to be rate limiting to fructolysis

(Bode et al., 1980). DHAP is then able to proceed into step five of the glycolytic pathway, and is

isomerized to glyceraldehydes-3-phosphate (G3P) by triose phosphate isomerase (Enzyme 5,

Figure 2). GA needs to be phosphorylated in order to enter glycolysis, which is accomplished by

the third enzyme of fructolytic pathway, triokinase (Enzyme 9, Figure 2). G3P is then processed

normally through the exergonic half of the glycolysis pathway (Sun and Empie, 2012).

Two isoforms of KHK are known in mammals: KHKA and KHKC (a.k.a. central/liver

ketohexokinase) (Diggle et al., 2009; Patel et al., 2015). KHKC is the isoform associated mostly

with liver as well as the kidney and intestine (Patel et al., 2015). KHKA (a.k.a. peripheral

ketohexokinase) is found in low quantities in a variety of peripheral tissues, including skeletal

muscle and fat. The Km of KHKA for fructose is approximately ten times greater than that of

KHKC, making the functional significance of this isoform unclear (Diggle et al., 2009).

Only one isoform of KHK has been identified in bird genomes, including Anna’s hummingbirds

(Calypte anna), and it has 73% protein homology to both mammalian isoforms (NCBI

GenBank). The activity of KHK in hummingbird tissues has not yet been measured.

4.3 Hummingbirds Challenge Models of Fructose Metabolism

The behaviour of hummingbirds challenges the vertebrate model of fructose metabolism. As

described above, hummingbirds can fuel their hovering behaviour just as rapidly with fructose as

they can with glucose (Chen and Welch, 2014). This is not what one would expect if fructose

metabolism to facilitate muscle contraction requires fructolysis and gluconeogenesis in the liver,

and then subsequent glycolysis in the muscle, more than twice as many enzymatic steps as

needed for dietary glucose (Sun and Empie, 2012). As stated above, vertebrates studied thus far

express the enzymes of fructolysis in their muscles, albeit at very low levels (Bais et al., 1985).

5 Questions and Hypotheses

In order to understand the diversity of ways in which animals deal with the challenges of energy

homeostasis we can look to animals with extreme challenges and unusual solutions, such as

hummingbirds. There has only been one study addressing the potentially rate-limiting step of

facilitated sugar transport from the extracellular space into the muscle of hummingbirds (Welch

13

et al., 2013). Respirometry and stable isotope tracking indicate that hummingbirds can fuel their

activity entirely with either glucose or fructose (Chen and Welch, 2014), but how do they

transport these sugars from circulation to muscle fibers at rates fast enough to sustain hovering

flight?

I hypothesized that hummingbird flight muscles express higher protein densities of GLUT1 and

GLUT5 than other vertebrates examined, in order to facilitate rapid uptake of glucose and

fructose, respectively. While GLUT1 western blot results exist for ruby-throated hummingbirds

(Welch et al., 2013) these results are difficult to quantify via densitometry due to the presence of

a doublet band. Commercially available antibodies are not available for avian GLUT5, which is

approximately 65% divergent from human and mouse GLUT5 (NCBI GenBank). Further, highly

accurate protein quantification is a very technically challenging endeavour that cannot match the

accuracy of transcript quantification with current quantitative PCR methods (Twyman, 2013).

To better understand capacities for the flux of sugars among hummingbird tissues I have

performed quantitative real-time PCR analysis (Q-PCR) on GLUT1 and GLUT5 using a variety

of tissues from ruby-throated hummingbirds (Archilochus colubris). I predicted that relative

transcript densities of GLUT1 and GLUT5 would be uniquely high in pectoralis when compared

to other vertebrates. I also predicted that relative levels of GLUT1 would be highest in pectoralis

and that relative levels of GLUT5 would be highest in either pectoralis or intestine.

Suarez et al. (2009a) have demonstrated that hexokinase phosphorylation of glucose is extremely

rapid in hummingbirds, but little is known about how they approach the problem of metabolizing

fructose to fuel exercise (Welch and Chen, 2014). Is the pathway of fructolysis enhanced in

hummingbirds flight muscles, as glycolysis appears to be?

I hypothesized that hummingbird flight muscles express higher protein levels of the enzymes

KHK and ALDOB than other vertebrates that have been examined. I further hypothesized that

upregulation of GLUT5 and upregulation of fructolytic enzymes are complementary in order to

jointly facilitate rapid catabolism of fructose in tissues where caloric demand is highest – the

primary flight muscles.

To begin deciphering the catabolic fate of recently ingested fructose during hummingbird flight,

I have also performed Q-PCR on KHK and AldoB in multiple hummingbird tissues. As

14

predicted protein homology between hummingbird and human KHK and AldoB is high (NCBI

GenBank), commercial antibodies likely to cross-react with hummingbirds were available for

these proteins. I have used western blots and densitometry to investigate KHK and AldoB

protein expression across tissues, to examine whether protein expression mirrors mRNA

template quantity for these rate-limiting enzymes.

I predicted that relative transcript densities of KHK and AldoB would be higher in hummingbird

pectoralis than has been previously observed in vertebrate muscles. I also predicted that relative

levels of these transcripts would be highest in pectoralis and liver. Further, I predicted that KHK

and AldoB protein density would reflect Q-PCR results.

15

Chapter 2 Materials and Methods

1 Ethics Statement and Animal Handling

The protocols for this study (20010081 and 20010627) were approved by and performed in

accordance with the requirements of the University of Toronto Animal Care Committee.

Wild adult male ruby-throated hummingbirds (Archilochus colubris) were captured at the

University of Toronto Scarborough using modified box traps. Birds were housed in the

University of Toronto Scarborough vivarium and fed NEKTON-Nectar-Plus (Nekton, Tarpon

Springs, FL, USA) ad libitum. Birds were sacrificed after ad libitum feeding by an overdose of

isoflurane followed by asphyxiation with helium between March 2014 and June 2015. Tissues

were sampled immediately after euthanization. It was noted that one bird was ill at the time of

sacrifice and that multiple tissues of interest showed severe pathology and marked signs of

infection. Chicken (Gallus gallus) pectoralis tissue was obtained commercially. Liver tissue of a

mouse (Mus musculus) strain C57Bl was obtained from another research project taking place at

the University of Toronto Scarborough.

2 Hummingbird DNA Extraction

32 mg of frozen ruby-throated hummingbird (Archilochus colubris) pectoralis was obtained from

a previous project that took place at the University of Toronto Scarborough. The sample had

been collected in May 2012 and stored at -80ºC. DNA extraction was performed using an

OMEGA Bio-tek E.Z.N.A. Tissue DNA Kit (Omega Bio-tek Inc. Norcross Georgina). The 30

mg of tissue was placed in 200 µl of TL buffer (OMEGA Bio-tek) and homogenized. 25µl of OB

Protease Solution (OMEGA Bio-tek) was added to the homogenate and the sample was then

incubated at 55ºC with agitation for one hour and vortexed every 20 minutes. Following

incubation, RNAse A (OMEGA Bio-tek) was added to a concentration of 0.21µg/µl and

incubated at room temperature for five minutes. The sample was centrifuged at 18000g at room

temperature for five minutes and the supernatant was collected. 220 µl BLbuffer (OMEGA Bio-

tek) was added to the supernatant and the sample was vortexed and incubated at 70ºC for ten

16

minutes. The sample was combined with 220 µl of 100% ethanol, vortexed, and transferred to a

HiBind DNA Mini Column and Collection Tube (OMEGA Bio-tek). The Mini Column was

centrifuged at 18000g at room temperature for one minute. 500 µl HBC buffer (OMEGA Bio-

tek) was then added to the HiBind DNA Column and centrifuged at room temperature for 30

seconds. Twice, 700 µl of DNA Wash buffer (OMEGA Bio-tek) was added to the HiBind DNA

Column and, using a fresh Collection Tube, the sample was centrifuged at room temperature at

18000g for 30 seconds. With a fresh Collection Tube, the HiBind DNA Column was centrifuged

at room temperature at 18000g for five minutes. The dried HiBind DNA Column was placed in a

1.5 ml nuclease-free microcentrifuge tube for elution. Twice, 100 µl of DNase free water was

added to the column, incubated at room temperature for ten minutes, and centrifuged at room

temperature at 18000g for five minutes and the filtrate was collected. Concentration and purity

were determined using NanoDrop 1000 (Thermo Scientific, Waltham, Massachusetts)

spectrophotometry analysis. Purity was evaluated using 260nm/280nm and 260nm/230nm UV

absorbance. Integrity was assessed using 1% agarose gel electrophoresis. The filtrate was stored

at -20ºC.

3 RNA Isolation and cDNA Synthesis

3.1 Column Method

RNA was extracted using an Ambion Life Technologies Purelink RNA Mini Kit (Life

Technologies, Carlsbad, California). From one bird, 100-200 mg of pectoralis, liver, heart, brain,

and ankle flexor and extensor muscles were collected and homogenized in Lysis Buffer (Life

Technologies) at 4ºC using an RNase free rotor-stator electric homogenizer. Homogenates were

centrifuged at room temperature at 26000g for five minutes and supernatants were collected. One

volume of RNase free 70% ethanol was combined with the supernatant and vortexed. 700 µl of

this sample was placed in a Spin Cartridge and collection tube (Life Technologies) and

centrifuged at room temperature for 15 seconds at 12000g. This step was repeated as needed to

filter an entire sample through a Spin Cartridge. 700 µl of Wash Buffer I (Life Technologies)

was added to the Spin Cartridge, which was then centrifuged at room temperature at 12000g for

15 seconds. Twice, 500 µl of Wash Buffer II (Life Technologies) was added to the Spin

Cartridge and it was centrifuged as before. The Spin Cartridge was centrifuged at 12000g at

room temperature for two minutes and the dry Spin Cartridge was placed in a 1.5 ml

17

microcentrifuge tube. 100 µl of RNase free water was added to the Spin Cartridge, which was

incubated at room temperature for one minute, then centrifuged for two minutes at room

temperature and 12000g. The eluted RNA was chilled to 4ºC and DNase I digestion and first

strand cDNA synthesis occurred immediately afterwards. Two samples each, from two birds, of

ankle muscle and kidney RNA were excluded from study due to degradation during the isolation

procedure, as determined by agarose gel electrophoresis.

3.2 Tri Reagent RNA Isolation Method

Samples of pectoralis, liver, heart, brain, and ankle flexor and extensor muscles were collected

from one bird during an initial trial of this procedure. Six samples of pectoralis, liver, heart, and

brain, ankle flexor and extensor muscles, whole intestine, and kidneys were then collected from

six birds. Tissues were homogenized at 4ºC in 1 ml cold Tri Reagent (Sigma Aldrich

Corporation, St. Louis, Missouri) using an RNase free glass tissue homogenizer and RNase free

syringes of increasing needle gauge. Up to 100 mg of tissue was used per 1 ml of Tri Reagent,

depending on the size of available tissue. Homogenized samples were stored at -80ºC for up to

four weeks. Homogenate was thawed at 4ºC then incubated at room temperature for five

minutes. To each sample, 200 µl of chloroform were added, followed by agitation. Samples were

incubated at room temperature for three minutes then centrifuged at 4ºC at 12000g for 15

minutes and the aqueous phase was collected. To further wash and purify the RNA, this

chloroform extraction was performed on the aqueous phase twice. RNA was precipitated with

the addition of 500 µl of isopropanol and a ten-minute incubation at room temperature. Samples

were then centrifuged at 4ºC for ten minutes at 12000g. The pellet was washed three times with 1

ml of RNAse free 75% ethanol. Each ethanol wash consisted of vortexing and centrifugation at

4ºC for five minutes at 7500g. After the third wash, pellets were vacuum dried for one hour with

1.5 mbar of suction. Each pellet was resuspended in 20 µl of RNAse free water and incubated at

60ºC for 15 minutes to complete RNA resuspension. RNA concentration was determined using

NanoDrop (Thermo Scientific). 260nm/280nm and 260nm/230nm UV absorbance was used to

assess purity. DNase I digestion and first strand cDNA synthesis were performed immediately

afterwards. Two samples each, from two birds, of ankle muscle and kidney RNA were excluded

from study due to degradation during the isolation procedure, as determined by agarose gel

electrophoresis.

18

3.3 DNase I Digestion

RNA samples were digested with Deoxyribonuclease I, Amplification Grade (Life

Technologies). Each µg of RNA (or less, depending on RNA yield) was combined with reagents

to 1X DNase I Reaction Buffer (Life Technologies) and 0.1 U/µl of DNase I, Amplification

Grade (Life Technologies). The reaction was incubated at room temperature for 15 minutes

before being terminated by the addition of EDTA (pH 8.0) (Life Technologies) to 2.27 mM and

ten minutes of incubation of 65ºC. RNA integrity and purity were checked with 1.5% agarose gel

electrophoresis. Concentration was assessed using NanoDrop (Thermo Scientific) and purity was

determined using 260nm/280nm and 260nm/230nm UV absorbance. Undigested excess RNA

was stored at -80ºC.

3.4 cDNA First Strand Synthesis

First strand cDNA synthesis from whole RNA was performed using SuperScript III First-Strand

Synthesis Supermix for qRT-PCR (Life Technologies). Each µg of DNase I treated RNA (or less

depending on the original RNA yield) was combined with reagents to obtain 1X RT Reaction

Mix (Life Technologies) and 10% v/v RT Enzyme Mix (Life Technologies). The reaction was

incubated at 50ºC for 30 minutes then stopped with a five-minute incubation at 85ºC and chilled

to 4ºC. 0.095 U/µl of E. coli RNase H (Life Technologies) was added to the cDNA sample,

which was incubated at 37ºC for 20 minutes to remove mRNA template from cDNA. Whole,

single-stranded cDNA was aliquoted and stored at -80ºC prior to use in Q-PCR. Due to the

presence of excess dNTPs and primers from the RT Reaction Mix, it was not possible to

determine cDNA concentration or purity using NanoDrop (Thermo Scientific). The

concentration of template whole RNA used in cDNA synthesis was used as an estimate of cDNA

concentration. cDNA integrity was examined using agarose gel electrophoresis.

4 Primer Design

4.1 Multiple Sequence Alignment Primer Design

MEGA 5.2.2 with ClustalW alignment was used to construct multiple sequence alignments for

GLUT5, GAPDH, and Elf1a1 and Beta-Actin mRNA from avian sequences available in NCBI

GenBank (National Center for Biotechnology Information, Bethesda, Maryland) during 2013

(Appendices). Due to high variation in intron sizes between species, gap opening penalty was set

19

at 15 and gap extension penalty was set at 0.1. This ensured that the software would extend long

gaps in sequences with shorter intron lengths. Sequences of high homology across birds were

chosen as targets of primer design using Genious 7.0.6. Areas of high homology were located

predominantly in the interior of the mRNA sequences. A partial mRNA sequence was available

for ruby-throated hummingbird GLUT1 (NCBI GenBank Accession Number KF492985), so

multiple alignments were not made for this gene. GLUT1 primers were designed using NCBI

Primer-BLAST (National Center for Biotechnology Information; Basic Local Alignment Search

Tool). Three primer sets were designed for each gene (Table 2). NCBI Primer-BLAST was used

to check for non-specific amplicons within the “Aves” taxid (8782).

4.2 GLUT1 Primer Design using a Calypte anna Sequence

The initial GLUT1 and GLUT5 primer sets did not amplify targeted sequences with sufficient

specificity and efficiency, so alternative primer sets were designed. First, four more GLUT1

primer sets were designed using an Anna’s Hummingbird (Calypte anna) GLUT1 (NCBI

Accession # XM_008503695) sequence that, along with the rest of the genome, became

available during the course of the study (Table 3). NCBI Primer-BLAST software was used to

design these primers and check for specificity.

4.3 GLUT5 and Further GLUT1 Primer Design using Calypte Anna Sequences

Additional primer sets were designed for both GLUT1 and GLUT5. Anna’s Hummingbird

(Calypte anna) sequences, which became available during the course of this study (NCBI

Accession # XM_008503695.1 and XM_008503671.1 respectively), were used rather than

consensus sequences derived from multiple sequence alignments. The assumption was made that

these sequences would be highly conserved between hummingbird species and, therefore, that

they would be closer to the actual ruby-throated hummingbird sequences than consensus

sequences from multiple alignments.

NCBI Primer-BLAST was used to design exon-spanning and, if possible, intron-spanning

primers for GLUT1 and GLUT5 based on the Anna’s Hummingbird genome (NCBI ID # 32060)

(Table 4). Here, exon-spanning refers to a primer set in which the forward and reverse primers

are on different, but adjacent, exons. An intron-spanning primer set describes one in which one

20

primer’s binding site starts on one exon and finishes on an adjacent exon. NCBI Primer-BLAST

was also utilized to check for non-specific amplicons within the “Aves” taxid (8782). Next,

OligoAnalyzer 3.1 (Integrated DNA Technologies Inc.) was used to further assess the stability of

secondary structures that might be formed by either or both primers. Finally, the Mfold Web

Server (The RNA Institute, College of Arts and Science, University of Alabama) was used to

assess the likelihood of template or amplicons forming secondary structures that might affect the

efficiency of primer binding. Primers were chosen such that secondary structures were as

unstable as possible and areas of template likely to form secondary structures were avoided when

choosing primer binding locations. All of these primer sets were exon-spanning and GLUT5

primer sets 4-6 were intron-spanning as well.

4.4 KHK and AldoB Primer Design

Three primer sets each for KHK and AldoB were designed using Anna’s Hummingbird (Calypte

anna) sequences (NCBI Accession # XM_008492930.1, and XM_008496441 respectively)

(Table 5). These were designed using NCBI Primer-BLAST, OligoAnalyzer and Mfold as

described above for GLUT1 and GLUT5 sequences. KHK set 1 and AldoB sets 1 and 2 were

exon-spanning. AldoB set 2 was also intron-spanning.

4.5 PCR for Primer Testing

Desalted primers were obtained from Life Technologies Inc. and eluted in TE buffer (10 mM

Tris-HCL and 1 mM EDTA, pH 8.0). PCR was used to assess reaction specificity. PCR reaction

volumes were 20 µl and, other than template and primers, reaction components came from

Promega GoTaq PCR Core System 1 (Promega, Madison, Wisconsin). Both forward and reverse

primers were included at 0.5 µM, MgCl2 was added to 2.5 mM, dNTPs were added to 200 µM,

and GoTaq DNA polymerase was added to 1 unit/µl. Template was included at 34 ng/µl for

hummingbird genomic DNA or 0.05 ng/µl for hummingbird liver or pectoralis cDNA. Green

GoTaq Reaction Buffer was included at 1X. PCR was performed with an Applied Biosystems

Veriti Thermal Cycler. Templates were denatured at 94ºC for four minutes. The protocol then

consisted of 35 cycles of 94 ºC for 30 seconds, the appropriate annealing temperature for 60

seconds, and extension at 72 ºC for 90 seconds. With the exception of temperature gradient PCR,

described below, the annealing temperature was 60ºC. A final extension step consisted of 72 ºC

for ten minutes followed by cooling to 4ºC. Products were separated on agarose gel with 1X

21

GelRed (Biotium, Hayward, California). Bands were visualized with UV light. Product size was

assessed against Biotium Ready-to-Use 100 bp DNA Ladder. Bands were sized using

GelAnalyzer 2010a.Temperature gradient PCR was used to determine optimal annealing

temperature of the primer sets. Hummingbird genomic DNA was used as the template, and both

template and no-template controls were amplified at six temperatures, ranging from 58ºC to 63ºC

and varying by 1ºC intervals.

4.6 Efficiency Testing

Efficiency of specific primer sets was assessed by amplification of a dilution series of

hummingbird liver cDNA. Liver cDNA was used for primer testing because all genes of interest

were expected to be stably expressed in this tissue and because RNA yield was very high for this

tissue. Q-PCR of this dilution series was performed as described below. The dilution series

started at cDNA derived from approximately 0.05ng/ul of whole RNA and included five to six

more ten-fold dilutions. Dilution factor was plotted against Ct value and analyzed by linear

regression in Microsoft Excel. An efficiency of 90-105% was considered to be satisfactory.

4.7 Primers Chosen for Q-PCR

The most specific and efficient primers were identified and used for Q-PCR: Elf1a1 set 1,

GAPDH set 2, Glut1 set 10, GLUT5 set 4, KHK set 2, and AldoB set 1 (Table 6).

5 Sequencing

5.1 Gel Extraction

Primer sets that showed specificity were chosen for product sequencing. PCR product bands

were excised from 2% agarose gel and processed using Omega Bio-tek E.Z.N.A. Gel Extraction

Kit (Omega Bio-tek). Volume of a gel slice was estimated based on mass and the gel slice was

combined with an equal volume of Binding Buffer (XP2) (Omega Bio-tek). The gel slices were

then incubated at 60ºC for seven minutes and vortexed every other minute during incubation. pH

of the dissolved gel was checked using a pH indicator included in the Binding Buffer and

adjusted to approximately 8.0 with 5M sodium acetate (pH 5.2) if necessary. 700 µl of dissolved

gel was added to a HiBind DNA Mini Column and Collection Tube (Omega Bio-tek) and

centrifuged at room temperature at 10000g for one minute. This step was repeated until all

22

dissolved gel had been filtered through the column; filtrate was discarded. 300 µl of Binding

buffer was added to the HiBind DNA Mini Column and spun through at room temperature at

13000g for one minute. Twice, 700 µl of SPW Wash Buffer (Omega Bio-tek) was spun through

the column at room temperature at 18000g for one minute. A fresh Collection Tube was used

and the HiBind DNA Mini Column was centrifuged at room temperature at 18000g for five

minutes to dry. The HiBind DNA Mini Column was transferred to a 1.5 ml microcentrifuge tube

and 30 µl of DNase free water was added to the column. The column was incubated at room

temperature for ten minutes then centrifuged at room temperature at 18000g for five minutes.

Concentration and purity were determined using NanoDrop (Thermo Scientific)

spectrophotometry analysis. 260nm/280nm and 260nm/230nm UV absorbance was used to

estimate purity and the elution was stored at -20ºC.

5.2 Sample Preparation for Sanger Sequencing

Samples were sent to The Center for Applied Genomics at The Hospital for Sick Children

(Toronto, Ontario) for Sanger Sequencing. Submitted samples contained 1.43 ng/µl of PCR

product and 0.71 pmols/µl of the appropriate primer and were suspended in DNase free water.

The “difficult template” reaction mixture was selected for sequencing and independent

sequencing reactions were performed in the forward and reverse directions.

6 Q-PCR Assays

6.1 Reaction Conditions

Real time PCR was performed in triplicate using SYBR green chemistry. A Bio-Rad PTC-200

Peltier Thermal Cycler and a Chromo 4 Continuous Fluorescence Detector were used in

conjunction with Bio-Rad Opticon Monitor 3 software (Bio-Rad Laboratories, Hercules,

California). 20 µl reactions were performed with IQ SYBR Green Supermix (Bio-Rad). Primers

were included at a final concentration of 0.5µM each and template was added to a final

concentration of 0.05ng/µl. Reactions took place in white, low profile PCR tubes with optical flat

caps (Bio-Rad). The program began with one cycle of 94ºC for 4 min, followed by 40 cycles of

94ºC for 30s, 60ºC for 1min and 72ºC for 1min 30s. A two-second fluorescence measurement

was taken at the end of each cycle using the Opticon Monitor 3 software. One triplicate was

immediately removed for later assessment of product integrity and specificity by gel

23

electrophoresis. This ensured that cDNA samples separated on agarose gel would be double-

stranded and representative of the state of the cDNA following amplification. A melting curve

was then immediately performed from 45-95ºC on the remaining two replicates, with a two-

second fluorescence measurement being taken by Opticon Monitor 3 software at one degree

intervals.

Elf1a1, GLUT1, and GLUT5 cDNA sequences were simultaneously quantified in each of six

birds, and GAPDH cDNA was simultaneously quantified in each of five of the same five birds as

only a very limited quantity of RNA was available from one bird. One bird was excluded from

the data set as the results differed dramatically from consistent results across the other five birds.

This was the bird that was observed to have severe pathology and infection during dissection.

KHK, AldoB, Elf1a1, and GAPDH were quantified in cDNA from four of the five healthy birds,

as cDNA from one was exhausted and cDNA from the diseased bird was excluded. Product

integrity and specificity were checked by running aliquots of the removed triplicate (one

technical replicate per biological replicate) on 1.5% agarose gel. Product size was assessed

against Biotium Ready-to-Use 100 bp DNA Ladder. Bands were sized using GelAnalyzer 2010a.

The remainder of the third technical replicate was later used to generate a melting curve

following the same melting protocol as above.

6.2 Data Analysis

Ct values of triplicates were averaged for each gene in each tissue of each bird. Ct values were

then compared using the delta-delta Ct method. Elf1a1 and GAPDH genes were used to

normalize the data both individually and when the two were averaged. GLUT1, GLUT5, KHK,

and AldoB were examined independently and the tissue with the lowest expression of each was

chosen as the calibrator for that gene (liver for GLUT1 and brain for GLUT5, KHK, and AldoB).

Calibration was achieved by dividing all normalized tissue measurements by that of the

calibrator tissue in a given bird. One-way ANOVA and Tukey HSD Post-hoc tests were

performed to assess differences in transcript level among tissues independently for each of the

four genes of study. A P-value of less than 0.05 was considered significant. Statistical analyses

were performed in R ver. 3.1.2 (R Foundation for Statistical Computing, Vienna, Austria).

24

7 Protein Isolation

Samples were homogenized in RIPA buffer (50mM Tris HCl, ph 7.4, 150mM NaCl, 1mM

EDTA, 0.1% SDS, 1% Triton X-100, 1mM DTT) with Sigma-Aldrich Protease Inhibitor

Cocktail for use with mammalian cells and tissue extracts, DMSO solution, such that final

protease inhibitor concentrations were approximately: 1.04 mM AEBSF, 0.8 µM Aprotinin, 0.04

mM Bestatin, 0.014 mM E-64, 0.02 mM Leupeptin, and 0.015 mM Pepstatin A. Samples were

then incubated at 4ºC with agitation for two hours and the homogenate centrifuged at 16000g for

20 minutes at 4ºC. The supernatants were obtained and stored at -80ºC.

8 Western Blotting

Total protein concentration was determined using a Pierce BCA Protein Assay Kit (Thermo

Scientific). BCA Reagent A and BCA Reagent B (Thermo Scientific) were combined in a 50:1

ratio to prepare BCA Working Reagent. BSA Albumin Standard (Thermo Scientific) was used to

prepare fresh dilutions series from 0 to 2000 µg/ml. Samples and standards were combined with

Working Reagent at a ratio of 1:20 and incubated at 37ºC for 30 minutes before being cooled to

4ºC. Absorbance was measured at 562 nm using a Beckman Coulter DU 730 Spectrophotometer.

The standards were used to construct a standard curve with R2 greater than 0.99, and all

measurements, including of standards, were taken within 15 minutes of each other.

10µg of each sample was diluted with one part Laemmli (loading) buffer (4% SDS, 10% 2-

mercaptoethanol, 20% glycerol, 0.004% bromophenol blue, 0.125M Tris-HCl, pH 8.0) and

separated using a Bio-Rad Mini Protean Tetra Cell (Bio-Rad) at 110V for 100 minutes. 4% and

10% acrylamide stacking and separating gels were used respectively. The running buffer

consisted of 192mM glycine, 33mM Tris, and 0.1%SDS. Bio-Rad Precision Plus Unstained

Protein Ladder was run in one lane.

Prior to transfer, the gel and nitrocellulose was allowed to equilibrate in transfer buffer (192mM

glycine, 24.8mM Tris, 1% SDS, 20% methanol) for 15 minutes. Protein was transferred to

nitrocellulose using a Hoefer TE22 Mighty Small Transphor Unit (Amersham Biosciences) at

70V for 75 minutes. After transfer the gel was stained with 0.1% amido black stain (20%

methanol, 7.5% acetic acid, 0.1% amido black), photographed, and then destained with deionized

water.

25

The membrane was blocked overnight at 4ºC in 5% skim milk in TBST buffer (150mM NaCl,

50mM Tris, 0.1% Tween-20, pH 7.5). Anti-Aldolase B (Abcam Anti-ALDOB antibody - N-

terminal ab138760 Abcam Inc., Cambridge, Massachusetts) or Anti-Ketohexokinase (Abcam

Anti-ketohexokinase antibody ab38281) primary antibody was used at a 1/100 dilution in 5%

skim milk TBST and incubated for 90 minutes. Following incubation the membrane was washed

for five minutes, five times, with TBST. The secondary antibody (Fisher Scientific Goat anti-

Rabbit IgG, HRP, Polyclonal R&D Systems RDSHAF008) was then used at a 1/25000 dilution

and incubated for one hour. Bio-Rad Precision Protein StrepTactin-HRP was also included in

this incubation mixture in order to visualize the ladder during later exposure. Following

incubation the membrane was washed as before followed by a five-minute wash with TBS

(150mM NaCl, 50mM Tris, 0.1% Tween-20, pH 7.5). Finally, the membrane was incubated with

Clarity Western ECL Blotting Substrate (Bio-Rad) for five minutes and visualized using a Bio-

Rad ChemiDoc XRS+ with Image Lab Software (Bio-Rad).

After visualization the membrane was stripped with 1X Antibody Stripping Buffer (Gene Bio-

Application L.T.D., Kfar-Hanagid, Israel) for ten minutes with agitation and washed with TBST.

The membrane was incubated with Clarity Western ECL Blotting Substrate and visualized as

described above to confirm absence of secondary antibody, then washed with TBST. The

membrane was next blocked as above, but for only one hour, and incubated with secondary

antibody and visualized as described above to confirm absence of primary antibody. Stripping

was repeated and the membrane was then blocked with 5% Bovine Serum Albumin (BSA) in

PBS (2.7 mM KCl, 137 mM NaCl, 1.8 mM KH2PO4 and 10 mM Na2HPO43-

, pH 7.4) for one

hour. The membrane was incubated with HRP-conjugated anti-GAPDH (ab9482 Abcam) at a

1:5000 dilution in 5% BSA in PBS at 4ºC overnight. Precision Protein StrepTactin-HRP was

included in the incubation mixture to visualize the ladder. The membrane was washed for 15

minutes three times with PBS and antibody, then incubated with Clarity Western ECL Blotting

Substrate and visualized as described above.

The experiment was replicated without the use of Bio-Rad Precision Protein StrepTactin-HRP to

check for non-specific binding of the Precision Protein conjugate. The experiment was also

performed using only HRP-conjugated anti-GAPDH antibody and omitting stripping and

detection of proteins other than GAPDH. This was done in order to examine the effect of

stripping on protein integrity.

26

Bands were sized using GelAnalyzer 2010a. Densitometry was performed using GelQuant.NET

software.

27

Chapter 3 Results

1 Primer Testing on Hummingbird Genomic DNA

I began my investigation by asking how relative transcript density of GLUT1 and GLUT5 varies

across hummingbird tissues. Three primer sets each were designed for three different

housekeeping genes, GAPDH, Beta-Actin, and Elf1a1, in order to compensate for potential

variation in expression of any given one of these genes among tissues (Table 2). At the

beginning of this study hummingbird genomic sequences were not available for my genes of

interest, with the exception of a partial GLUT1 mRNA sequence (NCBI GenBank: KF492985.1)

from a previous study by Welch et al. (2013). The choice to design multiple primer sets for each

gene was made in order to increase the likelihood of obtaining a specific and efficient primer set

during a single round of primer design. Each primer set was tested on ruby-throated

hummingbird DNA using thermal gradient PCR.

All three Beta-Actin primer sets amplified what appeared to be primer dimers, or failed to

amplify the intended product. Of the Elf1a1 primer sets, one set (Elf1a1 set 1) was specific and

the optimum annealing temperature was 60ºC. At lower temperatures less amplicon was

produced and at higher temperatures significant amplification of primer dimers was observed in

the no-template control. The other two primer sets for Elf1a1 successfully amplified a product of

the expected size but also reliably amplified primer dimers in the no-template controls (Figure

3).

Of the three GAPDH primer sets, the first amplified primarily primer dimers, while the second

and third showed patterns of amplification similar to that of Elf1a1 set 1, with an optimal

annealing temperature of about 60ºC (Figure 4). The product of GAPDH primer set 2 was

approximately 200 base pairs in size, twice the size that was expected, perhaps due to an

unanticipated intron.

Given that specific primers had successfully been produced for two housekeeping genes –

barring measurement of amplification efficiency – and that primers for both genes had the same

28

optimum annealing temperature, which is necessary for Q-PCR, I decided further attempts at

primer design for beta-actin would not be necessary.

Of the GLUT1 primer sets, set 1 was non-specific, but the other two produced an amplicon of the

expected size (Figure 5). Both GLUT1 primer sets 2 and 3 amplified a band, potentially a primer

dimer, in the no-template controls. These primer sets were therefore considered likely to be

unreliable and were used with caution.

In the case of GLUT5, primer set one was highly non-specific (Figure 6). Both primer sets two

and three were specific but showed some evidence of weak amplification of what appeared to be

primer dimers in the no-template controls (Figure 6). Primer dimer amplification appeared to be

minimal in the presence of template, and was weak in the no-template controls, so both primer

sets two and three were chosen for further testing. Amplification by these primers produced a

strong band across a broad range of temperatures. Since these two primer sets produced an

intense band of the intended size at 60ºC, they were considered compatible with the other primer

sets.

Sequencing of promising amplicons was attempted through The Center for Applied Genomics at

The Hospital for Sick Children, but reliable sequences were not obtained and a high degree of

“noise” was observed in the chromatograms.

2 RNA Extraction Protocol Development

The next step in testing these primers was to assess amplification using cDNA as template, so I

proceeded to RNA extraction. I first attempted RNA extraction using Ambion RNA-binding

Spin Cartridges, as this is a quick method with few steps at which RNase contamination can

occur. However, RNA yields using this method were low, particularly for fibrous tissues (Table

7).

Compounded with evidence of RNA degradation, as determined by gel electrophoresis, these

yields proved insufficient to synthesize enough cDNA for Q-PCR optimization. As suggested by

a colleague experienced in extracting RNA from warbler muscles, I next used a Tri Reagent

extraction procedure in order to increase RNA yield. Yields were magnitudes greater with this

procedure (Table 7).

29

RNA still showed degradation with the Tri-Reagent method despite stringent practices to ensure

sterile working conditions. While this RNA was too degraded to be used to obtain reliable Q-

PCR results, it was suitable for testing the effect of my primers on cDNA.

3 Primer Testing on Hummingbird cDNA

The cDNA was tested with primer sets for GAPDH, Elf1a1, GLUT1, and GLUT5. Primers for

the housekeeping gene were tested first as they would be expected to produce an amplicon

regardless of the tissue source of mRNA. This allowed the success of cDNA synthesis to be

verified. Interestingly, GAPDH set 2 produced an amplicon of the expected product size of

~100bp when cDNA template was used, but continued to produce a ~200bp product when the

template was genomic DNA, suggesting the presence of a small intron in the template.

While GAPDH, Elf1a1, and GLUT1 primers worked on cDNA, problems were encountered with

GLUT5 primers. GLUT5 primer set 2 was not sensitive enough to amplify much product from

cDNA, which was more dilute than the hummingbird genomic DNA used previously.

GLUT5 primer set 3 did produce a product (Figure 7). The band produced from pectoralis cDNA

was brighter than that produced from ankle muscle cDNA, which presented preliminary evidence

for my prediction of high GLUT5 mRNA expression in flight muscle. A band of the expected

product size was produced from muscle and heart RNA samples, suggesting incomplete DNA

digestion by DNAse1; however, these bands were less intense than those produced with cDNA.

Evidence of RNA degradation could be seen in liver and brain samples, but PCR was successful

with the corresponding cDNA.

Sanger sequencing of cDNA derived amplicons was attempted with minimal success. One

legible partial sequence each, of GAPDH (product of primer set 2) and Elf1a1 (product of primer

set 1) was obtained. Sequencing quality was very low with a low signal/noise ratio. Nearly all

base calls had a quality value lower than 20. Nonetheless, the two sequences that could be

interpreted, Elf1a1 produced from liver cDNA, and GAPDH produced from pectoralis cDNA,

were BLASTed against the hummingbird taxid (9242) (Figure 8 and Figure 9 respectively).

Based on BLAST results, both sequences appeared to be the intended products of amplification,

30

but low sequencing quality and mostly illegible chromatogram traces made the results difficult to

interpret with confidence.

Troubleshooting was performed in order to remove degradation from the RNA extraction

procedure. The problem was found to be the use of an electric tissue homogenizer and/or

ultrasonic tissue homogenizer. In order to isolate RNA in as nearly intact a state as possible, all

homogenization had to be performed manually and on ice, using scissors and syringes. This

technique yielded high-integrity RNA suitable for Q-PCR (Figure 10).

4 Initial Primer Efficiency Measurements

Having obtained cDNA and functional primers for two housekeeping genes as well as for

GLUT5, I moved on to using Q-PCR to measure amplification efficiency for each primer set. I

selected my most promising primer sets for this measurement. I used Elf1a1 primer set 1 and

GLUT5 primer set 3 as these performed the best of the three primer sets tested for each of these

genes. For GAPDH I used set 2 as it produced stronger bands than GAPDH set 3, and because

product band size could be used to differentiate between cDNA and genomic DNA

contamination, due to difference in product size obtained for the two templates. I tested GLUT1

primer set 2 as well, in the hope that primer dimer amplification would not occur in the presence

of template. Ruby-throated hummingbird liver cDNA was used as template.

Elf1a1, GAPDH, and GLUT5 had amplification efficiencies of 103.9%, 100.2%, and 104.7%

respectively, and all trends were consistent across the entire dilution range (Figure 11, Figure 12,

and Figure 13). As all three had efficiencies between 90% and 105%, these primer sets were

considered suitable for Q-PCR (Bio-Rad, 2006).

GLUT1 amplification had an efficiency of 172.1% up to a 1000 fold dilution, suggesting non-

specific amplification or amplification of primer dimers (Figure 14). The Ct values did not

increase at greater dilutions of template, suggesting a basal level of dimer amplification. Melting

curve analysis confirmed that primer dimers were being amplified. This GLUT1 primer set was

therefore unsuitable for quantitative detection of mRNA.

31

5 Tests of Calypte Anna-Informed Primers on Hummingbird cDNA

As previous GLUT1 primers were unacceptable due to primer dimer formation, new GLUT1

primers were designed. By this point the first hummingbird genome (Anna’s hummingbird:

Calypte anna) had been sequenced and many predicted gene sequences were henceforth

available on NCBI GenBank. Thus, I used NCBI Primer-BLAST to design new primers based on

the Calypte anna GLUT1 sequence (NCBI GenBank Accession XM_008503695.1) (Table 3).

As the entire predicted mRNA sequence was available, unlike for the ruby-throated

hummingbird sequence, less problematic areas of the sequence could be targeted. These primer

sets were tested with a temperature gradient PCR as described above; however, liver cDNA,

rather than genomic DNA, was used as the template in order to better simulate the reaction

conditions that would be used during Q-PCR. Regrettably, all of these primer sets proved to be

non-specific, so focus was instead placed on the study of GLUT5.

I reevaluated my primers by using NCBI Primer-BLAST to examine their predicted efficacy

based on this genome. Elf1a1 set 1 was both exon-spanning and intron-spanning based on this

genome. The expected product size was 95 base pairs as previously predicted (Figure 15). Most

of the intron-spanning (reverse) primer sequence bound to the downstream exon of the two exons

containing the target sequence of the primer, suggesting that product amplification using

genomic template might be possible. This was seemingly the case when genomic DNA was used

as template (Figure 3).

GAPDH set 2 was also exon- and intron-spanning for this hummingbird, with an expected

product size of 96, which again matched what had been predicted initially (Figure 16). Only one

base pair of the intron-spanning (reverse) primer occurred on the downstream exon. By chance,

this last base pair complemented both the downstream exon and the upstream end of the intron,

such that both templates would be expected to amplify with these primers. The intron was

approximately 150 base pairs, corresponding closely to what was observed during amplification

of ruby-throated hummingbird genomic DNA (Figure 4). This suggested that this primer set

could be used to visually assess the extent of genomic contamination on an agarose gel, by

checking for the presence of the genomic DNA specific product.

32

GLUT5 primer set 3 produced a 102 base pair product, which was consistent with the original

prediction (Figure 17). These primers were not exon-spanning.

Unfortunately, the above GLUT5 inter-tissue PCR result could not be replicated. In future

rounds of PCR using the GLUT5 primers, and in future rounds of Q-PCR, a band of the expected

product size was consistently observed in both the cDNA template reactions and the no-template

controls. This problem persisted despite the use of freshly ordered stock of all reagents, including

primers and water, and occurred only with the GLUT5 primers. Go-Taq polymerase (Promega)

was substituted for Phusion High-Fidelity DNA Polymerase (ThermoFisher Scientific), but the

PCR results remained unchanged. Given the presence of bands in the no-template controls of the

original thermal gradient PCR of GLUT5, as well as having ruled out other possibilities, the

problem was attributed to the GLUT5 primers. It was unclear why this problem did not occur

when measuring efficiency of amplification. It is possible that a primer-derived product formed

in only a percentage of reactions and that it was similar in size to the intended product.

It was decided that new GLUT5 primers would be designed (Table 4). New GLUT1 primers

were designed simultaneously (Table 4). Calypte anna predicted mRNA sequences were used as

templates for these designs, unlike previous primers, most of which were designed using

multiple sequence alignments and were limited to areas of high homology across birds. A

combination of three pieces of software was used to assess the primers (Primer-BLAST, Mfold,

and OligoAnalyzer). These were the most stringent primer design conditions used thus far and

four primer sets were designed for each of GLUT1 and GLUT5.

A thermal gradient test was performed as before, using hummingbird liver cDNA as template. At

60ºC, the optimal annealing temperature of my primers for housekeeping genes, all eight primer

sets produced a specific product of the expected size (Figure 18). Upon testing primers on

hummingbird genomic DNA, two GLUT1 primer sets produced a very intense band; therefore,

the other GLUT1 primer sets, sets 10 and 11, were chosen for efficiency testing. GLUT5 primer

sets 4 and 7 were chosen for efficiency testing as they produced smaller amplicons than did sets

5 and 6, and smaller amplicons are favourable for rapid product extension. Amplification of a

primer dimer was evident in the no-template control of GLUT5 set 4, but it was a weak signal

and did not appear in the presence of template.

33

6 Efficiencies of new GLUT1 and GLUT5 Primers Primer set 10 for GLUT1 had 108.8% efficiency up to a 1000 fold dilution (R = -0.999) (Figure

19). At dilutions greater than 1000 fold, cycle threshold did not significantly increase, suggesting

primer dimer amplification at those concentrations of template. Glut1 primer set 11 had an

efficiency of 101.2% (R = -0.971) up to a 10 000 fold dilution, but amplicon could not be

reliably detected at greater dilutions than 10 000 fold (Figure 20). Although these primer sets

cannot quantify very dilute cDNA, I expected my working range of template concentration

during Q-PCR to be well within the range of ideal efficiency.

I decided to use GLUT1 set 10 for Q-PCR data collection as its R-value was much closer to -1.0

in the expected working range of transcript concentration. This suggested that amplification

efficiency would be more consistent for set 10 than for set 11, which is important for making

accurate quantitative comparisons. This was decided to outweigh the amplification efficiency

being slightly above the ideal range of 95%-105%. Although primer dimers were evident at low

concentrations of template, no such amplification was observed in no-template controls when

visualized on agarose gel.

GLUT5 primer sets 4 and 7 had similar efficiencies and R-values (101.5%, R = -0.975 and

101.2%, R = -0.970 respectively) (Figure 21 and Figure 22 respectively). Neither primer set had

sensitivity beyond a 10 000 fold dilution, but this was not thought to be an impediment, as

expected working concentration range was not this dilute. Primer set 4 was chosen for Q-PCR

data collection as the data could be better fit with linear regression, suggesting more consistent

amplification efficiency than primer set 7.

Amplicon sequencing was attempted, but reliable sequences could not be obtained due to low

signal to noise ratio.

34

7 GLUT1 and GLUT5 Relative Q-PCR

Ct values observed in no reverse transcriptase controls were two to three orders of magnitude

greater than those seen in corresponding cDNA samples, suggesting that genomic contamination

amounted to less than 1% of available template in cDNA samples, which was considered

negligible. The RNA preparation procedure was therefore deemed to have produced sufficiently

pure RNA for cDNA synthesis.

Normalization to either GAPDH or Elf1a1 yielded differing results (Table 8). Ratio of

Elf1a1/GAPDH Ct values was highest in pectoralis, suggesting that at least one of these genes

might vary in copy number across tissues. A higher ratio is indicative of less Elf1a1 template

relative to GAPDH template. To shed light on how “housekeeping” gene expression might vary

between tissues and impact the results, data was normalized and assessed in three ways:

normalization to GAPDH, Elf1a1, and the average of GAPDH and Elf1a1, respectively.

GLUT5 was quantified in blood from one bird in order to check if variation between amounts of

blood in tissue homogenates might have impacted results. No amplification was detected,

suggesting that this was not a source of error. Insufficient RNA was available for the

examination of GLUT1 due to a very low yield of RNA from the blood sample collected.

7.1 GLUT1 Transcript Densities

GLUT1 mRNA was normalized to Elf1a1 and calibrated to liver (Figure 23: A). Expression

levels were significantly different among tissues (F5,21 = 4.767, P = 0.005). GLUT1 mRNA was

most highly expressed in heart and brain, and levels were similar between the two tissues (P =

0.999). Expression in heart and brain was significantly greater than in the two tissues with the

lowest levels of GLUT1 transcript: intestine and liver (brain-intestine P = 0.054, all other pairs P

< 0.048). Average GLUT1 expression was next-most abundant in muscle tissues, and expression

was not significantly different between pectoralis and ankle muscles (P = 0.983). As variation in

expression levels among muscle tissue samples was relatively high, average levels were not

significantly different from that in heart and brain (P > 0.0816), or from that in liver and intestine

(P > 0.865).

35

When normalized to GAPDH mRNA expression patterns remained qualitatively similar overall

with a few notable differences (Figure 23: B). Expression of GLUT1 in pectoralis was relatively

lower than when normalized to Elf1a1. Variation in expression was again statistically significant

(F5,16 = 3.936, P = 0.016). Heart and brain expressed more GLUT1 transcript than intestine,

pectoralis or liver but this was not statistically significant (P > 0.076).

When expression of both housekeeping genes was averaged and used for normalization (Figure

23: C), variation in expression levels remained significant (F5,17 = 3.627, P = 0.021). Patterns of

significance were largely the same as for GAPDH normalization. Heart and brain still expressed

more GLUT1 than pectoralis, liver or intestine, though differences were not significant (P >

0.094).

7.2 GLUT5 Transcript Densities

GLUT5 mRNA levels, normalized to Elf1a1, were calibrated to brain (Figure 24: A). Expression

varied significantly among tissues (F5,21 = 29.36, P < 0.001). GLUT5 mRNA was significantly

more highly expressed in intestine than in other tissues (P < 0.001). Pectoralis expressed

significantly more GLUT5 mRNA than any tissue other than intestine (P < 0.010). Other tissues

had much lower expression, with comparable levels of GLUT5 mRNA (P > 0.694).

Relative expression was similar for normalization to GAPDH, but pectoralis expressed relatively

less transcript, while intestine expressed relatively more (Figure 24: B). GLUT5 transcript level

variation was significant (F5,17 = 12.14, P < 0.001). Intestine expressed the highest quantity of

GLUT5 transcript (P < 0.001). On average, pectoralis did express more transcript than remaining

tissues, but these differences were not statistically significant (P > 0.990).

When expression of both housekeeping genes was averaged and used for normalization (Figure

24: C), variation in expression levels was significant for normalization to the averaged

expression of both housekeeping genes (F5,16 = 17.24, P < 0.001). Patterns more closely reflected

GAPDH than Elf1a1 normalization. Intestine expressed the greatest levels of template (P <

0.001). Variation in GLUT5 expression among other tissues was not statistically significant (P >

0.604).

36

8 KHK and AldoB Primer Testing on Hummingbird cDNA

Observed high relative expression of GLUT5 transcript in hummingbird pectoralis, as well as

high relative GLUT1 transcript expression in hummingbird muscles, supported my hypothesis

that these transporters are upregulated in hummingbird flight muscle. If mRNA expression is

taken to be indicative of protein expression for these genes, then glucose and fructose flux into

the flight muscle could be very rapid. Observing such high relative expression of GLUT5 led me

to investigate how fructose might be rapidly phosphorylated in flight muscle. Hypothesizing an

upregulation of the fructolysis pathway, I performed Q-PCR on the rate-limiting enzymes of this

pathway, ketohexokinase and aldolase B.

Primers were designed using the final method described above for GLUT1 and GLUT5 primers.

Three primer sets each were designed for KHK and AldoB using Calypte anna sequences (NCBI

Accession # XM_008492930.1, and XM_008496441 respectively) (Table 5). The exon-spanning

property was only included if it did not significantly increase the stability of primer secondary

structures. KHK set 1 and AldoB sets 1 and 2 were exon-spanning. AldoB set 2 was also intron-

spanning. These primers were tested by PCR at 60ºC using ruby-throated hummingbird liver

cDNA, pectoralis cDNA, and genomic DNA. Pectoralis cDNA was included to ensure that

amplification occurred specifically in the tissue of greatest interest to this experiment. Reaction

optimization by temperature gradient PCR was omitted because primers needed to amplify

successfully at exactly 60ºC in order to be used with the GAPDH and Elf1a1 primers. KHK set 1

failed to amplify template (Figure 25). Both KHK sets 2 and 3 amplified a template of the

expected size, though KHK set 2 produced a likely primer dimer amplicon in the no-template

control (Figure 25). All three AldoB primer sets produced an amplicon of appropriate size

(Figure 26).

9 KHK and AldoB Primer Efficiencies

All new primers save KHK set 1 (which failed to amplify the target) were tested for efficiency

using the method used previously for assessing efficiency. Up to a 10 000 fold dilution, KHK

sets 2 and 3 had amplification efficiencies of 100.7% (R = -0.998) (Figure 27) and 91.9% (R = -

0.999) (Figure 28) respectively. Dilutions greater than 10 000 fold lacked amplification

37

sensitivity. KHK primer set 2 was chosen for Q-PCR due to preferable efficiency. The working

range of template concentration was considered acceptable.

AldoB primer set 1 had an amplification efficiency of 90.5% (R = -0.999) (Figure 29). Primer set

2 had an efficiency of 91.3% (R = -0.999) up to a 1000 fold dilution (Figure 30) and efficiency

dropped at greater dilutions of template. Primer set 3 had the same properties as primer set 1 up

to a 100 000 fold dilution (Figure 31). AldoB set 1 was chosen for Q-PCR because, unlike set 3,

it did not amplify an intended product from genomic DNA, suggesting specificity to cDNA.

Sequencing of amplicons was not attempted due to the lack of reliable sequence reads that had

been produced by my previous attempts at sequencing small products.

10 KHK and AldoB Relative Q-PCR

As was the case during GLUT1 and GLUT5 transcript quantification, ratios of Elf1a1/GAPDH

varied between tissues (Table 9). As before, pectoralis had the highest ratio. In contrast to

previous results, intestine had a low ratio, as did kidney. Given potential variation in expression

of one or more housekeeping genes, data was separately normalized and assessed relative to

Elf1a1, GAPDH, and the average of the two genes, as had been done for GLUT data.

10.1 KHK Transcript Densities

Normalized to Elf1a1 and calibrated to brain, KHK transcript levels across tissues differed

significantly (F6,17 = 38.11, P < 0.001) (Figure 32: A). Compared to other tissues, transcript level

was highest in kidney (P < 0.001). Liver had significantly higher expression than pectoralis,

heart, or brain (P < 0.016). Expression in kidney and liver was significantly different (P < 0.001).

Among other tissues (pectoralis, intestine, heart, brain, and ankle), expression did not differ

significantly (P > 0.938).

When normalized to GAPDH, expression in liver and kidney was comparatively greater than

when normalized using Elf1a1 (Figure 32: B). Variation in KHK expression among tissues was

statistically significant (F6,17 = 12.22, P<0.001). Expression was greatest in kidney (P < 0.001)

and differences among tissues were otherwise not significant (P > 0.913).

38

When normalized to the averaged densities of Elf1a1 and GAPDH, significant variation was

observed between tissues expressing KHK (F6,17 = 21.66, P < 0.001) (Figure 32: C). Kidney still

showed the highest expression (P < 0.001) but variation among other tissues was not significant

(P > 0.354).

10.2 AldoB Transcript Densities

AldoB transcript levels were significantly different across tissues (F6,17 = 3.322, P = 0.024)

(Figure 33: A). As with KHK, kidney exhibited a significantly higher level of transcript than did

other tissues (P < 0.050). There were no significant differences in expression level among other

tissues (P > 0.998).

Relative expression was higher in kidney and liver with normalization to GAPDH. Variation

among tissues was statistically significant (F6,17 = 2.963, P = 0.036) (Figure 33: B). Kidney

expressed the highest levels of AldoB transcript (P < 0.071) but no significant variation was

observed (other pairs: P > 0.998).

Variation in AldoB expression among tissues was significant when normalized to the average

densities of Elf1a1 and GAPDH (F6,17 = 3.114, P = 0.030) (Figure 33: C). Expression was highest

in kidney (P < 0.035), but not significantly higher than ankle muscle (P = 0.061), and was similar

among other tissues (P > 0.998).

11 KHK and Aldo-B Relative Protein Expression

Contrary to predictions, KHK and AldoB transcript levels in flight muscle were barely above

background. Unlike GLUT5, the predicted sequences of these proteins based on putative Anna’s

hummingbird sequences exhibited high homology. Thus, commercially available primary

antibodies with possible cross-reactivity to these hummingbird proteins could be identified. A

polyclonal anti-ketohexokinase primary antibody (Abcam ab38281) with 80% homology

between the immunogen used to generate the antibody (last 50 amino acids of human KHK,

NCBI GenBank reference CAA55347.1) and the corresponding sequence on Calypte anna

(NCBI GenBank reference XP_008491152.1) was identified (Figure 34).

A polyclonal anti-aldolase B antibody (Abcam ab138760) was identified that shared 88%

homology between the original immunogen (amino acids 30-60 of human Aldolase B NCBI

39

GenBank reference NP_000026.2) and the relevant Calypte anna sequence (NCBI GenBank

reference XP_008494663.1) (Figure 35).

These antibodies were used for western blots to examine whether protein expression

corresponded with transcript expression for these genes.

Mouse liver was included as a positive control and chicken pectoralis was included for

comparison to hummingbird pectoralis. Many non-specific bands were observed in the anti-KHK

western Blot (Figure 36). Although some of these bands were approximately the expected

product size (33 kDa), it was not possible to determine with certainty which, if any,

corresponded to KHK. This is true, in part, due to the lack of a clear band of expected size in the

positive control. The primary antibody did not appear to specifically bind to the intended target,

so a conclusion about relative KHK protein expression could not be reached.

Amido black nitrocellulose staining indicated that similar quantities of protein were loaded in

each lane of the AldoB western blot (Figure 37). On the western blot a single band was observed

at the expected size-range (Figure 38). The same controls were used as above. A band was seen

in all samples, although it was very faint in pectoralis. A clear difference in band intensity

suggested that AldoB expression was highest in hummingbird liver and lowest in hummingbird

pectoralis, although a loading control was not successfully visualized (Table 10). Hummingbird

liver had a band intensity of over 16.7 times that of hummingbird pectoralis. Intensity was

second-most abundant in mouse liver, which was over 2.9 times more intense than chicken

pectoralis. Intensity in the liver of hummingbird was over 1.5 times greater than in the liver of

mouse, and intensity in chicken pectoralis was over 3.5 times greater than in hummingbird

pectoralis. These results supported Q-PCR data, which had indicated minimal AldoB expression

in pectoralis. This suggests that, for AldoB, relative mRNA and proteins levels among tissues are

positively correlated.

For both western blots, antibodies were successfully stripped and the membranes were probed

with anti-GAPDH, HRP-conjugated antibody as a loading control. Although Q-PCR results had

raised concerns about the usefulness of GAPDH as a housekeeping gene in this system, this

antibody had previously been successfully used as a loading control for ruby-throated

hummingbird protein (Welch et al., 2013), eliminating the possibility of having to test multiple

40

antibodies to find one that cross-reacted with hummingbirds. No bands were detected using this

antibody, and to rule out the possibility of protein damage during stripping and to test the

viability of the antibody, a new membrane was prepared and treated with anti-GAPDH prior to

any other antibody treatment or stripping. As before, no bands were detected, suggesting that this

batch of antibody was either not binding or not maintaining HRP activity. The procedure was

repeated with the unconjugated version of this antiobody (Abcam ab9484) and a secondary

antibody, but the result was the same. The experiment was then repeated using an anti-actin

primary antibody as a loading control, but this failed to cross-react with chicken or hummingbird

protein.

To test for cross-reactivity of the Protein StrepTactin-HRP Conjugate (Bio-Rad) used to

visualize the ladder, the experiment was repeated without including the conjugate along with the

secondary antibody. Non-specific bands at high molecular weights were not present in the

absence of this conjugate. In particular, the anti-AldoB antibody bound to only a single band in

the absence of ladder visualization, suggesting that this antibody was highly specific to the

intended target, AldoB. Band visualization at the expected size range of the target proteins was

not affected by the presence of the ladder conjugate.

41

Chapter 4 Discussion

1 Impact of the Expression of Reference Genes on Relative Quantification

The validity of relative quantification of mRNA is dependent on the consistent expression of a

housekeeping gene in all tissues of study. The data I collected suggested at least one of the two

housekeeping genes varied in expression across tissues, particularly for pectoralis (Table 8 and

Table 9). Before drawing quantitative conclusions, this variation in housekeeping gene

expression needed to be addressed. Which gene, if either, could be relied upon for normalization

had to be carefully considered.

Normalization to GAPDH yielded lower values for relative expression of both GLUT1 and

GLUT5 in pectoralis (Figure 23 and Figure 24). This may be due to the high metabolic activity

of this muscle. Maximal hexokinase activity in rufus hummingbird (Selasphorus rufus) pectoralis

is 18.4 µmol/min per gram of wet tissues, compared to only 2.2 µmol/min per gram of wet

tissues in rat soleus (Suarez et al., 2009b). Such high HK activity is indicative of very high

glycolytic activity in hummingbird pectoralis. Pectoralis had a higher ratio of Elf1a1/GAPDH

than any other tissue (0.972 vs. 0.955 to 0.967, and 1.159 vs 1.009 to 1.090), as seen in Table 8.

During normalization to a housekeeping gene, a smaller Ct value for the housekeeping gene

results in a larger calculated quantity of the gene of interest. A large ratio of Elf1a1/GAPDH is

indicative of either an especially high abundance of GAPDH transcript or an unusually low

abundance of Elf1a1 transcript. The question is: Which one is it? The physiology of avian

pectoralis, as well as previous attempts to validate these two housekeeping genes, leads one to

suspect the former – that pectoralis is especially abundant in GAPDH transcript.

Fast oxidative glycolytic muscle fibers exhibit properties intermediate between fast glycolytic

(white) and slow oxidative (red) muscle fibers (Welch and Altshuler, 2009). They are able to

achieve rapid muscle contraction yet are resistant to the onset of fatigue (Welch and Altshuler,

2009). Little wonder then that hummingbird pectoralis, which must maintain incredibly high

contraction frequencies for extended bouts of hovering, is uniformly composed of such fibers. In

order to supply pyruvate at rates high enough to satisfy oxidative demand, glycolysis is very

active in these muscles (Suarez et al., 1986).

42

GAPDH may be an inappropriate housekeeping gene for flight muscle, given that it has been

shown to vary temporally in expression in chicken pectoralis (Doherty et al., 2004). Chicken

(Gallus gallus) muscle is primarily composted of fast twitch glycolytic fibers (Doherty et al.,

2004). A number of chicken glycolytic enzymes have been found to vary temporally in density,

with GAPDH showing the most marked variation (Doherty et al., 2004). GAPDH in layer

chicken pectoralis has been shown to rise over 40 times in protein concentration during the first

30 days of a chick’s life (Doherty et al., 2007).

Another concern regarding GAPDH comes from likely variation in absolute levels of GAPDH in

the pectoralis of different breeds of chickens. Other enzymes of glycolysis have been linked to

the density of muscle fibers of chicken pectoralis. Among breeds of chicken, triose phosphate

isomerise, phosphoglyceromutase, and phosphofructokinase 2 have been shown to be more

concentrated in “tougher,” stronger pectoralis muscles (Mekchay et al., 2010). Overall, studies of

chicken proteomics suggest the possibility that capacity for glycolysis may be a marker for flight

muscle development and strength (Doherty et al., 2007; Mekchay et al., 2010; Teltathum and

Mekchay, 2009). Caution must be taken when extrapolating proteomics to other organisms and

to transcriptomics, but unfortunately such data for birds is currently only abundant for chicken.

In light of the unique aerobic feats accomplished by hummingbirds, it is reasonable to

hypothesize that their primary flight muscles may express glycolytic enzymes, including

GAPDH, at comparatively high levels, impacting the appropriateness of GAPDH as a

housekeeping gene for comparisons including this particular tissue. Any variation among tissues

in GAPDH units per total protein, as is seen when comparing the aforementioned enzymes

among breasts from different breeds of chicken, could invalidate GAPDH as a housekeeping

gene for avian pectoralis.

GAPDH transcript has been shown to be abundant in human skeletal muscle relative to other

tissues as well. In human skeletal muscle, GAPDH copy number is nearly twice as high as liver,

brain, or small intestine, nearly four times greater than in the pelvis of the kidney, and is twice or

more that of heart, depending on the part of heart in question (Barber et al., 2005).

Relative KHK and AldoB expression in kidney varied greatly depending on whether it was

normalized to Elf1a1 or GAPDH (Figure 32 and Figure 33). In contrast to pectoralis, kidney has

been shown to express low copy numbers of GAPDH relative to other tissues in humans,

43

including human pectoralis (Barber et al., 2005). Low relative copy number of GAPDH in

hummingbird kidney may account for increased calculated relative expression of KHK and

AldoB when normalized to GAPDH rather than to Elf1a1.

In many systems across a variety of organisms, Elf1a1 has been shown to be among the most

stable of potential reference genes (eg., Henn et al., 2013; Nakayama et al., 2014; Nicot et al.,

2005; Rodrigues et al., 2014). In particular, Hsiao et al. (2001) demonstrated Elf1a1 to be one of

the most stably expressed reference genes across a variety of human tissues, including heart,

brain, muscle, liver and kidney. While no reference gene is perfect across all tissues (Yijuan,

2015), available literature gives far less reason to suspect that observed variation in expression of

my housekeeping genes was in Elf1a1 rather than GAPDH. For these reasons, further discussion

will focus upon data normalization to Elf1a1 expression levels.

2 Facilitated Diffusion of Glucose Across the Sarcolemma may be Very Rapid

2.1 GLUT1 Relative Transcript Expression in Hummingbird Muscles may exceed that of Other Vertebrates Examined

Qualitative among-tissue comparisons have been made for GLUT1 mRNA expression levels in

pigs, cows, cod, and also sparrows (Aschenbach et al., 2009; Hall et al., 2014; Sweazea and

Braun, 2006; Zhao, Glimm, and Kennelly, 1993). In pigs, GLUT1 in skeletal muscle was hardly

detectable compared to a strong signal in liver. The pig duodenum also had a stronger GLUT1

signal than did muscle (Aschenbach et al., 2009). In cows there was not sufficient resolution to

distinguish among tissues (Zhao, Glimm, and Kennelly, 1993). Quantitative GLUT1 mRNA

levels were comparable between the aforementioned four tissues in Atlantic cod (Hall et al.,

2014). Finally, sparrows had a weak signal in pectoralis relative to brain, but a stronger signal in

gastrocnemius than in pectoralis (Sweazea and Braun, 2006).

I observed much higher average GLUT1 transcription in muscle samples than in liver (Figure 23:

A and Figure 39). Such high levels of GLUT1 mRNA suggest that hummingbird muscle has

evolved elevated capacities for glucose transport into the sarcoplasm in support of its rapid

oxidation, which is corroborated by previous findings (Chen and Welch, 2014).

44

In each individual bird analyzed both pectoralis and ankle muscle tissue had more GLUT1

transcript than did liver. This is in stark contrast to mammals and sparrows, where much more

GLUT1 mRNA is seen in liver than in muscle (Aschenbach et al., 2009; Sweazea and Braun,

2006; Zhao, Glimm, and Kennelly, 1993), and in contrast to cod, where GLUT1 mRNA levels

are similar between muscle and liver (Hall et al., 2014). Although GLUT1 expression in muscle,

as normalized to Elf1, was not significantly different from that in liver and intestine, it was also

not significantly different from the expression in heart and brain, where GLUT1 transcript

abundance is highest in mammals (Zhao and Keating, 2007). This suggests that expression of

GLUT1 transcript may be greater in hummingbird muscle than in liver and intestine but sample

size may be limiting. Qualitatively, the differences in average expression are, nonetheless,

apparent and compelling. The qualitative hummingbird pattern of much higher GLUT1

expression in pectoralis than in liver is the opposite of what is seen in other vertebrates such as

pigs and sparrows, strongly suggesting that GLUT1 is upregulated in hummingbird muscle

relative to other vertebrates (Figure 39; Aschenbach et al., 2009; Sweazea and Braun, 2006).

2.2 GLUT1 may play a Novel Role in Systems Lacking GLUT4

In nectarivorous mammals such as nectar bats, glucose uptake into muscle is enhanced by

upregulation of GLUT4 relative to other vertebrates. Indeed, there is preliminary evidence to

suggest that GLUT4 is upregulated in nectar bat muscle relative to other vertebrate muscles

(Lee-Young et al., unpublished). Birds lack GLUT4 and therefore lack an insulin-mediated

means of blood sugar regulation (Carver et al., 2001; Braun and Sweazea, 2008; Seki et al.,

2003; Sweazea and Braun, 2006; Welch et al., 2013). As such, hummingbird upregulation of

glucose flux cannot be explained by the same mechanisms used by mammals.

Although a great deal of variation was seen in muscle GLUT1 levels among individuals (Figure

23: A), it is qualitatively clear that hummingbird muscles have the capacity to transcribe high

levels of GLUT1, and this may facilitate observed rapid glucose uptake and subsequent oxidation

in vivo (Welch and Chen, 2014). How birds regulate blood sugar remains to be characterized,

and, in this context, the observation that muscle GLUT1 transcription is highly variable is a

tantalizing finding.

The relatively high level of variation in pectoralis and ankle GLUT1 transcript levels seen among

biological replicates may be indicative of regulation of gene expression in response to

45

environment or behaviour (Figure 23: A). Several growth factors, thyroid hormones, protein

kinase C, and AMP-activated protein kinase (AMPK) have been suggested to stimulate GLUT1

transcription in some mammalian tissues (Ismail-Beigi, 1993). GLUT1 transcript expression is

upregulated in response to prolonged hypoxia in mammalian models (Ismail-Beigi, 1993; Zhang

et al., 1999). While these are not necessarily stimuli encountered on a daily basis in most

mammals, they do demonstrate the presence of preexisting mechanisms for the control of

GLUT1 transcription in response to changing conditions. Which of these mechanisms, if any, are

related to high inter-individual variation in hummingbird muscle GLUT1 transcript density

remains to be determined.

It is interesting that GLUT1 transcription is high in not just the pectoralis, but also the ankle

muscles (Figure 23: A). Given that birds are not known to express GLUT4 (Carver et al., 2001;

Braun and Sweazea, 2008; Seki et al., 2003; Sweazea and Braun, 2006; Welch et al., 2013), it

may be that all of their muscles need a relatively high density of GLUT1 to achieve sufficient

rates of glucose uptake. Welch et al. (2013) demonstrated that GLUT3 mRNA, another glucose

transporter, is also present in hummingbird muscle, though quantification of transcript level was

not attempted. Quantification of GLUT3 mRNA and comparison to GLUT1 would be a very

interesting future study.

If mRNA quantity is indicative of protein quantity for this gene in this tissue, another possibility

is that the lower relative GLUT1 expression in pectoralis than in ankle may be due to the

reallocation of cellular resources in pectoralis toward high expression of another GLUT. This,

however, remains to be investigated.

Unlike mammals, birds have nucleated erythrocytes (Stier et al., 2013), and thus would be

expected to transcribe mRNA, including GLUT1. As GLUT1 protein is relatively abundant in

non-nucleated mammalian erythrocytes (Carruthers et al., 2009; Welch et al., 2013) one would

expect relatively high transcript levels in avian erythrocytes. Muscles, especially flight muscles,

are highly vascularized (Mathieu-Costello, 1994), and during dissection the quantities of blood

included during muscle sampling may have varied.

Even taking blood into consideration, all of the tissues I sampled were highly vascularized, yet

not all tissues had such great variation in GLUT1 mRNA levels. It is difficult to convince oneself

46

that blood alone could have accounted for so much variation in some highly vascularized tissues,

but not others, such as the liver.

3 Capacity for Facilitated Diffusion of Fructose across the Hummingbird Pectoralis Sarcolemma may be Among the Highest in Vertebrate Muscles

3.1 Relative Expression of GLUT5 Transcript in Flight Muscle is among the Highest Observed in Vertebrate Muscles

Inter-tissue comparisons of GLUT5 transcription have been made in rats, pigs, and cows

(Aschenbach et al., 2009; Rand et al., 1993; Zhao, Glimm, and Kennelly, 1993). In these

mammals, GLUT5 transcription in skeletal muscle was minimal in comparison to very strong

signals in both intestine and kidney (Aschenbach et al., 2009; Rand et al., 1993; Zhao, Glimm,

and Kennelly, 1993). The quantity of GLUT5 transcript that I observed in hummingbird

pectoralis was approximately half that seen in intestine (Figure 24: A and Figure 40). Relative to

intestine, GLUT5 transcript was more abundant in hummingbird flight muscle than in the

muscles of mammals examined (Figure 40).

I observed GLUT5 transcription levels in pectoralis that were much greater than in any other

tissue I investigated except for whole intestine (Figure 24: A). The level of relative GLUT5

transcription in hummingbird flight muscle is beyond what has been observed in any other

animal in any tissue other than intestine or kidney. Taken together with previously published

respirometry and stable isotope tracking data (Chen and Welch, 2014), GLUT5 expression data

suggest that the primary flight muscle of the ruby-throated hummingbird possesses the greatest

relative capacity for uptake of fructose observed in skeletal muscle tissue.

Although fructose uptake from the intestinal lumen of hummingbirds is known to occur readily

by paracellular transport, cellular mediated transport still plays a major role (Karasov et al.,

1986). The hummingbird gut is under selective pressure to absorb as much sugar as possible

from the diet as rapidly as possible. Rapid uptake is maximized through the use of both

paracellular and cellular transport, and the observation of such high GLUT5 transcript density in

intestine (Figure 24: A) is evidence of an intestinal epithelium that is adapted to very rapid

uptake of fructose.

47

The observation of very high GLUT5 mRNA expression in pectoralis relative to other tissues is

exciting evidence of high rates of fructose transport in flight muscle. Under all normalization

strategies used, only intestine, which served as my positive control for GLUT5 mRNA, had

higher average levels of GLUT5 than did pectoralis. This is consistent with previous findings

that hummingbirds do not seem to differentiate between glucose and fructose when it comes to

maintaining hovering flight and maximum metabolic activity (Chen and Welch, 2014). If protein

quantity is similar to transcript abundance in these tissues, then it appears that transport rate from

the blood, across the cellular membrane of the pectoralis, may not be rate limiting to fructose

oxidation. Furthermore, Welch et al. (2013) have shown that GLUT2 is absent in hummingbird

muscle; therefore, it is reasonable to hypothesize that the observed high level of GLUT5

transcript, and thus presumably protein levels, support proposed high rates of fructose uptake

into hummingbird muscle (Welch and Chen, 2014).

The difference between GLUT5 mRNA expression in flight versus non-flight muscle was

compelling (Figure 24: A). When normalized to Elf1a1, expression in pectoralis was

significantly greater than that of the ankle muscle group. This difference remained qualitatively

clear when normalized to GAPDH. It is not surprising to observe such contrast between flight

and non-flight muscle, given the enormous energetic needs of the former, versus those for the

metabolically less costly activity of perching. The lack of such high GLUT5 mRNA expression

in ankle musculature suggests that high expression in the pectoralis is a direct response to the

energetic demands of hovering flight and a nectarivorous, fructose-rich diet.

I must acknowledge that reliable sequencing results were not obtained for GLUT5 or GLUT1,

and that sequence read quality for GAPDH and Elf1a1 was very low; however, amplicon product

sizes were exactly as expected and high expression of GLUT1 or GLUT5 was seen in tissues

where these proteins are most abundant in most other animals. Based on available evidence I

have no reason to believe that my primers did not amplify their intended templates.

Direct Sanger sequencing of small products (100bp and less) is possible in theory, but very

difficult in practice. Sequencing data is often of very poor quality for the first 100 base pairs or

so of a read (The Gene Pool, 2009) and my products ranged from 70 to 116 base pairs in size

(Table 6). This problem can occur when secondary structures form in a template and is

compounded by any primer dimers that form, creating noise during the initial part of the

48

sequence read (The Gene Pool, 2009). This is less of a problem for a long sequence, but for such

short sequences, primer dimer sequencing would obstruct a large portion of the sequence read.

To further confirm amplicon identities through sequencing in future, an amplicon could be

cloned into a larger vector in order to work around the problem of low sequencing quality early

on in a sequencing reaction. Use of a commercial vector would also allow commercially

available primers to be used for sequencing, potentially reducing the probability of any primer

dimer formation.

3.2 Relation of Findings to Other Nectarivores

What is perhaps even more exciting is that this adaptation may not be limited only to

hummingbirds. Much like hummingbirds, nectarivorous bats maintain hovering flight and feed

on and rapidly metabolize nectar of high fructose content (Welch and Chen, 2014; Welch et al.,

2008; Suarez et al., 2011; Voigt and Speakman, 2007). High GLUT5 transcription in flight

muscles may prove to be a widespread adaptation to hovering flight on a high-fructose diet.

Moreover, hovering flight is the most energetically expensive form of exercise (Welch and Chen,

2014). In a broader sense, high GLUT5 transcription in active muscles, at least those lacking

GLUT4, may be a fundamental adaptation to energetically demanding exercise on a high-

fructose diet.

4 KHK and AldoB appear Upregulated in Some Hummingbird Tissues Relative to other Vertebrates, but not in Muscle

4.1 KHK and AldoB Transcript Levels in Flight Muscle are Surprisingly Low

While hexokinase phosphorylation rates have been estimated for glucose, fructose

phospohorylation rates have never been measured in hummingbirds (Suarez et al., 2009a).

Relative quantification of KHK and AldoB transcript gives us our first glimpse into the pathways

of fructose breakdown in hummingbirds (Figure 32: A and Figure 33: A).

Contrary to my hypothesis, the transcripts of KHK and AldoB, enzymes that are rate-limiting to

the pathway typically considered to metabolize most dietary fructose (Bode et al., 1980), were

49

expressed at very low relative levels in muscle. AldoB Q-PCR results were confirmed at the

protein level (Figure 38), barring the absence of a loading control. Further, there was no

discernible difference in transcript quantity between muscle types, which is in stark contrast to

GLUT5 results (Figure 24: A).

That AldoB protein expression appeared to be lower in the pectoralis of hummingbirds than in

that of chicken (Figure 38) suggests that these enzymes may in fact be down-regulated relative to

the muscles of other birds; however, this result is uncertain in the absence of a loading control.

Hummingbird muscles do not appear to fuel hovering flight by employing the fructolysis

pathway in flight muscle, but fructose may be phosphorylated within flight muscle via another

pathway. High GLUT5 transcript density in pectoralis suggests that uptake capacity of fructose is

likely relatively high. It would be very puzzling, and likely detrimental, for large volumes of

fructose to accumulate in flight muscle and not be catabolized.

KHK protein expression data either supporting or rejecting the above conclusions based on

transcript levels remains to be successfully produced. While cross-reactivity of anti-KHK

antibody with hummingbird tissue was never guaranteed, a band of expected size was never

observed in mouse liver positive control (Figure 36). After I communicated the procedure and

observed problems with the manufacturer (Abcam), representatives indicated that the stock of

anti-KHK may have been faulty. They also offered to provide a new batch of antibody if the

procedure continues to fail with minor modifications. A KHK western blot may be attempted in

future using new stock of the antibody. Given the stark contrast between KHK transcript

expression in hummingbird muscle and liver, the lack of protein expression data is not sufficient

to detract from my conclusion that most fructose in hummingbird muscle is not processed

through the fructolysis pathway.

Anti-GAPDH/HRP loading control western blotting was unsuccessful in all samples. Given that

the antibody and protocol have previously been used successfully on ruby-throated hummingbird

protein (Welch et al., 2013), the cause may lie with the stock of antibody used. Prior to this

study, anti-GAPDH/HRP had been stored at -20ºC for a prolonged period. Abcam has found that

HRP-conjugated antibodies are not stable when subjected to freeze-thaw cycles, losing

significant activity after even one to two cycles (Abcam). It is possible that this antibody was

damaged by improper storage, and AldoB western blots, normalized with new fresh stock of

50

anti-GAPDH loading control, will be performed in future. It is unfortunate that a loading control

was not successfully visualized; however all protein samples had been diluted to the same

approximate concentration using a BCA assay. If levels of protein expression were very similar

between tissues, lack of a loading control could be a serious problem, but given the large

observed difference in expression of AldoB between liver and pectoralis (Figure 38), slight

variations in whole protein concentration would not be expected to skew the overall conclusion.

We know, however, that fructose is somehow being rapidly metabolized in order to fuel hovering

flight, likely within the actual flight muscle (Chen and Welch, 2014).The question of what

enzymes could be accomplishing this feat remains unanswered.

4.2 Fructolysis in Hummingbird Liver is at Least as Abundant as it is in Mammalian Liver

The observation of high KHK and AldoB transcript levels in liver was expected and is consistent

with observations in other vertebrates (Figure 32: A and Figure 33: A; Cirillo et al., 2009; Cook

and Jacobson, 1970; Mayes, 1993). In most vertebrates, the liver is responsible for processing

fructose into other forms of fuel (Cirillo et al., 2009). In humans and other mammals, the bulk of

fructose taken up by the liver is converted into glucose, lactate, and lipid (Cook and Jacobson,

1970). Even though the hummingbird system is one where skeletal muscle is hypothesized not to

discriminate between glucose and fructose, one would expect these birds to have multiple

systems of dealing with such high quantities of ingested sugar.

In the context of life history, finding that AldoB protein is more highly expressed in

hummingbird liver than mouse liver (Table 10) is consistent with the idea that a fructose-rich

diet would necessitate relatively high fructolytic capacity in the hummingbird liver. The diet of

mice contains a very small proportion of fructose in comparison to that of hummingbirds,

reducing the overall demand on the fructolytic pathway. Normalized to total protein via

bicinchroninic acid assay, hummingbird liver has even more AldoB protein than mouse liver.

While keeping in mind the clear difference in band intensity in samples diluted according to a

BCA assay, it must be noted that this result does not yet have a loading control. Acknowledging

this limitation, the stark difference in band intensity suggests protein expression consistent with

the adaptation of these birds to a very high load of fructose intake and the need for fat

accumulation prior to nighttime or migration (Carpenter and Hixon, 1988). Even if the flight

51

muscles may be metabolizing large quantities of fructose by a different enzymatic means, one

would expect to observe upregulation of fructose metabolism in all tissues involved in

homeostasis and circulation of metabolites given the unusual circumstance of roughly half of

ingested calories being in the form of fructose (Baker, Baker, and Hodges, 1998).

4.3 Fructose Catabolism Starts at the Intestine

While low in comparison to liver and kidney, intestine showed higher transcript levels of KHK

and AldoB than muscle, heart or brain (Figure 32: A and Figure 33: A). Such a finding is in

keeping with the minor, but significant, role of intestine as a fructose-processing organ in most

animals (Adelman et al., 1967; Bjorkman et al., 1984; Froesh and Ginsberg, 1962). Very high

relative GLUT5 transcript quantity in hummingbird intestine, in addition to large gaps between

enterocytes (Caviedes-Vidal et al., 2007), indicate that fructose is available in this tissue at high

concentration before it travels anywhere further in the bird. It can be argued that fructose

processing at this location is an economical start to dietary fructose metabolism throughout a

hummingbird.

4.4 A High Concentration of Fructose may reach Hummingbird Kidneys

High quantities of KHK and AldoB transcripts in kidney was not an unexpected finding (Figure

32: A and Figure 33: A). Following absorption of most fructose by the liver in mammalian

models, most of the remainder is taken up and metabolized by the kidney to save it from being

lost in urine (Bjorkman and Felig, 1982; Cirillo et al., 2009). A novel observation was that these

transcripts were far more abundant in kidney than in liver. In contrast, among mammals studied,

both of these enzymes exhibit much higher activity in liver than in kidney (Adelman et al., 1967;

Bjorkman and Felig, 1985; Cirillo et al., 2009). In rats, KHK activity in kidney is only about

25% that of liver (Adelman et al., 1967) and in humans, kidney accounts for only up to 20% of

removal of fructose from the blood, with the liver handling the majority of fructose removal

(Bjorkman and Felig, 1982).

Comparatively low expression of fructolysis-related transcripts in the liver relative to the kidney

may indicate that the hummingbird kidney has adapted to process or recirculate as much fructose

as possible, rather than have much be excreted in urine, which is produced very frequently in

52

these birds (Calder and Hiebert, 1983). Seemingly high fructolytic activity in the hummingbird

kidney may serve, in part, to convert excess fructose into other metabolites, such as glucose,

prior to recirculation. Adaptation of the kidney to high fructose load reinforces the notion that

hummingbirds have had to modify and adapt multiple different systems in order to process the

fructose content of their diet. The question of how hummingbirds manage high concentrations of

blood fructose is unlikely to have a simple answer. It is, however, significant that fructose

concentrations in hummingbird blood may be great enough to warrant high expression of

fructolytic enzymes in the kidney. In humans, little fructose is expected to escape the liver into

general circulation, thus finding itself in the kidney (Cirillo et al., 2009).

5 Is There a New Role for Hexokinases in Hummingbirds?

A possible candidate for fructose phosphorylation in hummingbird muscle is an HK. While HK

affinities for fructose have always been found to be very low compared to glucose in most

animals (Cardenas et al., 1997), fructose cannot be metabolized if it is not first phosphorylated

(Cirillo et al., 2009). Ruling out phosphorylation by KHK leaves the HK family of enzymes as a

possible candidate. Hummingbird blood fructose concentrations may be high enough to saturate

HK despite the high Km, although these concentrations remain unmeasured. Alternatively,

hummingbirds may prove to possess a HK with high binding affinity for fructose.

No HK isoform has previously been identified with high affinity for both sugars (Cardenas et al.,

1997). Vertebrate HKs have always been found to be “generalist” kinases that can phosphorylate

glucose, mannose, 2-DG, and fructose, but with a much stronger affinity for glucose than for

other sugars. HKs with high affinity for fructose or other non-glucose sugars are only known in

bacteria (Cardenas et al., 1997). It would be fascinating to discover that hummingbirds have

evolved a new and unique isoform of HK with a high affinity for fructose at the cost of a lower

affinity for glucose.

The generation of a new HK geared toward fructose is not implausible in the context of the HK

gene family’s history of duplication, fusion, duplication and fusion again, and more duplication,

to create HKs of a variety of sizes and binding affinities (Cardenas et al., 1997). Recently, the

50kDa glucokinase has been postulated to have occurred by exon deletions of a 100kDa

hexokinase, resulting in an even more complex gene family tree (Irwin and Tan, 2014). More

53

recent duplications have been discovered since the sequencing of the human genome. The

generation of hexokinase domain containing 1, the fifth vertebrate HK, serves uncharacterized

functions, yet the gene is conserved among vertebrate genomes (Irwin and Tan, 2014). Yet

another duplication is known in some primates, which has generated a pseudogene that is a

reversed version of the HKB gene. This duplication is thought to have occurred recently in

primate evolution, demonstrating that such conserved HK duplications are by no means limited

to early eukaryote evolution (Irwin and Tan, 2014). A more fructose-specific isoform of HK

would complement HK1-HK3 to create a system in which HKs rapidly phosphorylate both

glucose and fructose within the same tissue.

While it is surprising that hummingbird muscle may rapidly phosphorylate fructose without the

aid of high densities of rate-limiting fructolytic enzymes, the existence of a unique HK isoform

is, in itself, an exciting possibility fully deserving of further investigation. Although the Anna’s

hummingbird genome is available, it is thus far incompletely annotated, and the existence of an

as yet unannotated HK isoform in the genome cannot be ruled out.

6 A New Synthetic Model of Sugar Flux in Hummingbirds

Capacity for facilitated transport of glucose out of the intestine is the highest measured in

vertebrates (Karasov et al., 1986), suggesting that this step is not rate-limiting for glucose. The

observation of high GLUT5 transcript abundance in the intestine suggests that facilitated fructose

uptake from the gut is very rapid. Taken together with evidence of rapid paracellular transport

(Karasov et al., 1986), glucose and fructose uptake by blood from the gut does not appear to be

the rate-limiting step in sugar oxidation, at least in the case of fructose.

As has been discussed, the supply of exogenous sugar to the sarcolemma of hummingbird flight

muscle is enhanced by a number of different adaptations and this step is unlikely to be rate-

limiting to sugar flux (Step 1: Figure 41; Bishop, 1997; Mathieu-Costello et al., 1992; Vock et

al., 1996). At present, it is not known whether calculated time-averaged rates of sugar oxidation

in hummingbirds (Welch and Chen, 2014) are supported by sugar uptake rates high enough to

match hovering oxidation rates in real time (Step 2: Figure 41). If this is the case, then hovering

powered exclusively by carbohydrate metabolism could, in theory, be supported for as long as

food intake is maintained. Alternatively, the time-averaged rates of sugar oxidation (Chen and

54

Welch, 2014) may mask glycogen buffering, wherein sugar uptake is limiting to rates of sugar

oxidation during hovering, and a bird must alternate hovering bouts with periods of perching to

allow for glycogen resynthesis (Suarez et al., 1990).

Relative GLUT1 transcription in hummingbird muscle exceeded that of the liver, which is in

stark contrast to known mammalian, avian, and fish models (Aschenbach et al., 2009; Hall et al.,

2014; Sweazea and Braun, 2006; Zhao, Glimm, and Kennelly, 1993). Future increases to sample

size may strengthen this clear, but qualitative, finding. Based on this observation I propose that

rapid facilitated uptake of glucose is enhanced by high sarcolemmal densities of GLUT1

transporters (Step 2: Figure 41). Whether this uptake is further enhanced by high GLUT3 density

remains to be investigated.

With regard to fructose, the observation of very high transcript level for GLUT5 in pectoralis

relative to other tissues suggests that facilitated diffusion may not be a rate-limiting step in

hummingbird fructose oxidation (Step 2: Figure 41). I have observed relative GLUT5 transcript

levels in hummingbird flight muscle that, relative to other tissues, far exceeds what has been

suggested in other animals (Aschenbach et al., 2009; Rand et al., 1993; Zhao, Glimm, and

Kennelly, 1993). From this observation I propose that rapid uptake of fructose is accomplished

by a high sarcolemmal of GLUT5 transporters in flight muscle.

Phosphorylation of glucose by hexokinase has been calculated to be rapid enough to keep up

with mitochondrial sugar oxidation (Step 3: Figure 41; Suarez et al., 2009a), but the mechanism

of suspected rapid fructose phosphorylation remains elusive. From my observations of very low

relative KHK and AldoB transcript in hummingbird flight muscle, as well as low AldoB protein

expression in this muscle, I can conclude that fructose phosphorylation in flight muscle is not

enhanced by high densities of fructolytic enzymes. I suggest that fructose may instead be rapidly

phosphorylated not by KHK, but rather by an isoform of HK, and subsequently processed

through glycolysis. The presence of an HK isoform in flight muscle with high affinity for

fructose may enhance phosphorylation of fructose in flight muscle (Step 3: Figure 41).

Given that control of sugar flux is distributed across multiple steps in humans and other

mammals (Figure 1), one would expect that each of these steps would be enhanced in

hummingbirds. While the evidence suggests that fructose is not phosphorylated by KHK in flight

muscle, an alternate mechanism of phosphorylation has not been ruled out. Upregulation is

55

evident throughout the Sugar Oxidation Cascade and the maintenance of food intake may be one

of the only limitations on sustained hovering by these birds.

Future investigation will examine the possible role of HKs in hummingbird fructose catabolism

in flight muscle. This work will be directed toward measuring rates of fructose phosphorylation

in hummingbird tissue homogenates. Experiments will assess fructose phosphorylation kinetics

by KHK, as well as those of fructose phosphorylation by HKs. The different products produced

by the two enzymes will make this possible. While fructose-1-phosphate is produced by KHK,

HK produces fructose-6-phosphate (Figure 2).

My observations support a growing body of evidence that hummingbirds have the highest sugar

uptake rates ever studied, and build upon a story of how these animals meet such incredible

challenges to energy homeostasis.

56

References

Abcam. Abcam Conjugated Antibody Stability Testing Data. PDF file. Retrieved from

http://docs.abcam.com/pdf/protocols/Maximal-performance.pdf

Adelman, R. C., F. J. Ballad, and S. Weinhouse. 1967.”Purification and Properties of Rat Liver

Fructokinase.” The Journal of Biological Chemistry 242(14): 3360-3365.

Aschenbach, J. R., K. Steglich, G. Gabel, and K. U. Honscha. 2009. “Expression of mRNA for

Glucose Transport Proteins in Jejunum, Liver, Kidney and Skeletal Muscle of Pigs.” The

Journal of Physiology and Biochemistry 65(3): 251-266

Bais, R., H. M. James, A. M. Rofe, and R. A. J. Conyers. 1985. “The Purification and Properties

of Human Liver Ketohexokinase. A Role for ketohexokinase and fructose-bisphosphate

aldolase in the metabolic production of oxalate from xylitol.” Biochemical Journal 230:

53-60.

Baker, H. G., I. Baker, and S. A. Hodges. 1998. “Sugar Composition of Nectars and Fruits

Consumed by Birds and Bats in the Tropics and Subtropics.” Biotropica 30(4): 559–586.

Barber, R. D., D. W. Harmer, R. A. Coleman, and B. J. Clark. 2005. "GAPDH as a

Housekeeping Gene: Analysis of GAPDH mRNA Expression in a Panel of 72 Human

Tissues." Physiological Genomics 21(3): 389-395.

Barnes, K., J. C. Ingram, O. H. Porras, E. R. Hudson, L. G. Fryer, F. Foufelle, D. Carling, D. G.

Hardie, and S. A. Baldwin. 2002. “Activation of GLUT1 by Metabolic and Osmotic

Stress: Potential Involvement of AMP-Activated Protein Kinase (AMPK).” Journal of

Cell Science 115(11): 2433-2442.

Bazzoni, G. 2006. “Endothelial Tight Junctions: Permeable Barriers of the Vessel Wall.”

Thrombosis and Haemostasis 95(1): 36-42.

Berg, J. M., J. L. Tymoczko, and L. Stryer. “Biochemistry” 5th

Edition. New York. W. H.

Freeman: Section 16.2.

57

Beuchat, C. A, and C. R. Chong. 1998. “Hyperglycemia in Hummingbirds and its Consequences

for Hemoglobin Glycation.” Comparative Biochemistry & Physiology-Part A: Molecular

& Integrative Physiology 120(3): 409–416.

Bio-Rad. 2006 “Real-Time PCR Applications Guide.” Bulletin 5279 (Revision B).

Bishop, C.M. 1997. “Heart Mass and the Maximum Cardiac Output of Birds and Mammals:

Implications for Estimating the Maximum Aerobic Power Input of Flying Animals.”

Philosophical Transactions B 352(1352): 447-456.

Bjorkman, O., M. Crump, and R. W. Phillips. 1984."Intestinal Metabolism of Orally

Administered Glucose and Fructose in Yucatan Miniature Swine." The Journal of

Nutrition 114: 1413-1420.

Bjorkman, O., and P. Felig. 1982. "Role of the Kidney in the Metabolism of Fructose in 60-Hour

Fasted Humans." Diabetes 6(1): 516-520.

Bode, C., J. C. Bode, W. Ohta, and G. A. Martini. 1980. "Adaptive Changes of the Activity of

Enzymes Involved in Fructose Metabolism in the Liver and Jejunal Mucosa of Rats

Following Fructose Feeding." Research in Experimental Medicine 178: 55-63.

Braun, E. J, and K. L Sweazea. 2008. “Glucose Regulation in Birds.” Comparative Biochemistry

& Physiology Part B: Biochemistry and Molecular Biology 151(1): 1–9.

Brooks, G. A., and J. Mercier. 1994. “Balance of Carbohydrate and Lipid Utilization During

Exercise: The “Crossover” Concept.” Journal of Applied Physiology 76(6): 2253-2261.

Calder, W. A. 1994. “When Do Hummingbirds Use Torpor in Nature?” Physiological Zoology.

67(5): 1051-1076.

Calder, W. A., and S. M. Hiebert. 1983. "Nectar Feeding, Diuresis, and Electrolyte Replacement

of Hummingbirds." Physiological Zoology 56: 325-334.

Cardenas, M. L., A. Cornish-Bowden, and T. Ureta. 1998. "Evolution and Regulatory Role of the

Hexokinases." Biochimica et Biophysica Acata 1401: 242-264.

58

Carpenter, E. L., and Hixon, M. A. 1988. “A New Function for Torpor: Fat Conservation in a

Wild Migrant Hummingbird.” The Condor 90(2): 373-378.

Carruthers, A., J. DeZutter, A. Ganguly, and S. U. Devaskar. 2009. “Will the Original Glucose

Transporter Isoform Please Stand Up!” 297(4): American Journal of Physiology

Endocrinology and Metabolism E836-E848.

Carver F., I. Shibley, J. Pennington, and S. Pennington. 2001. "Differential Expression of

Glucose Transporters During Chick Embryogenesis." Cell and Molecular Life Sciences

58: 645–652.

Chen, C. C. W., and K. C. Welch. 2013. “Hummingbirds Can Fuel Expensive Hovering Flight

Completely with Either Exogenous Glucose or Fructose.” Functional Ecology 28(3):

589-560.

Cirillo, P., M. S. Gersch, W. Mu, P. M. Scherer, K. M. Kim, L. Gesualdo, G. N. Henderson, R. J.

Johnson, and Y. Y Sautin. "Ketohexokinase-Dependent Metabolism of Fructose Induces

Proinflammatory Mediators in Proximal Tubular Cells." Journal of the American Society

of Nephrology 20(3): 545-553.

Cook, G. C., and J. Jacobson. 1971. "Individual Variation in Fructose Metabolism in Man." 26:

British Journal of Nutrition: 187-195.

Caviedes-Vidal, E., T. J. McWhorter, S. R. Lavin, J. G. Chediack, C. R. Tracy, and W. H.

Karasov. 2007. “The Digestive Adaptation of Flying Vertebrates: High Intestinal

Paracellular Absorption Compensates for Smaller Guts.” Proceedings of the National

Academy of Sciences of the United States of America 104(48): 19132-19137.

Deng, D., C. Xu, P. Sun, J. Wu, C. Yan, M. Hu, and N. Yan. 2014. "Crystal Structure of the

Human Glucose Transporter GLUT1." Nature 510(7503): 121- 125.

Diamond, J. M., W. H. Karasov, D. Phan, and F. L. Carpenter. 1986. “Digestive Physiology is a

Determinant of Foraging Bout Frequency in Hummingbirds.” Nature 320(6057): 62-63

Diggle, C. P., M. Shires, D. Leitch, D. Brooke, I. M. Carr, A. F. Markham, B. E. Hayward, A.

Asipu, and D. T. Bonthron. 2009. "Ketohexokinase: Expression and Localization of the

59

Principle Fructose-Metabolizing Enzyme." Journal of Histochemistry and Cytochemistry

57(8): 763-774.

Doherty, M. K., L. McLean, and R. J. Beynon. 2007. “Avian Proteomics: Advances, Challenges

and New Technologies.” Cytogenetic and Genome Research 117(1-4): 358–69.

Doherty, M. K., L. McLean, J. R. Hayter, J. M. Pratt, D. H. L. Robertson, A. El-Shafei, S. J.

Gaskell, and R. J. Beynon et al. 2004. “The Proteome of Chicken Skeletal Muscle:

Changes in Soluble Protein Expression during Growth in a Layer Strain.” Proteomics

4(7): 2082–93.

Douard, V. and R. P. Ferraris. 2008. "Regulation of the Fructose Transporter GLUT5 in Health

and Disease." American Journal of Physiology - Endocrinology and Metabolism 295(2):

E227-E237.

Drozdowsi, L. A., and A. B. R. Thomson. 2006. “Intestinal Sugar Transport.” World Journal of

Gastroenterology 12(11): 1657-1670.

Egert, S., N. Nguyen, and M. Schwaiger. 1999. “Myocardial Glucose Transporter GLUT1:

Translocation Induced by Insulin and Ischemia.” Journal of Molecular and Cellular

Cardiology 31(7): 1337-1344.

Froesch, E. R., and J. L. Ginsberg. 1962. "Fructose Metabolism of Adipose Tissue." The Journal

of Biological Chemistry 237(11): 3317-3324.

Gaster, M., A. Handberg, H. Bech-Nielson, and H. D. Schroder. 2000. "Glucose Transporter

Expression In Human Skeletal Muscle Fibers." The American Journal of Physiology -

Endocrinology and Metabolism 279(3): 529-538.

Gene Pool, the,. 2009. “Sanger Troubleshooting Guide V1.” Edinburgh Genomics. PDF file.

Retrieved from

http://genepool.bio.ed.ac.uk/sanger/Sanger_troubleshooting_guide_v1.pdf.

Guglielmo, C. G. 2010. “Move That Fatty Acid: Fuel Selection and Transport in Migratory Birds

and Bats.” Integrative and Comparative Biochemistry 50(3): 336-345.

60

Hall, J. R., K. A. Clow, C. E. Short, and W. R. Driedzic. 2014. " Transcript Levels of Class I

GLUTs within Individual Tissues and the Direct Relationship Between GLUT1

Expression and Glucose Metabolism in Atlantic Cod (Gadus morhua)." Comparative

Physiology Part B: Biochemistry & Molecular Biology 183: 483-496.

Hazelwood, R. L. 1973. “The Avian Endocrine Pancreas.” American Zoologist 13: 699-709.

Henn D., D. Bander-Risch, H. Perttunen, W. Schmied, C. Porras, F. Ceballos, N. Rodriguez-

Losada, and H. J. Schafers. 2013. "Identification of Reference Genes for Quantitative

RT-PCR in Ascending Aortic Aneurysms." PLoS ONE 8(1): e54132. doi:

10.1371/journal.pone.0054132.

Hoppeler, H. and Weibel, E. R. 1998. “Limits for Oxygen Substrate Transport in Mammals.”

The Journal of Experimental Biology 201(8): 1051-1064.

Hsiao, L. L., F. Dangond, T. Yoshida, R. Hong, R. V. Jensen, J. Misra, W. Dillon, K. F. Lee, K.

E. Clark, P. Haverty, Z. Weng, G. L. Mutter, M. P. Frosch, M. E. MacDonald, E. L.

Milford, C. P. Crum, R. Bueno, R. E. Pratt, M. Mahadevappa, J. A. Warrington, G.

Stephanopoulos, G. Stephanopoulos, and S. R. Gullans. "A Compendium of Gene

Expression in Normal Human Tissues." Physiological Genomics 7(2): 97-104.

Irwin, D. M. and H. Tan. 2014. "Evolution of glucose utilization: Glucokinase and Glucokinase

Regulator Protein." Molecular Phylogenetics and Evolution 70: 195-203.

Ismail-Beigi, Faramaraz. 1993. “Metabolic Regulation of Glucose Transport.” The Journal of

Membrane Biology 135: 1-10.

Jentjens, R. L. P. G., M. C. Venables, and A. E. Jeukendrup 2004. “Oxidation of Exogenous

Glucose, Sucrose, and Maltose During Prolonged Cycling Exercise.” Journal of Applied

Physiology 96(4): 1285–1291.

Joost, H. G., and B. Thorens. 2001. "The Extended GLUT-Family of Sugar/Polyol Transport

Facilitators: Nomenclature, Sequence Characteristics, and Potential Function of its Novel

Members (review)." Molecular Membrane Biology 18(4): 247-56.

61

Kamata, K., M. Mitsuya, T. Nishimura, J. Eiki, and Y. Nagata. 2004. "Structural Basis for

Allosteric Regulation of the Monomeric Allosteric Enzyme Human Glucokinase."

Structure 12(3): 429-438.

Karasov, W. H., and J. M. Diamond. 1988. “Interplay Between Physiology and Ecology in

Digestion. Intestinal Nutrient Transporters Vary Within and Between Species According

to Diet.” Bioscience 38(9): 602-611.

Karasov, W. H., D. Phan, J. M. Diamond, and F. L. Carpenter. 1986. "Food Passage and

Intestinal Nutrient Absorption in Hummingbirds." The American Ornithologists' Union

103(3): 453-464.

Laybutt, D. R., A. L. Thompson, G. J. Cooney, and E. W. Kraegen. 1997. “Selective Chronic

Regulation of GLUT1 and GLUT4 Content by Insulin, Glucose, and Lipid in Rat Cariac

Muscle In Vivo.” American Journal of Physiology 3(2): H1309-1316.

Lopez-Calleja, M. V., and F. Bozinovic. 2003. “Dynamic Energy and Time Budget in

Hummingbirds: A Study in Sephanoides sephanoides.” Comparative Biochemistry &

Physiology Part A: Molecular & Integrative Physiology. 134: 283-295.

Mathieu-Costello, O. 1994. "Morphometry of the Size of the Capillary-to-Giber Interface in

Muscles." Advances in Experimental Medicine and Biology 345: 661-668.

Martinez del Rio, C. 1990. “Dietary, Phylogenetic, and Ecological Correlates of Intestinal

Sucrase and Maltase Activity in Birds.” Physiological Zoology 63(5): 987-1011.

Mathieu-Costello, O., R. K. Suarez, and P. W. Hochachka. 1992. “Capillary-to-Fiber Geometry

and Mitochondrial Density in Hummingbird Flight Muscle.” Respiratory Physiology

89(1):113-132

Mayers, P. A. 1993. "Intermediary Metabolism of Fructose." The American Journal of Clinical

Nutrition: 58(5 Suppl): 754S-765S.

Mekchay, S., T. Teltathum, S. Nakasathien, and P. Pongpaichan. 2010. “Proteomic Analysis of

Tenderness Trait in Thai Native and Commercial Broiler Chicken Muscles.” The Journal

of Poultry Science 47(1): 8–12.

62

Mqokeli, B. R., and C. T. Downs. 2012. “Blood Plasma Glucose Regulation in Wahlberg’s

Epauletted Fruit Bat.” African Zoology 47(2):348-352.

Nicot, N., J. F. Hausman, L. Hoffmann, and D. Evers. 2005. "Housekeeping Gene Selection for

Real-Time RT-PCR Normalization in Potato During Biotic and Abiotic Stress." Journal

of Experimental Botany 56(421): 2907-2914.

Parks, E. J., L. E. Skokan, M. T. Timlin, and C. S. Dingfelder. 2008. “Dietary Sugars Stimulate

Fatty Acid Synthesis in Adults.” The Journal of Nutrition 138(6): 1039–1046.

Patel, C., V. Douard, S. Yu, P. Tharabenjasmin, N. Gao, and R. P. Ferraris. 2015. “Fructose-

Induced Increases in Expression of Intestinal Fructolytic and Gluconeogenic Genes are

Regulated by GLUT5 and KHK." American Journal of Physiology-Regulatory,

Integrative and Comparative Physiology 309(5): 499-509.

Planinsic, G., and M. Vollmer. 2008. “The Surface-to-Volume Ratio in Thermal Physics: From

Cheese Cube Physics to Animal Metabolism.” European Journal of Physics. 20(2): 369-

384.

Polakof, S., T. P. Mommsen, and J. L. Soengas. 2011. “Glucosensing and Glucose Homeostasis:

From Fish to Mammals.” Comparative Biochemistry & Physiology. Part B, Biochemistry

& Molecular Biology 160(4): 123–149.

Postic, C., M. Shiota, and M. A. Maqnuson. 2001. "Cell-Specific Roles of Glucokinase in

Glucose Homeostasis." Recent Progress in Hormone Research 56: 195-217.

Powers, D. R. 1991. "Diurnal Variation in Mass, Metabolic Rate, and Respiratory Quotient in

Anna's and Costa's Hummingbirds." Physiological Zoology 64: 860-880.

Rand, E. B., A. M. Depaoli, N. O. Davidson, G. I. Bell, and C. F. Burant. 1993. "Sequence,

Tissue Distribution, and Functional Characterization of the Rat Fructose Transporter

GLUT5." The American Journal of Physiology 264(6): 1169-76.

Robergs, R. A., F. Ghiasvand, and D. Parker. 2004. “Biochemistry of Exercise-Induced

Metabolic Acidosis.” American Journal of Physiology – Regulatory, Integrative, and

Comparative Physiology 287(3): R502-R516.

63

Rodrigues, T. B., C. Khajuria, H. Wang, N. Matz, D. C. Cardoso, F. H. Valicente, X. Zhou, and

B. Siegfried. 2014. "Validation of Reference Housekeeping Genes for Gene Expression

Studies in Western Corn Rootworm (Diabrotica virgifera virgifera)." PLoS ONE 10(4):

e0124187. doi: 10.1371/journal.pone.0124187.

Schlattner, U., M. Tokarska-Schlattner, and T. Wallimann. 2006 “Mitochondrial Creatine Kinase

in Human Health and Disease.” Mitochondria in Diseases and Therapeutics 1172(2):

164-180.

Schondube, J. E., and C. Martinez del Rio. 2004. "Sugar and Protein Digestion in Flower

Piercers and Hummingbirds: A Comparative Test of Adaptive Convergence."

Comparative Physiology Part B: Biochemistry & Molecular Biology 174: 263-273.

Seki Y., K. Sato, T. Kono, H. Abe, and Y. Akiba. 2003. “Broiler Chickens (Ross strain) lack

Insulin-Responsive Glucose Transporter GLUT4 and have GLUT8 cDNA.” General and

Comparative Endocrinology 133: 80–87.

Shepherd, P. R., and B. B. Kahn. 1999. “Glucose Transporters and Insulin Action--Implications

for Insulin Resistance and Diabetes Mellitus.” The New England Journal of Medicine

341(4): 248–257.

Sholtis, K. M., R. M. Shelton, and T. L. Hedrick. 2015. “Field Flight Dynamics of

Hummingbirds During Territory Encroachment and Defense.” PLos ONE 10(6):

e0125659 doi: 10.1371/journal.pone.0125659.

Stier, A., P. Bize, Q. Schull, J. Zoll, F. Singh, B. Geny, F. Gros, C. Royer, S. Massemin, and F.

Criscuolo. 2013. " Avian Erythrocytes Have Functional Mitochondria, Opening Novel

Perspectives for Birds as Animal Models in the Study of Ageing." Frontiers in Zoology

10: 33.

Stipank, M. H., and Caudill, M. A. 2013. “Biochemical, Physiological, and Molecular Aspects of

Human Nutrition.” 3rd

Edition. St. Louis. Elsevier Health Sciences: 213-219

64

Sturkie, P. D., and A. Abati. 1975. “Blood Flow in Mesenteric, Hepatic Portal and Renal Portal

Veins of Chickens.” Pflugers Archiv-European Journal of Physiology 359(1-2): 127-

135.

Suarez, R. K. 1992. “Hummingbird Flight: Sustaining the Highest Mass-specific Metabolic

Rates Among Vertebrates.” Experientia 48(6): 565–570.

Suarez, R. K. 1986. “Metabolic Sources of Energy for Hummingbird Flight.” American Journal

of Physiology-Regulatory, Integrative and Comparative Physiology 251(3): R537-R542.

Suarez, R. K., L. G. Herrera, and K. C. Welch Jr. 2011. “The Sugar Oxidation Cascade: Aerial

Refueling in Hummingbirds and Nectar Bats.” The Journal of Experimental Biology

214(2): 172–178.

Suarez R.K., K. C. Welch Jr., S. K. Hanna, and M. L. G. Herrera. 2009a. “Flight Muscle

Enzymes and Metabolic Flux Rates during Hovering Flight of the Nectar Bat,

Glossophaga soricina: Further Evidence of Convergence with Hummingbirds.”

Comparative Biochemistry & Physiology A: Molecular and Integrative Physiology. 153:

136–140.

Suarez, R. K., K. C. Welch, S. K. Hanna, and L. G. M. Herrera. 2009b. “Flight Muscle Enzymes

and Metabolic Flux Rates During Hovering Flight of the Nectar Bat, Glossophaga

soricina: Further Evidence of Convergence with Hummingbirds.” Comparative

Biochemistry and Physiology Part A: 153: 136-140.

Suarez, R. K., J. R. Lighton, C. D. Moyes, G. S. Brown, C. L. Gass, and P. W. Hochachka. 1990.

“Fuel Selection in Rufous Hummingbirds: Ecological Implications of Metabolic

Biochemistry.” Proceedings of the National Academy of Sciences of the United States of

America 87(23): 9207–9210.

Suarez, R. K., R. W. Brownsey, W. Vogl, G. S. Brown, and P. W. Hochachka. 1988.

“Biosynthetic Capacity of Hummingbird Liver.” American Journal of Physiology-

Regulation, Integrative and Comparative Physiology 255(5): R699-R702.

65

Sun, S. Z., and M. W. Empie. 2012. "Fructose Metabolism in Humans – What Isotopic Tracer

Studies Tell Us." Nutrition and Metabolism 9(1): 89-103.

Sweazea, K. L., and E. J. Braun. 2006. “Glucose Transporter Expression in English Sparrows

(Passer domesticus).” Comparative Biochemistry & Physiology Part B, Biochemistry &

Molecular Biology 144: 263–270.

Teltathum, Tawatchai, and Supamit Mekchay. 2009. “Proteome Changes in Thai Indigenous

Chicken Muscle during Growth Period.” International Journal of Biological Sciences

5(7): 679–85.

Thorens, B. 2015. "GLUT2, Glucose Sensing and Glucose Homeostasis." Diabetologia 58(2):

221-231.

Thorens, B., and M. Mueckler. 2010. "Glucose Transporters in the 21st Century." American

Journal of Physiology - Endocrinology and Metabolism 298: E141-E145.

Twyman, R. 2013. “Principles of Proteomics.” 2nd

edition. New York. Garland Science Chapter

4: Strategies for Protein Quantification.

Uldry, M., and B. Thorens. 2004. “The SLC2 Family of Facilitated Hexose and Polyol

Transporters.” Pflügers Archiv 447 (5): 480–489.

Vock R., E. R. Weibel, H. Hoppeler, G. Ordway, J. M. Weber, and C. R. Taylor. 1996. “Design

of the Oxygen and Substrate Pathways. V. Structural Basis of Vascular Substrate Supply

to Muscle Cells.” The Journal of Experimental Biology 199(8): 1675-1688.

Voigt, C. C., and J. R. Speakman. 2007. " Nectar-Feeding Bats Fuel their High Metabolism

Directly With Exogenous Carbohydrates." Functional Ecology 21(5): 913-921.

Weber, J. M. 2011. “Metabolic Fuels: Regulating Fluxes to Select Mix.” Journal of

Experimental Biology 214(2): 289-294.

Weber, J. M., G. Brichon, G. Zwingelstein, G. McClelland, C. Saucedo, E. R. Weibel, and C. R.

Taylor. 1996. “Design of Oxygen and Substrate Pathways. IV. Partitioning Energy

Provision from Fatty Acids.” The Journal of Experimental Biology 199(8): 1667-1674.

66

Weibel, E. R., C. R. Taylor, J. M. Weber, R. Vock, T. J. Roberts, and H. Hoppeler. 1996.

“Design of the Oxygen and Substrate Pathways. VII. Different Structural Limits for

Oxygen and Substrate Supply to Muscle Mitochondria.” Journal of Experimental Biology

199(8): 1699-1709.

Welch, K. C., and C. C. W. Chen. 2014. "Sugar Flux Through Flight Muscles of Hovering

Vertebrate Nectarivores: A Review." Comparative Physiology Part B: Biochemistry &

Molecular Biology 184(8): 945-959.

Welch, K. C., A. Allalou, P. Sehgal, J. Cheng, and A. Ashok. 2013. “Glucose Transporter

Expression in an Avian Nectarivore: The Ruby-Throated Hummingbird (Archilochus

Colubris).” PLoS ONE 8(10): e77003 doi:10.1371/journal.pone.0077003.

Welch, K. C., and D. L. Altshuler. 2009. " Flight Muscle Enzymes and Metabolic Flux Rates

during Hovering Flight in the Nectar Bat, Glossophaga soricina: Further Evidence of

Convergence with Hummingbirds. Comparative Biochemistry & Physiology Part A 153:

136-140

Welch, K. C., L. G. Herrera, and R. K. Suarez. 2008. “Dietary Sugar as a Direct Fuel for Flight

in the Nectarivorous Bat Glossophaga Soricina.” Journal of Experimental Biology 211

(3): 310–316.

Willmer, P. 2011. “Pollination and Floral Ecology.” Princeton, N. J. Princeton University Press:

338.

Wilson, J. E. 2003. "Isozymes of Mammalian Hexokinase: Structure, Subcellular Localization

and Metabolic Function." The Journal of Experimental Biology 206: 2049-2057.

Wolf, L. L., and R. Hainsworth. 1971. “Time and Energy Budgets of Territorial Hummingbirds.”

Ecology 52(6): 980-988.

Zhang, J. Z., A. Behrooz, and F. Ismail-Beigi. 1999. “Regulation of Glucose Transport by

Hypoxia.” American Journal of Kidney Diseases 34(1): 189-202.

Zhang Y. and D. Li. In. Prep. "Genome Evolution of Avian Class" NCBI GenBank.

67

Zhang, Y., D. Li, and B. Sun. 2015. "Do Housekeeping Genes Exist?" PLoS ONE 10(5):

e0123691. doi: 10.1371/journal.pone.0123691.

Zhao, F. Q., D. R. Glimm, and J. J. Kennelly. 1993. "Distribution of Mammalian Facilitative

Glucose Transporter Messenger RNA in Bovine Tissues.” The International Journal of

Biochemistry 25(12): 1897-1903.

Zhao, F., and A. F. Keating. 2007. “Functional Properties and Genomics of Glucose

Transporters.” Current Genomics 8: 113-128.

Zierath R. J., L. A. Nolte, E. Wahlstrom, D. Galuska, P. R. Shepherd, B. B. Kahn, and H.

Wallberg-Henriksson. 1995. “Carrier-mediated Fructose Uptake Significantly

Contributes to Carbohydrate Metabolism in Human Skeletal Muscle.” The Journal of

Biological Chemistry 311(2): 517-521.

68

Figures and Tables

Figure 1: Potential rate-limiting steps in mammalian transport of sugar from circulation to the

initiation of oxidation in muscle tissue. Step 1: Transport of circulating sugar to the extracellular

space. Step 2: Facilitated transport across the sarcolemma. Step 3: Phosphorylation of sugar prior

to further catabolism. Individual white hexagons symbolize glucose and blue pentagons

symbolize fructose. Glycogen is indicated by linked glucose hexagons. Contractile stimulation of

translocation is represented by a lightning bolt. Reprinted with permission from Welch and Chen

(2014).

69

Table 1: Glucose transporters — distribution and substrates. (Zhao and Keating, 2007). Main

tissues of expression are restricted to those investigated in this study. Approximate Km values for

human proteins were obtained from Uldry and Thorens (2004). The Km presented for GLUT3

was obtained using 2-deoxyglucose. N/A indicates Km values too high to serve a major function

at physiological concentrations of sugar.

Isoform Km(mM) for

Glucose

Km(mM) for

Fructose

Main Tissues Preferred

Substrate

GLUT1 3 N/A Everywhere Glucose

GLUT2 17 76 Liver, Intestine,

Kidney

Glucose and

Fructose

GLUT3 1.4 N/A Brain Glucose

GLUT4 5 N/A Muscle, Heart Glucose

GLUT5 N/A 6 Intestine, Kidney Fructose

70

Figure 2: Metabolic pathways of glucose and fructose prior to the exergonic reactions of

glycolysis. The endergonic reactions of glycolysis are indicated by red arrows and the reactions

of fructolysis are indicated by purple arrows. The dashed-arrow indicates a reaction that is

unfavourable due to high Km. Adapted by K. Welch from Sun and Empie (2012) under the terms

of the Creative Commons Attribution License.

71

Table 2: Initial primer sets first tested for Q-PCR. Product sizes are presented in base pairs and

refer to expected product sizes. These were determined using Primer-BLAST to compare primers

to the Aves taxid (8782) (NCBI GenBank http://www.ncbi.nlm.nih.gov).

Primer set Forward Sequence Reverse Sequence Product

Size

Elf1a1-1 GTGTCTGTGAAAGATGTTCGCC ATAATGACCTGTGCAGTGAAGC 95

Elf1a1-2 TCAAGGAGAAGATTGATCGTCG CAGGGATCATATCAACGATGGC 96

Elf1a1-3 GCCCCAAATTCCTGAAATCTGG AGAGGAGGATAATCAGAGAAGC 94

GAPDH-1 TGCTGAATATGTTGTGGAGTCC CAGAGATGATCACACGCTTGG 95

GAPDH-2 GTGCTGCTCAGAACATTATCCC AAGCCATTCCAGTAAGTTTCCC 96

GAPDH-3 ATGGGAAACTTACTGGAATGGC ATCGTACTTGGCTGGTTTTTCC 95

Beta-Actin-1 CATGTTTGAGACCTTCAACAC CCATCACAATACCAGTGGTACG 95

Beta-Actin-2 GAAATTGTGCGTGACATCAAGG TTCTCCAGGGAAGAGCTAGAGG 95

Beta-Actin-3 AGAGCTATGAACTCCCTGATGG CATACCCAAGAAAGATGGCTGG 95

GLUT1-1 TGTACGTGGGAGAGGTGTCC CAAACACCTGTGCGATGAGG 99

GLUT1-2 CTTTCTCCTTTGAGATGCTCATCC CACCTCTCCCACGTACATGG 95

GLUT1-3 TCCTTTGAGATGCTCATCCTCG CACCTCTCCCACGTACATGG 90

GLUT5-1 GGCTGGTGTTCCTCTACATGG AAGAAGATGTAAAGCGTGGTGG 94

GLUT5-2 CAGCTTCCTCATCTTCTGTGC GGCCATGATCCTGTTGATTTCC 109

GLUT5-3 CTCATCTTCTGTGCCATCTGC GGCCATGATCCTGTTGATTTCC 102

Table 3: Second batch of GLUT1 primer sets tested for Q-PCR. Product sizes are presented in

base pairs and refer to expected product sizes. These are based on the Calypte anna putative

sequence (NCBI Accession # XM_008503695) (NCBI GenBank http://www.ncbi.nlm.nih.gov).

Primer set Forward Sequence Reverse Sequence Product

Size

GLUT1-4 GGTTCATCTTCGTGCCTGCC TGTTCTCCTCGTTGCGGTTG 96

GLUT1-5 CTTCGTGCCTGCCCTGCTG CTTTGTTCTCCTCGTTGCGGTTG 92

GLUT1-6 TCACCGTCGTGTCGCTCTTT TCCAGCAGTGTCAGGGCAAT 118

GLUT1-7 TCAGTTGGAGGCATGGTTGG TGAGGACAGCAGCCAGGAAG 112

Table 4: GLUT1 and GLUT5 primer sets designed for Q-PCR using Primer-BLAST, Mfold, and

OligoAnalyzer. Product sizes are presented in base pairs and refer to expected product sizes.

These are based on Calypte anna putative sequences (NCBI Accession # XM_008503695.1 and

XM_008503671.1 respectively) (NCBI GenBank http://www.ncbi.nlm.nih.gov).

Primer set Forward Sequence Reverse Sequence Product

Size

GLUT1-8 CCACAGAAGGTGATTGAGGATTTC AACTGAGAAGATGGCAACAGAGAGG 117

GLUT1-9 TCATCAATGCCCCACAGAAGGTG CAACCATGCCTCCAACTGAGAAG 141

GLUT1-10 CACAGAAGGTGATTGAGGATTTCTAC AACTGAGAAGATGGCAACAGAGAG 116

GLUT1-11 CTGACACTGCTGGATCAAATG GCCACAATAAACCAAGGGATG 113

GLUT5-4 TATCAGCATAGTGTGTGTCATCGTT CACCGAAGGGATTGGACTGGC 70

GLUT5-5 CTTTGGGCTGTGCTGTCTCTCCTG CGAAGGGATTGGACTGGCTCCTA 145

GLUT5-6 TTTGGGCTGTGCTGTCTCTCCTGTG CCGAAGGGATTGGACTGGCTCC 145

GLUT5-7 TGCAGTCCTTATGGGAACCTCAGA CACGTTGGAAGCCAGACCAGCATA 100

72

Table 5: KHK and AldoB primer sets designed for Q-PCR using Primer-BLAST, Mfold, and

OligoAnalyzer. Product sizes are presented in base pairs and refer to expected product sizes.

These are based on putative Calypte anna sequences (NCBI Accession # XM_008492930.1 and

XM_008496441 respectively) (NCBI GenBank http://www.ncbi.nlm.nih.gov).

Primer set Forward Sequence Reverse Sequence Product

Size

KHK-1 GGAGCCTATGACCCTGAGACG GGCTGAGCTGTACCCAAAGTG 84

KHK-2 TGATGGGGAGATACTGCACTCG GAAGATGACGGCAGCGTTGAA 94

KHK-3 AGATACTGCACTCGGATGCCT GAAGATGACGGCAGCGTTGA 86

AldoB-1 CTCTTCTCCGCACTGTTCCTG AGCCTCCTCTTCACTCTGACC 74

AldoB-2 GGAGAAACCACCATCCAAGGG ACGCCACTTGCCAAAGTCTAC 87

AldoB-3 AGGCTTCTGTCAACCTGAATGC ACTCTTGCCCAACCATGCTG 119

Table 6: Primer sequences used for Q-PCR. Product sizes are presented in base pairs and refer to

expected product sizes. These are based on putative sequences from Anna’s hummingbird

(Calypte anna) (NCBI GenBank http://www.ncbi.nlm.nih.gov).

Gene Forward Sequence Reverse Sequence Product

Size

Elf1a1 GTGTCTGTGAAAGATGTTCGCC ATAATGACCTGTGCAGTGAAGC 95

GAPDH GTGCTGCTCAGAACATTATCCC AAGCCATTCCAGTAAGTTTCCC 74

GLUT1 CACAGAAGGTGATTGAGGATTTCTAC AACTGAGAAGATGGCAACAGAGAG 116

GLUT5 TATCAGCATAGTGTGTGTCATCGTT CACCGAAGGGATTGGACTGGC 70

KHK TGATGGGGAGATACTGCACTCG GAAGATGACGGCAGCGTTGAA 94

AldoB CTCTTCTCCGCACTGTTCCTG AGCCTCCTCTTCACTCTGACC 74

73

Figure 3: Temperature gradient PCR of Elf1a1 primers. Elf1a1 primer set 1 (A) and primer set 3

(B) were used to amplify ruby-throated hummingbird genomic DNA. DNA is separated on 1%

agarose gel with GelRed and Ready-To-Use DNA ladder (Biotium) (L). Annealing temperatures

are in ºC. Primer set 3 is presented as an example of significant primer dimer amplification

Figure 4: Temperature gradient PCR of GAPDH primers. GAPDH primer set 2 (A) and primer

set 3 (B) were used to amplify ruby-throated hummingbird genomic DNA. DNA is separated on

1% agarose gel with GelRed and Ready-To-Use DNA ladder (Biotium). Annealing temperatures

are in ºC.

74

Figure 5: Temperature gradient PCR of GLUT1 primers. GLUT1 primer set 2 (A) and primer

set 3 (B) were used to amplify ruby-throated hummingbird genomic DNA. DNA is separated on

1% agarose gel with GelRed and Ready-To-Use DNA ladder (Biotium). Annealing temperatures

are in ºC.

Figure 6: Temperature gradient PCR of GLUT5 primers. GLUT5 primer set 2 (A) and primer

set 3 (B) were used to amplify ruby-throated hummingbird genomic DNA. DNA is separated on

1% agarose gel with GelRed and Ready-To-Use DNA ladder (Biotium). Annealing temperatures

are in ºC.

Table 7: RNA yields from ruby-throated hummingbirds using a column extraction (Ambion

Purelink RNA Mini Kit) and a phenol/chloroform extraction method (Tri Reagent, Sigma

Aldrich).

Tissue Column RNA Yield (ng/µl) Tri Reagent RNA Yield (ng/µl)

Pectoralis 3.7 556.8

Ankle Muscles 7.0 216.0

Liver 132.1 4330.0

Heart 6.6 607.4

Brain 26.1 2014.6

75

Figure 7: GLUT5 PCR amplification of cDNA (derived from 0.005 ng/µl of RNA) and genomic

DNA contamination of RNA (0.005 ng/µl) from various tissues of a ruby-throated hummingbird.

GLUT5 is amplified in cDNA and RNA samples of pectoralis (A)/(F), ankle muscle (B)/(G),

liver (C)/(H), heart (D)/(I), and brain (E)/(J). A no template control is included (K), and

hummingbird genomic DNA is used as a positive control (L). DNA is separated on 2% agarose

gel with GelRed and Ready-To-Use DNA ladder (Biotium) (Ld).

Partial Amplicon AGAACGATCCTCCCAYKSGAAGCTSGCARGGCTYCACYGCACARG

||||||||||||||| |||||| ||| |||| ||| ||||| |

Hummingbird AGAACGATCCTCCCA-TGGAAGCT-GCA-GGCTTCACTGCACAGG

Figure 8: Partial alignment of an amplicon produced using Elf1a1 primer and Anna’s

hummingbird Elf1a1 mRNA (NCBI Reference XM_008490615.1). The sequences share 82%

identity. Elf1a1 primer set 1 reverse primer was used and the template was ruby-throated

hummingbird liver cDNA. Average Q-value was less than 20 for base calls during Sanger

sequencing. Some base calls are ambiguous due to poor sequence read quality: Y = C or T, S = G

or C, R = A or G.

Partial Amplicon TTTTCCACAGCCTTAGCAGCCCCAGTAGACGCTGGGATAATGTTCTGAGCAGCACC

||| |||||||||||||||||||||||||||||||||| |||||||||||||||||

Hummingbird TTTCCCACAGCCTTAGCAGCCCCAGTAGACGCTGGGATTATGTTCTGAGCAGCACC

Figure 9: Partial alignment of an amplicon produced using GAPDH primers and Anna’s

hummingbird GAPDH mRNA (NCBI Reference XM_008499176.1). The sequences share 96%

identity. GAPDH primer set 2 forward primer was used and the template was ruby-throated

hummingbird pectoralis cDNA. Average Q-value was less than 20 for base calls during Sanger

sequencing.

76

Figure 10: Representative gel of RNA following extraction but prior to DNase I treatment. The

RNA is separated by 2% agarose gel electrophoresis with GelRed. RNA was sized using Ready-

To-Use 1kb and 100bp DNA Ladders (Biotium) (KBL and BPL, respectively). RNA was

extracted from pectoralis (P), ankle muscle (A), thigh muscle (T), heart (H), liver (L), brain (B),

kidney (K), and intestine (I). Clear 28S and 18S rRNA bands indicate high RNA integrity.

Samples with very low RNA yield could not be clearly visualized as the same volume of sample

was loaded in each lane.

77

Figure 11: Amplification of a dilution gradient of Elf1a1 template with primer set 1. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Reactions

were performed in triplicate and averaged and the equation of the line of best fit is shown. R = -

0.999 and percent efficiency is 103.9%.

Figure 12: Amplification of a dilution gradient of GAPDH template with primer set 2. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Reactions

were performed in triplicate and the equation of the line of best fit is shown. R = -0.999 and

percent efficiency is 100.2%.

78

Figure 13: Amplification of a dilution gradient of GLUT5 template with primer set 3. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA starting at a dilution of cDNA derived from 0.005 ng/µl of RNA.

Reactions were performed in triplicate and averaged and the equation of the line of best fit is

shown. R = -0.999 and percent efficiency is 104.7%.

Figure 14: Amplification of a dilution gradient of GLUT1 template with primer set 2. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. One

technical replicate is plotted and the equation of the line of best fit is shown. R = -0.625 and

percent efficiency is 172.1%. Red points are not included in the linear regression.

79

product length = 95

Forward primer GTGTCTGTGAAAGATGTTCGCC

Calypte anna ...........G..C.......

Reverse primer ATAATGACCTGTGCAGTGAAGC

Calypte anna .....A................

Figure 15: Alignment of Elf1a1 primer set 1 primers with Anna’s hummingbird (Calypte anna)

sequence (NCBI GenBank reference XM_008490615.1). Sequences are aligned using NCBI

Primer-BLAST. Conserved nucleotides are indicated with a dot. Expected PCR product size is

indicated.

product length = 96

Forward primer GTGCTGCTCAGAACATTATCCC

Calypte anna ................A.....

Reverse primer AAGCCATTCCAGTAAGTTTCCC

Calypte anna ......................

Figure 16: Alignment of GAPDH primer set 2 primers with Anna’s hummingbird (Calypte

anna) sequence (NCBI GenBank reference XM_008499176.1). Sequences are aligned using

NCBI Primer-BLAST. Conserved nucleotides are indicated with a dot. Expected PCR product

size is indicated.

product length = 102

Forward primer CTCATCTTCTGTGCCATCTGC

Calypte anna .....................

Reverse primer GGCCATGATCCTGTTGATTTCC

Calypte anna ............A.........

Figure 17: Alignment of GLUT5 primer set 3 primers with Anna’s hummingbird (Calype anna)

sequence (NCBI GenBank reference XM_008503671.1). Sequences are aligned using NCBI

Primer-BLAST. Conserved nucleotides are indicated with a dot. Expected PCR product size is

indicated.

80

Figure 18: PCR of exon-spanning GLUT1 and GLUT5 primers. Ruby-throated hummingbird

liver cDNA (derived from 0.005 ng/µl of RNA) was amplified and separated on 1% agarose gel

with GelRed and Ready-To-Use DNA ladder (Biotium). Eight primer sets are tested which are

designated 8, 9, 10, and 11 for GLUT1 and 4, 5, 6, and 7 for GLUT5. Primer set used is

designated above each lane. “L” designates DNA ladder (Biotium).

Figure 19: Amplification of a dilution gradient of GLUT1 with primer set 10. Cycle threshold

(Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.999 and percent efficiency is 108.8%. Red points are not included in the linear regression.

81

Figure 20: Amplification of a dilution gradient of GLUT1 with primer set 11. Cycle threshold

(Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.971 and percent efficiency is 101.2%.

Figure 21: Amplification of a dilution gradient of GLUT5 with primer set 4. Cycle threshold

(Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.975 and percent efficiency is 101.5%.

82

Figure 22: Amplification of a dilution gradient of GLUT5 with primer set 7. Cycle threshold

(Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.970 and percent efficiency is 101.2%.

Table 8: Averaged Elf1a/GAPDH Ct values in ruby-throated hummingbird tissues used for Q-

PCR analysis of GLUT1 and GLUT5. The cycle threshold of Elf1a is divided by that of GAPDH

for each tissue. n = sample size. Data shown as mean ± standard deviation.

Tissue n Elf1a1/GAPDH

pectoralis 4 0.972±0.146

ankle muscles 2 0.956±0.079

liver 4 0.955±0.083

heart 4 0.961±0.093

brain 4 0.955±0.097

intestine 4 0.967±0.040

83

Figure 23: GLUT1 mRNA expression in ruby-throated hummingbird tissues normalized to

Elf1a1 (A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C). Samples are

calibrated to liver tissue. Error bars represent standard deviation and tissues with different letters

are significantly different P < 0.048. Sample size of A is indicated below each column. Sample

size of all tissues, save intestine, is one less in B and C.

84

Figure 24: GLUT5 mRNA expression in ruby-throated hummingbird tissues normalized to

Elf1a1 (A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C). Samples are

calibrated to brain tissue. Error bars represent standard deviation and tissues with different letters

are significantly different and P-values are indicated. Sample size of A is indicated below each

column. Sample size of all tissues, save intestine, is one less in B and C.

85

Figure 25: PCR with KHK primer sets. Primers were used to amplify ruby-throated

hummingbird pectoralis cDNA (P), liver cDNA (L), and genomic DNA (G). C = no-template

control and Ld = Ready-To-Use DNA ladder (Biotium). DNA was separated on 2% agarose gel

with GelRed.

Figure 26: PCR with AldoB primer sets. Primers were used to amplify ruby-throated

hummingbird pectoralis cDNA (P), liver cDNA (L), and genomic DNA (G). C = no-template

control and Ld = Ready-To-Use DNA ladder (Biotium). DNA was separated on 2% agarose gel

with GelRed.

86

Figure 27: Amplification of a dilution gradient of KHK transcript with primer set 2. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.998 and percent efficiency is 100.7%.

Figure 28: Amplification of a dilution gradient of KHK transcript with primer set 3. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.999 and percent efficiency is 91.9%.

87

Figure 29: Amplification of a dilution gradient of AldoB transcript with primer set 1. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. Linear regression was performed in Microsoft

Excel. The equation of the line of best fit is shown. R = -0.999 and percent efficiency is 90.5%.

Figure 30: Amplification of a dilution gradient of AldoB transcript with primer set 2. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.999 and percent efficiency is 91.3%. Red points are not included in linear regression.

88

Figure 31: Amplification of a dilution gradient of AldoB transcript with primer set 3. Cycle

threshold (Ct) is plotted against the dilution factor of the template. The template is ruby-throated

hummingbird liver cDNA. Dilutions start at cDNA derived from 0.005 ng/µl of RNA. Three

technical replicates were averaged and plotted. The equation of the line of best fit is shown. R = -

0.999 and percent efficiency is 90.5%.

Table 9: Averaged Elf1a/GAPDH transcript ratios in ruby-throated hummingbird tissues used

for KHK and AldoB Q-PCR analysis. The cycle threshold of Elf1a is divided by that of GAPDH

for each tissue. n = sample size. Data shown as mean ± standard deviation.

Tissue n Elf1a1/GAPDH

pectoralis 4 1.159±0.027

ankle muscles 2 1.062±0.057

liver 4 1.058±0.038

heart 4 1.083±0.018

brain 4 1.090±0.020

intestine 4 1.007±0.043

kidney 4 1.009±0.006

89

Figure 32: KHK mRNA expression in ruby-throated hummingbird tissues normalized to Elf1a1

(A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C). Samples are calibrated

to brain tissue. Error bars represent standard deviation and tissues with different letters are

significantly different and P-values are indicated. Sample size is indicated below each column.

90

Figure 33: AldoB mRNA expression in ruby-throated hummingbird tissues normalized to

Elf1a1 (A), GAPDH (B), and the average expression ELF1a1 and GAPDH (C). Samples are

calibrated to brain tissue. Error bars represent standard deviation and tissues with different letters

are significantly different and P-values are indicated. Sample size is indicated below each

column.

91

Human KHK-C VVDTLGAGDTFNASVIFSLSQGRSVQEALRFGCQVAGKKCGLQGFDGIV

Conservation VVDTLGAGDTFNA+VIF+LS+G+S+QEAL FGC++AGKKCG+QGFDG+V

Hummingbird KHK VVDTLGAGDTFNAAVIFALSEGKSLQEALTFGCRIAGKKCGIQGFDGLV

Figure 34: BLASTP (NCBI GenBank) alignment of the immunogen used to generate polyclonal

anti-KHK primary antibody (last 50 amino acids of human KHK, GenBank reference

CAA55347.1) and Anna’s hummingbird predicted KHK (NCBI GenBank reference

XP_008491152.1). Conserved amino acids are shown between these two sequences. A “+”

constitutes an amino acid substitution with conserved properties. 97% of amino acid locations

were conserved either in amino acid identity or amino acid properties. Exact amino acid

homology was 80%.

Human AldoB ILAADESVGTMGNRLQRIKVENTEENRRQFR

ILAADESVGTM NRLQRI VENTEENRR FR

Human AldoB ILAADESVGTMANRLQRINVENTEENRRAFR

Figure 35: BLASTP (NCBI GenBank) alignment of the immunogen used to generate polyclonal

anti-AldoB primary antibody (amino acids 30-60 of human AldoB, GenBank reference

NP_000026.2) and Anna’s hummingbird predicted AldoB (NCBI GenBank reference

XP_008494663.1). Conserved amino acids are shown between these two sequences. Exact amino

acid homology was 90%.

92

Figure 36: Western blot of ketohexokinase in ruby-throated hummingbird pectoralis and liver

and chicken pectoralis. Mouse liver is included as a positive control. Samples include Bio-Rad

Precision Plus Protein Ladder (L), ruby-throated hummingbird pectoralis (HP), ruby-throated

hummingbird liver (HL), chicken pectoralis (P), and mouse liver (ML). Whole protein is

separated on 10% polyacrylamide gel and 10ug of whole protein was loaded in each lane. The

expected band size was 33 kDa. Bands size was assessed using gel_analyser software.

93

Figure 37: Amido black stain of membrane bound proteins used for western blotting. Mouse

liver is included as a positive control. Samples include Bio-Rad Precision Plus Protein Ladder

(L), ruby-throated hummingbird pectoralis (HP), ruby-throated hummingbird liver (HL), chicken

pectoralis (P), and mouse liver (ML). Whole protein was separated on 10% polyacrylamide gel

and 10ug of whole protein was loaded in each lane. Bands were sized using gel_analyser

software.

Figure 38: Western blot of aldolase B in ruby-throated hummingbird pectoralis and liver and

chicken pectoralis. Mouse liver is included as a positive control. Samples include Bio-Rad

Precision Plus Protein Ladder (L), ruby-throated hummingbird pectoralis (HP), ruby-throated

hummingbird liver (HL), chicken pectoralis (P), and mouse liver (ML). Whole protein is

separated on 10% polyacrylamide gel and 10ug of whole protein was loaded in each lane. Protein

bands are 39kDA. Bands were sized using gel_analyser software.

94

Table 10: Densitometry results of aldolase B western blot depicted in Figure 11. GelQuant.NET

was used to compare relative band intensities of the target band in each lane of the western blot.

A BCA assay was used to ensure loading of equal concentrations of total protein in each well.

Band intensity is represented as a fraction of 1.0 where 1.0 is the combined intensity of the target

bands in all four sample lanes.

Lane Fraction of Overall Intensity (out of 1.0) 1 - hummingbird pectoralis 0.031 2 - hummingbird liver 0.527 3 - chicken pectoralis 0.112 4 - mouse liver 0.329

Figure 39: Enlarged view of the relationship between pectoralis and liver from Figure 23.

GLUT1 mRNA expression in ruby-throated hummingbird tissues is normalized to Elf1a1. Error

bars represent standard deviation. Liver does not have error bars as it was used as the calibrator.

Sample size is five.

95

Figure 40: Enlarged view of the relationship between pectoralis and intestine from Figure 24.

GLUT5 mRNA expression in ruby-throated hummingbird tissues is normalized to Elf1a1. Error

bars represent standard deviation and the tissues are significantly different (P < 0.01). Sample

size is five for pectoralis and four for intestine.

96

Figure 41: Potential rate-limiting steps in mammalian (A) and ruby-throated hummingbird (B)

transport of sugar from circulation to the initiation of oxidation in muscle tissue. Step 1:

Transport of circulating sugar to the extracellular space. Step 2: Facilitated transport across the

sarcolemma. Step 3: Phosphorylation of sugar prior to further catabolism. An “*” indicates a step

where adaptation is proposed. Individual white hexagons symbolize glucose and blue pentagons

symbolize fructose. Glycogen is indicated by linked glucose hexagons. Contractile stimulation of

translocation is represented by a lightning bolt. Modified by K. Welch from Welch and Chen

(2014) with permission.

97

Appendices

Appendix 1: Aves GAPDH mRNA Coding Strand Multiple Sequence Alignment

Sequences were aligned in MEGA 5.2.2 using ClustalW and the alignment was presented using

Geneious 7.0.5. A consensus sequence “C…” and a bar graph of % sequence identity “Id…” are

displayed above the alignment. Nucleotide numbering is included on the consensus sequence.

Nucleotides differing from the consensus sequence are highlighted in colour and gaps are

indicated by dashes “-”. 12 sequences are aligned and described in the table below. All of these,

save 1, 3, 4, and 12, are predicted sequences. Binding locations for GAPDH primer sets designed

using this alignment are indicated by coloured boxes on the consensus sequence. Primer set 1 is

red, set 2 is purple, and set 3 is blue.

Number in Alignment Species NCBI Accession Number

1 Taeniopygia guttata NM_001198610.1

2 Meleagris gallopavo XM_003202670.1

3 Passer domesticus AF416452.1

4 Gallus gallus NM_204305.1

5 Anas platyrhynchos XM_005016745.1

6 Ficedula albicollis XM_005038447.1

7 Melopsittacus undulatus XM_005146692.1

8 Falco peregrinus XM_005235383.1

9 Falco cherrug XM_005434364.1

10 Zonotrichia albicollis XM_005486061.1

11 Geospiza fortis XM_005420667.1

12 Columba livia NM_001282835.1

98

99

100

101

102

103

104

Appendix 2: Aves Elf1a1 mRNA Coding Strand Multiple Sequence Alignment

Sequences were aligned in MEGA 5.2.2 using ClustalW and the alignment was presented using

Geneious 7.0.5. A consensus sequence “C…” and a bar graph of % sequence identity “Id…” are

displayed above the alignment. Nucleotide numbering is included on the consensus sequence.

Nucleotides differing from the consensus sequence are highlighted in colour and gaps are

indicated by dashes “-”. 11 sequences are aligned and described in the table below. All of these

save Gallus gallus are predicted sequences. The 5’ end of Gallus gallus has been trimmed as it

extended well beyond the other sequences. Binding locations for Elf1a1 primer sets designed

using this alignment are indicated by coloured boxes on the consensus sequence. Primer set 1 is

red, set 2 is purple, and set 3 is blue.

Number in Alignment Species NCBI Accession Number

1 Taeniopygia guttata XM_002190770.2

2 Gallus gallus NM_204157.2

3 Anas platyrhynchos XM_005009711.1

4 Ficedula albicollis XM_005044132.1

5 Melopsittacus undulatus XM_005153166.1

6 Falco peregrinus XM_005240288.1

7 Falco cherrug XM_005431940.1

8 Zonotrichia albicollis XM_005491152.1

9 Geospiza fortis XM_005421148.1

10 Columba livia XM_005506744.1

11 Pseudopodoces humilis XM_005518141.1

105

106

107

108

109

110

111

112

113

Appendix 3: Aves Beta-actin mRNA Coding Strand Multiple Sequence Alignment

Sequences were aligned in MEGA 5.2.2 using ClustalW and the alignment was presented using

Geneious 7.0.5. A consensus sequence “C…” and a bar graph of % sequence identity “Id…” are

displayed above the alignment. Nucleotide numbering is included on the consensus sequence. All

are actual observed sequences as opposed to predicted sequences. Nucleotides differing from the

consensus sequence are highlighted in colour and gaps are indicated by dashes “-”. Nine

sequences are aligned and described in the table below. Binding locations for Beta-actin primer

sets designed using this alignment are indicated by coloured boxes on the consensus sequence.

Primer set 1 is red, set 2 is purple, and set 3 is blue.

Number in Alignment Species NCBI Accession Number

1 Anas platyrhynchos EF667345.1

2 Anser anser M26111.1|

3 Corvus macrorhynchos AB561857.1

4 Limosa limosa JF913946.1

5 Calidris canutus JN122335.1

6 Dromaius novaehollandiae JN663391.1

7 Passer domesticus AF416454.1

8 Meleagris gallopavo AY942620.1

9 Coturnix coturnix japonica AF199488.1

114

115

116

117

118

119

120

121

122

Appendix 4: Aves GLUT5 mRNA Coding Strand Multiple Sequence Alignment

Sequences were aligned in MEGA 5.2.2 using ClustalW and the alignment was presented using

Geneious 7.0.5. A consensus sequence “C…” and a bar graph of % sequence identity “Id…” are

displayed above the alignment. Nucleotide numbering is included on the consensus sequence.

Nucleotides differing from the consensus sequence are highlighted in colour and gaps are

indicated by dashes “-”. The ends of the alignment are trimmed because the sequences were

highly divergent in length at the 5’ and 3’ ends. All sequences are predicted. 11 sequences are

aligned and described in the table below. Binding locations for GLUT5 primer sets designed

using this alignment are indicated by coloured boxes on the consensus sequence. Primer set 1 is

red, set 2 is purple, and set 3 is blue.

Number in Alignment Species NCBI Accession Number

1 Taeniopygia guttata XM_002187376.1

2 Gallus gallus XM_004947446.1

3 Pseudopodoces humilis XM_005530449.1

4 Anas platyrhynchos XM_005015431.1

5 Ficedula albicollis XM_005057858.1

6 Melopsittacus undulatus XM_005143258.1

7 Falco peregrinus XM_005237843.1

8 Falco cherrug XM_005437102.1

9 Zonotrichia albicollis XM_005493803.2

10 Geospiza fortis XM_014309771.1

11 Columba livia XM_005514278.1

123

124

125

126

127

128

129

130

131

132