While Tinkering With the β-Cell… Metabolic Regulatory Mechanisms and New Therapeutic Strategies: American Diabetes Association Lilly Lecture, 2001

One very important need in our research is to be able to test the functional impact of genes in a rapid and efficient fashion. The use of recombinant adenovirus for gene transfer into islet cells was first demonstrated in our laboratory in the early 1990s (6,7). Figure 2 shows that adenovirus-mediated transfer of the β-galactosidase gene into isolated rat islets results in expression of the gene in 70–80% of the islet cells (6). This compares very favorably to traditional techniques such as electroporation, which only allow gene transfer efficiencies on the order of 10–20% in isolated islets, which is inadequate for testing hypotheses about stimulus/secretion coupling pathways. Our success in obtaining rapid and efficient transfer of genes into islets and β-cell lines has allowed us to prepare and test a number of viruses containing genes relevant to β-cell signaling (rev. in 8–10), some of which will be discussed in more detail below.

A second important tool for our work is a new cellular model for studying insulin secretion. The start-point for this work was a pre-existing rat β-cell line, INS-1, created by Claes Wollheim and associates in Geneva (11). This cell line has been widely used in the field of β-cell biology because it secretes insulin in response to physiological glucose concentrations. However, two problems with these particular cells have emerged. One is that insulin secretion is stimulated by only three- to fourfold as the glucose concentration is raised from 3 to 15 mmol/l (11–13). This is far less than that seen for freshly isolated pancreatic islets, which exhibit a 15-fold response to a similar increment in glucose concentrations. A second problem is that with continued growth of the cells, even the three- to fourfold response is lost as a function of time in culture.

In an attempt to address these issues, we isolated a large number of independent clonal cell lines from the parental INS-1 stock, using a stable transfection strategy (14). Once the clones were available, they were expanded and screened for their capacity for glucose-stimulated insulin secretion (GSIS). Figure 3 summarizes this screen of 58 independent INS-1-derived clones, expressed as fold-stimulation of insulin secretion as glucose is raised from 3 to 15 mmol/l. Fully 70% of the clones isolated in this fashion are very poorly glucose responsive or not responsive to glucose at all. Another subgroup of ∼20% of the clones are moderately responsive and a few clones are robustly responsive with 10- to 15-fold increases in insulin secretion in response to high glucose. Further study of clones of the highly responsive class, such as line 832/13, reveals that these clones retain a long-term functional stability, such that robust glucose responsiveness can be demonstrated after 9 months or more of continuous culture (14). This observation is important for three reasons. First, the development of cell lines such as 832/13 provides us with a stable model for probing the biochemical mechanisms involved in control of insulin secretion. Second, procurement of cell lines that are truly clonal allows maintenance of reliable and predictable functional characteristics. Our findings in this regard are consistent with the work of others in the field, notably Knaack et al. (15), who have developed stable mouse cell lines by dilution cloning strategies. This type of functional consistency will be a critical component of any cell-based insulin-replacement therapy and will be required for acceptance of these strategies by regulatory agencies and patients. Finally, the isolation of stably glucose responsive and unresponsive cell lines from a single starting source allows us to apply genetic and biochemical approaches for comparing these two discrete populations, thereby allowing key mechanistic determinants of robust GSIS to be identified, as detailed below.

Immunological attack on islet β-cells in type 1 diabetes involves direct interaction of islet cells and immune cells, as well release of a complex mixture of toxic soluble factors such as inflammatory cytokines and reactive oxygen species (16–20). A key obstacle that must be overcome for development of cell-based insulin-replacement strategies is the development of cells that are resistant to cell killing via the latter mechanisms. To this end, we recently developed a selection strategy for isolating cell lines that are resistant to the cytotoxic effects of multiple cytokines (21). This involved incubation of INS-1 cells in the combined presence of gradually increasing doses of interleukin-1β (IL-1β) and interferon-γ (IFN-γ), culminating with the isolation of a population of cells able to survive in the presence of very high concentrations of these cytokines. As shown in Fig. 4, unselected INS-1 cells exhibited ≤20% viability after 48 h of treatment with 10 ng/ml IL-1β + 100 units/ml IFN-γ or with conditioned media from activated rat peripheral blood mononuclear cells (PBMCs). Following the selection procedure, cells were completely resistant to the cytotoxic effects of the cytokine mixture or media from conditioned PBMCs. Note that the PBMC media contains cytokines and toxic factors in addition to IL-1β and IFN-γ, but the cells still remain fully protected. The protective effect is only partially dependent on the continual presence of cytokines in the culture medium. Thus, cells taken through the 8-week selection protocol and then cultured for an additional 2 months in the absence of cytokines remain 70% viable following 48 h of exposure to IL-1β + IFN-γ, as compared with only 20% in the unselected controls (Fig. 4). Studies on the molecular basis for cytokine resistance in the selected cell lines are summarized later in this article. In summary, this new model allows us to gain insight into the genes and biochemical pathways that are responsible for cytokine-mediated β-cell killing, possibly leading to development of methods for protecting transplanted insulin-secreting cells.

Glucose causes insulin to be released from β-cells via the generation of signals induced through metabolism of the sugar (22,23; Fig. 5). Glucose enters the pancreatic islet β-cell via facilitated glucose transporters, primarily GLUT-2 in rat islets. Once inside the cell, glucose is converted to glucose-6-phosphate in the glucokinase reaction, and the glucose-6-phosphate then enters glycolysis to be converted to pyruvate, with some ATP produced as a byproduct. Once pyruvate is generated it can enter the mitochondria, where it flows into the tricarboxylic acid (TCA) cycle at several points. Oxidation of pyruvate in the TCA cycle results in further production of ATP. A third way by which ATP can be produced during glucose metabolism is via various reducing equivalent shuttles, where the reducing power of NADH in the cytosol is converted to reducing power in the mitochondria (23–27).

Metabolic pathways that generate ATP have received major attention from islet biologists because a rise in the ATP-to-ADP ratio has been shown to inhibit ATP-sensitive K⁺ (K_ATP) channels in β-cells, resulting in activation of voltage-gated calcium channels (28). The resultant influx of extracellular calcium causes the activation of certain protein kinases and other enzymes, leading to exocytosis of insulin from preformed secretory granules. While regulation of K_ATP channels is undoubtably an important signal for regulation of insulin secretion, it is clearly not the only mechanism. Various laboratories have shown that GSIS still occurs when the ATP-sensitive K⁺ channel is taken out of play by pharmacologic agents or the application of very high concentrations of potassium to the cell (29,30). However, the biochemical mechanism of this so-called “K_ATP channel-independent” pathway of glucose sensing remains to be defined.

The general approach that our group has taken for unraveling this complex network of metabolic signaling in the β-cell is to use the recombinant adenovirus tool described earlier to introduce genes that perturb metabolism in defined ways. An example of this approach will be highlighted (12). The carbohydrate fuel, glycerol, is not metabolized in β-cells and is therefore unable to stimulate insulin secretion. β-Cells are unable to metabolize glycerol because they lack the first enzyme of glycerol metabolism, glycerol kinase (12). These observations raised the following set of questions: 1) Does adenovirus-mediated expression of glycerol kinase allow glycerol to be metabolized, and does it lead to glycerol-stimulated insulin secretion? and 2) if the answer to the first question is yes, can we gain insight into the mechanisms by which insulin secretion is regulated by comparing the metabolic fates of glycerol and glucose?

The answer to the first question is clear from the data in Fig. 6. In these experiments, rat pancreatic islets were treated with a control adenovirus (AdCMV-βGAL) or a virus containing a bacterial gene encoding glycerol kinase (AdCMV-GlpK). Increasing the glycerol concentration from 0 to 5 mmol/l caused a sharp rise in insulin secretion in glycerol kinase-expressing cells, but not in control cells. However, upon exposure of cells to 20 mmol/l glucose, potent stimulation of insulin secretion occurred in both groups. This experiment shows that it is possible to make glycerol equal in potency to glucose as a stimulator of insulin secretion simply by expressing glycerol kinase.

We next investigated the metabolic fate of glycerol in these cells. We first addressed whether the capacity of glycerol to serve as the backbone for esterification of fatty acids to make triglycerides is important for regulation of insulin secretion. This was done by monitoring the incorporation of radioactive glycerol into cellular lipids in the presence or absence of triacsin C, an inhibitor of long-chain acyl CoA synthetase. We observed that INS-1 cells engineered for glycerol kinase expression and incubated in the absence of triacsin C increased their incorporation of radiolabeled glycerol into lipids as a function of glycerol concentration. In contrast, triacsin C-treated cells exhibited a complete block in the incorporation of glycerol into the lipid pool. Despite the potent effects of the drug on lipid esterification, glycerol-stimulated insulin secretion remained intact, suggesting that the esterification of fatty acids into glycerol lipids is not an important metabolic pathway for controlling insulin release (12).

We continued the studies by comparing the metabolic fates of glycerol and glucose in glycerol kinase-expressing β-cells. We found that glycerol is more effectively converted to lactate than is glucose. Conversely, when we compared the oxidation of glucose and glycerol, glucose appeared to be the more efficiently oxidized fuel (12).

The following conclusions arise from these experiments. First, expression of glycerol kinase is clearly sufficient to confer glycerol-stimulated insulin secretion. This serves as added proof that regulation of insulin secretion by simple carbohydrate fuels is a metabolism-driven process, since without the expression of glycerol kinase there is no glycerol metabolism or regulation of insulin secretion by glycerol. The experiments just cited plus others that will not be summarized due to space limitations (13,31) also suggest that a fully active link between glucose and lipid metabolism is not required for glucose-stimulated insulin secretion. Last, glycerol stimulates insulin secretion despite its preferential conversion to lactate. This suggests that oxidation of these simple fuels may not be the critical metabolic pathway that regulates insulin release.

The results of the glycerol kinase studies are somewhat surprising in light of good evidence from several laboratories that mitochondrial metabolism of glucose generates important signals for insulin secretion (32,33). This raises the issue of what actually happens to pyruvate as it enters the mitochondria and whether pathways other than the classical pathway of glucose oxidation may be important.

To answer this question, we have recently been comparing the glucose-responsive and -unresponsive INS-1-derived clones described earlier at two levels. The first is to analyze the metabolic differences between strongly responsive and poorly responsive lines, using the powerful technique of ¹³C NMR. The second has been the identification of genes that are differentially expressed in the strongly responsive versus poorly responsive lines.

The application of NMR-based analysis of metabolic pathways has been made possible via a collaboration with Professor Dean Sherry of the Rogers NMR Center at UT Southwestern Medical Center, Dallas. Dr. Sherry and his colleague, Dr. Craig Malloy, have developed NMR-based methods for analyzing metabolism of pyruvate in mitochondria (34–37). As shown in Fig. 7, there are two sights of entry for pyruvate into the TCA cycle. The more familiar one is through the pyruvate dehydrogenase complex, which converts pyruvate to acetyl CoA. In β-cells, another important entry point for pyruvate is through the enzyme pyruvate carboxylase (PC), which converts pyruvate to oxaloacetate. The work of the groups of Michael MacDonald, Marc Prentki, and others has highlighted the abundant expression of PC in β-cells and has shown that this mode of entry into the cycle, known as anaplerosis, is an active metabolic event (38–46). In liver, PC pairs with phosphoenolpyruvate carboxykinase (PEPCK) to catalyze early steps in gluconeogenesis. In islets, PEPCK is lacking, suggesting that PC-catalyzed conversion of pyruvate to oxaloacetate has a specific function in β-cells distinct from that in liver. Radioisotopic methods have been used to estimate that 40% of the pyruvate generated during glucose stimulation of β-cells enters mitochondrial metabolism via PC-catalyzed conversion to oxaloacetate (42–45). It has been further proposed that PC-catalyzed anaplerotic influx of pyruvate into the TCA cycle is linked to efflux of other intermediates from the mitochondria, including malate (45) or citrate (46), resulting in synthesis of important coupling factors. Cytosolic malate can be reconverted to pyruvate via the malic enzyme, completing a pyruvate-malate cycle. An alternate cycle occurs when citrate leaves the mitochondria to be cleaved to acetyl-CoA and oxaloacetate by citrate lyase. Acetyl-CoA so formed can be converted to malonyl-CoA, which has been proposed as a coupling factor (17,48), although evidence against this idea has also been presented (13,31). The oxaloacetate formed via citrate cleavage can in turn be converted to malate via cytosolic malate dehydrogenase activity and then back to pyruvate via malic enzyme to complete a pyruvate-citrate cycle (Fig. 7). A cofactor common to both the pyruvate-malate and pyruvate-citrate cycles is NADPH produced as a byproduct of the malic enzyme (41).

The question that arises is whether one can discriminate between pyruvate dehydrogenase catalyzed entry of pyruvate into the TCA cycle versus PC-catalyzed entry and, if so, whether either one of these pathways has special significance for regulation of insulin secretion? The approach that we have taken to this problem is to incubate our robustly and poorly glucose responsive INS-1-derived cell lines with ¹³C-labeled glucose, followed by extraction of glutamate from the cells and NMR analysis of its labeling pattern (49). Glutamate is the intermediate chosen for analysis because it is present at high concentrations in cells and it is in equilibrium with an intermediate of the TCA cycle, α-ketoglutarate.

While the NMR spectra generated can be quite complex, certain signature features allow us to track the mode of entry of pyruvate into the TCA cycle, as is illustrated by examination of the spectra predicted in two vastly different metabolic circumstances (Fig. 8). In the first scenario, we assume that 100% of the ¹³C-labeled glucose is converted to pyruvate and that all of the pyruvate enters the TCA cycle via the pyruvate dehydrogenase (PDH) reaction. In an opposite scenario, we assume that the PDH and PC reactions each contribute 50% of the pyruvate entry into the TCA cycle. Certain peaks in the NMR spectra are vastly different in the two metabolic circumstances, as indicated by the arrows in Fig. 8. Computer-based deconvolution of the NMR spectra for all carbons of glutamate allows one to gain an estimate of the relative contributions of the two pathways, using the methods developed by Malloy, Sherry, and colleagues (34–37).

As shown in Fig. 9, there is a remarkable correlation (r = 0.92) between PC-catalyzed pyruvate cycling activity and GSIS in four independent INS-1-derived cell lines with different degrees of glucose responsiveness. In contrast, entry of pyruvate into the TCA cycle via PDH does not correlate with glucose sensing (49).

To further investigate the relationship between pyruvate cycling and GSIS, we measured the effects of stimulators and inhibitors of this pathway. First, we tested the cell permeant ester of malate, dimethylmalate (DMM), reasoning that this agent should increase anaplerosis and hence stimulate pyruvate cycling. Addition of DMM to our most glucose responsive cell line, 832/13, nearly doubled insulin secretion at 12 mmol/l glucose (49). Remarkably, this increase in insulin secretion was correlated with an increase in pyruvate cycling, such that the new data point fits precisely on the line relating pyruvate cycling and insulin secretion for the four variously responsive INS-1-derived cell lines (Fig. 9). As a second test, phenylacetic acid (PAA), a well-known inhibitor of PC (16,50), was added to 832/13 cells in the presence of stimulatory glucose (12 mmol/l), resulting in 50% inhibition of insulin secretion and a parallel decrease in pyruvate cycling of 25% (49). Again, the data point created for 832/13 cells treated with PAA falls on the line relating pyruvate cycling and GSIS for the four independent INS-1-derived cell lines (Fig. 9). Thus, under both stimulatory (DMM) and inhibitory (PAA) conditions, there is a linear relationship between GSIS and pyruvate cycling as measured by NMR.

One potential concern with regard to our conclusions about the link between pyruvate cycling and insulin secretion is that pyruvate cycling flux, as estimated by the NMR isotopomer method, is expressed as a ratio relative to TCA cycle flux. If absolute flux of pyruvate through PDH was different in glucose-responsive versus -unresponsive cells, this could mean that no real change in PC-catalyzed pyruvate cycling flux had actually occurred. If PDH-catalyzed glucose oxidation differed in the two cell lines, this should be reflected by a change in O₂ consumption. However, direct O₂ consumption measurements on actively respiring cells showed no difference between robustly or poorly glucose-responsive cells (49).

The following key summary points emerge from the NMR experiments. First, PC-catalyzed pyruvate cycling activity correlates with GSIS in INS-1-derived cell lines, while no correlation is observed between glucose responsiveness and PDH-catalyzed entry of pyruvate into the TCA cycle. Second, the PC inhibitor PAA impairs GSIS while preferentially inhibiting pyruvate cycling relative to TCA cycle influx. Third, a cell-permeant cycling intermediate, DMM, is a potent potentiator of GSIS in a glucose-responsive INS-1-derived cell line. These findings provide strong support for the idea that PC-catalyzed pyruvate cycling is a critical pathway for generation of coupling factors that mediate GSIS. Current work is focused on further exploration of this concept, with the following immediate goals: 1) to identify the coupling factor that links pyruvate cycling to exocytosis of insulin containing secretory granules; 2) to determine if pyruvate cycling activity is altered in models of β-cell dysfunction; and 3) to identify genes that may be used to enhance pyruvate cycling activity, possibly serving to improve glucose responsiveness of candidate human cell lines derived from stem cells or other sources.

While biochemical comparison of glucose-responsive and -unresponsive INS-1-derived cell lines has been quite informative, a genetic comparison of these same lines could yield additional information about genes that participate in determining the fully differentiated functions of the mature β-cell. We have taken a three-step approach to achieving this goal. The first step has been to compare the expression levels of a set of candidate genes that includes key metabolic regulatory proteins such as GLUT2 and glucokinase, as well as various transcription factors implicated in β-cell development such as PDX-1, Nkx 2.2 and 6.1, and neuro D. The second approach has been to perform a subtractive hybridization procedure known as representational difference analysis (RDA) (51). This method has identified approximately a dozen genes that are preferentially expressed in glucose-responsive cell lines relative to poorly responsive lines and another dozen that are preferentially expressed in the poorly responsive lines relative to the strong responders (P. Jensen, C.B.N., unpublished observations). Finally, we have entered into a collaboration with a biotechnology company, CuraGen, Inc. (New Haven, CT), in which their GeneCalling technology (52) has been applied to our INS-1-derived cell lines. This analysis has identified a set of ∼130 genes that discriminate our glucose-responsive and poorly responsive cell lines. We are now entering a phase of the analysis in which candidate genes identified by each of the three approaches are being prepared as recombinant adenoviruses to allow the effect of altered expression of individual genes to be assessed in the various INS-1-derived cell lines. This analysis is also likely to involve suppression of expression of certain candidate genes via antisense oligonucleotides or RNAi technologies.

The final section of this article focuses on our efforts to identify genes involved in protection of β-cells against damage induced by inflammatory cytokines and other soluble toxins. The development of a selection method for isolation of cytokine-resistant cell lines was summarized earlier. Cells with complete resistance to the cytotoxic effects of IL-1β and IFN-γ were selected via an 8-week regimen of exposure to gradually increasing concentrations of these inflammatory cytokines (21).

Recent efforts have been focused on understanding the mechanisms by which the selection strategy has caused stable resistance to IL-1β and transient but reinducible resistance to IFN-γ. Analysis of components of the known IL-1β signaling pathway reveals that selected cell lines have a defect in activation of the transcription factor nuclear factor-κB (NF-κB) (21). Consistent with this finding, selected lines also fail to increase expression of inducible nitric oxide synthase (iNOS), the NF-κB target gene. They also fail to increase nitric oxide production in response to acute exposure to IL-1β, while unselected cell lines have large increases in both variables. More recently, in collaboration with the laboratory of Zhijian (James) Chen at UT Southwestern Medical Center, Dallas, we have determined that the defect in NF-κB activation in selected lines occurs downstream of degradation of the NF-κB regulatory protein, IκB, since the rate of degradation of IκB is identical in selected and unselected cell lines acutely exposed to IL-1β. Although this narrows the search for the factor(s) involved in IL-1β resistance in selected lines, the precise mechanism remains unknown and is the subject of ongoing investigation in our laboratory.

We have also investigated the mechanism by which IFN-γ-mediated cytotoxicity is impaired in the selected cell lines (21,53). The IFN-γ receptor is a member of the JAK/STAT receptor family. The JAK kinase activity associated with the IFN-γ receptor phosphorylates and activates the transcription factor STAT-1α. When the STAT-1α transcription factor is phosphorylated and dimerizes, it translocates to the nucleus where it acts upon several genes. As shown in Fig. 10, phosphorylation of STAT-1α in response to acute cytokine exposure is similar in selected and unselected cell lines, but the total amount of STAT-1α protein is dramatically increased in the selected cells (53). Furthermore, cytokine withdrawal, which results in loss of IFN-γ resistance in selected lines, also results in a fall in STAT-1α expression to the same levels as those found in unselected cells. These data suggest that an increase in STAT-1α expression may be a mechanism by which the cytokine selection procedure results in IFN-γ resistance.

To test this hypothesis, we prepared a recombinant adenovirus containing the STAT-1α gene and demonstrated that treatment of unselected INS-1 cells with this virus caused a large increase in STAT-1α protein expression (Fig. 11). The effect of this maneuver was as hypothesized—cells with overexpressed STAT-1α gained resistance to IFN-γ-mediated cytotoxicity and, more importantly, to the combined effects of IL-1β and IFN-γ (53). While this resistance was not complete, it should be noted that STAT-1α was expressed in the background of unselected cell lines with no resistance to IL-1β. Studies to investigate the effect of STAT-1α expression in cell lines with stable resistance to IL-1β are ongoing, as are experiments attempting to define the mechanisms by which high levels of STAT-1α expression interfere with cytokine-induced cytotoxicity. Importantly, overexpression of STAT-1α did not impair GSIS in robustly responsive INS-1-derived cell lines such as 832/13 (53). In conclusion, the resistance to IL-1β in selected cell lines appears to involve impairment of NF-κB signaling, while resistance to IFN-γ is due at least in part to upregulation of STAT-1α. Further, expression of STAT-1α in INS-1 cells confers cytokine resistance without causing impairment of GSIS. Thus, STAT-1α emerges as a gene product that may be important in conferring protection against cytokine-induced damage of transplanted islet cells or that may be exploited in preserving β-cell mass in diabetes. However, much more remains to be learned about the mechanism of action of overexpressed STAT-1α in prevention of cytotoxicity. We will also seek to learn more about other genes whose expression may be affected in our new model of cytokine resistance and β-cell survival.

A great deal has been learned about the islet β-cell in recent years via the application of the tools of molecular biology. Many laboratories, including our own, are engaged in gene discovery experiments using various models of β-cell differentiation, expansion, survival, and dysfunction, and the field will soon be confronted with lists of hundreds, even thousands, of genes that are differentially expressed in islet β-cells under different conditions. Identification of the small subset of these genes that constitute therapeutic targets in type 2 diabetes, or that can be exploited to encourage β-cell growth, differentiation, and survival in development of surrogate β-cells for therapy of type 1 diabetes are perhaps the next great challenges of this area of research. Ultimate success will require the very careful selection of appropriate cellular models, the use of interdisciplinary approaches for phenotypic analysis, and the ability to screen the functional impact of candidate genes in a rapid and reliable fashion.

Work from the author’s laboratory described in this article was supported by National Institutes of Health Grants R01-DK-42582, P01-DK-58398, and RO1-DK-55188. The author is deeply grateful to an extraordinary group of mentors, colleagues, and collaborators who have contributed to the work performed in his laboratory over the past 15 years. This includes current lab members Hans Hohmeier, Tom Becker, Danhong Lu, Ruojing Yang, Mette Valentin Jensen, Jie An, Anne Boucher, Katsuhito Murase, Jonathan Schissler, Catherine Clark, Veronique Tran, Brent Pressley, Paul Anderson, and Lisa Poppe and former lab members Joseph Milburn, Ward Coats, Steve Hughes, Christian Quaade, Richard Noel, Laura Noel, Sarah Ferber, Hector BeltrandelRio, Anna Maria Gomez-Foix, Chisato Miyaura, Carol Minassian, Peter Antinozzi, Andrea Sestak, Wolfgang Schnedl, Khiet (Denny) Trinh, Robert O’Doherty, Hindrik Mulder, Per Bo Jensen, Sabine Telemaque Potts, Rosa Gasa, Hal Berman, Guoxun Chen, Teresa Eversole, Kim Ross, Donna Lehman, Sarah Lampert, Filip Knop, Lee Bryant, and Kazimiera Mizier. Throughout its existence, our laboratory has benefited from the expert administrative assistance of Kay Naughton. We have enjoyed fruitful collaboration on the projects described in this article with Dr. A. Dean Sherry and Dr. Shawn Burgess of UT Southwestern Medical Center, Dr. Marc Prentki of the University of Montreal, and Dr. Lee Witters of Dartmouth College. We have also had productive industrial collaborations with Dr. Mark Butler and his team at Gore Hybrid Technologies, Flagstaff, Arizona, and with a dedicated and talented group of scientists and business leaders at BetaGene, Inc., Dallas, Texas. The author has benefited from the extraordinary wisdom, friendship, and guidance of his key mentors at UT Southwestern Medical Center in Dallas, Dr. J. Denis McGarry, Dr. Roger Unger, and Dr. Daniel Foster, and has been enriched by the friendship and support of Gifford and Carol Touchstone and their family. This article is dedicated to the memories of the author’s father, Mr. John J. Newgard, and to his graduate mentor, Dr. J. Denis McGarry.