Optimization of an Untargeted Metabolomic Platform for Adherent Cell Lines

Catalyst Volume 7

ABSTRACT

Metabolites are the end products of all cell function. Metabolomics is the study of these small molecules such as sugars, lipids, and amino acids using analytical techniques including liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR). These instruments can measure differences between disease states and drug treatments as well as other physiological effects on cells. Ideal sample preparation is highly precise, accurate, and efficient with minimal metabolite loss. However, there is currently no universal method for isolating and analyzing these metabolites efficiently. Additionally, there is a lack of effective normalization strategies in the field of metabolomics, making comparison of results from different cell types or treatments difficult. The traditional method for data normalization uses protein concentration; however, the buffers used to extract metabolites effectively are not compatible with proteins, leading to protein precipitation and erroneous protein concentration measurements. Although time-consuming, cellular imaging would provide the best results in normalizing for cell number. We conducted a study comparing alternative filtration methods during sample preparation. Two types of filtration plates were analyzed for a variety of compound classes, features, and levels of sensitivity. Data normalization was tested by linearly increasing cell number and comparing whether protein concentration, cell debris weight, or DNA concentration increased at a rate that was proportional to cell number. Sample preparation was found to be more efficient using protein precipitation plates compared to phospholipid removal plates during lysate filtration. After testing several techniques to normalize data by cell number, DNA extraction from the cell debris demonstrated the most time-efficient, consistent, and reproducible results.

INTRODUCTION

Metabolomics is the study of small molecules, such as lipids, amino acids, and sugars present within the cell.^1,2 This field gives insight into cellular phenotype and function.³ Untargeted metabolomics is the common method used to repeatedly and quickly identify as many small molecules as possible at a specific occurrence using high resolution/high mass accuracy mass spectrometry.^1-4 As a response to variations in environment or drug treatment, changes in metabolites can give insight into what is happening within the cell. Detection of these changes can facilitate identification of possible cancer oncogenesis.¹

Despite the potential of metabolomics to provide new insights into cancer, it suffers from a lack of robust methodology. For example, at the time of this writing, the largest number of metabolites reported for a single study is approximately 400, whereas the number of metabolites in the human metabolome is estimated to be on the order of 40,000.² Since the amount and types of measured metabolites are largely influenced by the method chosen for sample preparation, one focus of this report is sample preparation-related factors that could improve both the number of measured metabolites and the accuracy or precision with which particular metabolites are measured.²

Ideal sample preparation involves a procedure that is reproducible, fast, simple, and free of metabolite loss or degradation during extraction.² During collection, the noise-to-signal ratio is problematic, but there are several ways to decrease it. One method focuses on protein precipitation. In this technique, metabolites are extracted from cells using solvents that quench metabolism and are filtered to remove protein and cell debris.⁵ Using a different approach, Ferreiro-Vera et al. found that using solid-phase extraction (SPE) to remove phospholipids yielded three- to four-fold up-regulation of metabolite signals compared to liquid-liquid extraction (LLE).⁵ We chose to focus on SPE due to its significant increase in metabolite signals and its simpler workflow.

Another problem associated with metabolomic data is the absence of robust methods for data normalization. Because raw metabolite measurements exhibit variation attributable to a number of sources, identification of a normalizer capable of reducing or eliminating the effects of such variation is important. Typically, total protein content or cell numbers are used.⁶ However, measuring total protein is not straightforward, as the buffers needed to extract metabolites are not compatible with proteins; for example, methanol in the buffers causes protein precipitation. While cell imaging as a surrogate for quantifying cell number is a good option, it is time-consuming and therefore not preferred because of the time-sensitivity of several drug treatments.⁶ Thus, an alternative, faster method that employs the same sample for both metabolite extraction and data normalization is needed.

Mass spectrometry (MS) was preferred over NMR spectrometry because MS has greater sensitivity, selectivity, and range of identifiable metabolites.¹¹ One weakness of MS relative to NMR, however, is that the sample cannot be recovered. MS allows the user to easily identify the metabolite by comparing the molecular weight of the molecule to the Tandem mass spectrometry (MS/MS) spectra and retention time of the sample.¹¹

The overall objectives of our research were to improve metabolomic data quality by 1) identifying a robust surrogate for the cell number method to facilitate data normalization and 2) optimizing sample preparation methods. Two different sample filtration strategies were tested—one using a protein precipitation plate and a second using a new phospholipid-depleting filter plate. Additionally, several data normalization strategies were evaluated. Finally, we applied the resulting optimizations to investigate the mechanism of action of L-asparaginase through its metabolic response.

MATERIALS AND METHODS

Cells Lines

OVCAR-8 and OVCAR-4 are ovarian cancer cell lines from the subpanel of the NCI-60, one of the most highly profiled sets of cell lines in the world. Unlimited, easily obtained, and manipulated, OVCAR-8 cells are sensitive to L-asparaginase, while OVCAR-4 cells are resistant.¹⁰ The U251 central nervous system line and MDA-MB-435 melanoma line were also used.⁹ All cells were routinely maintained in RPMI-1640 media containing 5% fetal bovine serum (FBS) and 1% (2 mM) L-glutamine. Cultures were grown in an atmosphere of 5% CO₂ and 90% relative humidity at 37°C.

Sample Preparation

Samples were prepared through filtration of the supernatant, vacuum-assisted evaporation to dryness, and reconstitution of the dried sample. Cell samples in 10 cm dishes were placed on dry ice and washed twice with 4-5 mL of 60% CH₃OH, 40% HPLC-grade H₂O, and 0.85% (NH_4)HCO3, then extracted using 500 µL of CH₃OH:CHCl₃:H₂O (7:2:1) buffer.^7,8 The cells were homogenized for 30s to lyse the cells. The cells were then centrifuged at 20,000 x g at 4°C for 2 min. The lysate was removed and inserted into the protein removal plate while the remaining debris in the cell was saved. In order to prepare for the DNA extraction, the cell debris is air dried because speed vacuuming makes it difficult for the pellet to fully solubilize.

Filtration

A phospholipid removal plate was used because Ferreiro-Veraet al. suggested it as a better way to effectively remove noise- or suppression-imparting metabolites.⁵ We compared the protein removal plate to the phospholipid removal plate. Each well was filled with 500Lfresh CH₃OH with 10 µM xylitol. 200 L of the lysate was then added to each well. After using the vacuum to filter the lysate through the plates, the samples were dried using a speed vacuum and later reconstituted and analyzed in a hybrid ion trap MS.

Metabolite Analysis

Samples stored at -80°C were reconstituted in 50 µL of mobile phase A (deionized H2O + 0.1% Formic Acid (FA)) immediately prior to analysis with LC-MS. The LC parameters were as follows: 5µL of metabolomic sample were injected onto a C18 column (1.8µm particle size, 2.1 x 50mm), and analytes were eluted using a 25 min linear gradient of 100% H₂O + 0.1% FA to 100% ACN + 0.1% FA. Analytes were detected in positive ion MS mode, and data was analyzed using XCMS Online software (Scripps Research Institute, La Jolla, CA). Averaged metabolite intensities for identified compounds were then plotted against cell number.

DNA Extraction

As a surrogate protocol for counting cell number, total genomic DNA was extracted and DNA concentration was quantified using a DNA extraction kit from IBI Scientific, following the Red Blood Cell Preparation protocol. After the cell pellets were completely air dried at room temperature, the pellet was reconstituted in 150 L of lysis buffer. The pellets were then stored at room temperature in the buffer for at least 24 h to allow for maximum resolubilization. DNA preparation was performed by adding GB buffer, shaking vigorously, and incubating at 60 °C for at least 10 min to allow for complete solubilization of the pellet. Afterward, the solution was treated with 100% C2H5OH and transferred to a GD maxi column. For large cell numbers, the solution was split between two columns to avoid saturation of the column. The columns were centrifuged, washed twice with wash buffer, and eluted with 50 L of elution buffer.

RESULTS

Filtration

A phospholipid removal plate was evaluated against a protein precipitation filter plate in order to determine which plate yielded the highest intensity metabolites as well as the greatest variety of compound classes. As shown in Figure 1, both filtration plates yielded similar metabolite intensities for most of the known metabolites. However, in the case of the polyaminesputrescine and spermidine (Figure 1C), the protein removal plate significantly outperformed the phospholipid removal plate. Only one metabolite, glucosamine, displayed greater signal intensity with the phospholipid removal plate (Figure 1B). This result suggests that the phospholipid removal plate may also be useful for future analysis of other carbohydrates.

Protein Concentration and Cell Debris Mass

To attempt to normalize cell number, we tried measuring protein concentration. As shown in Figure 2, extraction of proteins with lysis buffer (Figure 2A) worked well in comparison to extraction with CH₃OH/acetonitrile (Figure 2B), which showed low protein concentration. These were compared using levels of vehicle and L-asparaginase. To obtain better results, we tried another method for cell normalization using cell debris mass. Figure 3 shows that, in systems with fewer cell numbers, cell debris mass showed a low increase in weight; however, in samples with higher cell counts, the mass increased greatly. Neither cell debris nor protein concentration produced a linear relationship with cell number when using conditions and buffers required for metabolomics (Figure 2B and 3).

DNA

DNA concentration was measured for four different cell lines to evaluate its association with cell number. First, cell dilutions ranging from 0.25 – 4 million cells/dish were seeded and processed to determine DNA concentration. Figure 4 illustrates a positive linear correlation between cell number and DNA concentration among all four-cell lines investigated. Cell number was then evaluated by DNA concentration throughout a 48-hour incubation period. DNA concentration was recorded at 0, 4, 8, 24, and 48 hours after incubation. Analysis of the OVCAR-8 and OVCAR-4 cell lines suggests that a greater number of cells yielded faster growth (Figure 5). However, because cell doubling was not seen within the first 24 hours, these data suggest a slight lag phase in growth after feeding. This observation suggests that proximity between cells affects their growth (i.e., some lines grow faster once confluence exceeds a threshold level).

To test this hypothesis, OVCAR-4 cell growth was monitored by cell count (Figure 6) and DNA concentration (Figure 5B). Our results were consistent with the published doubling time of 39 hours for this cell line. However, the results also indicated a lag phase in the doubling of DNA concentration (Figure 5B) and cell number (Figure 6), although the latter lag phase was subtle.¹¹ To validate the existence of the growth lag observed in OVCAR-4 cells, we investigated cell growth by DNA concentration for two additional cell lines: U251 and MDA-MB-435. Because cells of these two lines tend to grow individually, we expected to observe no lag phase. However, as shown in Figure 5C and 5D, both lines exhibited a growth lag. This lag is smaller than that of the OVCAR-4 line but greater than the growth lag of the OVCAR-8 line, as shown by the differences in DNA concentration between the 0 and 8 hour time points.

DISCUSSION

Metabolomic data quality can be dramatically affected by sample preparation and data normalization methods amongst many other factors. Ferreiro-Vera et al. reported that removing phospholipids provided improved signal intensity for majority of retrieved metabolites. Based on these results, we hypothesized that phospholipid depletion would improve metabolite signal intensity across a diverse set of metabolites extracted from the ovarian cancer cell line OVCAR-8.² However, we found that a protein removal plate yielded greater signal intensity than the phospholipid removal plate for most metabolites. Specifically, the polyamines were significantly depleted in the phospholipid removal plate. Nevertheless, the phospholipid removal plate yielded a notable amount of glucosamine, suggesting that this plate may be helpful for the examination of carbohydrate metabolites.

Initial attempts to use protein concentration and cell debris mass from metabolomics samples failed to provide a solution for normalization. We initially tried normalizing cell number using protein. However, it was an issue because we miss the soluble proteins by measuring the debris. Protein concentration works well with bicine-chaps buffer, which keeps the proteins in solution. However, the buffers required for metabolite extraction do not preserve the proteins. Also, the methanol used for extraction causes precipitation of the proteins, making the concentrations measured extremely low. Realizing that the cell pellet could be put to use, we attempted to normalize cell number by cell debris mass. This method proved to be inefficient because the analytical scale was not precise enough to accurately give the same weight in repeated measurements. In the end, DNA extraction from the cellular debris proved to be an extremely effective method of normalization. Using OVCAR-8 and OVCAR-4 cell lines, DNA concentration was shown to increase proportionally to cell number. For time course experiments, cell number can therefore be normalized using DNA extraction from the metabolomic cellular debris.

In conclusion, this study has shown that protein precipitation plates are superior to phospholipid removal plates for most metabolite classes. However, for sugars, it may be more advantageous to use phospholipid removal plates. Rather than manually counting cells through a microscope, DNA extraction can efficiently normalize cell number without affecting the time required for metabolomic analysis.

ACKNOWLEDGMENTS

I would like to thank Dr. John Weinstein for allowing me to have this wonderful research experience, Dr. Phil Lorenzi for supporting me and providing feedback to help make me a better scientist, and Dr. Leslie Silva for her daily guidance, advice, and support through my whole experience at MD Anderson Cancer Center. This research was supported by the University of Texas, MD Anderson Cancer Center.

References

1. Dehaven, C.D., et al.; Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J. Cheminform, 2010, 2, 9.
2. Vuckovic, D.; Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal BioanalChem, 2012, 403, 1523-48.
3. Jankevics, A. et al.; Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets. Metabolomics, 2012, 8, 29-36.
4. Patti, G.J.; Separation strategies for untargeted metabolomics. J Sep Sci, 2011, 34, 3460-9.
5. Ferreiro-Vera, C.; F. Priego-Capote; M.D. Luque de Castro; Comparison of sample preparation approaches for phospholipids profiling in human serum by liquid chromatography-tandem mass spectrometry. J Chromatogr A, 2012, 1240, 21-28.
6. Cao, B. et al.; GC-TOFMS analysis of metabolites in adherent MDCK cells and a novel strategy for identifying intracellular metabolic markers for use as cell amount indicators in data normalization. Anal BioanalChem, 2011, 400, 2983-93.
7. Xiao, J.F.; B. Zhou; H.W. Ressom; Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends AnalytChem, 2012, 32, 1-14.
8. Lorenzi, P.L. et al.; DNA fingerprinting of the NCI-60 cell line panel. Mol Cancer Ther, 2009, 8, 713-24.
9. Carnicer, M. et al.; Development of quantitative metabolomics for Pichiapastoris. Metabolomics, 2012, 8, 284-298.
10. deJonge, L.P. et al.; Optimization of cold methanol quenching for quantitative metabolomics of Penicilliumchrysogenum. Metabolomics, 2012, 8, 727-735.
11. Shankavaram, U.T. et al.; CellMiner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics, 2009, 10, 277.
12. Roberts, D. et al.; Identification of genes associated with platinum drug sensitivity and resistance in human ovarian cancer cells. Br J Cancer, 2005, 92, 1149-58.

Statins May Hold the Answer to Eternal Youth

Catalyst Volume 7

Abstract

The signs and symptoms of aging are mostly a consequence of impaired antioxidant function in the body. Statins are drugs that have recently been discovered to counter these age-related changes. These drugs are typically prescribed for long-term control of plasma cholesterol levels in patients with atherosclerotic coronary artery disease. Statins have been found to enhance the enzyme paraoxonase, a potent antioxidant molecule; as a result, statins are able to alleviate manifestations of aging and effectively retard the aging process. Supplementing statins with dimercaprol and restriction of calorie consumption may also prove helpful in decelerating aging. However, use of statins frequency causes myopathy. In order to establish statins as an effective anti-aging medication, the exact pathophysiology of this side-effect must be determined.

Keywords: statins, anti-aging, HMG Co-A reductase, paraoxonase, antioxidant, ROS, sulfhydryl, dimercaprol, caloric restriction

Introduction

Statins decrease low-density lipoprotein (LDL) cholesterol while simultaneously elevating high-density lipoprotein (HDL) cholesterol levels.³ Rise in serum LDL level is correlated to increased cellular uptake of cholesterol, especially in the endothelia.² During atherosclerosis, these LDL molecules undergo oxidation and amplify the inflammatory process through macrophage induced cytokine release. Cytokines then give rise to a prothrombotic state that leads to coronary artery thrombosis.³ HDL molecules, however, remove the excess cholesterol from body tissues and transport them to the liver for final degradation.² Since statins reduce the serum LDL levels and increase serum HDL concentration, the risk of morbidity and mortality associated with coronary artery disease (CAD) can be significantly reduced by taking this drug.

However, recent research has revealed that the typical mechanism of action used by statins to control atherosclerosis might be useful in synthesizing future anti-aging medication.⁵ Aging brings about inevitable physiological changes. Among these, cardiopulmonary and renal disorders are the leading causes of death in the geriatric population, while aging skin spoils the beauty of an eternal youth. Aging imposes a significant health burden on the economy of a country, especially in developing nations. Degradation of beauty, although less important, possesses significant social implications. Hence, discovery of a drug to minimize age-related health complications and maintain external beauty can be a boon to health welfare authorities of every country and contribute to the economic prosperity of their nations.

The Mechanism of Statins in Opposing Atherosclerosis

The predominant mechanism of action of statins in preventing atherosclerotic CAD is by competitive inhibition of hexamethyl glutaryl Co-A (HMG Co-A) reductase enzyme, the key enzyme in the pathway of cholesterol biosynthesis. Statins also increase the expression of LDL receptors in the hepatocytes in order to clear plasma LDL molecules. In addition, the drugs increase the activity of paraoxonase-HDL enzyme complex.⁴ The functions of HDL and LDL molecules are exactly opposite to each other. LDL delivers cholesterol to the body tissues, while HDL removes the cellular cholesterol and presents them to the liver. Since low plasma cholesterol level is equivalent to low serum LDL concentration, LDL availability at the site of atherosclerosis is significantly decreased. As a result, LDL oxidation is reduced.

The alloenzyme PON 1 192 QQ of the paraoxonase-HDL complex is reported to possess the most prominent antioxidant action among all the enzymatic variants. It catalyzes the hydrolysis of phospholipid hydroperoxides in LDL.⁴ Paraoxonase plays an important role in preventing lipid peroxidation elsewhere in the body as well. Statins have been found to boost the activity of paraoxonase.⁵ Statins oppose atherosclerosis by using paraoxonase-HDL complex as the mediator.

Aging and Impaired Antioxidant Activity in the Body

Aging involves a significant deterioration in antioxidant activity of the body. There is a significant increase in mitochondrial activity as the body ages.⁶ As the site of oxidative phosphorylation, mitochondria produce reactive oxygen species (ROS) that are neutralized by superoxide dismutase (SOD), an important antioxidant molecule. With aging, the mitochondrial DNA may undergo mutations that amplify ROS generation.⁶ The free radicals alter the structure of mitochondrial membrane lipids and bring about undesirable changes in the organelle and other parts of the cell. This oxidative stress creates a functional deficiency of the SOD enzyme.⁶

ROS concentration has been found to rise in older age groups with unrestricted intake of calorie-rich foods (fats and carbohydrates). Metabolism of calorie-rich food increases the rate of transfer of electrons in oxidative phosphorylation, resulting in increased generation of ROS.⁷ Additionally, excessive calorie consumption leads to a dysregulation of cellular autophagy that, in turn, results in dolichol accumulation in the cells.⁸ Higher concentrations of dolichol lead to higher HMG Co-A reductase enzyme activity, thereby increasing serum cholesterol level in old age.⁸ Elevated serum cholesterol concentration increases rate of LDL oxidation and generates highly reactive LDL free radicals. Accumulation of free radicals and faulty SOD activity causes the loss of oxidant-antioxidant balance in the body tissues. The high level of oxidants then causes age-related changes in the different organ systems. Figure 1 below summarizes this relation between ROS production, excess calorie intake, and aging.

Statins as Possible Anti-aging Drugs

Their mechanism of action suggests that statins may be able to halt the natural process of aging by using HDL as a mediator. HDL downregulates LDL oxidation through paraoxonase;⁴ there is a time-regulated fall in the paraoxonase activity due to the accumulation of lipid peroxides, which are generated as byproducts of LDL oxidation.⁹ This decreased PON 1 192 QQ function is caused by interactions between its sulfhydryl groups and the metabolic by-products.⁹ Statins can reduce this interaction by decreasing the concentration of LDL molecules in the blood stream. If LDL levels are low, oxidation rate also drops, resulting in significantly reduced generation of oxidized byproducts that interact with paraoxonase.

Reduction of LDL oxidation, however, does not seem to be the definitive solution to this complex problem. Other non-high density lipoproteins such as intermediate-density lipoprotein (IDL) are independent risk factors for age-related CAD. Accumulation of IDL in high levels can bring about adverse effects despite low levels of LDL molecules. Nevertheless, new generations of statins have shown remarkable effects in reducing IDL levels in the blood stream.¹⁰

Statins can be combined with the drug dimercaprol to prevent decrease in paraoxonase activity. Dimercaprol is typically used as a chelating agent in heavy metal poisoning, such as arsenic poisoning. The drug possesses a large number of free sulfhydryl groups that attract metals; this unique property is used to free respiratory enzymes from inhibitory complexes with heavy metals (Figure 2). Because oxidized LDL products bind with the sulfhydryl radical of paraoxonase, dimercaprol can serve as a more attractive substrate for these oxidized products; this competition reduces paraoxonase inhibition (Figure 3). Unfortunately, dimercaprol is a nephrotoxic drug that can exacerbate the already-declining renal function of old age. Dimercaprol also leads to dose-related emesis, hypertension, and palpitation. To combat this, statins can be used in conjunction with dimercaprol to exert a synergistic effect in increasing paraoxonase activity. Therefore, the dose of dimercaprol can be decreased to a level where it will cause only minimal physiological distress.

In addition to these drug therapies, restricted calorie consumption can improve antioxidant effects in the body. Caloric restriction lowers both the HMG Co-A reductase activity and cholesterol biosynthesis. Low cholesterol production means a lower concentration of LDL and its subsequent oxidization, significantly enhancing paraoxonase activity and working with statin therapy to dampen generation of ROS.

Statins can be helpful in reducing incidence of atherosclerotic CAD, hypertension, hypertensive nephropathy, renal artery atherosclerosis, cerebral artery sclerosis, diabetic nephropathy, and dermatological changes. Slow progression of atherothrombotic changes in cerebral artery can lower incidences of cerebrovascular disease (stroke), improve cognition; the antioxidant activities of statins may also prevent cellular death, thus reducing development of skin wrinkles. Death of pancreatic beta cells due to ROS overproduction can be effectively limited and insulin sensitivity in the body tissues may be improved, which can decrease the risk of developing diabetic nephropathy and other micro and macrovascular changes.

A significant side effect, however, is the tendency of statins to cause myopathies.¹¹ This adverse effect must be reduced or eliminated before statins can be introduced as an anti-aging pill in the global market. Drug trials have failed to establish any specific dose-response relationship for this pathological condition.¹² However, lipophilic statins (simvastatin, lovastatin, atorvastatin) have been associated with a greater number of reported myopathy cases.¹³ As a result, statin-induced myopathy may be prevented by prescribing the lowest therapeutic dose of this group of drugs.

Conclusion

The antioxidant property of statins may be effective not only in treating dyslipidemia but also as a potential anti-aging medication. Although the combination of statins, dimercaprol, and caloric restriction seems promising towards reducing or even reversing the process of aging, the additive role of these therapies has not yet been studied fully. More intensive research is necessary to fill the gaps in our knowledge about aging mechanisms and to develop anti-aging drugs.

References

1. Malloy, M. J.; Kane, J. P. Agents Used in Dyslipidemia. In Basic & Clinical Pharmacology, 11th ed.; Katzung, B. G. et al. Eds.; Tata McGraw-Hill: New Delhi, 2009; p 605-620.
2. Botham, K. M.; Mayes, P. A. Lipid Transport and Storage. In Harper’s Illustrated Biochemistry, 27th ed.; Murray, R. K. et al. Eds.; McGraw-Hill: Singapore, 2006; p217-229.
3. Mitchell, R. N.; Schoen, F. J. Blood Vessels. In Robbins and Cotran’s Pathological Basis of Disease, 8th ed.; Kumar, V. et al., Eds.; Elsevier: Haryana, 2010; p 487-528.
4. Mahley, R. W.; Bersot, T.P. Drug Therapy for Hypercholesterolemia and Dyslipidemia. In Goodman and Gilman’s The Pharmacological Basis of Therapeutics, 10th ed.; Hardman, J. G. et al., Eds.; McGraw-Hill: New York, 2010; p 971-1002.
5. Durrington, P. N. et al. Arterioscler. Thromb., Vasc. Biol. 2001, 21, 473-480.
6. Pollack, M.; Leeuwenburgh, C. Molecular Mechanisms of Oxidative Stress in Aging: Free Radicals, Aging, Antioxidants and Disease. In Handbook of Oxidants and Antioxidants in Exercise; Sen, C. K. et al., Eds.; Elsevier Science: Amsterdam; p 881-923.
7. de Grey, A. D. N. J. History of the Mitochondrial Free Radical Theory of Aging, 1954-1995. In The Mitochondrial Free Radical Theory of Aging; de Grey, A. D. N. J., Ed.; R. G. Landes: Austin, Texas; p 65-84.
8. Cavallini, G. et al. Curr. Aging Sci. 1999, 1, 4-9.
9. Aviram, M. et al. Free Radic. Biol. Med. 1999, 26, 892-904.
10. Stein, D. T. et al. Arterioscler. Thromb. Vasc. Biol. 2001, 21, 2026-2031.
11. Amato, A. A.; Brown Jr, R. H. Muscular Dystrophies and Other Muscle Diseases. In Harrison’s Principles of Internal Medicine, 18th ed.; Longo, D.L. et al., Eds.; McGraw-Hill: New York, 2012; p 3487-3508.
12. Stewart, A. PLoS Curr. [Online] 2013, 1. http://currents.plos.org/genomictests/article/slco1b1-polymorphisms-and-statin-induced-myopathy/ (accessed March 14, 2013).
13. Sathasivam, S; Lecky, B. Statin Induced Myopathy. BMJ [Online] 2008, 337:a2286. 

Hands-free driving: A Roadmap to the Future

Catalyst Volume 7

The simple act of driving can be an unproductive, dangerous, and time consuming activity, one that can be solved through the installation of autonomous technology within vehicles. This technology is considered to be among the most crucial breakthroughs in human travel that is being developed today; it is believed to have the capacity to create an improved and efficient driving experience by limiting fuel consumption, decreasing traffic congestion, and reducing wasted time during road trips.

One of the driving forces behind the creation of autonomous vehicles is safety. Autonomous technology promises safer travel compared to human-operated vehicles, as the cars are equipped with laser and video detection systems to control the car's speed and steering mechanisms while avoiding obstacles in the roadway. This blend of autonomous technologies promises to make driving 99% safer while also allowing the travelers to focus on other activities.¹

These cars must detect and make rapid decisions to avoid objects in the roadway; the simple act of crossing an intersection requires the robotic cars to account for the inertias, right-of-way, and velocity of approaching vehicles.² A major problem facing autonomous vehicles is the idea of real-time communication. As humans correspond face-to-face, these autonomous cars need to interact in real-time, allowing the cars to work together safely. However, this type of communication is unpredictable and extremely hard to maintain.³ Autonomous technology presents near endless benefits to automobile commuters; however, this technology faces not only current mechanical and software problems but also major legal and social issues. This technology needs to be perfected in every way possible before being released into city streets. Through my review of the autonomous technology within these computer-driven cars, I will explore the type of technology that operates these cars, how it operates the vehicle, the benefits created from this technology, and any possible legal and social concerns that arise from their use.

Developing Technologies: Seeing, Thinking, Steering

The ability for autonomous cars to see and judge risks in the roadway is vital to safe operation of the vehicle. An outstanding prototype of autonomous technology was created in 2007 by the Stanford Racing Team. Their robotic car Stanley, which won the DARPA Grand Challenge, operated solely on a software system that processed and converted visual data into appropriate driving commands.⁴ This software system uses an onboard sensors including lasers, cameras, and radar instruments to gather outside information from the road, allowing the robotic vehicle to observe and judge the approaching roadway;⁴ these sensors are placed on top of the vehicle. The combination of lasers and cameras allows for increased detection of obstacles by allowing both short and long range detection, respectively.⁴ As the cameras receive the long range images, the lasers allow the vehicle to detect the dimension of approaching objects that could harm the vehicle. Detection of hazardous obstacles is one of the easier aspects of autonomous driving; split second decision-making based on the detection system is harder to accomplish. An autonomous vehicle must use the information from the detection systems to determine if the road surface is safe for driving. Measuring the dimensions of detected objects allows the car to determine if they are true obstacles, such as roadway debris, or non-obstacles, such as grass and gravel. The researchers who helped build Stanley stated that the robot had trouble determining the difference between tall grass and rocks, which poses obvious difficulties in application.⁴ In addition to obstacle recognition software, autonomous vehicles require extensive algorithms to accomplish and maintain velocity, steering, acceleration, and braking—functions all controlled by the same system of detection and decision making.

Dynamically Guided Routes

Route guidance is core to autonomous vehicle technology, which is not safe and effective without a computed path. The purpose of route guidance is to gather information from outside sources (e.g. other vehicles, fleet signals) and stored data to create the most efficient route. However, this technology is hindered by the limited amount of information that can be stored within the vehicle due to static map conditions.⁵ Static conditions are defined as the basic components of individual roadways, such as the length of the road, speed limit, and pre-existing intersection signals. Using static systems can result in unreliable and slower routes due to an inability to account for dynamic road situations; for example, these static routes can be highly ineffective once an accident occurs on the roadways.

Generating accurate routes while on the road is another computationally challenging problem for autonomous technology.⁵ Due to the mobile condition of autonomous vehicles, current onboard computational power cannot compute and translate both long algorithms and dynamic conditions at the same time. Researchers attempting to create an algorithm must balance quick execution and efficient route creation with low computational power.

An additional problem arises from dynamic roadways. Dynamic roads are defined as streets that are always changing due to traffic jams, accidents, and construction.⁵ In his article on route guidance, Yanyan Chen stated that a good route is one that, although possibly not the fastest, is both reliable and acceptable to the driver’s needs. As a solution, Chen and his team created the Risk-Averse A' Algorithm (Figure 1). This algorithm suggests a risk-averse strategy that pre-computes factors that affect traffic (such as weather and time of day), accounts for dynamic traffic flow and accidents, and computes a low-risk and reliable route. The Risk-Averse A' Algorithm is widely accepted in the field of autonomous research as the most efficient form of computing reliable and adaptive directions. In fact, Stanley used this algorithm in the DARPA Challenge.⁴

Navigating Intersections Proves Difficult

The task of navigating an autonomous car through an intersection is not simple. The vehicles must be able to use algorithms to derive not only the distance from the car to the intersection but also its current inertia. Simultaneously, this information must be constantly compared with that of other vehicles. The two main challenges in crossing an intersection are establishing reliable communication with other vehicles as well as the dynamic, convoluted environment of intersections. For autonomous navigation to be possible, vehicles must communicate with each other to determine which car has right of way. When approaching an intersection, each car should propagate signals to the other vehicles, a failsafe in case oncoming cars are not detected by the visual and laser system (Figure 2). In theory, autonomous vehicles will discharge signals containing position and velocity information. At an intersection, approaching cars can detect and process this information to determine the appropriate mechanical move.

The dynamic environment of an intersection creates a whole new series of problems with the introduction of unknown variables. An autonomous system must be able to adapt, sense, and make decisions in short periods of time. The proposed ideas on how to navigate intersections use a decentralized navigation function, a method that has no need for long-range communication between vehicles. It enables cars to navigate independently while maintaining network connectivity and an overall goal. This function allows the car to account for dynamic traffic and improves the use of algorithms.²

Robotic Communication

The problem of real-time coordination between vehicles is a major obstacle that must be overcome for this technology to function safely on city streets and highways. Without reliable and fast communication, autonomous vehicles cannot navigate intersections, conserve energy, drive in safe formations, or create efficient routes. However, communication through wireless networks is not always reliable. Dr. Mélanie Bouroche from Trinity College, Dublin, stated that a “vehicle intending to cross an un-signaled junction needs to communicate in an area wide-enough to ensure that other vehicles … will receive its messages.”³ Figure 3 illustrates how the cars should disperse signals to communicate with other vehicles.

In the article “Real-Time Coordination of Autonomous Vehicles,” Bouroche, Hughes, and Cahill found a solution to this communication issue by creating a space-elastic communication model. A coordination model for autonomous cars allowed autonomous vehicles to adapt their behavior depending on the state of communication, ensuring safety constraints were never violated.³

Conclusion

Autonomous technology should improve daily travel by decreasing fuel consumption, traffic congestion, and accidents. The construction of new highways and streets to accommodate this technology would modernize and improve the efficiency of cities. Daily life could be enhanced, as driving time could be spent more productively. Autonomous technology can greatly improve everyday vehicular travel—but only if it is correctly implemented into society. Many problems still remain in the realization of autonomous vehicles: detection systems must be improved to effectively identify and avoid obstacles, algorithms need to be refined to quickly compute dynamic routes, and communication between vehicles needs to be drastically improved in order to avoid accidents. The legal and societal issues must also be addressed: will all vehicular travel be converted to automated travel? If so, will all citizens be forced to use technology that controls their movement? If not, will separate highways and roads be built? Who will fund this new creation of streets and roads? Who will ultimately control and maintain such a system? Autonomous technology has the potential to vastly improve travel, but it can introduce system vulnerabilities and malfunction. Self-directed vehicles must be thoroughly researched and tested before the technology can be implemented on city, state, and national streets.  

References

1. Hayes, B. Am. Sci. 2011, 99, 362-366.
2. Fankhauser, B. et al. CIS 2011: IEEE 5th International Conference, Qingdao, China, Sept 17-19, 2011; pp.392-397.
3. Bouroche, M. et al. IEEE Conference on Intelligent Transportation Systems, 2006, 1232-1239.
4. Thrun, S. et al. J. Field Robot. 2006, 23, 661-692.
5. Chen, Y. et al. J. Intell. Transport. S. 2010, 14, 188–196.
6. Bergenhem, C. et al. Sartre, 2008, 1-12.
7. Dahlkamp, H. et al. In Proceedings of the Robotics Science and Systems Conference, 2006, 1-7.
8. Elliott, C. et al. In The Royal Academy of Engineering, 2009, 1-19.
9. Douglas, G. W. Unmanned Systems, 1995, 13, 3.
10. Laugier, C. et al. In Proceedings of the IFAC Symposium on Intelligent Autonomous Vehicles. 2001, 10-18.
11. Wright, A. Comm. ACM 2011, 54, 16-18. 

Aquaporin-4 and Brain Therapy

Catalyst Volume 7

Introduction

One of the tasks of modern medicine is to address the many diseases affecting the brain. These maladies come in various forms – including neurodegenerative complications, tumors, vascular constriction, and buildup of intracranial pressure.^1,2,3 Several of these disease classes are caused, in part or in full, by a faulty waste clearance or water flux. Although a pervasive system of slow cerebrospinal fluid (CSF) movement in the brain’s ventricles is present, a rapid method for clearing solutes from the cortex’s interstitial space, which contains neural tissue and the surrounding extracellular matrix, was unknown.^4,5 Recently, Iliff et al discovered a new mechanism for the flow of CSF in the mouse brain – the “glymphatic” system.^5,6 This pathway provides an accelerated mechanism to clear dissolved materials from the interstitial space, preventing a buildup of solutes and toxins.^5,6 At the center of this system is the water transport protein aquaporin-4 (AQP4), and the extent of this water channel’s various roles are only now being identified. New perspectives on the mechanism by which the brain is “cleansed” may lead to breakthroughs in therapeutics for brain disorders such as Alzheimer’s Disease (AD), which is the sixth leading cause of death in the US each year.⁷

The Infrastructure

To understand AQP4 and its role in the brain, the environment in which it operates must be examined. As seen in Figure 1, surrounding the brain are three meninges, protective layers between the skull and the cortex.⁸ Between these layers – the dura, arachnoid, and pia maters – are cavities, including the subarachnoid space that lies just below the arachnoid layer.⁹ As the figure shows, directly underneath the pia mater are the cortex and interstitial space. Within the cortex, CSF flows through a system of chambers called the ventricles, illustrated in Figure 2.10 CSF suffuses the brain and has several vital functions, namely shock absorption, nutrient provision, and waste clearance.¹¹ CSF is produced in a mass of capillaries called the choroid plexus and flows through the ventricles into the subarachnoid space, bathing the brain and never crossing the blood-brain barrier.¹⁰ After circulating through the brain’s interstitial space and ventricles, the CSF then leaves the brain through aquaporin channels surrounding the cephalic veins.⁶

A New Plumbing System

A team of researchers has discovered an alternate pathway for CSF that clears water-soluble materials from the interstitial space.⁶ CSF in this so-called “glymphatic pathway” starts in the subarachnoid cavity and then seeps into the cortex, as seen in Figure 3.⁶ This fluid eventually leaves the brain, carrying with it the waste generated by cells. CSF enters the parenchyma from the subarachnoid cavity and travels immediately alongside the blood vessels.⁶ This route, which forms a sheath around the blood vessels, is dubbed the “paravascular” pathway, and CSF enters and exits the interstitial space through these avenues.⁶ The pia membrane guides this pathway until the artery penetrates the cortex, as seen in Figure 3. From there, the endfeet of astrocytes bind the outer wall.⁶ Astrocytes are glial cells that play structural roles in the nervous system, and endfeet are the enlarged endings of the astrocytes that contact other cell bodies and contain AQP4 proteins.^6,12

Iliff et al found that AQP4 are highly polarized at the endfeet of astrocytes, which suggested that these proteins provide a pathway for CSF into the parenchyma.⁶ To test this hypothesis, they compared wild type and Aqp4-null mice on the basis of CSF influx into the parenchyma.⁶ They injected tracers such as radiolabeled TR-d3 intracisternally, finding that that tracer influx into the parenchyma was significantly reduced in Aqp4-null mice.⁴ According to their model, AQP4 facilitates CSF flow into the parenchyma. There, CSF mixes with the interstitial fluid in the parenchyma; AQP4 then drives these fluids out and into the paravenous pathway by bulk flow.⁶ The rapid clearance of tracer in wild type mice and the significantly reduced clearance in Aqp4-null mice demonstrated the pathway’s ability to clear solutes from the brain. This finding is important because the build-up of Aβ is often associated with the onset and progression of AD.

AQP4 and Aβ

To facilitate Aβ removal, astrocytes become activated at a threshold Aβ, but undergo apoptosis at high concentrations.^13,14,15 Thus, the concentration of astrocytes has to be in a narrow window. A study by Yang et al further explored the role of AQP4 in the removal of Aβ.¹⁶ They found that AQP4 deficiency reduced the astrocytic activation in response to Aβ in mice, and Aqp4-knockout reduced astrocyte death at high Aβ levels.¹⁶ Furthermore, AQP4 expression increased as Aβ concentration increased, likely due to a protein synthesis mechanism. Further investigation demonstrated that lipoprotein receptor-related protein-1 (LRP1) is directly involved in the uptake of Aβ, and knockout of Aqp4 reduced up-regulation of LRP1 in response to Aβ.^15,16 Finally, AQP4 deficiency was found to alter the levels and time-course of MAPKs, a family of protein kinases involved in the response to astrocyte stressors.¹⁶ The role of AQP4 in cleansing the parenchyma as well as modulating astrocytic responses to Aβ thus pinpoint it as a major target for the following potential therapies: repairing defects in toxin clearance from the interstitial space, increasing expression in AQP4-deficient patients to increase astrocyte response, and knocking out Aqp4 in patients with high levels of Aβ to prevent astrocyte damage.

Sleep and Aβ Clearance

Interestingly, there is a link between Aβ clearance and sleep. Xie et al. studied Aβ clearance from the parenchyma in sleeping, anesthetized, and wakeful rodents, obtaining evidence that sleep plays a role in solute clearance from the brain. The researchers found that glymphatic CSF influx was suppressed in wakeful rodents compared to sleeping rodents.¹⁷ Glymphatic CSF influx is vital because it clears solutes from the brain in a way somewhat analogous to the way kidneys filer the blood. Real-time measurements showed that the parenchymal space was reduced in wakeful rodents, which led to increased resistance to fluid influx.¹⁷ Moreover, Aβ clearance was faster in sleeping rodents. Adrenergic signaling was hypothesized as the cause of volume reduction, implicating hormones such as norepinephrine.¹⁷ AQP4 is implicated in this phenomenon, as constricted interstitial space resists the CSF influx that this protein enables.

Aquaporin Therapy

If future treatment will target AQP4 function, then researchers must learn to manipulate its expression. However such regulatory mechanisms are not well understood. It is well-known that cells can ingest proteins in the plasma membrane and thus modulate the membrane protein landscape. Huang et al studied this phenomenon with AQP4, utilizing the fact that occluding the middle cerebral artery mimics ischemia and alters AQP4 expression in astrocyte membranes.¹⁸ They found that artery occlusion down-regulates AQP4 expression and discovered various mechanisms behind this response.¹⁸ Specifically, they determined that AQP4 co-localized in the cytoplasm with several proteins involved in membrane protein endocytosis, after the onset of ischemia.¹⁸ They posited that this co-localization indicates the internalization of AQP4.¹⁸ These correlations indicate that AQP4 is intimately connected with fluctuations in brain oxygen and nutrient levels, which are limited when blood flow is restricted.

Future Research

Aquaporin-4 is vital to many processes in the brain, but the range and details of these roles are not yet fully understood. As demonstrated, this protein is the central actor in the newly defined glymphatic system responsible for clearing solutes from CSF in the interstitial space. This function implicates AQP4 in the progression of AD and suggests other brain states and neurological conditions may have links to the protein’s function. Studies have demonstrated that AQP4 expression is dynamic, indicating that it can be regulated. The hope is that modulation of aquaporin expression or function could be used in brain therapy. Future research will no doubt focus on these mechanisms, and discoveries will aid in developing a treatment for various brain disorders.

References

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5. Scientists Discover Previously Unknown Cleansing System in Brain, University of Rochester Medical Center. http://www.urmc.rochester.edu/news/story/index.cfm?id=3584 (Accessed February 11, 2014).
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