Commentary on Some Recent Theses Relevant to Combating Aging: October 2014

Abstract

In this column, we continue the series, begun in issue 10(1), of surveys highlighting a small selection of recently completed doctoral theses with particular relevance to the fields covered by Rejuvenation Research.^1

–7 While it has become common for thesis work to appear in the general academic literature, it remains valuable to scan the thesis databases for important advances that one might otherwise overlook.

Age-Related Macular Degeneration: Interventional Tissue Engineering and Predictive Modeling of Disease Progression

Kevin McHugh, PhD, Boston University

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in people over the age of 50. As many as 50 million people are affected by AMD worldwide, and prevalence is expected to continue to rise due to an aging population. There are two forms of the disease—dry (geographic atrophy) and wet (choroidal neovascularization)—both of which result in retinal degeneration and central vision loss. Although anti-vascular endothelial growth factor therapies are moderately successful at treating the wet form, there are no treatments currently available for the more common dry form. Pharmacological therapies have been extensively explored for the treatment of dry AMD, but have achieved little success because the pathogenesis underlying AMD is unknown and likely varies among patients. Recently, tissue engineering has emerged as a promising approach to restore function by replacing diseased retinal tissue with healthy retinal pigment epithelium (RPE). While AMD-associated vision loss occurs when photoreceptors degenerate, this process arises as a consequence of earlier RPE dysfunction. In the healthy retina, the RPE acts as a critical regulator of the microenvironment for both photoreceptors and the nearby vasculature. However, in AMD, the RPE no longer performs these essential homeostatic functions, leading to photoreceptor apoptosis and vision loss. This dissertation describes the development and in vitro characterization of a tissue-engineering scaffold for RPE delivery as potential treatment for dry AMD. First, a novel microfabrication-based method, termed “pore casting,” was developed to produce thin scaffolds with highly controlled pore size, shape, and spacing. Next, human RPE were cultured on pore-cast poly([varepsilon]-caprolactone) (PCL) scaffolds and compared to cells on track-etched polyester, the standard RPE culture substrate. RPE on porous PCL demonstrated enhanced maturation and function compared to track-etched polyester, including improved pigmentation, barrier formation, gene expression, growth factor secretion, and phagocytic degradation. Last, this study established a patient-specific method for predicting AMD progression using retinal oxygen concentration. This approach differs from current diagnosis techniques because it uses physiologically relevant mechanisms rather than generalized clinical associations that have little, if any, prognostic value.

Comment: Vertebrates perceive light through a chemical process known as the visual cycle, the general form of which has remained unchanged since the divergence of jawless and jawed taxa around half a billion years ago.⁸ The photoreceptor cells of the eye contain the pigment rhodopsin, a compound of a G-protein coupled receptor of the opsin family and the chromophore 11-cis retinal. The absorption of a photon causes 11-cis retinal to photoisomerize to all-trans retinal, following which a conformational change of the host opsin triggers a signaling cascade and firing of the photoreceptor cell. The expended retinal molecule is then transported into the underlying pigmented epithelium where it is recycled back to its original 11-cis form, ready to repeat the cycle. In rare cases, however, two molecules of 11-cis retinal can instead cross-react with the membrane phospholipid phosphatidylethanolamine, forming the retinoid A2E (N-retinyl-N-retinylidene ethanolamine). A2E, which accumulates with age in the lysosomes of human RPE cells as a major component of the “age pigment” lipofuscin and which stimulates apoptosis of RPE cells at physiologically relevant concentrations by inhibition of cytochrome oxidase, is suspected to be the primary causal agent in the development of dry AMD. The SENS Research Foundation is actively pursuing therapies able to stimulate the clearance of A2E in the healthy eye,⁹ preventing its accumulation long before pathology develops, and others are pursuing alternative solutions to lysosomal clearance.¹⁰ However, for the millions already afflicted with advanced disease, regeneration of the lost tissue—both the RPE, as discussed in this work, and the photoreceptors themselves—will be a requirement to restore normal function.

Biomimetics Through Nanoelectronics: Development of Three-Dimensional Macroporous Nanoelectronics for Building Smart Materials, Cyborg Tissues, and Injectable Biomedical Electronics

Jia Liu, PhD, Harvard University

Nanoscale materials enable unique opportunities at the interface between physical and life sciences. The interface between nanoelectronic devices and biological systems makes communication possible between these two diverse systems at the length scale relevant to biological functions. The development of a bottom-up paradigm allows the nanoelectronic units to be synthesized and patterned on unconventional substrates. In this thesis, I will focus on the development of three-dimensional (3D) nanoelectronics that mimic the structure of porous biomaterials to explore new methods for seamless integration of electronics with other materials, with a special focus on biological tissue. First, I introduce the fabrication of an ultra-flexible macroporous nanoelectronic with 3D structure, porosity larger than 99%, hundreds of addressable nanodevices, and feature sizes ranging from 10 μm to 10 nm. Second, I demonstrate that these nanoelectronics as nanoelectronic scaffolds (nanoES) may be easily integrated with organic gel, polymers, and biomaterials without altering their physical/chemical properties. Notably, these devices can have multiple functions in hybrid materials as photodetectors, electrical and chemical sensors, and strain sensors. Third, I present the culture of synthetic tissues within these nanoES to generate “cyborg” tissues, introducing a fundamentally new way to seamlessly integrate nanoelectronics with tissues in 3D to interrogate tissue activity. The response of cyborg tissue to external drug stimulation or microenvironment pH change can be monitored in real time by the embedded devices. Finally, I report a method by which freestanding macroporous nanoelectronics can be manipulated by syringe injection and self-restore their geometric configuration. The electronics can be injected into in vivo systems to facilitate in a minimally invasive way chronic neuron communications, electrode implantation, and active tissue scaffold implantation. Multiplexed recording of brain signals from nanodevices on the scaffold shows promise for studying functional brain activity. The macroporous structure of the scaffold allows reorganization of the tissue within the scaffold and promotes migration of adult neural stem cells from the subventricular zone to the electronics network. Together, these results open up new directions in merging nanoelectronics with living tissue, organs, and other systems, bringing many opportunities to fields ranging from smart materials design and brain–machine interface to regenerative medicine.

Comment: In vivo, the autonomic nervous system provides continual monitoring and regulation of tissue composition and activity throughout development and adult life. The replication of such feedback loops in vitro might therefore reasonably be expected to be necessary for—or at least, to assist in—the production of bioidentical engineered tissues and organs. However, most contemporary electronic sensor/effector technologies are poorly suited for use inside the body, especially at the densities required to interact effectively with large volumes of cells, and historically such work has generally been conducted in the unrealistic environment of 2D culture on an electrically active surface. This innovative thesis introduces a promising new approach to artificial innervation, employing an electronically functionalized nanowire mesh as part of the cell culture scaffold to permit sampling of variables such as electrical charge, pH, oxygen tension, mechanical strain, and more. Such “smart scaffolds” seem certain to be increasingly widely used in the future. The ability to inject such a scaffold into existing tissue with minimal disruption is perhaps even more exciting, giving clinicians access to real-time data on current function that could be used to rapidly assess response to therapy and detect undesirable side effects earlier in treatment. Where anomalous electrical activity forms part of the pathology itself, an injected mesh might even serve directly as a therapeutic tool—detecting and compensating for the problematic signal without interfering excessively with other local electrochemistry. The ability of the nanoES to self-assemble also implies the potential for its use in the context of de-cellularized scaffolds,¹¹ which are likely to be easier to infiltrate than native tissue. The long-term durability of such nanoelectronic scaffolds in vivo—and the extent to which this property can be adjusted at the time of preparation—will determine whether their role will be as temporary diagnostic aids, as permanent prosthetics, or both.

Live Cell Lithography and Non-Invasive Mapping of Neural Networks

Anna Linnenberger, PhD, University of Colorado at Boulder

This research explores two applications that both require the ability to redirect a laser dynamically and efficiently to a volume of focal points in a sample. In the first application, a fabrication technique is presented to microlithographically patterned three-dimensional (3D) cellular structures in polymer hydrogels, with a particular focus on patterning C2C12 cells in linear arrangements to study the formation of muscle fibers. By exploiting the precision and control of microlithography to fabricate artificial tissues, this research aims to develop and demonstrate use of a tool that can be used to answer fundamental questions of developmental cell biology that cannot be addressed with existing randomly arranged 3D tissue scaffolds or 2D plated cells. In the second application a spatial light modulator (SLM)-based microscope is presented that offers a new optical method of stimulating and monitoring interconnected cells in 3D environments. By non-invasively stimulating and imaging such 3D arrangements, including artificial, ex vivo, or in vivo tissue volumes, this research further aims to extend understanding of cellular interconnectivity, particularly in neural networks of brain slices. This highly interdisciplinary work was completed in collaboration with a diverse group of active international collaborators from the fields of micromanipulation, microlithography, cellular biology, and neuroscience. The tissue engineering work presented in this thesis merges holographic optical tweezers for cell positioning with step-and-repeat 3D additive manufacturing to fabricate complex polymer microstructures of arbitrary scale containing internal cellular arrangements organized with micron-scale precision. These polymer micro-scaffolds support the cellular network and also provide channels for nutrient flow and directed cell growth. This research is the foundation of a new discipline of live cell lithography, which is used to enable biologists to study cell to cell signaling and cellular growth as a function of 3D cellular positioning in an environment that more realistically represents living tissue. The second portion of the research extends the understanding of 3D cell networks using optogenetics. The project aims to develop a SLM-based microscope to enable optical monitoring and manipulation of the activity of neuronal ensembles in vitro and in vivo. The outcome is a compact commercially available microscope that enables fast, 3D imaging and photoactivation of neurons. This can be used for imaging intact neural network activity, optical manipulation of neuronal firing, functional mapping of brain connectivity, investigating neurovascular coupling, and assaying neuronal activity in animal models of brain disease.

Comment: Reproducibility is the key to both quality scientific research and translational regulatory approval, and consequently methods for spatially patterning biological materials with extremely high consistency continue to be in great demand. Bioprinting—the positioning of cells and other biomaterials via a mechanism similar to that of an inkjet printer—has dramatically improved the available options for producing customized 3D tissue constructs, but bioprinters generally lack the fidelity to position individual cells on the micrometer scale (relying instead on droplets containing multiple cells), while their products can suffer geometric distortion as a result of the deposition and setting of consecutive drops of material. In this ingenious work, the authors employ a bank of optical tweezers to precisely constrain the position of up to several hundred live cells in a layer of photosensitive monomer, while monitoring the process under bright-field illumination to confirm accurate placing. With the optical traps still active, a spatial light modulator is then used stereolithographically to trigger gelation in a programmed pattern throughout the layer. Although the latter step is limited to a resolution of around 10 μm, the approach retains the positional accuracy of the optical traps, enabling ∼1 μm precision for final cell placements. Finally, translation of the stage in three dimensions enables the production of large constructs without loss of precision.

Peto's Paradox and the Evolution of Cancer Suppression

Aleah Caulin, PhD, University of Pennsylvania

To successfully build and maintain a multicellular body, somatic cells must be constrained from proliferating uncontrollably and destroying the organism. If all mammalian cells were equally susceptible to oncogenic mutations and had identical tumor suppressor mechanisms, one would expect that the risk of cancer would be proportional to the body size and life span of a species. This is because a greater number of cells and cell divisions over a lifetime would increase the chance of accumulating mutations that result in malignant transformation. Peto's paradox is the clash between the theory that cancer incidence should increase with body size and life span and the observation that it does not. In this thesis, I present the first comprehensive survey of empirical evidence across mammals in support of Peto's paradox in addition to computational models that explore the numerous hypotheses that may help resolve the paradox. I provide a detailed examination of tumor suppression in African elephants (Loxodonta africana) and show that this species' genome contains redundant copies of the tumor suppressor gene TP53. I give evidence that these redundant copies are actively transcribed and also observe an increased apoptotic response after exposure to ionizing radiation, which may be linked to the expression of these genes. Few genomes of large, long-lived organisms are currently available, which motivated my work to provide the sequence and de novo assembly of the humpback whale (Megaptera novaeangliae) genome. In this genome, I discovered a set of tumor suppressor genes that have evolved at an accelerated rate along the whale lineage, which is suggestive of adaptation. Additionally, I find one gene that has undergone convergent evolution between the African elephant and the humpback whale. The overarching goal of my research is to gain a better understanding of how evolution has suppressed cancer in large, long-lived organisms in the hopes of ultimately developing improved cancer prevention in humans.

Comment: Given the limited fidelity of DNA replication and repair processes, there is a certain sense in which cancer seems “inevitable” for humans—a problem that only worsens with longer life spans as other causes of death are rendered curable. Existing treatment options rarely result in full remission, principally due to the genetic heterogeneity and high mutation rate of mature tumors. An ideal anti-cancer therapy would thus either take effect far earlier in the disease process, before the pre-neoplastic cells are able to fully activate their evolutionary advantage, or would exploit a distinctive feature of cancer cells that cannot be mutated away.^12,13 Progress on the former strategy has been slow due to our poor understanding of early carcinogenesis and of the healthy cell cycle from which it develops. Fortunately, as this work highlights, evolution has partially solved the problem for us; the biochemical machinery employed by unusually longevous mammals¹⁴ to control cancer might possibly be adopted directly, by gene therapy, to confer similar resilience on human cells. This seems particularly feasible when the nature of the “upgrade” is as simple as providing additional copies of genes with close human homologs, such as TP53. It remains to be seen whether the regulatory environment will advance swiftly enough to enable such prophylactic gene therapies to be tested efficiently (a particularly non-trivial task, given the timescales involved and the difficulty of measuring pre-cancerous changes) and licensed for widespread use.¹⁵

Predictive Optimization of Pharmaceutical Efficacy

Hann Wang, PhD, University of California, Los Angeles

Drug combinations significantly expanded the opportunity space of druggable genome in cancer therapeutics, but the discovery of novel combinations is still limited by the capacity of our current drug screening technology. To address the challenge, we introduced a data-driven search method called the Predictive Optimization of Pharmaceutical Efficacy, or PROPHECY, for the selection of drugs in combinatorial cancer therapeutics. The user provides the genetic profile, of cancer cell lines or primary cells, and PROPHECY selects optimal drug combinations from a comprehensive list of drugs to meet clinical objectives. The decision-making is accomplished by in silico drug screening in which the sensitivities of a cell on different drug combinations are ranked. The predictive model of sensitivity is trained to recognize signatures of information spread in the protein–protein interaction network. Once a comprehensive dataset of drug screening experiment is supplied, the computer could automatically learn interactions between drug targets and disease genes in the information signatures and infer sensitivity for unseen drug and cell line pairs. We showed that the predictions have high correlation with experimental data by cross-validation performed on a dataset of 40,000 entries, which represents 100 cancer drugs applied on 450 cell lines. We also verified the applicability of PROPHECY by performing an in vitro experiment with 36 two-drug pairs suggested by the program and a panel of six cell lines. PROPHECY not only predicted the sensitivity with high accuracy, but also discovered novel high-efficacy combinations and reproduced existing drug combinations. Unlike currently predominant approach of reductionist drug development, the prediction of drug efficacy is based on network view of proteomic scale data, and so can accurately reflect modular activity of the proteome and elucidate target gene interactions in de novo drug combinations.

Comment: The wide range of cancer drugs now available, combined with the variation in cancer genomes between patients with the same clinical diagnosis, makes the selection of the ideal therapeutic(s) for a given case intensely challenging. It is already impractical for a human doctor to evaluate even a small proportion of the available data on healthy metabolism and drug effects, leading inevitably to the conclusion that many patients are currently being treated with sub-optimal medication. Efforts to tackle this via patient-led research initiatives,¹⁶ while laudable, are impractical to implement globally. Combination therapy remains the most promising available strategy for tackling cancer, because attacking the tumor via multiple biochemical routes minimizes the chance of any individual neoplastic cell possessing resistance to all the agents employed; but including drug interactions in the already complex clinical analysis expands the space of possible solutions even further beyond unassisted human capacity. Handling this volume of data efficiently will require artificial intelligence techniques such as the one introduced in this work, and which have already been applied successfully in related fields.¹⁷ The success of PROPHECY in predicting both known and novel efficacious drug combinations is a highly promising step forward in the field, and one that we hope will lead to its eventual adoption as a tool in day-to-day oncological practice.

Rapid Detection of Lipid Biomarkers in Three-Dimensional Hybrid Microfluidic/Nanofluidic Devices

Larry Gibson, PhD, University of Notre Dame

Current biomarker measurements using benchtop analytical systems strongly suggest that real-time monitoring of the concentration of specific lipids and/or their derivatives in patient biofluids would enable physicians to more accurately track disease progression and swiftly administer therapeutic treatments. Although fluid-borne lipids constitute a severely underexplored class of biomolecules, primarily due to poor solubility in aqueous media, restrictive studies of a select few subclasses, using current bioanalytical methods, namely enzyme-linked immunosorbent assays (ELISAs), high-performance liquid chromatography (HPLC), and capillary electrophoresis (CE), have afforded invaluable correlations between these analytes and a number of neuroinflammatory and neurodegenerative diseases such as Parkinson's disease, multiple sclerosis, amyotropic lateral sclerosis, atherosclerosis, lupus, and Niemann–Pick type C. However, lipid analysis using benchtop ELISAs, HPLC, and CE systems in real-time has yet to be realized, because execution of these techniques requires highly specialized technicians within the confines of well-equipped laboratories. Moreover, analysis times are on the order of several hours. Fortunately, the development of point-of-care medical diagnostic systems has progressed tremendously over the last decade, aggressively replacing conventional laboratory-based clinical tests with the ability to rapidly provide diagnostic information to a patient at the bedside. This technology enables faster administration of care and improved analysis of the efficacy of therapeutic methods, thus extending the duration that patients suffering from life-threatening diseases enjoy a good quality of life. This dissertation introduces a three-dimensional hybrid microfluidic/nanofluidic device that performs electrophoretic separations of lipid biomarkers and discusses the development of this technology into a promising alternative to current analytical methods using benchtop CE separation units. Taking advantage of a newly devised approach, non-aqueous microchip electrophoresis coupled to mass spectrometry via nanospray ionization (NAME-NSI-MS), this hybrid device successfully executes label-free characterization of lipid biomarkers in a matter of minutes. Additionally, the highly versatile architecture is compatible with in vivo sampling methods such as microdialysis, and future iterations may incorporate additional capabilities such as sample pre-processing and analyte preconcentration. Collectively, these features constitute the invention of a powerful new medical diagnostic system with the potential to significantly improve the quality of health care administered to society at large.

Comment: Around 15%–20% of the mass of a healthy human body is composed of fat; yet even this figure probably underestimates the biological significance of lipids, sheets of which form the fundamental membrane barriers dividing any organism into distinct aqueous compartments. Interactions between membrane lipids and membrane-bound proteins determine which molecules can traverse a particular cellular barrier, whereas free lipids have critical roles in both short- and long-range signaling. Despite this significance, lipidomics has lagged considerably behind related fields, principally because the hydrophobicity of lipids makes typical biochemical techniques impractical, and because the features distinguishing one species from another are often structurally minor or repetitive and thus difficult to label accurately. The development of rapid, water- and label-free analytical approaches, as described in this work, is long overdue and promises to fundamentally expand the range of physiological variables available to a diagnosing clinician. Widespread use of such technology will also produce large datasets of lipidomic data across the population, enabling researchers for the first time to determine the extent and significance of age-related variation in systemic lipid levels. Given the increasing evidence that much of “normal” aging might be better described as pre-clinical disease,¹⁸ we anticipate that such datasets will greatly facilitate the early detection and prophylactic treatment of numerous conditions.

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