Translational Systems Medicine and Oral Disease

By Stephen T. Sonis

Translational Systems Medicine and Oral Disease bridges the gap between discovery science and clinical oral medicine, providing opportunities for both the scientific and clinical communities to understand how to apply recent findings in cell biology, genomic profiling, and systems medicine to favorably impact the diagnosis, treatment and management of oral diseases. Fully illustrated chapters from leading international contributors explore clinical applications of genomics, proteomics, metabolomics, microbiomics and epigenetics, as well as analytic methods and functional omics in oral medicine. Disease specific chapters detail systems approaches to periodontal disease, salivary gland diseases, oral cancer, bone disease, and autoimmune disease, among others.

In addition, the book emphasizes biological synergisms across disciplines and their translational impact for clinicians, researchers and students in the fields of dentistry, dermatology, gastroenterology, otolaryngology, oncology and primary care.

• Presents the work of leading international researchers and clinicians who speak on the clinical applications of genomics, proteomics, metabolomics, microbiomics, and epigenetics, as well as analytic methods and functional omics in oral medicine
• Provides full-color, richly illustrated chapters that examine systems approaches to periodontal disease, salivary gland diseases, oral cancer, bone disease and autoimmune diseases
• Includes clinical case studies that illustrate examples of oral disease diagnostics and management, highlighting points of key importance for the reader
• Emphasizes biological synergisms across disciplines and their translational impact for clinicians, researchers, and students in the fields of dentistry, dermatology, gastroenterology, otolaryngology, oncology, and primary care

• Language: English
• Publisher: Academic Press
• Release date: Sep 14, 2019
• ISBN: 9780128137635

Book preview

Translational Systems Medicine and Oral Disease

Editors

Stephen T. Sonis, DMD, DMSc

Allessandro Villa, DDS, PhD, MPH

Table of Contents

Cover image

Title page

Copyright

List of contributors

Preface

1. Introduction to systems medicine

Reductionism

Systems biology and medicine

Systems medicine and oral disease

2. System biology

Introduction to system biology

Cell biology and networks

Omics data

Practical implications

3. Genomic foundation for medical and oral disease translation to clinical assessment

Introduction

The basics of the genomic system

RNAs: other useful functions in genomic expression

Darwinism influence on genomic expression and risk for disease

Present status of genomic analysis and determination of disease risk

4. Proteomics

Introduction

Proteomic technologies

Clinical applications of salivary proteomics

Conclusions

5. Metabolomics in head and neck cancer: A summary of findings

Introduction

Saliva metabolomics

Blood and urine metabolomics

Cell and tissue metabolomics

Conclusions and future perspectives

Abbreviations

6. Microbiomics

The human microbiome: a general overview

The oral microbiome

The oral microbiome and systemic disease

Conclusion

7. Epigenetics and oral disease

General introduction

Craniofacial development

Dental caries

Periodontal disease

Salivary gland disease

Oral mucosal lesions

Orofacial pain

Conclusion

8. Systems pharmacology

Introduction and historical context

Systems approaches

9. Functional omics for systems medicine

10. Analytic methods for systems medicine

Introduction

Data sources used in systems medicine analytics and the need for integration

Challenges regarding big data

Data preprocessing

Data mining

Initiatives and perspectives

11. Systems medicine and periodontal diseases

Systems thinking in periodontal medicine

Genomics, epigenomics, and metagenomics of oral host–biofilm interactions

Inflammation resolution failure and oral dysbiosis

Periodontal osteoimmunology

Mechanisms linking periodontal and systemic diseases

Precision periodontal medicine—proteogenomic and metabolomic strategies for biomarker discovery

Conclusions

12. Bone translational medicine

Basic biology—bone and cartilage

Disorders of bone

13. Systems medicine and salivary gland diseases

Introduction

Autoimmune: Sjögren's syndrome

Biomarkers and Sjögren's syndrome

Radiation induced: gene therapy (recovery) and stem cell transfer (regeneration)

Infection: role of the microbiome

Epilogue

14. Systems medicine, neuropathic oral diseases, and orofacial pain

Introduction

Neurobiology of inflammatory pain phases

Chronic inflammatory orofacial pain

Neuropathic pain

Molecular basis of neuropathy in systems medicine

Neuroscience of neuropathic pain

Psychosocial dimensions of neuropathic pain

Conclusion and future directions

15. Chronic systemic symptoms in cancer patients

Introduction

Chronic systemic symptoms

16. Oral toxicities of cancer treatment

Introduction

Oral mucositis

Salivary gland hypofunction

Osteonecrosis of the jaws

Conclusion

17. Stem cells and regenerative medicine

Introduction

General features of stem cells from pluripotency and multipotency to oligopotency and unipotency

Aging and senescence versus regenerative medicine

New theoretical regenerative assumptions: the role of aging and senescence

Connecting metabolism, biomaterials, and stem cells in the tissue regeneration procedures

Conclusion

18. Translating concepts of systems medicine to the clinic

Putting the pieces together

Systems medicine and the clinic

Bringing systems medicine into the clinic

Connecting the dots—application of diverse data points to clinically actionable outcomes

What's next

Index

Copyright

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List of contributors

Andrea Ballini,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Andrea Burke,     Oral and Maxillofacial Surgery, University of Washington School of Dentistry, Seattle, WA, United States

Danila De Vito,     Department of Basic Medical Sciences, Neurosciences and Sense Organs (SMBNODS), School of Medicine, University Aldo Moro, Bari, Italy

Gianna Dipalma,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Joel Epstein,     Division of Otolaryngology and Head and Neck Surgery, Department of Surgery, City of Hope, Duarte, CA, United States

Camile S. Farah

UWA Dental School, University of Western Australia, Nedlands, WA, Australia

Australian Centre for Oral Oncology Research & Education, Nedlands, WA, Australia

Simon A. Fox,     UWA Dental School, University of Western Australia, Nedlands, WA, Australia

Isacco Ciro Gargiulo

Human Stem Cells Research Center, Ho Chi Minh City, Vietnam

Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Pham Chau Trinh University of Medicine, Danang City, Vietnam

Vassilis Gorgoulis

Molecular Carcinogenesis Group, Department of Histology and Embryology, Medical School, National and Kapodistrian University of Athens, Athens, Greece

Faculty Institute for Cancer Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, United Kingdom

Biomedical Research Foundation, Academy of Athens, Athens, Greece

Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece

Francesco Inchingolo,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Angelo Michele Inchingolo,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Alessio Danilo Inchingolo,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Karolina Elżbieta Kaczor-Urbanowicz

Center for Oral and Head/Neck Oncology Research, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, United States

UCLA Section of Oral Biology, Division of Oral Biology & Medicine, Center for the Health Sciences, UCLA School of Dentistry, Los Angeles, CA, United States

UCLA Section of Orthodontics, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, United States

UCLA Institute for Quantitative and Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, United States

Pachiyappan Kamarajan,     Division of Periodontology, Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States

Yvonne L. Kapila,     Division of Periodontology, Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States

Ravi Kasiappan

Division of Periodontology, Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States

Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India

Pagona Lagiou

Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece

Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States

Richard M. Logan,     Oral and Maxillofacial Pathology, Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia

Gkikas Magiorkinis,     Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece

Timothy J. Maher,     Sawyer Professor of Pharmaceutical Sciences, MCPHS University, Boston, MA, United States

Irina Makeeva,     Department of Therapeutic Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow, Russia

Rashmi Mishra,     Oral Medicine, University of Washington School of Dentistry, Seattle, WA, United States

Barbara Murphy,     Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN, United States

Cao đẳng Kinh Nguyen,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Manuel Nuzzolese,     University Hospitals Birmingham – NHS Foundation Trust, Birmingham, United Kingdom

Gregorio Paduanelli,     Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

Caitlin S.L. Parello,     Biomodels, LLC, Watertown, MA, United States

Salvatore Scacco,     Department of Basic Medical Sciences, Neurosciences and Sense Organs (SMBNODS), School of Medicine, University Aldo Moro, Bari, Italy

Joel L. Schwartz,     College of Dentistry, University of Illinois at Chicago, Chicago, IL, United States

Rachel Shparberg

Clinical Stem Cells Pty Ltd, Sydney, NSW, Australia

University of Sydney, School of Medical Sciences, Discipline of Physiology, Sydney, NSW, Australia

Corneliu Sima

Center for Clinical and Translational Research, The Forsyth Institute, Cambridge, MA, United States

Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, United States

Stephen T. Sonis

Division of Oral Medicine and Dentistry, Brigham and Women's Hospital; Division of Oral Medicine and Oral oncology, Dana-Farber Cancer Institute and Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA, United States

Biomodels, LLC, Watertown, MA, United States

Frederik K.L. Spijkervet,     Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Herve Sroussi,     Department of Surgery, Brigham and Women's Hospital and Dana Farber Cancer Institute, Harvard School of Dental Medicine, Boston, MA, United States

Marco Tatullo

Tecnologica Research Institute, Marrelli Health, Crotone, Italy

Department of Therapeutic Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow, Russia

Lalima Tiwari,     UWA Dental School, University of Western Australia, Nedlands, WA, Australia

Thomas E. Van Dyke

Center for Clinical and Translational Research, The Forsyth Institute, Cambridge, MA, United States

Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, United States

Panagiotis V.S. Vasileiou,     Molecular Carcinogenesis Group, Department of Histology and Embryology, Medical School, National and Kapodistrian University of Athens, Athens, Greece

Edward Russell Vickers

Clinical Stem Cells Pty Ltd, Sydney, NSW, Australia

University of Sydney, Department of Anaesthesia & Pain Management, Sydney, NSW, Australia

Alessandro Villa,     Division of Oral Medicine and Dentistry, Brigham and Women’s Hospital; Division of Oral Medicine and Oral oncology, Dana-Farber Cancer Institute and Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA, United States

Arjan Vissink,     Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

David T.W. Wong

Center for Oral and Head/Neck Oncology Research, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, United States

UCLA Section of Oral Biology, Division of Oral Biology & Medicine, Center for the Health Sciences, UCLA School of Dentistry, Los Angeles, CA, United States

UCLA’s Jonsson Comprehensive Cancer Center, Los Angeles, CA, United States

Preface

Although health and disease are the phenotypic manifestations of complex relationships between molecules, cells, tissues, and organs, we have largely elected to study diseases in a fragmented and reductionist way in which we rationalized that the best way to learn about the whole is to dissect it down to its most fundamental parts. Consequently, laboratories have become ever more focused as generations of scientists each investigate increasingly smaller pieces of the pie. And funding agencies, convinced of the necessity of specialization to best understand the pathogenesis and biology of disease, have encouraged such an approach. Scientific meetings either run specialized parallel session or themselves are highly concentrated. Clinical medicine has, in many ways, followed a similar path with subspecialists being supplanted by sub-subspecialists. And while much of this compartmentalization has resulted in meaningful discoveries, the clinical application of many findings has been impaired by the lack of reintegration and assembly back to the whole to truly judge the potential impact on patients. As we have seen over and over again, the dramatic impact of a drug on a cultured cancer cell might totally fall apart when that cell is exposed to the influences of its true in vivo environment. While influenced by a range of omics, the historical reductionist approach to understanding pathogenesis and druggable interactions neglects the intricacies of multiply occurring activities which change over time. Systems biology and medicine, by comprehensively assessing connectivity in a dynamic way, provide opportunities to understand disease processes in ways that are holistic, clinically relevant, and actionable.

As reflected by the Precision Medicine and Cancer Moonshot initiatives, there has been an increasing mandate and expectation for clinicians to effectively integrate and apply advances in cell biology to the care of their patients. Although the complexities and advances in systems biology have been embraced by many in the scientific community, their translational utility has been stymied by their seeming disconnect to clinical medicine. The primary objective of this book is to provide a conduit to bridge the gap between discovery science and clinical medicine as applied to oral disease.

Stephen T. Sonis

Alessandro Villa,     Boston, MA, USA

1

Introduction to systems medicine

Stephen T. Sonis ¹ , ² , and Alessandro Villa ¹       ¹ Division of Oral Medicine and Dentistry, Brigham and Women’s Hospital; Division of Oral Medicine and Oral oncology, Dana-Farber Cancer Institute and Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA, United States      ² Biomodels, LLC, Watertown, MA, United States

Abstract

Health and disease are the phenotypic manifestations of complex relationships molecules, cells, tissues, and organs. Although influenced by a range of omics, the historical reductionist approach to understanding pathogenesis and druggable interactions neglects the intricacies of multiply occurring activities which change over time. Systems biology and medicine, by comprehensively assessing connectivity in a dynamic way, provide opportunities to understand disease processes in ways that are holistic and clinically relevant and actionable.

Keywords

Bone disease; Epigenetics and oral diseases; Functional omics; Genomics; Human microbiome; Metabolomics; Microbiomics; Periodontal disease; Proteomics; Systems medicine

Reductionism

Beginning in the 1600s, much of medical and biological research has been characterized by an approach which conceptualized that the most effective way to understand the workings of a complex entity (i.e., cell, tissue, organ) was to study each of its constituent parts. Once the workings of each part was defined, the theory went, the whole could be defined by simply assembling the components—sort of a 1   +   1   +   n   =   the whole. This approach, termed reductionism, has transcended much of biomedical research and has proved to be a widely effective tool in providing answers to very specific questions. Examples abound throughout the literature and include many of the critical pieces of knowledge on which clinical science is based. Areas of molecular biology have been particularly fruitful; characterization of DNA, understanding the cellular basis of glucose metabolism, and transcription factor impacts on pathway activation are three of thousands of snippets of information that help characterize biological function. From the standpoint of disease, comparative studies of molecular function in normal and diseased states have helped provide targets for new drugs.

However, there are significant limitations to a reductionist approach, especially since it has become entrenched and the norm for medical science over such a long period of time. In a case of reverse evolution, the whole has been subdivided and resubdivided into smaller and smaller parts as generations of scientists and their professional off-spring have progressed. Thus, it is no longer adequate to be a cell biologist. Instead, it is the cell biologist specializing in one component of the cell or one pathway. Indeed, a look at representative titles of prestigious Gordon Research Seminars for this year (2018) is illustrative of the categorical investigative silos in which many of us find ourselves working: cell polarity signaling, phosphorylation and G-protein mediated signaling networks, and cyclic nucleotide phosphodiesterases. Each group of scientists working in their space is passionate about their area and topic, and it would not be a shocker if each believed that their molecule or pathway was the key to determining health or disease.

Not unexpectedly, reductionist approaches to clinical science has transgressed into oral health. Microbiologists have spent decades identifying, categorizing, and describing the bacteria which are associated with caries and periodontal disease. The biologic features of dysplastic cells have been investigated with gusto. Differential inflammatory cell responses have been compared between healthy and diseased tissue.

And reductionism has taken on a whole new layer of specificity and granularity with the explosion of omic science and the rapid progression of new analytical technologies. Whole gene description has given way to single-nucleotide polymorphisms (SNPs) and gene sequencing. The discovery of epigenetics and miRNAs has provided a whole new level of detail in describing disease risk and behavior. Aside from genomics, the omics' roster has proliferated greatly, limited only by the imagination of enthusiastic investigators seeking to emphasize the critical aspects of their research passions. Lipidomics, metabolomics, proteomics, transcriptomics, microbiomics, and inflammomics have joined genomics as areas of productive research. Journals have arisen to provide increasingly specific platforms to present data in each area.

Not only has reductionism played a major role in how basic medical science is approached, but in some ways the approach is noted in the clinic. As technical aspects of medicine have become more complex, subspecialties have evolved (i.e., there are at least 17 for anesthesia, 21 for internal medicine, and 14 for surgery). Even a specialty as defined as ophthalmology has nine subspecialties. And it does not stop there, there are now sub-subspecialties. For example, at major medical centers, and now even in community venues, cardiology (a subspecialty of medicine) now includes practices which focus on only one aspect of cardiac medicine. The evolution of the superspecialist has been undeniable and has resulted in some remarkable clinical benefits as seen in transplant medicine.

Consistent with a reductionist approach has been the emphasis on the maintenance of homeostasis as a predicate for health. First expounded by Claude Bernard in the mid-1800s, homeostasis was viewed as a motivator for clinicians to correct to a defined norm. Interestingly, as the concept of personalized or precision medicine has evolved, it is becoming clear that normal ranges are in some ways artificial and that for certain outlier patients, being outside the norm is not necessarily a reflection of disease. The clinical and fiscal implications of this approach are impressive. For example, the American Heart Association recently modified the normal values for blood pressure, dropping the thresholds for abnormality designation. Now thousands of individuals who were thought to be normal are in the position of requiring intervention at a potential cost of billions of dollars in treatment for which value has been questioned.

Incumbent in assessing standard measures of homeostatic behavior and values is the importance of recognizing dynamic stability. That is, an abandonment of the concept that physiologic functions are constant and linear. We know biological systems do not work that way. Changes occur over time or in response to external stressors of challenges. Trying to describe a movie by looking at two frames (OK—I know speaking to old technology) or even a 10   min stretch will not be informative.

So while this traditional reductionist approach has been of marked value, it has shortcomings which are especially apparent in limiting our understanding of the pathobiology and management of complex diseases. Imagine a group of individuals charged with understanding how a modern jet functioned in a scenario in which each group selected one aspect of the plane for study. The left aileron mavens could come up with a thorough analysis of that piece of the plane, metallurgy, wiring, structure, etc., and the fan blade group might understand the placement, angulation, and materials (there are 420,000 Google hits for keywords airplane fan blade design) and so on and so forth, but at the end of the day how these parts integrated and worked to result in a functioning airplane would not happen unless, as a component of the plan, there was the capability to integrate the desperate parts into the whole and understand how they interact and impact each other. (Table 1.1).

Table 1.1

Reprinted with permission from: DOI:10.1371/journal.pmed.0030208.t001.

From Ahn A et al. with permission.

Systems biology and medicine

While reductionism is based on studying fragmented components of the whole, we all recognize that many normal and abnormal physiologic phenotypes are the result of complex and complementary interactions. In the case of diseases, this is almost always true and it is the case at multiple levels. Although the level of complexity may vary, it is nonetheless apparent that even the most simple and acute condition does not occur in biological or physiological isolation, especially in the patients with comorbid conditions. For example, while an odontogenic infection meets the requirements for a simple, linear condition in an otherwise healthy individual, the biological platform on which the infection occurs and reacts is entirely different in a patient with poorly controlled diabetes or a cancer patient receiving myeloablative therapy.

In marked contrast to viewing each component of a disease as a distinct entity, systems biology as it is applied to medicine and clinical care recognizes two critical features of disease and health. First, the multiple elements which simultaneously contribute to a healthy or unhealthy phenotype do not act independently. Rather they affect the functions and manifestations of each other and how impactful one element is on another is not uniform; within each contributing level, the individual components do not function alone. For example, in the case of changes in gene expression impacting response to an external challenge such as radiation, it seems most likely that the resulting phenotype represents not just changes in expression of a single gene but more probably the cumulative effect of multiple genes interacting as a network and in a hierarchical way. Thus, while convention would suggest that if two genes acted together the resulting phenotype could be represented simply as 1   +   1   =   2, the biological reality, depending on the consequences of the gene interaction, might not be purely additive.

Furthermore, it is also naïve to think that each omics element functions independently. Rather not only are there horizontal interactions taking place within the genomic, metabolomics, microbiomic, and other omic worlds but there are also vertical cross talk across platforms (Fig. 1.1).

The other element for which systems biology/medicine accounts is based on the conclusion that health and disease states are the effect of cumulative but ever-changing interrelationships. Relationships are not static, but dynamic. While some changes occur within seconds such as the tumor or normal tissue response to radiation or chemotherapy, others are more plodding. Importantly, the rate with which different omics systems respond and interact may not be uniform. Therefore, in contrast to the linear expectation which has characterized most molecular studies, there are starts, stops, and variation in the velocity of reactions.

Figure 1.1 Multiplex networks recognize the complexities of diseases and provide a conduit to comprehensively assess the interaction of multiple layers of disease features and risk contributors. In this example, four networks which contribute to disease risk can be assessed, not only independently, but in the aggregate so that overlapping features can be identified. The same figure is being used also in Chapter 18.

Technological advancements have been catalytic to enabling many of the methods and analyses which are critical to facilitating systems biology and medicine. In 1965, Gordon Moore noted that the complexity of integrated circuits doubled annually (Moore's law). We have seen the impact of this in many ways. Think of the memory and speed of the first personal computers or laptops compared with what are now available even on your smartphone. At times, the application of technology conflicted with conventional approaches as is dramatically captured in Shreeve's book, The Genome War, which documents the Human Genome Project. Ultimately, the success of the Human Genome Project created an opportunity for the development of gene chips which could capture massive amounts of data in ways totally unimaginable a couple of decades ago where gene expression relied on laboratory-based methods like PCR. Likewise, biological and technical advancements have resulted in automated DNA analysis to identify SNPs with arrays that contain as many as four million probes making vast amounts of array data to be readily available. As the process and technology became more and more commoditized, costs decreased, utilization increased, and the NIH created the National Human Genome Research Institute. For basic and translational scientists, the costs for doing studies decreased, while the yield increased creating vast amounts of data, and the need to manage, analyze, and interpret big data was born.

Data trickling from a garden hose was replaced by data bursting from a fire hydrant. It is not surprising, therefore, that an opportunity for computational scientists to be attracted to biology and medicine blossomed and with it the need to integrate of analytics into systems biology and medicine.

Systems medicine and oral disease

The mouth is perhaps one of the better examples of, for want of a better term, how anatomic reductionism has been applied both biologically and clinically. Not only did dentistry become autonomous and isolated in practice, the same approach affected dental education, science, and even reimbursement. Following trends and discoveries in molecular biology, oral biologists focused exclusively on the cells and tissues of the mouth and its contiguous structures and on the local oral environment. In fact, differentiating and emphasizing the uniqueness of many aspects of the oral cavity became a strategic opportunity, not only for science but for agencies, funding sources, and journals. Strangely, in the academic world segregation of basic scientists studying issues related to oral health was evident as parallel investigational efforts took place in dental schools independent of medical basic science departments. There was little or no attempt to integrate what happened in the mouth with what happened to the rest of the patient or vice versa.

But there were exceptions. Perhaps the most visible was the rise, fall, and resurrection of the focal infection theory. Peaking in popularity a little over a century ago, loosely stated the theory hypothesized that many chronic diseases (i.e., arthritis and cardiovascular diseases) were often the consequence of oral infection. American dentistry reacted at the time with an approach that advocated extraction of teeth with pulpal necrosis or signs of infection. Subsequently, the impact of oral disease on systemic health lost support as evidence supporting an etiologic relationship between oral and systemic disease was questioned or refuted. However, beginning in the 1990s, there was renewed interest (for review see Newman HN. Focal Infection. J Dent Res 1996; 75: 1912–19) as epidemiological data supported relationships between biological aspects of periodontal disease and a wide range of systemic conditions.

However, although this more holistic view of oral and systemic health has reemerged, it is by no means universally accepted. Many query whether oral and systemic diseases are parallel manifestations of systemic changes or directional or causative. What does seem to be clear, however, is that molecular or cellular changes occurring in the tissues of the mouth are likely to represent or demonstrate responses to systemic challenges or disease. Physiologic isolationism simply does not exist.

The chapters that follow are meant to provide a broad picture of how the application of systems biology and medicine provide an opportunity to better integrate the pathogenesis and clinical relevance of oral and systemic health.

2

System biology

Alessandro Villa ¹ , and Stephen T. Sonis ¹ , ²       ¹ Division of Oral Medicine and Dentistry, Brigham and Women’s Hospital; Division of Oral Medicine and Oral oncology, Dana-Farber Cancer Institute and Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA, United States      ² Biomodels, LLC, Watertown, MA, United States

Abstract

Human diseases are dynamic and complex, and their behavior may be difficult to predict. System biology focuses on the study of biological components using systematic measurement technologies such as genomics, bioinformatics, and proteomics and mathematical and computational models to better understand the behavior of a particular condition.

Keywords

Networks; Omics data; System biology

Introduction to system biology

System biology developed at the beginning of the 21st century as an evolution of molecular biology. ¹ Although many definitions have been proposed over the years, no precise characterization exists yet. ² System biology focuses on the study of biological constituents including genes, protein, and cellular and metabolic components using mathematical and computational systems with a multidisciplinary and integrative approach ³,⁴ (Fig. 2.1). Over the past two decades, clinicians and scientists learnt how to familiarize with genomic, transcriptomic, miRnomic, proteomic, and metabolomic data which are obtained from technologies including quantitative polymerase chain reaction, oligonucleotide microarrays, mass spectrometry (MS), and next-generation sequencing (NGS). Through sophisticated computational models and simulations, information is extracted and interpreted with an interdisciplinary approach.

Specifically, system biology allows us to study how a phenotype is generated and affected by the genotype. To understand the interaction among different biological components, it is important to familiarize with four steps in the implementation of system biology ⁵ :

Figure 2.1 System biology.

- First, define and list the biological components that participate in a cellular mechanism

- Second, the formation of genome-scale maps in a stepwise manner

- Third, conversion of reconstructed gene networks into a mathematical model

- Finally, the use of models in a prospective and predictive manner.

System biology systematically studies the properties of cells and their constituents using experimental methods to provide helpful information to both bench scientists and clinicians. The aim of this chapter is to provide a clearer understanding of the nature of system biology and elucidate the mechanisms of cellular communication.

Cell biology and networks

System biology aims to recognize how information is received, transmitted, and interpreted by cells through new innovative methods that examine the level of RNA and DNA on a gene and proteins. Cells are constituted by a variety of molecular components that interact with each other to form networks. Each network is made of multiple nodes (the chemical constituents) and links (or edges) that connect the nodes to form a dynamical system. Arcs (or directed edges) typically have a starting node and an end node (target), which represent regulatory relationships or transformations. Nondirected edges, including protein to protein binding, are usually more suitable for mutual interactions. ⁶ Edges are then characterized by positive (for activation) or negative (for inhibition) signs, strengths, reaction speed, and weights. For example, in protein interaction figures, the nodes are proteins and the proteins may be connected by nondirected edge when there is a strong association between the two.

Cells use signaling and regulatory pathways generate a large amount of raw data that are assembled into heat maps, diagrams, and network. Depending on the function, there are three main networks: signaling, metabolic, and regulatory. ⁷,⁸

Signaling networks

Cells are subjected to continuous external and internal stimuli to which they typically react with a physiological response regulated by proteins and genes that interact in complex molecular networks. Signal networks involve both biochemical and protein reactions with their edges being mostly directed.

Metabolic networks

Metabolism is a necessary and vital mechanism for cellular function. Since the beginning of the previous century, genes and enzymes implicated in cellular metabolisms have been well characterized. Genome-scale metabolic pathways and networks are now available ⁸,⁹ (Fig. 2.2).

Regulatory networks

Regulatory networks represent a number of molecules, mainly RNA and protein, which regulate gene expression through a variety of chemical interactions. The key players in regulatory networks are DNA-binding proteins, which control the initial step in gene expression.

All these networks represent dynamical systems for better interpretation of the omics data obtained from cellular processes, which are then modeled into large mathematical databases. These are often available to the public on the Web.

Omics data

Data analysis and interpretation are fundamental aspects of system biology. System biology aims to provide a better understanding of the biological system and to provide predictive information on molecular processes. As for any research method, first the cycle begins with a generation of a hypothesis. Second, knowledge is generated and a model is constructed based on biological data present in the scientific literature. Finally, data are generated, analyzed, and measured simultaneously. Omics data include information from the genome, proteome, and transcriptome ¹⁰,¹¹ (Fig. 2.3). To correct interpret omics data and therefore reach a biological conclusion, a variety of measurement platforms and computational analyses exist. The three most commonly used are genome sequencing, MS-based proteomics, and transcriptomics (RNA sequencing and microarrays).

Figure 2.2 Schematic diagram of part of the metabolic network of Escherichia coli.

Genome sequencing aims to define the order of nucleotides in a given organism. Resequencing is the analysis of the genome of a strain or multicellular organism compared with a genome of reference. The resequencing includes large structural variation (e.g., copy number alterations or genome rearrangements) or small mutations such as single-nucleotide polymorphisms (SNPs). ¹² Exome sequencing determines the sequence of all of the exons in the genome and whole genome sequencing the complete genomic sequence. ¹³ Exome resequencing and whole genome sequencing are specifically used to study diseases and, in cancer, the set of somatic and germline mutations. ¹⁴

Figure 2.3 The analytic process of multi-omics data.

Transcriptomics is a gene expression analysis based on RNA-seq where RNA is reverse-transcribed into complementary DNA (cDNA) and then sequenced. ¹⁵ From the results of this analysis, it is possible to estimate the expressed mRNA and functional noncoding RNA. Resequencing produces a massive amount of data that require computational analysis and interpretation. Similar to transcriptomics, proteomics is the study of gene expression at a protein level with generation of data on the peptide sequences and their abundance in the sample. ¹⁶

The tools mostly used to survey the sequence of the chromosomes and the expression of thousands of genes are microarrays and NGS.

Microarray is a laboratory tool that was introduced a few decades ago and is based on fluorescence-tagged cDNA that is hybridized to unique gene-specific probes distributed over a chip. A laser scanner extracts information on the amount of DNA captured by each probe. Microarrays have been largely used in transcriptomics studies and to identify SNPs, and large datasets have been generated in the past. Although microarrays were provided important information, compared with NGS-based techniques, they have a lower resolution and have high technical variation. ¹⁷

NGS is widely used for DNA characterization and in proteomics (deep sequencing of RNA [RNaseq]). ¹⁸ NGS allows analyzing billions of DNA or RNA fragments instantaneously in a rapid and efficient manner. In medicine, genome-wide association studies on the association of SNPs and certain diseases have had important repercussions for the diagnosis, treatment response, disease progression, and prognosis. ¹⁹

Practical implications

While the complexities and findings in system biology have been embraced by the scientific community, their translational utility has been stymied by their seeming disconnect with clinical medicine. System biology has led to significant discoveries in the past few years with important clinical practical implications and a better understanding of complex diseases.

The first disease that has been studied using a system-based approach is neuroblastoma. Specifically, the oncogene MYCN was integrated into a regulatory network along with the drugs 13-cis-retinoic acid and fenretinide, to investigate the response of neuroblastomas to retinoids. ²⁰ Verma et al. employed a systems-based regulatory network to study the effects of miRNAs that were involved in the expression and phosphorylation of BCR-ABL oncoprotein in chronic myeloid leukemia imatinib-resistant cell lines. This study allowed investigating the effects of tyrosine kinase inhibitors and BCR-ABL-specific miRNAs on cell lines with differing expression profiles and chemoresistance properties. ²¹ Sarmady et al. used a systems-based computational analysis to identify key molecular pathways for human immunodeficiency virus.

System biology has been employed also for oral diseases. One example comes from oral mucositis, a common complication in patients who receive radiation treatment for head and neck cancers. ²² For example, Sonis et al. studied the relationship of expressed genes in peripheral blood samples from patients with head and neck cancer who received chemoradiation. Microarray analysis was performed using PBM-derived cRNA. The results showed a significant concordance between oral mucositis pathogenic mechanisms in relation to the genes, canonical pathways, and functional networks. ²³ System biology is also used to identify genetic markers that predispose for the development of toxicities secondary to drugs and for the development of new therapies. A good understanding of genetic and nongenetic determinants of drug-related toxicity may help to optimize drug treatment in individual patients. One example in the oral mucositis literature comes from the study by Van Erp et al. Results have shown that patients receiving a single agent sunitinib were at risk for mucosal inflammation in the presence of the G allele in CYP1A1 2455   A/G. ²⁴ In another study, Sonis et al. identified 82 SNP Bayesian network, which was associated with the development of severe mucositis in patients who received conditioning chemotherapy before autologous hematopoietic stem cell transplants. This study was a good example of the significance of system biology in the prediction of oral toxicities (in this case mucositis) secondary to cancer therapy.

The value of system biology is expanding, and it now comprises several areas that include genomics, epigenomics, proteomics, metabolomics, and microbiomics. System biology has led to the development of new drugs in a quicker way, improved diagnostics, and even the production of healthier foods and biocompatible materials. ²⁵–²⁸

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3

Genomic foundation for medical and oral disease translation to clinical assessment

Joel L. Schwartz ¹ , and Herve Sroussi ²       ¹ College of Dentistry, University of Illinois at Chicago, Chicago, IL, United States      ² Department of Surgery, Brigham and Women's Hospital and Dana Farber Cancer Institute, Harvard School of Dental Medicine, Boston, MA, United States

Abstract

We describe the chemical and genetic contributors for genomic expression as a product of complex molecular and gene expression, translation to produce functional development of proteins. We also discuss specific genes, factors, and molecular assistants to produce genomic expression in oral disorders. Genomic expression in the head and neck cells and tissues bears a high degree of similarity to distant tissues from the oral cavity. For example, products of genomic expression regulate physiology and cell biology to release factors of immune and endocrine function, regulate growth, neural, metabolic activity, and cell vesicle structures (e.g., exosome, multilamellar vesicle). Understanding the genomic relationships between oral microbiome and oral mucosa is critical to produce novel methods to detect disorders early, improve diagnosis, and enhance therapy effectiveness. Further expected is improvements of specificity and sensitivity of genomic profile change during pathogenesis of oral diseases will accelerate development of personalized oral medicine and dentistry.

Keywords

Chromosome; Darwinism; DNAs; Genomic; Mutation; Nucleotide bases; Protein; RNAs; Transcription; Translation

Introduction

Our current state of knowledge and assessment of gene expression is morphing as we learn more about mechanisms of regulation. Basic knowledge of organization of genetic systems is undergoing continuous review and update. Understanding the complexity and function of genetic systems will lead hopefully to innovative methods to improve the prognosis, early diagnosis, and treatment of both oral and systemic diseases. Advancement in knowledge will delineate new candidate regulatory factors and pathways that promote early detection. Expected is an expansion of our knowledge of specific regulation of gene elements to provide an opportunity to understand better the risks for disease and pathogenesis at an individual and personalized basis.

Genetic materials, structures, and arrangements are crucial elements influencing the presentation of diseases. Associations between gene activity and pathogenesis of oral and/or systemic disease are not simple. Gene–tissue interaction complexity is a product of either of the following: (1) inappropriate timing of normal regulatory expression and function; (2) depressed levels of activity of normal regulatory expression and function; (3) variety of damages from regulatory expression; (4) suppressed regulatory expression; or (5) inappropriate timing; expression, or function from damages to each segment of a genetic system but not directly linked to target to create a bystander event. In addition, a gene bystander activated to produce a time-bomb effect. An example found among are muscular dystrophies is DUX4 gene that creates a toxic protein that damages neuromuscular activity. We suggest although rare this Trojan horse can factor in loss of oral-facial function and requires consideration.

Gene expressions show individual variability in different characteristics (e.g., penetrance, prevalence). A range of physical appearances, trait characteristics, or phenotypes occurs in all human populations and increasingly, using statistical association, some of these population differences link to the presence of unique gene coding. We will describe this relationship later in this chapter, but in brief, inheritance of traits can be Mendelian or in accordance with Mendelian determination of genetic transfer of inheritance. In simple terms, this concept describes dominance or recessive gene locations on portions of chromosome structures designated alleles to produce similar, homologous, or nonsimilar, nonhomologous gene arrangements at identical sites on specific chromosomes (Fig. 3.1). These regions characterize a genetic motif designated, genotype. In Mendelian terms, gene allelic sites manifest a predominant or reduced (recessive) number of predicted phenotype presentations based on probability for specific genotypes. Gene copies of these sites can be either be homozygous (somatic, nongerm cell, or germ cell; maternal and paternal inheritance of similar genes of predominant or recessive activity) or heterozygous presentations (at least one copy of predominance). Many gene expressions result in phenotypic traits (e.g., height) not inherited in a Mendelian fashion but in a more complex manner that we will be described later in this chapter.

It is important to recognize that there are different levels of disruption that occur to genetic systems in cells. The cell-gene expression organization is derived from genetic material and structures located in nucleus and nucleolus, cytoplasm, and mitochondrion. The composite of genomic function is a product of chemical components, described as bases that compose gene code. Bases altered by chemistry (e.g., phosphorylation) or sequence of coded bases are under external or internal stresses in cells (Fig. 3.2).

Figure 3.1 Multifactorial inheritance for darwinian genetic relationships.Figure shows the relationship of Mendelian genetics to penetrance of trait in a population. This relationship describes genotype allelic expression or trait with a low frequency in a population and a