\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"279",leadTitle:null,fullTitle:"Aortic Stenosis - Etiology, Pathophysiology and Treatment",title:"Aortic Stenosis",subtitle:"Etiology, Pathophysiology and Treatment",reviewType:"peer-reviewed",abstract:"Currently, aortic stenosis (AS) is the most prevalent valvular disease in developed countries. Pathological and molecular mechanisms of AS have been investigated in many aspects. And new therapeutic devices such as transcatheter aortic valve implantation have been developed as a less invasive treatment for high-risk patients. Due to advanced prevalent age of AS, further discovery and technology are required to treat elderly patients for longer life expectancy. This book is an effort to present an up-to-date account of existing knowledge, involving recent development in this field. Various opinion leaders described details of established knowledge or newly recognized advances associated with diagnosis, treatment and mechanism. Thus, this book will enable close intercommunication to another field and collaboration technology for new devices. 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Over the years that tiny stroke of luck has rolled on and become a vastly sophisticated industry that demands state-of-the-art technology, and even political and social changes to keep humanity on the move. Today, developments in one field set up ripples that affect the development of other fields. Like seismic waves, the repercussions of that change are felt all over. So, in a time defined largely by software, it is no surprise that technology today is the next second away and about to disrupt the practice of our profession. As with any change, it remains to be seen whether the growing dependence on computers will be for better or for worse.
According to Miller [1], diagnosis is way more than connecting the name of a disease or syndrome with the findings for a patient. It is a recurring process in which the details of a patient such as history, symptoms, signs and how the disease process has unfolded over time, and eventually how that process affects the patient’s life, count [1]. The diagnosis of an individual involves a series of information which includes history, symptoms, physical exams, laboratory tests and clinical image interpretations which potentially coincides with the etiology of the patient’s illness. The diagnosis of some of the diseases may involve the response of an individual to “therapeutic intervention” and the response is studied to show characteristics of a particular disease [2]. For example, the dentist might prescribe antibiotic for 1 week to a patient with decayed tooth and tooth pain. The antibiotic resolves the infection and pain which shows that the patient had secondary infection going on in the carious tooth. The ultimate feature in the diagnosis of disease is to see if the planned therapy is working or if the disease is getting better or getting worse over a period of time, and have there been considerable side effects to the therapy [2]. Evaluation of the diagnoses is not only limited to living individuals. Post-mortem and autopsy examinations have proved to be a great tool in designing the diagnostic parameters. Artificial intelligence and computer learning have cashed upon the complex series of diagnosis in these days to assist humans in coming up with a module for diagnostic processes. In the current scenario, the possibility that artificial intelligence (abbreviated henceforth as AI) and computer aided systems may supersede humans in the diagnostic process, is an impending reality [1].
The diagnostic process is considered to be a complex transition process which begins with the illness of the patient and ends into a result which serves as a data for reference that can be categorized. The diagnoses start from the doctor asking the patient about signs and symptoms of a disease/illness. A doctor’s treatment specific to a patient’s symptoms and its outcome is important for both the doctor and the patient to see the efficacy of the treatment provided.
The diagnoses are an amalgamation of various processes that broadly depends on three main factors:
The doctor’s knowledge serves as data set for the human brain to process the likes and similarities from the previous acquired data. This helps in differentiating the two processes. And thus helps to form an opinion regarding a process. This is really critical for diagnosis.
For example, while diagnosing a Central Giant Cell Granuloma of the jaw, the doctor must also be aware of the differential diagnoses, and their appearance to rule those out.
The experience of a doctor plays an important role in the diagnosis of a disease. These experiences contribute in enriching the quality of the data and help in refining the data set and recreating subsets in the data. It is here that heuristics step into the picture. These subsets help in simplifying the data and make it comprehensible. The experiences can also be governed by the amount of different cases seen by the doctor, which in turn is greatly influenced by the geo-fencing of the same.
For example, the diseases that are predominant in some areas of the world, like Lyme’s disease in the North Eastern American region, the physicians practicing there will have more experience of those cases and will form a more accurate diagnosis in comparison to the physicians in any other part of the world. Basically the age old dictum at work here is that—if you hear hooves think horses, not zebras.
Human brain is a complex structure which plays a pivotal role in making a diagnosis. According to Charles Sherigton (c. 1920), some of the deepest mysteries facing science in the twenty-first century concern the higher functions of the central nervous system: perception, memory, attention, learning, language, emotion, personality, social interaction, decision-making, motor control, and consciousness. Nearly all psychiatric and many neurological disorders are characterized by a dysfunction in the neural systems that mediate these neural processes. In fact, all aspects of human behavior and hence human society are controlled by the human brain: economics and decision making, moral reasoning and law, arts and esthetics, social and global conflict, politics and political decision making, marketing and preference, etc. These functions are greatly altered by the level of stress and the mood that the person has. Thus, a diagnosis is also greatly influenced by the doctor’s state of mind, and stress level (Figure 1).
Diagnostic paradigm.
AI and how to use it in CAD has become one of the hottest research topics in medical radiology both in imaging and diagnostics. Although, research in CAD is pretty much established and growing but most radiologists do not as yet, use CAD in their daily routine. The basics of AI and how to use it in CAD for detection and for quantification is defined by the various requirements such as performance, regulatory compliance, reading time reduction and cost efficiency are even today not as sophisticated/dependable as the human mind. Overall the performance of the CAD systems is still a major bottleneck for adaption. However, the usual machine learning and AI strategy can be used to improve CAD by using past and public databases for training and validation. This will create cognitive AI that will help tackle corner cases in CAD and eventually create superior algorithms [3].
Yet all said and done, there is a global consensus that the advent of computer simulation is a crisis in the making for radiology. Not only has the number of imaging studies gone up, but also the number of images per study has drastically increased [4]. Radiology is becoming a victim of its own success, i.e., the disparity and the gap between the overall workload and the number of radiologists has increased dramatically which has resulted in a cost increase. Therefore, new solutions are needed to handle the spurt of data and workload. Computer simulation may be the answer to this evident problem. The key is to use AI and CAD to quicken the diagnostic process and minimize diagnostic errors.
Although, CAD is far much more than just a detection tool but CAD is now widely used as a general term for detection that includes aided extraction of quantitative data from radiology images. A very interesting fact about the algorithm development is that detection and quantification both use the same underlying principle for algorithm creation.
The overall growth in computer simulation or CAD is driven by Moore’s law, i.e., the computational power doubles every 2 years [5]. This has been true for the last 5 decades and should continue for at least another decade. Futurists like Kurzweil [6] talk about singularity of AI, i.e., within a decade, a $1000 computer will have the computational strength of a human brain and eventually the power of hundreds of human brains by 2040. The availability of cheaper and faster hardware has allowed for quicker computations and bigger and cleaner databases for algorithm training. All this has led to quicker and better CAD performance and results.
In the 1980s, the Kurt Rossmann Laboratories for Radiologic Image Research in the Department of Radiology at the University of Chicago first started the systematic research in developing and designing the CAD systems for the diagnosis of the diseases. (Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential). Before this there was a significant amount of studies and researches going on in the picture archiving and communication system (PACS) [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25].
As a matter of fact, the PACS were useful in storing the pictures and reducing the cost for the storage to the hospitals but at that time, it was not thought that the stored pictures, nowadays referred to the data, might be of any clinical significance to the doctors or the clinicians? The storage was one of the fringe benefits of PACS, but the major value addition was the formation of a sample set or data. The researchers started thinking how this data could help the doctors in diagnostic process. This led to the theory of computer-aided diagnosis (CAD) and artificial intelligence (AI) led diagnosis [26].
The sophistication of artificial intelligence (AI) in doing what humans do has increased by leaps and bounds in the last one decade. In 2019, AI is a fact today and we have seen a shift in the conversation. We are no longer answering the question—what is AI? Today, the primary concern is answering the question—how can we utilize the plethora of information to replicate the human actions in a more efficient and faster way? No other sector is answering this question better than Healthcare. Artificial intelligence and computer simulations are no longer a novelty and if things progress the way they are, these may soon be the norm.
The use cases for AI in healthcare are vast and ever evolving. Just like AI has become a seminal part of our daily lives, AI is also transforming our healthcare ecosystem. When AI is applied strategically to this ecosystem, it not only has the ability to deeply impact the way healthcare is delivered but also how that the healthcare impacts the overall cost structure.
Today, AI has pushed innovation in healthcare to the next level by combining the training data sets with cognitive computing to draw new insights and correlations. This has been possible because of predictive capabilities, complex algorithms and analytics to deliver real time data that is clinically relevant, i.e., transforming healthcare in new ways.
First and foremost, is to help people stay healthy and eventually reducing the frequency of patient and doctor interaction. The new health apps encourage people to live a healthy lifestyle. AI equips healthcare professionals to better understand everyday health patterns and the needs of their patients. The increased use of consumer hardware technologies such as Apple Watch and other medical devices combined with AI is used in pilot projects such as detecting early-stage heart disease. Thus, helping healthcare professionals to better detect and monitor underlying life-threatening events at early, more treatable stages. With more and more money being invested in projects like Apple Health and Common Health of Android platform, the upheavals in how we practice as diagnosticians, are going to be tectonic.
Recently, life threatening diseases such as cancer are being detected more accurately by AI in their early stages. Based on the study done by American Cancer Society, a large proportion of mammograms eventually result in false positives, i.e., 1 in 2 healthy women are diagnosed with cancer when they have none. Using AI in review and translation process of mammograms may help to avoid unnecessary biopsies.
AI has to meet several demands to be used widely in clinical practice. The major four requirements that we think, are of paramount importance for AI guiding computer simulations, to be helpful in the field of Oral Radiology, are:
Most AI systems, and therefore computer simulations used in diagnostics that are based on these, today do not meet all requirements, and this is why most applications described in the rapidly growing body of scientific literature on CAD are not widely used in clinical practice.
Presently, the existing CAD systems are pushed as complementary tools for radiologists to further evaluate certain images that need attention. CADs have a limitation though, i.e., it does not detect all potential lesions and would limit the radiologist to focus only on the areas that the CAD system has identified. Therefore, it is imperative that the radiologist does the evaluation of the complete image. But the CAD system can help detect lesions that the radiologist might have missed [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41].
It is very complicated and difficult for computers to decipher radiologic images. To understand an image, the CAD system breaks down the issue into multiple parts and does a step-by-step process to conclude whether a specific area on a radiology image looks suspicious. Therefore, it is important that radiologists have a basic understanding to comprehend why the output of the CAD is off from the usual even if it is because of human error.
Most of the AI systems have an input or baseline dataset and starts preprocessing the data before it undergoes further changes through the scanning software. Series of calibrations are done to refine the data. These include resampling the data and removing the noises in the image. The basic reason for this process is to make sure that the existing dataset evolves. Since the AI system works on the knowledge from the previous data set which in turn comes from a binary data set (numbers 0 and 1) so the basic changes in the aberrancies from the data set can be easily pointed out for the doctors/radiologist to bring attention to. These aberrancies can be studied by the doctors (Figure 2) [3].
(a) The CBCT reconstruction image with the crude image that was just captured from the patient and stored in to the system. (b) Enhanced image after the preprocessing with the color defects fixed. (c) Enhanced image after the preprocessing with the noise defects fixed.
The second step is segmentation or demarcation of the normal structures in the data set. This step includes the demarcation, and consequent categorization of anatomic regions. This is the toughest step in the process and it is the most studied area. This also decides the accuracy of the AI system. As compared to the human knowledge which greatly relies on the differentiation of the structures and the artifacts from the prior knowledge the AI system greatly depend on the data sets. The more the data set the more refined algorithms are going to be (Figure 3) [28].
CBCT panoramic reconstruction with demarcation of the anatomic structure showing the lower border of the mandible.
The next big step is the aberrancy detected and identified matching the system to the subsets from the normal. These locations are called as the
CBCT panoramic reconstruction of a Cherubism showing aberrancy from the normal data set.
As mentioned previously, the extracted data has to be very sensitive for the next stage, which is scrutinizing the aberrancy. In this step each area is closely analyzed to rule out the normal variations. This is done by using the
The learning patterns and the recognitions are classified and stratified in the space where the normal and the abnormal candidates exist. The next step is training the classified data set. Consistency is a big deciding factor here. The normal candidates are classified and stratified consistently with the in one subsets whereas the abnormal candidates are classified with consistency in the other data set. This provides training for the AI system to form an opinion regarding the classified and stratified data. This training is done with the help of person who had prior knowledge and could feed the data top provide reference standard with correct information. For example, the doctor can point out to the location of the cherubism on the CBCT pantographic reconstruction view with prior knowledge of its location. Also, data, like age, can be a deciding factor which is fed into the system by the doctor and this too aids the diagnostic process [29, 30, 31, 32].
This is a very complicated step because some of the basic aberrancies which might not be the disease may also have the same locations or some diseases may not be in their classical locations. Therefore, data enrichment and stratification should be done on a regular basis.
The AI system, as discussed before, learns and adapts from the quality of the data set. So, we need more exposure to the data set to refine the results. AI researchers are always in the ongoing process of learning and experimenting with the classification schemes.
The process comes to completion when a degree of suspicion is assigned to each and every candidate in the strata or the group. The threshold decided by the doctor is the parameter used to test the degree of suspicion. These degrees of suspicion crossing the threshold are demarcated with the identifiers which can be circles or arrows. The AI system learns from the threshold and the final outcomes and enriches its data and subsets of the data. The results that come from the previous learning go to the computer’s learning curve. For example, in the CBCT shown below if features of cherubism are detected, and the doctor diagnosed it as a case of cherubism, then the result is saved in the system for future reference for other cases. If CBCT for the case of a swollen angle of jaw presents a similar data, then the computer uses its previous knowledge to make sure whether the indicated region is considered true or false positive [3].
There is a big range of applications of the AI and CAD in various fields of medicine. These applications have received a premarket approval (PMA). This encompasses the devices which have been shown to pose serious levels of risk for the users. In these cases the FDA guidelines recommend newer devices could be safer and more effective in the near future.
In the last decade there has been a considerable increase in their accuracy as systems for diagnostic help [25, 26]. The AI systems in mammography have been successful in detecting differences between the mass lesions and micro-calcifications [42]. For micro-calcifications it has shown that the AI systems show high performance and sensitivity which is greatly used by doctors in making an educated decision during diagnosis making. The data from multiple views and combination of different modalities like ultrasonography (US), magnetic resonance (MR) imaging, and digital breast tomosynthesis have shown to be really promising in enhancing the diagnosis and enriching the data for future diagnoses [34].
In 1996, the American Journal of Roentgenology published a report of three cases of diagnostic errors in radiology. After assessing the clinicians’ defense of their decisions, the author concluded that the radiologists missed out on the diagnosis because they did not think of the lesion rather than not know of it. It is popularly known as the
Populations in countries have increased but investments in health and education have not kept pace with the same. Add to that an increasingly unstable world both politically and environmentally. All of these changes mean shifting populations and overburdened hospital based care provider. With the rise in longevity and polypharmacy, one diagnostician may not be and indeed, cannot be held accountable, for missing out on a few pertinent points here and there, while assessing a case. In today’s outpatient medicine practice, especially, that of Oral Medicine and Radiology, there is hardly any time to think. And much to our chagrin, the reduced pay of practicing doctors, and increased work time of a hospital based doctor only means, what the New England Journal of Medicine, recently referred to as
Where computer simulation steps in, is this very ripe environment of piling information and very few humans, qualified to process it. As of now, there are gaping holes in the way AI processes the information it collects, and human mind is, as yet, ahead of it. AI is heavily reliant on data sets that its algorithms work on. The stark problem with these data sets is that they are not representative of the wide gamut of humanity out there that needs treatment. For AI to pick a patient on its scanner, first of all the patient should have access to a device that lets this patient connect to its virtual world. Disadvantaged populations or displaced populations may not have that. This inherent bias of the system leaves it firmly in the field of speculation over its accuracy and therefore dependability.
Algorithms, however, tend to improve themselves over time; deleting redundancies and communicating across other platforms as they pick more and more data, as has been discussed earlier in the chapter. With the advent of quantum computing, in what has been defined by The Economist as the field’s Sputnik moment, Google has recently demonstrated its ability to perform a task in little over 3 min, what might take most powerful classical computers about 10,000 years to complete [46]. In a world, where Common Health of Android and Apple Health of iOS, will increasingly guide the logistics of collecting and collimating data, medical or otherwise, from Electronic health records, medical devices, software, apps, and smartphones, the art of diagnosis will witness seismic changes. The quality of information being used to create data sets is one of the many hurdles.
Yet, as the horse carriage eventually gave way to sophisticated cars, we will have to yield a part of the field to computer simulation. But as we do that, we have to remember, the algorithm, as yet, does not have a totalitarian power over the mind. At the end of the day, the most important part of patient care is after all, care. And a responsive warm doctor in the field of Oral Medicine and Radiology is still very much preferred over subservience to any cold computing device. Computer simulations, then, are just another set of tools in our armamentarium and we need to research how to use the same for the benefit and better experience of everyone involved.
In a world, increasingly driven on ever evolving computation, diagnostic medicine has to adapt to change. Harnessing the power of AI to prevent logjams of channeling information collected into a cohesive whole will benefit both the doctor providing care and the patient receiving it. Computer simulation is a vital tool and how, and how much will it change the topography of diagnostic medicine, remains to be seen.
To maintain hemostasis, new blood cells must be constantly generated to replace those lost through injury, disease, or age. Hematopoiesis, is the process where hematopoietic stem cells (HSC) differentiate into mature blood cells and is tightly regulated by the bone marrow (BM) micro-environment (or stem cell niche; reviewed in [1]), signal transduction pathways (reviewed in [2]), cytokines (reviewed in [3]), transcription factors (reviewed in [4]), epigenetics, (reviewed in [5]) and metabolic pathways (reviewed in [6]). HSCs are rare, constituting only 0.001% of peripheral blood (PB) and 0.05% of BM cells, but are responsible for producing a lifetime supply of blood cells. HSCs are cells that able to durably self-renew whilst also being multipotent. This differentiation is generally considered to occur via several intermediate progenitor cells, ultimately terminating in the specific mature blood cell through a process termed fate restriction or lineage commitment.
The compartmentalization of HSC, their progenitors and terminally differentiated blood cells, into different stages of differentiation, is traditionally based on the expression of cell surface proteins (Figure 1). The recent emergence of single cell technologies such as fluorescent in situ hybridization, high-throughput single-cell quantitative PCR, single cell mass spectrometry and mass cytometry however, have led to re-analysis of these models of hematopoietic differentiation [7]. Discrete progenitor cell populations, as determined by cell surface markers, have been shown to consist of heterogenous populations with different fates [8]. Recently, a study by Velten
Human hematopoiesis. Schematic diagram showing classical model of hematopoietic lineage commitment, with phenotypical cell surface markers (red), transcription factors determining differentiation (green box) and growth factors involved in myelopoiesis (blue). Hematopoietic stem cell (HSC), cluster of differentiation (CD), hematopoietic progenitor cell (HPC), common myeloid progenitor (CMP), common lymphoid progenitor (CLP), interleukin (IL), granulocyte macrophage (GM) colony-stimulating-factor (CSF), stem cell factor (SCF), thrombopoietin (TPO), erythropoietin (EPO), granulocyte myeloid progenitor (GMP), runt-related transcription factor 1 (RUNX1), transcription factor stem cell leukemia (SCL), ccaat enhancer binding proteins (C/EBP), friend of GATA protein 1 (FOG-1).
Regardless of provenance, leukemogenesis is characterized by a block in differentiation and an accumulation of immature white blood cell blasts with a rapid increase in these blasts, characteristic of the acute leukemias. Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are heterogenous diseases with a block in lymphoid or myeloid differentiation, respectively. They occur due to one or more genetic insults. Whilst ALL is predominantly a disease of children (80%), with a greater than 90% 5 y survival rate [10], in adults long term survival stands at only 30–40% [11]. AML in contrast is primarily a disease of the elderly, and like adult ALL it’s 5 y survival rate is around 30%, however this falls in the over 60’s to a particularly bleak 10% [12]. In ALL, recent advances for example in the use of tyrosine kinase inhibitors and CAR-T cell therapy, have started to suggest improvements to overall survival [10]. However, in patients fit enough to tolerate chemotherapy, the standard treatment for AML since 1973 has been a seven-day continuous intravenous infusion of cytarabine (Ara-C) (100–200 mg/m2) and 3 daily doses of daunorubicin (45–90 mg/m2), sometimes followed by allogeneic or autologous stem cell transplantation, and despite some recent advances (reviewed in [13, 14]), current treatments appear to have reached their efficacious limits and new therapies are required.
One potential therapeutic opportunity involves exploiting the metabolic differences that exist between malignant and non-malignant cells [15]. Differences that, in AML at least, appear exacerbated by cellular levels of reactive oxygen species (ROS) [16].
ROS is the collective term for several oxygen containing free radicals and other reactive molecules, such as hydrogen peroxide (H2O2). Physiologically, ROS are initially generated via the univalent reduction of molecular oxygen which generates superoxide (O2•−). Superoxide (t1/2 = 1 μs) subsequently dismutates to H2O2 (t1/2 = 1 ms) [17], either spontaneously or via the catalytic action of the enzyme superoxide dismutase (SOD), or reacts with other ROS molecules, forming a variety of other ROS (Figure 2). Functionally, ROS is important in innate immunity, protein folding in the endoplasmic reticulum and as a cell signalling molecule involved in cellular proliferation, survival, differentiation and gene expression [18].
Formation of reactive oxygen species (ROS). Diatomic oxygen (O2) is univalently reduced by peroxisomes (PO), xanthine oxidase (XO), the electron transport chain (ETC), or NADPH oxidase (NOX) to generate superoxide (O2•−). PO may also reduce O2 directly to form H2O2. O2•− may then dismutate to H2O2 either spontaneously or through the enzymatic action of superoxide dismutase (SOD). Hydroxyl radicals (OH•) may then be formed from H2O2 via the formation of hypochlorous radical (HOCl) in the PO, or via Fenton chemistry. Reactive nitrogen species (RNS) may also be formed through the reaction of nitric oxide radical (NO•) with O2•−.
There are several sources of cellular ROS, including the mitochondria, the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase family of enzymes (NOX), the cytochrome P450 enzymes, peroxisomes and the metabolic enzyme xanthine oxidase (XO).
Generation of ROS by the mitochondria is primarily a function of ‘electron leakage’ from the electron transport chain (ETC), however, mitochondrial ROS may also be generated as a result of numerous enzymes including monoamine oxidase, cytochrome b5 reductase, glycerol-3-phosphate dehydrogenase, aconitase, pyruvate dehydrogenase and α-ketoglutarate dehydrogenase (reviewed in [19]). Mitochondrial ROS production resulting from the ETC generates O2•−, and is thought to occur as result of one of three mechanisms. The first mechanism is a consequence of a high NADH/NAD+ ratio, and results from oxygen interacting with fully reduced FMN. Mitochondrial ROS generated by this mechanism has been observed due to mitochondrial mutation, physiological damage such as ischemia or aging, and only small amounts of ROS are thought to be generated via these mechanisms in normally respiring cells [20]. The second mechanism occurs when there is a high level of reduced co-enzyme Q (CoQH2) in complex II, which in the presence of a high proton motive force generated by the proton pump, force electrons back into complex I in a process known as reverse electron transport (RET). Whilst RET generated ROS has also been implicated in diseases such as ischemia, it is now also thought to be involved as a cell signalling molecule in metabolic adaptation, myeloid differentiation and response to bacterial infection [21]. The third mechanism of ROS generation by the ETC occurs at complex III and has also been implicated in ROS signalling. The formation of O2•− occurs at the ubiquinol oxidation centre (Qo) site of the cytochrome bc1 complex, in which fully oxidized CoQ supports formation of O2•−, through the transfer of electrons from reduced heme b1 to molecular oxygen [22]. Generation of O2•− by complex I and II occurs exclusively in the mitochondrial matrix, whereas O2•− generated by complex III also occurs in the intermembrane space. O2•− generated in the mitochondrial matrix is rapidly converted to H2O2 by mitochondrial SOD (Mn-SOD), whereas O2•− generated in the intermembrane space travels through the outer mitochondrial membrane prior to conversion to H2O2 by cytosolic SOD (Cu/Zn-SOD).
Whilst mitochondrial oxidative phosphorylation is a major source of intracellular ROS, the main source of extracellular ROS involves the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase family of enzymes (NOX). The NOX family of enzymes comprise of seven members, NOX1–5 and dual oxidase (DUOX) 1 and 2. NOX enzymes are transmembrane proteins that transfer electrons from NADPH to molecular oxygen, generating O2•− (or H2O2), which can then be converted to other forms of ROS. Different NOX isoforms share conserved structural features comprising of six helical transmembrane domains (TM) (with helix III and helix V containing two heme-binding histidines), and a C-terminus cytosolic domain (DH), which allows binding of FAD and NADPH (Figure 3). Difficulties in obtaining suitable levels of NOX proteins mean that to date relatively little crystal structure data is available. However, a recently published report [23], has elucidated the structure of the TM and DH domains (common to all NOX isoforms) of
Generation of superoxide (O2•−) by NADPH Oxidase (NOX). Schematic diagram showing the major structural features of NOX2, it’s activation by phosphorylation (P) of p67phoxand p47phox and the assembly of the major subunits of the NOX complex, and the generation of superoxide via electron transfer from NADPH to flavin adenine dinucleotide (FAD) to heme groups to diatomic oxygen. Guanosine triphosphate (GTP), guanosine diphosphate (GDP), homology domain (DH), RAS-related C3 botulinum toxin substrate 2 (Rac2).
From a metabolic perspective, one source of NOX2 activation results when cells experience intermittent hypoxia. Under this condition activation of the metabolic enzyme XO, an enzyme important in the catabolism of purines and a major source of cellular ROS, occurs [24]. XO activation leads to increased ROS, which induces Ca2+ activation of protein kinase C, an enzyme important in cell signalling, migration of p47phox and p67phox to the cell membrane, resulting in activation of the NOX2 complex (Figure 3). Finally it is important to note, from a cell signalling perspective, that extracellular H2O2 (which is rapidly formed from O2•−) is readily transported across the cell membrane via the transmembrane water permeable channel protein family of aquaporins [25, 26].
ROS has been implicated in both HSC quiescence and hematopoietic differentiation. HSC reside in the bone marrow and their quiescence is known to be negatively regulated by ROS. Forkhead box O (FOXO) transcription factors are involved in cell-cycle arrest and apoptosis and are activated in response to oxidative stress whereupon they translocate to the nucleus [27]. Translocation of FOXO4 to the nucleus has been shown to be a function of redox signalling, where oxidation of cys-239 by ROS mediates the formation of disulphide bonds with nuclear import receptor transportin-1, which in turn allows nuclear localization [28]. FOXO deactivation occurs as a result of phosphorylation in response to activation of the regulatory cell cycle PI3K/AKT/mTOR pathway, resulting in their export from the nucleus and subsequent degradation in the cytoplasm [29]. Studies in murine HSC have shown that deletion of FOXO3a, which upregulates transcription of Mn-SOD [30], results in decreased HSC renewal [31] which is mediated by the tumor suppressor protein ataxia-telangiectasia mutated (ATM) and is accompanied by elevated ROS levels and myeloid lineage expansion [32]. Deletion of ATM in mice resulted in BM failure which was restored following treatment with antioxidants [33]. In a different study, isolation of murine HSC into ROS high and ROS low populations showed that the ROS low population maintained self-renewal capacity following serial transplantations, whilst the self-renewal capacity of the ROS high population was exhausted following the third serial transplantation. Treatment of the ROS high HSC with the antioxidant N-acetyl cysteine (NAC), the p38 inhibitor SB203508 or rapamycin (a mTOR inhibitor), restored self-renewal activity [34]. Interestingly, the ROS high population in this study also exhibited a decreased ability to adhere to cells containing calcium sensing receptors, whilst NOX generated ROS has additionally been implicated in osteoclast differentiation in human mesenchymal cells, further emphasizing a potential regulatory role of ROS, in the BM niche [35].
Whilst these increased ROS levels are associated with HSC losing quiescence, it has also been shown, in the human megakaryocytic cell line MO7e, that hematopoietic cytokines, such as granulocyte macrophage-colony stimulating factor, interleukin-3, stem cell factor and thrombopoietin all increase ROS levels [36]. In megakaryopoiesis, ROS has been shown to increase platelet production and maturation in the chronic myeloid leukemia (CML) cell line MEG-01 and primary human megakaryocytes [37], which in murine models is mediated by the transcription factor NF-E2 [38]. Following lineage commitment, megakaryocyte progenitors undergo endomitosis (chromosomal replication in the absence of cell division), which in murine cells is potentially mediated by NOX1-derived ROS [39]. In human HSC, NOX-derived ROS has also been shown to be crucial for megakaryocyte differentiation via activation of ERK, AKT and JAK2 signalling pathways [40], whilst another study revealed the importance of cytochrome P450 2E1-generated ROS in megakaryocyte differentiation in human HSC [41]. As noted above, increased ROS in HSC has been associated with expanded myelopoiesis. Interestingly, a recent study using murine CMP, showed that higher levels of ROS impeded megakaryopoiesis, instead directing differentiation of CMP into GMP [42]. Finally, ROS has also been shown to induce differentiation of the promonocytic cell line, U937, into macrophages [43], and the differentiation of primary human monocytes into dendritic cells [44].
One of the first studies implicating ROS in carcinogenesis was performed in mice subcutaneously injected with C3H mouse fibroblasts, that had been previously cultured
In leukemia, a study which collected blood samples from ALL and CML patients samples and compared them with normal blood samples showed elevated levels of ROS in both ALL and CML patients [52], whilst elevated levels of NOX generated ROS, are observed, alongside increased proliferation in both AML models and AML patient samples when compared with healthy controls [53]. Reactions of ROS with DNA can generate numerous oxidised bases, including 8-hydroxy-2-deoxyguanosine (8-OHdG) which causes G:C to T:A DNA transversions (reviewed in [54]). Increased levels of 8-OHdG have been observed in patients with breast cancer [55], gastric carcinomas [56], lung cancer [57] and colorectal cancer [58]. In leukemia, a study of 116 Chinese children with either ALL or AML revealed significantly elevated levels of 8-OHdG, whilst 8-OHdG levels were also significantly elevated in relapsed AML adult patients [59].
As a signalling molecule, ROS can lead to hyperactivation of the PI3K pathway, a common feature of many cancers, resulting in increased cell survival, VEGF production, secretion of MMP (reviewed in [60]) and inactivation of FOXO [32]. In AML, constitutive activation of the PI3K/AKT pathway is frequently observed [61, 62], however the role of FOXO is less clear. A recent study revealed that FOXO1 expression in osteoblasts mediated β-catenin initiated AML [63], whilst a study of AML patient samples showed that 40% exhibited FOXO activation, that upon inhibition resulted in myeloid differentiation and AML cell death [64]. Additionally, in both CML and AML the BCR-ABL fusion protein and FMS-like tyrosine kinase receptor 3 internal tandem duplications (FLT3-ITD) have been shown to lead to phosphorylation of AKT resulting in increased activation of NOX, and increased ROS production (reviewed in [65]), which may in turn reinforce PI3K/AKT activation.
Broadly defined, cellular metabolism involves a series of catabolic or anabolic chemical reactions which generate or use energy as part of this process. In chemotrophs this energy is obtained through the oxidation of nutrients, with the energy typically stored in the form of ATP. Whilst in higher organisms a plethora of enzymatically catalyzed metabolic reactions occur, which are all part of different interconnecting metabolic pathways with multitudinous feedback mechanisms. These pathways are evolutionarily highly conserved with the citric acid cycle, for example, essentially a feature in all terrestrial life. There are three main classes of molecules involved in metabolism; carbohydrates, proteins and lipids that are either catabolized to generate energy or energy stores or used by anabolic pathways in the synthesis of, for example, nucleotides and structural molecules such as cell membranes. In mammals, a triumvirate of glycolysis, citric acid cycle and the ETC are central to the generation of ATP, with glycolysis and the citric acid cycle contributing 2 ATP molecules each and the ETC generating up to 34 ATP molecules in a process collectively termed aerobic respiration (reviewed in [66]).
Given the skew towards ATP production in the ETC, Otto Warburg’s observation in 1956 that aerobic glycolysis was a hallmark feature of cancer cells [15], was initially attributed to being the result of defective mitochondria in malignant cells, and initially raised little interest. However, this hypothesis is now known in most cases to be incorrect (reviewed in [67]) and instead, it has been shown that mitochondrial respiration is often necessary in tumorigenesis [68]. However, given its ubiquity and despite its inefficiency when compared with ETC, it is clear that the phenomenon of increased aerobic glycolysis (eponymously titled ‘The Warburg Effect’), must offer cancer cells some competitive advantage, although its exact ontology remains unclear. One hypothesis contends that whilst inefficient, aerobic glycolysis generates ATP at a rate 10–100 times faster than oxidative phosphorylation, therefore supplying cancer cells with energy at a faster rate. This increased glycolytic flux could then, potentially generate more nucleotides, amino acids and lipids for biosynthesis as well as generating the reducing agent NADPH, to deal with the increased levels of ROS common in many cancer cells [69]. Alternatively, increases in excreted lactate as a result of aerobic glycolysis would likely generate a more acidic microenvironment, breaking down stromal membrane structures and potentially increasing cancer cell motility and metastasis [70].
It has been shown that activation of the tumor suppressor protein ATM by ROS promotes glucose-6-phosphate dehydrogenase (G-6-PD) activity, the first step of the pentose phosphate pathway (PPP), which in turn generates NADPH [71]. Given that major cellular antioxidant systems, ultimately rely on NADPH to provide their reducing power, it is perhaps not surprising that ROS in both normal and aberrant cellular processes is inextricably linked with metabolism. In the cytosol, NADPH is primarily generated through the PPP, whilst a number of mechanisms exist for mitochondrial NADPH generation [72], which include the serine synthesis pathway (SSP) (via the folate cycle) [73] and the action of the citric acid cycle enzyme isocitrate dehydrogenase (IDH). IDH1 and IDH2 are commonly mutated in AML [74], although in this context NADPH is consumed, and the D-2-hydroxyglutarate generated leads to stabilization of the hypoxia regulator, hypoxia inducible factor alpha (HIF-1α) [75].
HIF-1α as a target of ROS is controversial [76], however it is overexpressed in many cancers where it induces expression of numerous glycolytic genes. The ROS regulated transcription factor nuclear-related factor 2 (NRF2) has also been shown to modulate metabolism in lung cancer cell lines, through the upregulation of enzymes involved in the NADPH production, notably G-6-PD, IDH1 and malic enzyme 1 [77] and high NRF2 levels have previously been reported in AML [78]. Furthermore, the tumor suppressor protein TP53 is also important in regulating metabolism. Homozygous deletion of TP53 in mice results in decreased oxygen consumption arising from decreased mitochondrial respiration [79]. TP53 expression has been shown to inhibit, both glucose transporter (GLUT) 1 and 4 and the glycolytic enzyme phosphoglycerate mutase (PGAM) (reviewed in [80]) leading to decreased glycolysis and potentially increased metabolism via the PPP and SSP. Finally, TP53 also upregulates the apoptosis regulator (TIGAR) an enzyme which has an active domain similar to 6-Phosphofructo-2-kinase/fructoste-2,6-bisphosphatase (PFKFB). TIGAR catalyzes the reaction of fructose-2,6-bisphosphate (F-2,6-BP) to fructose-6-phosphate (F-6-P), which inhibits glycolysis, redirects metabolites into the PPP, generating NADPH [81].
Changes of cellular ROS levels in both normal signalling as well cell signalling following cellular transformation result in changes in numerous signalling pathways controlling multiple cellular functions including growth, proliferation and differentiation. A number of these signalling pathways, exercise regulatory control over various metabolic pathways, which in turn modulate ROS levels via several feedback mechanisms (Figure 4). In leukemia, mutations in the
Regulation of metabolic pathways. Schematic illustration outlining some of the regulatory mechanism involved in glycolysis and other key metabolic pathways. Transcription factors are in pink and signalling pathways in blue. Reactive oxygen species (ROS), forkhead box O (FOXO), pyruvate kinase muscle 2 (PKM2), signal transducer and activator of transcription (STAT), nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB), glucose transporter (GLUT) hypoxia inducible factor-1 alpha (HIF-1α), tumour suppressor protein 53 (TP53), glycogen synthase kinase 3β (GSK-3β), isocitrate dehydrogenase (IDH), succinate dehydrogenase (SDH), fumarate hydratase (FH), protein kinase B (AKT), mammalian target of rapamycin (mTOR), phosphoinositide 3-kinase (PI3K), synthesis of cytochrome c oxidase 2 (SCO2) and prolyl-hydroxylase domain (PHD).
Nuclear localization of the glycolytic enzyme pyruvate kinase muscle 2 (PKM2) is also ROS mediated, where it acts as a co-factor in the activation of the transcription factor, c-MYC. RAS also activates c-MYC which is overexpressed in greater than 50% of human cancers and c-MYC has been shown to activate glycolysis via the upregulation of GLUT, the glycolytic enzymes hexokinase (HK), phosphoglucose isomerase (PGI), phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), PKM2, as well as lactate dehydrogenase A (LDHA), pyruvate dehydrogenase kinase 1 (PDK1) and PFKFB3 (reviewed in [87]). Increased glutaminolysis is also a target of c-MYC, which upregulates the glutamine transporter ASCT2 and a key enzyme glutaminase. Additionally, c-MYC was shown to upregulate both phosphoglycerate dehydrogenase (PHGDH) which catalyzes the first step of the SSP, serine hydroxymethyltransferase, part of the folate cycle as well as several genes involved in fatty acid metabolism and the citric acid cycle (reviewed in [67]). In contrast TP53 is known to inhibit glycolysis through inhibition of GLUT1, GLUT4 and PGAM and through activation of TIGAR and synthesis of cytochrome c oxidase 2 (SCO2). Inhibition of glycolysis also occurs due to the regulatory role of miRNA. For example, miR-195-5p inhibits GLUT3, miR-143 inhibits HK2 and miR-155 inhibits HIF-1α. Furthermore, TP53 induces miR-34a which suppresses HK1, HK2, GPI and PDK1, as well as sirtuin 1, which activates FOXO1, NF-κB and in a positive feedback loop TP53 (reviewed in [80]).
Given the role that ROS plays in regulating metabolism, it is not surprising that expression of nearly all enzymes associated with glycolysis have been shown to be altered in solid tumors, a pattern also observed in leukemia. In ALL, micro-array analysis showed significant upregulation of PFK as well as the glucose transporters GLUT1 and GLUT4 in pediatric B-ALL samples [88], whilst deletion of GLUT1 in primary human B-ALL cells suppressed leukemic progression
The citric acid cycle is a series of metabolic reactions involving oxidation/reduction reactions, which generate nicotinamide adenine dinucleotide (NAD)H and flavin adenine dinucleotide (FAD)H via the transfer of hydride ions, thus providing electrons for the ETC which is a major source of cellular ROS (reviewed in [106]). Mutations of IDH, which catalyzes the decarboxylation of isocitrate to alpha-ketoglutarate are frequently reported in AML (reviewed in [107]). Characterization of the inhibitor AG-221, which has been shown to inhibit mutant IDH2 in AML cells
The SSP branches from the glycolytic pathway at the glycolytic intermediate 3-PG, where it is converted into 3-phosphohydroxypyruvate by the enzyme PHGDH, followed by conversion to phosphoserine by phosphoserine aminotransferase 1 and finally to serine by the action of the enzyme phosphoserine phosphatase (reviewed in [73]). Regulation of the SSP is achieved through 2-phosphoglycerate (2-PG) which activates PHGDH whilst serine activates the tetrameric form of PKM2 leading to increased glycolysis and decreased levels of 2-PG. Importantly serine can enter the folate cycle, which provides another route for the generation of NADPH, which has been shown to contribute to tumor growth
The PPP generate nucleotides for biosynthesis and is a major source of cellular NADPH, an important cellular antioxidant. The first step involves the dehydrogenation of G-6-P to 6-phosphogluconolactone (6-PG) catalyzed by G-6-PD and the conversion of NADP+ to [115]. Gluconolactonase catalyzes the hydrolysis of 6-PG to 6-phosphogluconate, which is then catalyzed by 6-phosphogluconate dehydrogenase (6-PGD) to ribulose-5-phosphate (Ru-5-P) alongside the generation of a second NADPH. Ru-5-P can then be converted into ribose-5-phosphate (R-5-P) by the enzymatic action of ribulose-5-phosphate isomerase. R-5-P can then be used in the synthesis of nucleotides. Alternatively, where redox homeostasis and not nucleotide synthesis is the major requirement of the cell Ru-5-P can be catalyzed by ribulose-5-phosphate epimerase, into xyulose-5-phosphate (X-5-P) and via a series of further metabolic reactions back into the glycolytic intermediates F-6-P and glyceraldehyde-3-phosphate. G-6-PD is the rate limiting step of the PPP and is regulated by the NADP+/NADPH ratio, RAS/PI3K signalling and phosphorylation by Src, whilst 6-PGD is inhibited by 3-PG [99]. In cancer, aberrant RAS signalling or activation of Src can promote activation of the PPP. In AML, a recent study showed upregulation of
Lipid metabolism has also been shown to be dysregulated in both solid tumors and hematological malignancies (reviewed in [120]). Increased fatty acid oxidation (FAO) allows cancer cells to overcome metabolic and oxidative stress through the generation of ATP and NADPH. Significant changes to lipid metabolite levels are seen in AML patient samples with either high levels or low levels of ROS [16], whilst suppression of NOX2 has also been shown to increase FAO [121]. Furthermore, inhibition of the FAO using Avocatin B results in decreased NADPH levels and ROS dependent cell death in primary human AML samples but not normal mononuclear cells [122]. In ALL, use of L-asparaginase has been shown to increase FAO activity as a metabolic escape mechanism, however use of the FAO inhibitor etomoxir in combination with L-asparaginase has been shown to increase sensitivity of both leukemic cell lines and patient samples [123].
In the last twenty years, it has become increasingly clear that ROS play a significant role in cellular signalling, particularly pathways associated with growth, differentiation and survival, whilst its roles in HSC quiescence and normal hematopoiesis have started to be delineated. In many cancers including hematological malignancies, ROS levels have been shown to be elevated, leading to aberrant signalling in these pathways. Previously, arguments for both the use of anti-oxidant and pro-oxidant treatments in leukemia have been made (reviewed in [124]). Despite the transformation of survival rates in patients with acute promyelocytic leukemia using arsenic trioxide [125] cancer cells often upregulate the production of antioxidants, and downregulate pro-apoptotic pathways such as TP53, as a response to high ROS, allowing them to escape apoptosis. In addition, it has been shown that both cancer stem cells [126, 127] and leukemic stem cells [128] exhibit low ROS levels, suggesting that even if treatment with pro-oxidants eliminates the bulk of cancer cells, cancer/leukemic stem cells may survive and relapse occur. Conversely, studies involving the use of antioxidants in treatment and epidemiological studies of antioxidant use, have shown mixed results (reviewed in [129, 130]). Increasingly it is becoming apparent that increased levels of ROS are leading to changes in signalling pathways directly or indirectly controlling metabolism, as a mechanism for managing oxidative stress. Whilst, it has long been known that cancer cells exhibit greatly altered metabolism, only recently have the purposes behind this altered metabolism, started to be elucidated. Consequently, synergistic treatments involving the use of metabolic inhibitors, alongside classical treatments for leukemias are being explored. Future work, elucidating the intricate mechanisms governing the interplay between ROS and metabolism, alongside new and more specific metabolic inhibitors provide much promise for the future treatment of leukemia.
We are grateful to Blood Cancer UK for programmatic funding and to Tenovus Cancer Care for funding Andrew Robinson. We are grateful to Wellcome ISSF for funding aspects of ROS research. We are grateful for support from the NCRI AML trials cell bank and the AML patients for providing primary samples used in several of our studies.
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