Summary of major allergen databases and their relevant features.
\r\n\tThis book intends to explore the domain of Concurrent Computing in computer science with special emphasis on insight and deeper understanding, not just on formalisms. An attempt has been made to present the material in a clear and simple style which encompasses many challenges and opportunities in the area of Concurrent Computing.
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Harkut",publishedDate:null,coverURL:"//cdnintech.com/web/frontend/www/assets/cover.jpg",keywords:"Concurrent Programming, Concurrent Algorithms, Concurrent Data Structures, Multi-Processor System, Networked Computer Systems, Deterministic Parallelism, Message Passing, Concurrent Server Architectures, Real Time System, Cluster Computing, High-Speed Networks",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 2nd 2019",dateEndSecondStepPublish:"October 23rd 2019",dateEndThirdStepPublish:"December 22nd 2019",dateEndFourthStepPublish:"March 11th 2020",dateEndFifthStepPublish:"May 10th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"216122",title:"Dr.",name:"Dinesh G.",middleName:null,surname:"Harkut",slug:"dinesh-g.-harkut",fullName:"Dinesh G. 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Immunity is basically divided into two types such as innate immunity and adaptive immunity [1]. Innate immunity also referred to as natural, native, or nonspecific immunity acts as a first line of defense against common harmful agents. Innate immune response provides immediate protection and involves number of components such as monocytes, macrophages, neutrophils, cytokines, complement, and epithelial barriers. Adaptive or acquired immunity comprises highly specific immune responses that are elicited against particular pathogens or antigens. These immune responses are either cell mediated or antibody mediated (humoral) and executed by specialized lymphocytes or immunoglobulins, respectively. On certain occasions, the immune system produces immune responses that are harmful for the host organism. Autoimmunity denotes one such case wherein the body elicits immune responses against its own cells and tissues (self-antigens) which lead to development of autoimmune diseases. In some cases, immune system produces inappropriate immune responses known as hypersensitivity, which has deleterious effects on the host organism. Hypersensitivity reactions are categorized into four groups based on the type of immune response and the effector mechanism involved. These are (i) immediate hypersensitivity (type I), (ii) antibody-mediated hypersensitivity (type II), (iii) immune complex–mediated hypersensitivity (type III), and (iv) cell-mediated hypersensitivity (type IV) [2].
\nAllergic reactions are type I hypersensitivity reactions, which are characterized by induction of specific class of antibodies known as immunoglobulin E (IgE). These reactions are elicited against specific type of antigens commonly referred to as allergens. An allergic reaction involves specialized cells and specific molecules of the immune system [3]. IgE antibodies induced by allergens upon allergic sensitization bind to effector cells such as basophils and mast cells via specific Fc receptors present on the surfaces of those cells. Subsequent exposure to the allergen causes cross-linking of membrane-bound IgE on these effector cells, which leads to their degranulation and release of pharmacologically active agents such as histamine. These pharmacological mediators are responsible for clinical manifestations of allergic reactions in the affected individuals. Immunogenicity in general refers to the potential of an antigen to elicit an immune response, while in case of allergens, allergenicity is considered as a reflection of its allergenic potential. Allergenicity indicates the capability of an allergen to induce clinical symptoms of allergy as well as to induce and bind to IgE antibodies [4]. The prevalence of allergic reactions has increased significantly in the last few years, especially in the developing countries [5]. This has resulted in considerable increase in disease burden as well as economic issues due to costs associated with these diseases. Therefore, the study of allergic diseases has gained tremendous importance as they represent one of the major health problems in urban and rural regions.
\nThe field of allergy research has rapidly progressed in the last few years [6]. Recent advances in genomic, proteomic, and analytical methods have given rise to large amounts of data relevant to allergens. This data can be correlated with pathology of various allergic diseases based on experimental, clinical, and epidemiological data for allergic reactions. The continuous growth of data calls for the efficient archival, management, and analysis of data. This has led to the development of the field of allergen informatics, which comprises allergen specific databases/resources and computational methods/tools. Allergen informatics constitutes an important branch of immunoinformatics [7]. In this chapter, a review of the existing status of allergen informatics with respect to important aspects such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment has been presented (Figure 1).
\nTopics covered for allergen informatics in the current review.
Allergens represent the most critical component of an allergic reaction, although IgE antibody, Fc receptors, mast cells, and basophils as well as pharmacological mediators such as histamine and heparin also play very significant roles. Allergens are ubiquitous substances, which arise from a variety of sources such as foods, plants, animals, or environment. An allergen can either be a chemical substance (e.g., penicillin) or a protein (e.g., albumin, profilin, etc.). Majority of the allergens are proteins or glycoproteins that possess high water solubility. Several biochemical and structural features of allergens such as stability, hydrophobicity, and ligand-binding domains are known to contribute to their allergenicity [8]. However, common molecular and structural features of allergens that are responsible for allergenicity have not yet been conclusively discovered.
\nAllergens are provided with a unique, unambiguous, and systematic nomenclature which has been developed and maintained by the World Health Organization (WHO) and International Union of Immunological Societies’ (IUIS) “Allergen Nomenclature Sub-committee” [9, 10]. The nomenclature is based on the Linnean system, and an allergen, which satisfies certain biochemical and immunological criteria, is included in the WHO/IUIS nomenclature. An allergen name consists of an abbreviation of the scientific name of the allergen source organism. First 3–4 letters denote the genus name, while the subsequent 1–2 letters represent species, followed by an Arabic numeral that denotes the order of its identification. For instance, Der p 1 represents the first allergen to be characterized from the house dust mite
Allergens display important features such as epitopes and cross-reactivity that are very critical with respect to understanding of allergic reactions and developing newer approaches for diagnosis and treatment of allergic diseases. Epitope or antigenic determinant refers to the immunologically active region of the allergen. An epitope can be an IgE-binding epitope or a T-cell epitope depending on whether it interacts with an IgE or a T-lymphocyte. An IgE epitope can be either sequential (linear) that consists of contiguous stretch of amino acids or conformational (discontinuous), which comprises amino acids present at different loci in an antigen. An antibody is said to be cross-reactive when it recognizes and binds to multiple antigens.
\nIgE-binding epitopes refer to the IgE recognition sites in allergens that are involved in specific interaction of allergens and IgE antibody. Inferences drawn from allergen–antibody complexes and other important studies have shown that majority of IgE-binding epitopes are conformational in nature [14]. IgE epitopes possess some defining structural and immunological features such as they are more cross-reactive in nature and have higher intrinsic flexibility. These features make them distinct from other antibody epitopes and contribute significantly in the allergenicity [15, 16]. Identification and in-depth analysis of IgE-binding epitopes has the potential to contribute immensely in accurate diagnosis and allergen-specific immunotherapy of allergies, especially the food allergy [17, 18]. Large amount of data on allergen epitopes are generated by employing strategies based on the use of overlapping synthetic peptides, recombinant allergenic fragments, cocrystal structure complexes, etc. However, it is believed that insights obtained from study of allergen–antibody complexes will be the most helpful in understanding the role these epitopes play in allergic reactions [19–21].
\nT-cell epitopes are the antigenic determinants of allergens that interact with T-lymphocytes via specific T-cell receptors. T-cell epitopes of allergens have shown to be very important for the modulation of allergic response and thereby contributing to symptoms associated with allergic diseases [22]. They have enormous potential in the development of allergy vaccines as well as newer strategies in allergen immunotherapy, considering their fundamental role in allergic response [23, 24]. Recent findings have indicated that T-cell epitope repertoire in allergens is diverse than IgE epitopes, and it can be very useful in specific immunotherapy in allergy [25]. An analysis carried out on available epitope data has shown that T-cell epitopes are known to occur more commonly in the airborne allergens as compared to food allergens [26].
\nCross-reactivity denotes a clinically and immunologically critical phenomenon displayed by allergens from various sources and is the cause of pollen-food syndromes, such as the one seen in case of birch and apple. Cross-reactivity is considered as a property of antibodies and it arises when an antibody or a subgroup of antibodies recognizes more than one allergen or epitope [27]. Two allergens are considered cross-reactive if they are recognized by a single antibody (or T-cell receptor). It has been stated that cross-reactivity among allergens at the level of B cells, T cells, and mast cells reflects clinical sensitivities and contributes very significantly in the regulation of allergic sensitization [28].
\nCross-reactivity is predominantly an antibody defined phenomenon and IgE antibodies are shown to be more cross-reactive in nature. Affinity of the antibodies toward the allergen is known to play an important role in cross-reactivity. However, the properties of the allergenic protein are also very important and shared features on the level of both primary and tertiary structures of the cross-reactive proteins are found to be responsible for cross-reactivity [4]. Similarity at the level of sequence is an important indicator and cross-reactivity seems to require more than 70% sequence identity. In addition to this, other factors such as the host immune response against the allergen, dosage of allergen, and mode of exposure also contribute in clinical relevance of allergic cross-reactivity. Inferences drawn from studying a large number of allergens have led to the conclusion that structural similarity among proteins from diverse sources is the molecular basis of allergic cross-reactivity [29]. Considering the role it plays in the development of allergic symptoms, a detailed analysis of cross-reactivity has the potential to contribute in the development of new strategies in diagnosis and therapy of allergic diseases.
\nLast few years have witnessed substantial technological advances in the field of genomics and proteomics along with tremendous improvements in analytical methods. This has led to a significant progress in the area of allergy research. As a result of this, there has been a steady and continuous increase in the number of characterized protein allergens over the last few years. Efficient storage and management of data has become very important because of such incessant accumulation of molecular and clinical data on allergens. Therefore, allergen databases represent very crucial resources for basic allergy research as they are involved in archival of available allergen knowledge.
\nDatabase | \nDeveloped by (URL) | \nType of data archived | \nComputational tools (if any) | \nUpdates | \n
---|---|---|---|---|
IUIS Allergen [36] | \nWHO/IUIS Allergen Nomenclature Sub- committee (http://www. allergen.org) | \nSequence (isoallergens/ isoforms), structure, allergenicity | \n– | \nUpdated continuously | \n
Allergome [39] | \nCentre for Clinical and Experimental Allergology, Italy (http://www.allergome. org) | \nSequence (isoallergens/ isoforms), structure, clinical, epidemiological, cross-reactivity, etc. | \n– | \nUpdated continuously | \n
Structural Database of Allergenic Proteins (SDAP) [41] | \nSealy Centre for Structural Biology, University of Texas, USA (https://fermi.utmb.edu) | \nSequence, structure, structural models, IgE epitopes | \nYes | \n2013 | \n
Allergen Database For Food Safety (ADFS) [44] | \nNational Institute of Health, Japan (http://allergen.nihs. go.jp/ADFS/) | \nSequence, structure, IgE epitopes, small molecule allergens | \nYes | \n2016 | \n
AllergenOnline [46] | \nFood Allergy and Resource Program (FARP) (http://www.allergenonline. org) | \nSequence, allergenicity | \nYes | \n2016 | \n
AllFam [48] | \nDepartment of Pathophysio logy and Allergy Research, Medical University of Vienna, Austria (http://www.meduniwien.ac. at/allfam) | \nAllergen family data, cross-link to Pfam database | \n– | \n2011 | \n
AllergenPro [53] | \nThe National Agricultural Biotechnology Information Centre, Korea (http://nabic.rda.go.kr/ allergen) | \nSequence, IgE epitopes | \nYes | \n2015 | \n
AllerBase [61] | \nBioinformatics Centre, Savitribai Phule Pune University, India (www.bioinfo.net.in/ AllerBase/Home.html) | \nSequence and structure (cross-links), IgE epitopes, IgE antibody, IgE cross-reactivity, experimental evidences of allergenicity | \n– | \nUpdated continuously | \n
Summary of major allergen databases and their relevant features.
Many allergen-specific databases have been developed in the past few years although they differ from each other with respect to their objectives, type of data archived, accessibility of contents, and the level of annotation and applications [30]. In addition to dedicated allergen databases, primary bioinformatics databases also document significant data on allergens. Examples of these databases include GenBank/GenPept [31, 32], UniProtKB [33], and Protein Data Bank (PDB) [34], which archive sequence and structure data on allergens along with its annotation. The Summary of allergen-specific databases is provided in Table 1. In the following section, the existing allergen databases are described.
\nThe IUIS Allergen Nomenclature Sub-Committee, under the auspices of the WHO, provides the systematic nomenclature of allergenic proteins and it has developed and maintained Allergen database [35, 36]. The database archives all of the WHO/IUIS–recognized allergens along with their isoallergens and isoforms (variants). In order to maintain a consistent allergen nomenclature for newly discovered allergens, researchers are required to submit newly described allergens to the Allergen Nomenclature Sub-Committee before submitting their manuscript to a journal for consideration for publication.
\nEach allergen in this database is provided with annotation that includes biochemical name, molecular weight, information on its allergenicity, reference, etc. Additionally, sequence data for allergens and isoallergens/isoforms are also stored in the database, along with cross-references to GenBank [31], GenPept [32], and UniProtKB [33], as well as to PDB [34], for nucleotide, protein sequences, and 3D structure data, respectively. Allergen database can be searched by using allergen name, biochemical name, allergen source organism, taxonomic group, etc., as search criteria. The database is updated continuously with specific names assigned to newly discovered allergens and isoallergens/variants [37]. Allergen database does not exemplify the comprehensive allergen data although it documents majority of the characterized allergens. This is because there are a large number of allergens that have been reported in literature which are not recognized by IUIS-Allergen. The database does not archive data on allergen epitopes and cross-reactivity.
\nAllergome [38] represents an extensive repository of information on allergen molecules causing IgE-mediated (allergic, atopic) diseases [39]. The database comprises comprehensive data on WHO/IUIS-approved allergens along with other non-recognized allergens. These allergenic molecules are selected and curated from the published literature and web-based resources. It also contains data on allergenic sources based on whether they possess identified molecules or not. Allergome documents information on allergen and isoallergens/isoforms along with their sequences. Cross-links to sequence and structure databases like UniProtKB [33] and PDB [34] are also provided.
\nAllergome can be searched by using basic and advanced search options. Basic search employs numerous search criterions such as allergen name, biochemical name, source organism, etc., while advanced search enables the user to search using specific attributes. Each allergen molecule is represented by a monograph which represents information about the three parts of allergen such as basic information, data on the native form, and its recombinant form. The most important and unique feature of Allergome platform is the presence of several support modules that deal with archival of specific aspects of allergen data. A couple of important modules are RefArray, for easy access to references stored in the Allergome, and Real Time Monitoring of IgE sensitization (ReTiME), for real-time data collection and storage of IgE sensitization data and the number of other utilities. Allergome is updated regularly and allergen data curated from literature is documented.
\nStructural Database of Allergenic Proteins (SDAP) [40] is an allergen database that prominently deals with structural aspects of allergens [41]. It houses comprehensive cross-referenced sequence data on allergens, IgE-binding epitopes, 3D structures, and models of allergens. Each allergen in SDAP is provided with cross-links to primary databases such as UniProtKB [33], PDB [34], as well as to important resources such as NCBI Taxonomy Browser [42] and PubMed [42] for literature references. SDAP also has a utility as a web server that integrates various computational tools, which assist structural biology–related studies dealing with allergens and their epitopes. It employs an algorithm based on the conserved properties of amino acid side chains to detect regions associated with allergenicity in novel sequences. The database consists of number of tools that can be used to assess potential cross-reactivity of allergens and also help in screening of IgE epitopes in novel proteins. The last update of the database was carried out on February 25, 2013. SDAP does not archive complete data for allergens that are not recognized by IUIS while data on allergen cross-reactivity is also not documented.
\nAllergen Database for Food Safety (ADFS) [43] is developed as a project of the Division of Biochemistry and Immunochemistry of National Institute of Health Sciences (Japan). The aim of the database is to archive allergenic proteins and their IgE epitopes with a special emphasis on food allergens and food safety [44]. Allergens archived in ADFS are grouped into eight categories such as pollen, mite, animal, fungus, insect, food, latex, and others, and each allergen entry is provided with the primary database accession numbers of their genes and 3D structure information. The database is also equipped with homology-based sequence search tool for the evaluation of allergenicity. One of the most distinct features of ADFS is the archival of data on small molecule, nonprotein (chemical) allergens. The database does not archive data on allergen cross-reactivity.
\nAllergenOnline [45] is a well curated allergen database that documents a peer reviewed allergen list, which is compiled from various resources such IUIS-Allergen, PubMed, scientific publications, and other allergen databases. The database was developed within the Food Allergy Research and Resource Program (FARRP) at the University of Nebraska [46]. For each allergen, data on source organism, common name, IUIS official nomenclature, protein length, class of allergen like food allergen, contact allergen, etc., and a link to the NCBI protein (GenPept) [32] database are provided. AllergenOnline also provides the utility for sequence-based searches for allergens, which include alignments by FASTA and an eight-amino acid short-sequence identity search. This utility can be very useful in the identification of proteins that may present a potential risk of allergenic cross-reactivity. AllergenOnline is updated every year and the last update that resulted in version 16 of the database was reported on January 27, 2016. It does not archive data on allergen epitopes as well as on allergenic cross-reactivity.
\nAllFam [47] represents a very important resource for allergens as it is involved in classification of allergens into protein families [48]. This study has shown that allergens are distributed into relatively few protein families and possess a limited number of biochemical functions. The structural classification of allergens in AllFam is performed by using family information from PFam [49] and the Structural Classification of Proteins (SCOP) database [50], while biochemical functions of allergens were extracted from the Gene Ontology annotation database [51]. The database provides the option of browsing lists of allergen families based on allergen source (plants, animals, and fungi) and route of exposure (inhalation, ingestion, etc.) while search for specific protein families can also be performed. Each allergen family in AllFam is linked to a family fact sheet that describes the biochemical properties of the family members as well as a list of key references related to this family. The last update of AllFam was reported on September 12, 2011. AllFam does not archive data on molecular features of individual allergens although cross-link to IUIS-Allergen and Allergome is provided for each documented allergen.
\nAllergenPro [52] is a recently developed allergen database that archives data on allergen sequences, structures, and epitopes from various sources. It is an integrated database which provides information about allergens in foods, microorganisms, fungi, animals, and plants [53]. It has been provided with a utility to search for allergens based on keywords as well as the sequence. AllergenPro is also equipped with a computational tool for the prediction of allergenicity. Prediction is based on three different approaches such as FAO/WHO guidelines (sequence)–based approach, motif-based approach, and epitope-based approach. The database was last updated on June 4, 2015. AllergenPro does not archive data on allergen cross-reactivity while the literature references for documented allergens and epitopes have also not been provided.
\nAs mentioned earlier, epitopes denote very important feature of allergens as they play vital role in allergic diseases. Because the molecular characterization of allergens has risen immensely in recent years, the data on allergenic epitopes has also increased significantly. Therefore, it has become necessary to store and manage the epitope data for its efficient utilization.
\nSome of the existing allergen databases described above, such as SDAP, ADFS, and AllergenPro are involved in storage of allergen epitope data. There are few databases available that are dedicated for epitope data from all types of antigens, which also document information on allergen epitopes [54–56]. However, the allergy-associated epitope data stored in these databases may not be comprehensive. The Immune Epitope Database (IEDB) [57], which is a repository of immune epitope reactivity data, is also a major database of allergy-derived epitope data [58]. It archives extensive allergen epitope data along with biological assays associated with them, including IgE-binding as well as T-cell epitopes curated and compiled from allergy-related references. IEDB is also equipped with several strategies for efficient searching and visualization of data on allergy-related epitopes [59]. Therefore, it represents a very useful and user friendly platform to access and retrieve allergy-related epitope data for the community of allergists. In a study involving classification of all the epitope-specific literature in various immunological domains, it is stated that IEDB comprises relatively fewer references for allergy-derived epitopes as compared to Cancer and Infectious Diseases [60]. This indicates that there is considerable scope for more in-depth archival of allergen epitope data from literature. Another study on meta-analysis of the allergy-associated epitope data in IEDB has indicated that relatively lesser data is archived for allergen T-cell epitopes as compared to IgE epitopes [26].
\nObservations from the study of the existing allergen databases indicated that they archive significant data on various aspects of allergen and allergenicity, although the level of completeness of data differs considerably for diverse allergen features. AllerBase [61] is a recently developed comprehensive database of allergens and allergen features which addresses some of the limitations associated with the existing allergen databases [Kadam et al. 2016, unpublished]. The database comprises extensive data on experimentally validated allergens and allergen specific features such as IgE-binding epitopes, IgE cross-reactivity, IgE antibodies, and evidences for experimental validation of allergens. AllerBase is provided with basic and advanced search utilities along with browse database option to retrieve desired allergen data. The Completeness Index, which represents availability of data for various features for each allergen and a structure visualization utility, denote important features of the database. AllerBase also provides cross-references to several immunological and allergen databases and represents a notable instance of integration of allergen data from number of resources.
\nAllergens mainly comprise commonly occurring proteins in foods, pollens, and other biological entities in the environment. It has become necessary to assess the potential allergenicity of these proteins considering the health hazards associated with allergic reactions to them. In recent years, genetic engineering and food processing methods are routinely employed for modifying the existing proteins or introducing new ones. Analysis of allergenicity of such proteins/products along with newly introduced biopharmaceuticals is absolutely essential in order to avoid transfer of an allergenic molecule. Computational assessment or prediction of allergenicity represents the major approach to test for allergenicity, and numerous bioinformatics tools/methods have been employed successfully for this purpose [62]. The majority of these methods utilize the amino acid sequence of allergens along with its different features, while a very few approaches use structure information. Table 2 denotes the list of computational tools/servers available for the prediction of allergens/allergenicity. In the following section, the prominent approaches used for the computational assessment or prediction of allergens/allergenicity are described briefly.
\nNo. | \nMethod (URL) | \nApproach used | \nEfficiency | \n
---|---|---|---|
1 | \nSDAP (http://fermi.utmb.edu/) [41] | \nFAO/WHO guidelines | \n– | \n
2 | \nAllergenOnline (http://www.allergenonline.org) [46] | \nFAO/WHO guidelines | \n– | \n
3 | \nAllergenPro (http://nabic.rda.go.kr/allergen) [53] | \nFAO/WHO guidelines, sequence motifs, epitopes | \n– | \n
4 | \nAllermatch (http://allermatch.org) [66] | \nFAO/WHO guidelines | \n– | \n
5 | \nAllerTool (http://research.i2r.a-star.edu.sg/AllerTool/) [67] | \nFAO/WHO guidelines, global representation of protein sequence and SVM | \nSP = 86% | \n
6 | \nWebAllergen (http://weballergen.bii.a-star.edu.sg/) [73] | \nSequence motifs | \n– | \n
7 | \nAlgPred (http://www.imtech.res.in/raghava/algpred/) [75] | \nSequence features and SVM, sequence motifs, epitopes, allergen representative peptides | \nAccuracy = 85%, SE = 88%, SP = 81% | \n
8 | \nAllerTOP (http://www.ddg-pharmfac.net/AllerTOP) [83] | \nSequence based descriptors, auto and cross-covariance, machine learning | \nAccuracy = 85.3%, SE = 82.5%, SP = 88.1% | \n
9 | \nEVALLER (http://bioinformatics.bmc.uu.se/evaller.html) [86] | \nDFLAP algorithm and SVM | \n– | \n
10 | \nAllerHunter (http://tiger.dbs.nus.edu.sg/AllerHunter/) [89] | \nIterative pairwise sequence similarity and SVM | \nSE = 83.4%, SP = 96.4% | \n
11 | \nAPPEL (http://jing.cz3.nus.edu.sg/cgi-bin/APPEL) [91] | \nSequence based features and SVM | \nMCC = 0.95, SE = 93%, SP = 99.9% | \n
12 | \nSORTALLER (http://sortaller.gzhmu.edu.cn/) [93] | \nAFFP dataset, normalized BLAST SVM | \nMCC = 0.97, SE = 98.6%, SP = 98.4% | \n
13 | \nPREAL (http://gmobl.sjtu.edu.cn/PREAL/index.php) [96] | \nBiochemical and physicochemical descriptors, sequence features, subcellular locations, mRMR, SVM | \nAccuracy = 93.42% | \n
14 | \nAllerdictor (http://allerdictor.vbi.vt.edu/) [99] | \nSequences as text documents, Naive Bayes classifier and SVM | \n– | \n
15 | \nproAP (http://gmobl.sjtu.edu.cn/proAP/main.html) [100] | \nIntegration of methods based on FAO/WHO guidelines, sequence motifs and SVM | \n– | \n
16 | \nAllergenFP (http://ddg-pharmfac.net/AllergenFP/) [103] | \nAuto and cross-covariance, descriptor-based fingerprints of residues | \nAccuracy = 88%, MCC = 0.759 | \n
17 | \nFuzzyApp (http://fuzzyapp.bicpu.edu.in/fuzzyapp.php) [107] | \nFuzzy rule based system | \n– | \n
List of computational tools/servers for allergen/allergenicity prediction.
One of the first studies dealing with analysis of allergenicity was put forth by Metcalfe et al. [63]. They have proposed a decision tree–based approach for allergenicity assessment of foods derived from genetically modified crops. The first computational approach for the assessment of allergenicity was provided by “Codex Alimentarius Commission” of FAO/WHO [64, 65]. It stated that a protein can be regarded as an allergen if it consists of an exact match with at least six contiguous amino acids or showed more than 35% similarity over a window of 80 amino acids when compared with a sequence of known allergen. This approach has been widely used to predict allergenicity and there are number of web servers for allergen prediction, which are based on it. Allermatch [66], AllerTool [67], and AllergenPro [53] are some of the prominent web servers which employ these FAO/WHO guidelines for allergen prediction. Additionally, some of the major allergen databases such as SDAP [41] and AllergenOnline [46] also utilize this strategy for allergenicity prediction. A recent study performed by Verma et al. [68] has shown that the sequence similarity-based approach gives substantially better results when used in combination with other bioinformatics methods. However, results obtained by certain studies indicated that approaches based on these guidelines are not highly efficient for identifying allergenic proteins and many of times they lead to false or irrelevant allergenicity estimations [69–71]. As a result of these observations, it became necessary to discover and employ other strategies for the prediction of allergenicity.
\nIn a study carried out by Stadler and Stadler [71], it was observed that the use of sequence motifs, which represent the secondary structures of proteins, performs significantly better than the approach based on FAO/WHO guidelines. This method employs MEME motifs of a length of 50 residues for the prediction of allergenicity by using pairwise sequence alignment with certain threshold. WebAllergen [72] is a web server for the prediction of allergenic proteins which is also based on specific detectable allergenic motifs in known allergens [73]. Furthermore, a study carried out by Kong et al. showed that an approach based on search of multiple motifs is more specific and efficient than the conventional single motif search [74]. AlgPred [75] and AllergenPro [53] are important web servers for allergen prediction in which one of the prediction approaches is based on allergen-derived motifs. A recent study that employs computational approaches for comparison of allergens and metazoan parasite proteins stated that significant sequence and structure similarity exists between parasite proteins and allergenic proteins [76]. The analysis was carried out using sequence and structural motifs in allergens and a workflow was developed for the computational analysis of parasite proteins.
\nRecent years have witnessed tremendous increase in the application of machine learning methods for solving biological problems. Machine learning–based approaches have been widely used for predicting various aspects of protein function [77]. These methods are also employed routinely for the development of algorithms to predict allergenicity of novel proteins.
\nAlthough Support Vector Machine (SVM) is the most commonly used machine learning method for allergen prediction, other methods have also been frequently employed. One of the earliest methods was developed by Zorzet et al. [78] that utilizes a k-Nearest-Neighbor (kNN) classification algorithm for the prediction of allergenicity, while a Bayesian classifier was employed by Soeria-Atmadja et al. [79] for the same purpose. An approach based on the combination of hidden Markov model (HMM) and conserved motifs in allergen was also used to successfully predict protein allergenicity [80]. Dimitrov et al. [81] developed two artificial neural network (ANN)-based algorithms for allergenicity prediction, which utilize descriptors derived from amino acids that denote their structural and physicochemical properties. AllerTOP [82] is an online bioinformatics tool to perform the computational prediction of allergens [83]. This algorithm employs descriptors that denote the chemical properties of amino acids in allergen sequences and auto- and cross-covariance transformation along with five machine learning methods for classification. These methods are random forest, multilayer perceptron, logistic regression, decision tree, naïve Bayes, and kNN.
\nThere are number of web-based tools/servers developed which use SVM for performing classification/prediction of allergens. AlgPred [84] is one of the earlier web servers developed for the prediction of allergenic proteins [75]. It employs SVM with amino acid and dipeptide composition as features of allergens to achieve accuracy of 85.02 and 84.00%, respectively. EVALLER [85] is another web server created for
A web-based tool APPEL [90] is developed for the prediction of allergenic proteins that employs physicochemical and structural features derived from allergen sequence in combination with SVM [91]. Zhang et al. have developed an online allergen prediction tool titled SORTALLER [92], which is based on allergen family featured peptide (AFFP) dataset and employs SVM as a classifier [93]. An algorithm developed by Mohabatkar et al. [94] for the prediction of allergenic proteins utilizes pseudoamino acid composition (PseAAC) along with SVM and provides an accuracy of 91.19%. PREAL [95] is web-based tool that performs allergen prediction by using SVM along with feature selection methods such as maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS) [96]. A combination of hydrophobicity amino acid index and discrete Fourier transform along with an SVM classifier is employed for highly efficient prediction of allergenicity in a signal-processing bioinformatics approach [97]. Allerdictor [98] is web server that specializes in large-scale allergen discovery. It models protein sequences as text documents and employs SVM in text classification for carrying out allergen prediction [99].
\nA study carried out by Wang et al. evaluated sequence-, motif-, and SVM-based approaches for the computational prediction of allergens and also performed parameter optimization to obtain better performance [100]. The resulting methods from this study are integrated and made available as a web application titled proAP [101]. AllergenFP [102] is a recently developed web server for allergenicity prediction that utilizes alignment-free descriptor-based fingerprint approach [103]. The descriptors used here are important properties of amino acid such as size, hydrophobicity, relative abundance, helix, and beta-strand forming propensities, etc. In a structure-based approach proposed by Bragin et al. [104], information derived from protein 3D structure is used for the representation of protein surface as patches designated as discontinuous peptides. It is observed that prediction of allergenic proteins based on this approach gave better accuracy. Vijayakumar and Lakshmi have developed a fuzzy inference system–based algorithm for allergenicity prediction that utilizes five different modules [105]. These modules consist of a machine learning classifier, motif search, sequence similarity, FAO/WHO evaluation scheme, etc. FuzzyApp [106], a web server based on fuzzy rule–based system, is then developed for the prediction of allergenicity [107]. Jiang et al. performed an analysis of food allergens using a computational model that simulates gastric fluid digestion [108]. This study stated that food allergens could be classified as alimentary canal-sensitized and nonalimentary canal-sensitized allergens based on the digestibility of these allergens in simulated gastric fluid.
\nEpitopes represent distinctive amino acid residues on the antigens and are important determinants of an immune response. Identification of epitopes is considered a key aspect of designing highly effective multiple-subunit vaccines and developing efficient diagnostic and therapy methods against allergens. Although experimental methods have been very useful for the identification of epitopes, their usefulness is restricted because of their time- and cost-intensive nature and inability in dealing with large-scale elucidation of epitopes. Hence, computational approaches are considered to be very beneficial alternative as they are cost and time effective.
\nLarge number of highly efficient algorithms and tools have been developed over the years for the computational prediction of epitopes. These methods deal with the prediction of both B-cell and T-cell epitopes as well as sequential (linear) and discontinuous (conformational) epitopes. Based on the information (data) utilized for performing prediction, the methodologies can be grouped as sequence-based or structure-based approaches. Many sequence-based linear epitope prediction methods for B cells have been developed and used since long time and majority of them are propensity scale and machine learning–based methods [109, 110]. Some of the major tools/servers that deal with the prediction of linear B-cell epitopes are listed in Table 3.
\nNo. | \nMethod (URL) | \nApproach used | \nEfficiency | \n
---|---|---|---|
1 | \nABCPred (http://www.imtech.res. in/raghava/abcpred/) [111] | \nFixed length epitope patterns, recurrent ANNs | \nAccuracy = 65.93%, SE = 67.14%, SP = 64.71% | \n
2 | \nAPCpred (http://ccb.bmi.ac. cn/APCpred/) [112] | \nAmino acid anchoring pair composition (APC) and SVM | \n= 72.94% | \n
3 | \nBCPreds (http://ailab.cs. iastate.edu/bcpreds/) [113] | \nSVM classifiers with string kernels | \n|
4 | \nBcePred (http://www.imtech.res. in/raghava/bcepred/) [114] | \nPhysicochemical properties of epitope residues | \nAccuracy = 58.7% | \n
5 | \nBepiPred (http://www.cbs.dtu.dk/ services/BepiPred/) [115] | \nParker’s hydrophilicity scale and HMM | \n– | \n
6 | \nBEST (http://biomine.ece. ualberta.ca/BEST/) [116] | \nAntigen sequence features, SVM | \n|
7 | \nBayesb (http://www.immunopred.org/ bayesb/index.html) [117] | \nBayes feature extraction and SVM | \naccuracy = 74.5% | \n
8 | \nCOBEpro (http://scratch.proteomics. ics.uci.edu) [118] | \nAntigen fragment score and SVM | \n|
9 | \nEPMLR (http://www.bioinfo.tsinghua. edu.cn/epitope/EPMLR/) [119] | \nSequence features and multiple linear regression (MLR) | \nSE = 81.8%, SP = 64.1% | \n
10 | \nIEDB Analysis Resource (http://tools. iedb.org/bcell/) [120] | \nA collection of tools based on various methods | \n– | \n
11 | \nLBtope (http://www.imtech.res. in/raghava/lbtope/) [121] | \nLarge datasets of epitopes, KNN, SVM | \nAccuracy = 86% | \n
12 | \nSVMTriP (http://sysbio. unl.edu/SVMTriP/) [122] | \nTri-peptide similarity, propensity scores and SVM | \nSE = 80.1%, SP = 55.2% | \n
List of major tools/servers for sequential (linear) epitope prediction.
Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as solvent accessibility of surface residues [123, 124], solvent accessibility with propensity scores [125], and propensity scores with packing density of amino acids [126]. An account of major tools/servers that are involved in conformational epitope prediction is provided in Table 4.
\nNo. | \nMethod (URL) | \nApproach used | \nEfficiency | \n
---|---|---|---|
1 | \nBEpro (formerly PEPITO) (http://pepito.proteomics.ics.uci.edu/) [127] | \n3D structure of antigen, amino acid propensity scores | \n|
2 | \nB-Pred (http://immuno.bio.uniroma2.it/bpred) [128] | \n3D structure or model of antigen, solvent exposure of residues | \nSE = 0.70 | \n
3 | \nCBTOPE (http://www.imtech.res.in/raghava/cbtope/) [129] | \nSequence features and SVM | \n|
4 | \nCEP (http://196.1.114.49/cgi-bin/cep.pl) [124] | \n3D structure of antigen, solvent accessibility of amino acids | \nAccuracy = 75% | \n
5 | \nDiscoTope 2.0 (http://www.cbs.dtu.dk/services/DiscoTope/) [125] | \n3D structure of antigen, epitope propensity scores, surface accessibility | \n|
6 | \nElliPro (http://tools.immuneepitope.org/tools/ElliPro) [130] | \n3D structure of antigen, Thornton\'s method, residue clustering algorithm | \n|
7 | \nEpitopia (http://epitopia.tau.ac.il/) [131] | \nAntigen sequence or 3D structure, Naïve Bayes classifier | \n|
8 | \nEPSVR (http://sysbio.unl.edu/EPSVR/) [132] | \n3D structure of antigen, Support vector regression (SVR) | \n|
9 | \nEPMeta (http://sysbio.unl.edu/EPMeta/) [132] | \nMeta server integrating EPSVR with other methods | \n|
10 | \nEPCES (http://sysbio.unl.edu/EPCES/) [133] | \n3D structure of antigen, surface features | \n– | \n
11 | \nSEPPA 2.0 (http://badd.tongji.edu.cn/ seppa/) [126] | \n3D structure of antigen, subcellular localization of antigen, residue propensity, etc. | \n
List of major tools/servers for conformational (discontinuous) epitope prediction.
Studies have shown that the analysis of antigen–antibody complex structures is very useful for the characterization of conformational epitopes [134]. A dedicated resource titled AgAbDb [135] that archives the interactions derived from antigen–antibody complexes is available, which can be very useful for the analysis of epitopes [20, 21]. Several algorithms have also been developed for the prediction of T-cell epitopes in antigens. These methodologies deal with the prediction of peptides that possess the ability to interact with specific major histocompatibility complex (MHC) molecules [136]. Machine learning–based approaches are very commonly employed for this purpose and are found to be very efficient [137]. The details of epitope prediction methods/tools for B cells and T cells have been reviewed elsewhere [138, 139]. Some of the important tools/servers that perform the prediction of T-cell epitopes are listed in Table 5. Recently, it has been shown that epitope prediction can be performed over the whole proteome by integrating multiple epitope prediction methods [149]. Antibody-specific epitope prediction has emerged as a significant alternative to the traditional antibody-independent epitope prediction methods [150].
\nNo. | \nMethod (URL) | \nApproach used | \nEfficiency | \n
---|---|---|---|
1 | \nCTLPred (http://www.imtech.res. in/raghava/ctlpred/index.html) [140] | \nCytotoxic T-lymphocyte epitopes, SVM, ANN | \nAccuracy = 75.2% | \n
2 | \nEpiJen (http://www.ddg-pharmfac. net/epijen/EpiJen/EpiJen.htm) [141] | \nMulti-step algorithm that employs integrated approach | \nAccuracy = 60% | \n
3 | \nEpiTOP (http://www.pharmfac. net/EpiTOP/) [142] | \nQSAR approach based on proteochemometrics | \nAccuracy = 89% | \n
4 | \nNetCTLpan (http://www.cbs.dtu. dk/services/NetCTLpan/) [143] | \nIntegrated method employing proteasomal cleavage, TAP transport efficiency, and MHC class I binding affinity | \n|
5 | \nNetMHCIIpan-3.0 (http://www.cbs.dtu.dk/ services/NetMHCIIpan-3.0/) [144] | \nA method for all HLA class II molecules based on peptide- binding MHC environment | \n|
6 | \nPREDIVAC (http://predivac. biosci.uq.edu.au/) [145] | \nBased on specificity-determining residues | \n– | \n
7 | \nSYFPEITHI (http://www.syfpeithi.de/bin/ MHCServer.dll/EpitopePrediction.htm) [146] | \nScoring system based on position of residue in the epitopes | \n– | \n
8 | \nTEPITOPEpan (http://www.biokdd.fudan. edu.cn/Service/TEPITOPEpan/) [147] | \nAlgorithm based on HLA-DR binding pocket similarity | \n– | \n
9 | \nWAPP (http://abi.inf.uni-tuebingen.de/ Services/WAPP/) [148] | \nCombination of methods based on proteasomal cleavage, TAP transport and MHC binding | \n– | \n
List of major tools/servers for T-cell epitope prediction.
Epitopes represent critical components of allergens from the perspective of allergic reactions and development of new diagnosis and treatment strategies. Therefore, the computational prediction of these epitopes in allergens is of immense importance. Due to limitations associated with detailed archival of allergen epitope data and highly heterogeneous nature of the data, the number of tools available for allergen epitope prediction are far less, especially when compared with the number of tools available for allergen/allergenicity prediction. Therefore, the general epitope prediction methods listed above can be employed for epitope prediction studies in allergens. Kleter and Peijnenburg [70] developed a strategy to screen for the potential linear IgE-epitopes using sequence comparison with a minimal length of six amino acids. The approach was moderately effective and it showed that further verification of IgE binding of epitopes by experimental tests is necessary. AlgPred [84] developed by Saha and Raghava is one of the first and major tools for the computational assessment of IgE epitopes [75]. Here, a database of known IgE epitopes is created and it is used to accurately predict allergenic proteins. AllerPred is a SVM-based computational system for the assessment of overlapping continuous and discontinuous B-cell epitope binding patterns in allergenic proteins [151]. This approach is successfully used to predict allergenicity of novel proteins. Dall’Antonia et al. [152] have developed a software tool titled Surface comparison–based Prediction of Allergenic Discontinuous Epitopes (SPADE). The algorithm consists of a structure-based comparison of allergen surfaces and IgE cross-reactivity data and is able to predict IgE epitopes from three important allergen families. A recent work performed by Lollier et al. [153] on meta-analysis of IgE-binding epitopes provided some important findings regarding these epitopes. They computed the fraction of allergen amino acids that are involved in epitopes and modeled a relationship between the rising number of literature references and the amino acid fractions to assess the possibility of binary classification of epitopes and nonepitopes. A web-based tool LocAllEpi [154] is also developed for the visualization of allergen epitopes along the protein sequence and their structural features.
\nCross-reactivity plays an important role in allergic reaction from the immunological and clinical context. Therefore, the computational prediction of allergenic cross-reactivity has been considered of substantial significance. The prediction of cross-reactivity in allergens is associated with the prediction of allergenicity for the majority of the cases. This is mainly because the antigenic determinants that contribute to the cross-reactivity in allergens are also responsible for their allergenicity. As a result of this, many of the tools/algorithms that have been developed for the prediction of allergens/allergenicity also perform cross-reactivity prediction.
\nThe criteria defined by FAO/WHO experts, which have been mentioned earlier, help to identify cross-reactivity in allergens [155]. AllerTool [87] is a web server that performs cross-reactivity prediction based on amino acid sequence and WHO/FAO guidelines [67]. It also provides a graphical representation of the published and predicted cross-reactivity patterns of allergens. Stadler and Stadler [71] developed a sequence-based approach and stated that motif-based strategy provides better results for the computational assessment of cross-reactivity than the FAO/WHO guidelines. SDAP [40], which is a specialized allergen database described before, also comprises a sequence-based tool for the identification of cross-reactivity among allergens [41]. AllerHunter [88] is a SVM-based web server that deals with efficient assessment of allergic cross-reactivity in proteins [89]. A recently developed fuzzy inference system–based algorithm for allergenicity prediction is also able to predict cross-reactivity in allergens [105].
\nAllergy represents a serious problem, as allergic diseases are known to affect millions of people worldwide. Advancements in genomic, proteomic, and analytical techniques have led to the generation of large amount of data related to allergy and allergens. Archival and analysis of these data denotes a major challenge in allergen bioinformatics. Data integration is one of the key limitations for efficient and useful storage of allergy associated data. This is mainly due to the heterogeneous nature of the data, which is derived from various sources such as molecular data from experimental characterization of allergic reactions, clinical, and epidemiological data from patients/populations. Bioinformatics resources and tools have an important role to play in overcoming this problem. In the wake of ever-expanding volume of data, it is vital to focus on developing databases/resources that will integrate information from different sources as well as from literature and provide rapid access to it. Analysis of such data can be further utilized to obtain important insights to understand allergic reactions. Structural features of allergens contribute significantly to their allergenicity and therefore this knowledge can be employed for developing more efficient methods for allergen/allergenicity and allergic cross-reactivity prediction. Recent advances in epitope prediction methodologies focus on antibody-specific epitope prediction approaches [150]. Application of such approaches for predicting IgE-binding epitopes will be extremely important in the development of newer and effective strategies for diagnosis and treatment of allergic diseases. Allergen immunotherapy (AIT), which is an individualized and allergens-based treatment approach, has been considered as a prototype of precision medicine or personalized medicine [156]. Bioinformatics has the potential to play an important role in the development of novel approaches in AIT as well as contribute for further enrichment of the field of allergen informatics. This will surely aid in gaining better understanding of allergic diseases and positively influence upcoming research in the field.
\nThe work was supported under the Senior Research Fellowship (SRF) granted to KK by the University Grants Commission (UGC), New Delhi, India. UKK and SS would like to acknowledge the Centre of Excellence grant from the Department of Biotechnology (DBT), New Delhi, India. The authors would also like to acknowledge the Bioinformatics Centre, Savitribai Phule Pune University, for providing the infrastructure and resources.
\nCardiomyopathies (CMs) encompass a heterogeneous group of structural and functional (systolic and diastolic) abnormalities of the myocardium and are either confined to the cardiovascular system or are part of a systemic disorder. CMs represent a leading cause of morbidity and mortality and account for a significant percentage of death and cardiac transplantation [1]. The 2006 American Heart Association (AHA) classification grouped CMs into primary (genetic, mixed, or acquired) or secondary (i.e., infiltrative or autoimmune). In 2008, the European Society of Cardiology classification proposed subgrouping CM into familial or genetic and nonfamilial or nongenetic forms. In 2013, the World Heart Federation recommended the MOGES nosology system, which incorporates a morpho-functional phenotype (M), organ(s) involved (O), the genetic inheritance pattern (G), an etiological annotation (E) including genetic defects or underlying disease/substrates, and the functional status (S) of a particular patient based on heart failure symptoms [2, 3, 4]. Rapid advancements in the biology of cardio-genetics have revealed substantial genetic and phenotypic heterogeneity in myocardial disease. Given the variety of disciplines in the scientific and clinical fields, any desired classification may face challenges to obtaining consensus. Nonetheless, the heritable phenotype-based CM classification offers the possibility of a simple, clinically useful diagnostic scheme (for an example, see [5]). In this chapter, we will describe the genetic basis of dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), arrhythmogenic cardiomyopathy (ACM), LV noncompaction cardiomyopathy (LVNC), and restrictive cardiomyopathy (RCM). Although the descriptive morphologies of these types of CM differ, an overlapping phenotype is frequently encountered within the CM types and arrhythmogenic pathology in clinical practice. CMs appear to originate secondary to disruption of “final common pathways.” These disruptions may have purely genetic causes. For example, single gene mutations result in dysfunctional protein synthesis causing downstream dysfunctional protein interactions at the level of the sarcomere and a CM phenotype. The sarcomere is a complex with multiple protein interactions, including thick myofilament proteins, thin myofilament proteins, and myosin-binding proteins. In addition, other proteins are involved in the surrounding architecture of the sarcomere such as the Z-disk and muscle LIM proteins (Figure 1). One or multiple genes can exhibit tissue-specific function, development, and physiologically regulated patterns of expression for each protein. Alternatively, multiple mutations in the same gene (compound heterozygosity) or in different genes (digenic heterozygosity) may lead to a phenotype that may be classic, more severe, or even overlapping with other disease forms.
\nSchematic image of the sarcomere featuring thick/thin filaments and surrounding protein architecture [
DCM is mainly characterized by left or biventricular dilatation, increased LV mass, and decreased systolic function (Figure 2) [6]. DCM can present with the clinical syndrome of systolic heart failure or with or without associated arrhythmias or thrombo-embolic disease. Additionally, DCM can be detected in asymptomatic individuals. Globally, DCM is the most common form of CM and the leading cause of heart transplantation in children and adults. The estimated incidence in the pediatric population is between 0.34 to 1.13 cases per 100,000 children per year with differences in demographic characteristics [7]. DCM has many known etiologies with many more to be discovered. Unfortunately, in many cases, no etiology can be found, and the CM is deemed idiopathic. Still, 25 to 50% of patients with idiopathic DCM have a positive family history, suggesting an underlying genetic predisposition [8]. The majority of genetically triggered cases of DCM are transmitted in an autosomal dominant pattern exhibiting variable penetrance. Other forms of inheritance include autosomal recessive, X-linked, and mitochondrial (maternally inherited), which are more frequent in the pediatric population [2]. Familial DCM occurs in 20 to 60% of cases, where approximately 40% of those cases may have a primary monogenic basis. However, this percentage is a variable approximation as a more critical evaluation of the genes linked to DCM continues to evolve and certain types of variations are excluded from being certified as pathogenic [8]. Another conventional classification of DCM is based on the presence or absence of systemic disease. Thus, dividing DCM into syndromic and non-syndromic forms is a practical approach to evaluating this highly heterogeneous disease. The diagnostic rate for gene testing in non-syndromic DCM is 46 to 73% [9], but this estimation may likely be confounded by insufficient control for population variation. Over the past decade, 47 new genes (for a total of 60 different genes) have been linked to DCM in the Human Gene Mutation Database (HGMD), see Table 1. From these genes, a large-scale analysis revealed truncating variants in the titin gene (
Two-dimensional, apical 4-chamber echocardiographic image depicting an enlarged ventricle with spherical geometry and biatrial enlargement secondary to atrioventricular valve insufficiency in a patient with dilated cardiomyopathy.
Gene | \nProtein | \nPattern of Inheritance | \nDisease Association | \nOMIM# | \nLocus | \n
---|---|---|---|---|---|
ABCC9 | \nATP-Binding Cassette, Subfamily C, Member 9 | \nAD | \nDCM | \n601439 | \n14q12-q22 | \n
ACTC1 | \nActin, Alpha, Cardiac Muscle | \nAD | \nDCM, LVNC, ACM, HCM | \n102540 | \n5q31.1 | \n
ACTN2 | \nActinin, Alpha-2 | \nAD | \nDCM, HCM | \n102573 | \n6q22.1 | \n
AKAP9 | \nA-Kinase Anchor Protein 9 | \nAD | \nDCM | \n604001 | \n2q32.1-q32.3 | \n
ALMS1 | \nCentrosome and Basal Body Associated Protein | \nAR | \nDCM | \n606844 | \n10p14-p12 | \n
ALPK3 | \nAlpha Kinase 3 | \nAR | \nDCM, HCM | \n617608 | \n1p36.32 | \n
ANKRD1 | \nAnkyrin Repeat Domain-Containing Protein 1 | \nAD | \nDCM, HCM | \n609599 | \n7q36.1 | \n
BAG3 | \nBcl2-Associated Athanogene 3 | \nAD | \nDCM, RCM, HCM | \n603883 | \n14q24.3 | \n
CASQ2 | \nCalsequestrin 2 | \nAR, AD | \nDCM, LVNC | \n114251 | \n6q22.31 | \n
CAV3 | \nCaveolin 3 | \nAD | \nDCM, HCM | \n601253 | \n12q24.13 | \n
CHRM2 | \nCholinergic Receptor, Muscarinic, 2 | \nAD | \nDCM | \n118493 | \n10q25.2-q26.2 | \n
CRYAB | \nCrystallin, Alpha-B | \nAD | \nDCM | \n123590 | \n11q23.1 | \n
CSRP3 | \nCysteine- And Glycine-Rich Protein 3 | \nAD | \nDCM, HCM | \n600824 | \n12p12.1 | \n
CTF1 | \nCardiotrophin 1 | \nAR, AD | \nDCM | \n600435 | \n1p13.1 | \n
DES | \nDesmin | \nAD,AR | \nDCM, ACM, RCM | \n125660 | \n17q21 | \n
DMD | \nDystrophin | \nXL | \nDCM | \n300377 | \n3p25.3 | \n
DOLK | \nDolichol Kinase | \nAR | \nDCM | \n610746 | \n7q33 | \n
DSC2 | \nDesmocollin 2 | \nAD, AR | \nDCM, ACM | \n600271 | \nXq22 | \n
DSG2 | \nDesmoglein 2 | \nAD | \nDCM, ACM | \n125671 | \n15q24.1 | \n
DSP | \nDesmoplakin | \nAD, AR | \nDCM, ACM | \n125485 | \n11p15.5 | \n
DTNA | \nDystrobrevin Alpha | \nAD | \nDCM, LVNC | \n601239 | \n2q31 | \n
EMD | \nEmerin | \nXL | \nDCM | \n300384 | \n11q23.1 | \n
EYA4 | \nEyes Absent, Drosophila, Homolog Of, 4 | \nAD | \nDCM | \n603550 | \n11p15.1 | \n
FHL1 | \nFour-And-A-Half LIM Domains 1 | \nXL | \nDCM, HCM | \n300163 | \n15q22.31 | \n
FHL2 | \nFour-And-A-Half LIM Domains 2 | \nUnknown | \nDCM | \n602633 | \n16p11.2 | \n
FKRP | \nFukutin-Related Protein | \nAR | \nDCM | \n606596 | \n10q21.3 | \n
FKTN | \nFukutin | \nAR | \nDCM | \n607440 | \n2q35 | \n
FLNC | \nFilamin C | \nAD | \nDMC, RCM, HCM, ACM | \n102565 | \n10q22.2 | \n
GATA4 | \nGata-Binding Protein 4 | \nAD | \nDCM | \n600576 | \nXq21.2-p21.1 | \n
GATAD1 | \nGata Zinc Finger Domain-Containing Protein 1 | \nAR | \nDCM | \n614518 | \n9q34.11 | \n
GLA | \nGalactosidase, Alpha | \nXL | \nDCM, HCM | \n300644 | \n18q11.2 | \n
ILK | \nIntegrin-Linked Kinase | \nAD | \nDCM | \n602366 | \n18q12.1 | \n
JUP | \nJunction Plakoglobin | \nAD, AR | \nDCM, ACM | \n173325 | \n2p13.1 | \n
LAMA4 | \nLaminin, Alpha-4 | \nAD | \nDCM | \n600133 | \n18q12.1 | \n
LAMP2 | \nLysosome-Associated Membrane Protein 2 | \nXL | \nDCM, HCM | \n309060 | \n3p25.2 | \n
LDB3 | \nLim Domain-Binding 3 | \nAD | \nDCM, LVNC, ACM, HCM | \n605906 | \n2p22.1 | \n
LMNA | \nLamin A/C | \nAD, AR | \nDCM, LVNC, ACM, HCM | \n150330 | \n1q22 | \n
LRRC10 | \nLeucine-Rich Repeat-Containing Protein 10 | \nAD, AR | \nDCM | \n610846 | \n4q21.3 | \n
MURC/CAVIN4 | \nMuscle-Related Coiled-Coil Protein/Caveolae-Associated Protein 4 | \nAD | \nDCM | \n617714 | \n18q12.1 | \n
MYBPC3 | \nMyosin-Binding Protein C, Cardiac | \nAD | \nDCM, LVNC, RCM, HCM | \n600958 | \nXq28 | \n
MYH6 | \nMyosin, Heavy Chain 6, Cardiac Muscle, Alpha | \nAD | \nDCM, HCM | \n160710 | \n10q25.2 | \n
MYH7 | \nMyosin, Heavy Chain 7, Cardiac Muscle, Beta | \nAD | \nDCM, LVNC, RCM, HCM | \n160760 | \n7p14.2 | \n
MYL2 | \nMyosin, Light Chain 2, Regulatory, Cardiac, Slow | \nAD | \nDCM, HCM | \n160781 | \n3p21.3-p21.2 | \n
MYL3 | \nMyosin, Light Chain 3, Alkali, Ventricular, Skeletal, Slow | \nAD, AR | \nDCM, HCM, RCM | \n160790 | \n1q32 | \n
MYLK2 | \nMyosin Light Chain Kinase 2 | \nAD | \nDCM, HCM | \n606566 | \n20q13.31 | \n
MYOT | \nMyotilin | \nAD | \nDCM | \n604103 | \nXq28 | \n
MYOZ2 | \nMyozenin 2 | \nAD | \nDCM, RCM, HCM | \n605602 | \n3p25.1 | \n
MYPN | \nMyopalladin | \nAD | \nDCM, RCM, HCM | \n608517 | \n12q23.1 | \n
NEBL | \nNebulette | \nAD | \nDCM | \n605491 | \n6q23.2 | \n
NEXN | \nNexilin (F Actin Binding Protein) | \nAD | \nDCM, HCM | \n613121 | \n1q22 | \n
NKX2-5 | \nNk2 Homeobox 5 | \nAD | \nDCM | \n600584 | \nXq26.3 | \n
PDLIM3 | \nPdz And Lim Domain Protein 3 | \nAD | \nDCM, HCM | \n605899 | \n1q43 | \n
PKP2 | \nPlakophilin 2 | \nAD | \nDCM, ACM | \n602861 | \n11p15.4 | \n
PLN | \nPhospholamban | \nAD | \nDCM, ACM, HCM | \n172405 | \n4q12 | \n
PRDM16 | \nPr Domain-Containing Protein 16 | \nAD | \nDCM, LVNC | \n605557 | \n6q21 | \n
PRKAG2 | \nProtein Kinase, Amp-Activated, Noncatalytic, Gamma-2 | \nAD | \nDCM, HCM | \n602743 | \n4q26-q27 | \n
RBM20 | \nRNA-Binding Motif Protein 20 | \nAD | \nDCM | \n613171 | \n2q12.2 | \n
RYR2 | \nRyanodine Receptor 2 (Cardiac) | \nAD | \nDCM, HCM, ACM | \n180902 | \n12p11 | \n
SCN5A | \nSodium Channel, Voltage-Gated, Type V, Alpha Subunit | \nAD | \nDCM, ACM | \n600163 | \n20q13.12 | \n
SGCA | \nSarcoglycan Alpha | \nAR | \nDCM | \n600119 | \n1q25.2 | \n
SGCB | \nSarcoglycan Beta | \nAR | \nDCM | \n600900 | \n15q22.1 | \n
SGCD | \nSarcoglycan, Delta (35kda Dystrophin-Associated Glycoprotein) | \nAD, AR | \nDCM | \n601411 | \n19q13.32 | \n
SLC25A4 | \nSolute Carrier Family 25, Member 4 (Mitochondrial Carrier Adenine Nucleotide Translocator) | \nAD, AR | \nDCM | \n103220 | \n7q21-q22 | \n
TAZ | \nTafazzin | \nAR, XL | \nDCM, LVNC | \n300394 | \nXq24 | \n
TBX20 | \nT-Box 20 | \nAD | \nDCM, LVNC | \n606061 | \n10q22.3-q23.2 | \n
TCAP | \nTitin-Cap (Telethonin) | \nAR | \nDCM, HCM | \n604488 | \n3p21 | \n
TMEM43 | \nTransmembrane Protein 43 | \nAD | \nDCM, ACM | \n612048 | \n10q23.3 | \n
TMPO | \nThymopoietin | \nAD | \nDCM | \n188380 | \n9q31.2 | \n
TNNC1 | \nTroponin C Type 1 (Slow) | \nAD | \nDCM, HCM | \n191040 | \n17q21.33 | \n
TNNI3 | \nTroponin I Type 3 (Cardiac) | \nAD | \nDCM, RCM, HCM | \n191044 | \n3p21.1 | \n
TNNT2 | \nTroponin T Type 2 (Cardiac) | \nAD | \nDCM, LVNC, RCM, HCM | \n191045 | \n17q12 | \n
TOR1AIP1 | \nTorsin-1a-Interacting Protein 1 | \nAR | \nDCM | \n614512 | \n7q32.1 | \n
TPM1 | \nTropomyosin 1 (Alpha) | \nAD | \nDCM, RCM, HCM | \n191010 | \n19q13.4 | \n
TRDN | \nTriadin | \nAR | \nDCM | \n603283 | \n17q25.3 | \n
TTN | \nTitin | \nAD, AR | \nDCM, ACM, HCM | \n188840 | \n5q33-q34 | \n
TTR | \nDCM | \nAD | \nDCM | \n176300 | \n18q12.1 | \n
TXNRD2 | \nThioredoxin Reductase 2 | \nAD, AR | \nDCM | \n606448 | \n8p23.1 | \n
VCL | \nVinculin | \nAD | \nDCM, LVNC, HCM | \n193065 | \n10q25.2 | \n
List of common genes and patterns of inheritance in DCM, modified from Tayal et al. [9].
AD – Autosomal dominant; AR – Autosomal Recessive; XL – X-linked; DCM – Dilated cardiomyopathy; HCM – Hypertrophic cardiomyopathy; LVNC – Left ventricular non-compaction cardiomyopathy; ACM – Arrhythmogenic cardiomyopathy; RCM – Restrictive cardiomyopathy.
Regardless of the mode of inheritance, pathogenic gene variants result in a cardiomyocyte milieu susceptible to stress, leading to downstream dysfunction of the contractile apparatus and heart failure, “the final common pathway” hypothesis [14]. The term “familial DCM” is frequently applied in the presence of DCM in two or more first-degree relatives. The incidence is likely underestimated due to the diversity of inheritance patterns, timing of presentation, variable penetrance, and lack of symptoms in subclinical disease [15, 16].
\nThe most common form of familial DCM is inherited in an autosomal dominant pattern [6]. In this sub-type, arrhythmias associated with DCM (DCM-A) are frequently encountered [17]. Genetic heterogeneity exists with at least 30 unique genes identified in familial non-arrhythmogenic DCM and five genes for DCM-A [17, 18].
\nXLCM has been reported as an isolated disease of the heart or associated with skeletal myopathy such as with Duchenne muscular dystrophy (DMD) or Becker muscular dystrophy (BMD). All skeletal myopathies are frequently associated with the development of DCM and/or DCM-A. The causative gene codes for the protein dystrophin located at the short arm of the X chromosome at Xp21. Dystrophin is a cytoskeletal protein that provides structural support to the cardiomyocyte and plays a major role in linking the sarcomeric contractile apparatus to the sarcolemma and extracellular matrix (ECM) [19, 20]. DMD and BMD are severe muscular dystrophies of childhood, affecting ~1 in 3,500 males for DMD and 1 in 300,000 males for BMD. Typically, DMD and BMD are characterized by skeletal myopathy, elevated serum creatine kinase, and calf pseudo-hypertrophy. DMD is the more severe form due to the absence of functional dystrophin, leading to muscle weakness by 3 years of age and wheelchair dependence by 12 years of age [21]. Cardiac involvement varies with age but is nearly universal by 20 years in all DMD patients. The onset of clinical features starts later in life in BMD than in DMD. Histologic studies show cardiac muscle replacement with fibrosis. This fibrosis eventually leads to ventricular dysfunction/enlargement and is associated with conduction system abnormalities and ventricular arrhythmias. Molecular analysis of the
Isolated XLCM is characterized by consistent early expression and rapid progression of CM in males during childhood, later onset with slower progression in females, and no male-to-male transmission [26]. Linkage analysis of X-chromosome-specific DNA markers performed in suspected individuals demonstrated preferential involvement of cardiac muscle and normal dystrophin by Western blotting in skeletal muscle of the same affected individuals [27]. The phenotype and pathologic features described in this population do not differ from those in patients with DCM. Hence, the medical management should be provided according to the current heart failure guidelines.
\nEmery-Dreifuss muscular dystrophy (EDMD), also known as humeroperoneal muscular dystrophy, is a heterogeneous disorder with X-linked recessive, autosomal dominant, and autosomal recessive forms of inheritance [28]. Several forms of this disease are considered nuclear envelopathies because they are associated with mutations in genes encoding nuclear membrane proteins, including the
Barth syndrome (BTS) is another X-linked cardioskeletal myopathy that encompasses abnormal mitochondrial function, short stature, cyclic neutropenia, cardiolipin deficiency, and variable degrees of 3-methylglutaconic aciduria. BTS is caused by mutations in the
HCM is the second most prevalent CM in children, representing 40% of cases, with an estimated incidence of 0.47 in 100,000 children [35]. HCM is more prevalent in boys than in girls and in African American children than in Caucasian or Hispanic children. In the pediatric population, the incidence of HCM is 10 times higher in patients under 1 year of age than in older children [36]. HCM is a primary myocardial disorder with mainly an autosomal dominant pattern of inheritance characterized by hypertrophy of the left ventricle (with or without hypertrophy of the right ventricle) and histologic features of myocyte hypertrophy, myofibrillar disarray, and interstitial fibrosis. While asymmetric septal hypertrophy is the most common pattern of hypertrophy, the degree and location of hypertrophy vary. Some patients exhibit concentric hypertrophy, harbored in other walls or confined to the left ventricular apex (Figure 3) [37].
\nTwo dimensional images of HCM in the parasternal short axis (A) exhibiting concentric hypertrophy with significant involvement of the interventricular septum (IVS) and corroborated by the parasternal long axis view (B). Cardiac MRI also shows significant thickening of the IVS (C).
The clinical presentation of HCM is highly variable, ranging from asymptomatic hypertrophy, to symptomatic arrhythmias, to refractory heart failure due to diastolic dysfunction, or “burned-out HCM” with the development of systolic dysfunction. Notably, diastolic dysfunction can even be detected in individuals with HCM who have normal LV wall thickness, suggesting that diastolic dysfunction is an early feature of HCM rather than a secondary consequence of hypertrophy [38]. Categorization of HCM includes non-syndromic HCM (without other systemic involvement) and the syndromic form of HCM (in association with inborn errors of metabolism, malformation syndromes, and neuromuscular disorders) [39].
\nApproximately 20–30% of individuals with non-syndromic HCM and no family history of HCM harbor a pathogenic variant in a known gene encoding a component of the sarcomere. However, 50–60% of adults and children with a positive family history of HCM harbor a pathogenic gene variant. Furthermore, 3–5% of affected individuals have more than one sarcomere gene variant (either biallelic variants in 1 gene or heterozygous variants in >1 gene) [40, 41].
\nMore than two decades ago, the first chromosome locus (14q11.2-q12) encoding components of the sarcomere (beta-myosin heavy chain) was elucidated as the pathogenic basis for familial HCM [42]. Since then, more than 1,400 mutations in 27 identified genes have been associated with HCM, see Table 2 [43]. The vast majority have autosomal dominant transmission, but mitochondrial and autosomal recessive patterns have been also described [44, 45, 46]. Most of the disease-causing mutations implicated in HCM include mutations in the
Gene | \nProtein | \nPattern of Inheritance | \nDisease Association | \nOMIM# | \nLocus | \n
---|---|---|---|---|---|
ACTC1 | \nActin, Alpha, Cardiac Muscle | \nAD | \nHCM, DCM, LVNC, ACM | \n102540 | \n5q31.1 | \n
ACTN2 | \nActinin, Alpha-2 | \nAD | \nHCM, DCM | \n102573 | \n6q22.1 | \n
ALPK3 | \nAlpha Kinase 3 | \nAR | \nHCM, DCM | \n617608 | \n1p36.32 | \n
ANKRD1 | \nAnkyrin Repeat Domain-Containing Protein 1 | \nAD | \nHCM, DCM | \n609599 | \n7q36.1 | \n
BAG3 | \nBcl2-Associated Athanogene 3 | \nAD | \nHCM, DCM, RCM | \n603883 | \n14q24.3 | \n
BRAF | \nV-Raf Murine Sarcoma Viral Oncogene Homolog B1 | \nAD | \nHCM | \n164757 | \n12q15 | \n
CAV3 | \nCaveolin 3 | \nAD | \nHCM, DCM | \n601253 | \n12q24.13 | \n
CSRP3 | \nCysteine- And Glycine-Rich Protein 3 | \nAD | \nHCM, DCM | \n600824 | \n12p12.1 | \n
FHL1 | \nFour-And-A-Half LIM Domains 1 | \nXL | \nHCM | \n300163 | \n15q22.31 | \n
FLNC | \nFilamin C | \nAD | \nHCM, ACM, DMC, RCM | \n102565 | \n10q22.2 | \n
GAA | \nGlucosidase, Alpha, Acid | \nAR | \nHCM | \n606800 | \n19p13.3 | \n
GLA | \nGalactosidase, Alpha | \nXL | \nHCM | \n300644 | \n18q11.2 | \n
HRAS | \nV-Ha-Ras Harvey Rat Sarcoma Viral Oncogene Homolog | \nAD | \nHCM | \n190020 | \n9q31.1 | \n
JPH2 | \nJunctophilin 2 | \nAD | \nHCM | \n605267 | \n11p11.2 | \n
KRAS | \nV-Ki-Ras2 | \nAD | \nHCM | \n190070 | \n14q12 | \n
LAMP2 | \nLysosome-Associated Membrane Protein 2 | \nXL | \nHCM, DCM | \n309060 | \n3p25.2 | \n
LDB3 | \nLim Domain-Binding 3 | \nAD | \nHCM, DCM, LVNC, ACM | \n605906 | \n2p22.1 | \n
LMNA | \nLamin A/C | \nAD, AR | \nHCM, DCM, LVNC, ACM | \n150330 | \n1q22 | \n
MAP2K1 | \nMitogen-Activated Protein Kinase Kinase 1 | \nAD | \nHCM | \n176872 | \n14q12 | \n
MAP2K2 | \nMitogen-Activated Protein Kinase Kinase 2 | \nAD | \nHCM | \n601263 | \n12q24.11 | \n
MYBPC3 | \nMyosin-Binding Protein C, Cardiac | \nAD | \nHCM, DCM, LVNC, RCM | \n600958 | \nXq28 | \n
MYH6 | \nMyosin, Heavy Chain 6, Cardiac Muscle, Alpha | \nAD | \nHCM, DCM | \n160710 | \n10q25.2 | \n
MYH7 | \nMyosin, Heavy Chain 7, Cardiac Muscle, Beta | \nAD | \nHCM, DCM, LVNC, RCM | \n160760 | \n7p14.2 | \n
MYL2 | \nMyosin, Light Chain 2, Regulatory, Cardiac, Slow | \nAD | \nHCM | \n160781 | \n3p21.3-p21.2 | \n
MYL3 | \nMyosin, Light Chain 3, Alkali, Ventricular, Skeletal, Slow | \nAD, AR | \nHCM, RCM | \n160790 | \n1q32 | \n
MYLK2 | \nMyosin Light Chain Kinase 2 | \nAD | \nHCM | \n606566 | \n20q13.31 | \n
MYOM1 | \nMyomesin 1 | \nAD | \nHCM | \n603508 | \n18p11.31 | \n
MYOZ2 | \nMyozenin 2 | \nAD | \nHCM, DCM, RCM | \n605602 | \n3p25.1 | \n
MYPN | \nMyopalladin | \nAD | \nHCM, DCM, RCM | \n608517 | \n12q23.1 | \n
NEXN | \nNexilin (F Actin Binding Protein) | \nAD | \nHCM, DCM | \n613121 | \n1q22 | \n
NRAS | \nNeuroblastoma Ras Viral Oncogene Homolog | \nAD | \nHCM | \n164790 | \n5q31.2 | \n
PDLIM3 | \nPdz And Lim Domain Protein 3 | \nAD | \nHCM, DCM | \n605899 | \n1q43 | \n
PLN | \nPhospholamban | \nAD | \nHCM, DCM, ACM | \n172405 | \n4q12 | \n
PRKAG2 | \nProtein Kinase, Amp-Activated, Noncatalytic, Gamma-2 | \nAD | \nHCM | \n602743 | \n4q26-q27 | \n
PTPN11 | \nProtein-Tyrosine Phosphatase, Nonreceptor-Type, 11 | \nAD | \nHCM | \n176876 | \n10q21.3 | \n
RAF1 | \nV-Raf-1 Murine Leukemia Viral Oncogene Homolog 1 | \nAD | \nHCM | \n164760 | \n10p12 | \n
RIT1 | \nRas-Like Without Caax 1 | \nAD | \nHCM | \n609591 | \n1p31.1 | \n
RYR2 | \nRyanodine Receptor 2 (Cardiac) | \nAD | \nHCM, ACM | \n180902 | \n12p11 | \n
SHOC2 | \nSoc-2 Homolog | \nAD | \nHCM | \n602775 | \n5q35.1 | \n
SOS1 | \nSon Of Sevenless, Drosophila, Homolog 1 | \nAD | \nHCM | \n182530 | \n1p13.2 | \n
TCAP | \nTitin-Cap (Telethonin) | \nAR | \nHCM, DCM | \n604488 | \n3p21 | \n
TNNC1 | \nTroponin C Type 1 (Slow) | \nAD | \nHCM, DCM | \n191040 | \n17q21.33 | \n
TNNI3 | \nTroponin I Type 3 (Cardiac) | \nAD | \nHCM, DCM, RCM | \n191044 | \n3p21.1 | \n
TNNT2 | \nTroponin T Type 2 (Cardiac) | \nAD | \nHCM, DCM, LVNC, RCM | \n191045 | \n17q12 | \n
TPM1 | \nTropomyosin 1 (Alpha) | \nAD | \nHCM, DCM, RCM | \n191010 | \n19q13.4 | \n
TTN | \nTitin | \nAD, AR | \nHCM, DCM, ACM | \n188840 | \n5q33-q34 | \n
TTR | \nTransthyretin | \nAD | \nHCM | \n176300 | \n4q35.1 | \n
VCL | \nVinculin | \nAD | \nHCM, DCM, LVNC | \n193065 | \n10q25.2 | \n
List of common genes and patterns of inheritance in HCM.
AD – Autosomal dominant; AR – Autosomal Recessive; XL – X-linked; DCM – Dilated cardiomyopathy; HCM – Hypertrophic cardiomyopathy; LVNC – Left ventricular non-compaction cardiomyopathy; ACM – Arrhythmogenic cardiomyopathy; RCM – Restrictive cardiomyopathy.
Mouse models of sarcomeric mutations have shown changes in cardiac chemistry and diastolic function well before myocardial hypertrophy is observed [48]. Moreover, the genetic defect in a gene encoding for a sarcomeric protein may disrupt normal contraction and relaxation with dysregulation of calcium in the sarcomere. Thus, reduced calcium reuptake and decreased stores in the sarcoplasmic reticulum will trigger a remodeling process by several transcription factors, resulting in the hypertrophy of the cardiomyocytes and increased energy demand, which eventually results in ischemia, fibrosis, and death [44]. There is no reliable correlation between the genotype and phenotype among the identified sarcomeric mutations, except for those patients harboring multiple mutations [49].
\nHCM has been associated with multiple phenotypically distinct disorders. Improvements in sequencing technologies and phenotypic characterization and the incorporation of epigenetics have expanded our understanding of syndromic CMs.
\nSince the discovery of the first gene (
The management of RASopathies should involve a multidisciplinary team with expertise in the assessment of cardiac structural defects, HCM, and arrhythmias. Surveillance with periodic echocardiography (HCM), electrocardiography (rhythm disturbances), neurologic and eye examination, evaluation for scoliosis, and assessment of growth and cognitive development is also recommended.
\nNoonan syndrome is relatively common with a prevalence of ~1 in 3500 people. This disease is inherited in an autosomal dominant pattern, although new cases are common because the
Surgical relief of right ventricular outflow tract obstruction (RVOTO) is recommended in patients with more than a mild degree of obstruction. Septal myectomy is also advised when left ventricular outflow tract obstruction (LVOTO) is associated with heart failure symptoms, although re-growth of the LVOTO is common when myectomy is performed in patients younger than one year of age. In some children, heart transplantation is necessary.
\nLEOPARD syndrome, also called Noonan syndrome with multiple lentigines, is a rare autosomal dominant disorder caused by mutations in the protein tyrosine phosphatase gene,
Costello syndrome is a rare disorder with substantial clinical overlap with other RASopathy syndromes. This disorder is caused by mainly
Cardiofaciocutaneous (CFC) syndrome also has substantial clinical overlap with other RASopathy syndromes because of its common ectodermal involvement as well as findings of intellectual impairment and cardiac anomalies. Skin abnormalities can be extensive and include hyperkeratosis, eczema, palmoplantar hyperkeratosis, and keratosis pilaris. The hair is typically sparse and curly. CFC syndrome is characterized by cardiac abnormalities (pulmonary valve stenosis, other valve dysplasias, septal defects, HCM, and rhythm disturbances). HCM is identified in approximately 40% of cases and presents more commonly during infancy, but it can develop at any age [59]. Neoplasia, mostly acute lymphoblastic leukemia (ALL), has been reported in some individuals [50, 60]. Diagnosis is based on clinical findings and molecular genetic testing. Common genes associated with CFC syndrome include
Congenital metabolic disorders result from absent or abnormal enzymes—or their cofactors—which can lead to accumulation or deficiency of a specific metabolite. Although these disorders exhibit different modes of inheritance, most are transmitted in an autosomal recessive or mitochondrial pattern. The possibility of an inborn error of metabolism should be considered in infants, children, and young adults who present with any of the cardiovascular phenotypes or laboratory features described below. Optimal outcomes for children with these disorders depend upon early recognition of the signs and symptoms of metabolic disease, prompt evaluation, and referral to a center with expertise in cardiovascular genetics. Delay in diagnosis may result in acute metabolic/hemodynamic decompensation, progressive neurologic injury, or death.
\nPompe disease, also known as glycogen storage disease type II, is an autosomal recessive metabolic disorder that affects muscle and nerve cells throughout the body. This condition occurs secondary to accumulation of glycogen in lysosomes due to a deficiency of the lysosomal acid alpha-glucosidase enzyme. The build-up of glycogen leads to progressive myopathy and weakness throughout the body affecting various tissues including the liver, nervous system, and—most notably—skeletal muscle and myocardium. The Pompe phenotype varies widely [64]. In the infantile form, muscles appear normal but are limp and weak, preventing normal development. Elevated creatine kinase, lactate dehydrogenase, and aspartate aminotransferase (AST) are common. ECG reveals a short PR interval with giant QRS complexes in all leads, suggesting biventricular hypertrophy. As the disease progresses, HCM may result in cardiorespiratory failure. Without treatment, death usually occurs due to heart failure and respiratory weakness within the first year of life [65]. The juvenile and adult forms present with a variable age of onset. The primary clinical finding is skeletal myopathy with a more protracted course, leading to respiratory failure. Affected children usually present with delayed gross-motor development and progressive weakness in a limb-girdle distribution. Early involvement of the diaphragm is a common feature leading to death in the second or third decade of life. In contrast to the infantile form, mild and non-specific cardiac abnormalities can be detected in patients with late-onset disease [66]. Enzyme replacement therapy usually results in decreased ventricular hypertrophy, reduced LV outflow tract obstruction, and normalization of the conduction system [67].
\nDanon disease, also known as glycogen storage disease type IIb, is an X-linked lysosomal and glycogen storage disorder associated with skeletal muscle weakness and intellectual disability. Danon disease involves a genetic defect in the
Fabry disease is considered the most prevalent lysosomal storage disorder. This disease is an X-linked inborn error of the glycosphingolipid metabolic pathway and involves deficiency of the lysosomal hydrolase alpha-galactosidase A (alpha-Gal A) mapped to the long arm of the X chromosome (Xq22.1) [72]. Several hundred mutations in the
Friedreich’s ataxia is an autosomal recessive inherited disease with an estimated incidence of 1 in 50,000 in the general population. The genotype is characterized by trinucleotide repeat expansion of a normal codon affecting the protein frataxin, a mitochondrial inner membrane protein important for iron homeostasis. As the defect lies within an intron (which is removed from the mRNA transcript between transcription and translation), this mutation does not result in the production of abnormal frataxin. Instead, the mutation decreases the transcription of the gene through gene silencing. Low frataxin levels lead to insufficient biosynthesis of iron–sulfur clusters that are required for the mitochondrial electron transport chain to ultimately generate adenosine triphosphate (ATP). The major clinical manifestations of Friedreich’s ataxia include progressive neurologic dysfunction (gait ataxia, optic atrophy, loss of position and vibration sense), diabetes mellitus, and myocardial involvement. The cardiac phenotype is manifested by arrhythmias and HCM. Heart failure remains the leading cause of death in this population [77, 78]. HCM is seen in approximately two-thirds of patients with Friedreich’s ataxia, and one-third of those cases develop during childhood [79].
\nMitochondria are the main energy source in cells due to the ability to perform oxidative phosphorylation via proteins in the mitochondrial respiratory chain. Several genes are involved in the role of cellular energy production. Mutations in these genes may result in severe involvement in organs that are heavily dependent on energy production, such as the brain, heart, and skeletal muscle. Mitochondrial DNA (mtDNA) is exclusively maternally inherited, whereas nuclear DNA follows Mendelian inheritance. The frequency of cardiac involvement in mitochondrial disease is 17–40%, and the estimated prevalence of inherited mitochondrial disease is at least 1 in 5,000 births [80]. More than 40 different types of mitochondrial disease have been associated with the development of HCM. Many forms of mitochondrial disease associated with HCM present during infancy. Because diagnosing mitochondrial disease can be challenging for clinicians, it is recommended that a multidisciplinary team (including a geneticist or mitochondrial specialist) be involved in the diagnosis and management [81]. Mitochondrial CM is characterized by abnormal heart-muscle structure, function, or both. These abnormalities result from genetic defects involving mitochondrial activity in the absence of concomitant coronary artery disease, hypertension, valvular disease, or CHD. The typical cardiac manifestations of mitochondrial disease include the presence of arrhythmias, hypertrophic HCM, LVNC and DCM. Worsening cardiovascular disease may occur during a metabolic crisis [80, 81, 82].
\nBarth syndrome, described earlier in this chapter, is an X-linked disorder caused by pathogenic variants in the
MELAS (mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes) is a multisystem clinical syndrome. Cardiac involvement is manifested by nonobstructive concentric hypertrophy (HCM), although DCM, Wolff-Parkinson-White (WPW) syndrome, and atrial tachycardia have also been reported [83, 84, 85]. Several genes have been postulated to cause MELAS, including the ones listed in Table 3.
\nGene/locus | \nGene location | \n
---|---|
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
\n | \nMitochondrial | \n
LVNC is characterized by the presence of trabeculations, deep intertrabecular recesses, and a thin compacted myocardial layer in the left, right, or both ventricles. The incidence of LVNC is unknown, but some studies estimate 0.014% to 1.3% in the general population [4]. However, with improved echocardiographic and cardiac MRI quality and increasing awareness of LVNC in recent years, the incidence is likely underestimated [34]. Clinically, nine forms of LVNC have been described as follows: [1] the “benign” form of LVNC with normal systolic function, normal chamber sizes and thickness, and no history of arrhythmias; [2] the arrhythmogenic form of LVNC; [3] the DCM form of LVNC; [4] the HCM form of LVNC; [5] the mixed/undulating CM form of LVNC; [6] the RCM form of LVNC; [7] the biventricular noncompaction CM form; [8] the right ventricular noncompaction form (RVNC); and [9] LVNC associated with congenital heart disease [34, 81, 86, 87]. The various phenotypes are depicted in Figure 4. The clinical presentation may range from asymptomatic to a severe course accompanied by heart failure requiring heart transplant, arrhythmias, sudden cardiac death, and thromboembolic phenomena [88]. Familial cases are well-documented, and autosomal dominant transmission is the most common inheritance pattern (with variable penetrance and phenotypic heterogeneity). Other modes of inheritance include X-linked, autosomal recessive, and mitochondrial [43]. In pediatric and adult cohorts, the diagnostic rate of gene testing in patients with LVNC ranges from 17–41% depending on patient selection and the number of genes screened. An estimated 18 to 50% of probands have a family member with LVNC [89, 90]. One of the first genetic causes of isolated LVNC was described in 1997 in the gene
LVNC phenotypes. (A) Echocardiographic 4-chamber view displays the benign type of LVNC characterized by the cardinal feature of left ventricular trabeculations (arrow) with normal anatomy and function; (B) cardiac magnetic resonance (cMRI) 4-chamber view displays the dilated type of LVNC, notice the enlargement of the LV and the presence of apical and lateral trabeculations (arrow); (C) echocardiographic 4-chamber view shows the hypertrophic type of LVNC represented by asymmetric hypertrophy of the interventricular septum and the presence of lateral LV trabeculations (arrow); (D) echocardiography displaying the restrictive type of LVNC, notice the significant bilateral atrial enlargement (arrows) and the left ventricular dysfunction showing spontaneous cavitary contrast; (E) echocardiography shows features suggestive of bilateral ventricular hypertrabeculations (arrows); (F) cMRI in a short axis view displays a mixed LVNC phenotype represented by dilated and dysfunctional ventricles in a patient with ventricular arrhythmias and biventricular trabeculations (arrows).
Gene | \nProtein | \nPattern of Inheritance | \nDisease Association | \nOMIM# | \nLocus | \n
---|---|---|---|---|---|
ACTC1 | \nActin, Alpha, Cardiac Muscle | \nAD | \nLVNC, ACM, HCM, DCM | \n102540 | \n5q31.1 | \n
CASQ2 | \nCalsequestrin 2 | \nAR, AD | \nLVNC | \n114251 | \n6q22.31 | \n
DTNA | \nDystrobrevin, Alpha | \nAD | \nLVNC | \n601239 | \n2q31 | \n
HCN4 | \nHyperpolarization-Activated Cyclic Nucleotide-Gated Potassium Channel 4 | \nAD | \nLVNC | \n605206 | \n18q12.1 | \n
LDB3 | \nLim Domain-Binding 3 | \nAD | \nLVNC, ACM, HCM, DCM | \n605906 | \n2p22.1 | \n
LMNA | \nLamin A/C | \nAD, AR | \nLVNC, ACM, HCM, DCM | \n150330 | \n1q22 | \n
MIB1 | \nE3 Ubiquitin Protein Ligase 1 | \nAD | \nLVNC | \n608677 | \n22q11.21 | \n
MYBPC3 | \nMyosin-Binding Protein C, Cardiac | \nAD | \nLVNC, RCM, HCM, DCM | \n600958 | \nXq28 | \n
MYH7 | \nMyosin, Heavy Chain 7, Cardiac Muscle, Beta | \nAD | \nLVNC, RCM, HCM, DCM | \n160760 | \n7p14.2 | \n
PRDM16 | \nPr Domain-Containing Protein 16 | \nAD | \nLVNC, DCM | \n605557 | \n6q21 | \n
TAZ | \nTafazzin | \nAR, XL | \nLVNC, DCM | \n300394 | \nXq24 | \n
TBX20 | \nT-Box 20 | \nAD | \nLVNC, DCM | \n606061 | \n10q22.3-q23.2 | \n
TNNT2 | \nTroponin T Type 2 (Cardiac) | \nAD | \nLVNC, RCM, HCM, DCM | \n191045 | \n17q12 | \n
VCL | \nVinculin | \nAD | \nLVNC, HCM, DCM | \n193065 | \n10q25.2 | \n
List of common genes and patterns of inheritance in LVNC.
AD – Autosomal dominant; AR – Autosomal Recessive; XL – X-linked; DCM – Dilated cardiomyopathy; HCM – Hypertrophic cardiomyopathy; LVNC – Left ventricular non-compaction cardiomyopathy; ACM – Arrhythmogenic cardiomyopathy; RCM – Restrictive cardiomyopathy.
Additionally, LVNC has been associated with several genetic syndromes and inborn errors of metabolism such as Coffin-Lowry syndrome, Sotos syndrome, Charcot–Marie–Tooth disease, Noonan syndrome, and BTS [100, 101, 102, 103]. A recent study also demonstrated a higher prevalence of LVNC among patients with heterotaxy than among the general population, suggesting possible common genetic mechanisms [104].
\nThis CM is an arrhythmogenic myocardial disorder not explained by ischemia, hypertension, or valvular heart disease. ACM was previously referred to as arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD, ARVC). The reported prevalence of ACM is as common as 1 in 1,000-5,000 people [105]. The clinical diagnosis may be supported by evidence of conduction disease, supraventricular arrhythmias, and/or ventricular arrhythmias originating from any cardiac structure. ECG abnormalities include right bundle branch block pattern, an epsilon wave (defined as a low-amplitude deflection located between the end of the QRS and the onset of the T wave in leads V1–V3), and T wave inversion(s) recorded in leads V1–V4. Classically, the RV is dilated and contains fibro-fatty infiltration of the myocardium. The left ventricle is overtly affected with less frequent involvement. Notably, ACM clinically overlaps with other CM types, particularly DCM. However, ACM is distinct in that it is marked by arrhythmia at presentation with or without biventricular dilation and/or impaired systolic function [106]. This heritable disorder is usually transmitted in an autosomal dominant pattern (with variable penetrance), although autosomal recessive patterns reportedly affect junctional plakoglobin (JUP) and desmoplakin (DSP) in families with cardiocutaneous disease from Greece, Italy, India, Ecuador, Israel, and Turkey [107]. The most notable autosomal recessive diseases include Naxos disease (a homozygous pathogenic variant in the gene encoding the protein plakoglobin characterized by ACM, a non-epidermolytic palmoplantar keratoderma, and wooly hair) and Carvajal syndrome (caused by a homozygous pathogenic gene variant that truncates the DSP protein) [107, 108]. Analysis of first- and second-degree relatives of patients with ACM suggest that up to 50% of ACM cases are familial [109]. Pathogenic gene variants within the desmosomal proteins are the main cause of “classic” ACM [110]. Pathogenic gene variants in the three main classes of desmosomal proteins account for 60% of affected patients [111]. Overall, the three groups of desmosomal proteins include transmembrane desmosomal cadherins (including DSC2 and DSG2), DSP (a plakin family protein that attaches directly to the intermediate filament desmin in the myocardium), and linker proteins such as armadillo family proteins (including JUP and PKP2 that mediate interactions between the desmosomal cadherin tails and DSP) [112]. Pathogenic variants in the
Gene | \nProtein | \nPattern of Inheritance | \nDisease Association | \nOMIM# | \nLocus | \n
---|---|---|---|---|---|
ACTC1 | \nActin, Alpha, Cardiac Muscle | \nAD | \nACM, HCM, DCM, LVNC | \n102540 | \n5q31.1 | \n
ARVD3 | \nArrhythmogenic Right Ventricular Dysplasia, Familial, 3 | \nAD | \nACM | \n602086 | \n12p12.1 | \n
ARVD4 | \nArrhythmogenic Right Ventricular Dysplasia, Familial, 4 | \nAD | \nACM | \n602087 | \n15q14 | \n
ARVD6 | \nArrhythmogenic Right Ventricular Dysplasia, Familial, 6 | \nAD | \nACM | \n604401 | \n1q42-q43 | \n
CTNNA3 | \nCatenin Alpha 3 | \nAD | \nACM | \n607667 | \n7q21.2 | \n
DES | \nDesmin | \nAD,AR | \nACM, RCM, DCM | \n125660 | \n17q21 | \n
DSC2 | \nDesmocollin 2 | \nAD, AR | \nACM, DCM | \n600271 | \nXq22 | \n
DSG2 | \nDesmoglein 2 | \nAD | \nACM, DCM | \n125671 | \n15q24.1 | \n
DSP | \nDesmoplakin | \nAD, AR | \nACM, DCM | \n125485 | \n11p15.5 | \n
FLNC | \nFilamin C | \nAD | \nACM, DMC, RCM, HCM | \n102565 | \n10q22.2 | \n
JUP | \nJunction Plakoglobin | \nAD, AR | \nACM | \n173325 | \n2p13.1 | \n
LDB3 | \nLim Domain-Binding 3 | \nAD | \nACM, HCM, DCM, LVNC | \n605906 | \n2p22.1 | \n
LMNA | \nLamin A/C | \nAD, AR | \nACM, HCM, DCM, LVNC | \n150330 | \n1q22 | \n
PKP2 | \nPlakophilin 2 | \nAD | \nACM, DCM | \n602861 | \n11p15.4 | \n
PLN | \nPhospholamban | \nAD | \nACM, HCM, DCM | \n172405 | \n4q12 | \n
RYR2 | \nRyanodine Receptor 2 (Cardiac) | \nAD | \nACM, HCM | \n180902 | \n12p11 | \n
SCN5A | \nSodium Channel, Voltage-Gated, Type V, Alpha Subunit | \nAD | \nACM, DCM | \n600163 | \n20q13.12 | \n
TGFB3 | \nTransforming Growth Factor Beta 3 | \nAD | \nACM | \n190230 | \n\n |
TMEM43 | \nTransmembrane Protein 43 | \nAD | \nACM | \n612048 | \n10q23.3 | \n
TTN | \nTitin | \nAD, AR | \nACM, HCM, DCM | \n188840 | \n5q33-q34 | \n
List of common genes and patterns of inheritance in ACM.
AD – Autosomal dominant; AR – Autosomal Recessive; XL – X-linked; DCM – Dilated cardiomyopathy; HCM – Hypertrophic cardiomyopathy; LVNC – Left ventricular non-compaction cardiomyopathy; ACM – Arrhythmogenic cardiomyopathy; RCM – Restrictive cardiomyopathy.
RCM is rare, accounting for approximately 5% of all CMs. RCM is characterized by normal or decreased volume of both ventricles associated with atrial enlargement (left or bi-atrial), normal LV wall thickness, normal atrioventricular valve function/structure, impaired ventricular filling with restrictive physiology, and normal (or near normal) systolic function, please see Figure 5 [4, 116].
\nTwo-dimensional, apical 4-chamber echocardiographic image depicting small, restrictive ventricles and significant biatrial enlargement in a patient with restrictive cardiomyopathy.
The clinical course is defined by the inability to fill the ventricles due to poor ventricular relaxation, which limits the cardiac output. The disease may manifest with exercise intolerance, dyspnea, edema, atrial fibrillation, syncope, or sudden cardiac death. The hallmark of non-invasive imaging is atrial or bi-atrial enlargement. Normal or mild concentric hypertrophy with normal or reduced ventricular cavity can also be seen. Familial disease has been reported in 30% of cases and usually exhibits autosomal dominant inheritance. However, autosomal recessive, X-linked, and mitochondrial-transmitted disease have also been reported [117]. Most patients with RCM harbor gene mutations in sarcomere-encoding genes, such as
Gene | \nProtein | \nPattern of Inheritance | \nDisease Association | \nOMIM# | \nLocus | \n
---|---|---|---|---|---|
BAG3 | \nBcl2-Associated Athanogene 3 | \nAD | \nLVNC, HCM, DCM | \n603883 | \n14q24.3 | \n
DES | \nDesmin | \nAD, AR | \nRCM, DCM, ACM | \n125660 | \n17q21 | \n
FLNC | \nFilamin C | \nAD | \nRCM, HCM, ACM, LVNC | \n102565 | \n10q22.2 | \n
MYBPC3 | \nMyosin-Binding Protein C, Cardiac | \nAD | \nRCM, HCM, DCM, LVNC | \n600958 | \nXq28 | \n
MYH7 | \nMyosin, Heavy Chain 7, Cardiac Muscle, Beta | \nAD | \nRCM, HCM, DCM, LVNC | \n160760 | \n7p14.2 | \n
MYL3 | \nMyosin, Light Chain 3, Alkali, Ventricular, Skeletal, Slow | \nAD, AR | \nRCM, HCM | \n160790 | \n1q32 | \n
MYOZ2 | \nMyozenin 2 | \nAD | \nRCM, HCM, DCM | \n605602 | \n3p25.1 | \n
MYPN | \nMyopalladin | \nAD | \nRCM, HCM, DCM | \n608517 | \n12q23.1 | \n
TNNI3 | \nTroponin I Type 3 (Cardiac) | \nAD | \nRCM, HCM, DCM | \n191044 | \n3p21.1 | \n
TNNT2 | \nTroponin T Type 2 (Cardiac) | \nAD | \nRCM, HCM, DCM, LVNC | \n191045 | \n17q12 | \n
TPM1 | \nTropomyosin 1 (Alpha) | \nAD | \nRCM, HCM, DCM | \n191010 | \n19q13.4 | \n
List of common genes and patterns of inheritance in RCM.
AD – Autosomal dominant; AR – Autosomal Recessive; XL – X-linked; DCM – Dilated cardiomyopathy; HCM – Hypertrophic cardiomyopathy; LVNC – Left ventricular non-compaction cardiomyopathy; ACM – Arrhythmogenic cardiomyopathy; RCM – Restrictive cardiomyopathy.
RCM can be classified based on the underlying process: non-infiltrative; infiltrative; associated with storage diseases; idiopathic; or combined with DCM, HCM, and LVNC [116]. As with DCM, many previous cases deemed idiopathic are later found to harbor causative pathogenic variants in sarcomeric genes. Non-infiltrative causes of RCM include scleroderma and systemic sclerosis with well-described polymorphisms in genes coding for ECM proteins [121]. Pseudoxanthoma elasticum is an inherited disorder associated with accumulation of mineralized elastic fibers that may lead to blindness, coronary arterial occlusive disease, and RCM. The
Lysosomal storage disorders are characterized by abnormal lysosomal metabolism leading to accumulation of various glycosaminoglycans, glycoproteins, or glycolipids within lysosomes of various tissues, including the myocardium. Gaucher disease and Fabry disease (two of the most common lysosomal disorders) may manifest as CM (HCM or RCM), valvular disease, coronary artery disease, and/or aortic enlargement [127].
\nMucopolysaccharidoses (Hurler and Hunter diseases) are characterized by the deficiency of enzymes required for the breakdown of glycosaminoglycans. Thus, these diseases are considered lysosomal storage disorders. Cardiac manifestations start from childhood and include RCM, endocardial fibroelastosis, and valvular disease including thickening of the leaflets with resultant stenosis and/or insufficiency. Storage diseases such as hemochromatosis (mutation in the
In summary, CM is a widely variable disease process with a similarly variable pattern of genetic inheritance. Our understanding of the interplay between genetic mutation and disease phenotype is ever-evolving and merits much deeper investigation.
\nIntechOpen publishes different types of publications
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\\n\\nINTRODUCTORY CHAPTER – An introductory chapter states the purpose and goals of the book. The introductory chapter is written by the Academic Editor.
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\n\nEdited Volumes can be comprised of different types of chapters:
\n\nRESEARCH CHAPTER – A research chapter reports the results of original research thus contributing to the body of knowledge in a particular area of study.
\n\nREVIEW CHAPTER – A review chapter analyzes or examines research previously published by other scientists, rather than reporting new findings thus summarizing the current state of understanding on a topic.
\n\nCASE STUDY – A case study involves an in-depth, and detailed examination of a particular topic.
\n\nPERSPECTIVE CHAPTER – A perspective chapter offers a new point of view on existing problems, fundamental concepts, or common opinions on a specific topic. Perspective chapters can propose or support new hypotheses, or discuss the significance of newly achieved innovations. Perspective chapters can focus on current advances and future directions on a topic and include both original data and personal opinion.
\n\nINTRODUCTORY CHAPTER – An introductory chapter states the purpose and goals of the book. The introductory chapter is written by the Academic Editor.
\n\nMonographs is a self-contained work on a particular subject, or an aspect of it, written by one or more authors. Monographs usually have between 130 and 500 pages.
\n\nTYPES OF MONOGRAPHS:
\n\nSingle or multiple author manuscript
\n\nCompacts provide a mid-length publishing format that bridges the gap between journal articles, book chapters, and monographs, and cover content across all scientific disciplines.
\n\nCompacts are the preferred publishing option for brief research reports on new topics, in-depth case studies, dissertations, or essays exploring new ideas, issues, or broader topics on the research subject. Compacts usually have between 50 and 130 pages.
\n\nCollection of papers presented at conferences, workshops, symposiums, or scientific courses, published in book format
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