SFC actions.
\r\n\t
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"abf31c9873fc2d88b8ee05c6adb53a29",bookSignature:"Dr. David Bienvenido-Huertas",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10104.jpg",keywords:"Sustainable Construction, Innovative Construction, Construction Processes, Sustainable Design, Design Optimization, Maintenance Minimization, Energy Efficiency, Energy Conservation Measures, Thermal Comfort, Socio-cultural Integration, Urban Environment, Visual Impact",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 26th 2020",dateEndSecondStepPublish:"September 23rd 2020",dateEndThirdStepPublish:"November 22nd 2020",dateEndFourthStepPublish:"February 10th 2021",dateEndFifthStepPublish:"April 11th 2021",remainingDaysToSecondStep:"5 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"David Bienvenido-Huertas has completed his Ph.D. as an Architect, currently, he is a researcher of the Building Construction II Department at Universidad de Sevilla, Spain",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"320815",title:"Dr.",name:"David",middleName:null,surname:"Bienvenido-Huertas",slug:"david-bienvenido-huertas",fullName:"David Bienvenido-Huertas",profilePictureURL:"https://mts.intechopen.com/storage/users/320815/images/system/320815.jpg",biography:"PhD Architect. Researcher of the Building Construction II Department at Universidad de Sevilla, Spain. Active member of the Research Group TEP970: Technological Innovation, 3d Modeling Systems and Energy Diagnosis in Heritage and Building at the Universidad de Sevilla. His area of expertise covers climate change in the building sector, adaptive thermal comfort, heat transfer, fuel poverty, energy conservation measures, and design of nearly zero energy buildings. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"10842",title:"Adaptive Implementation of Discrete Event Control Systems based on Sequential Function Charts",doi:"10.5772/9528",slug:"adaptive-implementation-of-discrete-event-control-systems-based-on-sequential-function-charts",body:'\n\t\tThe discrete-event system (DES) is a class of dynamic systems whose behaviour is governed by discrete events and they state occupy a discrete symbolic-valued state at each time instant. These discrete events occur asynchronously and instantaneously at discrete instants of time and lead to a change of the state. Between event occurrences, the state of DES is unaffected. The DES behaviour is described by the sequence of events that occur and the sequence of states. Examples of DES abound in the industrial world as automated manufacturing systems, monitoring and control systems, supervisory systems; in building automation; in control of aircraft systems, railway systems…(Cassandras 1993).
\n\t\t\tAn example of a discrete event system is the classic programmable logic controller (PLC) controlling a sequential machine. The PLC acts as a discrete event control system (DECS). The DECS acts through the outputs over the actuators of the machines, and receives information of the state of the machines or events that happen in them through sensors. In the design of a DECS is neccesary to specify its dynamic behaviour, that is, the form of generating its outputs in response to the inputs. This specification can be carried out in different forms and will be a model of the desired behaviour of the system. There may be various desired behaviours for the same machine if the actions to be performed are different. The specification for the desired behaviour can be performed using the formalism of Petri nets. The technology translation can be done in a PLC in Sequential Function Chart language (SFC).
\n\t\t\tProgrammable Logic Controllers are extensively used in the control of production systems and their use is, at the present, widespread in most industrial sectors. The combination of the PLCs intelligence with the development of sensors and actuators, ever more specialized, allows a greater number of processes to be automated. These devices offer a series of advantages that meet some of the most important manufacturing industry requirements in recent years, such as low cost, capacity to control complex systems, flexibility (they can be quickly and easily re-programmed), reduced downtime and easier programming, and reliable and robust components ensuring their operation for a long time.
\n\t\t\tThe reaction time of a PLC is a fundamental matter in discrete event control systems. The PLC reads the inputs, executes the SFC and writes the output in a cyclic or periodic manner. In this chapter, we are interested in the execution time of algorithms that make the SFC of a control application evolve. We will show that the reaction time of a PLC depends greatly on the SFC structure, on the events sequence and also on the algorithm that executes the SFC. With the objective of minimizing the reaction time, we decided to design a Supervisor controller, which we have called the Execution Time Controller (ETC). The aim of the ETC is to determine in real time which algorithm executes the SFC the fastest and to change the execution algorithm when necessary.
\n\t\t\tWe propose to adapt the classical implementation techniques of Petri nets to execute SFCs. Thus, we have developed execution algorithms derived, on the one hand, from the Deferred Transit and the Immediate Transit SFC evolution models and, on the other hand, from Petri net implementation techniques (Brute Force, Enabled Transitions and Representing Places).
\n\t\t\tThe organization of this chapter is as follows. Section 2 is devoted to Discrete Event Systems, and Section 3 to Sequential Function Charts. Section 4 shows several implementation techniques of the SFC whose execution time is analyzed in Section 5. In Section 6 we present the Execution Time Controller. In Section 7 the technique is evaluated. The section describes the tests run to evaluate the estimation techniques and the working of the ETC in real time. Finally, in Section 8, we present the main conclusions.
\n\t\tAn example of a discrete events system is the classic PLC controlling a sequential machine. The PLC acts as a discrete event control system (DECS) (see Fig. 1). The DECS acts on the machines by sending output signals to the actuators and receives information about the state of the machines or events occurring in them through sensors. The DECS receives input signals not only from the machine sensors, but also from the commands of the control panel, from supervision systems and even from other DECS. An output signal can be a signal sent to an actuator to act on a physical process, to increase a variable or to send a message.
\n\t\t\tThe main function of discrete event control systems is to govern the workings of a machine in such a way that the desired behaviour is achieved. This is based on the coordination between the information received and the actions ordered to be carried out. A machine carries out the action ordered by the control system until the system decides that the action has been completed at which point it orders the machine to cease the action. In order that the control system can decide to end the action, it needs to obtain information indicating that the action should finish. This information can come from the sensors placed in the machine. With this information, the control system knows that it must execute an evolution. It has to pass from the state in which it performs the action to the subsequent state which could be one of many (perform another action, await material, etc.).
\n\t\t\tAn approach to the design of a DECS involves specifying its dynamic behaviour, in other words the way it generates its outputs in response to the inputs. This specification can be carried out in various ways and will be a model of the desired functioning of the system. The same machine may have different ways of functioning if the actions to be performed are different. The specification of the desired behaviour can be carried out using formalisms such as Petri nets. The technology translation can be done in a PLC using the Sequential Function Chart language (see Fig. 2).
\n\t\t\tDiscrete Event Control System
In 1975, one of the working groups of the now defunct AFCET (Asociation Francaise pour la Cibernétique Economique et Technique), the Logic Systems group, decided to establish a commission for the standardization of the representation of logic controller specifications. In August 1977 a commission comprising 12 academics and researchers and 12 representatives of companies such as EDF, CEA, Merlin-Gerín, and Telemecanique signed the final report.
\n\t\t\tIn brief, the group was looking for a model for the representation and specification of the functioning of systems controlled by logic controllers, through automatisms. The specification model only describes the desired behaviour, without detailing the technology with which the real implementation is effected. The model was named Grafcet (David 1995) and is recognised by standard IEC-848 (IEC 1988).
\n\t\t\tSimilar to Grafcet, the Sequential Function Chart (SFC) are standardized in IEC 61131 (ISO/IEC 2001) where is defined as one of the main PLC programming languages. A SFC program is organized into a set of steps and transitions connected by direct links. Associated with each step is a set of actions, and with each transition a transition predicate.
\n\t\t\tPLC programming in Sequential Function Chart
The SFCs are binary Petri nets with an interpretation for the control of industrial systems (Silva 1985)
\n\t\t\tImmediate actions are associated with the deactivation and activation of the steps (e.g., control signal changes, code execution).
Level control signals are associated with active steps.
Predicates are associated with transitions, as are additional preconditions for the firing of enabled transitions. Predicates are functions of system inputs or internal variables.
We take as an example the SFC shown in Fig. 3. The initial step (Automatic_star) is drawn with a double rectangle. The two output transitions of the initial step ( move_piece and NOT move_piece) are in conflict. The default priority rule for solving a conflict is a left to right precedence. The standard does not require a priority relation between transitions or that the transitions predicates are in mutual exclusion.
\n\t\t\tWhen all the input steps of a transition are active and the transition predicate or condition is true, the transition is fired, the input steps are deactivated and the output steps are activated. In the example of the Fig. 3: if the step named handgotoup is active and the transition hand_up is true, the step handgotoup is deactivated and the step named handgotopiece is activated.
\n\t\t\tActions can be programmed in a step. The type of programmed action is defined by the action qualifier. For example, a type N action is executed in all the cycles in which the step is active. The S, SD, SL, and SD actions are activated when the step in which they are programmed is activated, stored in an action buffer and from this point on are independent of the state of the step. They can only be deactivated by a type R action. Time limited actions can be programmed with type L or D qualifiers. There are also impulse type actions such as type P that are executed only when the step is activated.
\n\t\t\tSequential Function Chart example
\n\t\t\t\tTable 1 shows the actions that can be programmed in a SCF. In a PLC cycle, the following must be executed:
\n\t\t\tActions which depend on the state of a step: action qualifiers N, L, D, P, P0, P1.
The step in which is programmed the storage of the stored actions (S, SL, SD, DS) and their cancellation (R).
the stored actions (S, SL, SD, DS)
The action types and qualifiers are the standard ones of the IEC 61131 (ISO/IEC 2001).
\n\t\t\tQualifier | \n\t\t\t\t\t\tDescription | \n\t\t\t\t\t
N | \n\t\t\t\t\t\tNon-stored, executes while step is active. | \n\t\t\t\t\t
L | \n\t\t\t\t\t\tLimited, executes only a limited time while step is active. | \n\t\t\t\t\t
D | \n\t\t\t\t\t\tDelayed, starts executing after the step has been active. | \n\t\t\t\t\t
S | \n\t\t\t\t\t\tStored, starts executing when the step is activated until reset. | \n\t\t\t\t\t
R | \n\t\t\t\t\t\tReset stored action. | \n\t\t\t\t\t
SL | \n\t\t\t\t\t\tStored and limited | \n\t\t\t\t\t
SD | \n\t\t\t\t\t\tStored and delayed | \n\t\t\t\t\t
DS | \n\t\t\t\t\t\tDelayed and stored | \n\t\t\t\t\t
P | \n\t\t\t\t\t\tPulse, executes when the step is activated. | \n\t\t\t\t\t
P1 | \n\t\t\t\t\t\tPulse, positive flank, executes once when the step is activated. | \n\t\t\t\t\t
P0 | \n\t\t\t\t\t\tPulse, negative flank, executes once when the step is deactivated. | \n\t\t\t\t\t
SFC actions.
In the last 25 years, researchers have devoted considerable attention to the software implementation of Petri Nets (PN); see for example (Colom, Silva et al. 1986) (Briz and Colom 1994) (Taubner 1988) (Bruno & Marchetto 1986) (Garcia & Villarroel 1999) (Piedrafita & Villarroel 2006a). A PN implementation can be hardware or software. However, we are interested in the second approach, the software implementation. A software implementation is a program which fires the PN transitions, observing marking evolution rules, i.e., it plays the “token game”. An implementation is composed of a control part and an operational part. The control part corresponds to the structure, marking and evolution rules of the PN. On the other hand, the operational part is the set of actions and/or codes of the application, associated with the PN elements.
\n\t\t\tAccording to different criteria, a PN implementation can be mainly classified as compiled or interpreted, as sequential or concurrent and as centralized or decentralized.
\n\t\t\tAn implementation is interpreted if the SFC PN and the marking are codified as data structures. These data structures are used by one or more tasks called interpreters to make the PN evolve. The interpreters do not depend on the implemented PN. A compiled implementation is based on the generation of one or more tasks whose control flow corresponds to PN evolutions.
\n\t\t\tA sequential implementation is composed of only one task, even in PN with concurrency. This kind of implementation is common in applications whose operational part is composed by impulse actions without significant execution time. A concurrent implementation is composed of a set of tasks whose number is equal to or greater than the actual concurrency of the PN. Examples of concurrent implementations can be seen in (Colom, Silva et al. 1986) or in (Taubner 1988).
\n\t\t\tIn a centralized implementation the full control part is executed by just one task, commonly called the token player or coordinator. The operational part of the implementation can be distributed in a set of tasks to guarantee the concurrence expressed by the PN (see for example (Colom, Silva et al. 1986)).
\n\t\t\tThe problem of implementing a SFC is very similar to implementing a PN. Currently most industrial PLCs run their programs in an interpreted and centralized manner. The PLC reads the inputs, runs the SFC interpreter (also called coordinator in this paper) and writes the outputs. In the execution of the SFC it is necessary to determine which transitions can fire, and fire them making the state of the SFC evolve. It will also make the actions programmed in the steps.
\n\t\t\tThe algorithm to determine which transitions are enabled and can fire is important because it introduces some overhead in the controller execution and the reaction time is affected. In the present work we have implemented and study several algorithms in which different enabled transition search techniques are developed:
\n\t\t\tBrute Force (BF). PN implementation technique.
Deferred transit evolution model (DTEVM). SFC implementation technique.
Immediate transit evolution model (ITEVM). SFC implementation technique.
Static Representing Places (SRP). PN implementation technique.
Enabled Transitions (ET). PN implementation technique.
With the objective of carrying out a comparative study, all of these techniques have been uniformly implemented.
\n\t\t\tIn the Brute force algorithm all the transitions are tested for firing. Brute Force algorithms do not try to improve the search of enabled transitions. Works such as (Peng & Zhou 2004) (Uzam & Jones 1996) (Klein, Frey et al. 2003) belong to this implementation class.
\n\t\t\tThe IEC-61131 standard is not very precise in the definition of the SFC execution rules. Different execution models have been proposed to interpret the standard. As with BF, in the Immediate Transit Evolution Model (ITEVM) algorithm all the SFC transitions are tested for firing (Hellgren, Fabian et al. 2005). However, the Deferred Transit Evolution Model (DTEVM) (Hellgren, Fabian et al. 2005) only performs the testing of the transitions descending from the active steps, improving in this way the Brute Force operation.
\n\t\t\tIn (Lewis 1998) the IEC-61131 standard is interpreted and the following tasks are proposed to run an SFC:
\n\t\t\tDetermine the set of active steps
Evaluate all transitions associated with the active steps
Execute actions with falling edge action flag one last time
Execute active actions
Deactivate active steps that precede transition conditions that are true and activate the corresponding succeeding steps
Update the activity conditions of the actions
Return to step 1
These tasks are implemented in the DTEVM algorithm. In DTEVM, the transition conditions of all transitions leading from active steps (marked places in Petri net terminology) are evaluated first. Then, the transitions that were found to be fireable are executed one by one. In ITEVM, the transition conditions of all transitions of SFC are evaluated one by one. In the case of a transition condition being true, i.e., the corresponding transition is fireable, this transition is fired immediately.
\n\t\t\tIn the Static Representing Places (SRP) algorithm, only the output transitions of some representative marked steps are tested (Colom, Silva et al. 1986). Each transition is represented by one of its input steps, the Representing Place. The remaining input steps are called synchronization steps. Only transitions whose Representing step is marked are considered as candidates for firing.
\n\t\t\tIn the Enabled Transitions algorithm, only totally enabled transitions are tested. A characterization of the enabling of transitions, other than marking, is supplied, and only fully enabled transitions are considered. This kind of technique is studied in works such as (Silva & Velilla 1982) and (Briz. 1995).
\n\t\t\tAll implementation techniques are based on a treatment cycle which processes steps or transitions commonly stored in lists. The implementation of treatment the cycle is based on two kinds of lists that make an SFC evolve: treatment lists to be processed in the present treatment cycle and formation lists to be processed in the next cycle. The fundamental difference between each of the implementation techniques lies in the way in which the formation lists are built, and hence in the transitions which are considered in each treatment cycle.
\n\t\t\t\tOne of the most expensive operations in execution time is the search and insertion in lists. The time cost of such operations depends directly on the size of the lists. Therefore, it is stated in the algorithms where it carries out such operations.
\n\t\t\t\tThe basic treatment cycle of a SFC interpreter consists of three phases: (1) Enabling Test, (2) Transition firings (with two sub-phases: start and end), and (3) Lists update.
\n\t\t\t\tThe Enabling Test phase verifies the enabling of the transitions belonging to the treatment list. A transition is enabled if all of the input steps are active. An enabled transition will be fired in the next phase if the associated condition is true.
\n\t\t\t\tAll algorithms present two separate phases in the firing of transitions:
\n\t\t\t\tStart of transitions firing: deactivation of input steps of each fired transition.
End of transitions firing: activation of output steps of fired transitions.
The TransitionsFired list links both phases. In this way, the SFCs are executed step by step and avalanche effects are avoided. At the end of firing, the formation list is built with places or transitions being candidates for treatment in the next cycle.
\n\t\t\t\tFinally, at the end of the cycle, the elements of the formation list are analyzed and can become part of the treatment list for the next cycle.
\n\t\t\t\tIn the following paragraphs we show the ET (Silva & Velilla 1982) (Briz. 1995), SRP (Colom, Silva et al. 1986) and the DTEVM (Hellgren, Fabian et al. 2005) algorithms in more detail to illustrate how all the techniques have been coded. The ITEVM algorithm can be consulted in (Hellgren, Fabian et al. 2005). The procedures for the execution of the actions programmed in the SFC have been included, with the update of the activity conditions of the actions (ISO/IEC 2001).
\n\t\t\tProgram 1 presents the basic treatment cycle of the coordinator for the ET technique. This treatment cycle is also illustrated in Fig. 4. The following data structures will be available (see Fig. 4):
\n\t\t\t\tEnabled Transitions List (ETL). Treatment list made up of the transitions with all active input steps.
Almost Enabled Transitions List (AETL). Formation list which is built with the output transitions of the steps activated in the firing of the transitions, i.e., the transitions that can become enabled in the next cycle.
\n\t\t\t\t\t
Program 1. ET Coordinator Treatment Loop
\n\t\t\t\tFor each transition of the SFC a data structure is necessary that stores:
\n\t\t\t\tList of input steps
List of output steps
At the start of transitions firing Demark_input_steps (T, ETL) encapsulates the deactivation of the input steps of the transition fired, and the update of the ETL list. In this technique, the ETL (the treatment list) contains all transitions enabled at the beginning of the cycle. From this list each fired transition must be extracted and also the disabled transitions belonging to effective conflicts.
\n\t\t\t\tIn the function Update(ETL, AETL) the treatment list is prepared for the next cycle. The transitions in AETL are verified for enabling and, if positively verified, are added to the ETL (if they do not already belong). At this point, the algorithm performs search and insertion in list operations.
\n\t\t\tTreatment List and Formation List of the Enabled Transitions Technique
Program 2 presents the basic treatment cycle of the coordinator for the SRP technique. This treatment cycle is also illustrated in Fig. 5.
loop forever\n\t\t\t\t\t
Program 2. SRP Treatment Loop
\n\t\t\t\tThe following data structures will be available (see Fig. 5):
\n\t\t\t\tActive Representing Steps list (ARSL) and Active Synchronization Steps list (ASSL). Treatment lists containing the active Representing and Synchronization Steps.
Active Representing Steps list (ARSLnext) and Active Synchronization Steps list (ASSLnext). Formation lists with the Steps that will be active in the next cycle by the firing of the transitions.
For each representing step a data structure is necessary that contains:
\n\t\t\t\tList of transitions represented by the Step
In all the transitions of the SFC a data structure will be necessary that stores:
\n\t\t\t\tRepresenting step
List of synchronization steps
List of transitions in conflict
List of active representing steps after firing
List of active synchronization steps after firing
In each cycle only the output transitions of an active representing step are verified for enabling. If a represented transition fires, the verification process for the representing step ends because the rest of the represented transitions become disabled (they are in effective conflict).
\n\t\t\t\tAt the start of the transitions firing phase the function Demark_input_steps (T, ARSL, ASSL) encapsulates the deactivation of the input steps of the transition fired, and the updating of the ARSL and ASSL lists. The deactivated steps should be removed from the ARSL (if it is the representing step of the transition) or from ASSL (if it is a synchronization step of the transition). These fired transitions are added to the list Transitionsfired.
\n\t\t\t\tAt the end of the transitions firing phase the function Mark_output_steps (T, ARSLnext, ASSLnext) encapsulates the activation of the output steps of the transition fired and the building of the lists ARSLnext and ASSLnext. The output steps of the transitions in the Transitionsfired list are activated. The activated steps should be added to the list ARSLnext (if it is the representing step) or to ASSLnext (if it is a synchronization step). At this point, the algorithm performs search and insertion in list operations.
\n\t\t\t\tAt the end of the cycle, the ARSL list is updated in Update (ARSL, ARSLnext). The ARSLnext elements are added to the ARSL (if they do not already belong). The ASSL list is also updated in Update(ASSL, ASSLnext). The ASSLnext elements are added to the ASSL (if they do not already belong). At this point, the algorithm also performs search and insertion in list operations.
\n\t\t\t\tTreatment List and Formation List of the Representing Places Technique
Program 3 presents the basic treatment cycle of the coordinator for the DTEVM technique. The following data structures will be available (see Fig. 6):
\n\t\t\t\tActive Steps list (ASL). Treatment lists containing all the active steps.
Enabled Transitions List (ETL). Treatment lists containing the transitions with their input steps active and with their predicate condition true.
This treatment cycle is also illustrated in Fig. 6. The search of the Active Steps is carried out in DTEVM at the start of each cycle, in the function computeactivesteps. The execution time of this search function is proportional to the number of steps of the SFC.
\n\t\t\t\tTreatment List and Formation List of the DTEVM Technique
The enabling test of the transitions is carried out in two phases. First, it finds the enabled transitions with true predicates that are output of the steps in the ASL list, drawing up the ETL list. It then goes through this list and fires the transitions. The enabling must be re-evaluated to prevent the firing of transitions in conflict. This algorithm does not perform any search and insertion in list operations.
loop forever\n\t\t\t\t\t
Program 3. DTEVM Treatment Loop
\n\t\t\tAn analysis of SFC implementation algorithms was carried out in (Piedrafita & Villarroel 2008). Brute Force (BF), Enabled Transitions (ET), Static Representing Places (SRP) Inmediate Transit Evolution Model (ITEVM) and Deferred Transit Evolution Model (DTEVM) were analyzed. The main ideas obtained in (Piedrafita & Villarroel 2008) are:
\n\t\t\tThe implementation of the Enabled Transitions and Static Representing Places algorithms can lead to enormous savings in execution time compared to the Brute Force algorithm.
The choice of the most suitable type of algorithm to execute a SFC depends on the SFC behavior (effective concurrency vs. effective conflicts).
The presented tests show that the relative performance of implementation algorithms depends on the model concurrency structure but also on the dynamics imposed by the controlled system. In most of the cases, the SRP and the ET algorithms coming from PN field have good behaviors. The PN implementation techniques provide an improvement in the development of industrial controllers based on SFC language.
\n\t\t\tThe execution of SFCs without a suitable algorithm can suppose an increasing of the computing time, and a worse and slower answer in control applications. It is very difficult to estimate what algorithm will run faster an SFC. In real-time control only one algorithm can run the SFC, thus it must be possible to estimate what would be the execution time of the other alternative non executed algorithms
\n\t\t\tThe execution time, given its ease of measuring, is the physical parameter that most easily allows the performance of an algorithm to be evaluated. However, the execution time must be considered as an explicit measure of the performance of an algorithm, where it directly reflects the influence of the other parameters.
\n\t\t\tThe execution time of the algorithms described in the previous section will depend on the number of transitions tested for enabling in each cycle, and on the number of search and insertion in list operations. The computation time of the test for enabling operations does not depend on the size of the SFC. However, the computation time of the search and insertion in list operations does depend directly on the size of the algorithm lists.
\n\t\t\tThe number of transitions tested for enabling in the ET technique is the sum of the sizes of ETL and AETL. For the SRP technique, the number of transitions tested for enabling start from a minimum, being the number of Active Representing Steps (if firing even the first transition represented) to a maximum, being the total of the transitions represented by the Active Representing Steps (if firing even the last transition represented or if there is no firing transition). For the DTEVM technique, the number of transitions tested start from a minimum, the total of the output transitions of the Active Steps, to a maximum, twice the total of the output transitions of the Active Steps (if all predicates are true).
\n\t\t\tOne of the most expensive operations in execution time is the search and insertion in lists. The presented techniques frequently use this type of operation, especially in the real time building of formation lists and in the final phase of updating lists. The execution time of such operations depends directly on the size of the lists. There are techniques that abound in the use of search and insertion list operations, such as Representing Places. In other techniques such as Brute Force this type of operation is not performed since the lists are not updated.
\n\t\t\tThe search and insertion in list operations are performed in techniques such as ET or SRP because of the managing in real time of the treatment and formation lists. In the algorithms such operations are performed at the end of the firing of transitions and in the final update of the lists. Hence, if no transitions fire, the number of such operations is null. In the ET technique, the number of this kind of operation is the number of transitions of AETL that are enabled and become part of ETL. In SRP, it is twice the number of Steps that become active in the transitions firing, because SRP manages four lists. The computation time of the search and insertion in list operations depends directly on the size of the lists.
\n\t\t\tThe SFC implementation techniques are based on a cyclic treatment (see Program 1 to 3). The main loop goes through the treatment and formation lists using an algorithm that depends on the executed technique. The algorithm cycle execution time depends on the size of the treatment and formation lists. The size of the treatment lists in the case of ET and SRP depends on the current SFC state. This determines the number of enabled transitions and the number of active representing steps. The size of the formation lists depends on the number of transitions that fire in the cycle. Thus, the execution time depends on the evolution of the SFC state, the SFC structure and the sequence of events.
\n\t\t\tAs algorithms use different lists, their execution times will be different. The estimation of the algorithm execution time is based on the measurement of the mean time taken by these loops and on the estimation in real time of the size of the treatment and formation lists.
\n\t\t\tFirst, we study the SRP algorithm. The cycle execution time (CET) can be estimated by the following expression:
\n\t\t\tWhere FTnumber is the number of fired transitions; Trtested is the mean represented transitions tested of an active representing step; Tenabl is the time for the enabling test operation of one transition; Tfiring is the mean time for firing one transition; TinsertStep is the mean time necessary for the search and insertion in list operation of one Step in a List of size one (performed in the final phase of updating list).
\n\t\t\tThe ET algorithm is also analyzed. The cycle execution time can be estimated by the following expression:
\n\t\t\t\n\t\t\tWhere TinsertStep is the mean time necessary for the search and insertion in list operation of one transition in a List of size one (performed in the final phase of updating list).
\n\t\t\tEstablishing expressions for other implementation techniques is not complicated. Let us consider, for example, the brute force technique. The cycle execution time expression of the BF algorithm is:
\n\t\t\tTL is the list with all transitions of the SFC.
\n\t\tWith the objective of minimizing SFC execution time, we decided to design a Supervisor controller which we have called the Execution Time Controller (ETC).
\n\t\t\tThe main function of the ETC is to determine in real time which algorithm executes a SFC fastest. The ETC executes the algorithm chosen and estimates the execution time of the other non-executed algorithms, choosing the best algorithm in line with the controlled system. If necessary, the ETC changes the algorithm. In the next section we present in detail how the execution time (ExT) of the running and the alternative algorithms are estimated. To avoid the overload of continuous algorithm changes, an integral cost function is used:
\n\t\t\tThe change is made when I(k) is greater than half of the execution time of the executed algorithm. When a change happens, I (k-1) = 0.
\n\t\t\t\n\t\t\t\t
Program 4. Execution Time Controller
\n\t\t\tThe Tenabl, Tfiring, TinsertStep and Tinserttran times are measured in an offline execution test. For this purpose, the required measurement instrumentation is incorporated into the program. This instrumentation comprises the instructions required for reading the real time system clock and the necessary time difference calculations.
\n\t\t\t\tExecution Time Controller
The Time measuring test consists of the execution of the SFC with one of the algorithms carrying out the firing of 2000 transitions without executing the programmed actions. The condition associated with the transitions is considered true so that the firing is immediate. If there are conflicts, the transition that fires is chosen at random.
\n\t\t\t\tFor example, if the execution is done with the SRP algorithm during the test, the measurement of the Tenabl, Tfiring and TinsertStep times will proceed. The test is then repeated with the ET algorithm, and the Tinserttran time measurement is carried out.
\n\t\t\t\tThe cycle times of each algorithm are also measured in these tests and the algorithm with the shortest cycle time is chosen for the first execution of the SFC with control actions.
\n\t\t\tIn the real time execution of the ETC (see Program 4), the execution time of the executed algorithm can be measured by reading the system clock. To avoid an overload of the control actions, the execution time of the executed algorithm (runnig_alg) is then calculated with equations (1) to (3) (this depends on the algorithm being executed). In this case FTnumber and the sizes of the lists (ASRL, ASRLnext, …) are known by the ETC.
\n\t\t\t\tThe ETC should obtain enough information to determine what would be the computation time of the other algorithms not executed at that moment. There is no problem with the algorithm being executed. For the other algorithms, should be obtained the size in real time of the treatment and formation lists if they were being executed. From this data it can be estimated what would be their computation time. This estimation should be carried out in real time and with an small overload in the execution time of the algorithm.
\n\t\t\t\tThe execution times of the alternative algorithms should also be estimated with equations (1) to (3). The number of transitions fired is known given that it is the same as for the algorithm that is being executed. The value of the times measured in the test is also known. However, the size of the other lists must be estimated.
\n\t\t\t\tFor example, in the execution of the ET algorithm, the size of the lists of the SRP algorithm must be estimated. The mean number of active representing steps and active synchronization steps is more or less constant in most SFCs; therefore, the size (ARSL) and the size (ASSL) will be the mean value estimated in the offline time measurement test.
\n\t\t\t\tConsequently, it can be stated that, on average, the firing of a transition involves the unmarking of its representing Step and the marking of a new one. The size (ARSLnext) can be approximated by the number of transitions fired.
\n\t\t\t\tThe size(ASSLnext) can be approximated by the expression:
\n\t\t\t\tfp is the average parallelism factor (number of output places of a transition) of the fired transitions.
\n\t\t\t\tTo estimate the size of the lists of the SRP algorithm when the algorithm executed is FB, the same technique is used given that in real time it is only necessary to know the number of transitions fired and the mean parallelism factor of these transitions.
\n\t\t\t\tIf the SFC is executed with the SRP algorithm, it should be estimated what would be the computation time of the execution of the SFC with the ET algorithm. Therefore the sizes of the ETL and AETL lists should be estimated. The FTnumber is known because the two algorithms make the SFC evolve in the same way and therefore the number of fired transitions will be the same for both.
\n\t\t\t\tThe size of the ETL list is estimated in the SRP execution when the sensibilization of the transitions represented by the active representing steps is tested. When the SRP algorithm finds an enabled transition it fires it and continues with the next active representing step. If, therefore, it is necessary to know how many transitions there are enabled among those represented by the representing place, two possible solutions are adopted:
\n\t\t\t\tA first option is that the algorithm runs over all the transitions represented by a marked representing place, estimating their enabling.
When the SRP algorithm finds an enabled transition it fires it, and the rest of represented transitions are not verified for enabling. An approximation is carried out considering enabled half of the rest of transitions.
The second solution was chosen for the tests given that the computation time is shorter. The size of the AETL list is estimated at the firing of the transitions, when the output steps are activated, as is the size of the set of output transitions of the output steps of the fired transitions.
\n\t\t\t\tfp is the parallelism factor (number of output steps of a transition) of the transitions fired and fd is the descendants factor (average number of output transitions of a step) of the steps activated in the transitions firing.
\n\t\t\t\tA different technique is used to estimate the size of the ET algorithm lists when the FB algorithm is executed. Because with FB all the transitions in the SFC are covered in the enabling test, the size of the ETL list can be accurately known. The same technique as that used for the SRP algorithm is used to estimate the AETL almost sensibilized transitions list.
\n\t\t\t\tSFCs Library
We have implemented the techniques in the Java language using the Java Real-time Specification (Bollella & Gosling 2000.) and following some ideas presented in (Piedrafita & Villarroel 2006a), (Piedrafita & Villarroel 2006b) and (Piedrafita & Villarroel 2007). In our implementations, we used the Real Time Java Virtual Machine JamaicaVM v2.7 (Aicas 2007). The target hardware was a personal computer with a Pentium IV processor at 1.7GHz, running Red Hat Linux 2.4. The Coordinator is implemented as a Periodic Real Time Thread of high Priority. The execution is made in a single processor and threads are scheduled following a static priorities policy without round-robin.
\n\t\t\t\tIn the implementations developed here, the program loads the SFC structure from an XML file generated by an SFC editor. The implementation is independent of the SFC, and is therefore an interpreted implementation.
\n\t\t\t\tA library of Sequential Function Charts has been developed for carrying out the tests. The library is based on four base models which can be scaled using a parameter. These models represent most of the cases developed in industrial control: sequential systems and concurrent systems. The library comprises the following SFCs:
\n\t\t\t\tSEQ. SFC with one sequential process composed of 1 to 100 steps (Fig. 8.a).
PAR. SFC with p (1..100) sequential processes with 20 steps (Fig. 8.b).
The ETC controller has been tested with all the SFCs in the library and also with a real control application. This is a Flexible Manufacturing Cell in the Computer Science Department of the University of Zaragoza. The ETC obtains a high degree of success in all of the performed experiments, but here for the sake of brevity we present the results of the three most representative experiments. However, an exhaustive report of the experiments can be consulted in (Piedrafita 2008), accessible via the Internet.
\n\t\t\t\tThe execution of the ETC takes place in the Real Time Java Virtual Machine Jamaica. This is implemented as Periodic Real Time Thread of high Priority with 20 ms of period. The execution is made in a single processor and threads are scheduled following a static priorities policy without round-robin.
\n\t\t\t\tThe first experiment that we present is over a sequential SFC of 35 steps (see Fig. 9 left) and illustrates that the SRP algorithm is always the best in this experiment (Piedrafita & Villarroel 2007).
\n\t\t\t\tFig. 9.a, shows the Real Time execution of The Execution Time Controller (ETC), the Real Time estimation of the same algorithm (SRP), and the Real Time estimation of one alternative algorithm( ET in this case). Fig. 9.b, shows also the Real Time execution of the algorithm SRP, and the Real Time execution of the algorithm ET.
\n\t\t\t\tThe ETC chooses the SRP from the start since the estimation of the execution time of this algorithm is smaller than that of the ET algorithm. Because SRP is always better than ET, the integral cost function I(k) remains permanently null and therefore no algorithm change is performed.
\n\t\t\t\tThe second experiment that we present is over a concurrent SFC compound of 10 sequential SFCs (see Fig. 9 c and d) and illustrates that the SRP algorithm is the best in this experiment (Piedrafita & Villarroel 2007).
\n\t\t\t\tFig. 9.c, shows the Real Time execution of The Execution Time Controller (ETC), the Real Time estimation of the same algorithm (SRP), and the Real Time estimation of one alternative algorithm( ET in this case) and the integral cost function I(k). Fig. 9.e, shows also the Real Time execution of the algorithm SRP and the Real Time execution of the algorithm ET.
\n\t\t\t\tAs in the first experiment, the ETC chooses the SRP from the start since the estimation of the execution time of this algorithm is smaller than that of the ET algorithm. Because SRP is always better than ET, the integral cost function I(k) remains permanently null and no algorithm change is performed.
\n\t\t\t\tReal Time Execution of the ETC with a sequential SFC of 35 states and a concurrent SFC compound of 10 sequential SFCs.
The third experiment that we present is over a concurrent SFC compound of 40 sequential SFCs (see Fig. 10 a and b) and illustrates that the SRP algorithm is the best algorithm in this experiment when not firing transitions, and ET is the best algorithm when all possible transitions are firing.
\n\t\t\t\tIn the first 0.4 seconds all possible transitions fire and the best algorithm is ET, while in the next 0.4 seconds no transitions fire and the best algorithm is SRP. The event sequence is cyclically repeated.
\n\t\t\t\tAt the beginning, the ETC executes the ET algorithm which, as we have seen, is the better algorithm in the first 0.4 seconds. However, at the instant 0.4 no events reach the SFC and SRP becomes better, therefore I(k) increases and the ETC changes to SRP at instant 0.48. At instant 0.8, the events reach the SFC and ET becomes better, therefore I(k) increases again and the ETC changes to ET at instant 0.9. For the whole evolution, ETC changes to SRP at instants 0.48, 1.28 and 2.08 and to ET at instants 0.9, 1.7 and 2.5. With the observed behaviour, the ETC achieves the minimum possible execution time of the SFC.
\n\t\t\t\tIn industrial control it is very common that, in many cycles, events do not reach the SFC, and so no transition is fired, and when they are fired their quantity is variable. We can therefore differentiate between two operation regimes:
\n\t\t\t\tWithout events regime (static). No transitions are fired and the algorithm only runs the enabling test.
With events regime (dynamic). Transitions are fired and the algorithm must run all the phases: enabling test, firing and updating of lists.
If desired, the ETC can choose the algorithm that has the shortest computation time in the enabling test (without events regime). The integral cost function is only calculated when no transitions are fired. The integral cost function is:
\n\t\t\t\tIf (FTnumber==0)
\n\t\t\t\tThe execution of the ETC with this cost function can be seen in Fig. 10 c and d. It can be observed that the integral is only calculated when no transitions fire. At the instant 0.48 the ETC changes to the SRP algorithm, which has the shortest computation time in the without events regime.
\n\t\t\t\tIf it is required that the ETC achieve the shortest reaction time for events, the integral cost function is only calculated when transitions fire. In this way the ETC chooses as the best algorithm that which has the shortest computation time in the firing of transitions. The integral cost function is:
\n\t\t\t\tIf (FTnumber >0)
\n\t\t\t\tThe execution of the ETC with this function is shown in Fig. 10 e and f. It can be seen that the integral is only calculated when transitions are fired. At the instant 0.48 el ETC chooses the ET algorithm, which has the shortest computation time in the with events regime.
\n\t\t\t\tReal Time Execution of the ETC with a concurrent SFC compound of 40 sequential SFCs.
In this work we have developed an adaptive implementation of Discrete Event Control Systems, the Execution Time Controller, which allows choosing in real time the most suitable algorithm to execute a Sequential Function Chart. The main function of the ETC will be to determine which algorithm executes a SFC the fastest. The proposed technique is analyzed with the two most important algorithms (from the point of view of performance): the enabled transitions and the static representing places. However, the ETC can work with any SFC implementation algorithm.
\n\t\t\tThe ETC executes the chosen algorithm and estimates the execution time of other non-executed algorithms, deciding the best one in line. The execution of a SFC without a suitable algorithm can lead to a significant increase in the execution time, together with a less satisfactory and slower answer in control applications. The technique has been tested on a wide SFC library. Moreover, the ETC has also been tested in a real control application. The technique has a high success rate in the choice of the best implementation algorithm.
\n\t\t\tThe ETC allows faster reaction times in SFC based control systems and also minimizes the power consumed by the controller.
\n\t\tCardiomyopathies are diseases with primary defects associated with the structure and function of the heart. They are commonly classified into 5 different categories: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and left ventricular noncompaction (LVNC). HCM and DCM are the most common of the cardiomyopathies, with an incidence of 1:500 and 1:2500, respectively. Although there are variations in phenotypes and etiologies, there are also similar symptoms among the cardiomyopathies. For example, HCM, DCM, and RCM often present with signs and symptoms that are common in heart failure with reduced ejection fraction, including peripheral edema, fatigue, dyspnea on exertion, syncope, and cardiac ischemia [1, 2]. The focus of this article will be on HCM and DCM, the two most common cardiomyopathies.
\nHypertrophic cardiomyopathy (HCM) is defined as left and/or right ventricle hypertrophy in the absence of external load, and without chamber dilation. Interventricular septal thickening predominates and may cause left ventricular outflow tract obstruction and/or mitral valve dysfunction. Other common features include myocyte disarray, fibrosis, alterations in calcium sensitivity of myofilaments, and cardiac arrhythmias that may lead to premature sudden death and/or heart failure. Phenotypic expression is variable, with some genetically-identified HCM individuals dying in their late teens/early twenties, whereas others have a normal life span with minimal disability dependent upon the specific mutation within the affected gene. In addition, modifier genes and environmental factors can influence disease progression and phenotype.
\nThe genes associated with HCM can be roughly divided into several distinct categories: (1) genes definitively established as causing HCM via large family pedigrees; (2) genes likely causing HCM via small family pedigrees; and (3) genes associated with HCM via small families and sporadic cases [3]. However, as more genetic information is obtained on incidence of diseases, these categorizations may become blurred. Usually, HCM is inherited as an autosomal dominant disease where a single missense point mutation in the affected gene is sufficient to cause the disease, although there is variability in the phenotype. This variability in phenotype is also manifest by the numerous different point mutations that occur within a specific gene; for example, the myosin heavy chain 7 (MYH7) R403Q mutation is associated with a severe pathological phenotype, whereas other MYH7 mutations, such as V606M, are relatively benign [4]. Studies also show that modifier genes and their polymorphisms, such as angiotensin II type 2 receptor and calmodulin, can influence the HCM pathology [4]. In addition, mutations that occur in different HCM-causing genes have dramatically different pathologies, with some being severe and others being relatively benign.
\nThe genes primarily associated with the HCM phenotype are sarcomeric contractile protein genes associated with both thick and thin cardiac myofilaments, along with the Z discs (Table 1). There are over 1500 mutations in these genes that are associated with HCM. The pioneering studies that revealed the molecular genetic basis of HCM and its association with sarcomeric protein genes were conducted by Drs. Christine and Jonathan Seidman [5]. These initial studies led to the discovery that mutations within most of the thick and thin filament sarcomeric protein genes of the heart can cause HCM (Table 1). In the United States, the most common genes associated with HCM are β-myosin heavy chain (MYH7) and myosin-binding protein C (MYBPC3); other thick filament protein genes which cause HCM are the regulatory light chain (MLC2) and the essential light chains (MLC 1/3). Most MYH7 mutations occur in the globular head and hinge region of the myosin heavy chain, although mutations in the rod domain also cause HCM. Although most HCM mutations in the contractile protein genes are missense mutations, there is a bias for insertion/deletion mutations and premature truncation mutations in the MYBPC3 gene; these insertion/deletion mutations often result in translational reading frame shifts which lead to premature stop codons with subsequent degradation of the mRNA by nonsense mediated decay mechanisms or degradation of a truncated polypeptide. Thin filament protein genes associated with HCM are α-tropomyosin (TPM1α), troponin T, I, and C (TNNT2, TNNI3, TNNC1), and cardiac actin (ACTC1). The muscle LIM protein CSRP3, found in the Z-disc, also is a causal gene for HCM. Interestingly, HCM mutations in cardiac troponin T often have a relatively mild pathological phenotype but can lead to sudden cardiac death. Over the years, the identification and verification of many of these sarcomeric genes with HCM has been primarily through large family pedigrees, and often confirmed through experimental animal systems.
\nGene | \nProtein | \nProtein function | \nCardiomyopathy | \n
---|---|---|---|
\nMYH7\n | \nβ-Myosin heavy chain | \nATPase activity and Force generation in sarcomere | \nHCM, DCM | \n
\nMYH6\n | \nα-Myosin Heavy Chain | \nPrinciple protein of the thick filament with low expression in adult human ventricles | \nHCM, DCM | \n
\nMYL2\n | \nRegulatory myosin light chain | \nBinds myosin heavy chain | \nHCM | \n
\nMYL1/3\n | \nEssential myosin light chain | \nBinds myosin heavy chain | \nHCM | \n
\nACTC1\n | \nCardiac α-actin | \nPrinciple component of the thin filament | \nHCM, DCM | \n
\nTPM1\n | \nα-Tropomyosin | \nBlocks myosin interaction with actin in sarcomere | \nHCM, DCM | \n
\nTNNT2\n | \nCardiac troponin T | \nHolds troponin complex on tropomyosin | \nHCM, DCM | \n
\nTNNI3\n | \nCardiac troponin I | \nInhibits actomyosin interaction | \nHCM, DCM | \n
\nTNNC1\n | \nCardiac troponin C | \nBinds calcium to regulate sarcomeric contraction | \nHCM, DCM | \n
\nMYBPC3\n | \nMyosin binding protein C | \nStructure & Contraction in the sarcomere | \nHCM, DCM | \n
\nTTN\n | \nTItin | \nStructural component of the sarcomere | \nHCM, DCM | \n
\nCSRP3\n | \nCysteine-and glycine-rich protein 3 | \nMuscle LIM protein located in the Z disc | \nHCM, DCM | \n
\nACTN2\n | \nActinin | \nAttaches actin filaments to the Z lines in muscle | \nHCM, DCM | \n
\nTCAP\n | \nTcap (telethonin) | \nCapping protein for titin | \nHCM, DCM | \n
\nMYOZ2\n | \nMyozenin 2 (calsarcin 1) | \nTethers calcineurin to the Z disc via actinin | \nHCM | \n
\nPLN\n | \nPhospholamban | \nRegulates calcium entry into the sarcoplasmic reticulum | \nHCM, DCM | \n
\nLDB3\n | \nLim domain binding 3 | \nStabilizes the sarcomere during muscle contraction | \nHCM, DCM | \n
\nFHL1\n | \nFour-and-a-half LIM domains 1 | \nMuscle development and cardiac hypertrophy | \nHCM | \n
\nMYLK2\n | \nMyosin light chain kinase 2 | \nPhosphorylates myosin light chain 2 | \nHCM | \n
\nNEXN\n | \nNexilin | \nActin binding protein, part of T-tubule complex and Z discs | \nHCM, DCM | \n
\nJPH2\n | \nJunctophilin-2 | \nStructural protein linking the plasma membrane with the sarcoplasmic membrane. | \nHCM | \n
\nCASQ2\n | \nCalsequestrin 2 | \nCalcium binding protein | \nHCM | \n
\nVCL\n | \nVinculin | \nCytoskeletal protein | \nHCM, DCM | \n
\nANKRD1\n | \nAnkyrin repeat domain 1 | \nTranscriptional repressor of cardiac genes | \nHCM, DCM | \n
\nTRM63\n | \nMuscle ring finger protein | \nInvolved in proteasome-ubiquitin system for protein degradation | \nHCM, | \n
Genes found to cause cardiomyopathies.
In addition to those genes mentioned that have a strong association with causing HCM, there are other cardiac muscle protein genes that when mutated are likely candidates for HCM. These genes include four-and-a-half LIM domains 1 (FHL1), myozenin 2 (MYOZ2), phospholamban (PLN), titin (TTN), titin capping protein (TCAP), and muscle ring finger protein 1 (TRM63) (Table 1) [3, 6, 7]. Although some of these associated proteins are located in the sarcomere (titin, titin capping protein), others are found peripherally, such as phospholamban which is in the sarcoplasmic reticulum membrane, and myozenin 2, located in the Z disc. There are also genes that are associated with HCM but occur more sporadically [3, 6, 7]. Some of these proteins are associated with cardiac muscle, such as troponin C, myosin light chain kinase 2, actinin 2, vinculin, nexilin, α-myosin heavy chain, and Lim domain binding 3 protein; other proteins are found globally, such as caveolin, junctophilin-2, and calsequestrin (Table 1). The fact that a vast array of different genes encoding proteins with diverse functions can all trigger the HCM pathological response demonstrates a common end point in the development of cardiovascular disease. However, we must also consider HCM is a large phenotypic category and that with more detailed pathological and physiological analyses, an improved diagnostic system might be developed. This has already been demonstrated by the addition of other cardiomyopathic classifications, such as restrictive cardiomyopathy, storage and metabolic cardiomyopathies, dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, and mitochondrial cardiomyopathy.
\nDilated cardiomyopathy (DCM) is defined as dilation of the left or both ventricles that is not explained by coronary artery disease or abnormal loading of the heart. The cardiac enlargement occurs with either normal thickness or thinning of the ventricular walls and varying amounts of fibrosis. Oftentimes, DCM leads to heart failure with reduced ejection fraction, tachyarrhythmias, and increased risk of sudden death. All four cardiac chambers may be dilated with increased end-systolic volumes in both ventricles. The incidence of DCM is less well defined, with numbers varying between 1/250 individuals to 1/2500 individuals [6, 7]. Some of this variability can be attributed to the increased number of causes associated with DCM which include familial, viral myocarditis, cardiac toxins (i.e. alcohol, cocaine, amphetamine, cancer chemotherapeutic agents), peripartum cardiomyopathy, and prolonged tachycardia-related cardiomyopathy.
\nThe genes associated with familial DCM are numerous and varied in their function (Table 1); however, the most common mode of inheritance is autosomal dominant. Over 50 genes have been identified that are linked to familial DCM, which encode proteins in the sarcomeres, ion channels, cytoskeleton, nuclear envelope, and mitochondria [6, 7]. Familial DCM comprises 30–50% of the DCM population. There is also allelic heterogeneity with mutations occurring in multiple regions within a specific gene. In fact, many genes are associated with causing both DCM and HCM, dependent upon the specific mutation (Table 1).
\nTitin, lamin A/C, and β-myosin heavy chain account for >25% of genetically-inherited DCM [7, 8]. Titin, the longest human protein, is composed of 34,350 amino acids with a mass of 3,816,030 Da. This sarcomeric protein functions as a scaffold for both thick and thin filaments in striated muscle. Many of the DCM-associated mutations in titin encode premature stop codons, resulting in truncated forms of the protein. These truncations often map to the A band of the sarcomere, rather than the I band, and are associated with phenotypically mild DCM. Other titin mutations result in sarcomere instability, decreased binding to its cap-binding protein (TCAP), decreased binding to the Z-disc, and a decreased stretch response during sarcomere contraction.
\nMutations in the lamin A gene account for approximately 6% of all DCM mutations, and is oftentimes associated with a high incidence of sudden cardiac death [8]. Lamin proteins are associated with intermediate filaments which support the nuclear membrane, along with a role in chromatin structure and possibly gene transcription. Mutations in the lamin gene often lead to nuclear membrane damage and/or chromatin disorganization and impaired gene transcription. Because of lamin’s diverse function, mutations lead to a wide variety of disease conditions, including premature aging and various myopathies, including DCM. In the heart, LMN A mutations often lead to dysrhythmias including sinus node and AV node dysfunction, atrial and ventricular fibrillation, and sudden cardiac death [8].
\nMutations in genes involved in calcium/sodium handling are also associated with the onset of DCM. Phospholamban (PLN), a regulator of the sarcoplasmic reticulum Ca2+-ATPase pump, has several autosomal dominant mutations that result in DCM. In fact, the R14del mutation in PLN is associated with a founder effect in the Netherlands which results in a severe phenotype [9]. However, a milder DCM phenotype may also occur with the R14del mutation which demonstrates that modifying genes may play a role in the disease pathology. Another ion channel gene associated with DCM is SCN5A, a major sodium channel expressed in the heart. DCM mutations in this gene increase the risk for arrhythmias, whereas other SCN5A mutations result in channelopathies [6].
\nAn examination of the genes associated with DCM and HCM clearly demonstrates commonality in causing cardiomyopathies (Table 1). A clear example of this are the numerous sarcomeric protein genes, including titin, α- and β-myosin heavy chains, troponin T, I, and C, α-tropomyosin, α-actin, and titin capping protein, vinculin, desmin, and nexin (Table 1). There are also genes which appear more specific in causing only a single phenotype; genes associated with HCM are myosin light chain 2 and 1/3, myosin light chain kinase, and myozenin, whereas genes associated with only DCM include laminin α4, presenilin 1 and 2, and numerous others [6, 7]. The multitude of mutated genes that can result in DCM and HCM would infer that a continuum of phenotypes may exist for these cardiomyopathies dependent upon when the diseased heart is examined, which gene is mutated, where the mutation occurs, the type of mutation, associated modifying genes, and environmental influences. In fact, there are many cases where HCM hearts transition to DCM and heart failure as the disease progresses.
\nTropomyosin (TPM) is an essential component of the sarcomeric thin filament that regulates muscle contraction and relaxation through its interactions with actin and the troponin complex. More specifically, striated muscle TPM, along with the troponin complex, regulates Ca2+-mediated actin-myosin crossbridges. As stated previously, the Seidman laboratory discovered through pedigree analysis and gene mapping that HCM was associated with mutations in myosin heavy chains [5]. The association of TPM with HCM was also reported by the Seidman laboratory in 1994 which confirmed that HCM was a disease of the sarcomere and not solely confined to the thick filament [10]. In the United States, the percentage of HCM attributed to mutations in TPM is ~5%, with most of these cases exhibiting benign symptoms, oftentimes first displayed in later years in life. However, in Japan, the phenotype is severe, but the incidence is low [11, 12]. Interestingly, TPM-associated cases are the most prevalent of all contractile proteins in causing HCM in Finland, with a severe pathological phenotype [13, 14]. The variability in incidence and pathology in the different populations is most likely due to allelic variants, modifier genes, founder effects, and environmental influences.
\nMutations in the TPM1α gene are known to cause both HCM and DCM. There are at least 17 mutations that have been found to cause HCM and 11 mutations that can give rise to DCM (Table 2) [15, 16]. The striated muscle α-tropomyosin protein encodes 284 amino acids; this TPM isoform is the predominant TPM found in the adult human heart. The mutations that cause HCM are scattered throughout the gene/protein with a significant number located in the troponin-T binding regions, around amino acids 170–190 (Ile172Thr; Asp175Asn; Glu180Gly; Glu180Val; Leu185Arg; Glu192Lys) and amino acids 270–284 (Met281Thr; Ile184Val). A number of these mutations lead to a change in amino acid charge which may disrupt the dimerization of TPM with itself, or TPM’s interactions with actin and/or troponin T [17]. Also, most, if not all, of the HCM mutations occurring in thin filament sarcomeric proteins lead to increased calcium sensitivity of the myofilaments, coupled with decreased systolic and diastolic cardiac function which may be causative for the development of this cardiomyopathy.
\nAmino Acid Mutation | \nPhenotype | \n
---|---|
Met8Arg | \nDCM | \n
Lys15Asn | \nDCM | \n
Arg21His | \nHCM | \n
Ala22Ser | \nHCM | \n
Glu23Gln | \nDCM | \n
Glus40Lys | \nDCM | \n
Glu54Lys | \nDCM | \n
Asp58His | \nHCM | \n
Glu62Gln | \nHCM | \n
Ala63Val | \nHCM | \n
Lys70Thr | \nHCM | \n
Asp84Asn | \nDCM | \n
Ile92Thr | \nDCM | \n
Val95Ala | \nHCM | \n
Ala107Thr | \nHCM | \n
Ile172Thr | \nHCM | \n
Asp175Asn | \nHCM | \n
Glu180Gly | \nHCM | \n
Glu180Val | \nHCM | \n
Leu185Arg | \nHCM | \n
Glu192Lys | \nHCM | \n
Thr201Met | \nDCM | \n
Ser215Leu | \nHCM | \n
Asp230Asn | \nDCM | \n
Ala239Thr | \nDCM | \n
Ala277Val | \nDCM | \n
Met281Thr | \nHCM | \n
Ile284Val | \nHCM | \n
To understand the role of TPM in the development of HCM, our laboratory generated animal models of HCM. We produced the first in vivo transgenic mouse models expressing TPM with known HCM human mutations (Asp175Asn; Glu180Gln) [18, 19, 20]. Since there is a 100% amino acid sequence identity and comparable expression in the heart between mouse and human TPM, the mutations used in these transgenic mice reflect mutations and expression found in HCM patients. In addition, the exogenous cardiac-specific TPM transgene expression leads to a reciprocal decrease in endogenous TPM levels so that the total amount of TPM protein expression is unchanged in the hearts of these transgenic mice. Histological analyses demonstrate that the Asp175Asn transgenic mouse hearts show a moderate hypertrophic response; in contrast, the Glu180Gln mice demonstrate a severe cardiac hypertrophy with significant fibrosis and atrial enlargement (Figure 1) [18, 19, 20]. Physiologically, mice from both models display significant systolic and diastolic dysfunction, coupled with increased sensitivity to Ca2+ in the myofilaments. The pathological and physiological disease state progresses rapidly in the HCM Glu180Gln mice, with the mice dying between 4 and 6 months postpartum.
\nHCM TPMα180 and NTG control hearts at the designated 1-month time intervals. (A) Cross-section of a three-month-old TPMα180 heart. (B and C) trichrome stain of left ventricular wall from control and TPMα180 hearts. B NTG control. C TPMα180. Note, blue fibrous staining in panel C.
To understand the molecular mechanisms associated with the development of cardiomyopathy, we conducted a detailed comparative microarray analyses of hearts obtained from mild and severe HCM mice [21]. Ventricular tissue was obtained from 2.5-month-old TPMα175 and TPMα180 hearts, along with control (NTG) samples. Results show 754 genes (from a total of 22,600) were differentially expressed between the NTG and HCM hearts; 178 between NTG and TPMα175, and 388 between NTG and TPMα180. There are 266 differentially expressed genes between HCM TPMα175 and TPMα180. The genes that exhibit the largest increase in expression are associated with “secreted/extracellular matrix” category, and the most significant decrease in expression are in the “metabolic enzyme” category. This work illustrates the diverse array of genes that are activated and repressed during the early signaling processes of mild and severe cardiac hypertrophy.
\nThe development of mouse models that mimic human HCM physiological and pathological conditions afford researchers the opportunity to examine various methods for rescuing these mice from cardiomyopathy. Studies demonstrate cardiac thin filaments with HCM mutations exhibit an increased sensitivity to calcium. As calcium is a prime regulator of muscle contraction, we hypothesized that by normalizing myofilament calcium sensitivity, we could phenotypically rescue the HCM phenotype in our TPMα180 mice. Previously, we generated transgenic mice that exchanged the carboxyl terminal region of TPMα with that of TPMβ (Chi 1) [22]; these mice exhibit a decreased myofilament calcium sensitivity. By mating mice from the HCM TPMα180 with the Chi 1 mice, we tested the hypothesis that attenuation of myofilament calcium sensitivity would modulate the severe physiological and pathological consequences of the HCM mutation. Results show the double-transgenic mice “rescue” the hypertrophic phenotype by exhibiting a normal morphology with no pathological abnormalities, improved cardiac function, and normal myofilament calcium sensitivity [23, 24]. These results demonstrate that alterations in calcium response by modification of contractile proteins can prevent the pathological and physiological effects of this disease.
\nTo extend our studies on rescuing HCM TPMα180 mice by modulation of cytosolic calcium, we crossbred the TPMα180 mice with phospholamban knockout mice (PLNKO) [25]. PLN is a Ca2+-handling protein that regulates calcium uptake into the sarcoplasmic reticulum. Previous studies show that PLNKO mice exhibit hypercontractility with no change in morphology or heart rate, no alterations in myofilament Ca2+ sensitivity, and myosin ATPase activity [26]. Results show that PLN ablation in the TPMα180 mice rescues cardiac function and morphological abnormalities for up to one year [25]. There was a reversal of the cardiac hypertrophy, fibrosis, and abnormal physiological function in these rescued mice. This work shows that by modulating sarcoplasmic reticulum calcium cycling, many of the deleterious aspects of HCM caused by a mutation in the thin filament protein TPM can be reversed.
\nWe investigated whether oxidative myofilament modifications can reverse the diastolic dysfunction associated with HCM. The TPMα180 hearts display early signs of oxidative stress in the form of increased oxidative modifications of myosin binding protein C and activation of the MAPK signaling cascade. We hypothesized that treatment with the glutathione precursor N-acetylcysteine (NAC) may reverse the oxidative stress in the TPMα180 mice and improve the cardiomyopathic condition and diastolic dysfunction. To address this, NAC was administered for 30 days to control and TPMα180 mice. After NAC administration, the morphology, diastolic dysfunction, and myofilament Ca2+ sensitivity of the TPMα180 mice was similar to controls, indicating that NAC had reversed the abnormal pathology and physiology associated with HCM [27]. These studies indicate that oxidative myofilament modifications are an important mediator in diastolic function which can be of potential use in the treatment of HCM.
\nTPM is phosphorylated at a single site in the protein, located at the penultimate amino acid, serine 283. To address the significance of TPM phosphorylation, we generated transgenic mice where this serine residue is exchanged for alanine [28, 29]. These transgenic mice (S283A) exhibit a compensated hypertrophic response with significant increases in SERCA2a expression and phosphorylation of PLN. Having obtained these results, we postulated that decreasing TPMα phosphorylation may be beneficial in the context of a chronic, intrinsic stressor, such as HCM. To test this hypothesis, we generated mice expressing both the TPMα180 and S283A mutations and found the HCM phenotype was rescued [29, 30]. The double mutant transgenic mice exhibit no signs of HCM, displayed improved cardiac function, and have normal myofilament Ca2+ sensitivity. Changes in Ca2+ handling proteins may be responsible for the improved functional performance found in the double transgenic hearts. Also, changes in local flexibility of the TPM molecule conferred by the replacement of the Serine residue with an Ala residue in the S283A mice, and the significant loss of phosphorylation, may be responsible for the restoration of TPM to proper flexibility. Structural alterations in actin-TnT-TPM protein interactions could play a vital role, however, the precise mechanism whereby decreased TPM phosphorylation rescues the HCM phenotype remains to be elucidated.
\nDCM, a disease often associated with heart failure, is characterized by depressed systolic function, cardiomegaly, and ventricular dilation. As mentioned previously, DCM is caused by a variety of conditions, including idiopathic, viral and cardiotoxins. Mutations in genes associated with DCM include sarcomeric proteins, the cytoskeleton, and the sarcolemma. Sarcomeric protein genes that harbor DCM mutations include α- and β-myosin heavy chain, myosin binding protein C, actin, TPM, troponin T, I, and C, desmin, vinculin, and muscle LIM protein (Table 1).
\nTPM mutations known to cause DCM are located throughout the TPM1 gene, from the 5′ to 3′ end of the associated transcript (Table 2). Some of the corresponding amino acid changes are positioned in the inner regions of the TPM coiled-coil dimer where electrostatic charge interactions between specific amino acids may alter the TPM dimerization and/or binding to actin [31]. These non-conserved amino acid substitutions are thought to disrupt force transmission through the sarcomere leading to DCM.
\nTo investigate the structural and physiological consequences of known DCM mutations in TPM with cardiac morphology and performance, we generated the first mouse model of a sarcomeric thin filament protein that leads to DCM (TPMαGlu54Lys) [32]. As with the transgenic HCM mice that were generated, the increase in transgenic TPM protein expression led to a reciprocal decrease in endogenous wildtype TPMα levels, with the total myofilament TPM levels remaining unchanged. Also, since there is 100% amino acid identity between human and mouse TPM, the Glu54Lys DCM mutation is the same manifest in human DCM patients. Histological and morphological analyses of these transgenic mice revealed development of DCM with progression to heart failure, and death often ensuing by 6 months (Figure 2) [32]. Echocardiographic analyses confirmed the dilated phenotype of the heart with significant decreases in left ventricular fractional shortening. There was also impaired systolic and diastolic function, coupled with a decreased Ca2+ sensitivity and tension generation in cardiac myofilaments. Results indicate the Glu54Lys mutation decreases TPM flexibility, which may influence actin binding and myofilament Ca2+ sensitivity. In summary, the pathological and physiological phenotypes exhibited by these mice are consistent with those seen in human DCM and heart failure patients.
\nHistopathology of DCM TPMα54 hearts. Masson trichrome staining of whole-heart longitudinal sections (i and ii) and cross sections (iii and iv) from 5-month-old NTG and moderate-copy TPMα54 mice; longitudinal sections (v and vi) and cross sections (vii and viii) from 1-month-old NTG and high-copy TPMα54 mice. Note the severe dilation of right and left ventricles in both the moderate and high-copy mice. Images in i through viii are all enlarged at the same magnification.
Phosphorylation of cardiac sarcomeric and non-sarcomeric proteins play a major role in the regulation of the physiological performance of the heart. Phosphorylation of the thin filament proteins, such as troponin T and I, dramatically affect myofilament Ca2+ sensitivity, along with systolic and diastolic function. Less is known about the physiological effect of TPM phosphorylation on cardiac performance. To address this issue, we generated transgenic mice having a phosphorylation mimetic substitution in the phosphorylation site of TPM (Ser283Asp) [33]. Previous work in our laboratory demonstrated that ablating the ability of TPM phosphorylation in transgenic mice (TPMαS283A) leads to a compensated physiological hypertrophy [28]. Our results show that high expression of the TPM Ser283Asp transgene leads to an increased heart:body weight ratio, coupled with a severe dilated cardiomyopathic phenotype resulting in death within 1 month of birth [33]. Moderate TPM Ser283Asp expression mice causes a mild myocyte hypertrophy and fibrosis, without affecting lifespan; physiological analysis revealed diastolic dysfunction, without changes in systolic performance. Surprisingly, there were no alterations in Ca2+ sensitivity of the myofibers, cooperativity, or calcium-ATPase activity in the myofibers. This work revealed for the first time that constitutive phosphorylation of TPM could result in a DCM phenotype with its severity dependent upon the extent of the posttranslational phosphorylation modification.
\nStudies demonstrate that during embryonic and fetal cardiogenesis, the murine heart expresses both TPMα and TPMβ isoforms, with the TPMα isoform being predominant in the adult heart [34, 35, 36]. During developmental, the ratio of TPMα:TPMβ changes from 5:1 to 60:1 in the embryonic to adult transition in the murine heart [36]. To address whether the TPMβ isoform could substitute for the TPMα protein, we generated transgenic mice that overexpressed TPMβ in the heart. Results show that with 60% TPMβ expression, there were no morphological changes in the heart [37]. However, there were physiological differences; although there were no systolic alterations, diastole was impaired in both the time and rate of relaxation, coupled with an increase in myofilament Ca2+ sensitivity. Additional studies demonstrated that when the TPMβ transgene was expressed at high levels (80% TPMβ, 20% TPMα) in the heart, the mice developed a severe DCM phenotype and die with 14 days postpartum [38]. In these high expression TPMβ hearts, there is significant chamber dilation, thrombus formation in the atria and ventricles, and diastolic dysfunction.
\nAn extension of the research on the high expression TPMβ mice entailed treatment with cyclosporin and FK506, inhibitors of calcineurin. Calcineurin is a calcium-regulated phosphatase, which can initiate cardiac hypertrophy in hearts of transgenic mice that overexpress calcineurin [39]. Results show that treatment with cyclosporin or FK506 in various mouse models of cardiac hypertrophy, including the high expression TPMβ DCM mice, led to phenotypically rescued hearts [40]. This work suggests that in certain cases, inhibitors of calcineurin may play a potential therapeutic role in the treatment of heart disease.
\nThere are 4 distinct tropomyosin genes, each one subject to alternative splicing which generates multiple isoforms of TPM. Our investigation into striated muscle TPM isoform content in the adult human heart found there is 92% TPMα1, 4% TPMβ, and 4% TPMα1κ [41]. TPMα1k is a unique human cardiac-specific TPM isoform which is normally not expressed in rodents [41, 42]. Additional studies show the associated protein is expressed and incorporated into organized myofibrils and that its level is increased in human dilated cardiomyopathy and heart failure patients [41]. To investigate the role of TPMα1κ in sarcomeric function, we generated transgenic mice overexpressing this cardiac-specific isoform. Incorporation of increased levels of TPMα1κ protein in myofilaments leads to DCM, coupled with systolic and diastolic dysfunction and decreased myofilament Ca2+ sensitivity [41, 43]. Additional biophysical studies demonstrate less structural stability and weaker actin-binding affinity of TPMα1κ protein compared with TPMα1. This functional analysis of TPMα1κ provides a possible mechanism for the consequences of the TPM isoform switch observed in DCM and heart failure patients.
\nCalcium plays a pivotal role in the regulation of muscle contraction and relaxation. As seen in the TPM mouse models, the HCM and DCM phenotypes all exhibit abnormalities in myofilament Ca2+ sensitivity and Ca2+ handling. As mentioned, when mice harboring a phospholamban (PLN) knockout are crossed with the HCM TPMα180 mice, the pathological phenotype is rescued from their offspring [25]. To extend our work, studies were conducted to more fully examine the role of calcium and calcium-handling proteins in the development of HCM. To test whether improvements in the hypertrophic phenotype can be achieved through increased Serca2 expression, the HCM TPMα180 mice were treated with exogenous Serca2a, the protein involved in sequestering calcium from the cytoplasmic space into the sarcoplasmic reticulum [44, 45]. We implemented a gene transfer approach using an adenoviral vector to express Serca2a in HCM TPMα180 hearts. Results showed that injection of a single dose improved heart morphology and cardiac function. As the mice aged, there was a significant decrease in heart:body weight ratio, and a decrease in fibrosis when compared with controls. Additional work demonstrated that parvalbumin, a calcium buffer, may also play a role in ameliorating HCM; when parvalbumin transgenic mice were crossed with HCM TPM1α180 mice, there was improvement in cardiovascular performance [45, 46].
\nWith improvements in cardiac morphology and performance in the HCM and DCM mouse models that occur with modification in calcium handling proteins, therapeutic gene therapy trials in patients utilizing Serca2a expression as a potential treatment for cardiac disease were initiated. Using adeno-associated viruses to drive extended Serca2a expression, Phase 2 studies were conducted in patients with advanced heart failure [47, 48]. Results show there was a striking reduction in cardiovascular events that persisted through the 36 months of follow-up compared to patients who received the placebo. Additional work in this area is in progress.
\nRecently, investigators have examined the potential of the C-terminal end peptide of troponin I as a novel reagent to selectively facilitate cardiac muscle relaxation [49]. This is a highly conserved protein fragment across numerous vertebrate species. Protein binding studies found that this terminal fragment retains its binding affinity for TPM similar to intact cardiac troponin I. Addition of this fragment to skinned cardiac muscle preparations reduces myofibril Ca2+ sensitivity without decreasing maximum force production. Using this short peptide, studies were initiated to address whether it would be of therapeutic value in the treatment of HCM [50]. Recent work demonstrates that myofilament Ca2+ sensitivity isolated from TPMα180 hearts exhibit a more normalized decrease in myofilament Ca2+ sensitivity when treated with the C-terminal troponin I fragment. This demonstrates the C-terminal peptide of troponin I as a potential therapeutic reagent for the treatment of diastolic dysfunction in the heart.
\nThere is no scientific doubt that CRISPR-Cas9-base targeting has revolutionized how research is being conducted. With the ability to modify genomes, there is the potential to conduct precise gene-editing in animal models along with correcting human disease mutations. With respect to cardiomyopathic diseases, Ma et al. corrected a human heterozygous germline HCM mutation in the myosin binding protein C gene using the CRISPR-Cas9 system [51]. This targeting strategy was employed on preimplantation human embryos; following targeting, the embryos were genetically analyzed for correctly targeted nucleotide changes and then allowed to develop to the 8-cell stage. Results show that over 50% of blastomeres were correctly targeted, but used the wildtype allele as the correcting genetic template. This work demonstrates that CRISPR has the potential for usage as a corrective therapeutic system of heritable mutations; however, additional research needs to be conducted and ethical considerations need to be addressed.
\nMany lessons have been learned about HCM and DCM in the examination and usage of TPM and associated mouse model systems [52, 53]. Multiple mutations within the TPM1α gene lead to HCM and DCM. Surprisingly, for both disease conditions, the mutations are scattered throughout the gene, and are not confined to one or two specific regions or domains. The severity of the disease phenotype appears dependent upon the specific mutation, modifying genes, and environmental factors. The genetic animal models of HCM and DCM TPM mutations accurately reflect the disease process with respect to structural and functional abnormalities as they occur in humans. For HCM, the thickening of the left ventricular wall and interventricular septum with significant fibrosis is pronounced in these animal models. For DCM, the thinning of the ventricular walls and dilation of the ventricular cavities reflect the pathological features observed in patients. For both HCM and DCM, the functional abnormalities in systole and diastole are similar to those experienced by patients. More importantly, these basic research studies have been translated into potential therapeutic modalities, especially for investigations into the usage and modification of calcium-handling proteins as treatments of cardiovascular disease. Expansion of potential treatments utilizing phosphatases and kinases, along with sarcomeric protein peptides, may also prove beneficial for the treatment of specific cardiovascular conditions. An area of future expansion will be to focus on the identification and modification of protein expression for genes which are signaling agents for the development of cardiac HCM, DCM, and heart failure.
\nThe author would like to gratefully recognize and thank the numerous members of the laboratory for their invaluable contributions to this research. The author also thanks the funding of this research provided by the NIH NHLBI, American Heart Association, and the University of Cincinnati College of Medicine.
\nThere are no conflicts of interest to report.
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