Simulation parameters.
\r\n\t
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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:"56541",title:"Routing Protocols for Wireless Sensor Networks (WSNs)",doi:"10.5772/intechopen.70208",slug:"routing-protocols-for-wireless-sensor-networks-wsns-",body:'\nThe routing protocol is a process to select suitable path for the data to travel from source to destination. The process encounters several difficulties while selecting the route, which depends upon, type of network, channel characteristics and the performance metrics.
\nThe data sensed by the sensor nodes in a wireless sensor network (WSN) is typically forwarded to the base station that connects the sensor network with the other networks (may be internet) where the data is collected, analyzed and some action is taken accordingly.
\nIn very small sensor networks where the base station and motes (sensor nodes) so close that they can communicate directly with each other than this is single-hop communication but in most WSN application the coverage area is so large that requires thousands of nodes to be placed and this scenario requires multi-hop communication because most of the sensor nodes are so far from the sink node (gateway) so that they cannot communicate directly with the base station. The single-hop communication is also called direct communication and multi-hop communication is called indirect communication.
\nIn multi-hop communication the sensor nodes not only produce and deliver their material but also serve as a path for other sensor nodes towards the base station. The process of finding suitable path from source node to destination node is called routing and this is the primary responsibility of the network layer.
\nThe design task of routing protocols for WSN is quite challenging because of multiple characteristics, which differentiate them, from wireless infrastructure-less networks. Several types of routing challenges involved in wireless sensor networks. Some of important challenges are mentioned below:
It is almost difficult to allocate a universal identifiers scheme for a big quantity of sensor nodes. So, wireless sensor motes are not proficient of using classical IP-based protocols.
The flow of detected data is compulsory from a number of sources to a specific base station. But this is not occurred in typical communication networks.
The created data traffic has significant redundancy in most of cases. Because many sensing nodes can generate same data while sensing. So, it is essential to exploit such redundancy by the routing protocols and utilize the available bandwidth and energy as efficiently as possible.
Moreover wireless motes are firmly restricted in relations of transmission energy, bandwidth, capacity and storage and on-board energy. Due to such dissimilarities, a number of new routing protocols have been projected in order to cope up with these routing challenges in wireless sensor networks.
There are some major design challenges in wireless sensor networks due to lack of resources such as energy, bandwidth and storage of processing. While designing new routing protocols, the following essentials should be fulfilled by a network engineer.
\nWireless sensor networks are mostly battery powered. Energy shortage is a major issue in these sensor networks especially in aggressive environments such as battlefield etc. The performance of sensor nodes is adversely affected when battery is fallen below a pre-defined battery threshold level. Energy presents a main challenge for designers while designing sensor networks. In wireless sensor network, there are millions of motes. Each node in this network has restricted energy resources due to partial amount of power. So, the routing protocol should be energy efficient [1].
\nThe complexity of a routing protocol may affect the performance of the entire wireless network. The reason behind is that we have inadequate hardware competences and we also face extreme energy limitations in wireless sensor networks.
\nAs sensors are becoming cheaper day by day, hundreds or even thousands of sensors can be installed in wireless sensor network easily. So, the routing protocol must support scalability of network. If further nodes are to be added in the network any time then routing protocol should not interrupt this.
\nSome applications require instant reaction or response without any substantial delay such as temperature sensor or alarm monitoring etc. So, the routing protocol should offer minimum delay. The time needed to transmit the sensed data is required to be as little as possible in above cited WSN applications.
\nWireless sensor networks are deployed in very crucial and loss environments frequently. Occasionally, a sensor node might be expire or leaving the wireless sensor network. Thus, the routing protocol should be capable to accept all sorts of environments including severe and loss environments. The functionality of the routing protocol should be fine also [2].
\nThere are four modes of data transmission depending on the applications in wireless sensor networks namely as query driven, event driven and continuous type and hybrid type. A node begins to transmit the data only when sink creates the query or an event occurs in query driven model and event driven model. The data is sent out periodically in continuous transmission mode. The performance of the routing protocol is a function of network size and transmission media. So, transmission media of good quality enhances the network performance directly [3].
\nAnother major challenge that is faced by wireless sensor network designers is to correctly locate of the sensor nodes. Most routing protocols use some localization technique to obtain knowledge concerning their locations. Global positioning system (GPS) receivers are used in some scenario.
\nThe routing protocols define how nodes will communicate with each other and how the information will be disseminated through the network. There are many ways to classify the routing protocols of WSN. The basic classification of routing protocols is illustrated in Figure 1.
\nBasic classification of routing protocols.
In node centric protocols the destination node is specified with some numeric identifiers and this is not expected type of communication in Wireless sensor networks. E.g. Low energy adaptive clustering hierarchy (LEACH).
\nLEACH is a routing protocol that organizes the cluster such that the energy is equally divided in all the sensor nodes in the network. In LEACH protocol several clusters are produced of sensor nodes and one node defined as cluster head and act as routing node for all the other nodes in the cluster.
\nAs in routing protocols the cluster head is selected before the whole communication starts and the communication fails if there is any problem occurs in the cluster head and there is much chances that the battery dies earlier as compare to the other nodes in cluster as the fix cluster head is working his duties of routing for the whole cluster.
\nLEACH protocol apply randomization and cluster head is selected from the group of nodes so this selection of cluster head from several nodes on temporary basis make this protocol more long lasting as battery of a single node is not burdened for long.
\nSensor nodes elect themselves as cluster head with some probability criteria defined by the protocol and announce this to other nodes
\nIn most of the wireless sensor networks, the sensed data or information is far more valuable than the actual node itself. Therefore data centric routing techniques the prime focus is on the transmission of information specified by certain attributes rather than collecting data from certain nodes.
\nIn data centric routing the sink node queries to specific regions to collect data of some specific characteristics so naming scheme based on attributes is necessary to describe the characteristics of data. Examples are as follows:
\nSPIN is abbreviation of sensor protocol for information via negotiation. This protocol is defined to use to remove the deficiency like flooding and gossiping that occurs in other protocols. The main idea is that the sharing of data, which is sensed by the node, might take more resources as compare to the meta-data, which is just a descriptor about the data sensed, by the node. The resource manager in each node monitors its resources and adapts their functionality accordingly.
\nThree messages namely ADV, REQ and DATA are used in SPIN. The node broadcast an ADV packet to all the other nodes that it has some data. This advertising node ADV message includes attributes of the data it has. The nodes having interests in data, which the advertising node has requested by sending REQ message, to the advertising node. On receiving the REQ message the advertising node send data to that node. This process continues when the node on reception of data generate an ADV message and send it. The whole model SPIN is shown in (Figure 2).
\nSPIN routing protocol.
Protocols are called destination initiated protocols when the path setup generation originates from the destination node. Examples are directed diffusion (DD) & LEACH.
\nDirected diffusion is a data centric routing technique. It uses this data centric technique for information gathering and circulating. This routing protocol is also energy efficient and energy saving protocol so that’s why life time of the network is increased. All the communication in directed diffusion routing protocol is node to node so there is no need of addressing in this protocol.
\nIn these types of protocols the source node advertises when it has data to share and then the route is generated from the source side to the destination. Examples is SPIN.
\nIn order to transmit data in sensor networks, there are two techniques being used. The one is referred to as Flooding and the other one is gossiping protocol. There is no need to use any routing algorithm and maintenance of topology. In the flooding protocol, upon reception of a data packet by sensor nodes, this data packet is broadcast to all other neighbors. The process of broadcasting is continued till any one of two following conditions is satisfied; the packet has reached successfully to its destination. And second condition is; maximum number of hops of a packet has reached [4].
\nThe main advantages of flooding are ease of implementation and simplicity. The drawbacks are blindness of resources and overlapping and implosion. The gossiping protocol is somewhat advanced version of flooding protocol. In gossiping protocol, the sensor node, which is getting a data packet, transmits it to the arbitrarily selected neighbor. At the next turn, the sensing nodes again randomly pick another nodes and sends data to it. This process is continued again and again. The broadcasting is not used in gossiping protocol as it was used in flooding. In this way, implosion issue can be avoided easily. But delay is enhanced in this way. The main categories of the routing protocols are depicted in Figure 3.
\nCategories of routing protocols.
Routing protocols are classified on the basis of process they used to discover the routes.
\nReactive routing protocols do not maintain the whole network topology they are activated just on demand when any node wants to send data to any other node. So the routes are created on demand when queries are initiated. The most commonly used reactive routing protocols are as follows:
\nAd-hoc on-demand distance vector (AODV) is reactive on request protocol. AODV is engineered for Mobile infrastructure-less networks. It employs the on-demand routing methodology for formations of route among network nodes. Path is established solitary when source node want to direct packs of data and pre-set route is maintained as long as the source node needs. That’s why we call it as On-Demand. AODV satisfies unicast, multicast and broadcast routing. AODV routing protocol directs packets among mobile nodes of wireless ad-hoc network. AODV permits mobile nodes to pass data packets to necessary destination node via nodes of neighbor that are unable to connect link openly. The material of routing tables is switched intermittently among neighbor nodes and prepared for sudden updates [3].
\nAODV chooses shortest but round free path from routing table to transmit packets. Suppose if errors or variations come in nominated path, then AODV is intelligent enough to make a fresh new route for rest of communication.
\nDynamic source routing (DSR) is a routing protocol used in wireless sensor networks developed at CMU in 1996. Dynamic source routing can be reactive or on demand. As its name shows that it uses source routing instead of routing tables. Routing in DSR is divided into two parts, route discovery and route maintenance.
\nSource node will initiate a route discovery phase and this phase consist of route request and route reply (RREP) messages. In DSR only destination node will reply with route reply RREP message to the source node unlike in AODV where every intermediate node would reply with route reply message RREP. And the purpose of next phase route maintenance is to avoid flooding of RREP messages and used for shortening of nodes between source and destination [6, 8].
\nThey are also known as table driven routing protocols, because they maintains the routing tables for the complete network by passing the network information from node to node and the routes are pre-defined prior to their use and even when there is no traffic flow. The most commonly used algorithm is as follows:
\nOptimized link state routing (OLSR) belongs to the category of proactive routing protocols and it uses table focused practice. The main drawback of OLSR is that it has a massive overhead. To compensate this delay, multipoint relays (MPRs) are used to overcome the large overhead. For data transmission, three adjutant nodes are used as MPRs by every node. No consistent control information is required as each node sends it alternatingly [6, 8].
\nHybrid Routing Protocols have the merits of proactive and reactive routing protocols by neglecting their demerits.
\nFollowing protocols are based on the network organization of wireless sensor network.
\nFlat topology treats all nodes equally. Flat topology is mainly for homogeneous networks where all nodes are of same characteristics and have same functionality. Examples are:
Gradient based routing (GBR)
Cougar
Constrained anisotropic diffusion routing (CADR)
Rumor routing (RR)
Mostly heterogeneous networks apply hierarchical routing protocols where some nodes are more advance and powerful than the other nodes, but not always this is the case, sometimes in hierarchical (clustering) protocols sometimes the nodes are grouped together to form a cluster and the cluster head is assigned to every cluster, which after data aggregation from all the nodes, communicates with the base node .The clustering scheme is more energy efficient and more easily manageable. Examples are:
Threshold sensitive energy efficient sensor network (TEEN)
Adaptive threshold sensitive energy efficient sensor network (APTEEN)
Low energy adaptive clustering hierarchy (LEACH)
The power-efficient gathering in sensor information systems (PEGASIS)
Virtual grid architecture routing (VGA)
Self-organizing protocol (SOP)
Geographic adaptive fidelity (GAF)
In location based routing the nodes have capability to locate their present location using various localization protocols. Location information helps in improving the routing procedure and also enables sensor networks to provide some extra services. Examples are:
SPEED
Geographical and energy aware routing (GEAR)
SPAN
According to the operational basis the routing protocols are classified as:
Multipath routing protocols
Query based routing
Negotiation based routing
QoS-based routing
Coherent routing
Multi-path routing protocols provide multiple paths for data to reach the destination providing load balancing, low delay and improved network performance as a result. The multiple routing protocol also provide alternate path in case of failure of any path. Dense networks more interested in multiple path networks. To keep the paths alive some sort of periodic messages have to a send after some specific intervals hence multiple path routing is not more energy efficient. Multipath routing protocols are: [6]
Multi path and Multi SPEED (MMSPEED)
Sensor protocols for information via negotiation (SPIN)
These type of routing protocols are mostly receiver-initiated. The sensor nodes will only send data in response to queries generated by the destination node. The destination node sends query of interest for receiving some information through the network and the target node sense the information and send back to the node that has initiated the request. The examples are [6]:
Sensor protocols for information via negotiation (SPIN)
Directed diffusion (DD)
COUGAR
In these types of protocols to keep the redundant data transmission level at minimum, the sensor nodes negotiate with the other nodes a and share their information with the neighboring nodes about the resources available and data transmission decisions are made after the negotiation process. Examples are [6]:
Sensor protocols for information via negotiation (SPAN)
Sequential assignment routing (SAR)
Directed diffusion (DD)
To get good Quality of Service these protocols are used. QoS aware protocols try to discover path from source to sink that satisfies the level of metrics related to good QoS like throughput, data delivery, energy and delay, but also making the optimum use of the network resources.
\nSequential assignment routing (SAR)
SPEED
Multi path and Multi SPEED (MMSPEED)
In coherent data processing routing protocol the nodes perform minimum processing (time stamping, data compression etc.) on the data before transmitting it towards the other sensor nodes or aggregators. Aggregator performs aggregation of data from different nodes and then passes to the sink node.
\nA detailed comparison of WSN routing protocols is given below in tabular form is shown in Figure 4 [5].
\nSimulation parameters | \nValues | \n
---|---|
No. of nodes | \n20, 40, 80 | \n
Simulation time | \n120 s | \n
Simulation area | \n1000 m2 | \n
Data rate of nodes | \n11 Mbps | \n
Traffic | \nFTP (high load) | \n
Routing protocols | \nAODV, DSR and OLSR | \n
Simulation parameters.
Comparison of routing protocols.
OPNET Modeler 14.5 network simulator is used to analyze AODV, DSR and OLSR routing protocols in WLAN based WSNs. These protocols are compatible in WLAN based WSNs and previous reseraches indicated that they have better performnace.Here, the perforrmance of these protocols will be evaluated in small, medium and large scale network against delay, throughput and network load. Small scale network contains 20 nodes, medium scale with 40 nodes and large scale network takes 80 nodes. The simulation model is represented in Figure 5. The general parameters for simulation scenarios are given in Table 1.
\nSimulation model.
Now three network metrics are defined; End-to-End delay, throughput and network load. ETE delay is described by way of time engaged by an envelope to be communicated through a network from source to destination. It comprises retransmission delays on media access layer (MAC), packet transfer time and broadcast delay plus other delays at route discovery and conservation. The quantity of data transmission from source to destination network node in a given specified amount of time. It is dignified in byte per second. Network load (NL) shows net load, which indicates, in bits per second. Work load is sometimes also called as Network Congestion. When traffic load exceeds than link capacity then it is almost impossible for network to handle the traffic thus creating congestion in the network.
\nIn simulations, there sensor networks are considered, firstly in a small scale network, 20 nodes are selected with one stationary WLAN server. These nodes are interconnected in star topology. Area of the network is 1000 × 1000 m. IPv4 scheme is applied to entirely nodes and File Transfer Protocol is used as great traffic load. Each WLAN node has data rate of 11 Mbps. Similarly, a medium scale network is with 40 nodes and large scale network is consisted of 80 nodes.
\nAfter running simulations, the following results are obtained. Figures 6–8 depicts simulation results of delay, network load and throughput for AODV in small, medium and large scale networks, respectively. Delay is represented in seconds while throughtput and network load in bits per seconds.
\nSimulation Results for AODV.
Simulation Results for DSR.
Simulation Results for a OLSR.
The entire results of small, medium and large scale networks are mentioned below in Table 2. It is cocluded from the table that in terms of delay, the efficiency of OLSR is more than 100% in small and medium scale network as compared to the other two protocols while AODV is significantly (>50%) better in large networks. In case of network load, OLSR gives minimum load in all three scenarios. However, AODV gives best throughput in small scale network which is 40% more than DSR and 86% higher than OLSR. DSR is better than AODV and OLSR by a factor of 13 and 40% respectively, in medium scale network. Similarly, in large scale network it is better by a margin of 47 and 18%.
\nNodes | \nParameters | \nAODV | \nDSR | \nOLSR | \n
---|---|---|---|---|
20 | \nDelay (s) Network load (Kbps) Throughput (Kbps) | \n0.020 2500 2800 | \n0.024 1700 2000 | \n0.011 1300 1500 | \n
40 | \nDelay (s) Network load (Kbps) Throughput (Kbps) | \n0.033 3000 3700 | \n0.060 3000 4200 | \n0.013 2000 3000 | \n
80 | \nDelay (s) Network load (Kbps) Throughput (Kbps) | \n0.10 3100 6200 | \n0.17 2900 13,000 | \n0.015 2800 11,000 | \n
Simulation results.
The same comparison can be made for a ZigBee based Wireless Sensor Network using AODV. ZIGBEE nodes use in lower data rates applications where we need a longer battery life. Through wireless sensor nodes provides higher data rates but their disadvantage is that they require higher power. So in those applications where we don’t need higher data rates we use ZIGBEE because they increase the life of the network [7].
\nFigure 9 depicts that the end-to-end delay is higher in a network where we use ZIGBEE nodes. End-to-end delay starts from 0.060 s and then step up in the starting and then gets saturated at approximately 0.070 s. While in WSN nodes, End-to-end delay hardly increase from 0.010 s and throughput is lower in a ZIGBEE network as we can see in the Figure 10. From Figure 10, throughput increases linearly in the start and then gets stable at 6300 bits/s. So ZIGBEE nodes are used when there are concerns with the life span of network and economic issues because ZIGBEE is a low power, low cost devices.
\nEnd-to-end delay in ZigBee.
Throughput in ZigBee.
Routing protocols plays a very significant part to produce interruption less and efficient communication between source and destination nodes. The performance, service and reliability of a network mostly depend on the selection of good routing protocol. Protocols being used in Wireless sensor networks and ad hoc networks must be round-free. The routing protocols in WSN are classified in many different ways.
\nThe categories of routing protocols are network based organization, operation and route discovery. Most of the applications of WSN uses route discovery base routing protocols e.g. AODV, DSR & OLSR. The performance of these protocols is compared in different scenarios on the basis of throughput, delay and congestion.
\nIn small scale network with 20 nodes, OLSR gives less jitter & less congestion/load as matched with AODV and DSR. AODV & DSR give high throughput than OLSR. In medium scale network with 40 nodes, OLSR again give less delay and less network load when compared with AODV and DSR. On the other hand, DSR provides high throughput as compared to AODV and OLSR. In large scale network with 80 nodes, OLSR shows same behavior as in small and medium scale networks. In large scale network, OLSR has less delay and network load than DSR and AODV. Interestingly, DSR give highest value for throughput. AODV has least value of throughput in large scale network.
\nDistinct diseases have different etiology pattern and this chapter covers the chromosomal diseases, cancer, neurodegenerative diseases, pulmonary diseases, obesity-induced insulin resistance, lymphoblastic leukemia, viral immunology and infectious diseases. These communicable and non-communicable diseases negatively affect structure-function of the organism and specific symptoms are associated with these conditions. Pathogens or internal dysfunctions may lead these diseases. The chapter provides pathology of selected diseases from each class along with the molecular mechanisms.
\nDown syndrome (DS) is the most common chromosomal genetic disorder. The disease is caused by the trisomy of human chromosome 21 (HSA21) and is also the most genetic mental disability [1]. The HSA21 mosaic can also lead to DS. Maternity age is an important aspect in the formation of an individual with DS [2]. The main cause of this disease is the absence of normal chromosome separation during meiosis and the production of gametes with two copies of chromosome copies instead of a single copy. As a result, DS individuals have trisomy 21 in some body cells, and a normal number of chromosomes in others. This is called mosaicism and is seen in approximately 4% of DS individuals. The term mosaicism was first reported in 1961 [3] and can occur in two ways: either a normal zygote is exposed to an early mitotic error following fertilization, which results in trisomy 21 in some cells, or an early mitotic error in some cells allows it to return to normal karyotype [4].
\nHSA21 is the most studied human chromosome, and since the long arm of chromosome 21 has been fully sequenced, a significant progress has been made in understanding its functional genomic units. HSA21 is the smallest chromosome and the overall gene density per megabase is about 15 genes per Mb (for the human genome) [5]. HSA21 is also very rich in long encoding RNA (lncRNA) genes, and, one of the poorest for genes encoding microRNA (miRNA). Also, the gene density is average for pseudogenes encoding the protein per Mb [6]. HSA21 is a weak chromosome in non-encoding RNAs (ncRNAs) and long nuclear elements (LINE). Interestingly, HSA21 shows significant enrichment for proteins found in cytoskeleton structures. These cytoskeletal proteins are known to play a role in neurological disorders, especially Alzheimer’s neuropathology [7].
\nIndividuals with DS occasionally develop the myeloproliferative disorder (TMD), a disease that is mostly unique to DS. Almost all TMD cases were found to contain somatic mutations on the X chromosome, in the GATA1 transcription factor [8]. Certain features of DS contain genes on other chromosomes causing gene and trisomy mutations and working together to reveal the disorder in HSA21. Studies have shown that the formation of Trisomy 21 precedes the formation of GATA1 mutations [1]. This may indicate that Trisomy 21 either increases genomic discrepancy leading to GATA1 mutations, or it supplies a selected medium for hematopoietic cells containing GATA1 mutations.
\nMany hypotheses have been proposed to explain the genotype–phenotype relationship in DS. One of these is the ‘gene dosage effect’ hypothesis putting forward that the phenotypes arise directly from the dosage imbalance of the genes. Overlapping this hypothesis, the ‘DS Critical Region’ (DSCR) was announced in the 1990s. [9, 10]. Many of the DS features can be called into a subset of the critical genes in the DSCR region, suggesting that DS phenotypes are mainly caused by the dosage imbalance of only a few genes on HSA21. Genomic regions affecting the presence of certain DS phenotypes have been identified and high-resolution genetic maps of DS features have been created [11]. Olson et al. studied the DSCR regions in mice to test its hypothesis. They concluded that dosage imbalance of some individual genes on HSA21 directly affects certain phenotypes, but they stated that more studies are needed [12].
\nThe “amplified developmental instability” hypothesis suggests that dosage imbalance of the HSA21 gene leads to a non-specific impairment of cellular homeostasis [10]. Extra chromosome materials may also contribute to phenotypes by disrupting chromosomal regions. Some data on monozygotic twins for TS21 suggest that differential expression between normal and trisomic twins can be regulated across chromosome domains. This study shows that some DS phenotypes can be enlightened by the modification of the chromatin structure in the nucleus [13]. Monozygotic twins affected by DS but showing incompatible phenotypes have been reported in some cases, suggesting the role of epigenetics in the phenotypic variability of DS. For example, DNA methylation (controlling gen expression) has been shown to change in Trizomy of chromosome 21 (TS21) samples [14].
\nTurner syndrome (TS) is a disorder in mosaic karyotypes associated with complete or partial loss of the X chromosome. Seen especially in women, TS is associated with short stature, delayed puberty, ovarian dysgenesis, infertility, congenital malformations of the heart, type 1 and type 2 diabetes mellitus, osteoporosis, and autoimmune disorders. It occurs in almost every 2500 live female births. Fetuses affected by TS are 99% estimated to result in fetal death. Approximately half have monosomy X (45, X) and 10% have a repeat (isochromosome) of the long arm of the X chromosome. Most of the rest has a mosaic in more cell lines for 45X. TS, which is associated with a missing X chromosome, was first identified about 100 years ago [15].
\nRelated genes: Shox gene (short length homeobox protein-coding) located on X and Y chromosomes, it is a gene responsible for TS phenotype. This gene does not undergo X inactivation, and a decrease in the expression of SHOX explains some of the TS-related growth deficits. The gene product controls the expression of natriuretic peptide B (NPBB) and FGFR3 (fibroblast growth factor receptor 3) and regulates the proliferation and of chondrocytes, and also cooperates with SOX5, SOX6 and SOX9 and some other genes [16].
\nThe TS genome is hypo-methylated with less hypermethylation sites and there are RNA expression changes that affect the X chromosome genes and autosomal genes compared to women who are 46 XX. Known escape genes are expressed differently in individuals with TS and other X chromosome genes such as RPS4X and JPX (CD40LG and KDM5C) in particularly, KDM5C (encoding lysine-specific demethylase 5C) can participate in the transcriptional profile of neuronal genes and play role in different neurocognitive profiles [17]. 40S ribosomal protein S4 (RPS4X) also plays an important role in TS, bringing together multiple protein complexes. In addition, the Y paralog of RPS4X (RPS4Y) may also have a role since it is normally expressed as duplicates [18].
\nMany different studies show that women with TS have increased mortality compared to the pool of a wide variety of related diseases [19]. The most obvious increase in morbidity is caused by autoimmunities like diabetes mellitus or thyroiditis, osteoporosis, cardiovascular diseases, hypertension, congenital malformations, especially endocrine diseases including heart diseases, digestive system and anemia [20].
\nIt is still unclear which chromosomal regions or genes make up the phenotypical properties of TS. The physical symptoms of TS were thought to be due to the absence of normal sex chromosomes before inactivation of the X chromosome, or the haplo-insensitivity of the genes in the pseudo-autosomal regions of the aneuploidy [21]. It is thought that a complete phenotype results in the loss of short arm (Xp) in the X chromosome. Aneuploidy itself can cause growth failure. Loss of a region in Xp22.3 was found to be related to neurocognitive problems in TS [22]. Loss of the SRY gene locus in the short arm of the Y chromosome leads to the phenotype of TS, even if it does not cause a population of 45 X cells. It has also been suggested that an area in Xp11.4 is important for the development of lymphedema [23].
\nCancer can be defined as the uncontrolled cell growth with the most basic explanation. Cell stacks that grow uncontrollably are called tumors. Benign tumors grow much slower and usually do not metastasize, while malignant tumors can spread to other organs through metastasis, and lead to multiple organ damage and eventually death. Tumor cells acquire characteristic features such as sustaining growth signals in the process of cancer, avoiding growth suppressors, resisting cell death, ensuring replicative immortality, initiating angiogenesis, and activating invasion and metastasis [24].
\nCancer cells acquire these abilities in the process due to genetic instability and inflammation caused by environmental and hereditary effects. Many studies show that viruses, in addition to many environmental factors such as radiation and chemicals, induce cancer. Chronic inflammation has been shown to trigger oncogenic mutations, genetic instability, tumor growth, and angiogenesis through angiogenesis and cause local immunosuppression [25].
\nTwo types of gene groups involved in cancer are oncogenes, which trigger cellular growth and uncontrolled proliferation, causing increased genetic instability with increased expression and tumor suppressor genes that cause cancer as a result of decreased control of their expression, cell division, and growth. Proto-oncogenes include RAS, WNT, MYC, ERK, and TRK genes. A mutation that may occur on a proto-oncogene or a regulatory region of the gene (e.g., promoter region) can cause an increase in the amount of protein with the change in protein structure [26]. Expressions of oncogenes can also be regulated with miRNAs [27]. Mutations occurring in these regulatory miRNAs can cause activation of oncogenes [28]. Cancer cells increase cell growth-division by activation of oncogenes, as well as suppress preventive control mechanisms of tumor suppressor genes that control this process.
\nMutations in tumor suppressor genes cause loss of function. Therefore, they occur in both alleles. To inactivate the gene and its protein, wide-ranging effects, such as deletions, frame-shift mutations, insertions, should be seen rather than point mutations [29]. Tumor suppressor genes include retinoblastoma (RB) [30], TP53, BRCA1, BRCA2, APC, and PTEN. Many side factors such as transcription complexes, changes in cellular metabolism, microenvironment can guide the course of cancer [31].
\nThe development of cancer is a multi-stage process consisting of initiation, promotion, and progression. Cancer-inducing events are usually caused by genetic mutations. Mutant cell proliferates rapidly in the promotion stage and acquires features that allow malignant behavior in the progression stage. Production of telomerase and expression of p53 are examples of malignant behavior [32]. Then, the process proceeds in the form of dysplasia formation, where new blood vessels are formed (angiogenesis) with cellular transformation. Angiogenesis facilitates the intravasation of cancer cells after undergoing an epithelial-mesenchymal transition (EMT) [33]. EMT gives an invasive phenotype to cancer cells and is managed by various transcription factors (such as SNAI, SLUG, ZEB2, ETS1, TWIST) [34]. These transcription factors also regulate each other for the protection of EMT [35].
\nNormal cells only use anaerobic glycolysis when oxygen is absent or limited, while cancer cells can convert glucose to lactate in the presence of oxygen. Otto Warburg discovered that cancer cells exhibit a differentiated metabolism ability [36]. Warburg effect is biochemical properties that help identify cancer cells. On the other side, cancer cells are generally highly glucose-dependent. Glucose intake of cells is enabled by overexpression of different isoforms of membrane glucose transporters in cancer cells [37]. It has been shown that the benefit of the Warburg effect for cancer cells is not just the formation of glycolytic ATP, but also the production of many glycolytic intermediates before anabolic processes such as NADPH and amino acids [38]. Cancer cells are also able to metabolize glutamine to synthesize some amino acids they need, use it as a nitrogen source and for fatty acid synthesis in hypoxic conditions [39]. Therefore, blood glutamine levels increase in some cancer cases [40]. Lactic acid is used to produce citric acid and maintain cancer progression in neighboring cancer cells. This is called the “Reverse Warburg effect” [41].
\nTumor micro-environment, consisting of fibroblasts, adipocytes, endothelial cells, and macrophages, is a good source for tumor growth. Tumors “steal” energy-rich metabolites from their micro-environment [42]. Monocarboxylate carriers (MCTs) are used for L-lactate transfer between cancer cells and their microenvironment [43]. Tumors have heterogeneous structures with hypoxic and aerobic regions. A “metabolic symbiosis” behavior has recently been found between the two regions [44]. Lactate is produced by glycolysis in hypoxic tumor cells. This product is obtained by aerobic cancer cells by MCT1. Aerobic cells convert lactate to pyruvate with lactate dehydrogenase isoform B (LDH-B) enzyme.
\nWhen glucose consumption is not enough to meet the energy need of cancer cells, they begin the fatty acid oxidation (FAO) [45]. For example, prostate cancer, leukemia, and large B-cell lymphoma, increasing palmitate and FAO uptake in cells are among the most commonly used bioenergetic pathways [46, 47, 48]. Normal cells usually receive fatty acids by diet, while tumors show an increase in de novo fatty acid synthesis [45].
\nPyruvate plays a pivotal role in the regulation of metabolic reprogramming, especially in tumors [49]. Pyruvate dehydrogenase (PDH) converts cytosolic pyruvate into mitochondrial acetyl-CoA, which is the first substrate of the Krebs cycle. Pyruvate dehydrogenase kinase (PDK) negatively regulates PDH. This reaction slides glucose from oxidative to glycolytic metabolism [50]. Lactate dehydrogenase (LDH) is the primary metabolic enzyme converting pyruvate into lactate. LDH plays an important role in arranging food interchange between stroma and tumor. Studies have shown that inhibition of LDH is important for treating advanced carcinomas [51]. Mitochondrial hyperpolarization is a mutual property of several tumor cells [52]. Tumor cells, which have more negative mitochondrial structures, are more selective targets in drug therapies [53].
\nBrain tumors are cancer tissues that grow abnormally and prevent the brain or central spinal system from performing its normal functions. Primary brain tumors originating from brain tissue can usually spread only to other parts of the brain, and occasionally to other organs. Tumors that form in another tissue in the body migrate to the brain are called metastatic or secondary brain tumors. These types of tumors occur more frequently than primary brain tumors. They are termed after their tissue of origin [54].
\nThe most prevalent primary tumor types in adults are glioma, astrocytomas, oligodendroglioma, meningioma, schwannoma, pituitary tumors, and central nervous system (CNS) lymphoma.
\nRetinoblastoma mutations are found in almost 75% of brain tumors and are mostly associated with glioblastoma, and Tp53 mutations are found in more than 80% of advanced gliomas [55]. Primary glioblastomas have EGFR tyrosine kinase mutations, tumor suppressor PTEN gene mutations, DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) protein abnormalities [56, 57]. While IDH1 mutations in the control mechanism of the citric acid cycle are seen in advanced glioblastomas, IDH2 mutations are usually shown in oligodendroglioma [58]. Mutations in the BRAF oncogene are common in pilocytic astrocytomas, pleomorphic xanthoastrocytomas, and gangliogliomas [55]. In some glioblastoma tumors, telomere length is maintained by mutations in the TERT promoter and ATRX gene [59].
\nWHO groups glioma patients based on the presence of two genetic changes; first, mutations [60] in the family of genes encoding isocitrate dehydrogenase (IDH), and second, loss of two specific parts of the genome (1p and 19q co-deletion) [61]. The presence or absence of these changes gives a clue about the patient’s prognosis and appropriateness of various kinds of treatments.
\nApproximately 40% of people with astrocytoma, oligodendroglioma, or IDH mutation bear a hereditary variation. This variation is a single nucleotide polymorphism (SNP) in the 8q24 region of the genome [62]. There is another SNP in the 11q23 region, which enhances the risk of IDH-mutant brain cancer. Approximately 5–8% of gliomas are familial, POT1 gene mutations have been found in 6 of 300 families with glioma [63].
\nNon-coding RNAs (ncRNAs) play important roles in regulating tumor malignancy in glioma [64, 65, 66]. According to healthy brain tissue, mir-21 expression increases in glioma and mir-21 acts as an oncogene [67, 68]. It has been reported that mir-124 and mir-137 act as tumor suppressors in glioblastoma multiform cells [69]. Hotair, SOX2ot, CRNDE, Malat1, H19, GAS are lncRNAs that have been recently shown to regulate glioma [70, 71]. Glioma cells also express the circRNAs, for example, circBRAF, bircFBXW7, circSMARCA5. These regulate proliferation, migration, and invasion of glioma cells [72, 73, 74]. The exosomal ncRNAs, mir-21, mir-148a, lncRNA PU03F3, lncRNACCAT2 can be used as circulating biomarkers of glioma patients [75, 76, 77, 78]. circRNAs and the exosomal ncRNAs were also reported as potential biomarkers for the diagnosis and prognosis of glioma patients.
\nAs a characteristic of almost all neurodegenerative diseases, abnormal protein assembly gathers these diseases under the prion concept [79]. Prion protein, known as PrP, was introduced to define protein pathogens and distinguish them from viruses and was identified as a proteinaceous infectious particle known to resist inactivation. Even back at that time, its importance was foreseen in terms of shedding light on the etiologies of chronic degenerative diseases [80]. Self-propagation is an important characteristic of prions that is also observed in abnormal protein assembly in Alzheimer’s Disease (AD) [81, 82]. Aggregation of proteins in neurodegenerative diseases was believed to occur spontaneously in autonomous cells, however, it was later understood that this aggregation begins in a particular region and propagates across other regions developing the disease further. Transmission of these prion proteins across neuronal cells takes place trans-synaptically [82].
\nAs described more than a 100 years ago, abnormal protein assembly forms the basis of neurodegeneration with AD being one of the most common neurodegenerative diseases. The pathology of abnormal protein assembly starts with misfolding of native proteins that gather to form seeds which eventually lead to aggregation and development of protein fibrils. The pathophysiology of AD involves amyloid plaque inclusions of β-amyloid (Aβ) peptides and neurofibrillary lesions of tau protein. Tau inclusions may also be characteristics of other neurodegenerative diseases, which do not necessarily show the same implications. Altering the native forms of this protein may contribute to its pathology and cause damage to its host cell.
\nMost cases of this disease are sporadic, while dominantly inherited mutations are also seen to a lesser extent. Back in the 1990s, missense mutations of APP, encoding amyloid precursor, were shown to cause AD [83, 84, 85, 86, 87]. Mutations in this gene also increase the aggregation tendency of encoded proteins. Many studies have demonstrated phenotypes associated with neurodegeneration when this protein is overexpressed.
\nThere are six isoforms of microtubule-associated protein tau ranging from 352 to 441 amino acids, encoded by the MAPT gene as a result of alternative mRNA splicing. One half has three repeats and the other has four repeats, altogether establishing the microtubule-binding domain and also the core of tau filaments in case of pathology [88]. All isoforms have been observed in the brains of AD patients. Diseases that have isoforms with only three or four repeats, but not both, lack the Aβ peptides seen in AD and therefore do not carry the symptoms specific to the disease [70]. Tau inclusions may be of a variety of conformations, which can also be caused by different mutations on the MAPT gene, explaining the existence of numerous tauopathies [89, 90, 91, 92, 93].
\nAβ peptides are encoded by the amyloid precursor protein gene, APP, and are widely expressed as type 1 transmembrane glycoproteins. As a result of alternative mRNA splicing, there are three major transcripts named APP695, APP751, and APP770 [94, 95]. β- and γ-secretase enzymes take part in the production of Aβ peptides in sequential endoproteolytic cleavage. β-secretase is responsible for cleaving the N-terminus of the peptide thus removing the portion that remains on the extracellular side. This cleaved peptide is endocytosed and intracellular aggregation builds up which is later released into the extracellular space [79]. γ-Secretase is a membrane-embedded enzyme that is able to cleave many transmembrane proteins including C-terminus of the Aβ peptide. It a complex enzyme of four proteins; presenilin (PS) forming the catalytic core, presenilin enhancer-2 (Pen-2) enabling maturation of PS, anterior pharynx-defective (Aph-1) stabilizing the complex, and nicastrin possibly being the receptor for the enzyme’s substrate [96, 97]. PS and Aph-1 each have two variants resulting in at least four different enzyme complexes, which give rise to various cleaved Aβ peptides. Additionally, γ-secretases cleave the peptide in three different sites. Different protein variants and cleavage sites produce Aβ peptides of different profiles, some of which may be more susceptible to aggregation [98].
\nOverall, it is important to target the pathways leading to abnormal protein assembly and only then treatments may be proposed based on these mechanisms. Once the first protein inclusion is formed, it is essential to keep an eye on the time frame until the disease symptoms come forth. When techniques sensitive enough to catch the first protein inclusion are developed, then tracking its transformation into filaments can be helpful in designing novel preventive approaches. Understanding this cascade will also contribute to planning more efficient therapeutic methods.
\nAsthma and chronic obstructive pulmonary disease (COPD) are common disorders characterized by progressive chronic inflammation in the lungs. They have unique characteristics with dissimilarly involved cells, mediators, and inflammation. They also have distinct responses to corticosteroid treatment. Roughly 15% of COPD patients have characteristics of asthma [99]. Also, a comparable ratio of asthma patients has traits of COPD that is currently the fifth leading cause of death worldwide [100]. Many risk factors are linked to COPD including smoking tobacco, air pollution, indoor cooking while tobacco smoking (including passive smoking) making up around 80% of the cases [101]. There are many types of cells and mediators that have a significant effect during the pathogenesis of asthma and COPD.
\nMacrophages have a crucial role in coordinating the inflammatory response activated by cigarette smoke extract in COPD cases [102]. They discharge inflammatory mediators including tumor necrosis factor (TNF)-α, IL-8, other CXC chemokines, monocyte chemotactic peptide (MCP)-1, LTB4 and reactive oxygen species (ROS) [103]. However, the role of macrophages in asthma is not certain. Allergens via low-affinity IgE receptors may activate macrophages causing an inflammatory response through the discharge of a definite arrangement of cytokines. On the other hand, macrophages also excrete anti-inflammatory mediators, such as IL-10 that is thought to decrease in subjects with intense asthma [104].
\nActivated neutrophils were shown to be enhanced in some subjects with severe asthma and COPD in their sputum and airways [105]. Among the serine proteases secreted by neutrophils are neutrophil elastase (NE), cathepsin G, proteinase-3, matrix metalloproteinase (MMP)-8 and MMP-9, leading to alveolar destruction [103]. The mechanisms of neutrophilic inflammation in asthma and COPD are not clear. Demonstration of priming in COPD occurs at neutrophils in the peripheral circulation. Many chemotactic signals exhibit the capacity for neutrophil recruitment in COPD. These include LTB4, IL-8 and related CXC chemokines, comprising GRO-α (growth-related oncoprotein) and ENA-78 (epithelial neutrophil activating protein of 78 kDa) which are enhanced in COPD airways [106]. Although the mentioned mediators might be sourced from alveolar macrophages and epithelial cells, neutrophils have the capacity of being a vital source of IL-8 [107].
\nAirway and alveolar epithelial cells in COPD can be a vital point of source of inflammatory mediators and proteases 5. Cigarette smoke activates epithelial cells which produce inflammatory mediators, including TNF-α, IL-1β, GM-CSF and IL-8 [108]. Epithelial cells play an important role in airways defense and tissue repair processes. Goblet cells, a type of epithelial cell, in mucus catch bacteria and inhaled particulates [109]. Epithelial cells release antioxidants and antiproteases. Immunoglobulin A is carried by epithelial cells, hence involved in adaptive immunity [110]. On a side note, native and adaptive immune reactions of the airway epithelium are triggered by cigarette smoke and damage by other harmful agents, increasing sensitivity to infection.
\nThe main role of dendritic cells is to introduce innate and adaptive immune reaction by activating macrophages, neutrophils, T and B lymphocytes among others [103].
\nLymphocytes are directly involved in the pathogenesis of both asthma and COPD. Both airway and parenchymal inflammation exist in asthma and COPD patients [111]. Most lung lymphocytes are T cells which are in the respiratory tract of ordinary humans. Activated T lymphocytes are characteristic in both asthma and COPD, but CD4+ type-2 T lymphocytes are the major player in asthma whereas CD8+ type-1 lymphocytes are specific to COPD [111]. CD4+ T lymphocytes can generate many cytokines involved in mediating cell functions and cell–cell communications. This is done through impressing physiologic cell properties such as proliferation, differentiation and activation of other immunocompetent cells, chemotaxis, and connective tissue metabolism [112]. On the other hand, CD8+ T lymphocytes exist in the respiratory mucosa and are activated in response to foreign antigens [111]. Specifically, the cells in the respiratory mucosa have an important role in anti-viral immunity. Another lymphocyte type is B cells which are the minority (<5%) lymphocytes. The main function of B cells located in the lungs is the production of immunoglobulins for local defense mechanisms [113].
\nApart from these mentioned cells, there are crucial molecular mediators in the pathogenesis of asthma and COPD. The first family of mediators is transforming growth factor (TGF) family. The TGF-β subfamily is composed of five parts that exhibits plenty of effects pertaining to asthma and COPD. A recent study shows that overexpression of TGF-β1 in mice causes Smad3-dependent pulmonary expression of procollagen, antiproteases and fibrosis [114]. TGF-β exhibits chemotactic signatures for monocytes, macrophages and mast cells. Research shows an abnormal pulmonary expression of TGF-β1 in subjects suffering from COPD. Protein and mRNA expression of TGF- β1 are abundant in the lung tissue, including airway epithelial cells, of mild to moderate COPD patients. TGF-β1 has the role in pathogenesis of COPD because of its increased expression in parallel to the number of macrophages [115].
\nAnother mediator family is the fibroblast growth factor (FGF) family with 23 members in humans. Their functional receptors are named from FGFR1 to FGFR5 [116]. FGFs have many functions such as development, tissue homeostasis, and repair. In addition to further growth factors, FGF-1, FGF-2, and FGF-7 and their receptors FGFR1 and FGFR2, are located abundantly in the lungs [101]. Research shows that increased expression degrees of FGF-1, FGF-2, and FGFR1 were detected in vascular and epithelial areas in the lungs of COPD patients. FGF-1 causes higher collagenase expression and lower collagen I expression in lung fibroblasts which prompt tissue remodeling.
\nAnother family of mediators is the vascular endothelial growth factor (VEGF) family. There are seven units in this family capable of attaching to related cellular receptors. VEGFs have many functions including paracrine acting, angiogenic factors, prompting mitogenesis, emigration, and permeabilization of the vascular endothelium [101]. VEGF and its receptors assist in tissue remodeling as well as disease intensity in incessant lung diseases such as asthma [117]. COPD patients have increased pulmonary VEGF expression in bronchial and alveolar epithelial located around the vascular smooth muscle and alveolar macrophages. Additionally, unlike healthy subjects, COPD patients exhibit elevated levels of VEGFR-1 and VEGFR-2 expression inside the endothelium [118]. Furthermore, VEGFR-2 and VEGF expressions are decreased in COPD patients. Compared to VEGFR-2, VEGFR-1 has a higher affinity for VEGF which leads to VEGFR-1 scavenging VEGF from VEGFR-2. This phenomenon culminates VEGFR-1 activation and in the case of endothelial apoptosis, increased MMP activity as well as vascular and alveolar decimation [101]. This suggests the importance of harmony among VEGF, VEGFR-1, and VEGFR-2 during the pathogenesis of COPD subordinary types.
\nFinally, cytokines and chemokines are mediators supplying a chemotactic gradient which has the potential to activate macrophages, CD8+ T cells and neutrophils for COPD patients. It is known that inflammatory cells of both native and gained immune systems are significant in the COPD pathophysiology. This is where cytokines and chemokines are the key drivers [103, 119]. Different types of cytokines arrange chronic inflammation in asthma and COPD. T2 cytokines which are IL-4, IL-5, IL-9 and IL-13 interfere with allergic inflammation. Other types of cytokines including TNF-α and IL-1β accelerate the inflammatory response [120]. In asthma and COPD patients, chemokines are instrumental in drawing inflammatory cells from the circulation into the lungs [121].
\nObesity is a serious health problem that has become epidemic all over the world, especially in developed countries. It is characterized by hypertrophied adipocytes that secrete various adipokines and hormones, chronic inflammation in all tissues, and systemic insulin resistance resulting in type 2 diabetes, hypertension, and hyperlipidemia. In addition to these metabolic diseases, it can cause diseases such as cancer, atherosclerosis, obstructive sleep apnea syndrome, steatohepatitis, and musculoskeletal problems [122]. The obesity rate is 20% in women and 18% in men in developed countries [123]. It affects complex metabolic pathways in all tissues as a result of chronic and progressive inflammation, leads to insulin resistance, endothelial dysfunction and lipotoxicity.
\nThe pathophysiology of obesity includes complex interactions of numerous adipokines, hormones and pro-inflammatory cytokines with the central nervous system and metabolic organs (such as liver, pancreas, and muscle) as a result of genetic-environmental interactions.
\nGenetic etiology: Obesity is generally present in a polygenic etiology. Many studies have investigated the genetic background of body mass index (BMI) and waist/hip ratio (WHR), which are the best measurements of obesity. The results of these studies have been presented collectively in genome-wide association studies (GWAS) [124]. Although, single gene defects (monogenic) are rare in obesity, including especially melanocortin-4 receptor, leptin and leptin receptor genes [125].
\nDysregulation in hypothalamic control: The center of food intake and energy regulation in the central nervous system is the arcuate nucleus (ARC) in the hypothalamus besides the autonomic nervous system and brain stem. The balance between the opposing effects of orexigenic and anorexigenic neurons is important. Agouti-related protein (AgRP) and neuropeptide Y (NPY) (AgRP/NPY) neurons are orexigenic that promotes appetite and eating. Pro-opiomelanocortin–producing (POMC) peptide and cocaine-and-amphetamine–regulated transcript (CART), collectively known as POMC/CART neurons are anorexigenic that suppress appetite and eating. Oxygenic pathways that increase energy balance become more effective in obesity [126].
\nAdipose tissue dysfunction and systemic inflammation; The most important pathophysiological mechanisms of obesity and obesity-related insulin resistance are adipocyte dysfunction (visceral adipose tissue; VAT) and low-grade chronic systemic inflammation. In particular, white adipocyte tissue in obese subjects contributes to the regulation of food intake, energy metabolism and other functions by secreting adipokines from adipose tissue, which provide the necessary signals to the central nervous system, hypothalamus, liver, pancreas, muscle tissue, and other systems to regulate appetite, food intake, and energy balance [125]. Leptin is the most important adipokine that stimulates anorexigenic POMC/CART neurons and induces production of pro-inflammatory cytokines (TNF-alpha and IL-6) by macrophages and monocytes. In the case of hyperleptinemia, leptin resistance develops by the inhibition of the JAK2/STAT3 signaling pathway, which later increases oxidative stress and inflammation, causing insulin resistance, hyperlipidemia and hypertension [127]. Resistin is a pro-inflammatory adipokine produced by the resistin gene (RETN), which activates SOCS3, causing the insulin signaling pathway to be inhibited and consequently induces insulin resistance [128]. Other adipokines like Retinol binding protein 4 (RBP4), Angiopoietin-like protein 2 (ANGPTL2), Visfantin, Adiponectin, Lipocalin 2, Serum Amyloid A, Angiotensinogen, Renin, Angiotensin-Converting Enzyme, Acylation-Stimulating Protein, and Vaspin, are increased, and adiponectin, and Apelin are decreased in obesity, altogether stimulating inflammation, lipolysis, releasing free fatty acid (FFA) and causing insulin resistance as a result [129].
\nGastrointestinal hormones and microbiota: Gastrointestinal hormones and gut microbiota play a significant role in the complex pathophysiology of obesity. Ghrelin produced in the stomach induces starvation and food intake by stimulating orexigenic AgPR/NPY neurons in the hypothalamus. Although the effect of ghrelin cannot be fully explained, it is thought to increase in obesity, stimulate growth hormone release (GH), increase gastrointestinal motility and insulin secretion [130]. Decreased GLP-1, Peptide YY, pancreatic polypeptide, and increased amylin and cholecystokinin cause appetite inhibition and gastric emptying delay, resulting in excess energy [129]. Besides hormones in the gastrointestinal tract, changes in microbiota-gut-brain axis and their effects on metabolic organs are also important. Occurring as a result of nutrition and gene–environment interactions; chronic systemic inflammation resulting from intestinal microbiota dysbiosis (increase in Firmicutes-Bacteroides ratio), microbial fermentation products, increase in short-chain fatty acid formation and intestinal permeability, decrease in butyrate-producing bacteria rate, leads to an increase in proinflammatory response in metabolic organs, impaired fat metabolism and glucose metabolism [131, 132].
\nİmpaired insulin sensitivity and oxidative stress; The beginning of insulin resistance is the first step in the pathophysiology of T2D. Anabolic effects such as glycogen and protein synthesis, glucose transport, adipogenesis are formed by phosphatidylinositol-3-kinase (PI3K)/Akt pathway activation as a result of insulin binding to its receptor (INSR) synthesized in the pancreas [133]. On the other hand, insulin shows mitogenic effects with mitogen-activated protein kinases/Ras pathway (MAPK/Ras).
\nAdipokines, FFA’s, pro-inflammatory cytokines (TNF-a, IL-18, IL-1β, IL-6), synthesized as a result of inflammation in adipose tissue in obesity, also cause systemic inflammation in metabolic tissues such as liver and muscle. As a result, decreased GLUT-4 expression, activation of Ser/Thr kinases with insulin receptor substrate (IRS) phosphorylation, production of ceramides and proinflammatory cytokines, suppressing of cytokine signaling-3 (SOCS-3) expression, insulin pathways and effects. On the other hand, increased production of reactive oxygen radicals and production of toxic doses NO with inducible nitric oxide synthase (iNOS) activation, affect mitochondrial and endoplasmic reticulum functions. Activation of pro-inflammatory pathways increased oxidative stress, mitochondrial dysfunction, ER stress affects lipid metabolism, insulin mechanisms of action and other metabolic pathways, causing insulin resistance, Type 2 diabetes, hypertension, and hyperlipidemia [134].
\nBeta-cell dysfunction: In addition to peripheral insulin resistance in obesity, serious reductions in beta cell function are also observed. An increase in fat accumulation in islet cells due to chronic lipotoxicity disrupts the function of beta cells by blocking calcium channels. Chronic hyperglycemia due to disruption in glucose metabolism and systemic inflammation due to an increase in oxidative damage and lipotoxicity, disrupt insulin secretion pathways and cause changes in apoptosis gene expression. Hyperinsulinemia in obesity, impaired insulin signaling pathway, oxidative stress, lipotoxicity in islet cells, loss of beta-cell function and apoptosis may lead to the formation of type 2 diabetes [122, 135].
\nObesity has become a pandemic all over the world as a result of rapidly changing lifestyles and genetic heritage in the last century. Despite the findings in recent studies on the development and complications of obesity, it is difficult to say that the subject of etiology and pathophysiology is still not fully understood. Especially omics technologies, big data on environmental gene interactions, neuroendocrinology, and neuropsychological studies will reveal findings that open up different horizons. However, due to its complications from deadly metabolic diseases to cancer, rapid preventive measures should be taken, and effective treatment models should be developed.
\nAcute lymphoblastic leukemia (ALL) is a heterogeneous malignancy emerging from lymphoid precursors. It is characterized by the proliferation of immature lymphoid cells with somatic mutations including chromosomal rearrangements, and aneuploidy [136]. ALL has two peak points; first point occurs at ~5 years of age (80%), and the second point occurs at the age of ~50 (20%) [137]. The basic mechanism underlying the development of ALL is similar in children and adults, while they have the frequency of different genetic subtypes. Molecular analysis of genetic changes in leukemia disease provides a great advantage in order to understand prognosis and pathogenesis of ALL [138].
\nThe diagnosis of ALL depends on the presence of at least 20% lymphoblast in bone marrow. Immunophenotyping by flow cytometry (FCM) identifies the subtype of ALL that may be B-cell precursor (BCP), mature B-cell types, or T-cell ALL. Chromosomal abnormalities are a characteristic of lymphoblastic leukemia, which are found in B or T cell lineage. The most common abnormality found in adult B precursor ALL is the t(9;22) BCR-ABL translocation, while the t(12;21)(p13;q22) TEL-AML1 translocation is most commonly found in childhood B precursor ALL [139]. On the other hand, the discovery of mutations in the receptor tyrosine kinase FLT3 contributes to the understanding of leukemogenesis mechanism in hyperdiploid ALL (20% of cases). Based on this finding, targeting specific tyrosine kinase inhibition may be useful in the management of leukemia [140].
\nSmall-molecule kinase inhibitors have a clear benefit in the treatment of many cancer types including leukemia. Imatinib mesylate, a small-molecule inhibitor of BCR-ABL kinase, is highly effective in the treatment of chronic myelogenous leukemia (CML) [141]. Although the single kinase inhibitor is a remarkable treatment option in a different type of leukemia, it will need to be combined with either other targeted therapy or chemotherapy because of the resistance to small-molecule inhibitor [142]. Unlike ALL, Chronic Lymphocytic Leukemia (CLL) is defined the accumulation of monoclonal B cell with a special immunophenotype in the bone marrow, blood, and other lymphoid organs where B lymphocytes express CD19, CD23, CD5, low-level CD20 and surface immunoglobulins [143]. The standard treatment procedure of ALL and CLL includes consolidation therapy following chemotherapy in pediatric patients. For adult patients, unlike pediatric patients, the allogeneic hematopoietic stem cell transplantation is frequently preferred as consolidation therapy [144]. Because the patients resistant to chemotherapy are not respond to treatment well enough, novel therapy approaches such as Chimeric antigen receptor-modified T cell (CAR-T) therapy have developed in order to overcome chemotherapy resistance and improving the outcome of patients [145]. CAR-T cells, as immunotherapeutic tools, are genetically engineered to express a chimeric antigen receptor recognizing an antigen that is located in the special cells such as a tumor [146]. CD19 antigen on B lymphocytes was considered the initial target for CAR-T cell therapy. However, specific antigen loss might cause the failure of CAR-T cell therapy in CLL. CD19–20 co-targeting CAR-T cells were designed to kill both CD19-positive and CD19-negative CLL and it was shown that these cells were very effective in killing CLL cells. In one of the first reported in pediatric ALL the clinical trials, CAR-T cells targeted the CD19 antigen of B cells are designed with CD3ζ and CD28 costimulatory domain [147].
\nThe origin of a pathogen has a crucial role in developing vaccines and blocking transmission. This may last many years due to its elusiveness as seen in HIV-1, SARS, and MERS [148, 149, 150]. According to a recent report, it was emphasized that SARS-CoV-2 is able to infect T cells, which are targeted by HIV [151]. Another report alleged that the motif insertions of spike glycoprotein, similar to HIV-1, may help increase the range of host cells of SARS-CoV-2. HIV-1 envelope glycoprotein contains mutable insertions and deletions not necessary for biological function. Only 1 and 2 insertions are matched in only a few HIV-1 strains and this reveals that four insertions are scarce. Thus, HIV-1 cannot be assumed as the source for those insertion sequences in the SARS-CoV-2 genome due to their inefficient identities and scarceness in the HIV-1 sequences [152].
\nThe reported cases showed that there have been 3,162,284 COVID-19 cases in at least 212 countries and approximately 7.1% of which was resulted in death as of April 30, 2020 [153]. It is known that SARS-CoV, MERS-CoV, and SARS-CoV-2 are the members of coronoviridae family of the Nidovirales order, which comprises a relatively positive-sense, single-stranded RNA genome of around 26–32 kb [154]. 5o-methylguanosine cap at the beginning, a 3o-poly-A tail at the end, and a total of 6–10 genes in between exist in their genome [155, 156].
\nThis family has extremely expressive instability and recombination rate, which is similar to RNA viruses, so it is practically unfeasible to prevent their distribution among humans and animals worldwide; nevertheless, the fact that the virus is exceedingly pathogenic to humans is closely related to random genetic recombination in the host. Although there is a strict genetical relation between SARS-CoV-2 and SARS-CoV, it is explicit that SARS-CoV-2 has a unique feature providing rapidly spread worldwide [157].
\nSARS-CoV-2 genome sequence is much more resembles a SARS-like bat rather than SARS-CoV [158, 159]. Two open reading frames translating the replication- and transcription-related gene into two large non-structural polyproteins [156]. Ribosomal frameshifting contributes to translate two different but overlapping open reading frames. Besides these nonstructural proteins, the subgenomic RNA also encodes the viral genome packaging protein N (nucleocapsid), and the viral coating proteins M (membrane), E (envelope), and S (spike) as the structural proteins. Viral coating proteins, which interact with host surface receptors, is generally preferred as the therapeutic target blocking protein–protein interaction [160, 161]. TMPRSS2, the human serine protease, enables S Protein of both SARS-CoV and SARS-CoV-2 to prime, and these two viruses use the angiotensin-converting enzyme 2 (ACE2) receptor in order to bind the host cell as the first step of the viral entry mechanism. Unlike SARS-CoV and SARS-CoV-2, the cell entry of MERS-CoV depends on the binding of its own spike protein to DPP4 (dipeptidyl peptidase 4). The RT-PCR analysis of the throat swabs is essential to the diagnosis of COVID19 pneumonia, and it takes 3.5 h to provide the results [162].
\nClinical management puts emphasis on the importance of supportive care and prevention of complications due to a lack of specific treatment for COVID-19 pneumonia. On the other hand, potential antiviral therapies for the purpose of rapidly dealing with this pandemic are taking place on several clinical trials. These trials focused on three main targets that include enhancing the host immune system, blocking the virus spike protein-host cell surface receptor interaction, and vaccine development [163].
\nHPV genome, which is a double-stranded circular DNA, has the early (E) genes that are responsible for replication and transcription, and the late (L) genes that are responsible for viral capsid proteins. In the early stage of HPV infection, the highly expressed E1 and E2 proteins provide the maintaining of viral replication and transcription within the cervical cell [164].
\nHPVs, unlike SARS coronaviruses, are non-enveloped viruses and don’t have a specific host cell receptor that initiates the viral infection. Additionally, HPVs have many different genotypes such as HPV type 16 and type 18 which are known as the reason for cervical cancer. HPV infection may cause low-grade cytological changes on Papanicolaou smears, or low-grade squamous intraepithelial lesions [165]. When malignant conversion considered, viral oncoproteins E6 and E7 attach, respectively, tumor suppressor protein p53 and Rb have a crucial role [166]. Until today, many vaccine developments studies have been carried out to protect HPV malignant type 16 and 18. For example, the clinical vaccine Gardasil 9 provides effective protection against vaginal, cervical, and vulvar diseases caused by HPV type 16,18 and also its 5 other different types [167].
\nThe chapter outlined the unique mechanism of each disease. Depending of the origin of the disease; deficiency, hereditary, infectious and physiological diseases may be treated diversely but the perturbation effect can only be eliminated with proper intervention. Current amelioration may be improved by biochemical methods only if the molecular mechanism is clearly understood. Therefore, molecular medicine provides unique solutions to diagnose and treat disease by elucidating macromolecular interaction and abnormalities in cells and tissues. The chapter summarizes current findings and methods to alleviate and cure the diseases.
\nBYK, EK, KUC and ENYT acknowledge YOK100/2000 bursary and thanks to Turkish Council of Higher Education (YOK).
\nNone.
Thanks to Assistant Prof. Dr. Lütfi Tutar for carefully reading the manuscript.
\nIntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
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