Vegetation indices that were used derived from the images.
\r\n\t
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Shaheer Akhtar completed his Ph.D. in Chemical Engineering, 2008, from Jeonbuk National University, Republic of Korea. Presently, he is working as Associate Professor at Jeonbuk National University, the Republic of Korea. 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He is a Professor in the School of Chemical Engineering, Jeonbuk National University, and also President of Korea Basic Science Institute (KBSI), Gwahak-ro, Yuseong-gu, Daejon, Republic of Korea. He has been a promising researcher and visited several universities as a visiting professor/invited speaker worldwide. 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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:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"66585",title:"Endoplasmic Reticulum Stress-Mediated Cell Death",doi:"10.5772/intechopen.85401",slug:"endoplasmic-reticulum-stress-mediated-cell-death",body:'The endoplasmic reticulum (ER) is an intracellular organelle which has many roles in calcium storage, protein synthesis, degradation and transport, and carbohydrate and lipid metabolism. The ER has different types of domains in its specialized units to ensure its multifunction. The main function of the ER is the synthesis of secreted, cytosolic and membrane proteins. These processes are controlled by ribosomes that are localized in the cytosolic site of the ER. Initially, ribosomes and mRNA are united to form a translational complex on the inner surface of the cytosolic site of the ER. The protein synthesis starts in the cytosol and continues in mRNA-ribosome-signal recognition particle (SRP) complexes that are located on the ER membrane. Proteins classified simultaneously with protein synthesis are guided to the membrane or Golgi apparatus for secretion. The terminal step of the protein synthesis is the cleavage of the signal peptide; after this phase, proteins are secreted from the ER membrane to the cytosol via ribosomes [1, 2]. Between synthesis and secretion, exocrine proteins require folding and modifications through folding enzymes and chaperons. N-linked glycosylation, disulfide bond formation and oligomerization of proteins are determinants of the secretory proteins which indicates if they are or not [3, 4]. Hereby, the ER is one of the most crucial and multifunctional organelles for cell survival.
Since alterations of the ER’s functions leads to unfolded and/or misfolded proteins in the cell, ER stress-mediated cell death underlie several serious diseases such as cardiovascular disease, neurodegeneration, ischemia and diabetes. Stress conditions are captured by transmembrane receptors which are localized on the ER and unfolded protein response (UPR) initiated by these receptors. Under chronic stress conditions, the adaptive response of the ER fails and the cells undergo mechanisms of cell death. In the ER stress conditions ATP, calcium and oxidizing environment are important factors for protein folding and disulfide bond formation. UPR is the major protective mechanism against deleterious and toxic effects of ER stress. Protein RNA-like ER kinase (PERK), activating transcription factor 6 (ATF6) and inositol-requiring enzyme 1 (IRE1α) are the tree main modulators of the ER stress response pathway [5, 6].
In a normal functioning cell PERK, ATF6 and IRE1 are in an inactive phase that is maintained by a specific ER chaperone, GRP78. When the ER stress is triggered, GRP78 is released to activate these three receptors as UPR. Unfolded protein stress has a crucial role in cell survival. However, the internal ribosomal entry site (IREs) bypasses the eukaryotic translation initiation factor 2α (eIF2α) controlling pathway. In the PERK-eIF2 pathway, activating transcription factor 4 (ATF4) is the key element which encodes cAMP response element-binding transcription factor (C/EBP) and promotes cell survival via modulation of redox reactions, stress response, protein synthesis and secretion. On the other hand ATF4 promotes C/EBP homologous protein (CHOP) which triggers apoptotic cell death. In the ER stress-mediated cell death, mitochondrial apoptotic pathway initiates the autophagy while other cell death mechanisms play a smaller role (Figure 1). It has been concluded that the ER stress-mediated cell death is associated with severe diseases including nervous system disorders, diabetes and cancer [7, 8, 9, 10].
ER stress-mediated pathways via PERK, IRE1α and ATF6 which stimulates apoptosis and suppress anti-apoptotic proteins.
Several endogenous and exogenous factors may interfere with the ER protein folding mechanism and generate stress conditions which have been an issue of importance, strongly focused on in recent years. Chronic stress conditions may result with pathological perturbations in different systems in the organisms. Once the ER stress is triggered, UPR mechanisms strive to restore the ER homeostasis. If the UPR system does not be sufficient, apoptosis inducing signals are increased in the cell; and ultimately, cell death signaling pathways are activated [9, 10]. Enduring unfolded protein response (UPR) and ER stress in the cell cause dysfunctions of some mechanistic pathways which may in turn stimulate cell death. In the center of the UPR and ER stress response mechanism, IRE1α is placed as a key regulatory molecule. It has been demonstrated that IRE1α can directly bind to unfolded proteins and start signaling. IRE1α and its signaling pathway determine the fate of the cell between survival and death based on the longevity of ER stress [12].
PERK, ATF6 and IRE1 are the three main initiating proteins of UPR signaling due to ER stress. ATF6 is synthetized as an inactive precursor, and it contains bZIP transcription factor in its cytoplasmic domain. Under stress conditions, ATF6f, which is the active component of ATF6s, is released in the Golgi apparatus after cleavage by S1P and S2P proteases. ATF6f plays an important role as a transcription factor on ER homeostasis genes which include ER chaperons and ER-associated protein degradation (ERAD) [12, 13, 14]. PERK which is a transmembrane protein kinase gets in dimerization and auto-phosphorylation under ER stress conditions and phosphorylates eIF2α. Phosphorylated eIF2α have effects on initiating the selective translation of ATF4, protein folding factors genes expression regulation and plays a role in oxidative stress and amino acid metabolisms [14, 15, 16].
IRE1 is the most conserved signaling pathway in the ER stress mechanism. IRE1α and IRE1β are the main two isoforms of the IRE1. These isoforms have kinase and endoribonuclease activities at their cytoplasmic domain. Under stress conditions IRE1α goes into dimerization and auto-phosphorylation with a conformational change in its cytoplasmic part and activates the endoribonuclease domain. Active IRE1α catalyzes the splicing of X box-binding protein 1 (XBP-1) in its 26-nucleotide intron that result with active transcription factor XBP-1s which regulates protein folding, targeting to ER, ERAD and biogenesis of Golgi etc. [9, 16, 17]. IRE1α is a transmembrane protein that includes an N-terminal sensor domain, single transmembrane domain and C-terminal cytosolic effector domain. The C-terminal domain of IRE1α has both protein kinase and endoribonuclease activities. IRE1α oligomerization is induced by unfolded protein stress in the cell and following this cytosolic domain auto-phosphorylation occurs. With UPR control, IRE1α has an important cytoprotective effect [18, 19].
UPR signaling in the cell play an important role for restoring cellular homeostasis; however, chronic ER stress may result with cell death [11]. Apoptosis is the main cell death mechanism in ER stress; however, other types of cell death, such as necrosis, necroptosis or deregulated autophagy may contribute to ER stress too. Also autophagy that is a mechanism which enables the elimination of unfolded or misfolded proteins under ER stress conditions is one of the most studied issues in recent studies [20]. mRNA of IRE1α is regulated through X-box binding protein as well IRE1α controls its own mRNA expression by self-cleavage [21].
Under ER stress conditions, after activation of PERK, eIF2α phosphorilated by PERK and this phosphorylated eIF2α trigger translational arrest as a pro-survival response. This prosurvival response is important checkpoint step before cell death. Deficiencies of PERK expression or phosphorylation problems of eIF2α make cells more sensitive to ER stress conditions. PERK associated signaling pathway regulates important mechanisms such as autophagy, ATF4-mediated transcription pathway, and protein folding and redox metabolism [22, 23].
In different cell types it has been demonstrated that cell death is induced via the PERK signaling pathway under chronic stress conditions. The key molecule for initiating cell death is C/EBP homologous protein (CHOP), also named as growth arrest and DNA-damage-inducible 153 (GADD153). The expression of CHOPs is increased by ATF4. PERK activation induces eIF2α phosphorylation which in turn increases the selective transcription of ATF4 that increases CHOP level. Pro-apoptotic proteins such as GADD34, ERO1α (ER oxidase 1 alpha) and BH3-only proteins (BIM, PUMA and NOXA) expressions is increased by CHOP. PERK signaling pathway initiates mitochondrial apoptotic pathway. GADD34 and Ero1α increase cellular ROS production and calcium. Calcium release is regulated by IPR3 and the increase of cytosolic calcium triggers PTP related apoptosis in the cell. On the other hand, BIM, PUMA and NOXA induction cause cytochrome-c release from mitochondria via BAX and BAK activation [23, 24, 25, 26].
CHOP, main member of Bcl-2 family, is one of the ER stress associated regulator molecule which downregulates Bcl-2. Another member of Bcl-2 family is BH3-only proteins which are pro-apoptotic proteins and upregulated by CHOP. Moreover, CHOP induces BIM, PUMA and NOXA expression levels [27, 28, 29]. CHOP has another important role in the ER as a modulator of oxidative status in the organelle. Increase in the level of CHOP and ERO1α in the cell causes a decrease of the glutathione (GSH) level which leads to ROS formation. ERO1α induces reduction of hydrogen peroxide (H2O2) in the ER lumen via reconstitution of the active state of proteins through re-oxidation of protein disulfide isomerases (PDIs). Increased ROS conditions in the cell via ER stress make cells sensitive to cell death. Moreover, CHOP increase in the cell triggers the activation of inositol- 1,4,5-trisphosphate receptor (IP3R) through ERO1α and calcium release from ER to cytosol which contribute to apoptosis. Thus, PERK plays a key role in inducing cell death via ROS production and calcium release. It has been demonstrated that, apoptosis can be induced without activation of the PERK signaling pathway; because, there are several triggers of apoptosis in different conditions. It has been reported in different studies that, PERK signaling deficiencies may play a role in different types of diseases such as Parkinson disease, diabetes, atherosclerosis, ALS, cardiac dysfunction and liver damage induced by alcohol. Further and detailed studies are needed to clarify the full mechanism of the ER stress dependent disease occurrence [24, 26, 30, 31, 32, 33, 34, 35].
IRE1α/XBP-1 pathway plays a balancing role in survival-cell death homeostasis and also takes part in the gene regulation of the protein folding elements. With ER stress ASK1/JNK or NF-κB signaling pathways get activated by IRE1α-TRAF2 complex and afterwards cell death processes as apoptosis or autophagy starts in the cell [36, 37, 38, 39]. IRE1α-dependent decay (RIDD) is IRE1α’s endoribonuclease activity on several mRNAs. RIDD mechanism has a defensive role for degradation of proteins which have a misfolding potential and also RIDD takes part in pro-apoptotic mechanisms too. RIDD mechanism shows its pro-apoptotic effects through ER chaperons BiP/Grp78 mRNA degradation, effecting JNK signaling pathway or XBP-1 mRNA splicing. Thus, RIDD is placed in the center of the ER stress-mediated cell death and cell survival. It has been recently demonstrated that IRE1α show its endoribonuclease activity on different microRNAs (miRNA), caspase-2 and TXNIP which have role in cell death processes [40, 41, 42].
Dimerization, auto-phosphorylation and endoribonuclease domain engaging of IRE1α occurs under stress conditions. Active IRE1α activates transcription factor XBP1 which is named as XBP1s. XBP1 has a regulative role in protein folding [11, 12].
IRE1 contains a serine-threonine kinase and an endoribonuclease domain. With its endonuclease activity, IRE1 splices the 26-nucleotide intron from ATF6-induced XBP1 mRNA which generates the frameshift splice variant as sXBP1 and this variant encodes stable and active transcription factor. zXBP1 targets are ER chaperons and P58IPK which belongs to the HSP40 family. P58IPK plays a role in the negative feedback mechanism of PERK through binding and inhibiting PERK. P58IPK activity has the power to finish the UPR if the UPR could evade the ER stress, if not, the P58IPK activity gets suppressed and the apoptotic mechanism starts in the cell. Generally, IRE1 release has a strong pro-survival effect during stress conditions via UPR; however, long term active IRE induces kinase activities through the c-Jun N-terminal kinase (JNK) pathway and recruitment of TNF-receptor-associated factor 2 molecule (TRAF2). The IRE1-TRAF2 complex causes recruitment of apoptosis-signal-regulating kinase (ASK1) which in turn activates MAPKs JNK and p38. JNK activation associated with Bcl2 family members’ regulation in different stress conditions. During ER stress JNK phosphorylates the Bcl2 and inhibits its anti-apoptotic function, however while JNK phosphorylates Bcl-2 homology domain 3 (BH3) and Bim, their pro-apoptotic features gets activated. As an important initiator of apoptosis, IRE1 is the last resorts for the ER stress regulated UPR after PERK and ATF6. IRE1 is the top step for modulation of pro-surviving or cell-death in the cell via ASK1 and JNK [5, 43].
GRP78 is one of the main regulatory proteins for the ER stress pathways, while ATF6 is separated from GRP78, ATF6 translocate to Golgi apparatus to get spliced from its active sites. Active ATF6 in turn enhance the gene expressions of stress response elements in the nucleus. GRP78, GRP94, protein disulfide isomerase, CHOP and XBP1 are some of the targets of ATF6. However, ATF6-mediated cell death mechanism has not been clarified yet and further studies are needed to explain detailed intracellular protein interactions [5].
ATF6 proteins ATF6α and ATF6β are regulatory proteins which belong to the bZIP transcription factor family. ATF6 binds to the ER membrane via its hydrophobic sequence. The ATF6 activation process during ER stress is different from the PERK and IRE1 activation processes. After GRP78 releases from ATF6, it translocates to the Golgi apparatus from the ER and the site1 and site2 proteases splice the ATF6s juxtamembrane site. After that, ATF6 translocates to the nucleus as a transcription factor for gene expression regulation. ATF6 stimulates homodimerization or heterodimerization of the ER stress related genes such as XBP1, IRE1, PDI, α-mannosidase-like protein 1 (EDEM1) as a result of misfolded protein degradation. In the literature it has been not clarified yet whether AFF6 regulates calcineurin 1 (RCAN1) which has calcium dependent pro-apoptotic functions [44, 45].
RCAN1’s important substrates are pro-apoptotic Bcl-2 family members and Bcl-2 antagonist of cell death (BAD). Calcineurin dephosphorylates BAD and in turn BAD dimerizes with the anti-apoptotic protein Bcl-Xl and inhibits its function. Cyclic AMP responsive element binding proteins such as CREB3l1 (oasis), CREB3l2, CREB3 (luman), CREB4, CREB-H are the other known ER stress transcription factors, however their mechanisms are not yet well detailed [46].
C/EBP homologous protein (CHOP or DDIT3 or GADD153) is a bZIP transcription factor that regulates IRE1, PERK and ATF6 under ER stress conditions. ATF4, ATF6 and XBP1, important elements of UPR signaling, can bind to the CHOP gene promoter sequences to regulate its transcription [43, 44].
It has been demonstrated that with knock-out PERK and ATF4, CHOP induction was disrupted under ER stress, and also ATF2 and IRE1-ASK-p38 signaling pathways upregulated the activity of CHOP. CHOP induces apoptosis via inhibition of Bcl-2. CHOP and ERo1α together enhance the ER stress dependent protein loading in the cell which also enhances the apoptosis mechanism. Moreover, CHOP interacts with pro-apoptotic Bim to activate it and also inhibits Bcl-2, thus apoptosis occurs under ER stress condition. However, CHOP is not the main protein for ER stress-mediated cell death, it was demonstrated that ER stress-mediated apoptosis can occur without CHOP expression in PERK−/− and EIF2α (Ser51Ala) knock-in cells [27, 30, 46, 47, 48].
GADD34 expression in the cell is associated with apoptotic cell death. As a protein phosphatase 1 (PP1)-interacting protein GADD34, dephosphorylates eIF2α which results with protein translation inhibition. The detailed mechanistic pathway which lays under GADD34-induced apoptosis has not been clarified yet. GADD34 expression may increase the pro-apoptotic proteins. It has been demonstrated in studies that blocking the GADD34 pathway in the cell can cause inhibition of R-stress-mediated apoptosis [5, 24].
Bcl2 family members are regulatory proteins of apoptosis which especially modulate the mitochondrial apoptotic pathway. In resting cells, on the mitochondria and ER membrane, the Bcl2 protein interacts with the pro-apoptotic proteins Bax and Bak and inhibits their functions. Moreover, dynein interacts with the pro-apoptotic protein Bim and inhibits its function. ER stress affects mitochondria and follows the same mechanism of the mitochondrial apoptotic pathway. During prolonged ER stress after activation of CHOP and JNK, Bim phosphorylates and releases from dynein. At the same time Bax and Bak unbound from Bcl2 and the execution phase of apoptosis initiates. During ER stress-mediated apoptosis cytochrome-c releases from mitochondria and apoptosome formation occurs [49, 50].
Detailed molecular information about the ER stress modulated apoptosis mechanism needs further investigations to clarify the different genes and proteins which play role in this mechanism, thus, new generation therapy models generation can be designed for ER stress-mediated apoptosis related diseases. It has been well known that CHOP and JNK play a central role in the activation of ER stress-mediated apoptosis. Bcl2 gene expression is inhibited by CHOP, which leads to the pro-apoptotic Bcl2 family members’ activation. The functions of the Bcl2 family members also are regulated by JNK through phosphorylation. The IRE1-ASK1 pathway activates the JNK and JNK first phosphorylates the Bcl2 which is localized on the ER membrane. The BH3-only proteins on ER membrane gets activated and thus intracellular calcium flux become uncontrollable [51]. On the other hand JNK targets to BH3-only proteins are known as the “orchestrators of apoptosis.” It has been reported that, the p53-upregulated modulator of apoptosis (PUMA), Noxa and Bim play roles in ER stress-mediated cell death. The Bim protein have three isoforms as short (BimS), and long (BimL and BimEL). Bim tethers dynein in the cell via its long isoforms BimL and BimEL under normal conditions, however under ER stress JNK phosphorylates Bim and stimulates its release from dynein leading to apoptosis initiation. Moreover, IRE1 induction directly activates Bax and Bak during ER stress-mediated apoptosis [52, 53].
All these activation processes are figured out with caspase activation. However, the caspase activation phase has not been defined clearly yet. It has been reported in different studies that caspases 12, 3, 6, 7, 8 and 9 do not play role in ER stress-mediated cell death. It has been speculated that caspase-4 maintains caspase-12’s function in mammalians; however, it is not clarified yet [5]. It has been recently reported that in mammalians caspase-4, which is activated by ER stress, induces Bap31 and Bap20 proteins which play a role in the activation of the mitochondrial apoptotic pathway [54].
The Bcl2 family members (BCL-2 and BCL-XL) and transmembrane BAX inhibitor motif (TMBIM) (BI-1/TMBIM6 and GRINA/TMBIM3) interact and regulate IP3R activity. While cytosolic calcium level increases, BAX/BAX oligomerization (MOMP) occurs in the mitochondria and promotes the apoptotic pathway. ER foldase enzymes such as BiP and Protein disulfide isomerases (PDIs) translocate to the cytosol through BAX/BAX pores and in turn affect the plasma membrane pro-apoptotic protein Par-4 which results with apoptosis [11].
Caspase 12 and its polymorphic variant caspase 4 play an important promoting step role for ER stress-induced apoptosis. Caspase 12 activates pro-caspase-9, which belongs to the mitochondrial apoptotic pathway, without cytochrome-c release. Cytoplasmic calcium-activated protease calpain can activate caspase-12 by its cleavage and IRE1α also activates caspase-12 directly. Caspase-12 is located in the cell as a high molecular weight complex that includes apoptosis-linked gene-2 protein and p97 (ERAD mediator). P97 plays a crucial role for this pro-apoptotic stabilization of caspase-12. Once caspase-12 is activated, it activates the downstream caspases such as caspase-9 and caspase-7 which ultimately activates caspase-3; and hereby, apoptosis execution phase starts [55, 56].
Endoplasmic reticulum (ER) stress is also closely related to the autophagy mechanism in the cell to maintain cellular homeostasis. Generation of autophagosomes occurs during the ER stress-induced autophagy process which encapsulates protein and damaged protein aggregates. As in the apoptosis process, PERK, IRE1 and ATF6 signaling pathways have role in initiating autophagy during ER stress, in addition Atg40/FAM134B takes part too [57]. Autophagy has crucial roles in maintaining optimum cellular activity, elimination of unfolded and misfolded proteins, elimination of defective organelles, protection against pathogens, balancing cellular energy storage, tumor suppression, biosynthesis of new molecules and cell death mechanisms [58].
ER stress UPR activating molecules PERK and IRE1α also activates the autophagy process by activating autophagy-related genes (Atg). Autophagosome formations generated by Atg proteins interact with the LC3II-PE complex. IRE1α-JNK pathway activates Bcl2 by phosphorylation and subsequently stimulates Beclin1, ATG5 and ATG7. Thus, the Bcl2-Beclin complex dissociates and protein kinase-C activation phosphorylates LC3 and other autophagosome proteins. Another regulative process for autophagy is the mTOR pathway through AMP-dependent protein kinase (AMPK) that in turn induces the autophagy activating genes [59].
Autophagy process initiates EIF2AK3 activation which results with mTOR inhibition. Active EIF2AK3 upregulates ATF4 and subsequently SESN2, DDIT3 and DDIT4 are upregulated. These three active proteins inhibit the mTOR activity. AMPK pathway is activated via several types of metabolic stress, especially cellular energy state disorders and intracellular Ca level imbalance. AMPK pathway induces ULK1 and at the same time AMPK inhibits mTOR which has an inhibitory role on ULK1. MAPK8 and DAPK1 induction processes include formation of PtdIns3K and phosphorylation of the Bcl2-Beclin1 complex (Figure 2). All UPR sensory proteins have the potential to induce Beclin1 and Atg proteins for autophagy initiation. All UPR initiating molecules (IRE1, PERK, ATF4 and ATF6) activate the autophagy related protein Atg5-Atg12-Atg16 through CHOP activation. In mammalians, the ER stress-mediated autophagy mechanism is well defined and as an interesting point, under normal conditions the autophagy process plays a role for maintaining cellular homeostasis, however under stress conditions cellular mechanisms may inhibit the autophagy process with unknown regulative pathways. To understand the whole mechanism which takes part in ER stress-mediated autophagy, further studies are needed [57, 58].
ER stress-mediated autophagy induction via PERK, IRE1α, ATF6 and IPR3. PERK induce autophagy through eIF2α with Atg12 and Atg5 interaction, however ATF6 dependent pathway have not been clarified yet. Autophagy initiate by another pathway as Ca2+ influx through IPR3 transmembrane protein which inhibits mTOR. IRE1α pathway induce beclin-1 during autophagy induction processes.
ER is pivotal organelle for cellular protein, lipid synthesis and Ca storage. During cell cycle process, ER is very active due to these physiological processes and addition due to its highly powerful stress response mechanism as UPR [60]. ER stress induces several different complex molecular pathways in the cell which may conclude physiological or pathological conditions. UPR signaling mechanism is one of the important cell protective homeostasis provider factor. UPR signaling rapidly initiate with IRE1α signaling after stress stimulus, secondly ATF6 pathway become a part of activity because of its slow kinetics and finally PERK mechanism step in. UPR mechanism have balancing role between cytoprotective and proapoptotic systems. Molecular features of ER stress and UPR mechanism is crucial for delivering targeted drugs for diseases which are associated with these signaling pathways [35]. There are several ongoing studies about ER stress mechanism to clarify signaling pathways, however many unknown mechanisms about pathway remain. As discussed in this chapter different signaling mechanisms play role in ER stress-mediated cell death however; pro-apoptotic mechanism components different from PERK, CHOP, Bcl-2 family members are not identified fully yet [11, 61, 62].
The ER stress mechanism plays an important role in several different diseases such as neurodegenerative diseases, ophthalmology disorders, inflammation diseases, viral infections, cancer, metabolic diseases, and atherosclerosis. It is important to understand the detailed mechanism which plays a role in ER stress-mediated diseases to provide more effective therapeutics. Alzheimer disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), prion diseases, retinitis pigmentosa, glaucoma, macular degeneration, as inflammatory bowel diseases, multiple sclerosis, rheumatoid arthritis, heart failure, cardiac hypertrophy, myocardial infraction and type I autoimmune diabetes are ER stress dependent diseases. In AD, mutant presenilin 1 interferes with the UPR mechanism and causes disruption in IRE1α, PERK and ATF6 signaling pathway and increase CHOP activity as a consequence of amyloid β-precursor protein (APP) accumulation in the neuron cells. Parkin is an important E3 ubiquitin ligase and it is also associated with ER stress-mediated cell death. The Mutant parkin gene causes accumulation of Lewy bodies in neurons that associated with defective UPR mechanism which result as Parkinson disease. ER stress mechanism depression is a very important strategy for cancer therapy. ER stress mechanism helps the tumor cells to adapt to its microenvironment. UPR plays a protective role for tumor cells and thus inhibition of ER stress could provide reducing in tumorigenesis. IRE1α/XBP1 has a crucial role in tumor angiogenesis. Considering all this together, ER stress and UPR pathways are important targets for chemotherapeutics [8, 44, 63].
ER stress-mediated cell death has a crucial role in several diseases pathophysiology. In recent years several studies have been done on the mechanism of ER stress, pathway details, its role in diseases and therapy. However, all these information are just the tips of the iceberg. To put forward effective therapeutic strategies, mechanistic pathway details should be defined well with further studies. ER stress seems to be a central mechanism to cell survival and cell death. Pathway associations with the other intracellular mechanisms are also needed to be clarified in order to understand the complexity.
The authors would like thank to Prof. Dr. Gül Özhan from Istanbul University, Faculty of Pharmacy, Department of Toxicology.
For valuable critiques and contributions, Sinem Beyaz for technical and graphical support and MSc. Ayşenur Günaydın and Pharm. Enes Bişirir for linguistic support.
This research received no specific grant from any funding agency in the public, commercial, private, or not-for-profit sectors.
The authors declare that there is no conflict of interest.
Mangroves act as frontiers that protect the coastal land against destruction of ocean waves, tsunamis and storms. Mangroves also provide habitat for various aquatic life forms and function as natural filter, which improves the quality of water. Mangroves also play important roles as a significant carbon sink in coastal environment. It is interesting fact that despite only 0.05% of plant biomass stored in the ocean and coastal areas out of the total plant biomass on land, it can absorb a comparable amount of carbon every year. A study demonstrated that primary productivity in mangroves is higher than other types of forests. Biomass carbon in mangroves stands is among the highest in the tropics. Mangroves can store up to four times more carbon (C) as compared to other tropical forests around the world [1].
\nA mangroves ecosystem has an ability to absorb carbon dioxide (CO2) and store carbon 40% more than the dry land forest ecosystem. Due to this ability, the total carbon deposited in a square kilometer of mangrove ecosystem is 50 times faster than those of the same area in a dryland tropical forest ecosystem. The absorbed CO2 is stored not only in the plants, but in layers of soils underneath [2]. Therefore, mangroves are playing a crucial role in global carbon budgets and thus mitigating climate change.
\nHowever, despite being realized the importance of mangroves in the global carbon cycle and climate change, the extents of mangroves have inevitably declined since the last few decades. Unfortunately, the declines have been resulting mainly from human activities such as aquaculture expansion, coastal development, and over-harvesting [3]. Malaysia is one of the countries in South East Asia that has among the largest extents of mangroves. Despite its extensive distribution of mangrove ecosystem, this forest is inevitable from threats by various land use activities. The total area of mangrove forest was approximately 2% (650,000 ha) of the total land area in Malaysia in the 1990s [4].
\nHowever, the mangroves in Malaysia have been gradually diminishing, where the total area of mangrove forest has reduced to approximately 580,000 ha in the last decade [5]. Other reports indicated that the extent of mangrove areas in Malaysia is decreasing, from about 700,000 ha in 1975 to 572,000 ha in 2000 due to the intensive harvesting and natural wave actions [6, 7]. Globally, mangroves have also declined from 18.8 million ha to 15.6 million ha between years 1980 and 2005 [8]. Overall Asia was the largest net loss of mangroves since 1980, with about 1.9 million ha have loss, mainly due to conversion of mangrove forest to other land uses. However, there has been a slowdown in the annual rate of mangrove loss, from about 187,000 ha in the 1980s to 102,000 ha between 2000 and 2005. This reflects an increased awareness and an improved management system in mangroves ecosystem.
\nMajor threats towards the mangroves that are triggered by human activities can generalized into six [9], which are (i) conversion to other uses, (ii) overharvesting, (iii) overfishing, (iv) pollution, (v) sedimentation and (vi) alteration of flow regimes. Direct conversion to other uses was identified as the major factor that changes the world’s mangroves. This includes conversions to (i) urban and industrial areas, (ii) aquaculture, and (iii) agriculture. Additionally, natural phenomena such as coastal erosion, storm and lightning strikes are also the natural impacts that kill mangroves in Peninsular Malaysia, including the tragic tsunami on 24 December 2004.
\nDespite widespread concern and numerous case studies describing local issues and challenges, comprehensive information on the global extent of mangroves and trends of deforestation is largely lacking [10]. It is because determining the precise area of mangroves is not always easy. Measurement is affected by varying definitions of what constitutes mangroves; inclusion only on the basis of official recognition such as gazetted forest reserves; scattered or sparse areas considered too inconsequential for inclusion; and the accuracy of the returns made by the responsible authorities. Each of these can create uncertainty and produce significant variation depending on the timing and purpose of the assessment exercise.
\nRecently, RS satellites have been widely used for mangrove monitoring. They greatest reasons why is because the RS can (i) acquire information over large areas, (ii) produce repeated measurement over a place, and (iii) make full use of electromagnetic spectrum for quantitative and qualitative measurements over mangroves [11]. Satellites also provide information on spatial distribution and temporal changes of mangrove forests. When this information is gathered over decades, the mangrove monitoring over the large area will become possible. There are studies on the assessment of mangroves changes and identifying threats, for example in Terengganu [12], Selangor [13], and Peninsular Malaysia [14]. However, these studies are unable to represent the holistic conditions at national level. Therefore, this study was conducted to provide the information pertaining status of mangroves and changes that occurred since the last three decades.
\nThe study area covers the entire mangroves ecosystem in Malaysia, which can be divided into two regions, which are Peninsular Malaysia and East Malaysia (i.e. Malay Borneo). Forests in these regions can be divided into three major types, which are inland dipterocarps (dryland), peat swamp, mangrove forests (wetlands). The mangrove forest is a unique ecosystem and the second largest wetland forest type after the peat swamp forest. Ecologically based on elevation the mangrove forest is located at the lowest elevation, which is equivalent to the sea level. The mangrove forest is generally found along sheltered coasts where it grows abundantly in saline soil and brackish water dominated mainly by trees from the
Images from Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper (ETM+), and Landsat-8 Operational Land Imager (OLI) satellite were used in this study. Images from three different epochs, which are 1990, 2000 and 2017 were acquired to conduct the work. For the respective years were utilized in this study. All images are available at https://earthexplorer.usgs.gov/ and were downloaded free of charge. At least 23 scenes of Landsat images were used for a single epoch (Figure 1). Therefore, to complete the series, the study has acquired at least 69 scenes of Landsat images, assuming that all images are free from cloud cover. However, cloud cover are presence on some of the images, hence, more than one scene of images over the same year were acquired to remove the clouds.
\nLandsat scenes that were used for the classification. Numbers within the scene boundary indicates path/row ID of Landsat satellites.
Cloud cover is inevitable on the images acquired by the satellites. However, cloud patching process can eliminate the cloud covers that appear on a single-date observation data. Images of particular scenes that were acquired on different dates were used for cloud patching process as shown in Figure 2. F_mask algorithm was used to perform this process [15, 16]. Seamless mosaics product (i.e. images without cloud covers and atmospherically corrected) were used as input for subsequent processes.
\nCloud detection and removal process. Individual Landsat scene that was captured on 26 January 2017 (a) was merged with that captured on 14 June 2017 (b), where both produced a cloud-free images for the year 2017 (c).
Appropriate enhancement techniques were applied to the images to make the mangroves appear better on the images [17]. In addition to the individual spectral bands of Landsat images, vegetation indices such as Normalized Different Vegetation Index (NDVI), Green Atmospherically Resistant Index (GARI), and Normalized Difference Infrared Index (NDII) were also derived from the images to improve quality of classification. The vegetation indices that were used in this study are summarized in Table 1.
\nVegetation indices that were used derived from the images.
Most spectral-based image classifications are performed using traditional methods such as maximum likelihood, linear discriminant analysis, and spectral angle mapper classifiers. These methods are applied to the spectral bands to produce a classified feature in images [18]. Instead of using these approaches, this study attempted a new approach to classify the images. R Package, which is free, open source software with the RandomForest algorithm [19] was used.
\nRandomForest implements Breiman’s RandomForest algorithm, based on Breiman and Cutler’s original FORTRAN code for classification and regression [20]. It can also be used for assessing proximities among data points without necessarily a training set. All sampling points that were collected on the ground were connected to the corresponding pixels on the image through this algorithm. Classification was done by searching the most important variables i.e. which spectral bands are used in decision tree approach [21, 22, 23]. RandomForest applies four major steps of looking at the importance of variables as follow:
Step 1: to determine the significance of the mth variable. In the left out cases for the kth tree, randomly permute all values of the mth variable. Put these new covariate values down the tree and get classifications.
Steps 2 and 3: for the nth case in the data, its margin at the end of a run is the proportion of votes for its true class minus the maximum of the proportion of votes for each of the other classes. The 2nd measure of importance of the mth variable is the average lowering of the margin across all cases when the mth variable is randomly permuted as in Step 1. Step 3 then count the margins that was shrank.
Step 4: the splitting criterion used in RandomForest is the Gini criterion, a mechanism that can measure the most to least importance of variables used in decision tree. At every split, one of the mth variables is used to form the split and there is a resulting decrease in the Gini. The sum of all will decrease the forest due to a given variable, normalized by the number of trees.
All images have been classified to distinguish mangroves from the other land uses. The classification results were transformed into vector shapefile for further refinement and editing. The accuracy of the classification results were assessed by using a number of ground truth points. The GIS platform was used to carry out post-classification analysis. Post-classification analysis is usually used for quantifying changes of land uses. Changes of mangroves were identified from the conversions of mangroves to other landuse classes, which are (i) urban, settlement, and industrial areas, (ii) agricultural, (iii) aquaculture activities, and (iv) coastal erosion.
\nCarbon dioxide (CO2) is defined as natural, colorless and odorless greenhouse gas that is emitted when fossil fuels (i.e. natural gas, oil, coal, etc.) are burnt. In this study, the CO2 emission is expressed as C loss, assuming that the gas is emitted when deforestation occur. The units of metric tons C was converted to CO2 by multiplying the ratio of the molecular weight of carbon dioxide to that of carbon (44/12 = 3.67) [24].
\nThe CO2 resulted from deforestation is one of the important elements in greenhouse gases emissions. Therefore, it is also essential to quantify the contribution of mangrove deforestation towards the CO2 emission. Net emission as resulted from deforestation of mangroves can be estimated based stock-difference method, which can be expressed as Eq. (1) as follow [24];
\nwhere ∆
F mask algorithm successfully removed almost 100% of cloud covers and their shadows on the images. The algorithm also managed to detect thin, low temperature clouds in the high altitude by thermal sensors onboard the Landsat TM, ETM+ and OLI. The algorithm somehow failed to detect small scattering clouds that occurred in small patches on the images. Nevertheless, the algorithm has facilitated the cloud removal process and make the mangroves mapping and monitoring work at landscape-level practical. Figure 2 shows a portion of mangroves on two different images that were captured on different dates with clouds. These images were used to produce seamless mosaic of images without cloud covers.
\nThe study indicated that the suitable spectral bands for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important spectrum across all scales and the visible bands (437–700 nm) were more important at pixel and crown scales. By using the RandomForest algorithm, the most important bands in the classification were represented by a mean decrease Gini values. The most important bands in mangroves discrimination, from most to least, are; MidIR, NIR-2, NIR, Green, Blue, Red. Spectral profile of the images also showed that the NIR channels separate the mangroves from the other land covers very well (Figure 3). On the other hands, the vegetation indices that were used in this study played similar important role in mangroves classification.
\nSpectral profiles of several land covers extracted from the images. Channel 1 through 6 on the y-axis are blue, green, red, NIR, NIR-2 and MidIR, respectively.
The image classification approach that has been applied in this study was found to be effective only at large coverage of mangroves. The accuracy for all classifications were ranging from 83 to 91%, which were acceptable and reliable for monitoring purpose. Mangroves are normally appear dark on any combination of spectral bands of multispectral image. This is due to the natural ecosystem of mangroves, which is covered by swamps and sometimes inundated by tidal water. The chlorophyll content of the mangrove leaves, which is higher than those of trees and crops, tends to make them appear darker on satellite images [25], as depicted in Figure 4. Each mangrove species has a unique configuration of trunks, prop roots and pneumatophores that works as a different drag force therefore resulting in a different reduction rate of sea waves (Figure 5). Not only this, the wet floor of the forest gives special spectral characteristics on satellites images that can be differentiated easily from other features (Figure 6).
\nImages showing (a) combination of bands 5, 6 and 4 of Landsat-8 OLI and (b) combination of vegetation indices, NDVI, GARI and NDII. These images were selected for the classification process.
Roots and successive stands of
Mangroves as they appeared on Landsat-8 image. The dark green areas represent the mangrove areas. The image classification process, either automated or manual digitizing, is usually easier for mangrove areas than for other vegetation. The image is displayed using a combination of bands 543 (RGB) over the Kapar area in Klang, Selangor. The central bottom is Klang port complex and the bottom left is Pulau Klang, which is predominantly covered by mangroves.
The classification results were further edited to refine the shapes and accuracy. This process was conducted manually on the vector shapefile by visual interpretation on GIS platform. Finally the spatial distribution of the mangroves were mapped properly (Figure 7). The mangroves in Malaysia were mostly found in Sabah (60%), followed by Sarawak (22%) and Peninsular Malaysia (18%). Table 2 summarizes the total extents of mangroves in the respective regions that have been produced from the classification. It is notable that the total extents of mangroves have been decreasing throughout the monitoring period. Figure 8 shows spatially explicit map of mangroves distribution in Malaysia as of year 2017. Mangroves are found mainly along the west coast of Peninsular Malaysia, west coast of Sarawak and the east coast of Sabah.
\nMangroves appear dark green on the original image (left) and the classified mangroves, indicated as red polygons (right).
Region | \nMangroves 1990 (ha) | \nMangroves 2000 (ha) | \nMangroves 2017 (ha) | \n
---|---|---|---|
Peninsular Malaysia | \n116,746 | \n114,353 | \n110,953 | \n
Sabah | \n385,630 | \n382,448 | \n378,195 | \n
Sarawak | \n147,936 | \n145,263 | \n139,890 | \n
Total | \n650,311 | \n642,063 | \n629,038 | \n
Extents of mangroves in Malaysia.
Distribution of mangroves in Malaysia over the year 2017.
Table 3 reports the changes of mangroves that occurred over the 27 years of monitoring period. The total loss of mangroves was about 21,274 ha where majority of the mangroves loss were outside the Permanent Forest Reserve or within the stateland areas. These areas are actually the land bank for the states developments, which are principally included in the State’s Structural Planning. Example of mangroves changes detected from the multi-temporal mapping process is shown in Figure 9. From this information, it can be concluded that the annual decrease rate of mangroves was about 788 ha per year or about 0.13% per annum since year 1990. Major factors that contributed to these changes have been identified as: (i) direct conversion to other land uses (Figure 10), predominantly for commercial-scale agriculture (Figure 11) and aquaculture (Figure 12), and (ii) coastal erosion (Figure 13). The other factors such as overharvesting and pollution affect the mangroves to a lesser degree.
\nRegion | \nMangrove loss 1990–2000 (ha) | \nMangrove loss 2000–2017 (ha) | \nRate of deforestation 1990–2000 (ha yr−1) | (% yr−1) | \nRate of deforestation 2000–2017 (ha yr−1) | (% yr−1) | \nAverage rate of deforestation 1990–2017 (ha yr−1) | (% yr−1) | \n
---|---|---|---|---|---|
Peninsular Malaysia | \n2393 | \n3400 | \n239 | 0.20 | \n200 | 0.17 | \n215 | 0.19 | \n
Sabah | \n3182 | \n4253 | \n318 | 0.08 | \n250 | 0.07 | \n275 | 0.07 | \n
Sarawak | \n2673 | \n5373 | \n267 | 0.18 | \n316 | 0.22 | \n298 | 0.21 | \n
Total | \n8227 | \n13,190 | \n823 | 0.13 | \n776 | 0.12 | \n793 | 0.13 | \n
Mangroves deforestation in Malaysia between years 1990 and 2017.
Changes of mangroves that occurred between 1990 and 2017 overlaid on GIS platform.
Land developments on mangroves. Reddish color represents newly opened areas for development purposes that were cleared from the original mangroves areas (dark green color).
Expansion of oil palm plantation on mangroves. Reddish color represents newly opened plantations from the original mangroves areas (dark green color). The bright green represents existing plantations.
Expansion of aquaculture industries on mangroves. Dark blue patches represents newly opened aquaculture ponds from the original mangroves areas (dark green color).
Shoreline changes that resulted from coastal erosion along the coast of south Pontian, Johor. The study indicated that 14.2 km stretches have been facing serious coastal erosion within the last two decades with the rate of erosion ranging from 3.2 to 12.5 m per year.
Although coastal erosion was identified as one of the factors of mangroves loss, there were some accretions occurred in some other places. Erosion and accretion is a dynamic process and takes place along the coastlines and major estuaries, where suspended sediments are likely to settle. These phenomena also lead to species succession when the existing plant species die due to unsuitable soil and new species emerge. Besides, mangrove roots can act as wave breaker and promote flocculation and sedimentation, eventually forming mudflats that allow positive accretion (Figure 14). Coastal erosion occurs when the waves hit perpendicular to the coastlines and when the rapid flow of sea currents wash away the sand or soil particles. The frequency and height of waves hitting the coastlines contribute to the harshness of coastal erosion. Thus, the presence of mangroves can reduce the coastal erosion significantly. This condition is obvious particularly in the areas facing the sea [26, 27].
\nPositive accretion of mangroves at estuaries. The new formations at the river mouths were colonized by mangroves trees forming a naturally generated forest.
A study has indicated that the average C stock (aboveground and belowground) in mangroves in Malaysia is about 181 Mg C ha−1 [28]. The extents of mangroves loss for each epoch were multiplied by this average carbon stocks. The study demonstrated that the total loss of carbon due to the loss of mangroves was about 2.6 million Mg C. Subsequently, this has led to the CO2 emission at about 14.2 million Mg CO2, with an average of about 0.5 million Mg CO2 emission per year, along the monitoring period. Table 4 summarizes the impact of mangroves loss in terms of CO2 emission. Although the figures are generally crude, the study provided some ideas for further studies, especially which related to carbon cycles and climate change.
\nRegion | \nMangrove loss (ha) | \nCarbon loss (Mg C) | \nCO2 emission (Mg CO2) | \nRate of CO2 emission (Mg CO2 yr−1) | \n
---|---|---|---|---|
Peninsular Malaysia | \n5793 | \n1,048,567 | \n3,848,242 | \n142,527 | \n
Sabah | \n7435 | \n1,345,672 | \n4,938,617 | \n182,912 | \n
Sarawak | \n8046 | \n1,456,288 | \n5,344,578 | \n197,947 | \n
Total | \n21,417 | \n3,876,409 | \n14,226,422 | \n526,905 | \n
CO2 emission resulted from mangroves loss between years 1990 and 2017.
This study has successfully assessed the current state of mangroves and determined the rate of mangroves loss in Malaysia since the last decade. Total mangroves in Malaysia has decreased from 650,311 ha in 1990 to 629,038 ha in 2017. Total deforestation was accounted at 21,274 ha or 3.3% with the annual rate of deforestation of 788 ha yr−1 or 0.13% yr−1, between 1990 and 2017. The study also quantified the C stock changes and estimated CO2 emission due to the loss of mangroves in Malaysia. Total emission caused by the mangroves deforestation was accounted at about 14 million Mg CO2 with annual emission rate of around 0.5 million Mg CO2 yr−1.
\nThe study found that the Landsat-based mapping and monitoring of mangroves was very practical. It provides a reliable information on mangroves distribution, both qualitatively and quantitatively. Landsat missions provide a very useful RS tool for monitoring changes of mangroves over time. The study suggests that appropriate actions should be taken by the Government of Malaysia to protect the mangroves and keep their ecosystem intact forever. The most effective way to conserve the mangroves is to gazette the remaining stateland forest as Permanent Reserved Forests (PRFs). These PRFs should then be maintained as amenity for current and future generations, while contributing to the mitigation of climate change impacts at the local level. Any development in PRFs should be prohibited or implemented with caution.
\nOverall, there is great potential in the application of Landsat-based data with appropriate GIS technique for mapping and monitoring of mangroves in Malaysia. Although there are cloud covers problems on some of the images, this has not hindered the assessment of mangroves at landscape and regional levels. The accuracy and precision also vary depending on the objective of the application. However, the ability to detect major changes in the ecosystem that can cause profound and irreversible damage far outweighs a perfectly or highly accurate and precise RS based method at this point.
\nCurrently, Malaysia has reserved about 85% (~535,000 ha) out of the total areas of mangroves as Permanent Forest Reserve and State/National Parks. The remaining 15% is under the state-lands and alienated lands. By far, the most effective way to preserve these mangroves is through gazzeting into permanent forest reserves.
\nThis work has been carried out under the Research and Development Committee on Mangroves (JTRD) led by FRIM. Special thanks for the Forestry Department Peninsular Malaysia (JPSM), Sabah Forestry Department (SFD), and Forest Department Sarawak (FDS) for the supports on the ground data collection activities.
\nThe authors declare no ‘conflict of interest’ for this chapter.
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