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1. Introduction
1.1. Trypanosomiasis
A group of animal and human diseases caused by parasitic protozoan trypanosomes is called trypanosomiases. The final decade of the 20th century witnessed a frightening revival in sleeping sickness (human African trypanosomiasis) in sub-Saharan Africa. Meanwhile, Chagas\' disease (American trypanosomiasis) remains one of the most widespread infectious diseases in South and Central America. Arthropod vectors are responsible for the spread of African and American trypanosomiases, and disease restraint through insect control programs is an attainable target. However, the existing drugs for both illnesses are far from ideal. The trypanosomes are some of the earliest diverging members of the Eukaryotae and share several biochemical oddities that have inspired research into discovery of new drug targets. Nevertheless, discrepancies in mode of interactions between trypanosome species and their hosts have spoiled efforts to design drugs effective against both species. Heightened awareness of these neglected diseases might result in progress towards control through increased financial support for drug development and vector eradication [1].
Trypanosome is a group of unicellular parasitic flagellate protozoa which mostly infects the vertebrate genera. A number of trypanosome species cause important veterinary diseases, but only two cause significant human diseases. In sub-Saharan Africa, Trypanosoma brucei causes sleeping sickness or human African trypanosomiasis whilst in America, Trypanosoma cruzi causes Chagas\' disease (Figure 1) [2]. Meanwhile, the life cycle of these parasitic protozoa engage insect vectors and mammalian hosts (Figure 2) [1]. All trypanosomes require more than one obligatory host to complete their life cycle and are transmitted via vectors. Most of the species are transmitted by blood-feeding invertebrates, however there are distinct mechanisms among the varying species. In the invertebrate hosts they are generally found in the intestines as opposed to the bloodstream or any other intracellular environment in the mammalian host. As trypanosomes develop through their life cycle, they undergo a series of morphological changes [3] as is typical of trypanosomatids.
Figure 1.
Geographic distribution of Trypanosoma brucei and Trypanosoma cruzi, showing endemic countries harboring these diseases [2].
Figure 2.
Life cycles of (A) Trypanosoma cruzi and (B) Trypanosoma brucei. Upper cycles represent different stages that take place in the insect vectors. Lower cycles represent different stages in man and other mammalian hosts [1].
The life cycle often consists of the trypomastigote form in the vertebrate host and the trypomastigote or promastigote form in the gut of the invertebrate host. Intracellular lifecycle stages are normally found in the amastigote form. The trypomastigote morphology is unique to species in the genus Trypanosoma.
The genome organization of T. brucei is splitted into nuclear and mitochondrial genomes. The nuclear genome of T. brucei is made up of three classes of chromosomes according to their size on pulsed-field gel electrophoresis, large chromosomes (1 to 6 megabase pairs), intermediate chromosomes (200 to 500 kilobase pairs) and mini chromosomes (50 to 100 kilobase pairs) [4]. The large chromosomes contain most genes, while the small chromosomes tend to carry genes involved in antigenic variation, including the variant surface glycoprotein (VSG) genes. Meanwhile, the mitochondrial genome of the Trypanosoma, as well as of other kinetoplastids, known as the kinetoplast, is characterized by a highly complex series of catenated circles and minicircles and requires a cohort of proteins for organisation during cell division. The genome of T. brucei has been completely sequenced and is now available online [5].
1.3. Nuclear transport
Nuclear transport of proteins and ribonucleic acids (RNAs) between the nucleus and cytoplasm is a key mechanism in eukaryotic cells [6]. The transport between the nucleus and cytoplasm involves primarily three classes of macromolecules: substrates, adaptors, and receptors. The transport complex is formed when the substrates bind to an import or an export receptor. Some transport substrates require one or more adaptors to mediate formation of a transport complex. Once assembled, these transport complexes are transferred in one direction across the nuclear envelope via aqueous channels that are part of the nuclear pore complexes (NPCs). Following dissociation of the transport complex, both adaptors and receptors are recycled through the NPC to allow another round of transport to occur. Directionality of either import or export therefore depends on the formation of receptor-substrate complex on one side of the nuclear envelope and the dissociation of the complex on the other. The Ran GTPase is vital in producing this asymmetry. Modulation of nuclear transport generally involves specific inhibition of the formation of a transport complex, however, more global forms of regulation also occur [7]. The general concept of import and export process is shown in Figure 3 [8].
1.4. In silico approach
In silico study is defined as an analysis which is performed using computer or via computer simulation. It involves the strategy of managing, mining, integrating, and interpreting information from biological data at the genomic, metabalomic, proteomic, phylogenetic, cellular, or whole organism levels. The bioinformatics instruments and skills become crucial for in silico research as genome sequencing projects have resulted in an exponential growth in protein and nucleic acid sequence databases. Interaction among genes that gives rise to multiprotein functionality generates more data and complexity. In silico approach in medicine is not only reducing the need for expensive lab work and clinical trials but also is possible to speed the rate of drug discovery. In 2010, for example, researchers found potential inhibitors to an enzyme associated with cancer activity in silico using the protein docking algorithm EADock [9]. About 50 % of the molecules were later shown to be active inhibitors in vitro [9]. A unique advantage of the in silico approach is its worldwide accessibility. In some cases, having internet access or even just a computer is sufficient enough. Laboratory experiments either in vivo or in vitro both require more materials. In protein sequence analysis, in silico approach gives highly reproducible results in many cases or even exactly the same results because it only relies on comparison of the query sequence to a database of previously annotated sequences. However, in sophisticated analysis such as development of the 3-D structure of proteins from their primary sequences, discrepancies in results are to be expected due to the manual optimization which must consider several crucial steps such as template selection, target-template alignment, model construction and model evaluation.
Figure 3.
For import of molecules, cytoplasmic cargo is identified by Importin a, which then binds to Importin b (1). This ternary complex translocates through the nuclear membrane and into the nucleus. Once there, RanGTP binds to Importin b and causes a dissociation of the complex, which releases cargo to the nucleus (2). Import receptors are then recycled back to the nucleus (3) through binding of RanGTP and export to the cytosol. RanGTP is then hydrolyzed to the GDP-bound state and causes the release of the import receptors (4) and the cycle starts over again. Export of cargo undergoes a similar mechanism. Exported molecules will bind to the export receptor with RanGTP and exit the nucleus (5). Next RanGTP is hydrolyzed to cause release of cargo into the cytoplasm (6). NTF2 specifically identifies RanGDP and returns it to the nucleus (7) for RCC1 to then exchange it to RanGTP (8) [8].
1.5. Problem statements
Considering the importance of nuclear shuttling in many cellular processes, proteins responsible for the nuclear transport are vital for parasite survival. The presence of nuclear transport machinery was highlighted in the eukaryotic parasites such as Plasmodium falciparum, Toxoplasma gondii and Cryptosporidium parvum. However, the nuclear transport in T. brucei has not been established. Nuclear shuttling is one of the overlooked aspects of drug design and delivery. Exploitation of macromolecules movement across the nuclear envelope promises to be an exciting area of drug development. Furthermore, the divergence between host and parasite systems is always exploited as a strategy in drug development. Therefore, the exploitation of peculiarities of T. brucei nuclear transport machinery as compared to its host might be a promising strategy for the control of trypanosomiasis, which remains to be further investigated.
1.6. Objectives
This study is carried out to investigate the nuclear transport constituents of T. brucei by determining the functional characteristics of the parasite proteins. This includes functional protein domain, post translational modification sites and protein-protein interaction. The parasite proteins identified to exhibit the relevant functional protein domains, post translational modification sites and protein-protein interaction, are predicted as the true components for nuclear transport mechanism. This study also aims to evaluate the unique characteristics of proteins responsible for nuclear transport machinery between the parasites and human by determining the degree of protein sequence similarity. The information on the sequence level divergence between T. brucei proteins and their human counterparts may provide an insight into drug target discovery.
2. Materials and methods
Our in silico analyses were carried out using the public databases and web based programs (Table 1). The programs were employed to identify and annotate the parasite proteins involved in the nuclear transport mechanism. The identified parasite proteins were then compared with the human counterparts.
Analysis
Programme name
URL and Reference where available
Protein sequence retrieval
National Centre for Biotechnology Information (NCBI)
www.ncbi.nlm.nih.gov/
Universal Protein Knowledgebase/SwissProt (UniProtKB/ SwissProt)
http://www.uniprot.org/
TriTrypDB
http:// tritrypdb.org/ tritrypdb/
Clustering of protein sequences
BLASTClust
www.vardb.org/vardb/analysis/blastclust.html
Identification of protein domains
Conserved Domain Database (CDD)
http://www.ncbi.nlm.nih.gov/cdd/
Simple Modular Architecture Research Tool (SMART)
http://smart.embl-heidelberg.de/
InterPro
http://www.ebi.ac.uk/interpro/
Identification of post translational modification sites
PROSITE
http://prosite.expasy.org/
Sequence similarity search
BLASTp (NCBI)
http://blast.ncbi.nlm.nih.gov/
Table 1.
Databases and web-based programs used in the analysis of nuclear transport of T. brucei.
We utilized a personal computer equipped with AMD Turion 64x2 dual-core processor, memory size of 32 gigabytes and NVIDIA graphics card to perform the analyses. Our in silico work is summarized in Figure 4.
The nuclear transport refers to a process of entry and exit of large molecules from the cell nucleus. To identify T. brucei proteins of nuclear transport, the protein sequences of other various eukaryotic organisms were retrieved in FASTA format from National Centre for Biotechnology Information (NCBI) server and Universal Protein Knowledgebase/SwissProt (UniProtKB/ SwissProt) database based on biological processes and protein name search. The number of hits obtained for the query was recorded after manual inspection. The retrieved protein sequences were clustered into groups with more than 30% similarity using BLASTClust [10] to reduce non-redundant protein sequences. The non-redundant data set was subjected to BLASTp [11] analyses against an integrated genomic and functional genomic database for eukaryotic pathogens of the family Trypanosomatidae, TriTrypDB. The analysis was using cutoff point with E-value of less than 1e-06 and score of more than 100. Hits that pointed to the same location or overlapped location were removed manually. The identified protein sequences then were then retrieved from the TriTrypDB.
Figure 4.
In silico analysis workflow.
A portion of protein that can evolve, function, and exist independently is called protein domain. It is a compact three dimensional structure, stable and distribution of polar and non-polar side chains contribute to its folding process. To determine the functional protein domains, all identified protein sequences of T. brucei from TriTrypDB were subjected to functional annotation which makes use of Conserved Domain Database (CDD) [12], Simple Modular Architecture Research Tool (SMART) [13] and InterPro [14] programs. The protein sequences were submitted in FASTA format as queries.
Posttranslational modification (PTM) is the chemical modification of a protein after its translation. It is one of the later steps in protein biosynthesis, and thus gene expression, for many proteins. In this part of study, in relation to regulatory aspects of nuclear transport mechanism, we focused on potential glycosylation and phosphorylation sites. To analyze the post translational modification sites, all protein sequences of T. brucei from TriTrypDB were subjected to PROSITE [15] programme. The proteins sequences were submitted in FASTA format as queries.
Protein–protein interactions occur when two or more proteins bind together, often to carry out their biological function. Proteins might interact for a long time to form part of a protein complex, a protein may be carrying another protein, or a protein may interact briefly with another protein just to modify it. To analyze the participation of parasite proteins in protein-protein interactions, all protein sequences of T. brucei from TriTrypDB were subjected to mining of STRING 8.2 database [16]. The STRING 8.2 database integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections. The proteins sequences were submitted in FASTA format as queries. All information on protein-protein interaction were recorded and evaluated accordingly.
The degree of similarity between amino acids occupying a particular position in the protein sequence can be interpreted as a rough measure of how conserved a particular region or sequence motif is. To compare the parasite proteins with human homologues, all protein sequences of T. brucei from TriTrypDB were subjected to BLASTp analysis against Homo sapiens proteins. The proteins sequences were submitted in FASTA format as queries. The criteria such as cutoff point with E-value of less than 1e-06 and score of more than 100 were used.
3. Results and discussions
3.1. Parasite proteins involved in the nuclear transport machinery
Table 2 shows a summary of protein sequences used in this in silico analysis. A total of 904 and 642 protein sequences were retrieved in FASTA format from NCBI server and UniProt/SwissProt database respectively. A total of 18 protein sequences with less than 100 amino acid residues were excluded from the study as they were considered not completely functional [17]. Hence, 1528 protein sequences were used for protein sequence clustering. The 30% identity and above at the amino acid level is considered sufficient to imply functional relatedness [17]. Therefore, protein clustering with more than 30% similarity on the retrieved protein sequences produced a non-redundant data set of 248 protein sequences.
Protein sequences
Total
Raw protein sequences retrieved from NCBI and UniProtKB
1546
Raw protein sequences subjected to BLASTClust programme
1548
Non redundant protein sequences resulting from BLASTClust analysis
248
Query sequences for BLASTp analysis against TritrypDB database
248
Table 2.
Summary of protein sequences retrieved in in silico analysis.
The BLASTp analyses against TriTrypDB using cut off point with E-value of less than 1e-06 and score of more than 100 for the whole 248 query protein sequences resulted in 34 hits of parasite proteins. However our approach failed to identify a Ran GTPase-activating protein (RanGAP) protein in this parasite. In reference [18] also reported that sequence similarity searches have been unable to identify a RanGAP protein in any protozoan. Keyword searches among annotated proteins in the T. gondii genome database identified one candidate which was shown to have strong similarity to Ran-binding protein 1 (RanBP1) based on sequence analysis. Perhaps the RanGAP function in apicomplexans is performed by a single protein with multiple cellular responsibilities (i.e., a fusion of Ran binding protein 1 and RanGAP). It is also possible that a completely unique parasite protein possesses the RanGAP function.
Table 3 shows the identified and characterized parasite proteins involved in the nuclear transport machinery. The functional annotation based on protein domains, showed that, out of 34, only 22 parasite protein sequences were predicted with high confidence level to be involved in the nuclear transport mechanism with the presence of relevant protein domains. This includes guanine triphosphate (GTP)-binding domain, Nucleoporin (NUP) C terminal domain, Armadillo repeat, Importin B N-terminal domain, regulator of chromosome condensation 1 (RCC1) repeat and Exportin domain (Table 4). All these protein domains were experimentally verified to regulate the nuclear transport mechanism in eukaryotes. There were seven T. brucei proteins that exhibited functional features of the Importin receptor. This finding is consensus with the number of Importin receptors in another eukaryotic pathogen, Toxoplasma gondii [8]. In addition, our results of other nuclear transport constituents in T. brucei such as RCC1, Ran, nuclear transport factor 2 (NTF2), cell apoptosis susceptibility (CAS), Exportin and Ran binding proteins were also in agreement with reference [18].
The nuclear and cytoplasmic compartments are divided by the nuclear envelope in eukaryotes. By using this compartmentalization and controlling the movement of molecules between the nucleus and the cytosol, cells are able to regulate numerous cellular mechanisms such as transcription and translation. Proteins with molecular size lower than 40 kDa are able to passively diffuse through the nuclear pore complex (NPC), whereas larger proteins require active transport through the assistance of Karyopherins, specific transport receptors that shuttle between the nucleus and cytosol. Karyopherins which are able to distinguish between the diverse proteome to target specific cargo molecules for transport, can be subdivided into those that transport molecules into the nucleus (Importins) and those that transport molecules out of the nucleus (Exportins). It has been reported that more than 2000 proteins are shuttled between the nucleus and the cytoplasm in yeast [19]. From our result, with the identification of Karyopherin and Nucleoporin proteins in T. brucei, we expect that the parasite employs the typical components for the nuclear transport machinery.
Subject sequences
E-value
Score
Functional protein domains
1.70E-72
718
Ran GTPase, GTP-binding domain
5.50E-33
Karyopherin Importin Beta, Armadillo repeat
9.30E-149
Exportin-1 C terminal, Importin Beta N terminal domain
3.20E-16
Ran binding domain
2.40E-15
Exportin-like protein
9.10E-83
Karyopherin Importin Beta, Armadillo repeat
1.10E-75
CAS/CSE domain, Importin Beta N terminal domain
6.90E-20
172
RCC1 repeat
5.80E-09
NUP C terminal domain
2.10E-28
NUP C terminal domain
2.50E-48
281
Ran-binding protein Mog1p
8.20E-42
Armadillo repeat, Karyopherin Importin Beta
2.60E-26
Armadillo-like helical
Tb11.01.8030
1.70E-18
218
HEAT repeat, Armadillo repeat, Importin Beta N terminal domain
7.10E-07
Nsp1-like
Tb927.7.6320
1.20E-11
136
RCC1 repeat
3.70E-08
149
Armadillo-like helical
Tb09.160.2360
1.40E-36
WD40 repeat
8.50E-14
RNA recognition motif
1.30E-08
Nuclear transport factor 2 domain
4.60E-77
Importin Beta N terminal domain, Karyopherin domain
2.90E-08
112
Nuclear transport factor 2 domain
Table 3.
Identified and characterized T. brucei proteins of nuclear transport. Protein domain identification involved CDD, SMART, InterPro and PROSITE programs.
Protein domain
Accession
Description
Ran GTPase
SM00176
Ran is involved in the active transport of proteins through nuclear pores.
Ran binding domain
PDOC50196
This domain binds RanGTP and increases the rate of RanGAP1-induced GTP hydrolysis.
Armadillo
IPR000225
The Armadillo (Arm) repeat is an approximately 40 amino acid long tandemly repeated sequence motif first identified in the Drosophila melanogaster segment polarity gene armadillo involved in signal transduction through wingless. Animal Arm-repeat proteins function in various processes, including intracellular signalling and cytoskeletal regulation, and include such proteins as beta-catenin, the junctional plaque protein plakoglobin, the adenomatous polyposis coli (APC), tumour suppressor protein, and the nuclear transport factor importin-alpha, amongst others
Importin beta
IPR001494
Members of the Importin-beta (Karyopherin-beta) family can bind and transport cargo by themselves, or can form heterodimers with importin-alpha. As part of a heterodimer, Importin-beta mediates interactions with the pore complex, while Importin-alpha acts as an adaptor protein to bind the nuclear localisation signal (NLS) on the cargo through the classical NLS import of proteins.
HEAT
IPR000357
Arrays of Huntingtin, elongation factor 3 (EF3), protein phosphatase 2A (PP2A), and the yeast PI3-kinase TOR1 (HEAT) repeats consists of 3 to 36 units forming a rod-like helical structure and appear to function as protein-protein interaction surfaces. It has been noted that many HEAT repeat-containing proteins are involved in intracellular transport processes.
Exportin 1-like protein
pfam08389
The sequences featured in this family are similar to a region close to the N-terminus of yeast exportin 1 (Xpo1, Crm1). This region is found just C-terminal to an importin-beta N-terminal domain (pfam03810) in many members of this family. Exportin 1 is a nuclear export receptor that interacts with leucine-rich nuclear export signal (NES) sequences, and Ran-GTP, and is involved in translocation of proteins out of the nucleus.
CAS/CSE
IPR005043
In the nucleus, cell apoptosis susceptibility (CAS) acts as a nuclear transport factor in the importin pathway. The Importin pathway mediates the nuclear transport of several proteins that are necessary for mitosis and further progression. CAS is therefore thought to affect the cell cycle through its effect on the nuclear transport of these proteins
WD40
IPR001680
WD-repeat proteins are a large family found in all eukaryotes and are implicated in a variety of functions ranging from signal transduction and transcription regulation to cell cycle control and apoptosis. Repeated WD40 motifs act as a site for protein-protein interaction, and proteins containing WD40 repeats are known to serve as platforms for the assembly of protein complexes or mediators of transient interplay among other proteins.
RCC1
PDOC00544
The regulator of chromosome condensation (RCC1) is a eukaryotic protein which binds to chromatin and interacts with ran, a nuclear GTP-binding protein (see <PDOC00859"/>), to promote the loss of bound GDP and the uptake of fresh GTP, thus acting as a guanine-nucleotide dissociation stimulator (GDS)
NUP C-terminal
PDOC51434
Communication between the nucleus and cytoplams of an eukaryotic cell is mediated by the nuclear pore complexes (NPCs), which act as selective molecular gateways. Through these gateways, ribonucleic acids (RNAs) and proteins are exported into the nucleus. Each NPC consists of ~30 distinct proteins termed Nucleoporins, each present in at least eight copies, reflecting the octagonal symmetry of the complex.
NSP 1
IPR007758
The NSP1-like protein appears to be an essential component of the nuclear pore complex, for example preribosome nuclear export requires the Nup82p-Nup159p-Nsp1p complex.
NTF 2
IPR002075
Nuclear transport factor 2 (NTF2) is a homodimer which stimulates efficient nuclear import of a cargo protein. NTF2 binds to both RanGDP and FxFG repeat-containing Nucleoporins.
Table 4.
Summary of protein domains
3.2. Regulatory aspect of the parasite nuclear transport
Table 5 shows the presence of phosphorylation and glycosylation sites in the parasite proteins. The phosphorylation sites were found to be present in all parasite proteins. It was predicted that the parasite proteins could be phosphorylated at Serine, Threonine and Tyrosine amino residues. However, the O-glycosylation sites were not present in three parasite proteins, namely Tb11.02.0870, Tb927.8.3370 and Tb927.7.5760.
Subject sequences
Phosphorylation site
Glycosylation site
+
+
+
+
+
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
Tb11.01.8030
+
+
+
+
Tb927.7.6320
+
+
+
+
Tb09.160.2360
+
+
+
+
+
-
+
+
+
+
+
+
Table 5.
Phosphorylation and O-glycosylation sites in the T. brucei proteins. Identification of these functional sites involved ScanProsite programme.
Most of the parasite proteins were predicted to be involved in O-linked glycosylation. In eukaryotes, the O-linked glycosylation takes place in the Golgi apparatus. It also occurs in archaea and bacteria. Phosphorylation was reported to be crucial in the regulation of protein-protein interactions of the NADPH oxidase in the phagocytic cells [20]. The phosporylation-based signaling in T. brucei has been reported by reference [21]. Thus we believe that the phosphorylation could also regulate the nuclear transport components of T. brucei to participate in various functional interactions. Meanwhile, it was suggested that O-linked glycosylation may be analogous to protein phosphorylation. According to [22], phosphorylation by proline-directed kinases share the same sites with those potentially O-glycosylated by O-linked N-acetylglucosamine transferase (OGT). From this it is possible that O-glycosylation and phosphorylation may compete for sites of modification. Therefore, it is a strong likelihood that the nuclear transport of T. brucei could be regulated by both phosphorylation and O-glycosylation.
Apart from acting simply as an architectural structure which facilitates nuclear transport, the NPC may also play a more dynamic role in regulating transport. The specificity of import and export may be influenced by recognition of different substrates and alteration of the Nucleoporin expression. This would allow different interaction between the NPC and Karyopherins and modulate the nuclear import and export. However, the most common impact on nucleocytoplasmic movement stems comes from the post translational modifications of the cargo proteins themselves [23]. The post translational modification of NPC was reported by [24]. Post-translational modification of NUPs by ubiquitylation and phosphorylation can affect NUP turnover and pore disassembly, respectively. Our study identified four parasite proteins containing the Nucleoporin-related domain. We anticipate that the assembly and disassembly of the parasite Nucleoporin proteins might also be modulated by phosphorylation.
The NPC becomes an ideal target for inhibition of nuclear import or export. One of the most common features of Nucleoporins is the presence of conserved FG or FXFG repeats that bind to the Importin family members [25]. The monoclonoal antibodies such as mAb414 and RL2 can interrupt translocation through the NPC by blocking the FG and FXFG epitopes of the Nucleoporins. Consequently, several Nucleoporin proteins were identified by their reactivity against the anti-FG antibodies. Most of these FG repeat proteins exist as the cytoplasmic fibrils or projections on the nuclear side of the NPC. The monoclonal antibodies prevent cargo from associating with the edge of an NPC so it cannot cross the membrane [26]. Thus, there is a possibility that the pathogenesis of T. brucei could be controlled by inhibiting its Nucleoporin proteins.
3.3. Participation of parasite proteins in functional interaction network
Figure 5 illustrates the protein interaction data obtained from STRING 8.2 database. The mining of protein interaction data which is useful in contextual annotation of protein function showed that, out of 22 parasite homologues, only nine parasite proteins were interacting with each other. Out of the seven identified T. brucei Importins, only two namely Tb927.6.2640 and Tb10.70.4720 were found to be involved in that protein interaction network. This database mining approach indicated that T. brucei nuclear transport is typical of eukaryotic organisms. Importins initially recruit cargo at low RanGTP concentrations in the cytoplasm and release cargo at high RanGTP levels in the nucleus. Importin–RanGTP complexes return afterwards to the cytoplasm, where the Ran-bound GTP is finally hydrolysed and Ran dissociates from the receptor. The Importin can then bind and import another cargo molecule, while nuclear transport factor 2 (NTF2) recycles RanGDP back to nucleus. The cargo binding to exportins is controlled in a reverse manner compared to Importins; they recruit cargo at high RanGTP levels in the nucleus and release cargo at low RanGTP concentrations in the cytoplasm.
Figure 5.
Protein functional interaction network in the nuclear transport of T. brucei. This protein interaction data was obtained from STRING 8.2 database. The letters (a-i) indicate the parasite proteins involved in the nuclear transport.
Table 5 shows evaluation of the obtained protein interaction data of the parasite nuclear transport. There were 13 functional interactions between parasite proteins identified from the mining of STRING 8.2 database. The score values of functional interactions range from 0.45 to 0.976. The Importin alpha (Tb927.6.2640) was found to be the most interactive parasite proteins by participating in six functional interactions. Based on the relevant protein domains and previous reports, four out of 13 functional interactions were considered with high confidence level. It should be emphasized that our approach only considered the protein interaction data derived from experiments, gene fusion and text mining. To our knowledge, this is the first report of functional protein interactions in the nuclear transport of the eukaryotic parasites. Whether other eukaryotic parasites share the common protein interaction network for the nuclear transport machinery remains to be elucidated.
Subject sequence
Interacting partner
Source
Score
Confidence level
Reference
Tb927.3.1120
Tb11.01.5940
Experiment
0.45
High
Lounsbury and Macara (1997)
Tb927.3.1120
Tb11.02.0870
Experiment,Text mining
0.512
Moderate
None
Tb927.3.1120
Tb927.8.4280
Experiment
0.534
High
Fried and Kutay (2003)
Tb11.01.5940
Tb11.02.0870
Experiment,Text mining
0.88
High
Lounsbury and Macara (1997)
Tb11.01.5940
Tb927.6.2640
Experiment,Text mining,Co-expression
0.812
Moderate
None
Tb11.01.5940
Tb927.6.4740
Text mining,Co-expression
0.46
Moderate
None
Tb11.02.0870
Tb927.6.2640
Experiment,Text mining
0.453
Moderate
None
Tb927.6.2640
Tb927.6.4740
Experiment,Text mining,Co-expression
0.976
Moderate
None
Tb927.6.2640
Tb09.160.2360
Experiment,Text mining
0.647
Moderate
None
Tb927.6.2640
Tb10.70.4720
Experiment,Text mining
0.769
High
Fried and Kutay (2003)
Tb927.6.2640
Tb927.8.4280
Experiment,Text mining
0.641
Moderate
None
Tb10.70.4720
Tb927.8.4280
Experiment,Text mining
0.535
Moderate
None
Tb927.8.4280
Tb927.8.3370
Experiment
0.502
Moderate
None
Table 6.
Evaluation on protein interaction data obtained from STRING 8.2 database. The evaluation was based on the identified protein domains.
To gain an insight into nuclear transport, understanding on interactions between transport receptors and proteins of the nuclear pore complex (NPC) is essential. According to [27], the fluorescence resonance energy transfer (FRET) can be employed between enhanced cyan and yellow fluorescent proteins (ECFP, EYFP) in living cells in order to explain the transport of receptor through the NPC. A FRET assay has been used to analyze a panel of yeast strains expressing functional receptor--ECFP and nucleoporin-EYFP fusions. Based on this approach, points of contact in the NPC for the related Importin Pse1/Kap121 and Exportin Msn5 were successfully characterized. That study proved the advantage of FRET in mapping dynamic protein interactions in a genetic system. In addition, both Importin and Exportin have overlapping pathways through the NPC. However, our database mining approach did not reveal any functional interaction between Nucleoporin and Karyopherin proteins of T. brucei.
3.4. Sequence similarity between parasite proteins and their human counterparts
Table 6 shows the degree of protein sequence similarity between parasite and human proteins. The similarity search for the sequence was carried out with the help of BLASTp tool. All the parasite proteins of nuclear transport machinery were found to have their counterparts in human. The degree of sequence similarity between parasite proteins and human counterparts range from 19% to 72%. The resulting score values range from 49.3 to 558. Meanwhile, all the identified human proteins contain the same protein domains involved in the nuclear transport.
Subject sequence
Human counterparts
Score
E-value
Sequence similarity (%)
NP_006316.1
313
1.00E-109
72%
NP_694858.1
221
6.00E-62
25%
NP_003391.1
558
0
33%
AAA85838.1
79.3
5.00E-20
40%
AAH20569.1
79.3
2.00E-16
29%
NP_036448.1
360
4.00E-119
42%
AAC50367.1
368
9.00E-113
29%
AAI42947.1
453
2.00E-27
27%
AAH45620.2
258
2.00E-09
38%
NP_705618.1
134
1.00E-33
28%
AAF36156.1
70.9
2.00E-17
27%
NP_002262.3
207
2.00E-56
23%
NP_006382.1
156
9.00E-28
24%
Tb11.01.8030
NP_002262.3
101
3.00E-23
21%
CAA41411.1
59.7
4.00E-10
19%
Tb927.7.6320
NP_001041659.1
146
4.00E-17
28%
NP_006381.2
65.9
2.00E-12
20%
Tb09.160.2360
NP_003601.1
142
2.00E-39
30%
NP_001073956.2
75.5
8.00E-18
31%
NP_037380.1
49.3
6.00E-11
26%
NP_002256.2
277
2.00E-81
28%
NP_005787.1
73.6
1.00E-19
31%
Table 7.
Comparison of the identified parasite proteins with human counterparts at protein sequence level. This comparison involved BLASTp programme.
A study reported by [28] showed that despite the high degree of similarity in the primary structure of human and T. cruzi ubiquitins, the three amino acid difference is sufficient to distinguish parasite versus host proteins. In this study, a simplified one step purification procedure to partially purify T. cruzi ubiquitin was performed. Following this preparation, ELISA and Western blots were carried out to show that chagasic sera recognise T. cruzi but not human or Leishmania ubiquitin indicating a species-specific response. Thus, it is probable that the T. brucei proteins could also be distinguished from human counterparts at primary sequence level by using the immunodetection method.
4. General discussions
4.1. Transport of cargoes
In RanGTPase system, Ran-binding protein 1 (RanBP1) which is cytoplasmic localized binds RanGTP and eases the RanGAP-dependent conversion of RanGTP to RanGDP [29]. This indicates that RanBP1 catalyses the cytoplasmic disassembly of RanGTP–transport receptor complexes. These complexes are kinetically so stable that RanGAP alone fails to trigger GTP hydrolysis [30-32]. RanBP2 [33] is a major constituent of the cytoplasmic filaments of NPCs and exhibits similar functions as RanBP1. It has four RanBP1 homology domains and forms a stable complex with sumoylated RanGAP [34,35], in order to dismantle the RanGTP–transport receptor complexes that exit the nucleus. Importin- and exportin-mediated transport cycles can accumulate cargoes against gradients of chemical activity, which is an energy-dependent process. The RanGTPase system hydrolyses one GTP molecule per transport cycle, and a number of evidences suggest that this contains the sole input of metabolic energy [36-39]. We have successfully identified all the required key components in the T. brucei nuclear transport. Whether their functionalities in vivo are consensus with the known ones still remains to be further investigated.
4.2. Relationship between signaling pathways and nuclear transport
Many aspects of cell physiology are greatly dependent on the signaling pathways. This includes members of the mitogen activated protein (MAP) kinase family as well as phosphatidyl inositol 3 (PI3) and adenosine monophosphate (AMP) kinases which are crucial in controlling the cell growth, proliferation, apoptosis and the response to stress. By activating the signaling pathway through multiple kinase cascades, various stressors are able to regulate the nuclear transport. For example, oxidative and heat stress activate both MAP kinase kinase (MEK)-extracellular signal regulated kinase 1/2 (ERK1/2) and PI3 kinase-Akt pathways [40]. Based on these observations and the fact that many of the transport components are modified post-translationally, it was sensible to investigate whether these modifications are regulated by stress. A study reported by [41] showed that oxidant treatment induced phosphorylation and/or GlcNAc modification of soluble transport factors and nucleoporins. Interestingly, changes in transport factor modifications are not limited to stress conditions, as modifying ERK or PI3 kinase activities in unstressed cells also affect the transport factors. This is exemplified by the regulation of RanBP3 through ERK1/2-ribosomal S6 kinase (RSK) signaling, a regulatory link which ultimately controls the Ran concentration gradient. Furthermore, phosphorylation of Nup50 which is dependent on ERK, reduces its association with importin-β1 and transportin in vitro, and ERK2 is responsible to the oxidant-induced collapse of the Ran gradient [42]. It remains unknown how much modulating individual transport factors contributes to the overall regulation of nuclear trafficking. However, it is noteworthy that the kinase inhibitor PD98059, which targets ERK1/2 and ERK5, significantly increases classical nuclear import, both under normal and stress conditions. Taken together, these results highlight a critical role of ERK activity in nuclear transport, with ERK kinases targeting both soluble factors and nucleoporins [41]. Thus, there is an urgent need to investigate the possible connection between upstream signaling apparatus with nuclear transport components in T. brucei.
4.3. In silico approach for drug target discovery
We have provided interpretation of heterologous data sets for nuclear transport system of T. brucei from various resources. With the availability of protein databases and computer-aided softwares, we are able to explain various functional interactions between identified parasite proteins and how these functional interactions give rise to functionality and behavior of the parasite nuclear transport. This would partially facilitate the exhausted effort to obtain system-level understanding of T. brucei pathogenesis. Our in silico approach has the potential to speed up the rate of drug target discovery while reducing the need for expensive lab work and clinical trials. The conventional approaches in vivo and in vitro have high tendencies to produce inefficient results when investigating complex large scale data such as proteins associated with nuclear shuttling of macromolecules across the nuclear envelope. Therefore, the systematic in silico approach from this study provides a tremendous opportunity of cost effective drug target discovery for the pharmaceutical industry.
4.4. Experimental validation of in silico data
Experimental techniques such as yeast two-hybrid assay and affinity purification combined with mass spectrometry are useful to investigate the possible protein-protein interaction. However, they have their limitations in detecting certain types of interactions. They also have technical problems to scale-up for high-throughput analysis. In conjunction with this, in silico approach may solve those problems in inferring the protein function. The scope of experimental data can be expanded to increase the confidence of certain interacting protein pairs with the availability of databases containing in silico data such as protein domain and 3D structure. The databases integrate information from various resources such as computational prediction methods and public text collections. Since in silico and experimental approaches are complementary to each other, the combination of these different approaches is very useful to obtain a more accurate picture of T. brucei nuclear transport.
4.5. Our further direction
In silico approach offers various advantages over in vivo and in vitro approaches such as non-use of animals, low costs, and reduced execution time. This approach allows identification of proteins of interest from a particular biological study. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. Many bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. Thus, in addition to data mining for protein-protein interaction, further in silico approach should also consider structural alignment, molecular docking and pathway modeling in order to obtain a comprehensive and more reliable insight into protein-protein interaction of T. brucei nuclear transport.
5. Conclusion
The availability of protein databases and computer-aided softwares to identify probable components of cellular mechanisms has become a new trend in the present scientific era. We demonstrate here a computational analysis of nuclear transport in T. brucei as an initial step and proof of concept for further investigation. Our approach successfully identified 22 T. brucei proteins essential for nuclear transport. All those parasite proteins were found to contain relevant functional domains that drive the translocation of macromolecules in the parasite. The phosphorylation and O-glycosylation sites were also detected in all identified parasite proteins. This has given us an insight into the regulatory aspect of parasite nuclear transport. The database mining of protein interaction has shown that nine out of 22 parasite proteins possess relevant functional interactions for nuclear transport activities. However, more functional interactions from nuclear transport constituents of T. brucei are required to elucidate the exact mechanism. The homology between the parasite proteins and human counterparts was shown by BLASTp analyses. Whether there are structural differences between them remain unknown.
The nuclear transport in T. brucei has been characterized by using the in silico approach. The predicted functionalities and regulatory aspects of parasite nuclear transport constituents were in agreement with the previous reports. Moreover, the protein interaction data derived from the public database has made the participation of parasite proteins in the mechanism more convincing. Thus, we have laid a path for understanding the nuclear transport machinery in T. brucei. The development of drugs that target as well as alter nuclear import and export will undoubtedly become beneficial in controlling Trypanosomiasis in future. Drugs that have a direct effect on a single protein must be able to localize to the same site as the protein and interact with one or more of its domains. Alternatively, a drug that effectively blocks the target protein from reaching its proper organelle can also inhibit the protein’s function.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/41234.pdf",chapterXML:"https://mts.intechopen.com/source/xml/41234.xml",downloadPdfUrl:"/chapter/pdf-download/41234",previewPdfUrl:"/chapter/pdf-preview/41234",totalDownloads:1640,totalViews:172,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 4th 2011",dateReviewed:"April 24th 2012",datePrePublished:null,datePublished:"November 28th 2012",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/41234",risUrl:"/chapter/ris/41234",book:{slug:"bioinformatics"},signatures:"Mohd Fakharul Zaman Raja Yahya, Umi Marshida Abdul Hamid and Farida Zuraina Mohd Yusof",authors:[{id:"139063",title:"Mr.",name:"Mohd Fakharul Zaman",middleName:null,surname:"Raja Yahya",fullName:"Mohd Fakharul Zaman Raja Yahya",slug:"mohd-fakharul-zaman-raja-yahya",email:"fakharulzaman@salam.uitm.edu.my",position:null,institution:{name:"Universiti Teknologi MARA",institutionURL:null,country:{name:"Malaysia"}}},{id:"156401",title:"Dr.",name:"Umi Marshida",middleName:null,surname:"Abdul Hamid",fullName:"Umi Marshida Abdul Hamid",slug:"umi-marshida-abdul-hamid",email:"marshida@salam.uitm.edu.my",position:null,institution:null},{id:"161136",title:"Dr.",name:"Farida Zuraina",middleName:null,surname:"Mohd Yusof",fullName:"Farida Zuraina Mohd Yusof",slug:"farida-zuraina-mohd-yusof",email:"fzuraina@salam.uitm.edu.my",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Trypanosomiasis",level:"2"},{id:"sec_2_2",title:"1.3. Nuclear transport",level:"2"},{id:"sec_3_2",title:"1.4. In silico approach",level:"2"},{id:"sec_4_2",title:"1.5. Problem statements",level:"2"},{id:"sec_5_2",title:"1.6. Objectives",level:"2"},{id:"sec_7",title:"2. Materials and methods",level:"1"},{id:"sec_8",title:"3. Results and discussions",level:"1"},{id:"sec_8_2",title:"3.1. Parasite proteins involved in the nuclear transport machinery",level:"2"},{id:"sec_9_2",title:"3.2. Regulatory aspect of the parasite nuclear transport",level:"2"},{id:"sec_10_2",title:"3.3. Participation of parasite proteins in functional interaction network",level:"2"},{id:"sec_11_2",title:"3.4. Sequence similarity between parasite proteins and their human counterparts",level:"2"},{id:"sec_13",title:"4. General discussions",level:"1"},{id:"sec_13_2",title:"4.1. Transport of cargoes ",level:"2"},{id:"sec_14_2",title:"4.2. Relationship between signaling pathways and nuclear transport",level:"2"},{id:"sec_15_2",title:"4.3. In silico approach for drug target discovery",level:"2"},{id:"sec_16_2",title:"4.4. Experimental validation of in silico data",level:"2"},{id:"sec_17_2",title:"4.5. Our further direction",level:"2"},{id:"sec_19",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'BarrettM. P.BurchmoreR. J.StichA.2003The trypanosomiases. Lancet 3629394146980'},{id:"B2",body:'MilesM.2003American trypanosomiasis (Chagas disease). GC Cook, A Zumla (Eds.), Manson’s tropical disease (21st edn.), Elsevier Science, London, 13251337'},{id:"B3",body:'HeckerH.BohringerS.1977Morphometric analysis of the life cycle of Trypanosoma brucei. Ann. Soc. belge Med. trop., 57465470'},{id:"B4",body:'OgbadoyiE.ErsfeldK.RobinsonD.SherwinT.GullK.2000Architecture of the Trypanosoma brucei nucleus during interphase and mitosis. Chromosoma 108850113'},{id:"B5",body:'Acosta-SerranoA.VassellaE.LinigerM.RenggliC. K.BrunR.RoditiI.Englund. P. T.2001The surface coat of procyclic Trypanosoma brucei: Programmed expression and proteolytic cleavage of procyclin in the tsetse fly. PNAS, 9815131518'},{id:"B6",body:'GorlichD.MattajI. W.1996Nucleocytoplasmic transport. Science 27115131518'},{id:"B7",body:'MattajI. W.EnglmeierL.1998Nucleocytoplasmic transport: The Soluble Phase. Annual Review of Biochemistry, 67265306'},{id:"B8",body:'FriedH.KutayU.2003Nucleocytoplasmic transport:taking an inventory. Cell Mol Life Sci 6016591688'},{id:"B9",body:'RöhrigU. F.AwadL.GrosdidierA.LarrieuP.StroobantV.ColauD.CerundoloV.AndrewJ. G.2010Rational Design of Indoleamine 2,3-Dioxygenase Inhibitors. Journal of Medicinal Chemistry 533117289\n\t\t\t'},{id:"B10",body:'Hayes, C N2008varDB: a pathogen-specific sequence database of protein families involved in antigenic variation, Bioinformatics.\n\t\t\t'},{id:"B11",body:'AltschulS. F.GishW.MillerW.MyersE. W.LipmanD. J. .1990Basic local alignment search tool. J. Mol. Biol, 215403410'},{id:"B12",body:'Marchler-BauerA.AndersonJ. B.CherukuriP. F.De Weese-ScottC.GeerL. Y.GwadzM.HeS.HurwitzD. I.JacksonJ. D.KeZ.LanczyckiC. J.LiebertC. A.LiuLu. C. F.MarchlerG. H.MullokandovM.ShoemakerB. A.SimonyanV.SongJ. S.ThiessenP. A.YamashitaR. A. J. J.YinD.ZhangBryantS. H.2005CDD: a Conserved Domain Database for protein classification. Nucleic Acid Research, 33192196'},{id:"B13",body:'LetunicI.DoerksT.BorkP.2009SMART 6: recent updates and new developments. Nucleic Acid Research. 37229232'},{id:"B14",body:'HunterS.ApweilerR.AttwoodT. K.BairochA.BatemanA.BinnsD.BorkP.DasU.DaughertyL.DuquenneL.FinnR. D.GoughJ.HaftD.HuloN.KahnD.KellyE.LaugraudA.LetunicI.LonsdaleD.LopezR.MaderaM.MaslenJ.Mc AnullaC. J.Mc DowallMistry. J.MitchellJ. A.MulderN.NataleD.OrengoC.QuinnA. F.SelengutJ. D.SigristC. J.ThimmaM.ThomasP. D.ValentinF.WilsonD.WuC. H.YeatsC.2009InterPro: the integrative protein signature database. Nucleic Acids Research, 37211215'},{id:"B15",body:'HuloN.BairochA.BulliardV.CeruttiL.De CastroE.Langendijk-GenevauxD. S.PagniM.SigristC. J. A.2006The PROSITE database. Nucleic Acid Research, 34227230'},{id:"B16",body:'JensenL. J.KuhnM.StarkM.ChaffronS.CreeveyC.MullerJ.DoerksT.JulienP.RothA.SimonovicM.BorkP.VonMering. C.2009STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acid Research. 7412416'},{id:"B17",body:'SchmidM.1998Novel approaches to the discovery of antimicrobial agents. Curr. Opin. Chem. Biol. 2529534'},{id:"B18",body:'FrankelM. B.KnollL. J.2009The Ins and Outs of Nuclear Trafficking: Unusual Aspects in Apicomplexan Parasites. DNA and Cell Biology 28277284'},{id:"B19",body:'Macara, I G2001Transport into and out of the nucleus. Microbiol Mol Biol Rev 65570594'},{id:"B20",body:'Babior B M1999NADPH oxidase: an update. Blood 935146476\n\t\t\t'},{id:"B21",body:'NettI. R. E. D.MartinD. M. A.Miranda-SaavedraD.LamontD.BarberJ. D.MahlertA.2009The phosphoproteome of bloodstream form Trypanosoma brucei, causative agent of African sleeping sickness. Molecular & Cellular Proteomics, 815271538'},{id:"B22",body:'MillerM. W.CaraccioloM. R.BerlinW. K.HanoverJ. A.1999Phosphorylation and Glycosylation of Nucleoporins. Archives of Biochemistry and Biophysics, 367(1), 51-60.'},{id:"B23",body:'GasiorowskiJ. Z.DeanD. A.2003Mechanisms of nuclear transport and interventions. Advanced Drug Delivery Reviews, 55703716'},{id:"B24",body:'SchuldtA.2012Post-translational modification: A monoubiquitylation pore anchor. Nature Reviews Molecular Cell Biology, 13, 66.'},{id:"B25",body:'RaduA.BlobelG.MooreM. S.1995Identification of a protein complex that is required for nuclear protein import and mediates docking of import substrate to distinct nucleoporins, Proc. Natl. Acad. Sci. USA 9217691773'},{id:"B26",body:'GasiorowskiJ. Z.DeanD. A.2003Mechanisms of nuclear transport and interventions. Advanced Drug Delivery Reviews, 55703716'},{id:"B27",body:'DamelinM. S. P.2000Mapping interactions between nuclear transport factors in living cells reveals pathways through the nuclear pore complex. Mol Cell., 5(1), 133-40.'},{id:"B28",body:'TéllesS.AbateT.SlezyngerT. C.1999Trypanosoma cruzi and human ubiquitin are immunologically distinct proteins despite only three amino acid difference in their primary sequence. FEMS Immunol Med Microbio, 24(2), 123-30.'},{id:"B29",body:'BischoffF. R.KrebberH.SmirnovaE.DongW. H.PonstinglH.1995Coactivation of RanGTPase and inhibition of GTP dissociation by Ran GTP binding protein RanBP1. EMBO J, 14705715'},{id:"B30",body:'BischoffF. R.GörlichD.1997RanBP1 is crucial for the release of RanGTP from importin β-related nuclear transport factors. FEBS Lett, 419249254'},{id:"B31",body:'FloerM.BlobelG.RexachM.M.1997Disassembly of RanGTP-karyopherin β complex, an intermediate in nuclear protein import. J Biol Chem, 2721953819546'},{id:"B32",body:'LounsburyK. M.MacaraI. G.1997Ran-binding protein 1 (RanBP1) forms a ternary complex with Ran and karyopherin β and reduces Ran GTPase-activating protein (RanGAP) inhibition by karyopherin β. J Biol Chem, 272551555'},{id:"B33",body:'YokoyamaN.1995A giant nucleopore protein that binds Ran/TC4. Nature, 376184188'},{id:"B34",body:'MahajanR.DelphinC.GuanT.GeraceL.MelchiorF.1997A small ubiquitin-related polypeptide involved in targeting RanGAP1 to nuclear pore complex protein RanBP2. Cell, 8897107'},{id:"B35",body:'MatunisM. J.CoutavasE.BlobelG.1996A novel ubiquitin-like modification modulates the partitioning of the Ran-GTPase-activating protein RanGAP1 between the cytosol and the nuclear pore complex. J Cell Biol, 13514571470'},{id:"B36",body:'EnglmeierL.OlivoJ. C.MattajI. W.1999Receptor-mediated substrate translocation through the nuclear pore complex without nucleotide triphosphate hydrolysis. Curr Biol, 93041'},{id:"B37",body:'KoseS.ImamotoN.TachibanaT.ShimamotoT.YonedaY.1997Ran-unassisted nuclear migration of a 97 kD component of nuclear pore- targeting complex. J Cell Biol, 139841849'},{id:"B38",body:'RibbeckK.KutayU.ParaskevaE.GörlichD.1999The translocation of transportin-cargo complexes through nuclear pores is independent of both Ran and energy. Curr Biol, 94750'},{id:"B39",body:'WeisK.DingwallC.LamondA. I.1996Characterization of the nuclear protein import mechanism using Ran mutants with altered nucleotide binding specificities. EMBO J, 1571207128'},{id:"B40",body:'KodihaM.BanskiP.StochajU.2009Interplay between MEK and PI3 kinase signaling regulates the subcellular localization of protein kinases ERK1/2 and Akt upon oxidative stress. FEBS Lett, 583198793'},{id:"B41",body:'KodihaM.CramptonN.ShrivastavaS.UmarR.StochajU.2010Traffic control at the nuclear pore. Aging, 237244'},{id:"B42",body:'CzubrytM. P.AustriaJ. A.PierceG. N.2000Hydrogen peroxide inhibition of nuclear protein import is mediated by the mitogen-activated protein kinase, ERK2. J Cell Biol, 148716'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Mohd Fakharul Zaman Raja Yahya",address:null,affiliation:'
School of Biology, Faculty of Applied Sciences, MARA University of Technology Shah Alam, Shah Alam Selangor, Malaysia
'},{corresp:null,contributorFullName:"Umi Marshida Abdul Hamid",address:null,affiliation:'
School of Biology, Faculty of Applied Sciences, MARA University of Technology Shah Alam, Shah Alam Selangor, Malaysia
The development in the forest industry has progressed to the use of solid-like materials rather than solid wood materials. The main reason behind this progress is thought to be the demand and the concern on the capability of meeting this demand. Although wood is an important raw material, it has become more difficult to meet the demand in every passing year [1]. Because the formation of wood-based raw material obtained from forests needs a quite long period of time. The growth of wood-based industries all over the world has made the use of new substitute materials instead of wood inevitable [2, 3, 4]. On the other hand, neither the diversity in substitute materials nor the use of both wood-based materials and other materials at the same time has reduced the demand for wood. In this respect, the wood industry is subject to a constant development and change [5]. The wood-based panel industry is an important forest-based one in China. At this point, for example, wood-based panels have high economic importance in China economy. Some projections show that the production of the wood-based panel industry has expanded considerably in recent years and is expected to increase with an average annual growth rate of 1.05% from 2015 to 2030 [6, 7]. One of the most leading sectors in terms of this mentioned change and development has been the board industry.
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In fact, the forest industry has been an essential leading branch of the industry since old days in terms of social development [8, 9, 10, 11, 12]. Therefore, the use of wood in the industry continues as it was in the past. Similarly, the use of wood as an industrial material is still an important source of income for both those who produce wood and treat wood to produce wood products [13, 14]. When the subject is approached in terms of the industry, another reality is that the particleboard and fiberboard industries have developed particularly in recent years and a heavy raw material demand exists [15]. When the issue is considered from a historical perspective, the industrial production of particleboard started in 1941 in Germany and showed a rapid development after 1948 [16]. Although the fiberboard industry started in the early 1900s, the large-scale commercial production emerged between the two world wars in the United States [17, 18, 19]. The main reason for the fact that the particleboard industry first emerged and developed in Central European countries is the desire to substitute wood with a new and more economical construction material with more convenient dimensions and to make savings from wood use just like the other construction materials as a result of the destruction caused by the Second World War [20].
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The fact that the private sector completely dominated the forest product industry since the early 1990s has been accompanied by huge investment in the field by private companies. When considered from this point of view, it can be stated that the wood-based panel industry has carried out a great development in the past 20 years in particular [21]. However, rapid growth and development have brought adverse effects as well. The difficulty in meeting raw material demand comes at the top of these adverse effects. The most serious bottleneck in meeting the demand for raw material is considered to be the prices and the amount of the demand [12, 22, 23]. Public dominated production in Turkey has usually been a problem in meeting the raw material demand of the private sector. This situation has led the sector to import, but as a result of the recent changes in the raw material exporting policies of the countries and the economic events, this option has become insufficient in solving the problem.
\n
The estimated production capacity of the board industry in Turkey is 5.1 million m3 of particleboard per year. As for fiberboard, the production amount is 6.8 million m3 per year [24]. The total number of production facilities in the sector is 35 of which 19 produce particleboard and 16 produce fiberboard. The total production capacity of the industry is approximately 12 million m3 per year, whereas the actual production is 8.6 million m3 per year (Particle Board Industry Association [25]). Therefore, in addition to particleboard and fiberboard purchased by the sector from General Directorate of Forestry (OGM), fuelwood has also been added to the demand list of the sector. Latest investments in the fiberboard and particleboard industries and capacity enhancement attempts are expected to move the sector further. However, the expectation of low raw material supply for the sector is assumed as the biggest obstacle for the companies by the representatives of the sector against production enhancement (particularly in 2013). The expectations by the industry, the production amount of the forestry organization, and the changes in related policies shall directly affect the future of the industry. At this point, it is crucially important to estimate the changes in the raw material supply of the wood-based industry in the forthcoming period.
\n
This study aims to introduce suggestions on meeting the raw material demand, which is considered to be the main problem of the wood-based panel industry. The raw material supply amount of OGM, which is the main raw material supplier for the industry, has been projected by considering the particle-fiber wood, which is an important kind of raw material for the sector, and fuelwood production amount between the years 1977 and 2017. Certainly, the presence of various social and economic factors has been taken into consideration while making the projection. The main factors taken into consideration might be listed as follows: population; gross national product per capita; and afforestation fields, which are important for the sustainability of the forestlands and the unit sale price of the particle-fiber wood and fuelwood. One of the important variables to be considered in terms of the results of the study is the number of companies operating in the board industry and their production capacities. However, these variables have not been able to be evaluated under findings as there are not any regular statistics on the issue, but they have been evaluated in the suggestions provided under the conclusion part instead. The fact that no data can be found on the particle-fiber wood production of OGM until the year 1977 has been effective in gathering data starting from the year 1977.
\n
\n
\n
2. Material and method
\n
As it is known, the dependent variable in an economic event is sometimes affected by a single independent variable and sometimes by more than one independent variable. When the dependent variable is explained by more than one independent variable, multidimensional decision-making methods are used [26]. Multidimensional decision-making methods are suitable for the structure of forest resources, and with the use of the method, more significant decisions and solution offers in forest resources management can be created [27, 28]. In this respect, multidimensional decision-making methods are of the most frequently used methods in forestry studies. Regression analysis is one of the appropriate multidimensional decision-making methods for the study.
\n
In regression modeling, the intended use has to be well defined in order to find the most appropriate regression model [29, 30, 31]. Since the long-term data of the previous years (between 1977 and 2017) had been obtained regularly on a yearly basis and the purpose was to estimate the raw material production to meet the demand, regression modeling has been preferred to use. Two techniques are used in regression analyses. They are simple regression analysis and multiple regression analysis. Multiple linear regression modeling has been determined as the most appropriate modeling technique for the study as it provides the chance of evaluating multiple data. Multiple linear regression analysis has been formed for the purposes of revealing how the production amount of particle-fiber wood and fuelwood changes depending on the specified independent variables and determining the raw material amount that can be provided by country resources for the industry. Future projections concerning supply and demand equilibrium have been made regarding the established capacity of the industry (taking into account the available quantitative data range and the data quantity) as well. In terms of research technique, Durbin Watson (DW) statistic and coefficient have been utilized first, in order to test the autocorrelation among the independent variables used in the multiple linear regression analysis.
\n
While determining the particle-fiber wood and fuelwood supply amounts of the wood-based panel industry according to the data by OGM, particle-fiber wood production amount (Y1) and fuelwood production amount (Y2) have been specified as dependent variables. Unit sale price of the particle-fiber wood (X1), unit sale price of fuelwood (X2), afforestation rate (X3), population (X4), and current producer prices in the US dollar basis with the gross national product (X5) have been accepted as independent variables. The data related to the mentioned variables have been derived from the databases of OGM, Turkish Statistical Institute (TÜİK), İstanbul Chamber of Commerce (ITO), State Planning Organization (DPT), and the World Bank. The data including the number of facilities in the industry and their production capacity have also been obtained from the databases of the aforementioned institutions and from their reports related to the sector. All the obtained data are given in Table 1.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Years
\n
Particle-fiber wood productions (m3)
\n
Fuel wood productions (m3)
\n
Particle-fiber wood unit prices ($USD/m3)
\n
Fuel wood unit prices ($USD/m3)
\n
Afforestation (ha)
\n
Population
\n
Gross national product per person (GNPP) ($USD)
\n
\n
\n
Y1
\n
Y2
\n
X1
\n
X2
\n
X3
\n
X4
\n
X5
\n
\n\n\n
\n
1977
\n
1
\n
171,000
\n
20,309,000
\n
15.24
\n
7.70
\n
37,985
\n
41,316,300
\n
1427
\n
\n
\n
1978
\n
2
\n
184,000
\n
20,071,000
\n
14.84
\n
8.72
\n
34,050
\n
42,206,200
\n
1550
\n
\n
\n
1979
\n
3
\n
173,000
\n
20,046,000
\n
19.66
\n
10.85
\n
27,867
\n
43,132,600
\n
2079
\n
\n
\n
1980
\n
4
\n
164,000
\n
21,949,000
\n
18.63
\n
11.25
\n
20,969
\n
44,347,719
\n
1564
\n
\n
\n
1981
\n
5
\n
180,000
\n
20,192,000
\n
12.75
\n
10.99
\n
45,943
\n
45,130,000
\n
1579
\n
\n
\n
1982
\n
6
\n
439,000
\n
20,372,000
\n
12.06
\n
8.35
\n
53,680
\n
45,353,405
\n
1402
\n
\n
\n
1983
\n
7
\n
742,000
\n
19,851,000
\n
16.06
\n
7.48
\n
66,210
\n
46,965,156
\n
1310
\n
\n
\n
1984
\n
8
\n
953,000
\n
16,659,000
\n
22.38
\n
8.46
\n
87,627
\n
48,735,507
\n
1247
\n
\n
\n
1985
\n
9
\n
884,000
\n
14,289,000
\n
20.83
\n
12.52
\n
100,400
\n
50,664,458
\n
1368
\n
\n
\n
1986
\n
10
\n
1,071,000
\n
12,138,000
\n
15.08
\n
11.05
\n
108,354
\n
51,706,684
\n
1511
\n
\n
\n
1987
\n
11
\n
913,000
\n
12,503,000
\n
23.59
\n
8.18
\n
114,132
\n
52,770,350
\n
1706
\n
\n
\n
1988
\n
12
\n
1,137,000
\n
12,942,000
\n
21.16
\n
12.72
\n
119,369
\n
53,855,897
\n
1745
\n
\n
\n
1989
\n
13
\n
1,193,000
\n
13,062,000
\n
15.20
\n
11.14
\n
113,639
\n
54,963,775
\n
2022
\n
\n
\n
1990
\n
14
\n
1,113,000
\n
12,145,000
\n
17.20
\n
13.47
\n
78,884
\n
56,473,035
\n
2794
\n
\n
\n
1991
\n
15
\n
1,104,000
\n
11,503,000
\n
15.69
\n
12.70
\n
56,752
\n
57,512,139
\n
2736
\n
\n
\n
1992
\n
16
\n
1,177,000
\n
11,146,000
\n
29.50
\n
17.19
\n
24,519
\n
58,570,362
\n
2842
\n
\n
\n
1993
\n
17
\n
1,004,000
\n
10,846,000
\n
36.25
\n
25.44
\n
27,058
\n
59,648,057
\n
3180
\n
\n
\n
1994
\n
18
\n
1,363,000
\n
8,379,000
\n
16.60
\n
10.94
\n
39,652
\n
60,745,581
\n
2270
\n
\n
\n
1995
\n
19
\n
1,320,000
\n
9,539,000
\n
19.46
\n
13.59
\n
24,257
\n
61,863,300
\n
2898
\n
\n
\n
1996
\n
20
\n
1,362,000
\n
10,402,000
\n
32.75
\n
20.20
\n
37,927
\n
63,001,585
\n
3054
\n
\n
\n
1997
\n
21
\n
1,406,000
\n
9,246,000
\n
20.04
\n
14.88
\n
32,031
\n
64,160,814
\n
3144
\n
\n
\n
1998
\n
22
\n
1,278,000
\n
8,372,000
\n
24.17
\n
15.97
\n
25,959
\n
65,341,373
\n
4497
\n
\n
\n
1999
\n
23
\n
1,252,000
\n
8,167,000
\n
21.73
\n
14.25
\n
11,529
\n
66,543,654
\n
4108
\n
\n
\n
2000
\n
24
\n
1,371,209
\n
7,861,442
\n
21.64
\n
14.43
\n
24,494
\n
67,803,927
\n
4317
\n
\n
\n
2001
\n
25
\n
1,254,599
\n
7,576,683
\n
15.10
\n
9.38
\n
25,672
\n
68,064,972
\n
3120
\n
\n
\n
2002
\n
26
\n
1,821,253
\n
7,586,725
\n
22.91
\n
13.61
\n
28,647
\n
68,327,022
\n
3660
\n
\n
\n
2003
\n
27
\n
2,073,150
\n
7,815,932
\n
34.83
\n
22.10
\n
36,914
\n
68,590,081
\n
4718
\n
\n
\n
2004
\n
28
\n
2,329,897
\n
8,119,555
\n
38.67
\n
24.96
\n
34,016
\n
68,854,153
\n
6041
\n
\n
\n
2005
\n
29
\n
2,409,446
\n
7,667,026
\n
42.51
\n
26.85
\n
21,439
\n
69,119,242
\n
7384
\n
\n
\n
2006
\n
30
\n
2,964,647
\n
7,003,026
\n
41.23
\n
25.85
\n
25,319
\n
69,729,967
\n
8035
\n
\n
\n
2007
\n
31
\n
3,265,092
\n
6,834,024
\n
47.64
\n
27.66
\n
18,228
\n
70,586,256
\n
9710
\n
\n
\n
2008
\n
32
\n
3,816,522
\n
7,303,889
\n
52.59
\n
31.71
\n
39,467
\n
71,517,100
\n
10,851
\n
\n
\n
2009
\n
33
\n
4,033,257
\n
7,427,596
\n
41.37
\n
25.86
\n
46,872
\n
72,561,312
\n
9036
\n
\n
\n
2010
\n
34
\n
4,608,171
\n
7,194,372
\n
43.32
\n
27.99
\n
41,857
\n
73,722,988
\n
10,672
\n
\n
\n
2011
\n
35
\n
4,662,578
\n
6,778,101
\n
44.31
\n
26.95
\n
39,964
\n
74,724,269
\n
11,341
\n
\n
\n
2012
\n
36
\n
5,424,794
\n
6,432,674
\n
51.88
\n
36.82
\n
42,009
\n
75,627,384
\n
11,720
\n
\n
\n
2013
\n
37
\n
5,551,397
\n
5,981,703
\n
43.13
\n
27.35
\n
46,656
\n
76,667,864
\n
12,543
\n
\n
\n
2014
\n
38
\n
6,608,416
\n
5,257,995
\n
38.39
\n
25.60
\n
40,325
\n
77,695,904
\n
12,127
\n
\n
\n
2015
\n
39
\n
6,866,355
\n
5,022,986
\n
34.56
\n
21.32
\n
38,986
\n
78,741,053
\n
10,985
\n
\n
\n
2016
\n
40
\n
7,201,462
\n
4,877,067
\n
34.09
\n
19.86
\n
48,230
\n
79,814,871
\n
10,863
\n
\n
\n
2017
\n
41
\n
6,494,372
\n
4,359,646
\n
29.06
\n
20.29
\n
46,935
\n
80,810,525
\n
10,541
\n
\n\n
Table 1.
Depended and independent variables used in the study and their values.
\n
While carrying out the analyses, first of all, the changes in the independent variables (X1, X2, X3, X4, and X5) according to years have been tested using mathematical methods, a method of time-series analysis. In other words, mathematical formulas that represent the correlation and then the probability model have been reached by using the diagram that shows the correlation among the dependent and independent variables. The correlation among all the variables has been tested by means of diagrams in the study. Thus, estimated independent variable values that are to be used in explaining the values of the dependent variables in the upcoming years have been obtained at the first stage. At the second stage, multiple linear regression analysis was use. Two alternative models have been exploited for the purpose of numbering the estimations. In the first alternative model, all the independent variables were integrated regardless of reliability. Also, in the second alternative model, the reliability, which is below 0.05, was integrated, and thus, the model was constituted. The result of two alternative models was presented, and the resulting difference in number was put forward. Finally, the resulting differences were evaluated to be whether neglected or not. The numeric data analyses have been carried out using the software SPSS.
\n
\n
\n
3. Findings and discussion
\n
The data between the years 1977 and 2017 have been analyzed through this method with the help of the software SPSS. The results, the formulas for each regression model, and R2 values are given in Table 2. The coefficient for the variable “year” has been assumed as 1 for the initial year, which is 1977, and 54 for the year 2030. Therefore, a 54-year trend has been composed with the study.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Years
\n
\n
Estimation of particle-fiber wood unit sale price ($USD/m3)
\n
Estimation of fuel wood unit sale price ($USD/m3)
\n
Estimation of afforestation (ha)
\n
Estimation of population
\n
Estimation of gross national product per person (GNP) ($USD)
Estimated values of independent variables through regression modeling.
\n
As it can be seen in Table 2, except for the variable “afforestation rate,” an increase trend is estimated for all variables. After estimating the possible future values of the independent variables, models related to the production amount of particle-fiber wood and fuelwood, which are dependent variables, have been formed using multiple linear regression modeling. Multiple linear regression modeling has been preferred because all variables show a linear relation. Table 3 indicates not only the results of multiple linear regression analysis, which has been used to estimate the production amount of particle-fiber wood and fuelwood, but also the models obtained and the independent variable coefficients involved in the models.
Projection modeling results related to particle-fiber wood and fuelwood production.
Statistically significant at \n\n≤\n\n0.05.
\n
When the values of the dependent variable “particle-fiber wood and fuelwood production amount” (Y1A) in Table 3 are observed, it can be understood that 94.4% of the dependent variable (Y1A1) in Model 1 is explained by the independent variables involving in the model. The remaining 5.6% is explained by the variables that are not involved in the model due to the term “error.” As for the second model, it is understood that 91.5% of the dependent variable (Y1A2) is explained by the independent variables involving in the model, and the remaining rate is explained by the variables that are not involved in the model. In this case, it can be concluded that the variables picked for the model are highly effective. It is understood from the DW test scores that autocorrelation does not exist in estimating the particle-fiber wood production in the first and second models of which DW test scores are 0.961 and 0.441, respectively in Table 3. On the other hand, both the first model, where the modeling is significant at every level as a whole (F = 118.659/Sig = 0.000) and the second model (F = 133.369/Sig = 0.000) can be stated to be significant (Significance = Sig).
\n
Two alternative models that have been formed according to the coefficients obtained from multiple linear regression analysis in order to estimate particle-fiber wood production are given in Eqs. (1) and (2) as follows:
With the help of the model formed, particle-fiber wood production average of Turkey has been estimated to be 6,657,294 m3 for 2018 (average 1 and 2 models). According to the OGM [32] records, the production was 7,131,469 m3 at the end of October 2018. When the estimated and actual production amounts are compared, a difference of 474,000 m3 can be seen, which means an error margin of 6% meaning that the reliability of the projection has been proved with the rate of 94%. Table 4, on the other hand, indicates the estimated amounts covering the years between 2018 and 2030 by both of the alternative models and quantitative difference between the models.
\n
\n
\n
\n
\n
\n
\n\n
\n
Years
\n
Y1A (first model)
\n
Y1A (second model)
\n
Difference between estimates (Y1A1–Y1A2)
\n
Estimates percentage error (%)
\n
\n\n\n
\n
2018
\n
7,056,371
\n
6,258,217
\n
798,154
\n
11.31
\n
\n
\n
2019
\n
7,238,005
\n
6,433,786
\n
804,218
\n
11.11
\n
\n
\n
2020
\n
7,415,121
\n
6,604,032
\n
811,090
\n
10.94
\n
\n
\n
2021
\n
7,586,465
\n
6,767,472
\n
818,993
\n
10.80
\n
\n
\n
2022
\n
7,750,702
\n
6,922,535
\n
828,167
\n
10.69
\n
\n
\n
2023
\n
7,906,417
\n
7,067,554
\n
838,863
\n
10.61
\n
\n
\n
2024
\n
8,052,121
\n
7,200,771
\n
851,350
\n
10.57
\n
\n
\n
2025
\n
8,186,243
\n
7,320,337
\n
865,906
\n
10.58
\n
\n
\n
2026
\n
8,307,135
\n
7,424,307
\n
882,828
\n
10.63
\n
\n
\n
2027
\n
8,413,070
\n
7,510,648
\n
902,422
\n
10.73
\n
\n
\n
2028
\n
8,502,244
\n
7,577,231
\n
925,013
\n
10.88
\n
\n
\n
2029
\n
8,572,773
\n
7,621,836
\n
950,937
\n
11.09
\n
\n
\n
2030
\n
8,622,694
\n
7,642,150
\n
980,544
\n
11.37
\n
\n\n
Table 4.
The estimated amount of fiber-particle wood production 2018–2030.
\n
On the other hand, when the dependent variable “fuelwood production amount” (Y2A) is examined in Table 3, we come to the result that 96.1% of the dependent variable (Y2A1) is explained by the independent variables involved in the first model. The remaining 8.5% is explained by the variables that are not involved in the model due to the term “error.” As for the second model, it is understood that 95.7% of the dependent variable (Y2A2) is explained by the independent variables involving in the model, and the remaining rate is explained by the variables that are not involved in the model. In this case, it can be concluded that the variables picked for the model are highly effective. As seen in the DW test scores, autocorrelation does not exist in estimating the fuelwood production in the first and second models of which DW test scores are 1.062 and 0.758, respectively in Table 3. On the other hand, both the first model, where the modeling is significant at every level as a whole (F = 172.491/Sig = 0.000) and the second model (F = 274.206 / Sig = 0.000) can be stated to be significant (Significance = Sig).
\n
The models related to the projection of fuelwood production according to the coefficients obtained through multiple linear regression analysis are given in Eqs. (3) and (4) as follows:
With the help of the model formed, fuelwood production average of Turkey has been estimated to be 5,957,586 m3 for 2018 (average 1 and 2 models). According to the OGM [32] records, the production was 5,866,939 m3 at the end of October 2018. When the estimated and actual production amounts are compared, a difference of 90,647 m3 can be seen, which means an error margin of 1.5% meaning that the reliability of the projection has been proved with the rate of 98.5%. Table 5, on the other hand, indicates the estimated amounts covering the years between 2018 and 2030 by both of the alternative models and quantitative difference between the models.
\n
\n
\n
\n
\n
\n
\n\n
\n
Years
\n
Y2A1 (first model)
\n
Y2A2 (second model)
\n
Difference between estimates (Y2A1–Y2A2)
\n
Estimates percentage error (%)
\n
\n\n\n
\n
2018
\n
5,815,096
\n
6,100,077
\n
−284.982
\n
−4.01
\n
\n
\n
2019
\n
5,302,710
\n
5,577,000
\n
−274.290
\n
−4.15
\n
\n
\n
2020
\n
4,797,647
\n
5,060,584
\n
−262.937
\n
−4.31
\n
\n
\n
2021
\n
4,301,941
\n
4,552,681
\n
−250.739
\n
−4.46
\n
\n
\n
2022
\n
3,817,757
\n
4,055,259
\n
−237.501
\n
−4.62
\n
\n
\n
2023
\n
3,347,385
\n
3,570,402
\n
−223.016
\n
−4.77
\n
\n
\n
2024
\n
2,893,243
\n
3,100,309
\n
−207.066
\n
−4.90
\n
\n
\n
2025
\n
2,457,874
\n
2,647,295
\n
−189.420
\n
−4.99
\n
\n
\n
2026
\n
2,043,952
\n
2,213,791
\n
−169.839
\n
−5.02
\n
\n
\n
2027
\n
1,654,274
\n
1,802,342
\n
−148.068
\n
−4.95
\n
\n
\n
2028
\n
1,291,768
\n
1,415,611
\n
−123.844
\n
−4.72
\n
\n
\n
2029
\n
959,485
\n
1,056,376
\n
−96.891
\n
−4.24
\n
\n
\n
2030
\n
660,606
\n
727,528
\n
−66.922
\n
−3.38
\n
\n\n
Table 5.
The estimated amount of firewood production 2018–2030.
\n
According to the calculations by DPT [33] related to the energy and fuel required for production, 1.9 m3 wood is required for the production of 1 m3 particleboard. As stated in Section 1, in the event that the established capacity of the industry remains constant, approximately 10.2 million m3 of wood per year shall be required for the production of 5.1 million m3/year of particleboard. Similarly, according to the calculations by DPT in 2007, 1.2 m3 wood is required for the production of fiberboard. Under the circumstance that fiberboard production capacity remains constant, approximately 8.2 million m3 of wood per year shall be required for the production of 6.8 million m3 per year, and the total need for wood shall be 18.4 million m3 for a full-capacity production. When the capacity of the factories is kept at 80%, the amount shall be 14.7 million m3. According to the projections made in the study, the possibility of meeting this amount under these conditions is not considered to be favorable. Because the average annual production is 11–12 million m3 in the short term, whereas the long-term production decreases to 8–9 million m3 following the decrease in fuelwood production.
\n
\n
\n
4. Conclusion
\n
In the light of findings obtained within the scope of the study, it has been clearly found out that the raw material supply for wood-based panel industry may turn into a problematic issue. Considering that the supply of raw material shall mainly be provided by the OGM, the raw material problem shall begin to increase within the next 20 years. Several studies carried out on the issue present similar conclusions ([4, 23, 34, 35]). At this point, the sectors where panel products are used are also of great importance. Construction sector and furniture industry are the leading ones among those sectors. Since the study focuses on raw material supply, the demands on a sectoral basis have not been discussed. The following suggestions, on the other hand, have been suggested as a result of the findings by approaching the board industry as a whole:
\n
Raw material supply is one of the most important issues affecting the structural development of the sector. Therefore, demand projection should be made, and these projections should be revised on a yearly basis in order to provide the sector with a sustainable growth through accurate planning. Diversification of supply sources for balancing sectoral demand shall be the most important policy change as well. Considering the economic balances, not only the planning by the sector but also the involvement of the state, which holds 99.9% of the country’s forest assets, in this planning shall be crucial. Besides, the state should increase the incentive opportunities.
\n
Although the projection for raw material production does not point out any serious problems for the present but alarms for the possible ones in the future. Because the actual average production of particleboard and fiberboard is 11–12 million m3 per year. The raw material demand for such a production is calculated to be 18 million m3. While the current demand by the sector is hardly met, a greater bottleneck shall be created with a reduction in the fuelwood production amounts. From the point of view, in addition to the need to keep fuelwood production amount constant, it may be appropriate for OGM to focus on the production of wood that meeting the needs of the sector.
\n
Another problem that Turkey might encounter in providing the sector with raw material is the fact that wood-based energy generation emerges with the energy agenda of the country. Particularly, the countries’ tendency toward wood-based energy generation as an alternative way in order to provide the security of supply is likely to create a new kind of raw material bottleneck. The board industry, which has a slight chance of competing with the energy sector in raw material supply, is expected to encounter problems such as a shrink or capacity slow down. Moreover, the fact that energy forestry does not become widespread in the country seems to cast a shadow over the sector in the short term rather than the long term.
\n
Based on the projections that sectoral demand shall increase and new conditions of competition shall occur, it is of great importance not only for the private sector but also for the state to engage in afforestation activities using fast-growing species, particularly around the factories with great production capacities.
\n
As stated above, different alternatives or new policies may be identified for the solution of the raw material problem. One of these possible solutions might be the prioritization of the practices that are important to particularly meet the quantitative wood demands of the forest industry with a silvicultural technique. Here it is possible to consider the expansion of the afforestation using fast-growing species such as red pine or a reduction in the management period.
\n
Another important policy might be the designation of areas for the production of wood within the framework of functional planning, implementation of these plans, and reviewing the forest management plans. At this point, a policy to be followed might be the expanding the forestlands designated for wood production.
\n
\n\n',keywords:"fiberwood, fuelwood, demand, supply, forestry, Turkey",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65672.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65672.xml",downloadPdfUrl:"/chapter/pdf-download/65672",previewPdfUrl:"/chapter/pdf-preview/65672",totalDownloads:160,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 25th 2018",dateReviewed:"November 20th 2018",datePrePublished:"February 14th 2019",datePublished:"December 4th 2019",readingETA:"0",abstract:"The wood-based panel industry is one of the fast developing and growing sectors in the world. As of the year 2017, Turkey is the fourth biggest wood-based panel producer with a share of 3.9%. The fast sectoral development is considered as a positive indicator, although unplanned growth is not desirable. In this scope, the raw material Turkey possesses, and the opportunity to meet the future demand of the sector has been investigated. The estimated production capacity of Turkey for the year 2018 is calculated as 6,657,294 m3/year for particle board using two average alternative models. The sector’s possible yearly demand concerning the production capacity is approximately 11–12 million m3 besides the 8–9 million m3 production from the local production import gain ground. Providing a solution concerning the raw material supply, increasing the industrial afforestation, amplifying the state aid in the local products, and taking the necessary measures in order to decrease the cost is crucial. The said measures might have a significant role to offer a solution for the problems of the sector. The future projections should aim at reaching a solution to the raw material problem and the technical problems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65672",risUrl:"/chapter/ris/65672",signatures:"Hasan Tezcan Yildirim",book:{id:"8299",title:"Timber Buildings and Sustainability",subtitle:null,fullTitle:"Timber Buildings and Sustainability",slug:"timber-buildings-and-sustainability",publishedDate:"December 4th 2019",bookSignature:"Giovanna Concu",coverURL:"https://cdn.intechopen.com/books/images_new/8299.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"108709",title:"Dr.",name:"Giovanna",middleName:null,surname:"Concu",slug:"giovanna-concu",fullName:"Giovanna Concu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"277498",title:"Dr.",name:"Hasan Tezcan",middleName:null,surname:"Yildirim",fullName:"Hasan Tezcan Yildirim",slug:"hasan-tezcan-yildirim",email:"htezcan@istanbul.edu.tr",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Material and method",level:"1"},{id:"sec_3",title:"3. Findings and discussion",level:"1"},{id:"sec_4",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'FAO, 2018: Global Demand for Wood Products. http://www.fao.org/docrep/pdf/011/i0350e/i0350e02a.pdf (Visited date: 20 November 2018)'},{id:"B2",body:'Lata K, Dubey B, Misra AK. Modeling the effects of wood and non-wood based industries on forestry resources. Natural Resource Modeling. 2016;29(4):559-580'},{id:"B3",body:'Souza AM, Nascimento MF, Almedia DH, Lopes Silva DA, Almedia TH, Christoforo AL, et al. Wood-based composite made of wood waste and epoxy based ink-waste as adhesive: A cleaner production alternative. Journal of Cleaner Production. 2018;193:549-562'},{id:"B4",body:'Yıldırım HT, Candan Z, Korkut S. Wood-based panels industry in Turkey: Future raw material challenges and suggestions. Maderas Ciencia y Tecnologia. 2014;16(2):175-186'},{id:"B5",body:'Loučanová E, Palus H, Dzian M. A course of innovations in wood processing industry within the forestry-wood chain in Slovakia: A Q methodology study to identify future orientation in the sector. Forests. 2017;8(210):1-13'},{id:"B6",body:'Kun Z, Wenming L, Hashiramoto O. Demand and Supply of Wood Products in China. FAO Forest Products Working Paper 1; 2007'},{id:"B7",body:'Wang S, Zhang H, Nie Y, Yang H. Contributions of China’s wood-based panels to CO2 emission and removal implied by the energy consumption standards. Forests. 2017;8(273):1-16'},{id:"B8",body:'İlter E, Ok K. Marketing Principles and Management in Forestry and Forest Industry. Ankara, Turkey: Improvement Second Press. Form Ofset Bookstore; 2007'},{id:"B9",body:'Mahapatra A, Mitchell CP. Sustainable development of non-timber forest products: Implication for forest management in India. Forest Ecology and Management. 1997;94:15-29'},{id:"B10",body:'Ok K. Idea marketing in forestry: Some implications from the Turkish forestry experience. Forest Policy and Economics. 2005;7:493-500'},{id:"B11",body:'Paul S, Chakrabarti S. Socio-economic issues in forest management in India. Forest Policy and Economics. 2011;13(1):55-60'},{id:"B12",body:'Yıldırım HT. Turkish Wood Based Panels Industry: Future Challenges and Suggestions. Proceedings of the 55th International Convention of Society of Wood Science and Technology, August 27-31, 2012 - Beijing, China; 2012'},{id:"B13",body:'Cubbage FW, Newman DH. Forest policy reformed: A United States perspective. Forest Policy and Economics. 2006;9:261-273'},{id:"B14",body:'Damette O, Delacote P. Unsustainable timber harvesting, deforestation and the role of certification. Ecological Economics. 2011;70(6):1211'},{id:"B15",body:'Hosseini M, Brege S, Nord T. A combined focused industry and company size investigation of the internationalization-performance relationship: The case of small and medium-sized enterprises (SMEs) within the Swedish wood manufacturing industry. Forest Policy and Economics. 2018;97:110-121'},{id:"B16",body:'Jochem D, Janzen N, Weimar H. Estimation of own and cross price elasticities of demand for wood-based products and associated substitutes in the German construction sector. Journal of Cleaner Production. 2016;137:1216-1227'},{id:"B17",body:'Bozkurt AY, Göker Y. Chipboard Industry İ.Ü. Publication Number: 3614, İ.Ü. Faculty of Forestry Publication Number: 413, İstanbul; 1990'},{id:"B18",body:'Erol YS. Differences between urban and rural population with respect to demand on forestry aspects, in a case study of the Turkish province of Balıkesir. Ciência Rural, Santa Maria. 2012;42(3):436-443'},{id:"B19",body:'Suchsland O, Woodson GE. Fiberborad Manufacturing Practices in the United States. United States Department of Agriculture Forest Service Agriculture Handbook No: 640; 1987'},{id:"B20",body:'Huey BM. Problems of timber products procurement during world war II, 1941-1945. Graduate Student Theses. Dissertations, & Professional Papers. 3314; 1951'},{id:"B21",body:'Garcia R, Freire F. Environmental assessment of wood-based panels: A comparison of life-cycle-based tools. International Journal of Sustainable Construction. 2012;1:2182-2743'},{id:"B22",body:'Gerasimov Y, Seliverstov A. Industrial round-wood losses associated with harvesting systems in Russia. Croatian Journal of Forest Engineering. 2010;31(2):111-126'},{id:"B23",body:'Yıldırım HT. Examination of wood production-consumption relations in terms of Forest policy in Turkey, doctoral thesis. Istanbul, Turkey: Istanbul University Institute of Science; 2010 (in Turkish)'},{id:"B24",body:'AIMSAD. 2017. The World\'s 5th Largest Producer of Turkey Attacks Grows Through New Investments in the Sector Plate. https://www.aimsaddergisi.com/dunyanin-5-buyuk-ureticisi-turkiye-levha-sektoru-yeni-yatirimlarla-buyume-ataginda/ (Visited Date: 12.10.2018)'},{id:"B25",body:'Particle Board Industry Association, 2017. Statistical database 2012, Istanbul (in Turkish)'},{id:"B26",body:'Oubedda L, Erraha B, Khalfaoui. Multidimensional analysis data to create a decision support system dedicated to the university environment. Global Journal of Computer Science and Technology Software & Data Engineering. 2012;12(13):10-16'},{id:"B27",body:'Daşdemir İ, Güngör E. Multivariate decision-making methods and their using areas in forestry. The Journal of ZKÜ Bartın Faculty of Forestry. 2002;(4):1-19'},{id:"B28",body:'Guarini MR, Battisti F, Chiovitti A. A methodology for the selection of multi-criteria decision analysis methods in real estate and land management processes. Sustainability. 2018;10(507):1-28'},{id:"B29",body:'Altunışık R, Coşkun R, Yıldırım E, Bayraktaroğlu S. Sosyal Bilimlerde Araştırma Yöntemleri SPSS Uygulamalı, Sakarya Kitabevi. İkinci Baskı, Sakarya; 2002. ISBN 975-8644-07-6'},{id:"B30",body:'Yazıcıoğlu, Y. ve Erdoğan S. Spss uygulamalı bilimsel araştırma yöntemleri. Ankara: Detay Yayıncılık; 2004'},{id:"B31",body:'Kalaycı S. SPSS Applied Multivariate Statistical Techniques. Ankara: Second Press, Asil Bookstore; 2006, 975-9091-14-3'},{id:"B32",body:'OGM. 2018. Official Forestry Statistics https://www.ogm.gov.tr/ekutuphane/Sayfalar/Istatistikler.aspx?RootFolder=%2Fekutuphane%2FIstatistikler%2FOrmanc%C4%B1l%C4%B1k%20%C4%B0statistikleri&FolderCTID=0x012000301D182F8CB9FC49963274E712A2DC00&View={4B3B693B-B532-4C7F-A2D0-732F715C89CC} (Visited time: 20 November 2018)'},{id:"B33",body:'DPT. 9th Development Plan (2007-2013). Specialized Commission Report on Wood Products and Furniture. Ankara; 2007'},{id:"B34",body:'Aras U, Kalaycıoğlu H. Wood Based Composites and Application Areas. Uluslararası Hakemli Mühendislik ve Fen Bilimleri Dergisi. Sayı; 2016. p. 6'},{id:"B35",body:'Başyiğit C, Çankıran O, Taş HH. The raw materials used in the production of chipboard and the use of wooden waste materials for this aim. SDÜ Journal of Science Institute. 2000;4(1):26-31'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Hasan Tezcan Yildirim",address:"htezcan@istanbul.edu.tr",affiliation:'
Faculty of Forestry, Department of Forest Engineering, İstanbul University Cerrahpaşa, İstanbul, Turkey
'}],corrections:null},book:{id:"8299",title:"Timber Buildings and Sustainability",subtitle:null,fullTitle:"Timber Buildings and Sustainability",slug:"timber-buildings-and-sustainability",publishedDate:"December 4th 2019",bookSignature:"Giovanna Concu",coverURL:"https://cdn.intechopen.com/books/images_new/8299.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"108709",title:"Dr.",name:"Giovanna",middleName:null,surname:"Concu",slug:"giovanna-concu",fullName:"Giovanna Concu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"22193",title:"Prof.",name:"Peng",middleName:null,surname:"Chen",email:"chen@bio.mie-u.ac.jp",fullName:"Peng Chen",slug:"peng-chen",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{title:"Intelligent Methods for Condition Diagnosis of Plant Machinery",slug:"intelligent-methods-for-condition-diagnosis-of-plant-machinery",abstract:null,signatures:"Huaqing Wang and Peng Chen",authors:[{id:"20738",title:"Prof.",name:"Huaqing",surname:"Wang",fullName:"Huaqing Wang",slug:"huaqing-wang",email:"wanghq_buct@hotmail.com"},{id:"22193",title:"Prof.",name:"Peng",surname:"Chen",fullName:"Peng Chen",slug:"peng-chen",email:"chen@bio.mie-u.ac.jp"}],book:{title:"Intelligent Mechatronics",slug:"intelligent-mechatronics",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"5525",title:"Dr.",name:"Luis",surname:"Hernandez",slug:"luis-hernandez",fullName:"Luis Hernandez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Central University of Las Villas",institutionURL:null,country:{name:"Cuba"}}},{id:"17976",title:"Dr.",name:"Claudio",surname:"Urrea Oñate",slug:"claudio-urrea-onate",fullName:"Claudio Urrea Oñate",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/17976/images/3356_n.jpg",biography:"Claudio Urrea was born in Santiago, Chile. He received the M.Sc. Eng. and the Dr. degrees from Universidad de Santiago de Chile, Santiago, Chile in 1999, and 2003, respectively; and the Ph.D. degree from Institut National Polytechnique de Grenoble, France in 2003. Dr. Urrea is currently Professor at the Department of Electrical Engineering, Universidad de Santiago de Chile, from 1998. 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Moubarak is currently a Doctorate student at the Department of Mechanical and Aerospace Engineering at the George Washington University. His main research interests include robotics and robotic applications, especially in the field of mobility, manipulation and autonomy. Prior to joining GW in 2009, he had completed his M.Sc. in 2007 at the University of Maryland with research focus on bioinstrumentation technology. His thesis work involved the development of an autonomous gait analysis system that analyzes gait patterns in rodents for medical and pharmaceutical applications.",institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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