*2.4. Analyses*

Using the information obtained during the weekly phone calls and exit interviews, participant views on the perceived advantages, disadvantages, and challenges to adopting the TRF regimen were explored through qualitative analyses to better understand how older adults can successfully adopt TRF. A constant-comparison coding process was used to categorize and compare interview data for analysis purposes [23]. A trained researcher (SL) open-coded each interview and mapped data within the three primary constructs of the biopsychosocial model (biological, psychological, socio-environmental) to systematically categorize these three primary factors in their interactions in the TRF intervention. Open coding was completed by hand instead of using data mining software in order to take on the full context of the interviews. Positive and negative categories within each of the three primary themes were documented.


*Porphyromonas gingivalis* **Mfa1 Induces Chemokine and Cell Adhesion Molecules in Mouse Gingival Fibroblasts via Toll-Like Receptors**

**Yuhei Takayanagi 1, Takeshi Kikuchi 1,\* , Yoshiaki Hasegawa 2, Yoshikazu Naiki 2, Hisashi Goto 1, Kousuke Okada 1, Iichiro Okabe 1, Yosuke Kamiya 1 , Yuki Suzuki 1 , Noritaka Sawada 1, Teppei Okabe 1, Yuki Suzuki 1 , Shun Kondo 1, Tasuku Ohno 1, Jun-Ichiro Hayashi 1 and Akio Mitani 1**

Received: 6 November 2020; Accepted: 7 December 2020; Published: 10 December 2020

**Abstract:** *Porphyromonas gingivalis* Mfa1 fimbriae are thought to act as adhesion factors and to direct periodontal tissue destruction but their immunomodulatory actions are poorly understood. Here, we investigated the effect of Mfa1 stimulation on the immune and metabolic mechanisms of gingival fibroblasts from periodontal connective tissue. We also determined the role of Toll-like receptor (TLR) 2 and TLR4 in Mfa1 recognition. Mfa1 increased the expression of genes encoding chemokine (C-X-C motif) ligand (CXCL) 1, CXCL3, intercellular adhesion molecule (ICAM) 1 and Selectin endothelium (E) in gingival fibroblasts, but did not have a significant effect on genes that regulate metabolism. Mfa1-stimulated up-regulation of genes was significantly suppressed in *Tlr4* siRNA-transfected cells compared with that in control siRNA-transfected cells, which indicates that recognition by TLR4 is essential for immunomodulation by Mfa1. Additionally, suppression of *Tlr2* expression partially attenuated the stimulatory effect of Mfa1. Overall, these results help explain the involvement of *P. gingivalis* Mfa1 fimbriae in the progression of periodontal disease.

**Keywords:** *Porphyromonas gingivalis*; Mfa1; Toll-like receptors; gingival fibroblast

### **1. Introduction**

Periodontitis is a chronic inflammatory disease caused by multiple periodontal pathogenic bacterial species [1]. Socransky et al. indicated that three bacteria, *Porphyromonas gingivalis*, *Tannerella forsythia,* and *Treponema denticola*, are the main cause of the onset and progression of periodontitis and they suggested that these three species be called "red complexes" [2].

Among the three species, *P. gingivalis* is the major pathogen associated with periodontitis. Riviere et al. found that *P. gingivalis* was more prevalent at diseased sites than at healthy sites in diseased subjects [3]. The number of *P. gingivalis* in plaque samples was associated with the plaque score and clinical attachment level [4]. From the perspective of oral flora, *P. gingivalis* is considered to act as a keystone pathogen that creates a dysbiosis between the host and the dental biofilm. This altered

oral commensal microbiota is responsible for initiating pathological bone loss [5]. *P. gingivalis* expresses a number of potential virulence factors, including lipopolysaccharides, gingipines, and fimbriae [6].

Fimbriae are filamentous proteinaceous appendages on the surface of *P. gingivalis* bacteria that play a pivotal role in colonization through association with other bacteria and host tissues [7,8]. *P. gingivalis* ATCC strain 33,277 has fimbriae consisting of either FimA (*fimA* gene product) or Mfa1 (*mfa1* gene product), with apparent molecular masses of approximately 38 and 75 kDa, respectively [9].

Mfa1, encoded by the *mfa1* gene, is a structural subunit of a protein complex whose length varies from 60 to 500 nm [10]. In addition to the primary Mfa1 protein, mature fimbriae also have affiliated Mfa2–5 proteins. Mfa2 plays an anchor role, while Mfa3 can bind with Mfa1/2/4/5 in vitro to connect with other fimbrial subunits [11]. Recent data indicate that the C-terminal domain of Mfa1, rather than Mfa3, affects the aggregation and maturation of downstream fimbrial proteins [12].

Several studies have demonstrated different roles for FimA and Mfa1 fimbriae. FimA fimbriae act as an adhesive that mediates periodontal tissue colonization and host cell invasion [8,13,14]. They also induce inflammatory processes in periodontal tissues through several mechanisms [15–17]. FimA fimbriae promote early biofilm formation in a single species of *P. gingivalis*, whereas Mfa1 plays an inhibitory role in the formation of a homotypic biofilm in *P. gingivalis* [18]. However, although there are few reports on the host immune response to Mfa1 fimbriae, an Mfa1-deficient strain causes almost no alveolar bone resorption in a mouse model of oral infection [19].

The present study aimed to examine the effect of *P. gingivalis* Mfa1 stimulation on the immune and metabolic mechanisms of mouse gingival fibroblasts (MGFs) and to examine the effects of Toll-like receptor (TLR) 2 and TLR4 knock down on *P. gingivalis* Mfa1-stimulated MGFs. Our results show that Mfa1 fimbriae had a large effect on immunomodulation exerted by gingival fibroblasts, but did not have a significant effect on metabolic regulation. Our results also indicate that recognition of Mfa1 by TLR4 on MGFs is essential for the expression of genes related to cell migration and cell adhesion. Overall, these results help to explain how *P. gingivalis* Mfa1 fimbriae are involved in the progression of periodontal disease.

### **2. Materials and Methods**

### *2.1. Cell Culture*

MGFs were isolated from healthy gingival tissue from the palate of BALB/c mice which were purchased from CLEA Japan, Inc. (Tokyo, Japan). The MGFs were cultured in Minimum Essential Medium α (Thermo Fisher Scientific, Wilmington, DE, USA) containing 10% fetal bovine serum (Hyclone Laboratories Inc, Logan, UT, USA), 100 U/mL penicillin, and 100 μg/mL streptomycin at 37 °C in a CO2 incubator. When the cells reached confluency, they were separated by treatment with 0.25% trypsin-EDTA (Thermo Fisher Scientific) and collected by centrifugation. This study was approved by the Institutional Animal Care and Use Committees of Aichi Gakuin University (AGUD438; approved date 31 March 2019) and all animal experiments were conducted following the national guidelines and the relevant national laws on the protection of animals. Ultrapure lipopolysaccharide from *P. gingivalis* was purchased for experiments (InvivoGen, San Diego, CA, USA).

### *2.2. Purification of Mfa1 Fimbriae*

In this study, we used *P. gingivalis* mutant strains derived from ATCC 33,277. Mfa1 fimbriae were purified from JI-1, in which *fimA* was deleted, as described previously [20,21]. Mfa1 fimbriae were also purified from *mfa5* mutant FMFA5, in which *mfa5* was disrupted by *ermF-B*, and genetic complementation strain FMFA5C, as described previously [21,22]. Briefly, *P. gingivalis* cells were collected from 2 L of culture, suspended in 40 mL of 20 mM Tris/HCl buffer at pH 8.0, supplemented with protease inhibitors (1 mM phenylmethylsulfonyl fluoride and 1 mM Nα-p-tosyl-L-lysine chloromethyl ketone). The cells were disrupted by a French press. Unbroken cells were removed by centrifugation. The supernatant was subjected to precipitation at 50% ammonium sulfate saturation. The precipitate was dialyzed with 20 mM Tris/HCl buffer at pH 8.0, and then applied to DEAE Sepharose Fast Flow chromatography (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) with 50 mL of bed volume. After washing thoroughly with the buffer, sample was fractionated by a linear gradient elution with 400 mL of NaCl (0 to 0.3 M) in the buffer. Purity and identity were verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (Bio-Rad Laboratories, Hercules, CA, USA) and transmission electron microscopy. Details are described in the Supplementary Material.

### *2.3. RT2 Profiler PCR Array Analysis*

MGF (1 × 106 cells/dish) were seeded onto 60-mm dishes. When the cells reached confluency, they were incubated for 2 hours in the presence or absence of 1 μg/mL Mfa1, FMFA5, FMFA5C, FimA, or LPS of *P. gingivalis*. Total RNA was then collected. Complementary deoxyribonucleic acid (cDNA) was synthesized using the RT2 First Strand Kit (Qiagen, Germantown, MD, USA) according to the manufacturer's instructions and then applied to theMouse Antibacterial Response RT2Profile PCR Array (Qiagen) and the Mouse Extracellular Matrix & Adhesion Molecules RT2Profile PCR Array (Qiagen). Amplification was performed using the RT2 SYBR Green/ROX qPCR master mix (Qiagen) and the StepOnePlusTM Real-time PCR system (Thermo Fisher Scientific) with associated software (version 2.3; Thermo Fisher Scientific). CT values were transferred to an Excel file to build a table of CT values, which was then uploaded onto the data analysis web portal at http://www.qiagen.com/geneglobe.

### *2.4. Real-Time PCR*

MGFs (1 × 106 cells/dish) were seeded onto 60-mm dishes. When the cells reached confluency, they were incubated for 2 hours in the presence or absence of 1 μg/mL JI-1, FMFA5, FMFA5C, FimA or LPS of *P. gingivalis*. Total RNA was then extracted with a Nucleospin RNA kit (Macherey-Nagel Inc., Bethlehem, PA, USA) according to the manufacturer's instructions, and purity and concentration were assessed by calculating the A230/A260 and A260/A280 ratios using a NanoDrop Lite (Thermo Fisher Scientific). To quantify mRNA, quantitative PCR was performed using the TaqMan gene expression assay (Thermo Fisher Scientific) for mouse *Cxcl1* (Mm04207460\_m1), *Cxcl3* (Mm01701838), *Icam1* (Mm00516023\_m1), *Sele* (Mm00441278\_m1), *Tlr2* (Mm00442346\_m1) and *Tlr4* (Mm00445273\_m1) with the TaqMan Universal PCR Master Mix (Thermo Fisher Scientific). mRNA levels were normalized to the level of eukaryotic 18S rRNA (Hs99999901\_s1). Quantitative PCR was performed using the StepOnePlus Real-Time System. PCR conditions were 10 minutes at 95 ◦C, followed by 40 cycles of 15 seconds at 95 ◦C and 1 minute at 60 ◦C. The relative amounts of target mRNAs were determined by subtracting the cycle threshold (CT) value for 18S rRNA from that for the gene (ΔCT). Then, the ΔCT value for the control group was subtracted from that for the experimental group (ΔΔCT). The results are expressed as the fold change (2-ΔΔCT) between the mRNA levels of control and experimental groups, where ΔΔCT was calculated as follows: ((CT for the target mRNA − CT for 18S rRNA) for the experimental group)–((CT for the target mRNA − CT for 18S rRNA) for the control group).

### *2.5. siRNA Transfection*

MGFs were transfected with siRNA targeting TLR2 and TLR4 (Silencer Select Pre-designed siRNAs, Ambion, Austin, TX, USA) or non-targeting control siRNA (Ambion) using Lipofectamine RNAiMAX (Thermo Fisher Scientific) according to the manufacturer's protocol. Twenty-four hours after transfection, cells were stimulated with 1 μg/mL Mfa1, FMFA5, FMFA5C, FimA or LPS of *P. gingivalis* for 2 hours. The cells were then collected and TLR2 and TLR4 protein levels were determined by flow cytometry. Similarly, gene expression levels were determined by Real-Time PCR.

### *2.6. Flow Cytometry*

MGFs (1 × 106 cells in 100 μL) were incubated with anti-mouse CD282 (TLR2) phycoerythrin (PE) (BioLegend, San Diego, CA, USA, Cat: 148604), anti-mouse CD284 (TLR4) phycoerythrin (PE) (BioLegend, Cat: 117605), or isotype control antibody phycoerythrin (PE) (BioLegend, Cat: 400508) and analyzed by flow cytometry using a MACSQuant analyzer and MACSQuantify software version 2.4 (Miltenyi Biotec, Tokyo, Japan).

### *2.7. Statistical Analysis*

Data were analyzed using PASW Statistics software (version 18.0; SPSS Japan, Tokyo, Japan). Differences among groups were examined by one-factor analysis of variance (ANOVA) and Bonferroni's multiple comparison test. Comparisons of two independent groups were performed using Student's t-test. Data are expressed as the mean ± standard deviation (SD). Significance was accepted at *p* < 0.05.

### **3. Results**

### *3.1. Analysis of Antibacterial Response-Associated Genes in Gingival Fibroblasts to Various Fimbriae*

A mouse antibacterial response PCR array was used to investigate differences in the expression of 84 genes involved in bacteria-cell interactions. Figure 1 shows the fold changes in expression between control and 1 μg/mL various fimbriae or *P. gingivalis* LPS after stimulation for 2 hours. Among the 84 genes, *Cxcl1* and *Cxcl3* were upregulated in common by 4-fold in *JI-1*, *FMFA5* or *FMFA5C*-stimulated cells compared with non-stimulated cells (Figure 1A–C). Other genes that were elevated include *Nfkbia*, *Jun*, *Ccl5*, *Tlr6*, *Nod2* in *JI-1* stimulated cells, Nfkbia, Jun, Irf5, Birc3 in FMFA5-stimulated cells, and Hsp90aa1, Nfkb1, Nfkbia, Jun, Nod1, Slpi, Tirap, Rela, Ccl5, Tlr2, Irf5, Irf7, Nod2, Birc3, Lcn2 in FMFA5C-stimulated cells. Many genes (*Tnfrsf1a, Mapk1, Tollip, Irak1, Fadd, Map3k7, Map2k4, Slpi, Ripk2, Il18, Rela, Irak3, Card6, Camp, Irf5, Birc3, Slc11a1, Tlr9, Casp1, Crp*), including *Cxcl3* were up-regulated in FimA-stimulated cells compared with non-stimulated cells (Figure 1D). *P. gingivalis* LPS stimulation changed several genes expression (*Ccl5, Tlr6, Irf5, Nod2, Casp1*) compared with non-stimulated cells (Figure 1E).

**Figure 1.** Analysis of antibacterial response-associated genes in mouse gingival fibroblasts in response to Mfa1, FimA, or *Porphyromonas gingivalis* LPS. Graphs show the fold changes of gene expression in cells stimulated with JI-1 (**A**), FMFA5 (**B**), FMFA5C (**C**), FimA (**D**), and LPS (**E**) compared with non-stimulated cells. *Cxcl1* and *Cxcl3* were up-regulated over 4-fold in JI-1, FMFA5 or FMFA5C-stimulated cells.

### *3.2. Confirmation of PCR Array Data for Selected Antibacterial Response-Associated Genes by Quantitative Real Time-PCR*

To validate the PCR array data, Real Time-PCR showed that JI-1 stimulation increased the gene expression of the cell migration factors, *Cxcl1* and *Cxcl3*, compared with FimA stimulation (Figure 2). Furthermore, the highest increase in gene expression was observed with FMFA5 stimulation (Figure 2).

**Figure 2.** Mfa1 induces *Cxcl1* and *Cxcl3* in mouse gingival fibroblasts. Mouse gingival fibroblasts (MGFs) were cultured for 2 hours in the presence or absence of 1 μg/mL JI-1, FMFA5, FMFA5C, FimA, or LPS of *P. gingivalis* and then mRNA levels were examined using real-time PCR. Values are expressed as fold changes. Differences among groups were analyzed by one-way ANOVA. Data represent the mean + SD (*n* = 3). \*\* *p* < 0.01, \*\*\*\* *p* < 0.0001.

### *3.3. Analysis of Extracellular Matrix and Adhesion Molecule-Associated Genes in Gingival Fibroblasts in Response to Various Fimbriae*

A mouse extracellular matrix and adhesion molecules PCR array was used to investigate differences in the expression of 84 genes involved in cell–cell and cell–matrix interactions. Figure 3 shows the fold changes in expression between control and 1 μg/mL various fimbriae or *P. gingivalis* LPS after stimulation for 2 hours. Among the 84 genes, *Icam1* expression was upregulated 4-fold in FMFA5-stimulated cells compared with non-stimulated cells (Figure 3B). FMFA5C stimulation similarly induced *Icam1* and also *Selectin E (Sele)* (Figure 3C). No obvious changes in gene expression were observed with JI-1, FimA or *P. gingivalis* LPS stimulation compared with unstimulated cells (Figure 3A,D,E).

**Figure 3.** Analysis of extracellular matrix and adhesion molecule-associated genes in mouse gingival fibroblasts in response to Mfa1, FimA, or *P. gingivalis* LPS. Graphs show fold changes of gene expression in cells stimulated with JI-1 (**A**), FMFA5 (**B**), FMFA5C (**C**), FimA (**D**), and LPS (**E**) compared with non-stimulated cells. *Icam1* and *Sele* were up-regulated over 4-fold in FMFA5C-stimulated cells.

### *3.4. Confirmation of PCR Array Data for Selected Extracellular Matrix and Adhesion Molecule-Associated Genes by Quantitative RT-PCR*

Expression of cell adhesion factors, *Icam1* and *Sele*, was increased to a greater extent by JI-1 stimulation compared with FimA stimulation (Figure 4). The highest increase in gene expression was observed with FMFA5 stimulation (Figure 4).

**Figure 4.** Mfa1 induces *Icam1* and *Sele* in mouse gingival fibroblasts. MGFs were cultured for 2 hours in the presence or absence of 1 μg/mL JI-1, FMFA5, FMFA5C, FimA, or LPS of *P. gingivalis* and then mRNA levels were examined using real-time PCR. Values are expressed as fold changes. Differences among groups were analyzed by one-way ANOVA. Data represent the mean + SD (*n* = 3). \*\*\*\* *p* < 0.0001.

### *3.5. Induction of Tlr2 and Tlr4 Gene Expression by Various Fimbriae and LPS Stimulation in Gingival Fibroblasts*

*Tlr2* showed the same change in expression as the other factors, such as *Cxcl1*, but *Tlr4*, the receptor for LPS, did not show any significant variation in gene expression in response to various stimulants (Figure 5).

**Figure 5.** Mfa1 induces TLR2 but not TLR4 in mouse gingival fibroblasts. MGFs were cultured for 2 hours in the presence or absence of 1 μg/mL JI-1, FMFA5, FMFA5C, FimA or LPS of *P. gingivalis* and then mRNA levels were examined using real-time PCR. Values are expressed as fold changes. Differences among groups were analyzed by one-way ANOVA. Data represent the mean + SD (*n* = 3). \*\*\*\* *p* < 0.0001.

### *3.6. Transfection of Tlr2 and Tlr4 siRNA into Gingival Fibroblasts*

*Tlr2* and *Tlr4*siRNA-transfected gingival fibroblast cells showed obvious knockdown of *Tlr2* and *Tlr4*mRNA, respectively, compared with control siRNA-transfected gingival fibroblast cells (Figure 6A). FACS analysis confirmed a decrease in the surface expression of *Tlr2* and *Tlr4* in the respective siRNA-transfected gingival fibroblast cells compared with control cells (Figure 6B).

**Figure 6.** Suppression of *Tlr*2 and *Tlr4* in mouse gingival fibroblasts using siRNA. (**A**) *Tlr2* (left) and *Tlr4* (right) mRNA levels were examined using real-time PCR. *Tlr2* and *Tlr4* siRNA-transfected cells showed clear knockdown of *Tlr2* and *Tlr4* mRNAs, respectively, compared with control siRNA-transfected cells. (**B**) TLR2 (left) and TLR4 (right) protein levels were examined using flow cytometry. *Tlr2* or *Tlr4* siRNA transfected cells (red line) showed decreased levels of the respective receptors compared with control siRNA-transfected cells (black line). \*\*\*\* *p* < 0.0001.

### *3.7. Expression of Selected Genes in Tlr2 and Tlr4 siRNA-Transfected Cells*

Expression of the cell migration-related factor and cell adhesion factor genes in JI-1, FMFA5, and FMFA5C-stimulated *Tlr4* siRNA-transfected cells were significantly suppressed compared with control siRNA-transfected cells (Figure 7). Also, the suppression of *Tlr2* expression partially attenuated the stimulation effect (Figure 7).

**Figure 7.** Involvement of TLR2 and TLR4 in the expression of Mfa1-induced cell migration and cell adhesion-related factors. *Tlr2* siRNA, *Tlr4* siRNA, or control siRNA-transfected mouse gingival fibroblasts were cultured for 2 hours in the presence or absence of 1 μg/mL JI-1, FMFA5, or FMFA5C and then mRNA levels were examined using real-time PCR. Values are expressed as fold changes. Differences among groups were analyzed by one-way ANOVA. Data represent the mean + SD (*n* = 3). \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001, \*\*\*\* *p* < 0.0001.

### **4. Discussion**

In this study, we demonstrated that Mfa1 fimbriae stimulation markedly increased the expression of cell migration/cell adhesion-related genes in mouse gingival fibroblasts, and that the increase was more pronounced than with FimA stimulation. In addition, the ability of Mfa1 to regulate cell migration and cell adhesion was significantly attenuated in mouse gingival fibroblasts in which *Tlr4* expression was suppressed.

When the effect of JI-1 on gingival fibroblasts, which constitute the gingival connective tissue, was examined, a remarkable increase in the expression of *Cxcl1* and *Cxcl3*, which are involved in cell migration, was observed. Higher levels of CXCL1 were found in human and rat gingiva from periodontitis sites compared with periodontally healthy sites [23]. There was also a significant difference in the level of CXCL1 in gingival crevicular fluid between healthy and periodontitis subjects [24]. CXCL1 stimulates gingiva fibroblast migration [25] and it may be related to periodontal tissue healing. There is no literature reporting the relationship between CXCL3 and periodontitis, but its activity is similar to that of CXCL2, which may play an important role in the initiation of inflammation and subsequent periodontal tissue destruction [26], which suggests that it is involved in the progression of periodontitis. JI-1 stimulation produced a higher increase in *Cxcl1* and *Cxcl3* expression compared with FimA stimulation. FimA induces *Cxcl1* expression in mouse peritoneal macrophages [27]. It is possible that the immunomodulatory capacity of Mfa1 outperforms that of FimA for some factors in certain cell types.

Next, we investigated which part of the fimbriae structure is important for the immunomodulatory ability of Mfa1. FMFA5, with the Mfa3–5 tip structure of JI-1 removed, and FMFA5C, with the tip structure was complemented in FMFA5, were compared for their ability to induce immunomodulation. Compared with JI-1, a markedly higher increase in gene expression was observed with FMFA5 stimulation, while that of FMFA5C was significantly reduced. From this result, it is indicated that the incomplete fimbriae deficient in tip structure increase the stimulation ability.

When we confirmed the effects of Mfa1 on extracellular matrix and adhesion molecules, we found a marked increase in the expression of the cell adhesion factor genes, *Icam1* and *Sele*. ICAM1 deficiencies increase susceptibility to and severity of alveolar bone loss after *P. gingivalis* infection [28]. Selectin Platelet/Selectin E-deficient mice exhibit spontaneous early onset alveolar bone loss [29]. FimA and Mfa1 induce ICAM1 and Selectin E in human aortic endothelial cells [30]. These fimbriae appear to have similar effects on gingival fibroblasts. Similar to the results for cell migration factors, a markedly higher increase in cell adhesion factor gene expression was observed with FMFA5 stimulation and the stimulation ability of FMFA5C was significantly reduced compared with JI-1 stimulation. These results indicate that the regulation of cell adhesion factors by Mfa1 is also greatly influenced by the Mfa1 molecule in the shaft portion.

Cells use pattern recognition receptors, such as TLRs, to recognize pathogen-associated molecular patterns (PAMPs). Ten TLRs have been identified in humans and 12 in mice and the host innate immune response to pathogens is largely mediated via TLR signaling [31]. TLR2 and TLR4 are the most widely studied extracellular innate receptors that recognize various PAMPs and are likely to play a role in the pathogenesis of periodontitis [32]. TLR2 has been shown to be important for *P. gingivalis* to produce inflammatory cytokines [33,34]. TLR4 recognizes LPS (from *Escherichia coli*), which is a bacterial cell wall component [31]. Uniquely, *P. gingivalis* LPS is recognized by both TLR2 and TLR4 [35]. The LPS1435/1449 and LPS1690 isoforms can produce opposite effects on TLR2 and TLR4 activation [36,37]. *P. gingivalis* LPS and *E. coli* LPS regulate cytokine production differently in human gingival fibroblasts [38]. The heterogeneity of *P. gingivalis* LPS might contribute to one of the strategies used by *P. gingivalis* to evade the innate host defense in gingival tissues [39]. FimA activates human peripheral blood monocytes via TLR2 and CD14 [40]. TLR2-dependent signaling leads to CD11b-CD18 activation in human monocytes upon recognition of FimA through CD14 [41]. However, the receptor for Mfa1 is still unclear. The gingival fibroblasts constitutively express TLR2 and TLR4 [42]. When we examined the expression of *Tlr2* and *Tlr4* genes in MGFs stimulated by various fimbriae and LPS, *Tlr2* showed the same changes in expression as the other factors, but *Tlr4* did not show any significant changes in expression in response to various fimbriae or LPS. Therefore, to clarify the receptor of Mfa1, MGFs in which *Tlr2* and *Tlr4* were knocked down were stimulated with Mfa1 and their reactivity was assessed. Control siRNA, *Tlr2* siRNA and *Tlr4* siRNA were introduced into MGFs, and cells were stimulated with JI-1, FMFA5, and FMFA5C. Gene expression of cell migration-related genes, *Cxcl1* and *Cxcl3*, and cell adhesion genes, *Icam1* and *Sele*, were analyzed. The suppression of TLR4 was significantly reduced by Mfa1 fimbriae stimulation. Also, suppression of *Tlr2* partially attenuated this stimulation. Recognition of Mfa1 by TLR4 is suggested to be essential for the expression of genes related to cell migration and cell adhesion. This result is different from previous reports in which anti-TLR2 antibody pretreatment significantly inhibited pro-inflammatory cytokines in mouse macrophages stimulated with Mfa1 fimbriae [43]. Conversely, Hajishengallis et al. reported that native Mfa1 induced proinflammatory cytokines in a CD14- and TLR2-dependent mode, which was likely due to a fimbriae-associated 12-kDa lipoprotein [44]. There is a similar report on FimA-like lipoproteins or lipopeptides associated with FimA that could, at least in part, account for signaling via TLR2 and subsequent TNF-α production in macrophages [45]. Furthermore, the following reports demonstrate the stimulation ability of LPS. A lipoprotein from *P. gingivalis* LPS was shown to be a principal component for TLR2-mediated cell activation [46]. A lipopolysaccharide preparation extracted from a *P. gingivalis* lipoprotein-deficient mutant showed a marked decrease in TLR2-mediated signaling [47]. Recombinant FimA stimulated cytokine release in THP-1 mononuclear cells via CD14 and TLR4 but not TLR2 [44], while recombinant FimA induced an inflammatory response via the TLR4/NF-kB signaling pathway in human peripheral blood mononuclear cells [48]. *P. gingivalis* lipid A and its synthetic counterpart activate cells through a TLR4-dependent pathway [46,49]. From our results, we speculate that wild-type purified Mfa1 is mainly recognized by TLR4, but TLR2 might recognize the lipoprotein of fimbriae and contributes to the overall action. In the future, it is necessary to confirm the reactivity of purified Mfa1 from which lipoprotein has been removed.

There are several reports on the importance of TLR4 in periodontitis. TLR4- but not TLR2-mediated stimulation, was positively associated with plaque score and bleeding on probing score of teeth from which the plaque samples were taken [4]. The ratio of TLR4/TLR2-mediated stimulation activity was also positively associated with probing depth and clinical attachment level [4]. TLR4- but not TLR2-stimulation of subgingival plaque is associated with plaque index [50]. Therefore, TLR4 may play an important role in the progression of periodontitis, in which stimulation by Mfa1 may play a role.

Recent reports suggest that intracellular DC-SIGN, an intracellularly expressed pattern recognition receptor, could be critical for recognition of Mfa1 by dendritic cells [51]. We assayed expression of DC-SIGN in MGFs after stimulation with various fimbriae, but we could not detect its expression. This may be because of differences between immunocompetent cells and periodontal tissue constituent cells.

In conclusion, Mfa1 fimbriae have a significant effect on immunomodulation in gingival fibroblasts of periodontal tissue. We also suggest that recognition of Mfa1 by TLR4 on MGFs is essential for the expression of genes related to cell migration and cell adhesion. More detailed analysis, such as using an animal infection model, is needed to assess the immunomodulatory capacity of Mfa1 fimbriae in the progression of periodontitis.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0383/9/12/4004/s1, Figure S1: Purity of Mfa1 fimbriae.

**Author Contributions:** Conceptualization, Y.T., T.K., Y.H., and A.M.; methodology, Y.T., T.K., Y.H., and A.M.; validation, Y.T., T.K., and A.M.; formal analysis, Y.T., H.G., I.O., Y.S. (0000-0002-7536-5931), N.S., and T.O. (Tasuku Ohno); investigation, Y.T., Y.N., H.G., K.O., I.O., Y.K., Y.S. (0000-0002-7536-5931), N.S., T.O. (Teppei Okabe), and Y.S.(0000-0003-3660-6867); data curation, Y.K., Y.S. (0000-0003-3660-6867), and S.K.; writing—original draft preparation, Y.T., T.K., and A.M.; writing—review and editing, Y.T., T.K., Y.H., J.-I.H., and A.M.; visualization, Y.T. and T.K.; supervision, T.K., Y.H., J.H., and A.M.; project administration, A.M.; funding acquisition, T.K., Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by a Grant-in-Aid for Scientific Research (No.20K09985, No. 20K09931 and No.17K11999) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.

**Acknowledgments:** We thank Jeremy Allen, PhD, from Edanz Group (https://en-author-services.edanzgroup.com/) for editing a draft of this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

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**3. Results**

#### *3.1. Spatiotemporal Variations of Phenology Metrics in the QMs*

Figure 4 shows the spatial variation of the annual mean LSP and their corresponding standard deviations (Std) over the study period 2001–2019. Earlier (<90 days) sites of SOS (14.4%) were located at low elevations in the central QMs, and later (>130 days) sites (6.8%) were located at high elevations in the western QMs (Figure 4a). The earliest occurrence of SOS was for SL, with a mean SOS of 97 ± 14 days, and the latest occurrence was for GL, with a mean SOS of 109 ± 12 days (Figure 4a). The Std of SOS has significant spatial variation, with larger areas located in the southwestern QMs (57.2%) having higher Std

(>9 days) (Figure 4b). The overall spatial variation in multi-year average EOS was not significant, and in the southeastern QMs, EOS was mainly concentrated in 290–300 days, accounting for 43.0% of the entire study area (Figure 4c). The earliest EOS occurred in GL, with a mean EOS of 287 ± 11 days, and the latest occurred in EBF, with a mean EOS of 295 ± 12 days (Figure 4c). There was higher Std (>13 days) in the southern QMs (25.2%) compared with the northern QMs (Figure 4d). LOS had clear spatial differences, with the central QMs (15.5%) having the longest LOS (>210 days) and the western high-altitude areas (8.5%) had the shortest LOS (<150 days). For SL in the southern QMs (27.9%), its mean LOS was 193 ± 20 days and the std (>18 days) was also the largest (Figure 4e,f).

**Figure 4.** (**a**,**c**,**e**) Spatial distribution of the average phenology metrics from 2001 to 2019 and (**b**,**d**,**f**) standard deviation (Std) of the phenology metrics. Insets at bottom left show the histogram of the average pixel values for different vegetation types.

We also characterized the spatial distribution of LSP trends for different vegetation types from 2001 to 2019 (Figure 5). For the whole QMs, SOS was advanced in 67.8%, the average rate of advance was 1.5 days/decade, and 27.5% of the area (mostly located in the northern QMs) was significant (Figure 5a,b). DBF advanced at a rate of 1.9 days/decade and was the fastest compared to other vegetation types (Figure 5a). EOS was delayed in 72.1% of the region and significant for 42.1% of the region (mostly located in the southern QMs), with an average delay rate of 2.4 days/decade across the region (Figure 5c,d). EBF had the fastest delay rate of 3.3 days/decade (Figure 5c). The average rate of LOS lengthening across the study area was 3.9 days/decade, and 74.6% of the areas (mostly in the southwestern QMs) were lengthened (Figure 5e). The rate of LOS lengthening was 4.7 days/decade for EBF, fastest among the seven vegetation types. Of these areas that

were lengthened, the change was found to be significant in 40.3% (mostly in the western QMs) of cases (Figure 5f).

**Figure 5.** (**a**,**c**,**e**) Spatial distribution of phenology metrics trends from 2001 to 2019 and (**b**,**d**,**f**) significant (*p* < 0.1) changes in phenology metrics trends for the study periods. Insets at bottom left show the average pixel values of the trends of the phenology metrics for the different vegetation types.

Data of interannual variation trends and the significance of LSP for different vegetation types are shown in Figure 6. Overall, the *Sen's slope* of SOS is −0.09 days/year from 2001 to 2019, but this advance is insignificant. There was a trend of significant SOS advancement for DBF and GL, at 0.16 and 0.13 days/year, respectively. EOS shows a significant delay trend over the entire region of 0.29 days/year. The trend of EOS delay was more significant for both EBF and CL compared to other vegetation (*p* < 0.05), and the rate of EBF delay was the fastest (0.37 days/year). LOS is significantly lengthened at a rate of 0.48 days/year. ENF, EBF, DBF, MF, and CL show a more significant trend for lengthened LOS (*p* < 0.05), with the fastest being for EBF (0.68 days/year).

**Figure 6.** Interannual variations and significance of the mean phenology metrics for the entire study area and the areas covered by different vegetation types. The unit of *Sen's slope* is days/year.

#### *3.2. Change Pattern of LSP and Relative Attribution Analysis*

For all vegetation types, the dominant change pattern of the growing season was Type I, with a proportion of 48.4%, which implies that most vegetation in the QMs had an advanced SOS, delayed EOS, and lengthened LOS (Table 4). Type V showed the second largest proportion (15.2%) which meant that there were also many plant species on the QMs having delayed SOS, delayed EOS, and lengthened LOS. Types III, IV, and VI had the smallest proportions (all lower than 10.0%), which indicated that the probabilities of shortened LOS were very low for all plants on the QMs. For five of these vegetation types (ENF, EBF, MF, SL, and CL), the dominant change pattern of the growing season was Type I, followed by Type V. This indicates that these types of plants on the QMs had delayed EOS and lengthened LOS. The main change pattern for DBF and GL was also Type I, with Type II being the second most prevalent. This implies that there is some DBF and GL showing advanced SOS, advanced EOS, and lengthened LOS.

Figure 7 shows the significance of each pattern. For all vegetation types, lengthened LOS, advanced SOS, and delayed EOS were significant in terms of Type I, II, and V change patterns, respectively. Types I, II, and V were the top three patterns in terms of percentage, as seen in Table 4, which were also the three patterns of LOS lengthening. In the Type I, lengthened LOS is significant for most vegetation types (EBF, MF, SL, GL, and CL). In terms of Type II change, the SOS of ENF, DBF, MF, GL, and CL were all significantly advanced, and advanced SOS resulted in lengthened LOS. Fewer changes in LSP trends were significant in terms of Type III, IV, and VI changes, only SL and GL showed significant Type IV changes, and SL (15.5%) and GL (12.2%) accounted for a large proportion of Type IV changes. In terms of Type V, the EOS of all six vegetation types (ENF, EBF, DBF, MF, GL, and CL) was significantly delayed. Delayed EOS resulted in lengthened LOS, a pattern which also happens to be the second largest in terms of proportion, and this pattern is also the one we should be concerned about.

**Table 4.** The percentage of phenology metrics datasets consisting of each pixel showing different change patterns in the growing seasons for all the vegetation types. Change patterns refer to the trend groupings in Table 2.

**Figure 7.** Trends and significance of each pattern during the growing season for different vegetation types. (**a**) growing season pattern I. (**b**) growing season pattern II. (**c**) growing season pattern III. (**d**) growing season pattern IV. (**e**) growing season pattern V. (**f**) growing season pattern VI. Note: "All" refers to all vegetation in the QMs.

For the entire study area, the calculated C-index values were negative for 106,385 pixels and positive for 112,514 pixels (Table 5 and Figure S1). Pixels with C-index values less than 0 are mainly distributed in the easternmost and southernmost parts of the QMs, which indicates that their LOS variations are mainly controlled by SOS shifts. All other regions have pixels with values over 0, which indicates that they are controlled by EOS shifts (Figure S1, in Supplementary Materials). The percentage of LOS changes controlled by SOS and EOS is 48.6% and 51.4%, respectively (Table 5). This also shows that LOS trends, except for DBF and GL, were mainly controlled by the shift in EOS for each vegetation type. The percentage of LOS changes controlled by SOS shifts was 53.4% and 52.0% for DBF and GL, respectively. The largest percentage of changes in LOS of all vegetation types controlled by EOS was 58.3% (SL), and the smallest was 46.6% (DBF).

**Table 5.** The percentage of datasets in which LOS change was primarily attributable to the shift in SOS or EOS. For most vegetation types, the percentages show that the trend in LOS was mainly controlled by the shift in EOS.

*3.3. Drivers of Interannual Variations in LSP*

For the QMs, different drivers affect the interannual variability in SOS and EOS (Figure 8 and Table 6). The SWP, MD, and STP are the three most important factors influencing the interannual SOS variation, and the relative importance accounts for 54.4% of total (Figure 8a and Table 6). The total percentage of TG, PP, TP, STG, and PG was 39.2%, and the effect of TG and PP on the interannual SOS variation was almost the same. The remaining four variables (SWG, SMG, SMP, and MN) have a very small effect on interannual SOS variation, and their combined percentage was only 6.4%. Figure 8b and Table 6 also show that SWP, PP, and MD are the three most important factors influencing the interannual EOS variation, with a total relative importance of 54.0%, and the influence of SWP is much stronger than that of PP and MD. The effects of TP, SWG, STP, PG, TG, and STG on the interannual EOS variation totaled 41.9%, and the effects of TP and SWG on the interannual EOS variation were not very different, with a relative importance of 10.2% and 9.9%, respectively. There was also little difference in the relative importance of PG, TG, and STG. The effect of the single variable of STP on the interannual EOS variation (7.2%) is much larger than the sum of SMP, SMG, and MN (4.1%).

**Table 6.** The top three dominant drivers affecting interannual variations in LSP. These three dominant factors are derived from the ranking of the importance scores of the variables (VI). Different vegetation types have different dominant drivers.

**Table 6.** *Cont.*

**Figure 8.** Relative importance of drivers of affecting interannual variations in LSP for the entire study area, in decreasing order. (**a**) The ranking of the drivers of affecting interannual variations in SOS. (**b**) The ranking of the drivers of affecting interannual variations in EOS. The specific values for the relative importance of each driver are in Table S1. Note: The abbreviated variable names are the same as in Table 3.

The drivers influencing interannual variations in LSP of different vegetation types were assessed (Figure 9 and Table 6). Figure 9a and Table 6 show that the main drivers affecting the interannual SOS variation of ENF, MF, and GL were SWP, MD, and STP, and the relative importance of SWP in these three vegetation types was ranked as GL (28.6%) > MF (22.1%) > ENF (21.2%). The interannual SOS variation of EBF, DBF, and CL was mainly influenced by MD and SWP. The main drivers influencing the interannual SOS variation of SL were STP, TP, and SWP, and STP (29.2%) was the most important factor influencing the interannual SOS variation of SL. As shown in Figure 9b and Table 6, in terms of the interannual EOS variation, SWP had the strongest effect on GL (36.4%) and the slightest effect on SL (20.5%). The main drivers of interannual EOS variation of ENF, DBF, and CL are SWP, TP, and PP. SWP, PP and MD are the main drivers of interannual variations in EOS for EBF, MF and SL. Besides SWP, which is the most important driver, PP is the second factor affecting MF and SL, and MD is the second factor affecting EBF with a relative importance of 14.5%. Moreover, the main factors affecting the interannual EOS variation of GL are SWP, SWG, and TP, and the relative importance of SWG and TP is 11.5% and 11.2%, respectively.

**Figure 9.** The relative importance of drivers that affect interannual variations in LSP for different vegetation types. Relative importance was derived from the importance scores of variables (VI) based on RF models established for different vegetation types: (**a**) SOS. (**b**) EOS. Different colors indicate different factors. Note: SOS or EOS as response variable in the RF model. The specific values for the relative importance of each driver are in Table S1.

We used 19 years of data to construct an RF model for seven vegetation types (Table S2), and we randomly sampled 1/3 of the pixel dataset to assess their linear relationships (Figure S2). The actual and predicted SOS displayed good linear relationships, with their correlation coefficient R2 values ranging from 0.900 to 0.938, RMSE values ranging from 4.90 to 6.52, and MAE values ranging from 3.93 to 5.15 (Figure S2a). Both the actual and predicted EOS also show good linear relationships, with their correlation coefficients R2 values ranging from 0.911 to 0.942, RMSE values ranging from 4.23 to 5.63, and MAE values ranging from 3.26 to 3.74 (Figure S2b). These results indicate that it is appropriate to use RF models to analyze interannual variations in LSP in the QMs.


*2.4. Analysis of Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX)*

The microstructures and elemental compositions of the bonded *Rhizophora* spp. samples were obtained and studied using a SEM (FEI Quanta FEG-650, Eindhoven, The Netherlands) and EDX at the Centre for Global Archeological Research, Earth Material Characterisation Laboratory, Universiti Sains Malaysia. The samples with the measurement of (5.0 × 5.0 × 0.5) cm3 were taped to a specimen holder with two-sided adhesive tape, and coated with gold by a sputter coater (Quorum Q150T S, Quantum Design GmbH, Darmstadt, Germany). The images were taken at a magnification of 4000×. For EDX analysis, the elements were preset to focus only on carbon, nitrogen and oxygen.


**2. Analysis of thermal insulation materials for fencing bodies of refrigerated wagons and containers**

In recent years, the most commonly used insulation is made of polyurethane foam, which is applied to finished surfaces by spraying [21, 22]. **Table 1** shows some characteristics of the materials used.

*Experimental Research of New Design Solutions for Fencing Refrigerated Wagon Bodies… DOI: http://dx.doi.org/10.5772/intechopen.109744*

#### **Table 1.**

*Characteristics of insulation materials.*

To ensure a low value of the heat transfer coefficient, the dimensions of the thickness of the body fence with these materials are in the range of 160–240 mm. The fences of the vehicle body with the specified materials do not protect against the formation of fogging of the body fence elements and the formation of moisture inside the body, which is harmful to the transported goods.

When repairing refrigerated wagons and containers, it is necessary to replace almost all the elements of the body fence due to moisture damage [13, 21].

The consumption of materials for body fencing is quite high, which undoubtedly affects the cost of vehicles. The search for materials for thermal insulation of the body of vehicles that could become ideal continues [27–29].

To protect building structures from moisture and heat preservation indoors at low temperatures, the thermal insulation material "Corundum" [30], created in 2008, and

#### **Table 2.**

*Comparison of technical characteristics of traditional and new thermal insulation.*

produced by Russian manufacturers, is used. This material is a one-component polymer suspension, to which the smallest ceramic heat-sealed granules are added, which are characterized by the physical principle of reflection and heat transfer. The content of such granules (microspheres) in the suspension reaches 70%. Depending on the intended purpose, pigmenting fire-resistant, anticorrosive and inhibitory components are added to the composition. All this makes it possible to obtain thermal insulation with high adhesion and elasticity, which does not change under the influence of external factors: humidity, ultraviolet rays, temperature changes, mold and fungi. The basis of corundum is an Aqueous-F acrylic solution, to which the above components are added [31]. In addition, a number of new materials have appeared. Basic information about these materials is given in **Table 2**. Therefore, the task arises to assess the possibility of using these materials in the construction of fencing of wagons and containers [32].


**3. Pathophysiology**

When ICH occurs, blood leaks into the brain parenchyma, leading to the formation of hema‐ toma, which quickly increases intracranial pressure. The accumulated blood and high intracranial pressure cause immediate primary damage to the brain. This initial injury is followed by secondary damage mainly resulting from inflammatory responses [102, 103]. The exposure of brain parenchyma to blood proteins (e.g., proteases and hemoglobin) and cells (red blood cells and leukocytes) results in activation of microglia, and the secretion of proinflammatory cytokines/chemokines [104, 105], including TNF-α, IL-1β, and MCP1/CCL2. These inflammatory mediators, by forming a concentration gradient, activate and attract more microglia and other inflammatory cells to the injury site [106]. These cells then accumulate around the hematoma, forming a barrier to prevent the spread of injury to other sites. The released pro-inflammatory cytokines/chemokines and possibly activated microglia also act on BMECs, pericytes and astrocytes, leading to compromise of BBB integrity. Through the disrupted BBB, peripheral leukocytes infiltrate into the brain. The infiltrated leukocytes together with activated microglia produce more pro-inflammatory mediators, which induce cell death in the penumbra area [107, 108]. In addition, hemolysis of red blood cells causes iron deposition in the brain parenchyma and subsequent lipid peroxidation [109]. Free radicals generated during lipid peroxidation also lead to cell death and contribute to ICH-induced brain injury [110, 111]. With the progress of disease, microglia and infiltrated leukocytes change their gene expression profile from pro-inflammatory to anti-inflammatory and clear up the dead cells via phagocytosis [110, 112]. The clearance of cell debris finally leads to the resolution of the hematoma and repair of damaged tissue. At this stage, the activated inflam‐ matory cells revert to a resting state again. Due to the limited regenerative ability of neurons, however, most neurological functions cannot be recovered, which explains the high extent of disability after ICH.


**4. Continent's natural hazards: 1900–2022**

An estimated 45,000 people die yearly due to natural disasters, representing about 0.1% of global deaths. The number and proportion of fatalities caused

**Figure 1.** *The diagram shows the increased and completed of recorded natural hazards during 1900–2022.*

#### **Figure 2.**

*(a) Frequency, (b) deaths, (c) total affected people, (d) direct economic losses (US dollars) per natural hazard types, based on the top ten countries in the world (1900–2022).*

#### **Table 1.**

*Statistical results on natural hazards, based on the top ten countries in the world (1900–2022).*

by natural disasters have fluctuated in recent decades. While the number of deaths often falls below 10,000, the impact of major events, such as the famine and drought in Ethiopia in 1983–1985, the Indian Ocean 2004 earthquake and


**Introduction**

Finally,

VIII Preface

I offer special thanks to Author Service Managers Romina Skomeršić, Irina Štefanić,

**Dr. Didem Gokce** Department of Biology Faculty of Science and Letters

> Inonu University Malatya, Turkey

Kristina Jurdana, and Anja Filipović for their help in publishing the book in its present form. I am also grateful for IntechOpen Publishers for their concern, efforts, and encouragement.

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Wetland Importance and**

**Introductory Chapter: Wetland Importance**

DOI: 10.5772/intechopen.82456

Water is an important resource for all living beings. Therefore, the use of water and its supply from sources are very important. Wetlands are an ecosystem from mangrove to subarctic peatlands that have affected human. The earliest civilizations were established near the river, lake, and floodplains [1]. The Mesopotamian civilization is authoritatively accepted to have started around 4000–3500 BC between the Euphrates and Tigris River. The other ancestral civilization, Egypt, commenced in the Nile Valley at around 3200 BC. This represents the importance of the water and wetlands. The fact that people are in these regions is a reflection of how important it is for biotic diversity. Therefore, wetlands are a very critical ecosystem,

Wetlands occur where the water table is at or near the surface of the land or where the land is covered by water [2]. Wetlands are the only ecosystems for whose conservation an international convention (Ramsar Convention) had been adopted as early as 1971. Ramsar Convention defined wetlands as "areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters" [2]. Wetlands have about 6% of the earth although they play an important role in hydrology and include mangroves, peatlands and marshes, rivers and lakes, deltas, floodplains and flooded forests, and even coral reefs. A wetland is a generalized concept including coastal wetlands. It

Many wetlands are transitional area between aquatic and terrestrial ecosystems. These ecosystems are divided into two groups depending on the quantity of water: permanent and temporal flooded. Since wetlands are distributed in many different habitats on earth, they

exists in every climatic region, ranging from the polar zones to the arid zones.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Additional information is available at the end of the chapter

and some of them are the most productive habitats.

Didem GokceAdditional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.82456

**Management**

**and Management**

Didem Gokce

**1. Introduction**

#### **Introductory Chapter: Wetland Importance and Management Introductory Chapter: Wetland Importance and Management**

DOI: 10.5772/intechopen.82456


**Triple Bonds between Bismuth and Group 13 Elements: Theoretical Designs and Characterization**

Jia-Syun Lu, Ming-Chung Yang, Shih-Hao Su, Xiang-Ting Wen, Jia-Zhen Xie and Ming-Der Su

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67220

#### Abstract

The effect of substitution on the potential energy surfaces of RE13≡BiR (E13 = B, Al, Ga, In, and Tl; R = F, OH, H, CH3, SiH3, Tbt, Ar\*, SiMe(SitBu3)2, and SiiPrDis2) is investigated using density functional theories (M06-2X/Def2-TZVP, B3PW91/Def2-TZVP, and B3LYP/LANL2DZ+dp). The theoretical results suggest that all of the triply bonded RE13≡BiR molecules prefer to adopt a bent geometry (i.e., ∠RE13Bi ≈ 180° and ∠E13BiR ≈ 90°), which agrees well with the bonding model (model (B)). It is also demonstrated that the smaller groups, such as R = F, OH, H, CH3, and SiH3, neither kinetically nor thermodynamically stabilize the triply bonded RE13≡BiR compounds, except for the case of H3SiB≡BiSiH3. Nevertheless, the triply bonded RʹE13≡BiRʹ molecules that feature bulkier substituents (Rʹ = Tbt, Ar\*, SiMe(SitBu3)2, and SiiPrDis2) are found to have the global minimum on the singlet potential energy surface and are both kinetically and thermodynamically stable. In other words, both the electronic and the steric effects of bulkier substituent groups play an important role in making triply bonded RE13≡BiR (Group 13–Group 15) species synthetically accessible and isolable in a stable form.

Keywords: bismuth, group 13 elements, triple bond, multiple bond, density functional theory

## 1. Introduction

Triply bonded molecules are of great interest in structural and synthetic inorganic chemistry as well as in fundamental science. Molecules that have triple bonds, however, pose a more difficult challenge than analogous doubly bonded molecules from a synthetic viewpoint [1–8]. Acetylene is one of the most commonly triply bonded molecules in traditional organic chemistry. Thanks to Kira, Power, Sekiguchi, Tokitoh, Wiberg and many coworkers, the stable

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

homonuclear alkyne analogues of all of the heavier group 14 elements have now been isolated and characterized [9–19]. Recently, heteronuclear ethyne-like molecules that possess C≡Ge [20, 21], C≡Sn [22], and C≡Pb [23] triple bonds have also been theoretically predicted and have been published elsewhere.

Nevertheless, to the authors' best knowledge, neither experimental nor theoretical studies have been performed on acetylene-like compounds that feature an E13≡Bi (E = B, Al, Ga, In, and Tl) triple bond. It is surprising how little is known about the stability and molecular properties of E13≡Bi, considering the importance of bismuth compounds [24] that contain group 13 elements in inorganic chemistry [25–35] and material chemistry [36–45].

The aim of this study is to theoretically determine the existence and relative stability of RE13≡BiR triply bonded molecules, which can be synthesized as stable compounds when they are properly substituted. For the first time, the structures of RE13≡BiR with various substituents are reported. That is, theoretical calculations of RE13≡BiR are performed, using both smaller ligands (such as, R = F, OH, H, CH3, and SiH3) and larger ligands with bulky aryl and silyl groups (i.e., Rʹ = Tbt, Ar\*, SiMe(SitBu3)2, and SiiPrDis2; Dis = CH(SiMe3)2; Scheme 1) [46–51]. As a result, the effect of substituents on these bismuth-group-13 element triple bonds is systematically investigated using density functional theory (DFT) calculations. It is expected that the theoretical interpretations of the effect of substituents, presented in this work, will help in the experimental preparation of the many precursors of RE13≡BiR.