**6. General tips to achieve atomic resolution**

Atomic resolution images can be obtained by controlling tip-surface interactions as discussed above. In addition to tip-surface interactions, the following suggestions can be made to achieve atomic resolution:

#### **7. Artifacts and reproducibility**

The topographs obtained by AFM should be reproducible and represent the real surface structure of the sample. Artifacts at the atomic scale are topographic features by which uncertainties and errors enter the surface structure determination. There are numerous

properties of the materials and the medium, and the tip geometry. In most cases, vdW forces are attractive between tip and surface of interest. The capillary force arises when tip approaches the surface in air. The water molecules on the surface forms a bridge with the tip and an increased force must be applied in order to detach the tip from the surface. This increased force is called the capillary force, and depends on the surface properties, humidity, temperature and geometry of the tip. The capillary force is usually more longranged than the van der Wall force under moderate humidity conditions. Short-range forces become important when the tip-surface distance is less than 1 nm. Short-range force may originate from Born repulsion, chemical bonding, and electrostatic and vdW interactions between atoms. The electrical double layer force arises when two surfaces approach each other in solution. The surfaces develop charges either by protonation/de-protonation, adsorption, and specific chemical interaction, which attracts counterions and co-ions from solution. Lastly, contaminants, particularly organic materials adheres either to surface or tip, even in trace amounts, can significantly affect the tip-surface interaction. Therefore, a clean tip and surface are highly desirable prior to and throughout the experiments. This is a brief review of tip-surface interactions, and readers are advised to review classic textbooks (Butt

In order to achieve atomic resolution image, the external load on the tip must counteract the tip-surface interactions discussed above. The external load is a function of spring constant of the tip and its bending. It is highly desirable to keep the tip load as low as possible to

Atomic resolution images can be obtained by controlling tip-surface interactions as discussed above. In addition to tip-surface interactions, the following suggestions can be

 Use sharper tips. Weih *et al.* (Weihs *et al.*, 1991) showed by calculations that the lateral resolution increased by a factor of 4 by reducing the tip radius from 200 to 20 nm. The sharper tip also reduces the adhesion force between a tip and the surface, which also

 Use stiffer tips. The elastic modulus could be increased by using a stiffer tip to achieve a smaller contact area between a tip and the surface. The smaller contact area between a tip and the surface is desired so as to realize only few atoms in contact. Ideally, a single atom of the tip should interact with each surface atom to obtain atomic resolution. Reduce tip load. By applying the cantilever bending force, the contact area between a

Reduce adhesion. The work of adhesion can be minimized by immersing the tip and

The topographs obtained by AFM should be reproducible and represent the real surface structure of the sample. Artifacts at the atomic scale are topographic features by which uncertainties and errors enter the surface structure determination. There are numerous

tip and the surface could be reduced by lowering the tip load.

*et al.*, 2003; Israelachvili, 1985; Masliyah & Bhattacharjee, 2006).

**6. General tips to achieve atomic resolution**

produce high resolution image.

made to achieve atomic resolution:

decrease the tip load.

sample in liquid.

**7. Artifacts and reproducibility**

types of AFM artifacts, including missing atoms/molecules/vacancies, ghost atoms, and fuzzy steps etc. Most artifacts are caused by multiple-tip surface contacts and high tip loads. Ideally, a single atom tip interacts with the surface to obtain atomically resolved topographs. In reality, however, the structure, geometry, and surface chemistry of the AFM tips are usually poorly defined. During imaging, the AFM tip may get deformed and cause multiple point contacts. It is therefore highly desirable to monitor the structural and chemical modification of the tip before and after experiments. Equally, the low tip load is desirable for achieving high resolution atomic images. Ohnesorge and Binnig (Ohnesorge & Binnig, 1993) have demonstrated the dramatic change in topograph by carefully controlling the tipsurface interaction. Sokolov and Henderson (Sokolov & Henderson, 2000) also showed that an increased tip load destroys the atomically resolved images determined from the vertical force contrast and only improves lattice resolution images determined from the friction forces. Cleveland *et al.* (Cleveland *et al.*, 1995) also showed through atomic imaging of calcite and mica surfaces in water, that surface atoms could only be unambiguously identified when the tip load was attractive. It is thus highly recommended that one be cautious in interpreting AFM images before systematic studies of the tip load effect are carried out.

The AFM images should show the real surface structure and be reproducible. The surface structure should remain unchanged with varying probes, scanning directions, different location on the same surface, different sample of same material, tip-surface forces, and even different instruments and techniques if possible.

Finally, more confidence in the recorded AFM topographs will be gained if the same surface can be analyzed with other techniques such as STM, high resolution transmission electron microscopy, x-ray crystallography etc. Electron microscopy requires complex surface preparation procedures, but they are free from artifacts introduced in AFM images. These alternative techniques may compliment AFM in obtaining and verifying the atomic images.

## **8. Case study: Crystal lattice imaging of silica face and alumina face of kaolinite**

Kaolinite naturally exists as pseudo-hexagonal, platy-shaped, thin particles generally having a size of less than one micron extending down to 100 nm. The crystallographic structure of kaolinite suggests that there should be two types of surface faces defined by the 001 and the 001 basal planes. In this way, one face should be described by a silica tetrahedral surface and the other face should be described by an aluminum hydroxide (alumina) octahedral surface as shown in figure 1. The objective of this case study is to demonstrate the bi-layer structure of kaolinite – a silica tetrahedral layer and an alumina octahedral layer, through atomic resolution obtained using AFM.

#### **8.1 Materials and methods**

#### **8.1.1 Sample preparation**

A clean English kaolin (Imerys Inc., UK) was obtained from the St. Austell area in Cornwall, UK. The sample was cleaned with water and elutriation was used to achieve classification at a size of less than 2 µm. No other chemical treatment was done. Further details about the kaolinite extraction and preparation are given in the literature (Bidwell *et al.*, 1970).

Crystal Lattice Imaging Using Atomic Force Microscopy 9

The kaolinite suspension (1000 ppm) was prepared in high purity Milli-Q water (Millipore Inc.) with a resistivity of 18.2 MΩ-cm. The pH was adjusted to 5.5 using 0.1 M HCl or 0.1 M

Two substrates – a mica disc (ProSciTech, Queensland, Australia) and a fused alumina substrate (Red Optronics, Mountain View, CA), were used to order the kaolinite particles (Gupta & Miller, 2010). The kaolinite particle suspension (1000 ppm) was sonicated for 2 minutes, and about 10 µl of the suspension was air-dried overnight on a freshly cleaved mica substrate under a petri-dish cover in a laminar-flow fume hood. In this way, the kaolinite particles attach to the mica substrate with the alumina face down exposing the silica face of kaolinite, as shown from previous surface force measurements (Gupta & Miller, 2010), i.e., the positively charged alumina face of kaolinite is attached to the negatively

The fused alumina substrate was cleaned using piranha solution (a mixture of sulfuric acid and hydrogen peroxide in a ratio of 3:1) at 1200C for 15 minutes, followed by rinsing with copious amounts of Milli-Q water, and finally blown dry with ultra high purity N2 gas. A 10 µl kaolinite suspension was applied to the alumina substrate and dried in the same manner as the mica. It was found that the alumina face of kaolinite was exposed on the fused alumina substrate based on previous surface force measurements (Gupta & Miller, 2010), i.e., the negatively charged silica face of kaolinite is attached to the positively charged fused-

The samples were prepared the night before AFM analysis and stored in a desiccator until their use. Just prior to the AFM experiments, the substrates were sonicated for a minute in Milli-Q water to remove loosely adhered kaolinite particles, washed with Milli-Q water, and gently blown with N2 gas before AFM investigation. All substrates were attached to a

A Nanoscope AFM with Nanoscope IV controller (Veeco Instruments Inc., Santa Barbara, CA) was used with an E-type scanner. Triangular beam silicon nitride (Si3N4) cantilevers (Veeco Instruments Inc., Santa Barbara, CA), having pyramid-shaped tips with spring constants of about 0.58 N/m, were used. The cantilevers were cleaned using acetone, ethanol, water in that order, and gently dried with ultra high purity N2 gas. The cantilevers were subsequently cleaned in a UV chamber for 30 minutes prior to use. The substrates were loaded on AFM equipped with a fluid cell. The contact mode imaging was done in Milli-Q water. The AFM instrument was kept in an acoustic and vibration isolation chamber. The imaging was commenced 30 minutes after sample loading to allow the thermal vibration of the cantilever to equilibrate in the fluid cell. First, an image of the particles was obtained at a scan rate of 1 Hz and scan area of 1 µm. Subsequently, the atomic resolution imaging was completed using the zoom-in and offset feature of the Nanoscope vs. 5.31R1 software (Veeco Instruments Inc., Santa Barbara, CA) to scan an area of 12 nm on the particle surface. The atomic imaging was obtained at a scan rate of 30 Hz at scan angle of 800–900 with very low

KOH solutions.

**8.1.2 Substrate preparation**

charged mica substrate.

alumina substrate.

standard sample puck using double-sided tape.

**8.1.3 Atomic Force Microscopy**

Fig. 1. Side view (A) and Top view (B) of kaolinite (001) surface structure. The silica tetrahedra (red: oxygen, blue: silicon) and alumina octahedra (yellow: aluminum, green: hydroxyl) bilayers thought to be bound together via hydrogen bonding are illustrated in (A).

The kaolinite suspension (1000 ppm) was prepared in high purity Milli-Q water (Millipore Inc.) with a resistivity of 18.2 MΩ-cm. The pH was adjusted to 5.5 using 0.1 M HCl or 0.1 M KOH solutions.

#### **8.1.2 Substrate preparation**

8 Atomic Force Microscopy – Imaging, Measuring and Manipulating Surfaces at the Atomic Scale

Fig. 1. Side view (A) and Top view (B) of kaolinite (001) surface structure. The silica tetrahedra (red: oxygen, blue: silicon) and alumina octahedra (yellow: aluminum, green: hydroxyl) bilayers thought to be bound together via hydrogen bonding are illustrated in (A).

**A**

**B**

Two substrates – a mica disc (ProSciTech, Queensland, Australia) and a fused alumina substrate (Red Optronics, Mountain View, CA), were used to order the kaolinite particles (Gupta & Miller, 2010). The kaolinite particle suspension (1000 ppm) was sonicated for 2 minutes, and about 10 µl of the suspension was air-dried overnight on a freshly cleaved mica substrate under a petri-dish cover in a laminar-flow fume hood. In this way, the kaolinite particles attach to the mica substrate with the alumina face down exposing the silica face of kaolinite, as shown from previous surface force measurements (Gupta & Miller, 2010), i.e., the positively charged alumina face of kaolinite is attached to the negatively charged mica substrate.

The fused alumina substrate was cleaned using piranha solution (a mixture of sulfuric acid and hydrogen peroxide in a ratio of 3:1) at 1200C for 15 minutes, followed by rinsing with copious amounts of Milli-Q water, and finally blown dry with ultra high purity N2 gas. A 10 µl kaolinite suspension was applied to the alumina substrate and dried in the same manner as the mica. It was found that the alumina face of kaolinite was exposed on the fused alumina substrate based on previous surface force measurements (Gupta & Miller, 2010), i.e., the negatively charged silica face of kaolinite is attached to the positively charged fusedalumina substrate.

The samples were prepared the night before AFM analysis and stored in a desiccator until their use. Just prior to the AFM experiments, the substrates were sonicated for a minute in Milli-Q water to remove loosely adhered kaolinite particles, washed with Milli-Q water, and gently blown with N2 gas before AFM investigation. All substrates were attached to a standard sample puck using double-sided tape.

#### **8.1.3 Atomic Force Microscopy**

A Nanoscope AFM with Nanoscope IV controller (Veeco Instruments Inc., Santa Barbara, CA) was used with an E-type scanner. Triangular beam silicon nitride (Si3N4) cantilevers (Veeco Instruments Inc., Santa Barbara, CA), having pyramid-shaped tips with spring constants of about 0.58 N/m, were used. The cantilevers were cleaned using acetone, ethanol, water in that order, and gently dried with ultra high purity N2 gas. The cantilevers were subsequently cleaned in a UV chamber for 30 minutes prior to use. The substrates were loaded on AFM equipped with a fluid cell. The contact mode imaging was done in Milli-Q water. The AFM instrument was kept in an acoustic and vibration isolation chamber. The imaging was commenced 30 minutes after sample loading to allow the thermal vibration of the cantilever to equilibrate in the fluid cell. First, an image of the particles was obtained at a scan rate of 1 Hz and scan area of 1 µm. Subsequently, the atomic resolution imaging was completed using the zoom-in and offset feature of the Nanoscope vs. 5.31R1 software (Veeco Instruments Inc., Santa Barbara, CA) to scan an area of 12 nm on the particle surface. The atomic imaging was obtained at a scan rate of 30 Hz at scan angle of 800–900 with very low

Crystal Lattice Imaging Using Atomic Force Microscopy 11

Fig. 2. Crystal lattice imaging of mica substrate showing (A) Flattened height image, (B) FFT spectra, (C) FFT transformed flattened height image, and (D) Zoomed-in image of (C) of scan area of 36 nm2. The six light spots in (D) show the hexagonal ring of oxygen atoms

substrates (compare Figure 8.2D, Figure 8.4D, and Figure 8.5D). The image shown in Figure 8.5D is similar to the octahedral sheet of lizardite (Wicks *et al.*, 1992), the internal octahedral sheets of micas and chlorite (Wicks *et al.*, 1993), and the brucite-like layers of hydrotalcite (Cai *et al.*, 1994). The octahedral sheet of kaolinite consists of a plane of hydroxyls on the surface. The average hydroxyl-hydroxyl distance of the octahedral sheet is 0.36 ± 0.04 nm which is in reasonable agreement with the literature value of 0.29 nm (Wyckoff, 1968). For a kaolinite pellet, Kumai *et al.* (Kumai *et al.*, 1995) observed the distance between the hydroxyl

around the dark spots representing a hole. Adapted from (Gupta *et al.*, 2010).

**A B**

**C D**

atoms as 0.33 nm.

integral and proportional gain (0.06). The online filters (low pass and high pass) were turned off during the online crystal lattice imaging.

During offline image processing, flattening and low pass filtering were applied to obtain clear images using Nanoscope vs. 5.31R1 software. The images were further Fourier-filtered (2D FFT) to obtain the crystal lattice images using SPIP software (Image Metrology A/S, Denmark).

#### **8.2 Results and discussion**

In order to obtain the crystal lattice imaging of the silica face and alumina face of kaolinite, the scanner was first calibrated using a mica substrate. Figure 8.2 presents the crystal lattice imaging of mica, which shows the height image, fast-Fourier transform (FFT) spectra and the FFT transformed height image. In order to make sure that the image is real, the imaging was acquired from other locations on the mica substrate and also with varying scan size and scan angle. The repeated pattern of dark and light spots was reproducible and the dark spots observed were scaled appropriately with the scan size and angle. The images showed some drift in both x and y direction during imaging. The dark spots in Figures 8.2C and 8.2D correspond to a hole surrounded by the hexagonal lattice of oxygen atoms. The light spots are attributed to the three-surface oxygen atoms forming a SiO4 tetrahedron or pairs of SiO4 tetrahedra forming a hexagonal ring-like network. Similar images were reported for the 1:1 type clay mineral, lizardite (Wicks *et al.*, 1992) and other 2:1 type clay minerals (Drake *et al.*, 1989; Hartman *et al.*, 1990; Sharp *et al.*, 1993) from AFM observations on a single crystal. The fast-Fourier transform showed the intensity peaks of oxygen atoms arranged in a hexagonal ring network (see Figure 8.2B). The crystal lattice spacing between neighboring oxygen atoms was calculated as 0.51 ± 0.08 nm, from the average of 10 neighboring atoms. This is in very good agreement with the literature value of 0.519 nm (Wicks *et al.*, 1993).

Figure 8.3 shows an image of a kaolinite particle on a mica substrate. The image shows the platy nature and the pseudo-hexagonal shape of the kaolinite particle. The scanning was sequentially zoomed on the particle. Figure 8.4 shows the crystal lattice imaging of the silica face of a kaolinite particle on the mica substrate. The flattening and low pass filtering was applied to the height image in an offline mode (see Figure 8.4B). The FFT spectra showed the similar intensity of peaks of oxygen atoms arranged in a hexagonal ring network as observed for the mica substrate. As expected, the silica face of kaolinite showed the similar hexagonal ring-like network of oxygen atoms as observed on the mica substrate (compare Figure 8.2D and Figure 8.4D). Note that the scan scale for the image of the silica face of kaolinite was twice that used for the mica substrate (12 nm vs. 6 nm), which shows the reproducibility of the crystal lattice images obtained on different substrates. The crystal lattice spacing between neighboring oxygen atoms was calculated as 0.50 ± 0.04 nm, from the average of 10 neighboring atoms. This lattice spacing is in good agreement with 0.53 nm as reported in the literature (Kumai *et al.*, 1995).

The crystal lattice imaging of the alumina face of kaolinite on a fused alumina substrate is shown in Figure 8.5. The FFT spectra shows the intensity peaks of the hydroxyl atoms forming a hexagonal ring network similar to that obtained on the silica face of kaolinite (see Figure 8.5C). Notice that the hexagonal ring of hydroxyls shows the inner hydroxyl in the center of the ring instead of a hole as observed for the silica face of kaolinite and mica

integral and proportional gain (0.06). The online filters (low pass and high pass) were turned

During offline image processing, flattening and low pass filtering were applied to obtain clear images using Nanoscope vs. 5.31R1 software. The images were further Fourier-filtered (2D FFT) to obtain the crystal lattice images using SPIP software (Image Metrology A/S,

In order to obtain the crystal lattice imaging of the silica face and alumina face of kaolinite, the scanner was first calibrated using a mica substrate. Figure 8.2 presents the crystal lattice imaging of mica, which shows the height image, fast-Fourier transform (FFT) spectra and the FFT transformed height image. In order to make sure that the image is real, the imaging was acquired from other locations on the mica substrate and also with varying scan size and scan angle. The repeated pattern of dark and light spots was reproducible and the dark spots observed were scaled appropriately with the scan size and angle. The images showed some drift in both x and y direction during imaging. The dark spots in Figures 8.2C and 8.2D correspond to a hole surrounded by the hexagonal lattice of oxygen atoms. The light spots are attributed to the three-surface oxygen atoms forming a SiO4 tetrahedron or pairs of SiO4 tetrahedra forming a hexagonal ring-like network. Similar images were reported for the 1:1 type clay mineral, lizardite (Wicks *et al.*, 1992) and other 2:1 type clay minerals (Drake *et al.*, 1989; Hartman *et al.*, 1990; Sharp *et al.*, 1993) from AFM observations on a single crystal. The fast-Fourier transform showed the intensity peaks of oxygen atoms arranged in a hexagonal ring network (see Figure 8.2B). The crystal lattice spacing between neighboring oxygen atoms was calculated as 0.51 ± 0.08 nm, from the average of 10 neighboring atoms. This is in very good agreement with the literature value of 0.519 nm (Wicks *et al.*, 1993).

Figure 8.3 shows an image of a kaolinite particle on a mica substrate. The image shows the platy nature and the pseudo-hexagonal shape of the kaolinite particle. The scanning was sequentially zoomed on the particle. Figure 8.4 shows the crystal lattice imaging of the silica face of a kaolinite particle on the mica substrate. The flattening and low pass filtering was applied to the height image in an offline mode (see Figure 8.4B). The FFT spectra showed the similar intensity of peaks of oxygen atoms arranged in a hexagonal ring network as observed for the mica substrate. As expected, the silica face of kaolinite showed the similar hexagonal ring-like network of oxygen atoms as observed on the mica substrate (compare Figure 8.2D and Figure 8.4D). Note that the scan scale for the image of the silica face of kaolinite was twice that used for the mica substrate (12 nm vs. 6 nm), which shows the reproducibility of the crystal lattice images obtained on different substrates. The crystal lattice spacing between neighboring oxygen atoms was calculated as 0.50 ± 0.04 nm, from the average of 10 neighboring atoms. This lattice spacing is in good agreement with 0.53 nm

The crystal lattice imaging of the alumina face of kaolinite on a fused alumina substrate is shown in Figure 8.5. The FFT spectra shows the intensity peaks of the hydroxyl atoms forming a hexagonal ring network similar to that obtained on the silica face of kaolinite (see Figure 8.5C). Notice that the hexagonal ring of hydroxyls shows the inner hydroxyl in the center of the ring instead of a hole as observed for the silica face of kaolinite and mica

off during the online crystal lattice imaging.

as reported in the literature (Kumai *et al.*, 1995).

Denmark).

**8.2 Results and discussion**

Fig. 2. Crystal lattice imaging of mica substrate showing (A) Flattened height image, (B) FFT spectra, (C) FFT transformed flattened height image, and (D) Zoomed-in image of (C) of scan area of 36 nm2. The six light spots in (D) show the hexagonal ring of oxygen atoms around the dark spots representing a hole. Adapted from (Gupta *et al.*, 2010).

substrates (compare Figure 8.2D, Figure 8.4D, and Figure 8.5D). The image shown in Figure 8.5D is similar to the octahedral sheet of lizardite (Wicks *et al.*, 1992), the internal octahedral sheets of micas and chlorite (Wicks *et al.*, 1993), and the brucite-like layers of hydrotalcite (Cai *et al.*, 1994). The octahedral sheet of kaolinite consists of a plane of hydroxyls on the surface. The average hydroxyl-hydroxyl distance of the octahedral sheet is 0.36 ± 0.04 nm which is in reasonable agreement with the literature value of 0.29 nm (Wyckoff, 1968). For a kaolinite pellet, Kumai *et al.* (Kumai *et al.*, 1995) observed the distance between the hydroxyl atoms as 0.33 nm.

Crystal Lattice Imaging Using Atomic Force Microscopy 13

Fig. 4. Crystal lattice imaging of the silica face of kaolinite showing (A) Theoretical atomic lattice structure, (B) Flattened-low pass filtered height image, (C) FFT spectra, and (D) FFT transformed flattened-low pass filtered height image of scan size 36 nm2. The six black circles in (D) show the hexagonal ring of oxygen atoms around the dark spots representing a

**A B**

**C D**

hole. Adapted from (Gupta *et al.*, 2010).

Fig. 3. (A) Topography, and (B) Deflection images of kaolinite particle on the mica substrate. Adapted from (Gupta *et al.*, 2010).

Fig. 3. (A) Topography, and (B) Deflection images of kaolinite particle on the mica substrate.

**A**

**B**

Adapted from (Gupta *et al.*, 2010).

Fig. 4. Crystal lattice imaging of the silica face of kaolinite showing (A) Theoretical atomic lattice structure, (B) Flattened-low pass filtered height image, (C) FFT spectra, and (D) FFT transformed flattened-low pass filtered height image of scan size 36 nm2. The six black circles in (D) show the hexagonal ring of oxygen atoms around the dark spots representing a hole. Adapted from (Gupta *et al.*, 2010).

Crystal Lattice Imaging Using Atomic Force Microscopy 15

Looking ahead, we must face several challenges to produce fast and reproducible atomic resolution images. One should be skeptical of high resolution topographs, and do diligent work in reporting data. The image acquisition procedures and filtering routines discussed in this chapter should be applied judiciously. One should be aware of artifacts introduced during real-time image acquisition or post processing should be dealt with cautiously, and must be reported. Probes play a key role in realizing high resolution topographs. The benefits of sharper tips are numerous, such as smaller contact area and reduced long range forces. Most conventional tips are made from silicon nitride and silicon. Polymers or diamond tips have also used in some applications (Beuret *et al.*, 2000). Recent developments in producing nano-tips through whiskers or carbon fiber may find potential application in

Recent developments on cantilever dynamic studies (Holscher *et al.*, 2006; Strus *et al.*, 2005) and new experimental techniques, such as Q-control (Ebeling *et al.*, 2006; Okajima *et al.*, 2003) and higher order vibration imaging (Martinez *et al.*, 2006) will very likely make AFM a powerful tool for high resolution characterization in the future. Despite recent developments in AFM instrumentation for precise control of tip movement, it is still highly desirable to confirm the reliability of AFM topographs with complimentary techniques such as transmission electron microscopy (Matsko, 2007). We can conclude that AFM is a

Albrecht, T. R. and Quate, C. F. (1987). Atomic Resolution Imaging of a Nonconductor by

Albrecht, T. R. and Quate, C. F. (1988). Atomic Resolution with the Atomic Force Microscope

Belikov, S. and Magonov, S. (2006). True Molecular-Scale Imaging in Atomic Force

Beuret, C., Akiyama, T., Staufer, U., De, R. N. F., Niedermann, P. and Hanni, W. (2000).

Bidwell, J. I., Jepson, W. B. and Toms, G. L. (1970). The Interaction of Kaolinite with

Cleveland, J. P., Radmacher, M. and Hansma, P. K. (1995). Atomic-Scale Force Mapping

Butt, H. J., Graf, K. and Kappl, M. (2003). *Physics and Chemistry of Interfaces* Wiley-VCH Cai, H., Hillier, A. C., Franklin, K. R., Nunn, C. C. and Ward, M. D. (1994). Nanoscale

2010 American Chemical Society (ACS). pp. (543-549), 0168-132X

Atomic Force Microscopy. *Journal of Applied Physics*, Vol. 62, No. 7, pp. (2599-2602),

on Conductors and Nonconductors. *Journal of Vacuum Science & Technology , A*, Vol.

Microscopy: Experiment and Modeling. *Japanese Journal of Applied Physics, Part 1 (Regular Papers, Short Notes & Review Papers)*, Vol. 45, No. 3B, pp. (2158-2165),

Conical Diamond Tips Realized by a Double-Molding Process for High-Resolution Profilometry and Atomic Force Microscopy Applications. *Applied Physics Letters*,

Polyphosphate and Polyacrylate in Aqueous Solutions - Some Preliminary Results.

Imaging of Molecular Adsorption. *Science (Washington, D. C.)*, Vol. 266, No. 5190,

with Atomic Force Microscope. *NATO ASI Ser., Ser. E*, Vol. 286, No. Copyright (C)

AFM for high resolution images (Marcus *et al.*, 1989; Marcus *et al.*, 1990).

powerful instrument, and could be used for studying a variety of surfaces.

**10. References**

0021-8979

0021-4922

pp. (1551-1555)

6, No. 2, pp. (271-274), 0734-2101

Vol. 76, No. 12, pp. (1621-1623), 00036951

*Clay Minerals*, Vol. 8, No. pp. (445–459)

Fig. 5. Crystal lattice imaging of the alumina face of kaolinite showing (A) Theoretical atomic lattice structure, (B) Flattened-low pass filtered height image, (C) FFT spectra, and (D) FFT transformed flattened-low pass filtered height image of (B). The seven black circles in (D) show the hexagonal ring of hydroxyl atoms with a central inner hydroxyl atom. Adapted from (Gupta *et al.*, 2010).


**2. Paraoxonase 1**

Oxidative stress due to reactive oxygen species results in oxidation of LDL particles and phospholipids, especially phosphatidylcholine, of the cell membrane of macrophages. This leads to a state of cellular damage and inflammation. It is possible that PON1 acts on oxidized phosphatidylcholine to produce lactone which further taken care by PON1 lactonase activity.

PON1 also exhibits homocysteine-thiolactonase activity. Homocysteine (Hcy) is a four-carbon amino acid with free thiol group formed by demethylation of methionine. Plasma Hcy levels are affected by both acquired and genetic factors [18, 19]. High levels of Hcy have been implicated in the development of cardiovascular and cerebrovascular disorders. Elevated Hcy has been shown to cause homocysteinylation, induction of oxidative stress and excitotoxicity, leading to atherosclerotic and thrombotic effects [20]. Hyperhomocysteinemia results in excess production of Homocysteine-thiolactone. This modifies proteins of coagulation, lipoproteins, endothelial receptors and is an important risk factor for adverse vascular events [21, 22]. Thus hyperhomocysteinemia, encompassing higher concentrations of homocysteine-thiolactone, may be an added risk factor for enhanced atherogenesis. PON1 has been postulated to detoxify the Hcy with its homocysteine-thiolactonase activity. At the same time, it can also protect macrophages from oxidation and prevents further inflammatory cascades. Hence the patients with low PON1 arylesterase and lactonase activity are more susceptible for the deleterious effects of lipid peroxidation, homocysteine-thiolactone toxicity and macrophage activation, which would increase the risk of neurovascular disease [23].

As a xenobiotic metabolizer, PON1 provides link between exposure to pesticides and adverse effects. The products formed by action of PON1 are considered to be the markers of pesticide exposure due to which they can be useful in the assessment of severity of pesticide exposure. Animal studies have demonstrated the potential application of PON1 in tackling the effect of pesticide poisoning. But more advanced and stringent clinical trials are required to support the definitive role in pesticide poisoning [5, 24]. PON1 response to pesticides depends upon the genetic polymorphism like Q192R, L55M. Literature reports that 192Q is more protective than 192R towards prevention of LDL oxidation.


**Meet the editor**

Dr Neville Kukreja is a Consultant Interventional Cardiologist at the East & North Hertfordshire NHS Trust and the Royal Brompton & Harefield NHS Foundation Trust. He did his undergraduate studies in Cambridge and London before completing his basic medical and cardiology training in London. He underwent training in Interventional Cardiology at St

Mary's Hospital and the Royal Brompton Hospital in London and also at the internationally renowned Thoraxcenter in Rotterdam. He is currently on the General Medical Council's specialist register with accreditation in both Cardiology and General Internal Medicine. He is a member of the British Cardiovascular Society, British Cardiovascular Intervention Society, European Society of Cardiology and European Association of Percutaneous Coronary Intervention. He has co-authored more than 40 original research articles, 10 review articles and 6 book chapters. He has also been a co-author of more than 45 scientific abstracts which have been presented at major national and international cardiology scientific meetings. He is a peer- reviewer for a number of high-impact journals. He has been an invited speaker and faculty member at major international cardiology meetings, including the American College of Cardiology (ACC), European Society of Cardiology (ESC) and American Heart Association (AHA). His clinical interests include general medicine, general cardiology with particular expertise in all aspects of Percutaneous Coronary Intervention. His main research interests are the evaluation of new and existing drug-eluting stents and invasive coronary imaging (intravascular ultrasound and optical coherence tomography). He currently contributes towards the general and interventional cardiology service at the East & North Hertfordshire NHS Trust, including the provision of daytime primary percutaneous coronary intervention for local patients suffering a heart attack. He also participates in the on call rota at the Harefield Heart Attack Centre, one of the busiest PPCI units in the country providing 24hr emergency treatment for heart attack patients from Northwest London, Hertfordshire, Bedfordshire and Berkshire.

Contents

Chapter 1 **Transradial Approach**

Antoine Guédès

Seung-Jin Lee

Chapter 4 **Therapeutic Hypothermia**

Roman Škulec

R. Ernesto Oqueli

Chapter 6 **Drug Eluting Balloon 127**

Chapter 7 **Cardiac Postconditioning:**

Chapter 8 **DES Overview: A Historical and**

Petros S. Dardas

**for Coronary Interventions:**

Chapter 2 **Percutaneous Coronary Intervention and**

Rohan Poulter and Jaap Hamburger

Chapter 3 **Complications of Coronary Intervention 47**

**in Cardiac Arrest Survivors 71**

**Disease – State of the Art 95**

Chapter 5 **Percutaneous Intervention Post Coronary Artery**

S. Sharma, N. Kukreja and D. A. Gorog

**An Additional Therapy to Limit**

Chuck Simonton and Krishna Sudhir

Chapter 9 **Rotablation in the Drug Eluting Stent Era 181**

**Graft Surgery in Patients with Saphenous Vein Graft**

**Cell Death Following Myocardial Infarction 139**  Sandrine Lecour, Lionel Opie and Sarin J. Somers

Susan Bezenek, Poornima Sood, Wes Pierson,

**Current Review of Pivotal Clinical Trial Programs 155**

**The New Gold Standard for Vascular Access? 1**

**30-Day Mortality: The CANADA Score 29**


*2.1. Patients*

Thirty patients with biochemical recurrence of prostate cancer scheduled for routine clinical Ga-68PSMA PET/CT were prospectively enrolled in this pilot study (NL52809.100. 16/R16.058/Ga-68 PSMA test-retest study) between January 2018 and July 2019. Their scans were screened for evaluable PSMA-positive tumor lesions by two board certified nuclear physicians (MJR and CB). Seven patients had no evaluable tumor lesions and were excluded from the study. The remaining twenty-three had their second (retest) scan within on average 7.9 days (range 6 to 23) of their initial (test) scan and were evaluated for measurements in assigned tumor lesions. Only one patient had a relatively long interval of 23 days between test and retest due to logistical issues, where the next highest value was 10 days. Tumor lesions were classified as suspected local recurrence, lymph node metastases or bone metastases. Both baseline scans were performed before any treatment had begun. The study was approved by the local institutional ethics review board, and all patients had

given written approval before any scanning was done (NL52809.100.16/R16.058/Ga-68 PSMA test-retest study, date of approval 22 March 2017).

PET images were acquired on a PET/CT scanner (GE Healthcare Discovery 710), with a 2.5 min acquisition per bed position, on average 58 (range 55 to 69) min after injection of on average 1.4 (range 0.9 to 1.7) MBq/kg dose of Ga-68 PSMA. Two datasets were reconstructed: one standard ordered subset expectation maximization (OSEM) reconstruction including both time-of-flight (TOF) and point spread function (PSF) modelling and a second one using the Bayesian penalized likelihood (BPL) algorithm, called Q.Clear [17]. For the OSEM reconstruction, 2 iterations and 24 subsets were used. BPL reconstruction also included TOF and PSF. BPL (including TOF and PSF) is subsequently referred to as BPL, same as for OSEM. A matrix size of 256 × 256 was used, resulting in voxels of 2.73 × 2.73 × 3.27 mm3. With respect to filtering, a 6.4 mm Gaussian filter and 1:4:1 filter in axial direction were applied. Both used low dose CT for attenuation correction. For the BPL algorithm, a beta value of 600 was used. This value was found optimal in a Ga-68 PSMA phantom study using spheres of 5–37 mm diameter (this special issue).

#### *2.2. PET and CT Analysis*

For both the test and the retest datasets, the PET and low-dose CT images were processed independently. Imaging reading was performed using dedicated software for PET/CT imaging (Philips IntelliSpace Portal 9.0, Eindhoven, The Netherlands).

In PET, tumor lesion size was measured in the axial plane using a fixed PET windowing upper level (UL) of 10 used for stretching of the greyscale. Both long and short axis were measured; lesion area was calculated according to the simple formula for round and oval lesions: A = π × half long axis × half short axis. Within this area, the pixel with the highest standardized uptake value is designated the SUVmax (injected dose/kg body weight). Thus SUVmax was measured in all tumor lesions. The small size of most of the lesions did not allow for measurement of other meaningful SUVs such as SUVpeak that need lesions of at least 1 cm3. Low dose CT was used to check for appropriateness of the lesion area measured in PET if possible (i.e., with the exception of some bone metastases not visible on CT).


**Abstract**

As electric vehicle market growing fast, lithium ion batteries demand is increasing rapidly. Sufficient battery materials supplies including cathode, anode, electrolyte, additives, et al. are required accordingly. Although layered cathode is welcome in high energy density batteries, it is challenging to balance the high energy density and safety beside cost. As consequence, olivine phosphate cathode is coming to the stage center again along with battery technology development. It is important and necessary to revisit the olivine phosphate cathode to understand and support the development of electric vehicles utilized lithium ion batteries. In addition, blend cathode is a good strategy to tailor and balance cathode property and performance. In this chapter, blend cathode using olivine phosphate cathode will be discussed as well as olivine phosphate cathode.

**Keywords:** phosphate, phosphate composite, blend cathode, lithium ion battery, electric vehicles, stability, rate capability, safety

### **1. Introduction**

Batteries are used as the power source in electric vehicles and take the critical role for driving milage improvement and transportation safety as well as cost control. Battery energy density is the important parameter related with the driving mileage. As battery material and technology development, energy density of lithium ion battery for electric vehicle has been increased to 300 Wh/kg at cell level, [1, 2] it has big progress compared with the lead acid battery at beginning period. Lithium ion battery is basically composed by cathode, anode, separator and electrolyte. Lithium ions move between cathode and anode to store and output energy through reversible chemical reaction. In present commercial lithium ion batteries, lithium ions are reserved in cathode side and the reversible lithium concentration in cathode mainly determine the battery energy density. Three type of cathodes have been widely utilized in commercial lithium ion batteries, layered oxide which has two-dimensional lithium ion diffusion pathway, spinel oxide that provides three-dimensional lithium ion transportation space, and olivine phosphate with one-dimensional lithium ion diffusion channels. Each of them has their merit and are servicing the specialized portable power source market.

Lithium ion battery utilized on electrical vehicles has high requirement on safety, long lifespan, high energy density, high power density as well as low cost. Olivine phosphate cathode has stable crystal structure and present advantage on safety, long cycling stability and cost effective. In this chapter, lithium iron phosphate, lithium manganese phosphate and related composite cathodes are reviewed to understand material technology development. Blend cathode demonstrates the possibility to tailor and balance the cathode property and performance. Binary and ternary blend cathodes using olivine phosphate are summarized and discussed.


**Author details**

Ina Shaw1 , Musa L. Mathunjwa2 and Brandon S. Shaw1 \*

1 School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, UK

2 Department of Human Movement Science, University of Zululand, Kwadlangezwa, South Africa

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 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.


**Disrupted VLDL Features and Lipoprotein Metabolism in Sepsis**

Patricia Aspichueta, Nerea Bartolomé, Xabier Buqué, María José Martínez, Begoña Ochoa and Yolanda Chico *Department of Physiology, Faculty of Medicine, University of the Basque Country Spain*


**4.1 Existing TNE partnerships in TEIs in Ghana**

The first research question sought to identify TNE partnerships that exist in the Ghanaian tertiary education institutions. **Figure 1** presents TEIs in Ghana that are engaged in TNE partnerships disaggregated by public and private TEIs.

**Figure 1** indicates as at 2019 when this study was conducted, 28 TEIs were found to be engaged in TNE partnerships and majority (71%) of them are private TEIs.

**Figure 1.** *TEIs with TNE partnerships.*

**Figure 2.** *Year of establishment of TEIs with TNE partnership(s).*

From **Figure 2**, six of the TEIs engaged in TNE partnerships were established and accredited as such before 1996. 22 of the TEIs with TNE partnership arrangements were established and accredited from 1996 to 2019.


**Contents**

**Yago Chamoun F. Soares, Elyff Cargnin, Monica ˆ Feijo´ Naccache and Ricardo Jorge E. Andrade** Influence of Oxidation Degree of Graphene Oxide on the Shear Rheology of Poly(ethylene glycol) Suspensions Reprintedfrom:*Fluids***2020**,*5*,41,doi:10.3390/fluids5020041**163**

.....................

## **About the Editors**

**Maria Teresa Cidade** is a member of the Polymeric and Mesomorphic Materials Group of the Faculty of Sciences and Technology of the New University of Lisbon (FCT NOVA), Portugal. She graduated in Chemical Engineering (IST/Technical University of Lisbon, 1983) and obtained a Ph.D. degree from FCT NOVA (1994). In 2006, she was appointed with the Habilitation in Polymer Engineering. Currently, she is an Assistant Professor with Habilitation at the Materials Science Department (DCM) of FCT NOVA. She is the Coordinator of the Polymeric and Mesomorphic Materials Group of DCM, Coordinator of the Rheology Sub-Group of the Soft and Bifunctional Materials Group of the Materials Research Centre (Cenimat) of DCM, Coordinator of the Doctoral Program in Materials Science and Engineering, and Coordinator of FCT NOVA in the Doctoral Program in Advanced Materials and Processing (a Doctoral Program in Association with six other Portuguese Universities: Lisbon, Coimbra, Beira Interior, Aveiro, Porto, and Minho, supported by the Portuguese Foundation for Science and Technology). She is the President of the Portuguese Society of Rheology, Associate Editor of *Physica Scripta* (IOP), and a member of the Editorial Board of *Fluids* (MDPI). Her main scientific interests include the rheology (including electrorheology and rheooptics) of complex systems (polymers and polymeric base systems, liquid crystals, nanocomposites, biomaterials, building materials, etc.), the mechanical characterization of polymers and polymer composites, and polymer processing. During her career, she has supervised more than 30 researchers, coauthored four book chapters and 79 papers in international refereed journals, lodged 1 patent, and presented more than 100 communications in conferences.

**Jo˜ao Miguel N´obrega** (Associate Professor) works at the Polymer Engineering Department of the University of Minho and is a member of the Institute for Polymers and Composites. In 2004, he received his Ph.D. degree from the University of Minho in Polymer Science and Engineering. He is the Editor of the OpenFOAM-R Journal and OpenFOAM-R Wiki, a founder member of the Iberian OpenFOAM-R Technology Users, and the lead faculty of the Digital Transformation in Manufacturing area from the MIT Portugal Program. His research activities encompass three overlapping areas: product development, polymer processing, and material rheology. For this purpose, he has been developing computational rheology tools to model the flow of complex fluids in various polymer processing techniques. Regarding the product development area, he has been involved in the design and manufacture of polymeric products across several fields, comprising applications for health, textiles, sensoring/monitoring, construction, and mobility. In 2014, he joined the OpenFOAM-R Extend community, and has focused, since then, on the main numerical developments in this open-source computational library. In 2016, he was the chair of the 11th Workshop OpenFOAM, which took place in Guimaraes, Portugal. During his career, he was involved in the supervision of ˜ more than 50 researchers, working both in fundamental and applied research projects; he has coedited 4 books, and published more than 80 papers in international refereed journals and 22 book chapters, lodged 9 patents (3 international), and presented approximately 200 communications in conferences.

*Editorial*

#### **Editorial for Special Issue "Advances in Experimental and Computational Rheology, Volume II"**

**Maria Teresa Cidade 1,\* and João Miguel Nóbrega 2,\***

Received: 21 September 2020; Accepted: 24 September 2020; Published: 25 September 2020

Rheology, defined as the science of the deformation and flow of matter, is a multidisciplinary scientific field, covering both fundamental and applied approaches. The study of rheology includes both experimental and computational methods, which are not mutually exclusive. Its practical relevance embraces many daily life processes, like preparing mayonnaise, spreading an ointment, or shampooing, and industrial processes, like polymer processing and oil extraction, among several others. Practical applications also include formulation and product development.

Following a successful first volume, the Special Issue entitled "Advances in Experimental and Computational Rheology", the editorial team decided to launch a second volume.

The Special Issue "Advances in Experimental and Computational Rheology, Volume II" comprises 10 papers covering some of the latest advances in the fields of experimental and computational rheology, applied to a diverse class of materials and processes, which can be grouped into three main topics: rheology [1–5], rheometry and processing [6,7], and theoretical modeling [8–10].

The characterization of rheological behavior is the main topic of five contributions, covering the following materials/systems: S-aureus cultures (Portela et al. [1]), in which antibiotic activity was screened by rheometry; natural hydraulic lime grouts filled with polypropylene fibers (Baltazar et al. [2]), with a particular focus on the effect of the measurement methods on the obtained yield stress; wheat flour dough (Macedo et al. [3]), where rheology was used as a tool to study the impact of whey powder addition on the dough and breadmaking performance; human milk (Alatalo et al. [4]), covering the influence of external factors on its characteristics; and graphene oxide/poly(ethylene glycol) suspensions (Soares et al. [5]), where the authors studied the influence of the oxidation degree of graphene oxide on the suspensions' shear rheology.

Two of the Special Issue papers are dedicated to rheometry and processing. Ibañez et al. [6] analyzed the ability of different machine learning techniques, able to operate under a low data limit, to create a model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. Parlato et al. [7] applied the so-called Couette analogy concept, in order to achieve a reduction in the complex, non-viscometric rotational geometry to a virtual concentric cylinder analogue, allowing for the determination of the flow curve of non-Newtonian fluids in complex geometries.

Theoretical modeling is the main topic of the remaining three works. The work of Lopéz Aguilar et al. [8] put forward a modeling framework that was experimentally validated, with a focus on the circular abrupt contraction flow of two highly elastic constant shear viscosity Boger fluids, with various contraction ratio geometries. Pedro et al. [9] numerically studied the filling stage of thermoplastic injection molding with a solver implemented in the open-source computational library OpenFOAM® and compared the new solver performance and accuracy with a proprietary code. In the

work of OIshi et al. [10], the authors studied the gravitational e ffects of elasto-viscoplastic drops colliding on vertical planes and proposed a classification for the observed behaviors.

Finally, it is very important to recognize and acknowledge the e ffort put forth by the large number of anonymous reviewers, which was essential to assuring the high quality of all the contributions of this Special Issue.

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


2.7.2. Enrichment Factor (EF)

Enrichment factor (*EF*) was evaluated to determine the degree of anthropogenic factors on trace elements accumulation in the soil. It reflects the disturbance degree of human activities on the natural environment [44]. *EF* is calculated as follows:

$$EF = \frac{\left(\frac{\mathbb{C}\_i}{\mathbb{C}\_{ref}}\right)sample}{\left(\frac{\mathbb{B}\_i}{\mathbb{B}\_{ref}}\right)background}} \tag{5}$$

where *Ci* is the concentration of trace elements in the sample; *Cref* is the concentration of reference element in the sample; *Bi* is the background value of interest element and *Bref* is the background value of reference elements in the study area. Fe was selected as the reference element for this study because Fe is a major sorbent and it is a quasi-conservative tracer of natural elements in fluvial and coastal sediments [12]. Wuhan soil background was used as the reference. The following EF class were given for elements; *EF* < 2, 2 ≤ *EF* < 5, 5 ≤ *EF* < 20, 20 ≤ *EF* < 40 and *EF* ≥ 40 indicated low, moderate, significant, very high and extremely high enrichment factor, respectively.


*4.8. Western Blotting*

Exponentially growing cells were lysed in lysis bu ffer (50 mM Tris-HCl (pH 7.2), 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, and 0.1% SDS) containing 1 mM 4-(2-aminoethyl)-benzensulfonyl fluoride hydrochloride. The cell lysate was cleared by centrifugation at 15,000 rpm for 10 min at 4 ◦C, and then supernatant was used as the total cellular protein. Total protein concentration was determined by the BCA protein assay (Pierce, Rockford, IL). Protein samples (8 or 16 μg) were electrophoresed on SDS-polyacrylamide gel and were electrophoretically transferred to a polyvinyl difluoride membrane in a transfer bu ffer (100 mM Tris, 192 mM glycine). After overnight incubation with blocking solution (10% skim milk), the membrane was incubated with the primary antibodies, a biotinylated anti-mouse or anti-rabbit IgG antibodies, and streptavidine-alkaline phosphatase. The bands were visualized after addition of nitroblue tetrazolium/5-bromo-4-chloro-3-indolyl phosphate as a substrate. The primary antibodies used in this study are anti-adiponectin (clone 19F1, Abcam Co. Ltd., Tokyo, Japan), anti-FABP4 (Abcam Co. Ltd., Tokyo, Japan), anti-PPARγ (clone 81B8, Cell Signaling technology Japan, Tokyo, Japan), and antiα/β-tubulin (Cell Signaling technology Japan).


**Abstract**

The chronic colonisation of the lower airways by bacterial pathogens is the leading cause of morbidity and mortality in patients with cystic fibrosis (CF). The use of novel culture-independent techniques such as next-generation sequencing (NGS) to analyse the lungs has allowed us to further understand the diversity, the complexity, the effects of acute exacerbations and the use of antibiotics on the bacterial communities. The understanding of the CF microbiome to airway disease remains a fascinating area of research; it presents new opportunities for disease management in CF and has the potential to explore the effects of cystic fibrosis transmembrane conductance regulator (CFTR) modulators. It also allows further appreciation regarding the roles played by anaerobic organisms within the CF airways. It is also of interest that a number of studies have demonstrated that the fluctuations of microbiome are not necessarily associated with the patient's clinical status. Despite the available evidence, there remain many challenges that must be overcome if microbiome profiling is going to influence future clinical practice. The effects of fungus and the emergence of nontuberculous mycobacteria in CF are also briefly discussed in this chapter.

**Keywords:** cystic fibrosis, microbiome, CFTR modulators, nontuberculous mycobacteria, *Aspergillus*

#### **1. Introduction**

Traditional culture techniques rely on growing bacteria on media in laboratory conditions often optimised for growth of specific organisms so that they can then subsequently be identified. In the last 20 years, novel techniques utilising nextgeneration sequencing (NGS) to identify bacteria have become available, enabling detection and description of bacterial communities without the need for conventional culture. These technologies have allowed a greater understanding of bacterial communities throughout the human body and have revealed functional roles in both health and disease.

A healthy human gut, for example, is home to a highly diverse community of bacteria, termed as microbiome, which has symbiotic functions including metabolism of otherwise indigestible compounds and defence against opportunistic pathogens [1, 2]. Furthermore, bacteria in the gut influence the stimulation and development of the innate mucosal immune system [3]. In addition to the roles in health, there has been significant interest in the relationship between microbiomes and diseases such as obesity, inflammatory bowel disease and diabetes mellitus [4–6].

Studies utilising culture-independent techniques to analyse the lungs have identified the presence of bacterial communities that are much more complex than the previously appreciated. The lungs were long considered to be an inherently sterile environment, in part due to the fact that conventional culture techniques often yielded negative results during health and it was only during disease that pathogens were detected. However, the advent of culture-independent techniques has demonstrated that multiple organisms comprise a community, termed the 'microbiome', in the lungs of patients, both healthy and diseased [7–9]. In this chapter, we discuss the techniques employed in 16S rRNA sequencing and the evidence these techniques have generated so far in relation to cystic fibrosis.

#### **2. 16S rRNA gene sequencing**

The 16S rRNA gene codes for a ribosomal subunit present in nearly all bacteria. The gene itself is approximately 1.5 kb long and consists of conserved regions, similar in nearly all microorganisms, and nine variable regions labelled, V1–V9, which are practically specific to each microorganism [10]. The identification of a specific DNA sequence that corresponds to the known variable region of 16S rRNA gene can allow discrimination of the presence and relative abundance of different microorganisms (**Figure 1**).

Once the samples have been processed, DNA is extracted, and the 16S rRNA gene is amplified using polymerase chain reaction (PCR). Next-generation sequencing allows elucidation of the precise gene sequences, and online reference databases can then be used to match each sequence to an organism and quantify its relative proportion within a multispecies population. However, it is important to note that sequencing of the 16S rRNA gene has limited resolution and often cannot distinguish species with similar gene sequences apart. Therefore, instead of distinct species, sequences are referenced against and assigned into operational taxonomic units (OTU) (see **Table 1**).

Given the large number of species identified even in healthy lungs, ecological theory and analyses are often employed to understand community dynamics [9]. According to ecological principles, the composition of the lung microbiome is determined by three factors:

3.Regional growth factors [11]

#### **Figure 1.**

*Schematic illustration of structure of the 16S rRNA gene. Orange regions—variable regions; blue region conserved region.*

**3**

the last decade.

Operational taxonomic unit (OTU)

*Glossary of terms and definitions.*

**Table 1.**

**3. CF respiratory microbiota in early life**

early estimation of the neonatal lower respiratory tract.

*The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

**Term Definition**

Microbiome The microorganisms in a particular environment

Richness A measure of the number of species in a community

relative abundance?

Alpha diversity Within-sample diversity Beta diversity Between-sample diversity

The lung microbiome in healthy individuals is dictated largely by immigration

16S rRNA gene A gene which codes for a ribosomal subunit. Present in all prokaryotes and has

Evenness A measure of similarity of the relative abundance for each species in a

Diversity A measure of variety in a community. Combination of richness and evenness

variable regions, which differ slightly between bacterial species

Group of strains/species with similar 16S rRNA gene sequences

community That is, does one species dominate, or do all species have similar

Understanding the development of the CF respiratory microbiota in early life has attracted interest in order to appreciate the driving factors behind the distinct microbiota seen later in life and also to identify potential opportunities for intervention. Neonates and infants cannot expectorate sputum independently, and bronchoalveolar lavage (BAL) is only used sparingly; hence, upper respiratory tract samples are often used as surrogates. The imperfection of this approach was recently highlighted where large differences in concordance between BAL samples and upper respiratory tract (URT) samples were observed in some taxa [12]. Nevertheless, concordance was high for some important taxa such as *Moraxella* and *Staphylococcus,* and in the absence of less invasive techniques, URT sampling enables

The composition of CF nasopharyngeal microbiota diverges from that of non-CF infants as early as the first few months of life [13, 14]. Newborn healthy infants appear to have nasopharyngeal microbiota dominated by *Moraxella* spp*., Corynebacterium spp.* and *Haemophilus* spp., a community structure that persists for at least the first 6 months of life. Conversely the CF nasopharynx microbiome is initially dominated by *Staphylococcus aureus* before a gradual increase in *Streptococcus* spp*.* and *Moraxellaceae* at 3 months of age [13]. Despite the increased *S. aureus* seen in CF, there were no decreases in measures of richness or diversity indicating that changes are due

to differing microenvironment rather than interspecies competition [15].

and elimination and hence generally consists predominantly of those Gramnegative anaerobes also resident in the oral flora such as *Prevotella* and *Veillonella* spp. [11]. However, in disease the regional growth conditions are altered, and niches for other species to thrive are created. In CF, for example, viscous secretions, altered pH, nutrient availability and architectural disturbance may all help select for a community of altered composition to that of healthy individuals. How this community changes over time, in response to the intensive antibiotic treatment that people with CF are exposed to, has been the subject of much interest in

*The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

#### **Table 1.**

*Cystic Fibrosis - Heterogeneity and Personalized Treatment*

niques have generated so far in relation to cystic fibrosis.

**2. 16S rRNA gene sequencing**

microorganisms (**Figure 1**).

units (OTU) (see **Table 1**).

determined by three factors:

3.Regional growth factors [11]

1.Immigration of organisms into the lung

2.Elimination of microbes from the airways

Studies utilising culture-independent techniques to analyse the lungs have identified the presence of bacterial communities that are much more complex than the previously appreciated. The lungs were long considered to be an inherently sterile environment, in part due to the fact that conventional culture techniques often yielded negative results during health and it was only during disease that pathogens were detected. However, the advent of culture-independent techniques has demonstrated that multiple organisms comprise a community, termed the 'microbiome', in the lungs of patients, both healthy and diseased [7–9]. In this chapter, we discuss the techniques employed in 16S rRNA sequencing and the evidence these tech-

The 16S rRNA gene codes for a ribosomal subunit present in nearly all bacteria.

Once the samples have been processed, DNA is extracted, and the 16S rRNA

Given the large number of species identified even in healthy lungs, ecological theory and analyses are often employed to understand community dynamics [9]. According to ecological principles, the composition of the lung microbiome is

*Schematic illustration of structure of the 16S rRNA gene. Orange regions—variable regions; blue region—*

gene is amplified using polymerase chain reaction (PCR). Next-generation sequencing allows elucidation of the precise gene sequences, and online reference databases can then be used to match each sequence to an organism and quantify its relative proportion within a multispecies population. However, it is important to note that sequencing of the 16S rRNA gene has limited resolution and often cannot distinguish species with similar gene sequences apart. Therefore, instead of distinct species, sequences are referenced against and assigned into operational taxonomic

The gene itself is approximately 1.5 kb long and consists of conserved regions, similar in nearly all microorganisms, and nine variable regions labelled, V1–V9, which are practically specific to each microorganism [10]. The identification of a specific DNA sequence that corresponds to the known variable region of 16S rRNA gene can allow discrimination of the presence and relative abundance of different

**2**

**Figure 1.**

*conserved region.*

*Glossary of terms and definitions.*

The lung microbiome in healthy individuals is dictated largely by immigration and elimination and hence generally consists predominantly of those Gramnegative anaerobes also resident in the oral flora such as *Prevotella* and *Veillonella* spp. [11]. However, in disease the regional growth conditions are altered, and niches for other species to thrive are created. In CF, for example, viscous secretions, altered pH, nutrient availability and architectural disturbance may all help select for a community of altered composition to that of healthy individuals. How this community changes over time, in response to the intensive antibiotic treatment that people with CF are exposed to, has been the subject of much interest in the last decade.

### **3. CF respiratory microbiota in early life**

Understanding the development of the CF respiratory microbiota in early life has attracted interest in order to appreciate the driving factors behind the distinct microbiota seen later in life and also to identify potential opportunities for intervention. Neonates and infants cannot expectorate sputum independently, and bronchoalveolar lavage (BAL) is only used sparingly; hence, upper respiratory tract samples are often used as surrogates. The imperfection of this approach was recently highlighted where large differences in concordance between BAL samples and upper respiratory tract (URT) samples were observed in some taxa [12]. Nevertheless, concordance was high for some important taxa such as *Moraxella* and *Staphylococcus,* and in the absence of less invasive techniques, URT sampling enables early estimation of the neonatal lower respiratory tract.

The composition of CF nasopharyngeal microbiota diverges from that of non-CF infants as early as the first few months of life [13, 14]. Newborn healthy infants appear to have nasopharyngeal microbiota dominated by *Moraxella* spp*., Corynebacterium spp.* and *Haemophilus* spp., a community structure that persists for at least the first 6 months of life. Conversely the CF nasopharynx microbiome is initially dominated by *Staphylococcus aureus* before a gradual increase in *Streptococcus* spp*.* and *Moraxellaceae* at 3 months of age [13]. Despite the increased *S. aureus* seen in CF, there were no decreases in measures of richness or diversity indicating that changes are due to differing microenvironment rather than interspecies competition [15].

The divergence observed in the first few months of life usually precedes antibiotic administration and demonstrates that CF itself is associated with compositional changes in the microbiota, but as CF infants grow older, exposure to antibiotics, either via acute treatment for respiratory illnesses or prophylaxis against classic CF pathogens, becomes inevitable. Mika et al. investigated the relationship between antibiotics and the nasal microbiota by prospectively following 30 newborn infants with CF with fortnightly sampling for the first 12 months of life [14]. Antibiotic administration was associated with an increase in the Shannon diversity measure (a measure of the richness and evenness of a community), but this was judged to be most likely secondary to an increase in transient colonisers. Interestingly, antibiotic therapy was staphylococcal directed, but decreases in *Staphylococcus* OTUs were not seen. Instead significant reductions in *Moraxellaceae* were observed, and when oligotyping was used to observe changes in *Staphylococcus* at the species level, mild reductions in *S. aureus* were offset by increases in *S. epidermidis*, leading the authors to suggest that *S. epidermidis* may act as a reservoir of resistance. These findings were supported by Prevaes et al. who conducted a similar study in a slightly older population (mean age 2 years old) [13]. Antibiotic treatment was observed to be associated with reductions in *Moraxellaceae* and *S. aureus* OTUs, with increases in other staphylococcal OTUs, although more specific oligotyping was not performed.

As babies grow older, sampling from the lower airways becomes more common, and comparisons between the lower and upper airways become feasible. Given the close proximity and interrelated spaces of the nose, throat and lungs, it could be expected that they all may share similar community structures; however, the reality is that the nasal community appears different from that of the throat and lung, which are much more closely aligned. Boutin et al. found differences in the community structure of the nasal cavity compared to throat and sputum samples; in that diversity, richness and evenness were significantly higher in nasal samples, and up to 21 of the 76 most abundant nasal OTUs were not present in the throat or sputum samples [16]. Interestingly, the authors also found that subjects could be broadly defined into one of two ecotypes based on the presence or absence of *Pseudomonas*, and the similarities between throat and sputum samples began to diminish once *Pseudomonas* was present. Muhlebach et al. supported these findings in a recently published study, which for the first time included routine sequential BAL sampling in young children as part of the large AREST-CF cohort in Australia and the USA. They showed that lower airway cultures mirrored that of the oral cavity until approximately age 2, when increasing predominance of known CF pathogens was observed and communities diverged [17]. This has a number of clinical implications in that, firstly, throat swabs can provide adequate representation of the lower airways in very young children and, secondly, prevention or delay of this transition point by manipulation of the microbiota could theoretically be a strategy to improve outcomes later in life.

#### **4. Progressive loss of diversity**

Once the lungs are colonised with CF pathogens, a pattern of progressively uneven community structures ensues. Cox et al. examined biobanked sputum samples from a cohort of 63 clinically stable people with CF of ages ranging from 9 months to 72 years [7]. This cross-sectional approach identified the loss of community richness, evenness and diversity as age increased. *Pseudomonas* and *Burkholderia* OTUs began to progressively dominate in older subjects, and the changes in community structure were inversely associated with pulmonary function. In a similar study design with 269 patients, Coburn et al. also found sample

**5**

*The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

richness and diversity [21].

clinical outcomes [24].

predictor for decreased diversity [29].

which notably increased after the age of 25 [18].

diversity inversely correlated with age and disease stage. Progressive loss of diversity was particularly correlated with *Pseudomonas* and *Burkholderia* abundance,

Zhao et al. were the first to confirm these findings longitudinally when they followed up six patients over a 9-year period with serial sputum collections. It was observed that the three patients with what they termed as more 'progressive' disease had significant decreases in community diversity over the course of several years. Decreasing lung function and increasing age were also associated with decreasing community diversity [19]. This study was soon followed by Fodor et al. who focussed more on changes in the microbiota associated with acute changes in clinical status but did observe a strong correlation between low species richness and poor lung function [20]. Stokell et al. followed up a single patient up to over 3 years and observed increasing total bacterial load as well as diminishing community

Contrastingly, Whelan et al. recently published a study of six patients who submitted thrice-weekly sputum samples for a year [22]. No overall changes in community structure were observed over the course of the year, and the authors concluded that the respiratory microbiome is unique to each patient and the previously reported associations between community structure and clinical parameters may be true on a cohort/population level but not at an individual level. There is some merit in this argument, but it is also worth noting that the six patients in the study appeared relatively stable with a median of only one exacerbation in the 12-month study period. It is also therefore a possibility that the follow-up period was not long enough to capture the more indolent changes likely to be present in those patients [23]. A much longer study period was adopted by Acosta et al. [24] who analysed samples from matched patients with biobanked sputum samples in three historic cohorts spanning nearly 20 years at a single centre. Across all the three cohorts, the core microbiome constituents were preserved, but the proportion of *Pseudomonas*-dominated communities was reduced, and overall diversity increased in the more recent cohorts. Community structure improved gradually from the most historic cohort to the most recent, and these changes appeared to correlate with the generally improving clinical status of people with CF, confirming the previously described observed association between community structure and

The association reported in most studies between community structure and clinical outcomes has inevitably led to the question of whether a less diverse or even rich microbiome is simply a marker of increased pulmonary disease or is itself a driver in disease pathogenesis [25]. If the latter were true, efforts to promote a more diverse community could have the potential to slow pulmonary disease progression. An Italian group has led efforts to find patterns or signatures in the microbiome that may predispose patients to accelerated lung function decline; however, no causal association has been elucidated [26–28]. Instead, Zhao et al. found that the relationship between age, lung function and community diversity disappeared once controlled for antibiotic use, thus suggesting antibiotic therapy is the predominant driver of reducing community diversity [19]. The same group later developed a statistical approach to more precisely correct the antibiotic exposure when examining relationships between microbiota and clinical outcomes. The approach was applied to 478 sputum samples and confirmed that antibiotic use was an independent

Accurately recording antibiotic use is troublesome in longitudinal studies due to the frequent episodic use of antibiotics in CF which is often self-directed by patients themselves, due to the widespread use of long-term antibiotics for which compliance may be heterogeneous and also due to the retrospective nature of a number

#### *The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

*Cystic Fibrosis - Heterogeneity and Personalized Treatment*

The divergence observed in the first few months of life usually precedes antibiotic administration and demonstrates that CF itself is associated with compositional changes in the microbiota, but as CF infants grow older, exposure to antibiotics, either via acute treatment for respiratory illnesses or prophylaxis against classic CF pathogens, becomes inevitable. Mika et al. investigated the relationship between antibiotics and the nasal microbiota by prospectively following 30 newborn infants with CF with fortnightly sampling for the first 12 months of life [14]. Antibiotic administration was associated with an increase in the Shannon diversity measure (a measure of the richness and evenness of a community), but this was judged to be most likely secondary to an increase in transient colonisers. Interestingly, antibiotic therapy was staphylococcal directed, but decreases in *Staphylococcus* OTUs were not seen. Instead significant reductions in *Moraxellaceae* were observed, and when oligotyping was used to observe changes in *Staphylococcus* at the species level, mild reductions in *S. aureus* were offset by increases in *S. epidermidis*, leading the authors to suggest that *S. epidermidis* may act as a reservoir of resistance. These findings were supported by Prevaes et al. who conducted a similar study in a slightly older population (mean age 2 years old) [13]. Antibiotic treatment was observed to be associated with reductions in *Moraxellaceae* and *S. aureus* OTUs, with increases in other staphylococcal OTUs, although more specific oligotyping was not performed. As babies grow older, sampling from the lower airways becomes more common, and comparisons between the lower and upper airways become feasible. Given the close proximity and interrelated spaces of the nose, throat and lungs, it could be expected that they all may share similar community structures; however, the reality is that the nasal community appears different from that of the throat and lung, which are much more closely aligned. Boutin et al. found differences in the community structure of the nasal cavity compared to throat and sputum samples; in that diversity, richness and evenness were significantly higher in nasal samples, and up to 21 of the 76 most abundant nasal OTUs were not present in the throat or sputum samples [16]. Interestingly, the authors also found that subjects could be broadly defined into one of two ecotypes based on the presence or absence of *Pseudomonas*, and the similarities between throat and sputum samples began to diminish once *Pseudomonas* was present. Muhlebach et al. supported these findings in a recently published study, which for the first time included routine sequential BAL sampling in young children as part of the large AREST-CF cohort in Australia and the USA. They showed that lower airway cultures mirrored that of the oral cavity until approximately age 2, when increasing predominance of known CF pathogens was observed and communities diverged [17]. This has a number of clinical implications in that, firstly, throat swabs can provide adequate representation of the lower airways in very young children and, secondly, prevention or delay of this transition point by manipulation of the microbiota could theoretically be a strategy to improve

**4**

outcomes later in life.

**4. Progressive loss of diversity**

Once the lungs are colonised with CF pathogens, a pattern of progressively uneven community structures ensues. Cox et al. examined biobanked sputum samples from a cohort of 63 clinically stable people with CF of ages ranging from 9 months to 72 years [7]. This cross-sectional approach identified the loss of community richness, evenness and diversity as age increased. *Pseudomonas* and *Burkholderia* OTUs began to progressively dominate in older subjects, and the changes in community structure were inversely associated with pulmonary function. In a similar study design with 269 patients, Coburn et al. also found sample

diversity inversely correlated with age and disease stage. Progressive loss of diversity was particularly correlated with *Pseudomonas* and *Burkholderia* abundance, which notably increased after the age of 25 [18].

Zhao et al. were the first to confirm these findings longitudinally when they followed up six patients over a 9-year period with serial sputum collections. It was observed that the three patients with what they termed as more 'progressive' disease had significant decreases in community diversity over the course of several years. Decreasing lung function and increasing age were also associated with decreasing community diversity [19]. This study was soon followed by Fodor et al. who focussed more on changes in the microbiota associated with acute changes in clinical status but did observe a strong correlation between low species richness and poor lung function [20]. Stokell et al. followed up a single patient up to over 3 years and observed increasing total bacterial load as well as diminishing community richness and diversity [21].

Contrastingly, Whelan et al. recently published a study of six patients who submitted thrice-weekly sputum samples for a year [22]. No overall changes in community structure were observed over the course of the year, and the authors concluded that the respiratory microbiome is unique to each patient and the previously reported associations between community structure and clinical parameters may be true on a cohort/population level but not at an individual level. There is some merit in this argument, but it is also worth noting that the six patients in the study appeared relatively stable with a median of only one exacerbation in the 12-month study period. It is also therefore a possibility that the follow-up period was not long enough to capture the more indolent changes likely to be present in those patients [23]. A much longer study period was adopted by Acosta et al. [24] who analysed samples from matched patients with biobanked sputum samples in three historic cohorts spanning nearly 20 years at a single centre. Across all the three cohorts, the core microbiome constituents were preserved, but the proportion of *Pseudomonas*-dominated communities was reduced, and overall diversity increased in the more recent cohorts. Community structure improved gradually from the most historic cohort to the most recent, and these changes appeared to correlate with the generally improving clinical status of people with CF, confirming the previously described observed association between community structure and clinical outcomes [24].

The association reported in most studies between community structure and clinical outcomes has inevitably led to the question of whether a less diverse or even rich microbiome is simply a marker of increased pulmonary disease or is itself a driver in disease pathogenesis [25]. If the latter were true, efforts to promote a more diverse community could have the potential to slow pulmonary disease progression. An Italian group has led efforts to find patterns or signatures in the microbiome that may predispose patients to accelerated lung function decline; however, no causal association has been elucidated [26–28]. Instead, Zhao et al. found that the relationship between age, lung function and community diversity disappeared once controlled for antibiotic use, thus suggesting antibiotic therapy is the predominant driver of reducing community diversity [19]. The same group later developed a statistical approach to more precisely correct the antibiotic exposure when examining relationships between microbiota and clinical outcomes. The approach was applied to 478 sputum samples and confirmed that antibiotic use was an independent predictor for decreased diversity [29].

Accurately recording antibiotic use is troublesome in longitudinal studies due to the frequent episodic use of antibiotics in CF which is often self-directed by patients themselves, due to the widespread use of long-term antibiotics for which compliance may be heterogeneous and also due to the retrospective nature of a number

of CF microbiome studies [30]. However, Pittman et al. were able to prospectively perform bronchoscopy and record antibiotic exposure of 32 subjects as part of the AREST-CF study. In that study, community diversity was much lower in the BAL of those patients receiving antibiotics [31].

Thus it appears likely that the strong association between community structure and degree of lung disease is related to the inevitable prolonged and aggressive use of antibiotics in CF rather than direct pathogenesis from a less diverse microbiome.

### **5. Community changes with acute pulmonary exacerbations**

Despite the importance of exacerbations on long-term outcomes of people with CF, the pathophysiology of these events remains undefined [32, 33]. Clinically, exacerbations are frequent and are characterised by rapid changes in symptoms such as an increase in sputum volume or purulence, shortness of breath and fatigue. The precise mechanisms underlying these important events remain elusive, and studies looking for answers using culture-independent techniques have not found consistent answers. For example, one may expect to find evidence of increases in known pathogens at the time of exacerbations, yet there is no consistent evidence of this [23]. In fact, a number of studies have found the CF microbiota to be extremely stable over time and resilient to change at exacerbation and following subsequent treatment [19, 20, 34, 35].

However, when the community structure as a whole is considered, a number of larger studies have found reduced diversity or richness at the times of exacerbation compared to clinical stability. Coburn et al. found small decreases in Shannon diversity in exacerbation samples compared to their baseline study of 269 people with CF [18]. Similarly, Filkins et al. found that samples taken during exacerbations had significantly lower diversity than samples taken when patients were stable [36]. Perhaps most convincingly, Li et al. collated data from 18 previous studies to analyse over 700 sputum samples and found that there were significant reductions in community richness at exacerbation [37].

Whilst increases in *P. aeruginosa* at the time of exacerbation have not been seen consistently, they have been observed in some cases. Carmody et al. followed up four patients for 3 months with daily sputum sampling and observed daily stability between exacerbations but increased *P. aeruginosa* abundance at the time of exacerbation in some patients and increases in *Prevotella* in others [38]. These findings help introduce two new concepts: firstly, the potential for exacerbations to appear similar phenotypically but have different underlying aetiology with only some being due to changes that can be observed in the microbiota and, secondly, that previously overlooked anaerobes may play a pathogenic role.

The first concept is supported by Whelan et al. who found in longitudinal sampling of six patients that some but not all exacerbations were associated with changes in the microbiota [22]. Attempts to identify different types of exacerbations in COPD have identified four distinct aetiological clusters, bacterial, viral, eosinophilic predominant and 'paucinflammatory', and even though these clusters may not be mirrored in CF, it is plausible that not all exacerbation clusters would be associated with changes apparent in either individual taxa or overall bacterial community structure [39].

Changes in the metabolic activity of specific taxa or the community as a whole triggering an exacerbation could be another explanation for an apparent lack of change in the community structure seen in some studies. The metabolites lactate and putrescine were found by Twomey et al. to increase during exacerbation in the absence of clear changes in the community structure [40]. Quinn et al. used

**7**

*The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

pathogenesis of some exacerbations [43, 44].

abundant OTUs were all reduced [20].

community structure.

**exacerbations**

infections.

the ecological functional networking to identify the non-mevalonate pathway of isoprenoid synthesis as a 'keystone' pathway in CF infections. Intriguingly fosmidomycin, an antimalarial agent, is known to be effective at targeting this pathway [41]. The second concept to emerge from the study of Carmody et al. relates to the changes in *Prevotella* abundance at the time of exacerbation and raises the prospect that species not considered conventional CF pathogens may play a role in exacerbations [42]. Anaerobic species are easily overlooked in conventional selective culturing due to the requirement for anoxic culture yet are identified frequently in culture-independent analyses of the CF lower airways. In addition to *Gemella*, both *Prevotella* and *Streptococcus anginosus (milleri)* have been found to have associations with clinical stability [36, 38, 42]. Anaerobes have been shown to have the potential to modulate *P. aeruginosa* gene expression in the polymicrobial setting; hence, even if they are not directly pathogenic, they may still play a contributory role to the

To summarise, the aetiologies underpinning the transition from a stable state to an acute exacerbation are not well understood. It is likely that there multiple aetiological clusters but only some of which may be associated with changes in

**6. Community changes associated with treatment for acute pulmonary**

Traditional dogma would dictate that intensive, targeted antimicrobial therapy with dual anti-pseudomonal agents will result in significant reductions in abundance of *P. aeruginosa*; however, in a similar vein to the findings from studies of the onset of exacerbations, microbiota responses to treatment for acute pulmonary exacerbations in CF have not aligned with this conventional understanding of

One of the predominant themes that has emerged from studies of the respiratory microbiota response to acute antibiotics is that *P. aeruginosa* is not impacted to the same degree as other members of the community. For example, Daniels et al. studied 12 adult CF subjects across the cycle of an exacerbation and treatment and found that following initiation of anti-pseudomonal antimicrobials, the relative abundance of *P. aeruginosa* actually increased, alongside a reduction in the total number of species detected [45]. Cuthbertson et al. also found no evidence of reduced *P. aeruginosa* abundance in a study of 12 CF patients receiving treatment for pulmonary exacerbations [45]. Instead, reductions in *Streptococcus sanguinis*, *Prevotella* and *Porphyromonas* OTUs were observed [35]. Similarly, Li et al., in their analysis of over 700 sputum samples, found that antibiotic treatment had no effect on *Pseudomonas* abundance but did have significant effects on *Gemella*, *Staphylococcus*, *Actinomyces, Moraxellaceae* and *Fusobacterium* [37]. Further, Fodor et al. again found that dominant taxa such as *Pseudomonas* and *Burkholderia* were unchanged when compared at the beginning and end of an exacerbation but the relative abundances of *Gemella, Streptococcus* and a small number of other less

In contrast, two studies have found reductions in *P. aeruginosa* following treatment. Firstly, Zemanick et al. investigated the association between inflammation and changes to the airway microbiota during treatment for exacerbations and found that although bacterial load did not change, the relative abundance of *P. aeruginosa* was observed to decrease and that these changes correlated with improved lung function [46]. A reduction in *P. aeruginosa* abundance following treatment was also reported by Smith et al., who noted rapid decreases in *P. aeruginosa* abundance and

#### *The Pulmonary Microbiome in Cystic Fibrosis DOI: http://dx.doi.org/10.5772/intechopen.91765*

*Cystic Fibrosis - Heterogeneity and Personalized Treatment*

those patients receiving antibiotics [31].

treatment [19, 20, 34, 35].

community structure [39].

in community richness at exacerbation [37].

previously overlooked anaerobes may play a pathogenic role.

of CF microbiome studies [30]. However, Pittman et al. were able to prospectively perform bronchoscopy and record antibiotic exposure of 32 subjects as part of the AREST-CF study. In that study, community diversity was much lower in the BAL of

Thus it appears likely that the strong association between community structure and degree of lung disease is related to the inevitable prolonged and aggressive use of antibiotics in CF rather than direct pathogenesis from a less diverse microbiome.

Despite the importance of exacerbations on long-term outcomes of people with CF, the pathophysiology of these events remains undefined [32, 33]. Clinically, exacerbations are frequent and are characterised by rapid changes in symptoms such as an increase in sputum volume or purulence, shortness of breath and fatigue. The precise mechanisms underlying these important events remain elusive, and studies looking for answers using culture-independent techniques have not found consistent answers. For example, one may expect to find evidence of increases in known pathogens at the time of exacerbations, yet there is no consistent evidence of this [23]. In fact, a number of studies have found the CF microbiota to be extremely stable over time and resilient to change at exacerbation and following subsequent

However, when the community structure as a whole is considered, a number of larger studies have found reduced diversity or richness at the times of exacerbation compared to clinical stability. Coburn et al. found small decreases in Shannon diversity in exacerbation samples compared to their baseline study of 269 people with CF [18]. Similarly, Filkins et al. found that samples taken during exacerbations had significantly lower diversity than samples taken when patients were stable [36]. Perhaps most convincingly, Li et al. collated data from 18 previous studies to analyse over 700 sputum samples and found that there were significant reductions

Whilst increases in *P. aeruginosa* at the time of exacerbation have not been seen consistently, they have been observed in some cases. Carmody et al. followed up four patients for 3 months with daily sputum sampling and observed daily stability between exacerbations but increased *P. aeruginosa* abundance at the time of exacerbation in some patients and increases in *Prevotella* in others [38]. These findings help introduce two new concepts: firstly, the potential for exacerbations to appear similar phenotypically but have different underlying aetiology with only some being due to changes that can be observed in the microbiota and, secondly, that

The first concept is supported by Whelan et al. who found in longitudinal sampling of six patients that some but not all exacerbations were associated with changes in the microbiota [22]. Attempts to identify different types of exacerbations in COPD have identified four distinct aetiological clusters, bacterial, viral, eosinophilic predominant and 'paucinflammatory', and even though these clusters may not be mirrored in CF, it is plausible that not all exacerbation clusters would be associated with changes apparent in either individual taxa or overall bacterial

Changes in the metabolic activity of specific taxa or the community as a whole triggering an exacerbation could be another explanation for an apparent lack of change in the community structure seen in some studies. The metabolites lactate and putrescine were found by Twomey et al. to increase during exacerbation in the absence of clear changes in the community structure [40]. Quinn et al. used

**5. Community changes with acute pulmonary exacerbations**

**6**

the ecological functional networking to identify the non-mevalonate pathway of isoprenoid synthesis as a 'keystone' pathway in CF infections. Intriguingly fosmidomycin, an antimalarial agent, is known to be effective at targeting this pathway [41].

The second concept to emerge from the study of Carmody et al. relates to the changes in *Prevotella* abundance at the time of exacerbation and raises the prospect that species not considered conventional CF pathogens may play a role in exacerbations [42]. Anaerobic species are easily overlooked in conventional selective culturing due to the requirement for anoxic culture yet are identified frequently in culture-independent analyses of the CF lower airways. In addition to *Gemella*, both *Prevotella* and *Streptococcus anginosus (milleri)* have been found to have associations with clinical stability [36, 38, 42]. Anaerobes have been shown to have the potential to modulate *P. aeruginosa* gene expression in the polymicrobial setting; hence, even if they are not directly pathogenic, they may still play a contributory role to the pathogenesis of some exacerbations [43, 44].

To summarise, the aetiologies underpinning the transition from a stable state to an acute exacerbation are not well understood. It is likely that there multiple aetiological clusters but only some of which may be associated with changes in community structure.