**4.2 Subclinical keratoconus**

Xu et al. reported UHR-OCT epithelial vertical thickness profiles in the diagnosis of subclinical keratoconus. Data showed statistically significant thinning of the central corneal epithelium; 53.48 μm in normal eyes and 51.92 μm in those with subclinical keratoconus. There was no significant inferior epithelial thinning in subclinical keratoconus; 54.94 μm in normal eyes and 54.85 μm in eyes with subclinical keratoconus [51]. However, our unpublished data found that the epithelium in patients with subclinical keratoconus had localized thinning of inferior epithelium quantified with minimum thickness. We also found that the epithelium has relative superior thickening by maximum thickness and that standard deviation of epithelial thickness was increased significantly in all regions.

#### **4.3 Keratoconus**

Corneas with keratoconus show epithelial remodeling, which minimizes local topographic irregularities and improves corneal curvature [52]. Epithelial thinning precedes other corneal changes in keratoconus [53, 54], and the location of the thinnest zone of the epithelium corresponds with the steepest zone seen on Scheimpflug tomography [10]. Xu et al. reported no significant thinning of the inferior cornea in eyes with keratoconus as compared to normal eyes. However, there was significant thinning of the central epithelium; 53.48 μm in normal eyes and 46.10 μm in eyes with keratoconus [51]. Yadav et al. reported that variation in epithelial thickness across the central 3 mm was significantly larger in eyes with keratoconus. This finding was supported by Pircher et al. who wrote that "epithelial thickness, irregularity, and asymmetry seem to be the most promising diagnostic factors in terms of discriminating between keratoconic eyes and healthy eyes" [55].

### **4.4 Ocular surface pathology**

UHR-OCT can be used for the diagnosis of ocular surface squamous neoplasia (OSSN) and detection of sub-clinical disease [16, 56, 57]. OSSN has several classical features on anterior segment OCT, including thickened, hyper-reflective epithelium with an abrupt transition from normal to abnormal epithelium [16, 57, 58]. The gold standard for diagnosis of OSSN is examination of pathology, but non-invasive methods of diagnosis are helpful as topical chemotherapy becomes increasingly utilized [16]. UHR-OCT provides high-resolution imaging with cross-sectional views; dynamic non-contact scanning modality reduces need for technical expertise compared to UBM and confocal microscopy. However, it has poor penetrance with thicker lesions and cannot reliably detect

invasion [16]. UHR-OCT is especially useful as it can non-invasively detect OSSN in the presence of other ocular surface diseases. Co-existing conditions such as mucus membrane pemphigoid or limbal stem cell deficiency make it difficult to diagnosis OSSN based on clinical exam [59].

Corneas with scarring and Salzmann's nodular degeneration have a normalthickness epithelium overlying a dense, hyper-reflective lesion overlying Bowman's layer on UHR-OCT [16]. Epithelial hypo-reflective cysts without basement membrane thickening are seen in Meesman's dystrophy [60]. Eyes with secondary corneal amyloidosis show deposits of amyloid above Bowman's layer, and destruction of Bowman's layer as the disease progresses [61].

#### **4.5 Physiologic changes after contact lens wear**

UHR-OCT can show corneal changes after soft contact lens wear. Epithelial thickness increased by 3.5% and total corneal thickness increased by 10% after 3 hours of patching with soft contact lens wear [62]. Endothelium and Descemet membrane showed no significant change in thickness. Long-term hydrogel lens wearers have been shown to have uniform epithelial thinning [63]. Orthokeratology lenses caused the central epithelium to thin in vertical and horizontal meridians, while the mid-peripheral nasal and temporal epithelium became thicker and the superior mid-peripheral epithelium became thinner. Bowman's layer showed no change from orthokeratology lenses [64].

#### **4.6 Monitoring corneal epithelial defects**

UHR-OCT is a useful way to monitor corneal epithelial healing as it provides an objective and three-dimensional evaluation [65]. Corneal wound healing was assessed after epithelial-off corneal collagen cross-linking, and it was noted that epithelium surrounding the fluorescein stained abrasion was not fully settled to the underlying basement membrane [10, 65]. UHR-OCT can also help monitor corneal epithelial healing under a bandage contact lens and can determine the appropriate time for lens removal after pterygium excision [11].

#### **4.7 Post-operative monitoring**

UHR-OCT revealed a significant correlation between epithelial thickening and the extent of refractive correction after myopic small incision lenticule extraction. Epithelial thickening of approximately 10% was observed during the first six postoperative months and stabilized after 3 months [66]. Epithelial thickness in eyes treated with photorefractive keratectomy was significantly higher than that of normal eyes at (68.2 vs. 55.8 μm) [24]. This difference was thought to be caused by non-uniformly altered Bowman's layer [24].

Rocha et al. reported a reduction in peripheral epithelial thickness and decreased regional variation in epithelial thickness consistent with increased corneal curvature after corneal collagen crosslinking [52]. UHR-OCT of corneal wound healing after epithelial-off cross-linking correlated well with fluorescein photographs and visualized the stromal demarcation line [65]. Most of the data about the demarcation line seen in cross-linking comes from use of the SD-OCT and further research is needed [67].

Zarei-Ghanavati et al. showed that epithelium covers the Boston type I keratoprosthesis edge and seals the potential space in the interface. They proposed that failure to epithelialize this interface and lack of epithelial sealing around the keratoprosthesis edge might be associated with endophthalmitis [68].

**9**

**Figure 2.**

*Corneal Microlayer Optical Tomography Review DOI: http://dx.doi.org/10.5772/intechopen.84750*

**5. Bowman's membrane pathology**

Vertical thickness of Bowman's layer in subclinical keratoconus was decreased inferiorly compared with normal control eyes [51]. Our group demonstrated that Bowman's layer in patients with subclinical keratoconus was significantly thinner centrally and inferiorly, and could be quantified with mean thickness, minimum

Bowman's layer shows thinning, disintegration and breakage on pathological specimens of eyes with keratoconus (**Figure 2**). Histopathological light and electron microscopy studies of these eyes can be helpful for the diagnosis of keratoconus [70]. Interestingly, these changes happen before stromal changes [71]. Abou Shousha et al. demonstrated that vertical topographic thickness maps of keratoconus patients had characteristic localized relative inferior thinning of Bowman's layer. Inferior average thickness, inferior minimum thickness, Bowman's ectasia index and Bowman's ectasia index-max were all correlated with the severity of keratoconus. The inferior average thickness of Bowman's layer in eyes with keratoconus was 12 μm compared with 15 μm in normal eyes [72]. Light scatter from Bowman's layer in eyes with keratoconus was significantly higher but did not correlate with

UHR-OCT can detect early secondary corneal amyloidosis as a dense spot in Bowman's layer [61]. Corneas with Thiel-Behnke Dystrophy have extensive deposits of hyper-reflective material in a saw-tooth pattern on the surface of Bowman's layer [60]. UHR-OCT images may become adjunct to clinical evaluation and provide an optical biopsy image in other conditions. Eyes with spheroidal degeneration show cystic structures in Bowman's layer and superficial stroma, and Salzmann's nodular degeneration has hyper-reflective material which replaces anterior stroma and Bowman's layer with thin epithelium [60] (**Figure 2**). Limbal stem cell deficiency exhibits a hyper-reflective material which replaces Bowman's layer as well as the

*A prototypical cross-sectional UHR-OCT image of a human cornea with keratoconus; focal disintegration of*

anterior stroma, with irregular overlying epithelium (**Figure 3**).

*Bowman's layer (white arrow), focal stromal thinning (yellow arrow).*

thickness, Bowman's ectasia index and Bowman's ectasia index-max [69].

**5.1 Subclinical keratoconus**

**5.2 Keratoconus**

disease severity [13].

**5.3 Corneal and ocular surface pathology**


**4. Purification of antifungal metabolites from soil bacteria: A practical approach**

#### **4.1. Culture conditions for metabolite production**

The PCR mixtures were denatured at 95°C for 5 min, which was followed by 35 cycles of 94°C for 30s, 55°C for 30s, and 72°C for 90s and then a final extension at 72°C for 5 min. Am‐ plification was checked for purity by electrophoresis on a 1.0% agarose gel. The bands of in‐ terest were excised from the gel, and the DNA was purified using QIAquick PCR purification columns (Qiagen, Inc., Valencia, CA). Purified DNA fragments were sequenced using the same sets of primers that were used for amplification by an ABI Prism Big Dye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). Bacteria were identified based on sequence similarities to homologous 16S rRNA gene fragments in the Ribosomal Data‐ base Project database (Cole et al., 2005) (accessed at http://rdp.cme.msu.edu/index.jsp).

Cell free culture supernatants of inhibitory bacteria were used in an antagonistic assay sys‐ tem. Table 2 represents the strongest antagonistic bacteria which were identified by a combi‐

> **% of growth inhibition**

 Maize 57.7 95.7 + Maize 48.9 78.2 – Peanuts 60.4 63.4 + Maize 64.5 85.3 + Maize 55.6 74.7 + Pistachio 55.7 73.6 + Maize 59.3 65.3 + Maize 59.0 87.6 + Peanuts 35.0 84.4 + Maize 62.6 96.9 + Maize 69.3 70.3 +

298 Pistachio 70.6 18.7 + 295 Pistachio 56.0 43.0 +

**% of AFB1 inhibition** **Surfactant production on blood agar**

nation of biochemical and molecular methods in relation to their source of isolation.

**3.4. Antagonistic activity against aflatoxigenic** *A. parasiticus* **NRRL 2999**

**Field**

*P. aeruginosa* 320 Maize 63.9 95.3 +

*P. chlororaphis* 236 Peanuts 15.3 65.9 – *P. fluorescens* 82 Pistachio 72.7 91.1 – *B. subtilis* 248 Maize 52.0 19.1 +

*B. amyloliquefaciens* 296 Maize 66.7 24.4 +

growth rate of 51.17 mg and an AFB1 amount of 697.78 ng/mg fungal dry weight.

**Table 2.** Inhibitory effects of the strongest antagonistic bacteria selected from screening plates of visual agar plate assay on *A. parasiticus* NRRL 2999 growth and AF production in Potato dextrose broth. Control fungal culture had a

**Strain number**

**Antagonistic bacteria**

30 Aflatoxins - Recent Advances and Future Prospects

As the first step for production of bioactive antifungals, different culture conditions includ‐ ing medium, incubation time and aeration should be optimized. In order to initial purifica‐ tion of inhibitory metabolites, the selected bacterium with strongest antifungal activity in initial screening was cultured on suitable liquid media such as GY (2% glucose, 0.5% yeast extract), SCD (2% bacto dextrose, 20% potato infusion), PDB (potato dextrose broth) or even KB (King´s B). The cultures were checked for optimal conditions of aeration (stationary cul‐ tures to shaking at different rpm from 100 to 250), incubation times (for at least 1 to maxi‐ mum 7 days) and temperature (from 20 to 40°C). After culturing the bacterium at optimized condition, the whole culture as the main source of secretory metabolites was centrifuged at 8,000 x *g* for 30 min at room temperature. The cell free culture filtrate was then sterilized by filtration through a 0.22-µm-pore-size Millipore membrane (Millex-GV; Millipore) and kept at -20°C before use. The heat stability of the inhibitory metabolites can be examined by incu‐ bating the bacterial culture filtrate at 60, 80 and 100°C for 120 min or autoclaving at 121°C for 15 min. The acid and alkaline stabilities of the inhibitory metabolites can be checked by changing the pH of the culture medium to 1.5 and 11 by adding 1 M HCl or 1 M NaOH and incubating the solution at room temperature for 3 h.

*4.2.1. Metabolite production at pre-optimized culture conditions*

natant was used for purification of the inhibitory metabolites.

*4.2.2. Ion exchange column chromatography*

for further purification (Fig. 5A).

*4.2.3. Preparative thin layer chromatography*

*4.2.4. High performance liquid chromatography (HPLC)*

The selected bacterium with strongest antifungal activity was cultured in 1000 ml capacity flasks contained 250 ml GY as selected medium from section 4.1. The cultures were incubat‐ ed at pre-optimized conditions (28°C for 5 days with shaking at 120 rpm). The whole culture (2 liters totally) was then centrifuged at 8,000 × g at room temperature for 30 min. The super‐

Terrestrial Bacteria from Agricultural Soils: Versatile Weapons against Aflatoxigenic Fungi

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33

A glass column (2.5 × 60.0 cm) was equilibrated with MeOH. Five hundred grams of Diaion HP20 resin was suspended in MeOH and then packed onto the glass column. After remov‐ ing of MeOH, the column was equilibrated with distilled water. The culture broth of select‐ ed bacterium (500 ml) was loaded onto the column. The resin was washed with 3 liters of distilled water, and the substances bound to the resin were then stepwise eluted by using 2 liters each of 40, 60, 80, and 100% methanol (MeOH) in water. Each elution was concentrated to dryness with a rotary evaporator and dissolves in desirable amounts of 100% MeOH. The 80% MeOH fraction which showed the highest growth and/or AF inhibitory activity against NA-mutant of *A. parasiticus* NRRL 2999 in microtiter agar plate assay (MPA), was selected

The 80% MeOH fraction from section 4.2.2 (an approximate of 250 mg dry weight) was ap‐ plied to Silica gel 60F254 TLC plate and then developed with a mixture of chloroform/metha‐ nol/water (65:25:4, v/v/v) as mobile phase. Total area developed on the TLC plate was divided into at least 5 regions under 365 nm UV light, and the silica gel was scraped sepa‐ rately from each region. The substances presented in the silica gel were extracted with tenfold amounts of 100% MeOH. Each fraction was concentrated to dryness, dissolves in a small amount of MeOH, and subjected to the MPA on 96-well microplates. The fraction "b" (75.6 mg dry weight) which contained the strongest inhibitory activity against fungal

The fraction "b" from section 4.2.3 was finally purified by HPLC equipped with a Cosmosil 5C18-AR column (4.6 × 150 mm; 5 µm). After injecting the sample, the column was washed with MeOH/water (50:50, v/v) for 80 min. The flow rate was adjusted at 1.0 ml/min, and elu‐ tion was monitored at 290 nm wavelength. The number of 6 separated peaks (P1 to P6) were collected from the ODS column as shown in Fig. 5. Based on the MPA results, two peaks i.e. P2 and P3 were able to inhibit fungal growth and pigment production by *A. parasiticus* NRRL 2999 (Fig. 5C). These peaks were selected for further characterization by LC-MS and MALDI-TOF.

growth and/or AF production was selected for further purification (Fig. 5B).

#### **4.2. Purification of antifungal metabolites**

Consecutive steps of purification of bioactive metabolites from bacterial culture filtrate are summarized in Fig. 5. As the first step, the inhibitory bacterium should be cultured at opti‐ mized culture conditions from section 4.1. The next steps are Ion exchange column chroma‐ tography on Diaion HP20 resin, preparative thin layer chromatography on silica gel 60F254 and finally HPLC purification of bioactive metabolites.

**Figure 5.** Sequential steps of purification of *A. parasiticus* growth inhibitory metabolites from bacterial culture fil‐ trate:A) Stepwise elution of culture broth from a Diaion HP20 resin column using 40-100% aqueous MeOH. Fungal growth inhibition was reported for only 80% MeOH elution in microtiter agar plate assay (MPA).B) Further purification of fungal growth inhibitory metabolites from active Diaion HP20 column fraction (80% MeOH from step A) by thin layer chromatography (TLC). According to MPA result, section "b" was scrapped from TLC gel contained inhibitory compounds and thus, it was selected for further study.C) Final purification of inhibitory metabolites from section "b" of TLC in step B by normal-phase HPLC. Among 6 separated peaks shown (P1 to P6), two peaks i.e. P2 and P3 showed fungal growth inhibition in MPA.

#### *4.2.1. Metabolite production at pre-optimized culture conditions*

The selected bacterium with strongest antifungal activity was cultured in 1000 ml capacity flasks contained 250 ml GY as selected medium from section 4.1. The cultures were incubat‐ ed at pre-optimized conditions (28°C for 5 days with shaking at 120 rpm). The whole culture (2 liters totally) was then centrifuged at 8,000 × g at room temperature for 30 min. The super‐ natant was used for purification of the inhibitory metabolites.

#### *4.2.2. Ion exchange column chromatography*

mum 7 days) and temperature (from 20 to 40°C). After culturing the bacterium at optimized condition, the whole culture as the main source of secretory metabolites was centrifuged at 8,000 x *g* for 30 min at room temperature. The cell free culture filtrate was then sterilized by filtration through a 0.22-µm-pore-size Millipore membrane (Millex-GV; Millipore) and kept at -20°C before use. The heat stability of the inhibitory metabolites can be examined by incu‐ bating the bacterial culture filtrate at 60, 80 and 100°C for 120 min or autoclaving at 121°C for 15 min. The acid and alkaline stabilities of the inhibitory metabolites can be checked by changing the pH of the culture medium to 1.5 and 11 by adding 1 M HCl or 1 M NaOH and

Consecutive steps of purification of bioactive metabolites from bacterial culture filtrate are summarized in Fig. 5. As the first step, the inhibitory bacterium should be cultured at opti‐ mized culture conditions from section 4.1. The next steps are Ion exchange column chroma‐ tography on Diaion HP20 resin, preparative thin layer chromatography on silica gel 60F254

**Figure 5.** Sequential steps of purification of *A. parasiticus* growth inhibitory metabolites from bacterial culture fil‐ trate:A) Stepwise elution of culture broth from a Diaion HP20 resin column using 40-100% aqueous MeOH. Fungal growth inhibition was reported for only 80% MeOH elution in microtiter agar plate assay (MPA).B) Further purification of fungal growth inhibitory metabolites from active Diaion HP20 column fraction (80% MeOH from step A) by thin layer chromatography (TLC). According to MPA result, section "b" was scrapped from TLC gel contained inhibitory compounds and thus, it was selected for further study.C) Final purification of inhibitory metabolites from section "b" of TLC in step B by normal-phase HPLC. Among 6 separated peaks shown (P1 to P6), two peaks i.e. P2 and P3 showed

incubating the solution at room temperature for 3 h.

and finally HPLC purification of bioactive metabolites.

**4.2. Purification of antifungal metabolites**

32 Aflatoxins - Recent Advances and Future Prospects

fungal growth inhibition in MPA.

A glass column (2.5 × 60.0 cm) was equilibrated with MeOH. Five hundred grams of Diaion HP20 resin was suspended in MeOH and then packed onto the glass column. After remov‐ ing of MeOH, the column was equilibrated with distilled water. The culture broth of select‐ ed bacterium (500 ml) was loaded onto the column. The resin was washed with 3 liters of distilled water, and the substances bound to the resin were then stepwise eluted by using 2 liters each of 40, 60, 80, and 100% methanol (MeOH) in water. Each elution was concentrated to dryness with a rotary evaporator and dissolves in desirable amounts of 100% MeOH. The 80% MeOH fraction which showed the highest growth and/or AF inhibitory activity against NA-mutant of *A. parasiticus* NRRL 2999 in microtiter agar plate assay (MPA), was selected for further purification (Fig. 5A).

#### *4.2.3. Preparative thin layer chromatography*

The 80% MeOH fraction from section 4.2.2 (an approximate of 250 mg dry weight) was ap‐ plied to Silica gel 60F254 TLC plate and then developed with a mixture of chloroform/metha‐ nol/water (65:25:4, v/v/v) as mobile phase. Total area developed on the TLC plate was divided into at least 5 regions under 365 nm UV light, and the silica gel was scraped sepa‐ rately from each region. The substances presented in the silica gel were extracted with tenfold amounts of 100% MeOH. Each fraction was concentrated to dryness, dissolves in a small amount of MeOH, and subjected to the MPA on 96-well microplates. The fraction "b" (75.6 mg dry weight) which contained the strongest inhibitory activity against fungal growth and/or AF production was selected for further purification (Fig. 5B).

#### *4.2.4. High performance liquid chromatography (HPLC)*

The fraction "b" from section 4.2.3 was finally purified by HPLC equipped with a Cosmosil 5C18-AR column (4.6 × 150 mm; 5 µm). After injecting the sample, the column was washed with MeOH/water (50:50, v/v) for 80 min. The flow rate was adjusted at 1.0 ml/min, and elu‐ tion was monitored at 290 nm wavelength. The number of 6 separated peaks (P1 to P6) were collected from the ODS column as shown in Fig. 5. Based on the MPA results, two peaks i.e. P2 and P3 were able to inhibit fungal growth and pigment production by *A. parasiticus* NRRL 2999 (Fig. 5C). These peaks were selected for further characterization by LC-MS and MALDI-TOF.

#### **4.3. Structural elucidation of antifungal metabolites**

With a combination of Liquid chromatography-Mass spectrometry (LC-MS) and Matrix-as‐ sisted laser desorption/ionization (MALDI-TOF), we will be able to elucidate the chemical structure of a protein or peptide in a best way. LC-MS spectrum determines retention time and an approximate mass of a purified compound, while complementary MALDI-TOF ena‐ ble us to explain chemical formula and precise mass of the compound as the final step of identification. LC-MS and MALDI-TOF spectra of purified antifungal are shown in Fig. 6.

*4.3.2. MALDI-TOF*

Matrix-assisted laser desorption ionization-time of flight spectrometer (MALDI-TOF) is a soft ionization technique used in mass spectrometry, allowing the analysis of biomolecules (bio‐ polymers such as DNA, proteins, peptides and sugars) and large organic molecules (poly‐ mers, dendrimers and other macromolecules), which tend to be fragile and fragment when ionized by more conventional ionization methods. The MALDI-TOF is a two step process. First, desorption is triggered by a UV laser beam. Matrix material heavily absorbs UV laser light, leading to the ablation of upper layer of the matrix material. The second step is ioniza‐ tion which takes place in the hot plume. Aside from peptide mass fingerprinting and useful application in identifying of microorganisms such as bacteria and fungi, MALDI-TOF is used for the rapid identification of proteins isolated by using gel electrophoresis: SDS-PAGE, size exclusion chromatography, affinity chromatography, strong/weak ion exchange, isotope cod‐ ed protein labeling (ICPL), and two-dimensional gel electrophoresis. MALDI-TOF analysis of inhibitory compounds with defined retention time and an approximate mass from LC-MS step reveals valuable data about chemical formula and exact mass and provides finally identifica‐ tion of the absolute configuration of the purified inhibitory bacterial metabolite (Fig. 6).

Terrestrial Bacteria from Agricultural Soils: Versatile Weapons against Aflatoxigenic Fungi

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AF contamination of food and feed remains a major risk for human and animal health all over the world. Despite the long history of our knowledge about AF, little has been documented on how we can virtually combat the global distress of AF contamination of crops and agricultur‐ al commodities. AF-producing fungi can infect grains from pre-harvest conditions in the field through to post-harvest stages in the stores. Several pre- and post-harvest strategies have be‐ ing tested to reduce risk of AF contamination. One of the management strategies being devel‐ oped is biological control using various antagonistic microorganisms such as fungi, bacteria, and actinomycetes by a competitive exclusion mechanism. Biological control in conjunction with other management practices has potential to dramatically reduce AF contamination. Nat‐ ural population of *A. flavus* consists of toxigenic strains that produce considerable amount of AF and atoxigenic strains that lack the capacity to produce AF. Nowadays, introducing atoxi‐ genic strains has been successfully used to compete and exclude toxigenic strains in the field thereby reducing AF production in contaminated crops. However, there are some important limitations from the type of vegetative compatibility groups which shows the progeny of the fungus for AF-producing ability to geographic limitations in selection of atoxigenic strains. Considerable tolerance of *B. subtilis* and *P. chlororaphis* to environmental stresses, their large ca‐ pacity for producing diverse array of beneficial antifungal metabolites and their readily pro‐ ducing by current fermentation technology make them promising tools for biocontrol of aflatoxigenic fungi in practice. Bacterial population from the genera *Bacillus* and *Pseudomonas* identified in pistachio, maize and peanut fields in the present study with potent antagonistic activity against aflatoxigenic *Aspergillus parasiticus* can potentially be developed into new bio‐ control agents for combating AF contamination of crops in the field. These bacteria must be evaluated for a set of selection criteria for further use in biocontrol field experiments. Inabili‐

**5. Concluding remarks and future prospective**

#### *4.3.1. Liquid chromatography-Mass spectrometry (LC-MS)*

The LC-MS system usually consists of a LC-10Avp separation module equipped with a SPD-M10Avp photodiode array detector and LC-MS2010A single quadruple mass spectrometer with atmospheric pressure photo ionization (APPI) source. The probe can be operated in the positive/negative mode under the condition of defined probe voltage, temperature of 300°C, CDL temperature of 200°C, nabulization gas (N2) flow 2.5 1/min, and scan range 900-1600 m/z (sec/scan). The amount of 2 µl of each inhibitory peak purified from HPLC separation was injected to an Ascentis C18 column (150 mm × 2.1 mm, 5 µm) and washed with MeOH (65% aqueous solution) acidified with 0.1% acetic acid in a flow rate of 0.2 ml/min. The col‐ umn temperature should be maintained at 40°C during the operation. Approximate mass and retention time of the compound were recorded at the end of analysis.

**Figure 6.** Liquid chromatography-Mass spectrometry (LC-MS) analysis of a HPLC purified inhibitory metabolite for *A. parasiticus* growth shows an approximate retention time of 17.0 min and a mass of 1042.0 m/z (A), while MALDI-TOF data indicates a structural formula of C48H76N12O14 and an exact mass of 1042.5447 m/z (B).

## *4.3.2. MALDI-TOF*

**4.3. Structural elucidation of antifungal metabolites**

34 Aflatoxins - Recent Advances and Future Prospects

*4.3.1. Liquid chromatography-Mass spectrometry (LC-MS)*

With a combination of Liquid chromatography-Mass spectrometry (LC-MS) and Matrix-as‐ sisted laser desorption/ionization (MALDI-TOF), we will be able to elucidate the chemical structure of a protein or peptide in a best way. LC-MS spectrum determines retention time and an approximate mass of a purified compound, while complementary MALDI-TOF ena‐ ble us to explain chemical formula and precise mass of the compound as the final step of identification. LC-MS and MALDI-TOF spectra of purified antifungal are shown in Fig. 6.

The LC-MS system usually consists of a LC-10Avp separation module equipped with a SPD-M10Avp photodiode array detector and LC-MS2010A single quadruple mass spectrometer with atmospheric pressure photo ionization (APPI) source. The probe can be operated in the positive/negative mode under the condition of defined probe voltage, temperature of 300°C, CDL temperature of 200°C, nabulization gas (N2) flow 2.5 1/min, and scan range 900-1600 m/z (sec/scan). The amount of 2 µl of each inhibitory peak purified from HPLC separation was injected to an Ascentis C18 column (150 mm × 2.1 mm, 5 µm) and washed with MeOH (65% aqueous solution) acidified with 0.1% acetic acid in a flow rate of 0.2 ml/min. The col‐ umn temperature should be maintained at 40°C during the operation. Approximate mass

**Figure 6.** Liquid chromatography-Mass spectrometry (LC-MS) analysis of a HPLC purified inhibitory metabolite for *A. parasiticus* growth shows an approximate retention time of 17.0 min and a mass of 1042.0 m/z (A), while MALDI-TOF

data indicates a structural formula of C48H76N12O14 and an exact mass of 1042.5447 m/z (B).

and retention time of the compound were recorded at the end of analysis.

Matrix-assisted laser desorption ionization-time of flight spectrometer (MALDI-TOF) is a soft ionization technique used in mass spectrometry, allowing the analysis of biomolecules (bio‐ polymers such as DNA, proteins, peptides and sugars) and large organic molecules (poly‐ mers, dendrimers and other macromolecules), which tend to be fragile and fragment when ionized by more conventional ionization methods. The MALDI-TOF is a two step process. First, desorption is triggered by a UV laser beam. Matrix material heavily absorbs UV laser light, leading to the ablation of upper layer of the matrix material. The second step is ioniza‐ tion which takes place in the hot plume. Aside from peptide mass fingerprinting and useful application in identifying of microorganisms such as bacteria and fungi, MALDI-TOF is used for the rapid identification of proteins isolated by using gel electrophoresis: SDS-PAGE, size exclusion chromatography, affinity chromatography, strong/weak ion exchange, isotope cod‐ ed protein labeling (ICPL), and two-dimensional gel electrophoresis. MALDI-TOF analysis of inhibitory compounds with defined retention time and an approximate mass from LC-MS step reveals valuable data about chemical formula and exact mass and provides finally identifica‐ tion of the absolute configuration of the purified inhibitory bacterial metabolite (Fig. 6).


**4. Calibration and validation**

During the model calibration parameters are varied within an acceptable range, until a satisfactory correlation is achieved between measured and simulated data. Usually, the parameters values are changed uniformly on the catchment level. However, certain

Modelling of Surface Water Quality by Catchment Model SWAT 117

Official measurements of a flow showed that on certain days the flow was not present or it was negligible. Model does not neglect extremely low flows, as is evident from the cumulative distribution of the flow (Fig. 2). Errors in flow measurements, in the worst case

The ENS values for total flow fall into the category of satisfactory results (Moriasi et al., 2007, Henriksen et al., 2003), R2 values fall into the category of good results, RMSE into the category of very good results (Henriksen et al., 2003) and PBIAS into the category of very good and good results (Moriasi et al., 2007). The reasons for lower results of the objective functions in the validation lie in the representation of the soil, rainfall and in the river flow

may be upto 42 % and in best case upto 3 % of the total flow (Harmel et al., 2006).

**Reka Dragonja**

1993 - 1997

(Total Flow)

**ENS** 0.61 0.61 0.39 0.69 0.55 0.57 0.45 0.42 **R2** 0.72 0.64 0.57 0.70 0.66 0.59 0.49 0.49 **RMSE** 0.13 0.82 1.21 0.74 0.35 1.06 1.98 1.50 **PBIAS** -12.79 7.04 -14.19 19.40 1.49 4.69 23.15 -3.31 Table 5. Daily time step river flow performance statistics for the rivers Dragonja and Reka

2006 - 2008

Base Flow

Calibration Validation

Total Flow (Total Flow)

2006 - 2008

1994 - 1996

Calibration Validation

Total Flow

for the calibration (2001-2005) and validation periods

data uncertainty.

**Objective function**

**River Flow - Reka (m3 s-1)**

**01/01/01**

**0.00 0.01 0.10 1.00 10.00 100.00**

**River Flow - Reka (m3 s-1)**

**04/01/01**

**07/01/01**

**10/01/01**

**01/01/02**

**04/01/02**

**07/01/02**

**10/01/02**

**01/01/03**

**04/01/03**

**07/01/03**

**Month/Day/Year**

**1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Percentile (River Flow)**

**10/01/03**

**01/01/04**

**04/01/04**

**07/01/04**

**10/01/04**

**01/01/05**

**04/01/05**

**07/01/05**

**Measured Simulated (c)**

**Measured Simulated (a)**

**10/01/05**

**01/01/06**

**River Flow - Dragonja (m3 s-1)**

**01/01/01**

**0.00 0.01 0.10 1.00 10.00 100.00**

Fig. 2. Comparison between simulated (SWAT) and measured daily flows (m3 s-1) (a, b) and cumulative distribution (c, d) of daily river flows for the calibration period (2001-2005)

**River Flow - Dragonja (m3 s-1)**

**04/01/01**

**07/01/01**

**10/01/01**

**01/01/02**

**04/01/02**

**07/01/02**

**10/01/02**

**01/01/03**

**04/01/03**

**07/01/03**

**Month/Day/Year**

**1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Percentile (River Flow)**

**10/01/03**

**01/01/04**

**04/01/04**

**07/01/04**

**10/01/04**

**01/01/05**

**04/01/05**

**07/01/05**

**Measured Simulated (d)**

**Measured Simulated (b)**

**10/01/05**

**01/01/06**

Base Flow

parameters (Sol\_Awc, Cn2, Canmx) are exceptions, because of the spatial heterogeneity. Firstly manual calibration, parameter by parameter, should be carried out with gradual adjustments of the parameter values until a satisfactory output results (ENS and R2> 0.5) (Moriasi et al., 2007, Henriksen et al., 2003). This procedure may be time consuming for inexperienced modellers. In the process of autocalibration only the most sensitive parameters are listed that showed the greatest effect on the model outputs. For each of the parameter a limit range (max, min) has to be assigned.

Validation is performed with parameter values from the calibrated model (Table 4) and with the measured data from another time period. Due to the data scarcity, the model was validated only for the hydrological part (flow). The river Reka water quality data covers only one year of daily observations, which was only enough for the calibration. For the river Dragonja a 14 years long data series of water quality was available, but the data was scarce in the number of observations (for sediment, NO3- and TP only 92, 73, 75, 78 measurements). It should be pointed out that samples taken during monitoring represents only the current condition of the river in a certain part of the day (concentration in mg l-1), while the simulated value is a total daily transported load (kg day-1) in a river.

Calibration of the daily flow for the rivers Reka and Dragonja catchments was performed for the period from 1998 to 2005. According to the availability of data we selected different periods for the daily flow validation of the Reka (1993–1997, 2006–2008) and Dragonja (1994–1996, 2006–2008). Due to the lack of data, on sediment, NO3- and TP, we performed only the calibration for Kožbanjšček (1. 7. 2008 – 30. 6. 2009) and Dragonja (1994–2008).

Legend: 1 - forest, permanent crops, grassland, arable; 2 - subcatchment 1-2-5, subcatchment 3-4-6-7-8-9; D - default value - depends on soil type, land use and modeller set up

Table 4. Hydrological parameters, ranges and final values selected for the calibration of models (SWAT) for the rivers Reka and Dragonja catchments

#### **4.1 Hydrology calibration and validation**

Objective functions show that the simulated total flows are within the acceptable range (Table 5, Fig. 2). Correlation coefficient (R2) for a daily flow is influenced by low flows.

parameters (Sol\_Awc, Cn2, Canmx) are exceptions, because of the spatial heterogeneity. Firstly manual calibration, parameter by parameter, should be carried out with gradual adjustments of the parameter values until a satisfactory output results (ENS and R2> 0.5) (Moriasi et al., 2007, Henriksen et al., 2003). This procedure may be time consuming for inexperienced modellers. In the process of autocalibration only the most sensitive parameters are listed that showed the greatest effect on the model outputs. For each of the

Validation is performed with parameter values from the calibrated model (Table 4) and with the measured data from another time period. Due to the data scarcity, the model was validated only for the hydrological part (flow). The river Reka water quality data covers only one year of daily observations, which was only enough for the calibration. For the river Dragonja a 14 years long data series of water quality was available, but the data was scarce in the number of observations (for sediment, NO3- and TP only 92, 73, 75, 78 measurements). It should be pointed out that samples taken during monitoring represents only the current condition of the river in a certain part of the day (concentration in mg l-1), while the

Calibration of the daily flow for the rivers Reka and Dragonja catchments was performed for the period from 1998 to 2005. According to the availability of data we selected different periods for the daily flow validation of the Reka (1993–1997, 2006–2008) and Dragonja (1994–1996, 2006–2008). Due to the lack of data, on sediment, NO3- and TP, we performed only the calibration for Kožbanjšček (1. 7. 2008 – 30. 6. 2009) and Dragonja (1994–2008).

**Calibrated values Parameter Default Range Reka Dragonja**

Legend: 1 - forest, permanent crops, grassland, arable; 2 - subcatchment 1-2-5, subcatchment 3-4-6-7-8-9;

Objective functions show that the simulated total flows are within the acceptable range (Table 5, Fig. 2). Correlation coefficient (R2) for a daily flow is influenced by low flows.

Table 4. Hydrological parameters, ranges and final values selected for the calibration of

D - default value - depends on soil type, land use and modeller set up

models (SWAT) for the rivers Reka and Dragonja catchments

**4.1 Hydrology calibration and validation**

1 Alpha\_Bf 0.048 0–1 0.30058 0.45923 2 Canmx1 0 0–20 8, 4, 2 8, 4, 2 3 Ch\_K2 D 0–150 7.0653 3.7212 4 Ch\_N D 0–1 0.038981 0.04363 5 Cn2 D –25/+25% –8, –15 2 +14 6 Esco 0.95 0–1 0.8 0.75 7 Gw\_Delay 31 0–160 131.1 60.684 8 Gw\_Revap 0.02 0–0.2 0.19876 0.069222 9 Gwqmn 0 0–100 100 0.79193 10 Sol\_Awc D +50% no change no change 11 Surlag 4 0.01–4 0.28814 0.13984 ENS 0.61 0.57

parameter a limit range (max, min) has to be assigned.

simulated value is a total daily transported load (kg day-1) in a river.

Official measurements of a flow showed that on certain days the flow was not present or it was negligible. Model does not neglect extremely low flows, as is evident from the cumulative distribution of the flow (Fig. 2). Errors in flow measurements, in the worst case may be upto 42 % and in best case upto 3 % of the total flow (Harmel et al., 2006).

The ENS values for total flow fall into the category of satisfactory results (Moriasi et al., 2007, Henriksen et al., 2003), R2 values fall into the category of good results, RMSE into the category of very good results (Henriksen et al., 2003) and PBIAS into the category of very good and good results (Moriasi et al., 2007). The reasons for lower results of the objective functions in the validation lie in the representation of the soil, rainfall and in the river flow data uncertainty.

Table 5. Daily time step river flow performance statistics for the rivers Dragonja and Reka for the calibration (2001-2005) and validation periods

Fig. 2. Comparison between simulated (SWAT) and measured daily flows (m3 s-1) (a, b) and cumulative distribution (c, d) of daily river flows for the calibration period (2001-2005)

Modelling of Surface Water Quality by Catchment Model SWAT 119

**Parameter Default Range Calibrated values**

Nitrate nitrogen (NO3-N)

Total phosphorus (TP)

Sediment *Reka – Kožbanjšček Dragonja* 1 SpCon 0.0001 0.0001–0.01 0.002 0,002 2 SpExp 1 1–1.5 1.3 1 3 Ch\_Erod 0 0–1 0.092 0,06 4 Ch\_Cov 0 0.05–0.6 0.1 0,1

**ENS 0.23 0.70 ENS percentile 0.83 0.73 R2 0.24 0.80 RMSE 10.35 19.81 PBIAS –0.15 –6.33**

1 Nperco 0.2 0.01–1 1 0,2 2 Al1 0.08 0.07–0.09 0.071 0,08 3 CMN 0.0003 0.0001–0.001 - 0,0001 4 HLIFE\_NGW 0 0–200 - 0,02

**ENS 0.40 0.10 ENS percentile 0.72 0.78 R2 0.46 0.17 RMSE 79.89 5.11 PBIAS 21.24 –3.43**

1 Pperco 10 10–17.5 15 10 2 Phoskd 175 100–200 175 200 3 Al2 0.015 0.01–0.02 0.003 0,001 4 PSP 0.4 0.01–0.7 0.22 0,04 5 ERORGP 0 0.001–5 0 0,003 6 BC4 0.35 0.01–0.7 0.1 0,1 7 RS2 0.05 0.001–0.1 0.1 0,1 8 RS5 0.05 0.001–0.9 0.08 0,001

**ENS –0.05 0.36 ENS percentile 0.95 0.85 R2 0.11 0.46 RMSE 48.17 0.18 PBIAS 3.43 49.21**

models calibration periods (Reka 2008 - 2009; Dragonja 1994 - 2008)

5 FRT\_surface 0.2 0–1 management

9 FRT\_surface 0.2 0–1 management

5 USLE\_P 1 0–1 slope dependent slope dependent

dependent

dependent

Table 6. SWAT water quality parameters, their ranges and the final values chosen for the

management dependent

management dependent

#### **4.2 Sediment, nitrogen and phosphorus calibration**

Sediment calibration is essential for the proper P calibration, as P is preferentially transported adsorbed on the sediment particles. Parameters used for the calibration were USLE\_P, SPCON, SPEXP, CH\_EROD, CH\_COV. Simulation results for the river Reka show lower ENS = 0.23 and a good result in predicting the variability of ENSpercentile = 0.83 (Table 6). In the case of Dragonja, model achieved good results for ENS = 0.70 and ENSpercentile = 0.73. PBIAS values fall within the category of very good results as deviation is less than 15% (Moriasi et al., 2007).

Parameters with impact on the N calibration results were FRT\_SURFACE, NPERCO, AL1, CMN, HLIFE\_NGW. The river Dragonja statistic is lower (ENS = 0.10, ENspercentile = 0.78) and for the river Reka is in satisfactory range with ENS = 0.40 and ENspercentile = 0.72 (Table 6). The PBIAS results fall into the very good (Dragonja) and satisfactory (Reka) category (Moriasi et al., 2007). The lower performance of the objective functions is connected to data scarcity in the Dragonja catchment with only 73 measurements in 14 years and in river Reka with only one year of daily data. Therefore, it is difficult to say whether the model is a good predictor of nitrate nitrogen (NO3-N ) loads and dynamics. Monthly sampling rate leads to inaccurate estimates of the transported loads of nutrients in rivers (Johnes, 2007); especially NO3- (Harmel et al, 2006).

#### **4.3 Model performance indicators**

An important step before calibrating sediment and water quality parameters is to look at other model performance indicators. Three main parameters are crop growth, evapotranspiration (ET) and Soil Water Content (SWC), as all of them have a great effect on the water balance. Evapotranspiration is a primary mechanism by which water is removed from the catchment. It depends on air temperature and soil water content. The higher the temperature, the higher is potential evapotranspiration (PET) and consequently ET, if there is enough of water in the soil. A simple monthly water balance between monthly precipitation and PET showed that average monthly water balance in the Reka catchment (station Bilje) is negative between May and August (Fig. 3). In the Dragonja catchment (station Portorož) water balance is negative from April to August (growing season) (Fig. 3).

Fig. 3. Comparison of simulated and measured (Environment Agency of Republic of Slovenia - EARS) water balance (mm) for the Reka subcatchments 8 and Dragonja subcatchment 14

Sediment calibration is essential for the proper P calibration, as P is preferentially transported adsorbed on the sediment particles. Parameters used for the calibration were USLE\_P, SPCON, SPEXP, CH\_EROD, CH\_COV. Simulation results for the river Reka show lower ENS = 0.23 and a good result in predicting the variability of ENSpercentile = 0.83 (Table 6). In the case of Dragonja, model achieved good results for ENS = 0.70 and ENSpercentile = 0.73. PBIAS values fall within the category of very good results as deviation is less than 15%

Parameters with impact on the N calibration results were FRT\_SURFACE, NPERCO, AL1, CMN, HLIFE\_NGW. The river Dragonja statistic is lower (ENS = 0.10, ENspercentile = 0.78) and for the river Reka is in satisfactory range with ENS = 0.40 and ENspercentile = 0.72 (Table 6). The PBIAS results fall into the very good (Dragonja) and satisfactory (Reka) category (Moriasi et al., 2007). The lower performance of the objective functions is connected to data scarcity in the Dragonja catchment with only 73 measurements in 14 years and in river Reka with only one year of daily data. Therefore, it is difficult to say whether the model is a good predictor of nitrate nitrogen (NO3-N ) loads and dynamics. Monthly sampling rate leads to inaccurate estimates of the transported loads of nutrients in rivers (Johnes, 2007); especially

An important step before calibrating sediment and water quality parameters is to look at other model performance indicators. Three main parameters are crop growth, evapotranspiration (ET) and Soil Water Content (SWC), as all of them have a great effect on the water balance. Evapotranspiration is a primary mechanism by which water is removed from the catchment. It depends on air temperature and soil water content. The higher the temperature, the higher is potential evapotranspiration (PET) and consequently ET, if there is enough of water in the soil. A simple monthly water balance between monthly precipitation and PET showed that average monthly water balance in the Reka catchment (station Bilje) is negative between May and August (Fig. 3). In the Dragonja catchment (station Portorož) water balance is negative from April to August (growing season) (Fig. 3).

Fig. 3. Comparison of simulated and measured (Environment Agency of Republic of Slovenia - EARS) water balance (mm) for the Reka subcatchments 8 and Dragonja

**-250 -200 -150 -100 -50 0 50 100 150 200 250 300**

**Water Balance (mm)**

**1 2 3 4 5 6 7 8 9 10 11 12 Month**

**Measured EARS Portorož (1971 -2000) Simulated Dragonja (2001-2005) Sim. Max. W.B. Sim. Min. W.B.**

**4.2 Sediment, nitrogen and phosphorus calibration**

(Moriasi et al., 2007).

NO3- (Harmel et al, 2006).

**-250 -200 -150 -100 -50 0 50 100 150 200 250 300**

subcatchment 14

**Water balance (mm)**

**4.3 Model performance indicators**

**1 2 3 4 5 6 7 8 9 10 11 12 Month**

**Measured EARS Bilje (1971-2000) Simulated Reka (2001-2005) Sim. Max. W.B. Sim Min. W.B.**

Table 6. SWAT water quality parameters, their ranges and the final values chosen for the models calibration periods (Reka 2008 - 2009; Dragonja 1994 - 2008)

Modelling of Surface Water Quality by Catchment Model SWAT 121

The aim of this scenario was to investigate possible effects of the agri-environmental measures on the river water quality. To achieve the aim seven different scenarios were

The field erosion buffer strips scenario (EVP) is a function of how to minimize influences of diffuse pollution resulting from agricultural activities without drastic management changes. They are planted or indigenous bands of vegetation that are situated between source areas and receiving waters to reduce surface runoff velocities and to remove pollutants from surface and subsurface runoff. The effectiveness of strips is closely correlated with their slope and width (Dillaha et al., 1989). An option of 3 m wide strips was modelled on all arable (AGRC, AGRR), vineyard (VINE), orchard (ORCI, ORCE) in olive grove (OLEA)

Organic farming scenarios on 20 % of the area (EKO20) and on the 100 % area (EKO100) aim to reduce the use of mineral fertilizers and to reduce the intensity of production. Special organic rotations with green manure and composted farmyard manure were created. The lack of P was compensated with the use of triple-superphosphate that is allowed in organic production. Both organic scenarios were designed to ensure normal production for the

Steep meadows, being an agricultural landscape, should be cut regularly, but due to the steep slopes and the associated costs and risks, are abandoned and overgrown. Scenarios having steep meadows with slope inclination above 35 % (S35) and 50 % (S50) should prevent overgrowth. To verify the effects of scenarios on water quantity and nutrients transport, meadows (TRAV) of both case studies located on slopes greater than 35 % and 50 % were changed into the forest (FRSD) (Fig. 6). In the S35 scenario 18 % (Reka) and 3.6 % (Dragonja) of grassland was changed into forest, which is equivalent to 1.43 % (Reka) and 0.67 % (Dragonja) of the total catchments. In the S50 scenario only 2 % (Reka) and 0.3 % (Dragonja) of grassland was changed into forest, which is equivalent to 0.16% (Reka) and

than 35 % and 50 % for the Reka and Dragonja catchment

Fig. 6. Hydrological response units with the grassland land use (TRAV) and slopes greater

Conservation of vineyards on steep slopes has proved to be difficult because of unprofitable production. Economic reasons were followed by a trend of wine production abandonment.

applied to the study area EVP, EKO20, EKO100, S35, S50, STV35, ETA.

**5. Agri-environmental scenarios**

0.06% (Dragonja) of the total catchments.

HRUs.

market.

Water that enters the soil may move along one of the several different pathways. It may be removed by plant uptake or evaporation; it may percolate past the bottom of the soil profile or may move laterally in the profile. However, plant uptake removes the majority of water that enters the soil profile (Neitsch et al., 2005). The soil water content will be represented correctly if crops are growing at the expected rate and soils have been correctly parameterized. Figure 4 shows the average of HRU for both catchments, with a silt clay soils, with the prevailing surface runoff and slow lateral subsurface flow. Soils exit the field capacity in the spring and return to that state in the autumn (Fig. 4). Soils in the summer are often completely dry with occasional increasing induced by storms.

Fig. 4. Comparison of simulated soil water content (mm) for the HRU No. 38 (Reka) and HRU No. 182 (Dragonja) and observed precipitation (mm) in the calibration period (2001−2005)

The plant growth component of SWAT is a simplified version of the plant growth model. Phenological plant development is based on daily accumulated heat units, leaf area development, potential biomass is based on a method developed by Monteith, a harvest index is used to calculate yield, and plant growth can be inhibited by temperature, water, N or P stress. (Neitsch et al., 2005). In the crop database a range of parameters can be changed to meet the requirements for optimal plant growth. We used default SWAT database parameters that were additionally modified (Frame, 1992). An example crop growth profile for development of leaf area index (LAI) and plant biomass (BIOM) for vineyard is presented on figure 5.

Fig. 5. Simulated vineyard biomass growth (kg ha-1) and leaf area index (m2 m-2) for the HRU No. 38 in the river Reka catchment


**2. Clinical perspective, diagnosis, and treatment**

Despite *Cryptosporidium* species infecting the epithelial cells of the gastrointestinal and respiratory epithelium tracts, human cryptosporidiosis is a usually self-limiting infection in immunocompetent individuals with a low fatality rate [39–41]. In general, onset of the symptoms occurs 5–7 days following exposure and resolves in 2–3 weeks [42]. Clinical manifestations vary from subclinical infection to watery diarrhoea, sometimes profuse. Other common symptoms include abdominal cramps, fever, flatulence, nausea, vomiting, and low-grade fever [43–45]. Clinical presentation of cryptosporidiosis in individuals with impairment of T-cell functions, mainly those with HIV infection, varies according to the level of immunosuppression, from asymptomatic disease, to transient disease, to relapsing chronic diarrhoea or even cholera-like diarrhoea that is debilitating and potentially life-threatening [46]. Spreading of infection beyond the extra-intestinal site (in the biliary or respiratory tract) has been documented in children and immunocompromised people, resulting in a potentially life-threatening disease [47, 48]. Sclerosing cholangitis and other biliary involvements are common in AIDS patients with cryptosporidiosis. Both innate and adaptive immunity of the host have major impacts on the severity of cryptosporidiosis and its prognosis.

*Cryptosporidium* has been diagnosed using a variety of approaches, such as microscopy, immunofluorescent antibody (IFA), enzyme-linked immunosorbent assay (ELISA), and DNA-based detection methods [18]. However, identification of the parasite's morphologic features through examination of stool smears is widely employed in diagnostic laboratories, particularly in resource-limited health systems. The oocysts are shed intermittently [49]; therefore, three faecal samples collected on alternate days are recommended. To maximise the recovery of oocysts, Sheather's sucrose flotation, saturated salt flotation, and Allen and Ridley's formol-ether method are the stool concentration techniques most frequently used prior to the use of the microscopy staining technique [50, 51]. Stain differential is required due to the small size of the specimen (ranging from 4 to 6 μm), similar in shape to yeasts and faecal debris [52]. Safranin-methylene blue, Kinyoun Ziehl-Neelsen, and dimethyl sulfoxide-carbol fuchsin are the most commonly used stain methods [11, 53–55]. However, in the absence of staining solution, phase contrast microscopy has proven to be highly specific for the detection of *Cryptosporidium* oocysts in human stool

**335**

*State of the Art and Future Directions of* Cryptosporidium *spp.*

samples [56]. In general, conventional microscopy lacks sensitivity, is time-consum-

Direct fluorescent antibody tests (DFAs), enzyme-immunoassays (EIAs), and

rapid immunochromatographic assays (dipsticks) are commercially available [60–63]. The EIA kits have been evaluated with human stool specimens only, presumably from patients infected with *C. hominis* or *C. parvum*. The direct fluorescent antibody tests have been widely used for the detection of *Cryptosporidium* in faecal smears, water, and food [60, 62–66]. However, the antigenic variability of oocyst wall epitopes contributes to reducing specificity, and the sensibility of all immunological-based methods is low. High specificity (99–100%) has been generally reported for EIA kits. Sensitivities, however, have been reported to range from 70 to 100% [62–65]. Dipsticks and EIAs are available for individual and for all-in-one tests for *Giardia*, *Cryptosporidium*, and *Entamoeba histolytica* [66–69]. The tests are fast and easy to perform. However, EIA kits and rapid format assays present a potential problem with false positives, so results need to be interpreted and evaluated with caution [70]. To overcome these barriers, one of the most notable advances in public health in recent decades has been the development of tools based on molecular biology for the diagnosis of infectious diseases. These polymerase chain reaction (PCR) techniques have enabled specific sensitive detection of oocysts (a single oocyst) in clinical and environmental samples [71–77]. Examples of such techniques include conventional PCR, quantitative PCR real time, and high-resolution melt. A wide variety of PCR methods targeting different genes have been developed for the detection of *Cryptosporidium* at the species/genotype/subtype levels. However, no targeted tests have been patterned for the detection of *Cryptosporidium* in clinical laboratories. Recently, the simultaneous qualitative detection and identification of multiple viral, parasitic (including *C. parvum* and *C. hominis*), and bacterial nucleic acids in human stool specimens were approved by the Food and Drug Administration (FDA) [78]. In general, PCR tools solely amplify the DNA of *C. parvum*, *C. hominis*, *C. meleagridis*, and species/genotypes closely related to *C. parvum* [18]. For genotyping, nested PCR-RFLP was the most commonly used method in the past. Nowadays, DNA sequencing of 18S has been required to reliably detect all *Cryptosporidium* spp. The HSP70 and COWP targets fail to detect the DNA of *C. felis*, *C. canis*, and *C. muris* [79]. Subtyping tools are indispensable from the epidemiological point of view and are helpful in knowing the possible transmission routes of *Cryptosporidium* species and zoonotic potential of the parasite. Several subtyping tools have been developed to evaluate the diversity within *C. parvum* or *C. hominis*, including analysis of the microsatellite, GP-60 gene, HSP70 gene, 47-kDa protein, small double-stranded (ds) RNA virus, serine repeat antigen, and T-rich gene fragment [73, 80–85]. The 18S ribosomal RNA (rRNA) gene and the hypervariable 60-kDa glycoprotein (gp60) gene have been widely used as targets to identify species and track transmission [18, 86, 87]. The 60-kDa glycoprotein (gp60, also known as gp40/gp15) gene presents a wide genetic heterogeneity in the number of trinucleotide repeats (TCA, TCG, or TCT). This gene encodes a precursor protein that is cleaved to produce mature cell surface glycoproteins (gp45/gp40 and gp15) implicated in the attachment to, and invasion of, enterocytes [18, 87]. Identification of subtypes using GP60 subtype families has revealed the subtype families (Ia-Ik) in *C. hominis* [87–91] and two zoonotic subtypes (IIa, IId), subtypes (IIb, IIc, IIe, IIf, IIi, IIj-IIt) in *C. parvum* [4, 87, 92–94], and subtype families (IIIa to IIIg) in *C. meleagridis* have been acknowledged [87, 95, 96]. Subtyping tools targeting the gp60 gene have been developed recently for several other human-pathogenic *Cryptosporidium* species [87]. Species and subtype identification are not necessary for clinical care and therapeutic options but are important for epidemiological surveillance and for drug investigations and clinical trials. Novel diagnostic tools and biomarkers for

ing, and requires a skilled and well-experienced microscopist [57–59].

*DOI: http://dx.doi.org/10.5772/intechopen.88387*

#### *State of the Art and Future Directions of* Cryptosporidium *spp. DOI: http://dx.doi.org/10.5772/intechopen.88387*

*Parasitology and Microbiology Research*

perspective.

to inactivation by chlorine; and without the use of filtration, it is challenging to remove it from drinking water [19–21]. *Cryptosporidium* prevalence is higher in areas lacking a sanitation infrastructure, mainly drinking water and sewage, which led the World Health Organization (WHO) to include it in the water sanitation and health programme [22]. The scarcity of sustained access to safe water creates tremendous burdens of *Cryptosporidium* diarrhoea in developing countries [23]. Treatment and diagnosis options are still not totally effective [2, 24–26]. No fully effective drug therapy or vaccine is available for *Cryptosporidium*, and the diagnosis of cryptosporidiosis has been based on the demonstration of oocysts in faeces, which present low sensibility [25]. However, the ability to culture relevant *Cryptosporidium* isolates in vitro, the development of novel gene-editing tools (knockout genes, CRISPR/ Cas9, and RNAi) [26–30], and 'omic' research (genomics, transcriptomics, and proteomics) represent essential paths towards significant advancements in the control of cryptosporidiosis [30–38]. In the future, those approaches will show a holistic view of the biology of *Cryptosporidium*. In this chapter, we present recent advances and remaining challenges regarding human cryptosporidiosis under a public health

Despite *Cryptosporidium* species infecting the epithelial cells of the gastrointestinal and respiratory epithelium tracts, human cryptosporidiosis is a usually self-limiting infection in immunocompetent individuals with a low fatality rate [39–41]. In general, onset of the symptoms occurs 5–7 days following exposure and resolves in 2–3 weeks [42]. Clinical manifestations vary from subclinical infection to watery diarrhoea, sometimes profuse. Other common symptoms include abdominal cramps, fever, flatulence, nausea, vomiting, and low-grade fever [43–45]. Clinical presentation of cryptosporidiosis in individuals with impairment of T-cell functions, mainly those with HIV infection, varies according to the level of immunosuppression, from asymptomatic disease, to transient disease, to relapsing chronic diarrhoea or even cholera-like diarrhoea that is debilitating and potentially life-threatening [46]. Spreading of infection beyond the extra-intestinal site (in the biliary or respiratory tract) has been documented in children and immunocompromised people, resulting in a potentially life-threatening disease [47, 48]. Sclerosing cholangitis and other biliary involvements are common in AIDS patients with cryptosporidiosis. Both innate and adaptive immunity of the host have major impacts on

*Cryptosporidium* has been diagnosed using a variety of approaches, such as microscopy, immunofluorescent antibody (IFA), enzyme-linked immunosorbent assay (ELISA), and DNA-based detection methods [18]. However, identification of the parasite's morphologic features through examination of stool smears is widely employed in diagnostic laboratories, particularly in resource-limited health systems. The oocysts are shed intermittently [49]; therefore, three faecal samples collected on alternate days are recommended. To maximise the recovery of oocysts, Sheather's sucrose flotation, saturated salt flotation, and Allen and Ridley's formol-ether method are the stool concentration techniques most frequently used prior to the use of the microscopy staining technique [50, 51]. Stain differential is required due to the small size of the specimen (ranging from 4 to 6 μm), similar in shape to yeasts and faecal debris [52]. Safranin-methylene blue, Kinyoun Ziehl-Neelsen, and dimethyl sulfoxide-carbol fuchsin are the most commonly used stain methods [11, 53–55]. However, in the absence of staining solution, phase contrast microscopy has proven to be highly specific for the detection of *Cryptosporidium* oocysts in human stool

**2. Clinical perspective, diagnosis, and treatment**

the severity of cryptosporidiosis and its prognosis.

**334**

samples [56]. In general, conventional microscopy lacks sensitivity, is time-consuming, and requires a skilled and well-experienced microscopist [57–59].

Direct fluorescent antibody tests (DFAs), enzyme-immunoassays (EIAs), and rapid immunochromatographic assays (dipsticks) are commercially available [60–63]. The EIA kits have been evaluated with human stool specimens only, presumably from patients infected with *C. hominis* or *C. parvum*. The direct fluorescent antibody tests have been widely used for the detection of *Cryptosporidium* in faecal smears, water, and food [60, 62–66]. However, the antigenic variability of oocyst wall epitopes contributes to reducing specificity, and the sensibility of all immunological-based methods is low. High specificity (99–100%) has been generally reported for EIA kits. Sensitivities, however, have been reported to range from 70 to 100% [62–65]. Dipsticks and EIAs are available for individual and for all-in-one tests for *Giardia*, *Cryptosporidium*, and *Entamoeba histolytica* [66–69]. The tests are fast and easy to perform. However, EIA kits and rapid format assays present a potential problem with false positives, so results need to be interpreted and evaluated with caution [70]. To overcome these barriers, one of the most notable advances in public health in recent decades has been the development of tools based on molecular biology for the diagnosis of infectious diseases. These polymerase chain reaction (PCR) techniques have enabled specific sensitive detection of oocysts (a single oocyst) in clinical and environmental samples [71–77]. Examples of such techniques include conventional PCR, quantitative PCR real time, and high-resolution melt. A wide variety of PCR methods targeting different genes have been developed for the detection of *Cryptosporidium* at the species/genotype/subtype levels. However, no targeted tests have been patterned for the detection of *Cryptosporidium* in clinical laboratories. Recently, the simultaneous qualitative detection and identification of multiple viral, parasitic (including *C. parvum* and *C. hominis*), and bacterial nucleic acids in human stool specimens were approved by the Food and Drug Administration (FDA) [78]. In general, PCR tools solely amplify the DNA of *C. parvum*, *C. hominis*, *C. meleagridis*, and species/genotypes closely related to *C. parvum* [18]. For genotyping, nested PCR-RFLP was the most commonly used method in the past. Nowadays, DNA sequencing of 18S has been required to reliably detect all *Cryptosporidium* spp. The HSP70 and COWP targets fail to detect the DNA of *C. felis*, *C. canis*, and *C. muris* [79]. Subtyping tools are indispensable from the epidemiological point of view and are helpful in knowing the possible transmission routes of *Cryptosporidium* species and zoonotic potential of the parasite. Several subtyping tools have been developed to evaluate the diversity within *C. parvum* or *C. hominis*, including analysis of the microsatellite, GP-60 gene, HSP70 gene, 47-kDa protein, small double-stranded (ds) RNA virus, serine repeat antigen, and T-rich gene fragment [73, 80–85]. The 18S ribosomal RNA (rRNA) gene and the hypervariable 60-kDa glycoprotein (gp60) gene have been widely used as targets to identify species and track transmission [18, 86, 87]. The 60-kDa glycoprotein (gp60, also known as gp40/gp15) gene presents a wide genetic heterogeneity in the number of trinucleotide repeats (TCA, TCG, or TCT). This gene encodes a precursor protein that is cleaved to produce mature cell surface glycoproteins (gp45/gp40 and gp15) implicated in the attachment to, and invasion of, enterocytes [18, 87]. Identification of subtypes using GP60 subtype families has revealed the subtype families (Ia-Ik) in *C. hominis* [87–91] and two zoonotic subtypes (IIa, IId), subtypes (IIb, IIc, IIe, IIf, IIi, IIj-IIt) in *C. parvum* [4, 87, 92–94], and subtype families (IIIa to IIIg) in *C. meleagridis* have been acknowledged [87, 95, 96]. Subtyping tools targeting the gp60 gene have been developed recently for several other human-pathogenic *Cryptosporidium* species [87]. Species and subtype identification are not necessary for clinical care and therapeutic options but are important for epidemiological surveillance and for drug investigations and clinical trials. Novel diagnostic tools and biomarkers for

cryptosporidiosis, which could also be used for therapeutic or vaccine trials, are necessary for accurate identification.

Current treatment options for cryptosporidiosis are limited. So far, there is no vaccine against *Cryptosporidium* [97]*,* and nitazoxanide (NTZ) is the only drug approved by the FDA for treatment of cryptosporidiosis in children and immunocompetent adults [98]. However, it is not effective without an appropriate immune status and, consequently, is ineffectual for the treatment of immune-compromised patients, particularly those with AIDS [25, 99]. NTZ is a nitrothiazole benzamide compound with a broad spectrum of activity against a wide range of parasites, bacteria, and viruses. In protozoa, NTZ inhibits the enzyme pyruvate ferredoxin oxidoreductase, which is essential to anaerobic energy metabolism [100]. Due to the prevalence of cryptosporidiosis, the development of novel therapeutic targets and vaccines against *Cryptosporidium* spp. is a public health priority. The ongoing need to develop new anti-cryptosporidial drugs has spurred the process of finding new uses for existing drugs. Repurposing drug provides an attractive alternative to drug development [101]. Two compounds, 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitor, pitavastatin and auranofin (approved for the treatment of rheumatoid arthritis), have been shown to be effective against *Cryptosporidium* in vitro [102]. Auranofin has been shown to be 10 times more potent than metronidazole against *Entamoeba histolytica*, the protozoan agent of human amoebiasis [103]. HMG-CoA and auranofin have particular promise in fasttracking for further in vivo testing in animals and humans.


**3.1 GDP-ManA biosynthesis pathway**

It is known that bacterial alginate is initially synthesized as a linear poly-Dmannuronic acid homopolysaccharide before undergoing chemical modifications and epimerization in the periplasmic space, which implies in mannuronic acid (ManA) being the main precursor molecule specifically involved in the biosynthesis of bacterial alginate. However, prior to its utilization to form the poly-D-ManA chain that initiates alginate biosynthesis, ManA must first be synthesized from fructose-6-phosphate (F6P), which is derived from the cell's central carbon metabolism, and then activated with a high-energy bond to yield the sugar nucleotide GDP-ManA [109]. All steps leading to the conversion of F6P to GDP-ManA, the basic building block of alginate, occur in the cytosol and have been extensively analyzed and reported in the past, and a summary of this process is shown in **Figure 3**.

Three enzymes are directly involved in GDP-ManA biosynthesis, namely, AlgA (isomerase/pyrophosphorylase, AlgC (phosphomutase), and AlgD (dehydrogenase). GDP-ManA biosynthesis begins with the conversion of F6P to mannose-6-phosphate (M6P). This reaction is catalyzed by the bifunctional enzyme AlgA through its phosphomannose isomerase (PMI) activity [110]. Through the formation of M6P,

#### **Figure 3.**

*Biosynthesis of guanosine diphosphate-mannuronic acid (GDP-ManA), the precursor molecule for bacterial alginate biosynthesis. AlgA: bifunctional phosphomannose isomerase/GDP-Mannose pyrophosphorylase; AlgC: phosphomannomutase; AlgD: GDP-mannose dehydrogenase; PMI: phosphomannose isomerase activity; GMP: guanosine-diphosphomannose pyrophosphorylase activity; F6P: D-fructose-6-phosphate; M6P: D-mannose-6-phosphate; M1P: D-mannose-1-phosphate; GDP-Man: guanosine diphosphate D-mannose; GDP-ManA: guanosine diphosphate D-mannuronic acid.*

F6P-derived carbon molecules are committed to the alginate biosynthetic pathway, and M6P is rapidly converted to mannose-1-phosphate (M1P) by the Mg2+-dependent phosphomannomutase AlgC [111], a crucial enzyme not only for the biosynthesis of alginate but also required for the production other exopolysaccharides [112], lipopolysaccharides, and rhamnolipids [113, 114]. AlgA is required once again in the next reaction step, however, it will now employ its guanosine-diphosphomannose pyrophosphorylase (GMP) activity to convert M1P to GDP-mannose, simultaneously hydrolyzing GTP and pyrophosphate (PPi). GDP-ManA is finally obtained by the irreversible oxidation of GDP-mannose by AlgD, an NAD+ -dependent guanosinediphospho-D-mannose dehydrogenase that catalyzes the limiting step and major control point of alginate biosynthesis [41, 115, 116].

#### **3.2 Biosynthesis of the poly-D-ManA linear chain**

Polymerization of GDP-ManA precursors to polymannuronate is carried out by two enzymes located at the inner membrane of bacteria, interfacing the cytosolic material with the other periplasmatic proteins in the alginate biosynthetic complex. Both enzymes, namely Alg8 and Alg44, are indispensable for alginate production [65, 69, 117]. Alg8 is believed to be the catalytic subunit of alginate polymerase since it shares homologies with class II glycosyltransferases [118], which are responsible for the transfer of sugar units from an activated monosaccharide donor to an acceptor molecule. Similarities in the polymerization mechanism of alginate with the biosynthesis of other polysaccharides have been suggested [119], and since the catalytic mechanism of class II glycosyltransferases requires an inversion of the anomeric configuration at the reaction center, in order to yield β-linked products such as the nascent alginate polymer (poly-β-D-ManA) the donor substrates must be and α-linked sugar nucleotide (i.e.: GDP-α-D-ManA) [69] (**Figure 4**). Membrane topology analysis of Alg8 has shown that this protein has at least four transmembrane helices, a short periplasmic loop, and an extended cytoplasmic loop containing the glycosyltransferase domain [118–120]. It has been reported that *alg8* overexpression in both *A. vinelandii* and *P. aeruginosa* greatly increases alginate production in comparison to wild-type strains [80]. The molecular weight of alginate polymers has also been shown to be higher when *alg8* expression increases in *A. vinelandii* [121].

The copolymerase Alg44 is the most controversial protein in the entire alginate biosynthesis complex. It is mostly accepted as being a transmembrane protein localized in the bacterial inner membrane and bears a c-di-GMP-binding PilZ domain at its cytosolic N-terminal portion [79, 103, 104]. However, because its complete structure has not yet been determined, and due to its homology with MexA, a periplasmic membrane fusion protein involved in the multi-drug efflux system [122], some reports have proposed that Alg44 lacks transmembrane domains and instead participates in the multi-protein scaffold formation of the alginate biosynthetic complex bridging Alg8 in the inner membrane to the porin (AlgE or AlgJ) in the outer membrane [79, 112, 117]. As previously stated, however, Alg44 is required for the polymerization of ManA together with Alg8, and its regulatory function via the second messenger c-di-GMP in the Alg8-Alg44 complex has been demonstrated [45, 123]. One particular c-di-GMP synthesizing protein, namely MucR, has been shown to specifically influence the alginate biosynthesis in both *P. aeruginosa* [124] and *A. vinelandii* [125], and mutations in the PilZ domain of Alg44 has resulted in the loss of alginate production [112].

**Figure 4.**

*Proposed mechanism reaction for the polymer-level epimerization of β-D-ManA into α-L-GulA by the periplasmic C5-mannuronan epimerase AlgG.*

#### **3.3 Periplasmic chemical modifications of the polymannuronic acid**

After polymerization by the Alg8-Alg44 complex, the nascent poly-D-ManA chain is directed across a multi-enzymatic scaffold structure situated in the periplasmic space, where ManA units may suffer epimerization or O-acetylation [10, 65, 80]. The enzymes involved in these processes are the best-analyzed proteins in the biosynthesis process of alginate [126, 127], and the understanding of their reaction mechanisms is an important factor to enable the production of tailored polymers [10, 128]. Although neither epimerization nor O-acetyl-esterification is essential for the bacterial production of alginate, these chemical modifications significantly alter the physicochemical properties of the polymer [31, 32, 38].

The O-acetylation of alginate is a process naturally occurring only in bacteria. During its transit through the periplasm, some ManA units of the polymannuronate chain are O-acetyl-esterified at positions C-2 and/or C-3 by the combined activity of four enzymes: AlgX, AlgF, AlgI, and AlgJ/AlgV [10, 65, 69, 77, 110]. The acetyl groups from the donor molecules (probably acetyl-CoA, but still undetermined), are transported from the cytosol to the periplasm by AlgI, a 7-helical transmembrane protein with homology to DltB (D-alanyltransferase) in *Bacillus subtilis* [76], to AlgJ (in *P. aeruginosa*) or AlgV (in *A. vinelandii*). AlgJ/AlgV is found on the periplasm, closely associated with AlgI, and also anchored in the cytoplasmic membrane presumably by its hydrophobic signal peptide [129]. It shows high homology with AlgX, another protein present in the alginate biosynthesis complex. The structure of AlgX has been shown to have an N-terminal hydrolase domain involved in the acetylation of alginate, and a C-terminal portion where a sugar-binding domain is possibly involved in the binding and orientation of the polymannuronate chain [78, 130]. AlgJ/AlgV and AlgX show about 70% analogy [76, 77], very similar topology [129], and are both required for the esterification of alginate. AlgF has no sequence homology with any other proteins involved in the O-acetylation [10], and *algF*− mutants of *A. vinelandii* have been shown to produce non-acetylated alginate [131]. The order of transfer of the acetyl donor to each of the acetylation-related enzymes is uncertain, but the fact is that AlgJ/V performs is directly involved in the O-acetylation of ManA residues and that AlgF serves presumably an accessory role due to the lack of identifiable catalytic residues [129]. Moreover, it is clear that AlgX is also able to catalyze the direct O-acetylation of alginate, receiving the acetate or acetyl donor molecules either from AlgJ/V or AlgF [129]. AlgX has also been shown to interact with MucD in some sort of undetermined regulatory mechanism, which affects the formation of alginate [65, 69, 132].

The esterification of ManA units on hydroxyl groups at C-2 and/or C-3 prevents degradation of the growing alginate polymer by AlgL, an alginate lyase present in the periplasm which will not be discussed in detail herein. O-acetylation also hampers the epimerization of esterified ManA units by AlgG, which is a C5-mannuronan epimerase of the alginate biosynthesis complex and catalyzes the epimerization of β-D-mannuronate (M-blocks) to α-L-guluronate (G-blocks) at polymer level [92]. Non-acetylated ManA units may undergo epimerization by AlgG, which causes the monosaccharide unit to suffer a dramatic change in both its anomeric configuration (from β to α) and pyranose chair conformation (from 4 C1 to 1 C4) (**Figure 4**). Such considerable structural changes have very important effects on the physicochemical properties of the mature alginate depending on the proportion and sequence of M-blocks that go through epimerization [69, 112, 133]. AlgG adopts a right-handed parallel β-helix fold, and the catalytic mechanism proposed for this epimerase is based on the β-elimination (not hydrolysis) reaction of polysaccharide lyases [134], which involves the neutralization of the carboxylate group of the uronic acid, removal of the proton at the C-5 position and cleavage of the glycosidic bond with the formation of a double bond between C-4 and C-5 for the formation of a transient glycal intermediate. For the epimerization reaction, however, glycosidic linkage remains intact, and a proton is added to the opposite side of the C-5 to form the epimer [135]. Functional analysis of AlgG mutants suggests that His319 is the catalytic base and that Arg345 is responsible for neutralizing the carboxylic groups during the epimerase reaction. It has also been proposed that water is the likely catalytic acid [134].

Polymer-level epimerization of monosaccharide units is rarely found in nature, and thus far has only been described for the biosynthesis of alginate (both microbial and algal) and glycosaminoglycans (animal origin) [136]. Hence, AlgG is a very exclusive and extraordinary enzyme found in both *P. aeruginosa* and *A. vinelandii*. These enzymes show an optimum pH for activity between 6.0 and 7.5 and share about 60% sequence identity [134, 135]. However, differently from Pseudomonads, the *A. vinelandii* genome also encodes a family of seven extracellular Ca2+-dependent epimerases (AlgE1–AlgE7), which will be discussed later in this chapter.

### **3.4 Secretion of the mature alginate chain**

The two remaining proteins that are part of the alginate biosynthesis complex are AlgK and AlgE/AlgJ (*Pseudomonas*/*Azotobacter*). AlgK is a lipoprotein of unclear function found anchored into the inner leaflet of the bacterial outer membrane [137]. It is hypothesized that AlgK may have a protective role, guiding the nascent alginate polymer through the biosynthetic multi-protein complex in the periplasmic space, and preventing it from being degraded by lyases, since the lack of AlgK has been shown to result in the secretion of free uronic acids and/or short-chain alginate products [78, 132], showing that AlgK an essential element for the biosynthesis of full-length alginate and the development of the mucoid phenotype in *P. aeruginosa* [138]. Additionally, AlgK is proposed to interact with both Alg44, stabilizing the Alg8-Alg44 polymerase complex, and the porin protein AlgE/AlgJ [112, 137]. There is some evidence to suggest that AlgK is involved in the localization of AlgE/AlgJ to the outer membrane [139]. Also, it has been shown that AlgK may form complexes with AlgX and MucD in the periplasmic space, possibly halting the regulatory role of MucD due to its sequestration by an AlgK/AlgX complex in the biosynthetic multiprotein scaffold [65]. The large number of interactions of AlgK with other proteins may be due to its structure containing several tetratricopeptide-like helical motifs which allow unspecific protein-protein interactions [139].

AlgE was first described in the early 1990s as an integral outer membrane protein strongly associated with the mucoid phenotype of *P. aeruginosa* [140, 141]. Its counterpart in *A. vinelandii*, AlgJ, shares a high degree of similarity and is thought to perform the same function as in Pseudomonads [142]. Although AlgE is classified as a member of the general diffusion porin family [143], biochemical and electrophysiological analyses suggest that this protein is specifically responsible for the passage of alginate into the extracellular environment through the outer membrane, rather than allowing the passage of small molecules and ions, and that it forms a strongly positively charged anion-selective pore that can be partially blocked by GDP-ManA [144, 145]. AlgE/ AlgJ is a monomeric 18-stranded β-barrel protein homologous to the OprD family of substrate-specific porins. In addition to its alginate transport function, it has been shown to play an important role in the assembly of the multiprotein complex involved in alginate biosynthesis [145, 146].

Secretion usually indicates the end of the bacterial alginate biosynthetic pathway, especially in Pseudomonads, and a schematic representation of the entire biosynthetic/regulatory process can be found in **Figure 5**. After release into the environment, alginate may undergo further non-enzymatic structural modifications such as de-O-acetylation and/or depolymerization, albeit to a very modest extent, depending on the chemical conditions in the medium surrounding the cell. However, the alginate produced by *Azotobacter vinelandii* can undergo very significant enzymatic processing extracellularly due to the presence of C5-mannuronan epimerases and alginate lyases, which are also secreted by this bacterium.

### **3.5 Post-secretion chemical modifications of alginate**

### *3.5.1 Extracellular epimerases*

As previously described, *Azotobacter vinelandii* encodes seven different C5-ManA epimerases (AlgE1–AlgE7) that are exported to the extracellular environment and are able to promote polymer-level epimerization of D-ManA units into L-GulA on

**Figure 5.**

*Schematic representation of bacterial alginate biosynthesis, regulation, periplasmic transportation/modification, secretion, and post-secretion modification. Protein structures are merely illustrative (https://pdb101.rcsb.org/) and the nomenclature used is that established for P. aeruginosa encoded genes. (\*A. vinelandii counterparts or enzymes that are exclusively found in the Azotobacter genus are marked with an asterisk).*

alginate. Although the epimerization mechanism for these enzymes is very similar to the mechanism proposed for AlgG, extracellular epimerases have been found to be Ca2+-dependent, in contrast to the periplasmic counterpart. It has been considered that extracellular alginate epimerases are necessary for *A. vinelandii* cells for the creation of a diffusion barrier against O2 [84] as a protective mechanism for its oxygen-sensitive nitrogenase complex and that enzymes of the AlgE family play an important role in the encystment process induced by adverse environmental conditions [147]. Also, only alginate-producing cells may produce cysts that can germinate after storage [65]. Alginates produced by Pseudomonads show lower L-GulA content in comparison to the *A. vinelandii* polymers [38, 65, 70]. Furthermore, alginates isolated from *Azotobacter* spp. contain G-blocks (consecutive L-GulA residues), while in *Pseudomonas* spp. alginates have been found to contain only single L-GulA units on their backbone structure [70]. It is proposed that post-secretion epimerization of alginate by AlgE1–7 is the reason for the higher amounts of L-GulA found in the alginate produced by *A. vinelandii* [10, 65, 118]. As the G-block amounts increase, so does the affinity of this polymer for divalent ions, which are responsible for the formation of cross-links between alginate chains creating a more rigid gel surrounding *A. vinelandii* cells, being physicochemically much different from the lower viscosity alginate produced by bacteria of the genus *Pseudomonas* [10]. Even though consecutive GulA units have not been found in alginates produced by Pseudomonads to date, it has been reported that *P. syringae* pv *glycinea* encodes a C5-mannuronan epimerase designated PsmE, which was found to be able to introduce G-blocks on alginate *in vitro* [65, 148]. Differently from the *A. vinelandii* extracellular epimerases, PsmE is able to epimerize acetylated D-ManA units after removing O-acetyl groups, thus showing a bifunctional deacetylase/epimerase characteristic [148].

### *Bacterial Alginate Biosynthesis and Metabolism DOI: http://dx.doi.org/10.5772/intechopen.109295*

Isomerases of the AlgE family have been shown to be differentially expressed during the life cycle of *A. vinelandii* and to generate different patterns of G-blocks on alginate [147]. Five of the *algE* genes of *A. vinelandii* are found clustered in the genome, while *algE5* and *algE7* are found isolated elsewhere (see **Figure 1**), and each correspondingly encoded enzyme appears to have different specificities in terms of substrate preferences and non-random formation of G- and GM-blocks on the alginate backbone [149]. These extracellular alginate epimerases from *A. vinelandii* are about 70% identical but share less than 10% sequence identity with AlgG [134]. The characterization of AlgEs from *A. vinelandii* has been made at certain levels after cloning, heterogeneous expression, and purification, and three putative AlgE-like isomerases have also been described and characterized in *A. chroococcum* [58]. All of the AlgE isomerases are structurally organized into two types of alternating modules, each with different roles for these enzymes, namely: A-module and R-module (**Table 2**). It is known that the catalytic site is located in the A-modules, each comprising about 385 amino acids (with a primary amino acid sequence homology of ~85%). These are always followed by 1–4 R-modules (~155 amino acids each), which, in turn, are thought to modulate the epimerization rate by stabilizing the process with an extended alginate binding site [150, 151], and have also been shown to play a role in the epimerization pattern of the final alginate product [152]. A-modules have been calculated to bind 11 uronic acid units, while each of the R-modules can bind five units with different binding strengths, which also affects the processivity of a particular AlgE enzyme [151]. Four to seven copies of a nine amino acid long motif are located in the N-terminal region of the R-modules and are known to be Ca2+-binding sites [153]. Calcium ions are important not only for the structural integrity of the protein but also for neutralizing the charges of ManA during the epimerization reaction, in a similar manner Arg345 functions in AlgG [149, 150]. In addition, the last R-module of each extracellular AlgE usually contains an unstructured peptide involved in the secretion of the enzyme [151]. Despite the large sequence homology

*\*Bifunctional extracellular epimerase/lyase enzymes. A-modules: Location of the catalytic site; R-modules: Ca2+-binding sites and substrate-binding/stabilization extended sites.*

#### **Table 2.**

*Structural organization, substrate specificity, and products formed by extracellular C5-ManA epimerases of Azotobacter vinelandii.*

they share, different AlgE epimerases catalyze distinct residue sequences in the alginate product. It has been proposed that the different epimerization patterns depend on the concerted action of both the A- and R-modules, rather than only the catalytic activity of A-modules [150].

Although the extracellular C5-epimerases AlgE1–7 play an important role in the addition of L-GulA units to the alginate produced by *A. vinelandii*, these enzymes have also been cloned and isolated to be used for *in vitro* modification of macroalgal-derived alginate [128, 149]. AlgE1 and AlgE3 are the only epimerases that contain two catalytic A-modules, one of which introduces single G units that form alternating GM sequences, while the other can generate G-blocks with consecutive GulA units [65, 154]. AlgE2 and AlgE5 both consist of an A-module followed by four R-modules, and they primarily introduce short G-blocks on the alginate chain [149]. The same module organization is found in AlgE6, which, however, is able to introduce longer G-blocks on polymannuronic acid [147, 153]. AlgE4 consists of only one A-module and one R-module and is the smallest enzyme among the extracellular C5-epimerases. Although it inserts a high proportion of GulA units into the alginate chain, this enzyme is unable to attach successive G-blocks to the polymer [65, 149]. AlgE7 generally produces the same GM alternating products as AlgE1, AlgE3, and AlgE4. However, AlgE7 and AlgE2 have also been shown to have present lyase activity (breaks G-MM and G-GM bonds), which is thought to share the same active site with epimerase activity [86]. These bifunctional enzymes can degrade the alginate chain after the epimerization reaction, but at different rates. AlgE2 is thought to have low lyase activity and has been shown to perform 1 to 3 elimination reactions per thousand epimerized units [149, 155]. In contrast, AlgE7 has a much more pronounced lyase activity, with a predicted number of 3–4 glycosyl bond breaks per 26 conversions of D-ManA to L-GulA units [86].

Regarding the reaction mechanisms of Ca2+-dependent AlgE epimerases, two reaction modes have been proposed: (1) the preferred attack mode, in which the enzyme preferentially attacks M-units with G residues as the nearest neighbor and detaches from the substrate after each epimerization reaction [58, 149], and (2) the processive mode, in which there is a relative displacement of the enzyme and the alginate chain that allows the conversion of the nearest M-unit without dissociation of the enzyme-substrate complex [149, 153]. AlgE4 has been reported to have a processive mode of action by sliding along the substrate chain and epimerizing every other M residue [156], and this is thought to be due to the 180°-turn between the successive β-(1 → 4)-linked ManA units in the alginate polymer [153]. AlgE2 follows the mechanism of preferred attack mode and is related to the Ca2+ concentration and substrate type of the blocks found in alginate [149, 155]. The remaining AlgE epimerases appear to behave according to either a preferred attack mode or a combination of the two mechanisms [149].