Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against *Pseudomonas aeruginosa* and Its Biofilm Genes Expression

*Azza S. El-Demerdash and Neveen R. Bakry*

## **Abstract**

A total of 100 broiler chickens were examined for the presence of *Pseudomonas aeruginosa* by standard microbiological techniques. Susceptibility pattern for amikacin and cefotaxime was performed by Kirby-Bauer and microdilution assays. Then, checkerboard titration in trays was applied and FIC was measured to identify the type of interaction between the two antibiotics. The ability of isolates to form in vitro biofilm was detected by two methods, one qualitative (CRA) and the other quantitative (MTP), followed by investigating the effect of each antibiotic alone and in combination on the expression of biofilm genes. The overall isolation percentage of *P. aeruginosa* was 21%. Resistance to each antibiotic was more than 50%; the range of cefotaxime MIC was 8–512 μg/ml, while amikacin MIC range was 1–64 μg/ml. The FIC index established a synergistic association between tested two drugs in 17 (81%) of isolates and the remaining represent partially synergism. The qualitative technique showed that only 66.6% of the isolates were considered biofilm producers, while the quantitative technique showed that 90.4% of the isolates were biofilm producers. Further to RT-PCR investigation, significant repression against biofilm-forming genes (*filC*, *pelA*, and *pslA*) was observed for the combination of antibiotics when compared with monotherapy.

**Keywords:** *P. aeruginosa*, cefotaxime, amikacin, combination therapy, biofilm, gene expression

## **1. Introduction**

The infection with *Pseudomonas aeruginosa* is responsible for humanity in poultry and clinical signs including respiratory signs and septicaemia. *P. aeruginosa* produces dyspnea and cheesy deposits on the serous surfaces lining the air sacs and peritoneal cavity and also congestion of the internal organs, perihepatitis, and pericarditis [1]. *Pseudomonas* species are not related to disease entity except *Pseudomonas aeruginosa* that has been associated with infection in both man and animals. The disease of pseudomonas induces a significant economic loss to the farm by causing high mortality of newly hatched chickens and death of embryo at

a later stage [2]. Furthermore, *Pseudomonas aeruginosa* shows innate resistance to almost antibiotics in recent years [3, 4].

Due to this intrinsic resistance to antibiotics, its ability to easily develop new resistance, its ability to create biofilms, and the recent decline in drug discovery programs, *P. aeruginosa* infections have become an urgent worldwide health concern [3, 5]. Recent efforts to focus on this rising challenge comprise repositioning screens to recognize commercially permitted drugs with novel antimicrobial activity [6–9] and combinatorial drug screens to categorize combinations of traditional antibiotics and novel repositionable modulators [10, 11].

Concomitant use of antibiotics (combination therapy) is recommended for severe infections when *P. aeruginosa* is the suspected pathogen, to prevent the development of resistance during treatment and to achieve a wide spectrum of activity. In addition to preventing the development of resistance, the combined use of antibiotics (as cephalosporins and aminoglycosides) may have synergistic effects and may reduce the occurrence of side effects, since each drug is used at a lower dose than would be used for monotherapy [12].

Concerning bacterial biofilms, Batoni et al*.* [13] and Grassi et al. [14] proved a strong interaction between the effectiveness of combination therapy and biofilms formed by *P. aeruginosa*. Therefore, the present study concerned the effect of cefotaxime, amikacin singly, and in combination besides validating the activity of them on biofilm expression of the obtained *P. aeruginosa* isolates.

## **2. Material and methods**

## **2.1 Sampling and isolate characterization**

A total of 500 samples of the liver, heart, kidney, spleen, and lung (100 each) was aseptically collected from 100 freshly dead and diseased with respiratory manifestations broiler chickens from different ages and localities in Sharkia province, Egypt, from November 2018 to February 2019. All samples were subjected to conventional methods for isolation and identification of pseudomonas recommended by the Health Protection Agency [15]. *Pseudomonas aeruginosa* isolates were further identified with API20E kits (BioMérieux, France).

## **2.2 Antibiotic susceptibility testing**

## *2.2.1 Disk diffusion method*

The antimicrobial susceptibility test of the isolates was performed by Kirby-Bauer disk diffusion test [16]. In brief, each test isolate was swabbed uniformly onto the surface of Mueller-Hinton agar plates. Antibiotic sterile disks including cefotaxime (CTX: 30 μg) and amikacin (AK: 30 μg) were then placed on to the agar surface of the plate. Following incubation, the inhibition zones, in millimeters, were measured in duplicate and scored as sensitive, intermediate, and resistant categories by the critical breakpoints recommended by the Clinical and Laboratory Standards Institute (CLSI) [17].

#### *2.2.2 Preparation of antibiotic stock solution*

Standard powder forms of cefotaxime and amikacin were stored at 4°C till usage. The stock solution of each antibiotic was prepared by weighing and consequently

**123**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

*2.2.3 Determination of the minimum inhibitory concentration (MIC)*

was added to each well at a final concentration of 5 × 105

(in 0.5 mL of broth) should be approximately 2 × 105

to produce a final inoculum of 1 × 105

*2.2.5 Estimation of FIC index*

*2.3.1 Congo red agar test*

and < 4), and antagonistic (ƩFIC > = 4) [20].

**2.3 Phenotypic characterization of biofilm production**

dissolving suitable amounts of the antibiotics reaching a concentration of 1000 μg/mL

MIC values of antibiotics were determined by the microdilution method following the recommendations of Papich [18]. Stock solutions of antibiotics were prepared and added to the bottom of a 96-well microtiter plate (Nunc Inc., Roskilde, Denmark). 100 mL of this solution was added to the first well of the 96-well plate and serially diluted. 100 mL of an overnight culture of *P. aeruginosa*

units per milliliter). The microtiter plates were incubated at 35°C for 24 h and the MIC determined as the lowest concentration of antibiotics showing no visible

The synergistic effect of the antibiotic combinations was detected using a checkerboard dilution assay [19]. The initial concentration of each drug should be fourfold greater than the desired concentration (MIC concentration) and then diluted twofold. In a screw cap test tube, 0.25 mL of broth of each two drugs to be tested was added to 0.5 mL of broth containing a suspension of the organism to be tested to reach the final volume of 1 mL. The inoculum of the bacterial suspension

volume of the antimicrobial solutions. Each test composed of 36 tubes set horizontally and vertically, 6 rows in one direction contained twofold serial dilutions of antibiotic 1, and 6 rows in the other direction contained twofold serial dilutions of antibiotic 2; two additional rows contained twofold serial dilution of antibiotic 1 or antibiotic 2 alone. The tubes were incubated at 37°C for 24 and 48 h, the tubes were read as those showing turbidity (+) and those showing no turbidity (−). A fractional inhibitory concentration index was used to interpret the results.

FIC of each agent was calculated by dividing the MIC of the drug in combination by the MIC of the drug alone. The sum of both FICs (ƩFIC = FIC of antibiotic A + FIC of antibiotic B) in each well was used to categorize the combined activity of antimicrobial agents at the given concentrations as synergistic (ƩFIC <= 0.5), partially synergistic (ƩFIC >0.5 and < 1), additive (ƩFIC = 1), indifferent (ƩFIC >1

Freeman et al. [21] have described a simple qualitative method to detect biofilm production by using a Congo red agar (CRA) medium. CRA medium was prepared with brain heart infusion agar (Oxoid, UK) 37 g/L, sucrose 50g/L, and Congo red indicator (Oxoid, UK) 8 g/L. The first Congo red dye was prepared as a concentrated aqueous solution and autoclaved (121°C for 15 min) separately from the other medium constituents. Then, it was added to the autoclaved brain heart

CFU/mL (colony-forming

colony-forming unit (CFU)

CFU per mL after the addition of an equal

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

in Mueller-Hinton broth.

bacterial growth.

*2.2.4 Test for synergism*

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*

dissolving suitable amounts of the antibiotics reaching a concentration of 1000 μg/mL in Mueller-Hinton broth.

## *2.2.3 Determination of the minimum inhibitory concentration (MIC)*

MIC values of antibiotics were determined by the microdilution method following the recommendations of Papich [18]. Stock solutions of antibiotics were prepared and added to the bottom of a 96-well microtiter plate (Nunc Inc., Roskilde, Denmark). 100 mL of this solution was added to the first well of the 96-well plate and serially diluted. 100 mL of an overnight culture of *P. aeruginosa* was added to each well at a final concentration of 5 × 105 CFU/mL (colony-forming units per milliliter). The microtiter plates were incubated at 35°C for 24 h and the MIC determined as the lowest concentration of antibiotics showing no visible bacterial growth.

## *2.2.4 Test for synergism*

*Gene Expression and Phenotypic Traits*

almost antibiotics in recent years [3, 4].

ics and novel repositionable modulators [10, 11].

dose than would be used for monotherapy [12].

**2.1 Sampling and isolate characterization**

**2.2 Antibiotic susceptibility testing**

*2.2.2 Preparation of antibiotic stock solution*

*2.2.1 Disk diffusion method*

Institute (CLSI) [17].

**2. Material and methods**

them on biofilm expression of the obtained *P. aeruginosa* isolates.

further identified with API20E kits (BioMérieux, France).

a later stage [2]. Furthermore, *Pseudomonas aeruginosa* shows innate resistance to

Due to this intrinsic resistance to antibiotics, its ability to easily develop new resistance, its ability to create biofilms, and the recent decline in drug discovery programs, *P. aeruginosa* infections have become an urgent worldwide health concern [3, 5]. Recent efforts to focus on this rising challenge comprise repositioning screens to recognize commercially permitted drugs with novel antimicrobial activity [6–9] and combinatorial drug screens to categorize combinations of traditional antibiot-

Concomitant use of antibiotics (combination therapy) is recommended for severe infections when *P. aeruginosa* is the suspected pathogen, to prevent the development of resistance during treatment and to achieve a wide spectrum of activity. In addition to preventing the development of resistance, the combined use of antibiotics (as cephalosporins and aminoglycosides) may have synergistic effects and may reduce the occurrence of side effects, since each drug is used at a lower

Concerning bacterial biofilms, Batoni et al*.* [13] and Grassi et al. [14] proved a strong interaction between the effectiveness of combination therapy and biofilms formed by *P. aeruginosa*. Therefore, the present study concerned the effect of cefotaxime, amikacin singly, and in combination besides validating the activity of

A total of 500 samples of the liver, heart, kidney, spleen, and lung (100 each) was aseptically collected from 100 freshly dead and diseased with respiratory manifestations broiler chickens from different ages and localities in Sharkia province, Egypt, from November 2018 to February 2019. All samples were subjected to conventional methods for isolation and identification of pseudomonas recommended by the Health Protection Agency [15]. *Pseudomonas aeruginosa* isolates were

The antimicrobial susceptibility test of the isolates was performed by Kirby-Bauer disk diffusion test [16]. In brief, each test isolate was swabbed uniformly onto the surface of Mueller-Hinton agar plates. Antibiotic sterile disks including cefotaxime (CTX: 30 μg) and amikacin (AK: 30 μg) were then placed on to the agar surface of the plate. Following incubation, the inhibition zones, in millimeters, were measured in duplicate and scored as sensitive, intermediate, and resistant categories by the critical breakpoints recommended by the Clinical and Laboratory Standards

Standard powder forms of cefotaxime and amikacin were stored at 4°C till usage. The stock solution of each antibiotic was prepared by weighing and consequently

**122**

The synergistic effect of the antibiotic combinations was detected using a checkerboard dilution assay [19]. The initial concentration of each drug should be fourfold greater than the desired concentration (MIC concentration) and then diluted twofold. In a screw cap test tube, 0.25 mL of broth of each two drugs to be tested was added to 0.5 mL of broth containing a suspension of the organism to be tested to reach the final volume of 1 mL. The inoculum of the bacterial suspension (in 0.5 mL of broth) should be approximately 2 × 105 colony-forming unit (CFU) to produce a final inoculum of 1 × 105 CFU per mL after the addition of an equal volume of the antimicrobial solutions. Each test composed of 36 tubes set horizontally and vertically, 6 rows in one direction contained twofold serial dilutions of antibiotic 1, and 6 rows in the other direction contained twofold serial dilutions of antibiotic 2; two additional rows contained twofold serial dilution of antibiotic 1 or antibiotic 2 alone. The tubes were incubated at 37°C for 24 and 48 h, the tubes were read as those showing turbidity (+) and those showing no turbidity (−). A fractional inhibitory concentration index was used to interpret the results.

## *2.2.5 Estimation of FIC index*

FIC of each agent was calculated by dividing the MIC of the drug in combination by the MIC of the drug alone. The sum of both FICs (ƩFIC = FIC of antibiotic A + FIC of antibiotic B) in each well was used to categorize the combined activity of antimicrobial agents at the given concentrations as synergistic (ƩFIC <= 0.5), partially synergistic (ƩFIC >0.5 and < 1), additive (ƩFIC = 1), indifferent (ƩFIC >1 and < 4), and antagonistic (ƩFIC > = 4) [20].

#### **2.3 Phenotypic characterization of biofilm production**

#### *2.3.1 Congo red agar test*

Freeman et al. [21] have described a simple qualitative method to detect biofilm production by using a Congo red agar (CRA) medium. CRA medium was prepared with brain heart infusion agar (Oxoid, UK) 37 g/L, sucrose 50g/L, and Congo red indicator (Oxoid, UK) 8 g/L. The first Congo red dye was prepared as a concentrated aqueous solution and autoclaved (121°C for 15 min) separately from the other medium constituents. Then, it was added to the autoclaved brain heart

infusion agar with sucrose at 55°C. In this test, the Congo red dye was used as a pH indicator, showing black coloration at pH ranges between 3.0 and 5.2. Plates with the Congo red agar medium were seeded and incubated in an aerobic environment for 24–48 h at 37°C. Isolates were interpreted according to their colony phenotypes. Black colonies with dry constancy and rough surface and edges were suspected as a positive sign of slime formation, while both black colonies with a smooth, round, and shiny surface and red colonies of dry texture and rough edges and surface were suspected as intermediate slime producers. Red colonies with smooth, round, and shiny surfaces were indicators for negative slime formation.

### *2.3.2 Quantitative detection of biofilm by microtiter plate method*

The biofilm assay is performed by using flat-bottom microtiter plates (Techno Plastic Products, Switzerland) as described by O'Toole [22]. *P. aeruginosa* isolates were grown at 37°C in tryptic soy broth (TSB; Oxoid, UK). The bacterial cells were then pelleted at 6000 g for 10 min, and the cell pellets were in 5 mL of fresh medium. The optical densities (ODs) of the bacterial suspensions were measured using a spectrophotometer (Model 6305, Jenway Ltd., Essex, UK) and normalized to an absorbance of 1:00 at 600 nm. The cultures were diluted 1:40 in fresh TSB, and 200 μL of cells were aliquoted into a 96-well polystyrene microtiter plate and inoculated for 24 h at 37°C without agitation. After incubation at 37°C for 24 h, the planktonic cells were aspirated, and the wells were washed three times with sterile phosphate-buffered saline (PBS). The plates were inverted and allowed to dry for an hour at room temperature.

For biofilm quantification, 200 μL of 0.1% aqueous crystal violet solution was added to each well, and the plates were allowed to stand for 15 min. The wells were subsequently washed three times with sterile PBS to wash off the excess crystal violet. Crystal violet bound to the biofilm was extracted with 200 μL of an 80:20 (v/v) mixture of ethyl alcohol and acetone, and the absorbance of the extracted crystal violet was measured at 545 nm on ELISA reader (stat fax 2100, USA). A negative control, crystal violet binding to wells was measured for wells exposed only to the medium with no bacteria. All biofilm assays were performed in triplicate. The interpretation of biofilm production was according to the criteria described by Stepanović et al. [23]. Based on these criteria, optical density cutoff value (ODc) is defined as an average OD of negative control +3 × SD (standard deviation) of the negative control. The ability to produce biofilm of each *P. aeruginosa* isolate was classified according to the following criteria: OD ≤ ODc = not a biofilm producer, ODc < OD ≤ 2x ODc = weak biofilm producer, 2x ODc < OD ≤ 4x ODc = moderate biofilm producer, and 4x ODc < OD = strong biofilm producer.

#### **2.4 Molecular evaluation**

#### *2.4.1 DNA extraction*

DNA extraction from isolates was performed using the QIAamp DNA Mini Kit (Qiagen, Germany, GmbH) with modifications from the manufacturer's recommendations. Concisely, 10 μL of proteinase K and 200 μL of lysis buffer were added to 200 μL of the sample suspension and incubated at 56°C for 10 min. Then, 200 μL of 100% ethanol was added to the lysate followed by washing and centrifugation according to the manufacturer's recommendations. Nucleic acid was eluted with 100 μL of elution buffer.

**125**

**3. Results**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

The obtained DNA was examined for the presence of biofilm in a 25 μL reaction comprising 12.5 μL of EmeraldAmp Max PCR Master Mix (Takara, Japan), 1 μL of each primer of 20 pmol concentration, 4.5 μL of water, and 6 μL of DNA template. The reaction was implemented in an Applied Biosystems 2720 Thermal Cycler for the investigation of the presence of biofilm genes. The properties of all used primers, as well as amplicon length and cycling conditions, were synopsized by

The products of PCR were separated by electrophoresis on 1.5% agarose gel (AppliChem, Germany, GmbH) in 1× TBE buffer at room temperature using gradients of 5 V/cm. For gel analysis, 20 μL of the products were loaded in each gel slot. A GelPilot 100 bp DNA ladder (Qiagen, Germany, GmbH) and GeneRuler 100 bp ladder (Fermentas, Germany) were used to verify the size of fragments. The gel was photographed by a gel documentation system (Alpha Innotech, Biometra), and the

Biofilm gene expression was analyzed by quantitative real-time PCR (qRT-PCR), and the 16S rRNA housekeeping gene of *Pseudomonas aeruginosa* served as internal control with primer sequence F: GGGGGATCTTCGGACCTCA, R: TCCTTAGAGTGCCCACCCG to normalize the expressional levels between samples. Primers were utilized in a 25 μL reaction containing 12.5 μL of the 2× QuantiTect SYBR Green PCR Master Mix (Qiagen, Germany, GmbH), 0.25 μL of RevertAid Reverse Transcriptase (200 U/μL) (Thermo Fisher), 0.5 μL of each primer of 20 pmol concentration, 8.25 μL of water, and 3 μL of RNA template. The reaction was performed in a Stratagene MX3005P real-time PCR machine with specific conditions mentioned in **Table 2**. To estimate the variation of gene expression on the RNA of the different samples, the Ct of each sample was compared with that of the positive control group according to the "ΔΔCt" method stated by Yuan et al. [25].

Data analysis was performed by SPSS version 22 for windows. A t-test was used to detect statistical differences of the experiments including antibiotic combination treatment versus single antibiotic therapy. Moreover, one-way ANOVA was used for contrasting the influence of these remedies on the fold change of biofilm gene

*Pseudomonas* spp. were isolated from 34 of 100 examined broiler chickens (34%) as shown in **Table 3**. They were further identified by standard microbiological techniques, and an API giving an overall prevalence of 21% was identified as

expression. A P ≤ 0.05 value was suspected as statistically significant.

**3.1 The recovery rate of isolation and identification**

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

Ghadaksaz et al. [24] and listed in **Table 1**.

data were assessed through computer software.

*2.4.4 Quantitative analysis of biofilm gene expression*

*2.4.3 Analysis of the PCR products*

**2.5 Statistical analysis**

*Pseudomonas aeruginosa.*

*2.4.2 PCR amplification of biofilm virulence genes*

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*

## *2.4.2 PCR amplification of biofilm virulence genes*

The obtained DNA was examined for the presence of biofilm in a 25 μL reaction comprising 12.5 μL of EmeraldAmp Max PCR Master Mix (Takara, Japan), 1 μL of each primer of 20 pmol concentration, 4.5 μL of water, and 6 μL of DNA template. The reaction was implemented in an Applied Biosystems 2720 Thermal Cycler for the investigation of the presence of biofilm genes. The properties of all used primers, as well as amplicon length and cycling conditions, were synopsized by Ghadaksaz et al. [24] and listed in **Table 1**.

## *2.4.3 Analysis of the PCR products*

*Gene Expression and Phenotypic Traits*

hour at room temperature.

biofilm producer.

*2.4.1 DNA extraction*

100 μL of elution buffer.

**2.4 Molecular evaluation**

infusion agar with sucrose at 55°C. In this test, the Congo red dye was used as a pH indicator, showing black coloration at pH ranges between 3.0 and 5.2. Plates with the Congo red agar medium were seeded and incubated in an aerobic environment for 24–48 h at 37°C. Isolates were interpreted according to their colony phenotypes. Black colonies with dry constancy and rough surface and edges were suspected as a positive sign of slime formation, while both black colonies with a smooth, round, and shiny surface and red colonies of dry texture and rough edges and surface were suspected as intermediate slime producers. Red colonies with smooth, round, and shiny surfaces were indicators for negative slime formation.

The biofilm assay is performed by using flat-bottom microtiter plates (Techno Plastic Products, Switzerland) as described by O'Toole [22]. *P. aeruginosa* isolates were grown at 37°C in tryptic soy broth (TSB; Oxoid, UK). The bacterial cells were then pelleted at 6000 g for 10 min, and the cell pellets were in 5 mL of fresh medium. The optical densities (ODs) of the bacterial suspensions were measured using a spectrophotometer (Model 6305, Jenway Ltd., Essex, UK) and normalized to an absorbance of 1:00 at 600 nm. The cultures were diluted 1:40 in fresh TSB, and 200 μL of cells were aliquoted into a 96-well polystyrene microtiter plate and inoculated for 24 h at 37°C without agitation. After incubation at 37°C for 24 h, the planktonic cells were aspirated, and the wells were washed three times with sterile phosphate-buffered saline (PBS). The plates were inverted and allowed to dry for an

For biofilm quantification, 200 μL of 0.1% aqueous crystal violet solution was added to each well, and the plates were allowed to stand for 15 min. The wells were subsequently washed three times with sterile PBS to wash off the excess crystal violet. Crystal violet bound to the biofilm was extracted with 200 μL of an 80:20 (v/v) mixture of ethyl alcohol and acetone, and the absorbance of the extracted crystal violet was measured at 545 nm on ELISA reader (stat fax 2100, USA). A negative control, crystal violet binding to wells was measured for wells exposed only to the medium with no bacteria. All biofilm assays were performed in triplicate. The interpretation of biofilm production was according to the criteria described by Stepanović et al. [23]. Based on these criteria, optical density cutoff value (ODc) is defined as an average OD of negative control +3 × SD (standard deviation) of the negative control. The ability to produce biofilm of each *P. aeruginosa* isolate was classified according to the following criteria: OD ≤ ODc = not

a biofilm producer, ODc < OD ≤ 2x ODc = weak biofilm producer, 2x

ODc < OD ≤ 4x ODc = moderate biofilm producer, and 4x ODc < OD = strong

DNA extraction from isolates was performed using the QIAamp DNA Mini Kit (Qiagen, Germany, GmbH) with modifications from the manufacturer's recommendations. Concisely, 10 μL of proteinase K and 200 μL of lysis buffer were added to 200 μL of the sample suspension and incubated at 56°C for 10 min. Then, 200 μL of 100% ethanol was added to the lysate followed by washing and centrifugation according to the manufacturer's recommendations. Nucleic acid was eluted with

*2.3.2 Quantitative detection of biofilm by microtiter plate method*

**124**

The products of PCR were separated by electrophoresis on 1.5% agarose gel (AppliChem, Germany, GmbH) in 1× TBE buffer at room temperature using gradients of 5 V/cm. For gel analysis, 20 μL of the products were loaded in each gel slot. A GelPilot 100 bp DNA ladder (Qiagen, Germany, GmbH) and GeneRuler 100 bp ladder (Fermentas, Germany) were used to verify the size of fragments. The gel was photographed by a gel documentation system (Alpha Innotech, Biometra), and the data were assessed through computer software.

### *2.4.4 Quantitative analysis of biofilm gene expression*

Biofilm gene expression was analyzed by quantitative real-time PCR (qRT-PCR), and the 16S rRNA housekeeping gene of *Pseudomonas aeruginosa* served as internal control with primer sequence F: GGGGGATCTTCGGACCTCA, R: TCCTTAGAGTGCCCACCCG to normalize the expressional levels between samples. Primers were utilized in a 25 μL reaction containing 12.5 μL of the 2× QuantiTect SYBR Green PCR Master Mix (Qiagen, Germany, GmbH), 0.25 μL of RevertAid Reverse Transcriptase (200 U/μL) (Thermo Fisher), 0.5 μL of each primer of 20 pmol concentration, 8.25 μL of water, and 3 μL of RNA template. The reaction was performed in a Stratagene MX3005P real-time PCR machine with specific conditions mentioned in **Table 2**. To estimate the variation of gene expression on the RNA of the different samples, the Ct of each sample was compared with that of the positive control group according to the "ΔΔCt" method stated by Yuan et al. [25].

#### **2.5 Statistical analysis**

Data analysis was performed by SPSS version 22 for windows. A t-test was used to detect statistical differences of the experiments including antibiotic combination treatment versus single antibiotic therapy. Moreover, one-way ANOVA was used for contrasting the influence of these remedies on the fold change of biofilm gene expression. A P ≤ 0.05 value was suspected as statistically significant.

## **3. Results**

#### **3.1 The recovery rate of isolation and identification**

*Pseudomonas* spp. were isolated from 34 of 100 examined broiler chickens (34%) as shown in **Table 3**. They were further identified by standard microbiological techniques, and an API giving an overall prevalence of 21% was identified as *Pseudomonas aeruginosa.*


#### **Table 1.**

**127**

**Target gene**

*16S rRNA*

*pslA* *pelA*

*fliC* **Table 2.** *Target genes and cycling conditions for SYBR green rt-PCR.*

50°C, 30 min

94°C, 15 min

94°C, 15 s

52°C, 30 s 60°C, 30 s 60°C, 30 s 56.2 °C, 30 s

72°C, 30 s

94°C, 1 min

52°C, 1 min 60°C, 1 min

60°C, 1 min 56.2 °C, 1 min

Ghadaksaz et al. [24]

94°C, 1 min

Spilker et al*.* [26]

**Reverse transcription**

**Primary denaturation**

**Amplification (40**

**Secondary denaturation**

 **cycles)**

**Annealing**

**Extension**

**Secondary denaturation**

**Dissociation curve (1**

 **cycle)**

> **Annealing**

**Final denaturation**

**Reference**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

*Primer sequences, target genes, amplicon sizes, and cycling conditions.*

### *Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*


**Table 2.**

*Target genes and cycling conditions for SYBR green rt-PCR.*

*Gene Expression and Phenotypic Traits*

**126**

**Target gene**

*PslA* *PelA* *FliC* **Table 1.** *Primer sequences, target genes, amplicon sizes, and cycling conditions.*

TCCCTACCTCAGCAGCAAGC

TGTTGTAGCCGTAGCGTTTCTG

CATACCTTCAGCCATCCGTTCTTC

CGCATTCGCCGCACTCAG

TGAACGTGGCTACCAAGAACG

180

94°C, 5 min

94°C, 30 s

56.2 °C, 30 s

72°C, 30 s

72°C, 7 min

786

94°C, 5 min

94°C, 30 s

60°C, 40 s

72°C, 45 s

72°C, 10 min

656

94°C, 5 min

**Primer sequences**

**Amplified segment (bp)**

**Primary** 

**Amplification (35**

 **cycles)**

**Annealing**

**Extension**

**Final** 

**extension**

**denaturation**

**Secondary** 

**denaturation**

94°C, 30 s

60°C, 40 s

72°C, 45 s

72°C, 10 min


#### **Table 3.**

*The incidence of* Pseudomonas aeruginosa *isolated from examined samples.*

## **3.2 Antimicrobial activity**

According to the disk diffusion method, 76.2% of isolates were resistant to cefotaxime, 14.3% were intermediate, and 9.5% were sensitive. Regarding amikacin, 57.2% of isolates were resistant, 9.5% were intermediate, and 33.3% were sensitive. Of interest, 57.2% of isolates were resistant to both tested antibiotics.

According to the microdilution assay, the range of cefotaxime MIC was 8–512 μg/mL, while the amikacin MIC range was 1–64 μg/mL as depicted in **Table4**.

In the checkerboard technique, the interaction between the combination of cefotaxime and amikacin against *Pseudomonas aeruginosa* was predominantly synergistic, although there were few partially synergistic. Thus no growth or turbidity clearly illustrated the extensive activity of aminoglycoside which was enforced by the second drug: cefotaxime resulting in an antibacterial effect. The synergistic activities of the antimicrobial combinations are detailed in **Table 4**. The combination of amikacin and cefotaxime exerted synergetic effect against 17 isolates, and 4 isolates were partially synergistic. FIC index values ranged from 0.18 to 0.75. Statistical analysis of one sample test revealed no significant difference between synergism effects among all isolates indicating strong synergy between both antibiotics where P-value = 0.088. Antagonism was not detected against any isolate in our study.

#### **3.3 Congo red test**

About 66.6% of the isolates were positive for biofilm production with varying degrees. Out of 21 *P. aeruginosa* isolates, 19%, 28.6%, and 19% were strong, intermediate, and negative biofilm producers, respectively. The morphology of all types of colonies is illustrated in **Figure 1**.

#### **3.4 Microtiter plate test (MTP)**

Biofilm quantification analyses showed that 90.4% of the isolates were biofilm producers, indicating that this technique was more efficient than Congo red agar for the detection of biofilm production. The obtained isolates of this study had the following results for the categories of biofilm production: 9.6% were non-adherent, 33.4% weakly adherent, 42.8% moderately adherent, and 14.2% strongly adherent as shown in **Figure 2**.

A comparison of results obtained by the CRA method versus that of MTP assay is declared in **Table 5**. Out of 21 biofilm *P. aeruginosa* isolates by the CRA method, 19 isolates were positive by the MTP approach but with various levels of

**129**

tive by both assays.

**Table 4.**

*method.*

shown in **Figure 3**.

**by conventional multiplex PCR**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

**MIC of AK in combination**

1 256 32 32 2 0.125 0.06 0.18 Synergistic 2 8 1 2 0.25 0.25 0.25 0.5 Synergistic 3 32 2 2 1 0.06 0.5 0.56 Partially

 128 64 32 16 0.25 0.25 0.5 Synergistic 32 64 8 16 0.25 0.25 0.5 Synergistic 32 64 8 8 0.25 0.125 0.375 Synergistic 64 64 16 4 0.25 0.06 0.31 Synergistic 8 4 2 1 0.25 0.25 0.5 Synergistic 64 64 16 16 0.25 0.25 0.5 Synergistic 128 64 16 16 0.125 0.25 0.375 Synergistic 32 64 2 32 0.06 0.5 0.56 Partially

 32 64 8 16 0.25 0.25 0.5 Synergistic 16 4 2 1 0.125 0.25 0.375 Synergistic 128 64 8 8 0.06 0.125 0.18 Synergistic 256 64 16 8 0.06 0.125 0.18 Synergistic 256 32 64 2 0.25 0.06 0.31 Synergistic 32 8 8 4 0.25 0. 5 0.75 Partially

18 16 4 4 2 0.25 0.5 0.75 Partially

19 16 4 2 1 0.25 0.25 0.5 Synergistic 20 256 64 64 16 0.25 0.25 0.5 Synergistic 21 512 64 64 8 0.125 0.125 0.25 Synergistic

**FIC of CTX**

**FIC of AK**

**Ʃ FIC Interpretation**

synergistic

synergistic

synergistic

synergistic

production (3 strong, 7 moderate, 9 weak), and only 2 isolates were factual nega-

*MIC of CTX and AK alone and in combination and FIC index against* P. aeruginosa *by the checkerboard* 

All strong biofilm producers *P. aeruginosa* isolates of code numbers (1, 4, 21) were harbored all examined biofilm genes and gave their characteristic bands as

By RT-PCR, comparing the amount of examining biofilm gene products before

and after each treatment with a sub-inhibitory concentration (SIC) of each antibiotic alone and combination, results revealed that the amount of examining

**3.5 Detection of biofilm genes in strong biofilm** *P. aeruginosa* **isolates** 

**3.6 Quantitative assessment effect of each antibiotic alone and in combination on biofilm gene expression**

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

**MIC of AK**

**MIC of CTX in combination**

**Isolates no.**

**MIC of CTX**


*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*

#### **Table 4.**

*Gene Expression and Phenotypic Traits*

**Sample No. of** 

**examined samples**

*The incidence of* Pseudomonas aeruginosa *isolated from examined samples.*

**3.2 Antimicrobial activity**

Diseased chicks Young (1–10 days)

Old broilers (11–35 days)

**Table 3.**

isolate in our study.

**3.3 Congo red test**

as shown in **Figure 2**.

of colonies is illustrated in **Figure 1**.

**3.4 Microtiter plate test (MTP)**

According to the disk diffusion method, 76.2% of isolates were resistant to cefotaxime, 14.3% were intermediate, and 9.5% were sensitive. Regarding amikacin, 57.2% of isolates were resistant, 9.5% were intermediate, and 33.3% were sensitive.

Freshly dead 28 11 39% 11 39%

Total 100 34 34% 21 21%

In the checkerboard technique, the interaction between the combination of cefotaxime and amikacin against *Pseudomonas aeruginosa* was predominantly synergistic, although there were few partially synergistic. Thus no growth or turbidity clearly illustrated the extensive activity of aminoglycoside which was enforced by the second drug: cefotaxime resulting in an antibacterial effect. The synergistic activities of the antimicrobial combinations are detailed in **Table 4**. The combination of amikacin and cefotaxime exerted synergetic effect against 17 isolates, and 4 isolates were partially synergistic. FIC index values ranged from 0.18 to 0.75. Statistical analysis of one sample test revealed no significant difference between synergism effects among all isolates indicating strong synergy between both antibiotics where P-value = 0.088. Antagonism was not detected against any

About 66.6% of the isolates were positive for biofilm production with varying degrees. Out of 21 *P. aeruginosa* isolates, 19%, 28.6%, and 19% were strong, intermediate, and negative biofilm producers, respectively. The morphology of all types

Biofilm quantification analyses showed that 90.4% of the isolates were biofilm producers, indicating that this technique was more efficient than Congo red agar for the detection of biofilm production. The obtained isolates of this study had the following results for the categories of biofilm production: 9.6% were non-adherent, 33.4% weakly adherent, 42.8% moderately adherent, and 14.2% strongly adherent

A comparison of results obtained by the CRA method versus that of MTP assay is declared in **Table 5**. Out of 21 biofilm *P. aeruginosa* isolates by the CRA method, 19 isolates were positive by the MTP approach but with various levels of

According to the microdilution assay, the range of cefotaxime MIC was 8–512 μg/mL,

*Pseudomonas* **spp. isolates** *P. aeruginosa* **isolates No. Frequency No. Frequency**

33 20 60% 9 27%

39 3 7.6% 1 2.5%

Of interest, 57.2% of isolates were resistant to both tested antibiotics.

while the amikacin MIC range was 1–64 μg/mL as depicted in **Table4**.

**128**

*MIC of CTX and AK alone and in combination and FIC index against* P. aeruginosa *by the checkerboard method.*

production (3 strong, 7 moderate, 9 weak), and only 2 isolates were factual negative by both assays.

## **3.5 Detection of biofilm genes in strong biofilm** *P. aeruginosa* **isolates by conventional multiplex PCR**

All strong biofilm producers *P. aeruginosa* isolates of code numbers (1, 4, 21) were harbored all examined biofilm genes and gave their characteristic bands as shown in **Figure 3**.

## **3.6 Quantitative assessment effect of each antibiotic alone and in combination on biofilm gene expression**

By RT-PCR, comparing the amount of examining biofilm gene products before and after each treatment with a sub-inhibitory concentration (SIC) of each antibiotic alone and combination, results revealed that the amount of examining

#### **Figure 1.**

*Investigation of biofilm producer* P. aeruginosa *using CRA method: (A) dry black colonies, (B) smooth black colonies, (C) dry red colonies, and (D) smooth red colonies.*

#### **Figure 2.**

*Microtiter plate method showing none, strong, moderate, and weak biofilm producers differentiated by crystal violet stain in 96-well tissue culture plate.*

gene products was relatively increased in untreated samples with drugs than those treated, which leads to high threshold cycle (Ct) value in treated than untreated. Interestingly, we found that drug combination was more effective in significantly reducing the expression of biofilm genes than each antibiotic alone.

Statistical data assessed that fold changes in *pslA*, *pelA*, and *filC* gene expression after treatment with SIC of cefotaxime and amikacin alone were (0.599:0.752:0.597

**131**

**Table 6.**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

1, 16, 20, 21 Dry black 4 2 2 0 0 4, 7, 9, 10, 14, 15 Smooth black 6 1 3 2 0 3, 5, 6, 17 Dry red 4 0 2 2 0 2, 8, 11, 12, 13, 18, 19 Smooth red 7 0 0 5 2

**Strong Moderate Weak None**

**Sample code no. CRA No. MTP**

*CRA versus MTP methods for detection of biofilm formation by* P. aeruginosa*.*

*isolates for* pelA *gene; and lanes 13–15, positive isolates for* pslA *gene.*

**Genes Isolate no. Fold change**

fold) and (0.348:0.354:0.296 fold), respectively, which were significantly higher (P ≤ 0.05) than a fold change in same gene expression after combination treatment

*Results of RT-PCR showing expression of biofilm genes in* P. aeruginosa *isolates before and after treatment* 

*Agarose gel electrophoresis of biofilm genes: Lanes 1, 6, and 12, positive controls; lanes 5, 11, and 16, negative controls; lane 8, DNA ladder (100 bp); lanes 2–4, positive isolates for* filC *gene; lanes 7, 9, and 10, positive* 

*PslA* 1 0.5212 0.3209 0.0890

*PelA* 1 0.7371 0.4506 0.2535

*FliC* 1 0.6071 0.3322 0.2176

2 0.6830 0.3121 0.1869 3 0.5946 0.4118 0.1216

2 0.8526 0.3276 0.2253 3 0.6690 0.2852 0.1550

2 0.5471 0.2643 0.1708 3 0.6373 0.2932 0.0884

**Cefotaxime Amikacin Cefotaxime-amikacin combination**

(0.132:0.211:0.158 fold) as shown in **Table 6** and **Figure 4.**

*with SIC of each antibiotic alone and in combination.*

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

**Table 5.**

**Figure 3.**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*


**Table 5.**

*Gene Expression and Phenotypic Traits*

**130**

**Figure 2.**

*violet stain in 96-well tissue culture plate.*

**Figure 1.**

gene products was relatively increased in untreated samples with drugs than those treated, which leads to high threshold cycle (Ct) value in treated than untreated. Interestingly, we found that drug combination was more effective in significantly

*Microtiter plate method showing none, strong, moderate, and weak biofilm producers differentiated by crystal* 

Statistical data assessed that fold changes in *pslA*, *pelA*, and *filC* gene expression after treatment with SIC of cefotaxime and amikacin alone were (0.599:0.752:0.597

reducing the expression of biofilm genes than each antibiotic alone.

*Investigation of biofilm producer* P. aeruginosa *using CRA method: (A) dry black colonies,* 

*(B) smooth black colonies, (C) dry red colonies, and (D) smooth red colonies.*

*CRA versus MTP methods for detection of biofilm formation by* P. aeruginosa*.*

#### **Figure 3.**

*Agarose gel electrophoresis of biofilm genes: Lanes 1, 6, and 12, positive controls; lanes 5, 11, and 16, negative controls; lane 8, DNA ladder (100 bp); lanes 2–4, positive isolates for* filC *gene; lanes 7, 9, and 10, positive isolates for* pelA *gene; and lanes 13–15, positive isolates for* pslA *gene.*


#### **Table 6.**

*Results of RT-PCR showing expression of biofilm genes in* P. aeruginosa *isolates before and after treatment with SIC of each antibiotic alone and in combination.*

fold) and (0.348:0.354:0.296 fold), respectively, which were significantly higher (P ≤ 0.05) than a fold change in same gene expression after combination treatment (0.132:0.211:0.158 fold) as shown in **Table 6** and **Figure 4.**

#### **Figure 4.**

*Expression curves of each biofilm gene after different treatments by SYBR green RT-PCR, (A) PslA gene, (B) PelA gene and (C) FilC gene.*

## **4. Discussion**

*Pseudomonas aeruginosa* is considered to be an opportunistic organism that produces respiratory infection, sinusitis, keratitis/keratoconjunctivitis, and septicemia, and it becomes an infection when it is introduced into tissues of susceptible hosts [27]. The bacterium is widely distributed in the environment, as it can utilize a wide range of materials for its nutrients while only requiring a limited amount of nutrients to survive [28]. Moreover, biofilm production has been considered to be an important determinant of pathogenicity in *P. aeruginosa* infections [29]. The formation of biofilms facilitates chronic bacterial infections and reduces the efficacy of antimicrobial therapy [29–31]. The situation is getting very concerning, the World Health Organization has declared it to be a "critical priority pathogen," on which research and development of novel antibiotics should be focused [32]. For this reason, this work designed to find repositionable candidate's antibiotics against *P. aeruginosa* biofilms, which are disreputable for their intensified drug resistance.

Here we isolated 21 *P. aeruginosa* from 100 broiler chickens suffering from respiratory manifestations (21%). These findings were close to that (20%) reported earlier in India [1]. Many studies showed different prevalence rates of *P. aeruginosa* isolates in broilers worldwide: in Iraq, a low rate of 6% was reported [33], while in Nigeria, a high rate of 75% was reported [34]. These differences in prevalence rates may reflect the considerable disparity in the sampling scheme, sample types, pseudomonas detection protocol, and geographic location.

In the current investigation, all the isolates were tested against cefotaxime and amikacin to determine the antibiotic susceptibility patterns. A high-resistance rate was detected for both antibiotics at which 76.2% were resistant to cefotaxime and 57.2% to amikacin. This might be due to the indiscriminate use of antibiotics in the feed of broiler breeders or other environmental possibilities [35].

The increased observance of multiple resistances (mainly to beta-lactam antibiotics) in pseudomonas isolates is making it increasingly difficult to treat infections caused by this pathogen. Resistance to antimicrobials in pseudomonas strains develops via several mechanisms, including the production of specific enzymes (b-lactamases, enzymes that modify aminoglycosides, for example), changes in cell-membrane permeability, and active efflux systems [36].

Interpretative reading was used to detect the bactericidal activity of each antibiotic against isolates with cefotaxime MICs of 8–512 and amikacin MICs of 1–64. These data are reinforced by findings from other countries, including Kuwait [37], Canada [38], China [39], and the USA [40].

Synergy testing has shown evidence of an interaction of two antibiotics in combination against pseudomonas bacterial isolates where statistical analysis provides important insights into drug synergism where the FIC index calculations

**133**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

exemplified a significant synergism of both drugs achieving an enhanced overall effect which is substantially greater than the sum of their ones. These results were consistent with the previous studies of Saiman [41], Dundar and Otkun [42], and Hawkey et al. [43]. The possible explanation for this synergism is the ability of beta-lactam cefotaxime to penetrate the outer membrane of pseudomonas bacteria which thereby increases the permeability of the bacterium to the aminoglycoside amikacin binding to 30S ribosome inhibiting the protein synthesis, thus leading to a

To investigate the effect of a synergistic combination of the repositionable drugs against *P. aeruginosa* biofilms, we detected their effect on the expression of screened

In this study, biofilm production was examined qualitatively, depending on colony morphology of 21 *P. aeruginosa* isolates inoculated on Congo red agar. Some differences between researches were apparent concerning the interpretation of CRA test results. In that respect, both bright black colonies [45] and black colonies [46] were considered as a positive result. However, Cucarella et al. [47] described the dry crystalline surface (rough colony morphology) as a positive result, disregarding the color (black or pink). Such discrepancy when interpreting the results may be possible since the test itself was not originally designed for investigating *P. aeruginosa* isolates as reported by Freeman et al. [21]. In this investigation, according to Osman et al. [48], isolates that produced black/rough colonies were verified as strong biofilmforming, while isolates producing red/smooth colonies were described as non-biofilm formers. The smooth black and dry red colonies were respected as indefinite findings. The qualitative technique revealed that only 66.6% of the isolates were considered biofilm producers, while the biofilm quantitative technique (MTP method) revealed that 90.4% of the isolates were biofilm producers, indicating that the quantitative technique was more efficient than the qualitative technique for the detection of biofilm production. There was also high biofilm production

Biofilms are surface-associated communities embedded within an extracellular matrix [49]. The extracellular matrix consists of polysaccharides, proteins, nucleic acids, and lipids and is a distinguishing feature of biofilms, capable of functioning as both a structural scaffold and a protective barrier [45]. Extracellular polysaccharides are a crucial component of the matrix and carry out a range of functions including promoting attachment to surfaces and other cells, building and maintaining biofilm structure, as well as protecting the cells from antimicrobials and host defenses [50, 51]. *P. aeruginosa* produces at least two extracellular polysaccharides that can be important in biofilm development and is accompanied by gene regulation [52–54]. Conventional PCR was carried out for detection of *pelA* and *pslA* genes which were involved in the formation of polysaccharide components of biofilm among tested isolates and were expressed heavily in all of them (100%). These data matched with previous studies of Wei and Ma [55], Vasiljević et al. [56], and Emami et al. [57]. Moreover, Suriyanarayanan et al. [58] mentioned that the effects of *fliC* phosphorylation on biofilm attachment and dispersal led to two conclusions. Both initial attachment and detachment during the dispersal stage were delayed by the loss of *fliC* phosphorylation in static and dynamic flow biofilms. As each of these processes still proceeded in the lack of phosphorylation, it suggested that *fliC* phosphorylation regulates the timing and rate of these processes without affecting biofilm architecture. These investigations were parallel with our results where *fliC* detected in all tested isolates. Regarding the qRT-PCR results, the suppressing effects in fold change of previously mentioned biofilm gene expression were detected for drug combination in comparison with each antibiotic alone. Exposure to each antibiotic caused a decreased level of biofilm expression ranging between 0.1- and 0.7-fold changes,

*DOI: http://dx.doi.org/10.5772/intechopen.91146*

synergistic effect in the in vitro studies [44].

by the evaluated tested isolates of *P. aeruginosa.*

biofilm genes.

### *Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas… *DOI: http://dx.doi.org/10.5772/intechopen.91146*

exemplified a significant synergism of both drugs achieving an enhanced overall effect which is substantially greater than the sum of their ones. These results were consistent with the previous studies of Saiman [41], Dundar and Otkun [42], and Hawkey et al. [43]. The possible explanation for this synergism is the ability of beta-lactam cefotaxime to penetrate the outer membrane of pseudomonas bacteria which thereby increases the permeability of the bacterium to the aminoglycoside amikacin binding to 30S ribosome inhibiting the protein synthesis, thus leading to a synergistic effect in the in vitro studies [44].

To investigate the effect of a synergistic combination of the repositionable drugs against *P. aeruginosa* biofilms, we detected their effect on the expression of screened biofilm genes.

In this study, biofilm production was examined qualitatively, depending on colony morphology of 21 *P. aeruginosa* isolates inoculated on Congo red agar. Some differences between researches were apparent concerning the interpretation of CRA test results. In that respect, both bright black colonies [45] and black colonies [46] were considered as a positive result. However, Cucarella et al. [47] described the dry crystalline surface (rough colony morphology) as a positive result, disregarding the color (black or pink). Such discrepancy when interpreting the results may be possible since the test itself was not originally designed for investigating *P. aeruginosa* isolates as reported by Freeman et al. [21]. In this investigation, according to Osman et al. [48], isolates that produced black/rough colonies were verified as strong biofilmforming, while isolates producing red/smooth colonies were described as non-biofilm formers. The smooth black and dry red colonies were respected as indefinite findings.

The qualitative technique revealed that only 66.6% of the isolates were considered biofilm producers, while the biofilm quantitative technique (MTP method) revealed that 90.4% of the isolates were biofilm producers, indicating that the quantitative technique was more efficient than the qualitative technique for the detection of biofilm production. There was also high biofilm production by the evaluated tested isolates of *P. aeruginosa.*

Biofilms are surface-associated communities embedded within an extracellular matrix [49]. The extracellular matrix consists of polysaccharides, proteins, nucleic acids, and lipids and is a distinguishing feature of biofilms, capable of functioning as both a structural scaffold and a protective barrier [45]. Extracellular polysaccharides are a crucial component of the matrix and carry out a range of functions including promoting attachment to surfaces and other cells, building and maintaining biofilm structure, as well as protecting the cells from antimicrobials and host defenses [50, 51].

*P. aeruginosa* produces at least two extracellular polysaccharides that can be important in biofilm development and is accompanied by gene regulation [52–54].

 Conventional PCR was carried out for detection of *pelA* and *pslA* genes which were involved in the formation of polysaccharide components of biofilm among tested isolates and were expressed heavily in all of them (100%). These data matched with previous studies of Wei and Ma [55], Vasiljević et al. [56], and Emami et al. [57].

Moreover, Suriyanarayanan et al. [58] mentioned that the effects of *fliC* phosphorylation on biofilm attachment and dispersal led to two conclusions. Both initial attachment and detachment during the dispersal stage were delayed by the loss of *fliC* phosphorylation in static and dynamic flow biofilms. As each of these processes still proceeded in the lack of phosphorylation, it suggested that *fliC* phosphorylation regulates the timing and rate of these processes without affecting biofilm architecture. These investigations were parallel with our results where *fliC* detected in all tested isolates.

Regarding the qRT-PCR results, the suppressing effects in fold change of previously mentioned biofilm gene expression were detected for drug combination in comparison with each antibiotic alone. Exposure to each antibiotic caused a decreased level of biofilm expression ranging between 0.1- and 0.7-fold changes,

*Gene Expression and Phenotypic Traits*

**4. Discussion**

*PelA gene and (C) FilC gene.*

**Figure 4.**

*Pseudomonas aeruginosa* is considered to be an opportunistic organism that produces respiratory infection, sinusitis, keratitis/keratoconjunctivitis, and septicemia, and it becomes an infection when it is introduced into tissues of susceptible hosts [27]. The bacterium is widely distributed in the environment, as it can utilize a wide range of materials for its nutrients while only requiring a limited amount of nutrients to survive [28]. Moreover, biofilm production has been considered to be an important determinant of pathogenicity in *P. aeruginosa* infections [29]. The formation of biofilms facilitates chronic bacterial infections and reduces the efficacy of antimicrobial therapy [29–31]. The situation is getting very concerning, the World Health Organization has declared it to be a "critical priority pathogen," on which research and development of novel antibiotics should be focused [32]. For this reason, this work designed to find repositionable candidate's antibiotics against *P. aeruginosa* biofilms, which are disreputable for their intensified drug resistance. Here we isolated 21 *P. aeruginosa* from 100 broiler chickens suffering from respiratory manifestations (21%). These findings were close to that (20%) reported earlier in India [1]. Many studies showed different prevalence rates of *P. aeruginosa* isolates in broilers worldwide: in Iraq, a low rate of 6% was reported [33], while in Nigeria, a high rate of 75% was reported [34]. These differences in prevalence rates may reflect the considerable disparity in the sampling scheme, sample types,

*Expression curves of each biofilm gene after different treatments by SYBR green RT-PCR, (A) PslA gene, (B)* 

In the current investigation, all the isolates were tested against cefotaxime and amikacin to determine the antibiotic susceptibility patterns. A high-resistance rate was detected for both antibiotics at which 76.2% were resistant to cefotaxime and 57.2% to amikacin. This might be due to the indiscriminate use of antibiotics in the

The increased observance of multiple resistances (mainly to beta-lactam antibiotics) in pseudomonas isolates is making it increasingly difficult to treat infections caused by this pathogen. Resistance to antimicrobials in pseudomonas strains develops via several mechanisms, including the production of specific enzymes (b-lactamases, enzymes that modify aminoglycosides, for example), changes in

Interpretative reading was used to detect the bactericidal activity of each antibiotic against isolates with cefotaxime MICs of 8–512 and amikacin MICs of 1–64. These data are reinforced by findings from other countries, including Kuwait [37],

Synergy testing has shown evidence of an interaction of two antibiotics in combination against pseudomonas bacterial isolates where statistical analysis provides important insights into drug synergism where the FIC index calculations

pseudomonas detection protocol, and geographic location.

feed of broiler breeders or other environmental possibilities [35].

cell-membrane permeability, and active efflux systems [36].

Canada [38], China [39], and the USA [40].

**132**

while the repression was strong and most significant with amikacin-cefotaxime combination treatment with fold change reaching 0.08, i.e., the consequence of treatment on the average expression profile among all biofilm involving genes constituting the bacterial communities studied. As described in this paper and by others [59–61], sub-MICs of combinations have potent effects on attenuating biofilm formation which are totally different from each antibiotic alone.

## **5. Conclusion**

The treatment of biofilm-related *P. aeruginosa* infections in the poultry industry has become an important part of antimicrobial chemotherapy because biofilms are not affected by therapeutic concentrations of antibiotics permitting attachment of other pathogens. Our study proved that using a combination of antimicrobial agents including cefotaxime and amikacin represents a profound synergism, significant antibiofilm, and a suitable candidate in combatting this fierce infection.

## **Conflict of interest**

The authors manifested that they have no conflicts of interest.

## **Abbreviations**


## **Author details**

Azza S. El-Demerdash1 \* and Neveen R. Bakry2

1 Agriculture Research Center (ARC), Animal Health Research Institute (AHRI), Zagazig, Egypt

2 Agriculture Research Center (ARC), Animal Health Research Institute (AHRI), Reference Laboratory for Veterinary Quality Control on Poultry Production (RLQCP), Dokki, Egypt

\*Address all correspondence to: dr.azzasalah@yahoo.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**135**

*Evaluation of the Synergistic Effect of Amikacin with Cefotaxime against* Pseudomonas…

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[9] Yssel AEJ, Vanderleyden J, Steenackers HP. Repurposing of nucleoside-and nucleobase-derivative drugs as antibiotics and biofilm

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*DOI: http://dx.doi.org/10.5772/intechopen.91146*

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## **References**

*Gene Expression and Phenotypic Traits*

**5. Conclusion**

**Conflict of interest**

**Abbreviations**

**Author details**

Zagazig, Egypt

Azza S. El-Demerdash1

(RLQCP), Dokki, Egypt

CRA Congo red agar MTP microtiter plate

while the repression was strong and most significant with amikacin-cefotaxime combination treatment with fold change reaching 0.08, i.e., the consequence of treatment on the average expression profile among all biofilm involving genes constituting the bacterial communities studied. As described in this paper and by others [59–61], sub-MICs of combinations have potent effects on attenuating

The treatment of biofilm-related *P. aeruginosa* infections in the poultry industry has become an important part of antimicrobial chemotherapy because biofilms are not affected by therapeutic concentrations of antibiotics permitting attachment of other pathogens. Our study proved that using a combination of antimicrobial agents including cefotaxime and amikacin represents a profound synergism, significant

biofilm formation which are totally different from each antibiotic alone.

antibiofilm, and a suitable candidate in combatting this fierce infection.

The authors manifested that they have no conflicts of interest.

RT-PCR reverse transcriptase-polymerase chain reaction

\* and Neveen R. Bakry2

\*Address all correspondence to: dr.azzasalah@yahoo.com

provided the original work is properly cited.

1 Agriculture Research Center (ARC), Animal Health Research Institute (AHRI),

2 Agriculture Research Center (ARC), Animal Health Research Institute (AHRI), Reference Laboratory for Veterinary Quality Control on Poultry Production

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

FIC fractional inhibitory concentration MIC minimum inhibitory concentration

**134**

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Enoch DA, Otter JA, et al. Treatment of infections caused by multidrug-resistant gram-negative bacteria: Report of the British Society for Antimicrobial Chemotherapy/healthcare infection society/british infection association joint working party. The Journal of Antimicrobial Chemotherapy.

[42] Dundar D, Otkun M. In-vitro efficacy of synergistic antibiotic combinations in multidrug resistant *Pseudomonas aeruginosa* strains. Yonsei Medical Journal. 2010;**51**:111-116. DOI:

[41] Saiman L. Clinical utility of synergy testing for multidrug-resistant *Pseudomonas aeruginosa* isolated from patients with cystic fibrosis. Paediatric Respiratory Reviews. 2007;**8**:249-255. Available from: https://www.ncbi.nlm.

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[52] Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: A common cause of persistent infections. Science. 1999;**284**:1318-1322. Available from: https://www.ncbi.nlm.nih.gov/ pubmed/10334980

[53] Drenkard E. Antimicrobial resistance of Pseudomonas aeruginosa biofilms. Microbes and Infection. 2003;**5**:1213-1219. Available from: https://www.ncbi.nlm.nih.gov/ pubmed/14623017

[54] Wei Q, Ma LZ. Biofilm matrix and its regulation in *Pseudomonas aeruginosa*. International Journal of Molecular Sciences. 2013;**14**:20983-21005. Available from: https://www.ncbi.nlm. nih.gov/pubmed/24145749

[55] Wei B, Cha S-Y, Kang M, Park I-J, Moon O-K, Park C-K, et al. Development and application of a multiplex PCR assay for rapid detection of 4 major bacterial pathogens in ducks. Poultry Science. 2013;**92**:1164-1170. Available from: https://academic.oup. com/ps/article/92/5/1164/1573914

[56] Vasiljević Z, Jovčić B, Ćirković I, DJukić S. An examination of potential differences in biofilm production among different genotypes of *Pseudomonas aeruginosa*. Archives of Biological Sciences. 2014;**66**:117-121. Available from: http://www.doiserbia.nb.rs/ Article.aspx?ID=0354-46641401117V#. XhmKw9QzbIU

[57] Emami S, Nikokar I, Ghasemi Y, Ebrahimpour M, Ebrahim-Saraie HS, Araghian A, et al. Antibiotic resistance pattern and distribution of pslA gene among biofilm producing *Pseudomonas aeruginosa* isolated from waste water of a burn center. Jundishapur Journal of Microbiology. 2015;**8**(11):e23669. DOI: 10.5812/jjm.23669

[58] Suriyanarayanan T, Periasamy S, Lin MH, Ishihama Y, Swarup S. Flagellin FliC phosphorylation affects type 2 protease secretion and biofilm dispersal in *Pseudomonas aeruginosa* PAO1. PLoS One. 2016;**11**(10):1-19. DOI: 10.1371/ journal.pone.0164155

[59] Gümücs D, Kalayci-Yüksek F, Yörük E, Uz G, Çelik E, Arslan C, et al. Alterations of growth rate and gene expression levels of UPEC by antibiotics at sub-MIC. Folia Microbiologia (Praha). 2018;**63**:451-457. Available from: https://www.ncbi.nlm.nih.gov/ pubmed/29327292

[60] Raymond B. Five rules for resistance management in the antibiotic apocalypse, a road map for integrated microbial management. Evolutionary Applications. 2019;**12**(6):1079-1091. DOI: 10.1111/eva.12808

[61] Shahat HS, Mohamed H, Al-Azeem A, Mohammed W, Nasef SA. Molecular detection of some virulence genes in *Pseudomonas aeruginosa* isolated from chicken embryos and broilers with regard to disinfectant resistance. SVU-International Journal of Veterinary Sciences. 2019;**2**:52- 70. Available from: https://svu. journals.ekb.eg/article\_40727\_ bc49d8a75991c214123ab7c8573d73a4.pdf

**141**

**Chapter 8**

*Jade Q. Clement*

adaptation responses.

microgravity

**1. Introduction**

**Abstract**

Gene Expression Profile of HDF in

SMG Partially Overlaps with That

Microgravity research is an important field in biomedical sciences not only due to our interest in exploring and living in space, but also because of the insights it gives on earthbound health conditions. Using a human dermal fibroblast (HDF) cell line cultured in simulated microgravity (SMG) in combination with high throughput cDNA microarrays and quantitative Northern analysis, 271 differentially regulated genes were identified and 72% of these genes were also reported in the high throughput gene expression data of the recent National Aeronautics and Space Administration (NASA) Twins Study. The identification of the large number of overlapping microgravity sensitive genes between the skin fibroblast in microgravity and astronaut's peripheral blood mononuclear cells (PBMCs) indicated that microgravity alone, without space radiation, was able to elicit an adaptive response involving a set of about 200 genes. Further analysis of the overlapping genes with the same direction of regulation (86 genes) and opposite direction of regulation (108 genes) revealed important pathways and cellular processes in the microgravity

**Keywords:** gene expression profiling, DNA microarray, northern blotting, rotating wall vessel (RWV), rotary cell culture system (RCCS), fibroblast,

deterioration [5–7], and muscular atrophy [8–10].

Humans have been traveling to space since 1961. During the close to 60-year period, hundreds of astronauts and cosmonauts have experienced microgravity as well as radiation in space. Exposure to microgravity environment has been shown to have potentially negative effects on human health. Some of the factors include cardiovascular deconditioning [1, 2], decline in immune response [3, 4], bone

Human immune response dysregulation has been shown during and after space flight [11]. Although acute onset life-threatening incidents directly caused by microgravity or spaceflight have not been reported, many conditions of health concern and symptoms related to immune dysfunction during orbital spaceflight have been described [12, 13]. Among the 70 tabulated clinical symptoms and medical conditions pertinent to immune dysfunction on board the International Space Station (ISS), skin rash and hypersensitivity account for 23 events; skin

in the NASA Twins Study

## **Chapter 8**

## Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study

*Jade Q. Clement*

## **Abstract**

Microgravity research is an important field in biomedical sciences not only due to our interest in exploring and living in space, but also because of the insights it gives on earthbound health conditions. Using a human dermal fibroblast (HDF) cell line cultured in simulated microgravity (SMG) in combination with high throughput cDNA microarrays and quantitative Northern analysis, 271 differentially regulated genes were identified and 72% of these genes were also reported in the high throughput gene expression data of the recent National Aeronautics and Space Administration (NASA) Twins Study. The identification of the large number of overlapping microgravity sensitive genes between the skin fibroblast in microgravity and astronaut's peripheral blood mononuclear cells (PBMCs) indicated that microgravity alone, without space radiation, was able to elicit an adaptive response involving a set of about 200 genes. Further analysis of the overlapping genes with the same direction of regulation (86 genes) and opposite direction of regulation (108 genes) revealed important pathways and cellular processes in the microgravity adaptation responses.

**Keywords:** gene expression profiling, DNA microarray, northern blotting, rotating wall vessel (RWV), rotary cell culture system (RCCS), fibroblast, microgravity

## **1. Introduction**

Humans have been traveling to space since 1961. During the close to 60-year period, hundreds of astronauts and cosmonauts have experienced microgravity as well as radiation in space. Exposure to microgravity environment has been shown to have potentially negative effects on human health. Some of the factors include cardiovascular deconditioning [1, 2], decline in immune response [3, 4], bone deterioration [5–7], and muscular atrophy [8–10].

Human immune response dysregulation has been shown during and after space flight [11]. Although acute onset life-threatening incidents directly caused by microgravity or spaceflight have not been reported, many conditions of health concern and symptoms related to immune dysfunction during orbital spaceflight have been described [12, 13]. Among the 70 tabulated clinical symptoms and medical conditions pertinent to immune dysfunction on board the International Space Station (ISS), skin rash and hypersensitivity account for 23 events; skin

infections, 6 events; cold sores (caused by herpes simplex virus 1 infection), 6 events. About 50% of the tabulated incidents are of or closely related to skin symptoms or abnormal skin conditions during the long duration space flights [12]. Skin is the essential outer cover that protects the internal tissues and organs from potential physical, chemical, and biological assaults of the environment. In addition to immune cells and keratinocytes, dermal fibroblasts also play an important immunomodulation role such as in antimicrobial defense [14]. Epidermal keratinocytes can sense the presence of pathogen invasion and other environmental stimuli such as the presence of UV light and foreign chemicals and produce cytokines, chemokines, and growth factors in response. Communication between keratinocytes and dermal fibroblasts through cytokines is fundamental in skin immunity. A recent report shows that in a 3D skin model-based study, just keratinocytes and fibroblasts alone embedded in a collagen matrix are able to activate CD4+ T cells in response to microbial invasion [15]. The dermal fibroblasts play an essential role in antimicrobial response by integrating signals among cells in the skin.

To date, much microgravity research work has been done in ground-based research using microgravity analogs. Due to cost and limits to the technology much less has been done directly in the space environment. The recent NASA Twins Study is a tremendously important study because it is the first study that uses an integrated approach to study human adaptation to a space environment by documenting the molecular, physiological and cognitive effects during long term spaceflight [16]. The study also highlights the need for further study on important aspects such as vascular changes and immunological stress associated with the weightlessness of space flight [16].

Sudden gravity change has altered gene expressions from many cell types [17–19]. High-throughput gene expression analysis have great potential for application to research involving changes in environmental conditions [20]. Various high throughput studies such as cDNA microarrays and transcriptome RNA sequencing have been increasingly used to assess the mRNA levels in microgravity research [16, 17]. This is an effective approach because the control of mRNA abundance of genes is efficiently adapted by cells through controlling transcription (especially transcription initiation), nuclear pre-mRNA processing, mRNA transport, mRNA stability, etc. The cellular abundance of mRNAs is critical to gene function and protein production which are intriguingly fine-tuned by non-coding regulatory RNAs such as miRNAs. There have been many gene expression studies done on various cell lines grown both in space and using ground-based microgravity analogs. Many of these studies have yielded valuable data, but correlation of gene expression data between studies has been relatively low [17, 18].

To further understand the cellular and molecular mechanisms by which space flight alters skin immune defense activities such as those analyzed from the ISS [12], the effects of microgravity on various human skin cell lines need to be studied to identify the genes whose functions are altered by microgravity. In our previous study, we found expression changes in certain genes (such as HLA-G and IL-1β among many others) in response to simulated microgravity [21]. The current report is on the gene expression profile of HDF in response to SMG. Interestingly, a substantial overlap in gene expression profiles between the HDF under SMG and that from the human blood cells of the NASA Twins Study, especially the peripheral blood mononuclear cells (PBMCs) from inflight in the ISS. The comparative analysis yielded 194 differently expressed genes in both studies, of which 86 genes were regulated in the same direction (trend) while 108 genes were regulated oppositely. The significance of these findings was discussed.

**143**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

The HDF cell line, AG 1522, was generously provided by Dr. Honglu Wu of NASA. The HDF cell line displayed regular monolayer spindle shaped growth in conventional 2-D cell culture flasks. When HDF cells were subjected to SMG treatment, in a 3-D culture environment, they formed spherical aggregates. Ground based simulated microgravity was achieved using the 50 ml high aspect ratio vessels (HARVs) or rotating wall vessels (RWVs) of a rotary cell culture system (RCCS-4D)

bioreactors from Synthecon, Inc. Cell viability and cell concentration were determined by Vi-Cell 1.01 cell counter of Beckman Coulter. For the three parallel experiments of 5 day modeled microgravity exposures, a density of 2.0 × 106 cells/ml with viability of 95.5% of the HDF AG1522 cells were cultured in RWVs at 20 rpm rotary setting to achieve the constant free-fall experience for cell aggregates. At the end of the five-day microgravity exposure period, the content of the bioreactor vessels was poured out into a 50 ml sterile centrifuge tube to collect cell pellet and 5 ml of the cell suspension from the bioreactor vessels were transferred to T75 flasks for morphological observation. Non-exposed stationary normal gravity control AG1522 cells were cultured in tissue culture flasks with vented caps (TPP

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

**2.1 Simulated microgravity and cell culture**

Techno Plastic) in the same incubator at 37°C, 5% CO2.

**2.2 Total RNA isolation and DNA microarray hybridization**

HDF cells cultured in three SMG bioreactor vessels and control flasks were removed at the end of the five-day SMG exposure period, washed with phosphate buffered saline three times and lysed in Guanidinium Isothiocyanate Buffer. The cell lysates were stored at −80°C prior to ultracentrifugation for total RNA isolation [22, 23]. Total cellular RNA was labeled using the Agilent Low RNA Input Fluorescent Linear Amplification Kit following manufacturer's protocols [24]. The fluorescently labeled cRNA probes were further purified and hybridized to Agilent 22 K Human Microarray V2 according to the specified procedures within the kit.

**2.3 Microarray scanning, feature extraction and functional grouping**

[26]. Cut-offs were set at a P-value ≤0.01 and fold change of ≥1.5.

**2.4 Northern blotting and quantitative gene expression analysis**

The microarrays were scanned using a ScanArray microarray scanner (Perkin-Elmer). The images generated from the scanning were imported into GenePix 6.0 (Molecular Devices, Sunnyvale, CA) for alignment and initial quantitation. The Gene Pix Results (GPR) files were then uploaded to CARMAweb [25] for normalization and statistical analysis. Background was subtracted and then each array was normalized using loess normalization within the array. A paired moderated T-Test was applied with Benjamini-Hochberg correction to control the false discovery rate

Some of the significantly regulated microgravity sensitive genes identified from the DNA microarray analysis were further verified using Northern blotting. Briefly, 10 ug total RNA was loaded per lane on a 1% formaldehyde agarose gel for electrophoresis separation of RNA species. RNA Ladders from Fermentas Life Sciences were used as RNA size markers. The gel-separated RNAs were capillary transferred onto a nylon membrane which was subjected to a hybridization procedure using

**2. Materials and methods**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

## **2. Materials and methods**

*Gene Expression and Phenotypic Traits*

in the skin.

space flight [16].

between studies has been relatively low [17, 18].

The significance of these findings was discussed.

infections, 6 events; cold sores (caused by herpes simplex virus 1 infection), 6 events. About 50% of the tabulated incidents are of or closely related to skin symptoms or abnormal skin conditions during the long duration space flights [12]. Skin is the essential outer cover that protects the internal tissues and organs from potential physical, chemical, and biological assaults of the environment. In addition to immune cells and keratinocytes, dermal fibroblasts also play an important immunomodulation role such as in antimicrobial defense [14]. Epidermal keratinocytes can sense the presence of pathogen invasion and other environmental stimuli such as the presence of UV light and foreign chemicals and produce cytokines, chemokines, and growth factors in response. Communication between keratinocytes and dermal fibroblasts through cytokines is fundamental in skin immunity. A recent report shows that in a 3D skin model-based study, just keratinocytes and fibroblasts alone embedded in a collagen matrix are able to activate CD4+ T cells in response to microbial invasion [15]. The dermal fibroblasts play an essential role in antimicrobial response by integrating signals among cells

To date, much microgravity research work has been done in ground-based research using microgravity analogs. Due to cost and limits to the technology much less has been done directly in the space environment. The recent NASA Twins Study is a tremendously important study because it is the first study that uses an integrated approach to study human adaptation to a space environment by documenting the molecular, physiological and cognitive effects during long term spaceflight [16]. The study also highlights the need for further study on important aspects such as vascular changes and immunological stress associated with the weightlessness of

Sudden gravity change has altered gene expressions from many cell types [17–19]. High-throughput gene expression analysis have great potential for application to research involving changes in environmental conditions [20]. Various high throughput studies such as cDNA microarrays and transcriptome RNA sequencing have been increasingly used to assess the mRNA levels in microgravity research [16, 17]. This is an effective approach because the control of mRNA abundance of genes is efficiently adapted by cells through controlling transcription (especially transcription initiation), nuclear pre-mRNA processing, mRNA transport, mRNA stability, etc. The cellular abundance of mRNAs is critical to gene function and protein production which are intriguingly fine-tuned by non-coding regulatory RNAs such as miRNAs. There have been many gene expression studies done on various cell lines grown both in space and using ground-based microgravity analogs. Many of these studies have yielded valuable data, but correlation of gene expression data

To further understand the cellular and molecular mechanisms by which space flight alters skin immune defense activities such as those analyzed from the ISS [12], the effects of microgravity on various human skin cell lines need to be studied to identify the genes whose functions are altered by microgravity. In our previous study, we found expression changes in certain genes (such as HLA-G and IL-1β among many others) in response to simulated microgravity [21]. The current report is on the gene expression profile of HDF in response to SMG. Interestingly, a substantial overlap in gene expression profiles between the HDF under SMG and that from the human blood cells of the NASA Twins Study, especially the peripheral blood mononuclear cells (PBMCs) from inflight in the ISS. The comparative analysis yielded 194 differently expressed genes in both studies, of which 86 genes were regulated in the same direction (trend) while 108 genes were regulated oppositely.

**142**

## **2.1 Simulated microgravity and cell culture**

The HDF cell line, AG 1522, was generously provided by Dr. Honglu Wu of NASA. The HDF cell line displayed regular monolayer spindle shaped growth in conventional 2-D cell culture flasks. When HDF cells were subjected to SMG treatment, in a 3-D culture environment, they formed spherical aggregates. Ground based simulated microgravity was achieved using the 50 ml high aspect ratio vessels (HARVs) or rotating wall vessels (RWVs) of a rotary cell culture system (RCCS-4D) bioreactors from Synthecon, Inc. Cell viability and cell concentration were determined by Vi-Cell 1.01 cell counter of Beckman Coulter. For the three parallel experiments of 5 day modeled microgravity exposures, a density of 2.0 × 106 cells/ml with viability of 95.5% of the HDF AG1522 cells were cultured in RWVs at 20 rpm rotary setting to achieve the constant free-fall experience for cell aggregates. At the end of the five-day microgravity exposure period, the content of the bioreactor vessels was poured out into a 50 ml sterile centrifuge tube to collect cell pellet and 5 ml of the cell suspension from the bioreactor vessels were transferred to T75 flasks for morphological observation. Non-exposed stationary normal gravity control AG1522 cells were cultured in tissue culture flasks with vented caps (TPP Techno Plastic) in the same incubator at 37°C, 5% CO2.

## **2.2 Total RNA isolation and DNA microarray hybridization**

HDF cells cultured in three SMG bioreactor vessels and control flasks were removed at the end of the five-day SMG exposure period, washed with phosphate buffered saline three times and lysed in Guanidinium Isothiocyanate Buffer. The cell lysates were stored at −80°C prior to ultracentrifugation for total RNA isolation [22, 23]. Total cellular RNA was labeled using the Agilent Low RNA Input Fluorescent Linear Amplification Kit following manufacturer's protocols [24]. The fluorescently labeled cRNA probes were further purified and hybridized to Agilent 22 K Human Microarray V2 according to the specified procedures within the kit.

## **2.3 Microarray scanning, feature extraction and functional grouping**

The microarrays were scanned using a ScanArray microarray scanner (Perkin-Elmer). The images generated from the scanning were imported into GenePix 6.0 (Molecular Devices, Sunnyvale, CA) for alignment and initial quantitation. The Gene Pix Results (GPR) files were then uploaded to CARMAweb [25] for normalization and statistical analysis. Background was subtracted and then each array was normalized using loess normalization within the array. A paired moderated T-Test was applied with Benjamini-Hochberg correction to control the false discovery rate [26]. Cut-offs were set at a P-value ≤0.01 and fold change of ≥1.5.

## **2.4 Northern blotting and quantitative gene expression analysis**

Some of the significantly regulated microgravity sensitive genes identified from the DNA microarray analysis were further verified using Northern blotting. Briefly, 10 ug total RNA was loaded per lane on a 1% formaldehyde agarose gel for electrophoresis separation of RNA species. RNA Ladders from Fermentas Life Sciences were used as RNA size markers. The gel-separated RNAs were capillary transferred onto a nylon membrane which was subjected to a hybridization procedure using

chemiluminescent (Pierce Biotechnology) labeled cDNA probe fragments. The blot was sequentially hybridized and striped and hybridized again with 12 different probe fragments. The cDNA probe fragments were generated from reverse transcription polymerase chain reaction (RT-PCR) using total cellular RNA as the templates. RT-PCRs were carried out using the Reverse Transcription System of Promega and the BioLine Red Polymerase PCR kit. Northern blot quantitation and association with the cDNA array results were done as described previously [21, 23].

## **2.5 Comparative analysis with the data from the NASA twins study**

The NASA Twin gene expression data was identified from Suppemental Table 2 in the NASA Twins Study [16]. Prior to further bioinformatics analysis, the high throughput gene expression data from transcriptome RNA sequencing analysis for the inflight, first half and in-flight, second half was extracted from the NASA Twin study [16]. This data was then compared to the gene expression data from the current study with HDF in SMG. Mainly the transcriptome RNA sequencing data of the PBMC RNA from NASA Twins Study was compared with the HDF SMG data here, since it offered the most abundant overlapping genes. The PBMC data was extracted from the Excel spreadsheets and combined with the HDF data for further comparative analysis using similar previously published method [17].

## **2.6 Pathway and gene ontology analysis**

To determine the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways [27, 28] and Gene Ontology, the genes that were determined to be differentially regulated were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) [29]. The pathway and gene ontology information were used to build the tables. The process was done for both the HDF SMG data alone, and for its comparative analysis with the data from the NASA Twins Study. Prior to further bioinformatics analysis, the gene expression data for the inflight, first half and in-flight, second half was extracted from Supplemental Table 2 of the NASA Twins study [16]. This data was then compared to the gene expression data from the current study with HDF in SMG.

## **2.7 Constructing heat map and Venn diagram**

The heat map was generated using Genesis 1.8.1 [30]. The Venn diagram was generated using the Excel plugin Array File Maker 4.0 [31].

## **3. Results**

## **3.1 Gene expression profiling and the identification of microgravity sensitive genes from SMG treated HDF**

HDF cells from each of the three SMG bioreactors and normal gravity controls were removed after 5-day SMG exposure for RNA extraction and microarray experiments. After normalization and statistical analysis (student *t* test), the gene expression data were used to identify the initial set of significantly differentially regulated genes at the statistically significance level of P ≤ 0.01 and cut off point of ≥1.5 fold up or down regulation. The volcano plot (**Figure 1**) shows the overall profile of the gene expression data from the three sets of parallel SMG experiments. Each dot on the volcano plot represents a gene selected in the initial set of differentially

**145**

**Figure 1.**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

expressed ones if the dot is to the left (down) or right (up) of the red lines. The genes found outside of the vertical parallel red lines correspond to the genes differentially expressed by 1.5 fold or greater in the SMG. A total of 271 genes were identified from these three SMG experiments on HDF to be the initial set of significant genes that were used for further analysis as follows. The two most notable differentially regulated genes by the SMG evident on the plot were the matrix metallopeptidase 1

*Volcano plot scattering the average M values (x axis) against the corrected p values (y axis) created using CARMAweb tool. Dots to the left of the red line and above the dashed line have a p-value of 0.01 or less and are down-regulated by at least 1.5 fold. The dots to the right of the red line and above the dashed lines have a* 

The significantly (≥ 1.5 fold change, P ≤ 0.01) differentially expressed genes under simulated microgravity were further analyzed using Heatmap and Venn diagram to visually display the directions and centralization of gene expression. A high level of consistency of both the Heatmap and Venn diagram results were found among the microarray data from the three SMG bioreactors (**Figure 2**). The Heatmap indicated that the expression levels of the three replicates were similar, with very minor variations in magnitude of expression. The Venn diagram further showed that there was a complete match among the three replicate microarrays from the three SMG bioreactor RNA samples. Thus, there was a very high level of consistence among data from the three SMG bioreactors. Among the 271 microgravity sensitive genes that differentially regulated by 1.5 fold or greater with a P value of ≤0.01, 129 were down-regulated and 142 were upregulated (**Figure 2B**).

(MMP1) and connective tissue growth factor (CTGF) genes (**Figure 1**).

**3.2 KEGG pathways of the microgravity sensitive genes from HDF**

genes in all the 23 KEGG pathways had >2 fold enrichment (**Table 1**).

The identified 271 SMG sensitive genes were subjected to further bioinformatics analysis using the DAVID v6.8, which uses a modified Fisher Exact P-value for gene enrichment analysis and statistically determines the over-representation of functional gene categories in a gene list. P values equal to or smaller than 0.05 are considered significantly enriched [29]. Through the DAVID analysis, 16 statistically significant (p ≤ 0.05) KEGG Pathways from the SMG gene list were identified and

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

*p-value of 0.01 or less and are up-regulated by at least 1.5 fold.*

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

#### **Figure 1.**

*Gene Expression and Phenotypic Traits*

chemiluminescent (Pierce Biotechnology) labeled cDNA probe fragments. The blot was sequentially hybridized and striped and hybridized again with 12 different probe fragments. The cDNA probe fragments were generated from reverse transcription polymerase chain reaction (RT-PCR) using total cellular RNA as the templates. RT-PCRs were carried out using the Reverse Transcription System of Promega and the BioLine Red Polymerase PCR kit. Northern blot quantitation and association with the cDNA array results were done as described previously [21, 23].

The NASA Twin gene expression data was identified from Suppemental Table 2 in the NASA Twins Study [16]. Prior to further bioinformatics analysis, the high throughput gene expression data from transcriptome RNA sequencing analysis for the inflight, first half and in-flight, second half was extracted from the NASA Twin study [16]. This data was then compared to the gene expression data from the current study with HDF in SMG. Mainly the transcriptome RNA sequencing data of the PBMC RNA from NASA Twins Study was compared with the HDF SMG data here, since it offered the most abundant overlapping genes. The PBMC data was extracted from the Excel spreadsheets and combined with the HDF data for further

To determine the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways

[27, 28] and Gene Ontology, the genes that were determined to be differentially regulated were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) [29]. The pathway and gene ontology information were used to build the tables. The process was done for both the HDF SMG data alone, and for its comparative analysis with the data from the NASA Twins Study. Prior to further bioinformatics analysis, the gene expression data for the inflight, first half and in-flight, second half was extracted from Supplemental Table 2 of the NASA Twins study [16]. This data was then compared to the gene expression data

The heat map was generated using Genesis 1.8.1 [30]. The Venn diagram was

HDF cells from each of the three SMG bioreactors and normal gravity controls were removed after 5-day SMG exposure for RNA extraction and microarray experiments. After normalization and statistical analysis (student *t* test), the gene expression data were used to identify the initial set of significantly differentially regulated genes at the statistically significance level of P ≤ 0.01 and cut off point of ≥1.5 fold up or down regulation. The volcano plot (**Figure 1**) shows the overall profile of the gene expression data from the three sets of parallel SMG experiments. Each dot on the volcano plot represents a gene selected in the initial set of differentially

**2.5 Comparative analysis with the data from the NASA twins study**

comparative analysis using similar previously published method [17].

**2.6 Pathway and gene ontology analysis**

from the current study with HDF in SMG.

**2.7 Constructing heat map and Venn diagram**

**sensitive genes from SMG treated HDF**

generated using the Excel plugin Array File Maker 4.0 [31].

**3.1 Gene expression profiling and the identification of microgravity** 

**144**

**3. Results**

*Volcano plot scattering the average M values (x axis) against the corrected p values (y axis) created using CARMAweb tool. Dots to the left of the red line and above the dashed line have a p-value of 0.01 or less and are down-regulated by at least 1.5 fold. The dots to the right of the red line and above the dashed lines have a p-value of 0.01 or less and are up-regulated by at least 1.5 fold.*

expressed ones if the dot is to the left (down) or right (up) of the red lines. The genes found outside of the vertical parallel red lines correspond to the genes differentially expressed by 1.5 fold or greater in the SMG. A total of 271 genes were identified from these three SMG experiments on HDF to be the initial set of significant genes that were used for further analysis as follows. The two most notable differentially regulated genes by the SMG evident on the plot were the matrix metallopeptidase 1 (MMP1) and connective tissue growth factor (CTGF) genes (**Figure 1**).

The significantly (≥ 1.5 fold change, P ≤ 0.01) differentially expressed genes under simulated microgravity were further analyzed using Heatmap and Venn diagram to visually display the directions and centralization of gene expression. A high level of consistency of both the Heatmap and Venn diagram results were found among the microarray data from the three SMG bioreactors (**Figure 2**). The Heatmap indicated that the expression levels of the three replicates were similar, with very minor variations in magnitude of expression. The Venn diagram further showed that there was a complete match among the three replicate microarrays from the three SMG bioreactor RNA samples. Thus, there was a very high level of consistence among data from the three SMG bioreactors. Among the 271 microgravity sensitive genes that differentially regulated by 1.5 fold or greater with a P value of ≤0.01, 129 were down-regulated and 142 were upregulated (**Figure 2B**).

#### **3.2 KEGG pathways of the microgravity sensitive genes from HDF**

The identified 271 SMG sensitive genes were subjected to further bioinformatics analysis using the DAVID v6.8, which uses a modified Fisher Exact P-value for gene enrichment analysis and statistically determines the over-representation of functional gene categories in a gene list. P values equal to or smaller than 0.05 are considered significantly enriched [29]. Through the DAVID analysis, 16 statistically significant (p ≤ 0.05) KEGG Pathways from the SMG gene list were identified and genes in all the 23 KEGG pathways had >2 fold enrichment (**Table 1**).

#### **Figure 2.**

*Heatmap and Venn diagram comparing the expression levels among the HDF cells cultured in three separate bioreactors. (A) The Heatmap shows the expression levels were very similar among the three replicate experiments. Green represents the down-regulated genes and red represents the up-regulated genes. (B) Venn diagram comparing the centralization level of the differentially regulated genes among each of the three replicates. The red represents the up-regulated genes and the green represents the down-regulated genes.*

The Ribosome KEGG Pathway included 27 of the SMG sensitive genes; 26 of these genes were down-regulated between 1.5 and 2 fold; only MRPS6 was upregulated (4.5 fold). Interestingly, a number of ribosomal protein genes have also been found to be down-regulated in other studies including our previous study on keratinocytes [21] as well as the NASA Twins Study [16]. The mineral absorption pathway had 10 genes which were all significantly up-regulated in SMG. ATP1A1 and ATP1B3 were both up-regulated by over 2.8 fold. The ferritin genes FTH1 and FTL were up-regulated by 6.6 and 1.6 fold, respectively. Ferritin genes have also been found to be up-regulated as a result of exposure to simulated microgravity in other studies [32]. The metallothionein genes were up-regulated from 7.5 to close to

**147**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

**Pathways P value Gene name FE**

(RPL10A), L11 (RPL11), L12 (RPL12), L18A (RPL18), L27 (RPL27), L27A (RPL27A), L31 (RPL30), L32 (RPL32), L34 (RPL34), L35 (RPL35), L36 (RPL36), L39 (RPL39), S2 (RPS2), S3 (RPS3), S3A (RPS3A), S7 (RPS7), S10 (RPS10), S15A (RPS15A), S17 (RPS17), S18 (RPS18), S19 (RPS19), S29 (RPS29), S23 (RPS23), S24 (RPS24), Mitochondrial Ribosomal ProteinS6 (MRPS6)

(ATP1A1), ATPase Na+/K+ Transporting Subunit Beta 3 (ATP1B3), Metallothionein 1A (MT1A), 1B (MT1B), 1G MT1G), 1H (MT1H), 1X (MT1X), 2A (MT2A), Ferritin Heavy Chain 1(FTH1), Ferritin Light Chain (FTL)

Caveolin 1 (CAV1), Collagen Type 3 Alpha 1 Chain (COL3A1), Type I Alpha 1 Chain (COL1A1), Type 1 Alpha 2 Chain (COL1A2), Type 6 Alpha 3 Chain (COL6A3) Actinin Alpha 1, Myosin Light Chain 9 (MYL9), 12A (MYL12A), 12B (MYL12B), Calpain 2 (CAPN2), Integrin Beta 1(ITGB1), Filamin A (FLNA),, Vascular endothelial growth factor B (VEGFB), Fibronectin 1(FN1)

leukocyte antigen A (HLA-A), B (HLA-B), C (HLA-C), G (HLA-G), Hepatocyte Growth Factor (HGS), Tubulin Beta 6 Class V (TUBB6), Tubulin alpha 1 B (TUBA1B), ITGB1,

HLA-C, HLA-G, Protein Family A (Hsp70) Member 1A Shock Protein (HSPA1A), Protein Family A (Hsp70) Member 8A (HSPA8)

ubiquinone oxidoreductase chain 1 (ND1), NADH–ubiquinone oxidoreductase chain 3 (ND3) ATP synthase F1 subunit epsilon (ATP5E), ATP Synthase Peripheral Stalk Subunit OSCP (ATP50), NADH:Ubiquinone Oxidoreductase Subunit B4 (NDUFB4), Ubiquinol-Cytochrome C Reductase, Complex III Subunit XI (UQCR11), Cyclooxygenase 1 (COX1) and 2 (COX2), Ubiquitin C-Terminal Hydrolase L1 (UCHL1), Ubiquitin B (UBB)

HLA-G

HLA-B, TUBA1B, ITGB1, HLA-G

(HSP90B1), Valosin Containing Protein (VCP), Defender Against Cell Death 1 (DAD1), DnaJ homolog subfamily A member 1 (DNAJA1), Protein Disulfide Isomerase Family A Member 4 (PDIA4) and 6 (PDIA6), HSPA1A, Heat Shock Protein Family A (Hsp70) Member 5 (HSPA5), CAPN2, HSPA8, Signal Sequence Receptor Subunit 1 (SSR1)

8.15 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, ARPC2, TUBB6, TUBA1B, ITGB1 4.73

9.38 × 10<sup>−</sup><sup>7</sup> ATPase Na+/K+ Transporting Subunit Alpha 1

1.30 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, Cathepsin L (CTSL), Human

2.60 × 10<sup>−</sup><sup>3</sup> Beta-2-Microglobulin (B2M), CTSL, HLA-A, HLA-B,

2.61 × 10<sup>−</sup><sup>3</sup> Peptidylprolyl Isomerase F (PPIF), NADH–

2.67 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, CAV1, HLA-A, HLA-C, HLA-B,

8.86 × 10<sup>−</sup><sup>3</sup> Heat Shock Protein 90 Beta Family Member 1

hsa04145: Phagosome 3.88 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, CTSL, HLA-A, HGS, TUBB6, HLA-C,

7.99

9.14

3.12

2.82

4.23

3.12

4.94

2.95

2.62

hsa03010: Ribosome 1.25 × 10<sup>−</sup><sup>16</sup> Ribosomal Protein L7(RPL7), L9 (RPL9), L10A

hsa04510: Focal adhesion 1.61 × 10<sup>−</sup><sup>4</sup> Actin Beta (ACTB), Actin Gamma 1(ACTG1),

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

hsa04978: Mineral absorption

hsa05205: Proteoglycans

hsa04612: Antigen processing and presentation

hsa05012: Parkinson's

hsa05416: Viral myocarditis

hsa05130: Pathogenic *Escherichia coli* infection

hsa04141: Protein processing in endoplasmic reticulum

in cancer

disease

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*


*Gene Expression and Phenotypic Traits*

**146**

**Figure 2.**

The Ribosome KEGG Pathway included 27 of the SMG sensitive genes; 26 of these genes were down-regulated between 1.5 and 2 fold; only MRPS6 was upregulated (4.5 fold). Interestingly, a number of ribosomal protein genes have also been found to be down-regulated in other studies including our previous study on keratinocytes [21] as well as the NASA Twins Study [16]. The mineral absorption pathway had 10 genes which were all significantly up-regulated in SMG. ATP1A1 and ATP1B3 were both up-regulated by over 2.8 fold. The ferritin genes FTH1 and FTL were up-regulated by 6.6 and 1.6 fold, respectively. Ferritin genes have also been found to be up-regulated as a result of exposure to simulated microgravity in other studies [32]. The metallothionein genes were up-regulated from 7.5 to close to

*Heatmap and Venn diagram comparing the expression levels among the HDF cells cultured in three separate bioreactors. (A) The Heatmap shows the expression levels were very similar among the three replicate experiments. Green represents the down-regulated genes and red represents the up-regulated genes. (B) Venn diagram comparing the centralization level of the differentially regulated genes among each of the three replicates. The red represents the up-regulated genes and the green represents the down-regulated genes.*


*Red indicates that the genes were up-regulated in the data and green indicates the genes were down-regulated in the data. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. Fold enrichment (FE) is a measure of the magnitude of gene enrichment. P value ≤0.05 and FE ≥1.5 were considered significant and interesting.*

## **Table 1.**

*KEGG pathway analysis of the 271 differential regulated genes from the HDF cells exposed to 5 days SMG.*

12 fold. In a previous study on human keratinocytes in SMG, metallothioneins were also up-regulated [21]. Metallothionein isoforms MT1 and MT2 have been identified as gravity sensitive genes [17, 18, 21, 33–35]. Focal adhesion pathway genes were found to be generally down-regulated by more than 2 fold in the present study. The only exceptions were ITGB1 and VEGFB which were up-regulated by 1.5 and 1.8 fold, respectively. There were 14 genes in the proteoglycans in cancer category, with 9 genes being up-regulated and 4 down-regulated; these included several major gravity sensitive genes identified previously, CAV1, FN1, DCN, and CD44 [17]. In the antigen processing and presentation pathway, the 8 genes represented were all up-regulated. These include the HLA genes, the closely related B2M, as well as CTSL, HSPA1A and HSPA8. In a previous study, HLA-G is also up-regulated in the SMG treated human keratinocytes [21]. In normal gravity environment, HLA-G has been shown to have direct inhibitory effect on T, APC, and NK cell functions and induces suppressor T-cells [36]. It has been found to be present in neurological

**149**

**Figure 3.**

*labeled along the left side of each panel.*

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

disorders [37]. HLA-G is considered a stress inducible gene [38]. HLA-G also plays a role in tumor-driven immune escape mechanism of cancer cells during the later phase in host and tumor cell interactions. When HLA-G is expressed, it can result in an immune suppressive function [38–40]. HLA-G expression has been found to correlate with low frequency of rejection in some forms of organ transplants [39]. Heat shock proteins HSPA1A and HSPA8 have been identified as being gravity sensitive in several studies. In some of these studies, HSPA1A has been shown to be

up-regulated [21, 32, 41]. They were also shown to be down-regulated [42].

**3.3 Validation of microarray results through northern blotting analysis**

The quality of a gene list from a high throughput study is essential for a successful functional analysis in DAVID [29]. The high throughput microarray data of HDF in SMG presented above was further validated by performing Northern blot analysis. Northern blotting measures the abundance as well as the size of the RNA of interest [21]. In agreement with the microarray data, the Northern results showed

*Microarray data validation using northern blot analysis. 10 μg of total RNA of the fibroblast cells from each of the three RWV bioreactors (3-D spheres) were loaded in lanes 2–4. Lanes 1 and 5 are control RNA samples from the cells grown at normal gravity. The northern blot was probed and striped repeatedly using cDNA probe fragments from genes indicated at the bottom of each panel (A–I). RNA size markers in nucleotides were* 

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

disorders [37]. HLA-G is considered a stress inducible gene [38]. HLA-G also plays a role in tumor-driven immune escape mechanism of cancer cells during the later phase in host and tumor cell interactions. When HLA-G is expressed, it can result in an immune suppressive function [38–40]. HLA-G expression has been found to correlate with low frequency of rejection in some forms of organ transplants [39]. Heat shock proteins HSPA1A and HSPA8 have been identified as being gravity sensitive in several studies. In some of these studies, HSPA1A has been shown to be up-regulated [21, 32, 41]. They were also shown to be down-regulated [42].

## **3.3 Validation of microarray results through northern blotting analysis**

The quality of a gene list from a high throughput study is essential for a successful functional analysis in DAVID [29]. The high throughput microarray data of HDF in SMG presented above was further validated by performing Northern blot analysis.

Northern blotting measures the abundance as well as the size of the RNA of interest [21]. In agreement with the microarray data, the Northern results showed

#### **Figure 3.**

*Gene Expression and Phenotypic Traits*

hsa00190: Oxidative phosphorylation

hsa04512: ECM-receptor

interaction

hsa04974: Protein digestion and absorption

hsa04670: Leukocyte transendothelial migration

hsa04260: Cardiac muscle contraction

hsa04611: Platelet activation

hsa05100: Bacterial invasion of epithelial

hsa05332: Graft-versus-

hsa05010: Alzheimer's

hsa05330: Allograft

hsa04940:Type I diabetes mellitus

hsa04918:Thyroid hormone synthesis

*significant and interesting.*

**Table 1.**

cells

host disease

disease

rejection

**148**

12 fold. In a previous study on human keratinocytes in SMG, metallothioneins were also up-regulated [21]. Metallothionein isoforms MT1 and MT2 have been identified as gravity sensitive genes [17, 18, 21, 33–35]. Focal adhesion pathway genes were found to be generally down-regulated by more than 2 fold in the present study. The only exceptions were ITGB1 and VEGFB which were up-regulated by 1.5 and 1.8 fold, respectively. There were 14 genes in the proteoglycans in cancer category, with 9 genes being up-regulated and 4 down-regulated; these included several major gravity sensitive genes identified previously, CAV1, FN1, DCN, and CD44 [17]. In the antigen processing and presentation pathway, the 8 genes represented were all up-regulated. These include the HLA genes, the closely related B2M, as well as CTSL, HSPA1A and HSPA8. In a previous study, HLA-G is also up-regulated in the SMG treated human keratinocytes [21]. In normal gravity environment, HLA-G has been shown to have direct inhibitory effect on T, APC, and NK cell functions and induces suppressor T-cells [36]. It has been found to be present in neurological

*KEGG pathway analysis of the 271 differential regulated genes from the HDF cells exposed to 5 days SMG.*

hsa05131: Shigellosis 7.30 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, CD44, ARPC2, ITGB1 3.14

*Red indicates that the genes were up-regulated in the data and green indicates the genes were down-regulated in the data. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. Fold enrichment (FE) is a measure of the magnitude of gene enrichment. P value ≤0.05 and FE ≥1.5 were considered* 

**Pathways P value Gene name FE**

hsa04530: Tight junction 2.03 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, ACTN1, MYL12B, MYL12A, member

1.69 × 10<sup>−</sup><sup>2</sup> ND1, ATP5E, NDUFB4, UQCR11, COX2, COX1, ND3,

2.03 × 10<sup>−</sup><sup>2</sup> CD44, COL3A1, COL6A3, COL1A2, COL1A1, ITGB1,

2.13 × 10<sup>−</sup><sup>2</sup> HSP90B1, VCP, DAD1, DNAJA1, PDIA6, HSPA1A,

2.34 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, ACTN1, MYL12B, MYL12A, ITGB1,

4.17 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, COL3A1, COL1A2, MYL12B, COL1A1,

5.59 × 10<sup>−</sup><sup>2</sup> tumor necrosis factor receptor superfamily member

3.75 × 10<sup>−</sup><sup>2</sup> UQCR11, ATP1B3, COX2, COX1, ATP1A1,

ATP5O, PPA1

RAS oncogene family(RAB13), MYL9

FN1

HSPA5, PDIA4, CAPN2, HSPA8, SSR1

MYL9, THY1

tropomyosin 2 (beta)(TPM2)

MYL12A, ITGB1

4.32 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, CAV1, ARPC2, ITGB1, FN1 3.09

4.71 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B, HLA-G 4.88

6.25 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B, HLA-G 4.35

8.45 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B, HLA-G 3.83

9.43 × 10<sup>−</sup><sup>2</sup> HSP90B1, ATP1B3, ATP1A1, HSPA5, PDIA4 2.87

1A(TNFRSF1A), ATP5E, NDUFB4, UQCR11, COX2, COX1, ATP5O, CAPN2, Calmodulin 2 (CALM2)

2.72

3.24

3.24

3.2

2.8

3.22

2.48

2.16

*Microarray data validation using northern blot analysis. 10 μg of total RNA of the fibroblast cells from each of the three RWV bioreactors (3-D spheres) were loaded in lanes 2–4. Lanes 1 and 5 are control RNA samples from the cells grown at normal gravity. The northern blot was probed and striped repeatedly using cDNA probe fragments from genes indicated at the bottom of each panel (A–I). RNA size markers in nucleotides were labeled along the left side of each panel.*

that the mRNA levels of β-actin, CTGF, and VIM were down regulated, while that of FTH1, HLA-A, HLA-B (data not shown), HLA-G, MMP1, and MT2A were upregulated (**Figure 3**). In addition, Northern analysis showed that the expression level of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was consistent in the presence and absence of microgravity (**Figure 3I**).

## **3.4 Comparison of HDF in SMG gene expression data with that from the NASA twins study**

The NASA Twins study is very substantial in undertaking with extensive data sets [14]. It was very appealing to compare the current data of HDF in SMG with the recently published gene expression data from the NASA Twins study. Since a large amount of data is available, it is more manageable to first focus on the gene expression data from the samples taken from inflight first half (up to 6 months) and the second half (up to 1 year), generated from the inflight astronaut's PBMCs. Such comparison of the two gene expression data sets gave 194 overlapping differentially expressed genes, or about 72%, of the genes found to be differentially regulated in the current study with HDF in SMG were also differentially regulated in the selected data set from the NASA study. Of these 194 genes, 86 had a similar expression pattern (regulated in the same direction) to the HDF in SMG data and 108 had the opposite expression pattern. A Heatmap was generated as a way of better visualizing the similarities between the data points (**Figure 4**).

## **3.5 KEGG pathways of the overlapping genes**

As a way of comparing and better understanding the potential relationships between the HDF SMG gene expression data and the NASA Twin gene expression data from **Table 2** [16], we uploaded the gene list of the genes that showed same direction expression patterns (**Table 2**) and the genes that showed opposite expression patterns (**Table 3**) to DAVID in order to generate KEGG Pathway information.

After processing the list of genes with the same expression patterns through DAVID, a total of 7 KEGG pathways were statistically significant (P ≤ 0.05) Interestingly, the genes enriched in all 9 KEGG pathways had >3.5 fold enrichment (**Table 2**). All the genes represented in the KEGG pathways were down-regulated in both sets of data. The Ribosome pathway had the greatest number of genes represented. The down-regulation of the ribosomal protein genes is consistent with our previous study in keratinocytes in SMG [21].

KEGG pathway analysis of the list of genes with opposite direction of expression regulation produced a total of 15 KEGG Pathways that were statistically significant (P ≤ 0.05). Interestingly, the genes in all the 21 KEGG pathways had >2.5 fold enrichment (**Table 3**).

The protein processing in endoplasmic reticulum (ER) pathway had the greatest number of genes represented. In the current study of HDF in SMG, the genes represented in the ER pathway were all up-regulated whereas they were down-regulated in the NASA Twin Study data [16]. Heat shock proteins HSPA1A and HSPA8 have been identified as being gravity sensitive in several studies. In some of these studies, HSPA1A has been shown to be up-regulated [21, 32, 41] and in one study in addition to the NASA Twin Study was shown to be down-regulated [42]. Collagens also seem to be mixed in differential regulation in microgravity. In our current study, COL1, 2, and 6 were down-regulated but were up-regulated in the Twin study. In some studies COL1 has been shown to be up-regulated [43] while in others it has been shown to be down-regulated [33, 44]. FN1, which we identified as a putative

**151**

**Figure 4.**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

*Heatmap comparison of HDF in SMG gene expression data with that from the NASA twins study Heatmap is used to compare the HDF SMG data (column A) with the inflight, first half of the twins study ribodepleted (column B), PolyA+ (column C), multivariant (column D), and the inflight, second half of the twins study ribodepleted (column E), PolyA+ (column F), and multivariant (column G). Green were down-regulated genes, red represented up-regulated genes, and gray indicates the presence of no corresponding data.*

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

#### **Figure 4.**

*Gene Expression and Phenotypic Traits*

**twins study**

points (**Figure 4**).

enrichment (**Table 3**).

presence and absence of microgravity (**Figure 3I**).

**3.5 KEGG pathways of the overlapping genes**

previous study in keratinocytes in SMG [21].

that the mRNA levels of β-actin, CTGF, and VIM were down regulated, while that of FTH1, HLA-A, HLA-B (data not shown), HLA-G, MMP1, and MT2A were upregulated (**Figure 3**). In addition, Northern analysis showed that the expression level of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was consistent in the

**3.4 Comparison of HDF in SMG gene expression data with that from the NASA** 

The NASA Twins study is very substantial in undertaking with extensive data sets [14]. It was very appealing to compare the current data of HDF in SMG with the recently published gene expression data from the NASA Twins study. Since a large amount of data is available, it is more manageable to first focus on the gene expression data from the samples taken from inflight first half (up to 6 months) and the second half (up to 1 year), generated from the inflight astronaut's PBMCs. Such comparison of the two gene expression data sets gave 194 overlapping differentially expressed genes, or about 72%, of the genes found to be differentially regulated in the current study with HDF in SMG were also differentially regulated in the selected data set from the NASA study. Of these 194 genes, 86 had a similar expression pattern (regulated in the same direction) to the HDF in SMG data and 108 had the opposite expression pattern. A Heatmap was generated as a way of better visualizing the similarities between the data

As a way of comparing and better understanding the potential relationships between the HDF SMG gene expression data and the NASA Twin gene expression data from **Table 2** [16], we uploaded the gene list of the genes that showed same direction expression patterns (**Table 2**) and the genes that showed opposite expression patterns (**Table 3**) to DAVID in order to generate KEGG Pathway information. After processing the list of genes with the same expression patterns through DAVID, a total of 7 KEGG pathways were statistically significant (P ≤ 0.05) Interestingly, the genes enriched in all 9 KEGG pathways had >3.5 fold enrichment (**Table 2**). All the genes represented in the KEGG pathways were down-regulated in both sets of data. The Ribosome pathway had the greatest number of genes represented. The down-regulation of the ribosomal protein genes is consistent with our

KEGG pathway analysis of the list of genes with opposite direction of expression regulation produced a total of 15 KEGG Pathways that were statistically significant (P ≤ 0.05). Interestingly, the genes in all the 21 KEGG pathways had >2.5 fold

The protein processing in endoplasmic reticulum (ER) pathway had the greatest number of genes represented. In the current study of HDF in SMG, the genes represented in the ER pathway were all up-regulated whereas they were down-regulated in the NASA Twin Study data [16]. Heat shock proteins HSPA1A and HSPA8 have been identified as being gravity sensitive in several studies. In some of these studies, HSPA1A has been shown to be up-regulated [21, 32, 41] and in one study in addition to the NASA Twin Study was shown to be down-regulated [42]. Collagens also seem to be mixed in differential regulation in microgravity. In our current study, COL1, 2, and 6 were down-regulated but were up-regulated in the Twin study. In some studies COL1 has been shown to be up-regulated [43] while in others it has been shown

to be down-regulated [33, 44]. FN1, which we identified as a putative

**150**

*Heatmap comparison of HDF in SMG gene expression data with that from the NASA twins study Heatmap is used to compare the HDF SMG data (column A) with the inflight, first half of the twins study ribodepleted (column B), PolyA+ (column C), multivariant (column D), and the inflight, second half of the twins study ribodepleted (column E), PolyA+ (column F), and multivariant (column G). Green were down-regulated genes, red represented up-regulated genes, and gray indicates the presence of no corresponding data.*


*The gene symbols in green color indicated down-regulation. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. FE is a measure of the magnitude of gene enrichment. P value ≤0.05 and FE ≥1.5 were considered significant and interesting, respectively.*

#### **Table 2.**

*KEGG pathways generated from comparison of genes differentially regulated in the same direction between the HDF and NASA twins data.*


**153**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

**Pathways P value Gene name FE**

hsa05332:Graft-versus-host disease 3.96 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B 4.06

hsa05330:Allograft rejection 4.87 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B 8.93 hsa04514:Cell adhesion molecules (CAMs) 5.37 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B, ITGB1 7.97 hsa04145:Phagosome 6.32 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B, ITGB1 3.46 hsa04940:Type I diabetes mellitus 6.60 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B 3.28 hsa03050:Proteasome 7.16 × 10<sup>−</sup><sup>2</sup> PSMB4, PSMB7, PSMA6 7.02 hsa05320:Autoimmune thyroid disease 9.55 × 10<sup>−</sup><sup>2</sup> HLA-A, HLA-C, HLA-B 6.7 hsa05145:Toxoplasmosis 9.79 × 10<sup>−</sup><sup>2</sup> PPIF, HSPA1A, ITGB1, HSPA8 5.67 *The green indicates genes that were down-regulated and the red indicates genes that were up regulated in the HDF data set. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. FE is a measure of the magnitude of gene enrichment. P value ≤0.05 and FE ≥1.5 were considered significant and interesting,* 

HLA-B

ITGB1, FN1

4.64

2.85

hsa05169:Epstein–Barr virus infection 2.92 × 10<sup>−</sup><sup>2</sup> CD44, VIM, HLA-A, HLA-C,

hsa05205:Proteoglycans in cancer 4.23 × 10<sup>−</sup><sup>2</sup> CAV1, CD44, CD63, DDX5,

"space gene" [17, 18] has also been shown to have variations in expression patterns.

*KEGG pathways generated from a comparison of genes differentially regulated in the opposite direction* 

In the current study, the gene expression profile of HDF grown in 5 day SMG was first displayed and validated (**Figures 1**–**3**, **Table 1**). The high throughput cDNA microarray data of HDF in 5 day SMG was then used to compare with the high throughput RNA sequencing data from an astronaut's PBMCs during a long term inflight ISS (**Figure 4**, **Tables 2** and **3**). Amazingly, about 72% of the 271 microgravity sensitive genes of HDF in SMG, were also differentially regulated in the NASA Twins 6- and 12-month inflight data. These 194 overlapping genes were identified as putative microgravity sensitive space genes, because the human dermal fibroblast cell line was only exposed to SMG, no radiation nor other space related environmental factor was involved. However, other factors such as cell type difference and space radiation, may also influence the expression of microgravity sensitive genes. Indeed, among these microgravity sensitive genes, 86 genes showed the same expression pattern in both simulated and real microgravity conditions while

It is remarkable that as many as 86 genes were found to have the same directions of expression regulation between very different settings of studies. It is most likely that these genes were the main players in cellular response to microgravity environment. When the 86 microgravity sensitive genes with the same expression regulation trends were subjected to KEGG pathway analysis, they were represented in seven significant pathways where they were all downregulated (**Table 2**). Most notably, both sets of global gene expression data showed the down regulation of 25 ribosomal protein genes. The genes in pathogenic Escherichia coli infection pathway and leukocyte trans-endothelial migration pathway were all down-regulated in

It has been shown to be down-regulated in several studies [34, 44, 45] and

the other 108 genes displayed opposite direction of regulation.

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

up-regulated in others [16, 43, 46].

*between the HDF and NASA twins data.*

**4. Discussion**

*respectively.*

**Table 3.**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*


*The green indicates genes that were down-regulated and the red indicates genes that were up regulated in the HDF data set. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. FE is a measure of the magnitude of gene enrichment. P value ≤0.05 and FE ≥1.5 were considered significant and interesting, respectively.*

#### **Table 3.**

*Gene Expression and Phenotypic Traits*

hsa05130:Pathogenic Escherichia

hsa04810:Regulation of actin

hsa04921:Oxytocin signaling

*and FE ≥1.5 were considered significant and interesting, respectively.*

coli infection

cytoskeleton

pathway

**Table 2.**

hsa04670:Leukocyte transendothelial migration

hsa04141:Protein processing in endoplasmic reticulum

*HDF and NASA twins data.*

hsa04612:Antigen processing and

presentation

**Pathways P value Gene name FE**

*KEGG pathways generated from comparison of genes differentially regulated in the same direction between the* 

hsa04611:Platelet activation 8.88 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, MYL12B, MYL12A 3.713 hsa05131:Shigellosis 9.53 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, ARPC2 5.657 *The gene symbols in green color indicated down-regulation. The significance of gene enrichment is evaluated chiefly by the p value with a modified Fisher's exact test. FE is a measure of the magnitude of gene enrichment. P value ≤0.05* 

**Pathways P Value Gene name FE**

hsa03010:Ribosome 3.93 × 10<sup>−</sup><sup>27</sup> RPL35, RPL27A, RPL36, RPS15A, RPS2,

hsa04530:Tight junction 5.32 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, MYL12B, MYL12A,

hsa04510:Focal adhesion 2.55 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, MYL12B, MYL12A,

hsa04512:ECM-receptor interaction 1.70 × 10<sup>−</sup><sup>4</sup> CD44, COL3A1, COL6A3,

hsa04978:Mineral absorption 7.86 × 10<sup>−</sup><sup>4</sup> ATP1B3, ATP1A1, MT1X, FTH1,

hsa04974:Protein digestion and absorption 1.48 × 10<sup>−</sup><sup>3</sup> ATP1B3, COL3A1, COL6A3,

hsa04918:Thyroid hormone synthesis 4.41 × 10<sup>−</sup><sup>3</sup> HSP90B1, ATP1B3, ATP1A1,

hsa04510:Focal adhesion 1.37 × 10<sup>−</sup><sup>2</sup> CAV1, COL3A1, COL6A3,

hsa04151:PI3K-Akt signaling pathway 1.67 × 10<sup>−</sup><sup>2</sup> HSP90B1, COL3A1, COL6A3,

hsa04144:Endocytosis 2.74 × 10<sup>−</sup><sup>2</sup> CAV1, ARF1, HLA-A, HLA-C,

hsa05134:Legionellosis 1.48 × 10<sup>−</sup><sup>2</sup> ARF1, VCP, HSPA1A, HSPA8 3.44

hsa05416:Viral myocarditis 1.71 × 10<sup>−</sup><sup>2</sup> CAV1, HLA-A, HLA-C, HLA-B 6.9

2.93 × 10<sup>−</sup><sup>5</sup> HSP90B1, VCP, DAD1, DNAJA1,

RPL39, RPS3, RPL32, RPL7, RPL31, RPS3A, RPL9, RPL34, RPL11, RPL10A, RPL12, RPS23, RPS24, RPL27, RPS7, RPS18, RPS19, RPL18A, RPS17, RPS10

MYL9

MYL9

CAPN2, MYL9

MYL12A, MYL9

3.32 × 10<sup>−</sup><sup>2</sup> ACTB, MYL6, ACTG1, CALM2, MYL9 4.023

8.07 × 10<sup>−</sup><sup>3</sup> ACTB, ACTG1, ARPC2, TUBA1B 9.465

1.40 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, MYL12B, MYL12A,

2.74 × 10<sup>−</sup><sup>2</sup> ACTB, ACTG1, ARPC2, MYL12B,

7.62 × 10<sup>−</sup><sup>4</sup> HLA-A, HLA-C, HSPA1A, HLA-B,

PDIA6, HSPA1A, HSPA5, PDIA4, HSPA8, SSR1

COL1A2, COL1A1, ITGB1, FN1

HSPA8, B2M

FTL

COL1A2, ATP1A1, COL1A1

HSPA5, PDIA4

COL1A2, COL1A1, ITGB1, FN1

COL1A2,YWHAQ, COL1A1, ITGB1, FN1, DDIT4

HSPA1A, HLA-B, HSPA8

5.81

22.18

6.936

5.247

3.515

3.448

7.91

7.76

11.2

6.7

9.1

7.02

3.34

2.56

**152**

*KEGG pathways generated from a comparison of genes differentially regulated in the opposite direction between the HDF and NASA twins data.*

"space gene" [17, 18] has also been shown to have variations in expression patterns. It has been shown to be down-regulated in several studies [34, 44, 45] and up-regulated in others [16, 43, 46].

## **4. Discussion**

In the current study, the gene expression profile of HDF grown in 5 day SMG was first displayed and validated (**Figures 1**–**3**, **Table 1**). The high throughput cDNA microarray data of HDF in 5 day SMG was then used to compare with the high throughput RNA sequencing data from an astronaut's PBMCs during a long term inflight ISS (**Figure 4**, **Tables 2** and **3**). Amazingly, about 72% of the 271 microgravity sensitive genes of HDF in SMG, were also differentially regulated in the NASA Twins 6- and 12-month inflight data. These 194 overlapping genes were identified as putative microgravity sensitive space genes, because the human dermal fibroblast cell line was only exposed to SMG, no radiation nor other space related environmental factor was involved. However, other factors such as cell type difference and space radiation, may also influence the expression of microgravity sensitive genes. Indeed, among these microgravity sensitive genes, 86 genes showed the same expression pattern in both simulated and real microgravity conditions while the other 108 genes displayed opposite direction of regulation.

It is remarkable that as many as 86 genes were found to have the same directions of expression regulation between very different settings of studies. It is most likely that these genes were the main players in cellular response to microgravity environment. When the 86 microgravity sensitive genes with the same expression regulation trends were subjected to KEGG pathway analysis, they were represented in seven significant pathways where they were all downregulated (**Table 2**). Most notably, both sets of global gene expression data showed the down regulation of 25 ribosomal protein genes. The genes in pathogenic Escherichia coli infection pathway and leukocyte trans-endothelial migration pathway were all down-regulated in microgravity (**Table 2**), which may contribute to the decreased immune resistant to microbial infection in spaceflight. In addition, genes in the cytoskeleton network (ACTB, ACTG1, ARPC2, MYL12B, MYL12A, MYL9), focal adhesion (ACTB, ACTG1, MYL12B, MYL12A, CAPN2, MYL9), as well as tight junction (ACTB, ACTG1, MYL12B, MYL12A, MYL9) pathways were also downregulated in both sets of data. In combination with the downregulation of extracellular matrix proteins (**Table 1**) such as COL1A1, COL1A2, COL3A1 in HDF, the data indicated an overall decrease in bone matrix and skeletal muscle synthesis and increased catabolism (e.g. MMP1 increased sharply). Furthermore, genes in the oxytocin signaling pathway (ACTB, MYL6, ACTG1, CALM2, MYL9), which is involved in smooth muscle contraction and stress management, were also down in their expression levels in both microgravity data sets. Malfunction of this pathway has been implicated in depression, autism, and schizophrenia [47]. Overall, the data in **Table 2** gave a strong mechanistic connection to the main symptoms, such as skin problems and immunological stress, vascular changes, muscle atrophy and bone density alteration that were associated with the weightlessness of space flight. Indeed, the altered expression of these 86 microgravity sensitive genes affected many fundamental molecular functions (data not shown), including structural constituent of ribosome, RNA binding, protein binding, metal ion binding, structural constituent of the cytoskeleton, cadherin binding involved in cell-cell adhesion, extracellular matrix binding, etc. Many biological processes in these cells (data not shown), such as SRP-dependent co-translational protein targeting to membrane, translation initiation and elongation, mRNA stability, muscle contraction, regulation of cell shape, among others, were also significantly impacted. These genes with common trend of expression regulation in microgravity would most likely expand the list of putative major space genes and microgravity sensitive pathways [17, 18]. A substantial amount of information was derived from the current work which may necessitate more detailed analysis and discussion in future communications.

The number of overlapping microgravity sensitive genes was substantial considering the many differences between the two study settings. The result from this comparative analysis further validated the effectiveness of the bioreactors for SMG cell culture. The identification of 86 genes (**Table 2**) with the same direction of regulation in two different study settings is very substantial and unique. These genes should be considered best candidates for major microgravity sensitive genes because one of the two studies, the current study, only involves simulated microgravity, while the other study, the NASA Twins Study involves true spaceflight environment with microgravity and space radiation. This comparative analysis here enabled the differentiation of the microgravity effect alone on the differentially expressed genes from the human astronaut spaceflight gene expression data. The identification of the overlapping significant genes regulated in the opposite direction rendered important insight into human gene activity changes in very different study systems. The HDF cell line in SMG versus the human astronaut in ISS, adjusted their expressions toward adaptation to the simple SMG as well as the true space environment of both space microgravity and space radiation. The 108 microgravity sensitive genes with opposite directions of expression regulation could also be of major significance in the microgravity adaptation process. Compared to the single cell line in SMG alone for the HDF cells, the cell samples from the inflight astronaut was exposed to various other factors such as space radiation, in addition to the microgravity of the ISS. The more complex space environment may require the significant genes to modify their expression toward adaptation. Expression patterns in this group of genes could provide insight into our understanding regarding the interplay among different cellular gene functions in human adaptation to microgravity and space radiation (**Table 3**).

**155**

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

Our previous studies on gene expression in HEK cells grown in SMG has 43 genes overlapping with the HDF data in the current communication; of which, 23 genes were regulated in the same direction and 20 were regulated in the opposite direction [21]. These two different types of cells require different culture conditions and perform different roles in the skin. It is understandable that they have their characteristic expression profiles in response to the simulated microgravity environment. However, the number of overlapping genes were also substantial. In a previous review paper comparing various microarray based gene expression studies on microgravity effects, an initial list of 129 genes were identified as putative microgravity sensitive genes or major space genes [17]. In the current study, 12 out of the 194 genes that were significantly differentially regulated in both the HDF cells and the PBMCs, are also in the group of the putative major spaces genes [17], with 4 genes regulated in the same direction (MMP1, GPNMB, RPL10A, and ANXA2) and 8 genes (CAV1, CD44, CD59, CYR61, FN1, HSPA1A, MT1X, and PDIA4) changed their expression in the opposite directions in response to microgravity. Continued microgravity research in space and the readily controlled simulated microgravity bioreactors would provide valuable information toward the identification of major gravity sensitive genes, or simply, the major space genes. With more data available, the molecular and cellular mechanisms underlying the microgravity response could be better understood. Elucidation the molecular mechanism of human space adaptation response is an important aspect toward safer space experience and human health in general. It is evident that continued microgravity research is beneficial to

The identification of 271 genes of HDF significantly differentially regulated by SMG provided a set of data for more detailed mechanistic studies; 72% of these microgravity sensitive genes were also reported in the high throughput gene expres-

The identification of the large number of overlapping genes between the HDF in SMG and astronaut's PBMCs indicates microgravity alone, without space radiation,

was able to elicit an adaptive response involving a set of about 200 genes.

This work was partially supported by NASA JSC Grant No. NCC9-165.

ATP1A1 ATPase Na+/K+ transporting subunit alpha 1 ATP1B3 ATPase Na+/K+ transporting subunit beta 3 ATP50 ATP synthase peripheral stalk subunit OSCP

ATP5E ATP synthase F1 subunit epsilon

B2M beta-2microglobulin

CALM2 calmodulin 2 CAPN2 calpain 2

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

healthy living in space and on earth.

sion data in the recent NASA Twins' Study.

**5. Conclusion**

**Acknowledgements**

**Abbreviations/glossary**

ACTB actin beta ACTG1 actin gamma 1 ANXA2 annexin A2

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

Our previous studies on gene expression in HEK cells grown in SMG has 43 genes overlapping with the HDF data in the current communication; of which, 23 genes were regulated in the same direction and 20 were regulated in the opposite direction [21]. These two different types of cells require different culture conditions and perform different roles in the skin. It is understandable that they have their characteristic expression profiles in response to the simulated microgravity environment. However, the number of overlapping genes were also substantial. In a previous review paper comparing various microarray based gene expression studies on microgravity effects, an initial list of 129 genes were identified as putative microgravity sensitive genes or major space genes [17]. In the current study, 12 out of the 194 genes that were significantly differentially regulated in both the HDF cells and the PBMCs, are also in the group of the putative major spaces genes [17], with 4 genes regulated in the same direction (MMP1, GPNMB, RPL10A, and ANXA2) and 8 genes (CAV1, CD44, CD59, CYR61, FN1, HSPA1A, MT1X, and PDIA4) changed their expression in the opposite directions in response to microgravity. Continued microgravity research in space and the readily controlled simulated microgravity bioreactors would provide valuable information toward the identification of major gravity sensitive genes, or simply, the major space genes. With more data available, the molecular and cellular mechanisms underlying the microgravity response could be better understood. Elucidation the molecular mechanism of human space adaptation response is an important aspect toward safer space experience and human health in general. It is evident that continued microgravity research is beneficial to healthy living in space and on earth.

## **5. Conclusion**

*Gene Expression and Phenotypic Traits*

microgravity (**Table 2**), which may contribute to the decreased immune resistant to microbial infection in spaceflight. In addition, genes in the cytoskeleton network (ACTB, ACTG1, ARPC2, MYL12B, MYL12A, MYL9), focal adhesion (ACTB, ACTG1, MYL12B, MYL12A, CAPN2, MYL9), as well as tight junction (ACTB, ACTG1, MYL12B, MYL12A, MYL9) pathways were also downregulated in both sets of data. In combination with the downregulation of extracellular matrix proteins (**Table 1**) such as COL1A1, COL1A2, COL3A1 in HDF, the data indicated an overall decrease in bone matrix and skeletal muscle synthesis and increased catabolism (e.g. MMP1 increased sharply). Furthermore, genes in the oxytocin signaling pathway (ACTB, MYL6, ACTG1, CALM2, MYL9), which is involved in smooth muscle contraction and stress management, were also down in their expression levels in both microgravity data sets. Malfunction of this pathway has been implicated in depression, autism, and schizophrenia [47]. Overall, the data in **Table 2** gave a strong mechanistic connection to the main symptoms, such as skin problems and immunological stress, vascular changes, muscle atrophy and bone density alteration that were associated with the weightlessness of space flight. Indeed, the altered expression of these 86 microgravity sensitive genes affected many fundamental molecular functions (data not shown), including structural constituent of ribosome, RNA binding, protein binding, metal ion binding, structural constituent of the cytoskeleton, cadherin binding involved in cell-cell adhesion, extracellular matrix binding, etc. Many biological processes in these cells (data not shown), such as SRP-dependent co-translational protein targeting to membrane, translation initiation and elongation, mRNA stability, muscle contraction, regulation of cell shape, among others, were also significantly impacted. These genes with common trend of expression regulation in microgravity would most likely expand the list of putative major space genes and microgravity sensitive pathways [17, 18]. A substantial amount of information was derived from the current work which may neces-

sitate more detailed analysis and discussion in future communications.

The number of overlapping microgravity sensitive genes was substantial considering the many differences between the two study settings. The result from this comparative analysis further validated the effectiveness of the bioreactors for SMG cell culture. The identification of 86 genes (**Table 2**) with the same direction of regulation in two different study settings is very substantial and unique. These genes should be considered best candidates for major microgravity sensitive genes because one of the two studies, the current study, only involves simulated microgravity, while the other study, the NASA Twins Study involves true spaceflight environment with microgravity and space radiation. This comparative analysis here enabled the differentiation of the microgravity effect alone on the differentially expressed genes from the human astronaut spaceflight gene expression data. The identification of the overlapping significant genes regulated in the opposite direction rendered important insight into human gene activity changes in very different study systems. The HDF cell line in SMG versus the human astronaut in ISS, adjusted their expressions toward adaptation to the simple SMG as well as the true space environment of both space microgravity and space radiation. The 108 microgravity sensitive genes with opposite directions of expression regulation could also be of major significance in the microgravity adaptation process. Compared to the single cell line in SMG alone for the HDF cells, the cell samples from the inflight astronaut was exposed to various other factors such as space radiation, in addition to the microgravity of the ISS. The more complex space environment may require the significant genes to modify their expression toward adaptation. Expression patterns in this group of genes could provide insight into our understanding regarding the interplay among different cellular gene functions in human adaptation to

**154**

microgravity and space radiation (**Table 3**).

The identification of 271 genes of HDF significantly differentially regulated by SMG provided a set of data for more detailed mechanistic studies; 72% of these microgravity sensitive genes were also reported in the high throughput gene expression data in the recent NASA Twins' Study.

The identification of the large number of overlapping genes between the HDF in SMG and astronaut's PBMCs indicates microgravity alone, without space radiation, was able to elicit an adaptive response involving a set of about 200 genes.

## **Acknowledgements**

This work was partially supported by NASA JSC Grant No. NCC9-165.

## **Abbreviations/glossary**



**157**

**Author details**

Jade Q. Clement

United States

Department of Chemistry, Texas Southern University, Houston, Texas,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: jade.clement@tsu.edu

provided the original work is properly cited.

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study*

*DOI: http://dx.doi.org/10.5772/intechopen.88957*

*Gene Expression Profile of HDF in SMG Partially Overlaps with That in the NASA Twins Study DOI: http://dx.doi.org/10.5772/intechopen.88957*

## **Author details**

*Gene Expression and Phenotypic Traits*

CTGF connective tissue growth factor

CYR61 cysteine rich angiogenic inducer 61 DAD defender against cell death 1

DNAJA1 DnaJ homolog subfamily A member 1

GAPDH glyceraldehyde-3-phosphate dehydrogenase

KEGG kyoto encyclopedia of genes and genomes

ND NADH–ubiquinone oxidoreductase chain NDUFB4 NADH:ubiquinone oxidoreductase subunit B4

PDIA Protein disulfide isomerase family A member

TNFRSF1A tumor necrosis factor receptor superfamily member 1A

UQCR11 ubiquinol-cytochrome C reductase, complex III subunit XI

MRPS6 mitochondrial ribosomal protein 6

PBMCs peripheral blood mononuclear cells

SSR1 signal sequence receptor subunit 1

UCHL1 ubiquitin C-terminal hydrolase L1

VEGFB vascular endothelial growth factor B

COL collagen COX cyclooxygenase

CTSL cathepsin L

FLNA filamin A FN1 fibronectin

GO gene ontology GPNMB glycoprotein NMB HARVs high aspect ratio vessels HDF human dermal fibroblast HLA human leukocyte antigen HSP heat shock protein

ITGB1 integrin beta 1

MT metallothionein MYL myosin light chain

FTH1 ferritin heavy chain 1 FTL ferritin light chain

ISS international space station

MMP1 matrix metallopeptidase 1

PPIF peptidylprolyl isomerase F RAB13 member RAS oncogene family RCCS rotary cell culture system RP ribosomal protein RWV rotating wall vessel SMG simulated microgravity

TPM2 tropomyosin beta chain TUBA1B tubulin alpha 1 B TUBB6 tubulin beta 6 class V

VCP valosin containing protein

UBB ubiquitin B

CARMAweb comprehensive R based microarray analysis web frontend

DAVID database for annotation, visualization and integrated discovery

**156**

Jade Q. Clement Department of Chemistry, Texas Southern University, Houston, Texas, United States

\*Address all correspondence to: jade.clement@tsu.edu

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[19] Bauer J, Bussen M, Wise P, Wehland M, Schneider S, Grimm D. Searching the literature for proteins facilitates the identification of biological processes, if advanced methods of analysis are linked: A case study on microgravity-caused changes in cells. Expert Review of Proteomics. 2016;**13**(7):697-705. DOI: 10.1080/14789450.2016.1197775

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[21] Clement JQ, Lacy SM, Wilson BL. Gene expression profiling of human epidermal keratinocytes in simulated

microgravity and recovery cultures. Genomics, Proteomics & Bioinformatics. 2008;**6**(1):8-28. DOI: 10.1016/S1672-0229(08)60017-0

[22] Clement JQ, Qian L, Kaplinsky N, Wilkinson MF. The stability and fate of a spliced intron from vertebrate cells. RNA. 1999;**5**(2):206-220. DOI: 10.1017/ s1355838299981190

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gmp041

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*Gene Expression and Phenotypic Traits*

[29] Huang d W, Sherman BT,

[30] Sturn A, Quackenbush J,

[31] Breitkreutz BJ, Jorgensen P, Breitkreutz A, Tyers M. AFM 4.0: A toolbox for DNA microarray analysis.

Genome Biology. 2001;**2**(8): SOFTWARE0001. DOI: 10.1186/ gb-2001-2-8-software0001

[32] Grosse J, Wehland M,

3-dimensional formation of

japplphysiol.90780.2008

fj.03-0419fje

Pietsch J, Schulz H, Saar K, Hubner N, et al. Gravity-sensitive signaling drives

multicellular thyroid cancer spheroids. The FASEB Journal. 2012;**26**(12): 5124-5140. DOI: 10.1096/fj.12-215749

[33] Allen DL, Bandstra ER, Harrison BC, Thorng S, Stodieck LS, Kostenuik PJ, et al. Effects of spaceflight on murine skeletal muscle gene expression. Journal of Applied Physiology. 2009;**106**(2):582-595. DOI: 10.1152/

[34] Nikawa T, Ishidoh K, Hirasaka K, Ishihara I, Ikemoto M, Kano M, et al. Skeletal muscle gene expression in space-flown rats. The FASEB Journal. 2004;**18**(3):522-524. DOI: 10.1096/

[35] Hammond TG, Benes E, O'Reilly KC, Wolf DA, Linnehan RM, Taher A, et al. Mechanical culture conditions effect gene expression: Gravity-induced changes on the space shuttle. Physiological Genomics. 2000;**3**(3):163-173. DOI: 10.1152/physiolgenomics.2000.3.3.163

[36] Mouillot G, Marcou C, Zidi I, Guillard C, Sangrouber D, Carosella ED,

10.1038/nprot.2008.211

bioinformatics/18.1.207

Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols. 2009;**4**(1):44-57. DOI:

et al. Hypoxia modulates HLA-G gene expression in tumor cells. Human Immunology. 2007;**68**(4):277-285. DOI:

[37] Wiendl H. HLA-G in the nervous

[38] Alegre E, Rizzo R, Bortolotti D, Fernandez-Landazuri S, Fainardi E, Gonzalez A. Some basic aspects of HLA-G biology. Journal of Immunology Research. 2014;**2014**:657625. DOI:

[39] Loustau M, Wiendl H, Ferrone S, Carosella ED. HLA-G 2012 conference: The 15-year milestone update. Tissue Antigens. 2013;**81**(3):127-136. DOI:

[40] Donadi EA, Castelli EC, Arnaiz-Villena A, Roger M, Rey D, Moreau P. Implications of the polymorphism of HLA-G on its function, regulation, evolution and disease association. Cellular and Molecular Life Sciences. 2011;**68**(3):369-395. DOI: 10.1007/

[41] Ward NE, Pellis NR, Risin SA, Risin D. Gene expression alterations in activated human T-cells induced by modeled microgravity. Journal of Cellular Biochemistry. 2006;**99**(4): 1187-1202. DOI: 10.1002/jcb.20988

[42] Chang TT, Hughes-Fulford M. Molecular mechanisms underlying

2014;**35**(7):2162-2171. DOI: 10.1016/j.

[43] Chopard A, Hillock S, Jasmin BJ. Molecular events and signalling pathways involved in skeletal muscle disuseinduced atrophy and the impact of countermeasures. Journal of Cellular and Molecular Medicine.

the enhanced functions of three-dimensional hepatocyte aggregates. Biomaterials.

biomaterials.2013.11.063

10.1016/j.humimm.2006.10.016

system. Human Immunology. 2007;**68**(4):286-293. DOI: 10.1016/j.

humimm.2006.10.021

10.1155/2014/657625

10.1111/tan.12053

s00018-010-0580-7

Trajanoski Z. Genesis: Cluster analysis of microarray data. Bioinformatics. 2002;**18**(1):207-208. DOI: 10.1093/

**160**

[44] Sheyn D, Pelled G, Netanely D, Domany E, Gazit D. The effect of simulated microgravity on human mesenchymal stem cells cultured in an osteogenic differentiation system: A bioinformatics study. Tissue Engineering. Part A. 2010;**16**(11):3403-3412. DOI: 10.1089/ten.tea.2009.0834

[45] Dapp C, Schmutz S, Hoppeler H, Fluck M. Transcriptional reprogramming and ultrastructure during atrophy and recovery of mouse soleus muscle. Physiological Genomics. 2004;**20**(1):97-107. DOI: 10.1152/ physiolgenomics.00100.2004

[46] Qian A, Di S, Gao X, Zhang W, Tian Z, Li J, et al. cDNA microarray reveals the alterations of cytoskeletonrelated genes in osteoblast under high magneto-gravitational environment. Acta Biochimica et Biophysica Sinica. 2009;**41**(7):561-577. DOI: 10.1093/abbs/ gmp041

[47] Shen H. Neuroscience: The hard science of oxytocin. Nature. 2015;**522**(7557):410-412. DOI: 10.1038/522410a

**163**

genetic level.

**Chapter 9**

Plants

**Abstract**

suitable plant species and varieties.

**1. Three types of bilateral asymmetry**

Environmental Factors

Affecting the Expression of

*Sergey Baranov, Igor Vinokurov and Lubov Fedorova*

In recent years, there has been a growing interest in the problem of asymmetry of bilateral traits in plants. Three types of bilateral asymmetry are found in the leaf blade, of interest to ecologists and evolutionists. A brief review of the methods used in testing bilateral asymmetry and developmental stability discusses their role in the development of homeostasis and ontogenesis. Intra- and interspecific differences are considered on the example of woody plants under the influence of factors influencing the expression of bilaterally symmetry. The influence of stress on the manifestation of asymmetric traits is considered. Apparently, the climate and topography of the area play a more important role, determining the plastic and fluctuating variability. The relationship of plasticity, evolutionary canalization, and development stability is considered on the example of woody and cultivated plants. Plasticity and fluctuation variability are in a relationship coordinated by climatic conditions, primarily lighting and temperature. This, in turn, determines the mechanisms of gene regulatory networks. Thus, phenogenetics, which studies the patterns and mechanisms of gene expression and ontogenesis, is based on the data from field botanical studies of plant shape and asymmetry. Epigenetic and population studies of phenotypic variations play a role in standardizing and finding

**Keywords:** bilateral asymmetry, fluctuating variability, gene regulatory networks

One of the promising areas of monitoring for the environment is bioindication by determining the developmental stability (DS) of plants, including woody ones. Database on the developmental stability of different species of plants is to be complementing other data sets, such as chemical contamination of air, soil, and water.

Fluctuating asymmetry (FA) is a kind of asymmetry used to assess the stability of development, as the organism's ability to regulate its development on the phylo-

The concept of developmental noise was introduced by Worthington [1], developed and completed in the works of foreign and Russian scientists at the end of the 20th century [2–6]. This term originally meant the factors that lead to deviations

Bilateral-Symmetrical Traits in

## **Chapter 9**
