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Quantitative Impact of Neutrophils on Bacterial Clearance in a Murine Pneumonia 1

Model 2

3

Beining Guo1,2

, Kamilia Abdelraouf1, Kimberly R. Ledesma

1, Kai-Tai Chang

1, Michael 4

Nikolaou3, Vincent H. Tam

1,3* 5

6

Department of Clinical Sciences and Administration, University of Houston College of 7

Pharmacy, Houston, Texas1; Institute of Antibiotics, Huashan Hospital, Fudan University, 8

Shanghai, China2; University of Houston, Department of Chemical and Biomolecular 9

Engineering, Houston, Texas3

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Running title: Bacterial clearance in a pneumonia model 12

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* Corresponding author and mailing address: 14

University of Houston College of Pharmacy 15

1441 Moursund Street, Houston, TX 77030 16

Phone: (713) 795-8316; Fax: (713) 795-8383; E-mail: [email protected] 17

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Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Antimicrob. Agents Chemother. doi:10.1128/AAC.00508-11 AAC Accepts, published online ahead of print on 1 August 2011

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ABSTRACT 26

The rapid increase in the prevalence of antibiotic-resistant pathogens is a global 27

problem that has challenged our ability to treat serious infections. Currently, clinical 28

decisions on treatment are often based on in vitro susceptibility data. The role of the immune 29

system in combating bacterial infections is unequivocal, but it is not well captured 30

quantitatively. In this study, the impact of neutrophils on bacterial clearance was quntitatively 31

assessed in a murine pneumonia model. In vitro time-growth studies were performed to 32

determine the growth rate constants of Acinetobacter baumannii ATCC BAA 747 and 33

Pseudomonas aeruginosa PAO1. The absolute neutrophil count in mice resulting from 34

different cyclophosphamide preparatory regimens was determined. The dynamic change of 35

bacterial (BAA 747) burden in mice with graded immunosuppression over 24 h was captured 36

by a mathematical model. The fit to the data was satisfactory (r2=0.945). The best-fit 37

maximal kill rate of the bacterial population by neutrophils was 1.743 h-1

, the number of 38

neutrophils necessary for 50% maximal killing was 190.8/mm3, and the maximal population 39

size was 1.8×109 CFU/g, respectively. Using these model parameter estimates, the model 40

predictions were subsequently validated by the bacterial burden change of PAO1 at 24 h. A 41

simple mathematical model was proposed to quantify the contribution of neutrophils to 42

bacterial clearance and predict the bacterial growth / suppression in animals. Our results 43

provided a novel framework to link in vitro and in vivo information, and may be used to 44

improve clinical treatment of bacterial infections. 45

46

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48

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The rapid increase in the prevalence of antibiotic-resistant pathogens is a global 51

problem that has challenged our ability to treat serious infections. To prevent further spread 52

of multidrug resistance and returning to the pre-antibiotic era, it is imperative that the current 53

approach to treatment is improved. Presently, clinical decisions on treatment are often based 54

on in vitro susceptibility data (MIC), which only provides information about anti-bacterial 55

activity at a single time point and potentially missing information on the change in bacterial 56

population dynamics over time. Drug selections solely based on MIC values may not always 57

correlate to patient outcomes (11). One major reason for the discrepancy may be due to in 58

vitro investigations neglecting the presence of immune system in patients, which is well 59

known to be significant. 60

61

The immune system (especially neutrophils) plays an important role in combating 62

bacterial infections. This is best exemplified by patients with neutropenia or after 63

transplantation, who have a significantly higher mortality than patients without neutropenia 64

(9, 10, 13). However, the effect of the immune system is not well accounted for when data 65

obtained from in vitro investigations are used to provide guidance for treatment. The overall 66

antimicrobial effect in a patient is the sum of the anti-bacterial activity from drug therapy and 67

the effect from the immune system. The functional contribution from the latter is often 68

overlooked and the quantitative contribution of the neutrophils to bacterial clearance is not 69

well established. 70

71

Acinetobacter baumannii and Pseudomonas aeruginosa are important Gram-negative 72

pathogens associated with serious nosocomial infections (5, 12). Multidrug resistance in both 73

species has been increasing over the past decades and correlates with significant morbidity 74

and mortality (3, 6). They have been implicated in numerous global outbreaks and pose 75


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serious challenges to clinicians (4, 7, 14). In this study, we investigated the impact of 76

neutrophils on the clearance of A. baumannii in an animal infection model. A simple 77

mathematical model was developed to characterize bacterial behavior over time under a 78

variable degree of immunosuppression. Model predictions of bacterial behavior were 79

subsequently validated using another important pathogen - P. aeruginosa. Our quantitative 80

results may provide useful information to improve clinical treatment of bacterial infections. 81

82

MATERIALS AND METHODS 83

Microorganisms. A wild-type strain of A. baumannii ATCC BAA 747 (American 84

Type Culture Collection, Rockville, MD) and a laboratory strain of P. aeruginosa PAO1 were 85

used in the study. The bacteria were stored at –70°C in Protect

® (Key Scientific Products, 86

Round Rock, TX) storage vials. Fresh isolates were subcultured twice on 5% blood agar

87

plates (Hardy Diagnostics, Santa Maria, CA) for 24 h at 35°C prior to each experiment. 88

89

In vitro time-growth studies. The in vitro growth rates of AB BAA 747 and PAO1 90

were determined in cation-adjusted Mueller-Hinton broth (Ca-MHB) (BBL, Sparks, MD). 91

Briefly, 1-2 medium-sized fresh colonies were inoculated in Ca-MHB until reaching 92

log-phase growth. The bacterial suspension was then diluted based on absorbance at 630 nm 93

and transferred to a 50 ml flask containing 20 ml of Ca-MHB. To be consistent with 94

subsequent animal experiments, the baseline inocula of AB BAA 747 and PAO1 used were 95

approximately 1×107 and 1×10

3 CFU/ml, respectively. The experiments were conducted for 96

24 h in a shaker water bath at 35 °C. Serial samples (baseline, 1, 2, 3, 4, 6, 8 and 24 h) were 97

obtained in triplicate, and bacterial burden was determined by quantitative culture on 98

Mueller-Hinton Agar (MHA) plates (BBL) after 10×serial dilutions. Colony counts were 99

enumerated after incubation at 35 °C in a humidified incubator for 24 h. The theoretical lower 100


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limit of detection was 100 CFU/ml. 101

102

Modeling of in vitro growth. The exponential growth of bacterial population over 24 103

h was analyzed using a mathematical model (18). Details of the model are shown in Figure 1. 104

Based on the best-fit model, the bacterial growth rate constants ( gK ) were determined by the 105

ADAPT II program (1). 106

107

Animals. Female Swiss-Webster mice weighing 21-25 g were used (Harlan 108

Laboratories, Indianapolis, IN). The animals were housed in ventilated micro-isolator cages 109

to decrease the risk of infection from extraneous pathogens. The mice were allowed to eat 110

and drink ad libitum. The experimental protocol was approved by the Institutional Animal 111

Care and Use Committee of the University of Houston. 112

113

Determination of absolute neutrophil count (ANC). A total of 16 mice were 114

randomized divided into 4 groups. One group was used for baseline ANC determination and 115

the other 3 groups were given different cyclophosphamide preparatory regimens to produce a 116

graded immunosuppression in the animals. Each cyclophosphamide regimen contained two 117

doses administered intraperitoneally; the first dose (50, 100 or 200 mg/kg) was given on day 118

-4 followed by the second dose (50, 50 or 150 mg/kg) on day -1. For each treatment group, 119

blood (approximately 40 µl) was drawn on day 0 via the tail vein and was collected into BD 120

microtainer®

tubes with EDTA (BD, Franklin Lakes, NJ). The blood samples were 121

transported in ice and were analyzed within two hours of collection using a HemaVet 950FS 122

multispecies blood analyzer (Drew Scientific Inc., Oxford, CT). The total number of white 123

blood cells, the number of neutrophils and the percentage of neutrophils were determined 124

following the instructions of the manufacturer. 125


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126

In vivo growth experiments. To examine the impact of neutrophils on bacterial 127

clearance, a total of 72 mice were randomly divided into 4 groups (18 mice in each group). 128

Each group was given a cyclophosphamide regimen as above. The mice were anesthetized by 129

a single intraperitoneal injection of 1.25% 2,2,2-tribromoethanol (Sigma-Aldrich, St Louis, 130

MO) at a dosage of 25 mg/kg. AB BAA 747 was grown to log-phase growth in Ca-MHB 131

broth; the suspension was washed once and concentrated in sterile saline based on absorbance 132

at 630 nm. The bacteria were inoculated into the trachea of anesthetized mice under 133

laryngoscopic guidance. The inoculum (approximately 1×106 CFU in 10 µl) used was 134

guided by previous investigations mimicking a clinical course of infection but no excessive 135

mortality by 24 h. In each group, 3 mice were sacrificed by CO2 asphyxiation at baseline to 136

ascertain the infective inoculum, and 3 mice each were sacrificed at 4, 8, 12, 20 and 24 h after 137

infection. The lungs from each mouse were aseptically collected for quantitative culture. 138

Prior to being cultured, lungs were homogenized in 10 ml of sterile saline. The homogenates 139

were centrifuged (4°C at 4000× g for 15 min), decanted, and reconstituted with sterile saline 140

at 10 times the original volume. The samples were subsequently serially diluted (10×) and 141

quantitatively plated on MHA plates. The reliable lower limit of detection was 1000 CFU/g. 142

143

In vivo growth dynamics model. The change of bacterial burden in lung tissues over 144

time was described using a mathematical model, as shown in Figure 1. The rate of change of 145

bacteria over time was expressed as the difference between the intrinsic bacterial growth rate 146

and the kill rate provided by the neutrophils. Previous investigations demonstrated the growth 147

of AB BAA 747 was not considerably impacted by nutrient depletion, so the gK value 148

derived from the in vitro time growth experiments was adopted (data not shown). Additional 149

model parameters were used to account for other relevant physiological phenomena such as 150


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contact inhibition (using a maximum population size) and non-linear (sigmoidal) kill rate to 151

account for saturable killing. The killing profiles in all animal groups were co-modeled by the 152

ADAPT II program (1). 153

154

Prospective validation of mathematical model. The above in vivo growth 155

experiment was repeated using PAO1. An inoculum of approximately 1×103 CFU (in 10 µl) 156

was used. Four mice were sacrificed at baseline to ascertain the infective inoculum and 4 157

mice in each group were sacrificed at 24 h as described above. 158

159

RESULTS 160

In vitro time-growth studies. The best-fit growth rate constant ( gK ) of AB BAA 161

747 in full-strength Ca-MHB was 1.463 ± 0.191 h-1

(mean ± standard deviation). Previous 162

experiments demonstrated the growth courses in various strengths (full-strength and 163

0.1-strength) of broth were similar, indicating that the growth of AB BAA 747 was not 164

considerably impacted by nutrient depletion (data not shown). In contrast, the growth rate 165

constants ( gK ) of PAO1 in full-strength and 0.1-stength Ca-MHB were found to be 1.509 ± 166

0.014 h-1

and 1.047 ± 0.015 h-1

, respectively. 167

168

Determination of absolute neutrophil count. The ANC values of animals given 169

cyclophosphamide are shown in Figure 2. Compared to the control animals, a graded 170

neutropenia was successfully induced by the cyclophosphamide regimens. The neutrophil 171

count was reduced approximately 90% after administrations of 200 mg/kg and 150 mg/kg of 172

cyclophosphamide. For the other two treatment groups, the percentages of ANC reduction 173

were approximately 70% and 20%, respectively. 174

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In vivo growth experiments. A consistent baseline tissue burden (ranged from 7.07 176

to 7.32 log CFU/g) was achieved. The dynamic change of bacterial burden in lung tissues 177

over time is displayed in Figure 3. A divergence of bacterial burden profiles was observed 178

after 8 h. At 24 h, the net change of bacterial burden from baseline ranged from -1.08 log 179

CFU/g to 1.72 log CFU/g. The bacterial burden observed was negatively correlated the ANC 180

achieved in the animals. A net growth in bacterial burden was observed in animal groups 181

given 200/150 and 100/50 mg/kg of cyclophosphamide (1.72 and 0.56 log CFU/g, 182

respectively). On the other hand, a net reduction of 0.52 and 1.08 log CFU/g was seen in 183

animals given the 50/50 mg/kg regimen and the control animals, respectively. 184

185

Mathematical modeling of in vivo data. Overall, the model fit to the data was 186

satisfactory (r2

= 0.945) based on the best-fit parameter estimates (Figure 4). The best-fit 187

maximal kill rate by neutrophils ( kK ) was 1.743 h-1

. In addition, the number of neutrophils 188

necessary for 50% maximal killing was 190.8/mm3 and the maximal population size was 1.82189

×109 CFU/g, respectively. The best-fit relationship between the killing rate and ANC is 190

shown in Figure 5A. 191

192

Prediction and validation of mathematical model. A baseline tissue burden ranging 193

from 3.10 to 3.51 log CFU/g was achieved. The behavior of PAO1 with similar 194

cyclophosphamide preparatory regimens was predicted using the best-fit parameter estimates. 195

The best-fit kill rate was compared to the growth rates of the bacteria, as shown in Figure 5A. 196

Qualitatively, PAO1 was expected to be suppressed in 2 out of 4 experimental groups (the 197

control group and the dosing group given 50/50 mg/kg cyclophosphamide), using the 198

gK value derived from experiments using full-strength Ca-MHB. Alternatively, it was 199

predicted that 1 out of 4 experiment groups (the dosing group given 200/150 mg/kg of 200


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cyclophosphamide) would not be suppressed, using the gK value derived from 0.1-strength 201

Ca-MHB experiments. Validation experiments revealed that bacterial growth was suppressed 202

in only 2 dosing groups (Figure 5B), attesting to the predicting performance of our 203

mathematical model. 204

205

DISCUSSION 206

The important role of the immune system (neutrophils) in combating bacterial 207

infections is indisputable. Several pre-clinical studies have demonstrated that depletion of 208

neutrophils would result in a serious deficiency in bacterial clearance (15, 16, 19). In addition, 209

retrospective cohort clinical studies have demonstrated severe neutropenia or 210

immunosuppression was associated with higher mortality in patients (8, 17). 211

212

The quantitative impact of the immune system on bacterial clearance is not well 213

established. Recently, an investigation delineated the impact of granulocytes on the bacterial 214

clearance in a mouse thigh infection model (2). To the best of our knowledge, this is the only 215

recent published study investigated the quantitative impact of granulocytes. In this study, 216

different inocula were injected into the thighs of immunecompetent mice, and the bacterial 217

burden changes were observed over 24 h. A mathematical model was used to elucidate the 218

impact of bacterial burden on bacterial clearance by granulocytes. The authors concluded that 219

bacterial kill by granulocytes was saturable and a higher reliance on chemotherapy would be 220

necessary to drive clearance of a high bacterial burden. 221

222

In this study, we attempted to capture the quantitative impact of neutrophils in 223

bacterial infections. Instead of just relying on an early and late observation, we observed the 224

growth course of bacteria serially over 24 h to more closely track the dynamic change of 225


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bacterial burden. Also, escalating cyclophosphamide regimens were used to produce graded 226

immunosuppression in the animals. This would allow us to assess the quantitative 227

relationship between neutrophil counts and bacterial killing with more precision. Finally, in 228

addition to fitting the experimental data, the mathematical model also provided a reasonable 229

prediction on the bacterial growth / suppression of another bacterium under similar 230

experimental conditions. 231

232

Several assumptions were made in the model. One assumption was the in vivo 233

bacterial growth rate was identical to that derived from our in vitro time-growth 234

investigations. Conceptually, it was regarded as the in vivo maximal growth rate in the 235

absence of neutrophils. The growth rate constant of AB BAA 747 was derived from 236

experiments using full-strength Ca-MHB, which was similar to that obtained using 237

0.1-strength Ca-MHB previously. However, nutrient depletion appeared to influence the 238

growth of PAO1 to a greater extent. We were unsure which growth rate constant would be 239

more realistic under in vivo conditions. Nonetheless, observations in the validation 240

experiments seemed more reliable using the value from full-strength Ca-MHB. Secondly, the 241

ANC data on day 0 was used solely to represent the impact of neutrophils. Although 242

neutrophil recruitment to the infection site is expected following bacterial inoculation, we did 243

not expect the neutrophil count would increase dramatically within a short period of time. 244

Thus, our assumption was deemed reasonable for the timeframe of the experiments (24 h). 245

Finally, we assumed that neutrophil was the only immune component responsible for 246

bacterial clearance, and there was no substantial residual killing from other immune 247

components such as antibodies or the complement system. Residual killing was previously 248

considered during the model development stage, with the use of additional model 249

parameter(s). However, these hierarchically more complex mathematical models did not 250


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improve predictions of the validation experiments and was thus abandoned. 251

252

The model predictions were reasonable in predicting bacterial behavior in vivo in a 253

qualitative sense. There were several limitations in this study, which could have affected the 254

predicting ability of the model. Observations of ANC on the day of infection could only be 255

made with ANC > 200 cell//mm3 (approximately 90% neutrophils reduction from baseline). 256

As such, it was difficult for us to fully define and assess the necessity to incorporate residual 257

killing from immune components other than neutrophils. We have briefly investigated a 258

cyclophophamide dosing regimen higher than 200/150 mg/kg (the highest reported in this 259

study). However, it was found to be associated with excessive mortality (likely due to drug 260

toxicity) and thus it was not further pursued (data not shown). Another limitation of the study 261

was the bacterial inocula used. In contrast to the previous investigation (2), we did not 262

evaluate the impact of neutrophils on the bacterial clearance for different inocula. Finally, 263

only two representative Gram-negative bacteria were examined. Further studies in other 264

clinically important pathogens (e.g., Staphylococcus aureus, Streptococcus pneumoniae) 265

could further validate the utility of the proposed mathematical model. 266

267

In conclusion, our quantitative results provided new insights of the impact of 268

neutrophil on bacterial clearance. The mathematical model established in the study may be 269

used as a foundation to investigate the impact of other immune components on various 270

bacterial species. Furthermore, the beneficial effect of neutrophils in combination with 271

antimicrobials should be investigated. 272

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ACKNOWLEDGMENT 276

The authors thank Alice Chen and Allison Rosen for technical assistance in blood cell 277

profiling. The study was partially supported by the National Science Foundation 278

(CBET-0730454). 279


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8. Lin, M. Y., R. A. Weinstein, and B. Hota. 2008. Delay of active antimicrobial 300

therapy and mortality among patients with bacteremia: impact of severe neutropenia. 301


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Mortality hazard functions as related to neutropenia at different times after marrow 315

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with Acinetobacter baumannii in mice. Infect Immun 75:5597-608. 335

336

337

338

339


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Figure 1. In vivo bacterial growth dynamics model 340

341

Population balance for a bacterial population: 342

Rate of change of bacteria over time = Intrinsic growth rate − Kill rate by neutrophils 343

( )

dt

tdN= ( )[ ]tNG - ( )[ ]tNANCK , 344

where: ( )[ ]tNG = gK ·( )

−

max

1N

tN· ( )tN 345

( )[ ]tNANCK , =

+

⋅

k

k

ANCANC

ANCK

50

· ( )tN 346

gK ─ growth rate constant for bacterial population 347

( )tN ─ concentration of bacterial population at time t 348

maxN ─ maximum population size 349

kK ─ maximal kill rate constant for bacterial population by neutrophils 350

kANC50 ─ ANC to achieve 50% of maximal kill rate for bacterial population 351


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Figure 2. The absolute neutrophil count (ANC) observed in mice with different 352

cyclophosphamide preparatory regimens 353

354

0

500

1000

1500

2000

2500

3000

Ab

solu

te n

eutr

op

hil

co

un

t (c

ell

/mm

3) Control

50/50 mg/kg

100/50 mg/kg

200/150 mg/kg

355

356

Data are shown as mean ± standard deviation. The mice were given corresponding 357

cyclophosphamide doses on day -4 and -1, respectively. 358


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Figure 3. Time course of pulmonary bacterial burden of A. baumannii AB BAA 747 in mice 359

administered different cyclophosphamide regimens (0/0, 50/50, 100/50 and 200/150 mg/kg of 360

bodyweight). 361

362

5

6

7

8

9

10

0 4 8 12 16 20 24

Time (h)

Lo

g C

FU

/g

200/150 mg/kg

100/50 mg/kg

50/50 mg/kg

Control

363

Data are shown as mean ± standard deviation. 364


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Figure 4. Performance of the best-fit model. 365

366

Best-fit = 0.959 × Observed + 0.314

r2 = 0.945

6.0

6.5

7.0

7.5

8.0

8.5

9.0

6.0 6.5 7.0 7.5 8.0 8.5 9.0

Observed bacterial burden (Log CFU/g)

Bes

t-fi

t b

acte

rial

bu

rden

(L

og C

FU

/g)

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Figure 5. Prediction and validation of bacterial growth / suppression. Comparison of the kill 368

rate by neutrophils and bacterial growth rates (A). Observed pulmonary bacterial (PAO1) 369

burden changes from baseline at 24 h (B). 370

372

374

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 500 1000 1500 2000 2500

Absolute neutrophil count (cell / mm3)

Kil

l ra

te (

h-1)

Kill rate

Growth rate (AB BAA 747)

Growth rate (PAO1 in full-strength)

Growth rate (PAO1 in 0.1-strength)

375

376

The solid curve depicts kill rate changes with absolute neutrophil count. The dotted 377

horizontal lines represent growth rates of AB BAA 747, and PAO1 in different strengths of 378

broth. 379

(A)


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381

383

-3

-2

-1

0

1

2

3

4

5

6

200/150 mg/kg 100/50 mg/kg 50/50 mg/kg Control

Bac

teri

al B

urd

en (

Lo

g C

FU

/g)

384

The shaded bars indicate conditions which bacterial growth was predicted; while the open 385

bars indicated conditions which bacterial suppression was predicted. 386

387

(B)


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