A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

Mohamad Hakam Tiba; Brendan M McCracken; Robert P Dickson; Jean A Nemzek; Carmen I Colmenero; Danielle C Leander; Thomas L Flott; Rodney C Daniels; Kristine E Konopka; J Scott VanEpps; Kathleen A Stringer; Kevin R Ward

doi:10.1371/journal.pone.0243577

. 2020 Dec 11;15(12):e0243577. doi: 10.1371/journal.pone.0243577

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

Mohamad Hakam Tiba ^1,^2,^*, Brendan M McCracken ^1,², Robert P Dickson ^2,³, Jean A Nemzek ^2,^4,⁵, Carmen I Colmenero ^1,², Danielle C Leander ^1,², Thomas L Flott ⁶, Rodney C Daniels ^2,^7,⁸, Kristine E Konopka ^2,⁵, J Scott VanEpps ^1,^2,^8,⁹, Kathleen A Stringer ^2,^3,⁶, Kevin R Ward ^1,^2,⁸

Editor: Anasuya Sarkar¹⁰

¹Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America

²Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan, United States of America

³Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan, United States of America

⁴Unit of Laboratory Animal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America

⁵Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States of America

⁶Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America

⁷Department of Pediatrics, Pediatric Critical Care Medicine, University of Michigan, Ann Arbor, Michigan, United States of America

⁸Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America

⁹Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, United States of America

¹⁰The Ohio State University, UNITED STATES

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: tibam@med.umich.edu

Roles

Mohamad Hakam Tiba: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Brendan M McCracken: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Robert P Dickson: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing – review & editing

Jean A Nemzek: Conceptualization, Data curation, Investigation, Methodology, Resources, Supervision, Writing – review & editing

Carmen I Colmenero: Data curation, Investigation, Project administration, Writing – review & editing

Danielle C Leander: Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – review & editing

Thomas L Flott: Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing

Rodney C Daniels: Conceptualization, Data curation, Investigation, Resources, Validation, Writing – review & editing

Kristine E Konopka: Investigation, Writing – review & editing

J Scott VanEpps: Conceptualization, Data curation, Investigation, Methodology, Resources, Validation, Writing – review & editing

Kathleen A Stringer: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Kevin R Ward: Conceptualization, Methodology, Resources, Supervision, Validation, Writing – review & editing

Anasuya Sarkar: Editor

PMCID: PMC7732124 PMID: 33306742

Abstract

Background

The systemic responses to infection and its progression to sepsis remains poorly understood. Progress in the field has been stifled by the shortcomings of experimental models which include poor replication of the human condition. To address these challenges, we developed and piloted a novel large animal model of severe infection that is capable of generating multi-system clinically relevant data.

Methods

Male swine (n = 5) were anesthetized, mechanically ventilated, and surgically instrumented for continuous hemodynamic monitoring and serial blood sampling. Animals were inoculated with uropathogenic E. coli by direct injection into the renal parenchyma and were maintained until a priori endpoints were met. The natural history of the infection was studied. Animals were not resuscitated. Multi-system data were collected hourly to 6 hours; all animals were euthanized at predetermined physiologic endpoints.

Results

Core body temperature progressively increased from mean (SD) 37.9(0.8)°C at baseline to 43.0(1.2)°C at experiment termination (p = 0.006). Mean arterial pressure did not begin to decline until 6h post inoculation, dropping from 86(9) mmHg at baseline to 28(5) mmHg (p = 0.005) at termination. Blood glucose progressively declined but lactate levels did not elevate until the last hours of the experiment. There were also temporal changes in whole blood concentrations of a number of metabolites including increases in the catecholamine precursors, tyrosine (p = 0.005) and phenylalanine (p = 0.005). Lung, liver, and kidney function parameters worsened as infection progressed and at study termination there was histopathological evidence of injury in these end-organs.

Conclusion

We demonstrate a versatile, multi-system, longitudinal, swine model of infection that could be used to further our understanding of the mechanisms that underlie infection-induced multi-organ dysfunction and failure, optimize resuscitation protocols and test therapeutic interventions. Such a model could improve translation of findings from the bench to the bedside, circumventing a significant obstacle in sepsis research.

Introduction

The immune system’s dysregulated response to infection (sepsis) remains a major public health threat. The annual incidence rate is over 48.9 million cases globally with 1.7 million in the U.S.A, and a global sepsis-related death rate of 11 million by conservative estimates [1–3]. Notably, the number of reported cases continues to increase [4, 5]. Despite the enormous impact on human health, sepsis, as a disease remains ill-defined with little progress made to further understand its pathogenesis or find effective diagnostics, prognostics or pharmacotherapy regimens. One of the major obstacles to advancing our knowledge of this disease is its unpredictability and the absence of translatable experimental pre-clinical models that accurately recapitulate the evolution of systemic responses to infection in humans [6].

Development of management strategies aimed at altering the inflammatory response due to acute infection starts at understanding the host’s response to such triggers, as well as the subsequent sequelae that lead to life threatening organ dysfunction (sepsis) and death. Attempts at decoding such host-dependent changes using pre-clinical and early clinical trials have been repeatedly disappointing and have failed to translate in larger clinical trials [7]. One reason for the decades-long discrepancy between pre-clinical and clinical trials might be the persistent reliance on small animal (especially murine) models in an attempt to identify the precise mechanisms of the systemic inflammatory response. Although rodent-based studies have led to promising mechanistic findings, rodents are phylogenetically distant and biologically divergent from humans, limiting their relevance to the study of certain human diseases such as severe infection and sepsis [8–13]. In addition, these models rarely simulate a natural time-course where comprehensive data are collected from insult to the development of clinically relevant symptoms, and they do not permit easy investigation of the impact of standard therapies such as intravenous fluids, mechanical ventilation, or vasopressor administration over clinically relevant time periods.

Novel pre-clinical models of infection that allow for proper latency and natural progression of the broad range of systemic responses as well as the incorporation of traditional supportive care are more likely to lead to high-fidelity models that mimic the human conditions of infection, sepsis and organ dysfunction. To this end, and consistent with the recently published Minimum Quality Threshold in Pre-Clinical Sepsis Studies (MQTiPSS) recommendations, more routine use of large animal models for the study of sepsis is emerging [14]. However, current attempts using sheep and swine to model sepsis have mainly utilized endotoxemia [15, 16], peritonitis [17, 18], or direct bacterial IV inoculation [19]. Although these investigations produced valuable insight, they may not be particularly clinically applicable and may not mirror the development and progression of the disease over time.

In this investigation, we present a pilot study aimed at creating a swine model of untreated infection and systemic inflammation that demonstrates the physiologic and immunologic features associated with development of human sepsis using the genitourinary system as the infection source. Such an approach is envisioned to lead to the development of more clinically relevant and reproducible models that will permit mechanistic interrogation and assessment of responses to a variety of treatment scenarios.

Materials and methods

Ethics statement

This study adhered to the principles of the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals [20]. The protocol was approved by the Institutional Animal Care and Use Committee of the University of Michigan (PRO00008551). All animals were procured from the Swine Teaching and Research Center, Michigan State University, East Lansing, MI. All research personnel in this research study received specialized training in animal care and use, swine handling, and surgery from the University of Michigan Unit for Laboratory Animal Medicine. All experiments were terminal.

Animal instrumentation

Five, male, Yorkshire mix swine weighing a mean (SD) of 45.0 (2) kg were fasted overnight with ad libitum access to water. Anesthesia was induced with an intramuscular injection of ketamine/xylazine (20/2 mg/kg) and maintained with 1–2% inhalant isoflurane balanced in room air for the duration of surgical instrumentation. Animals were intubated using a 7.5mm cuffed endotracheal tube and mechanically ventilated with a tidal volume of 7–8 ml/kg. Respiratory rate was adjusted as necessary to maintain a baseline end-tidal CO₂ (PetCO₂) level between 35-45mmHg (Biopac Systems Inc., Goleta, CA). Core body temperature was maintained between 37.5 and 39.0°C during instrumentation using a closed loop feedback temperature blanket (Blanketrol, Sub-Zero Medical, Cincinnati, OH). The ECG was monitored continuously using a standard 3-lead configuration (Biopac Systems Inc., Goleta, CA).

Under aseptic conditions, the common carotid artery was cannulated to measure mean arterial blood pressure (MAP) and collect blood samples. The internal jugular vein was cannulated for administration of intravenous anesthetics. The external jugular vein was isolated and cannulated using a pulmonary artery thermodilution catheter (CCOmbo V, 8F, Edwards Lifesciences, Irvine, CA) for measurements of pulmonary artery pressure (PAP), central venous pressure (CVP), and blood sampling. A midline, ~25 cm laparotomy was performed to access the bladder for placement of a Foley catheter. A small retroperitoneal window was opened to reach the kidney and the ureter. Surgical procedures were carried out concurrently within 1.5 to 2 hours.

At the end of surgical instrumentation, animals were transitioned to total intravenous anesthesia using midazolam (5–20 mcg/kg/min), fentanyl (0.1–0.5 mcg/kg/min), and propofol (10–1000 mcg/kg/min) to mitigate the effects of isoflurane on the cardiovascular system during the prolonged period of observation post inoculation. The level of anesthesia was monitored by assessing corneal reflex, jaw tension, and hemodynamics including blood pressure, heart rate and respiratory rate.

Inoculation

The clinical, uropathogenic isolate (CFT073) of Escherichia coli [21] from a cryopreserved glycerol stock (-80°C) was streaked onto Luria Bertani (LB) agar plates. Single colony inoculates were grown to stationary phase overnight (shaken at 200rpm at 37°C) in 200ml of Luria Bertani broth. The cells were pelleted by centrifugation (4000rpm, 6min), washed twice with 1x phosphate buffered saline (PBS), and resuspended in 6ml 1xPBS. A 22G catheter was inserted into the lower-most part of the kidney under direct visualization through the laparotomy. Inoculation was achieved by an infusion of 5ml of bacterial solution directly into the renal parenchyma over 15 minutes. This resulted in an average (SD) total inoculum per animal of 3.1x10¹¹ (2.5x10¹⁰) cells, quantified by incyto chip hemocytometer. The laparotomy was immediately closed following the inoculum infusion. The corresponding ureter was occluded (with an externalized vessel loop) for one hour then released.

Monitoring and sample collection

In order to observe the natural course of the infection, animals were observed with limited intravenous supportive care (30 mL/hour of normal saline) for the duration of the experiment, up to 24 hours. Hemodynamics and ventilation parameters such as MAP, PAP, CVP, heart rate (HR), core body temperature, PetCO₂ and fraction of oxygen in the inspired and expired air (FiO₂, FeO₂) were monitored and recorded every hour using a Biopac Data Acquisition System MP150 (Biopac Systems Inc., Goleta, CA). Arterial and mixed-venous blood gases were obtained every two hours and analyzed with temperature correction using ABL800 FLEX (Radiometer America, Brea, CA). Blood samples for complete blood count (CBC), comprehensive chemistry profiles, (Vetscan HM5 & VS2, Abaxis, Union City, CA) as well as for the cytokines, tumor necrosis factor (TNF)-α and interleukin (IL)-6 (Porcine TNFα DuoSet ELISA & Porcine IL-6 DuoSet ELISA, R&D Systems, Minneapolis, MN) were obtained and analyzed every 6 hours. In one animal, blood samples to measure TNF-α and IL-6 were collected every hour for the first 6 hours, and then every 6 hours to check for the acute phase response. Every 6 hours arterial and mixed venous blood samples were also collected for the measurement of metabolites by quantitative ¹H-nuclear magnetic resonance (NMR) spectroscopy (see S1–S4 Figs, S1 and S2 Files, S1 and S2 Tables).

The experiment was terminated and animals were euthanized using 1–2 mEq/kg of potassium chloride while under anesthesia if one of the following humane endpoint conditions was met: persistently low MAP (< 40 mmHg for 2 hours) or low PetCO₂ (< 25 mmHg for 2 hours) or 24 hours of total experiment duration. Upon completion of the study, tissue samples from the lungs (en bloc), liver, spleen, intestine, and kidneys were collected, placed in formalin, processed for pathology examination or stored for future analysis. Whole blood and plasma samples were also collected and stored for future analysis.

Statistical analysis

Descriptive data are presented as the mean and standard deviation (SD). Statistical analyses were performed using repeated measures analysis of variance (ANOVA) with post-hoc correction of multiple comparisons using Tukey’s test as applicable. In cases of missing data, a mixed-effects analysis was used. Whole blood metabolomics data were analyzed as described in the S1–S4 Figs, S1 and S2 Files, S1 and S2 Tables. In all cases, significance was considered at α = 0.05. Data were analyzed using MATLAB R2017a (The MathWorks, Inc., Natick, MA) and PRISM 8 (GraphPad Software, San Diego, CA).

Results

All animals used in this pilot study died or reached a priori study end points with a mean (SD) survival time of 11.6(1.5) hours with a survival time range between 9 and 13 hours. S1 Table in the supporting information file lists baseline and end of experiment hemodynamic, clinical chemistry, and oxygenation data.

Hemodynamic and physiologic parameters

There were noted changes in a number of hemodynamic and physiologic parameters as systemic response to infection progressed. Mean core body temperature increased by 13.5% (37.9(0.8) to 43.0(1.2) ^oC, p = 0.006; (Fig 1A) from baseline to the end of the experiments. This occurred rapidly at an average rate of 0.43 ^oC/h. Heart rate (Fig 1B) progressively increased from 74.2(6.0) at baseline to 142.0(43.2) beats per minute (BPM) at the termination of the experiment but this did not reach statistical significance. MAP decreased from 86(9) mmHg at baseline to a low of 28(5) mmHg (p = 0.005) at the end of the experimental time period (Fig 1C). The central venous pressure (Fig 1D) also followed a downward trend; it was lower at baseline than at the termination of the experiment (7.7(1.0) mmHg vs 4.7(0.48) mmHg; (p = 0.03). Conversely, pulmonary artery pressure (Fig 1E) varied between 20–28 mmHg over the course of the experiment. Respiratory rate did not change as these animals were deeply anesthetized and mechanically ventilated.

Fig 1 — From baseline (BL), core temperature (A) was increased at 4h (^{^}p = 0.03), 6h (⁺p = 0.01), 8h (^#p = 0.01) and at the end (*p = 0.006) of the experiments. Heart rate (B) also increased but the changes were not significant. Mean arterial pressure (C) progressively declined and was significantly lower at the end of the experiments compared to BL (*p = 0.005). There was a progressive decline in central venous pressure such that by the end of the experiments it was lower than BL (*p = 0.05) (D). Pulmonary artery pressure was highly variable but trended upward in the early phase of the experiment likely reflecting a compensatory increase in cardiac output driven by an increase in heart rate. Data are the mean(SE) of five animals. ^{^} Denotes significant difference between baseline and 4h. ⁺ Denotes significant difference between baseline and 6h. ^# Denotes significant difference between baseline and 8h. * Denotes significant difference between baseline and end of experiment.

Clinical chemistries

A number of organ function tests were measured over the experimental time course. Whole blood creatinine (Fig 2A) and BUN (Fig 2B) levels increased from 12(1.0) to 32(9) μmol/L (p = 0.02) and 2.6(1.2) to 7.6(2.3) mmol/L (p = 0.003), respectively. Glucose (Fig 2C) progressively declined over time from the 2h time point. The mean baseline value of 8.5(1.4) mmol/L was significantly higher than the mean of 3.7(1.2) mmol/L (p = 0.04) at the end of experiment. This is consistent with findings from the metabolomics data described below. Animals also developed higher lactate levels (Fig 2D) at the end of the experiment 8.0(3.8) when compared to baseline 1.5(0.9) mmol/L (p = 0.09). When taken together, the high level of lactate and low MAP (Fig 1C) at end of experiment, are consistent with the development of shock. Metrics of liver function such as total bilirubin (Fig 2E) and alanine aminotransferase (ALT; Fig 2F) did not show any statistically significant changes over the course of the experiment.

Hematologic parameters and white blood cell count and differential

The mean platelet count decreased from 292(114) to 203(102) (10⁹ cells/L) however, this difference was not significant (Fig 3A). Hemoglobin (Fig 3B) progressively increased from 10.8(1.6) to 14.6(1.8), which is most likely indicative of hemoconcentration. Hematocrit measurements further support this as they increased from 40.9(6.6) to 53.8(3.6) % (p = 0.05) (Fig 3C). Mean white blood cell count (WBC) increased from 17.0(3.6) to 18.4(8.1) 10⁹/L, but this was not significant (Fig 3D). However, there was a significant dynamic shift in WBC differential for which neutrophils increased from 37.5(8.7) % to 74.4(8.9) % (p = 0.01) and lymphocytes decreased from 61.6(8.5) % to 21.6(7.7) % (p = 0.004).

pH, blood gases and oxygenation

The arterial pH and HCO₃^- declined over time (Fig 4A and 4B), and the partial end tidal of carbon dioxide (PetCO₂) remained stable indicating development of a metabolic acidosis (Fig 4C). Overall, oxygen consumption (Fig 4D) remained stable but began a declining trend during the latter hours of the experiment. Notably, the arterial oxygen partial pressure (PaO₂) to fractional inspired oxygen (FiO₂) ratio progressively declined over time (Fig 4E) from 457(62) at baseline to 315(118) at experiment termination. This did not reach statistical significance (p = 0.20) likely due to the high variability at the terminal phase of the experiment. However, the difference between the mean PaO₂/FiO₂ ratio at baseline and 8h (286(33)) was markedly different (p = 0.01). Mixed venous oxygen saturation (Fig 4F) also declined over time from baseline 59(1.9) % reaching a critically low value of 17(10.2) % at the end of the experiment (p = 0.005).

Metabolic measurements

Blood samples for the acquisition of metabolomics data were available from 4 of the 5 animals. The concentration of a number of metabolites changed during infection progression. Metabolites with a false discovery rate (FDR)-corrected p value of < 0.10 are shown in Fig 5. The most significantly changed metabolite, as determined by FDR-corrected ANOVA p value (0.05), was creatine (Fig 5A). Notably, phenylalanine (Fig 5B) and tyrosine (Fig 5C), precursors of catecholamines, progressively increased. Broadly, with the exception of glucose (Fig 5G), all measured metabolite concentrations increased over time, including lysine (Fig 5D), histidine (Fig 5F), glutamine (Fig 5H) and methionine (Fig 5I). The concentration of inosine monophosphate (IMP) also progressively increased (Fig 5E). This suggests disruption of adenosine metabolism but the calculated adenylate energy charge did not change although energy balance (depicted by the ATP/ADP ratio) trended lower (see S1A Fig and S1B Fig in the online supporting information). Notably, increases in lactate concentration were modest but greatest towards the end of experimentation which is consistent with the clinical measurements (see S1C Fig in the online supporting information and Fig 2D). All metabolites with an FDR-corrected ANOVA p value of < 0.15 are shown in S1 Fig. The list of all detected metabolites can be found in S2 Table.

Fig 5 — Creatine (A), a non-proteinogenic amino acid which is abundant in skeletal muscle and critical to energy metabolism, increased in concentration from BL the end of the experiments (End: *p = 0.01). The essential amino acid, phenylalanine (B), increased from baseline (BL) as systemic infection progressed (6h: ⁺p = 0.02; End: *p = 0.01). In parallel, the non-essential amino acid, tyrosine (C), that is produced from phenylalanine, also increased from BL (End: *p = 0.005). Lysine (D), an essential amino acid, progressively increased over the course of infection (End: *p = 0.0002). Inosine monophosphate (E), which is a nucleotide monophosphate, also progressively increased from BL (6h: ⁺p = 0.006; End: *p = 0.03). The essential amino acid, histidine (F), which is a precursor of histamine, was increased compared to BL at the end of the experiment (End: *p = 0.04). Consistent with the clinical measurement, glucose (G), progressively declined from BL to the end of the experiment (*p = 0.05). Glutamine (H), a non-essential amino acid, and methionine (I), an essential amino acid, both of which are important in maintaining glutathione homeostasis, were higher at the end of the experiments than at BL (End: *p = 0.006 and *p = 0.0001, respectively). Data are the mean(SE) of four animals. Metabolites are presented in ascending rank order of their FDR-corrected ANOVA p value which in all cases was less than 0.10. ⁺ Denotes significant difference between baseline and 6h. * Denotes significant difference between baseline and end of experiment.

Plasma cytokines

Plasma IL-6 was elevated in all animals, increasing 15-fold from a baseline to 6 hours with a mean(SD) of 394.5(446) to 6339.4(5648) pg/mL respectively. (Fig 6 inset). Mean plasma TNF-α (Fig 6 inset) declined from a baseline of 2286(1259) to 741.3(549) pg/mL at the end of the experiment. The change in both IL-6 and TNF-α were not statistically significant. However, in one animal from which hourly blood samples were collected for 6 hours, the measured TNF-α peak occurred 1-hour post inoculation, 3.36 (0.03) x 10⁴pg/mL and IL-6 at 2hours post inoculation 4.26 (0.49) x 10⁴ pg/mL (Fig 6). The levels of TNF-α and IL-6 rapidly decreased by hour 6 to 973 pg/mL and 6031 pg/mL, respectively. This suggests that the early peak level of TNF- α and IL-6 were missed by sampling at 6-hour intervals.

Histopathology of vital organs

Representative sections of the kidneys, liver, and lungs were processed and hematoxylin and eosin-stained slides were made. These tissues showed histomorphologic evidence of organ injury. Significant histological findings in the inoculated kidneys included marked neutrophilic tubulointerstitial nephritis with evidence of vascular origin (Fig 7A and 7B). Inflammatory foci appeared to be preferentially centered on the venous vessels, but the extent of tissue damage, presence of bacteria in renal tubules, hemorrhage, and inflammation obscured the origin of histologic lesions. There were no significant histopathologic lesions observed in examined sections of the contralateral kidneys. The liver showed marked acute sinusoidal dilation (Fig 7C) consistent with acute vascular (agonal) congestion. Sections of the lung showed a patchy inflammatory infiltrate that included mild interstitial thickening by mononuclear inflammatory cells (Fig 7D) and focal acute bronchopneumonia. The acute bronchopneumonia was seen as neutrophils infiltrating the bronchiolar epithelium (Fig 7F), accompanied by a cellular intra-alveolar airspace exudate in which neutrophils predominated (Fig 7G and 7H). These findings were affiliated with variably dense perivascular inflammation, comprised mainly of lymphocytes and lymphoid aggregates, and rare fibrin thrombi (Fig 7E) in small blood vessels.

Fig 7 — (A) foci of suppurative inflammation (arrow) and degenerate cellular debris adjacent to a renal arteriole (arrowhead), (B) focus of renal tubules with intracytoplasmic eosinophilic droplets, and (C) Acute hepatic sinusoidal dilation (arrow) is seen around the central vein (*). Lung tissues show (D) perivascular interstitial inflammation, acute bronchopneumonia with neutrophilic infiltrates (F, G, H) within alveolar airspaces (H), focally in bronchiolar lumens (F), and in peribronchiolar airspaces (G); Rare fibrin thrombi (E) were identified in small precapillary vessels. Renal tissues were collected from the inoculated kidney.

Discussion

In this investigation, we studied the progression of untreated severe infection initiated by a direct inoculation of a virulent clinical isolate of E. coli (CFT073) [22, 23] into the kidney parenchyma of swine. This direct renal inoculation provoked a systemic inflammatory syndrome which led to the development of organ dysfunction and shock. Importantly, the model permitted the acquisition of simultaneous multi-organ system temporal data that enabled the tracking of key events of the progression of localized infection to systemic manifestation (Fig 8). This type of model may circumvent many of the challenges and limitations of current rodent models that have failed to widely translate to the human situation, including cecal ligation and puncture in which bacterial species involved are rarely defined, ongoing intestinal ischemia and necrosis is concurrently present, and the mouse microbiota is not the same as in human patients [24, 25].

Fig 8 — A “high-fidelity” longitudinal swine model of infection induced by injection of pathogenic E. *coli* into the kidney parenchyma leads to numerous events including but not limited to fever, altered clinical chemistries, increases in blood inflammatory cytokines, alterations in hematologic and immune cell numbers, disruption in metabolism and ultimately, loss of hemodynamic stability. These events culminate as end-organ dysfunction and injury. This model permits the measurement of numerous parameters as infection progresses which allows for the detailed study of key features that lead to, and mechanisms that underlie, organ dysfunction and failure. It also allows for the manipulation of host factors such as sex, age and diet. Improved understanding of the temporal relationship of these events will help drive well-informed testing of intervention strategies including fluid resuscitation and targeted therapeutics.

There are a number of important hallmarks that we identified in our model of systemic responses to a renal inoculation of uropathogenic E. coli. Despite a rapid rise in core body temperature, MAP was maintained for approximately 6 hours after which it began to rapidly decline; MAP is a measurement of the cardiovascular component of the sequential organ failure assessment (SOFA) score which is clinically used to diagnose and assess sepsis severity [26]. Heart rate followed a similar trajectory to body temperature. For most infections, heart rate increases by ~8 BPM for every 1°C rise in body temperature [27]; this is evident in our model. In aggregate, these changes and their temporal associations are difficult, if not impossible, to study in humans because most often patients self-administer anti-pyretic medications before presenting to the health care system. Studying fever during the natural history of infection is relevant because it is an important and common response and is generated by a number of mechanisms including increases in the concentrations of the inflammatory cytokines, TNF- α and IL-6 [28]. It also has significant physiologic implications. For example, fever is an important mediator of the host response and if untreated, can contribute to the translocation of gut bacteria and cause cell death and end-organ injury [28].

Changes in other parameters altered by infection were captured by the measurement of clinical chemistries including glucose and lactate. Blood glucose has been an intense focus of study in critically ill patients including those with sepsis. It is generally accepted that hyperglycemia is a stress response and it has been shown to be associated with illness severity [29]. However, the study of glucose in the clinical setting of sepsis is challenging due to concomitant illnesses like diabetes and variable nutritional support and in some cases, sepsis can present with hypoglycemia [30]. Here we demonstrate that glucose concentration declines during the course of infection as measured by both a clinical assay and metabolomics which may be counterintuitive given the elevation in levels of metabolic precursors to catecholamines. Other clinical chemistries directly related to organ function were also measured. As expected, whole blood creatinine, BUN, total bilirubin increased and these changes occurred in advance of a significant decline in MAP. Creatinine and total bilirubin represent the renal and liver components, respectively, of the SOFA score [26].

Other indications of infection were evident in measurements of platelet count, which declined over time. Acute thrombocytopenia can accompany sepsis and its persistence is associated with mortality [31]. As such, platelet count represents the coagulation component of the SOFA score [26]. In our experiments, the WBC count did not change. However, there was a rapid shift in the WBC differential in which neutrophils increased and lymphocytes declined. This pattern of change is consistent with physiologic stress, indicative of acute illness and often precedes an elevation in WBC count [32, 33]. It has been proposed that this phenomenon is due to an abrupt increase in catecholamines [34].

Arterial pH began to drop well before any change in blood lactate concentration and more closely coincided with an increase in whole blood creatinine. This is consistent with the finding that the metabolic acidosis of infection is attributable to renal dysfunction rather than hyperlactatemia [35]. Despite animals not being paralyzed, a compensatory hyperventilation was not observed likely due to the deep state of anesthesia produced. The progression of infection was also met with a rapid decline in lung function as measured by the PaO₂/FiO₂ ratio. By the 8 h time point, animals reached a PaO₂/FiO₂ ratio that is indicative of acute hypoxemic respiratory failure. The PaO₂/FiO₂ ratio represents the pulmonary component of the SOFA score [26].

Infection induced a number of metabolic changes that extend beyond those which have been traditionally studied (e.g., glucose and lactate). Notably, this included a number of both essential and non-essential amino acids (see S1–S4 Figs, S1 and S2 Files, S1 and S2 Tables) many of which are metabolically related (see S2 Fig in the online supporting information). These findings also highlight the rapid onset of these changes that occurred in advance of changes in lactate concentration, in particular, increases in phenylalanine and tyrosine. Phenylalanine is a precursor of tyrosine and both amino acids are required for the synthesis of catecholamines [36] which are necessary for brain function and to support blood pressure. Although we did not specifically measure catecholamines, this illustrates the utility of our model for the study of infection-induced changes in metabolism and the value of serial sampling that is afforded by a large animal model. We acknowledge that in this work, conclusions about a broad range of metabolic pathways is limited by the chosen analytical platform, in this case, quantitative ¹H-NMR. This approach primarily detects polar compounds. However, this is not a limitation of the model itself as any assay can be performed to assess components of metabolism including the energy demands induced by infection.

In concert with the pyretic response, TNF-α and IL-6 were elevated in response to infection. In addition to their role in the manifestation of fever, both are associated with severity of organ failure and are predictive of mortality in humans with sepsis [37, 38]. TNF-α increases in the early phases of the inflammatory response [37] then declines in the chronic phase partly due to the inhibitory effect of IL-6 [37]. We acknowledge that our initial sampling scheme was not optimal as we missed the early increases in both cytokines due to our 6-hour sampling frequency. However, in one animal with more frequent blood measurements (hourly for the first 6 hours), we were able to capture a more granular measurement and time course of both cytokines corroborating the systemic inflammatory response in this model. Of note, as shown in Fig 6, there was an early post-inoculum rise in concentrations of TNF-α and subsequently IL-6, that appears to occur independent of the invasive surgical procedures. This suggests that such increases are primarily due to the host’s response to the newly introduced bacteria into the kidney.

Swine have been used in biomedical research for more than 25 years to model human disease. Although phylogenetically distant from humans, unlike rodents, their anatomical, hemodynamic, and physiological similarities to humans [39] make them an ideal species for modeling the systemic responses of infection [40] due to their comparable endotoxin sensitivity and tissue antigenicity to humans. The large size of swine allows for comprehensive surgical instrumentation, adequate longitudinal blood and tissue sampling, and application of therapeutic interventions such as mechanical ventilation. Swine also have an established track record in sepsis and critical care research which includes studies that have shown hemodynamic changes and cytokine profiles that are similar to those of humans [41–44]. We have exploited these advantages and modernized the model by employing a clinically faithful organ specific infectious source (renal injection of E. coli) to acquire detailed, real-time hemodynamic, physiologic and biochemical data [14] that, as far as we know, has not been done to this extent in other swine sepsis models (see S1 Table). The kidney was chosen for inoculation due to its small size, ease of access, and the reproducibility of the insult location. Furthermore, there is clinical relevance since urinary tract infections that lead to pyelonephritis are common. Acute pyelonephritis occurs at a rate of 15 to 17 cases per 10,000 females and 3 to 4 cases per 10,000 males annually in the US [45]. It is a common source of gram negative infection and also accounts for 25% of sepsis cases annually [46]. Other conditions such as soft tissue, pulmonary, and GI tract infection are arduous to model in large animals due to the larger size of the organs, complexity of the microbiome, and the time needed for the manifestation of illness. These modifications make our model novel and distinct from other porcine models of sepsis particularly since, to date, most have induced sepsis via peritonitis [17, 18, 40, 42, 43] or by direct intravenous infusion of either a pathogen [19, 44] or endotoxin [41]. In summary, our model is closely aligned with the current MQTiPPS review Part II recommendations which advocate for the discontinuation of endotoxin challenge models as well as “modeling sepsis syndromes that are initiated at sites other than the peritoneal cavity (e.g., lung, urinary tract, brain)” [47].

Importantly, our model has implications for the study of sepsis and septic shock in relation to the components of the definition of sepsis [48], and all but one (neurological) of the components of the SOFA score, can be assessed [26]. Recently, the definition of sepsis has expanded beyond the systemic inflammatory response syndrome (SIRS) to a combination of a dysregulated immune response with development of life threatening organ dysfunction while also providing different criteria for septic shock based on changes in blood pressure and lactate levels [48]. Although, currently there has been a shift towards more emphasis on prognostication using SOFA grading and scores rather than SIRS criteria, we evaluated the systemic responses in these animals based on both doctrines. The late rapid increase in lactate combined with the decrease in blood pressure and increased heart rate raises the possibility that these animals developed systemic sepsis and succumbed to shock.

In this pilot study, we elected to assess the natural evolution of the systemic responses to untreated infection (minimal fluid supplementation and no antibiotics) so that the experimental timeline of disease progression and lethality could be established. This is necessary to direct the design of future planned studies. We envision that this model, and future derivations of it such as utilizing a full resuscitation protocol with fluids and antibiotics, use of a drug resistant E. coli to mitigate the effects of antibiotics, delaying treatment protocols, or multiple inoculations, will contribute to a better understanding of the pathogenesis of infection progression to multi-organ dysfunction, systemic inflammatory response, and immune dysregulation (Fig 8). The voluminous data that can be produced by this model may also be useful for identifying new biomarkers, gene expression and cytokine profiles, immune cell populations, and immune cell function. Further examination of the metabolome, microbiome, and other systems will greatly enhance the likelihood of the identification of prognostics, development of diagnostics, and treatment strategies of sepsis and septic shock helping to bring badly needed precision medicine approaches to a poorly misunderstood disease that continues to confound progress in its treatment. To summarize, the combination of this model’s longitudinal observation of temporal events, its breadth and magnitude of collected physiologic, metabolic, and organ specific data and most importantly, its clinical faithfulness, is what sets this model apart from murine or other large animal models of sepsis. The uniqueness of this model lies in its attempt to reproduce a complicated series of events leading to a recognizable clinical disease state in reproducible fashion within a realistic timeframe. This model will add to and enrich currently available models if utilized as a platform for further study of diagnostics, markers and therapeutics related to severe infection and sepsis.

Despite the value of our model, we acknowledge a number of limitations. First, in our study, we did not compare the results of our experimental group to an independent negative control group (no inoculation). Instead, we used each animal as its own control by comparing baseline parameters (pre-inoculum) to changes over time (post-inoculum). This design was employed because the primary aim of our work was to follow the progression of events and collect repeated measures data while observing and studying within-subject effects (compare baseline data to after inoculation). We acknowledge that the study of the progression of the systemic responses to infection in a large animal model necessitates prolonged anesthesia and mechanical ventilation and that these interventions, regardless of how well controlled, likely influence a number of the parameters. To minimize the impact of these factors, we took great care to be as consistent as possible in the execution of our detailed experimental protocol. This included participation of the same team members for all experiments and the use of standard operating procedures for preparation and injection of the inoculum and sample collection and processing. Notably, the magnitude of change of some of the hemodynamic and biochemical measurements (e.g., temperature, complete blood count, kidney function) cannot be explained in the absence of a direct effect of bacterial inoculation and are consistent with those that would be expected in a severe, untreated infection in humans. Finally, we recognize that the laparotomy used to visualize the kidney for direct inoculation could incite a surgical inflammatory response and peritoneal or retroperitoneal bacterial seeding could introduce variance into our measurements. We think the likelihood of this is negligible because of the careful procedure we used to inject the bacterial inoculum directly into the kidney. These limitations notwithstanding, our model advances the field of experimental sepsis because it establishes the much needed temporal course of the early systemic responses to severe infection.

Conclusion

A swine model of untreated infection that employs a renal injection of uropathogenic E. coli generates physiologic and biochemical data that are highly relevant to the study of human sepsis. This sets a road map for future assessment of therapeutic, diagnostic, and prognostic modalities while furthering the broader understanding of the pathophysiologic and mechanistic underpinnings of sepsis and septic shock. Thus, this novel model will enable the testing and validation of critically needed novel therapeutics against systemic infection that can often lead to sepsis and septic shock.

Supporting information

S1 Fig. Metabolic changes associated with the natural history of systemic infection.

The adenylate energy charge (A) did not change, but the ATP/ADP ratio (B) trended downward. Lactate, as measured by NMR (C), followed the same trend as the clinical measurement and was not significantly changed during the course of the experiment (ANOVA false discovery rate [FDR] corrected p-value = 0.3). In addition to the nine metabolites with FDR < 10%, there were an additional seven metabolites with FDR of < 15%. These were (D) ornithine (FDR = 13%), (E) threonine (FDR = 13%), (F) AMP (FDR = 13%), (G) alanine (FDR = 13%), (H) serine (FDR = 13%), (I) glutathione (FDR = 15%), and (J) creatinine (FDR = 15%). Data are the mean (SE) from four animals. BL = baseline.

(TIF)

Click here for additional data file.^{(1.7MB, tif)}

S2 Fig. Metscape generated network of the metabolites of progressive systemic infection.

Of the nine metabolites with an FDR-corrected ANOVA p value of < 0.10, six (inosine monophosphate, glutamine, methionine, tyrosine, phenylalanine and histidine) mapped into a single network. The figure was generated by uploading the Kyoto Encyclopedia of Genes and Genomes (KEGG) identification (ID) numbers of the nine metabolites into Metscape (3.1.3 metscape.ncibi.org/), a plugin application for Cytoscape (3.8.0 cytoscape.org). The human library was used. Metabolites are designated by red hexagons with dark red being those that were manually entered into Metscape. The gray squares represent reactions, green round-corner squares, enzymes and blue circles, genes.

(TIF)

Click here for additional data file.^{(587.8KB, tif)}

S3 Fig. Carnitine and acetylcarnitine are not detectable early in the course of infection.

Concentrations of (A) carnitine were not detectable in any samples at baseline (BL), only one sample had a detectable concentration at 6h but carnitine was detected in all samples by the end of the experiment. Acetylcarnitine (B) was only detectable on one sample at BL and 6h and only two samples at the end of the experiment. These metabolites were removed from the primary analysis because of data missingness but are shown here because they have previously been shown to be important as indicators of sepsis severity (see online supplement text). Data are the mean (SE, when applicable) from four animals.

(TIF)

Click here for additional data file.^{(822.4KB, tif)}

S4 Fig. Kaplan-Meier survival plot for experimental end times.

Plot of survival times for each animal used in the study.

(TIF)

Click here for additional data file.^{(356KB, tif)}

S1 Table. Baseline and end of experiment parameters for all animals.

Hemodynamic and biochemical parameters were measured in each animal at baseline (pre-infection) and at the termination of the experiment. These included core temperature, heart rate, mean arterial pressure, central venous pressure (CVP); pulmonary artery pressure (PAP); creatinine, blood urea nitrogen (BUN); glucose, lactate, total bilirubin, alanine transaminase (ALT); platelets, hemoglobin, hematocrit, white blood cells (WBC; with percent neutrophils and lymphocytes); arterial pH; bicarbonate (HCO₃^-); end tidal carbon dioxide pressure (PetCO₂); oxygen consumption, partial pressure of oxygen (PaO₂); fraction of inspired oxygen (FiO₂); and central venous oxygen saturation (ScvO₂).

(DOCX)

Click here for additional data file.^{(19.4KB, docx)}

S2 Table. ¹H-Nuclear Magnetic Resonance (NMR)-detected and quantified swine whole blood metabolites with Kyoto Encyclopedia of Genes and Genome (KEGG) Identifications (ID).

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S1 File. A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine.

(DOCX)

Click here for additional data file.^{(28.7KB, docx)}

S2 File. Sepsis-manuscript compiled-data.

(XLSX)

Click here for additional data file.^{(35.2KB, xlsx)}

Acknowledgments

The authors would like to thank Christopher Fry and the staff of Michigan Center for Integrative Research in Critical Care (MCIRCC) for their technical support. We would also like to thank Cora McHugh for her assistance with the acquisition of the metabolomics data.

Data Availability

All relevant data are within the manuscript and its Supporting Information files. The entire data set can be found on the NIH Metabolomics Workbench (https://www.metabolomicsworkbench.org/) under the accession # PR000953.

Funding Statement

This work is supported in full by the Michigan Center for Integrative Research in Critical Care (https://mcircc.umich.edu/); K.A. Stringer’s contribution was supported, in part, by grants from the National Institute of General Medical Sciences (NIGMS; R01GM111400 and R35GM13631201; https://www.nigms.nih.gov/); J. VanEpps’ effort was supported, in part, by a grant from the National Institute of Allergy and Infectious Diseases (NIAID; K08AI128006; https://www.niaid.nih.gov/); J. Nemzek’s effort was supported, in part, by a grant from the NIGMS (R01GM112799). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIGMS, NIAID, or the National Institutes of Health. With the exception of MCIRCC, none of the sponsors played any role in the study design. None of the sponsors played any role in data collection and analysis, decision to publish or the preparation of the manuscript. The authors declare no conflict of interest.

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PLoS One. doi: 10.1371/journal.pone.0243577.r001

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Anasuya Sarkar

30 Jul 2020

PONE-D-20-18461

Natural history of the systemic responses to a renal inoculation of uropathogenic E. coli in swine

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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Reviewer #1: Comments to the Authors:

General Comments:

Tiba et al. present a careful summary of the hemodynamic, immunologic and metabolic changes induced after injecting E. coli organisms into the renal pelvis of 5 intubated and anaesthetized pigs. Detailed kinetics over a 9-13 hour period are displayed including cytokine profiles, Hgb, blood cell counts, ETCO2, amino acid levels and essential metabolic measurements. The study provides a strong argument to support the need for better animal models (than murine models) to study sepsis and propose that their model provides a representative pattern of physiological changes that could make a better sepsis model than the murine model.

I have several issues with the work. First of all there is no control animal for the E coli challenge. That is, we cannot distinguish which of the changes seen are due to the infection and which are due to the 12 h instrumentation and anesthesia. This is particularly critical for the metabolic changes that are noted which may well relate to the effects of immobility and fasting as much as from sepsis.

Secondly, the major point is that this is a novel model. However, the paper provides no supportive information to explain why this “novel” model is more representative of sepsis than previously published studies using pigs. Neither does the work necessarily demonstrate that this is a better model. There is no comparison model like cecal ligation and puncture or i.p. injection of E coli.

Specific Comments:

1 Details in the description of the model that injects live E coli into the renal pelvis are lacking. This is the key uniqueness of the model yet hard to tell what was actually done here. The methods mention a 22 gauge catheter but don’t’ mention how this was done. Was it percutaneous under ultrasound? There is a mention in the discussion of the study limitations that argues that the “need for a laparoscopy to visualize the kidney could introduce variance into the measurements”. This is the first mention that I found of the model. If this was done for all 5 animals, there is concern that the laparoscopy itself may change the model dramatically by providing a peritoneal or retroperitoneal site for extra renal seeding of organisms. Was the wound closed or left open?

2 Line 353 mentions that one of the animals showed an early burst of serum TNF at about 90 min. The attribution to surgical procedures is possible but it should be noted that human studies have classically noted an early peak of TNF 90 min. after endotoxin infusions.

3 What was the timing of the surgical procedures and was laparotomy the main surgical procedure or does this refer to line placements and intubation?

4 This sepsis model is provided as a better “mouse trap” so to speak for sepsis work that would be more human-like. It would be helpful to show in a Table or Discussion format exactly the ways that this model improves upon what has already been published regarding animal models, to include cost, reproducibility, aspects that mimic the human condition, ability to develop biomarkers etc.

5 In this context, Figure 8 presents a typical model of the sepsis events. Might be better to use this space to provide a diagram or table that describes the summary advantages of this model over murine models.

Minor comments:

The title “Natural history of ….” seems odd word choice for a 12 hour study.

**********

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Reviewer #1: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Dec 11;15(12):e0243577. doi: 10.1371/journal.pone.0243577.r002

Author response to Decision Letter 0

1 Sep 2020

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

And

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

The manuscript and files names have been formatted to meet PLoS One style requirements

2. Please state where the swine used in this study were obtained.

Response: All animals used in this study were procured from the Michigan State University Swine Teaching & Research Center. The manuscript has been updated to reflect this. (redlined manuscript: lines 67-68)

3. Please also provide survival curves as a supplementary file.

Response: The survival curve has been submitted as supplementary file “S4 Fig”.

4. Also, please state the total length of time the experiment was run

Response: The total length of the experiment was designed to run for up to 24 hours from inoculation. The manuscript has been updated and this information is now included (redlined manuscript: lines 111 and 125).

5. Finally, please state any special training in animal care or handling provided for research staff.

Response: All staff participating in these experiments received specialized training and have completed the following courses from the University of Michigan’s Unit for Laboratory Animal Medicine:

-ULAM-C10000 Orientation to Animal Care and Use at the University of Michigan

-ULAM-C11400 Introduction to the Laboratory Swine

-ULAM-C11771 Non-Rodent Mammalian Survival Surgery

The Manuscript has been updated with this information (redlined manuscript: lines 68-70).

Reviewers' comments:

Reviewer's Responses to Questions

1. Is the manuscript technically sound, and do the data support the conclusions?

‘The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.”

Reviewer #1: Partly

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

“The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.”

Reviewer #1: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

“PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.”

Reviewer #1: Yes

Review Comments to the Author

Reviewer #1: Comments to the Authors:

General Comments: Tiba et al. present a careful summary of the hemodynamic, immunologic and metabolic changes induced after injecting E. coli organisms into the renal pelvis of 5 intubated and anaesthetized pigs. Detailed kinetics over a 9-13-hour period are displayed including cytokine profiles, Hgb, blood cell counts, ETCO2, amino acid levels and essential metabolic measurements. The study provides a strong argument to support the need for better animal models (than murine models) to study sepsis and propose that their model provides a representative pattern of physiological changes that could make a better sepsis model than the murine model.

I have several issues with the work.

Comment 1:

First of all, there is no control animal for the E coli challenge. That is, we cannot distinguish which of the changes seen are due to the infection and which are due to the 12 h instrumentation and anesthesia. This is particularly critical for the metabolic changes that are noted which may well relate to the effects of immobility and fasting as much as from sepsis.

Response: We thank the reviewer for this comment and we certainly appreciate this concern. Although we haven’t compared the results of our experimental group to control (no inoculation), the absence of a control group may be justified for this type of investigation. In this pilot study, we standardized all anesthetic and surgical manipulation for all five animals. This was done before inoculation and was then longitudinally tracked. In addition, we contend that in this type of observational study, for which the E. coli inoculation was the only experimental manipulation, a comparison to control is not necessary since the primary aim of the work was to follow the progression of events. Furthermore, we collected all data in a repeated measures fashion while observing and studying within-subject effects. This allowed us to compare baseline data (prior to inoculation) to those after inoculation.

Although some of the observed effects may be attributable to surgical manipulation and prolonged anesthesia, the magnitude of change in many of the hemodynamic and biochemical measurements (e.g., temperature, CBC, kidney function) cannot simply be explained by these factors. It is reasonable to conclude that these changes are due to the direct effect of bacterial inoculation like that which may be observed in a severe untreated infection in humans which, of course, is not ethically feasible. We acknowledged this point as a limitation in our original submission with a description of the mitigation effort to minimize such effects. We have expanded the details about this limitation by adding “…we used each animal as its own control by comparing baseline parameters (pre-inoculum) to changes over time (post-inoculum). This design was employed because the primary aim of our work was to follow the progression of events and collect repeated measures data while observing and studying within-subject effects (compare baseline data to after inoculation).” to the discussion section. We have also altered the title of the paper to remove the implication that the time course is a “natural history”.

The manuscript has been updated to address the reviewer’s comment (redlined manuscript: lines 420-428).

Comment 2:

Response: We thank the reviewer for this comment. The novelty of our model lies in its faithfulness to the clinical condition of sepsis. It describes the systemic events that occur during the manifestation of infection. This starts with the knowledge that pyelonephritis can progress to one of the leading causes of sepsis, urosepsis (second only to pneumonia); a cause of sepsis that is far more clinically common than bacterial peritonitis (the infection which is mimicked by cecal ligation and puncture or i.p. injection of bacteria). The model’s longitudinal observation of temporal events, its breadth and magnitude of physiologic, metabolic, and organ specific data collected, sets this model apart from murine or other large animal models of sepsis. The uniqueness of this model also lies in its utility for the identification of a complicated series of events that leads to a recognizable clinical disease state in reproducible fashion within a realistic timeframe. This model is envisioned to provide a more accessible platform for further study of diagnostics, markers and therapeutics related to severe infection and sepsis.

The manuscript has been updated to address the reviewer’s comment (redlined manuscript: lines 413-419)

Specific Comments:

Comment 3:

Details in the description of the model that injects live E coli into the renal pelvis are lacking. This is the key uniqueness of the model yet hard to tell what was actually done here. The methods mention a 22-gauge catheter but don’t’ mention how this was done. Was it percutaneous under ultrasound? There is a mention in the discussion of the study limitations that argues that the “need for a laparoscopy to visualize the kidney could introduce variance into the measurements”. This is the first mention that I found of the model. If this was done for all 5 animals, there is concern that the laparoscopy itself may change the model dramatically by providing a peritoneal or retroperitoneal site for extra renal seeding of organisms. Was the wound closed or left open?

Response: A 22-gauge catheter was placed in the kidney under direct visualization through the laparotomy. The seeding of bacterial organisms outside of the kidney was minimized by maintaining a slow and constant infusion rate (0.3 mL/min), and proper placement of the catheter, during the infusion. We also applied direct pressure using gauze to the infusion site of the kidney. As such, the primary inflammatory response is largely driven by the direct renal inoculation and less likely due to peritonitis. The laparotomy was closed immediately following the completion of the infusion, leaving the Foley catheter and Potts loop ureter occlusion externalized.

These details have been added to the methods section (redlined manuscript: lines 101-106) and limitations of this procedure has been addressed in the discussion (redlined manuscript: lines 431-439).

Comment 4:

Line 353 mentions that one of the animals showed an early burst of serum TNF at about 90 min. The attribution to surgical procedures is possible but it should be noted that human studies have classically noted an early peak of TNF 90 min. after endotoxin infusions.

Response: In this statement, we intended to suggest the burst of TNF-α immediately following bacterial infusion was more likely attributable to the host’s inflammatory response to the pathogen, and less likely related to the surgical procedures, consistent with the reviewer’s mention of previous studies. The manuscript has been updated to clarify this point. Specifically, we have added the statement: “Of note, as shown in Fig 6, there was an early post-inoculum rise in concentrations of TNF-α and subsequently IL-6, that appears to occur independent of the invasive surgical procedures. This suggests that such increases are primarily due to the host’s response to the newly introduced bacteria into the kidney.”

These details have been added to the discussion section (redlined manuscript: lines 371-374).

Comment 5:

What was the timing of the surgical procedures and was laparotomy the main surgical procedure or does this refer to line placements and intubation?

Response: Surgical procedures consisted of the insertion of catheters into surgically exposed vessels (i.e., arterial and venous cutdown), laparotomy, and Foley catheter placement. All procedures were carried out concurrently within 1.5 to 2 hours.

These details have been added to the methods section (redlined manuscript: lines 86-89).

Comment 6:

This sepsis model is provided as a better “mouse trap” so to speak for sepsis work that would be more human-like. It would be helpful to show in a Table or Discussion format exactly the ways that this model improves upon what has already been published regarding animal models, to include cost, reproducibility, aspects that mimic the human condition, ability to develop biomarkers etc.

Response: We thank the reviewer for this comment. We appreciate that a table would be helpful but we think it is much more efficient to address these elements in the discussion (and the introduction which sets the premise for the work). However, in doing so, we view that an extensive comparison of published experimental models and their advantages and disadvantages is beyond the scope of the paper. To generate this type of review would, in our view, require head-to-head comparisons across models as well as procurement of information that may not be readily or reliably available in the literature (e.g., costs). Of note, there have been numerous commentaries about the utility and translatability of experimental sepsis models to the human condition. So much concern has been raised about the translational gap in sepsis that it prompted the National Institute of General Medical Sciences of the U.S. National Institutes of Health to make rodent-based sepsis studies a low priority (https://grants.nih.gov/grants/guide/notice-files/NOT-GM-19-054.html).

Comment 7:

In this context, Figure 8 presents a typical model of the sepsis events. Might be better to use this space to provide a diagram or table that describes the summary advantages of this model over murine models.

Response: We would very much like to keep Figure 8 (please also see our response to Comment 6). It represents an overview of the timeline of the events of our model and some of its unique aspects. In our opinion, this provides a visual and important summation of the model for readers. We have updated it to make some aspects of it more clear.

Minor comments:

Comment 8:

The title “Natural history of ….” seems odd word choice for a 12-hour study.

Response: The title of the manuscript has been changed to “A Comprehensive Assessment of Multi-System Responses to a Renal Inoculation of Uropathogenic E. coli in Swine.”

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(28.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0243577.r003

Decision Letter 1

Anasuya Sarkar

12 Oct 2020

PONE-D-20-18461R1

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

PLOS ONE

Dear Dr. Tiba,

Please submit your revised manuscript by Nov 26 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Anasuya Sarkar

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The manuscript has been improved and the changes have largely answered the concerns that were expressed. However, the suggestion that this approach is novel deserves a supportive discussion. A paragraph in the discussion based upon what is already known about porcine models is needed to put the current work into context.

It is necessary that the manuscript make it explicit why this models is more representative of sepsis than previously published studies using pigs. Perhaps taking advantage of the reviews that the author as mentioned, the discussion could add a paragraph to put the work into better context

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. 2020 Dec 11;15(12):e0243577. doi: 10.1371/journal.pone.0243577.r004

Author response to Decision Letter 1

28 Oct 2020

Comments to the Author

Reviewer #1: (No Response)

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

6. Review Comments to the Author

Reviewer #1:

Comment: The manuscript has been improved and the changes have largely answered the concerns that were expressed. However, the suggestion that this approach is novel deserves a supportive discussion. A paragraph in the discussion based upon what is already known about porcine models is needed to put the current work into context.

Response: We thank the reviewer for this comment. We have added a paragraph to highlight the novelty of our model and what sets it apart from other models. In addition to our model’s faithfulness to the clinical course of severe infection, the model’s longitudinal observation of temporal events, its breadth and magnitude of physiologic, metabolic, and organ specific data collected, sets it apart from murine or other large animal models of sepsis. These features make our model distinct from other porcine models of sepsis particularly since, to date, most have induced sepsis via peritonitis or by direct intravenous infusion of either a pathogen or endotoxin. Furthermore, many have employed interventions such as fluid supplementation, antibiotics or vasopressors which mask the natural course of illness. We also added citation [47] which lists the MQTiPPS review Part II recommendations. Within other recommendations, it advocates for the discontinuation of endotoxin challenge use as well as modeling of the sepsis syndromes at sites other than the peritoneal cavity (e.g., lung, urinary tract, brain). Our model aligns closely with these recommendations.

The manuscript has been updated to address the reviewer’s comment and few other formatting issues (redlined manuscript: lines 423-426, 432-437, 671-679, and 685-687).

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(19.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0243577.r005

Decision Letter 2

Anasuya Sarkar

24 Nov 2020

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

PONE-D-20-18461R2

Dear Dr. Tiba,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Anasuya Sarkar

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0243577.r006

Acceptance letter

Anasuya Sarkar

1 Dec 2020

PONE-D-20-18461R2

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

Dear Dr. Tiba:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Anasuya Sarkar

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Metabolic changes associated with the natural history of systemic infection.

(TIF)

Click here for additional data file.^{(1.7MB, tif)}

S2 Fig. Metscape generated network of the metabolites of progressive systemic infection.

(TIF)

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S3 Fig. Carnitine and acetylcarnitine are not detectable early in the course of infection.

(TIF)

Click here for additional data file.^{(822.4KB, tif)}

S4 Fig. Kaplan-Meier survival plot for experimental end times.

Plot of survival times for each animal used in the study.

(TIF)

Click here for additional data file.^{(356KB, tif)}

S1 Table. Baseline and end of experiment parameters for all animals.

(DOCX)

Click here for additional data file.^{(19.4KB, docx)}

S2 Table. ¹H-Nuclear Magnetic Resonance (NMR)-detected and quantified swine whole blood metabolites with Kyoto Encyclopedia of Genes and Genome (KEGG) Identifications (ID).

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S1 File. A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine.

(DOCX)

Click here for additional data file.^{(28.7KB, docx)}

S2 File. Sepsis-manuscript compiled-data.

(XLSX)

Click here for additional data file.^{(35.2KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(28.7KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(19.5KB, docx)}

Data Availability Statement

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PERMALINK

A comprehensive assessment of multi-system responses to a renal inoculation of uropathogenic E. coli in swine

Mohamad Hakam Tiba

Brendan M McCracken

Robert P Dickson

Jean A Nemzek

Carmen I Colmenero

Danielle C Leander

Thomas L Flott

Rodney C Daniels

Kristine E Konopka

J Scott VanEpps

Kathleen A Stringer

Kevin R Ward

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Ethics statement

Animal instrumentation

Inoculation

Monitoring and sample collection

Statistical analysis

Results

Hemodynamic and physiologic parameters

Fig 1. Infection leads to progressive changes in hemodynamic and physiologic parameters.

Clinical chemistries

Fig 2. Infection induces changes in clinical chemistries.

Hematologic parameters and white blood cell count and differential

Fig 3. Hematologic parameters and white blood cell differentials are altered as infection progresses.

pH, blood gases and oxygenation

Fig 4. Infection disrupts acid-base and oxygenation homeostasis.

Metabolic measurements

Fig 5. Infection causes a large shift in host metabolism as measured by whole blood metabolomics.

Plasma cytokines

Fig 6. The inflammatory cytokines, tumor necrosis factor (TNF)-α and interleukin (IL)-6 increase in during infection.

Histopathology of vital organs

Fig 7. Representative hematoxylin and eosin stained sections of renal, hepatic, and lung tissues.

Discussion

Fig 8. Experimental modeling of the natural history of systemic responses to infection.

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Anasuya Sarkar

Roles

Author response to Decision Letter 0

Decision Letter 1

Anasuya Sarkar

Roles

Author response to Decision Letter 1

Decision Letter 2

Anasuya Sarkar

Roles

Acceptance letter

Anasuya Sarkar

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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