NNS 2015 Program & Abstract Book

versus sham APP/PS1. Histological analysis at 48h revealed increased

6E10

ve A-beta deposits, microglial activation (Iba-1), argyrophilic

fibre (silver staining), and axonal bulb-like structures (phospho-

neurofilaments) in injured versus sham APP/PS1. Together, these

results support the hypothesis that CHIMERA TBI induces inflam-

mation, white matter pathology, and A-beta deposition in acute post-

injury period. Future studies will reveal how CHIMERA TBI affects

disease progression in APP/PS1 mice.

Keywords: Novel TBI Model, Mild TBI, APP/PS1 Mouse Model of

AD, Inflammation, White Matter Pathology

D2-10

DYNAMIC PROFILING: OUTCOME PREDICTION IN TBI

BASED ON DEMOGRAPHICS, INJURY CHARACTERISTICS,

AND INFLAMMATORY MEDIATORS

Andrew Abboud

, Gregory Constantine, Ava Puccio, Marius Buliga,

Alexey Solovyev, Qi Mi, David Okonkwo, Yoram Vodovotz

University of Pittsburgh, Surgery, Pittsburgh, USA

Objectives:

Inflammation induced by traumatic brain injury (TBI)

can lead to both morbidity and mortality. The goal of the present study

was to develop dynamic, data-driven computational models in order to

predict the likelihood of mortality post-TBI.

Methods:

Thirteen inflammatory cytokines and chemokines were

determined using Luminex in serial cerebrospinal fluid samples

from 31 TBI patients (26 survivors [24 males/2 females] and 5 non-

survivors [4 males/1 female]). Overall, patients in the cohort were 33

–

years old, with a mean Glasgow Coma Scale (GCS) score of 6

–

0.2.

Data on each subject, consisting of ten clinical (one-dimensional) var-

iables, such as age, gender, GCS score, Glasgow Outcome Scale

(GOS) score, and presence of infection, along with inflamma-

tory mediator time series were used to develop a technique called

‘‘Dynamic Profiling’’. Dynamic Profiling attempts to recreate the

clinician’s decision making process, by clustering patients sequen-

tially over time with regard to likelihood to die, using Hartigan’s

k-means method, into disjoint groups at different stages based on

demographic, injury, and inflammation data.

Results:

Using the Dynamic Profiling method, we could segregate

patients over time with regard to their mortality odds. This model had

a predictive accuracy of 72% for non-survivors.

Conclusions:

A novel, data-driven method was developed to assess

the probability of morbidity and mortality following TBI. This method

incorporates both injury-specific and demographic data as well as a broad

panel of inflammatory markers. Outcome prediction in the setting of TBI

may be improved by use of the Dynamic Profiling method, which in

essence replicates physician decision making in the setting of TBI.

Keywords: Dynamic Profiling

D2-11

DIFFERENTIAL DYNAMIC NETWORKS OF INFLAMMA-

TION IN CEREBROSPINAL FLUID OF TRAUMATIC BRAIN

INJURY SEGREGATE SURVIVORS & NON-SURVIVORS

Andrew Abboud

, Gregory Constantine, Ava Puccio, Qi Mi, David

Okonkwo, Yoram Vodovotz

University of Pittsburgh, Surgery, Pittsburgh, USA

Objectives:

Though inflammation induced by traumatic brain injury

(TBI) is a mediator of morbidity and mortality, its complexity has

defied therapeutics and diagnostic applications. We have previously

suggested that inference of dynamic inflammation networks may aid

in filling this gap. Thus, we hypothesized that differential dynamic

inflammation programs characterize TBI survivors vs. non-survivors.

Methods:

Thirteen inflammatory cytokines and chemokines were

determined using Luminex in serial cerebrospinal fluid (CSF)

samples from 31 TBI patients over 5 days. In this cohort, 5 were non-

survivors (Glasgow Outcome Scale [GOS] score

1) and 26 were

survivors (GOS

1). Significant differences in the time courses of CSF

inflammatory mediators were determined by Two-Way ANOVA.

Principal Component Analysis (PCA) was used to identify signatures

and key drivers of the inflammatory response. Dynamic Bayesian

Network (DyBN) inference was used to define central nodes of positive/

negative feedback, as well as defining outcome-specific dynamic in-

terrelationships among inflammatory mediators.

Results:

A Pearson correlation analysis of GCS vs. GOS suggested

that survivors and non-survivors had distinct clinical response tra-

jectories to injury. Statistically significant differences (p

0.05) in

interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-

(TNF-

) were observed between TBI survivors vs. non-survivors by

Two-Way ANOVA. PCA suggested that IL-6 and IL-8 were hall-

marks of the post-TBI inflammatory response, whereas macrophage

inflammatory protein-1

(MIP-1

) and IL-10 were key component of

the inflammatory response in non-survivors. DyBN inference sug-

gested a core module of self-feedback and cross regulation among

IL-6, IL-8, and IL-1

in TBI survivors, with multiple mediators such

as TNF-

and IL-1

as output nodes. In contrast, DyBN suggested that

IL-6 and IL-8 alone were central nodes in TBI non-survivors.

Conclusions:

Differential dynamic trajectories and network pat-

terns elucidated by

in silico

modeling highlight the importance of IL-6

and IL-8 as principal drivers and central nodes in both survivors and

non-survivors following TBI, with a potential role for IL-1

in sur-

vivors.

Keywords: TBI

D2-12

DEVELOPMENT AND CHARACTERIZATION OF A ZEB-

RAFISH MODEL OF TBI

Victoria McCutcheon

, Eugene Park

, Elaine Liu

, Pooya

SobheBidari

, Jahan Tavakkoli

, Andrew Baker

2,3,1

University of Toronto, Institute of Medical Sciences, Toronto, Ca-

nada

University of Toronto, Anesthesia and Surgery, Toronto, Canada

St. Michael’s Hospital, Trauma Research, Toronto, Canada

Ryerson University, Physics, Toronto, Canada

Background:

Traumatic brain injury (TBI) is a leading cause of death

and morbidity in industrialized countries with considerable associated

direct and indirect healthcare costs. Animal models have been critical in

efforts to understand the pathophysiology of TBI, and to aid in the

identification of novel therapies. To date, numerous models of closed-

head trauma have been developed to address mTBI sequelae. However,

even with the use of rodent models, preclinical drug evaluation is a

lengthy process. In this regard, the zebrafish (ZF) has numerous advan-

tages to address the technical and time-dependent obstacles associated

with preclinical drug validation. There is a high degree of evolutionary

conservation between ZF and human homologue proteins, brain struc-

tures, and pathways. Furthermore, ZF offer advantages compared with

other vertebrate models, including the availability of rapid and efficient

tools for genetic manipulation and significant potential advantages in live

imaging and documentation of injury progression.

Methods:

We developed and characterized a ZF model of TBI

designed for high-throughput drug screening. In adult zebrafish, a

A-104