Djani_Presentation_Final_Updated

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Early neurochemical deficits in offspring of dams fed high fat diets: Focus on Brain Monoamines Dylan M. Djani Mentor: Nikolay M. Filipov Department of Physiology and Pharmacology College of Veterinary Medicine University of Georgia

Transcript of Djani_Presentation_Final_Updated

Early neurochemical deficits in offspring of dams fed high fat diets:

Focus on Brain Monoamines

Dylan M. DjaniMentor: Nikolay M. Filipov

Department of Physiology and PharmacologyCollege of Veterinary Medicine

University of Georgia

Presentation Outline• Obesity and Autism Spectrum Disorder (ASD)– Prevalence, key features

• ASD Etiology– Maternal environment

• Early Brain Monoamines

What are monoamines?

(Bear et. al., 2015)

Obesity Trends in the U.S.

National Health and Nutrition Examination Survey (NHANES)• Obesity Prevalence in U.S. Adults ≥ 25 years [2007 – 2012]

– Men: 35.04% Data published June 22, 2015 (Yang and Colditz, 2015)

– Women: 36.84%

Prevalence consistently high [2003 – 2010]

Data published February 26, 2014 (Ogden et. al., 2014)

Obesity – chronic inflammatory condition: risk mortality, physical, emotional, and mental conditions

OVER 1 in 3 AMERICANSRegardless of sex or race

(Monteiro and Azevedo, 2010; Nijhuis et. al., 2009)

Maternal Obesity Trends in the U.S.

Schlaff et. al., 2014

*Gestational weight gain; 1Archive for Research on Child Health

Overweight and obese women are more likely to gain excess weight during pregnancy.

>50% women gained excess weight during gestation. (2009 Institute of Medicine)

Maternal Obesity Adversely Impacts

Offspring

Rivera et. al., 2015

BMI:Body mass index

GWG:Gestational weight gain

HFD:High-fat diet

Rivera et. al., 2015Human research studies

Animal model research studies

Autism-Spectrum Disorder: Overview DSM-V: Neurodevelopmental Disorder Early onset and lifelong impact (APA, 2013)

Core symptoms: deficits in communication, social interaction, behavior

http://www.autismspeaks.org/Last updated January 6, 2015

Prevalence Facts:

*1 in 68 children in the U.S. *Male bias (M/F Sex Ratio 4:1) *Positive correlation with obesity

123% increase in ASD prevalence since 2002.

Analysis of 2010 data; published in 2014; studies ongoing (ADDM)CDC Autism and Developmental Disabilities Monitoring Network

Key Features: ASD ComorbiditiesChen et. al., 2015

Zerbo et. al., 2015

Increased odds ratios for psychiatric and immune-mediated comorbidities.

Maternal obesity as a perinatal environmental factor in ASD etiology.

Synaptic dysregulation leads to altered brain connectivity.Gastrointestinal and innate immune alterations – pro-inflammatory.

GENETICS: h2 = 52% (common variation ~48.4%)Neuroimmunologic Cell Adhesion

Cellular Function

EPIGENETICS: Environmental factors impact epigenetic load in utero.

ENVIRONMENT: Prenatal, perinatal, postnatal factors.Environmental toxicants.Maternal infection/inflammation,

maternal obesity.

Etiological Insights into ASDAnimal Models & Epidemiological Studies: Key Findings

(Banerjee et. al., 2014; Kana et. al., 2014; Hahamy et. al., 2015; Jaiswal et. al., 2015; Gaugler et. al., 2014; Loki et. al., 2015; Tamashiro and Moran, 2010)

Maternal Obesity in the Perinatal Period

Maternal obesity

(Rivera et. al., 2015; Sullivan et. al., 2015; Mehta et. al., 2014; Bolton and Bilbo, 2014)

Immunologic dysregulation

Altered placental function

Maternal circulation

Pro-inflammatory cytokinesGlucose and triglyceridesHormones (i.e. leptin)Serotonin (5-HT)

Altered fetal exposureduring perinatal period

Altered brain connectivity and neurotransmitter systems

Epigenetic, metabolic, neurobehavioral programming

Monoaminergic Involvement in ASD: Serotonin

Jaiswal et. al., 2015

Neuroimmunologic dysregulation in ASD involves serotonin.

Monoaminergic Involvement in ASD

Dopaminergic System

Noradrenergic System

ASD Psychiatric comorbidities

Cross-talk between DA and NEBrainstem

(VTA, LC)Dorsal

hippocampus

(Hara et. al., 2015; Kriete and Noelle, 2015) (Jellinger, 2011; Guiard et. al., 2008)

Prefrontal Cortex Valproic-acid mouse model: PND21 Computerized developmental modeling

Rivera et. al., 2015

Experimental Objectives

Evaluate the effects of maternal obesity and sex on monoamine systems in the early post-natal life of mice.

Hypothesis

Significant neurochemical differences will be observed in selected brain regions of post-natal day 10 mice due

to maternal high fat diet and sex.

Experimental Design and TimelineBrain Collection: PND10

N = 7; 1m/1f per group

Brain Region Collection:500 μm coronal sectioning; dry ice Regional micropunches obtained

Animals:

C57BL/6 female mice, 6-7 weeks

Assigned Diet*:

High fat: 60% kcal from fat HFDLow fat: 10% kcal from fat LFDPND = post-natal day; *Diets balanced for simple sugars and micronutrients.

*Assigned diets maintained throughout weaning (PND21).

WEEK 0Maternal dietsassigned

WEEK 6Mating with control males.

PND 0Pups born.

PND 10Pups selected for brain collection.

Brain region collection

Neurochemical analysis

Materials and Methods

Monoamine and Metabolite Analytes:

Dopaminergic System: DA, DOPAC, HVA, 3-MT

Serotonergic System: 5-HT, 5-HIAA

Noradrenergic System: NE, MHPG

Brain regions collected:

Prefrontal cortex (PFC)Striatum

(STR)Dorsal Hippocampus(dHIPP)Ventral Hippocampus (vHIPP)Cerebellum

(CER)

Neurochemical Analysis: HPLC-ECD + Bradford AssayData normalized per mg protein prior to statistical analysis

Data Processing and Presentation:Performed with Microsoft Excel, SigmaPlot, GraphPad Prism 5 software

Comparative Mammalian Neurodevelopment“Translating Time” Across Mammals – Why PND10?

Assumptions:Mouse gestation length: 18.5 daysHuman gestation length: 270 days (~38.5

weeks)

(Workman et. al., 2013)

Including synaptogenesis!Mouse PND10 equivalent to late third trimester in

terms of neurodevelopment.

Critical Windows – Why Post-Natal Day 10?

Significantly increased rates of AXONAL GROWTH and SYNAPTOGENESISduring the late third trimester of human gestation.

Vertes and Bullmore, 2015

Critical window for cortical wiring of neuronal circuitries and neurotransmitter systems.

Added benefit: offspring nutritional source is maternal lactation.

RESULTS

Dopaminergicdysregulation.

Possible behavioral consequences

(i.e. hyperactivity).

*Statistically significant main effect of diet. aStatistically significant effect of diet within females.#Statistical trend for main effect of diet. ^Statistical trend for effect of diet within females.

RESULTS

Serotonergicdysregulation

(female-restricted)

Possible communication or social interaction

deficits.

*Statistically significant main effect of sex. aStatistically significant effect of diet within females.#Statistical trend for main effect of diet. ^Statistical trend for effect of diet within females.

RESULTS

Sex-specific noradrenergic

differences.

*Statistically significant main effect of sex.#Statistical trend for main effect of sex.

Female: NE PFC, vHIPP, CER

NE dHIPPPositive correlation between

female-restricted maternal HFD effects on 5-HT and female-

specific NE differences.

Conclusion

Maternal HFD disrupts monoamine systems at PND10 in mice in a sex-specific manner, consistent with altered brain neurochemistry

and connectivity.

Data suggests maternal HFD may put offspring on a trajectory towards ASD.

Future Research:

Monoamine turnover rate analysis via NT/metabolite ratios.Biomarker analysis of dopaminergic dysfunction in PFC, dHIPP, vHIPP

Western Blot + qPCR (qPCR samples obtained for HIPP)

Integration with neurochemical data at other time points.Integration with behavioral data and immunologic assessment.

General and Funding AcknowledgementsDepartment of Physiology and PharmacologySaritha Krishna John J. Wagner Sadie E. Nennig Nikolay M. Filipov

Department of Infectious DiseasesDonald A. Harn

Department of Foods and NutritionClaire B. de La Serre

MiscellaneousAnnika Carter

The project described was supported by Grant Number 05 T35 OD010433-09 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH.

Funding was also provided by through a grant from the University of Georgia’s Obesity Initiative (http://obesity.ovpr.uga.edu).