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AExamGanda

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Erika Ganda

on 30 March 2017

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Transcript of AExamGanda

Background
A Exam
Understanding Mastitis Through a Metagenomic Approach
June 17th, 2016
Erika Ganda
Rodrigo Bicalho

Daryl Van Nydam

Georgios Oikonomou

Helene Marquis

Julio O. Giordano
Overview
Background

Courses

Accomplishments

PhD Research Focus

Methods

Current Studies

Future Goals
2012 - 2013

Research Assistant at Dr. Bicalho's lab


2013 - present
PhD student

Animal Science

Minors: Epidemiology
Microbiology
2015
AABP - New Orleans
Oral Presentation






2016
Cornell Dairy Center of Excellence Symposium
Poster







2016
Buiatrics – Dublin
Oral Presentation and Poster

Courses
Abstract Presentations
Epidemiology

BTRY 6010
Statistical Methods I

BTRY 6020
Statistical Methods II

VTPMD 6640
Intro to Epidemiology

VTPMD 6650
Study Designs

VTPMD 6660
Advanced Epidemiology

BTRY 4100
Multivariate Analysis

VTPMD 7080
Epidemiology Seminar Series

Microbiology


ANSC 3410
Biology of the Mammary Gland

BIOMS 4340
Cellular Microbiology

VETMI 7120
Seminar: Infection & Immunity

VTPMD 6250
Evolutionary Genomics of Bacteria

Animal Science

ANSC 6620
Grad Student Research Updates
"The application of Next Generation Sequencing to further understand the microbial dynamics of bovine clinical and subclinical mastitis"
Culture independent techniques for investigation of the microbiome
PhD
Chapter 1


Chapter 2


Chapter 3


Chapter 4

Chapter 5
Thesis Outline
Longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third-generation cephalosporin
Dynamics of milk microbiome in an experimental challenge with a known mastitis pathogen and the effect of extended antimicrobial treatment in mastitic and normal milk
Microbiome of dairy cows with elevated somatic cell counts
Literature Review
Bovine mastitis and the use of culture independent techniques for investigation of the mammary environment
1990
2007-2012
5
Publications
First Author
Co-author
• Lameness Prevalence and Risk Factors in Large Dairy Farms in Upstate New York. Model Development for the Prediction of Claw Horn Disruption Lesions.
Foditsch C, Oikonomou G, Machado VS, Bicalho ML, Ganda EK, Lima SF, Rossi R, Ribeiro BL, Kussler A, Bicalho RC.
PLoS One. 2016
Jan 21;11(1):e0146718. doi: 10.1371/journal.pone.0146718. eCollection 2016.


Oral Administration of Faecalibacterium prausnitzii Decreased the Incidence of Severe Diarrhea and Related Mortality Rate and Increased Weight Gain in Preweaned Dairy Heifers.
Foditsch C, Pereira RV, Ganda EK, Gomez MS, Marques EC, Santin T, Bicalho RC.
PLoS One. 2015
Dec 28;10(12):e0145485. doi: 10.1371/journal.pone.0145485. eCollection 2015.



Associations among Trueperella pyogenes, endometritis diagnosis, and pregnancy outcomes in dairy cows.

Bicalho ML, Lima FS, Machado VS, Meira EB Jr, Ganda EK, Foditsch C, Bicalho RC, Gilbert RO.

Theriogenology. 2016
Jan 15;85(2):267-74. doi: 10.1016/j.theriogenology.2015.09.043. Epub 2015 Sep 26.


The effect of intrauterine infusion of dextrose on clinical endometritis cure rate and reproductive performance of dairy cows.
Machado VS, Oikonomou G, Ganda EK, Stephens L, Milhomem M, Freitas GL, Zinicola M, Pearson J, Wieland M, Guard C, Gilbert RO, Bicalho RC.
J Dairy Sci. 2015
Jun;98(6):3849-58. doi: 10.3168/jds.2014-9046. Epub 2015 Mar 18.
• Prepartum and postpartum rumen fluid microbiomes: characterization and correlation with production traits in dairy cows.
Lima FS, Oikonomou G, Lima SF, Bicalho ML, Ganda EK, Filho JC, Lorenzo G, Trojacanec P, Bicalhoa RC.
Appl Environ Microbiol. 2015
Feb;81(4):1327-37.


Effect of trace mineral supplementation on selected minerals, energy metabolites, oxidative stress, and immune parameters and its association with uterine diseases in dairy cattle.
Bicalho ML, Lima FS, Ganda EK, Foditsch C, Meira EB Jr, Machado VS, Teixeira AG, Oikonomou G, Gilbert RO, Bicalho RC.
J Dairy Sci. 2014
Jul;97(7):4281-95. doi: 10.3168/jds.2013-7832. Epub 2014 May 16.

• Association of digital cushion thickness with sole temperature measured with the use of infrared thermography.
Oikonomou G, Trojacanec P, Ganda EK, Bicalho ML, Bicalho RC.
J Dairy Sci. 2014
Jul;97(7):4208-15. doi: 10.3168/jds.2013-7534. Epub 2014 May 16.


Subcutaneous immunization with inactivated bacterial components and purified protein of Escherichia coli, Fusobacterium necrophorum and Trueperella pyogenes prevents puerperal metritis in Holstein dairy cows.
Machado VS, Bicalho ML, Meira Junior EB, Rossi R, Ribeiro BL, Lima S, Santos T, Kussler A, Foditsch C, Ganda EK, Oikonomou G, Cheong SH, Gilbert RO, Bicalho RC.
PLoS One. 2014
Mar 17;9(3):e91734. doi: 10.1371/journal.pone.0091734. eCollection 2014.


90%
Antibiotics
USDA, NAHMS Dairy 2007
USDA, NAHMS Dairy 2007
1
14
Randomized Clinical Trial
Random Allocation
Clinical Score
PreTRT sample
8
Metagenome
qPCR
Metagenome
Metagenome
qPCR
3
Metagenome
qPCR
Metagenome
qPCR
Clinical Score
Aerobic Culture
Mastitic
Healthy
Mastitic
Healthy
Mastitic
Healthy
Mastitic
Healthy
Mastitic
Healthy
Clinical Score
Aerobic Culture
Clinical Score
Return to Herd
Experimental Design
10
5
4
2
6
9
11
12
13
7
Metagenome
Metagenome
Metagenome
Metagenome
Mastitic
Healthy
Mastitic
Healthy
Healthy
Mastitic
Treatment
Control
5 days
no antibiotic
E. coli
Negative
Klebsiella
Pseudomonas*
N=80
Why Study Mastitis?
Methods
qPCR Data
Repeated measures ANOVA
Teat nested in cow as a random effect
Clinical Cure
Bacteriological Cure
Contingency tables (TRT versus Control)

Statistical Analysis
Metagenomic Data
Response screening analysis - false discovery rate

#Pathogen relative abundance and Bacterial Diversity
Repeated measures ANOVA {Mastitic versus Healthy}
{TRT versus Control}
Effect of intrammary treatment with ceftiofur hydrochloride on cure of clinical mastitis in lactating dairy cows
DNA extraction
MoBIO Power Food

PCR amplification 16S rRNA
515F and Barcoded 815R primers

Standardization and Sequencing
Illumina MiSeq Platform
6 runs ~ 1400 samples

qPCR number of 16S rRNA copies
Plasmid containing V6 region as standard

Bioinformatics
QIIME - OTUs (de-novo)
Approach
Culture Independent Techniques to
Investigate Microbial Populations
Addis et al., 2016
16S rRNA
V4 region
Objectives

1)
Describe the microbiome
of milk in dairy cows affected by clinical mastitis associated with Gram-negative bacteria or negative culture.




2) Determine the bacterial load based on PCR quantification of 16S rRNA gene copies, and
compare microbial populations of affected and healthy quarters.




3) Assess the
effect of intramammary treatment
with ceftiofur hydrochloride on the milk microbiome, bacterial load, and clinical cure in quarters affected with clinical mastitis.
Illumina Technology - Sequencing by Synthesis
Enrollment criteria
Study 1
Clinical Mastitis
Gram - or culture negative
No IMM treatment in the 14 days preceding IMI
Case definitions
Clinical cure
cows without any clinical signs on both day 10 and day 14

Bacteriological cure
both the samples taken on day 10 and day 14 being negative for the organism present on day 0
Objectives



1)
Describe the microbiome
of milk before, during and after experimental challenge with a
E. coli
strain (C1) isolated from bovine mastitis.



2)
Compare microbial populations of challenged and non-challenged quarters.



3)
Evaluate the effect of extended intramammary therapy with ceftiofur or
untreated controls.
Study 2
-72 -60 -48 -36 -24 -12 0 6 12 18 24 30 36 42 48 54 60 72 78 84 96 102 108 120 126 132 144 150 156 168 174 180 192 198 204 216 384
-3 -2 -1 0 1 2 3 4 5 6 7 8 9 16
No Treatment, N=6
Ceftiofur, N=6
Conclusions

* Mastitic vs Healthy milk - microbial profile and diversity

*
E. coli
mastitis

* Culture negative mastitis

* Treatment effect - bacterial load, rate of pathogen clearance, and cure

* Diversity and disease

* Discrimination
treated and untreated
mastitic vs healthy controls
Longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third-generation cephalosporin
Study 1
Multivariate Analysis
a more universal approach
Does healthy milk have a different microbial profile when compared to mastitic milk?
Cluster Analysis
Is there a long term effect on the microbiome in the milk of treated quarters?
Last Sample
Mastitic
Treated versus Non-Treated
H0: There is no difference in the microbial profile between treated and non-treated quarters
E. coli
Supplementary Material
Phylum Level
Mastitic and Healthy milk
Experimental Design
Experimental Design
Treatment Random Allocation at Cow Level
Challenge and Treatments applied at Quarter Level
Heatmaps
Descriptive Statistics
Non-Challenged
Quarters
Descriptive Data
Enterobacteriaceae
CFU
SCC (x1000)
Diversity
Enterobacteriaceae
SCC
Multivariate Analysis
Exploring
Weighted Unifrac
41
1025 -
Challenged
and
Treated
Unifrac Discriminant Analysis
1048 -
Challenged
and
Treated
Unifrac Discriminant Analysis
PhD Future Goals
* Dynamics of milk microbiome in an experimental challenge with a known mastitis pathogen and the effect of extended antimicrobial treatment in mastitic and normal milk
* Microbiome of dairy cows with elevated somatic cell counts
qPCR - Bacterial Load
Multivariate Analysis
Unifrac - Compare all cows in a single analysis
Manuscript

Shotgun sequencing of selected timepoints
-investigate changes in functional profile and resistome
Data analysis
Manuscript
Thank you
ekg43@cornell.edu

Data and Samples Collected
Study 2
Methods
DNA extraction
MoBIO Power Food


PCR amplification 16S rRNA
515F and Barcoded 815R primers


Standardization and Sequencing
Illumina MiSeq Platform
6 runs ~ 1400 samples

Bioinformatics
QIIME - OTUs (de-novo)
Statistical Analysis
Metagenomic Data
Pathogen relative abundance and Bacterial Diversity
Repeated measures ANOVA {Challenged versus Not-Challenged}
{Treated versus Non-Treated}

Outcomes
Somatic Cell Count
Colony Forming Units
Rate of clearance of Enterobacteriaceae
Bacterial Diversity Indexes

UNIFRAC distances
weighted distances based on phylogenetic distances
Principal Component Analysis

UNIFRAC = ("unshared" branch lengths)/(all tree branch lengths (= shared+unshared))
Dynamics of milk microbiome in an experimental challenge with a known mastitis pathogen and the effect of extended antimicrobial treatment in mastitic and normal milk
Study 2
Longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third-generation cephalosporin
Study 1
Randomized Clinical Trial
Commercial Dairy Farm
Experimental Challenge Trial
LARTU
Large Animal Research and Teaching Unit
Study 2
Study 1
Study 1
Study 2
Milk Samples - Microbiome

Temperature

Somatic Cell Count

CMT

CFU

RAPD-PCR
confirmation of infection of C1 strain

100 CFU
E. coli
Bacterial Load and Diversity
Response Screening Analysis
Culture
Negative
Bacterial Load and Diversity
Culture negative
Response Screening Analysis
Culture negative
Culture negative
E. coli
E. coli
E. coli
E. coli
Enterobacteriaceae
Diversity and Clinical Cure
Diversity
and Clinical Cure
Enterobacteriaceae
and Clinical Cure
Control vs Cefiofur
Multiparous, no mastitis, mid lactation, SCC<100,000
Quality control:
Aliquots of challenge solution were cultured in selective media and had RAPD PCR performed
Submitted
No intervention
Sampled
No Challenge
Treated
Challenged
Treated
Ceftiofur group
No intervention
Sampled
No Challenge
Not Treated
Challenged
Not Treated
Control group
Not Treated
N= 18
Treated
N= 6
Heatmaps
Descriptive Statistics
Challenged

Quarters
Not Treated
N= 3
Treated
N= 6
Heatmaps
Descriptive Statistics
Challenged
Quarters - Infection
Failures
Not Treated
N= 3
Randomized Clinical Trial
Experimental Design
Healthy
Mastitic
Treatment
Control
5 days
no antibiotic
N=80
Chao1
Enterobacteriaceae
CFU
Shannon
Chao1
Enterobacteriaceae
CFU
SCC (x1000)
Shannon
Chao1
Enterobacteriaceae
CFU
SCC (x1000)
Shannon
Chao1
Enterobacteriaceae
CFU
SCC (x1000)
1081 -
Challenged
and
Treated
Unifrac Discriminant Analysis
Conclusion
16S rRNA
"Omics" Studies
Next Generation Sequencing
1118 -
Challenged
and Not Treated
Unifrac Discriminant Analysis
Diversity
Treatment
Effect
Infected Cows Only
1
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CFU
Chall
RelTime
Trt
Chall*RelTime
Chall*Trt
RelTime*Trt
Chall*RelTime*Trt
<.0001
<.0001
0.2091
<.0001
0.8753
0.2686
0.1786
Chall
RelTime
Trt
Chall*RelTime
Chall*Trt
RelTime*Trt
Chall*RelTime*Trt
0.0008
<.0001
0.7232
<.0001
0.9027
0.9782
0.8249
Chall
RelTime
Trt
Chall*RelTime
Chall*Trt
RelTime*Trt
Chall*RelTime*Trt
<.0001
<.0001
0.1278
<.0001
0.6148
0.9947
0.9981
Chall
RelTime
Trt
Chall*RelTime
Chall*Trt
RelTime*Trt
Chall*RelTime*Trt
0.1549
<.0001
0.0714
<.0001
0.2784
0.0001
<.0001
Full transcript