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Importance of nutritional management during the transition period in dairy cows from a genomics perspective
NUPEECParque AssisBrasilAugust 28, 2011
Juan J. Loor
Department of Animal Sciences & Division of Nutritional Sciences
University of Illinois, Urbana, USA
Outline
1. Background of transition period:– Physiology, metabolism, and immune function
– Disease incidence during transition period:• Impact of nutrition role of feed intake
2. Genomics:– Terminology and technical aspects
– Application to nutrition, metabolism, and physiology• Relevance to transition cows: what can be learned ?
3. Nutrition and genomics during transition:– The concept of “feed to fill” during the dry period:
• Adipose tissue
• Liver
4. Perspectives
0 1 2 3 4 5 6 7 8 9 10 11 12
Month
Dry Matter Intake
Body Weight
Milk
Production
Peak DMIPeak Milk Late Lactation
Dry period
Negative energy balance
From M. F. Hutjens
Metabolic physiology during transition
Dry Parturition Peak Mid Late
Tis
su
e w
eig
ht
(kg
)
0
10
20
30
40
50
60Subcutaneous
Omental
Mesenteric
Lactation
Adipose depots
(Butler-Hogg et al. 1985)
(-) Energy balance
Loor et al. 2005 Physiol. Genomics
Lipolysis (+)
Adipose
Tissue
NEFA
TG
NEFA NEFA
TG
TGVLDL
Milk
Fat
Mammary
Gland
CO2
Propionate
Liver
InsulinNE, Epi
Mitochondria
Modified from Drackley, 1999
Glucose
Healthy well-fed cow
Feed intake
Adipose
Tissue
NEFA
TG
NEFA NEFA
TG
TGVLDL
Ketone
Bodies
Milk
Fat
Mammary
Gland
CO2
Propionate
Liver
InsulinNE, Epi
Mitochondria
Modified from Drackley, 1999
Glucose
Amino acids,
glycerol
Cow in negative
energy balance
Feed intake
Haptoglobin
-14 -7 0 7 14 21 28 42 63
g/L
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Albumin
-14 -7 0 7 14 21 28 42 63
g/L
30
31
32
33
34
35
36
37
ROM
Day relative to parturition
-14 -7 0 7 14 21 28 42 63
mg H2O
2/100 mL
11.0
11.5
12.0
12.5
13.0
13.5
14.0Bilirubin
-14 -7 0 7 14 21 28 42 63
g/L
0
2
4
6
8
10
12
UP
INUP
INLO
LO
Bionaz et al., J Dairy Sci, 2007
LO = poor liver function
Inflammation is related to oxidative stress
Metritis
Mastitis
Laminitis
Acidosis
Tissue damageAcute Phase
Proteins
SAA
CGRP
CRP
Hp
LBP
Tf
RBP
Alb
Apo
TNF-a
IL-1
IL-6MØ
Hepatocyte
LIVER
[NEFA]
↑ Redox-sensitive
genes
Pro-oxidant
molecule
production
Nutritional management: the peripartal cow puzzle
• How do we feed cows during the dry period to minimize metabolic health problems at parturition, while still allowing high milk yield and good fertility?
• Goal: optimize feed intake……
• Minimize blood NEFA…..
Postpartum energy balance is highly correlated
with dry matter intake
r = 0.80
P < 0.0001
Drackley, 2006
Prepartal nutrition and postpartal liver metabolism
•Feed intake prepartum goals: Dogma
–Maximize intake improve health
–To increase nutrient intake increase dietary energy density (e.g. more fermtable carbohydrate)
–11 experiments from 1998-2003 yielded mixed results
–Illinois studies (J. K. Drackley et al.):
Controlled energy (“feed to fill”) vs. ad libitum feeding of moderate-energy diets leads to:
»Lower liver triglyceride post calving
»Positive effect on feed intake post-partum
Energy (NEL, Mcal) requirements 2 days before
versus 2 days after calving
725-kg Cow 570-kg Heifer
Function Pre Post Pre Post
Maintenance 11.2 10.1 9.3 8.5
Pregnancy 3.3 --- 2.8 ---
Growth --- --- 1.9 1.7
Milk production --- 18.7 --- 14.9
Total (Mcal) 14.5 28.8 14.0 25.1
Calculated from NRC (2001). Assumes milk production of 25 kg/d for cow and
20 kg/d for heifer, each containing 4% fat.
Typical intake 14-17 19-21
Energy balance is altered by prepartal energy intake
0
25
50
75
100
125
150
175
200
-10 -8 -6 -4 -2 0 2 4 6 8Weeks relative to parturition
En
erg
y i
nta
ke,
% r
eq
uir
ed
Overfed energy
Controlled energy
Diet, diet × week: P < 0.001
Diet: P < 0.002;
diet × week: P < 0.10
(Modified from Janovick et al., 2010)
(1.63 Mcal/kg)
(1.30 Mcal/kg)
Overfeeding of moderate-energy diets increases
postpartal hepatic lipid storage and metabolic
disorders
Day relative to parturitionPretrial -14 1 14 28
% o
f w
et
wt
0
1
2
3
4
5
6
7
Controlled
Overfed
*
Liver TAGVariable CON OVER P
DA 0 4 0.01
Ketosis 1 6 0.03
Mastitis 2 3 0.11
Cow>1 prob. 1 6 0.06
Janovick et al., 2011, J Dairy Sci
Why might too much energy in the dry period be bad?
• Cows respond metabolically as if they were too fat, even if they don’t appear to be (insulin resistance)
• Lower dry matter intake (DMI), more body fat loss, fatty liver, ketosis…
Overfeeding energy increased prepartal serum
insulin and glucose
Day relative to parturition
-40 -30 -20 -10 0 10 20 30 40 50 60
Seru
m I
nsu
lin
, m
IU/d
L
0
2
4
6
8
10
12
14
16
18
Control
Overfed
*
Insulin
Day relative to parturition
Pre-trial-35-28-21-14 -7 0 7 14 21 28 35 42 49 56
Glu
co
se,
mg
/dL
45
50
55
60
65
70
75
80
85
90
Controlled
Overfed
Glucose
*
Janovick et al., 2011, J Dairy Sci
Sign of insulin resistance!
Overfeeding energy increased postpartal serum
NEFA and BHBA
Day relative to parturition
-12 -9 -6 -3 0 3 6 9 12
Seru
m N
EF
A,
Eq
/L
0
200
400
600
800
1000
1200
Control
Overfed
*
NEFA
Day relative to parturition
-12 -9 -6 -3 0 3 6 9 12
Se
rum
BH
BA
, m
g/d
L2
4
6
8
10
12
14
16
Control
Overfed
*
BHBA
Janovick et al., 2011, J Dairy Sci
Most transition health problems are related to excessive negative nutrient balance and body fat
mobilization around parturition
Summary: nutrition, metabolism, and immune
function are linked
modified from Ingvartsen et al. (2003)
Metabolic Immune
Status Status
Disease
Susceptibility
Disease
Incidence
Genotype
Nutrition
Physiological state
Management
Conceptual framework
Effect
Cause
Abnormal mobilization
of body reserves
(Adipose, Muscle)
Immune competence
“Common currencies”
Blood metabolites/hormones
Metabolic end-products
Physical measures
Innate immune parameters
Specific immune parameters
Acute-phase response parameters
Indicators
Susceptibility Indicator Genes ??
Genomics: terminology,
techniques, and application in
transition cow biology
Use of “Omics” for advancing knowledge of
nutrition and metabolismTerminology:
Genome: all the DNA in an organism
Genomics: the study of genomes
Functional genomics: assessing gene function
mRNA expression:
Microarrays hundreds of genes
Bioinformatics: using computers to process and
understand large sets of gene/protein expression
data
Integrative physiology: understanding of
interactions among multiple pathways in the intact
organism
DNA Microarray Technology
• A collection of DNA sequences attached to a solid support
• mRNA expression analysis
• Simultaneous study of expression for thousands of genes in 1 experiment
Illinois cattle microarray chip
9 mm
60 mm
22 mm
“Omics” technology and physiological nutrition
Source: J. Nutr. 135:3016S, 2005
Benefits of approach:
Improve accuracy of nutrient
requirements
Identify genotype × nutrition
interactions
Identify disease-associated
genes
Design genotype-based diets
“Reverse Genetics”
Addressing complex metabolic phenotypes in
ruminants
Our view of potential approaches to identify gene
variations that contribute to metabolism and health in
response to dry period nutrition:
Determine transcript expression: adipose, liver, mammary,
immune cells (e.g., neutrophils)
Identify gene expression networks
Perturb system through nutrition: of practical importance
Identify regulatory pathways, causal interactions
Link expression networks or gene/s to metabolic/health
phenotypes (e.g., ketosis, mastitis): back to nutrition…..
Genomics in transition cows: learning about the whole system….………….
DietMilk
Tissue
composition
Fatty acids
Proteins
Carbohydrates
Lipid
Glycogen
Physiologic adaptations
J. J. Loor, 2009 (7th Pathways, Networks, and Systems Med. Conf., Corfu, Greece)
Bioinformatics
Fluxome
Network reconstruction
Nutrition and genomics during the
transition period
• Increased ketosis
• Increased displaced abomasum
• Impaired reproduction
• Decreased milk production
• Increased culling
• Increased death loss
Impacts of excessive liver fat accumulation:
The problem: elevated NEFA in blood increases fat
accumulation in liver, with peak content at about 10
days postpartum
Diet
Tissue
composition
Lipid
Glycogen
Physiologic adaptations
J. J. Loor, 2009 (7th Pathways, Networks, and Systems Med. Conf., Corfu, Greece)
Bioinformatics
Fluxome
Network reconstruction
Genomics in transition cows: learning about the whole system….………….
Visceral adipose tissue
The adipose tissue component
Over-nutrition
Liver
Drackley et al., 2005; Loor et al., 2006, 2007
Subcutaneous
adipose
TAG
Inflammatory
Cytokines??
Prepartal diet
Hypothesis
Visceral adiposeSubcutaneous
adipose
Supervised approach
▼Inflammation
Adipogenesis
Dietary energy level affects
adipose transcriptome
(Ji et al., 2009 JDS Suppl. 1:120
Mukesh et al., 2009 Domest. Anim. Endo.(Janovick et al., 2009 JDS Suppl. 1:709)
Unsupervised approach
▼Temporal or end-point adaptations
Adipose tissue depots in non-lactating non-
pregnant cows after 57 days on diets
Controlled energy Moderate energyVariable (1.31 Mcal/kg) (1.64 Mcal/kg) SE
Body weight, kg 736 735 24
Adipose tissue site
Omental, kg 17.5 28.1** 1.3
Mesenteric, kg 12.1 22.0** 2.4
Total visceral, kg 35.6 60.0 3.9
Insulin, uIU/mL 23.5 29.6 3.2
Glucose:insulin 2.6 3.5† 0.3
** P < 0.01† P = 0.05 [Nikkhah, Loor, Drackley et al. 2008 (unpublished)]
Peripartal subcutaneous adipose tissue transcriptomics
Janovick et al. 2009, JDS Suppl. 1
>3,400 DEG (FDR < 0.05)
Controlled
Energy
Overfed
Energy
Adipogenic/Lipogenic genes
DGAT2
SCD
THRSP
2 wk before parturition
Overfeeding energy:
Prolonged hyperinsulinemia
Promotes fat deposition
Insulin resistance ?
Inflammation ?
(1.63 Mcal/kg)(1.30 Mcal/kg)
Network among lipid-related genes (Overfed vs. Control at -14 d)
Lipogenic gene targets
Lipogenic transcription
regulators
Adipose tissue-secreted factors
(at least in non-ruminants)
Overfed vs. Control -14 dCentral role of the nutrient sensor
PPARγ
functions
Loor 2010 (Animal 4:1110-1139)
Are PPAR (,α) potential nutritional targets ??
Ligands
(Fatty acids, retinoic acid)
Co-activator
complex
(ligand-bound)(un-bound)
Classical nuclear receptor (PPAR,α) activation
mechanism Redrawn from Sonoda et al. (2008)
Target gene DNA response
element sequence
Co-repressor complex
dissociation
+Transcription
Biological
outcome
NEFA
Peroxisome
β-oxidation
Microsome
ω-oxidation
Mitochondria
β-oxidation
VLDL
TG
Ketone
Bodies
Adipocytes
Glucose
Insulin
Sensitivity
Hepatocyte
Gluconeogenesis
Immune cells
PPARα
PPAR
Cytokines
Muscle Tissue
Oxidation
PPAR
PPARαMammary Gland
PPAR
Milk fat synthesis
What practical knowledge have we gained from
genomics of adipose tissue ??
Quick and robust response of the transcriptome (mRNA) to
overfeeding energy:
Strong impact on metabolic pathways (energy metabolism,
lipogenesis)
Pivotal role for PPAR, a nutrient sensor
Manipulating PPAR could alter a large number of biological
functions:
Pros: Greater insulin sensitivity ? Quicker recovery of
adipose mass post-partum ? Lower NEFA concentrations
? Improved fertility ? etc
Little carryover effect after parturition, i.e. the last 2-3 weeks
prepartum are an important window from nutritional standpoint
Diet
Tissue
composition
Lipid
Glycogen
Physiologic adaptations
J. J. Loor, 2009 (7th Pathways, Networks, and Systems Med. Conf., Corfu, Greece)
Bioinformatics
Fluxome
Network reconstruction
Genomics in transition cows: learning about the whole system….………….
Peripartal liver gene networks and pathways: role of plane of
nutrition during late pregnancy
-65 -30 -141 14 28 49
Day relative to parturition
Over
Rest
• Multiparous Holstein cows (Loor et al. 2005, 2006 Physiol. Genomics)
• Energy intake during late pregnancy:
- Ad libitum (Over – ca. 150% of NRC requirements)
- Control (Con – ca. 100% of NRC requirements)
- Restricted (Rest – ca. 80% of NRC requirements)
• Aims: study the liver transcriptome and physiological
outcomes
Con
Physiological data confirmed potential negative effects of energy
overfeeding (Loor et al., 2006)
Non-esterified fatty acids, uEq/L
0
200
400
600
800
1000
1200
-65
-18 -9 -6 -3 0 3 6 9 21 56
Controlled Ad libitum
Beta hydroxybutyric acid, mg/dL
3
45
6
78
9
1011
12
-63
-18 -9 -6 -3 0 3 6 9 21 56
Controlled Ad libitum
Insulin, uIU/mL
0
1
2
3
4
5
6
7
8
9
-65
-18 -9 -6 -3 0 3 6 9 21 56
Controlled Ad libitum
Liver tissue composition, % by weight
0
1
2
3
4
5
6
-65 -30 -14 1 14 28 49
Controlled Ad libitum
**
**
**
**
**
Overfeeding energy prepartum hyperinsulinemia; more NEFA and liver TAG after calving
>130% NRC prepartum
Overfed energy
~100% NRC prepartum
Control diet
~80% NRC prepartum
Restricted energy
Bionaz et al. 2005 JDS Suppl. 1; Loor et al. 2005, 2006 Physiol. Genomics
Dietary energy prepartum affects liver transcriptome
4,790 genes with FDR ≤ 0.05 diet time
0
200
400
600
800Pentose phosphate pathway Glycolysis / Gluconeogenesis
-400
-200
0
200
400Citrate cycle (TCA cycle)
0
200
400
600
800Oxidative phosphorylation Synthesis and degradation
of ketone bodies
Dir
ec
tio
n o
f im
pa
ct
-400
-200
0
200
400Fatty acid metabolism
Imp
ac
t
0
200
400
600
800Steroid biosynthesis Glycerolipid metabolism
-400
-200
0
200
400PPAR signaling pathway
-30
-14
1
14
28
49
-3
0 -1
4 1 14 28
49
-3
0 -1
4 1 14 2
8 4
90
200
400
600
800Ribosome
Day relative to parturition
-30
-14
1
14
28
49
-3
0 -1
4 1 14 28
49
-3
0 -1
4 1 14 2
8 4
9
Cell cycle
-30
-14
1
14
28
49
-3
0 -1
4 1 14 28
49
-3
0 -1
4 1 14 2
8 4
9-400
-200
0
200
400
Antigen processingand presentation
Restrict Control Adlibitum Restrict Control Adlibitum Restrict Control Adlibitum
Ribosome
Terpenoid backbone biosynthesis
Sulfur metabolism
Phe, Tyr and Trp biosynthesis
Complement & coagulation
cascades
Synthesis & degrad. ketone bodies
Glycosphingolip bios - globo series
Pentose phosphate pathway
PPAR signaling pathway
Butanoate metabolism
Fatty acid metabolism
Folate biosynthesis
N-Glycan biosynthesis
Pyruvate metabolism
Fructose & mannose metabolism
O-Glycan biosynthesis
ECM-receptor interaction
Limonene and pinene degradation
Glycolysis / Gluconeogenesis
Steroid biosynthesis
Ubiquin &other terp-quinone bios
Vitamin B6 metabolism
Most impacted biological pathways22 most impacted
1
0
-1
1
0
-1
1
0
-1
1
0
-1
Log2
fold
ch
ange
rel
ativ
e to
-6
5 d
ay in
milk
(d
ry-o
ff)
Overfed Control Restricted
Overfed Control Restricted Overfed Control Restricted Overfed Control Restricted
Cluster analysis plus bioinformatics
applied to bovine liver longitudinal
transcriptomics
GOTERM_BP_FAT
activity of plasma protein involved in acute inflam. response
complement activation, classical pathway
humoral immune responseKEGG_PATHWAY
Complement and coagulation cascadesGOTERM_CC_FAT
extracellular region
GOTERM_BP_FAT
translationKEGG_PATHWAY
RibosomeGOTERM_CC_FAT
basement membrane
proteinaceous extracellular matrix
cytosolic ribosome
GOTERM_BP_FAT
ubiquitin-dependent protein catabolic process
response to protein stimulus
GOTERM_CC_FAT
mitochondrion
nuclear lumen
organelle membrane
What practical knowledge have we gained from the
bioinformatics approach ??
Overfeeding or restricting energy prepartum:
Coordinated inhibition of genes related with immune
system:
•Plasma inflammatory proteins
•Complement system activation
•Antigen processing and presentation
Restricting energy prepartum:
Coordinated upregulation of:
•Fatty acid oxidation and energy production: Mitochondrial
elements
Role for PPARα signalling pathway ?
Pros: long-chain fatty acid supplementation ?
Gene networks in liver from peripartal ketotic cows
Cholesterol metabolism
Fatty acid metabolism:
Oxidation
Transc. regulation
Glycolysis/gluconeogenesis
RED = UP with ketosis
GREEN = Down
Loor et al. 2007 Physiol. Genomics
Do bovine PPAR subtypes respond to long-
chain FA ?
Which FA are more potent activators of each
PPAR subtype and at which dose ?
Do bovine PPAR control the same target
genes as in non-ruminants (i.e., same
metabolic functions) ?
MDBK
150 μM WY or LCFA
6h incubation
RNA
Microarray (WY and 16:0)
Metabolites
media
12 LCFASaturated (16:0, 18:0, 20:0)
Unsaturated:
c9-18:1, c9,c12-18:2
3 (18:3, 20:5, 22:6)
t10-18:1, t11-18:1
CLA (c9,t11; t10,c12)
Fetal Bovine Serum
Transport and trafficking LCFA
Cholesterol synthesis
TAG synthesis
Oxidation LCFA
Other metabolism
Inflammation
Liver-adipose signaling
Most putative PPARα target
qPCR for 34 genes
Fatty Acid
C16:0 C18:1 C18:2 C18:3 CLA
mR
NA
fold
change r
ela
tive to C
TR
0.0
0.5
1.0
1.5
2.0
2.5
3.0
10 M
25 M
50 M
100 M
200 M
*
*
*
*
*
*
* *
ACOX1
0 6 12 18 24
% m
RN
A r
ela
tive to 0
h
-40
-20
0
20
40
60
80
10016:0
Wy-14643
CPT1A
0 6 12 18 24
0
100
200
300
400
500
600
700
ACADVL
Hour of incubation
0 6 12 18 24
% m
RN
A r
ela
tive to 0
h
0
20
40
60
80
100
120 ACSL1
0 6 12 18 24
0
50
100
150
200
250
300
*
*
#
*a a
bbb
*#
#
*
*a
#
Saturated are more potent activators of bovine PPARα in vitroBionaz et al., J Dairy Sci, 2008
Thering et al., J Dairy Sci, 2009
Bionaz et al., Br J Nutr, in press
• How do we feed cows during the dry period to minimize metabolic health problems at parturition, while still allowing high milk yield and good fertility ?
• A role for lipid supplementation ?
Nutritional management: the peripartal cow puzzle
Nutrigenomics of supplemental ipids in peripartal ows
0 1 14-21
Day Relative to Parturition
Saturated lipid, 250 g/d
Fish oil, 250 g/d
• Cows in second or greater lactation
• A subset of 5 cows/diet
• Collaboration with M. A. Ballou and E. J. DePeters
• UFPEL: E. Schmitt and M. N. Correa
Khan et al., 2010 JDS Suppl. 1
1% of as-fed
intake
1% of as-fed
intake
Control (no supplemental lipid)
Liver biopsy
-10
Schmitt et al., 2011 JDS
Performance response: positive effect of saturated lipid
0 1 14-21
Day Relative to Parturition
Saturated lipid, 250 g/d
Fish oil, 250 g/d
• Cows in second or greater lactation
• A subset of 5 cows/diet
• Collaboration with M. A. Ballou and E. J. DePeters
Khan et al., 2010 JDS Suppl. 1
1% of as-fed
intake
1% of as-fed
intake
Control (no supplemental lipid)
Liver biopsy
-10
Liver phospholipid fatty acid profiles (% of total FA) are
affected by supplemental lipid: data for day 1 postpartum
Control EB100 Fish
18:0 ~28 ~29 ~27
c9-18:1 ~11 ~11 ~7.0
c9,c12-18:2 14 14 10
c9,c12,c15-18:3 1.4 1.4 1.1
20:4n-6 1.1 1.0 0.9
20:5n-3 ~1.5 ~1.5 ~4.0
22:5n-3 4.0 4.0 5.5
22:6n-3 ~1.0 ~1.0 ~8.0
Trans-18:1 ~1.7 ~1.5 ~4.0
Ballou et al., 2009 JDS
0 500 1000 1500
-10
1
14
EB100 vs FISH
EB100 vs Con.
FISH vs ConDa
ys re
lative
to
p
art
uri
tio
n
Number of genes
Number of liver genes affected by lipid supplementation
1,280
810
1,140
1,082
1,257
630
180
519
362
At FDR P ≤ 0.04 treatment time
Saturated vs. Fish
Saturated vs. control
Fish vs. control
Most Impacted Metabolic Pathways
Term Impact Flux
D-Glutamine and D-glutamate metabolism
Cyanoamino acid metabolism
Folate biosynthesis
Fatty acid biosynthesis
Butirosin and neomycin biosynthesis
Lipoic acid metabolism
Taurine and hypotaurine metabolism
Primary bile acid biosynthesis
Limonene and pinene degradation
Ascorbate and aldarate metabolism
Thiamine metabolism
Vitamin B6 metabolism
Histidine metabolism
Biosynthesis of unsaturated fatty acids
Phenylalanine metabolism
Glycosaminoglycan biosynthesis - keratan sulfate
alpha-Linolenic acid metabolism
Steroid biosynthesis
Nitrogen metabolism
PPAR signaling pathway
Overall
Overall: encompass data from day -14, 1, and 14 across all treatment
comparisons
Amino acidmetabolism
Fatty acidmetabolism
Vitaminmetabolism
Summary and Perspectives
Substantial amount of resources already
invested in sequencing, annotation, and
functional genomics studies in bovine
A breadth of knowledge of biochemical
pathways, their “main” control points, and their
response to nutrition
Bioinformatics tools are ideal for generating
additional value from the existing knowledgebase
Functional studies of gene networks will shed
light on the applicability of nutrients and diets to optimize efficiency