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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


-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


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

＊


Sign of insulin resistance!

Overfeeding energy increased postpartal serum

NEFA and BHBA


-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


-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


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



Bioinformatics

Fluxome



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



Bioinformatics

Fluxome



Peripartal liver gene networks and pathways: role of plane of

nutrition during late pregnancy

-65 -30 -141 14 28 49


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


Insulin, uIU/mL

0

1

2

3

4

5

6

7

8

9

-65

-18 -9 -6 -3 0 3 6 9 21 56


Liver tissue composition, % by weight

0

1

2

3

4

5

6

-65 -30 -14 1 14 28 49


**

**

**

**

**

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


-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

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