Importance of nutritional management during the transition period in dairy cows from a genomics perspective

Importance of nutritional management during the transition period in dairy cows from a genomics perspective NUPEEC Parque Assis Brasil August 28, 211 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

Peak Milk Peak DMI Late Lactation Dry period Dry Matter Intake Milk Production Negative energy balance Body Weight 1 2 3 4 5 6 7 8 9 1 11 12 Month From M. F. Hutjens

Metabolic physiology during transition Lipolysis (+) Tissue weight (kg) 6 5 4 3 2 1 (-) Energy balance Adipose depots Subcutaneous Omental Mesenteric Dry Parturition Peak Mid Late Lactation (Butler-Hogg et al. 1985) Loor et al. 25 Physiol. Genomics

Adipose Tissue Healthy well-fed cow Liver NE, Epi NEFA Insulin NEFA NEFA TG Feed intake CO 2 Propionate Mitochondria Milk Fat Glucose TG Mammary Gland VLDL TG Modified from Drackley, 1999

Adipose Tissue Cow in negative energy balance Liver NE, Epi NEFA Insulin NEFA NEFA Feed intake TG Milk Fat CO 2 Propionate Ketone Bodies Glucose Mitochondria TG Amino acids, glycerol Mammary Gland VLDL TG Modified from Drackley, 1999

.8.7.6.5.4.3.2.1. 14. 13.5 13. 12.5 12. 11.5 11. g/l Haptoglobin -14-7 7 14 21 28 42 63 mg H 2 O 2 /1 ml ROM -14-7 7 14 21 28 42 63 37 36 35 34 33 32 31 3 g/l LO = poor liver function 12 1 8 6 4 2 Day relative to parturition Albumin -14-7 7 14 21 28 42 63 g/l Bilirubin -14-7 7 14 21 28 42 63 UP INUP INLO LO Bionaz et al., J Dairy Sci, 27

Inflammation is related to oxidative stress Tissue damage Metritis Mastitis Laminitis MØ Redox-sensitive genes TNF-a IL-1 IL-6 LIVER Acidosis [NEFA] Hepatocyte Pro-oxidant molecule production Acute Phase Proteins SAA CGRP CRP Hp LBP Tf RBP Alb Apo

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 =.8 P <.1 Drackley, 26

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 1998-23 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 57-kg Heifer Function Pre Post Pre Post Maintenance 11.2 1.1 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. 25.1 Typical intake 14-17 19-21 Calculated from NRC (21). Assumes milk production of 25 kg/d for cow and 2 kg/d for heifer, each containing 4% fat.

Energy intake, % required Energy balance is altered by prepartal energy intake 2 175 15 125 1 75 5 25 Diet, diet week: P <.1 Overfed energy Controlled energy (1.63 Mcal/kg) (1.3 Mcal/kg) Diet: P <.2; diet week: P <.1-1 -8-6 -4-2 2 4 6 8 Weeks relative to parturition (Modified from Janovick et al., 21)

Overfeeding of moderate-energy diets increases postpartal hepatic lipid storage and metabolic disorders % of wet wt 7 6 5 4 3 2 1 Liver TAG Controlled Overfed * Pretrial -14 1 14 28 Day relative to parturition Variable CON OVER P DA 4.1 Ketosis 1 6.3 Mastitis 2 3.11 Cow>1 prob. 1 6.6 Janovick et al., 211, 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 Serum Insulin, miu/dl 18 16 14 12 1 8 6 4 2 * Insulin Control Overfed -4-3 -2-1 1 2 3 4 5 6 Day relative to parturition Glucose, mg/dl 9 85 8 75 7 65 6 55 5 45 * Glucose Pre-trial -35-28-21-14 -7 7 14 21 28 35 42 49 56 Day relative to parturition Controlled Overfed Sign of insulin resistance! Janovick et al., 211, J Dairy Sci

Overfeeding energy increased postpartal serum NEFA and BHBA Serum NEFA, Eq/L 12 1 8 6 4 2 Control Overfed NEFA * Serum BHBA, mg/dl 16 14 12 1 8 6 4 Control Overfed BHBA * -12-9 -6-3 3 6 9 12 Day relative to parturition 2-12 -9-6 -3 3 6 9 12 Day relative to parturition Janovick et al., 211, 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 Cause Conceptual framework Genotype Nutrition Physiological state Management Metabolic Immune Status Status Common currencies Disease Susceptibility Disease Incidence Abnormal mobilization of body reserves (Adipose, Muscle) Immune competence Effect Blood metabolites/hormones Metabolic end-products Physical measures Indicators Innate immune parameters Specific immune parameters Acute-phase response parameters Susceptibility Indicator Genes?? modified from Ingvartsen et al. (23)

Genomics: terminology, techniques, and application in transition cow biology

Use of Omics for advancing knowledge of nutrition and metabolism Terminology: 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 Illinois cattle microarray chip 6 mm 9 mm 22 mm Simultaneous study of expression for thousands of genes in 1 experiment

Omics technology and physiological nutrition Benefits of approach: Improve accuracy of nutrient requirements Identify genotype nutrition interactions Identify disease-associated genes Design genotype-based diets Reverse Genetics Source: J. Nutr. 135:316S, 25

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.. Diet Milk Fatty acids Proteins Carbohydrates Network reconstruction Bioinformatics Tissue composition Lipid Glycogen Fluxome Physiologic adaptations J. J. Loor, 29 (7 th Pathways, Networks, and Systems Med. Conf., Corfu, Greece)

Nutrition and genomics during the transition period

The problem: elevated NEFA in blood increases fat accumulation in liver, with peak content at about 1 days postpartum Impacts of excessive liver fat accumulation: Increased ketosis Increased displaced abomasum Impaired reproduction Decreased milk production Increased culling Increased death loss

Genomics in transition cows: learning about the whole system.. Diet Network reconstruction Bioinformatics Tissue composition Lipid Glycogen Fluxome Physiologic adaptations J. J. Loor, 29 (7 th Pathways, Networks, and Systems Med. Conf., Corfu, Greece)

The adipose tissue component Prepartal diet Liver TAG Subcutaneous adipose Inflammatory Cytokines?? Over-nutrition Drackley et al., 25; Loor et al., 26, 27 Visceral adipose tissue

Hypothesis Dietary energy level affects adipose transcriptome Subcutaneous adipose Unsupervised approach Temporal or end-point adaptations Visceral adipose (Janovick et al., 29 JDS Suppl. 1:79) Supervised approach Inflammation Adipogenesis (Ji et al., 29 JDS Suppl. 1:12 Mukesh et al., 29 Domest. Anim. Endo.

Adipose tissue depots in non-lactating nonpregnant cows after 57 days on diets Controlled energy Moderate energy Variable (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.** 2.4 Total visceral, kg 35.6 6. 3.9 Insulin, uiu/ml 23.5 29.6 3.2 Glucose:insulin 2.6 3.5.3 ** P <.1 P =.5 [Nikkhah, Loor, Drackley et al. 28 (unpublished)]

Peripartal subcutaneous adipose tissue transcriptomics >3,4 DEG (FDR <.5) Adipogenic/Lipogenic genes Overfeeding energy: Prolonged hyperinsulinemia Promotes fat deposition Insulin resistance? Inflammation? 2 wk before parturition DGAT2 THRSP SCD Controlled Energy (1.3 Mcal/kg) Overfed Energy (1.63 Mcal/kg) Janovick et al. 29, JDS Suppl. 1

Network among lipid-related genes (Overfed vs. Control at -14 d) Lipogenic gene targets Adipose tissue-secreted factors (at least in non-ruminants) Lipogenic transcription regulators

Central role of the nutrient sensor PPARγ Overfed vs. Control -14 d functions Loor 21 (Animal 4:111-1139)

Are PPAR (,α) potential nutritional targets?? Classical nuclear receptor (PPAR,α) activation mechanism Redrawn from Sonoda et al. (28) Co-activator complex Ligands (Fatty acids, retinoic acid) Biological outcome + Transcription (un-bound) (ligand-bound) Target gene DNA response element sequence Co-repressor complex dissociation

Insulin Sensitivity Cytokines NEFA PPARα PPAR PPAR Adipocytes Immune cells Ketone Bodies Mitochondria β-oxidation Gluconeogenesis Glucose Peroxisome β-oxidation PPARα Microsome ω-oxidation TG Mammary Gland PPAR Oxidation Muscle Tissue VLDL Hepatocyte 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

Peripartal liver gene networks and pathways: role of plane of nutrition during late pregnancy Multiparous Holstein cows (Loor et al. 25, 26 Physiol. Genomics) Energy intake during late pregnancy: - Ad libitum (Over ca. 15% of NRC requirements) - Control (Con ca. 1% of NRC requirements) - Restricted (Rest ca. 8% of NRC requirements) Aims: study the liver transcriptome and physiological outcomes Over Con Rest 1 14 28 49-65 -3-14 Day relative to parturition

Physiological data confirmed potential negative effects of energy overfeeding (Loor et al., 26) 12 1 8 6 4 2 Non-esterified fatty acids, ueq/l -65-18 -9-6 -3 Controlled 3 ** 6 9 Ad libitum 21 56 12 11 1 9 8 7 6 5 4 3 Beta hydroxybutyric acid, mg/dl -63-18 -9-6 -3 Controlled 3 ** 6 9 Ad libitum 21 56 9 8 7 6 5 4 3 2 1-65 -18-9 ** -6-3 Controlled 3 Insulin, uiu/ml 6 9 Ad libitum 21 56 6 5 4 3 2 1 Liver tissue composition, % by weight ** ** -65-3 -14 1 14 28 49 Controlled Ad libitum Overfeeding energy prepartum hyperinsulinemia; more NEFA and liver TAG after calving

Dietary energy prepartum affects liver transcriptome 4,79 genes with FDR.5 diet time ~8% NRC prepartum Restricted energy ~1% NRC prepartum Control diet >13% NRC prepartum Overfed energy Bionaz et al. 25 JDS Suppl. 1; Loor et al. 25, 26 Physiol. Genomics

22 most impacted 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 Impact Most impacted biological pathways 8 6 4 2 8 6 4 2 8 6 4 2 8 6 4 2 Pentose phosphate pathway Oxidative phosphorylation Steroid biosynthesis Ribosome Glycolysis / Gluconeogenesis Synthesis and degradation of ketone bodies Glycerolipid metabolism Cell cycle Citrate cycle (TCA cycle) Fatty acid metabolism PPAR signaling pathway Antigen processing and presentation 4 2-2 -4 4 2-2 -4 4 2-2 -4 4 2-2 Direction of impact 14 28 49-3 -14 1 14 28 49-3 -14 1-3 -1411428 49 14 28 49-3 -14 1 14 28 49-3 -14 1-3 -1411428 49 Day relative to parturition 14 28 49-3 -14 1 14 28 49-3 -14 1-3 -1411428 Restrict Control Adlibitum Restrict Control Adlibitum Restrict Control Adlibitum 49-4

Log2 fold change relative to -65 day in milk (dry-off) 1-1 1-1 1-1 GOTERM_BP_FAT activity of plasma protein involved in acute inflam. response complement activation, classical pathway humoral immune response KEGG_PATHWAY Complement and coagulation cascades GOTERM_CC_FAT extracellular region GOTERM_BP_FAT translation KEGG_PATHWAY Ribosome GOTERM_CC_FAT basement membrane proteinaceous extracellular matrix cytosolic ribosome GOTERM_CC_FAT mitochondrion nuclear lumen organelle membrane GOTERM_BP_FAT ubiquitin-dependent protein catabolic process response to protein stimulus Overfed Control Restricted Overfed Control Restricted Overfed Control Restricted 1-1 Overfed Control Restricted Cluster analysis plus bioinformatics applied to bovine liver longitudinal transcriptomics

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 RED = UP with ketosis GREEN = Down Fatty acid metabolism: Oxidation Transc. regulation Glycolysis/gluconeogenesis Loor et al. 27 Physiol. Genomics

Do bovine PPAR subtypes respond to longchain 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)?

Fetal Bovine Serum MDBK 15 μm WY or LCFA 12 LCFA Saturated (16:, 18:, 2:) Unsaturated: c9-18:1, c9,c12-18:2 3 (18:3, 2:5, 22:6) t1-18:1, t11-18:1 CLA (c9,t11; t1,c12) Metabolites media 6h incubation RNA Microarray (WY and 16:) qpcr for 34 genes Transport and trafficking LCFA Cholesterol synthesis TAG synthesis Oxidation LCFA Other metabolism Inflammation Liver-adipose signaling Most putative PPARα target

Saturated are more potent activators of bovine PPARα in vitro Bionaz et al., J Dairy Sci, 28 Thering et al., J Dairy Sci, 29 Bionaz et al., Br J Nutr, in press % mrna relative to h mrna fold change relative to CTR 1 8 6 4 2-2 3. 2.5 2. 1.5 1..5. ACOX1 * * C16: C18:1 C18:2 C18:3 CLA * Fatty Acid 16: Wy-14643 7 6 5 4 3 2 1 * * * * * CPT1A * 1 M 25 M 5 M 1 M 2 M * # % mrna relative to h -4 6 12 18 24 6 12 18 24 12 ACADVL 3 ACSL1 1 8 6 4 2 * # * 25 * a 2 15 * a # a 1 b # b b 5 6 12 18 24 6 12 18 24 Hour of incubation

Nutrigenomics of supplemental ipids in peripartal ows 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 Control (no supplemental lipid) Saturated lipid, 25 g/d Fish oil, 25 g/d 1% of as-fed intake 1% of as-fed intake -21 Liver biopsy -1 1 14 Day Relative to Parturition Khan et al., 21 JDS Suppl. 1 Schmitt et al., 211 JDS

Performance response: positive effect of saturated lipid Cows in second or greater lactation A subset of 5 cows/diet Collaboration with M. A. Ballou and E. J. DePeters Control (no supplemental lipid) Saturated lipid, 25 g/d Fish oil, 25 g/d 1% of as-fed intake 1% of as-fed intake -21 Liver biopsy -1 1 14 Day Relative to Parturition Khan et al., 21 JDS Suppl. 1

Liver phospholipid fatty acid profiles (% of total FA) are affected by supplemental lipid: data for day 1 postpartum Ballou et al., 29 JDS Control EB1 Fish 18: ~28 ~29 ~27 c9-18:1 ~11 ~11 ~7. c9,c12-18:2 14 14 1 c9,c12,c15-18:3 1.4 1.4 1.1 2:4n-6 1.1 1..9 2:5n-3 ~1.5 ~1.5 ~4. 22:5n-3 4. 4. 5.5 22:6n-3 ~1. ~1. ~8. Trans-18:1 ~1.7 ~1.5 ~4.

Number of liver genes affected by lipid supplementation Days relative to parturition At FDR P.4 treatment time 1,28 14 81 1,14 1 362 1,82 1,257-1 18 519 63 EB1 Saturated vs FISH vs. Fish EB1 Saturated vs Con. vs. control FISH Fish vs. Con control 5 1 15 Number of genes

Most Impacted Metabolic Pathways Overall: encompass data from day -14, 1, and 14 across all treatment comparisons Amino acid metabolism Vitamin metabolism Fatty acid metabolism Overall 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

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