Bioinformatics and data mining: application in dairy cattle nutrition and physiology

Transcription

1 Bioinformatics and data mining: application in dairy cattle nutrition and physiology Juan J. Loor Associate Professor Department of Animal Sciences and Division of Nutritional Sciences University of Illinois, Urbana-Champaign, USA

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3 Outline 1. Biological context: a. Dairy cattle physiology and metabolism b. Nutrition c. Interactions outcomes at physiologic level 2. Bioinformatics, data mining, and systems biology 3. Application of these tools in dairy cattle

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5 Optimized nutrition depends on the target(s) Calving Re-bred Lactation TMR Dry period TMR 2+ Calving 0 10 months 2-4 months 12 months Common and important goals from a nutrition standpoint: Minimize incidence of disease around calving ( transition ) Enhance the ability of the cow to get pregnant Early lactation enhance nutrient use for milk synthesis Post-peak lactation enhance body tissue replenishment Dry period minimize body fat deposition Fetal growth and development?? (epigenetics and heredity)

6 The transition cow: a complex and dynamic system

7 Energy (NE L, Mcal) requirements 2 days before versus 2 days after calving 725-kg Cow 570-kg Heifer Function Pre Post Pre Post Maintenance Pregnancy Growth Milk production Total (Mcal) Typical intake Calculated from NRC (2001). Assumes milk production of 25 kg/d for cow and 20 kg/d for heifer, each containing 4% fat.

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

9 Incidence of transition period health phenotypes in high-producing herds (US National Animal Health Monitoring System) Mean (%) Range (%) 1 Clinical mastitis to 20 Milk fever to 44 Displaced abomasum* to 14 Clinical ketosis* to 20 Retained fetal membranes to 22 Metritis to 66 (modified from Goff, 2006) *Strong association with liver lipidosis Subclinical ketosis most predominant 1 Jordan and Fourdraine (1993)

10 Majority of cows leave herd soon after calving Timing of culling (% of cows culled) 12% 10% 25% of cullings 8% 624,614 Cows Leaving 5,749 Herds % 4% 2% 0% Week postcalving Godden et al. (2003) source - Overton and Waldron (2004)

11 How might nutrition and physiology impact incidence of health problems? Immune system (metritis, mastitis, retained placenta) Body fat mobilization (ketosis, fatty liver) Intakes of dry matter and energy (ketosis, milk fever, displaced abomasum) Rumen function (ketosis, displaced abomasum) Supply of glucose precursors (ketosis) Calcium and mineral balances (milk fever, subclinical hypocalcemia)

12 Cows can consume enough energy to meet requirements during transition period from a variety of diets Dietary NEL (Mcal/kg) DMI (kg) for 15 Mcal 1.30 (high straw) (typical close-up) 9.4 Dry cows will easily consume more energy than they require

13 Energy intake, % required Energy balance is altered by prepartal energy intake One-diet dry period feeding program Diet, diet week: P < Overfed energy Controlled energy (1.63 Mcal/kg) (1.30 Mcal/kg) Diet: P < 0.002; diet week: P < Weeks relative to parturition (Modified from Janovick et al., 2010)

14 Overfeeding of moderate-energy diets increases postpartal hepatic lipid storage and risk of metabolic disorders % of wet wt Liver TAG Controlled Overfed * 0 Pretrial Day relative to parturition Variable CON OVER P Displaced Ab Ketosis Mastitis Cow>1 prob Janovick et al., 2011

15 g/l TAKE HOME MESSAGE ON TRANSITION PERIOD 0.0 mg H 2 O 2 /100 ml 14.0 Haptoglobin Reactive Oxygen Species IU/mL Non-esterified fatty acids (NEFA) mmol/l Insulin Day relative to parturition Day relative to parturition Bionaz et al. (2008) Bionaz et al. (2012)

16 TAKE HOME MESSAGE ON TRANSITION PERIOD Haptoglobin Insulin 0.5 g/l 4.5 IU/mL METABOLIC, INFLAMMATORY Reactive Oxygen & Species OXIDATIVE mg H 2 O 2 /100 ml mmol/l Non-esterified fatty acids (NEFA) STRESS Day relative to parturition Day relative to parturition Bionaz et al. (2008) Bionaz et al. (2012)

17 Systems biology, functional genomics, and bioinformatics: terminology/concepts, techniques, and applications

18 NCBI (National Center for Biotechnology Information) What is bioinformatics? The unified discipline formed from the combination of biology, computer science, and information technology. The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information. Essential tool for understanding interrelationships among components of biological systems

19 Dogma of molecular biology as it relates to metabolism and physiology (Transcription regulators) Promoter CpG G1 Promoter G2 Promoter G3 Protein/DNA interactions CH 3 PPAR Metabolon Transcriptomics Protein/protein interactions CPT-1A ACACA FASN Proteomics Ac AcCoA P MalCoA 16:0 Metabolomics Metabolite/protein interactions S t o i c h i o m e t r y Metabolic K i n e t i c s control (Modified from Loor and Cohick, JAS 2009) Tissue function phenotype

20 Metabolomics Proteomics Nutrient Metabolites mirnaomics Protein Transcript omics Translation AAAA m7g TF1 TF2 TF1 RE TF2 TF1 TF2 TF1 TF2 RE Transcription mrna

21 Most-common transcriptomics tools Microarray dates back to ~1995 RNA-Sequencing newer (e.g. yeast transcriptome; 2008 Science 320:1344)

22 Transcriptome data analysis pipeline Quality evaluation of hybridizations GPRparser Perl: Pick only good spots 3 SD above median background Total number of good spots (min 20,000) Mean intensity per channel (dye), min 200 RFU SD per channel (compare to mean) Intensity among channels (check difference) Statistical analysis Lowess normalization and PROC Mixed in SAS Filtering Minimum P-value and False Discovery Rate (FDR) Minimum degree of freedom (= presence of data) Database for bioinformatics/data mining

23 What can we do with all the data?? Bovine mammary transcriptomics 6,579 DEG with FDR (Bionaz et al., 2012)

24 Individual datasets several objectives multiple approaches Unsupervised approach: Temporal transcriptomic adaptations Supervised approach: Known functions -adipogenesis -inflammation -energy generation - protein synthesis - lipid synthesis - vesicle transport - etc Affected genes Finding similar functions/gene regulation k-means clusters Promoter Motifs Metabolic, adipogenic, lactation gene sets Genes consistently affected by a physiological state, e.g., growth or lactation

25 Putting it all together. the Systems concept

26 The systems concept Every object that biology studies is a system of systems. Francois Jacob (1974) System network of interconnected components unified whole. Every system exhibits emergent behavior individual components Anthony Trewavas, The Plant Cell, 2006 Systems biology study of interactions between the components of biological systems function and behavior is about putting together rather than taking apart, integration rather than reduction.. Wikipedia Reductionism Holism Lee Hood, 2011 National Medal of Science

27 Critical for systems biology of cattle in the genome era Improvement and introduction of new technologies omics Bovine Genome Sequencing and Analysis Consortium Bovine genome annotation consortium Elsik et al. (2009) Reecy et al. (2010)

28 Number of scientific papers using transcriptomics (microarray) or proteomics approaches to study bovine has increased over time (PubMed, through May, 2012) (Loor et al., 2013)

29 Experiment Measurements Bioinformatics analyses -diet -physiology -etc. RNAseq Functional assays Biological functions Groupings and Networks Tissue composition Lipid Glycogen Blood Build an allencompassing model Milk Fatty acids Proteins Carbohydrates

30 Experiment Measurements Bioinformatics analyses -diet -physiology -etc. RNAseq Functional assays Biological functions Groupings and Networks Tissue composition Lipid Glycogen Blood Build an allencompassing model Milk Fatty acids Proteins Carbohydrates

31 Experiment Measurements Bioinformatics analyses -diet -physiology -etc. RNAseq Functional assays Biological functions Learning about the whole Groupings system and Networks and the dynamic interactions Tissue composition Lipid Glycogen Blood Build an allencompassing model Milk Fatty acids Proteins Carbohydrates

32 So, how do we put it all together??

33 Traditional analysis..before transcriptomics Gene 1 Apoptosis Cell-cell signaling Protein phosphorylation Mitosis Gene 2 Growth control Mitosis Oncogenesis Protein phosphorylation Gene 3 Growth control Mitosis Gene 4 Oncogenesis Nervous system Protein phosphorylation Pregnancy Oncogenesis Mitosis Gene 100 Positive ctrl. of cell prolif Mitosis Oncogenesis Glucose transport

34 There is a lot of biological research output You are interested in specific genes You get 1,893 results! How will you ever find what you want?

35 You work hard to read.. Help! more and more and more!!

36 The Gene Ontology (GO) consortium

37 Definition of mesoderm development Gene products involved in mesoderm development

38 Different ways of grouping genes: e.g. by biological process Apoptosis Gene 1 Gene 53 Mitosis Gene 2 Gene 5 Gene45 Gene 7 Gene 35 Glucose transport Gene 7 Gene 3 Gene 6 Positive ctrl. of cell prolif. Gene 7 Gene 3 Gene 12 Growth Gene 5 Gene 2 Gene 6 Annotations give function label to genes Ask meaningful questions of omics data e.g. genes/proteins involved in the same process, same/different expression patterns?

39 Biological process regulation of gluconeogenesis

40 Commercial software packages use GO annotation information but can be costly! ($10K/year)

41 Chronological: 0 vs. - 4 d Cellular Functions Cell-to-cell signaling and interaction Cell assembly and organization Cell morphology Cell signaling Cell growth and proliferation Molecular transport Nucleic acid metabolism Small molecular biochemistry Cellular development FA, anions, amino acids Secretion (exocytosis) Down-regulated Cellular function and maintenance Gene expression Carbohydrate metabolism Cellular movement Drug metabolism Cell death Lipid metabolism Amino acid metabolism Number of oligos Up-regulated Time course (all time points vs. previous) FDR< P < Overall Upregulated Downregulated Cellular compromise Post-translational modification Protein degradation Free radical scavenging (Tramontana et al., 2008)

42 (freely-accessible )

43 KEGG Pathway Database

44 There is a wealth of bioinformatics resources.

45 Bioinformatics tools for data mining

46 The enrichment analysis concept in bioinformatics Why useful? Tissue biological processes and functions many genes rather than an individual gene Enrichment tools systematically map a large number of affected genes in an experiment to an associated biological annotation term, function, and pathway Goal: Annotation terms with enriched gene members will give important insights to understand biological meaning behind the large gene list path towards comprehensive functional analysis of large gene lists. (Huang da et al., 2009)

47 Enrichment analysis: maps genes, metabolites, proteins to biological functions and pathways Association of biological terms to a gene/s Annotation Database Back-end annotation database User to input gene list Algorithms (sort and organize annotation terms) Statistics Calculate enrichment p-values with suitable methods Data mining Physiological context Nutrition Enriched terms Presentation of results

48 An example of affected pathways in a nutrigenomics experiment: liver N-Acetyl ornithine Oxidative Phosphorylation Propanoyl CoA Methylmalonyl CoA Amino Sugar and Nucleotide Sugar Metabolism D-Galactose D-Fructose D-Sorbitol α-d-glucose Fructose Metabolism Urea Ornithine Arginine Fumarate Succinate Urea Cycle Succinyl-CoA Citrulline L-Arginosuccinate S-Malate TCA Cycle Oxaloacetate Glycolysis/ Gluconeogenesis Citrate Isocitrate L-Aspartate Glycerate-3-P Glycerate-2-P Phosphoenolpyruvate Acetyl-CoA Acetate Galactose Metabolism D-Galactose-1-P UDP-Galactose D-Glucose-1-P α-d-glucose-6-p Glyceraldehyde-3-P Glycerate-1,3-2P Pyruvate Acetoacetyl CoA UDP-Glucose 1-D-myo- Inositol 3-P Formaldehyde Glycerone Methanol 3P-Hydroxypyruvate Phosphoserine Serine Treatments A vs control B vs control Identical regulation in both diets Unequal regulation in both diets 04 2-Oxoglutaramate L-Glutamine NH3 Carbamoylphosphate 2-Oxoglutarate L-Glutamate Glutamate Metabolism NH3 R-S- Cysteinyl glycine Oxalosuccinate R-S-Glutathione R X Glutathione Glutathione Metabolism Acetaldehyde Ethanol (S)-3-Hydroxy-butanoyl CoA Fatty Acid Metabolism

49 Existing pathway analysis methods

50 Existing pathway analysis methods Limitations of enrichment approach (ORA): Uses only the most-significant genes and discards others. : biological information is lost Assumes the behavior of each gene or pathway is independent from another. : biological control is not a function of a gene, but groups of genes Cannot handle time-course datasets.

51 What s new? Accounts for proportion of Differentially Expressed Genes (DEG), fold change, and p-value Allows to follow the impact of pathways/functions through time and between multiple treatments Provides the overall direction ( activation / inhibition ) of the impact on a pathway/function based on genes affected Can use any publicly available-annotation database

52 Mammary transcriptomics during lactation Illinois bovine oligoarray (>13,000 elements) 6,579 DEG with FDR 0.001

53 Functional analysis using ORA tools Functions Pathways Networks Transcription factors plus Gene Ontology 8 Cellular Assembly and Organization Cell Cycle Cell Death 6 -log P-value Lipid Metabolism Molecular Transport Protein Synthesis Almost no functions/pathways significant with False Discovery Rate (FDR) correction!

54 Data mining bioinformatics tools used with DIA Microarrays (Affy, Agilent, etc) RNA-Seq data also could be used Statistical cut-offs (e.g. FDR <0.05 and P value<0.05) Canonical Pathway Analysis Functional Annotation Analysis Kyoto Encyclopedia of Genes and Genomes (>200 manually-curated pathways) Database for Annotation, Visualization, and Integrated Discovery Biological Processes (2,300) Chromosomes (32) Lipid-related (100) Gene expression-related (141)

55 How to interpret DIA output Impact = Impact of the condition (diet, physiol. state) on the biological term Direction of the impact (or flux ) = Biological effect of the condition Impact/Direction of the impact 120 Direction of the impact 100 Impact Time

56 Bioinformatics analysis of functional adaptations of the mammary gland using DIA (Bionaz and Loor, 2012)

57 30 most impacted KEGG pathways by DIA PATHWAYS Galactose metabolism Glycosylphosphatidylinositol(GPI)-anchor biosynthesis PPAR signaling pathway Ascorbate and aldarate metabolism Proximal tubule bicarbonate reclamation Biosynthesis of unsaturated fatty acids Synthesis and degradation of ketone bodies O-Mannosyl glycan biosynthesis Citrate cycle (TCA cycle) Antigen processing and presentation Limonene and pinene degradation ABC transporters Hedgehog signaling pathway Sulfur metabolism Drug metabolism - other enzymes Adipocytokine signaling pathway Steroid biosynthesis TGF-beta signaling pathway Glutathione metabolism ECM-receptor interaction Phagosome Peroxisome Cell adhesion molecules (CAMs) Hematopoietic cell lineage Fc epsilon RI signaling pathway Jak-STAT signaling pathway Ether lipid metabolism Arachidonic acid metabolism Riboflavin metabolism Valine, leucine and isoleucine degradation vs120

58 Carbohydrate and energy metabolism Impact Galactose metabolism Glycolysis/ gluconeogenesis TCA cycle Pentose & glucuronate interconversions Oxidative phosphorylation Pyruvate metabolism Direction of the impact Day relative to parturition

59 Carbohydrate and energy metabolism Impact Kg/d Galactose metabolism Glycolysis/ gluconeogenesis 60 Curve of lactation TCA cycle Pentose & glucuronate interconversions Oxidative phosphorylation Pyruvate metabolism Direction of the impact Day relative to parturition

60 Application of DIA to liver transcriptome data

61 Application of DIA to liver transcriptome data

62 Dynamics of liver transcriptome in response to plane of nutrition during the dry period 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) Over Con Rest Aims: study the liver transcriptome and physiological outcomes Day relative to parturition

63 Dietary energy prepartum affects the liver transcriptome 4,790 genes with diet time effect ~80% NRC prepartum Restricted ~100% NRC prepartum Control >140% NRC prepartum Overfed (Bionaz and Loor, 2012)

64 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 using DIA 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 Direction of impact Day relative to parturition Restrict Control Adlibitum Restrict Control Adlibitum Restrict Control Adlibitum

65 Log2 fold change relative to -65 day in milk (dry-off) Overfed Control Restricted Overfed Control Restricted Overfed Control Restricted Overfed Control Restricted Cluster analysis plus ORA applied to bovine liver longitudinal transcriptomics

66 Log2 fold change relative to -65 day in milk (dry-off) 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 Overfed Control Restricted Cluster analysis plus ORA applied to bovine liver longitudinal transcriptomics

67 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?

68 The transition cow: a complex and dynamic system Lipolysis Adipokynes Lipogenesis Immune response Milk synthesis FA oxidation Glucose oxidation FA oxidation AA metabolism Immune response Gluconeogenesis Rumen/intestine Muscle Pancreas Brain Bone Others. Bionaz and Loor (2012) Loor et al. (2013)

69 The transition cow: a complex and dynamic system Lipolysis Adipokynes Lipogenesis Immune response Milk synthesis FA oxidation Glucose oxidation FA oxidation AA metabolism Immune response Gluconeogenesis Rumen/intestine Muscle Pancreas Brain Bone Others. Bionaz and Loor (2012) Loor et al. (2013)

70 Application of DIA for integrative systems physiology Y-axis: 100 Mammary Bionaz et al. (2012) 10 Number of DEG Number of Differentially Expressed Genes 5000 Mammary Adipose 4000 Loor Liver et al. (2005) Janovick et al. (2009) Mamamry up to 60 vs -15 Max Feb12, Default Interpretation Colored by: Time Gene List: FDR0.05 (9567) Y-axis: Liver vs-15 Max Feb12, Default Interpretation Colored by: Time Gene List: all genes (9004) 10 Liver Adipose Day relative to parturition Y-axis: to 60 vs Default Interpretation Mamamry up Max Feb12, Colored by: Time 16.94

71 Day relative to parturition Overall metabolism Carbohydrate Metabolism Lipid Metabolism Direction of the Impact PPAR signaling Mammary Adipose Liver Glycolysis/Gluconeogenesis Pyruvate metabolism Fatty acid biosynthesis Biosynt. of Unsaturated FA

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73 Summary and Perspectives Most value from nutrigenomics and metabolomics (i.e. expensive) from examining multiple tissues or biological fluids: Potential crosstalk e.g. visceral fat to liver (dairy); myocyte and adipocyte (beef) Plasma, serum, milk, ruminal fluid, etc If focused only on nutrition (beef or dairy) as management tools, diets: Must be applicable in the field (of practical value): Supplemental nutrients: Essential amino acids (rumen by-pass) Organic trace minerals Long-chain fatty acids Build up knowledge within a systems framework use of bioinformatics. Link expression networks or gene/s to: Blood indicators: metabolism, immune response Health status: ketosis, fatty liver, mastitis, metritis, etc

74 Summary and Perspectives Identify susceptibility/marker genes: Probably tissue-specific Field applications: Management strategies based on marker genes? Personalized nutrition? e.g. grouping cows/steers and feed accordingly How to deliver outcomes? Training packages for students and industry professionals Marker-assisted selection for more disease-resistant or more efficient animals Biologicals or metabolic modifiers that can be used in the short or long-term to modify metabolism and health: Dietary fatty acids, amino acids, trace minerals, etc.