Nuevas herramientas de secuenciación (RNA seq) para el ... 2011. Medrano_INIA2011_RNAseq… · RNA sequencing procedure ... genes responsible for variation of CITRATE content in

Nuevas herramientas de secuenciación (RNA seq) para el análisis de

características complejasDNA / Genes

Structural

Juan F. Medrano

Dept of Animal Science mRNA

Genomics

Transcriptome Dept. of Animal Science University of California, Davis

AAAA

Proteins

Transcriptome

AnnotationAnnotationQuantificationOligosaccharides

Metabolism

INIA, Madrid, España Octubre 14, 2011

J.F. Medrano / U.C. Davis

TemasAnálisis del transcriptoma con RNA sequencing

Transcriptoma de la leche a diferentes etapas lactanciaLactación temprana: proteínas de la lecheL t ió t dí i t líti Lactación tardía: enzimas proteolíticas

Características complejas/validación de reguladoresCaracterísticas complejas/validación de reguladoresOligosacáridos de la lecheContenido de citrato en la lecheNutrigenómica estudio en pez zebraNutrigenómica, estudio en pez zebra


RNA sequencing procedure

Sample i RNA ti d icollection RNA preparation and sequencing

~220 MRNA extraction

~220 M

Multiplex indexing Millions of readsMultiplex indexingadapter ligation

Tissue Lane 1 Lane 2

Brainstem 20.6 20.9

Cerebral Cortex 17.1 20.7

Hypothalamus 17.3 18.7

Gonadal fat 15.5 14.1

Pituitary 17.7 19.7


Pituitary 17.7 19.7

Liver 14.4 16.3Total reads 102.6 110.4

Mapping sequencing reads to exons

Assembled:- to a reference genome

Morozova et al. 2009,

Software used:

Measured by counting sequence reads

Gene expression

Measured by counting sequence reads RPKM value = Reads per kilobase of exon per million mapped reads


Gene structureSNP discovery

RNA-Sequence Analysis Workflow I

Sequence analysis

Importing sequence reads and QC

II

Assembly to Reference Genome

De novo assembly

SNP detection SNP discovery and Allelic differential expression

New transcript discovery using unmapped reads

DIP detection RNA-Seq analysis

SNP discovery and Allelic differential expression

Deletions, Insertions analysisTranscriptome (RPKM)Exons/genes discovery

New transcripts Exons/genes discovery in annotated gene regionsSplice variants

Experimental comparison Functional annotation, Blast2Go

III


p

Compare multiple samplesTransformation and normalizationStatistical analysis

Pathway Analysis, IPA

GeneMammary RPKM

RNA‐SeqReads

Affymetrix Expression values

CSN2 174686 1351852 14.31

RNA seq vs. Microarray

LGB 129059 737858 14.11CSN3 44255 271151 14.24LALBA 34007 177313 14.08CSN1S1 32345 277713 13.12GLYCAM1 22015 102009 13.92

Highly expressed genes(~180 genes)

Dynamic RangeCSN1S2 14670 120333 13.83MFGE8 4398 42588 12.55FASN 2332 130664 12.67LTF 817 14570 13.33AGPAT6 425 7269 9.47

RPKM: 817 - 174,686Affy 12.5 – 14.3

MUC1 411 4510 10.32SLC29A1 253 4480 8.49CIDEA 185 5352 11.12PTGDS 147 877 10.86TSTA3 125 1356 8.66FOLR1 113 1571 9 66

Medium expressed genes(~6,026)

Dynamic RangeFOLR1 113 1571 9.66BANF1 108 1400 8.90VAT1 93 1852 9.56DAP 86 1522 10.62MST1 1.18 21 3.20FGD1 1 17 29 4 10

y gRPKM: 86 - 425Affy 8.5 – 11.1

FGD1 1.17 29 4.10PTGS1 1.16 25 5.41MORC4 1.16 19 3.56TOR1AIP2 1.14 18 3.01CHRND 1.14 17 4.55ARID3B 1.14 20 3.78

Low expressed genes(~11,024)

Dynamic Range


ARID3B . 4 20 3.78TMEM59L 1.13 13 3.58RAMP1 1.12 15 4.17FUT1 1.10 19 3.21

RPKM: 1.10 – 1.18Affy 3.01 – 5.41

RPKM: Reads per kilo base of exon length / million reads

Nature 447:337-42, 2011

~40% of the variance in protein level is explained by


mRNA levels. Most of these 40% is due to differences in transcription rate.

Milk transcriptome at different stages of lactation

Experimental comparison

~18,000 of 26,000 genes are expressed~9,000 genes are ubiquitously expressed at all stages

D15 D90 D250Highly expressed 86 140 150>500 RPKM

10 genes represent 61% 11% 19% this % of reads

IPAnalysisD15 D250

IPAnalysis

Milk components antiapoptotic


Milk componentsCasein/whey proteinsGlycam1-mucin

antiapoptoticinmmune systemProteolytic enzymes

Gene expression pattern of highly expressed genes at day 15 representing 61% of all sequence reads.

200,000

250,000PK

M

100,000

150,000

Expr

essi

on in

RP

0

50,000

E

DAY 15 Day 90 Day 250

LGB CSN2 CSN1S1 LALBA CSN3 GLYCAM1 CSN1S2


Protein % 3.13±0.2Casein % 2.38±0.21

Protein in cow milk remains fairly constant

Milk transcriptome at different stages of lactation

Experimental comparison

~18,000 of 26,000 genes are expressed~9,000 genes are ubiquitously expressed at all stages

D15 D90 D250Highly expressed 86 140 150>500 RPKM

10 genes represent 61% 11% 19% this % of reads

IPAnalysisD15 D250

IPAnalysis

Milk components apoptotic


Milk componentsCasein/whey proteinsGlycam1-mucin

apoptoticinmmune systemProteolytic enzymes

RNA-Seq analysis

Proteolytic enzymes in milk: Plasmin (alkaline serum protease)Plasmin (alkaline serum protease)Cathespins (lysosomal proteases)

Role:Mammary development

Microbial interactions

5,000.00

6,000.00

7,000.00

RPK

M

CTSB

CTSD

CTSZ

CTSH

Effect on fermented products and cheese

Sensory quality of milk

2 000 00

3,000.00

4,000.00

ene

expr

essi

on R CTSH

CTSS

CTSC

CTSK

CTSA

y q y

Potential neutraceticals

0.00

1,000.00

2,000.00

Day 15 Day 90 Day 250

Ge

CTSF

CTSW

CTSL2

CTSO


SNP discovery in 14 Holstein cows107,639 SNP in coding regions

100

SNP validation with dbSNP

, g gCriteria• Quality score• >10 reads• Min 2 reads/SNP

80

90

100 • No SNP on read ends

(Canovas etalMammGen 2010)

50

60

70

20

30

40

0

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30


Validated SNP Unique SNP

Milk oligosaccharide structures

OOH

CH2OHO

CH2OH

[M+ Na]+=732.3

Lacto-N-Neohexose

[M+ Na]+=1097.4

C OOOH

CH2OH

CH OH

Lacto-N-Tetraose

Isomeric fucosylated Lacto-N-Hexose

OH

OH

OH

OOH

OH

OH

CH2OH

NHAc

OH

O

NHAc

OH

CH2OH

OOH

OH

CH2

CH2OH

OH

OH

OH

CH2OH

O

OO

O

O

OOH

OH

OH

CH2OH

O

OH

NHAc

CH2OH

OH

OH

CH2OH

OH

OH

OH

CH2OH

OO

O

y

OH C CH2OH

OOH

OH

CH2OH

O

OH

CH2OH

O

CH2OH

OH

OH

OH

CH2OHOOH

OH

CH2OH

OO

O CH2OHCH2

O

NHAc

CH2OH

O

OOH

OH

OH

CH2OH

O

[M+ Na]+=1389.5

[M+ Na]+=1243.4 Difucosyllacto-N-Hexaose

OHOH

OH

H3C

OOH

OH

CH2OH

OH

O

NHAc

CH2OH OH

NHAcO CH2OH

OH

OH

OH

OOH

OH

O

O

NHAc

CH2OHO

O

OH

OH

OH

H3C

OOH

OH

OH

CH3

O

OH

OH

OH

H3C

O

O

O

Sialic acidGlucosamine

J.F. Medrano / U.C. DavisZivkovic A M , Barile D Adv Nutr 2011

Syalic Acid Metabolism genes in milkg


Wickramsinghe et al PloSONE 2011

128 genes from 10 functional oligosaccharide metabolism categories in mammals

502 SNP in coding regions

↓Directly genotyped by

RNAseq-


Genotyping array↓

Association study

Wickramsinghe et al PloSONE 2011

Non-synonymous SNP in glycosyaltion-realted genes that showed aNon synonymous SNP in glycosyaltion realted genes that showed a damaging effect in the encoded protein (Polyphen analysis)


SNP detection Target ValidationPathway analysis

SNP selection (Canovas et al Mamm Genome, 2010)

Marker-trait association studies

Association Analysis

Definition of regulators

Example: genes responsible for variation of CITRATE content in cow milk (130-160mg/100ml).( g )

Citrate in milkInvolved in Ca and P balanceHeat StabilityAid i t i l ti fl dAids in protein coagulation, flavor and aroma Provides protein stabilityPrimary buffer in milk


Pathway of fatty acid synthesis in ruminant mammary tissue

NADP

NADPH


Numbers in parenthesis correspond to average expression values (RPKM) measured by RNA-seq in milk samples.

Zebrafish muscle tissue response to a plant protein diet♂ n= 440

Average weight = 52 mg Average weight = 228 mg5% 5%

Muscle from 8 males

pool RNA (4 fish/pool)

Muscle from 8 males

pool RNA (4 fish/pool)

2 RNA-seq libraries 2 RNA-seq libraries 17,227 expressed genes

54 differentially d

70 differentialyexpressed genesexpressed genes expressed genes

Low growth fish: proteinsynthesis, cellularmorphology, skeletal and

High growth fish: lipidmetabolism, vitamin and mineral metabolism and p gy,

muscle system development, and tissue morphology.

oxidation reduction.


Population fish (24 families)

RNA-seq RNA-seq

5%5%

%

Parents (48 fish)

Four low growth fish/ familyN= 96

Four high growth fish/ familyN= 96

165 SNP / 240 samples

Parents (48 fish)

ID Gen Gene SNP Minor allele

Minor allele frequency

p-value FDR slope Amino acids

ENSDARG000000 N A/T T 0 129 1 60E 05 0 001233 110 1670988 SynonymENSDARG000000 N A/T T 0.129 1.60E‐05 0.001233 ‐110.1670988 Synonym

ENSDARG000000 A T/C T 0.200 0.0033 0.172945 12.7210075 Synonym

ENSDARG000000 P T/A A 0.132 0.0050 0.195037 39.98901273 Synonym

ENSDARG000000 C A/C C 0.031 0.0056 0.17339 ‐134.1560644 Ile500Leu


ENSDARG00000045864 Tmod1 G/C C 0.223 0.0061 0.158305 61.38335784 Ser141Thr

Conclusiones

•El workflow analítico de RNAseq aplicado a caracteres complejos es una robusta herramienta para incrementar el conocimiento biológico de los mismos.

- cuantificación precisa del nivel de expresión génica con una lt l ió l i l d t íalta correlación a los niveles de proteína.

- el descubrimiento de nuevos tránscritos- la identificación de nuevos SNP y otras variantes a través de

un completo genotipado del exoma del organismoun completo genotipado del exoma del organismo- permitiendo la identificación de otros organismos presentes

en el material biológico

• La combinación de RNAseq en el análisis de vías metabólicas e identificación de SNP con estudios de asociación es una forma experimental para definir módulos reguladores clave de


p p gcaracteres complejos.

Acknowledgements

Medrano Lab

Alma Islas, Gonzalo Rincon, Saumya Wickramasinghe, Pilar Ulloa, Angela Canovas (IRTA, Spain)

UCDavis Genome Center

Colaboradores

Carlito Lebrilla (UCDavis)Bruce German (UCDavis)Rafael Jimenez-Flores (CalPoly, SLO)Armand Sanchez (UAB)Financiamiento


Genetic Principles Governing the Rate of

S ll W i h 1939

Genetic Principles Governing the Rate of Progress of Livestock Breeding, JAS 1939

“As a starting point suppose that we were given a reasonably complete map of all of the chromosomes, showing the location of all important genes affecting

Sewall Wright 1939

showing the location of all important genes affecting the character in question as well as of convenient marker genes. What could we do with it?”


Sewall Wright 1939

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Nuevas herramientas de secuenciación (RNA seq) para el ... 2011. Medrano_INIA2011_RNAseq… · RNA sequencing procedure ... genes responsible for variation of CITRATE content in