About OMICS Group OMICS Group is an amalgamation of Open Access publications and

worldwide international science conferences and events. Established in the year 2007 with the sole aim of making the information on Sciences and technology ‘Open Access’, OMICS Group publishes 500 online open access scholarly journals in all aspects of Science, Engineering, Management and Technology journals. OMICS Group has been instrumental in taking the knowledge on Science & technology to the doorsteps of ordinary men and women. Research Scholars, Students, Libraries, Educational Institutions, Research centers and the industry are main stakeholders that benefitted greatly from this knowledge dissemination. OMICS Group also organizes 500 International conferences annually across the globe, where knowledge transfer takes place through debates, round table discussions, poster presentations, workshops, symposia and exhibitions.

About OMICS International Conferences

OMICS International is a pioneer and leading science event organizer,

which publishes around 500 open access journals and conducts over 500

Medical, Clinical, Engineering, Life Sciences, Pharma scientific

conferences all over the globe annually with the support of more than

1000 scientific associations and 30,000 editorial board members and 3.5

million followers to its credit.

OMICS Group has organized 500 conferences, workshops and national

symposiums across the major cities including San Francisco, Las Vegas,

San Antonio, Omaha, Orlando, Raleigh, Santa Clara, Chicago,

Philadelphia, Baltimore, United Kingdom, Valencia, Dubai, Beijing,

Hyderabad, Bengaluru and Mumbai.

.

Genome-Wide Transcriptomes Integrate Animal Intra- and Inter-Organism Processes and Evoke the Ecoimmunological Concept in Animal Health

Yongming Sang, D.Sc., Ph.D.

College of Veterinary Medicine

Kansas State University, Manhattan, KS 66506, USA

(07/28/2015, Transcriptome-2015)

Antiviral regulation model (PRRSV, PEDV, SIV, DCoV, CSFV, ASFV)

Obesity model (Had-36)

Metabolism Immunity = Two Facets of

One task of animal health & nutrition

Google images

III. Integrative Strategy in Animal Health and Nutrition:

Current Projects

NIH: Rat obesity induced by high-fat-diet plus Had-36 infection (NIH pilot: $50,000; and R15 pending)

USDA: (1) Immunogenetics and immunoreagent development ($48,000, 2014-2018) (2) Macrophage-based antiviral regulation ($499,436, 2013-2017); (3) Molecular

and functional diversity of interferons (USDA pending)

Mediator: IFNγ LPS IL4/13 IL10 IFNα

Status: M1a M1b M2 IL10+ antiviral Classically activated Alternative activated Regulatory Antiviral state

Phenotype: ProinflammatoryHost defense Antitumor and antiviral (M1a); Antibacterial (M1b)

Wound healingSuppress host defense Suppress antitumor immunity

Anti-inflammatorySecrete IL-10Restore immune homeostasis

ProinflammatorySecrete type I IFNsAntiviral

++ + Tissue macrophage

CMP GMP CFU-M

Progenitor cells

RNA from pooled cells

mRNA selection

Library preparation

Cluster preparation

Illunima sequencing

Mapping clean reads

Data analysis and DEG confirmation

Read quality control

PR

RSV

5h

Our Macrophage Polarization and RNA-seq Procedure

IRF1 IRF2 IRF3 IRF4 IRF5 IRF6 IRF7 IRF8 IRF9

-10

-5

0

5

10

15

20 IFN

LPS

IL4

IL10

IFN

Exp

ressio

n in

de

x(2

-C

t )

rela

tive

to

DP

BS

co

ntr

ols

M0-PBS vs. M1-LPSM0-PBS vs. M2-IL4M0-PBS vs. M1-IFNγM0-PBS vs. M2-IL10M0-PBS vs. MaV-IFNα

From 6624 significant DEGs of >30,000 detected genesIR

F1IR

F2IR

F3IR

F5IR

F6IR

F7IR

F8IR

F9IR

F2B

P1

IRF2

BP

2

0.0

12

3.2

52

.6

M0-PBSM1-IFNγM2-IL4M1-LPSM2-IL10MaV-IFNα

0.0

97

.33

.2

CC

L1C

CL2

CC

L3C

CL4

CC

L5C

CL8

CC

L17

CC

L20

CC

L21

CC

L23

CC

L24

CC

L35

CC

L26

CC

L27

CX

CL2

CX

3C

L1C

XC

R3

CX

CL9

CX

CL1

0C

XC

L11

CX

CL1

3C

XC

L14

CX

CL1

6C

CR

L2C

CR

2C

CB

P2

CC

R4

CC

R5

CC

R7

CX

CR

1C

XC

R2

CX

CR

2L

CX

CR

4C

XC

R5

CX

CR

6C

XC

R7

CX

3C

R1

0.0

15

0.3

4.4

M0-PBSM1-IFNγM2-IL4M1-LPSM2-IL10MaV-IFNα

IL1A

IL1B

IL36

AIL1

F10

IL1R

NIL1

R2

IL2R

AIL2

RG

IL3R

AIL4

RA

IL4I1

IL5IL6IL6

RIL6

RB

IL7IL7

RA

IL8IL9

RIL1

0IL1

0R

AIL1

0R

BIL1

1R

AIL1

2A

IL12

RB

1IL1

2B

IL12

RB

2IL1

3R

AIL1

5IL1

5R

AIL1

6IL1

7B

IL17

RB

IL17

RC

IL17

RE

IL18

IL18

BP

IL19

IL20

RB

IL21

RIL2

3R

AIL2

2R

AIL2

3P

19

IL27

IL27

RA

IL27

BIL2

9IL3

3TN

FTN

FAIP

1M

CSF

CSF

CSF3

CSF2

RA

CSF1

RC

SF2R

BTG

FB

DEGs Related to Cytokines, Chemokines and IRFs, and RT-PCR Confirmation

IRF1

IRF2

IRF3

IRF5

IRF6

IRF7

IRF8

IRF9

IRF2

BP

1IR

F2B

P2

0.0

12

3.2

52

.6

M0-PBSM1-IFNγM2-IL4M1-LPSM2-IL10MaV-IFNα

pH 4.0 5.0 5.5 6.0 6.5 7.0 8.0 9.0

(kD

a)

6

13

4 5 6 7

89 10

11

2

12

18

19

1716

151413

37

22

2120

3029

28

272625

2423

36

353433

32

31

38

44

43

42

41

40

39

5051

59

49

4847

46

45

585756

5554

53

52

66

6564

6362

61

60

6768

69

75

7473

72

71

70

7978

77

7680

81

89

888786

85

848382

90

93

91

9495

9697

99

98

92

104

105

109

111

112

106

108

107

100

102

103

101

110

M1-I

FNγ

/ M0-P

BS

20

14

10

150 96

84 75

66

50

40

30

RP

KM

0

200

400

600

800

0

50

100

150

200

250

0

200

400

600

800

0

200

400

600

800

1000

1200

1400

*

*

*

*

*

*

*

* *

*

*

*

*

*

2-S

ND

13

0-W

AR

S4

7-P

LEK

77

-RA

N

42

-CK

B1

6-P

LOD

36

2-R

NF1

14

79

-HN

RN

PU

52

-AN

XA

16

6-A

TP6

V1

E19

1-U

BE2

D3

10

0-C

AN

X

18

-MX

14

9-M

x29

0-H

2B

3A

99

-MP

P5

M1-IFNγ vs.

M0-PBS

M2-IL4 vs.

M0-PBS

M2-IL10 vs.

M0-PBS

MaV-IFNα vs.

M0-PBS

Spence S, et al., Immunity. 2013 Jan 24;38(1):66-78.

KLF1

KLF2

KLF3

KLF4

KLF5

KLF6

KLF7

KLF8

KLF9

KLF1

0K

LF11

KLF1

2K

LF13

KLF1

4K

LF15

KLF1

6K

LF17

0.0

30

.72

.8

SOC

S1SO

CS2

SOC

S3SO

CS4

SOC

S5SO

CS6

SOC

S7

0.0

50

.51

0.6

M0-PBSM1-IFNγM2-IL4M1-LPSM2-IL10MaV-IFNα

PP

AR

AP

PA

RD

PP

AR

GP

PR

C1

PP

AR

GC

1B

CD

47

0.0

15

0.5

60

.2

Alder JK, et al. J Immunol. 2008 Apr 15;180(8):5645-52.

DEGs Related to Transcription Factors that are Important for Macrophage Activation, and Proteomic Confirmation

STRADA

STRADB

CAB39

PRKAA1

PRKAA2

PRKAB1

PLCB2

PLCB1

CAMKK1

CAMKK2

STK11

PRKACB

PRKACG

PRKAB2

PRKAG1

PRKAG2

PRKAG3 AMP kinase

HMGR

ACC1

ACC2

CPT1A (liver)

CPT1B (muscle)

CPT1C (brain)

HNF4

FASN LIPE

IFN

γ-M

1 v

s. P

BS

-M0

STRADA

STRADB

CAB39

PRKAA1

PRKAA2

PRKAB1

PLCB2

PLCB1

CAMKK1

CAMKK2

STK11

PRKACB

PRKACG

PRKAB2

PRKAG1

PRKAG2

PRKAG3AMP kinase

HMGR

ACC1

ACC2

CPT1A (liver)

CPT1B (muscle)

CPT1C (brain)

HNF4

FASN LIPE

STRADA

STRADB

CAB39

PRKAA1

PRKAA2

PRKAB1

PLCB2

PLCB1

CAMKK1

CAMKK2

STK11

PRKACB

PRKACG

PRKAB2

PRKAG1

PRKAG2

PRKAG3AMP kinase

HMGR

ACC1

ACC2

CPT1A (liver)

CPT1B (muscle)

CPT1C (brain)

HNF4

FASN LIPE

IFN

γ-M

1 v

s. P

BS

-M0

IL4-M

2 v

s. P

BS

-M0

PLCB1 PLCB2

CAMKK1 CAMKK2 STRADA STRADB

CAB39 STK11

PRKACB PRKAA2 PRKAB1 PRKAB2 PRKAG1 PRKAG2

HMGR ACC1

CPT1A CPT1B CPT1C

SREBP2 FASN

LIPE

0.0 70.9 20.9

M0

-PB

S M

1-I

FNγ

M2

-IL4

M

1-L

PS

M2

-IL1

0

MaV

-IFN

α

DEGs in AMPK Pathway in Mediation of Lipid Metabolism

Regulation of PRRSV infection via AMPK

Pathway and Lipid Metabolism

CTRL

Fluorescent

ToFA (2.5 μg/ml)

ToFA (5 μg/ml)

mDCs infected with DsRed-PRRSV for 48 h

Merged

ToFA (5-(Tetradecyloxy)-2-furoic acid), a competitive

inhibitor of acetyl-CoA carboxylase (ACC)

FL1 (PRRSV)

99.42 0.58

100 101 102 103 104

MФsMARC-145 mDCs

6.09 93.91

83.16 16.84

98.52 1.48

43.26 56.74

62.55 37.45

74.51 25.49

86.97 13.02

98.96 1.04

100 101 102 103 104 100 101 102 103 104

FS

C-H

1000

800

600

400

200

0

1000

800

600

400

200

0

1000

800

600

400

200

0

Mo

ck

PR

RS

VT

oF

A+

PR

RS

V

DN

A m

eth

yla

tran

sfe

rase

his

ton

e d

ea

ce

tyla

se

His

ton

e m

eth

yltra

nsfe

rase

MGMT DMAP

DNMT1 DNMT1L DNMT3A

DNMT3AL DNMT3B

0.0 10.2 3.6

M0

-PB

S M

1-I

FNγ

M2

-IL4

M

1-L

PS

M2

-IL1

0

MaV

-IFN

α

ASH1L DOT1

EHMT1 EHMT2

EZH1 MLL1 MLL2 MLL3 MLL4 MLL5 NSD1 NSD3

SETD1A SETD2 SETD8

SETDB1 SETDB2

SETMAR SUV39H1

SUV420H2 WHSC1

0.0 19.1 13.9

HADC1 HADC2 HADC3 HADC8 HADC5 HADC7 HADC9 HADC9L HADC6 HADC10 HADC11 SIRT1 SIRT2 SIRT3 SIRT4 SIRT5 SIRT6 SIRT7

0.0 42.9 6.7

M0

-PB

S M

1-I

FNγ

M2

-IL4

M

1-L

PS

M2

-IL1

0

MaV

-IFN

α

KDM2A KDM2B KDM3A KDM3B KDM4A KDM4B KDM4C KDM5A KDM5B KDM5C KDM6A KDM7 KDM8 NO66

0.0 21.0 11.0

His

ton

e d

em

eth

yla

se

DEGs Critical for Epigenetic Regulation

http://www.eusem.com/main/CH/epi

CT

RL

Azacytidine DNA (-methyltransferase)

BIX-01294(-histone methyltransferase)

Trichostatin A(-histone deacetylase)

UNC0638(-selective HMT G9a/GLP)

MA

RC

-14

5M

acro

ph

ag

es

10

μM

0.5

μM

10

μM

0.5

μM

0.5

μM

0.5

μM

10

μM

1 μ

M

Regulation of PRRSV infection via

Epigenetic Regulation

Tissue macrophages

Differential activation statuses PRRSV

Epigenetic regulation

Lipid metabolism

Interferon responses…

Deviation of activation statuses

Increase PRRSV permissiveness

Routes for preventing

Summary

1. Porcine monocytic cells at different activation statuses, interact differentially with PRRSV

2. Key genes in the pathways such as lipid metabolism, epigenetics and interferon signaling are revealed among significant DEGs.

3. Signature genes revealed in PRRSV—infected macrophages at each activation status infer the mechanism of PRRSV-cell interaction.

4. Understanding and modulating cell activation status of monocytic innate immune cells provides a framework for optimizing antiviral immunity

Objective and Approach

Aims:

1. Develop rat obesity model integratively induced by a high-fat diet and Had-36 infection

2. Validate obesity phenotypes including body weight, fat index, plasma leptin level, and

parameters associated with gut/adipose inflammation

3. Extract ileal RNA from LF lean, Had-36+LF, HF and Had-36+HF obese groups

4. Perform transcriptomic analyses using 8-wk ileal and hypothalamus samples

5. Analyze gene ontology and pathway integration, and determine obesity-linked innate

immune genes

Tissue samples taken

at 8, 15, 30 wks

Body weight measured and

blood samples taken weekly

High-fat diet (HF)* Control diet (LF)*

Male Wistar Rats: 6-wk old [Serum negative to Ad., Body Weight (BW): 125 5g]

Had-36 + HF

Negative control: 12 Integrative induction: 24 Positive control-II: 24

Had-36*+LF

Positive control-I: 24

(*human adenovirus 36

LF: LabDiet 5001 Fat 5.0%

HF: LabDiet 5SLA Fat 20.6%)

de La Serre CB, et al., Am J Physiol Gastrointest Liver Physiol. 2010 Aug;299(2):G440-8.

Results and Discussion

Total fat

8-week

Adip

osity index (

% o

f B

W)

2.0

4.0

6.0

8.0

10.0

Total fat EWAT

30-weekMAT PRAT

Low-fat diet (LF)

High-fat diet (HF)

LF + Had-36

HF + Had-36* **

*

**

*

* *

**

* p<0.05, ** p<0.005, n=6-7

EWAT, MAT and PRAT: epididymal , mesenteric, and perirenal white adipose tissue

1. LF 4.HF+36

HF + Had-36 infection synergistically increased adiposity indexes in total fat or visceral fats in three adipose depots

Results and Discussion Profiling of Significant Differential Expression Genes

Tlr2

Tlr3

Tlr4

Tlr5

Tlr6

Tlr7

Tlr1

0

Tlr1

1

Tlr1

2

Tlr1

3Lo

g2(f

old

ch

an

ge

to

LF

)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5HF

Had-36

HF+Had36

De

fa5

De

fa6

De

fa7

De

fa8

De

fa9

De

fa1

0

De

fa1

1

De

fa2

4

De

fa-r

s1Log

2(f

old

change t

o L

F)

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0HF

Had-36

HF+Had-36

* *

*

* *

* * *

*

* * * *

* p<0.05

Ifn

a5

Ifn

k

Ifn

ar1

Ifn

g

Ifn

gr1

Ifn

gr2

Isg

12

Isg

20

Isg

20

L2Lo

g2(f

old

change t

o L

F)

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

HF

LF+Had

HF+Had

*

* p<0.05

*

*

* *

* * * *

*

Results and Discussion Profiling of Significant Differential Expression Genes

Il1

a

Il1

b

Il1

f9

Il1

rn

Il1

r1

Il1

r2 Il4

Il4

ra Il6

Il6

ra

Il1

0

Il1

0ra

Il1

0rb

Il1

2a

Il1

2b

Il1

2rb

1

Il1

2rb

2

Il1

3

Il1

3ra

1

Il1

3ra

2

Il1

7b

Il1

7c

Il1

7d

Il1

7f

Il1

7ra

Il1

7rb

Il1

7rc

Il1

7rd

Il1

7re

Il1

8

Il1

8r1

Il2

0ra

Il2

2ra

1

Il2

2ra

2

Tn

fsf1

0

Tn

frsf1

1a

Tn

faip

1

Csf1

Csf2

Nfil3L

og

2(f

old

ch

an

ge

to

LF

)

-4

-3

-2

-1

0

1

2

3 HF

LF+Had

HF+Had * p<0.005

*

*

*

*

* * * *

* * * *

* *

* *

* *

* * * *

*

HF+Had

LF+Had

HF

LF

*

Log10

(p value)

-6.0 -4.0 -2.0 0.0

rhythmic processPPAR signaling pathwaynegative regulation of receptor biosynthetic processCircadian rhythm

(Dow

n-D

EG

enrich

ed

)

Log10

(p value)

-6.0 -4.0 -2.0 0.0

multi-organism process

response to biotic stimulus

response to other organism

organelle

membrane-bounded organelle

intracellular

intracellular part

cytoplasm

intracellular organelle

intracellular membrane-bounded organelle

nucleus

RNA binding

binding

heterocyclic compound binding

small molecule binding

nucleoside phosphate binding

nucleotide binding

BioGRID interaction data

SNARE complex

MI:mmu-miR-568

CREB regulated

TNF signaling pathway

Apoptosis

Legionellosis

Herpes simplex infection

Influenza A

NOD-like receptor signaling pathway

RIG-I-like receptor signaling pathway

Hepatitis C

Cytosolic DNA-sensing pathway

(Up-D

EG

enrich

ed

)

Results and Discussion Pathway analysis and gene ontology (GO) analysis

HF + Had-36 infected group compared with control group

All 30, including 10 disease/immunity related (highlighted), pathways show little direct relation with lipid metabolic pathways but are highly relevant to intracellular and inter-organism communication (p<0.05)

1. Infection of Had-36 synergizes with the induction of obesity by high-fat diet in rats; however, Had-36 infection has little effect in obesity induction in rats fed the control diet.

2. Either high-fat diet or Had-36 infection increases gut permeability and inflammation but has no synergistic effect (actually having antagonistic effect)

3. At 8-wk post treatment, rat ileal transcriptome shows significant down-regulation of multiple families of immune and metabolic genes in the HF and HF+Had-36 groups, as well as reprograming of circadian rhythm pathway.

Chawla A, et al. Nat Rev Immunol. 2011, 11:738-49.

Summary

Acknowledgements

• Laboratory colleagues:

•Funding resources: • The studies are supported in part by (1) USDA-NIFA-COOP-2010-002475 (Co-PI: Sang, KSU); USDA NIFA AFRI 2015-67015-23216 (Co-PI: Sang, KSU), and in particular (3) USDA-NIFA-2013-67015-21236 (PI: Sang, KSU) •NIH 1R15HD066377-01 (Co-PI: Sang KSU), Pilot project (PI. Sang, KSU) of NIH NCRR-COBRE-P20-RR017686 (PD: Daniel Marcus, KSU)

Andrew Schade

Amy Hanson

Joe Bergkamp Wyatt Brichalli

Qinfang Liu

Samantha Lyman

Wenjing S. Fausnett Jinhwa Lee Luca Popescu

Lyndie Holmes

Immunophysilogy Lab

2011-2015

Lab manager:

Dr. Barbara Lutjemeier

•Collaborators: Dr. Frank Blecha, KSU Dr. Raymond R. R. Rowland, KSU Dr. Joan Lunney, USDA-ARS Dr. Jishu Shi, KSU Dr. Wenjun Ma, KSU Dr. Laura Millar, USDA-ARS Dr. Zhihua Jiang, WSU Dr. Christopher Tuggle, ISU Dr. Chris Netherton (Pirbright, UK) Dr. Bryan Charleston (Pirbright, UK) Dr. Linda Dixon (Pirbright, UK) Dr. Ruth Welti (Kansas Lipdomics) Dr. Robert Goodband (KSU, Animal Science)

and Mr. Frank Jennings Jr (KSU, Animal farms)

Visiting Scholar:

Dr. Xiaorong Zhang

Thank You!

Classification of Raw Reads (M0: PAM-PBS)

0.0 0.2 0.4 0.6 0.8 1.0

Relative Position in Gene (5’-3’, 100 windows) N

um

be

r o

f re

ad

s (

×10

3)

30

20

10

0

93

4

95

82

4

10

09

69

1

13

28

31

84

42

57

96

4

94

4

10

09

78

5

72

6

16

38

12

28

2405

153

1461

4667

1489

3744

4592

5303

2320 1655

4397

1904

1491

2842

4884

0

1000

2000

3000

4000

5000

6000

M0

-M1

a

M0

-M2

c

M0

-M2

a

M0

-M1

b

M0

-Mav

M2

a-M1

a

M2

b-M

1a

M2

b-M

2a

M2

c-M1

a

M2

c-M2

a

M2

c-M1

b

Mav-M

1a

Mav-M

2c

Mav-M

2a

Mav-M

1b

up-regulated down-regulated total DEGs

RNA-seq Data Quality Control and DEG Analysis

Table 1: Numbers of potential gene makers identified in each activation status.

Status DEG

number

(1) IFNg-M1 44

(2) IFNa-MaV 72

(3) Co-IFNg & IFNa 88

(4) IL4-M2 234

(5) LPS-M1 72

(6) IL10-M2 9

Total 519

Profiling Signature Genes of Each

Activation Status

Background and Hypothesis

Mucosal antimicrobials,

and cytokines

Gut viruses

Gut bacteria

Bacterial and lipid

sensors, eg. TLR4

High-fat diet

Intestinal

epithelial

barrier

Viral

sensors

IRFs

IFN-a/b Cytokines

Tight

junction

?

Gut low-grade chronic

inflammation

Adipose low-grade

chronic inflammation Obesity Peripheral

circulation

Mitra AK, Clarke K. Viral obesity: fact or fiction? Obes Rev. 2010 Apr;11(4):289-96. Dhurandhar NV. Insulin sparing action of adenovirus 36 and its E4orf1 protein. J Diabetes Complications. 2013 Mar-Apr;27(2):191-9.

Hypothesis: Viral infection and high-fat diet can induce obesity independently, and in particular may contribute to prevalence of obesity synergistically.

Results and Discussion

Week

0 2 4 6 8 10

Avera

ge w

eig

ht in

cre

ase (

%)

0

50

100

150

200

Low-fat diet (LF)

High-fat diet (HF) LF + Had-36 HF + Had-36

*

**

*

HF + Had-36 infection synergistically increased BW after 5 wk Had-36 infection without HF had little effect

Ave. BW diff: 20-30 g between HF groups with LF groups

Body w

eig

ht

(g)

200

400

600

800

1 2 3 4

Low-fat diet (LF)

High-fat diet (HF) LF + Had-36 HF + Had-36

8 week post treatment

* p<0.05

Vander Wal JS, Huelsing J, Dubuisson O, Dhurandhar NV. An observational study of the association between adenovirus 36 antibody status and weight loss among youth. Obes Facts. 2013;6(3):269-78.

Results and Discussion

Either high-fat diet or Had-36 infection increases gut permeability but has no synergistic effect (actually having antagonistic effect) . Thus, the development of obesity is associated but may not proportionally correlated with an increase in gut permeability.

LF HF

LF + Had-36

HF + Had-36

Dextr

an

-FIT

C (

g/m

l)

0.0

1.0

2.0

3.0

4.0

5.0

6.0**

**

*

2.HF

* p<0.05, ** p<0.005, n=6-7

Lo

g2(f

old

ch

an

ge

to

LF

) -14

-12

-10

-8

-6

-4

-2

0

2

4

6 HF DEGs: Total: 1483, Up 15

Results and Discussion Profiling of Significant Differential Expression Genes

Log 2(fo

ld change to

LF)

-14 -1

2 -10

-8-6

-4-2

02

46

810

12

HF+Had DEGs: Total: 626, Up 23

Log2(fold change to LF)

-6 -4 -2 0 2 4 6 8 10

Let Us Meet Again

We welcome you all to our future conferences of OMICS International

Please Visit:

http://transcriptomics.conferenceseries.com/

http://conferenceseries.com/

http://www.conferenceseries.com/genetics-and-molecular-biology-conferences.php