6th German-Brazilian dialogue on science, research and innovation

FAPESP, November, 8-9th 2017

Paulo Arruda (parruda@unicamp.br

Health and food: Genomics science towards

the development of new drugs and stress

tolerant plants

HEALTH

The dilemma of pharmaceutic industry in

developing new drugs

Industry R&D return on investment (ROI) as calculated in

Deloitte's innovation report 2015

Dickson & Gagnon (2014) Nature Reviews Drug Discovery

The costs of developing new drugs are estimated at USD 2.6

billion, more than twice the value in 2013

DiMasi et al., (2016) Journal of Health Economics 47 (2016) 20–33

Lack of efficacy

• 2007-2010

- Phase II - 51%

- Phase III - 66%

• 2011-2012

- Phase II - 59%

- Phase III - 52%

• 2013-2015

- Phase II - 48%

- Phase III - 55%

• These failures are expensive

• These failures are duplicated in

multiple companies

• Biology is poorly understood

• Target was not properly validated

The increase in costs is because most drug candidates fail

for lack of efficacy

Prinz et al. (2011) Nature Reviews Drug Discovery

Work at Bayer found that out of 67 published target-validation

studies, only 20-25% were reproducible

• Urgent need for good quality tools

to help enable reproducible science

Most studies for target validation is not reproducible

FOOD

The drought and high temperature stress

imposed by climate changes constitute a

significant threat to food production

Global food consumption and its sources

Source: A world of insecurity (2017) Nature, 544: 57

• The main sources of energy for human are carbohydrates from cereals, fats from

leguminous plants and animal, milk and eggs, fruits and vegetables, meat and fish.

• Carbohydrates, fats, milk, eggs and meat, that together account for 80-90% of the energy

consumed by human, are directly or indirectly derived from 5 crops: rice, maize, soybean,

wheat, and sugarcane.

• To meet the energy needs of a population estimated at 9 billion by 2050, it will be

necessary to increase the productivity of these five crops by 50-60%.

An extreme drought (and heat) in the 2012/13 crop imposed a lost of 40 Million Tons of maize.

This is equivalent to the average of Brazilian production in the period 2005-2010 (49 Mt).

Impact of extreme climatic events for maize production in

the USA

Genome science will have a real impact on all our lives

-- and even more, on the lives of our children. It will

revolutionize the diagnosis, prevention and treatment of

most, if not all, human diseases

…In fact, it is now conceivable that our children's

children will know the term cancer only as a

constellation of stars.

…Researchers in a few years will have trouble

imagining how we studied human biology without the

genome sequence in front of us.

The -omics revolution

June 26, 2000

Would the -omics revolution help health and food?

How can -omics promote …

• Finding disease-linked gene variants

• Understanding mechanisms of disease

• New drug targets

• Genotyping – tailoring cures to patients

• Molecular diagnosis

• Opening up unexplored areas of human biology

• Genome editing

Health

Food production

• Finding disease-linked gene variants

• Understanding mechanisms of disease and stress response

• New traits associated genes

• Genotyping – tailoring custom varieties

• Molecular diagnosis

• Opening up unexplored areas of plant biology

• Genome editing

HEALTH

• The GSC was founded in late 2003 with funding from the Wellcome

Trust and GlaxoSmithKline to establish a Public Open Innovation

Consortium

• It commenced operations in June 2004 at the Universities of Oxford and

Toronto and has since expanded its PPP network

• In September 2017 the SGC network comprises + 280-researchers in

Oxford, Toronto, Stockholm, Campinas, Chapel Hill and Frankfurt

• The GSC is currently funded by large pharmaceutical companies and

governamental agencies

• Open Science Policy: Promptly placing results, reagents and know-

how in the public domain. SGC scientists dont file patents

Secrecy imposed by early stage IP protection delay the

process o Drug Discovery

The Structural Genomics Consotium (SGC) open innovation model to accelerate DDD

~20 kinase probes

SGC genome-scale work on Human Kinases

Kinases are potential targets for DD as it is involved in almost all cellular metabolism

and is linked to diseases like cancer, pain, inflammation, etc

Currently there are 42 Kinase targeted medicines in the market to treat these diseases

Target Selection

HTCloning & Test

Expression

Large-scale

ExpressionGO

NO

NO

NO

Co-crystallization /Structure

Determination

BiochemAssays

-potency-

BiochemAssays

-selectivity-

ChemicalSynthesis

GO/NO GO

NO

GO

Data released to the

community

end of open phase

Pharma internal

development

closed phase starts

NO

NO

GO/NO GO

GOGO/NO GO

GOGO/NO GO

HT CompoundScreening

GO/NO GO

GO

CellularAssaysActivity

CellularAssaysToxicity

Final Compound

GO/NO GO

GO

The SGC-UNICAMP platform: From the gene to the probe

through genomics and medicinal chemistry

2nd series of AAK1 inhibitors were potent, cell-permeable and did

not show toxic effects to cells

Approved SGC probe!

SGC-AAK1-1

The SGC-UNICAMP platform: In less than 2 years our

scientists developed the first probe for Kinase target

FOOD

Embrapa Centers

Agricultural

Informatics

Agroenergy

Biotechnology &

Genetic Resources

Cerrados

Instrumentation

Maize and

Sorghum

Soybean

Wheat

University of

Nottingham

University of

Campinas

University of

Delaware

VIB/Ghent University

Biotechnology

Startup:

Pangeia Biotech

Academic

Partners

Industry

Partners

Genomics

Functional Genomics

GWAS

Microbiome

Transformation

Genome Editing

Phenomics

Field trials

Elite Germplasm

Registration and Regulatory Affairs

Patent Science

Information Technology

Imp

roved

cu

ltiv

ars

Germ

pla

sm

, b

iod

ivers

ity,

new

tra

its

Gene

discovery

Proof of

concept

Breeding &

wide-area

testingPre-

launch Launc

h

UMiP GenClima EMBRAPA & partners

University of Western

Australia

Technical University

of Madrid (UPM)

Cotton

The Genomics for Climate Change Research Center

From the gene to the trait

(i) Sugarcane ratoon at 4th month after budding(ii and iii) Stalks(iv) Stalk before (left) and after (right)microbiome sampling

(v and vi) Leaf exophytic microbiome sampling(vii) Ratoon excavationviii) Exopyitic root microbiome sampling from soil(ix and x) Roots during sampling procedure

i ii iii iv v vi

vii viii ix x

Leaves

Bulk Soil

Upper stalk

Medium stalk

Bottom Stalk

Young Shoot

Root

Exophytic

Endophytic

Sample type CompartmentDevelopmental stage

4th

month

6th

month

8th

month

10th

month

Leaves

Organ

Stalk

Root

Exophytic

Endophytic

Exophytic

Endophytic

Exophytic

Endophytic

Endophytic

Exophytic

Endophytic

Bacterial community(16S – V4 region)

Fungal community (ITS – ITS2 region)

HiSeq 2500, 250bp paired end reads, average of 1MM reads per sample

Unlocking the bacterial and fungal communities

assemblages of sugarcane microbiome

Bulk soil, root and young shoot

(10,078)

Exophytic stalks

and leaves

(6,418)

Endophytic stalks

and leaves

(4,900)

4,581

535407

1,711332

718

3,443

Bulk soil, root and young shoot

(21,983)

Exophytic stalks

and leaves

(17,741)

Endophytic stalks

and leaves

(6,489)

5,181

792505

11,349384

531

5,069

Bacterial communities Fungal communities

16S and ITs massive amplicons sequencing revealed ~22K

bacteria and 10K fungi groups inhabiting the sugarcane

• A significant proportion of the microbial diversity is shared among samples

• But plant organs select their most preferable microbes probably through

functional enrichment

• Soil serves as reservoir for the assemblage of plant organ microbiota

B u lk s o il

R h iz o s p h e re

E n d o . ro o t

E n d o . yo u n g s h o o t

E n d o . bo tto

m s ta

lk

E n d o . me d iu m

s talk

E n d o . up p e r s

talk

E n d o . lea f

E x o . bo tto

m s ta

lk

E x o . me d iu m

s talk

E x o . up p e r s

talk

E x o . lea f

0

1 0

2 0

3 0

4 0

10

40

30

0

Endophytic Exophytic

(a)

20

Rela

tive

abundance

(%)

**

**

(b)

N I I

0

5

1 0

1 5

5

15

10

0

Fre

sh

weig

ht

(g)

N I I

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

0.2

1

0.4

0

Dry

weig

ht

(g)

0.6

0.8

Uninoculated plants

Inoculated plants

(c)i ii iii

iv v

(c)

Impact of an abundant-based synthetic community in plant

growthProof of concept: Synthetic community made with 20

highly abundant bacteria in sugarcane organs

OT

Us

(a)

20

1

Asticcacaulis

Microbacterium

Sphingomonas

Enterobacter

Bosea

u_Comamonadaceae

Chitinophaga

1.

2.

3.

4.

5.

6.

7.

u_Xanthomonadaceae

Lysobacter

Ensifer

Pseudoxanthomonas

Stenotrophomonas

Burkholderia

Pedobacter

Stenotrophomonas

Dyella

u_Streptomycetaceae20.

11.

12.

13.

14.

15.

16.

17.

18.

19.

Exo. End.

Root Stem Leaf Root Stem Leaf

Uninoculated Inoculated

Exo. End.Exo. End. Exo. End. Exo. End. Exo. End. Lower Higher

Relative abundance in row

Rhizobium8.

Pantoea

Pedobacter

9.

10.

R X R N O X

0

2 0

4 0

6 0

8 0

(b)

0

80

60

40

20

Re

lative

abu

nd

an

ce (

%)

Exo. root End. root Exo. stem

***

***

***

Uninoculated plants

Inoculated plans

Syn

the

tic c

om

mu

nity

...

Microbiome mapping revealed that the synthetic community made with highly abundant

sugarcane bacteria inoculated in maize plants displaced the natural maize microbiota,

efficiently colonize the plant organs, and stimulate the root system and plant growth

Highly abundant bacterial are robust colonizers

Robust colonizers are enriched in genes encoding specific

functions

• The complete genome

sequences of the synthetic

community revealed 26

bacteria;

• Robust colonizers share a set

of enriched functions;

• Some of the specific

functions are absent in non-

robust colonizers;

• Genes related to

carbohydrate metabolism,

energy production and

secondary metabolites are

among the top categories

that are enriched in the

robust colonizers;

Velloziaceae species growing in exposed rocks and shallow patches of soil in Brazilian campos

rupestres (rupestrian grasslands) (A,B,C). Example of a desiccated Vellozia nivea (ressurection) after

four months without rain and the same plant six days after rain, respectively (D). Vellozia intermedia

(ever green) after four months without rain and the same plant six days after rain, respectively (E-F).

A B

D

E F

C

Unlocking the mechanism of extreme drought stress

tolerance through genomics of Velloziaceae species

Our goal: Cross-talk between biomedical and plant

sciences to boost innovation for new medicine and

food production in a more sustainable way

GCCRCSGC

+

= Knoledge sinergism in using genomics tolls