Vol

September

doi

ARTICLES

Natural

history

and

evolutionary

principles

of

gene

duplication

in

fungi

Ilan

Avi

Nir

Aviv

Gene

duplication

and

loss

is

a

powerful

source

of

functional

innovation

However

the

general

principles

that

govern

this

process

are

still

largely

unknown

With

the

growing

number

of

sequenced

genomes

it

is

now

possible

to

examine

these

events

in

a

comprehensive

and

unbiased

manner

Here

we

develop

a

procedure

that

resolves

the

evolutionary

history

of

all

genes

in

a

large

group

of

species

We

apply

our

procedure

to

seventeen

fungal

genomes

to

create

a

genome-wide

catalogue

of

gene

trees

that

determine

precise

orthology

and

paralogy

relations

across

these

species

We

show

that

gene

duplication

and

loss

is

highly

constrained

by

the

functional

properties

and

interacting

partners

of

genes

In

particular

stress-related

genes

exhibit

many

duplications

and

losses

whereas

growth-related

genes

show

selection

against

such

changes

Whole-genome

duplication

circumvents

this

constraint

and

relaxes

the

dichotomy

resulting

in

an

expanded

functional

scope

of

gene

duplication

By

characterizing

the

functional

fate

of

duplicate

genes

we

show

that

duplicated

genes

rarely

diverge

with

respect

to

biochemical

function

but

typically

diverge

with

respect

to

regulatory

control

Surprisingly

paralogous

modules

of

genes

rarely

arise

even

after

whole-genome

duplication

Rather

gene

duplication

may

drive

the

modularization

of

functional

networks

through

specialization

thereby

disentangling

cellular

systems

Gene

duplication

and

loss

are

major

forces

of

evolutionary

innovation

facilitating

the

development

of

new

functions

and

pruning

of

old

Nonetheless

the

natural

history

of

gene

duplication

and

loss

is

poorly

understood

What

classes

of

genes

readily

evolve

through

duplication

and

loss

Do

whole-genome

duplication

events

reshape

the

genome

in

a

qualitatively

distinct

way

What

innovations

typically

arise

from

gene

duplication

events

Studies

addressing

such

have

been

limited

by

the

difficulty

of

tracing

the

exact

evolutionary

history

of

genes

The

growing

availability

of

sequenced

genomes

enables

the

direct

reconstruction

of

a

genome-wide

history

of

gene

duplication

and

loss

across

Here

we

describe

a

computational

method

for

reconstructing

this

history

and

apply

it

to

the

genomes

of

seventeen

Ascomycota

fungi

spanning

million

years

of

The

results

suggest

evolutionary

principles

applicable

for

fungi

and

possibly

more

generally

Method

for

identifying

orthologues

and

paralogues

Systematic

study

of

gene

duplication

and

loss

requires

reliable

resolution

of

gene

orthology

and

paralogy

a

notoriously

difficult

We

designed

SYNERGY

a

scalable

method

for

resolving

gene

ancestry

for

all

genes

across

multiple

genomes

-LRB-

Fig.

Supplementary

Fig.

-RRB-

The

input

is

a

species

phylogeny

and

for

each

extant

species

the

sequences

of

predicted

genes

and

their

chromosomal

positions

SYNERGY

partitions

these

genes

into

orthogroups

Each

orthogroup

consists

of

all

-LRB-

and

only

-RRB-

the

genes

descended

from

a

single

ancestral

gene

in

their

last

common

ancestral

species

and

is

associated

with

a

gene

tree

that

describes

the

history

of

speciation

duplication

and

loss

events

for

its

genes

-LRB-

Methods

-RRB-

An

orthogroup

catalogue

for

Ascomycota

fungi

We

applied

SYNERGY

to

the

complete

set

of

predicted

protein-coding

genes

from

seventeen

genomes

of

Ascomycota

fungi

including

the

model

organisms

Saccharomyces

cerevisiae

and

Schizosaccharomyces

pombe

-LRB-

Fig.

Methods

-RRB-

The

phylogeny

includes

a

whole-genome

duplication

-LRB-

WGD

-RRB-

-LRB-

Fig.

red

star

-RRB-

SYNERGY

produced

a

catalogue

of

orthogroups

-LRB-

Fig.

-RRB-

Of

these

were

singleton

genes

with

no

recognizable

orthologues

-LRB-

Supplementary

Note

-RRB-

We

further

analysed

the

multigene

orthogroups

The

orthogroups

and

trees

are

available

at

SYNERGY

made

high-quality

predictions

by

several

benchmarks

-LRB-

Methods

Supplementary

Notes

and

-RRB-

To

test

sensitivity

to

the

input

quality

we

applied

SYNERGY

to

different

subsets

of

organisms

and

of

genes

in

each

genome

We

examined

how

each

orthogroup

was

reconstructed

under

these

perturbations

deriving

four

confidence

measures

for

each

orthogroup

Overall

SYNERGY

was

remarkably

robust

-LRB-

Supplementary

Note

-RRB-

SYNERGY

's

predictions

also

agree

well

with

those

of

two

independent

manual

assignments

of

orthology

and

-LRB-

Supplementary

Note

-RRB-

Finally

SYNERGY

showed

high

specificity

and

sensitivity

on

data

attained

by

forward

simulated

evolution

Gene

duplication

and

loss

across

Ascomycota

evolution

The

reconstructed

orthogroups

show

a

range

of

evolutionary

patterns

These

are

summarized

by

the

extended

phylogenetic

profile

-LRB-

EPP

-RRB-

of

each

orthogroup

defined

as

the

number

of

genes

present

in

each

extant

and

ancestral

species

For

example

uniform

orthogroups

-LRB-

Fig.

-RRB-

with

no

duplication

or

loss

events

have

EPPs

consisting

only

of

ones

Other

orthogroups

exhibit

duplications

-LRB-

Fig.

red

star

-RRB-

or

losses

-LRB-

Fig.

blue

strikes

-RRB-

and

their

EPPs

may

consist

of

noughts

ones

twos

and

so

on

-LRB-

Fig.

-RRB-

An

orthogroup

with

at

least

one

gene

present

in

all

species

-LRB-

Supplementary

Fig.

-RRB-

is

persistent

From

the

EPP

we

can

derive

an

extended

copy-number

variation

profile

that

records

the

change

in

copy

number

at

each

position

in

the

species

tree

-LRB-

Supplementary

Fig.

-RRB-

Broad

Institute

of

MIT

and

Harvard

Cambridge

Center

Cambridge

Massachusetts

USA

Center

for

Systems

Biology

Harvard

University

Divinity

Avenue

of

Engineering

and

Applied

Sciences

Harvard

University

Oxford

Street

Cambridge

Massachusetts

USA

of

Computer

Science

and

Engineering

Hebrew

University

Jerusalem

Israel

of

Biology

Massachusetts

Institute

of

Technology

Massachusetts

Avenue

Cambridge

Massachusetts

USA

Nature

Publishing

Group

NATURE

Vol

September

ARTICLES

-LRB-

P

-RRB-

Nevertheless

essential

genes

are

not

ancestral

-LRB-

for

example

spindle

pole

body

proteins

P

-RRB-

suggesting

that

new

essential

functions

can

arise

albeit

rarely

-LRB-

Supplementary

Note

-RRB-

Orthogroups

that

are

not

ancestral

appear

at

specific

points

in

the

phylogeny

and

reveal

evolutionary

innovations

For

example

the

clade

spanning

S.

cerevisiae

and

Kluyveromyces

waltii

is

marked

by

appearing

orthogroups

with

S.

cerevisiae

genes

related

to

meiosis

and

sporulation

-LRB-

sporulation

genes

P

-RRB-

including

the

master

meiosis

regulator

-LRB-

Supplementary

Fig.

-RRB-

The

Euascomycota

clade

contains

many

appearances

-LRB-

orthogroups

of

all

appearing

orthogroups

-RRB-

roughly

half

show

no

similarity

to

other

orthogroups

or

to

a

more

distant

fungus

Cryptococcus

neoformans

-LRB-

Supplementary

Fig.

-RRB-

demonstrating

extensive

genomic

innovation

within

the

We

also

find

coordinated

gene

losses

indicating

major

changes

in

biological

processes

For

example

Yarrowia

lipolytica

has

significant

losses

in

orthogroups

containing

meiotic

recombination

genes

-LRB-

P

-RRB-

Interestingly

the

genes

lost

in

Candida

glabrata

extensively

overlap

those

independently

lost

in

the

ancestor

of

Candida

albicans

and

Debaryomyces

hansenii

-LRB-

P

-RRB-

possibly

reflecting

the

fact

that

these

are

all

opportunistic

or

occasional

human

pathogens

Copy

number

volatility

corresponds

to

a

functional

dichotomy

The

observed

variation

in

copy

number

changes

among

orthogroups

is

inconsistent

with

random

expectation

-LRB-

Fig.

Methods

-RRB-

We

assigned

a

volatility

score

to

each

orthogroup

depending

on

the

number

and

phylogenetic

position

of

duplication

and

loss

events

with

uniform

orthogroups

at

one

end

of

the

scale

and

volatile

orthogroups

-LRB-

score

s.d.

above

the

mean

-RRB-

at

the

other

-LRB-

Fig.

-RRB-

Evolutionary

forces

have

acted

very

differently

on

these

two

classes

the

uniform

and

volatile

orthogroups

show

diametrically

opposed

patterns

in

their

function

regulation

and

essentiality

in

S.

cerevisiae

-LRB-

Fig.

Supplementary

Table

-RRB-

We

first

tested

for

functional

distinctions

between

uniform

and

volatile

orthogroups

based

on

gene

ontology

annotations

in

S.

Volatile

orthogroups

are

enriched

-LRB-

P

-RRB-

for

genes

that

encode

peripheral

transporters

receptors

and

cell

wall

proteins

and

genes

that

participate

in

stress

responses

In

contrast

uniform

orthogroups

are

enriched

-LRB-

P

-RRB-

for

genes

involved

in

essential

growth

processes

genes

residing

in

the

nucleus

nucleolus

mitochondrion

endoplasmic

reticulum

and

Golgi

apparatus

and

genes

essential

for

viability

We

next

examined

whether

the

evolutionary

dichotomy

is

also

aligned

with

the

transcriptional

program

of

S.

cerevisiae

Using

data

from

gene

expression

profiles

we

organized

S.

cerevisiae

genes

into

a

hierarchy

of

transcriptional

modules

each

containing

functionally

related

genes

with

a

coherent

expression

-LRB-

Fig.

Supplementary

Fig.

Supplementary

Table

and

Figure

The

SYNERGY

algorithm

a

Orthogroup

construction

SYNERGY

starts

-LRB-

top

-RRB-

with

a

collection

of

genes

-LRB-

and

so

on

-RRB-

their

chromosomal

order

-LRB-

grey

lines

-RRB-

and

sequence

distances

-LRB-

blue

arrows

arrows

of

the

same

thickness

have

similar

sequence

distances

-RRB-

It

then

builds

orthogroups

as

it

climbs

the

species

tree

First

it

collects

the

genes

in

species

A

and

B

that

share

a

common

ancestor

in

species

X

-LRB-

second

panel

orange

ovals

-RRB-

Then

it

merges

orthogroups

formed

in

the

previous

stage

with

the

genes

in

C

resulting

in

new

orthogroups

representing

ancestral

genes

in

species

Y

-LRB-

third

panel

yellow

ovals

-RRB-

The

orthogroups

assembled

at

each

stage

are

associated

with

gene

trees

reflecting

divergence

duplication

and

loss

events

-LRB-

bottom

-RRB-

b

Gene

tree

reconstruction

and

refining

orthogroup

assignments

An

unrooted

phylogeny

is

reconstructed

for

the

genes

and

suborthogroups

in

each

putative

orthogroup

-LRB-

dashed

oval

-RRB-

Some

rootings

-LRB-

purple

arrow

-RRB-

indicate

that

all

the

genes

descended

from

a

common

ancestor

-LRB-

for

example

bottom

left

-RRB-

Others

-LRB-

green

arrow

-RRB-

show

that

a

duplication

occurred

at

the

root

of

the

gene

tree

-LRB-

for

example

and

bottom

right

-RRB-

In

the

latter

case

the

orthogroup

is

partitioned

before

proceeding

Nature

Publishing

Group

By

tallying

these

profiles

from

all

orthogroups

we

find

the

numbers

of

genes

appearances

duplications

and

losses

that

occurred

throughout

Ascomycota

evolution

-LRB-

Fig.

Supplementary

Fig.

-RRB-

The

-LRB-

-RRB-

orthogroups

present

in

the

clade

spanning

the

Hemiascomycota

and

Euascomycota

were

defined

as

ancestral

accounting

for

genes

in

the

reconstructed

last

common

ancestor

-LRB-

-RRB-

of

Saccharomyces

cerevisiae

genes

belong

to

these

ancestral

orthogroups

The

ancestral

orthogroups

are

enriched

in

essential

S.

cerevisiae

genes

of

genes

essential

for

growth

in

rich

medium

are

ancestral

-LRB-

P

Fischer

's

exact

test

-RRB-

as

are

of

genes

essential

only

for

growth

in

other

cona

A

X

Y

B

C

A

X

Y

B

C

A

X

Y

B

C

Duplication

Loss

b

Gene

tree

construction

Choosing

root

ARTICLES

NATURE

Vol

September

a

Hemiascomycota

Ascomycota

fungi

S.

cerevisiae

S.

paradoxus

S.

mikatae

S.

bayanus

C.

glabrata

S.

castellii

K.

lactis

A.

gossypii

K.

waltii

D.

hansenii

C.

albicans

Y.

lipolytica

N.

crassa

F.

graminearum

M.

grisea

A.

nidulans

Euascomycota

b

Singletons

Uniform

Number

of

orthogroups

Archeascomycota

S.

pombe

Orthogroup

size

-LRB-

number

of

genes

-RRB-

c

S.

cer

S.

par

S.

mik

S.

bay

C.

gla

S.

cas

K.

lac

A.

gos

K.

wal

D.

han

C.

alb

Y.

lip

N.

cra

F.

gra

M.

gri

A.

nid

d

S.

pom

c

S.

cer

S.

par

S.

mik

S.

bay

C.

gla

S.

cas

S.

cer

S.

par

S.

mik

S.

bay

C.

gla

S.

cas

K.

lac

A.

gos

K.

wal

D.

han

C.

alb

Y.

lip

N.

cra

F.

gra

M.

gri

A.

nid

S.

pom

e

c

f

WGD

S.

cer

S.

par

S.

mik

S.

bay

C.

gla

S.

cas

K.

lac

A.

gos

K.

wal

D.

han

C.

alb

Y.

lip

N.

cra

F.

gra

M.

gri

A.

nid

S.

pom

Figure

A

gene

ancestry

catalogue

for

Ascomycota

fungi

a

Species

tree

showing

the

Hemiascomycota

-LRB-

pink

-RRB-

Euascomycota

-LRB-

green

-RRB-

and

Archeascomycota

-LRB-

blue

-RRB-

clades

the

WGD

-LRB-

red

star

-RRB-

and

post-WGD

species

-LRB-

darker

pink

-RRB-

b

Distribution

of

orthogroup

sizes

Grey

bars

total

number

of

orthogroups

of

a

certain

size

-LRB-

number

of

genes

-RRB-

Black

bars

the

number

of

persistent

orthogroups

of

a

certain

size

The

uniform

arrow

points

to

orthogroups

which

are

persistent

orthogroups

with

exactly

genes

The

singleton

arrow

points

to

orthogroups

with

a

single

gene

c

A

uniform

orthogroup

The

topology

of

the

gene

tree

-LRB-

left

panel

-RRB-

is

identical

to

that

of

the

species

tree

d

A

non-uniform

orthogroup

with

a

single

duplication

event

-LRB-

red

star

-RRB-

and

two

loss

events

-LRB-

blue

strikes

-RRB-

e

The

extended

phylogenetic

profile

of

the

orthogroup

in

d

summarizes

the

number

of

genes

at

each

extant

and

ancestral

species

in

the

tree

-LRB-

numbered

boxes

-RRB-

f

Reconstructed

evolutionary

events

and

gene

counts

Each

species

is

annotated

with

the

number

of

known

or

reconstructed

genes

-LRB-

rectangles

-RRB-

Pie-charts

on

branches

denote

the

fraction

of

appearing

-LRB-

green

-RRB-

duplicated

-LRB-

red

-RRB-

and

lost

-LRB-

blue

-RRB-

genes

inferred

in

the

corresponding

branch

-LRB-

see

Supplementary

Fig.

for

exact

numbers

-RRB-

Nature

Publishing

Group

NATURE

Vol

September

ARTICLES

Methods

-RRB-

Analysing

the

volatility

scores

of

these

transcriptional

modules

we

see

that

the

evolutionary

dichotomy

follows

the

regulatory

branches

-LRB-

Fig.

d

Supplementary

Table

-RRB-

Modules

in

the

cell

cycle

and

meiosis

and

fundamental

processes

branches

are

enriched

-LRB-

P

-RRB-

for

uniform

and

persistent

orthogroups

whereas

modules

in

the

development

and

stress

and

carbohydrate

metabolism

branches

are

enriched

-LRB-

P

-RRB-

for

volatile

orthogroups

These

distinctions

indicate

a

limit

not

only

on

the

ability

to

lose

key

growth

genes

but

also

on

the

ability

to

maintain

them

in

duplicate

As

suggested

by

the

gene

balance

hypothesis

one

reason

for

this

a

Volatility

nt

rm

ste

le

fo

si

ti

ni

er

ola

U

P

V

Uniform

Persistent

Essential

-LRB-

rich

media

-RRB-

Organelle

lumen

TFIID

target

Complex

cores

RNA

metabolism

Primary

metabolism

RNA

processing

Nuclear

lumen

Non-TATA

Low

expression

divergence

Ribosome

biogenesis

mRNA

metabolism

Ribonuclear

complex

Essential

-LRB-

not

rich

media

-RRB-

Organellar

ribosome

Low

protein

noise

Mitochondrial

lumen

Complex

modules

Nucleolus

Spliceosome

complex

RNA

binding

Non-membrane-binding

organelle

Complex

attachment

General

Pol

II

transcription

factor

TFIIH

transcription

factor

Proteasome

Proteasome

complex

Lipoprotein

metabolism

up

DNA

replication

Protein

biosynthesis

Amino

acyl-tRNA

ligase

Endomembrane

system

Amino

acyl-tRNA

biosynthesis

Replication

fork

Translation

initiation

Ribosome

assembly

Golgi

vesicle

transport

Protein

modification

down

Cytoskeleton

organization

and

biogenesis

Ribosome

DNA

replication

initiation

Mitotic

cell

cycle

Lipid

biosynthesis

Pyrimidine

metabolism

High

expression

divergence

Very

high

protein

noise

Vacuole

High

protein

noise

Aryl-alcohol

dehydrogenase

Extracellular

region

haploid

up

Nucleobase

transport

Acid

phosphatase

Amino

acid

transport

rich

up

up

min

down

sorbitol

down

Drug

transporter

min

down

Pyridoxine

metabolism

haploid

up

up

heat

up

Thiamine

metabolism

up

SAGA-TFIID

target

up

hydroxyurea

up

Cell

wall

Oxidoreductase

Organic

acid

transport

SAGA

target

rich

up

haploid

down

min

up

TATA

Sugar

transporter

down

down

haploid

up

haploid

up

Volatile

b

may

be

that

such

genes

often

encode

components

of

essential

cellular

machineries

requiring

stoichiometric

balance

Indeed

we

found

that

S.

cerevisiae

genes

encoding

core

components

of

protein

are

enriched

in

uniform

orthogroups

-LRB-

complex

core

genes

P

-RRB-

Furthermore

uniform

and

persistent

orthogroups

are

enriched

for

S.

cerevisiae

genes

displaying

-LRB-

P

and

P

respectively

-RRB-

However

the

dichotomy

extends

beyond

protein

complexes

to

many

cellular

processes

and

includes

orthogroups

with

moderately

low

and

high

volatility

scores

-LRB-

Fig.

-RRB-

suggesting

a

general

principle

affecting

the

vast

majority

of

genes

in

the

genome

To

test

whether

differential

flexibility

in

copy

number

between

uniform

and

volatile

orthogroups

reflects

global

functional

constraints

we

examined

the

variation

in

their

respective

transcripts

and

proteins

We

found

that

the

volatile

orthogroups

are

enriched

in

genes

whose

expression

changes

significantly

in

response

to

many

single-gene

-LRB-

P

notably

deletions

of

chromatin

modifiers

-RRB-

genes

with

noisy

levels

of

protein

abundance

within

isogenic

S.

cerevisiae

-LRB-

P

-RRB-

genes

the

transcription

of

which

is

regulated

through

the

SAGA

complex

and

the

TATA

-LRB-

P

-RRB-

and

genes

with

variable

RNA

expression

across

-LRB-

P

-RRB-

-LRB-

Fig.

Supplementary

Table

-RRB-

Conversely

the

uniform

orthogroups

are

enriched

in

genes

whose

expression

is

largely

unchanged

in

response

to

single-gene

knockouts

genes

whose

protein

levels

tend

to

be

tightly

controlled

-LRB-

P

-RRB-

genes

whose

transcription

is

TATA-independent

and

regulated

through

-LRB-

P

-RRB-

and

genes

whose

RNA

expression

shows

less

variation

across

species

-LRB-

P

-RRB-

-LRB-

Fig.

Supplementary

Table

-RRB-

These

results

highlight

a

general

bipolar

principle

that

governs

tolerance

to

duplications

and

losses

Copy-number

variation

in

stress-responsive

genes

may

not

only

be

tolerable

but

beneficial

allowing

adaptation

to

diverse

ecological

niches

In

contrast

genes

essential

for

cell

growth

including

those

necessary

for

intricate

complexes

can

not

readily

tolerate

such

noise

and

tend

not

to

evolve

by

gradual

duplication

and

loss

This

evolutionary

dichotomy

aligns

closely

with

a

bipolarity

in

gene

function

transcriptional

program

and

expression

noise

across

cells

strains

and

all

reflecting

similar

functional

constraints

on

the

amount

of

gene

products

in

the

cell

Furthermore

shared

functional

constraints

on

copy-number

variation

also

manifest

in

remarkably

synchronized

and

concerted

patterns

of

specific

duplications

and

losses

in

many

orthogroups

harbouring

functionally

related

or

interacting

genes

-LRB-

Supplementary

Note

Supplementary

Figs

and

and

Supplementary

Table

-RRB-

Whole-genome

duplication

alters

the

nature

of

duplication

We

next

explored

whether

these

functional

principles

generalize

to

the

WGD

event

We

found

that

duplications

associated

with

the

WGD

show

a

strikingly

different

pattern

-LRB-

Fig.

Supplementary

Table

-RRB-

many

transcriptional

modules

that

maintain

little

duplication

elsewhere

in

the

phylogeny

are

associated

with

a

high

level

of

Figure

A

functional

dichotomy

of

uniform

persistent

and

volatile

orthogroups

a

Distribution

of

orthogroup

volatility

scores

Uniform

orthogroups

-LRB-

leftmost

blue

column

-RRB-

are

the

least

volatile

Orthogroups

whose

score

is

above

three

standard

deviations

from

the

expected

mean

are

labelled

as

volatile

-LRB-

red

columns

-RRB-

The

remainder

of

the

distribution

is

shown

-LRB-

grey

bars

-RRB-

and

the

distribution

of

scores

for

persistent

orthogroups

is

superimposed

-LRB-

blue

bars

-RRB-

The

expected

distribution

when

sampling

random

orthogroups

from

the

evolutionary

model

is

shown

as

a

black

line

b

Gene

class

annotations

that

are

significantly

enriched

among

uniform

persistent

or

volatile

orthogroups

-LRB-

purple

colour

scale

is

saturated

at

P

-RRB-

The

functional

and

mechanistic

dichotomy

between

volatile

and

non-volatile

orthogroups

largely

reproduces

along

the

range

of

volatility

scores

-LRB-

left

columns

are

bins

of

orthogroups

with

similar

volatility

scores

rows

are

significant

annotations

-RRB-

Higher

-LRB-

yellow

-RRB-

and

lower

-LRB-

blue

-RRB-

relative

enrichment

compared

to

the

expected

enrichment

in

the

class

is

shown

The

colour

scale

is

saturated

at

Class

sizes

are

shown

on

the

left

-LRB-

black

bars

-RRB-

Number

of

orthogroups

Nature

Publishing

Group

ARTICLES

NATURE

Vol

September

volatility

during

the

WGD

Examples

include

ribosome

biogenesis

in

all

post-WGD

lineages

-LRB-

P

-RRB-

and

ER

protein

modification

in

Saccharomyces

castellii

-LRB-

P

-RRB-

Thus

the

WGD

is

associated

with

a

qualitatively

different

pattern

of

duplication

The

gene

balance

hypothesis

postulates

that

this

effect

is

due

to

post-WGD

retention

of

paralogues

for

all

members

of

a

complex

Indeed

S.

cerevisiae

genes

with

either

or

phenotypes

are

enriched

in

orthogroups

that

duplicated

only

in

the

WGD

-LRB-

P

-RRB-

Furthermore

several

modules

representing

essential

machineries

are

enriched

for

WGD

-LRB-

for

example

rRNA

biogenesis

P

and

ribosome

P

-RRB-

However

the

expanded

scope

of

the

WGD

is

observed

beyond

complexes

within

more

volatile

modules

The

simultaneous

duplication

of

all

genes

in

a

module

in

a

WGD

may

permit

retention

of

paralogues

in

orthogroups

that

are

otherwise

constrained

against

duplication

and

may

be

a

principal

way

in

which

WGD

events

contribute

to

evolutionary

Gene

duplication

results

in

limited

biochemical

divergence

We

next

explored

the

types

of

functional

innovations

that

arise

from

gene

duplications

In

principle

both

paralogues

can

either

retain

a

the

same

function

-LRB-

Fig.

-RRB-

or

one

-LRB-

or

both

-RRB-

can

migrate

to

assume

a

distinct

function

-LRB-

Fig.

c

-RRB-

Migration

can

either

reflect

the

development

of

a

novel

function

-LRB-

Fig.

-RRB-

or

a

division

of

labour

in

which

each

paralogue

assumes

only

some

functions

of

the

ancestral

gene

-LRB-

Fig.

-RRB-

Given

the

longpostulated

importance

of

gene

duplication

in

we

hypothesized

that

migration

would

be

the

predominant

evolutionary

mode

We

quantified

the

extent

to

which

paralogous

gene

pairs

remain

in

or

migrate

from

a

variety

of

gene

classes

in

S.

cerevisiae

-LRB-

gene

ontology

functional

categories

genes

with

shared

regulatory

mechanisms

protein

complexes

and

transcriptional

modules

-RRB-

We

calculated

the

fraction

of

paralogous

pairs

that

are

retained

within

each

class

-LRB-

Methods

and

Supplementary

Figs

-RRB-

To

avoid

confounding

factors

we

studied

only

cases

in

which

both

paralogues

had

been

annotated

and

the

annotation

had

not

been

inferred

solely

from

sequence

similarity

Surprisingly

our

analysis

shows

that

paralogous

pairs

rarely

migrate

between

functional

gene

ontology

categories

-LRB-

Supplementary

Figs

and

Supplementary

Table

-RRB-

The

retention

rate

is

highest

for

the

molecular

function

categories

-LRB-

-RRB-

and

somewhat

lower

for

biological

process

-LRB-

-RRB-

and

cellular

component

-LRB-

-RRB-

categories

Development

Stress

and

carbohydrate

metabolism

Cell

cycle

and

meiosis

Amino

acid

and

nitrogen

metabolism

Fundamental

processes

b

Stress/starvation

Conditions

Sporulation

Transcription

knockouts

Phosphate/sulphate

depletion

Various

KOs

Cell

cycle

Long

stress

Nitrogen/amino

acid

starvation

c

d

e

Uniform

Persistent

Volatile

Not

essential

Essential-not

rich

media

Essential-rich

media

All

WGD

paralogues

WGD-only

paralogues

Non-WGD

paralogues

Modules

Figure

Evolutionary

profiles

correspond

to

the

hierarchical

modular

organization

of

the

yeast

transcriptional

system

a

A

functional

hierarchy

of

S.

cerevisiae

transcriptional

modules

b

Expression

patterns

of

modules

Shown

is

the

average

expression

of

module

genes

-LRB-

columns

-RRB-

in

each

expression

array

-LRB-

rows

with

main

condition

names

marked

on

right

-RRB-

Red

induced

green

repressed

black

no

significant

change

The

colour

scale

is

saturated

at

c

e

Evolutionary

characterization

of

modules

Enrichment

significance

-LRB-

negative

log

P-value

yellow

indicates

significant

the

colour

scale

is

saturated

at

-RRB-

for

the

projected

orthogroup

classes

from

each

module

-LRB-

columns

-RRB-

against

various

phylogenetic

attributes

-LRB-

rows

-RRB-

c

Growth

modules

are

enriched

for

uniform

and

persistent

genes

with

the

notable

exception

of

amino

acid

metabolism

modules

whereas

stress

modules

are

enriched

in

volatile

genes

d

Essential

genes

are

enriched

in

modules

from

the

growth

but

not

the

stress

groups

e

This

dichotomy

is

violated

in

the

WGD

Nature

Publishing

Group

NATURE

Vol

September

ARTICLES

To

reveal

innovation

at

a

finer

resolution

than

the

relatively

coarse

functional

classes

we

measured

the

fraction

of

shared

interaction

partners

for

each

pair

of

paralogues

in

molecular

networks

We

examined

both

biochemical

of

physically

interacting

proteins

-LRB-

reflecting

molecular

function

-RRB-

and

genetic

of

synthetic

phenotypes

in

double

mutants

-LRB-

reflecting

biological

processes

-RRB-

We

find

that

in

both

networks

roughly

half

of

the

paralogues

share

a

significantly

high

proportion

of

their

interaction

partners

-LRB-

pairs

in

the

genetic

network

and

in

the

biochemical

network

-RRB-

much

more

than

would

be

expected

in

comparable

-LRB-

degree-preserving

-RRB-

random

networks

-LRB-

Methods

Supplementary

Fig.

f

-RRB-

Thus

many

pairs

show

little

migration

from

their

pre-duplication

organization

-LRB-

Supplementary

Fig.

-RRB-

supporting

the

broader

result

at

both

the

biochemical

and

the

functional

level

The

remaining

paralogues

typically

share

no

interaction

partners

and

may

indicate

neofunctionalization

-LRB-

Supplementary

Fig.

e

-RRB-

Indeed

paralogous

pairs

had

more

biochemical

interaction

partners

than

would

be

expected

by

-LRB-

versus

P

Mann

Whitney

U-test

-RRB-

providing

global

evidence

for

neofunctionalization

Many

of

these

disjoint

paralogue

pairs

are

dispersed

in

the

biochemical

network

-LRB-

are

separated

a

Retention

by

four

or

more

proteins

-RRB-

implying

divergence

in

molecular

function

-LRB-

Supplementary

Fig.

-RRB-

In

contrast

half

of

them

are

immediate

neighbours

in

the

genetic

network

-LRB-

Supplementary

Fig.

-RRB-

suggesting

a

related

biological

process

This

is

consistent

with

the

role

of

duplicate

genes

as

either

genetic

back-ups

when

their

paralogues

are

or

with

division

of

labour

through

subfunctionalization

-LRB-

see

below

-RRB-

Gene

duplication

innovates

through

regulatory

divergence

Another

source

of

innovation

is

regulatory

divergence

We

inspected

the

migration

of

paralogous

pairs

with

respect

to

gene

classes

representing

regulatory

mechanisms

-LRB-

genes

that

are

targets

of

the

same

transcription

or

contain

the

same

cis-regulatory

or

RNA-binding

-RRB-

or

expression

patterns

-LRB-

transcriptional

modules

-RRB-

We

find

that

paralogous

genes

usually

migrate

with

respect

to

these

gene

classes

In

most

cases

-LRB-

-RRB-

regulatory

gene

classes

contain

no

retained

paralogy

relations

within

them

reflecting

either

novel

regulation

or

regulatory

specialization

between

paralogues

-LRB-

Supplementary

Figs

h

and

e

-RRB-

Transcriptional

modules

exhibit

an

intermediate

behaviour

with

of

paralogous

gene

pairs

having

migrated

between

modules

both

within

and

between

the

major

branches

of

the

module

hierarchy

-LRB-

Supplementary

Figs

and

-RRB-

Our

analysis

shows

that

paralogues

diversify

most

frequently

at

the

level

of

regulation

less

frequently

through

changes

in

their

cellular

component

biological

process

or

molecular

interactions

and

rarely

in

biochemical

function

Although

some

of

these

differences

may

stem

from

variations

in

the

quality

and

resolution

of

available

annotations

multiple

functional

and

regulatory

data

sources

support

this

broad

distinction

This

highlights

inherent

limitations

of

gene

duplication

in

accomplishing

molecular

innovation

It

also

emphasizes

the

overwhelming

importance

of

regulatory

divergence

in

driving

functional

divergence

and

reconfiguring

molecular

systems

after

Coordinated

migration

of

multiple

paralogues

is

rare

When

several

genes

in

a

class

are

duplicated

they

can

either

migrate

in

a

coordinated

manner

resulting

in

two

paralogous

classes

-LRB-

Supplementary

Fig.

-RRB-

or

disperse

into

different

classes

-LRB-

Fig.

-RRB-

We

expect

coordinated

migration

after

simultaneous

duplications

-LRB-

for

example

-RRB-

To

test

this

we

counted

the

number

of

paralogous

gene

pairs

that

connect

each

pair

of

gene

classes

-LRB-

Supplementary

Fig.

-RRB-

Surprisingly

coordinated

migration

is

rare

gene

classes

-LRB-

functional

regulatory

or

transcriptional

-RRB-

rarely

share

more

than

one

or

two

paralogous

relations

regardless

of

the

overall

proportion

of

paralogues

retained

-LRB-

Supplementary

Fig.

-RRB-

The

few

observed

paralogous

classes

are

very

small

and

formed

gradually

-LRB-

see

for

example

Supplementary

Fig.

-RRB-

Thus

paralogues

overwhelmingly

disperse

as

individuals

rather

than

migrate

in

a

coordinated

fashion

Although

theory

that

paralogous

classes

might

form

in

a

single

concerted

step

-LRB-

such

as

after

a

WGD

-RRB-

we

observed

little

evidence

of

this

here

The

same

patterns

of

retention

migration

and

interaction

are

observed

even

among

paralogues

derived

only

from

the

WGD

-LRB-

Supplementary

Fig.

data

not

shown

-RRB-

and

validated

by

manual

Thus

while

the

WGD

allows

qualitatively

different

gene

duplications

the

subsequent

patterns

of

innovation

-LRB-

or

lack

thereof

-RRB-

follow

the

same

general

trajectory

for

both

WGD

and

non-WGD

paralogues

Conclusions

We

set

out

to

uncover

the

evolutionary

potential

and

constraints

of

gene

duplication

and

loss

We

created

a

rich

resource

of

evolutionary

history

in

fungi

and

compared

these

evolutionary

patterns

with

a

wealth

of

functional

and

genomics

data

for

S.

cerevisiae

to

uncover

the

principles

that

govern

copy

number

changes

in

Ascomycota

Cytoplasmic

ribosomal

large

subunit

-LRB-

MIPS

-RRB-

genes

paralogue

pairs

-LRB-

retention

-RRB-

b

Migration/novel

function

DNA

replication

initiation

-LRB-

gene

ontology

-RRB-

genes

paralogue

pairs

-LRB-

retention

-RRB-

c

Migration/specialization

TFIID

complex

TFIID/SAS

complex

SAS

complex

Figure

Functional

conservation

and

innovation

of

paralogues

in

classes

and

networks

Shown

is

the

reconstructed

functional

history

of

several

paralogous

pairs

of

S.

cerevisiae

genes

-LRB-

circles

with

gene

name

-RRB-

After

duplication

-LRB-

arrow

-RRB-

paralogous

gene

pairs

can

be

either

retained

within

the

same

class

-LRB-

a

-RRB-

migrate

to

assume

new

functions

-LRB-

b

-RRB-

or

specialize

into

distinct

functions

resulting

in

modularization

-LRB-

c

-RRB-

Nature

Publishing

Group

ARTICLES

NATURE

Vol

September

What

is

the

contribution

of

gene

duplication

to

system

evolution

and

how

does

it

affect

the

modularity

of

molecular

systems

Earlier

studies

suggested

that

paralogous

modules

form

in

massive

duplication

but

we

found

that

paralogous

modules

are

rare

even

after

a

WGD

An

alternative

mechanism

is

suggested

by

the

fact

that

many

paralogous

pairs

genetically

interact

with

each

other

despite

having

no

shared

physical

interactions

-LRB-

Supplementary

Fig.

-RRB-

This

may

indicate

a

partial

division

of

labour

-LRB-

subfunctionalization

-RRB-

between

two

paralogous

proteins

that

become

physically

or

temporally

Such

specialization

could

modularize

a

molecular

network

by

severing

links

within

a

network

when

duplicating

a

For

example

a

single

ancestral

gene

may

have

participated

in

two

distinct

complexes

-LRB-

Fig.

-RRB-

If

each

of

the

paralogues

specialized

to

optimally

perform

one

of

the

functions

and

interact

only

with

the

members

of

one

of

the

complexes

the

resulting

network

will

be

more

modular

Thus

increasing

gene

copy

number

may

eventually

simplify

a

system

rather

than

making

it

more

complex

Modularization

could

relax

opposing

constraints

on

a

single

component

and

thus

set

in

motion

further

specialization

and

As

our

knowledge

of

molecular

and

genetic

networks

improves

further

studies

can

systematically

assess

this

possibility

Principles

similar

to

those

described

here

may

apply

to

copy

number

variation

in

metazoan

genomes

For

example

evidence

from

vertebrates

and

Arabidopsis

suggests

that

genes

encoding

signalling

molecules

and

transcription

factors

are

duplicated

in

WGD

events

but

rarely

The

reconstruction

algorithm

and

analytical

framework

here

make

it

possible

to

test

such

hypotheses

in

other

taxa

and

will

facilitate

other

novel

studies

of

the

evolution

of

genes

genomes

and

systems

METHODS

SUMMARY

Orthology

assignment

and

gene

tree

reconstruction

SYNERGY

assigns

orthologies

in

a

step-wise

bottom-up

fashion

solving

it

for

each

ancestral

node

in

a

species

tree

starting

at

the

leaves

and

concluding

at

the

root

At

each

stage

SYNERGY

first

clusters

together

the

genes

or

orthogroups

from

previous

stages

that

share

significant

sequence

similarity

into

new

putative

orthogroups

-LRB-

Fig.

-RRB-

It

then

constructs

a

phylogeny

of

these

intermediate

orthogroups

-LRB-

Fig.

-RRB-

using

a

modified

neighbour-joining

procedure

based

on

the

amino

acid

similarities

that

have

been

pre-computed

and

shared

synteny

scores

The

sub-trees

from

each

stage

are

based

on

the

reconstructions

at

earlier

stages

Each

tree

is

rooted

using

a

score

based

on

sequence

similarity

conserved

synteny

and

the

inferred

number

of

duplications

and

losses

Trees

that

invoke

fewer

unlikely

duplication

and

loss

events

will

be

favoured

over

those

that

incur

many

such

events

If

the

rooted

tree

indicates

that

all

the

orthogroups

-LRB-

genes

-RRB-

in

that

tree

descended

from

a

single

hypothetical

gene

at

the

current

stage

the

cluster

is

defined

as

an

orthogroup

along

with

its

tree

-LRB-

Fig.

left

-RRB-

Otherwise

the

orthogroup

is

partitioned

by

removing

the

inferred

root

of

the

gene

tree

-LRB-

Fig.

right

-RRB-

This

process

may

be

repeated

until

each

orthogroup

consists

of

genes

that

share

a

single

common

ancestor

at

the

current

level

of

reconstruction

Thus

after

each

stage

a

complete

orthology

assignment

and

gene

tree

reconstruction

for

the

genes

below

that

node

has

been

made

These

are

used

as

the

input

to

subsequent

stages

at

higher

nodes

in

the

species

tree

When

this

procedure

is

completed

at

the

root

of

the

species

tree

the

genes

for

all

species

have

been

assigned

to

their

orthogroups

and

placed

in

their

respective

trees

Full

Methods

and

any

associated

references

are

available

in

the

online

version

of

the

paper

at

Received

March

accepted

July

Ohno

S.

Evolution

by

Gene

Duplication

-LRB-

Allen

and

Unwin

London

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Lynch

M.

Conery

J.

S

The

origins

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genome

complexity

Science

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Blomme

T.

et

al.

The

gain

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loss

of

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during

million

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vertebrate

evolution

Genome

Biol

-LRB-

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Freeling

M.

Thomas

B.

C.

Gene-balanced

duplications

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predictable

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increase

morphological

complexity

Genome

Res

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Gu

Z.

Rifkin

S.

A.

White

K.

P.

Li

W.

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Duplicate

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between

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Nature

Genet

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Kafri

R.

Bar-Even

A.

Pilpel

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Berglund

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Lagergren

J.

Sennblad

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gene/species

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reconciliation

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Bioinformatics

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Suppl

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Chen

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Durand

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NOTUNG

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Halldorsson

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Vernot

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A

hybrid

micro-macroevolutionary

approach

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gene

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reconstruction

J.

Comput

Biol

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Fitch

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Distinguishing

homologous

from

analogous

proteins

Syst

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Jothi

R.

Zotenko

E.

Tasneem

A.

Przytycka

T.

M.

COCO-CL

hierarchical

clustering

of

homology

relations

based

on

evolutionary

correlations

Bioinformatics

-LRB-

-RRB-

Kellis

M.

Patterson

N.

Birren

B.

Berger

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identification

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discovery

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M.

Storm

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Mol

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BMC

Bioinform

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Regev

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Automatic

genome-wide

reconstruction

of

phylogenetic

gene

trees

Bioinformatics

doi

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Byrne

K.

P.

Wolfe

K.

H

The

Yeast

Gene

Order

Browser

combining

curated

homology

and

syntenic

context

reveals

gene

fate

in

polyploid

species

Genome

Res

-LRB-

-RRB-

Dudley

A.

M.

Janse

D.

M.

Tanay

A.

Shamir

R.

Church

G.

M

A

global

view

of

pleiotropy

and

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derived

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function

in

yeast

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Biol

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Tzung

K.

W.

et

al.

Genomic

evidence

for

a

complete

sexual

cycle

in

Candida

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Proc

Natl

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et

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ontology

tool

for

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unification

of

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Nature

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Friedman

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Kaminski

N.

Regev

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Koller

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models

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Nature

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A.

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survey

reveals

modularity

of

the

yeast

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machinery

Nature

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A.

M.

et

al.

Mechanisms

of

haploinsufficiency

revealed

by

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in

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T.

R.

et

al.

Functional

discovery

via

a

compendium

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Cell

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NATURE

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September

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J.

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et

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Single-cell

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the

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Nature

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Huisinga

K.

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Pugh

B.

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O.

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Supplementary

Information

is

linked

to

the

online

version

of

the

paper

at

Acknowledgements

A.R.

was

supported

by

a

Career

Award

at

the

Scientific

Interface

from

the

Burroughs

Wellcome

Fund

and

by

NIGMS

N.F.

was

supported

by

the

Israel

Science

Foundation

We

thank

E.

S.

Lander

for

discussions

and

D.

Peer

A.

Tanay

and

O.

Rando

for

their

comments

on

previous

drafts

of

this

manuscript

We

are

also

grateful

to

the

members

of

the

FAS

Center

and

the

Broad

Institute

for

their

scientific

and

technical

support

especially

A.

Daneau

M.

Ethier

and

B.

Mantenuto

Author

Information

Reprints

and

permissions

information

is

available

at

The

authors

declare

no

competing

financial

interests

Correspondence

and

requests

for

materials

should

be

addressed

to

A.R.

-LRB-

aregev@broad.mit.edu

-RRB-

Nature

Publishing

Group

doi

METHODS

Pre-processing

for

orthogroup

reconstruction

An

exhaustive

sequence

similarity

search

between

all

protein

sequences

was

performed

using

the

FASTA

sequence

alignment

FASTA

identifies

pairs

of

sequence

segments

with

significant

similarity

and

subsequently

performs

a

full

alignment

between

then

thus

returning

a

single

hit

per

pair

of

similar

genes

All

hits

whose

expectation

value

E

was

below

were

subsequently

treated

as

potential

homologues

This

lenient

threshold

allows

many

homology

relations

to

be

considered

We

then

computed

the

amino

acid

similarity

between

each

pair

of

potential

homologues

using

the

substitution

model

of

ref

The

SYNERGY

procedure

SYNERGY

assigns

orthologies

by

tracing

all

genes

in

the

species

below

a

given

node

in

the

species

tree

to

their

reconstructed

ancestral

genes

on

the

basis

of

sequence

similarity

and

shared

gene

Briefly

beginning

at

the

leaves

of

the

tree

and

traversing

backwards

chronologically

sets

of

orthogroups

for

two

daughter

species

that

share

significant

sequence

similarity

are

assembled

into

candidate

orthogroups

at

the

stage

pertaining

to

each

internal

node

-LRB-

The

orthogroups

for

the

daughter

species

were

identified

at

the

previous

stages

for

an

extant

species

each

gene

is

a

singleton

orthogroup

-RRB-

The

candidate

orthologues

are

then

positioned

on

a

phylogenetic

gene

tree

using

a

modified

neighbour-joining

procedure

that

reconstructs

the

phylogeny

of

orthogroups

at

that

stage

using

the

amino

acid

similarities

that

have

been

pre-computed

and

shared

synteny

Synteny

scores

are

computed

by

counting

the

fraction

of

neighbouring

genes

that

are

assigned

to

the

same

orthogroup

according

to

the

provisional

orthology

assignments

The

sub-trees

joined

at

each

stage

are

based

on

the

reconstruction

at

earlier

stages

Each

tree

is

rooted

using

a

combined

score

based

on

sequence

similarity

conserved

synteny

and

the

inferred

number

of

duplications

and

losses

Trees

that

invoke

fewer

unlikely

duplication

and

loss

events

will

be

favoured

over

those

that

incur

many

such

events

If

the

rooted

tree

determines

that

a

candidate

orthogroup

's

genes

are

not

all

descended

from

a

single

ancestral

gene

the

orthogroup

is

partitioned

to

two

candidate

orthogroups

by

removing

the

inferred

root

of

the

gene

tree

This

process

may

be

repeated

on

the

two

new

trees

until

each

tree

represents

genes

that

share

a

single

common

ancestor

at

the

current

level

of

reconstruction

The

orthology

assignments

and

trees

obtained

at

each

stage

of

the

algorithm

are

used

as

the

input

to

the

next

stages

involving

higher

nodes

in

the

species

tree

The

interim

trees

are

also

used

to

compute

the

distances

between

orthogroups

by

using

the

neighbour-joining

distance

update

rule

when

nodes

on

the

tree

are

joined

When

this

procedure

is

completed

at

the

root

of

the

species

tree

the

genes

for

all

species

have

been

assigned

to

their

orthogroups

and

placed

in

their

respective

trees

Full

details

of

the

procedure

are

available

in

the

companion

technical

Bootstrap

evaluation

To

assess

the

sensitivity

of

SYNERGY

's

orthology

assignments

to

both

the

choice

species

and

their

protein-coding

gene

predictions

two

bootstrap-based

confidence

measures

were

calculated

A

branch-bootstrap

was

performed

by

systematically

removing

branches

from

the

species

tree

and

counting

the

number

of

orthology

assignments

that

changed

A

gene-bootstrap

was

similarly

executed

by

hiding

each

predicted

open

reading

frame

in

the

data

set

with

a

fixed

of

probability

of

For

each

bootstrap

run

a

predicted

orthogroup

was

validated

for

the

number

of

orthology

assignments

that

were

not

designated

during

that

run

-LRB-

soundness

-RRB-

and

the

proportion

of

the

original

orthologous

pairs

among

the

new

predictions

-LRB-

completeness

-RRB-

For

each

pair

of

orthologues

within

an

orthogroup

from

a

bootstrap

run

an

assignment

was

considered

to

be

inconsistent

either

if

the

pair

was

assigned

as

orthologues

at

a

point

in

the

species

tree

that

deviated

from

the

original

assignment

or

if

they

were

not

originally

assigned

as

orthologues

at

all

The

average

number

of

consistent

assignments

across

each

experiment

was

used

as

the

branch

and

gene-bootstrap

confidence

measures

for

an

orthogroup

's

soundness

and

completeness

A

detailed

description

of

this

procedure

is

given

in

Supplementary

Note

Genome

sequences

The

predicted

protein

sequences

and

their

genomic

locations

were

downloaded

from

the

following

sources

From

the

Saccharomyces

Genome

S.

cerevisiae

-LRB-

downloaded

July

-RRB-

S.

paradoxus

-LRB-

accession

number

-RRB-

S.

mikatae

-LRB-

accession

number

-RRB-

S.

bayanus

-LRB-

accession

number

-RRB-

and

S.

castellii

-LRB-

accession

number

-RRB-

From

C.

glabrata

-LRB-

accession

number

-RRB-

K.

lactis

-LRB-

accession

number

-RRB-

D.

hansenii

-LRB-

accession

number

-RRB-

and

Y.

lipolytica

-LRB-

accession

number

-RRB-

From

the

Ashbya

Genome

A.

gossypii

-LRB-

accession

number

-RRB-

From

Kellis

et

al.

K.

waltii

-LRB-

accession

number

-RRB-

From

the

Candida

Genome

C.

albicans

-LRB-

accession

number

-RRB-

From

The

Broad

Institute

Fungal

Genome

Initiative

-LRB-

annotation/fgi

-RRB-

Aspergillus

nidulans

-LRB-

accession

number

-RRB-

Fusarium

graminearum

-LRB-

accession

number

-RRB-

Magnaporthe

grisea

-LRB-

accession

number

-RRB-

Neurospora

crassa

-LRB-

accession

number

-RRB-

and

Cryptococcus

neoformans

-LRB-

accession

number

-RRB-

From

S.

pombe

-LRB-

accession

numbers

-RRB-

Species

tree

The

species

tree

topology

representing

the

phylogenetic

relations

between

the

Ascomycota

fungi

was

compiled

from

several

and

subsequently

validated

using

the

results

from

our

orthogroup

assembly

by

concatenating

the

multiple

sequence

alignments

from

sampled

uniform

orthogroups

and

obtaining

the

maximum-likelihood

tree

topology

using

the

Phylip

package

's

default

This

sampling

procedure

was

repeated

ten

times

with

the

same

tree

topology

resulting

each

time

A

manual

correction

to

this

topology

was

introduced

as

previously

swapping

the

locations

of

S.

castellii

and

C.

glabrata

owing

to

chromosome-based

evidence

suggesting

that

C.

glabrata

is

in

fact

more

closely

related

to

the

Saccharomyces

sensu

stricto

clade

Branch

lengths

for

the

tree

presented

in

Fig.

were

estimated

using

a

maximum-likelihood

approach

using

multiple

site

rate

variation

with

the

default

Branch

lengths

were

not

used

in

any

of

the

subsequent

analyses

Functional

gene

classes

We

compiled

a

total

of

gene

classes

obtained

as

follows

from

the

Gene

hierarchy

from

the

Kyoto

Encyclopedia

of

Genes

and

-LRB-

KEGG

-RRB-

from

the

BioCyc

from

the

MIPS

database

of

manually

curated

protein

from

a

data

set

describing

the

targets

genes

bound

by

various

transcription

from

a

data

set

describing

the

target

genes

harbouring

a

given

cisregulatory

element

in

their

and

from

a

data

set

describing

the

targets

of

the

RNA

binding

proteins

from

the

PUF

These

classes

were

used

for

constructing

the

transcription

module

hierarchy

-LRB-

see

below

-RRB-

In

addition

the

following

gene

classes

were

included

in

various

analyses

but

were

not

included

in

the

construction

of

the

module

hierarchy

genes

controlled

by

the

SAGA

and/or

TFIID

transcription

genes

with

and

without

TATA

box

genes

with

large

levels

of

expression

variation

between

yeast

genes

with

high

and

low

levels

of

noise

in

protein

haploinsufficient

genes

whose

overexpression

reduces

and

genes

belonging

to

complex

cores

attachments

and

modules

based

on

high

throughput

Functional

orthogroup

classes

We

used

the

aforementioned

S.

cerevisiae-based

gene

classes

to

define

orthogroup

classes

by

projecting

the

S.

cerevisiae

annotations

onto

the

orthogroups

containing

S.

cerevisiae

genes

S.

cerevisiae

protein

interaction

networks

We

constructed

separate

biochemical

and

genetic

protein

interaction

networks

using

both

manually

curated

and

high-throughput

data

Defining

orthogroup

copy

number

variation

profiles

To

measure

the

changes

of

gene

copy

number

through

either

duplication

or

loss

events

we

assigned

a

copy

number

variation

profile

to

each

orthogroup

The

profile

is

defined

by

inspecting

the

extended

phylogenetic

profiles

belonging

to

an

orthogroup

and

subtracting

the

number

of

losses

observed

at

each

index

in

the

species

tree

from

the

number

of

duplications

We

increment

this

copy

number

variation

profile

at

the

last

common

ancestor

identified

for

the

orthogroup

indicating

its

age

Coherence

of

copy

number

variation

profiles

To

assess

the

coherence

in

gene

copy

number

variation

across

a

class

of

orthogroups

we

first

calculated

the

class

centroid

extended

copy

number

variation

profile

-LRB-

ECVP

-RRB-

by

averaging

the

ECVPs

from

all

the

orthogroups

belonging

to

a

class

This

centroid

is

then

applied

to

estimate

the

degree

of

deviation

between

the

orthogroups

belonging

to

a

class

by

summing

the

distance

from

each

of

the

class

orthogroups

to

it

We

compare

this

deviation

to

that

of

randomly

assigned

orthogroup

classes

each

containing

the

same

number

of

ECVPs

The

fraction

of

times

the

deviation

is

equal

to

or

less

than

that

of

the

orthogroup

class

is

the

measure

-LRB-

Pvalue

-RRB-

we

use

to

evaluate

the

coherence

of

that

class

Copy

number

variation

occurs

at

each

individual

branch

of

the

species

tree

so

we

similarly

define

a

coherence

profile

for

an

orthogroup

class

by

evaluating

the

copy

number

variation

coherence

for

each

position

along

the

species

tree

Copy

number

variation

in

protein

interaction

networks

To

test

the

relation

between

proximity

in

protein

interaction

networks

and

similarity

in

copy

number

variation

we

first

computed

the

difference

-LRB-

using

the

distance

-RRB-

between

the

ECVPs

for

each

pair

of

proteins

in

the

network

ignoring

pairs

that

belong

to

the

same

orthogroup

-LRB-

hence

sharing

the

same

profile

-RRB-

Next

we

averaged

these

differences

among

all

proteins

within

a

given

radius

in

the

network

To

determine

whether

these

averages

were

significant

we

repeated

this

computation

by

shuffling

profile

assignments

to

proteins

in

the

network

times

obtaining

the

expected

range

of

average

differences

between

pairs

of

proteins

in

the

network

for

each

radius

Statistical

benchmark

of

orthogroup

evolutionary

history

To

benchmark

the

volatility

in

gene

copy

number

for

each

orthogroup

we

used

the

estimated

rates

of

duplications

and

losses

along

each

branch

of

the

species

tree

to

calculate

the

log-probability

of

the

observed

number

of

such

events

in

each

orthogroup

assuming

that

they

occur

according

to

a

standard

Poisson

distribution

This

Nature

Publishing

Group

doi

statistic

is

used

as

a

measure

of

volatility

for

each

orthogroup

We

compare

this

volatility

metric

to

those

of

hypothetical

orthogroups

with

randomly

generated

duplications

and

losses

-LRB-

based

on

the

empirical

rates

-RRB-

In

our

analysis

we

label

those

orthogroups

the

volatility

of

which

deviates

more

than

three

standard

deviations

from

the

mean

of

the

random

distribution

as

being

significantly

volatile

Statistical

enrichment

tests

To

identify

the

annotations

in

which

each

orthogroup

class

was

significantly

enriched

we

projected

the

class

annotations

as

described

above

calculated

the

fraction

of

orthogroups

from

that

class

that

contained

a

given

annotation

and

used

the

hypergeometric

distribution

to

calculate

a

P-value

for

this

fraction

-LRB-

compared

with

the

hypothesis

of

choosing

the

same

number

orthogroups

at

random

-RRB-

We

corrected

for

multiple

tests

using

the

false

discovery

rate

correction

with

a

false

rate

Construction

of

S.

cerevisiae

transcription

module

map

We

constructed

the

hierarchy

of

transcriptional

modules

following

the

procedure

presented

by

Segal

et

al.

We

applied

this

procedure

to

a

yeast

data

set

and

followed

it

by

manually

selecting

which

modules

to

use

in

the

hierarchy

We

note

that

although

it

is

not

highlighted

in

their

manuscript

this

method

creates

modules

in

a

hierarchical

fashion

Full

technical

details

on

the

construction

of

the

map

are

at

the

end

of

this

section

Estimating

functional

divergence

of

paralogous

genes

in

hierarchical

annotation

data

We

estimated

the

functional

divergence

between

a

pair

of

paralogous

genes

by

considering

the

most

specific

gene

class

in

the

annotation

hierarchy

that

each

gene

was

assigned

to

-LRB-

for

gene

ontology

we

ignored

all

assignments

that

were

attributed

only

to

computational

sequence

analysis

-RRB-

We

regarded

a

pair

of

genes

as

functionally

diverged

only

if

both

genes

are

assigned

to

at

least

one

annotation

class

and

they

are

not

both

assigned

to

the

most

specific

annotations

of

either

of

the

two

genes

Computing

degree

of

conserved

interactions

between

paralogues

We

used

two

statistics

to

compute

the

degree

of

conserved

interactions

between

pairs

of

paralogous

proteins

The

first

was

simply

the

fraction

of

shared

interactions

between

both

proteins

For

this

we

counted

the

number

of

interactions

each

protein

takes

part

in

-LRB-

and

for

proteins

and

respectively

-RRB-

and

the

number

of

interactions

they

both

share

-LRB-

s

-RRB-

The

fraction

of

shared

interactions

is

thus

f

s

-LSB-

min

-LRB-

-RRB-

-RSB-

We

also

used

the

subfunctionalization

index

-LRB-

Isf

-RRB-

as

previously

to

characterize

how

diverged

a

pair

of

paralogues

interactions

are

This

is

calculated

as

Isf

-LRB-

s

-RRB-

t

where

s

is

as

above

and

t

is

equal

to

-LRB-

s

-RRB-

This

statistic

gives

a

reasonable

estimate

of

the

degree

of

subfunctionalization

in

the

absence

of

neofunctionalization

because

subfunctionalization

would

reduce

the

number

of

shared

interactions

This

measure

considers

the

proportion

of

ancestral

interactions

that

are

no

longer

shared

between

the

paralogues

and

the

extent

of

subfunctionalization

for

these

interactions

Estimating

significance

of

shared

protein

interaction

neighbourhood

To

estimate

the

significance

of

the

shared

protein

interaction

neighbourhood

between

pairs

of

paralogues

we

first

calculated

the

degree

of

conserved

interactions

and

compared

this

to

the

degree

of

shared

interactions

between

the

two

paralogues

in

a

degree-preserving

randomized

network

obtained

by

swapping

edges

between

random

pairs

of

nodes

times

We

repeated

this

procedure

times

and

assigned

a

P-value

to

the

shared

protein

interaction

neighbourhood

of

a

pair

of

proteins

according

to

the

number

of

times

the

fraction

of

shared

interacting

partners

between

paralogues

is

equal

to

or

greater

than

the

fraction

in

the

real

network

Homology

searches

To

identify

putative

homologies

between

orthogroups

and

the

C.

neoformans

genome

we

first

constructed

a

tree-assisted

multiple

sequence

alignment

from

each

orthogroup

's

protein

sequences

using

their

reconstructed

gene

trees

and

the

MUSCLE

alignment

We

then

constructed

a

sequence

profile

from

this

alignment

and

executed

a

homologous

protein

search

using

the

HMMSEARCH

profile

search

software

employing

an

E-value

cutoff

of

-LRB-

ref

-RRB-

DNA

microarray

data

set

We

compiled

a

collection

of

previously

published

microarray

experiments

-LRB-

Supplementary

Table

-RRB-

We

normalized

the

expression

of

each

gene

g

in

every

data

set

separately

as

in

ref

For

data

sets

generated

using

Affymetrix

chips

we

first

take

the

log

-LRB-

base

-RRB-

of

g

's

expression

value

in

each

array

-LRB-

truncating

expression

values

that

are

below

ten

-RRB-

For

data

sets

generated

using

spotted

complementary

DNA

chips

we

use

the

log-ratio

-LRB-

base

-RRB-

between

the

measured

sample

and

the

control

sample

In

both

types

of

data

sets

we

subsequently

normalize

the

-LRB-

log

-RRB-

expression

value

of

gene

g

in

each

array

to

its

average

expression

in

all

the

arrays

in

the

same

data

set

by

subtracting

its

average

in

that

data

set

from

each

of

its

expression

measurements

After

this

normalization

the

mean

value

of

a

gene

within

each

data

set

is

zero

Details

of

transcriptional

module

hierarchy

construction

The

transcription

module

hierarchy

was

constructed

in

a

step-wise

process

as

in

ref

Step

Identifying

arrays

where

gene

classes

significantly

change

in

expression

To

identify

the

arrays

where

each

gene

class

is

significantly

induced

-LRB-

or

repressed

-RRB-

we

defined

the

induced

-LRB-

or

repressed

-RRB-

genes

in

each

array

to

be

those

genes

whose

change

in

expression

is

greater

-LRB-

less

-RRB-

than

twofold

For

each

gene

class

and

each

array

we

calculated

the

fraction

of

genes

from

that

class

that

are

induced

-LRB-

or

repressed

-RRB-

in

that

array

and

used

the

hypergeometric

distribution

to

calculate

a

P-value

for

this

fraction

-LRB-

compared

to

the

hypothesis

of

choosing

the

same

number

genes

at

random

-RRB-

We

corrected

for

multiple

tests

using

the

false

discovery

rate

correction

with

a

false

rate

Step

Identification

of

gene

class

clusters

We

performed

-LRB-

bottom-up

-RRB-

hierarchical

clustering

of

the

gene

classes

in

the

matrix

of

all

significant

array

gene

class

pairs

We

manually

selected

a

hierarchy

of

gene

class

clusters

corresponding

to

the

cluster

boundaries

defined

automatically

and

assigned

a

biologically

meaningful

name

to

each

cluster

We

obtained

a

total

of

such

gene

class

clusters

-LRB-

excluding

the

root

node

-RRB-

The

transcriptional

modules

were

defined

from

the

genes

in

those

gene

class

clusters

-LRB-

below

-RRB-

and

organized

according

to

the

same

hierarchy

Step

Testing

consistency

of

a

gene

with

expression

of

a

gene

class

Given

a

class

of

genes

G

and

a

gene

g

we

test

whether

the

expression

of

g

is

consistent

with

the

significant

changes

in

the

expression

of

G

using

the

following

procedure

We

first

identify

the

subsets

of

arrays

I

and

R

where

G

is

significantly

induced

and

repressed

respectively

We

then

measure

the

extent

in

which

the

expression

of

g

changes

by

more

-LRB-

or

less

-RRB-

than

twofold

in

arrays

in

I

-LRB-

or

R

-RRB-

with

the

score

X

X

Score

-LRB-

g

-RRB-

-LCB-

log

-LRB-

pa

-RRB-

z

-LCB-

log

-LRB-

pa

-RRB-

fa

-LSB-

I

j

g

is

induced

in

ag

fa

-LSB-

R

j

g

is

repressed

in

ag

where

pa

is

the

fraction

of

genes

in

the

array

a

that

are

induced

-LRB-

or

repressed

-RRB-

by

more

than

twofold

for

arrays

in

I

-LRB-

or

in

R

-RRB-

This

score

assigns

more

weight

to

induction

in

arrays

where

there

are

fewer

induced

genes

-LRB-

and

respectively

for

repression

-RRB-

We

evaluate

the

significance

of

Score

-LRB-

g

-RRB-

with

respect

to

the

hypothesis

in

which

the

genes

in

each

array

are

randomly

permuted

Under

this

hypothesis

Score

-LRB-

g

-RRB-

is

the

sum

of

independent

binary

random

variables

one

for

each

array

in

I

and

R

The

random

variable

corresponding

to

array

a

attains

the

value

log

-LRB-

pa

-RRB-

with

probability

pa

and

the

value

of

with

probability

pa

Because

Score

-LRB-

g

-RRB-

in

this

model

is

a

sum

of

independent

random

variables

its

mean

m

and

variance

are

the

sum

of

the

means

and

variances

respectively

of

the

these

variables

and

can

be

computed

analytically

X

m

-LCB-

pa

log

pa

a

-LSB-

I

R

s

X

pa

-LRB-

-LCB-

pa

-RRB-

pa

a

-LSB-

I

R

By

the

central

limit

theorem

the

distribution

of

Score

-LRB-

g

-RRB-

under

the

hypothesis

can

be

closely

approximated

by

a

gaussian

distribution

with

mean

m

and

variance

We

use

standard

methods

for

computing

the

tail

probability

of

a

gaussian

distribution

to

compute

the

probability

of

attaining

a

score

as

large

as

the

observed

score

under

the

hypothesis

Step

Deriving

modules

from

clusters

of

gene

classes

For

each

cluster

of

gene

classes

we

define

G

to

be

the

union

of

the

gene

classes

in

the

cluster

We

then

test

each

gene

in

G

for

consistency

-LRB-

as

described

above

-RRB-

The

resulting

module

consists

of

genes

the

expression

of

which

is

significantly

consistent

with

the

expression

of

the

gene

class

-LRB-

after

false

discovery

rate

correction

for

multiple

hypotheses

using

a

false

rate

-RRB-

Pearson

W.

R.

Lipman

D.

J.

Improved

tools

for

biological

sequence

comparison

Proc

Natl

Acad

Sci

USA

-LRB-

-RRB-

Jones

D.

T.

Taylor

W.

R.

Thornton

J.

M

The

rapid

generation

of

mutation

data

matrices

from

protein

sequences

Comput

Appl

Biosci

-LRB-

-RRB-

Saitou

N.

Nei

M

The

neighbor-joining

method

a

new

method

for

reconstructing

phylogenetic

trees

Mol

Biol

Evol

-LRB-

-RRB-

Kurtzman

C.

P.

Robnett

C.

J.

Phylogenetic

relationships

among

yeasts

of

the

Saccharomyces

complex

determined

from

multigene

sequence

analyses

FEMS

Yeast

Res

-LRB-

-RRB-

Kuramae

E.

E.

Robert

V.

Snel

B.

Boekhout

T.

Conflicting

phylogenetic

position

of

Schizosaccharomyces

pombe

Genomics

-LRB-

-RRB-

Felsenstein

J.

PHYLIP

Phylogeny

Inference

Package

-LRB-

Version

-RRB-

Cladistics

-LRB-

-RRB-

Nature

Publishing

Group

doi

Ninio

M.

Privman

E.

Pupko

T.

Friedman

N.

Phylogeny

reconstruction

increasing

the

accuracy

of

pairwise

distance

estimation

using

Bayesian

inference

of

evolutionary

rates

Bioinformatics

-LRB-

-RRB-

Kanehisa

M

A

database

for

post-genome

analysis

Trends

Genet

-LRB-

-RRB-

Karp

P.

D.

et

al.

Expansion

of

the

BioCyc

collection

of

pathway/genome

databases

to

genomes

Nucleic

Acids

Res

-LRB-

-RRB-

Mewes

H.

W.

et

al.

MIPS

analysis

and

annotation

of

proteins

from

whole

genomes

in

Nucleic

Acids

Res

-LRB-

-RRB-

Edgar

R.

C.

MUSCLE

multiple

sequence

alignment

with

high

accuracy

and

high

throughput

Nucleic

Acids

Res

-LRB-

-RRB-

Eddy

S.

HMMER

Hidden

Markov

models

for

sequence

profile

analysis

http

hmmer.janelia.org

-LRB-

-RRB-

Nature

Publishing

Group

gene duplication
yeast
evolution
synteny
gene