Systems biology of yeast cell death

M IN I R E V I EW

Systems biology of yeast cell death

Ana Joyce Munoz, Kwanjeera Wanichthanarak, Eugenio Meza & Dina Petranovic

Department of Chemical and Biological Engineering, Chalmers University of Technology, Goteborg, Sweden

Correspondence: Dina Petranovic,

Department of Chemical and Biological

Engineering, Chalmers University of

Technology, Kemivagen 10, SE-412 96

Goteborg, Sweden. Tel.: +46(0)31 772 3836;

fax: +46(0)31 772 3801;

e-mail: [email protected]

Received 16 September 2011; revised 8

December 2011; accepted 9 December 2011.

Final version published online 23 January

2012.

DOI: 10.1111/j.1567-1364.2011.00781.x

Editor: Jack Pronk

Keywords

yeast; programmed cell death; apoptosis

systems biology.

Abstract

Programmed cell death (PCD) (including apoptosis) is an essential process,

and many human diseases of high prevalence such as neurodegenerative dis-

eases and cancer are associated with deregulations in the cell death pathways.

Yeast Saccharomyces cerevisiae, a unicellular eukaryotic organism, shares with

multicellular organisms (including humans) key components and regulators of

the PCD machinery. In this article, we review the current state of knowledge

about cell death networks, including the modeling approaches and experimen-

tal strategies commonly used to study yeast cell death. We argue that the sys-

tems biology approach will bring valuable contributions to our understanding

of regulations and mechanisms of the complex cell death pathways.

Introduction

Systems biology is an engineering-inspired approach to

study complex biological systems, that brings together

experimental and computational methods, and provide

new insights or generate new hypotheses. There are two

complementary paths of systems biology: the top–downapproach and the bottom–up approach. The top–downapproach is often data driven and includes the analysis

and integration of large datasets generated often by differ-

ent -omics tools. In this setup, the hypotheses are gener-

ated by data analyses. The bottom–up approach is often

hypothesis driven; there is a need for a starting hypothesis

and a collection of focused data (e.g. kinetic data), which

lead to a creation of a discrete mathematical model that

can be used for simulations to gain additional under-

standing of a complex biological system (Fig. 1). An ideal

approach would integrate cell and molecular biology

approaches, biochemistry and genetics together with -

omics datasets, bioinformatics, data analysis, and mathe-

matical modeling to provide the most comprehensive

description of the biological system.

One of the complex systems that we are interested in, is

the regulation of cell death in yeast Saccharomyces cerevisi-

ae. Yeast has been extensively used for understanding of

fundamental cellular and molecular processes (DNA repli-

cation and recombination, cell division, metabolism, pro-

tein homeostasis, and vesicular trafficking) (Fields &

Johnston, 2005). Some of the benefits of yeast as a model

organism include the fact that it grows fast, in cheap and

defined media; it can be cultivated in controlled condi-

tions; and over time, the toolbox to study and modify

yeast has been developed and populated with many useful

methods and protocols (Petranovic & Nielsen, 2008).

After the genome sequence was published, it was found

that 31% of the yeast genes have a mammalian homologue

and 30% of the genes known to be involved in human dis-

eases have a yeast orthologue (Foury, 1997). It is possible

to imagine that more conservation would be revealed at

the level of functionality or regulatory mechanisms that

might have similar inputs and outputs but the effectors do

not share significant sequence homologies, so we would

omit it in bioinformatics homology-based searches. Stud-

ies with yeast have contributed to unravel the molecular

mechanisms involved in the pathogenesis of many disor-

ders either by classical complementation assays or by

developing humanized yeast systems. For example, for sev-

eral neurological disorders such as Huntington’s disease

(Duennwald et al., 2006; Sokolov et al., 2006), Parkinson’s

disease (Cooper et al., 2006), and Alzheimer’s disease

FEMS Yeast Res 12 (2012) 249–265 ª 2011 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

YEA

ST R

ESEA

RC

H

(Vandebroek et al., 2005), studies in yeast contributed

with significant insights into the role of human proteins

that are involved in the mentioned neurodegenerative dis-

eases (reviewed by Winderickx et al., 2008). In addition to

the experimental studies in this field, recent advances in

modeling of programmed cell death (PCD) in mammalian

cells (Bialik et al., 2010; Calzone et al., 2010; Lavrik, 2010)

have provided us with a PCD network that proposes inter-

connectivity within and also between different PCD path-

ways (Bialik et al., 2010). This kind of approach could be

used in yeast studies as well, and we propose that this will

contribute not only to gain additional insight into yeast

fundamental biology, but also help in developing screens

(e.g. pharmaceutical screens) and setups for studies of

proteins and pathways related to human diseases.

Programmed cell death in multicellularorganisms

In early 19th century, after many observations suggesting

the cell as the basic element of living organisms, the cell

theory was officially established by Shleiden and Schwann

(1838 and 1839) (Mazzarello, 1999). Three years later,

Carl Vogt was the first to recognize that the cells die

naturally during vertebrate development. This phenome-

non of physiological cell death was initially observed dur-

ing amphibian metamorphosis and subsequently in many

different tissues of vertebrates and invertebrates (Clarke &

Clarke, 1996). The term ‘programmed cell death’ (PCD)

was introduced in 1964 by Lockshin (Lockshin & Wil-

liams, 1964) to define the programmed and controlled

cellular self-destruction process, locally and temporally

defined. PCD plays a crucial role during development,

particularly in morphogenesis, differentiation, prolifera-

tion, and epigenetic self-organization processes. For the

proper functioning of living organisms, PCD is essential

for the maintenance of tissue homeostasis, in the removal

of defective and harmful cells and defense against infec-

tions (reviewed by Miura, 2011).

The PCD may occur through different mechanisms that

are defined by morphological criteria. Apoptosis, the term

coined by Kerr et al. (1972), describes the active and

defined PCD characterized morphologically by cell shrink-

age and swelling of organelles in early stages, mitochon-

drial outer membrane permeabilization, condensation and

aggregation of the chromatin (globular, crescent-shaped),

nuclear fragmentation (karyorrhexis), and plasma mem-

brane blebbing (Kroemer et al., 2005). Biochemical fea-

tures associated with apoptosis include internucleosomal

cleavage of DNA, leading to an oligonucleosomal ‘ladder’

(Cohen et al., 1994), and the redistribution and external-

ization of phosphatidylserine (PS) to the external leaflet of

the plasma membrane that allows phagocytes to recognize

and engulf these cells (Martin et al., 1995). Finally, the cell

breaks into compact apoptotic bodies (containing cytosol,

condensed chromatin, and organelles).

Although apoptosis is possibly the most frequent form

of PCD, there are other (nonapoptotic) types of cell death

of biological significance (Leist & Jaattela, 2001). Necrosis

is the term used to describe pathological cell death, which

is a consequence of mechanical damage, exposure of cells

to toxins, and severe environmental perturbations. The

cell death morphology during necrosis is characterized by

cellular swelling, dilation of organelles, mechanical rup-

ture of the plasma membrane, and release of cellular con-

tent leading to an inflammatory response (Zong &

Thompson, 2006). Although necrosis is defined as a pas-

sive and accidental process with uncontrolled release of

cellular content, it has been suggested that the necrotic

cell death could follow a programmed route playing an

important role during embryogenesis, tissue regeneration,

and immune response (Proskuryakov et al., 2003; Zong &

Thompson, 2006; Vandenabeele et al., 2010).

Since the discovery that tumor necrosis factor (TNF)

can induce cell death in different cell types with

morphological features of apoptosis but without nuclear

Fig. 1. Systems biology for yeast cell death: top–down approach is

relying on experimental datasets obtained by high-throughput

experiments (e.g. by different -omics) that are analyzed and

integrated by bioinformatics. In this case, the discovery is data driven.

The bottom-up approach is based on focused information about

specific cellular modules (subnetworks) that are used to construct

mathematical models. This approach is hypothesis based. Both

approaches are used to generate the most complete description

(model) of the biological system.

ª 2011 Federation of European Microbiological Societies FEMS Yeast Res 12 (2012) 249–265Published by Blackwell Publishing Ltd. All rights reserved

250 A.J. Munoz et al.

fragmentation (Laster et al., 1988), more evidence has

accumulated to support the idea of programmed necrosis,

and it was in 2005 that the term ‘necroptosis’ was intro-

duced to define the regulated necrotic cell death (Degte-

rev et al., 2005). The activation of death receptors that

activate the apoptotic machinery, such as CD95, TNFR1

(TNF receptor 1), TNFR2, TNF-related apoptosis-induc-

ing ligand receptor 1 (TRAILRI), and TRAILR2 (Wilson

et al., 2009), can culminate in necroptosis, when apopto-

sis is blocked (e.g. with caspase inhibitors) in some cell

lines and primary cells (Fiers et al., 1995). Necroptosis

initiated by the ligation of TNFR1 is the best character-

ized pathway and requires the kinase activity of receptor-

interacting protein 1 (RIP1) and RIP3. Depending on the

cell type and environmental factors, the induction by

TNF can result in cell survival, apoptosis, or necroptosis

(Wilson et al., 2009). The ubiquitin-editing system and

initiator caspases are responsible for modulating the

switch between the different biological responses (Van-

denabeele et al., 2010). Several other necroptotic triggers,

including pathogen-associated molecular patterns and

DNA damage, as well as oxidative stress and mitochon-

drial dysfunction, contribute to the execution of necrop-

tosis (Vandenabeele et al., 2010). It has been reported

that necrotic cells lead to an inflammatory response

through the release of pro-inflammatory factors (Zitvogel

et al., 2010) but it has been reported that clearance of

cells dying by necrosis and exposing PS of the outer

membrane facilitates their recognition by phagocytic cells

and reduce the release of the pro-inflammatory cytokine

TNF from the macrophages (Brouckaert et al., 2004; Hirt

et al., 2004). Pathways leading to necrosis, necroptosis, or

secondary necrosis lead to very similar end phenotypes

but different molecular events are involved, so it is

important to be able to discern these cellular events.

In addition to the favorable roles of cell death, dys-

function or deregulation of this process may contribute

to a variety of diseases such as cancer, neurodegenerative

diseases (e.g. Parkinson’s, Alzheimer diseases) (Zhivotov-

sky & Orrenius, 2010), autoimmune disorders (Cacciapa-

glia et al., 2009), athrosclerosis, hematological disorders

(e.g. aplastic anemia), ischemic injury, viral (e.g. AIDS)

and bacterial infections, metabolic and development-asso-

ciated disorders, and cardiovascular diseases (Fadeel et al.,

1999). In addition, aging is linked to deregulation of the

apoptotic machinery (Joaquin & Gollapudi, 2001; Mura-

dian & Schachtschabel, 2001).

Apoptosis can be triggered by various stimuli from out-

side or inside the cell, and the death signals activate the

cell death machinery. Pioneering work in the nematode

Caenorhabditis elegans established that apoptosis is under

genetic control and has lead to the identification of several

genes that participate in the regulation and execution of

apoptosis. The central components of the apoptotic

machinery in C. elegans are encoded by four main genes:

ced-3, ced-4, ced-9, and egl-1. The gene ced-3 encodes a

caspase (cysteine-dependent aspartate-directed or cyste-

ine-aspartic protease) that oligomerizes with an adaptor

protein encoded by ced-4 gene. This interaction is

required for the activation and cleavage of cellular sub-

strates, leading to the molecular and morphological fea-

tures of apoptosis. Activity of the CED-3/CED-4 complex

is regulated by the apoptosis inhibitor and the apoptosis

inducer proteins encoded by ced-9 and egl-1 genes, respec-

tively (reviewed by Metzstein et al., 1998). The identifica-

tion of ced-3, ced-4, ced-9, and egl-1 provided much of the

understanding how the apoptotic machinery is engaged in

C. elegans as well as in other species. For the significance

of their discoveries concerning ‘genetic regulation of

organ development and PCD’ using C. elegans, the Nobel

Prize in Physiology or Medicine in 2002 was awarded

jointly to Sydney Brenner, Robert Horvitz, and John E.

Sulston. The C. elegans homologous genes were described

in subsequent studies in mammals and the fly Drosophila

melanogaster, indicating that the key elements that partici-

pate in caspase activation are highly conserved through

evolution (reviewed by Ameisen, 2002) (Fig. 2). Caspases

are of central importance in the execution of apoptosis. In

mammals and D. melanogaster, seven members of the cas-

pase family having an important role in apoptosis have

been identified (Chowdhury et al., 2008) and they are

generally divided in two classes: the initiator caspases and

the effector caspases. In C. elegans, the activation of pro-

CED-3, the only apoptotic caspase, is facilitated by the

tetrameric CED-4, forming a caspase-activating complex

called apoptosome. In D. melanogaster, the apoptosome

requires an octameric complex involving Dark, the CED-4

homologue, and the initiator caspase DRONC to eventu-

ally activate the effector caspase DRICE. The mammalian

homologue of CED-4 protein is the adaptor protein

APAF-1 (apoptotic protease activating factor) which is the

central element of the apoptosome. In mammals, the

cytochrome c and dATP are essential components for the

heptameric APAF-1 assembly with the initiator casapse-9

for the activation of the effector caspase-3 (Cain et al.,

2002; Riedl & Shi, 2004).

The caspase activation is regulated by inhibitor and

inducer apoptotic proteins. CED-9 and EGL-1 are the reg-

ulatory proteins in C. elegans. The inhibitor protein CED-

9 prevents CED-4/CED-3 assembling sequestering the

adaptor protein CED-4. It is the inducer protein EGL-1

that disrupts the CED-4/CED-9 interaction after an apop-

totic stimulus (reviewed by Lettre & Hengartner, 2006). In

mammals, the Bcl-2 and BH3-only proteins, homologues

of EGL-1 and CED-9, are members of the Bcl-2 family of

pro- or anti-apoptotic proteins that promote or inhibit


Systems biology of yeast cell death 251

the permeabilization and disruption of the outer mito-

chondrial membrane regulating the release of mitochon-

drial proteins involved in the activation of apoptosis. The

core elements of the apoptotic machinery in fruit flies,

nematodes, and mammals are compared in Fig. 2.

Although the caspase activation pathway is evolution-

ary conserved, the regulation of caspases is executed by

different proteins. In mammals, the mitochondria have

an essential role in the regulation of the intrinsic apopto-

tic pathway (also called mitochondrial pathway) activated

under a variety of apoptotic stimuli. The members of the

Bcl-2 family proteins that are localized in the cytosol or

associated with intracellular membranes including nuclear

envelope, the endoplasmic reticulum, and mitochondria

have been divided into the following classes (Antonsson

& Martinou, 2000):

(1)The pro-apoptotic members (Bax, Bak, and Bok) cru-

cial for inducing mitochondrial outer membrane permea-

bilization (MOMP) and the subsequent release of

apoptogenic molecules, such as cytochrome c (important

for the apoptosome activation), apoptosis-inducing factor

(AIF), second mitochondrial activator of caspases

(SMAC), endonuclease G, and Htr/Omi (implicated in

regulation or in execution of apoptosis and acting in a

caspase-independent form).

(2)The anti-apoptotic members (Bcl-2, Bcl-xl, Bcl-w,

Mcl-1, Bcl-B, and A1) that neutralize or inhibit the activ-

ity of pro-apoptotic members.

(3)The BH-3-only proteins (Bad, Bik, Bid, Hrk, Bim,

Bmf, Noxa, and Puma), also members of the proapototic

class, have a conserved BH3 domain that can bind and

regulate the anti-apoptotic Bcl-2 proteins to promote

apoptosis.

Some studies have demonstrated that, depending on

the stimulus, features of both apoptosis and necrosis may

be identified in the same cell and dead cells in the late

stages of apoptosis may present necrotic features (Zong &

Thompson, 2006). Although some researchers have pro-

posed apoptosis to be defined as caspase-mediated cell

death (Martin & Green, 1995; Samali et al., 1999; Blag-

osklonny, 2000), many other proteolytic enzymes, such as

serine proteases, cathepsins, granzymes, and calpains,

contribute to apoptosis (reviewed by Vandenabeele et al.,

2005). Furthermore, caspase-independent cell death pro-

cess, with morphological features of apoptosis, have been

identified, where MOMP can occur independent of cas-

pase activation leading to the release of mitochondrial

proteins, for example, endonuclease and chromatin modi-

fying factors such as endonuclease G and AIF (Susin

et al., 1999; Li et al., 2001) or decline in ATP production

and increase in reactive oxygen species (ROS) generation

(Kroemer & Martin, 2005). Leist & Jaattela (2001) classi-

fied the forms of PCD based on the different combina-

tions and degrees of apoptotic features observed in the

same cell population. In this review, the forms of cas-

pase-independent PCD are:

Fig. 2. Evolutionary conservation of the core apoptotic machinery in nematode, fruit fly, and mammal (adapted from Riedl & Shi, 2004). Yeast

metacaspase-dependent pathways lead towards the YCA1 activation but detailed mechanisms remain to be elucidated.



(1)Apoptosis-like PCD: PCD morphologically character-

ized by chromatin condensation (less compact than in

apoptosis) and in addition any other apoptotic feature.

(2)Necrosis-like PCD: active cellular process in the

absence of chromatin condensation, or with chromatin

clustering speckles, and in addition different degrees of

other apoptosis-like features which might occur before

the lysis (e.g. PS externalization).

Programmed cell death in yeast

Regulated cell death is also found in unicellular eukary-

otes (Trypanosome brucei brucei, Tetrahymena thermophil-

a, and Dictyostelium discoideum) and even prokaryotes

(Bacillus subtilis, Streptomyces and Myxobacteria) (Amei-

sen, 2002; Deponte, 2008). PCD has been observed in

yeast, playing a significant role in the physiological mech-

anism of aging (Laun et al., 2001; Herker et al., 2004)

and in the altruistic preservation of the colony (Frohlich

& Madeo, 2000; Vachova & Palkova, 2005).

Almost two decades ago, with the aim to study the

interactions between the proteins of the Bcl-2 family, a

yeast two hybrid screen was used (Sato et al., 1994; Hana-

da et al., 1995), and this study reported a lethal pheno-

type in yeast when human Bax was expressed, which

could be rescued by the co-expression of human Bcl-2.

These results suggested the possibility of a conserved

death pathway between mammalian and yeast cells. Fur-

ther studies with S. cerevisiae and Schizosaccharomyces

pombe (Zha et al., 1996; Jurgensmeier et al., 1997; Manon

et al., 1997) showed that, as in mammalian cells, heterol-

ogously expressed Bax must be targeted to mitochondria

and form homodimers to induce cytochrome c release

and therefore induce the lethal phenotype. However, nei-

ther oligonucleosomal DNA degradation (DNA fragmen-

tation) nor chromatine condensations was found. These

results showed that, when compared with mammals, the

events downstream of cytochrome c release were different,

but the effect of Bax on mitochondria was very similar

(Xu et al., 1999). An argument against apoptosis in yeast

was the fact that expression of Bax in yeast did not show

induction of chromatin fragmentation, DNA breakage,

and PS externalization (Hanada et al., 1995; Manon et al.,

1997). However, another study (Ligr et al., 1998) showed

that Bax was able to induce the apoptotic phenotype in

yeast such as loss of asymmetric distribution of PS in the

plasma membrane, membrane blebbing, chromatin con-

densation and DNA fragmentation, and the apoptotic

phenotype could be prevented by simultaneous overex-

pression of Bcl-XL. Until very recently, there was no

known orthologous of Bax protein or other members of

the Bcl-2 family in yeast but Buttner et al. have found

that the protein Ybh3 that contains a Bcl-2 family BH3

domain in its C-terminus was able to promote mitochon-

dria-dependent apoptosis upon induction with acetic acid

and H2O2. Interestingly, expression of Ybh3 induced

apoptotic events also in mammalian cells, pointing addi-

tionally toward evolutionary conservation of regulatory

mechanisms of cell death in eukaryal cells (Buttner et al.,

2011).

The first work that showed chromatin fragmentation,

DNA breakage, and PS externalization as apoptotic

features in S. cerevisiae was carried out in a strain

expressing a mutant version of the protein Cdc48,

Cdc48S565G (Madeo et al., 1997). Cdc48 is an ATPase first

described to be involved in cell cycle arrest during

G-phase (Moir et al., 1982). However, its role in the

endoplasmic reticulum–associated degradation process

(ERAD), where its ATPase activity is coupled to the ret-

rotranslocation of the misfolded protein, points to its

effect on induction of ubiquitin-mediated degradation of

cell cycle proteins (Ye, 2006; Vembar & Brodsky, 2008;

Baek et al., 2011). During the characterization of the

CDC48S565G mutant, DAPI and Annexin V staining as

well as terminal deoxynucleotidyl transferase dUTP nick-

end labeling (TUNEL) assay were used together with elec-

tron microscopy to characterize morphological changes

that are the hallmarks of the apoptotic PCD pathway

(Kerr et al., 1972). However, due to the fact that neither

caspases (Salvesen & Dixit, 1997) nor the standard apop-

tosis machinery (e.g. p53, Blc-2, Bax) homologues were

found at the time, there was still doubt about yeast phe-

notype being truly apoptotic.

More evidence was accumulating, supporting the idea

that yeast was able to undergo apoptosis. The induction

of apoptotic phenotype owing to the accumulation of

ROS (Pierce et al., 1991) was described in yeast when

exposed to hydrogen peroxide (Longo et al., 1997; Ma-

deo et al., 1999). In the last work, two PCD routines

were observed: one associated with low hydrogen perox-

ide concentration that was prevented by the arrest of

protein synthesis, and the other associated with high

hydrogen peroxide concentration, independent of pro-

tein synthesis. Interestingly, the PCD that depends on

protein synthesis showed features of apoptosis, while

the other showed necrotic features. The same apoptotic

phenotype was observed when S. cerevisiae was treated

with acetic acid (Ludovico et al., 2001): in this work, a

timeline was described in which chromatin condensa-

tion precedes DNA cleavage and PS externalization, as

found in other model systems (Sun et al., 1994; Dar-

zynkiewicz et al., 1997). Furthermore, using the same

approach, apoptotic features and ROS accumulation

were observed in aged cells, linking these three events

(Laun et al., 2001).



Apoptosis in yeast was still a matter of discussions

(Matsuyama et al., 1999; Fleury et al., 2002): even after

caspase-independent apoptosis was described in jurkat

cells, rat-1 fibroblasts, and mouse liver cells (Xiang et al.,

1996; McCarthy et al., 1997; Monney et al., 1998; Susin

et al., 1999), the burning question still was whether yeast

has a caspase-like activity responsible for the apoptotic

phenotype observed. An interesting finding emerged when

the human homologue of S. pombe Rad9 (involved in cell

cycle) was able to induce apoptosis in HeLa cells, and it

was therefore proposed that Rad9 function could be simi-

lar to p53, as a caspase-independent apoptosis inducer

(Komatsu et al., 2000). In addition, the identification of

paracaspases and metacaspases including Yor197w from

S. cerevisiae (Uren et al., 2000) and the subsequent char-

acterization of yeast metacaspase 1 (Yca1, Mca1) (Madeo

et al., 2002) allowed better understanding of yeast apop-

tosis. It was also found that the caspase activity increased

with the presence of hydrogen peroxide and that its dele-

tion prevents H2O2, acetic acid, and aging-induced apop-

tosis; thus, it was concluded that this caspase was the

executor of different apoptotic scenarios. It has recently

also been shown that the caspase activities of Esp1 and

Kex1 can be induced by H2O2 and N-glycosylation stress,

respectively (Hauptmann & Lehle, 2008; Yang et al.,

2008; Wilkinson & Ramsdale, 2011).

Several reviews provide more overviews on yeast PCD:

Matsuyama et al., 1999; Fleury et al., 2002; Jin & Reed

2002; Madeo et al., 2004; Jazwinski 2005; Ludovico et al.,

2005; Eisenberg et al., 2007; Perrone et al., 2008; Carmo-

na-Gutierrez & Madeo 2009; Madeo et al., 2009; Barros

et al., 2010; Carmona-Gutierrez et al., 2010.

Methods for yeast cell death studies

Different stimuli can trigger different cell death pathways

but, as mentioned before, there are specific hallmarks that

define an apoptotic phenotype, such as chromatin con-

densation, DNA fragmentation, cell shrinkage, PS exter-

nalization, membrane blebbing, and formation of

apoptotic bodies (Kerr et al., 1972; Hacker, 2000; Ziegler

& Groscurth, 2004; Kroemer et al., 2009). Different meth-

ods, with their benefits and drawbacks, can be used to

study apoptosis in yeast, and it is suggested to use several

methods for different cellular compartments to establish a

definitive apoptotic phenotype (Galluzzi et al., 2009)

(Fig. 3).

Cell growth and viability assays show if the stimulus

used is causing cell arrest (which might be due to cell

death). The fastest methods include growth on plate such

as the dilution spot-test determination of the colony for-

mation units (CFU) or growth assessment in the liquid

medium. It is possible to use fluorescent dyes that dis-

criminate between viable and nonviable cells and deter-

mine the viability of cells in a population using flow

cytometry. Fun 1 family of vital fluorescent probes are

transported into the vacuole where living cells form cylin-

drical intravacuolar structures that show an intense red

color at 480 nm. Metabolically inactive cells lack the

detoxification activity and show a diffused green color

evenly distributed in the cell (Millard et al., 1997).

Events in different cellular compartments (cytosol,

nucleus, mitochondria, and also the plasma membrane)

contribute to or are one of the markers for the apoptotic

phenotype. In the next section, we categorize different

events in cellular compartments and methods useful for

study of yeast cell death. ROS are mainly generated as a

result of the respiratory chain deficiency that produces

superoxide anion (O_�2 ). During the detoxification of this

radical, because of the superoxide dismutase activity,

H2O2 is generated that can thereafter oxidize Fe2+ to Fe3+

within the Fe-S clusters, generating the hydroxyl radical

OH� in the Fenton reaction (Balaban et al., 2005; Cash

et al., 2007; D’Autreaux & Toledano, 2007; Forkink et al.,

2010). These three ROS species have different reactivity

and can cause DNA damage, lipid peroxidation (both by

OH�), or protein damage (H2O2 and O_�2 can oxidize thi-

ols and cause misfolding). It has been reported that stress

that affects mitochondria can increase ROS generation by

impairing respiration, and this in turn can damage mito-

chondrial proteins and mtDNA that will contribute to the

respiration impairment and additional ROS production

in a vicious cycle. Furthermore, ROS has been associated

with apoptosis during aging, acetic acid, and H2O2 induc-

tion; there are also indications that ER-stress could also

contribute to generation of ROS (Perrone et al., 2008).

The measurement of ROS is an important hallmark to

follow in cells undergoing apoptosis. The use of fluores-

cent dyes is the most common way to determine ROS

production; the selection of the dye depends on the type

of ROS that is assessed. Dihydrorhodamine (DHR) can

be use to determine a broad range of ROS whereas di-

chloro-fluorescein diacetate (DC-FDA) is mostly used to

detect H2O2 and dihydroethidium (hydroethidine) to

detect O_�2 . The life span of OH� is short because of its

high reactivity, but it is possible to measure it using hy-

droxyphenyl fluorescein. These stains can be combined

with flow cytometry and florescence microscopy (Jaku-

bowski & Bartosz, 1997; Setsukinai et al., 2003; Soh,

2006; Miller & Chang, 2007; Hwang et al., 2011).

Caspases are proteases that contain a conserved penta-

peptide active site QACXG and need to be activated auto-

catalytically or by other caspases, to form heterodimers

that will then form active heterotetramers (Fan et al.,

2005; Kumar, 2007). So far, three caspases (Yca1, Esp1 and

Kex1) have been described in S. cerevisiae. Determination



of caspase activity in this organism is made using the cas-

pase inhibitor VAD-fmk (carbobenzoxy-valyl-alanyl-aspar-

tyl-[O-methyl]-fluoromethylketone) bounded to the

fluorogenic fluorescein isothiocyanate (FICT), which irre-

versibly binds to activated caspases and then can be visual-

ized using fluorescence microscopy or quantified by flow

cytometry (Sylte et al., 2000; Hauptmann & Lehle, 2008).

During apoptosis, the highly structured DNA in the form

of chromatin is damaged because of the proteolysis of

key nuclear proteins, which results in chromatin condensa-

tion in the vicinity of the nuclear envelope, leading to

visible crescent structures (Dobrucki & Darzynkiewicz,

2001; Martelli et al., 2001). This apoptotic feature is easily

observed using DNA dyes, such as 4′,6-diamidino-2-

phenylindole (DAPI). DAPI binds to AT sites within the

minor groove of DNA where its fluorescence can be mea-

sured at 468 nm (Tanious et al., 1992). The shape of the

nucleus can be observed by confocal microscopy, and it is

Fig. 3. Overview of methods to study apoptotic hallmarks in yeast Saccharomyces cerevisiae. Apoptosis was induced with ditiotreitiol (DTT) in

exponential phase, and after 5 h, cell shrinkage and membrane blebbing were observed with light microscopy. At this time, cells were harvested

and assayed for ROS determination (DHR), chromatin condensation (DAPI), DNA breakage (TUNEL), mitochondrial morphology (Mito Tracker), PS

externalization (Annexin V), and viability (FUN1). The cells were examined with fluorescence microscopy.



usually easy to discern normal looking cells with round

nuclei compared to cell with crescent shape nuclei (Fig. 3).

During chromatin condensation, the DNA strands get

exposed (Dobrucki & Darzynkiewicz, 2001) and are sus-

ceptible to degradation by the caspase-activated DNAse

(CAD) or endonuclease G (EndoG) (Wyllie, 1980; Samej-

ima & Earnshaw, 2005). In S. cerevisiae Nuc1, Aif1, and

Yca1 are the executors of this action (Madeo et al., 2009).

The fragmented DNA can be identified using the TUNEL

method (Gavrieli et al., 1992). This method uses the spe-

cific binding of terminal deoxynucleotidyl transferase

(TdT) to 3′OH of DNA nicks to incorporate deoxyuri-

dine bound to FICT, which can be detected by fluores-

cent microscopy or quantified by flow cytometry (the

cells that stain positive contain fragmented DNA). In

higher eukaryotes, it is possible also to identify the frag-

mented DNA by electrophoretic methods (Wyllie, 1980;

Duke et al., 1983; Ioannou & Chen, 1996). Due to the

fact that endonucleases cut between the nucleosomes (10–80 bp linker), a ladder of multiples of 200 bp is observed

in the electrophoresis gel. In yeast, it has not been possi-

ble implement this method and has been assumed that

the small linker (18 bp) between nucleosomes is the cause

(Madeo et al., 1997; Jansen & Verstrepen, 2011). How-

ever, it is possible to see DNA degradation using pulse

field gel electrophoresis (Ribeiro et al., 2006).

Mitochondria are key organelles in yeast apoptosis and

have been associated with aging and ROS formation

(Fannjiang et al., 2004; Balaban et al., 2005; Pereira et al.,

2008). In S. cerevisiae, these organelles are found as

dynamic intracellular networks that fuse and fragment

(fusion/fission) every 2 min in complex media during

exponential phase (Nunnari et al., 1997; Jensen et al.,

2000). These two events are in equilibrium and maintain

a balanced mitochondrial network. In yeast, Fzo1, Mgm1,

and Ugo1 regulate the fusion and Dnm1, Mvd1, and Fis1

regulate fission (Westermann, 2010). It has been observed

that fusion events are enhanced during cell stages that

require ATP synthesis (Egner et al., 2002). Furthermore,

the mitochondrial activity generates ROS, which can

damage the mitochondrion and impair respiration. The

damaged mitochondrion can undergo fusion with

‘healthy’ ones, and during DNA recombination events,

the mutated allele can be substituted and respiration can

be restored (Westermann, 2010). During apoptosis, the

fusion/fission equilibrium is misbalanced and the mito-

chondrial network gets fragmented (Fannjiang et al.,

2004), so the structure of the mitochondrial network can

be seen as one of the hallmarks of apoptosis. There are

several methods for study of mitochondrial networks

structures, such as staining with fluorescent dyes, labeling

of mitochondrial proteins or target green fluorescent pro-

tein with mitochondrial membrane spanning motifs (Poot

et al., 1996; Wiedenmann et al., 2009). Figure 3 shows

the mitochondrial network stained with MitoTracker

Green FM that accumulates in the mitochondrial matrix

because of the mitochondrial membrane potential where

its fluorescence is enhanced (Keij et al., 2000).

Another approach to study mitochondrial response to

apoptotic stimuli is the measurement of potential change

of the inner mitochondrial membrane (DΨm) that reflects

the change of the permeabilization of the membrane.

Mitochondria posses an outer and an inner membrane;

the latter is the boundary between the mitochondrial

matrix and intermembrane space. This membrane is

essentially impermeable to all ions, including protons.

This property allows the formation of a proton gradient

that is coupled to ADP phosphorylation (ATP synthesis)

(Mitchell & Moyle, 1965a, b). The formation of pores in

the mitochondrial inner membrane allows the flux of

protons down the gradient (DΨm dissipation), and some

apoptotic stimuli (chronological and replicative aging,

acetic acid, H2O2) have been described to induce mem-

brane permeabilization and the subsequent release of

mitochondrial contents, like cytochrome c and the apop-

tosis-inducing factor (Eisenberg et al., 2007; Kroemer

et al., 2007, 2009). It is possible to use lipophilic cations,

like rhodamine 123, to dye mitochondria and determine

DΨm (Grinius et al., 1970; Metivier et al., 1998; Ludovico

et al., 2001). The spectroscopic properties of the dye

change owing to the change in its environment (from in-

termembrane space to inner membrane) and hence can

be detected by fluorescent microscopy. It is also possible

to use flow cytometry (Emaus et al., 1986; Ludovico

et al., 2001) and obtain the percentage of the cells in the

population that have DΨm dissipation.

An important hallmark in mammalian apoptosis is the

externalization of PS. This negatively charged phospho-

lipid is especially abundant in the plasma membrane

where it is asymmetrically distributed on the cytosolic

side of the membrane. This asymmetry is attributed to

the activity of flippases (Cerbon & Calderon, 1991; Dale-

ke, 2007; van Meer et al., 2008). However, when intracel-

lular Ca2+ levels increase during apoptosis as a result of

Ca2+ release from the ER, an enzyme (scramblase, that

randomizes the lipid distribution in the membrane) gets

activated and some PS gets also transferred to the outer

side if the membrane (PS externalization) (Frasch et al.,

2000; Vance & Steenbergen, 2005). In mammalian, insect

and nematode models PS is recognized by phagocytes

with the subsequent engulfment of the apoptotic cell

(Henson et al., 2001). In S. cerevisiae, PS externalization

has also been observed, suggesting a fundamental role of

this event during apoptosis (Madeo et al., 1997); how-

ever, no scramblase activity has been described in yeast so

far. Additionally, most Ca2+ flux in yeast would probably



be due to calcium release from the vacuole, where its

concentration is higher than in the ER.

To assess PS externalization, the Annexin V assay is

used: Annexin V is a phospholipid binding protein with

affinity for PS, so by coupling this protein to a reporter

protein, it is possible to visualize or measure PS external-

ization by fluorescence microscopy or flow cytometry

(Schutters & Reutelingsperger, 2010). This technique,

combined with propidium iodide (PI) vital staining,

allows for the identification and discrimination of apop-

totic and secondary necrotic cells (Madeo et al., 1997;

Carmona-Gutierrez et al., 2010). PI is a dye that interca-

lates in DNA and enhances its fluorescence to 30-fold at

535 nm showing an intense red color. A hallmark that

allows the differentiation between early apoptosis and

necrosis is the fact that during early apoptosis, the cell

membrane is not compromised whereas necrotic cells suf-

fer ruptures in the membrane. PI is not able to diffuse

across cell membrane, so early apoptotic cells will not

show PI staining and necrotic cells will (Fig. 3). During

late apoptosis, the membrane is compromised so late

apoptotic cells show as well staining with PI. As well in

this case, the Annexin V test can differentiate between

late apoptotic cells and necrotic cells due to the fact that

apoptotic cell would be double positive for Annexin V

and PI whereas necrotic cells are only PI positive (Car-

mona-Gutierrez et al., 2010).

Systems biology of cell death

Cellular processes are complex and can be viewed as net-

works made of many components (nodes, e.g. proteins)

and interactions (edges), and to make it even more com-

plicated, some proteins have more than one role (and are

involved in several modules/pathways) and some proteins

are redundant (i.e. there is more than one protein for a

given function). Identifying and studying each component

(gene and protein) one at the time is necessary but might

not be enough to understand the networks underlying

biological systems, which need to be considered in toto.

The aim of systems biology is to study biological pro-

cesses at a system level by quantitatively analyzing data,

integrating genome-scale information, and building mod-

els by depicting connected components of cellular path-

ways of interest in silico (Kitano, 2002; Ge et al., 2003).

This approach allows biological insight into system topol-

ogies, system dynamics, and control mechanisms deter-

mining functions and phenomena of biological systems

(Kitano, 2002; Ng et al., 2006). The final goal of systems

biology approach would be creation of a useful model

that can simulate reality and also have predictive powers

that can be used to guide new hypotheses and design of

experiments. The newly obtained data should be fed-back

to the model, and experimental and computational

approaches should be iterative and complementary.

A model by definition is a reduced and simplified rep-

resentation of reality (Kohl et al., 2010). It is extensively

used in systems biology as a tool for predicting system

behaviors under specific conditions and as a scaffold for

integrated data analysis (Petranovic & Nielsen, 2008).

Model building is intrinsically an iterative process

(Fig. 4). Once initialized, the model is tested and refined

to better represent cellular processes. We propose four

basic steps of modeling strategy (4D):

(1)Define: one should begin by defining the scope and

the boundaries of the project to determine what/how it

will be performed, by asking ‘what is the biological ques-

tion?’, ‘what is the availability of data and what is the

level of knowledge at the moment?’, ‘what should be

answered?’, ‘what can be answered?’, and ‘which type of

model should be used?’.

(2)Design: this step results in creation of a static model

(map) of the pathway of interest. All genes, proteins, and

other components of the pathway should be identified and

connected using information from published literature

and databases. This draft model is used in the next step

for designing experiments and integrated data analysis.

Fig. 4. The modeling cycle illustrates the iterative process of model

building. In the first stage, the biological question to be studied is

defined, and then the draft model of the pathway of interest is

created from literature, databases and experimental data. The model

is completed through integration of information and is used for

simulation and prediction of the system. The model is further refined

with data from targeted experiments.



(3)Develop: more features (components and connections)

are added to the model, as a result of perturbation exper-

iments and integrated data analysis. A mathematical

model can be formalized to represent system dynamics,

and experimental data are considered to estimate key

parameters.

(4)Deploy and Debug: through model analysis and simu-

lation, testable predictions are obtained and novel proper-

ties are identified. Additional experimental data are often

required for model refinement and verification. The

obtained knowledge might give rise to a new hypothesis

(new cycle) that might further be studied through the

extension of the current model.

Systems biology approach has been applied in several

studies of different cellular pathways including PCD (Bia-

lik et al., 2010; Calzone et al., 2010; Lavrik, 2010). From

the systems point, apoptosis is a complex system com-

posed of several signaling cascades, regulatory pathways,

and complex crosstalks with other death and cellular reg-

ulatory modules. To understand such a complex pathway,

several mathematical models of apoptosis, in mammalian

cells, were proposed using systems biology strategies (Lav-

rik et al., 2009).

Mathematical models, either discrete models (such as

Boolean models) or continuous models using ordinary

differential equations (ODEs) and partial differential

equations, have been used to analyze apoptotic signaling

networks. An ODE model from Bentele et al. (2004) was

the first signaling model of CD95-induced apoptosis and

it was constructed by integrating information from data-

bases and literature, and the parameters (such as reaction

rates) were adjusted according to experimental data. The

model was analyzed to identify critical system parameters

and to design a set of experiments to collect more param-

eters for the model and to validate model predictions.

Through the iterative processes of model refinement, they

successfully showed strong predictions of systemic behav-

ior of CD95-induced apoptosis with experimental valida-

tion (Bentele et al., 2004). Additional dynamic models

include a model of apoptotic intrinsic pathway (Rehm

et al., 2006) and modeling of combined intrinsic and

extrinsic pathway (Albeck et al., 2008).

Most of the proposed models focus on one cell death

pathway (e.g. apoptosis); however, the routes of cell death

can be apoptosis, necrosis, and autophagy (Bialik et al.,

2010). By applying systems biology, Zalckvar et al. (2010)

developed a system-level platform to explore large-scale

interconnections between three cell death modules in a

mammalian cell. An initial static network of cell death

modules was manually constructed, yielding a model with

functional connectivity among cell death routines. A sys-

tem-level analysis of the established cell death network

model using RNA interference (RNAi)-mediated pertur-

bations and computational approaches revealed crosstalks

between modules and the new connectivity between pro-

teins involved in apoptosis and autophagy.

Another integrative model of mammalian cell death is

a discrete Boolean model from Calzone et al. (2010). An

integrated-regulatory network of cell fate decision was

reconstructed based on information from literature, and

its dynamics were formalized into a Boolean model that

shows mechanisms of cell fate decisions (apoptosis,

necrosis or survival), in response to signaling from death

receptors TNFR and Fas. The model was used to

enhance understanding of cell fate decision mechanisms,

predicting effects of novel perturbations and proposing

novel experiments that could also serve to validate the

model.

The mentioned studies showed successful uses of sys-

tems biology for understanding cell death in mammalian

cells. In yeast, even though several studies have identified

in detail important elements of the apoptotic machinery,

as well as triggers, regulators and mechanisms involved in

initiating or carrying out the apoptotic response (Carmo-

na-Gutierrez et al., 2010), and understanding of necrosis

and autophagy is also increasing, (Cebollero & Reggiori,

2009; Eisenberg et al., 2010; Kanki & Klionsky, 2010), we

are still lacking quantitative or mathematical models of

both individual and combined modes of yeast cell death

to investigate dynamic behaviors of these pathways. Fig-

ure 5b shows a static model of yeast cell death, in which

we represent some putative and unidentified proteins or

interactions that are to be identified experimentally. Sys-

tems biology approach has been used successfully in stud-

ies of mammalian PCD, and we believe that the similar

approaches would be useful in studying yeast cell death

by providing a more complete view of cell death modules

and their interplay.

Yeast has many benefits that suit the systems approach:

it is cheap and easy to grow in controlled conditions, and

we have the ability to easily change and control the

genetic and/or regulatory networks (genetically or envi-

ronmentally) so it can be ideal for designing different

experiments that can generate large sets of high quality

data. In addition, we already have datasets from large-

scale studies that have been performed such as transcrip-

tome, proteome, metabolome, interactome, locasome, and

phosphoproteome (Petranovic & Nielsen, 2008; Snyder &

Gallagher, 2009). We believe that one of the first needs in

this field will be to collect and organize the relevant data

and information into a repository, which will serve as a

community resource and database, but will be also used

as a bioinformatics and systems biology platform for data

mining and analysis, visualization, and modeling.



Fig. 5. PCD networks, generated with CellDesigner (Funahashi et al., 2003): (a) mammalian cell and (b) yeast Saccharomyces cerevisiae. PCD

networks are composed of nodes (molecular components, in this case proteins) and edges (interactions, activations, inhibitions). Three PCD

modules are shown: apoptosis (grey), necrosis (light purple), and autophagy (green), which can contribute to different phenotypes (dark purple).

Proteins marked in orange are involved in more than one module. Dashes denote putative proteins or interactions that have not been confirmed

experimentally yet. (a) Adapted from Bialik et al. (2010) and (b) created from Carmona-Gutierrez et al. (2010), Eisenberg et al. (2010).



Outlook

Yeast has been a model organism to study many cell path-

ways that are conserved along the evolutionary tree and

with the toolbox filled with molecular, cellular, biochemi-

cal, and genetic tool as well as with -omics datasets, we

have more resources in the field of yeast cell death. We

consider several additional tools would be helpful: (i) stan-

dardization of experimental protocols and methods for use

of different yeast strains, different stress/death inducers,

cultivation conditions, etc.; (ii) more focused large experi-

mental datasets, for example, transcriptome, proteome,

metabolome aimed specifically at conditions that are inter-

esting for cell death study; (iii) more quantitative methods

especially methods for earlier detection of activation of

pathways (such as Q-PCR). For example, so far little is

known about early events that lead to apoptosis, such as

transcriptional regulation that precedes caspase activation.

In a recent study (Meza E, Johansson M, Munoz AJ &

Petranovic D, unpublished data) using environmental

stress response transcription data (Gasch et al., 2000), we

separated the transcriptional responses for individual stres-

ses (e.g. oxidative, endoplasmic reticulum, heat shock,

hyper osmotic stresses among others) and filtered the data

to search for genes that were commonly overexpressed in

two or more stresses and for the genes that were overex-

pressed exclusively in one type of stress. On the basis of

this analysis, we designed a Q-PCR method and tested the

sets of unique genes, in different stress conditions and at

different levels (mild stress where cells are able to cope, or

induction of apoptosis which we monitored by previously

mentioned methods). We included in the test genes coding

for different effectors and markers of apoptosis and found

that they are not under a transcriptional control in these

conditions but that many other specific genes (involved in

stress response) do have a particular transcriptional

response prior to the initiation of apoptosis.

Last but not least, we think there is a need for a dedi-

cated and specific database that will collect and organize

relevant information, resources, protocols, references, and

published datasets that would provide support for experi-

mental and theoretical (e.g. modeling and bioinformatics)

efforts in the field.

We believe that studying yeast cell death from systems

biology perspective will provide a global view that is nec-

essary for better understanding of complex process in

yeast. As our knowledge about yeast cell death grows, it

will be possible to integrate these pathways with other

cellular networks, such as cell cycle/division, aging, prote-

ostasis, and metabolism, and a more complete picture will

additionally allow for better use of yeast in fundamental

and medical research, and biotechnology.

Acknowledgements

We would like to thank Dr Jin Hou for the photograph

of yeast viability test, and we thank The Wallenberg

Foundation, The Chalmers Foundation, and the Depart-

ment of Chemical and Biological Engineering at Chalmers

for funding and support.

Authors’ contribution

A.J.M., K.W. and E.M. have equally contributed.

References

Albeck JG, Burke JM, Spencer SL, Lauffenburger DA &

Sorger PK (2008) Modeling a snap-action, variable-delay

switch controlling extrinsic cell death. PLoS Biol 6: 2831–2852.

Ameisen JC (2002) On the origin, evolution, and nature of

programmed cell death: a timeline of four billion years. Cell

Death Differ 9: 367–393.Antonsson B & Martinou JC (2000) The Bcl-2 protein family.

Exp Cell Res 256: 50–57.Baek GH, Kim I & Rao H (2011) The Cdc48 ATPase

modulates the interaction between two proteolytic

factors Ufd2 and Rad23. P Natl Acad Sci USA 108:

13558–13563.Balaban RS, Nemoto S & Finkel T (2005) Mitochondria,

oxidants, and aging. Cell 120: 483–495.Barros MH, da Cunha FM, Oliveira GA, Tahara EB &

Kowaltowski AJ (2010) Yeast as a model to study

mitochondrial mechanisms in ageing. Mech Ageing Dev 131:

494–502.Bentele M, Lavrik I, Ulrich M, Stoßer S, Heermann DW,

Kalthoff H, Krammer PH & Eils R (2004) Mathematical

modeling reveals threshold mechanism in CD95-induced

apoptosis. J Cell Biol 166: 839–851.Bialik S, Zalckvar E, Ber Y, Rubinstein AD & Kimchi A (2010)

Systems biology analysis of programmed cell death. Trends

Biochem Sci 35: 556–564.Blagosklonny MV (2000) Cell death beyond apoptosis.

Leukemia 14: 1502–1508.Brouckaert G, Kalai M, Krysko DV, Saelens DX, Vercammen

D, Ndlovu MN, Haegeman G, D′Herde K & Vandenabeele

P (2004) Phagocytosis of necrotic cells by macrophages is

phosphatidylserine dependent and does not induce

inflammatory cytokine production. Mol Biol Cell 15: 1089–1100.

Buttner S, Ruli D, Vogtle FN et al. (2011) A yeast BH3-only

protein mediates the mitochondrial pathway of apoptosis.

EMBO J 30: 2779–2792.Cacciapaglia F, Spadaccio C, Chello M, Gigante A, Coccia R,

Afeltra A & Amoroso A (2009) Apoptotic molecular

mechanisms implicated in autoimmune diseases. Eur Rev

Med Pharmacol Sci 13: 23–40.



Cain K, Bratton SB & Cohen GM (2002) The Apaf-1

apoptosome: a large caspase-activating complex. Biochimie

84: 203–214.Calzone L, Tournier L, Fourquet S, Thieffry D, Zhivotovsky B,

Barillot E & Zinovyev A (2010) Mathematical modelling of

cell-fate decision in response to death receptor engagement.

PLoS Comput Biol 6: e1000702.

Carmona-Gutierrez D & Madeo F (2009) Tracing the Roots of

Death: Apoptosis in Saccharomyces cerevisiae. Essentials of

Apoptosis – A Guide for Basic and Clinical Research.

Humana Press, 325–354.Carmona-Gutierrez D, Eisenberg T, Buttner S, Meisinger C,

Kroemer G & Madeo F (2010) Apoptosis in yeast: triggers,

pathways, subroutines. Cell Death Differ 17: 763–773.Cash TP, Pan Y & Simon MC (2007) Reactive oxygen species

and cellular oxygen sensing. Free Radic Biol Med 43: 1219–1225.

Cebollero E & Reggiori F (2009) Regulation of autophagy in

yeast Saccharomyces cerevisiae. Biochim Biophys Acta 1793:

1413–1421.Cerbon J & Calderon V (1991) Changes of the compositional

asymmetry of phospholipids associated to the increment in

the membrane surface potential. Biochim Biophys Acta 1067:

139–144.Chowdhury I, Tharakan B & Bhat GK (2008) Caspases – an

update. Comp Biochem Physiol B Biochem Mol Biol 151: 10–27.

Clarke PG & Clarke S (1996) Nineteenth century research on

naturally occurring cell death and related phenomena. Anat

Embryol (Berl) 193: 81–99.Cohen GM, Sun XM, Fearnhead H, MacFarlane M, Brown

DG, Snowden RT & Dinsdale D (1994) Formation of large

molecular weight fragments of DNA is a key committed

step of apoptosis in thymocytes. J Immunol 153: 507–516.Cooper AA, Gitler AD, Cashikar A et al. (2006) Alpha-

synuclein blocks ER–Golgi traffic and Rab1 rescues neuron

loss in Parkinson’s models. Science 313: 324–328.Daleke DL (2007) Phospholipid flippases. J Biol Chem 282:

821–825.Darzynkiewicz Z, Juan G, Li X, Gorczyca W, Murakami T &

Traganos F (1997) Cytometry in cell necrobiology: analysis

of apoptosis and accidental cell death (necrosis). Cytometry

27: 1–20.D’Autreaux B & Toledano MB (2007) ROS as signalling

molecules: mechanisms that generate specificity in ROS

homeostasis. Nat Rev Mol Cell Biol 8: 813–824.Degterev A, Huang Z, Boyce M, Li Y, Jagtap P, Mizushima N,

Cuny GD, Mitchison TJ, Moskowitz MA & Yuan J (2005)

Chemical inhibitor of nonapoptotic cell death with

therapeutic potential for ischemic brain injury. Nat Chem

Biol 1: 112–119.Deponte M (2008) Programmed cell death in protists. Biochim

Biophys Acta 1783: 1396–1405.Dobrucki J & Darzynkiewicz Z (2001) Chromatin

condensation and sensitivity of DNA in situ to denaturation

during cell cycle and apoptosis – a confocal microscopy

study. Micron 32: 645–652.Duennwald ML, Jagadish S, Giorgini F, Muchowski PJ &

Lindquist S (2006) A network of protein interactions

determines polyglutamine toxicity. P Natl Acad Sci USA

103: 11051–11056.Duke RC, Chervenak R & Cohen JJ (1983) Endogenous

endonuclease-induced DNA fragmentation: an early event in

cell-mediated cytolysis. P Natl Acad Sci USA 80: 6361–6365.Egner A, Jakobs S & Hell SW (2002) Fast 100-nm resolution

three-dimensional microscope reveals structural plasticity of

mitochondria in live yeast. P Natl Acad Sci USA 99: 3370–3375.

Eisenberg T, Buttner S, Kroemer G & Madeo F (2007) The

mitochondrial pathway in yeast apoptosis. Apoptosis 12:

1011–1023.Eisenberg T, Carmona-Gutierrez D, Buttner S, Tavernarakis N

& Madeo F (2010) Necrosis in yeast. Apoptosis 15: 257–268.Emaus RK, Grunwald R & Lemasters JJ (1986) Rhodamine 123

as a probe of transmembrane potential in isolated rat-liver

mitochondria: spectral and metabolic properties. Biochim

Biophys Acta 850: 436–448.Fadeel B, Orrenius S & Zhivotovsky B (1999) Apoptosis in

human disease: a new skin for the old ceremony? Biochem

Biophys Res Commun 266: 699–717.Fan TJ, Han LH, Cong RS & Liang J (2005) Caspase family

proteases and apoptosis. Acta Biochim Biophys Sin

(Shanghai) 37: 719–727.Fannjiang Y, Cheng WC, Lee SJ, Qi B, Pevsner J, McCaffery

JM, Hill RB, Basanez G & Hardwick JM (2004)

Mitochondrial fission proteins regulate programmed cell

death in yeast. Genes Dev 18: 2785–2797.Fields S & Johnston M (2005) Cell biology. Whither model

organism research? Science 307: 1885–1886.Fiers W, Beyaert R, Boone E et al. (1995) TNF-induced

intracellular signaling leading to gene induction or to

cytotoxicity by necrosis or by apoptosis. J Inflamm 47: 67–75.Fleury C, Pampin M, Tarze A & Mignotte B (2002) Yeast as a

model to study apoptosis? Biosci Rep 22: 59–79.Forkink M, Smeitink JA, Brock R, Willems PH & Koopman

WJ (2010) Detection and manipulation of mitochondrial

reactive oxygen species in mammalian cells. Biochim Biophys

Acta 1797: 1034–1044.Foury F (1997) Human genetic diseases: a cross-talk between

man and yeast. Gene 195: 1–10.Frasch SC, Henson PM, Kailey JM, Richter DA, Janes MS,

Fadok VA & Bratton DL (2000) Regulation of phospholipid

scramblase activity during apoptosis and cell activation by

protein kinase Cdelta. J Biol Chem 275: 23065–23073.Frohlich KU & Madeo F (2000) Apoptosis in yeast – a

monocellular organism exhibits altruistic behaviour. FEBS

Lett 473: 6–9.Funahashi A, Morohashia M, Kitano H & Tanimura N (2003)

CellDesigner: a process diagram editor for gene-regulatory

and biochemical networks. BIOSILICO 1: 3.



Galluzzi L, Aaronson SA, Abrams J et al. (2009) Guidelines for

the use and interpretation of assays for monitoring cell

death in higher eukaryotes. Cell Death Differ 16: 1093–1107.Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB,

Storz G, Botstein D & Brown PO (2000) Genomic

expression programs in the response of yeast cells to

environmental changes. Mol Biol Cell 11: 4241–4257.Gavrieli Y, Sherman Y & Ben-Sasson SA (1992) Identification

of programmed cell death in situ via specific labeling of

nuclear DNA fragmentation. J Cell Biol 119: 493–501.Ge H, Walhout AJ & Vidal M (2003) Integrating ‘omic’

information: a bridge between genomics and systems

biology. Trends Genet 19: 551–560.Grinius LL, Jasaitis AA, Kadziauskas YP, Liberman EA, Skulachev

VP, Tsofina LM&Vladimirova MA (1970) Conversion of

biomembrane-produced energy into electric form. I.

Submitochondrial particles. Biochim Biophys Acta 216: 1–12.Hacker G (2000) The morphology of apoptosis. Cell Tissue Res

301: 5–17.Hanada M, Aime-Sempe C, Sato T & Reed JC (1995)

Structure–function analysis of Bcl-2 protein. Identification

of conserved domains important for homodimerization with

Bcl-2 and heterodimerization with Bax. J Biol Chem 270:

11962–11969.Hauptmann P & Lehle L (2008) Kex1 protease is involved in

yeast cell death induced by defective N-glycosylation, acetic

acid, and chronological aging. J Biol Chem 283: 19151–19163.

Henson PM, Bratton DL & Fadok VA (2001) The

phosphatidylserine receptor: a crucial molecular switch? Nat

Rev Mol Cell Biol 2: 627–633.Herker E, Jungwirth H, Lehmann KA, Maldener C, Frohlich

KU, Wissing S, Buttner S, Fehr M, Sigrist S & Madeo F

(2004) Chronological aging leads to apoptosis in yeast. J

Cell Biol 164: 501–507.Hirt L, Badaut J, Thevenet J, Granziera C, Regli L, Maurer F,

Bonny C & Bogousslavsky J (2004) D-JNKI1, a cell-

penetrating c-Jun-N-terminal kinase inhibitor, protects

against cell death in severe cerebral ischemia. Stroke 35:

1738–1743.Hwang B, Hwang JS, Lee J, Kim JK, Kim SR, Kim Y & Lee

DG (2011) Induction of yeast apoptosis by an antimicrobial

peptide, Papiliocin. Biochem Biophys Res Commun 408: 89–93.

Ioannou YA & Chen FW (1996) Quantitation of DNA

fragmentation in apoptosis. Nucleic Acids Res 24: 992–993.Jakubowski W & Bartosz G (1997) Estimation of oxidative

stress in Saccharomyces cerevisae with fluorescent probes. Int

J Biochem Cell Biol 29: 1297–1301.Jansen A & Verstrepen KJ (2011) Nucleosome positioning in

Saccharomyces cerevisiae. Microbiol Mol Biol Rev 75: 301–320.Jazwinski SM (2005) Yeast longevity and aging–the

mitochondrial connection. Mech Ageing Dev 126: 243–248.Jensen RE, Hobbs AE, Cerveny KL & Sesaki H (2000) Yeast

mitochondrial dynamics: fusion, division, segregation, and

shape. Microsc Res Tech 51: 573–583.

Jin C & Reed JC (2002) Yeast and apoptosis. Nat Rev Mol Cell

Biol 3: 453–459.Joaquin AM & Gollapudi S (2001) Functional decline in aging

and disease: a role for apoptosis. J Am Geriatr Soc 49: 1234–1240.

Jurgensmeier JM, Krajewski S, Armstrong RC, Wilson GM,

Oltersdorf T, Fritz LC, Reed JC & Ottilie S (1997) Bax- and

Bak-induced cell death in the fission yeast

Schizosaccharomyces pombe. Mol Biol Cell 8: 325–339.Kanki T & Klionsky DJ (2010) The molecular mechanism of

mitochondria autophagy in yeast. Mol Microbiol 75: 795–800.

Keij JF, Bell-Prince C & Steinkamp JA (2000) Staining of

mitochondrial membranes with 10-nonyl acridine orange,

MitoFluor Green, and MitoTracker Green is affected by

mitochondrial membrane potential altering drugs. Cytometry

39: 203–210.Kerr JF, Wyllie AH & Currie AR (1972) Apoptosis: a basic

biological phenomenon with wide-ranging implications in

tissue kinetics. Br J Cancer 26: 239–257.Kitano H (2002) Systems biology: a brief overview. Science

295: 1662–1664.Kohl P, Crampin EJ, Quinn TA & Noble D (2010) Systems

biology: an approach. Clin Pharmacol Ther 88: 25–33.Komatsu K, Miyashita T, Hang H, Hopkins KM, Zheng W,

Cuddeback S, Yamada M, Lieberman HB & Wang HG

(2000) Human homologue of S. pombe Rad9 interacts with

BCL-2/BCL-xL and promotes apoptosis. Nat Cell Biol 2: 1–6.

Kroemer G & Martin SJ (2005) Caspase-independent cell

death. Nat Med 11: 725–730.Kroemer G, El-Deiry WS, Golstein P et al. (2005)

Classification of cell death: recommendations of the

Nomenclature Committee on Cell Death. Cell Death Differ

12(suppl 2): 1463–1467.Kroemer G, Galluzzi L & Brenner C (2007) Mitochondrial

membrane permeabilization in cell death. Physiol Rev 87:

99–163.Kroemer G, Galluzzi L, Vandenabeele P et al. (2009)

Classification of cell death: recommendations of the

Nomenclature Committee on Cell Death 2009. Cell Death

Differ 16: 3–11.Kumar S (2007) Caspase function in programmed cell death.

Cell Death Differ 14: 32–43.Laster SM, Wood JG & Gooding LR (1988) Tumor necrosis

factor can induce both apoptic and necrotic forms of cell

lysis. J Immunol 141: 2629–2634.Laun P, Pichova A, Madeo F, Fuchs J, Ellinger A, Kohlwein S,

Dawes I, Frohlich KU & Breitenbach M (2001) Aged

mother cells of Saccharomyces cerevisiae show markers of

oxidative stress and apoptosis. Mol Microbiol 39: 1166–1173.Lavrik IN (2010) Systems biology of apoptosis signaling

networks. Curr Opin Biotechnol 21: 551–555.Lavrik IN, Eils R, Fricker N, Pforr C & Krammer PH (2009)

Understanding apoptosis by systems biology approaches.

Mol Biosyst 5: 1105–1111.



Leist M & Jaattela M (2001) Four deaths and a funeral: from

caspases to alternative mechanisms. Nat Rev Mol Cell Biol 2:

589–598.Lettre G & Hengartner M (2006) Developmental apoptosis in

C. elegans: a complex CEDnario. Nat Rev Mol Cell Biol 7:

97–108.Li LY, Luo X & Wang X (2001) Endonuclease G is an

apoptotic DNase when released from mitochondria. Nature

412: 95–99.Ligr M, Madeo F, Frohlich E, Hilt W, Frohlich KU & Wolf

DH (1998) Mammalian Bax triggers apoptotic changes in

yeast. FEBS Lett 438: 61–65.Lockshin AL & Williams MC (1964) Programmed cell death—

II. Endocrine potentiation of the breakdown of the

intersegmental muscles of silkmoths. J Insect Physiol 10:

643–649.Longo VD, Ellerby LM, Bredesen DE, Valentine JS & Gralla EB

(1997) Human Bcl-2 reverses survival defects in yeast

lacking superoxide dismutase and delays death of wild-type

yeast. J Cell Biol 137: 1581–1588.Ludovico P, Madeo F & Silva M (2005) Yeast programmed

cell death: an intricate puzzle. IUBMB Life 57: 129–135.Ludovico P, Sousa MJ, Silva MT, Leao C & Corte-Real M

(2001) Saccharomyces cerevisiae commits to a programmed

cell death process in response to acetic acid. Microbiology

147: 2409–2415.Madeo F, Frohlich E & Frohlich KU (1997) A yeast mutant

showing diagnostic markers of early and late apoptosis. J

Cell Biol 139: 729–734.Madeo F, Frohlich E, Ligr M, Grey M, Sigrist SJ, Wolf DH &

Frohlich KU (1999) Oxygen stress: a regulator of apoptosis

in yeast. J Cell Biol 145: 757–767.Madeo F, Herker E, Maldener C et al. (2002) A caspase-related

protease regulates apoptosis in yeast. Mol Cell 9: 911–917.Madeo F, Herker E, Wissing S, Jungwirth H, Eisenberg T &

Frohlich KU (2004) Apoptosis in yeast. Curr Opin Microbiol

7: 655–660.Madeo F, Carmona-Gutierrez D, Ring J, Buttner S, Eisenberg

T & Kroemer G (2009) Caspase-dependent and caspase-

independent cell death pathways in yeast. Biochem Biophys

Res Commun 382: 227–231.Manon S, Chaudhuri B & Guerin M (1997) Release of

cytochrome c and decrease of cytochrome c oxidase in Bax-

expressing yeast cells, and prevention of these effects by

coexpression of Bcl-xL. FEBS Lett 415: 29–32.Martelli AM, Zweyer M, Ochs RL, Tazzari PL, Tabellini G,

Narducci P & Bortul R (2001) Nuclear apoptotic changes:

an overview. J Cell Biochem 82: 634–646.Martin SJ & Green DR (1995) Protease activation during

apoptosis: death by a thousand cuts? Cell 82: 349–352.Martin SJ, Reutelingsperger CP, McGahon AJ, Rader JA, van

Schie RC, LaFace DM & Green DR (1995) Early

redistribution of plasma membrane phosphatidylserine is a

general feature of apoptosis regardless of the initiating

stimulus: inhibition by overexpression of Bcl-2 and Abl. J

Exp Med 182: 1545–1556.

Matsuyama S, Nouraini S & Reed JC (1999) Yeast as a tool for

apoptosis research. Curr Opin Microbiol 2: 618–623.Mazzarello P (1999) A unifying concept: the history of cell

theory. Nat Cell Biol 1: E13–E15.McCarthy NJ, Whyte MK, Gilbert CS & Evan GI (1997)

Inhibition of Ced-3/ICE-related proteases does not prevent

cell death induced by oncogenes, DNA damage, or the Bcl-2

homologue Bak. J Cell Biol 136: 215–227.Metivier D, Dallaporta B, Zamzami N, Larochette N, Susin SA,

Marzo I & Kroemer G (1998) Cytofluorometric detection of

mitochondrial alterations in early CD95/Fas/APO-1-

triggered apoptosis of Jurkat T lymphoma cells. Comparison

of seven mitochondrion-specific fluorochromes. Immunol

Lett 61: 157–163.Metzstein MM, Stanfield GM & Horvitz HR (1998) Genetics

of programmed cell death in C. elegans: past, present and

future. Trends Genet 14: 410–416.Millard PJ, Roth BL, Thi HP, Yue ST & Haugland RP (1997)

Development of the FUN-1 family of fluorescent probes for

vacuole labeling and viability testing of yeasts. Appl Environ

Microbiol 63: 2897–2905.Miller EW & Chang CJ (2007) Fluorescent probes for nitric

oxide and hydrogen peroxide in cell signaling. Curr Opin

Chem Biol 11: 620–625.Miura M (2011) Active participation of cell death in

development and organismal homeostasis. Dev Growth

Differ 53: 125–136.Mitchell P & Moyle J (1965a) Evidence discriminating

between the chemical and the chemiosmotic mechanisms

of electron transport phosphorylation. Nature 208: 1205–1206.

Mitchell P & Moyle J (1965b) Stoichiometry of proton

translocation through the respiratory chain and adenosine

triphosphatase systems of rat liver mitochondria. Nature

208: 147–151.Moir D, Stewart SE, Osmond BC & Botstein D (1982) Cold-

sensitive cell-division-cycle mutants of yeast: isolation,

properties, and pseudoreversion studies. Genetics 100: 547–563.

Monney L, Otter I, Olivier R, Ozer HL, Haas AL, Omura S &

Borner C (1998) Defects in the ubiquitin pathway induce

caspase-independent apoptosis blocked by Bcl-2. J Biol

Chem 273: 6121–6131.Muradian K & Schachtschabel DO (2001) The role of

apoptosis in aging and age-related disease: update. Z

Gerontol Geriatr 34: 441–446.Ng A, Bursteinas B, Gao Q, Mollison E & Zvelebil M (2006)

Resources for integrative systems biology: from data

through databases to networks and dynamic system models.

Brief Bioinform 7: 318–330.Nunnari J, Marshall WF, Straight A, Murray A, Sedat JW &

Walter P (1997) Mitochondrial transmission during

mating in Saccharomyces cerevisiae is determined by

mitochondrial fusion and fission and the

intramitochondrial segregation of mitochondrial DNA.

Mol Biol Cell 8: 1233–1242.



Pereira C, Silva RD, Saraiva L, Johansson B, Sousa MJ &

Corte-Real M (2008) Mitochondria-dependent apoptosis in

yeast. Biochim Biophys Acta 1783: 1286–1302.Perrone GG, Tan SX & Dawes IW (2008) Reactive oxygen

species and yeast apoptosis. Biochim Biophys Acta 1783:

1354–1368.Petranovic D & Nielsen J (2008) Can yeast systems biology

contribute to the understanding of human disease? Trends

Biotechnol 26: 584–590.Pierce GB, Parchment RE & Lewellyn AL (1991) Hydrogen

peroxide as a mediator of programmed cell death in the

blastocyst. Differentiation 46: 181–186.Poot M, Zhang YZ, Kramer JA, Wells KS, Jones LJ, Hanzel

DK, Lugade AG, Singer VL & Haugland RP (1996) Analysis

of mitochondrial morphology and function with novel

fixable fluorescent stains. J Histochem Cytochem 44: 1363–1372.

Proskuryakov SY, Konoplyannikov AG & Gabai VL (2003)

Necrosis: a specific form of programmed cell death? Exp

Cell Res 283: 1–16.Rehm M, Huber HJ, Dussmann H & Prehn JH (2006) Systems

analysis of effector caspase activation and its control by X-

linked inhibitor of apoptosis protein. EMBO J 25: 4338–4349.

Ribeiro GF, Corte-Real M & Johansson B (2006)

Characterization of DNA damage in yeast apoptosis induced

by hydrogen peroxide, acetic acid, and hyperosmotic shock.

Mol Biol Cell 17: 4584–4591.Riedl SJ & Shi Y (2004) Molecular mechanisms of caspase

regulation during apoptosis. Nat Rev Mol Cell Biol 5: 897–907.

Salvesen GS & Dixit VM (1997) Caspases: intracellular

signaling by proteolysis. Cell 91: 443–446.Samali A, Zhivotovsky B, Jones D, Nagata S & Orrenius S

(1999) Apoptosis: cell death defined by caspase activation.

Cell Death Differ 6: 495–496.Samejima K & Earnshaw WC (2005) Trashing the genome: the

role of nucleases during apoptosis. Nat Rev Mol Cell Biol 6:

677–688.Sato T, Hanada M, Bodrug S et al. (1994) Interactions among

members of the Bcl-2 protein family analyzed with a yeast

two-hybrid system. P Natl Acad Sci USA 91: 9238–9242.Schutters K & Reutelingsperger C (2010) Phosphatidylserine

targeting for diagnosis and treatment of human diseases.

Apoptosis 15: 1072–1082.Setsukinai K, Urano Y, Kakinuma K, Majima HJ & Nagano T

(2003) Development of novel fluorescence probes that can

reliably detect reactive oxygen species and distinguish

specific species. J Biol Chem 278: 3170–3175.Snyder M & Gallagher JE (2009) Systems biology from a yeast

omics perspective. FEBS Lett 583: 3895–3899.Soh N (2006) Recent advances in fluorescent probes for the

detection of reactive oxygen species. Anal Bioanal Chem

386: 532–543.Sokolov S, Pozniakovsky A, Bocharova N, Knorre D & Severin

F (2006) Expression of an expanded polyglutamine domain

in yeast causes death with apoptotic markers. Biochim

Biophys Acta 1757: 660–666.Sun XM, Snowden RT, Dinsdale D, Ormerod MG & Cohen

GM (1994) Changes in nuclear chromatin precede

internucleosomal DNA cleavage in the induction of

apoptosis by etoposide. Biochem Pharmacol 47: 187–195.Susin SA, Lorenzo HK, Zamzami N et al. (1999) Molecular

characterization of mitochondrial apoptosis-inducing factor.

Nature 397: 441–446.Sylte M, O’Brien M, Inzana TJ & Czuprynski C (2000)

CaspACETM

FITC-VAD-FMK in situ marker for

apoptosis: applications for flow cytometry. Promega Notes

75: 20–23.Tanious FA, Veal JM, Buczak H, Ratmeyer LS & Wilson WD

(1992) DAPI (4’,6-diamidino-2-phenylindole) binds

differently to DNA and RNA: minor-groove binding at AT

sites and intercalation at AU sites. Biochemistry 31: 3103–3112.

Uren AG, O’Rourke K, Aravind LA, Pisabarro MT, Seshagiri S,

Koonin EV & Dixit VM (2000) Identification of

paracaspases and metacaspases: two ancient families of

caspase-like proteins, one of which plays a key role in

MALT lymphoma. Mol Cell 6: 961–967.Vachova L & Palkova Z (2005) Physiological regulation of

yeast cell death in multicellular colonies is triggered by

ammonia. J Cell Biol 169: 711–717.van Meer G, Voelker DR & Feigenson GW (2008) Membrane

lipids: where they are and how they behave. Nat Rev Mol

Cell Biol 9: 112–124.Vance JE & Steenbergen R (2005) Metabolism and functions

of phosphatidylserine. Prog Lipid Res 44: 207–234.Vandebroek T, Vanhelmont T, Terwel D, Borghgraef P,

Lemaire K, Snauwaert J, Wera S, Van Leuven F &

Winderickx J (2005) Identification and isolation of a

hyperphosphorylated, conformationally changed

intermediate of human protein tau expressed in yeast.

Biochemistry 44: 11466–11475.Vandenabeele P, Galluzzi L, Vanden Berghe T & Kroemer G

(2010) Molecular mechanisms of necroptosis: an ordered

cellular explosion. Nat Rev Mol Cell Biol 11: 700–714.Vandenabeele P, Orrenius S & Zhivotovsky B (2005) Serine

proteases and calpains fulfill important supporting roles in

the apoptotic tragedy of the cellular opera. Cell Death Differ

12: 1219–1224.Vembar SS & Brodsky JL (2008) One step at a time:

endoplasmic reticulum-associated degradation. Nat Rev Mol

Cell Biol 9: 944–957.Westermann B (2010) Mitochondrial fusion and fission in cell

life and death. Nat Rev Mol Cell Biol 11: 872–884.Wiedenmann J, Oswald F & Nienhaus GU (2009) Fluorescent

proteins for live cell imaging: opportunities, limitations, and

challenges. IUBMB Life 61: 1029–1042.Wilkinson D & Ramsdale M (2011) Proteases and caspase-like

activity in the yeast Saccharomyces cerevisiae. Biochem Soc

Trans 39: 1502–1508.



Wilson NS, Dixit V & Ashkenazi A (2009) Death receptor

signal transducers: nodes of coordination in immune

signaling networks. Nat Immunol 10: 348–355.Winderickx J, Delay C, Klinger H, Pellens K, Vanhelmont T,

Van Leuven F & Zabrocki P (2008) Protein folding diseases

and neurodegeneration: lessons learned from yeast. Biochim

Biophys Acta 7: 1381–1395.Wyllie AH (1980) Glucocorticoid-induced thymocyte apoptosis

is associated with endogenous endonuclease activation.

Nature 284: 555–556.Xiang J, Chao DT & Korsmeyer SJ (1996) BAX-induced cell

death may not require interleukin 1 beta-converting

enzyme-like proteases. P Natl Acad Sci USA 93: 14559–14563.

Xu Q, Jurgensmeier JM & Reed JC (1999) Methods of assaying

Bcl-2 and Bax family proteins in yeast. Methods 17: 292–304.

Yang H, Ren Q & Zhang Z (2008) Cleavage of Mcd1 by

caspase-like protease Esp1 promotes apoptosis in budding

yeast. Mol Biol Cell 19: 2127–2134.

Ye Y (2006) Diverse functions with a common regulator:

ubiquitin takes command of an AAA ATPase. J Struct Biol

156: 29–40.Zalckvar E, Yosef N, Reef S, Ber Y, Rubinstein AD, Mor I,

Sharan R, Ruppin E & Kimchi A (2010) A systems level

strategy for analyzing the cell death network: implication in

exploring the apoptosis/autophagy connection. Cell Death

Differ 17: 1244–1253.Zha H, Fisk HA, Yaffe MP, Mahajan N, Herman B & Reed JC

(1996) Structure–function comparisons of the proapoptotic

protein Bax in yeast and mammalian cells. Mol Cell Biol 16:

6494–6508.Zhivotovsky B & Orrenius S (2010) Cell death mechanisms:

cross-talk and role in disease. Exp Cell Res 316: 1374–1383.Ziegler U & Groscurth P (2004) Morphological features of cell

death. News Physiol Sci 19: 124–128.Zitvogel L, Kepp O & Kroemer G (2010) Decoding cell death

signals in inflammation and immunity. Cell 140: 798–804.Zong WX & Thompson CB (2006) Necrotic death as a cell

fate. Genes Dev 20: 1–15.



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Systems biology of yeast cell death