Chemogenomics - University of Cambridge

4 Chemogenomics

Chapter 1

Introduction

Atomiclevel

Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6

Pathwaylevel

Proteomelevel

Cellularlevel

Spial Quorumsensing

Chemo-genomics

Descriptorrelationships

Conclusionsand

perspectives

Chapter 4: Chemogenomics

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Contents of chapter 4

Contents of chapter 4.................................................................................................. 1 Summary of chapter 4................................................................................................. 2 Introduction ................................................................................................................. 2

Chemogenomic experiments................................................................................... 4 Heterozygous deletions ....................................................................................... 5 Homozygous deletions......................................................................................... 5 Overexpression.................................................................................................... 6 Beyond deletion and overexpression libraries ..................................................... 7

Detection methods................................................................................................... 7 Non-competitive arrays ........................................................................................ 7 Competitive mutant pools .................................................................................... 7

Data interpretation and analysis .............................................................................. 8 Clustering............................................................................................................. 8 Matrix operations ................................................................................................. 9

Applications ........................................................................................................... 10 Chemogenomic networks.......................................................................................... 12

Results and discussion.......................................................................................... 13 Interpretation of a chemogenomic network........................................................ 14 Properties of the chemogenomic network.......................................................... 15

Materials and methods .......................................................................................... 16 Data acquisition ................................................................................................. 16 Construction of the chemogenomic network...................................................... 16 Computation of network parameters and overlap with other networks .............. 17

Linking the chemical and biological properties of small molecules ........................... 17 Results and discussion.......................................................................................... 18

What differentiates bioactive small molecules from others? .............................. 18 What differentiates small molecules that hit S. cerevisiae from those that hit Sc.

pombe?.............................................................................................................. 20 Conclusions ....................................................................................................... 22

Materials and Methods .......................................................................................... 22 References................................................................................................................ 23


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Summary of chapter 4

A robust knowledge of the interactions between small molecules and specific

proteins aids the development of new biotechnological tools, the identification of drug

targets, and the gain of specific biological insights. Such knowledge can be obtained

through chemogenomic screens. In these screens, each small molecule from a

chemical library is applied to each cell type from a library of cells, and the resulting

phenotypes are recorded. Chemogenomic screens have recently become very

common and will continue to generate large amounts of data. Therefore methods to

investigate these data become important. In this chapter, I develop computational

methods for the analysis of chemogenomic data. For this, I focus on the fungus

Saccharomyces cerevisiae, and, to a lesser extent, Schizosaccharomyces pombe.

Parts of this chapter appear in the following articles:

Wuster, A. and M. Madan Babu (2008). “Chemogenomics and biotechnology.”

Trends Biotechnol 26(5): 252-8.

Wuster, A. (2008). “Networking with drugs.” Mol BioSyst 4(1): 14-7

The section on linking the chemical and biological properties of SMs would not have

been possible without the help of Laura Schuresko-Kapitzky and Nevan Krogan of

the University of California, San Francisco, who generated much of the data this

section is based on.

Introduction

The effects of small molecules (SMs) on cells were central to the research of Paul

Ehrlich (1854-1915; figure 4-1). For much of his career he strived to identify 'magic

bullets', SMs that would allow him to target specific tissues or microbes whilst sparing

others (Ehrlich 1911). In order to find a cure for syphilis, he systematically screened a

library of hundreds of SMs for their effect against Treponema pallidum, the causative

agent of the disease. After testing 605 different compounds, he eventually identified

arsphenamine, which he later marketed as Salvarsan 606 (Gensini, Conti et al. 2007).

With this strategy, he initiated a whole new approach to drug discovery which has

persisted until today (Lipinski and Hopkins 2004). The aim of Paul Ehrlich's chemical

screen was to find an SM that was active against a single pathogen. The data


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structure created by his screen was one-dimensional and can be encoded by a single

vector of the 606 compounds tested. Nowadays, chemical screens are more complex

and wider in scope. In this chapter, I mostly work with the data obtained through two-

dimensional chemogenomic screens. As in Paul Ehrlich's screen, the first dimension

in such screens is a chemical library. The second dimension is a library of different

cell types. For the data I work with in this chapter, the cell types are well-defined

mutants in a library of S. cerevisiae deletion strains, where in each strain a different

gene has been deleted. Alternatively, the cell types could be defined in other ways,

such as in a library of cancer cell lines or of meiotic recombinants (Perlstein, Deeds

et al. 2007; Perlstein, Ruderfer et al. 2007). The resulting data structure is a two-

dimensional matrix where each data point has two coordinates and one specific

associated value. The chemical coordinate specifies the SM that was applied, whilst

the genetic coordinate specifies the cell type. The value of each data point is a

measurement of the phenotype of interest, such as viability, growth rate, or cell size

and shape. The results of a large number of chemogenomic screens with different

designs have been published (table 4-B). They all have in common the data structure

described above, but the experimental designs and aims, such as the identification of

cellular targets of SMs, or the characterisation of cellular pathways, vary between

them.

In this chapter, I describe the different ways in which chemogenomic data can be

created, and then introduce the methods that I have developed to extract information

from this data. For this purpose, I have divided the results of this chapter into two

parts. The first part, on chemogenomic networks, takes a gene-centric view, whilst

the second part, on linking the chemical and biological properties of SMs, takes an

SM-centric view. I have only tested the methods in this chapter on S. cerevisiae, and,

to a lesser extent, on Sc. pombe. Nevertheless, they can potentially also be applied

to other systems, such as humans.

Figure 4-1. Paul Ehrlich (1854-1915) on a former D-Mark banknote. The molecular structure to the left of the portrait symbolises arsphenamine. The six-membered ring in the centre consists of arsenic atoms. It was not shown until 16 years after the issue of the banknote that arsphenamine is actually a mixture of compounds with three- and five-membered arsenic rings (Lloyd, Morgan et al. 2005). Source of the image: http://tinyurl.com/l3r9np


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Chemogenomic experiments

An in-depth understanding of the subtly different ways in which chemogenomic

experiments are designed is necessary in order to develop the appropriate methods

to extract meaningful information from them. Although for all methods of carrying out

chemogenomic screens the resulting data structure is similar, its interpretation

depends on the design of the experiment. For yeast, there are at least three different

types of mutant libraries that can be generated, such as heterozygous deletions,

homozygous deletions, and overexpression libraries (table 4-1).

Screen design →

A. Homozygous deletants B. Heterozygous deletants

C. Overexpression

↓ Detection

Non-competitive

arrays

(Dudley, Janse et al. 2005) 21 conditions 4’710 mutants aim: determine gene function

(Baetz, McHardy et al. 2004) 1 SM > 5000 mutants aim: determine mode of action for drugs (Chang, Bellaoui et al. 2002) 1 SM 5’000 mutants aim: determine genes with certain function

(Luesch, Wu et al. 2005) 1’000 SMs 7’296 mutants aim: identify protein targets for SMs

Competitive

barcoded pools

(Hillenmeyer, Fung et al. 2008) 178 SMs and conditions 5’000 mutants aim: determine gene function

(Parsons, Lopez et al. 2006) 82 SMs 4’111 mutants aim: determine mode of action for drugs (Giaever, Flaherty et al. 2004) 10 SMs 6’000 mutants aim: determine functional interactions of SMs (Hillenmeyer, Fung et al. 2008) 354 SMs and conditions 6’000 mutants aim: determine gene function

(Butcher, Bhullar et al. 2006) 2 SMs 3’929 mutants aim: identify protein targets for SMs

Table 4-1. This table shows examples of chemogenomic screens carried out in the recent past. The columns correspond to three different types of mutant libraries (screen design). Expression of a gene product can either be avoided altogether by deleting the gene altogether (homozygous deletion; empty red arrows), or expression can be reduced by deleting the gene on only one chromosome (heterozygous deletion; filled and empty red arrows), or expression can be increased by introducing extra copies of the gene into the cell (overexpression; multiple red arrows). The rows of the table correspond to two different detection methods that can be used to observe the phenotype of the mutants when treated with SMs. The SM can be added to a well or agar plate where each mutant occupies a separate well or spot on the agar. The effect is then observed directly (non-competitive arrays). Alternatively, the barcoded mutants can all be placed into a flask containing an SM. The effect on the growth of each individual mutant is then observed by measuring the abundance of the different mutants using microarrays whose probes are complementary to the barcodes of the mutants. The intensities of specific probes are then thought to be proportional to the abundance of the mutant strains in the pool (competitive barcoded pools).

well or agar plate


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In the following sections I discuss how each of these library types can be used to

generate chemogenomic data. For each mutant library, the measured phenotype

refers to changes in growth rate as a proxy for viability or fitness. The measured

phenotypes can be caused by a variety of molecular interactions between proteins

and SMs, whose interpretation depends on the choice of experimental setup.

Heterozygous deletions

This library type consists of diploid yeast cells that have two copies of each

chromosome. Almost every single one of the 6’500 yeast genes can be deleted

independently, as long as the deletion is only on one of the two chromosomes of a

chromosome pair (heterozygous) (Deutschbauer, Jaramillo et al. 2005). In such a

library, even cells with a deletion in an essential gene will normally survive as they

still have another copy of the gene on the second chromosome. Data from a

heterozygous deletion library and a homozygous deletion library, which is described

in the next section, need to be interpreted in different ways (Baetz, McHardy et al.

2004; Giaever, Flaherty et al. 2004; Parsons, Lopez et al. 2006). The rationale

behind screening against a heterozygous deletion library is that a cell that has only

one copy of a particular gene produces less of the gene product. Such a cell is

rendered more sensitive to an SM that is capable of disrupting the function of this

gene product than a wildtype cell. This effect is also referred to as haploinsufficiency,

and the method has also been described as haploinsufficiency profiling (Giaever,

Flaherty et al. 2004). Data points obtained from such a screen that show decreased

viability might therefore directly point to an interaction between a chemical compound

and a gene product.

Homozygous deletions

This type of library consists of yeast cells in which each non-essential gene is deleted

from both chromosomes of a diploid chromosome pair. For obvious reasons, in such

a homozygous deletion library only non-essential genes can be deleted as the

deletion of any of the approximately 1000 essential yeast genes will by definition be

lethal (Deutschbauer, Jaramillo et al. 2005). The interpretation of such a

chemogenomics screen is more complex for a homozygous deletion library than for a

heterozygous deletion library. Clearly, an SM cannot act on a gene product after the

respective gene has been knocked out. Therefore, if a homozygous deletion strain is

sensitive to a particular SM, this can be interpreted in different ways. One possibility

is that the deleted gene may be different from the gene targeted by the SM but has a

similar function. In the absence of SMs, disruption of one of the two genes is


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expected to have little effect as the second gene provides a back-up function.

However, the disruption of the function of both genes, the one by the SM and the

other by deletion, might result in decreased viability. This has been verified in a study

which compared the effect of knocking out one gene and targeting the other with an

SM with the effect of knocking out both genes, a method which is also referred to as

a synthetic genetic array (SGA) (Parsons, Brost et al. 2004). This study considered

the biosynthesis gene ERG11, a target of the SM fluconazole, as an example. There

are 27 genes which cause synthetic lethality when mutated together with ERG11. 13

of these 27 mutants also cause lethality when ERG11 is not mutated but when

fluconazole is added. Comparing the lethality profile of other SMs to the synthetic

lethal profiles of their targets, it was found that SGA profiles generally tend to have a

significant overlap with the chemogenomic profiles (Parsons, Brost et al. 2004). This

shows that the effect of deleting ERG11 is similar to the effect of disrupting its

function with fluconazole. In an approach which is complementary to the

homozygous deletion library described above, it would be possible to screen for

deletions which confer resistance to a compound whose addition would be

deleterious to the wildtype strain. An example of this would be if a compound which is

not normally toxic to the cell becomes toxic due to the action of a cellular enzyme

modifying it. In this case, deletion of the genes encoding the modifying enzyme would

confer resistance to the cell. For example, azidothymine becomes active against

DNA polymerases and reverse transcriptases only after it has undergone several

cycles of phosphorylation (Brenner 2004). Mutating the genes responsible for this

phosphorylation could potentially decrease the sensitivity of the cell towards

azidothymine. In this way, genes that are responsible for causing side effects to

certain drugs could be identified.

Overexpression

A third approach investigates the effect of SMs under conditions of increased gene

dosage, which can be amended by increasing gene copy number (Butcher, Bhullar et

al. 2006; Luesch 2006). One such study (Luesch, Wu et al. 2005) used an

overexpression library to screen for genes which confer increased resistance to SMs

when multiple copies of a gene, and therefore presumably higher level of the gene

product, are present. In this way it was possible to show, amongst other things, that

the breast cancer therapeutic tamoxifen disrupts calcium homeostasis (Luesch, Wu

et al. 2005).


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Beyond deletion and overexpression libraries

Alternative approaches for generating libraries of different cell types include the

exploration of the relationship of essential genes with SMs via titrable promoter

alleles (Mnaimneh, Davierwala et al. 2004), and Decreased Abundance by mRNA

Perturbation (DAmP). DAmP enables fine-tuning of protein concentration by

disrupting the 3′ untranslated region of a gene by insertion of an antibiotic resistance

marker, which greatly destabilises the corresponding mRNA (Muhlrad and Parker

1999). DAmP leads to proteins that are synthesised at substantially lower levels than

in the wildtype but under their natural transcriptional regulation. DAmP can therefore

be used to gain insights into the deleterious and alleviating effects SMs have at

differential protein levels (Schuldiner, Collins et al. 2005).

Detection methods

Chemogenomic screens can be carried out in two fundamentally different designs:

Non-competitive arrays and competitive mutant pools (table 4-1).

Non-competitive arrays

In this design, a set of arrays is constructed that contain each of the deletion or

overexpression strains separate from the others, so that they can be observed

individually. This can either be achieved by locating each strain into a separate well

of a well plate, or by spotting them onto an agar surface. After each compound from

the chemical library is added, changes in growth can be measured independently for

each strain and each SM. Since each mutant strain is physically isolated, strains do

not interact or compete for resources in this design.

Competitive mutant pools

In this design, each mutant has a unique DNA sequence also referred to as a bar

code. All bar-coded mutant strains are initially grown together in the same flask. This

mutant pool is then divided into multiple flasks and each compound from the

chemical library is added to a different flask. In each flask, the different strains

compete for resources and fitter strains in the presence of the SM increase in

concentration. For analysis, the genetic material of the contents of the flasks is

hybridised to microarrays, which contain probes that are complementary to the bar

codes of the individual deletion strains. The observed differential intensities of the

probes correspond to the differential concentration and therefore the differential

viability of the respective strains in the presence of the SM. Alternatively, the

differential concentration of the strains could be established using high-throughput


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sequencing. However, in this screen design, the intrinsically different growth rates of

mutants compared to wild type cells need to be taken into account when interpreting

the obtained data. It is therefore important that the data are normalised by the growth

rate of each mutant without any SM treatment.

It has been observed that the results of the array-based design and the pool-based

design overlap but differ (Giaever, Shoemaker et al. 1999). A possible explanation for

this is interactions between strains in mutant pools. For example, certain deletion

strains could accumulate intermediates in a metabolic pathway from which an

enzyme has been deleted, and these intermediates, when released, might interfere

with the viability of other strains in the same flask. Alternatively, yeast strains grown

in flasks might behave differently from yeast strains grown in arrays due to the

selection pressures of interstrain competition for resources, which is not a limitation

in the array-based detection method.

Data interpretation and analysis

To understand and interpret chemogenomic data is not a trivial task, as the observed

phenotypic effects can have many causes and can also be indirect. Here I discuss

the most common methods to interpret chemogenomic data, before introducing my

own methods later in this chapter.

Clustering

Application of clustering algorithms is a standard way of analysing multidimensional

data. In chemogenomic screens, data clustering can be applied to both the chemical

and the genetic dimension. A cluster in the chemical dimension will result in groups

of SMs that have similar effects on the different cell types (table 4-2). It has been

shown that clusters in the chemical dimension often contain SMs which are

chemically similar (Giaever, Flaherty et al. 2004). Therefore, the action of untested

SMs could be predicted based on similarity to tested SMs from previous screens.

Accordingly, a cluster in the genetic dimension groups cell types that are affected by

the SMs in a similar way. Such a cell type cluster can then be expected to be

enriched for mutated genes (deletion or overexpression) that have a certain function

in common. In order to identify such clusters, a number of supervised or

unsupervised algorithms that are also applied to microarray data have been

described. They include hierarchical clustering, k-means, and self-organising maps

(Madan Babu 2004). Alternatively, biclustering, an approach that analyses both

dimensions at the same time, can be employed (Cheng and Church 2000; Madeira


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and Oliveira 2004). The biclusters obtained in this way contain a subset of mutated

genes which interact similarly with a subset of SMs, and vice versa. The aim of

biclustering is to sort both dimensions simultaneously in such a way that functionally

significant blocks can be identified amongst the overall pattern (figure 4-2C). Another

important advantage of biclustering is that it can identify mutated genes or SMs that

are associated with more than one cluster. This is a significant improvement over

simpler clustering procedures since many genes may have more than one function.

Biclustering approaches have successfully been applied to chemogenomic data

(Dudley, Janse et al. 2005; Parsons, Lopez et al. 2006). Non-negative or Probabilistic

sparse matrix factorisation (Greene, Cagney et al. 2008) are alternative algorithms to

biclustering and is has been shown that in some cases they are more sensitive than

hierarchical clustering. For example, the SMs verrucarin and neomycin sulphate are

clustered together by matrix factorisation but not by hierarchical clustering (Parsons,

Lopez et al. 2006). A detailed review of the various clustering methods and how they

are implemented, can be found in references (D'Haeseleer 2005; Xu and Wunsch

2005).

B. One-dimensionalclustering

C. Biclustering

∆ gene 1

small

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1

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.small

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3

. small

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∆ gene 2∆ gene 3

.

.

.

∆ gene n

.

A. Data structure

Figure 4-2. The chemogenomic data structure and clustering methods. (A) A hypothetical data structure obtained from a chemogenomic screen. One dimension (columns) refers to the SMs added, whilst the other dimension (rows) refers to cell type, in this case defined deletion (∆) mutants. The values, here represented as white, grey and black boxes, refer to the growth or viability of the specific mutant when treated with a specific SM. (B) The columns of the matrix (SMs) have been clustered according to the similarity of their effect on different cell types. (C) Rows and columns of the matrix have been biclustered (see text). The resulting biclusters are highlighted in green.

Matrix operations

Although clustering is a helpful tool for the interpretation of chemogenomic data, it is

not the only available method. Weinstein and coworkers (Scherf, Ross et al. 2000)

have taken an interesting approach by integrating two different two-dimensional

matrices obtained from a chemogenomics screen and this strategy could also prove

valuable for the interpretation of other datasets. Their first matrix had SMs as its first

dimension, cell lines as its second dimension, and the associated values referred to


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differential growth. In a second matrix, genes were represented in the first dimension,

cell lines in the second dimension, and the associated values referred to transcript

levels as measured by microarrays. The common dimension in both matrices was the

cell lines. The authors then correlated the expression of each gene in each cell line

with its response to each SM and thereby obtained a third two-dimensional matrix,

which has SMs as one dimension and genes as the other dimension. From this

matrix, the authors could identify the genes that are expressed at a differential level

in cell lines that have differential sensitivity to a specific SM. In this way, they could

show how the transcript levels of particular genes relate to sensitivity to specific SMs.

Matrices can in many cases also be visualised and interpreted in networks. SM-gene

networks have also been used to visualise and analyse chemogenomic-like datasets

(Haggarty, Koeller et al. 2003; Sharom, Bellows et al. 2004; Lamb, Crawford et al.

2006; Ma'ayan, Jenkins et al. 2007; Yildirim, Goh et al. 2007) (figure 4-3). One

conclusion that has been possible from the analysis of such drug-target networks is

that there has been a trend for more rational drug design over the last decade

(Yildirim, Goh et al. 2007; Wuster 2008). Later in this chapter I introduce the concept

of chemogenomic networks, which make use of the idea that matrices can be

visualised and interpreted as networks.

small molecule (SM)target protein

target proteinnetwork

SM-targetnetwork

SMnetwork

Figure 4-3. A bipartite network of drugs and their target proteins (drug-target network; centre panel) contains the information to derive both the target protein network and the drug network (Yildirim, Goh et al. 2007). In the target protein network (left panel) two proteins are linked if they are targeted by the same SM. In the drug network (right panel) two drugs are linked if they target the same protein. The giant component of each network is highlighted in light green.

Applications

The immediate purpose of a chemogenomic screen is to characterise the effect a set

of SMs has at the level of genes or proteins. From a biotechnological point of view,

such chemogenomics data can allow for the identification of proteins as novel drug

targets (Bredel and Jacoby 2004). In a screen of SMs against a library of

heterozygous yeast deletions, the gene products that interact with each of the SMs

can be identified. For example, in a chemogenomic screen of mammalian cell lines


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STAT3 could be identified as a potential target for apratoxin A, an SM that is

cytotoxic against tumour cells (Luesch, Chanda et al. 2006). Many yeast genes have

orthologues in humans (Hohmann 2005), which raises the possibility that interactions

with SMs might also be conserved and that orthologues could be potential drug

targets or might aid in gaining an understanding of the side effects of drugs at the

molecular level (Luesch 2006). Nevertheless, it is not clear how well the responses to

SMs are conserved between distantly related organisms. I address this question later

in this chapter. Rapamycin is an example of an SM for which it is known that the

molecular mechanism of action is conserved between S. cerevisiae and humans

(Butcher, Bhullar et al. 2006). This is of particular interest as the majority of drugs are

SMs (Lipinski and Hopkins 2004).

SMs that are found to be active against specific proteins could also serve as

alternatives to genetic methods. In reverse genetics, in order to disrupt the function of

a protein, the DNA sequence that encodes the protein is mutated. Although this

approach has generated a large variety of useful data, it has some drawbacks. First

of all, mutagenesis can be difficult to implement in higher animals such as mammals.

Furthermore, mutagenesis strategies do not exhibit a large degree of spatial and

temporal flexibility, since it is difficult to mutate a gene for only a transient period of

time or in a limited and clearly defined set of cells. An additional problem is that in

multifunctional proteins it can be difficult to disrupt only one protein function whilst

leaving others intact. Chemical genetics has the potential to overcome these

problems (Stockwell 2000; Stockwell 2004; Spring 2005; Florian, Hümmer et al.

2007). The ultimate goal of chemical genetics is to identify SMs that are able to

modulate the function of as many proteins in a cell as possible, either by simulating a

gain-of-function or of a loss-of-function mutation. It has been estimated that between

8 and 14 percent of proteins in eukaryotic genomes are 'druggable' in this way

(Hopkins and Groom 2002). Chemical genetics can be more easily employed in

mammalian cells than mutagenesis strategies and in a manner that is specific to a

set of clearly defined cells, or during a clearly defined time window by adding and

removing the compounds used in the screen. Furthermore, in a multifunctional

protein potentially only one specific function could be modulated without affecting the

others.

The potential of chemical genetics for drug development is well recognised. Recent

applications include the targeting of cancer cell lines that carry mutations in cancer

pathways with specific SMs (Dolma, Lessnick et al. 2003; Rognan 2007) or the use


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of SMs to direct differentiation of stem cells (Huangfu, Maehr et al. 2008; Borowiak,

Maehr et al. 2009; Chen, Borowiak et al. 2009). However, other areas of

biotechnology, including green biotechnology might also benefit from chemical

genetics. Crop plants could be improved with the use of SMs that target specific

proteins, for example those involved in drought resistance pathways (Raikhel and

Pirrung 2005). Since such SMs can be applied in a manner similar to pesticides, the

introduction of genetically modified crops that is often associated with environmental,

legal, or technical difficulties, could be avoided.

Besides these practical applications, chemogenomics data can also offer

fundamental biological insights. Grouping genes according to their chemogenomic

profiles can lead to the identification of clusters that are enriched for functional

categories as defined by the Gene Ontology (GO) database (Ashburner, Ball et al.

2000). If a gene of previously unknown function is found in such a cluster, it is likely

that it has a similar function to the other genes in the same cluster. Chemogenomics

could therefore contribute to the determination of the function of genes whose

function was previously unknown (Dudley, Janse et al. 2005). Moreover, it might

even be possible to define novel functional classes of genes. More specifically, if an

SM causes decreased viability when a set of genes is deleted, this set of genes can

be interpreted as being necessary for conferring resistance to the SM. Therefore,

these genes might have a previously unknown function in common. For example,

deletion of members of the protein complexes SAGA, Swi/Snf and Ino80 each

caused sensitivity to cadmium, cycloheximide, hydroxyurea, and glycerol (Dudley,

Janse et al. 2005). Therefore, it has been assumed that these complexes share a

function, which is to regulate genes that are required in the presence of these SMs.

Chemogenomic networks

Understanding and interpreting the data obtained from a chemogenomic screen is

not trivial, as often the observed effects are not direct. However, in most cases it is

true that if a defined mutant is less viable in the presence of an SM than in its

absence, then the gene that has been mutated has some importance for resistance

to the SM.

This makes a novel method for interpreting chemogenomic data possible that

involves the construction of chemogenomic networks (figure 4-4). Nodes in

chemogenomic networks are genes. Two genes are linked by an edge if they have

one or more SMs in common that cause decreased viability when either of the two


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genes is deleted. Because two genes can be deleterious when deleted in the

presence of more than one different SM, edges in a chemogenomic network have a

weight, which is an integer greater or equal to one. For example, if the edge

connecting two genes has a weight of four, this means that there are four different

SMs that cause a decrease in viability if either of the genes is deleted.

Although it is often assumed that drugs that have the highest selectivity for the

protein they bind are the most effective, this may not be the case (Hopkins, Mason et

al. 2006; Hopkins 2008; Nobeli, Favia et al. 2009). Instead, many of the most

effective drugs bind multiple proteins instead of single targets. Chemogenomic

networks may provide a way of rationally identifying clusters of gene products that

are targeted by the same SMs. If such a cluster is implicated in a certain disease,

SMs that target all or most of the genes of the cluster will be strong candidates for

further investigation, although they may not have been identified as being candidates

when only considering one SM-protein interaction at a time.

2 31

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Figure 4-4. As any network, chemogenomic networks consist of nodes and edges that connect the nodes. In chemogenomic networks, the nodes are genes or strains whilst the edges are relationships between these genes. Two genes are connected by an edge if they are sensitive to the same SM (SM). An edge is therefore an SM that causes a decrease in viability if either of the two genes it connects is deleted. Therefore, the edges in a chemogenomic network are weighted and undirected. This figure shows how a chemogenomic network is constructed. Starting with the chemogenomic data structure (panel 1; decreases in viability are symbolised by dark green), each SM is considered in turn. If the SM causes decreased viability in more than two strains, the edge weight between the two strains is increased by one.

Results and discussion

The properties of a chemogenomic network are dependent on the dataset that is

used to generate it. In a chemogenomic dataset that uses twenty different SMs, the

maximum possible weight of an edge is twenty. Furthermore, the network

constructed from such a dataset can be expected to have a lower graph density (the

proportion of all possible edges that is actually observed) than a network constructed

from a dataset which uses one thousand different SMs. For comparing

chemogenomic networks, it is therefore important to keep these issues in mind and

to counteract them, for example by filtering the network by only including edges

above a threshold weight.


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Interpretation of a chemogenomic network

Two genes are linked by an edge in a chemogenomic network if both are needed for

resistance to the same SM (see Materials and Methods). This suggests that the two

genes have complementary functions. Figure 4-5 shows a chemogenomic network

that has been generated using previously published data (Parsons, Lopez et al.

2006). In the figure, clusters of genes with a grey background are highlighted

because they share common functions: ARN1 and ARN2 both are transporters that

are specific to siderophore-iron chelates; RIP1 is cytochrome-c reductase whilst

COX9 is a cytochrome-c oxidase; and DUG2, APE3, and PCA1 all are peptidases or

hydrolases (http://www.yeastgenome.org/; last accessed 20 July 2009). The function

of open reading frame YBR284W is unknown, but due to its association with three

hydrolases in the chemogenomic network it is plausible to assume that it is one as

well. It is also interesting to note that all four genes of this cluster are encoded within

15 open reading frames of each other on chromosome II. This demonstrates that

edges in chemogenomic networks can be used to identify functional similarities

between genes.

PAN3

CTR1

PET130

OCA5

SRN2

LUG1

RIM21

ZAP1

CAF40

YGR122W

YPT6

YBR246W

COX9

YBR284WVPS21

LAS21

BUD25

SWA2

PEP8

DAL5

DUG2

MCH5

YIL077C

YIR016W

YKL023W

SMI1

PIN4

VPS52

CDC73

GOS1

VAM3

LSM1

SOP4

VAM7

ATG15

FTR1

ECM34VAM10

RBK1

TOM5

MIG1

FET3

MLP1

UNG1

RPL12B

SHU1

SUR4

ARN2

YPL105C

PHO86

END3

URE2

WSC4

CHS5

YPT7

COG6YPL158C

PAT1

SPO22

VPS71

SNF8

APL5

SHE4

VPS30

REG1

MDM20

YCR045C

RIP1

PEP7

PCA1

VPS9

ARN1

NUC1

VPS27

YIL092W

MOG1

POR1

APE3

TEF4

YAR029W

WHI3

VPS24

GMH1

APQ12

ADA2

BEM1

SNX3

SKT5

GCN5

YDJ1

RPS8A

Figure 4-5. Chemogenomic network filtered for edges with a weight of 20 or more. That is to say, two genes are only connected if they are important for resistance to the same 20 or more SMs. Only the largest connected component of the network is shown. The clusters of genes with a grey background are highlighted because it is known that they also share common functions.


4-15

Properties of the chemogenomic network

To ascertain the overlap between two networks, such as the chemogenomic network

and the protein-protein interaction network, I first created a list of nodes which appear

in both networks. Next, I went through both networks and retained only those edges

which connected two nodes which were both on the list. The overlap between the two

networks was then measured by counting the number of edges they had in common.

To assess the significance of this result, I generated a null distribution of overlapping

edge counts by permutating the node labels of the graphs. The Z score of the

observed overlapping edge count relative to this distribution is given below (table 4-2).

Table 4-2. Parameters of the chemogenomic network at different minimum edge weights. The edge weight refers to how many SMs both nodes connect by the edge interact with. Whilst it is clear that there is a larger than expected overlap between the chemogenomic network and the protein interaction network, no such effect can be seen for the transcription interaction network, in which two genes are connected if they are regulated by the same transcription factor (target gene co-regulatory network). In many cases a decrease in growth rate or fitness in a certain combination of SM

and heterozygous deletant will not be indicative of a direct and physical interaction

between the SM and the gene whose dosage has been increased. The gene may

instead encode a protein that compensates for the direct effect of the SM (Venancio,

Balaji and Aravind 2009; Mol BioSyst (in print)). The extent to which such “buffer

genes” contribute to the interactions observed in chemogenomic data is not known.

As a widespread occurrence of this compensation effect could severely skew the

interpretation of chemogenomic data, more research in this area is needed.

In the chemogenomic networks described above, genes that respond to the same

SMs are connected. Alternatively, one can envisage a network that has SMs as

nodes. These nodes are connected if they interact with the same gene (figure 4-3). In

such a way, it may be possible to identify clusters of SMs with similar biological

activities. In the next section (Linking the chemical and biological properties of SMs),

min edge weight ≥ 1 ≥ 2 ≥ 5 edge count 2’084’597 817’683 149’374 connected node count 3’189 2’497 1’391 graph density 0.41 0.26 0.16 min degree mean degree max degree

44 1’307.367

2’870

2 654.93

2’169

1 214.77

1’046 min weight mean weight max weight

1 1.916

29

2 3.336

29

5 6.977

29 clustering coefficient 0.67 0.58 0.54 protein interaction edge overlap – absolute – Z score

4’870 5.43

2’579 8.89

819

13.30 transcriptional interaction edge overlap – absolute – Z score

123’155

1.58

47’087

-0.30

7’615 -1.46


4-16

I approach this problem from a different angle by asking if SMs that have similar

chemical properties also have similar biological properties.

Materials and methods

Data acquisition

The chemogenomic data from which the chemogenomic network used in this

analysis was constructed was published by Parsons et al. (Parsons, Lopez et al.

2006). The dataset describes the sensitivity of a haploid S. cerevisiae mutant library

to 82 SMs.

The protein-protein interaction network was obtained from the BioGRID database

(Stark, Breitkreutz et al. 2006), which combines information on protein-protein

interaction from various sources and types of experiments. The transcription

regulatory network was based on ChIP-chip data (Lee, Rinaldi et al. 2002; Harbison,

Gordon et al. 2004; Balaji, Madan Babu et al. 2006), which identifies direct in vivo

DNA binding events for transcription factors. According to this network, 144

transcription factors regulate 4’347 target genes via 12’230 regulatory interactions. In

order to make the transcription regulatory network comparable to the chemogenomic

network, it was processed in such a way that target genes that are regulated by the

same transcription factor are connected by edges. The resulting network is, strictly

speaking, not a transcription regulatory network, but a target gene co-regulatory

network.

Construction of the chemogenomic network

The chemogenomic network is constructed by collapsing a bipartite network with two

types of nodes into a simple network with only one type of node. In the initial bipartite

network, the two types of nodes are SMs and genes or deletion strains. In the

resulting simple network, genes are the only type of node. In this network, two genes

are connected by an edge if they have one or more SMs they interacted with in the

initial network in common. The edge weight corresponds to the number of those

common SMs. The chemogenomic network can be constructed by considering one

SM at a time, and connecting all genes that interact with it to each other (figure 4-4).

This means that the chemogenomic network will be densely connected if SMs tend to

interact with many genes in the initial bipartite graph. Therefore, it can be useful to

filter edges that have a low weight (that is to say, that symbolise interaction of genes

with only a few common SMs).


4-17

Computation of network parameters and overlap with other networks

Network parameters were computed using the statistical language R and the

package igraph (http://cneurocvs.rmki.kfki.hu/igraph/). Overlap with the protein-

protein interaction network and the transcription regulatory network was assessed

using in-house Perl scripts. For this, all nodes that do not appear in both networks to

be compared were deleted. Then, the number of common edges between the

networks was counted. The significance of the overlap between networks was

assessed by randomising the networks by permutating the node labels and repeating

the analysis 1’000 times.

Linking the chemical and biological properties of small

molecules

Not all sections of chemical space are equally likely to contain SMs that have a

certain biological activity. This is the basic assumption of quantitative structure-

activity relationships (QSAR), which describes how chemical structure is associated

with biological activity. The central tenet of QSAR is that similar SMs bind similar

proteins (Klabunde 2007). This insight enables us to define areas of chemical space

that are occupied by SMs with specific affinity for certain protein families.

By focussing on those areas of chemical space that contain SMs with a biological

activity of interest, it is possible to make the process of drug lead identification more

rapid and efficient. By analysing the chemical properties of approved drugs, Lipinski's

rule of five was discovered (Lipinski, Lombardo et al. 2001). This “rule” states that

most drugs that have been approved by the American Food and Drug Administration

(FDA) have less than five hydrogen-bond donors, less than ten hydrogen-bond

acceptors, a log P value (a measure of hydrophobicity) of less than five, and a

molecular weight of less that 1’500 Da. Therefore, SMs with known structures can be

pre-screened in silico prior to a chemogenomic experiment to select those that are

most likely to have certain biological activities.

However, Lipinski’s rule is limited in its usefulness to chemogenomic screens carried

out in yeasts such as S. cerevisiae and Schizosaccharomyces pombe as it has been

developed to describe the druglikeness of approved drugs in humans. It is not clear if

the same limitations to molecular properties also apply to those SMs that show

bioactivity in yeasts. Here, I address this question by first computing the molecular

properties of SMs, including the predicted octanol-water partition coefficient (log P),


4-18

the polar surface area, the number of atoms, the molecular mass and volume, the

number of hydrogen bonds and acceptors, the number of rotable bonds, and the

number of violations of Lipinski’s rule. I then compare those properties between SMs

that are bioactive in S. cerevisiae and those that are not. Furthermore, I ask whether

there are properties that distinguish SMs that are active in S. cerevisiae from those

that are active in Sc. pombe. In the following, I use the term bioactivity to mean the

ability of a compound to inhibit the growth of a yeast colony.

In order to determine whether SMs that are bioactive in S. cerevisiae tend to have

different molecular properties from those SMs that are not bioactive, I analysed two

distinct datasets. The first dataset (dataset A) has until now not been published and

describes the IC50 values of 2’961 SMs in both S. cerevisiae and Sc. pombe. The

second dataset (dataset B) has been created by a high-throughput screen by an NCI

yeast anticancer drug screen and separates 87’264 SMs into those that have an

effect on the growth of S. cerevisiae and those that do not (see Materials and

Methods).

Results and discussion

In order to assess whether any molecular properties are significantly distinct for the

SMs that are bioactive in S. cerevisiae and Sc. pombe compared to those SMs that

are not, I analysed two distinct datasets, A and B (see Materials and Methods).

What differentiates bioactive small molecules from others?

For dataset A, there is a clear difference between active and non-active SMs for a

number of chemical descriptors. The strongest predictor of biological activity is log P,

a measure of hydrophobicity (table 4-3), which was more than 80% larger in active

SMs than in inactive ones (p < 5.54 · 10-12).

Bioactive SMs on average tend to be more hydrophobic, have a lower polar surface

area, have a higher molecular weight, a lower number of hydrogen bond acceptors, a

lower number of hydrogen bond donors, and a higher volume than the other SMs

screened. Furthermore, a bigger proportion of bioactive compounds, compared to

compounds that are not bioactive, violate one or more of Lipinski's rules.


4-19

Property Explanation Mean

active Mean inactive

Wilcoxon test p-value

logP predicted octanol-water partition coefficient log P (hydrophobicity)

3.59 1.98 5.5 · 10-12

PSA topological molecular polar surface area (important to predict drug transport properties)

55.32

80.97 1.7 · 10-9

natoms number of atoms 22.99 22.63 0.10 MW molecular weight 352.32 326.90 1.5 · 10-4 nON number of hydrogen bond acceptors 4.06 5.47 3.1 · 10-8 nOHNH number of hydrogen bond donors 1.15 2.14 6.3 · 10-9 nviolations how many of Lipinski's rules are violated 0.37 0.33 0.08 nrotb number of rotable bonds (measure of molecular

flexibility) 4.54 3.92 0.40

volume molecular volume in Å3 299.00 284.20 4.6 · 10-3 Table 4-3. Active SMs are defined as those that have an IC50 value of less than 100 μM in both species. Inactive SMs are those that have a higher IC50 value in either or both species. The Wilcoxon test p-value indicates how likely it is that the descriptor is the same for SMs that hit S. cerevisiae and Sc. pombe, and SMs that hit neither. These rules state that the majority of approved drugs have less than five hydrogen

bond donors, less than ten hydrogen bond acceptors, a log P value of less than five,

and a molecular weight of less than 500 Da (Lipinski, Lombardo et al. 2001). In all

cases, the majority of SMs that I defined as being bioactive against the two fungi

follow these rules. However, in the case of log P and molecular weight, the bioactive

SMs are closer to Lipinski's threshold values than the drugs that are not bioactive.

In order to confirm the chemical differences between SMs that are bioactive and

those that are not, I performed principal component analysis using the nine

descriptors mentioned above as initial dimensions. After dimensionality reduction, the

difference between the two types of molecules could be confirmed. It has previously

been shown that there are differences between SMs from combinatorial synthesis,

and drugs as well as natural products (Feher and Schmidt 2003). My analysis shows

that there are also significant differences within classes of SMs according to their

bioactivity.

In order to confirm these results, I repeated the analysis using a previously published

dataset that was larger than the one used above. In the initial stage of an anticancer

drug screen, 87’264 SMs were tested against six different strains of S. cerevisiae. Of

these, 12’068 had a strong effect on yeast growth and 75’196 did not. The results of

this screen have been made available online (http://dtp.nci.nih.gov/yacds/index.html;

last accessed 20 July 2009) and constitute dataset B. As before, I determined the

molecular properties of the tested drugs to see which ones are predictive of activity.


4-20

-800 -600 -400 -200 0 200 400

-200

-150

-100

-50

050

100

PCA of molecular properties

PC1

PC

2

Figure 4-6: Plot of the first two principal components (PCs) of the molecular properties of the 2’961 SMs of dataset A. Active SMs (IC50 < 100 μM) are in red, inactive SMs in grey. The first principal component (PC1) explains 92% of the variance in the data, PC2 explains 6%. The same descriptors are over- and underrepresented according to this dataset

compared to the 2’961 SMs in dataset A. Property Explanation Mean

active Mean inactive



3.41 2.44 < 2.2 · 10-16

PSA polar surface area (important to predict drug transport properties)

61.07 66.16 < 2.2 · 10-16

natoms number of atoms 23.40 20.54 < 2.2 · 10-16 MW molecular weight 351.91 297.96 < 2.2 · 10-16 nON number of hydrogen bond acceptors 4.42 4.70 < 2.2 · 10-16 nOHNH number of hydrogen bond donors 1.22 1.38 < 2.2 · 10-16 nviolations how many of Lipinski's rules are violated 0.39 0.25 < 2.2 · 10-16 nrotb number of rotable bonds (measure of molecular

flexibility) 4.90 4.39 < 2.2 · 10-16

volume molecular volume in Å3 300.91 262.36 < 2.2 · 10-16 Table 4-4. The Wilcoxon test p-value indicates how likely it is that the descriptor is the same for SMs that hit S. cerevisiae and those that do not. This table is similar to table 4-3, but is based on a different dataset (87’264 SMs from an NCI cancer drug screen).

What differentiates small molecules that hit S. cerevisiae from those that

hit Sc. pombe?

Having established that there are chemical properties that allow distinguishing

between SMs that are bioactive and those that are not, it is of interest to establish

whether these properties vary between organisms. I therefore compared the

chemical properties of SMs that are bioactive in S. cerevisiae to the properties of

SMs that are bioactive in Sc. pombe. There are several important differences

between the two species that would explain why such differences in sensitivity to


4-21

certain SMs exist, including differences in the length of cell cycle phases, or the

presence of RNAi machinery in Sc. pombe but not in S. cerevisiae.

5411559

2733

S.cerevisiae

S.pombe

Figure 4-7: Of 2’961 NCI SMs, the majority that has an IC50 value of less than 100 μM in one species also has an IC50 value of less than 100 μM in the other species. This indicates that an SM that is bioactive to one species is also likely to be bioactive in the other species.

IC50 < 1’000 μM IC50 < 100 μM IC50 < 10 μM IC50 < 1 μM hits S. cerevisiae only 84 59 20 2 hits Sc. pombe only 55 115 48 4 hits S. cerevisiae and Sc. pombe 132 54 48 29 hits neither 2’690 2’733 2’845 2’926 p-value < 2.2 · 10-16 < 2.2 · 10-16 < 2.2 · 10-16 1.89 · 10-7

Table 4-5: As figure 4-7, but at multiple different IC50 cut-offs. The strong overlap between SMs that hit Sc. pombe and S. cerevisiae does not depend on the IC50 cut-off used. It could be that some SMs are bioactive in one organism but not the other because

they have different molecular properties. As before, I obtained the molecular

structures of each of the SMs that have different bioactivity in the two species and

computed nine properties for each of them in order to assess whether one of these

properties was significantly distinct for the SMs that hit one organism but not the

other. No significant result could be found, indicating that bioactivity in S. cerevisiae

is a good predictor of bioactivity in Sc. pombe and vice versa. Property Explanation Mean S.

cerevisiae Mean Sc. pombe



2.83 3.13 0.46

PSA polar surface area (important to predict drug transport properties)

66.01 61.46 0.71

natoms number of atoms 22.75 23.04 0.85 MW molecular weight 334.30 336.76 0.97 nON number of hydrogen bond acceptors 4.52 4.21 0.66 nOHNH number of hydrogen bond donors 1.63 1.74 0.91 nviolations how many of Lipinski's rules are violated 0.27 0.43 0.09 nrotb number of rotable bonds (measure of molecular

flexibility) 3.62 4.28 0.99

volume molecular volume in Å3 288.12 296.88 0.56 Table 4-6. Properties of drugs that only hit S. cerevisiae versus those that only hit Sc. pombe (IC50 < 1’000 μM). The Wilcoxon test p-value indicates how likely it is that the descriptor is the same for drugs that hit S. cerevisiae and drugs that hit Sc. pombe.


4-22

Conclusions

Some of the above observations on the differences between biologically active and

inactive SMs could be due to the way the chemical libraries that were tested were

generated. The removal of chiral centres, introducing additional flexibility into the

molecule and decreasing its size often lead to lower activity (Feher and Schmidt

2003). As chemical libraries are often created doing exactly those things, this may

explain why, for example, the molecular weight is larger for SMs that are active

compared to those that are inactive. Filtering chemical libraries for SMs with

properties that are enriched in the bioactive set could therefore increase the

efficiency of such screens.

Materials and Methods

Two datasets measuring the bioactivity of SMs in yeast were used.

The first dataset (dataset A) was created by Laura Schuresko-Kapitzky of the

University of California, San Francisco and Scott Lokey of the University of California,

Santa Cruz (unpublished). It describes the IC50 values of 2’961 NCI compounds in

both wild-type S. cerevisiae and wild-type Sc. pombe. The data was obtained by

high-throughput yeast halo assays (Gassner, Tamble et al. 2007).

The second dataset (dataset B) describes the bioactivity of 87’264 NCI compounds

on S. cerevisiae strains. In the initial stage of the anticancer drug screen that

produced this dataset, 87’264 drugs were tested against six different strains of S.

cerevisiae. Of these, 12’068 had a strong effect on yeast growth and 75’196 did not

(http://dtp.nci.nih.gov/yacds/index.html).

I downloaded the molecular structures of each of the SMs

(ftp://ftp.ncbi.nih.gov/pubchem/) and computed nine properties for each of them in

order to assess whether one of these properties was significantly distinct for the SMs

that affect growth. SM properties were computed using Molinspiration's mib tool

(http://www.molinspiration.com/). A local version of the mib tool was kindly provided

by John J. Irwin of the University of California, San Francisco.

All data was analysed using a combination of in-house Perl scripts and the statistical

programming language R.


4-23

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Chemogenomics - University of Cambridge