Vol. 23 no. 13 2007, pages 1666–1673
Data and text mining
Phenotypic clustering of yeast mutants based on kinetochore
K. Jaqaman1,*,†, J. F. Dorn1,†, E. Marco2, P. K. Sorger2and G. Danuser1
1Department of Cell Biology, The Scripps Research Institute, 10550N. Torrey Pines Road, La Jolla, CA 92037 and
2Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
Received on February 17, 2007; revised on April 24, 2007; accepted on April 25, 2007
Advance Access publication May 5, 2007
Associate Editor: Martin Bishop
Motivation: Kinetochores are multiprotein complexes which mediate
chromosome attachment to microtubules (MTs) of the mitotic
spindle. They regulate MT dynamics during chromosome segrega-
tion. Our goal is to identify groups of kinetochore proteins with
similar effects on MT dynamics, revealing pathways through which
kinetochore proteins transform chemical and mechanical input
signals into cues of MT regulation.
Results: We have developed a hierarchical, agglomerative clustering
algorithm that groups Saccharomyces cerevisiae strains based on
MT-mediated chromosome dynamics measured by high-resolution
live cell microscopy. Clustering is based on parameters of
autoregressive moving average (ARMA) models of the probed
dynamics. We have found that the regulation of wildtype MT
dynamics varies with cell cycle and temperature, but not with the
chromosome an MT is attached to. By clustering the dynamics
of mutants, we discovered that the three genes IPL1, DAM1 and
KIP3 co-regulate MT dynamics. Our study establishes the clustering
of chromosome and MT dynamics by ARMA descriptors as a
sensitive framework for the systematic identification of kinetochore
protein subcomplexes and pathways for the regulation of MT
Availability: The clustering code, written in MATLAB, can be down-
loaded from http://lccb.scripps.edu. (‘download’ hyperlink at bottom
Supplementary information: Supplementary data are available at
The kinetochore is a multiprotein structure that establishes the
physical linkage between chromosomes and spindle microtu-
bules (MTs) during mitosis. It comprises more than 70 different
proteins (Cheeseman et al., 2002; Chen and Yuan, 2006; De
Wulf et al., 2003; Maiato et al., 2004; Meraldi et al., 2006), of
which several have been implicated in the regulation of the
attached kinetochore MTs (kMTs) (DeLuca et al., 2006;
Jaqaman et al., 2006). The regulation of kMT dynamics by
the kinetochore is likely an essential part of the process that
ensures accurate chromosome segregation. However, very little
is known about the pathways among kinetochore proteins that
ensure proper control of kMTs, and about the chemical and
mechanical signals involved in the regulation.
To reveal the interactions between kinetochore proteins that
are important for proper kMT regulation, we are pursuing
a quantitative genetics approach using the budding yeast
Saccharomyces cerevisiae as a model system. Loss-of-function
mutations are introduced into kinetochore proteins and the
resulting chromosome movement is determined using 3D
(Dorn et al., 2005; Thomann et al., 2002, 2003). Since
chromosome dynamics are stochastic, wildtype (WT) and
mutant behavior cannot be compared on a time point by time
point basis. Rather, they must be compared indirectly via
parameters that capture the properties of the dynamics. In
Jaqaman et al. (2006), we established autoregressive moving
average (ARMA) model parameters as sensitive descriptors of
chromosome and kMT dynamics. Their comparison via
statistical hypothesis testing allows the indirect comparison of
the measured dynamics and thus the detection of subtle
differences between dynamics in WT and in S. cerevisiae strains
carrying kinetochore protein mutations. Different ARMA
model parameters capture different aspects of the dynamics,
and their separate comparison gives insight into which
component of the dynamics changes as a result of kinetochore
Here, we advance this analysis by clustering chromosome
motion and kMT dynamics based on their ARMA descriptors.
This allows us to compare dynamics from large data sets
of mutants and identify groups of mutations that lead to similar
chromosome and kMT dynamics, a task not achievable by
pairwise comparison. We address the critical question whether
the clustering of chromosome and kMT dynamics based on
ARMA models can be used to derive insight into the
functions of and interactions between kinetochore proteins.
The quantitative comparison of genotype and phenotype is
a key step in assigning protein function: mutants whose
phenotypes cluster into a distinct group provide evidence for
the joint function of the corresponding proteins in a complex
*To whom correspondence should be addressed.
yThe authors wish it to be known that, in their opinion, the first two
authors should be regarded as joint first Authors.
? The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com
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Groups are formed by cutting the trees at the P-value levels
which indicate statistically significant differences between the
ARMA descriptors used for the clustering.
We have found that mutants of the genes IPL1, KIP3 and
DAM1 form a phenotypic group whose kMT dynamics are
significantly different from dynamics in WT. The correspond-
ing three proteins seem to function in a complex that regulates
kMT dynamics. We have also found that the regulation of kMT
dynamics in WT cells varies with cell-cycle and temperature,
but not between chromosomes. Interestingly, the change in
the temporal coupling of kMT dynamics between G1 and
metaphase disappears in mutants of the proteins Ipl1p and
Our results show that the clustering of chromosome and
kMT dynamics based on their ARMA descriptors is a sensitive
framework to reveal kinetochore proteins that form a pathway
or functional complex for the purpose of regulating kMT
dynamics. They demonstrate, for the first time, that systematic
readouts of a dynamic cell behavior and subsequent clustering
via sensitive parametersallow
in functionally relevant complexes and pathways. No other
approach exists to identify functional pathways in a multi-
protein complex, where structural analyses are rendered
impossible by the size of the complex and biochemical methods
are insufficient to reveal the functional consequences of
the grouping of genes
This work was supported in part by NIH R01 GM68956
to P.K.S and G.D. K.J. is a Paul Sigler/Agouron Fellow of the
Helen Hay Whitney Foundation. J.F.D. is a fellow of
the Roche Research Foundation. E.M. is a long-term fellow
of the Human Frontier Science Program.
Conflict of Interest: none declared.
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