![some mRNAs may vary in an oscillatory fashion (even in a
constant medium) not reaching a true steady state (13, 14).
2. TR and m should be expressed in units that cancel down appro-
priately (e.g. if m is in molecules/cell and TR in molecules/
cell/min, you will get kD in per minute). See Chapter 2 on how
to transform the GRO raw data to absolute units. Whenever TR
and m are determined on a per cell basis and the cells undergo
division in the particular conditions of the study, the calculated
kD includes an additive term due to the mRNA dilution into the
dividing cells. Therefore,
kD ¼ kDv þ kDg
where kDv is the dilution rate due to cell division and kDg is the
proper degradation rate of the mRNA. kDv can be estimated
from the cell doubling time of the culture (tD), as
kDv ¼
ln2
tD
0:693
tD
Thus, if tD is known, the contribution of cell division can be
subtracted from kD to obtain the net rate of mRNA degrada-
tion (kDg). For short half-life mRNAs this correction may be
negligible, but for stable mRNAs the dilution rate can make a
significant contribution to kD.
On the other hand, if TR and m are calculated on a culture
volume basis [e.g. m in molecules/(ml of culture) and TR in
molecules/(ml of culture)/min], the influence of cell division
is directly offset and the calculated kD reflects exclusively the
degradation rate.
3. Although mRNA half lives can be calculated in a straightfor-
ward manner from the kDs using Eq. 3, it seems advisable
to keep using kD values for quantitative comparisons and
gene clustering because the mathematical transformation to
half-lives may amplify substantially any associated error. This is
especially relevant for kDs close to zero (i.e. stable mRNAs).
4. Instead of typing the lengthy macro instructions you may
download them from the following URL: http:/
/scsie.uv.es/
chipsdna/chipsdna-e.html#datos. Lines beginning by an
apostrophe (‘) in both macros are not strictly needed for
running the program and may be deleted. These lines are
just comments, but they may be helpful if someone wants to
learn what the program is doing at each step.
5. Macro 2 may be run by itself with this shortcut key, thereby
calculating kD for the time, m and TR values directly intro-
duced in columns A–C of “Calk” (Fig. 2). You may want to
run Macro 2 separately to process single gene data or to check
for possible errors at program transcription or modification.
Global Estimation of mRNA Stability in Yeast 15](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-32-320.jpg)
![cell membranes and allows labeled UTP utilization for RNA
synthesis (1). This permits an instantaneous labeling of the physi-
ologically real RNA transcription. We adapted the run-on tech-
nique to the genomic scale [genomic run-on (GRO)] using
[a-33
P]rUTP labeling and nylon macroarray hybridization
(Figs. 1 and 2, and ref. 4). Using GRO, the nascent TRs for all
the genes of an organism have been calculated for the very first
time. Since the experiment includes a parallel RA determination,
the mRNA stabilities can be calculated at the genomic scale if
considering steady-state conditions (4, 5) or even under non-
steady-state conditions (6, 7). This utility of the GRO technique
will be discussed in a companion chapter of this book (8). Similar
protocols have been used in other eukaryotes, but without a real
determination of TRs (2, 3). The GRO technique has also been
adapted to massive parallel sequencing technologies but, again,
without TR calculation (9).
GRO
“in vivo”
RNA extraction
cDNA labeling
GRO experiment diagram
RNA extraction
Macroarray
stripping
Hybridization of
33
P-UTP
labeled RNA
Assuming, or not,
steady-state
Transcription
Data (TR)
mRNA amount
data (RA)
mRNA stability data
Hybridization of
33
P-dCTP
labeled RNA
labeling of nascent RNA
Fig. 1. Genomic run-on protocol for simultaneous TR and RA measurements. Grown
cells are subjected to two independent protocols: GRO for nascent RNA labeling (right)
and direct RNA extraction (left). The data from the GRO hybridized macroarrays are used
to obtain transcription rates (TR) after normalization and corrections. The data from
successive cDNA hybridization onto the same macroarray (after stripping it) are used to
obtain mRNA amounts (RA). If one assumes steady-state conditions for mRNA amounts,
it is possible to calculate mRNA stability data by dividing RA by TR. If there is no steady-
state, a mathematical approximation is also possible see ref. 15.
26 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-43-320.jpg)
![2. Materials
2.1. Run-On and
Macroarray
Hybridization
1. YDP medium: 1% w/v, yeast extract, 2% w/v, peptone, 2%
glucose. Store at room temperature (see Note 1).
2. 10 and 0.5% w/v, L-laurylsarcosine (sarkosyl, Sigma–Aldrich
Inc., St. Louis, MO)/in H2O. Store at room temperature.
3. 2.5 Transcription buffer: 50 mM Tris–HCl, pH 7.7, 50 mM
KCl, 80 mM MgCl2. Store at room temperature (see Note 2).
4. ACG mix (10 mM each ATP, CTP, GTP, Roche, Mannheim,
Germany). Store frozen.
5. 0.1 M DTT (Invitrogen, Carlsbad, CA). Store frozen.
6. [a-33
P]rUTP (~3,000 Ci/mmol, 10 mCi/mL, PerkinElmer,
Waltham, MA). Store at 4
C (see Note 3).
7. Transcription mix: 120 mL of 2.5 Transcription buffer,
16 mL AGC mix, 6 mL 0.1 M DTT, and 16 mL of [a-33
P]
rUTP. Prepare fresh (see Note 4).
8. LETS buffer: 100 mM LiCl, 10 mM EDTA, 10 mM Tris–HCl,
pH 7.5, 0.2% w/v, SDS. Store at room temperature.
9. Acid phenol:chloroform:isoamilic alcohol (125:24:1), equili-
brated with water, not buffered. Store at 4
C.
10. 5 M Lithium chloride. Store at room temperature.
11. Hybridization solution: 0.5 M sodium phosphate buffer,
1 mM EDTA, 7% w/v, SDS, pH 7.2, 100 mg/mL sonicated
salmon sperm DNA. Do not autoclave. Store at room tem-
perature. Add the DNA (stored frozen in 10 mg/mL solution
in small aliquots) just before use (see Note 5).
12. Wash buffer I 1 SSC, 0.1% w/v, SDS and wash buffer II
0.5 SSC, 0.1% w/v, SDS. 20 SSC is 300 mM Na citrate,
3 M NaCl, pH 7.0 adjusted with HCl. Store at room temper-
ature (see Note 5).
13. 1 M and 50 mM NaOH. Store at room temperature.
14. Neutralizing buffer: 50 mM Tris–HCl, pH 7.5, 0.1 SSC,
0.1% w/v, SDS. Store at room temperature.
15. Stripping solution: 5 mM sodium phosphate buffer, pH 7.0,
0.1% w/v, SDS. Store at room temperature.
16. Yeast nylon macroarrays. Described in (17).
2.2. cDNA Labeling 1. 5 First Strand Buffer (Invitrogen). Store frozen.
2. 0.1 M DTT (Invitrogen). Store frozen.
3. RNase OUT (Invitrogen). Store frozen.
4. DNase I (RNase free, 10/mL) (Roche). Store frozen.
28 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-45-320.jpg)
![5. Chloroform (Panreac, Barcelona). Store at room temperature.
6. 3 M Sodium acetate, pH 4.5. Store at room temperature.
7. Random Hexamers (3 mg/mL) (Invitrogen). Store frozen.
8. Oligo dT (T15VN) (500 ng/mL). Store frozen.
9. dNTP’s mix:16 mM each of dATP, dGTP, dTTP, and 1 mM
dCTP. Divide into small aliquots and store frozen.
10. [a-33
P]dCTP (~3,000 Ci/mmol, 10 mCi/mL) (PerkinElmer).
Store at 4
C.
11. SuperScript II Reverse Transcriptase (200 U/mL) (Invitro-
gen). Store frozen.
12. 0.5 M EDTA, pH 8.0 buffered with NaOH. Store at room
temperature.
13. ProbeQuant G-50 or SR-H300 columns (GE, Niskayuna,
NY). G-50 columns at room temperature and SR-H300 col-
umns at 4
C, according to the suppliers.
2.3. Chromatin
Immunoprecipitation
1. 37% w/v, formaldehyde solution in H2O (Sigma–Aldrich).
Store at room temperature.
2. 2.5 M Glycine. Store in small autoclaved aliquots at room
temperature.
3. TBS buffer: 20 mM Tris–HCl, 140 mM NaCl, pH 7.5.
4. Glass beads, acid-washed and autoclaved (425–600 mm,
Sigma–Aldrich). Store at room temperature.
5. 8GW16 antibody (Covance Inc., Berkeley, CA). Store frozen;
once thawed, keep at 4
C.
6. Dynabeads®
Protein G for immunoprecipitation (Invitrogen).
Store at 4
C.
7. 5 mg/mL bovine serum albumin (BSA) in PBS buffer:
140 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM
KH2PO4, pH 7.4. Divide into small aliquots and store frozen.
8. 10 mg/mL yeast tRNA (Applied Biosystems, Austin, TX).
Store frozen.
9. Lysis buffer: 50 mM HEPES–KOH, pH 7.5, 140 mM NaCl,
1 mM EDTA, 1% v/v, Triton X-100, 0.1% w/v, sodium
deoxycholate, 1 mM phenylmethylsulfonyl fluoride (PMSF),
1 mM benzamidine and one pill of complete protease inhibitor
cocktail (Roche) for 50 mL of buffer. Prepare fresh (see
Note 6).
10. Wash buffer:10 mM Tris–HCl, pH 8.0, 250 mM LiCl, 0.5%
w/v, Nonidet P-40, 0.5% w/v, sodium deoxycholate, 1 mM
EDTA, pH 8.0. Prepare fresh.
11. TE: 10 mM Tris–HCl, pH 8.0, 1 mM EDTA. Store at room
temperature.
Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 29](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-46-320.jpg)
![3.1.6. DNase I Digestion 1. Use a total of around 100 mg of total RNA (to prevent loss
after the phenolization and precipitation steps). Dissolve it in
17 mL of H2O.
2. Add 2 mL of 5 first strand buffer (Invitrogen), 1 mL of
RNase OUT (Invitrogen) and 0.6 mL of RNase free-DNase I.
3. Incubate at 37
C for 30 min. Once again, add 0.4 mL of
RNase free-DNase I, and incubate under the same conditions
for 30 min more.
4. Remove the RNase free-DNase I by extracting once with acid
phenol:chloroform and once with chloroform.
5. Precipitate the RNA with 0.1 volume of 3 M sodium acetate,
pH 4.8, and 2.5 volumes of 96% ethanol, incubating at
20
C for a minimum of 1 h.
6. Recover the RNA by centrifugation in a microcentrifuge at
13,400 g-force for 15 min. Remove the supernatant and
wash with 0.7 mL of 70% ethanol, and centrifuge again at
13,400 g-force for 5 min.
7. Dry the RNA for 1–2 min in a Speed-vac (see Note 13).
3.1.7. Labeling Reaction 1. Take 50 mg of total RNA (DNase I-digested or not, see
Note 12) in a volume of 12.3 mL, add 1 mL of RNase OUT
and, alternatively, 1.2 mL of random hexamers (3 mg/mL) or
1.2 mL of Oligo d(T) (500 ng/mL), depending on the labeling
option. The final volume of that mix must be 14.5 mL.
2. Incubate the mix at 70
C for 10 min and leave at room
temperature for 5–10 min. Then place it on ice.
3. To the previous sample, add 6 mL of the 5 first strand buffer,
3 mL of 0.1 M DTT, 1.5 mL of dNTP’s mix, 4 mL of
[a-33
P]-dCTP, and 1 mL of SuperScript II Reverse Tran-
scriptase. The final reaction volume must be 30 mL (see
Note 9).
4. Incubate at 42
C for 1 h and stop the reaction by adding 1 mL
of 0.5 M EDTA, pH 8.0.
5. Add water to the reaction to a final volume of 50 mL, and
eliminate the nonincorporated nucleotides by using Probe-
Quant G-50 or SH-300R columns according to the manu-
facturer’s instructions.
6. Estimate the radioactive incorporation by measuring 1 mL in
the scintillation counter to calculate the total dpm.
3.1.8. Hybridization
of cDNA Samples
1. Perform a prehybridization of the macroarray as for the run-
on samples (Subheading 3.1.2, step 1).
2. Denature the labeled sample at 95
C for 5 min and transfer to
an ice bath.
34 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-51-320.jpg)
![with agitation (600 rpm in a Thermomixer) to reverse the
cross-linking.
6. To digest the proteins, 142.5 mL TE and 7.5 mL proteinase K
(to 20 mg/mL) are added to each sample. Incubation is kept
at 37
C with agitation (600 rpm) for 1.5 h. Samples are
purified using QIAquick PCR purification columns (or simi-
lar) with two binding steps and the same column for each
sample. The sample is eluted in 50 mL. Up to 5 mL sample
should be used in this step to check IP efficiency by
performing a standard PCR analysis for an expressed control
gene (11, 21). These DNA samples are only stable for a few
days at 20
C. For this reason, the rest of the sample should
be used as soon as possible for the DNA amplification step
(next paragraph).
3.2.2. DNA Amplification
by LM-PCR
1. The ends of the DNA molecules are blunted. The entire IP
sample is used for this, but only 2 mL of the sample is used for
the WCE (4% of the total). The reaction is allowed to proceed
for 20 min at 12
C in the presence of 0.6 U of T4 DNA
polymerase in its buffer supplemented with 80 mM dNTPs.
Then, the sample is extracted twice with neutral phenol:chlo-
roform and precipitated with two volumes of ethanol in the
presence of 0.1 volume of sodium acetate and 12 mg of
glycogen.
2. Ligate the blunt-ended sample overnight at 16
C using 0.5 U
of T4 DNA ligase in a final volume of 50 mL in the presence of
the annealed linkers oJW102 and oJW103 (1.5 mM) (18).
Precipitate the ligated sample with ethanol and resuspend it
in 25 mL of milliQ sterile water.
3. Amplify the sample in a 50-mL PCR mix using 1 mM of
oJW102 primer. The PCR program is 2 min at 95
C, 30 (or
less) cycles (30 s at 95
C, 30 s at 55
C, and 2 min at 72
C),
with a final cycle of 4 min at 72
C. The number of PCR cycles
should be tested and kept as low as possible. Precipitate the
DNA with ethanol and resuspend it in 50 mL of milliQ water
(see Note 25). In this state, the sample can be kept at 20
C
for months.
3.2.3. Sample Labeling
and Macroarray
Hybridization
1. Label the sample by one additional cycle of PCR in the
presence of a-[33
P]-dCTP. 15 mL of sample containing
1–2 mg of DNA from LM-PCR in 50 mL final volume, includ-
ing: 1 Taq DNA pol buffer, 2 mM MgCl2, 0.2 mM dATP,
dTTP, and dGTP, 25 mM dCTP, 1 mM oJW102, 0.8 mCi
a-[33
P]-dCTP, and 5 U Taq DNA pol. Denature the mix for
5 min at 95
C, anneal for 5 min at 50
C, and amplify for 30 min
at 72
C (see Note 26). Purify the reaction product with a
ProbeQuant G-50 column following the manufacturer’s
Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 37](https://image.slidesharecdn.com/1074893-250604181343-4d75e078/85/Yeast-Genetic-Networks-Methods-And-Protocols-1st-Edition-Julia-Marnnavarro-54-320.jpg)
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Yeast Genetic Networks Methods And Protocols 1st Edition Julia Marnnavarro
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- 6. M E T H O D S I N M O L E C U L A R B I O L O G Y TM Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK For further volumes: http://www.springer.com/series/7651
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- 8. Yeast Genetic Networks Methods and Protocols Edited by Attila Becskei Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- 9. Editor Attila Becskei Institute of Molecular Life Sciences University of Zurich Zurich Switzerland attila.becskei@imls.uzh.ch ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-61779-085-0 e-ISBN 978-1-61779-086-7 DOI 10.1007/978-1-61779-086-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011923964 ª Springer ScienceþBusiness Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer ScienceþBusiness Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana press is a part of Springer Science+Business Media (www.springer.com)
- 10. Preface A gene changes the activity of the genes it interacts with. The entirety of these effects in a set of genes represents the dynamical behavior of a gene network. The analysis of this behavior can reveal how a network stabilizes the expression level of its components against perturbations, how it specifies the range of signaling intensity and frequency that can be efficiently transmitted in a pathway, or how it induces gene expression to oscillate. Regula- tion of gene expression a major determinant of gene activity occupies a central place in molecular biology. A detailed mechanistic description of the processes involved, methods for highly quantitative measurements, and an array of biotechnological tools are available to understand, to measure and to control gene expression. These favorable conditions explain why yeast genetic networks attracted the attention of many scientists in the nascent field of molecular systems biology. The book Yeast Genetic Networks: Methods and Protocols covers approaches to the systems biological analysis of small-scale gene networks in yeast. Gene expression is primarily determined by how activators and repressors bound to promoters set the level of mRNA production and how quickly the produced mRNA decays. Part I of the book discusses the methods to analyze gene expression quantitatively: identification of promoter regulatory functions, measurement of mRNA production rates, inference of mRNA decay rates based on mRNA production rates, and detection of oscillatory patterns in gene expression. Furthermore, approaches are presented how to control and analyze signaling in genetic networks by implementing self-regulatory syn- thetic networks and by using microfluidics to dynamically modulate the intensity of external signals. Part II is a collection of mathematical and computational tools to analyze stochasticity, adaptation, sensitivity in signal transmission, and oscillations in gene expression. Control of genetic circuits by synthetic elements and dynamical external stimulation are carefully designed for specific purposes. On the other hand, natural genetic variations in a species provide a gratuitous form of control of genetic networks. While the potential to explore the behavior of networks by natural mutations is more restricted, they offer the advantage of identifying the naturally occurring gene variants that shape the behavior of networks. In Part III, methods are presented how to use the tools of quantitative genetics to identify genes that regulate stochasticity and oscillations in gene expression. Genetic variations are even larger among related fungal species and evolution can shed a different light on network behavior. Thus, Part IVoutlines the analysis of conserved gene expression systems and networks in different fungal species: the galactose network in Kluyveromyces lactis, and transcriptional silencing is described in Candida glabrata. While the former two species are close relatives of the baker’s yeast, more diverged pathogenic fungi, Candida albicans and Cryptococcus neoformans were also included, to emphasize the medical aspects of fungal systems biology. In summary, Yeast Genetic Networks: Methods and Protocols contains a broad range of resources of significant value to both novices and experienced researchers. Zurich, Switzerland Attila Becskei v
- 11. .
- 12. Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix PART I EXPERIMENTAL ANALYSIS OF SIGNALLING IN GENE REGULATORY NETWORKS 1 Global Estimation of mRNA Stability in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Julia Marı́n-Navarro, Alexandra Jauhiainen, Joaquı́n Moreno, Paula Alepuz, José E. Pérez-Ortı́n, and Per Sunnerhagen 2 Genomic-Wide Methods to Evaluate Transcription Rates in Yeast . . . . . . . . . . . . . . . 25 José Garcı́a-Martı́nez, Vicent Pelechano, and José E. Pérez-Ortı́n 3 Construction of cis-Regulatory Input Functions of Yeast Promoters . . . . . . . . . . . . . 45 Prasuna Ratna and Attila Becskei 4 Luminescence as a Continuous Real-Time Reporter of Promoter Activity in Yeast Undergoing Respiratory Oscillations or Cell Division Rhythms. . . . . . . . . . 63 J. Brian Robertson and Carl Hirschie Johnson 5 Linearizer Gene Circuits with Negative Feedback Regulation. . . . . . . . . . . . . . . . . . . 81 Dmitry Nevozhay, Rhys M. Adams, and Gábor Balázsi 6 Measuring In Vivo Signaling Kinetics in a Mitogen-Activated Kinase Pathway Using Dynamic Input Stimulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Megan N. McClean, Pascal Hersen, and Sharad Ramanathan PART II MATHEMATICAL MODELLING OF NETWORK BEHAVIOR 7 Stochastic Analysis of Gene Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Xiu-Deng Zheng and Yi Tao 8 Studying Adaptation and Homeostatic Behaviors of Kinetic Networks by Using MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Tormod Drengstig, Thomas Kjosmoen, and Peter Ruoff 9 Biochemical Systems Analysis of Signaling Pathways to Understand Fungal Pathogenicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Jacqueline Garcia, Kellie J. Sims, John H. Schwacke, and Maurizio Del Poeta 10 Clustering Change Patterns Using Fourier Transformation with Time-Course Gene Expression Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Jaehee Kim vii
- 13. PART III ANALYSIS OF NETWORK BEHAVIOUR BY QUANTITATIVE GENETICS 11 Finding Modulators of Stochasticity Levels by Quantitative Genetics . . . . . . . . . . . . 223 Steffen Fehrmann and Gaël Yvert 12 Functional Mapping of Expression Quantitative Trait Loci that Regulate Oscillatory Gene Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Arthur Berg, Ning Li, Chunfa Tong, Zhong Wang, Scott A. Berceli, and Rongling Wu PART IV EXAMINATION OF NETWORK BEHAVIOUR IN RELATED YEAST SPECIES 13 Evolutionary Aspects of a Genetic Network: Studying the Lactose/Galactose Regulon of Kluyveromyces lactis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Alexander Anders and Karin D. Breunig 14 Analysis of Subtelomeric Silencing in Candida glabrata . . . . . . . . . . . . . . . . . . . . . . . . 279 Alejandro Juárez-Reyes, Alejandro De Las Peñas, and Irene Castaño 15 Morphological and Molecular Genetic Analysis of Epigenetic Switching of the Human Fungal Pathogen Candida albicans . . . . . . . . . . . . . . . . . . . 303 Denes Hnisz, Michael Tscherner, and Karl Kuchler 16 Quantitation of Cellular Components in Cryptococcus neoformans for System Biology Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Arpita Singh, Asfia Qureshi, and Maurizio Del Poeta Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 viii Contents
- 14. Contributors RHYS M. ADAMS • UT M. D. Anderson Cancer Center, Houston, TX, USA PAULA ALEPUZ • Facultad de Ciencias Biológicas, Departmento de Bioquı́mica y Biologı́a Molecular, Universitat de València, Burjassot, Spain ALEXANDER ANDERS • Institut f € ur Biologie, Martin-Luther-Universit€ at Halle-Wittenberg, Halle, Germany GÁBOR BALÁZSI • UT M. D. Anderson Cancer Center, Houston, TX, USA ATTILA BECSKEI • Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland SCOTT A. BERCELI • Department of Surgery, University of Florida, Gainesville, FL, USA ARTHUR BERG • Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA KARIN D. BREUNIG • Institut f € ur Biologie, Martin-Luther-Universit€ at Halle-Wittenberg, Halle, Germany IRENE CASTAÑO • Instituto Potosino de Investigación Cientı́fica y Tecnológica, San Luis Potosı́, SLP, Mexico ALEJANDRO DE LAS PEÑAS • Instituto Potosino de Investigación Cientı́fica y Tecnológica, San Luis Potosı́, SLP, Mexico MAURIZIO DEL POETA • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA TORMOD DRENGSTIG • Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway STEFFEN FEHRMANN • Laboratoire de Biologie Moléculaire de la Cellule Ecole Normale Superieure de Lyon, Lyon, France JACQUELINE GARCIA • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA JOSÉ GARCÍA-MARTÍNEZ • Facultad de Ciencias Biológicas, Sección de Chips de DNA-S.C.S.I.E, Universitat de València, Burjassot, Spain PASCAL HERSEN • Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA DENES HNISZ • Max F. Perutz Laboratories, Christian Doppler Laboratory for Infection Biology, Campus Vienna Biocenter, Vienna, Austria ALEXANDRA JAUHIAINEN • Department of Mathematical Statistics, Chalmers University of Technology and University of Gothenburg, Göteborg, Sweden CARL HIRSCHIE JOHNSON • Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA ALEJANDRO JUÁREZ-REYES • Instituto Potosino de Investigación Cientı́fica y Tecnológica, San Luis Potosı́, SLP, Mexico JAEHEE KIM • Department of Statistics, Duksung Women’s University, Seoul, South Korea ix
- 15. THOMAS KJOSMOEN • Department of Electrical Engineering, University of Stavanger, Stavanger, Norway; Department of Computer Science, University of Stavanger, Stavanger, Norway KARL KUCHLER • Max F. Perutz Laboratories, Christian Doppler Laboratory for Infection Biology, Campus Vienna Biocenter, Vienna, Austria NING LI • Department of Epidemiology and Biostatistics, University of Florida, Gainesville, FL, USA JULIA MARÍN-NAVARRO • Departmento de Biotecnologı́a, Instituto de Agroquı́mica y Tecnologı́a de Alimentos, Paterna, Spain MEGAN N. MCCLEAN • Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA JOAQUÍN MORENO • Facultad de Ciencias Biológicas, Departmento de Bioquı́mica y Biologı́a Molecular, Universitat de València, Burjassot, Spain DMITRY NEVOZHAY • UT M. D. Anderson Cancer Center, Houston, TX, USA VICENT PELECHANO • Facultad de Ciencias Biológicas, Departmento de Bioquı́mica y Biologı́a Molecular, Universitat de València, Burjassot, Spain JOSÉ E. PÉREZ-ORTÍN • Facultad de Ciencias Biológicas, Departmento de Bioquı́mica y Biologı́a Molecular, Universitat de València, Burjassot, Spain ASFIA QURESHI • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA SHARAD RAMANATHAN • Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA J. BRIAN ROBERTSON • Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA PRASUNA RATNA • Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland PETER RUOFF • Faculty of Science and Technology, Centre for Organelle Research, University of Stavanger, Stavanger, Norway JOHN H. SCHWACKE • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA KELLIE J. SIMS • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA ARPITA SINGH • Department of Biochemistry, Medical University of South Carolina, Charleston, SC, USA PER SUNNERHAGEN • Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Gothenburg, Sweden YI TAO • Key Lab of Animal Ecology and Conservational Biology, Centre for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China CHUNFA TONG • Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA MICHAEL TSCHERNER • Max F. Perutz Laboratories, Christian Doppler Laboratory for Infection Biology, Campus Vienna Biocenter, Vienna, Austria ZHONG WANG • Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA x Contributors
- 16. RONGLING WU • Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USA GAËL YVERT • Laboratoire de Biologie Moléculaire de la Cellule, Ecole Normale Superieure de Lyon, Lyon, France XIU-DENG ZHENG • Key Lab of Animal Ecology and Conservational Biology, Centre for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China Contributors xi
- 17. .
- 18. Part I Experimental Analysis of Signalling in Gene Regulatory Networks
- 19. .
- 20. Chapter 1 Global Estimation of mRNA Stability in Yeast Julia Marı́n-Navarro, Alexandra Jauhiainen, Joaquı́n Moreno, Paula Alepuz, José E. Pérez-Ortı́n, and Per Sunnerhagen Abstract Turnover of mRNA is an important level of gene regulation. Individual mRNAs have different intrinsic stabilities. Moreover, mRNA stability changes dynamically with conditions such as hormonal stimulation or cellular stress. While accurate methods exist to measure the half-life of an individual transcript, global methods to estimate mRNA turnover have limitations in terms of resolution in time and precision. We describe and compare two complementary approaches to estimating global transcript stability: (1) direct measurement of decay rates; (2) indirect estimation of turnover from determination of mRNA synthesis rates and steady-state levels. Since the two approaches have distinct strengths yet confer different cellular perturbations, it is valuable to consider results obtained with both methods. The practical aspects of the chapter are written from a yeast perspective; the general considerations hold true for all eukaryotes, however. Key words: 1-10-Phenanthroline, Microarray, Exponential decay, Transcription 1. Introduction Regulation of gene products occurs on multiple levels, from initiation of transcription to post-translational modifications. The post-transcriptional level, which starts once a primary tran- script has been formed, consists of several steps, including mRNA modification, transport, translation, and eventual degradation. All of these steps can be subject to regulation following, e.g. stress or hormonal stimulation. In this chapter, we describe existing methods to study mRNA turnover rates on a global scale. The abundance of an mRNA species is determined by the rates of its production (transcription) and its decay. However obvious, this relation is many times ignored, and changes in steady-state levels of a transcript are often taken to imply regulation at the level of transcription initiation. The extent of regulation at the level of mRNA stability is increasingly becoming appreciated. Attila Becskei (ed.), Yeast Genetic Networks: Methods and Protocols, Methods in Molecular Biology, vol. 734, DOI 10.1007/978-1-61779-086-7_1, # Springer Science+Business Media, LLC 2011 3
- 21. Quite precise methods for estimating the stability of individual mRNA species under physiologically relevant conditions exist, such as promoter shut-off followed by direct observation of tran- script decay. By contrast, methods for global estimation of mRNA stability have limitations regarding resolution in time as well as the physiological disturbances that are imposed on the cell by the respective experimental techniques. Two principally different approaches will be described. In the first, direct measurement of mRNA decay following arrest of transcription, RNA polymerase II is inactivated either by mutation (e.g. using the temperature-sensitive rpb1-1 allele in Saccharomyces cerevisiae (1)), or by chemical inhibitors. Both techniques suffer from the physiological impact of the necessary temperature shock or the side effects of the chemical, respectively. In an important array-based study, global estimates of mRNA stability using five different RNA pol II inhibitors (1-10-phenanthroline, thiolutin, 6-azauracil, ethidium bromide, and cordycepin) or an rpb1-1 allele were directly compared (2). It was concluded that there was good agreement between the estimates obtained by different methods, with the inhibitor 1-10-phenanthroline showing the best fit with the RNA pol II mutant. However, the study identified groups of mRNAs specifically affected by one or several inhibitors, which should consequently be excluded from the analysis. Another con- cern about this traditional approach is that of temporal resolution. If we want to study fundamental decay rates and to estimate the changes in mRNA stability that take place over time in the course of, e.g. a cellular stress response or hormonal stimulation, we may be interested in resolving data points separated by only one or a few minutes. However, the time required for inactivation of a temperature-sensitive allele, or for a chemical inhibitor to penetrate into the cell and fully inactivate its target may be several minutes. In addition, since the half-lives of eukaryotic mRNAs themselves on average are longer than the time course under study, it is intrinsically difficult to obtain data with high resolution in time by direct observation of mRNA decay. In a second, complementary approach, mRNA decay rates are instead estimated indirectly, from simultaneous measurement of both mRNA amounts (RA) and transcription rates (TR). An estimate of TRs is achieved by adding labelled RNA precursors to cells permeabilized by treatment with sarcosyl and subsequent hybridisation of the labelled nascent mRNA pool to DNA arrays (“genomic run-on” (GRO); see Chapter 2). Steady-state RA levels are estimated by conventional hybridisation of in vitro labelled mRNA to arrays. Both TR and RA data have to be converted to real units (molecules/minute and molecules/cell, respectively) by comparison with external standards in order to determine real mRNA half-lives. A distinct advantage of this approach is that higher resolution in time is possible because the 4 Marı́n-Navarro et al.
- 22. method provides instantaneous determination of TR and RA, and so time points in a measurement series as close as only 1 min apart are meaningful. Moreover, the indirect method obviates the dras- tic perturbations of cell physiology associated with blocking tran- scription. However, the indirect nature of the estimation introduces additional uncertainty, in particular, when the system is not at steady state (i.e. when transcription rates and/or degrada- tion rates are changing). In the following, we give an account of practical considera- tions when estimating mRNA turnover rates with either of these two complementary approaches, both concerning experimenta- tion, data treatment, and analysis. 2. Direct Estimation of mRNA Stability Using Transcriptional Arrest 2.1. Experimental Considerations When designing an experiment series for the determination of mRNA degradation rates, it is advantageous to include several time points if changing conditions are going to be studied. It has emerged that mRNA stability changes dynamically in the course of stress responses, where early stabilisation of mRNAs required for stress resistance is followed by later destabilisation (3–5). In order to capture these events, therefore, a time course is in order. It is a good idea to check the in vivo efficiency of the particular RNA pol II inhibitor to be used before large-scale experimenta- tion is commenced. This can be done by, e.g. sampling RNA at various times after the addition of inhibitor and analysing individual genes by Northern blot using probes for transcripts with known half-lives, preferably including at least one reference gene with a slow and one with a rapid decay rate. A 1-10- phenanthroline at a final concentration of 100 ng/ml works well for S. cerevisiae (5). This concentration works well also for Sz. pombe (Asp et al., in preparation) even though higher concentra- tions have been reported in the literature. Care should also be taken to store the inhibitor in question to prevent loss of efficacy between experiments. For instance, 1-10-phenanthroline is sensi- tive to oxidation, and stocks (100 mg/ml in ethanol) should be kept frozen at 20 C in sealed tubes under nitrogen gas. A typical mRNA stability experiment consists of one sample taken before application of RNA pol II inhibition, which provides the mRNA steady-state levels to be used as a reference. In addi- tion, several samples (usually 2–4) taken after different times after RNA pol II inactivation are included. These will result in one final estimate of the stability for every mRNA, under one set of condi- tions. Based on our experience, it is not meaningful to incubate yeast cells with 1-10-phenanthroline for a shorter time than 5 min, since it takes this long to achieve full RNA pol II Global Estimation of mRNA Stability in Yeast 5
- 23. inactivation. If a dynamic event is to be followed, then several time points representing different times after the stimulus in question are needed, each connected with samples representing a series of RNA pol II inactivation times. The total number of arrays needed for stability estimations is thus rather great. For mRNA stability measurements, yeast cells at a density around 5 107 /ml (10 ml of culture for S. cerevisiae; 20 ml for Sz. pombe) are divided into two fractions. To one fraction, the RNA pol II inhibitor is added and incubation is continued. From the other fraction, RNA is prepared and used for the determina- tion of steady-state levels of mRNA species. After different times, samples are taken from the fraction with inhibitor added and RNA prepared by the same method. For convenience, cell sam- ples can be flash frozen in liquid nitrogen and stored at 70 C and RNA prepared at a later time. For array hybridisations, the purified RNA is fluorescence labelled (with or without prior conversion to cDNA). If the two-dye approach is used, then it is convenient to pair samples on arrays representing steady-state levels from different time points of the experiment series with the time ¼ 0 sample, to obtain the steady-state mRNA levels. To obtain stability estimates, the samples taken after different times of RNA pol II inhibition are matched on arrays with the sample taken at the same time point of the experiment but without inhibitor added. 2.2. Microarray Data Processing All microarray experiments require some kind of normalisation procedure. For two-colour arrays, the purpose is often to remove intensity-dependent trends, and these methods are based on the prerequisite that there is no dependence between log2-ratios (M-values) of the two channels to the mean intensities (A-values), i.e. that an M/A plot has a cloud centred around zero. The most common normalisation is a loess smoother, used either globally or within print-tip groups. When applying the loess normalisation to arrays in a decay experiment, one should be aware that trends between mRNA length and decay rate will be removed, if such trends exist. In a typical microarray decay experiment, arrays showing steady-state transcript levels are used as a standard for calculation of decay rates (i.e. from cells treated with some transcriptional inhibitor). The steady-state level arrays can be pre-processed according to standard procedure; however, the decay arrays demand special attention. If a chemical inhibitor of RNA pol II is used, the levels of particular mRNA groups will be affected for reasons irrelevant to the decay measurement. For instance, 1-10-phenanthroline is a Zn2+ chelator, and many genes involved in zinc metabolism will be transcriptionally induced by this compound ((2) and our own 6 Marı́n-Navarro et al.
- 24. observations). If known, such genes should be excluded from further analysis. In each series of treatment with a transcriptional inhibitor, the arrays from different time points exhibit very different orders of magnitude for the M-values. Performing global scale normal- isation is therefore seldom appropriate and would result in loss of information. A better approach would be to perform scale normalisation (creating, for example the same median-absolute- deviation (MAD) across arrays) within groups of arrays measuring pools within the same transcriptional inhibitor time point across strains and stress conditions. The arrays measuring steady-state levels can also be scale normalised for comparability. 2.3. Modelling mRNA Stability The simplest model for mRNA decay is an exponential decay model. We assume that we are observing a single mRNA species, with N(0) copies in the steady-state condition. The number of copies over time, N(t), .under no transcription, would follow N(t) ¼ N(0) 2(t/t1/2) , where t1/2 is a the half-life of the mRNA transcript, often referred to in the literature. Ideally, in a decay experiment of a competitive fashion, the wanted quantity is N(t)/N(0), and since transformations on a log2 scale often is used, we would have log2 (N(t)/N(0)) ¼ t/t1/2. Unfortunately, this quantity is never observable in practice. Noise is added to the experiments, and the normalisation methods and/or hybridisation schemes cause a shift of the M-values of each decay time point. To extract approximate half-lives for the mRNA species, some transformation of the data is required. For the different mRNA microarray decay studies reported in the litera- ture, several normalisation methods have been employed. In some cases, external spike-in controls have been used, for example in microarray studies using Escherichia coli or Halobacterium sali- narium (6, 7). In these studies, the number of external controls was 64 and only one, respectively. Other studies have employed a more computational approach to deduce the decay rates of tran- scripts. In a study using the archaeon Sulfolobus (8), the arrays were loess normalised, followed by the assumption that around 10% of the transcripts were stable. The decay profiles were after- wards adjusted to fit this assumption. Another approach is to assume a mean half-life for the transcripts, and then adjust the decay profiles to match this half-life (2). However, whatever nor- malisation and decay profile adjustment scheme is employed, it comes with a price in the form of extra assumptions that need to be made on the data. Alternatively, instead of computing half-lives (which is difficult), the possibility to rely on the strength of multi-parallel (if such are made) is present, to detect differences in half-lives between time series. Systematic errors in parallel decay series (from different stress Global Estimation of mRNA Stability in Yeast 7
- 25. conditions for example) will be similar, and are likely to cancel when comparing decay slopes between series. By choosing not to transform the data, the extra assumptions are avoided, however, the global behaviour over each time series is assumed to be unchanged. The quantities which then are compared between time series (e.g. stress conditions) are stability indices, which may be positive or negative compared to a median transcript. 2.4. Statistical Analysis To estimate the stability indices from a decay experiment, a linear model is adopted to the M-values at each time point, with an origin at zero. The slopes for each decay profile are estimated via least-squares, and can be done in, e.g. the open source statistical software R or with Microsoft Excel. Differences in decay indices between parallel time series can be tested using different versions of two-sample t-tests. A possibility is to use moderated t-tests (9), in which the problem with spurious small variances, due to the small number of replicates, is circumvented. 3. Indirect Determination of mRNA Stability from Transcription Rate and RNA Amount Data In cases where experimental determination of mRNA decay rate is not feasible or convenient, there is still the possibility of an indi- rect estimation whenever both mRNA amount and synthesis rate are known. We shall consider two different situations. In the first instance, the cells, under more or less constant environmental conditions, are assumed to keep the unchanged mRNA levels in a dynamical steady-state (i.e. synthesis equals decay). In a second scenario, there is a cell response to an environmental shift leading to relatively fast changes in mRNA levels and steady-state condi- tions cannot be assumed. 3.1. Estimating mRNA Stability Under Steady-State Conditions The mRNA concentration (m) is thought to be established as a balance between a zero-order transcription rate (TR) and a first order decay rate with kinetic constant kD. Therefore, the rate of mRNA change is written as: dm dt ¼ TR kD m (1) Under steady-state conditions, m does not vary (i.e. dm/ dt ¼ 0). Thus, TR ¼ kD m and kD ¼ TR m (2) 8 Marı́n-Navarro et al.
- 26. According to Eq. 2, kD can be calculated as the ratio of TR to m determined at a steady state (see Notes 1 and 2). kD is related to the mRNA half-life (t1/2) by t1=2 ¼ ln 2 kD 0:693 kD (3) which allows mRNA decay to be expressed as a half-life (see Note 3). This procedure has been applied for the indirect estima- tion of mRNA half-lives of yeast cells growing under steady-state conditions in glucose and galactose media (10). 3.2. Estimating mRNA Stability Under Non-Steady State Conditions 3.2.1. Background In many interesting biological instances the levels of relevant mRNAs are changing with time. This is the habitual case after imposing a stress or an environmental shift to the cell culture, which results in an adaptation of the gene expression pattern to the new situation. Under these circumstances the steady-state relation between kD, TR, and m (Eq. 2) does not hold (at least, transitorily). Moreover, shifts in mRNA levels must result from changes in transcription rate, decay rate, or both. Consequently, for a detailed description of the process, the time course of kD, TR, and m should be monitored. It is currently possible to make a point-wise simultaneous measurement of TR and m, which may be frequently repeated (typically every few minutes) along the experiment, for a whole set of yeast genes by means of the GRO technique (see Chapter 2). Since Eq. 1 must hold at any time, it is still possible to find a relation to infer kD from the instantaneous values of TR and m determined by GRO. If TR values are sampled frequently enough, a linear variation between successive time points might be assumed. Under these circumstances, the following expression relating the experimental values of TR (TR1 and TR2) and m (m1 and m2) determined a consecutive time points (t1 and t2) with kD has been demonstrated to hold (11): ½ðTR2 TR1Þ=ðt2 t1Þ TR2 kD þ m2 kD 2 ¼ ½½ðTR2 TR1Þ=ðt2 t1Þ TR1 kD þ m1 kD 2 exp ½kDðt2 t1Þ (4) Here, kD represents an average value of the decay constant in between t1 and t2 (11). Equation 4 may be used to calculate kD values for each time interval in between successive GRO sampling time points. However, Eq. 4 cannot be analytically solved for kD and, therefore, a numerical approach should be considered. A relatively simple spreadsheet program, like the VBA “Marmor” program for Microsoft Excel (given in Appen- dix), can be used to perform this calculation. Indeed, this proce- dure has been already employed to estimate global changes in Global Estimation of mRNA Stability in Yeast 9
- 27. yeast mRNA stability from GRO data obtained under oxidative and hyperosmotic stress (3, 4). In the following sections, we describe how to prepare, load, and use this program. 3.2.2. Basic Features of the “Marmor” Program This program uses two separate Microsoft Excel books named “Calk” and “Data.” The actual program is written as two Visual Basic for Applications (VBA) macros inserted in “Calk.” The first macro operates sequentially, gene by gene, in a three-step cycle: (1) it transfers the data of a particular gene from the “Data” book to the “Calk” book, (2) it runs the second macro, which actually performs the kD calculation for each pair of consecutive time points for the given gene, and (3) it transfers the resulting kD values back to the “Data” book, proceeding to the next gene. Technically, kD is calculated by means of a bisection algorithm which approaches the solution up to a specified degree of precision. 3.2.3. Soft- and Hardware Requirements The program was originally written for Microsoft Excel 2002 but will run in later versions (such as the current Excel from Microsoft Office 2007). Running of the program (at the yeast genomic scale) requires a personal computer with a 2-GHz (or faster) processor and at least 512 MB of RAM memory. Typically, calculation of the kDs (to a 0.0001/min error) for seven GRO time points on the whole yeast genome (about 6,000 genes) takes some 3 min. 3.2.4. Preparing the Excel Books 1. Open a new Microsoft Excel book (to be saved with the name “Data”). 2. On sheet 1 of “Data” type in letters (see Fig. 1): – “Data book” on cell A1 – “Data sheet” on cell A2 – “Calc book” on cell D1 – “Calc sheet” on cell D2 – “# time points” on cell G1 – “# of genes” on cell G2 – “time” on cell B4 – “Gene number” on cell A5 – “Gene name” on cell B5 3. End saving changes in “Data.” 4. Open another new Microsoft Excel book (to be saved with the name “Calk”). 5. On sheet 1 of “Calk” type in letters (see Fig. 2): – “minimum m” on cell A1 – “precision” on cell A2 – “ # time points ¼” on cell D1 10 Marı́n-Navarro et al.
- 28. – “gene number ¼” on cell D2 – “ time” on cell A4 – “m” on cell B4 – “TR” on cell C4 – “k” on cell D4 6. End saving changes in “Calk.” If you are using the Microsoft Office 2007 version of Excel, you should choose to save in the “book containing macros” format, which will automatically affix the extension “.xlsm”. 3.2.5. Recording the Macros 1. Open the Microsoft Excel book “Calk.” 2. Open the Visual Basic Editor screen (i.e. Go to Tools ! Macro ! Visual Basic Editor or, if you are using Office 2007, click on the Developer tab and then on the Visual Basic icon). If the Developer tab is not visible in the Office 2007, you must previously activate it by clicking on the Fig. 1. Screen of the “Data” book. Light-grey fields contain permanent instructions of the program. Dark-grey fields denote Excel location and numerical parameters that may vary from experiment to experiment. Therefore, they have to be changed as needed for each data set. The figure shows data and results of an experiment with four time points. Numerical data are arranged in columns C–J (from row 6 downwards). Calculated kD results are displayed in columns L–N (also from row 6). Global Estimation of mRNA Stability in Yeast 11
- 29. Microsoft Office Button ! Excel Options, and selecting “Show Developer tab in the ribbon.” 3. Once in the Visual Basic Editor screen select on the left panel “VBA project (Calk.xls)” and on the upper menu go to Insert ! Module. 4. You will see that Module 1 is created in the Module folder within “VBA project (Calk.xls)” and an empty white panel will open on the right side (if not so, open Module 1 by double- clicking on the corresponding icon on the left panel). Copy carefully all lines given in the Appendix under “Macro 1” on this right side panel (see Note 4). 5. Save changes in Calk. If you are using the Microsoft Office 2007 version of Excel, you will be asked to save in the “book Fig. 2. Screen of the “Calk” book. Light-grey fields contain permanent instructions of the program. Dark-grey fields denote numerical parameters that may vary from experiment to experiment. Therefore, they have to be changed as needed for each data set. The figure shows m and TR data (columns B and C from row 5) for a single gene (number 20) taken at four time points (column A), and the corrresponding kD results (column D). At running the program, each gene (in numbering order) has its data imported into this “Calk” book sheet and, after performing the calculation, its kD results exported back to the “Data” book. Cells E1 and E2 display automatically the number of time points and the number of the gene being currently processed, respectively. 12 Marı́n-Navarro et al.
- 30. containing macros” format, which will automatically affix the extension “.xlsm”. 6. Repeat step 3 to create now Module 2. 7. Open Module 2 and copy carefully all lines given in the Appendix under “Macro 2” as in step 4 (see Note 4). 8. Save changes in Calk. 9. Go back to the “Calk” book in order to assign a shortcut key to Macro 1. Go to Tools ! Macro ! Macros (or directly click on the Macros icon if you are using Office 2007). In this window, select Macro 1 and click on Options: now select “Ctrl + t” as shortcut key. 10. You will not strictly need a shortcut key for Macro 2 since it will be automatically called from Macro 1. However, you may select a shortcut key (e.g. select “Ctrl + k” as in step 9) just in case you want to run the Macro 2 separately (see Note 5). 11. Save changes in Calk. 3.2.6. Running the Program Some parameters have to be previously filled in (on dark-shaded cells of Figs. 1 and 2) as indicated below. Afterwards, the data will be introduced in the “Data” book before starting the program. Let us suppose that the data consist in n time points (pairs of m and TR values) for N genes. 1. Open the “Calk” book and type in the following cells of sheet 1 (Fig. 2): Cell B1: Enter a number which is lower than the sensitivity of experimental detection for m (e.g. 0.000001). This is necessary because the program does not admit 0 as a plausible value for m and will replace all 0s in the m data by this number. Cell B2: Enter a number expressing the maximum error allow- able in the numerical calculation of kD (e.g. 0.0001). The program will approach the solution through iterative steps until closer than this limit deviation value. 2. Without closing “Calk,” open the “Data” book and type in the following cells of sheet 1 (Fig. 1): Cell B1: Enter the name (including file extension) of the Excel book that will contain the data. Initially, it will be “Data.xls” (or “Data.xlsx” if you are using Office 2007 version) (see Note 6). Cell B2: Enter the name of the sheet where the data will be pasted (e.g. “Sheet 1”). Cell E1: Enter the name (including file extension) of the Excel book containing the program macros. Initially, it Global Estimation of mRNA Stability in Yeast 13
- 31. will be “Calk.xls” (or “Calk.xlsm” if you are using Office 2007 version) (see Note 6). Cell E2: Enter the name of sheet in “Calk” where the kD will be calculated (e.g. Sheet 1). Cell H1: Enter the number of genes (i.e. N). Cell H2: Enter the number of time points (i.e. n). Row 4: Columns 3 to (3 + n 1): enter the times corres- ponding to the n successive time points (introduce only numbers; the units may be specified in cell B4). Row 4: Columns (3 + n) to (3 + 2n 1): repeat times (this is optional). 3. Label consecutively the cells of column A from row 6 to row (N + 5) as “Gene 1” to “Gene N.” You may also label the cells of column B with the names of the genes (Fig. 1). 4. In row 5, label columns 3 to (3 + n 1) as “m1” to “mn”; columns (3 + n) to (3 + 2n 1) as “TR1” to “TRn” and columns (3 + 2n + 1) to (3 + 3n) as “k12” to “k(n 1)n.” 5. Paste the data corresponding to each gene (rows 6 to N + 5) and each time point between columns 3 and (3 + 2n 1) (see Notes 2 and 7). The value in each cell must correspond to its “coordinates” as read in column A (or B) and row 5. 6. Making sure that the “Calk” book is open, start the program from the data screen (i.e., “Data” book, sheet 1) with Ctrl + t. During the program run you will see the “Calk” book (sheet 1) and you will be able to monitor the advance of the calculation through cell E2, which will display the number of the gene being currently processed. If you wish to abort the calculation at any time, use the Esc key. At the end of the run, kD results will be printed in the “Data” book, on the rows assigned to the corresponding genes, between columns (3 + 2n + 1) and (3 + 3n) (i.e. a void column is left between the data and the results) (Fig. 1) (see Notes 8–10). 4. Notes 1. A sound use of Eq. 2 requires that the amount of the particular mRNA considered does not vary significantly under the study conditions. This should be experimentally tested. Although steady state may apply to most mRNAs under stable environ- mental conditions (e.g. exponentially growing yeast cells in standard YPD medium (12)), under certain circumstances 14 Marı́n-Navarro et al.
- 32. some mRNAs may vary in an oscillatory fashion (even in a constant medium) not reaching a true steady state (13, 14). 2. TR and m should be expressed in units that cancel down appro- priately (e.g. if m is in molecules/cell and TR in molecules/ cell/min, you will get kD in per minute). See Chapter 2 on how to transform the GRO raw data to absolute units. Whenever TR and m are determined on a per cell basis and the cells undergo division in the particular conditions of the study, the calculated kD includes an additive term due to the mRNA dilution into the dividing cells. Therefore, kD ¼ kDv þ kDg where kDv is the dilution rate due to cell division and kDg is the proper degradation rate of the mRNA. kDv can be estimated from the cell doubling time of the culture (tD), as kDv ¼ ln2 tD 0:693 tD Thus, if tD is known, the contribution of cell division can be subtracted from kD to obtain the net rate of mRNA degrada- tion (kDg). For short half-life mRNAs this correction may be negligible, but for stable mRNAs the dilution rate can make a significant contribution to kD. On the other hand, if TR and m are calculated on a culture volume basis [e.g. m in molecules/(ml of culture) and TR in molecules/(ml of culture)/min], the influence of cell division is directly offset and the calculated kD reflects exclusively the degradation rate. 3. Although mRNA half lives can be calculated in a straightfor- ward manner from the kDs using Eq. 3, it seems advisable to keep using kD values for quantitative comparisons and gene clustering because the mathematical transformation to half-lives may amplify substantially any associated error. This is especially relevant for kDs close to zero (i.e. stable mRNAs). 4. Instead of typing the lengthy macro instructions you may download them from the following URL: http:/ /scsie.uv.es/ chipsdna/chipsdna-e.html#datos. Lines beginning by an apostrophe (‘) in both macros are not strictly needed for running the program and may be deleted. These lines are just comments, but they may be helpful if someone wants to learn what the program is doing at each step. 5. Macro 2 may be run by itself with this shortcut key, thereby calculating kD for the time, m and TR values directly intro- duced in columns A–C of “Calk” (Fig. 2). You may want to run Macro 2 separately to process single gene data or to check for possible errors at program transcription or modification. Global Estimation of mRNA Stability in Yeast 15
- 33. Otherwise, Macro 2 is always automatically called by Macro 1 to solve for kD when needed. 6. You can save the “Data” book with a different name, or copy and paste the pattern of sheet 1 from the “Data” book (Fig. 1) into anothersheetfromthesamebook,inorder tointroduceanewdata set. Running the program with this new data set requires only that entries in cells B1 and B2 containing the current book and sheet name, respectively. This sheet should be activated (i.e. on screen) and the “Calk” book should be open when starting the program. Similarly, entries in cells E1 and E2 allow to process the data with programs introduced in other books and/or sheets (different from “Calk” “Sheet 1”) whenever they exist. This is especially convenient if the macro instructions are modified to fit particular requirements, thereby creating program variants which may be saved in different books. By selecting book and sheet in cells E1 and E2 you may choose an adequate program variant to manage the data. 7. Avoid blank spaces before data numbers. Microsoft Excel may use either a comma (,) or a dot (.), to separate decimal parts depending on the particular configuration of the program (default configurations may also vary between versions for different countries). Make sure that data values are pasted into the sheet in an acceptable number format. 8. In some instances, the program may return a negative value of kD (see, for example cell N12 in Fig. 1). Obviously, negative values of a kinetic constant make no physical sense, but the message behind this result is that the program, at solving Eq. 4, has found “too much” mRNA at t2 (i.e. too high m2) for what was expected from the initial m1 and the transcrip- tion rates TR1 and TR2, assuming a linear time course between them. If kD is “weakly” negative (i.e. near zero) and occurs eventually in single genes, the negative value is most likely a result of experimental error (overestimation of m2 or underestimation of the TRs at the particular time point). Since these errors affecting GRO values appear to be ran- domly distributed between genes and time points, they are usually averaged out when considering a mean value of a relatively high number of genes (such as in gene clusters). Conversely, whenever significantly negative values persist after this averaging, this strongly suggests that the postulate of a linear progression between TR1 and TR2 does not actu- ally hold. Indeed, a prominent negative value of kD for the interval between t1 and t2 indicates that the TR value peaked between TR1 and TR2. Frequent negative values for many genes (or even clusters) are a clear symptom of excessively separated time points. In these cases, the cultures should be sampled for GRO more often in order to follow the time 16 Marı́n-Navarro et al.
- 34. course of TR and m with enough detail as to approach the linear postulate. 9. Occasionally, some values of TR and/or m may be missing for certain genes and/or time points because of experimental failures. In that instance, you may leave blank cells. Note that the program distinguishes “blank” from “0” with a totally different meaning (“0” means “nothing” while “blank” means “unknown”). Whenever an interspersed value of TR or m is given as a blank, the program looks for the next time point for which both data are available and calculates kD for the whole interval between the nearest consecutive fully documented time points, disregarding any intermediate incomplete pair of values. Consequently, it gives the same value of kD for all intermediate time points encompassed by this interval. To highlight this special circumstance, these values are printed in italics. For example, in Fig. 1 the second time point (4 min) is missing from gene 12 (cell D17). As a result, the program calculates kD for the interval between the first and third time point (from 0 to 11 min) giving a value of 0.098 which is printed in italics both under k12 and k23 (cells L17 and M17). In case that the missing data are at the beginning or the end of the time series, the program will accordingly leave blank cells corresponding to the initial or final intervals for which kD cannot be determined. For example, the missing value of TR at the fourth (and last) time point of gene 27 (cell J32) produces a blank for k34 of the same gene (cell N32) in Fig. 1. 10. The program calculates kD through an iterative method. Initial trials executing the “Marmor” program with experimental data (3, 4) have revealed that a kD value within a precision of 0.000001/min is usually achieved in less than 20 iterations. However, in order to prevent the program to get stalled between two time points (i and j) by an inconsistent data set, “Marmor” will stop the calculation after performing 1,000 iterations without reaching the required precision. The mes- sage “2 many” (meaning too many iterations) will be printed in the corresponding kij cell before resuming with the next point. Acknowledgments Work in the authors’ laboratories is supported by grants from the Spanish MEC (BIO2007-67708-C04-02) and MiCInn (BFU2009-11965, BFU2008-02114, BFU2007-67575-C03- 01/BMC), and by the Swedish Research Council (2007-5460). Global Estimation of mRNA Stability in Yeast 17
- 35. Appendix: The MARMOR Program Macro 1 18 Marı́n-Navarro et al.
- 36. Global Estimation of mRNA Stability in Yeast 19
- 37. Macro 2 20 Marı́n-Navarro et al.
- 38. Global Estimation of mRNA Stability in Yeast 21
- 39. References 1. Nonet, M., Scafe, C., Sexton, J., and Young, R. (1987) Eucaryotic RNA polymerase condi- tional mutant that rapidly ceases mRNA syn- thesis. Mol Cell Biol 7, 1602–11. 2. Grigull, J., Mnaimneh, S., Pootoolal, J., Robinson, M. D., and Hughes, T. R. (2004) Genome-wide analysis of mRNA stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis. factors. Mol Cell Biol 24, 5534–47. 3. Molina-Navarro, M. M., Castells-Roca, L., Bellı́, G., Garcı́a-Martı́nez, J., Marı́n-Navarro, J., Moreno, J., Pérez-Ortı́n, J. E., and Her- rero, E. (2008) Comprehensive transcrip- tional analysis of the oxidative response in yeast. J Biol Chem 283, 17908–18. 4. Romero-Santacreu, L., Moreno, J., Pérez- Ortı́n, J. E., and Alepuz, P. (2009) Specific and global regulation of mRNA stability dur- ing osmotic stress in Saccharomyces cerevisiae. RNA 15, 1110–20. 5. Molin, C., Jauhiainen, A., Warringer, J., Ner- man, O., and Sunnerhagen, P. (2009) mRNA stability changes precede changes in steady- state mRNA amounts during hyperosmotic stress. RNA 15, 600–14. 6. Bernstein, J. A., Khodursky, A. B., Lin, P. H., Lin-Chao, S., and Cohen, S. N. (2002) Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc Natl Acad Sci U S A 99, 9697–702. 7. Hundt, S., Zaigler, A., Lange, C., Soppa, J., and Klug, G. (2007) Global analysis of mRNA decay in Halobacterium salinarum NRC-1 at single-gene resolution using DNA microar- rays. J Bacteriol 189, 6936–44. 8. Andersson, A. F., Lundgren, M., Eriksson, S., Rosenlund, M., Bernander, R., and Nilsson, P. (2006) Global analysis of mRNA stability in the archaeon Sulfolobus. Genome Biol 7, R99. 9. Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differ- ential expression in microarray experiments. Stat Appl Genet Mol Biol 3, Article 3. 22 Marı́n-Navarro et al.
- 40. 10. Garcı́a-Martı́nez, J., Aranda, A., and Pérez- Ortı́n, J. E. (2004) Genomic run-on evaluates transcription rates for all yeast genes and iden- tifies gene regulatory mechanisms. Mol Cell 15, 303–13. 11. Pérez-Ortı́n, J. E., Alepuz, P. M., and Moreno, J. (2007) Genomics and gene transcription kinetics in yeast. Trends Genet 23, 250–7. 12. Pelechano, V., and Pérez-Ortı́n, J. E. (2010) There is a steady state transcriptome in exponentially growing yeast cells. Yeast 27, 413–22. 13. Tu, B. P., Kudlicki, A., Rowicka, M., and McKnight, S. L. (2005) Logic of the yeast metabolic cycle: temporal compartmentaliza- tion of cellular processes. Science 310, 1152–8. 14. Reinke, H., and Gatfield, D. (2006) Genome- wide oscillation of transcription in yeast. Trends Biochem Sci 31, 189–91. Global Estimation of mRNA Stability in Yeast 23
- 41. .
- 42. Chapter 2 Genomic-Wide Methods to Evaluate Transcription Rates in Yeast José Garcı́a-Martı́nez, Vicent Pelechano, and José E. Pérez-Ortı́n Abstract Gene transcription is a dynamic process in which the desired amount of an mRNA is obtained by the equilibrium between its transcription (TR) and degradation (DR) rates. The control mechanism at the RNA polymerase level primarily causes changes in TR. Despite their importance, TRs have been rarely measured. In the yeast Saccharomyces cerevisiae, we have implemented two techniques to evaluate TRs: run-on and chromatin immunoprecipitation of RNA polymerase II. These techniques allow the discrimi- nation of the relative importance of TR and DR in gene regulation for the first time in a eukaryote. Key words: Yeast, Saccharomyces cerevisiae, Transcription rate, Functional genomics, ChIP-on-chip, Run-on 1. Introduction Transcription rate (TR) is the rate at which RNAs are produced as molecules per time unit. Measurement of TRs is not as straight- forward as the measurement of mRNA amounts (RA). Even at the individual level, the TR of a given gene has been rarely measured because ofthe difficulty of quantifying nascent RNA molecules. One possibility of evaluating TR is by measuring the RNA polymerase densities in the transcribed regions of the genes. Since each elongat- ing enzyme has a single nascent RNA molecule, the number of RNA polymerases on a gene reflects the number of RNAs being pro- duced, while density reflects the TR if we assume a constant RNA polymerase speed. RNA polymerase II (Pol II) density can be counted by either the run-on (1) or the chromatin immunoprecipi- tation (chIP) techniques using specific antibodies (Abs). The run-on technique can be used in many kinds of eukary- otic cells prior to nuclei isolation (2, 3). However, whole cells can be used only in yeast because sarkosyl detergent permeabilizes Attila Becskei (ed.), Yeast Genetic Networks: Methods and Protocols, Methods in Molecular Biology, vol. 734, DOI 10.1007/978-1-61779-086-7_2, # Springer Science+Business Media, LLC 2011 25
- 43. cell membranes and allows labeled UTP utilization for RNA synthesis (1). This permits an instantaneous labeling of the physi- ologically real RNA transcription. We adapted the run-on tech- nique to the genomic scale [genomic run-on (GRO)] using [a-33 P]rUTP labeling and nylon macroarray hybridization (Figs. 1 and 2, and ref. 4). Using GRO, the nascent TRs for all the genes of an organism have been calculated for the very first time. Since the experiment includes a parallel RA determination, the mRNA stabilities can be calculated at the genomic scale if considering steady-state conditions (4, 5) or even under non- steady-state conditions (6, 7). This utility of the GRO technique will be discussed in a companion chapter of this book (8). Similar protocols have been used in other eukaryotes, but without a real determination of TRs (2, 3). The GRO technique has also been adapted to massive parallel sequencing technologies but, again, without TR calculation (9). GRO “in vivo” RNA extraction cDNA labeling GRO experiment diagram RNA extraction Macroarray stripping Hybridization of 33 P-UTP labeled RNA Assuming, or not, steady-state Transcription Data (TR) mRNA amount data (RA) mRNA stability data Hybridization of 33 P-dCTP labeled RNA labeling of nascent RNA Fig. 1. Genomic run-on protocol for simultaneous TR and RA measurements. Grown cells are subjected to two independent protocols: GRO for nascent RNA labeling (right) and direct RNA extraction (left). The data from the GRO hybridized macroarrays are used to obtain transcription rates (TR) after normalization and corrections. The data from successive cDNA hybridization onto the same macroarray (after stripping it) are used to obtain mRNA amounts (RA). If one assumes steady-state conditions for mRNA amounts, it is possible to calculate mRNA stability data by dividing RA by TR. If there is no steady- state, a mathematical approximation is also possible see ref. 15. 26 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 44. On the other hand, RNA polymerase molecules have been shown to be cross-linked to transiently bound DNA sequences (10, 11). The scaling of this method at the genomic level using DNA chips has been demonstrated for human cells (12) and yeast cells (13–15) using tiling arrays. These studies proved very power- ful in terms of the description of the RNA polymerase distribution within the genome and the genes, but they were not used to calculate TRs. However, the use of DNA arrays containing whole ORF probes enables the calculation of an average distribution of Pol II density over the genes. We call this method RNA Polymerase II ChIP-on-chip (RPCC) (Fig. 2). Although the RPCC technique may be used to calculate the TRs in yeast, it is technically more complex than the GRO technique and, moreover, is affected by a higher background due to the unavoidable amplification of co- precipitated nonbound DNA, which is typical of ChIP. This results in a narrower dynamic range than that seen in the GRO technique. Interestingly, the comparison of RPCC and GRO methods allows the detection and correction of technique-specific biases (V. Pelechano et al., in press). Moreover, the comparison between the presence ofPol II and the elongation activity measured by GRO allows the discovery of biological differences in the way in which the genes are transcribed (16). The RPCC can be done using any antibody that recognizes Pol II. However, the quality of the results depends on the antibody’s affinity. We have successfully used Abs against either a tagged Pol II or the different phosphorylation forms of the carboxy terminal domain (CTD) of its largest subunit. Abs against other Pol II subunits may also be used (13, 15). Fig. 2. Comparison of the GRO and RPCC methods. Different forms of Pol II molecules (ovals) are bound to a transcription unit (horizontal rectangle). Pol II molecules are represented with a CTD tail that can be, or not, modified in Ser5 and/or Ser2 (dashed circles) and with or without an mRNA molecule (long string with a filled circle, 50 cap). All of them are cross- linkable to the adjacent DNA sequences. If a “general” Ab is used in the RPCC method (such as 8WG16, which recognizes hypophosphorylated molecules, but also others (21) or Ab against tags is added to a Pol II subunit, different forms represented), all the cross-linked Pol II (all kinds of ovals) are immunoprecipitated. If specific Abs against the post- translational modifications are used, only those molecules will be precipitated. Run-on, however, only labels true elongating Pol II molecules (dark ovals), as well as the other nuclear RNA polymerases (I and III, not shown). Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 27
- 45. 2. Materials 2.1. Run-On and Macroarray Hybridization 1. YDP medium: 1% w/v, yeast extract, 2% w/v, peptone, 2% glucose. Store at room temperature (see Note 1). 2. 10 and 0.5% w/v, L-laurylsarcosine (sarkosyl, Sigma–Aldrich Inc., St. Louis, MO)/in H2O. Store at room temperature. 3. 2.5 Transcription buffer: 50 mM Tris–HCl, pH 7.7, 50 mM KCl, 80 mM MgCl2. Store at room temperature (see Note 2). 4. ACG mix (10 mM each ATP, CTP, GTP, Roche, Mannheim, Germany). Store frozen. 5. 0.1 M DTT (Invitrogen, Carlsbad, CA). Store frozen. 6. [a-33 P]rUTP (~3,000 Ci/mmol, 10 mCi/mL, PerkinElmer, Waltham, MA). Store at 4 C (see Note 3). 7. Transcription mix: 120 mL of 2.5 Transcription buffer, 16 mL AGC mix, 6 mL 0.1 M DTT, and 16 mL of [a-33 P] rUTP. Prepare fresh (see Note 4). 8. LETS buffer: 100 mM LiCl, 10 mM EDTA, 10 mM Tris–HCl, pH 7.5, 0.2% w/v, SDS. Store at room temperature. 9. Acid phenol:chloroform:isoamilic alcohol (125:24:1), equili- brated with water, not buffered. Store at 4 C. 10. 5 M Lithium chloride. Store at room temperature. 11. Hybridization solution: 0.5 M sodium phosphate buffer, 1 mM EDTA, 7% w/v, SDS, pH 7.2, 100 mg/mL sonicated salmon sperm DNA. Do not autoclave. Store at room tem- perature. Add the DNA (stored frozen in 10 mg/mL solution in small aliquots) just before use (see Note 5). 12. Wash buffer I 1 SSC, 0.1% w/v, SDS and wash buffer II 0.5 SSC, 0.1% w/v, SDS. 20 SSC is 300 mM Na citrate, 3 M NaCl, pH 7.0 adjusted with HCl. Store at room temper- ature (see Note 5). 13. 1 M and 50 mM NaOH. Store at room temperature. 14. Neutralizing buffer: 50 mM Tris–HCl, pH 7.5, 0.1 SSC, 0.1% w/v, SDS. Store at room temperature. 15. Stripping solution: 5 mM sodium phosphate buffer, pH 7.0, 0.1% w/v, SDS. Store at room temperature. 16. Yeast nylon macroarrays. Described in (17). 2.2. cDNA Labeling 1. 5 First Strand Buffer (Invitrogen). Store frozen. 2. 0.1 M DTT (Invitrogen). Store frozen. 3. RNase OUT (Invitrogen). Store frozen. 4. DNase I (RNase free, 10/mL) (Roche). Store frozen. 28 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 46. 5. Chloroform (Panreac, Barcelona). Store at room temperature. 6. 3 M Sodium acetate, pH 4.5. Store at room temperature. 7. Random Hexamers (3 mg/mL) (Invitrogen). Store frozen. 8. Oligo dT (T15VN) (500 ng/mL). Store frozen. 9. dNTP’s mix:16 mM each of dATP, dGTP, dTTP, and 1 mM dCTP. Divide into small aliquots and store frozen. 10. [a-33 P]dCTP (~3,000 Ci/mmol, 10 mCi/mL) (PerkinElmer). Store at 4 C. 11. SuperScript II Reverse Transcriptase (200 U/mL) (Invitro- gen). Store frozen. 12. 0.5 M EDTA, pH 8.0 buffered with NaOH. Store at room temperature. 13. ProbeQuant G-50 or SR-H300 columns (GE, Niskayuna, NY). G-50 columns at room temperature and SR-H300 col- umns at 4 C, according to the suppliers. 2.3. Chromatin Immunoprecipitation 1. 37% w/v, formaldehyde solution in H2O (Sigma–Aldrich). Store at room temperature. 2. 2.5 M Glycine. Store in small autoclaved aliquots at room temperature. 3. TBS buffer: 20 mM Tris–HCl, 140 mM NaCl, pH 7.5. 4. Glass beads, acid-washed and autoclaved (425–600 mm, Sigma–Aldrich). Store at room temperature. 5. 8GW16 antibody (Covance Inc., Berkeley, CA). Store frozen; once thawed, keep at 4 C. 6. Dynabeads® Protein G for immunoprecipitation (Invitrogen). Store at 4 C. 7. 5 mg/mL bovine serum albumin (BSA) in PBS buffer: 140 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4. Divide into small aliquots and store frozen. 8. 10 mg/mL yeast tRNA (Applied Biosystems, Austin, TX). Store frozen. 9. Lysis buffer: 50 mM HEPES–KOH, pH 7.5, 140 mM NaCl, 1 mM EDTA, 1% v/v, Triton X-100, 0.1% w/v, sodium deoxycholate, 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM benzamidine and one pill of complete protease inhibitor cocktail (Roche) for 50 mL of buffer. Prepare fresh (see Note 6). 10. Wash buffer:10 mM Tris–HCl, pH 8.0, 250 mM LiCl, 0.5% w/v, Nonidet P-40, 0.5% w/v, sodium deoxycholate, 1 mM EDTA, pH 8.0. Prepare fresh. 11. TE: 10 mM Tris–HCl, pH 8.0, 1 mM EDTA. Store at room temperature. Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 29
- 47. 12. Elution buffer: 50 mM Tris–HCl, pH 8.0, 10 mM EDTA, 1% w/v, SDS. Store at room temperature. 13. Proteinase K (Roche) stock solution: 1 mg/mL in water. Store frozen divided into aliquots. 14. QIAquick PCR purification columns (Qiagen, Valencia, CA). Store at room temperature. 15. Neutral phenol:chloroform: Phenol:chloroform:isoamilic alcohol (25:24:1, saturated with 50 mM Tris–HCl, pH 7.5 buffer). Store at 4 C. 2.4. Ligation-Mediated PCR (LM-PCR) DNA Amplification 1. T4 DNA polymerase. Store frozen. 2. T4 DNA ligase. Store frozen. 3. Linkers oJW102 (50 -GCGGTGACCCGGGAGATCTGA ATTC) and oJW103 (50 -GAATTCAGATC) (18). The linker oligonucleotides are mixed to a final concentration of 15 mM in the presence of 250 mM Tris–HCl (pH 7.9). The mixture is distributed into 50 mL aliquots and dena- tured for 5 min at 95 C. Then they are transferred to a 70 C heated block and allowed to cool down slowly to room temperature. Afterward, the block with the tubes is placed at 4 C and allowed to cool down again. The linkers are then stored frozen, and should always be thawed and kept on ice. 4. Glycogen 20 mg/mL (Roche). Store frozen. 2.5. Macroarray Hybridization 1. Hybridization, washing, and stripping solutions are identical to those described for GRO (see Subheading 1). 3. Methods 3.1. Genomic Run-On 3.1.1. Run-On 1. Allow cells to grow to the desired OD600 (we normally use 0.4–0.6). 2. Two aliquots of the culture are needed: 50 and 20 mL (corresponding to about 6 108 and 2.5 108 cells, respec- tively). Other volumes may be required if using different cell densities for the transcription rate (TR) and the mRNA amount (RA, see Subheading 3.1.5) measurements, respec- tively (see Note 4). 3. Cells are pelleted in a 50-mL falcon tube by centrifugation at 2,500 g-force for 3 min. 4. Eliminate the supernatant and resuspend the cells in 5 mL of 0.5% sarkosyl at room temperature (see Note 7). 30 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 48. 5. Pellet the cells as before. The aliquot for RA is directly frozen in dry ice (see Note 8) and the TR aliquot is resuspended in 1 mL 0.5% sarkosyl. 6. Transfer resuspended cells into a 1.5-mL tube, and pellet the cells in a microcentrifuge by centrifuging at 3,300 g-force for 30 s. Discard the supernatant and centrifuge again, if necessary, to eliminate any remains of sarkosyl. 7. Resuspend the cells in 120 mL (see Note 4) of RNase-free water. Pre-warm both cells and mix separately at 30 C for 5 min. Add 158 mL of the transcription mix: the final reaction volume should be ~300 mL (see Note 9). 8. Incubate the mix at 30 C for 5 min in a Thermomixer (Eppendorf, Hamburg, Germany), or similar, with 600 rpm agitation (see Note 10). 9. Stop the run-on reaction by adding 1 mL of ice-cold RNase- free water. Recover cells by centrifuging at 3,300 g-force for 1 min and discard the supernatant (which contains the non- incorporated radioactive nucleotide). 10. Start the RNA extraction by resuspending cells in 500 mL of LETS buffer. 11. Transfer the cells resuspended in LETS to a fresh tube con- taining 500 mL of glass beads and 500 mL of acid phenol: chloroform. 12. Break cells by vortexing tubes three times for 30 s at 5.5 intensity in a Fast-Prep 24 (MP Biomedicals, Solon, OH) (see Note 11). 13. Centrifuge tubes for 5 min at 13,400 g-force to separate the phases, and transfer the upper water phase to a fresh tube. Add one volume of acid phenol:chloroform, mix well by vortexing, and centrifuge as before. 14. Transfer the new upper aqueous phase to a fresh tube and add 0.1 volume of 5 M LiCl and two volumes of cold 96% ethanol. Mix and incubate at 20 C for at least 3 h (see Note 12). 15. Recover the total RNA by centrifugation at 13,400 g-force in a microcentrifuge for 15 min. Discard the supernatant and wash the pellet with 0.7 mL of 70% ethanol. Dry the pellet in a Speed-vac (Thermo Savant, Waltham, MA) for 2–3 min, and dissolve the RNA in 300 mL of RNase-free water (see Note 13). 16. Prepare a 1:100 dilution of the dissolved RNA in H2O. Quantify the extracted RNA by measuring at A260. A spectrophotometer that is capable of measuring low volumes (as 50 mL) will avoid losses of the valuable material. Use 5 mL of each one from the same dilutions to measure the Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 31
- 49. radioactivity incorporated into a scintillation counter. The radioactivity obtained ranges of between 0.8 and 3.5 107 dpm (see Note 14). All the labeled RNA is used in hybridization. 3.1.2. Hybridization of Run-On Samples 1. Prehybridize the yeast nylon macroarray (17) for a minimum of 30 min at 65 C with 5 mL of hybridization solution in a hybridization tube on a roller oven (see Note 15). 2. Hybridization is performed with fresh hybridization solution by adding the labeled RNA. The volume of fresh hybridization solution may be adjusted to obtain in a hybri- dization solution of between 1 and 7 106 dpm/mL. Allow to hybridize for 20–24 h at 65 C in a roller oven (see Note 15). 3. After hybridization, wash the macroarray once with washing buffer I at 65 C for 10 min, and twice with washing buffer II at 65 C for 10 min (see Note 5). 4. After washing, the membranes are saran-wrap sealed and exposed between 1 and 7 days to an Imaging Plate (Fujifilm BAS IP or similar), depending on the intensity of the signal measured with a Geiger counter (see Note 16). 3.1.3. Analysis of Run-On Hybridized Macroarrays 1. Scan the macroarrays in a suitable phosphorimager (such as a Fujifilm FLA, Fujifilm BAS, GE Storm, or GE Typhoon), with a resolution of at least 50 mm. 2. The macroarray image data are analyzed by using ArrayVision 7.0 (Imaging Research Inc., Ontario, Canada) or by other array analysis softwares. Biological replicates of the experi- ment should be done. We recommend at least three. 3. Before manipulating the raw data, we use genomic hybridi- zations to eliminate any differences due to the filter (see Note 17). Thus, each run-on hybridization dataset was divided by the corresponding genomic hybridization dataset done on the same nylon membrane. This procedure also serves to normalize the signals of the different probes, which enables comparable TR results for all the genes. 4. Values for each replicate are corrected by the number of cells used (see Note 18). 5. Hybridization values for each gene probe in each replicate are normalized and averaged by using ArrayStat 1.0 (Imaging Research Inc.), or other statistical array analysis softwares, in order to obtain a sure transcription value per cell for each gene (TR values). 6. Average values from step 5 are corrected for each gene by the percentage of U in each probe-coding strand. 32 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 50. 7. RNA polymerase densities reflect transcription rates if we consider they have a constant elongation speed (4). The TR values obtained are, however, in arbitrary units (radioac- tive intensities). In order to convert them into real rates (i.e., molecules/min) it is necessary to use a reference. We have used the known TR for HIS3 gene, 0.43 mRNAs/min (19). In this way, knowing the ratio of the radioactive intensities between HIS3 and a given gene, the real TR can be calcu- lated for that gene. Another possibility is to use the whole set of absolute values for mRNA concentrations (called m or RA) and mRNA half-lives t1/2, e.g., that described in ref. 20 to determine a set of indirect TR using the Eqs. 2 and 3 described in the companion chapter (8) and plot it against the arbitrary units set to obtain a conversion factor (V. Pelechano et al., in press). This last method is more robust than the one previously described. 3.1.4. Stripping Run-On Hybridizations Nylon macroarrays can be used several times (up to ten times in our hands). Therefore, it is necessary to strip them of the radioac- tive sample before they are reused. They should be stripped even if they are not to be used immediately (see Note 16). 1. Incubate the membrane inside the hybridization tube with 25 mL of 50 mM NaOH at 45 C for 45 min. 2. Wash once with the same volume of neutralizing buffer at 45 C for 15 min. 3. Transfer the filter to a plastic box and perform an additional washing step with boiling stripping solution for 5–10 min with agitation. 4. Membranes can be reused directly or stored after air-drying. 3.1.5. cDNA Labeling: RNA Extraction A cDNA labeling experiment requires a series of independent protocols that we describe independently (from Subhead- ings 3.1.5–3.1.10). Two different procedures can be followed depending on the primer used in the cDNA synthesis: random primers (RP labeling) or oligo d(T) (dT labeling). If RP labeling is used, it is necessary to perform a DNase I digestion of the RNA in order to eliminate any remains of contaminant DNA that co-extracted with the RNA. This is not necessary with dT labeling because it only primes at poly(A)-mRNAs (see Notes 19 and 20). 1. Total RNA is extracted from the 20-mL frozen culture aliquot for mRNA measurements as in an in vivo run-on protocol. The RNA extraction yield is evaluated by A260 (see Subhead- ing 3.1.1, steps 2 and 10–16, but also see Note 12). Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 33
- 51. 3.1.6. DNase I Digestion 1. Use a total of around 100 mg of total RNA (to prevent loss after the phenolization and precipitation steps). Dissolve it in 17 mL of H2O. 2. Add 2 mL of 5 first strand buffer (Invitrogen), 1 mL of RNase OUT (Invitrogen) and 0.6 mL of RNase free-DNase I. 3. Incubate at 37 C for 30 min. Once again, add 0.4 mL of RNase free-DNase I, and incubate under the same conditions for 30 min more. 4. Remove the RNase free-DNase I by extracting once with acid phenol:chloroform and once with chloroform. 5. Precipitate the RNA with 0.1 volume of 3 M sodium acetate, pH 4.8, and 2.5 volumes of 96% ethanol, incubating at 20 C for a minimum of 1 h. 6. Recover the RNA by centrifugation in a microcentrifuge at 13,400 g-force for 15 min. Remove the supernatant and wash with 0.7 mL of 70% ethanol, and centrifuge again at 13,400 g-force for 5 min. 7. Dry the RNA for 1–2 min in a Speed-vac (see Note 13). 3.1.7. Labeling Reaction 1. Take 50 mg of total RNA (DNase I-digested or not, see Note 12) in a volume of 12.3 mL, add 1 mL of RNase OUT and, alternatively, 1.2 mL of random hexamers (3 mg/mL) or 1.2 mL of Oligo d(T) (500 ng/mL), depending on the labeling option. The final volume of that mix must be 14.5 mL. 2. Incubate the mix at 70 C for 10 min and leave at room temperature for 5–10 min. Then place it on ice. 3. To the previous sample, add 6 mL of the 5 first strand buffer, 3 mL of 0.1 M DTT, 1.5 mL of dNTP’s mix, 4 mL of [a-33 P]-dCTP, and 1 mL of SuperScript II Reverse Tran- scriptase. The final reaction volume must be 30 mL (see Note 9). 4. Incubate at 42 C for 1 h and stop the reaction by adding 1 mL of 0.5 M EDTA, pH 8.0. 5. Add water to the reaction to a final volume of 50 mL, and eliminate the nonincorporated nucleotides by using Probe- Quant G-50 or SH-300R columns according to the manu- facturer’s instructions. 6. Estimate the radioactive incorporation by measuring 1 mL in the scintillation counter to calculate the total dpm. 3.1.8. Hybridization of cDNA Samples 1. Perform a prehybridization of the macroarray as for the run- on samples (Subheading 3.1.2, step 1). 2. Denature the labeled sample at 95 C for 5 min and transfer to an ice bath. 34 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 52. 3. Add the denatured labeled cDNA sample to the corres- ponding volume of hybridization solution to obtain a radio- activity concentration ranging between 5 and 10 106 dpm/mL. 4. Hybridization, washing, and scanning are performed as previ- ously described (Subheading 3.1.2, steps 2–5). 3.1.9. Stripping cDNA Hybridizations 1. Perform three washes in a dish with boiling stripping solution for 5–10 min in agitation. 2. Membranes can be reused directly or kept air-dried. 3.1.10. Analysis of the cDNA Hybridized Macroarrays 1. The hybridized macroarrays are scanned and the images are analyzed as before with the run-on samples. Biological repli- cates of the experiment should be done. Again, we recom- mend at least three. 2. As in Subheading 3.1.3, genomic hybridizations are used for eliminating any differences due to the filter; again, ArrayStat or a similar software was used to normalize and average the cDNA hybridization values (see Note 17). 3. When different conditions are analyzed, normalized, and averaged, the cDNA values are corrected by the combined factor of total RNA per cell (see Note 18) and the proportion of mRNA in the total RNA (see Note 21) in order to obtain the mRNA values per cell (RA values). 4. Average values from step 3 are corrected for each gene by the percentage of G in each probe-coding strand. 5. As for TR values, the RA values obtained are in arbitrary units (radioactive intensities). In order to convert them into real units (molecules/cell) it is necessary to use a reference. We have used the whole set of absolute values for mRNA con- centrations described in ref. 20, and plot it against the arbi- trary units to obtain a conversion factor, and transform the arbitrary units into real ones. 3.2. RNA Polymerase- ChIP-on-Chip The first step of this protocol, and the most critical one, is chro- matin immunoprecipitation (IP). To obtain reliable and reproduc- ible results, it is important to ensure that the Pol II IP is successful. It is advisable to perform a control PCR to check IP efficiency using a gene that is known to be expressed as a positive control before proceeding to the array hybridization (11, 21). The genomic RPCC data should be obtained using the IP data that have been normalized by a positive control of the total chromatin (whole cell extract, WCE). A negative control (such as an IP without a specific antibody) is highly variable between different technical replicates due to the low amount of contami- nant DNA. Therefore, although it is advisable to perform negative Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 35
- 53. control replicates to discard any nonspecific IP, they are not used to normalize the final IP data. 3.2.1. Chromatin Immunoprecipitation 1. For each IP reaction or for the negative control, 50 mL cells of yeast culture (OD600 ~ 0.5) are cross-linked by adding formaldehyde at a final concentration of 1% for 15 min at room temperature. Then the reaction is quenched by the addition of glycine at a final concentration of 125 mM (see Note 22). Cells are washed four times with 30 mL ice-cold TBS buffer, frozen in liquid N2, and stored at 20 C until use. Samples can be kept several weeks in this stage. 2. Thaw cells on ice and resuspend them in 300 mL lysis buffer. Then, transfer cells to an ice-cold 1.5 mL screw-capped tube with 0.2 mL of glass beads and break them by vortexing at the maximum power for 12 min at 4 C in a Genie 2 vortex (Scientific Industries Inc., Bohemia, NY) or similar. 3. Add 300 mL lysis buffer to the tubes and transfer the lysed cells to a new tube. Sonicate the chromatin at 4 C (see Note 23). 4. Remove the cell debris by centrifugation at 14,000 g at 4 C for 5 min. A 10 mL aliquot of this WCE is kept as a positive control. 5. The magnetic beads with the Ab should be prepared 1 day prior to their use. Beads (50 mL/sample) are washed twice with 600 mL PBS/BSA using a magnet (DynaMag™-2, Invi- trogen). Then they are resuspended with 15 mL 8WG16 Ab (2 mg/mL) and 1 mL yeast tRNA as a blocking agent. For a no-Ab negative control, the volume of Ab is changed by an equal volume of PBS/BSA. Beads are kept in a tube rotator overnight at 4 C (Roto-Torque, Cole-Parmer, Vernon Hills, IL). The next day, beads are washed four times with 600 mL PBS/BSA. Afterward, they are resuspended in 30 mL of PBS/ BSA and the sonicated chromatin obtained from 50 mL cells (step 4) is added. The samples with the beads are incubated in a rotator for 1.5 h at 4 C (see Note 24). Wash beads twice with 1 mL lysis buffer, twice with 1 mL lysis buffer supple- mented with 360 mM NaCl, twice with 1 mL wash buffer, and once with 1 mL TE. In order to elute the samples, beads are resuspended in 50 mL of elution buffer and incubated for 10 min at 65 C under agitation (600 rpm in a Thermomixer). Then 30 mL of eluted sample is recovered and an additional amount of 30 mL of elution buffer is added. Repeat this incubation and recover an additional amount of 30 mL of the eluted sample. It is important in this step to be careful not to touch beads excessively with the tip to avoid contami- nation or any bead carryover. Raise the final volume of the samples to 300 mL with TE and incubate overnight at 65 C 36 Garcı́a-Martı́nez, Pelechano, and Pérez-Ortı́n
- 54. with agitation (600 rpm in a Thermomixer) to reverse the cross-linking. 6. To digest the proteins, 142.5 mL TE and 7.5 mL proteinase K (to 20 mg/mL) are added to each sample. Incubation is kept at 37 C with agitation (600 rpm) for 1.5 h. Samples are purified using QIAquick PCR purification columns (or simi- lar) with two binding steps and the same column for each sample. The sample is eluted in 50 mL. Up to 5 mL sample should be used in this step to check IP efficiency by performing a standard PCR analysis for an expressed control gene (11, 21). These DNA samples are only stable for a few days at 20 C. For this reason, the rest of the sample should be used as soon as possible for the DNA amplification step (next paragraph). 3.2.2. DNA Amplification by LM-PCR 1. The ends of the DNA molecules are blunted. The entire IP sample is used for this, but only 2 mL of the sample is used for the WCE (4% of the total). The reaction is allowed to proceed for 20 min at 12 C in the presence of 0.6 U of T4 DNA polymerase in its buffer supplemented with 80 mM dNTPs. Then, the sample is extracted twice with neutral phenol:chlo- roform and precipitated with two volumes of ethanol in the presence of 0.1 volume of sodium acetate and 12 mg of glycogen. 2. Ligate the blunt-ended sample overnight at 16 C using 0.5 U of T4 DNA ligase in a final volume of 50 mL in the presence of the annealed linkers oJW102 and oJW103 (1.5 mM) (18). Precipitate the ligated sample with ethanol and resuspend it in 25 mL of milliQ sterile water. 3. Amplify the sample in a 50-mL PCR mix using 1 mM of oJW102 primer. The PCR program is 2 min at 95 C, 30 (or less) cycles (30 s at 95 C, 30 s at 55 C, and 2 min at 72 C), with a final cycle of 4 min at 72 C. The number of PCR cycles should be tested and kept as low as possible. Precipitate the DNA with ethanol and resuspend it in 50 mL of milliQ water (see Note 25). In this state, the sample can be kept at 20 C for months. 3.2.3. Sample Labeling and Macroarray Hybridization 1. Label the sample by one additional cycle of PCR in the presence of a-[33 P]-dCTP. 15 mL of sample containing 1–2 mg of DNA from LM-PCR in 50 mL final volume, includ- ing: 1 Taq DNA pol buffer, 2 mM MgCl2, 0.2 mM dATP, dTTP, and dGTP, 25 mM dCTP, 1 mM oJW102, 0.8 mCi a-[33 P]-dCTP, and 5 U Taq DNA pol. Denature the mix for 5 min at 95 C, anneal for 5 min at 50 C, and amplify for 30 min at 72 C (see Note 26). Purify the reaction product with a ProbeQuant G-50 column following the manufacturer’s Genomic-Wide Methods to Evaluate Transcription Rates in Yeast 37
- 55. Other documents randomly have different content
- 59. The Project Gutenberg eBook of Antique Works of Art from Benin
- 60. This ebook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. Title: Antique Works of Art from Benin Author: Augustus Henry Lane-Fox Pitt-Rivers Release date: October 22, 2013 [eBook #44014] Most recently updated: October 23, 2024 Language: English Credits: E-text prepared by Henry Flower and the Online Distributed Proofreading Team (http://www.pgdp.net) from page images generously made available by Internet Archive/Canadian Libraries (http://archive.org/details/toronto) *** START OF THE PROJECT GUTENBERG EBOOK ANTIQUE WORKS OF ART FROM BENIN ***
- 61. The Project Gutenberg eBook, Antique Works of Art from Benin, by Augustus Henry Lane-Fox Pitt-Rivers Note: Images of the original pages are available through Internet Archive/Canadian Libraries. See http://archive.org/details/antiqueworksofar00pittuoft Many of the illustrations can be enlarged by clicking on them. ANTIQUE WORKS OF ART FROM BENIN,
- 62. COLLECTED BY LIEUTENANT-GENERAL PITT RIVERS, D.C.L., F.R.S., F.S.A. Inspector of Ancient Monuments in Great Britain, c. PRINTED PRIVATELY. 1900.
- 63. LONDON: HARRISON AND SONS, PRINTERS IN ORDINARY TO HER MAJESTY, ST. MARTIN’S LANE, W.C.
- 64. WORKS OF ART FROM BENIN, WEST AFRICA. OBTAINED BY THE PUNITIVE EXPEDITION IN 1897, AND NOW IN GENERAL PITT RIVERS’S MUSEUM AT FARNHAM, DORSET. Benin is situated on the Guinea Coast, near the mouth of the Niger, in latitude 6·12 north, and longitude 5 to 6 east. It was discovered by the Portuguese at the end of the fourteenth or commencement of the fifteenth centuries. The Portuguese were followed by the Dutch and Swedes, and in 1553 the first English expedition arrived on the coast, and established a trade with the king, who received them willingly. Benin at that time appears by a Dutch narrative to have been quite a large city, surrounded by a high wall, and having a broad street through the centre. The people were comparatively civilized. The king possessed a number of horses which have long since disappeared and become unknown. Faulkner, in 1825, saw three solitary horses belonging to the king, which he says no one was bold enough to ride. In 1702 a Dutchman, named Nyendaeel, describes the city, and speaks of the human sacrifices there. He says that the people were great makers of ornamental brass work in his day, which they seem to have learnt from the Portuguese. It was visited by Sir Richard Burton, who went there to try to put a stop to human sacrifices, at the time he was consul at Fernando Po. In 1892 it was visited by Captain H. L. Galloway, who speaks of the city as possessing only the ruins of its former greatness; the abolition of the slave trade had put a stop to the prosperity of the place, and the king had prohibited
- 65. any intercourse with Europeans. The town had been reduced to a collection of huts, and its trade had dwindled down to almost nil. The houses have a sort of impluvium in the centre of the rooms, which has led some to suppose that their style of architecture may have been derived from the Roman colonies of North Africa. In 1896 an expedition, consisting of some 250 men, with presents and merchandise, left the British settlements on the coast, and endeavoured to advance towards Benin city. The expedition was conducted with courage and perseverance, but with the utmost rashness. Almost unarmed, neglecting all ordinary precautions, contrary to the advice of the neighbouring chiefs, and with the express prohibition of the King of Benin to advance, they marched straight into an ambuscade which had been prepared for them in the forest on each side of the road, and as their revolvers were locked up in their boxes at the time, they were massacred to a man with the exception of two, Captain Boisragon and Mr. Locke, who, after suffering the utmost hardships, escaped to the British settlements on the coast to tell the tale. Within five weeks after the occurrence, a punitive expedition entered Benin, on 18th January, 1897, and took the town. The king fled, but was afterwards brought back and made to humiliate himself before his conquerers, and his territory annexed to the British crown. The city was found in a terrible state of bloodshed and disorder, saturated with the blood of human sacrifices offered up to their Juju, or religious rites and customs, for which the place had long been recognised as the “city of blood.” What may be hereafter the advantages to trade resulting from this expedition it is difficult to say, but the point of chief interest in connection with the subject of this paper was the discovery, mostly in the king’s compound and the Juju houses, of numerous works of art in brass, bronze, and ivory, which, as before stated, were mentioned by the Dutchman, Van Nyendaeel, as having been constructed by the people of Benin in 1700.
- 66. These antiquities were brought away by the members of the punitive expedition and sold in London and elsewhere. Little or no account of them could be given by the natives, and as the expedition was as usual unaccompanied by any scientific explorer charged with the duty of making inquiries upon matters of historic and antiquarian interest, no reliable information about them could be obtained. They were found buried and covered with blood, some of them having been used amongst the apparatus of their Juju sacrifices. A good collection of these antiquities, through the agency of Mr. Charles Read, F.S.A., has found its way into the British Museum; others no doubt have fallen into the hands of persons whose chief interest in them has been as relics of a sensational and bloody episode, but their real value consists in their representing a phase of art—and rather an advanced stage—of which there is no actual record, although no doubt we cannot be far wrong in attributing it to European influence, probably that of the Portuguese some time in the sixteenth century. A. P. R. Rushmore, Salisbury, April, 1900.
- 67. DESCRIPTION OF PLATE I. Fig. 1.—Bronze plaque, representing two warriors with broad leaf- shaped swords in their right hands. Coral or agate head-dress. Coral chokers, badge of rank. Leopards’ teeth necklace. Coral scarf across shoulder. Leopards’ heads hanging on left sides. Skirts each ornamented with a human head. Armlets, anklets, etc. Ground ornamented with the usual foil ornament incised.
- 68. Fig. 2.—Bronze plaque, representing two figures holding plaques or books in front. Coral chokers, badge of rank. Reticulated head- dresses of coral or agate, similar to that represented in Plate XXI, Fig. 121. Barbed objects of unknown use behind left shoulders, ornamented with straight line diaper pattern. Ground ornamented with foil ornaments incised. Guilloche on sides of plaque.
- 69. Fig. 3.—Bronze plaque, representing three warriors, two with feathers in head-dress and trefoil leaves at top; one with pot helmet, button on top. The latter has a coral choker, badge of rank, and all have leopards’ teeth necklaces. The central figure has a cylindrical case on shoulder. Two have hands on their sword-hilts. All three have leopards’ heads on breast, and quadrangular bells hanging from neck. Leopards’ skins and other objects hang on left sides. Ground ornamented with foil ornaments incised.
- 70. Fig. 4.—Bronze plaque, figure of warrior with spear in right hand, shield on left shoulder. Head-dress of coral or agate, similar to that represented in Plate XXI, Fig. 121. Quadrangular bell hanging from neck. Chain-like anklets. Coral choker, badge of rank, and leopards’ teeth necklace. A nude attendant on right upholds a large broad leaf-shaped sword, with a ring attached to pommel. Another holds two sistri or bells fastened together by a chain. Small figure on left is blowing an elephant’s tusk trumpet. Figures above in profile are holding up tablets or books. The dress of one of them is fastened with tags or loops of unusual form. These figures have Roman noses, and are evidently not negro. Ground ornamented with the usual foil ornament incised.
- 71. DESCRIPTION OF PLATE II. Figs. 5 and 6.—Bronze plaque, representing a warrior in centre, turned to his left. He has a beard and a necklace of leopards’ teeth, but no coral choker. He has a high helmet, somewhat in the form of a grenadier cap. Quadrangular bell on neck. Dagger in sheath on right side, and various appurtenances hanging from his dress. He holds a narrow leaf-shaped sword in his right hand over an enemy who has fallen, and who has already a leaf-shaped sword thrust through his body. The victim has a sword-sheath on left side, with broad end, and a peculiar head-dress. His horse is represented below with an attendant holding it by a chain and carrying barbed darts in his left hand. On the right of the conqueror is a small figure blowing a tusk trumpet, and on his right a larger figure carrying a shield in his left hand and a cluster of weapons. He has a high helmet, ornamented with representations of cowrie shells of nearly
- 72. the same form as that of the central figure. Above are two figures, one blowing what appears to be a musical instrument and the other carrying a barbed pointed implement, and armed with a sword in sheath similar to that of the fallen warrior. The plaque appears to represent a victory of some kind, and all the conquerors have the same high helmet. The ground is ornamented with the usual foil ornament incised. Figs. 7 and 8.—Bronze plaque, representing a king or noble on horseback sitting sideways, his hands upheld by attendants, one of whom has a long thin sword in his hand in sheath. Two attendants, with helmets or hair represented by ribs, are holding up shields to shelter the king from the sun. The king or noble has a coral choker, badge of rank, with a coral necklace hanging on breast. Horse’s head-collar hung with crotals. A small attendant carries a “manilla” in his hand. The two figures above are armed with bows and arrows. Ground ornamented with foil ornaments incised. De Bry, “India Orientalis,” says that in the sixteenth century both the king and chiefs were wont to ride side- saddle upon led horses. They were supported by retainers, who held over their heads either shields or
- 73. umbrellas, and accompanied by a band of musicians playing on ivory horns, gong-gongs, drums, harps, and a kind of rattle.
- 74. DESCRIPTION OF PLATE III. Fig. 9.—Bronze plaque, naked figure of boy; hair in conventional bands; three tribal marks over each eye and band on forehead. Coral choker, badge of rank. Armlets and anklets. Four rosettes on ground and usual foil ornaments. De Bry says that all young people went naked until marriage.
- 75. Fig. 10.—Bronze plaque, figure of warrior with helmet or hair represented by ribs. Leaf-shaped sword upheld in right hand. A bundle of objects on head upheld by left hand. Object resembling a despatch case on left side, fastened by a belt over right shoulder. Human mask on left side. Four fishes on ground, and the usual foil ornaments incised.
- 76. Figs. 11 and 12.—Bronze plaque, representing a figure holding a ball, perhaps a cannon ball, in front. Coral choker, badge of rank. Three tribal marks over each eye. Crest on head-dress, feather in cap. Skirt wound up behind left shoulder. Skirt ornamented with a head and hands. Four rosettes on ground, and usual foil ornaments incised. Guilloche on sides of plaque.
- 77. DESCRIPTION OF PLATE IV. Fig. 13.—Bronze plaque, figure of warrior, feather in cap; broad leaf- shaped sword in right hand. Coral choker, badge of rank. Leopards’ teeth necklace. Coral sash; ground ornamented with leaf-shaped foil, ornaments incised.
- 78. Figs. 14 and 15.—Bronze ægis or plaque, with representations of two figures with staves in their right hands. Coral chokers, badge of rank. On the breasts are two Maltese crosses hanging from the necks, which appear to be European Orders. The objects held in left hands have been broken off. The hats are similar to that on the head of the figure, Fig. 91, Plate XV. Ground ornamented with the usual foil ornaments incised.
- 79. Fig. 16.—Bronze plaque, figure of warrior with pot helmet, button on top. Coral choker, badge of rank, on neck. Leopards’ teeth necklace. Quadrangular bell on breast. Armlets, anklets, c. Four rosettes on ground, and the usual foil ornaments incised.
- 80. Fig. 17.—Bronze plaque, figure of warrior with spear in right hand, shield in left hand; pot helmet, button on top. Quadrangular bell hanging from neck. Coral choker, badge of rank. Leopards’ teeth necklace. Leopard’s skin dress with head to front. On the ground are two horses’ heads below and two rosettes above. Ground ornamented with the usual foil ornaments incised.
- 81. Fig. 18.—Bronze plaque, figure of warrior. Peculiarly ornamented head-dress. Coral choker, badge of rank. Leopards’ teeth necklace. Broad leaf-shaped sword in right hand. Coral sash on breast. Leopard’s mask hanging on left side. Armlets, anklets, c. Small figure of boy, naked, to right, holding a metal dish with lid in form of an ox’s head. A similar object may be seen amongst the Benin objects in the British Museum.
- 82. DESCRIPTION OF PLATE V. Figs. 19, 20 and 21.—Stained ivory carving of figure on horse. Coral choker; spear in right hand, the shaft broken. Tribal marks on forehead incised. Chain-bridle or head-collar. Degenerate guilloche pattern on base. Straight line diaper pattern represented in various parts. The stand formed as a socket for a pole.
- 83. Figs. 22, 23 and 24.—Ivory carving of figure on horse, with spear in right hand and bell on neck, and long hair. The bridle formed as a head-collar. Degenerate guilloche pattern on base. The stand formed as a socket for a pole ornamented with bands of interlaced pattern and the head of an animal.
- 84. DESCRIPTION OF PLATE VI. Figs. 25 and 26.—Ivory carving of a human face. Eyes and bands on forehead inlaid. Straight line diaper pattern on head-dress, above which are conventionalised mud-fish. Four bands of coral across forehead. Ears long and narrow. Found hidden in an oaken chest inside the sleeping apartment of King Duboar.
- 85. Fig. 27.—Carved wooden panel, consisting of a chief in the centre; broad leaf-shaped sword, with ring attached to pommel, upheld in right hand, studded with copper nails, and ornamented with representations of itself. In left hand a fan-shaped figure terminating in two hands. Coral choker, badge of rank. Bell on neck and cross- belts. Skirt ornamented with three heads and a guilloche pattern of three bands with pellets. Anklets. Attendant on left holding umbrella over chief’s head. Serpent with human arm and hand in its mouth, head upwards; eyes of inlaid glass; body studded with copper nails. Leopard, drawn head upwards. On right, figure with jug in left hand and cup in right hand, standing in a trough or open vessel. Small attendant with paddle in right hand. At top a bottle bound with grass, and figure of some object, perhaps a stone celt bound with grass. Brass and iron screws are used for ornamentation in this carving. Guilloche pattern of two bands without pellets around the edge of the panel.
- 86. Figs. 28, 29 and 30.—Ivory carved tusk, 4 feet 1 inch long from bottom to point; traversed by five bands of interlaced strap-work. The other ornamentation consists of:—Human figures with hands crossed on breast; bird standing on pedestal; human figures with hands holding sashes; trees growing downwards; a rosette; mudfish; crocodiles with heads upwards; a serpent with sinuous body, head downwards; two cups; a serpent, head upwards; detached human heads. Some of the representations are so rude that it requires experience to understand their meaning. On this tusk the interlaced pattern is the prevailing ornament, and it passes into the guilloche pattern. This tusk is more tastefully decorated than the other tusk, Figs. 167 and 168, Plate XXVI, but with less variety in the carving. These carved tusks are said to represent gods in the Ju-ju houses.
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