Cavallaro et al., 1

Programs of gene expression during the laying down of memory formation as revealed by DNA

microarrays

Sebastiano Cavallaro1,2, Velia D’Agata1,2 and Daniel L. Alkon1

1Blanchette Rockefeller Neurosciences Institute, West Virginia University, Rockville, MD 20850, USA;

2Institute of Neurological Sciences, CNR, 95123 Catania, Italy.

Running head: Unveiling the program of gene expression during the laying down of memory.

Corresponding author: Sebastiano Cavallaro, M.D., Ph.D., Blanchette Rockefeller Neurosciences

Institutes, West Virginia University, JHU Academic and Research Building, 3rd Floor, Room 315, 9601

Medical Center Drive, Rockville, MD 20850, USA. Phone: 301-2947176; Fax: 301-2947007. E-mail:

sebi@brni-jhu.org.

Cavallaro et al., 2

Abstract

Many experiments in the past have demonstrated the requirement of de novo gene expression

during memory formation. In contrast to the initial reductionistic view that genes relevant to learning

and memory would be easily found and would provide a simple key to understand this brain function, it

is becoming apparent that the genetic contribution to memory is complex. Previous approaches have

been focused on individual genes or genetic pathways and failed to address the massively parallel

nature of genome activities and collective behavior of the genes that ultimately control the molecular

mechanisms underlying brain function. In view of the broad variety of genes and the cross talk of

genetic pathways involved in this regulation, only gene expression profiles may reflect the complete

behavior of regulatory pathways. In this review we illustrate how DNA microarray-based gene

expression profiling may help to dissect and analyze the complex mechanisms involved in gene

regulation during the acquisition and storage of memory in the mammalian brain.

Keywords: learning; memory, gene expression, cDNA microarray.

Cavallaro et al., 3

Regulation of gene expression is required for memory formation

Identifying the mechanisms responsible for memory formation and consolidation has long been

a goal of behavioral neuroscience. For more than a century, two forms of memory have been

distinguished by their duration: short term memory (STM), which is rapidly formed and can outlast

training for minutes or hours, and long-term memory (LTM), which lasts from hours to days, weeks or

even years (1). In both vertebrates and invertebrates, STM is based on transient modification of

preexisting molecules, capable of rapidly altering the efficacy of synaptic transmission. In contrast,

LTM can be blocked by inhibitors of transcription or translation indicating that it is dependent on de

novo gene expression (2-3). Proteins newly synthesized during memory consolidation may contribute

to restructuring processes at the synapse and thereby alter the efficiency of synaptic transmission

beyond the duration of short-term memory. Revealing the dependence of LTM on protein synthesis,

however, provides no information about the identity and specificity of the required proteins. Since the

quantity of a particular protein is often reflected by the abundance of its mRNA, a variety of methods

have been used to describe a limited number of differentially expressed mRNAs during LTM.

Increased or, less often, decreased expression of genes has been demonstrated during specific time

windows following learning (3).

In contrast to the initial reductionistic view that genes relevant to learning and memory would

be easily found and would provide a simple key to understand this brain function, it is becoming

apparent that the genetic contribution to memory is complex. In the past we have used RNA

fingerprinting to identify genes that were up-regulated in the hippocampus of water maze-trained rats

(4-5). In these studies, however, we screened only a small fraction of the genes that may have been

differentially expressed during long-term memory. Thus, the question remains of how many genes are

involved in memory. In addition, each of the genes that were identified acts not in a linear sequence but

in complex networks. Each gene, therefore, plays only a small role and may not contribute significantly

Cavallaro et al., 4

to memory in the absence of other genes. Even if no single gene proves to be the key that unlocks the

secrets of learning and memory, the search for these genes remains critically important to both basic

and clinical neuroscience. Successive screening, therefore, will be needed to uncover the spectrum of

genes involved in distinct temporal domains of memory storage.

Post-genomic research: a new dynamic and functional dimension

The era of examining the expression and effects of single genes in memory is now waning. In

higher metazoa, each gene or protein is estimated to interact on average with four to eight other genes

(6), and to be involved in 10 biological functions (7). The global gene expression pattern is, therefore,

the result of the collective behavior of individual regulatory pathways. In such highly interconnected

cellular signaling networks, gene function depends on its cellular context; thus, understanding the

network as a whole is essential.

The Human Genome Project is revolutionizing the way memory and cognition can be explored

and is opening the door to a new dynamic and functional dimension. Remarkable progress in genomic

research is leading to a complete map of the building blocks of biology. Knowledge of this map is, in

turn, setting the stage for a fundamental description of cellular function at the DNA level. Such a

description will entail an understanding of gene regulation, in which proteins often regulate their own

production or that of other proteins in a complex web of interactions.

As the human genome project moves toward its goal of sequencing the entire 3 billion base

pairs of the human genome, cDNA microarray technology has been devised to comparatively analyze

genome-wide patterns of mRNA expression. In this article we will highlight the use of DNA

microarrays for the study of learning and memory. We will begin with a general overview of the

methodology and we will then turn to its application by restricting ourselves to our recent studies. For a

more general description of microarray technology, the reader is referred to other reviews (8).

Cavallaro et al., 5

DNA microarray methodology

A DNA-microarray is a grid of DNA spots, called probes, each containing a unique DNA

sequence. Spots contain either DNA oligomers or a longer DNA sequence designed to be

complementary to a particular mRNA of interest. When a microarray is hybridized to fluorescence-

tagged complementary DNAs or RNAs derived from messenger or total RNA, each spot is a target for

the mRNA encoded by a gene. The bound cDNAs or cRNAs can then be excited by a laser and

fluorescence intensities from each spot on the slides are collected by a scanner. The intensity of the

fluorescence at each array element is proportional to the expression level of that gene in the sample.

The choice of having oligomers or longer cDNA sequences yields two different microarray

technologies: oligonucleotide and cDNA microarrays, respectively. With cDNA microarrays, two

fluor-labelled cDNAs can be combined and simultaneously hybridized to the same microarray, where

they competitively react with thousands of arrayed cDNA molecules. Oligonucleotide microarrays,

instead, require that each sample is hybridized onto separate arrays. The thing that makes microarrays

the most promising technology for genome-wide expression analysis is the number of DNA probes that

it is possible to place on a microarray. Already there are microarrays with probes for every gene in

Yeast, and others with over 30,000 human genes. This allows researchers to observe the response of

whole genomes to various stimuli instead of one gene at a time.

Microarray analysis result in large amounts of data that are difficult to interpret without

computational methods. The simplest analysis involves two samples, representing a test condition and a

control condition and yields a list of paired expression values, one pair for each gene. As illustrated in

Figure 1, these pairs can be represented graphically by a scatterplot, with the values of sample one

plotted on the x-axis and the values of sample two plotted on the y-axis. The resulting correlation plot

provides a visual image of the relationship between the two expression profiles. In this plot, genes with

similar expression levels in the two samples should have points on the identity line (y=x) and genes

Cavallaro et al., 6

that are expressed differentially should lie at some distance from this line. However, the problem is that

microarrays do not measure expression levels directly, but rather intensity levels, as represented by the

amount of phosphorescent dye that was recorded by a scanner. Many other factors, such as the overall

mRNA concentration of the two samples, the saturation effects in the hybridization or the quenching

effect of the phosphorescent dyes can affect these intensity values. In order to correct these differences

in intensity levels, the raw data can be “normalized”, for example, by using a normalization constant

derived from “housekeeping” or “spiked” control genes.

More complex computational methods are needed to monitor several expression profiles, such

as those arising from time course studies, and various clustering techniques have been applied to the

identification of patterns in gene-expression data. A commonly used method is to use hierarchical

clustering algorithms to order genes on the basis of similarity in their expression patterns and visualize

the data with a dendrogram in which the pattern and length of the branches reflects the relatedness of

the samples (Figure 1). Data are presented in a matrix format where rows represent the experiments

(e.g. different time points), columns correspond to the genes and the color of each cell reflects its

expression level. Studies in simple organisms support the notion that some of the genes with similar

expression patterns have common functions and regulatory systems (9). However, examples of

functionally related genes that are not co-expressed are readily found, as well as genes of unrelated

function with similar expression patterns. Thus, cluster analysis does not always reveal the final

answers. Rather, they are an exploratory tool that is meant to identify candidate genes for further study.

DNA microarray and the study of memory consolidation.

To begin a comprehensive survey of the molecular mechanisms that underlie long-term

memory, we have used the unprecedented experimental opportunities that genome sequences and the

development of cDNA array technology now provide to perform genome-wide expression analysis

Cavallaro et al., 7

after classical conditioning of the rabbit nictitating membrane response (NMR) (10). This uniquely

well-controlled associative learning paradigm involves the presentation of an innocuous stimulus such

as a tone followed by a noxious stimulus such as air puff to or electrical stimulation around the eye

(11). Extensive lesion and recording data have implicated the cortex of the cerebellum and in

particular, lobule HVI, in classical conditioning of the rabbit NMR (12-18). Although the hippocampus

may not be necessary for NMR conditioning, it does store contextual information as revealed by

sensory preconditioning. Furthermore, recording data do show consistent eye blink conditioning-

specific hippocampal changes (19-20). Imaging studies have implicated both structures in human eye

blink conditioning (21-24).

To relate changes in gene expression to a learning task, we used pairings of a tone and

periorbital electrical stimulation in a standard delay conditioning procedure, training rabbits to

asymptotic levels of conditioning over 3 consecutive days (10). Messenger RNA levels from cerebellar

lobule HVI and hippocampus of unpaired and paired rabbits were simultaneously analyzed with high-

density cDNA microarrays containing more than 8700 cDNA clones (10). A complete list of the

differentially expressed genes is available online at http://www.brni-jhu.org/sebi/microarray-

data/microarray-data.htm.

When gene expression patterns were compared using a cut-off of more than two-fold, mRNA

levels of 79 (0.9 %) and 17 (0.2 %) genes differed in lobule HVI and hippocampus, respectively

(Figure 2A). Approximately 50% (eight) of the genes differentially expressed in the hippocampus were

also differentially expressed in the HVI lobule, suggesting common mechanisms of memory storage in

the two areas.

A majority of genes that differed by a factor greater than 2 were down regulated, whereas only 2

genes that differed by a factor greater than 2 were up regulated in lobule HVI of paired animals. Given

that transcription and protein synthesis inhibitors can block LTM, most previous reports have focused

Cavallaro et al., 8

on the identification of proteins whose expression is up regulated (2-3). The preponderant reduction of

gene expression during LTM, therefore, would not have been predicted and provides new and

unexpected insights into the molecular mechanisms that underlie it. At present it is not clear how

silencing a number of genes plays a role in memory storage. This down regulation may be an end point

to a dynamic gene expression process recruited during the acquisition and retention of memory.

Alternatively, memory storage may require decreased expression of proteins that exert inhibitory

constrains (25).

To examine microarray-identified changes in gene expression at a regional and cellular level we

have used in situ hybridization in cerebellum and forebrain tissue sections from individual paired,

unpaired and sit rabbits. In addition to corroborating the microarray data, the in situ hybridization

analysis revealed distinct spatial distribution patterns of the genes. Figure 2B shows the regional

mRNA expression of two genes implicated by microarray analysis, Insulin-like growth factor-I (IGF-I)

and Bach 2. These mRNAs are abundantly expressed in the cerebellar cortex and are reduced in the

lobule HVI of paired rabbits. In paired animals, a marked down regulation of Bach 2 is also revealed in

the dentate gyrus, CA1, and CA3 areas of the hippocampus.

A majority of differentially expressed genes have no apparent homology to any gene whose

function is known. Complete nucleotide sequence determination, conceptual translation, expression

monitoring, and biochemical analysis will provide a detailed functional understanding of these genes.

Some of the genes with a known function, however, have been previously related to synaptic plasticity,

memory, or cognitive disorders. One of them, insulin-like growth factor I (IGF-I), is a peptide with

trophic and neuromodulatory actions. In the cerebellum, IGF-I is locally synthesized by Purkinje cells

but also originates from climbing fibers, which are thought to convey information to the cerebellum

about the reinforcing properties of the unconditioned stimulus. IGF-I modulates the size of dendritic

spines on Purkinje cells (26), and inhibits glutamate-induced GABA release by Purkinje cells (27).

Cavallaro et al., 9

Interestingly, IGF-I levels have been correlated with cognitive test performance in aging humans (28)

and administration of IGF-I has been shown to ameliorate age-related behavioral deficits in rats (29).

Another gene implicated by microarray analysis is similar to the CCAAT enhancer-binding protein

(C/EBP) family of transcription factors which have been implicated in long-term memory consolidation

after inhibitory avoidance learning (30) and long-term facilitation, a synaptic mechanism which in

Aplysia is thought to contribute to long term memory (25). Interestingly, selectively enhanced

contextual fear conditioning (24 hours after training) has been shown in mice lacking the

transcriptional regulator C/EBP delta, implicating some isoforms of this family of proteins in specific

types of learning and memory as memory suppressor genes (31). A third gene differentially expressed

after NMR is WBSCR11, a putative transcription factor gene that is commonly deleted in Williams-

Beuren syndrome and may contribute to the spectrum of developmental symptoms that includes mental

retardation and profound impairment of visuospatial cognition (32).

Summary

The data presented demonstrate the feasibility and utility of a cDNA microarray system as a

means of dissecting the molecular mechanisms of learning and memory. The value of these

experiments will progressively increase as more is learned about the function of each gene. Systematic

characterization of expression patterns associated with cognition and cognition related disorders will

provide a framework for interpreting the biological significance of the expression patterns observed at

one time point in the long-term retention of learning and memory. Identification of genes associated

with memory in health and disease may also provide valid drug targets for therapeutic intervention.

Cavallaro et al., 10

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Figure legends

Figure 1. Schematic representation of DNA microarray methodology. Total or messenger RNA is

extracted from two specimens (from brain areas of control and conditioned animals), reverse

transcribed and labeled with different fluorochromes (e.g Cy3 and Cy5), and simultaneously co-

hybridized to arrays. In other platforms, such as those using oligonucleotide probes, one sample is

hybridized to each array. At the end of the hybridization, the images produced by each dye are

produced and collected by a laser scanner. Intensity values from each spot are calculated and then

analyzed by specific software. Data can be represented graphically by a scatterplot, with the values of

sample one plotted on the x-axis and the values of sample two plotted on the y-axis. Data obtained

under different conditions (e.g. different time points) can be analyzed with different algorithms such as

hierarchical clustering.

Figure 2. Differential gene expression after classical conditioning of the rabbit nictitating membrane

response. A: gene expression signal values for paired and unpaired animals in cerebellar lobule HVI

and hippocampus. B: in situ hybridization validation and localization of differential gene expression.

Specific riboprobes labeled with [α-35S] for IGF-I and Bach 2 mRNAs were hybridized with horizontal

sections through the cerebellum and coronal sections through the forebrain of sit, paired and unpaired

rabbits. Labeled mRNA signals were revealed with autoradiography. The color spectrum on the right

side of the figure represents the pixel value of the grey levels.

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