Trends of Toxicogenomics
Trends of Toxicogenomics
Trends of Toxicogenomics
The field of toxicology is defined as the study of stressors and their adverse effects. One sub
discipline deals with hazard identification, mechanistic toxicology, and risk assessment.
Increased understanding of the mechanism of action of chemicals being assayed will improve
the efficiency of these tasks. However, the derivation of mechanistic knowledge traditionally
evolves from studying a few genes at a time in order to implicate their function in mediation
of toxicant effects. Undoubtedly, this process has to be accelerated to monitor and discern the
effects of the thousands of new compounds developed by the chemical and pharmaceutical
industries. There is a need for a screening method that can offer some insight into the
Duggan, et al., 1999), proteomic- (Lueking, et al., 1999; Page, et al., 1999; Rubin and
Merchant, 2000; Steiner and Anderson, 2000; Weinberger, et al., 2000; Huang, 2001), and
metabonomic- (Foxall, et al., 1993; Corcoran, et al., 1997; De Beer, et al., 1998) based
technologies has accelerated the application of gene expression for understanding chemical
advances have led to the development of the field of “toxicogenomics”, which proposes to
apply global mRNA, protein and metabolite analysis related technologies to study the effects
of hazards on organisms (Afshari, et al., 1999; Farr, 1999; Henry, 1999; Nuwaysir, et al.,
1999; Rockett and Dix, 1999; Hamadeh and Afshari, 2000; Pennie, et al., 2000; Rockett and
Dix, 2000; Hooker, 2001; Iannaccone, 2001; Olden, 2001; Smith, 2001; Tennant, 2001;
Hamadeh et al., 2001; Hamadeh et al., 2002d). These collective approaches will allow the
development of a knowledge base of compound effects that will aid in improving the
efficiency of safety and risk assessment of drugs and chemicals by facilitating better
Technologies in Toxicogenomics
Gene expression changes associated with signal pathway activation can provide compound-
method used to study changes in gene expression is the Northern blot (Sambrook et al.,1989).
expression level of all transcripts (including splice variants) for a particular gene. This
method, however, is labor intensive and is practical for examining expression changes for a
limited number of genes. Alternate technologies, including DNA microarrays, can measure
the expression of tens of thousands of genes in an equivalent amount of time (DeRisi, et al.,
1996; Duggan, et al., 1999; Hamadeh and Afshari, 2000; Hamadeh et al., 2001). DNA
patterns in dose and time contexts. There are two basic types of microarrays used in gene
expression analyses: oligonucleotide-based arrays (Lockhart, et al., 1996) and cDNA arrays
(Schena, et al., 1995). Both yield comparable results, though the methodology differs.
Oligonucleotide arrays are made using specific chemical synthesis steps by a series of
photolithographic masks, light, or other methods to generate the specific sequence order in
the synthesis of the oligonucleotide. The result of these processes is the generation of high-
density arrays of short oligonucleotide (~ 20-80 bases) probes that are synthesized in
predefined positions. cDNA microarrays differ in that DNA sequences (0.5-2 kb in length)
that correspond to unique expressed gene sequences, are usually spotted onto the surface of
treated glass slides using high speed robotic printers that allow the user to configure the
placement of cDNAs on a glass substrate or chip. Spotted cDNAs can represent either
sequenced genes of known function, or collections of partially sequenced cDNA derived from
expressed sequence tags (ESTs) corresponding to messenger RNAs of genes of known or
unknown function.
Any biological sample from which high quality RNA 46 Hamadeh et al. can be
isolated may be used for microarray analysis to determine differential gene expression levels.
For toxicology studies, there are a number of comparisons that might be considered. For
example, one can compare tissue extracted from toxicant treated organism versus that of
vehicle exposed animals. In addition, other scenarios may include the analysis of healthy
versus diseased tissue or susceptible versus resistant tissue. For spotted cDNA on glass
1995; DeRisi, et al., 1996). Multicolor based labels are currently being optimized for
conjugated with a chromophore. The two RNAs being compared are labeled with different
fluorescent tags, traditionally either Cy3 or Cy5, so that each RNA has a different energy
emission wavelength or color when excited by dual lasers. The fluorescently labeled targets
are mixed and hybridized on a microarray chip. The array is scanned at two wavelengths
using independent laser excitation of the two fluors, for example, at 632 and 532 nm
wavelengths for the red (Cy5) and green (Cy3) labels. The intensity of fluorescence, emitted
at each wavelength, bound to each spot (gene) on the array corresponds to the level of
expression of the gene in one biological sample relative to the other. The ratio of the
processed efficiently at a time. Processing and scanning samples may take several days and
generate large amounts of information that can take considerable time to analyze.
Automation is being applied to microarray technology, and new equipment such as the
automated hybridization stations and auto-loaded scanners will allow higher throughput
analysis. To overcome these limitations, one can combine microarrays with quantitative
(Kreuzer, et al., 1999; Tokunaga, et al., 2000) to monitor the expression of hundreds of genes
in a high throughput fashion. This will provide more quantitative output that may be crucial
for certain hazard identification processes. In the QPCR (Walker, 2001) assay one set of
primers is used to amplify both the target gene cDNA and another neutral DNA fragment,
engineered to contain the desired gene template primers, which competes with the target
cDNA fragment for the same primers and acts as an internal standard. Serial dilutions of the
neutral DNA fragment are added to PCR amplification reactions containing constant
amounts of experimental cDNA samples. The neutral DNA fragment utilizes the same primer
as the target cDNA but yields a PCR product of different size. QPCR can offer more
“real time” during the time of the amplification and within a linear dynamic range. The PCR
The validity and utility of analysis of gene expression profiles for hazard identification
al., 2001; Waring, et al., 2001; Hamadeh et al., 2002c) and whether defined profiles maybe
chemically treated biological models (Hamadeh et al., 2002b). Gene expression profiling may
aid in prioritization of compounds to be screened in a high throughput fashion and selection
of chemicals for advanced stages of toxicity testing in commercial settings. In one effort to
validate the toxicogenomic strategy, Waring and coworkers (Waring, et al., 2001; Waring, et
al., 2001) conducted studies to address whether compounds with similar toxic mechanisms
produced similar transcriptional alterations. This hypothesis was tested by generating gene
expression profiles for 15 known hepatotoxicants in vitro (rat hepatocytes) and in vivo (livers
of male Sprague-Dawley rats) using microarray technology. The results from the in vitro
studies showed that compounds with similar toxic mechanisms resulted in similar but
distinguishable gene expression profiles (Waring, et al., 2001). The authors took advantage of
the variety of hepatocellular injuries (necrosis, DNA damage, cirrhosis, hypertrophy, hepatic
carcinoma) that were caused by the chemicals and compared pathology endpoints to the
clustering output of the compounds’ gene expression profiles. Their analyses showed a strong
correlation between the histopathology, clinical chemistry, and gene expression profiles
induced by the various agents (Waring, et al., 2001). This suggests that DNA microarrays
chemical effects.
mechanisms of toxicity. Bulera and coworkers (Bulera, et al., 2001) identified several groups
phenobarbital, both of which are liver tumor promoters, induced a parallel set of genes
(Bulera, et al., 2001). Based on this information the authors speculated that liver tumor
promotion by both compounds may occur by similar mechanisms. Such observations derived
authors also confirmed toxicity in the animals using conventional methods such as
histopathology, modulations in liver enzymes and bilirubin levels and related these effects to
gene expression changes; however, it would have been advantageous to utilize gene
expression data to map relevant pathways depicting mechanism(s) associated with the
researchers may attempt to build “transcriptome” or “effector maps” that will help to
visualize pathway activation (Tennant, 2001). Finally, Huang and coworkers (Huang, et al.,
nephrotoxicity. In these studies, rats were treated daily for 1 to 7 days with cisplatin at a dose
that resulted in necrosis of the renal proximal tubular epithelial cells but no hepatotoxicity at
day 7. Gene expression patterns for transplatin, an inactive isomer, was examined and
revealed little gene expression change in the kidney, consistent with the lack of nephrotoxicity
histopathological changes in the kidney i.e. gene related to cellular remodeling, apoptosis,
and alteration of calcium homeostasis, among others which the authors describe in a putative
Protein Expression:
Gene expression alone is not adequate to serve the understanding of toxicant action and the
disease outcomes they induce. Abnormalities in protein production or function are expected
in response to toxicant exposure and the onset of disease states. To understand the complete
with that exposure and to understand how these changes affect protein/cellular function.
Unlike classical genomic approaches that discover genes related to toxicant induced disease,
proteomics can aid to characterize the disease process directly by capturing proteins that
participate in the disease. The lack of a direct functional correlation between gene transcripts
and their corresponding proteins necessitates the use of proteomics as a tool in toxicology.
Proteomics is the systematic analysis of expressed proteins in tissues, by isolation, separation,
(Lueking, et al., 1999; Page, et al., 1999;Anderson, et al., 2000; Rubin and Merchant, 2000).
domain modules and their effect on protein interactions, protein complexes, ligand binding
sites and structural representation. Currently, the most commonly used technologies for
proteomics research are 2-dimensional (2-D) gel electrophoresis for protein separation
followed by mass spectrometry analysis of proteins of interest (Rasmussen, et al., 1994; Shaw,
et al., 1999; Carroll, et al., 2000; Fountoulakis, et al., 2000; Kaji, et al., 2000; Watarai, et al.,
Liang, et al. 1996) has become a widely used method for determination of biomolecules
Desorption/Ionization (SELDI) (Kuwata, et al., 1998; Li, et al., 2000; Merchant and
Weinberger, 2000; Rubin and Merchant, 2000) and antibody arrays (Borrebaeck, et al., 2001;
Haab, et al., 2001; Paweletz, et al., 2001; Sreekumar, et al., 2001) are also proving to be
useful. Cutler and coworkers conducted a study aimed at the investigation of biochemical
changes and identification of biomarkers associated with acute renal injury following a single
PAGE, reversephase HPLC, mass spectrometry, amino acid analysis and 1H-NMR
spectroscopy of urine as well as routine plasma clinical chemistry and tissue histopathology
(Cutler et al., 1999). The 2-D PAGE of urine showed patterns of protein
change which were in accord with the limited profiles for glomerular toxicity derived by use
of other techniques and allowed a more detailed understanding of the nature and progression
of the proteinuria associated with glomerular toxicity. Interestingly, the 2-D PAGE approach
taken by the investigators, coupled with computational analysis of the accompanying data
gleaned on the collected samples, lead to the detection of proteinuria at a considerably earlier
time point than has typically been reported following puromycin aminonucleoside exposure,
thus potentially defining relatively early biomarkers which are superior to the traditional
challenging due to the presence of high abundant proteins such as albumin, immunoglobulin
heavy and light chains, transferring, and haptoglobin in the sera or actin, tubulin, and other
structural proteins when analyzing tissue. Selective removal of these proteins from protein
samples via column-based immunoaffinity procedures allows for more sample to be loaded
on gels thereby facilitating visualization of low abundant proteins that would otherwise be
Genomic and proteomic methods do not offer the information needed to gain understanding
of the resulting output function in a living system. Neither approach addresses the dynamic
metabolic status of the whole animal. The metabonomic approach is based on the premise
or cerebrospinal fluid (CSF), are in dynamic equilibrium with those inside cells and tissues,
compositions. An advantage of measuring changes in body fluids is that these samples are
much more readily available from human subjects. High resolution NMR spectroscopy (1H
NMR) has been used in a high-throughput fashion to simultaneously detect many cellular
biochemicals in urine, bile, blood plasma, milk, saliva, sweat, gastric juice, seminal, amniotic,
synovial and cerebrospinal fluids (Holmes, et al., 1995; Robertson, et al., 2000; Bundy, et al.,
2001; Griffin, et al., 2001; Nicholls, et al., 2001; Waters, et al., 2001). In addition, intact tissue
and cellular suspensions have also been successfully analyzed for metabolite content using
NMR spectra of urine samples from male Wistar rats treated with different hepatotoxicants
4-aminophenol) (Robertson, et al., 2000). Principal component analysis (PCA) of the urine
spectra was in agreement with clinical chemistry data observed in blood samples taken from
the chemically exposed animals at various time points of chemical exposure. Furthermore,
PCA analysis suggested low dose effects with two of the chemicals, which were not evident by
clinical chemistry or microscopic analyses. This conclusion was demonstrated with the 150
mg/kg 2- bromoethanolamine treated animals where only 5 of 8 of the animals had creatinine
or BUN levels, at day 1, that were outside the normal range, while all animals exhibited
diuresis and principal component analysis was clearly indicative of a consistent effect in all 8
animals. In another seminal study, 1H NMR spectroscopy was used to characterize the time-
Wistar or Sprague Dawley rats were treated with either control vehicle or one of 13 model
toxicants or drug that predominantly target liver or kidney. The resultant 1H NMR spectra
were analyzed using a probabilistic neural network approach (Holmes, et al., 2001). A set of
583 of the 1310 samples were designated as a training set for the neural network, with the
remaining 727 independent cases employed as a test set for validation. Using these
techniques, the 13 classes of toxicity, together with the variations associated with strain, were
highly distinguishable (>90%). An important aspect of this study is the sensitivity of the
methodology towards strain differences that will be useful in investigating the genetic
variation of metabolic responses across multiple animal models and may also prove useful in
alterations such as necrosis and vasculitis are often localized to specific regions of an organ
or tissue. It is not known whether subtle gene or protein expression alterations associated
with these events are detectable when the whole organ is used for preparation of samples for
further analyses. Laser capture microdissection (LCM) (Emmert-Buck, et al., 1996; Bonner,
et al., 1997; Fend, et al., 2000; Murakami, et al., 2000) is one method used to precisely select
affected tissue thereby enhancing the probability of observing gene or protein expression
changes associated with pathologically altered regions. For example, profiling specific
understanding how chronic chemical exposure leads to tumor development. However, for
some tissues or laboratories, LCM may not be technically feasible to discern gene expression
in cellular subtypes. A technical challenge may be that the affected area or region is too small
for enough RNA or protein to be extracted for later analysis, or the extra manipulation
compromises the quality of harvested samples. Therefore, when deriving samples from gross
organ or tissue samples for expression analysis, one often has no measure of specific gene or
protein expression alterations attributable to the pathological change that was diluted in the
assayed organ or tissue. When an organ, or part thereof, is harvested from a chemically
exposed animal, the response to the insult is almost always diluted to a certain extent because
not every area or cell is responsive to treatment. Similarly, tumor samples or other diseased
tissues may contain other significant cell types including stroma, lymphocytes, or endothelial
cells. Dilution effects are also involved when a heterogeneous expression response occurs. For
example, even in a homogeneous cell population, each individual cell may have a very
different quantitative response for each gene expression change. In order to address this
expression alterations thus simulating relatively minor changes in the context of total organ
between two cell lines (HaCaT and MCF- 7) that continued to be detected even after a 20-
fold dilution of original changes (Hamadeh et al., 2002a) showing that microarray analyses,
when conducted in a manner to optimize sensitivity and reduce noise, may be used to
determine gene expression changes occurring in only a small percentage of cells sampled.
Finally, once important biomarkers are hypothesized from genomics and proteomics
technologies, candidate target genes or proteins can then be monitored using more high-
Tissue microarrays are microscope slides where thousands of minute tissue samples from
normal and diseased organisms can be tiled in an array fashion. The tissue microarrays can
then be probed with the same fluorescent antibody to monitor the expression, or lack of,
Database Requirements:
relational database that will facilitate the query of data depending on different criteria.
Technical requirements of the database are beyond the scope of this discussion. From a
biological perspective, the ideal database will not only house the afore mentioned data, but
will also hold additional toxicology information describing various parameters of the
stressor-subjected biological systems. The parameters might include body and organ weights,
animal studies or cell viability, cell cycle analyses, cell density, culture conditions and cell
morphology reports in the case of in vitro studies. Chemical purity, solubility, stability, and
volatility, also are important to archive. These additional data are of importance when
others will aid in the interpretation of different profiles as suggested by pattern recognition
tools such as clustering algorithms or principal components analysis (Hamadeh et al., 2002b;
2002c).
Comparative/Predictive Toxicogenomics
There are two main applications for a toxicogenomic approach, comparative/predictive and
and types of genes, protein, and metabolites respectively that are present in normal and
Thus a biological sample derived from toxicant, or sham treated animals can be regarded as
an n-dimensional vector in gene expression space with genes as variables along each
dimension. The same analogy can be applied for protein expression or NMR analysis data
investigation. Thus, this aspect of toxicogenomics deals with automated pattern recognition
analysis aimed at studying trends in data sets rather than probing the individual genes for
mechanistic information. The need for pattern recognition tools is mandated by the volume
and complexity of data generated by genomic, proteomic and tools, and human intervention,
methods are very desirable and prediction models are well suited for this task. The data
profiles reflect the pharmacological or toxicological effects, such as disease outcome, of the
drug or toxicant being utilized. The underlying goal is that a sample from an animal exposed
some properties regarding the studied sample. These predictions, as we view them, fall into 2
major categories, namely, classification of samples based on the class of compound to which
animals were exposed to, or classification of samples based on the histopathology and clinical
chemistry that the treated animals displayed. Such data will allow insight into the gene,
toxic endpoints that ensue. If array data can be “phenotypically anchored” to conventional
indices of toxicity (histopathology, clinical chemistry etc.) it will be possible to search for
evidence of injury prior to it’s clinical or pathological manifestation. This approach could
lead to the discovery of potential early biomarkers of toxic injury. “Supervised” predictive
models (Zhou and Bennett, 1997; Jonic, et al., 1999; Tafeit and Reibnegger, 1999) have been
used for many years in the financial sectors for evaluating future economic prospects of
companies, and in geological institutes for predicting adverse weather outcomes using past or
historical knowledge. They have also been utilized to make predictions, using clinical and
time of presentation at a health-care facility that can be superior to physicians’ opinion (El-
Solh, et al., 1999). Predictive modeling will undoubtedly revolutionize the field of toxicology
by recognizing patterns and trends in high-density data, and forecasting gene-, protein-, or
and their corresponding profiles. During the development of a predictive model, a number of
issues must be considered. These include the representativeness of the variables to the entity
being modeled and the quality of databases consulted. The National Center for
Sciences, is building a database to store many variables (ex. dose, time, biological system)
and observations (ex. histopathology, body weight, cell cycle data) that accompany the
process of compound evaluation studies (in vivo or in vitro) (Tennant, 2001). Recording these
parameters will greatly enhance the process of parameter selection in subsequent efforts such
fragmented into a multistage process. The primary stage predictive modeling includes
hypothesis development, organization and data collection. Secondary stage modeling
includes initial model development and testing. Tertiary stagemodeling includes continued
application of the model, ongoing refinement, and validation. Ideally, tertiary stagemodeling
is a perpetual process whereby lessons learned from previous model applications are
incorporated into new and future applications maintaining or increasing the predictive
activities such as data collection strategies based on proposed hypotheses. Data can be
generated from in vivo or in vitro experiments, depending on the suitability of the biological
system for studying effects of the targeted compound. In the case of in vivo studies,
hypotheses must be generated regarding the compounds and endpoint effects so that other
measures, such as pathology, serum markers, and carcinogenicity potential, are made and
can contribute to the ensuing model development. Data on animal weight fluctuations, serum
study should be documented and be the primary source of such information for the
constructed predictive model. Pertinent data and analytically useful variables gathered from
other sources (ex. National Toxicology Program) can be evaluated and incorporated into the
model. These data are important in developing a theoretical framework in which to interpret
the results of the predictive model as well as to provide a guide for the data to be collected.
The next step in the predictive model construction involves a deductive phase that
incorporates collected data into the second stage of the model. The degree of correlation
relationships and dissimilarities among the variables studied. Neural networks, which have
been used in models predicting health status of HIV/AIDS patients (Giacomini, Figure 1.
subjected to various exposure conditions, based on the expression levels of gene, protein or
metabolite levels. Computational algorithms can form prediction zones (A) circumscribing
sets of samples derived from the same exposure conditions (in vivo, in vitro) or (B) zones that
encompass samples based on user defined endpoints associated with these samples et al.,
1997; Kwak and Lee, 1997; Ioannidis, et al., 1998), can be trained with a set of available
profiles from previously studied compounds or pathophysiological states. This allows the
automation of all the actions aimed at searching the interrelationships and producing
by various parameters describing its gene expression pattern. Thus, a pattern may be
represented by a vector in space whose components could represent various parameters that
drive the decision of classification. Dimensionality of this space is the number of vector
components or parameters involved and is based on the analysis of multiple parameters that
compound or adverse endpoint we are modeling to have only three attributes, these three
parameters can represent vector coordinates in a 3-dimensional space. Figure 1A shows how
the treated animals, or cells, could be spatially disposed, so that one can easily notice where
they are grouped, i.e. have similar parameters, for which reason they most probably belong
to the same group. Now we proceed to defining which objects are situated in particular nodes
ndimensional space based on computational approaches (ex. PCA). We can then construct
similarity zones around various preset chemical (Figure 1A) or adverse endpoint (Figure 1B)
nodes. Such similarity zones would allow the classification, with a defined level of confidence,
of the identity of unknown samples which neighbor samples in the training data set. Thus
possessing the map and information about the analyzed compounds, we can reliably judge
the compounds with which we are less familiar. The initial predictive model can be tested
using the data collected in the primary stage. Based upon the outcome of this exercise,
variables such as toxicant induced lesion severity or organ weight fluctuations can be
introduced or removed from the process, or the weighting of the variables can be adjusted
until the model is able to predict the highest percentage of chemicals possible. This highlights
the need for the consulted database to contain enough parameters such as histopathological
this dynamic model optimization process. Developed models should ideally allow the
outcome depending on the querying preferences of the user and the question being asked.
Once this has been achieved, tertiary stage modeling may begin.
will facilitate an emerging type of experimentation termed “in silico” testing. For example, if
compound A was found to bear similarity to compound B, and B had some aspects that were
based on the their common link to B. In silico experimentation can define this relationship
Developments in computer modeling and expert systems for the prediction of biological
activity and toxicity will revolutionize the process of drug discovery and development, by
reducing the need to use animals for the pre-screening of almost limitless numbers of
potential drug candidates. It is not foreseeable that in the near future predictive models will
take the place of actual testing. However, in the context of toxicogenomics, and with the
increasing number of chemicals to be tested, better prioritization canbe used to select the
compounds for animal testing. The most promising efficacious compounds with the least
Functional Toxicogenomics
Functional toxicogenomics is the study of genes’ and proteins’ biological activities in the
context of compound effects on an organism. Gene and protein expression profiles are
analyzed for information that might provide insight into specific mechanistic pathways.
Mechanistic inference is complex when the sequence of events following toxicant exposure is
viewed in both dose and time space. Gene and protein expression patterns can indeed be
highly dependent on the toxicant concentrations furnished at the assessed tissue and the time
of exposure to the agent. Expression patterns are only a snapshot in time and dose space.
requires establishing patterns at various combinations of time and dose. This will minimize
the misinterpretation of transient responses and allow the discernment of delayed alterations
of pathophysiological endpoints. Studies that target temporal expression of specific genes and
protein in response to toxicant exposure will lead to a better understanding of the sequence of
events in complex regulatory networks. Algorithms, such as self organizing maps (Kohonen,
1999), can categorize genes or proteins based on their expression pattern across a
continuum of time points. These analyses might suggest relationships in the expression of
some genes or proteins depending on the concerted modulation of these variables. An area of
may have regional variations in the tissue. Furthermore, very few compounds exist that
result in only one phenotypic alteration at a given coordinate in dose and time. Thus,
interest, one could tease out gene, protein, or metabolite modulations that are in common
between the studied compounds (Figure 2). Laser capture microdissection may also be used
to capture regional variations such as zonal patterns of hepatotoxicity. This concept will
allow the objective assignment of measurable variables to phenotypic observations that will
protein expression, or metabolite fluctuation analyses are not expected to produce decisive
However, these tools constitute powerful means to generate viable and testablehypotheses
that can direct future endeavors on proving or disproving the involvement of genes, proteins,
be validated by the use of traditional molecular biology techniques that include the use of
specific enzyme inhibitors, and the examination of the effects of overexpression or deletion of
specific genes or proteins on the studied toxic endpoint or mechanism of compound action.
variables that are of most interest in the toxicological evaluation process. Thus, this
reductionist strategy mandates the selection of subsets of genes, proteins or metabolites that
are and what data we need to achieve this knowledge. Election of these subsets by surveying
existing toxicology literature is inefficient because the role of most genes or proteins in
or unknown genes (ESTs) that might ultimately be key players in toxicological processes. We
propose the use of genes, proteins, or metabolites, that are found to be most discriminative
levels of these parameters across toxicant exposure scenarios. In the case of samples derived
from animals treated with one of few chemicals, the levels of one gene, protein, or metabolite
might be sufficient to distinguish samples based on the few classes of compounds used for the
exposures. However, multiple parameters are needed to separate samples derived from
requires the use of computational and mining algorithms that extract this knowledge from a
rats treated with either of compounds A, B, C, D, E, or F reveals an overlap among the effects
cluster analysis of gene, protein, or metabolite levels would indicate a potential association
between the altered parameters and the shared histopathological endpoint. Linear
Wichern, 1998) and single gene ANOVA (Neter, 1996) can be used to test single parameters
(ex. genes) for their ability to separate profiles corresponding to samples derived from
different exposure conditions (ex. chemical identity, biological endpoint). Higher order
are able to find a user defined number of parameters that would, as a set, highlight the most
difference between biological samples based on the levels of genes, proteins, or metabolites.
these discriminatory parameters are derived from historical data, it is possible that their
status might not hold once significant volumes of new data is inputted in the database that
Summary.
Toxicogenomic tools will inevitably improve the way data is extracted from classical
toxicology studies. Ultimately, through the use of computational tools encompassed within
performed in a high-through put and efficient fashion. These achievements will be facilitated
through the development of gene, protein, or metabolite markers whose levels can be
monitored in samples derived from exposed populations. Compound profiling will also
(pathological lesions, cell cycle alterations) by providing information about the underlying
molecular pathways that are involved inresponse to compound exposure. This knowledge will
lead to a more informed and precise classification of compounds for their safety evaluation.
Submitted by,
D.Pratap
(pratap66666@gmail.com)