Plexpress_Whitepaper

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www.plexpress.com

High throughput multiplex gene expression analysis from

discovery to in-depth investigation

www.plexpress.com

Gene expressionanalysis: A review


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Gene expression is the most fundamental level at which the

genotype of an organism (or its internal blueprint of genetic

information) gives rise to the phenotype (the outward physical

manifestation of this information). A good way to consider gene

expression is as a mediator that interprets the information stored

in a cells DNA to create a phenotypic output via gene transcription

and mRNA processing. The ultimate influence on phenotype is

predominantly exerted through the synthesis of proteins, some of

which are structural and control the shape and characteristics of the

organism, while others may be enzymes responsible for catalyzing

particular metabolic pathways.

However, recent results from the ENCODE project, a 10-year effort

by hundreds of scientists to characterize the human genome in

depth, have indicated that a much larger proportion of our DNA is

likely to be expressed and functional than previously estimated1.

This has put the focus back on RNA as a key component of organism

growth and development, meaning that the measurement of gene

expression continues to be a critical tool employed across drug

discovery, life science research and the optimization of bioproduction.

Indeed, we now have the technological ability to quantify the level at

which a particular gene is expressed within a cell, tissue or organism,

providing access to a wealth of information. For example, researchers

can determine an individuals susceptibility to cancer through the

analysis of oncogene expression, identify viral infection, monitor

cellular metabolism or assess whether a bacterium is resistant to an

antibiotic using gene expression as an indicative readout.

In particular, the development of targeted drug compounds using the

information gained from gene expression or transcriptional profiling

methods has revolutionized many areas of the drug discovery

process including biomarker development and validation, compound

selection, target identification, pre-clinical ADME-Tox studies

(Absorption, Distribution, Metabolism, Excretion, Toxicology) and

evaluation via clinical trials.

For example, biomarkers (such as specific DNA, RNA or protein

molecules) provide an indication of a particular biological state and

1) The importance of gene expression analysis

can be used to more accurately classify disease- and patient-subsets.

Changes in biomarker expression are also useful for the investigation

of drug effects and modes of action, the establishment of dosing

regimes, and the monitoring of patient response. This data is now

being used extensively to aid the development of personalized

therapeutic strategies. In particular, gene expression is becoming the

go-to biomarker approach in many studies, particularly following a

recent recommendation by the FDA2suggesting that it is the best

method for assessing drug toxicity and efficacy during pre-clinical

trials.

The bioproduction of useful proteins such as enzymes, hormones

and vaccines by recombinant bacteria or yeast can also be improved

via gene expression analysis. Biosynthesis in this way involves

transforming the host organism with the gene of choice, thereby

mediating the production of the target protein in large volumes

for commercial use. However, the expression of foreign molecules

inside bacterial or yeast cells can have undesired toxic effects, while

over-expressing a gene does not necessarily guarantee high protein

production. For this reason, gene expression analysis is often used

to optimize the bioproduction process. This includes measuring the

expression of the recombinant gene itself, as well as monitoring

components of the protein production machinery e.g. molecular

chaperones and secretory pathway factors.

Gene expression analysis can be roughly split into two separate

but overlapping approaches, depending on the needs of the study

in question. When dealing with biological systems where the key

genes of interest are not yet known, an initial discovery phase must

often be undertaken. This is usually achieved using genome-wide

approaches such as microarrays and RNA sequencing (Table 1). For

those studies requiring the analysis of many samples and where

the key genes of interest are already known, it is common to use

more targeted techniques. Many genome-wide and targeted

methodologies exist for carrying out gene expression analysis, the

most important of which will be discussed in the next few sections.

2.1 Microarrays

Microarray technology has yielded much important information

about the transcriptome (or the entire profile of transcripts in

a species) and as such has been invaluable in providing the link

between information encoded in the genome and phenotype.

The great benefit of this approach is that it allows a researcher

to investigate the expression of every gene in the genome in a

single experiment. Unfortunately, it can be time consuming and

potentially expensive to explore more than a handful of samples

per study.

Microarray analysis of gene expression utilizes a large set

of short oligonucleotide probes that represent every gene

in the genome, often with several probes per gene. These

probes are complementary to the transcripts being analyzed

and are immobilized on a solid substrate, where they bind to

target transcripts in the sample. These have been labeled with

fluorescent dyes, allowing the relative number of transcripts to

be quantified based on fluorescence intensity. The innovative

capabilities and importance of this technology are deftlydemonstrated by a simple search for the term microarray in

PubMed (as conducted by M. Reimers 20103) and from our search

in July 2012, which identified nearly 50,000 citations.

2) Genome-wide expression analysis

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Table 1: Relative merits of whole-genome gene expression methods

Parameter Microarrays RNA sequencing

Sensitivity

& detection

Considered a robust and reliable technology

Limited dynamic range due to background noise and

saturation of signals9, 15

Often misses single nucleotide polymorphisms (SNPs) Coverage and heterogeneity not an issue, thanks to

fixed nature of probes by hybridization

Can only hybridize to a microarray designed for another

species when full genome sequence is unavailable

Tiling microarrays where overlapping probes assay

sequences over entire genome now being developed

Cant assay between exon junctions, and therefore

misses isoforms

New technology that needs further development

Potentially unlimited dynamic range (more than ve

orders of magnitude)9, 10

SNPs reliably detected Free of background hybridization and has less

systematic bias11

Can be used where a full genome sequence is not

available

Direct access to sequence and gene expression level

Junctions between exons can be assayed & expression

of different isoforms detected 12, 13

Cost Far more cost eective than sequencing Currently 10 times more costly than microarray per

sample (owing to depth of sequencing needed to

sample the transcriptome)

High cost may impact accuracy as sucient coverage/

reads using enough biological replicates may not be

obtained14

Time Samples relatively easy to process and analyze Analysis requires signicant bioinformatics expertise,

and can be laborious / time-consuming

For genes expressed at lower levels, many reads

necessary, increasing analysis time

Data Image les of around 30MB per array (converted

into text files denoting fluorescence intensities and

expression levels for each gene)

Larger les generated (20-30GB) and specialist

bioinformatics scripting support (Python, Perl, UNIX etc)

necessary15, 16

Some of the technical variation seen in the early days of

microarrays has been largely eliminated, and data quality much

enhanced. This improvement has been aided by the efforts of

the Microarray Quality Control (MAQC) consortium, which has set

QC standards to ensure the efficacy of microarray experiments 4.

Now any systematic variation between research groups and

laboratories can be dealt with through experimental and

computational methods, making comparison much easier and

more insightful5, 6. As a well established technology, microarrays

provide an excellent method for the study of genome-wide gene

expression.

2.2 RNA sequencing

A relatively new and rapidly developing technology, RNA

sequencing (also termed Whole Transcriptome Shotgun

Sequencing (WTSS)7or RNA-Seq) uses high throughput deep

sequencing technologies to determine the expression level and

exact nucleotide sequence of each transcript expressed in a

sample. This is achieved by accurately quantifying the amount

of starting material, and then comparing the frequency of each

sequence read versus the number of total reads produced by the

sequencing run.

Other normalization steps are also required, for example to

account for differences in gene lengths or sequence read lengths,

before the expression level of each gene can be estimated. For

this reason, quantitative analysis of RNA-Seq data is undergoing

continual improvement8and in its current form may not be

as robust or reliable as other methods, especially those that

measure transcript numbers more directly. There are also issues

related to processing time and cost to consider, as well as the

analysis challenges associated with the accurate assembly and

interpretation of next generation sequence data. However, a

great benet of RNA-Seq datasets is that they can be used

to identify the existence of unexpected nucleotide variations,

such as those introduced by mutations in the DNA template,

alternative transcript splicing or RNA editing. No other technology

currently offers this level of nucleotide resolution or the ability to

detect de novo RNA variations.

In a typical RNA sequencing experiment, cDNA synthesis and

DNA fragmentation is used to convert the mix of RNAs into a

library of short cDNA fragments. Sequencing adaptors, attached

to one or both ends are then added to each cDNA fragment, and

a short sequence is obtained using high-throughput sequencing

technology. This yields a large number of sequence reads which

are aligned with the known genome and classified as either

exonic reads, junction reads, poly(A) end-reads or other reads (e.g.

intronic sequence). Via extensive analysis, these results are used

to generate a base-resolution expression profile for each gene 9.

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3) Targeted gene expression analysis

Accurate quantitative gene expression analysis became popular

at the start of the 1990s when the first real-time polymerase

chain reaction (PCR) machines were developed. 20 years later,

the technology is still the method of choice for performing gene

expression analysis in many laboratories across the world. However,

the last decade has seen an explosion in new technological

innovations that offer faster, cheaper and more accurate analysis,

thereby providing significant benefits over traditional qPCR-based

approaches. Many of these methods are described below, with a

particular focus on the relative merits and drawbacks of each.

3.1 qPCR (including multiplex qPCR)

The PCR technique was pioneered in 1983 by Kary Mullis 17

and provides a method for amplifying the copy number of DNAmolecules using DNA polymerase. Although initially developed for

DNA detection and eventually quantitation, the technique lends

itself well to RNA expression analysis. This can be achieved by

taking advantage of the viral enzyme reverse transcriptase, which

converts single stranded RNA molecules into double stranded DNA,

thereby allowing each individual transcript to be amplified via PCR.

The final amount of product generated by a PCR run is proportional

to the amount of starting material in the sample, allowing gene

expression levels to be estimated. However, any PCR reaction will

eventually be saturated at end-point, making comparisons at the

end of the reaction difficult and potentially limiting the accuracy of

the data.

To combat this problem, real time quantitative PCR (RT-qPCR)

methods were developed. These allow researchers to follow the

PCR process as it progresses, circumventing the problem of end

point saturation and providing a far more accurate approximation

of initial gene expression levels. However, as the process still relies

on DNA amplification, there is the potential that analyses will be

biased by technical variation caused by the efficiency of the PCR

reaction.

Modern RT-qPCR reactions are undertaken in a thermocycler, which

measures the light emitted by a fluorescent detector molecule. Thisis usually achieved using technologies such as:

a. Probe sequences that fluoresce once hydrolyzed or upon

hybridization

b. Fluorescent hairpins

c. Intercalating dyes (e.g. SYBR Green)

All of the above approaches require less RNA than traditional

end-point assays and possess a wider dynamic range than

gel-based densitometry18, 19. Some of these technologies can be

multiplexed to quantify multiple genes in a single reaction, whereas

others require melting curve analysis to determine specificity

of the amplication (uorescent hairpins and SYBR Green)20, 21.

Multiplexing offers the benefit of increasing the number of

genes that can be examined in an experiment, thereby increasing

throughput, while reducing the amounts of sample / reagents

required. However, competition between different PCR reactions

occurring in the same tube can lead to bias in the results. For this

reason, the optimization of multiplex RT-qPCR conditions can be

time-consuming and expensive.

At this point in time, RT-qPCR remains the method of choice for

validating genome-wide studies as it provides more accurate

quantitative data than current microarray and RNA-Seq

technologies22, and is relatively easy to design and set up. However,

as sample and/or gene target number increases, RT-qPCR tends to

become more expensive (Figure 1) and equally time-intensive.

Average qPCR TRACCompetingmultiplexmethod

Number of genes

Cost

2015105

Figure 1. Relative cost- and time-effectiveness of different

methods for targeted gene expression analysis

3.2 Microfluidic qPCR

One innovation that has helped improve traditional RT-qPCR is the

adaption of microfluidic formats to thermal cycling, which leads

to reduced reagent consumption, lower amplification times and

Hours

qPCR

TRAC

0 50 100 150

Total time Hands on time

Analysis: 4x96-well plates (i.e. 384 samples) and 20 genes

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increased analytical throughput. Microfluidic qPCR also allows

simpler integration with post-reaction sample analysis23. However,

of the platforms available, even the most efficient microfluidic qPCR

method is not entirely suitable for high throughput analysis; tens of

samples can be processed in one run, but manual sample pipetting

into small wells makes it very time consuming as sample number

increases. Due to these limitations, microfluidics is mainly being

used in academic research, but commercial use beckons through

the optimization of on-chip functions, integration of functional

components and better linking between the device and end user24.

3.3 Array qPCR

Depending on the proprietary platform, array RT-qPCR systems can

enable nearly 3,000 real-time qPCR assays to be run on a single,

high-density plate, making them a higher throughput but rather

expensive option. Each of the assays occupies a single well, which

are then thermocycled and imaged. The detection chemistry is

either SYBR Green or optimized probe-based primer sets, which canscreen an entire panel of genes. In this way expression profiling

capabilities are possible in labs where there is a block-based

RT-PCR instrument. The high costs of instrumentation purchase

are still there, and this system is not always cost effective for

routine use if the entire plate is used for gene expression profiling

one sample at a time. Lastly, although qPCR arrays can increase

sample throughput, they are still limited to one gene target per

reaction and rely upon amplification to infer gene expression levels,

impacting on their ability to provide precise, in-depth analysis.

3.4 Transcript Analysis and Affinity Capture (TRAC)

TRAC is a novel method of targeted gene expression analysis,

which enables the rapid and cost-effective detection of target

gene transcripts from a large number of samples in a single assay.

Unlike multiplex RT-qPCR, TRAC provides reliable and accurate

readouts of up to 30 transcripts per sample, including in-well

normalization for maximum data reliability. The approach utilizes

labeled oligonucleotide probes directed against known target

genes that hybridize directly to the target RNA without any need

for RNA extraction, cDNA synthesis or amplification (Figure 2).

This minimizes any technical bias caused due to variable reaction

efficiencies, as is the case for RT-qPCR sample preparation and

analysis (TRAC intra- and inter-assay CVs are typically < 10 %).

As far as instrument requirements are concerned, TRAC is

extremely flexible and easy to set up in any laboratory, without

the need for specialist equipment. This includes a magnetic bead

processor and capillary electrophoresis device, both of which are

common in molecular biology laboratories. This type of hardware

can often be automated, enabling high-throughput, walk-away

sample processing. This makes TRAC simple to set up, while the

technique is also more cost-effective than RT-qPCR thanks to

lower reagent usage and up to 10 times faster due to short hands-

on time with few manual steps (total time for 96 samples is 3-4 h

using TRAC, with only 1-2 h hands-on time).

Using TRAC, hundreds to thousands of samples can be processed

in parallel using standard 96 well plates, with one sample per well.

This allows researchers to generate a dynamic perspective of gene

Figure 2: The steps of TRAC analysis

RNA exposed for analysis

In a single step, biotin-oligo-dT is used to capture

targets and specific fluorescent probes hybridise

to each one (up to 30 genes per sample)

Probe-transcript complexes captured by

streptavidin-coated magnetic beads. The

unbound material is removed and probes eluted

ready for detection

Individual probes separated by capillary

electrophoresis and quantified by fluorescence

signal

Data analysed using our

proprietary software

1

2

3

4

5

Cell lysis

mRNA captureand hybridisation

Automatedsampleprocessing

Separationanddetection

Dataanalysis

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expression by studying many genes across numerous samples in

many different states (disease, biological cycle, time, response to

other stresses etc).

3.5 Focused probe microarrays

Gene expression microarray data can be complicated by the

presence of alternative transcript isoforms25, 26. If the hybridizingprobes dont sample all isoforms equally, expression differences

amongst them can result in excessive variation in summarized

probeset expression values27. To alleviate this, probeset re-mapping

efforts have recently centered on identifying unique probes which

map to gene regions that are constitutively expressed across

tissues and throughout all developmental stages28. This has lead

to improved interpretation of gene expression. However, such

focused DNA arrays can only house enough probes to cover a few

hundred genes at the overlap depth required, making this a targeted

approach for investigating a panel of known genes, rather than a

method for genome-wide discovery.

One of the technologies in this niche is the Illumina Focused Array

(Illumina Inc, San Diego, CA) which can be used to query groups

of up to 1,400 genes, with each focused analysis chip capable

of assessing up to 96 samples. However, the Illumina platform is

rather inflexible and does not allow modification for any design

of experiment changes. Precision is good over a limited dynamic

range (CV of


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4) Choosing a targeted gene expression analysis solution

Currently, the method of choice for focused target validation and

analysis is RT-qPCR, which has been used for many years and is

well-understood and validated. While this method offers higher

sample throughput than microarrays or NGS, costs and processingtime soon escalate as sample number increases. Perhaps more

importantly, the number of genes that can be studied using RT-

qPCR is limited, as multiplexing often causes technical bias, thereby

reducing data quality. This severely impacts on the penetration that

the method can provide, as most pathways involve many genes

that must be studied simultaneously to provide a realistic overview

of the process in question.

The ideal technology would provide significant and reliable target

multiplexing, with an optimal balance between gene number,

sample throughput and cost-effectiveness (Figure 3). Many options

are already available, with many in development (see Table 2,a comparison between methods of gene expression analysis).

However, the TRAC technology offered by Plexpress is uniquely

positioned to fill the gap between genome wide studies (e.g. using

arrays) and targeted approaches with only a narrow focus, such as

RT-qPCR.

TRAC has a wide range of user benefits (Section 4.4)

Pre-validated probe sets are availablecovering a wide range

of needs including ADME-Tox, cancer markers, metabolism genes

and more.

Custom probe creationmeans that TRAC can also be used to

investigate the expression of any known gene sequence, in any

organism.

The FastTRAC gene expression analysis serviceprovides

hassle-free analysis for any researcher. Samples are processed

in Plexpress ISO-9001-certied laboratory and the data sent to

the customer in an insightful, ready-to-use report.

TRAC can be carried out in any third party labwithout

the need for proprietary equipment, as it is compatible with

many third party magnetic bead processors and capillary

electrophoresis devices.

Figure 3. The gene expression analysis space

Arrays

qPCR

NumberofGenes

Number of Samples

TRAC

Parameters qRT-PCR Microfluidic

PCR

Array qPCR Focused

probe

microarrays

(Illumina)

Magnetic

bead

assays

(Panomics)

Molecular

bar-coding

QNTP

(HTG)

TRAC

Precision Good Poor Poor Fair Fair Very good Good Excellent

Costeffectiveness

Poor Fair Poor Poor Poor Poor Good Excellent

System

flexibility

Poor Fair Poor Poor Fair - New

gene panelsare laboriousto set up

Fair Fair Excellent

Timeefficiency

Fair Good Good Poor Poor Fair Fair Good

Samplestability

Poor Fair Fair Fair Good Good Good Good

Samples RNAconvertedto cDNA,introducingpotential bias

RNA convert-ed to cDNA,introducingpotential bias



Good flex-ibility (handlesFFPB and fro-zen samples)

Good. flexibil-ity (handlesFFPB andfrozen sam-ples)

Very good,handles RNA,DNA & proteinplus FFPB& frozensamples

Good, handlesRNA, tissueand celllysates

Process

simplicity

Poor. RNA

extractionand reversetranscriptionrequired

Poor. RNA


Poor. RNA


Poor. RNA


Poor (complex) Good, but a 2

Day process

Fair, but

requires nucle-ase enzymestep

Excellent,

simple to setup. Walk-awayprocessing.

SampleThroughput

Low High High Low High Low High High

Table 2: Relative merits of different targeted gene

expression methods

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5) Integrated gene expression analysis

In order to completely bridge the gap between genome-wide

discovery and targeted analysis, while providing high sample

throughput, Plexpress offers its OneTRACAnalysis service.

This combines both microarrays and TRAC into one integrated

workow (Figure 4). Starting with Phalanx Biotechs OneArray

profiling service, users can visualize global expression patterns

and identify significant genes. Targeted TRAC analysis panels can

then be assembled based on this information, to further validate

the target genes and investigate their function in depth. In this

way, the OneTRACsystem is a cost efficient, one-stop service

for all gene expression profiling needs, incorporating streamlined

and integrated methodology from screening to validation of gene

signatures.

6) Applications of TRAC

6.1 ADME-Tox and Drug Discovery

TRAC offers numerous cost, time and technical advantages over

alternative methods such as qPCR and is ideal for pre-clinical

ADME-Tox analysis, as it allows the profile of a drug to be

investigated in many thousands of samples, without sacrificing

the number of genes being analyzed. The approach is also

cost-effective and fast, allowing screening to be carried out very

efficiently. Drugs likely to be toxic or ineffective can be therefore

identified early in the process, before costly clinical trials have

taken place.

To aid with ADME-Tox analysis, Plexpress has created a library

of pre-validated probes for use in human and rat model systems,

including hepatocyte cell lines and rat liver tissue. As TRAC can

multiplex up to 30 genes per well, these probes can be combined

into highly informative panels for measuring genes involved in

hepatotoxicity, drug transport and drug metabolism (Figure 5).

More information can be found by visiting the Plexpress website ordownloading one of several application notes discussing research

programs that have used TRAC, including Human cytochrome

P450 expression screening in primary hepatocytes and Screening

of ADME-Tox markers from rat liver samples using TRAC.

Figure 4. The integrated OneTRACdiscovery, validation and analysis service.

OneArray

Whole Genome

Screening

Identify biomarker

signature

+TRAC

Focused Transcript

Analysis

Understand gene

function

NumberofGenes

Number of Samples

DISCOVER

with

OneArray

VALIDATE

with TRAC

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6.2 Using the integrated OneTRACservice to discover,

validate and utilize ADME-Tox biomarkers

In a recent study, the integrated OneTRACgene expression

service was used to discover, validate and analyze potential mRNA

and microRNA ADME-Tox markers for studying rat liver samples.

The Phalanx Biotech OneArray Proling Service was used to

perform a full genome-wide screen of mRNA and microRNA. The

full service included study design, protocol selection and sample

preparation advice, with results presented via a data package

including microarray images, raw and normalized intensity

data, hierarchical clustering, Venn diagram analysis and gene

classification. This analysis revealed overlapping sets of biomarker

candidates that would be ideal for use in the rat ADME-Tox model.

The newly discovered marker genes were then validated using

TRAC analysis across a large range of samples. This allowed

a custom panel to be created that precisely matched the

requirements of the experimental system and which reliably

produced the expected results when tested using well-known

inducers of drug metabolism and hepatotoxicity. Furthermore, this

study demonstrated that the OneTRACintegrated framework

can be used to rapidly and accurately perform toxicogenomic

screening programs based on streamlined biomarker selection and

validation. More information about OneTRACcan be found by

visiting the Plexpress website.

6.3 Cancer research

As TRAC can be used to screen up to 30 genes in one experiment,it is ideal for monitoring the key genes involved in a given disease

or pathway over a wide range of conditions or a large number of

samples. In particular, when used as part of the new Plexpress

OncoTRACintegrated gene expression service, important gene

markers can first be identified using the worlds largest unified

array database (using the IST Online Gene Expression Viewer

provided by bioinformatics specialist MediSapiens), and then

investigated in detail using TRAC. One significant application

of this approach is to measure gene expression changes during

cancer onset and progression with the possibility for rapid and

easy assay customization. This allows researchers to investigate

their own specific genes of interest where required.

In 2008, Dr Jari Rautio (expert in TRAC and now CEO of Plexpress)

and colleagues used TRAC to screen for expression signatures

of cancer related gene markers in cultured colon cell lines 32. The

team compared four cell lines and 20 genes, during six time points

over a 24 hour period. Expression signatures were detected

directly from 10-100 x 103cells grown on 96-well plates. One of

the 20 marker genes responded consistently between cell lines

to a specific drug candidate treatment being tested. Furthermore,

four of the marker genes had a consistently different expression

level between tested cell lines. The data showed good

reproducibility (with CVs less than 12% with biological replicates)

and functioned as a proof-of-concept study illustrating that

TRAC is ideal for screening purposes. More information and

further examples can be found by visiting the Applications and

References sections of the Plexpress website.

6.4 Metabolic diseases

Diseases that disrupt normal metabolism in the body can

have diverse, and often severe, effects. For example, energy

metabolism disorders such as diabetes are complex multi-

tissue and multi-genic diseases, involving a range of cell types

and biochemical pathways. In one recent study carried out by

Metabolex, a specialist in the treatment of type 2 diabetes

and other metabolic disorders, TRAC was used to investigate

the expression of a panel of genes likely to be important

during lipid formation and storage. These genes included

transcription factors implicated in the regulation of glucose and

lipid metabolism, key enzymes and kinases for glucose and lipid

homeostasis, as well as house-keeping genes for normalization.

Until now, this experiment had not been contemplated due to

resource constraints, prohibitive cost and comparability issues

across tests imposed by using traditional techniques such as

microarrays or qPCR.

Using TRAC, the researchers were able to study expression of 20

genes in more than 200 muscle, adipose and liver tissue samples,

generating over 10,000 data points quickly, efficiently and cost-

effectively. The results led to the development of a new model for

lipogenesis MOA (mechanism of action), including a new target for

potential therapeutic modulation, designated Gene X. This gene

is active in a pathway involving Receptor A, which had previously

identified by Metabolex as a potential target for metabolic

disease in lipogenesis models. Due to the quality and promise of

the data obtained, the company plans to investigate this pathway

in more detail in the future.

Figure 5. Pre-validated multi-gene panels for ADME-Tox gene expression profiling

Human ADME-Tox library

Over 6 reference genes and many targets to choose

from*, including:

14 CYP450 genes, including all FDA-recommended

CYP targets Glucuronosyl Transferases

Glutathione Transferases

ABC transporters

Rat ADME-Tox library

Over 14 reference genes and more than 120 targets

to choose from*, including:

Hepatotoxicity, necrosis, cholestasis and steatosis

markers Drug metabolism and transport pathways

* more information can be found at www.plexpress.com 9


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7) Summary

6.5 Yeast culture optimization

Yeasts are highly important model organisms in modern cell biology

research. Some yeast species are also used extensively in industry

for example in baking, in fermentation of alcoholic beverages and

the production of ethanol for biofuel. In recognition of this, Plexpress

offer a range of pre-validated TRAC libraries for investigating

gene expression in Saccharomyces cerevisiae, Saccharomyces

carlsbergensis and Pichia pastoris. The yeast pre-designed TRAC

probes are ideal for monitoring the fermentation process, tracking

metabolic processes, assessing stress caused by large-scale protein

production and validating the effects of gene repair. Using TRAC for

yeast studies has several cost, time and technical advantages over

similar techniques used to measure gene expression.

In one example, Brigitte Gasser and colleagues at the University

of Natural Resources and Applied Life Sciences in Vienna, Austria

used TRAC to monitor the expression of more than 50 genes in

four recombinant P. pastorisstrains and the wild type, grown under

various conditions. Temperature has a large impact on the speed

of microorganism growth, and can have unexpected effects on

The quantitative analysis of gene expression has become an

integral part of most modern biological investigations, ranging from

pure academic research through to drug discovery and healthcare.

Advances in molecular biology and bioinstrumentation have

been required in order to meet the demands of these differentenvironments. This has lead to the development of new techniques

offering a range of sensitivities, throughputs and quantitative

capabilities.

Methods that allow genome-wide analysis are excellent for

the initial discovery of interesting genes / biomarkers and non-

hypothesis driven studies. However, they are currently considered

unfeasible for in-depth studies or gene validation, as scaling

up such approaches for high sample throughput can be costly,

time-consuming and impractical. For this reason, researchers often

switch to using a more targeted method after they have identified

the genes important to their experimental system.

Traditionally, the method of choice for targeted gene expression

analysis has been qRT-PCR. However, even with current

multiplexing methodologies, there are concerns that this technique

cannot offer reliable cost- or time-effective analysis across

the efficiency of protein production. In this study, the reduction of

cultivation temperature from 25 C to 20 C unexpectedly led to

a 1.4-fold increase in product secretion rate, even though product

transcription at the mRNA level was actually reduced at this lower

temperature.

To probe this temperature response in more detail, TRAC was

performed to detect changes in the expression levels of a panel

of genes at 20 C and 25 C. These genes spanned a wide range

of biological functions, from roles in amino acid synthesis, protein

folding and secretion through to core metabolism, DNA repair

and stress response. Among the transcripts upregulated at the

lower temperature (i.e. higher protein production conditions) were

components of the secretory pathway, suggesting a boost in

secretion. In addition, a reduction in the expression of chaperones,

such as those of the Hsp70 family, was also observed. This led

the authors to speculate that at the lower temperature, a reduced

amount of folding stress is imposed on the cells, thereby leading to

an increase in the generation of correctly folded product.

enough samples to provide meaningful biological insight. For this

reason, the last decade has seen the emergence of several new

technologies that aim to combine increased sample throughput

with efficient gene-multiplexing, while reducing assay time and

cost.

One such option is the TRAC gene expression analysis technology

offered by Plexpress. Facilitating the multiplex of up to 30 genes

per sample, with samples processed using standard 96 well plates,

the technology allows researchers to examine panels of target

genes in many samples quickly, easily and cost-eectively. Such

studies provide a more dynamic perspective of gene expression

across many biological states. Providing high sample throughput

without comprising on target breadth, TRAC is perfect for studying

gene expression in a wide range of systems. Furthermore, when

TRAC is combined with microarrays as part of the OneTRAC

integrated workflow, it provides a full gene expression solution,from discovery to in-depth analysis.

For more information about using TRAC for your studies,

please contact Plexpress at [email protected] or call

+358 50 313 9427.

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11/12


12/12

Plexpress Oy

Finland

Viikinkaari 6

00790

Helsinki

Phone: +358 50 313 9427

[email protected]

TRAC Sales Enquiries

[email protected]+358 50 313 9427

TRAC Customer Service

[email protected]

Documents

Plexpress_Whitepaper