Development and Validation of a Dedicated Microarray for the Evaluation of Bovine Mammary Gland...

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Molecular BiotechnologyPart B of Applied Biochemistry andBiotechnology ISSN 1073-6085 Mol BiotechnolDOI 10.1007/s12033-012-9629-1

Development and Validation of aDedicated Microarray for the Evaluationof Bovine Mammary Gland Health Statusand Milk Quality

Francesco Broccolo, Valentina Maran,Massimo Oggioni, Barbara Matteoli,Gianfranco Greppi, Luca Ceccherini-Nelli & Lisa Fusetti

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RESEARCH

Development and Validation of a Dedicated Microarrayfor the Evaluation of Bovine Mammary Gland Health Statusand Milk Quality

Francesco Broccolo • Valentina Maran •

Massimo Oggioni • Barbara Matteoli • Gianfranco Greppi •

Luca Ceccherini-Nelli • Lisa Fusetti

� Springer Science+Business Media New York 2012

Abstract The purpose of this study was the output and set

up of the milk array, a dedicated array designed to investigate

the expression levels of many genes involved in cow mam-

mary gland inflammation and milk production regulation.

First, a new targeted genes panel was selected. Successively,

the microarray reliability was examined by yellow and dye

swap experiments using the normal and mastitic mammary

gland samples from the same cow. The sensitivity and reli-

ability were evaluated using different amounts of the same

mastitic mammary gland RNA: a good linear regression

(R2 = 0.758) was obtained also using only 3 lg of RNA. We

used both reverse transcriptase RT-qPCR and the microarray

to analyze 100 bovine genes (96 known to be involved in

inflammation and milk production regulation and four

housekeeping genes) in pooled total RNA isolated from

tissue samples. All genes were detectable by RT-qPCR and

microarray: a good mean correlation coefficient over all

samples of 0.885 showed that both methods were similarly

well suited to analyze gene expression in these samples. This

report describes the development of small DNA microarray

of fully defined genes suitable for analysis of expression of

many genes involved in cow mammary gland inflammation

and milk production regulation; this platform will prove

useful as diagnostic tool prototype to perform a more

in-depth analysis of the milk quality and mammary glands

health status.

Keywords Microarray � Expression � Milk

Cattle mammary gland � Mastitis � Inflammation

Introduction

Microarray technology has revolutionized the way to

approach fundamental biological issues allowing the study

of the biological issues in a more holistic perspective. In

the past 10 years, DNA microarrays have been widely

heralded and are becoming increasingly integrated into the

current research and future plans of many laboratories

[1–4]. Although the great diffusion of microarray, the use

of this new technology in veterinary research is still at the

beginning [5]. There are three main limits to the diffusion

of microarray technology in animal science research: the

high costs of the technology (which is still somewhat

prohibitive for some laboratories), the limited number of

species for which arrays are available (due to the lack of

Francesco Broccolo and Lisa Fusetti contributed equally to the

research.

F. Broccolo (&) � M. Oggioni � L. Fusetti

Laboratory of Molecular Microbiology and Virology,

Department of Health Sciences, Faculty of Medicine and

Surgery, University of Milano, Bicocca, Via Cadore, 48,

20900 Monza, MB, Italy

e-mail: [email protected]

V. Maran � G. Greppi

Department of Animal Science, CNBS Nanobiotechnology

Center-Sardegna, University of Sassari, Sassari, Italy

B. Matteoli � L. Ceccherini-Nelli

Retrovirus Centre of the Virology Section, Department

of Experimental Pathology, B.M.I.E., Faculty of Medicine

and Surgery, University of Pisa, Via Del Brennero, 2,

56127 Pisa, Italy

Present Address:L. Fusetti (&)

Retrovirus Centre of the Virology Section, Department

of Experimental Pathology, B.M.I.E., Faculty of Medicine

and Surgery, University of Pisa, Via Del Brennero, 2,

56127 Pisa, Italy

e-mail: [email protected]

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Mol Biotechnol

DOI 10.1007/s12033-012-9629-1

Author's personal copy

knowledge about genome sequences) and mostly the lim-

ited presence for array of genes really representative for a

pathological process.

Most commercially available off-the-shelf arrays contain

a cosmopolitan series of well-characterized genes expressed

across many cell types: as most researchers focus on one or

two tissues only, this global approach to array production is

somewhat wasteful [6]. In some cases, commercial arrays

containing many thousands of genes may be less efficient

than smaller scale application-targeted arrays since many

elements unrelated to the specific problem are present. From

an economic point of view arrays of distinct set of genes may

be more useful, given that they can be produced to contain

only the genes playing a pivotal role in the biological pro-

cesses of interest, thereby reducing the overall effort in

analyzing data from causally associated genes [5, 6]. Small

focused arrays have the big advantage that only the genes

relevant in the biologic or pathological process of interest are

represented; in addition, there are more replicates of the

genes, allowing a bigger statistical power and a simpler data

analysis [1, 6].

In this study, there was an interest in exploring the

expression profile associated to milk quality and mammary

glands health status, and therefore a suitable array has been

constructed.

Bovine mastitis is defined as inflammation of the

mammary gland and it is the most serious and economi-

cally important disease in dairy milk production world-

wide [7] and it is responsible of several production

effects: it causes changes of milk composition as the

primary results of impaired synthetic and secretory

activity of the udder epithelial cells as well as a change in

the permeability of the milk-blood barrier (the major milk

proteins as a- and b-Casein have been reported to

decrease while other proteins originating from the blood

as serum albumin and immunoglobulins increase) [8, 9].

Bovine mastitis reduces the yield and quality of milk:

these changes and their degrees depend on the infecting

agent and the inflammatory response and increase the rate

of culling and veterinary costs [10]. Furthermore, studies

in cattle using mastitis as a model indicate that one of the

causes of early embryonic loss is infectious disease or

activation of immune responses at the sites outside the

reproductive tract [11]. Infection of the mammary gland is

associated with a reduction in pregnancy rate: the mech-

anism is not clear although cytokines probably play a

crucial role giving anovulation, fertilization failure, and

embryonic mortality [11]. In addition, often cows have

mastitis without obvious clinical symptoms: these sub-

clinical infections also result in decreased milk production

and elevated leukocyte counts in milk [11]. It is very

important to understand all the mechanism leading to

milk production: milk protein and lipid synthesis and

transport, milk genes regulation, mammary gland devel-

opment and involution, and, very important, mammary

gland health status.

In this paper, we describe the optimization of a dedi-

cated milk array (MIA) to determine the expression levels

of bovine mammary gland genes involved in the immune-

endocrine responses.

Materials and Methods

Literature Search for Mammary Gland Transcriptome

Genes

The first step was the assembling of a list of genes repre-

senting part of the mammalian mammary gland transcrip-

tome. An extensive literature survey was conducted to find

human and bovine mammary gland expressed genes. The

NCBI UniGene-National Library of Medicine database or

the DFCI Cattle Gene Index were searched to identify

genes expressed in bovine mammary gland.

The bovine and human transcriptome gene lists were

brought together for a comparison looking for gene

sequence matching. Paring down the list to the final 100

genes was carried out sequentially according to the fol-

lowing criteria:

(i) all the genes present in both lists were retained;

(ii) all the genes of specific interest in inflammation and

immunity was retained;

(iii) genes involved in milk production were retained

over genes obtained from literature, which were in

turn selectively retained over those obtained from

database searching. In this way, we were able to

produce a final master list of 96 genes expressed in

the bovine mammary gland. Four housekeeping

genes and a negative control have also been

included.

Assembly and Printing of the Microarray

Each oligonucleotide spotted on the array was designed and

tested to be specific for the respective gene sequence with

bioinformatics tools (Oligos4array, MWG). The Oli-

gos4Array program (MWG-Biotech) designs specific 50mer

oligonucleotides within the defined coding regions. All oli-

gonucleotides are matched in GC-content, TM and are free

from secondary structures and self-annealing. Every probe

was compared by BLAST and Smith–Watermann analysis

with those of all known bovine coding region.

Oligonucleotides were synthesized with the high purity

salt-free (HPSF) method (MWG) that guarantees less than

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0.1 % of truncated chains. Oligos were modified in the 50

portion: they present a c6 codon to allow a better interac-

tion of the oligo itself with the probes during hybridization

and an amino-modification to bind covalently the slide

surface. Probes were suspended in an appropriate spotting

buffer (150 mM sodium phosphate, pH 8.5) at a final con-

centration of 50 mM. MIA was printed by a QArray2 spotter

machine on a 8 9 7 double spotted grid. Epoxy-coated slide

(MWG) were used. After oligo deposition MIA was incu-

bated overnight in a humidified chamber (75 % constant

humidity, room temperature) and residual reactive groups

were blocked using 19 blocking buffer for 1 h at room

temperature shaking. Coupled slides were stored at room

temperature in a desiccated chamber until use.

Sample Preparation, RNA Extraction, and cDNA

Synthesis

Bovine mammary gland tissues have been collected in a

local abattoir. Healthy and mastitic mammary gland tissues

were collected from the same animal and preserved

immediately in RNALater (Invitrogen, Milan, Italy). The

determination of the clinical status of the bovine was car-

ried out by a veterinary physician who has established a

manifest clinically acute mastitis. Tissue samples were

homogenized in 1 ml of TRIZOL reagent per 50-100 mg

of tissue using a glass-Teflon homogenizer.

Healthy tissues were processed using RNeasy Mini Kit

(Qiagen, Milan, Italy). After homogenization, RNA was

extracted from mammary gland tissues were treated with

proteinase K (Qiagen, Milan, Italy) and processed using

RNeasy Lipid Tissue Mini Kit (Qiagen, Milan, Italy)

according to the manufacturer’s instructions. In order to

eliminate residual genomic DNA, an on column DNase

digestion has been performed.

Quality of total RNA was assessed by running a non-

denaturing 1 % agarose tris–acetate buffer. RNA quantity

was assessed using a Nanodrop (Thermo-Scientific, Euro-

clone, Milan, Italy) and quality was evaluated using the

Agilent 2100 Bioanalyzer (Agilent Technologies, Milan,

Italy). All RNAs used in the present study were of high

quality and un-degraded (A260/280 [ 1.8 and A260/230 [ 1.7,

RNA integrity number (RIN) [ 8). All nucleic acid sam-

ples were stored at -80 �C until use.

The same RNA was used for both MIA and Reverse

Transcriptase-quantitative real-time PCR (RT-qPCR)

analyses.

RNA extracted was initially labeled by a direct incor-

poration method: 10 lg total RNA were primed with oligo

dT and incubated at 65 �C for 10 min followed by a brief

chilling on ice. Four ll of 59 first strand buffer, 2 ll of

0.1 M DTT, 1 ll of RNase OUT, 1 ll dNTP mix 10 lM

(Invitrogen, Milan, Italy), and 1 ll of Cy dCTP

(Amersham Corporation, Milan, Italy) were then added to

the primed RNA and incubated 2 min at 42 �C. Finally,

1 ll of 50 U/ll SuperScript III (Invitrogen, Milan, Italy)

was added at the reaction and the elongation was carried

out at 46 �C for 1 h. A final inactivation step was per-

formed at 70 �C for 15 min. Residual RNA was degraded

adding 1 ll RNAse H at 37 �C for 20 min. Labeled cDNA

was purified by ethanol precipitation.

MIA Processing and Data Analysis

An equal mixture of two dye-labeled DNA was resus-

pended in hybridization buffer Perfect Hyb plus (Sigma,

Milan, Italy) and applied on the array. Slides were incu-

bated over night at 42 �C in a humified chamber. Following

hybridization, slides were placed in 49 standard sodium

citrate (SSC) buffer to remove the coverslip and then

washed in buffers with SSC at decrescent concentrations

(from 29 to 0.19) at room temperature to remove residual

SDS and salts, and centrifuged dry at 800 g for 1 min.

Slides were visualized using ScanArray express with dual

laser (Perkin Elmer, Milan, Italy) and spot intensity nor-

malized to the median background intensity and LOWESS

algorithm using ScanArray software. Arrays were manu-

ally examined and spots with irregular morphology were

excluded from data analysis. A frequently used method for

the selection of differentially expressed genes was adopted

based on setting intensity thresholds and minimal fold

change to discard genes with low expression and non-

significant fold change, respectively.

Reproducibility and sensitivity of cDNA hybridization

to MIA was determined by comparing gene expression on

repeated sub-grids within and between duplicate slides,

evaluating gene expression following dye swapping and by

testing hybridisation of differentially labeled samples.

Gene Expression Analysis by RT-qPCR

The expression of 100 genes was assessed by a RT-qPCR.

SuperScript III Platinum two-Step RT-qPCR System with

ROX (Invitrogen, Milan, Italy) was used according to the

manufacturer’s instructions. Primers and a probe comple-

mentary to an internal region 1–5 bp downstream of the

forward primer were selected using the Primer Express

software (PE Biosystem, Foster City, CA, USA); the

thermodynamic features were predicted by Oligo 6 soft-

ware (Molecular Biology Insights Co, USA). Optimization

of the reactions was achieved by determining the concen-

trations of primers and probes, as well as the annealing

temperature yielding the highest intensity of reporter

fluorescent signal without a reduction in specificity or

sensitivity. All experiments were performed on an ABI

PRISM 7900HT Sequence Detection System (Applied

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Biosystems, Milan, Italy) using SYBR green chemistry

under the following conditions: 10 min at 95 �C, and 40

cycles of 15 s at 95 �C, and 30 s at specific annealing

temperature (59–60 �C). Each RT-qPCR contained (final

volume of 10 ll) 25 ng of reverse-transcribed RNA, each

primer at 150 nM, and 5 ll of 29 SYBRGreen PCR

Master Mix (Applied Biosystems), and each sample was

analyzed in triplicate. Results were evaluated by the

comparative cycle threshold (Ct) method (User Bulletin

No. 2, PerkinElmer Life Sciences) using GAPDH as

the invariant control gene. Following amplification, the

instrument software was used to set the baseline and

threshold for each reaction. A Ct was assigned at the

beginning of the logarithmic phase of PCR amplification

and the difference in the Ct values of the control and

experimental samples were used to determine the relative

expression of the gene in each sample. Before quantitative

analysis, a standard curve was constructed using serial

dilutions of RT product and the efficiency of each primer

set was determined using the equation [(10(-1/-slope)

- 1) 9 100]. Efficiencies of 90–110 % were required to

include the qPCR assay in array validation. Therefore, the

assay-specific efficiency was not used in the calculation of

relative expression levels.

These Ct values serve as indirect indicators of gene

expression so that samples with high expression of a given

gene will exhibit lower Ct than samples showing low level

gene expression. Average Ct values and standard devia-

tions were calculated for each gene. Expression of the

housekeeping gene GAPDH was used to control for input

cDNA in each amplification reaction. Once the Ct for the

GAPDH gene was determined for each cDNA sample, it

was used to normalize all other genes tested in the same

cDNA sample. Determination of fold increase or decrease

in expression for selected genes in the mastitic mammary

gland tissue relative to levels of expression in health

mammary gland tissue was accomplished using the fol-

lowing formula, as described previously.

The comparative Ct method (2-DDCT) was used to

quantify results obtained by RT-qPCR. Data were nor-

malized by determining differences in Ct values between

the target gene of interest and the reference gene: DCt =

(Ct target - Ct reference gene). For the mastitic mammary

gland tissue, evaluation of 2-DDCT was defined as the fold

change in gene expression relative to healthy mammary

gland tissue [12]. GAPDH was chosen as reference gene.

Statistical Evaluation

For statistical analyses, we used Prism 4 (GraphPad Soft-

ware) and Microsoft Excel 2003 software. We calculated

arithmetic means, medians, and CVs; regression analysis

was done to measure the relationship between two variable.

Pearson correlation coefficient (r) was utilized to calculate

the correlation between expression levels obtained by MIA

and RT-qPCR. To compare signal intensities of the MIA

with qPCR-derived Ct values, we converted the former to

logarithms.

Results

Microarray Design

A dedicated array has been designed to study the expres-

sion of many genes known to be involved in inflammation

and milk production regulation. The first decision in

planning a custom array is whether or not to include clones

of expressed sequence tags (ESTs) in addition to known

genes. Where resources are limited it is prudent to use only

named genes that have been previously characterized. It

may be possible to obtain several thousand such clones

depending on the species of interest. In most instances, we

did not use ESTs, in some cases, however, highly relevant

genes (YY1, Stat6) were only available as EST sequences.

Public database and literature were used to identify genes

expressed in the bovine mammary gland: selected gene

sequences were obtained from the National Center for

Biotechnology Information (NCBI), or the Dana–Farber

Cancer Institute (DFCI) Cattle Gene Index. Every selected

gene must be individually assessed for the following

factors:

(i) Availability of a publicly accessible clone. Although

suppliers such as Clontech, IGI, and IMAGE

consortium provide public access to a huge number

of full-length and partial clones from a number of

species, not all the genes are available from public

database. This problem is more often encountered

when newly discovered or characterized genes have

been selected from the literature.

(ii) Availability of a sequence verified clone. It is

important to select sequence-verified clones to insure

that the correct sequences are obtained.

(iii) 30 end clone. When possible the selection of a 30-end

clones is desirable, as the inclusion of the 30-UTR will,

in most cases, enhance the specificity of the gene

fragment for its gene in the hybridization process.

Details of the selected genes list for our bovine MIA are

shown in Table 1; the selected genes were divided

according to their families to which they belong.

Microarray Validation

Each microarray has been scanned using a ScanArray

express microarray scanner (Perkin Elmer) with dual laser.

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Table 1 Selected gene of different functional families

Gene ID Accession #

Genes encoding milk proteins

Beta-lactoglobulin MilkArray1 NM_173929

Alpha s1-Casein MilkArray2 NM_181029

Alpha s2-Casein MilkArray3 NM_174528

Beta Casein MilkArray4 NM_181008

Lactoferrin MilkArray5 NM_180998

Kappa Casein MilkArray6 NM_174294

Matrix metalloproteinases (MMPs) and tissue inhibitors of matrix

metalloproteinases (TIMPs)

Timp1 MilkArray7 NM_174471

Mmp14 MilkArray8 NM_174390








Signal transducer and activator of transcription (STAT family)

stat5a/mgf MilkArray16 Z72482

stat5b MilkArray17 NM_174617

stat6 MilkArray18 AB038383

stat3 MilkArray19 AJ276489

Genes involved in apoptosis

bcl2 MilkArray20 U92434

bax alpha MilkArray21 U92569

Mitogen-activated protein

(MAP) kinase

MilkArray22 NM_175793

p53 MilkArray23 AF525301

yy1 MilkArray91

NM_001076079.1

Interleukin (IL), interferon (IFN), tumor necrosis factor (TNF) and

transforming growth factor (TGF) receptors

IL8R-beta MilkArray30 NM_174360

IL2 R-alpha MilkArray31 NM_174358

IL2R-gamma MilkArray32 NM_174359

IFN-alpha receptor MilkArray39 NM_174552

IFN-alpha r2 MilkArray40 NM_174553

IL-12R-beta MilkArray41 NM_174645

TNF R1 MilkArray42 U90937

TGF beta r1 MilkArray43 NM_174621

Interleukin (IL), chemokines

Growth regulated oncogene

(gro)-alpha

MilkArray24 U95812

gro beta MilkArray25 U95813

gro gamma MilkArray26 U95811

cxcl6 MilkArray27 NM_174300

EotaxinE MilkArray28 AJ132003

csf1 MilkArray29 AY181987

Table 1 continued

Gene ID Accession #

cd14 MilkArray33 AF141313

cd69 MilkArray34 AF272828

cd44 MilkArray35 NM_174013

Lif MilkArray36 E11512

L-selectine MilkArray37 NM_174182

osteopontine MilkArray38 NM_174187

IL1 alpha MilkArray45 M37210

IL1 beta MilkArray46 NM_174093

IL 2 MilkArray47 M13204

IL 3 MilkArray48 NM_173920




IL 7 MilkArray52 AF348422


IL10 MilkArray54 U00799

IL12 35 kdal MilkArray55 U14416

IL12 40 kdal MilkArray56 U11815

IL13 MilkArray57 NM_174089

IL15 MilkArray58 U42433


IL18 MilkArray60 NM_174091

Gmcsf MilkArray61 U22385

INFgamma MilkArray62 M29867

TNF alpha MilkArray63 AF348421

Rantes MilkArray64 AJ007043

IL1 receptor antagonist MilkArray65 AB005148

Molecules of the inflammatory cascade

Pghs 2 MilkArray66 AF031699

cox 1 MilkArray67 AF004943

cox 2 MilkArray68 AF004944

lipoxygenase 5 MilkArray69 AJ306424

Carbonic anidrase VI MilkArray70 NM_173898

inducible i nos MilkArray87 AF340236

amyloid a3 MilkArray44 AF335552

Molecules of lipid metabolism

glycam 1 MilkArray71 NM_174828

Lactophorina MilkArray72 D26176

Butyrophilin MilkArray73 NM_174508

Scd MilkArray74 AF188710

Obese gene MilkArray75 U43943

Leptin receptor MilkArray76 U83512

Lipoprotein lipase MilkArray77 M16966

100 kdal coatt MilkArray92 AY273893

Acc MilkArray78 AJ132890

dgat 1 MilkArray79 NM_174693

Fabp 1 MilkArray80 NM_175817

Fabp 5 MilkArray81 NM_174315

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Low resolution horizontal line prescans were performed on

each microarray before higher resolution scanning to bal-

ance the overall fluorescence intensity of the whole

microarray between the two dyes. The laser power and

photomultiplier tube gain settings were assessed and

slightly adjusted for each individual microarray to achieve

optimal balance to minimize the post-scanning processing

and normalization. For each channel a high-resolution

image is generated, which is then analyzed in a process

referred to as ‘‘spot finding.’’ QuantArray (Perkin Elmer)

software has been used to quantitate the relative transcript

level for each spot of the microarray from the ScanArray

output TIF file. The spots are quantified into single inten-

sity window for each channel as absolute value. These two

intensity values are the estimators of the relative expression

level of the gene in the two samples. The spot finding of

scanning software also provides an estimator of the back-

ground intensity for a given spot and in both channels.

Local fluorescent background was subtracted and log2

transformed, the resulting data has been LOWESS nor-

malized. The correction of the data, according to those

factors introducing systematic errors, is an essential stage

before the analysis and biologic interpretation of the data.

In order to compare measurements within and across arrays

and to correct non-biologic variation masking meaningful

information, normalization is an essential task prior to any

further analysis. Among all the available normalization

techniques, LOWESS has proved useful for the

normalization of data generated from microarray platform

due to its ability to remove intensity dependent effects.

The reliability of MIA has been examined by two dif-

ferent experiments: (i) Yellow experiment and (ii) dye

swap experiment.

Yellow Experiment

The yellow experiment is an efficient tool for microarray

system optimization: it requires only one type of RNA

sample, but give information on all aspects of the micro-

array experiment. In this experiment, the RNA sample is

labeled with both fluorescent dyes, Cy3 and Cy5: hybrid-

ization of equal amounts of both probes onto a microarray

should produce equal hybridization signals for both colors.

As a computer screen image of microarray data should be

misleading, analyzing numerical data of the experiment

will be more informative. First spots have been visually

examined and those with irregular morphology or artefacts

were excluded from data analysis. In our experiments, we

eliminate 7.5 % spots for artifacts and irregular spot mor-

phology. Then, data were analyzed for fluorescence

intensity values and signal to noise ratio, low expressed

genes (13.5 %) were eliminated. Data obtained were used

for further statistical analysis.

RNA extracted from mastitic mammary gland has been

divided into two equal parts (10 lg per aliquot). One part

was labeled with Cy3 and the other part was labeled with

Cy5, then simultaneously hybridized to a microarray slide.

Cy3 and Cy5 normalized intensities were Log2 transformed

and converted in a diagram, respectively, in horizontal and

vertical axis (Fig. 1). As no differential gene expression is

expected, normalized Cy3 signal plotted against normal-

ized Cy5 signals should appear as a straight line. The result

reveals a strong linear relationship (R2 = 0.998).

Dye Swap

Reverse labeling designs are common steps in two-colors

microarray experiments, if comparison between the

co-hybridized samples is of interest. In two-colors micro-

array experiments, the so-called dye effect is one of the

most important sources of systematic errors. Several

properties are different in the two dyes, i.e., their quantum

efficiency and their gene specific incorporation properties.

These differences make it necessary to balance the inten-

sities of both channels before further analysis. Dye-swap

experiments are extended and well established in the

microarray community. The main advantage of dye-swap

normalization is the correction of the data preserving the

characteristics of every gene. In addition, it accounts for

the different incorporation rate of the two dyes to different

sequences.

Table 1 continued

Gene ID Accession #

Ppar alpha MilkArray82 AF229356

Ppar beta MilkArray83 AF229357

Ppar gamma MilkArray84 NM_181024

Hormones and their receptors

Er beta MilkArray85 NM_174051

Pr MilkArray86 AY116891

Prolactin receptor MilkArray88 NM_174155

prp3 MilkArray89 NM_174160

prp1 MilkArray90 NM_174159

igfr2 MilkArray93 NM_174352

Igfbp 1 MilkArray94 954979

Igfbp 2 MilkArray95 NM_174555

Igfbp 3 MilkArray96 NM_174556

Housekeeping genes

Beta actin Positive control 1 AY141970

rpl 24 Positive control 2 NM_174455

Gapdh Positive control 3 AF077815

Beta 2 myoglobin Positive control 4 BC118352.1

Arabidopsis thaliana Negative control NC_003075.4

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A real gene expression experiment was performed, two

different RNA species, from mammary gland of mastitic

and healthy bovine, were labeled separately with two col-

ors, Cy3 and Cy5, and hybridizations were performed with

both possible combinations (Fig. 2). Reverse color exper-

iment gives information about any inherent variation

between the quality of different arrays: variation in back-

ground fluorescence, different Dye incorporation.

Data analysis after MIA forward and reverse slide

scanning was performed. Spots with mean signal intensities

that were at least twice as high as the mean background

were accepted for further analysis. Local fluorescence

background was subtracted and log2 transformed, the

resulting data was LOWESS normalized. Sum of intensity

values were plotted against log2 (Cy30/Cy50)-log2(Cy500/Cy300). Mean spot intensity was evaluated and genes

CImean ± 2 standard deviation were eliminated (2.5 % of

total genes) (Fig. 3).

Sensitivity

In order to test the sensibility of MIA, we set up a series of

experiments using the same sample in different quantities.

The rationale of the experiment was to test the minimum

amount of sample that can be used to obtain reliable data.

We used a mastitic mammary gland labeled both with Cy3

and Cy5 starting from 1, 3, 5, and 10 lg of RNA: every

pair of Cy3/Cy5 labeled RNAs were then hybridized to

different MIA slides and, after scanning and correcting for

signal to noise ratio and low expressed genes elimination,

the signals were plotted on a diagram as log2 values

(Fig. 4). The good linear regressions visualized indicate

that also using only 3 lg of RNA the MIA has a very good

reliability as confirmed by the high R values. In fact only

the 1 lg sample shows a sensible shifting and scattering of

the signals from the diagonal, indicating that the milk array

does not have any bias for the Cy3 or Cy5 probe labeling.

Fig. 2 A representative example of MilkArray: images depict the

same sub-array of two slides of a reverse color experiment.

a Hybridization with mastitic and healthy bovine mammary gland

RNA labeled, respectively, with Cy5 and Cy3. b Hybridization with

the same RNA, but reverse labeled, the healthy one with Cy5 and the

mastitic one with Cy3. The two expansions highlight the opposite spot

fluorescence from the reverse labeling

Fig. 1 Yellow experiment: scatter plot of a self-against-self hybrid-

isation of mastitic mammary gland RNA labeled both with Cy3

and Cy5. Data were normalized and log2 transformed (linear

regression, R2 = 0.998)

Fig. 3 Dye Swap: scatter plot of sum of intensity values against the

difference of fluorescence intensity ratios, indicating a bias in

fluorophores incorporation. The diagram indicates that the majority

of spots lie within twofold lines

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Validation of Microarray Differential Expression Data

by RT-qPCR Analysis

Microarray results were validated for a large (96 candidate

bovine genes) number of differentially expressed genes

involved in inflammation and milk production regulation

utilizing pooled total RNA isolated from mastitic mammary

gland tissue sample (Fig. 5). All genes were detectable by

both methods (RT-qPCR and microarray). Using GAPDH

expression as a control for input cDNA, we directly com-

pared the levels of gene expression obtained with the use of

mRNA in microarray and RT-PCR analyses. A good corre-

lation coefficient over all evaluated genes of 0.885 showed

that both methods were similarly well suited to analyze gene

expression in these samples (Fig. 5). In particular, we

observed a good correlation between microarray and

RT-qPCR results for both evaluated upregulated (e.g., a

Fig. 4 Sensibility: scatter plots

of four yellow experiments

conducted with different

amounts of the same mastitic

mammary gland RNA. a 1 lg,

R2 = 0.674; b 3 lg,

R2 = 0.758; c 5 lg,

R2 = 0.797; d 10 lg,

R2 = 0.8847

Fig. 5 Correlation plot of fold-change values for the selected genes

analyzed by microarray (X-axis) and RT-qPCR methodologies

(Y-axis) data

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s1-Casein) and downregulated genes (e.g., Butyrophilin).

Moreover, the two methods performed similarly well with

respect to reproducibility; overall, the mean correlation

coefficient between replicates was r = 0.989 and r = 0.987

for RT-qPCR and the MIA, respectively. Of note is that the

replicates were measured on the same plate in the RT-qPCR

analysis.

Next, we choose four genes which were significantly

downregulated (e.g., Butyrophilin, Glycam 1, Lactophorin,

100 Kdal Coactivator) and four genes which were signifi-

cantly upregulated (e.g., a s1-Casein, a s2-Casein, b-Casein,

J-Casein) in mastitic mammary gland tissue (Fig. 6). In

Table 2, primers used for qPCR experiments and the fold

change calculated from microarray and qPCR data by

dividing average are also shown.

Discussion

The techniques of RT-qPCR to measure relative differen-

tial expression are highly labor intensive; in contrast,

microarray technology permits global assessment of gene

expression and by its nature generates both quantitative and

qualitative data that enable parallel, comparative analyses.

Datasets gathered from such experiments provide the

potential to follow pathways of immune reactivity,

metabolism as well as assessing disease states and breeding

patterns that can then be related to specific gene expression

signatures [13]. Many platforms have been developed for

mouse, rat, and human species and the resources available

for livestock species are advancing rapidly [2, 14]; the

Fig. 6 Comparison of microarray expression data and RT-qPCR data

for the selected differentially expressed genes in healthy and mastitis

mammary gland. The expression is normalized using GPDH house-

keeping gene

Table 2 Comparison of average microarray and qPCR results for eight selected genes (four downregulated and four upregulated) in mastitic

gland mammary (GM)

Gene Genbank

Acc.#

Microarray qPCR

Health

GM

Mastitic

GM

Fold Primers (forward/reverse) Health

GM

Mastitic

GM

Fold

Butyrophilin NM_174508 5.2 0.0021 Down 50-ACAGTGGCGAACTCACAGGG

50-TGGACTCTGACATGGATGGAA

5.6 0.003 -1867

Glycam 1 NM_174828 4.7 0.01 Down 50-AAACGGATGCTGCTACAGCC

50-AGAAGCAGGACGCAGAGGAA

5.2 0.011 -473

Lactophorin D26176 3.6 0.008 Down 50-ATCAGATCTTCCAGGCAACCA

50-GGACAGAGGGAGCTTGGGAT

4.7 0.009 -522

100 Kdal

Coactivator

AY273893 3.7 0.008 Down 50-CTACCTGGTCACGGTCATGCT

50-CGTCGAAAAGTTGGGCACTT

4.6 0.01 -460

a s1-Casein NM_181029 0.45 85 Up 50-CTGCCATCACCTTGATCATCA

50-CCAAGACTGGGAAGAAGCAGC

0.4 100 250

a s2-Casein NM_174528 0.88 10.5 Up 50-GAATGCTGTTCCCATTACTCCC

50-CTGGTGGAGAGCTGCTCTCTG

0.78 14 18

b-Casein NM_181008 0.65 8.5 Up 50-TCCTCCTCAGTCCGTGCTG

50-GGAACAGGCAGGACTTTGGA

0.45 10 22

K-Casein NM_174294 2 95 Up 50-TTGCTAGTGGCGAGCCTACA

50-TGCTCTCTACTGCTTCGGTGG

2.9 250 86

Primers used for qPCR experiments and the fold change calculated from microarray and qPCR data by dividing average are also shown

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genome sequences for the chicken, pig, and cattle are

currently available. Consequently, there are a number of

focused and whole genome oligonucleotide and EST arrays

for these species available commercially (for example the

Agilent bovine microarray) and academically [15–17].

However, our approach was to use oligonucleotides rep-

resenting previously sequenced genes from the public

databases when possible; conversely, ESTs sequences were

used when relevant genes sequences were unavailable from

databases. The advantages of an oligonucleotide-based

array compared to an EST array are increased specificity and

sensitivity and the ease of gene identification. The avoidance

of making libraries, cDNA clones, and sequencing of

selected ESTs to identify genes was an additional advantage.

In this paper, we describe the optimization of a dedi-

cated Milk Array (MIA) to measure the bovine mammary

gland gene expression levels. Results of few previous

studies indicate that microarrays are a sensitive and prac-

tical method to monitor differential gene expression in

cattle [2, 3, 18]. Although commercially available micro-

arrays contain several thousands of transcripts, they are not

usually aimed at a specific research purpose; conversely,

we selected a genes panel relevant in bovine mammary

gland immunobiology and milk production. Assembling

the list of genes to be included in a tissue-specific array is

perhaps the most challenging part of the process.

MIA contains probes able to investigate the expression

of many genes known to be involved in inflammation and

milk production regulation: those include the cytokine

network, the genes involved in induction/repression of

apoptosis (involution), cell cycle control (proliferation

before lactation), energy metabolism, and fatty acid

metabolism in particular. We aim that this small-scale

application-targeted array can be used to determine the

expression levels of genes involved in the stress-related

immune-endocrine responses of the bovine mammary

gland and their correlation to its product, the milk.

In particular, we focused our milk array on immune

system molecules, involved both in mammary gland

physiologic changes and immune-inflammatory response.

The cytokines and the networks more or less directly

associated could provide a reliable and highly sensitive

means of diagnosis or prognosis: subtle changes in the

cytokine network of mammary gland in health and disease

could help in detecting early infection and in monitoring

the effectiveness of treatment [19]. The added value of the

milk array is that it examines not only the cytokine network

but also all the pathways related to it and those related to

the normal physiology of the gland (as involution and

lactation): the focus is not only the inflammation status but

also the overall physiology of the gland with a particular

glance to the production and fertility problems. This per-

mits the detection of all the molecular changes (as gene

expression) happening in a normal or whatever (patho-

logical or subpathological) condition of the gland for a

deeper knowledge of the markers of the milk quality and

mammary gland health status.

Substantial effort was devoted to insure spot uniformity,

high signal to noise ratio, and good statistical reproduc-

ibility. Reported data demonstrate the achievement of these

objectives and that MIA can relate favorably to the com-

mercial array of other species. The power of MIA include

two replicates per probe per slide and high signal to noise

ratio. The last parameter is due both to the quality of the

slides, the hybridization procedures that we utilized and the

quality of the oligo printed.

Our experiments with pilot MIA show that the method-

ology of RNA transcription and labeling, slide preparation,

hybridization, and detection are both reproducible and

robust.

Separate experimentation must be done to address the

issue of whether biologic significance can be inferred from

microarray data. The low variability that we have seen

from spot-to-spot, from slide-to-slide, and from cDNA

synthesis make us confident that we can distinguish small

changes in gene expression between any two experimental

sample. These data show that the MIA performs well with

RNA isolated from tissue samples without requiring target

amplification and hence without the risk of introducing an

amplification bias and importantly it maintains a very good

correlation with the current state-of-the-art technology,

RT-qPCR.

In conclusion, this report describes the development of a

small DNA microarray of fully defined genes suitable for

projects requiring detailed analysis of expression of many

genes involved in bovine mammary gland inflammation

and milk production regulation; this platform will prove

useful as diagnostic tool prototype to perform a more

in-depth analysis of the milk quality and mammary glands

health status.

Acknowledgments This study was supported by grant from Regi-

one Lombardia grant. We thank Dr. Francesco Damin, Pietro Parma,

and Marcella Chiari for their technical support.

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