Strengthening insights into host reponses to mastitis infection in ruminants by combining heterogeneous microarray data sources

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DOI: 10.1186/1471-2164-12-225 · Source: PubMed
Abstract
Gene expression profiling studies of mastitis in ruminants have provided key but fragmented knowledge for the understanding of the disease. A systematic combination of different expression profiling studies via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of infections with mammary gland pathogens, including samples from cattle challenged in vivo with S. aureus, E. coli, and S. uberis, samples from goats challenged in vivo with S. aureus, as well as cattle macrophages and ovine dendritic cells infected in vitro with S. aureus. We combined different time points from those studies, testing different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific. Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the general disease response. In the overall response, pathways related to immune response and inflammation, as well as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk fat content depression commonly observed during mastitis infection. Complementarities between early and late stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by few genes, including XBP1 and SREBF1.The cattle-specific response was characterized by alteration of the immune response and by modification of lipid metabolism. Comparison of E. coli and S. aureus infections in cattle in vivo revealed that affected genes showing opposite regulation had the same altered biological functions and provided evidence that E. coli caused a stronger host response. This meta-analysis approach reinforces previous findings but also reveals several novel themes, including the involvement of genes, biological functions, and pathways that were not identified in individual studies. As such, it provides an interesting proof of principle for future studies combining information from diverse heterogeneous sources.
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RESEARCH ARTICLE Open Access
Strengthening insights into host responses to
mastitis infection in ruminants by combining
heterogeneous microarray data sources
Sem Genini
1,15*
, Bouabid Badaoui
1
, Gert Sclep
1
, Stephen C Bishop
2
, Dave Waddington
2
,
Marie-Hélène Pinard van der Laan
3
, Christophe Klopp
4
, Cédric Cabau
5
, Hans-Martin Seyfert
6
, Wolfram Petzl
7
,
Kirsty Jensen
2
, Elizabeth J Glass
2
, Astrid de Greeff
8
, Hilde E Smith
8
, Mari A Smits
9
, Ingrid Olsaker
10
,
Guro M Boman
10
, Giuliano Pisoni
11
, Paolo Moroni
11,16
, Bianca Castiglioni
12
, Paola Cremonesi
12
,
Marcello Del Corvo
1,12
, Eliane Foulon
13
, Gilles Foucras
13
, Rachel Rupp
14
and Elisabetta Giuffra
1,17
Abstract
Background: Gene expression profiling studies of mastitis in ruminants have provided key but fragmented
knowledge for the understanding of the disease. A systematic combination of different expression profiling studies
via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using
the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of
infections with mammary gland pathogens, including samples from cattle challenged in vivo with S. aureus,E. coli,
and S. uberis, samples from goats challenged in vivo with S. aureus, as well as cattle macrophages and ovine
dendritic cells infected in vitro with S. aureus. We combined different time points from those studies, testing
different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific.
Results: Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share
biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the
general disease response. In the overall response, pathways related to immune response and inflammation, as well
as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk
fat content depression commonly observed during mastitis infection. Complementarities between early and late
stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the
immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by
few genes, including XBP1 and SREBF1.
The cattle-specific response was characterized by alteration of the immune response and by modification of lipid
metabolism. Comparison of E. coli and S. aureus infections in cattle in vivo revealed that affected genes showing
opposite regulation had the same altered biological functions and provided evidence that E. coli caused a stronger
host response.
Conclusions: This meta-analysis approach reinforces previous findings but also reveals several novel themes,
including the involvement of genes, biological functions, and pathways that were not identified in individual
studies. As such, it provides an interesting proof of principle for future studies combining information from diverse
heterogeneous sources.
Keywords: Meta-analysis microarray analysis, mastitis infection, lipid metabolism, immune response
* Correspondence: geninis@vet.upenn.edu
Contributed equally
1
Parco Tecnologico Padano - CERSA, Via Einstein, 26900 Lodi, Italy
Full list of author information is available at the end of the article
Genini et al.BMC Genomics 2011, 12:225
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© 2011 Genini et al; licensee BioMed Central Ltd . This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creative commons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, pro vided the original work is properly cited.
Background
In the last decade, gene expression profiling microarrays
have been widely used in animal genomics and this
technique has enabled researchers to monitor, on a
broad scale, the effects of pathogens on host cells and
tissues, aiming to gain insight into the molecular
mechanisms that are involved in the host-pathogen
interactions. Mastitis is one of the most costly diseases
of the dairy industry, which makes it among the major
concerns for the livestock sector [1]. As a consequence,
numerous gene expression studies on mastitis in differ-
ent host species infected with various pathogens are
publicly available. However, due to the high costs of this
approach, most individual studies have been carried out
on limited numbers of technical and biological repli-
cates. Furthermore, different and improved microarray
platforms have been used over time, due to the
increased availability of improved microarray tools tai-
lored to the genome sequence of most livestock species.
Meta-analysis can be used to combine or integrate the
data or results of independent studies. It allows a more
objective appraisal of evidence than individual studies
and has been widely used to interpret contradictory
results from various studies or overcome the problem of
reduced statistical power in studies with small sample
sizes (reviewed by [2,3]). The applicability of meta-analy-
sis to microarrays was initially demonstrated by [4,5].
Subsequently, several different meta-analysis applications
have been developed in order enable the integration of
independent microarray expression studies, e.g. through
the combination of effect sizes [6], the comparison of
data intersections (comparative meta-profiling) [7,8], the
integration of data from Affymetrix arrays through re-
annotation and common pre-processing methods [9],
the quantification of similarities in the literature (with
an algorithm called LAMA, Literature-Aided Meta-Ana-
lysis) [10], the development of a ranking aggregation
approach [11], and the application of improved and
meta-analysis adapted normalization methods [12-14].
Meta-analysis methods have also been applied to charac-
terize the properties of promoters to regulate transcrip-
tion of up-regulated genes [15].
As p-values are usually available for each gene in each
study, the main focus of the current meta-analysis
approach was to increase the reliability of statistical evi-
dence, by combining p-values across several, often het-
erogeneous, experiments. Various statistics have been
suggested to combine p-values [2,4,16-19]. In particular,
the meta-analysis tool chosen for this study, Pointillist
[20,21], uses and extends the Fisher inverse chi-square
method for p-value combination (reviewed by [22]) by
calculating different weights (i.e. reliability/representa-
tiveness parameters which represent relative measures of
statistical power of all datasets analysed) that are used to
transform the p-values of each experiment. By doing so,
Pointillist takes into consideration the various experi-
mental design differences and the high heterogeneity of
the datasets, including the use of different platforms,
that has been a major hindrance to meta-analysis so far.
Thelargequantityofmicroarraydataavailablefor
mastitis in ruminants provides an attractive opportunity
for a meta-analysis approach. Gene expression common-
alities shared across pathogens and host species may
contribute to understanding the disease and its physiol-
ogy, as well as pinpoint the most promising direction of
research to identify effective biomarkers. Indeed, several
innate immune responses, especially to pathogen-asso-
ciated molecular patterns, show evolutionary conserva-
tion, thus increasing the feasibility of meta-analysis of
gene expression data across species [23]. In controlled
in vitro cultures of macrophages [24] and dendritic cells
[25], a similar shared induction of common gene expres-
sion patterns in responses to a broad range of bacteria
has been observed. Furthermore, previous meta-analysis
results [26] showed common clusters of affected genes
across larger numbers of pathogens and studies.
The aim of this project was to identify common sets
of differentially expressed genes regulated by three mas-
titis pathogens (S. aureus,S. uberis,andE. coli) in three
affected ruminant species (cattle, goat, and sheep). Econ-
omy-wise, these three species are by far the most impor-
tant for the dairy industry. For this purpose we used the
program Pointillist [20,21] and, by combining similar
time points of different experiments, we created four
main lists of genes differentially modulated by mastitis
infection. In vitro experiments were treated in the same
way as in vivo experiments as the weighting mechanism
of Pointillist provided protection against potential
response-dependant biases.
We then used the Ingenuity Pathways Analysis (IPA;
http://www.ingenuity.com) software to retrieve the cano-
nical pathways, biological functions and networks that
were most significantly associated with the lists of
affected genes. IPA is a curated database and web-based
analysis system that delivers an assessment of signaling
and metabolic pathways, molecular networks, as well as
key biological and disease processes that are most signifi-
cantly perturbed in a gene set of interest. For each meta-
analysis combination tested with IPA, the five most
affected canonical pathways and the five most affected
biological functions belonging to the sub-group molecu-
lar and cellular functionsare discussed in detail.
All the meta-analysis combinations highlighted a pre-
dominance of gene pathways and biological functions
related to immune response and to lipid metabolism.
The results show common but also combination-specific
affected genes and pathways and provide new avenues
for future studies.
Genini et al.BMC Genomics 2011, 12:225
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Results and discussion
Combination of time points of mastitis experiments with
Pointillist
Different combinations of time points from individual
experiments (Table 1) were selected to represent four
main categories of response to mastitis infection. These
combinations were performed with Pointillist and were
named: (I) overall response, (II) early stage response,
(III) late stage response, and (IV) cattle-specific response
(Table 2). No goat- or sheep-specific responses were
studied because of the more limited number of experi-
ments and time points for those species.
The combination (I) overall response included each
animal species (cattle, sheep, goat) and all the time
points (see Tables 1 and 2) in order to capture the het-
erogeneity of all datasets. In order to avoid bias towards
cattle, for which more datasets were available, the list of
combined p-values, or so-called Combined Effective
Significances, for each probe was obtained by a step-
wise process. First, species-specific p-value lists were
obtained. A single Pointillist run was applied to obtain
the goat-specific (combination of time points {12}+{13}
+{16}) and the sheep-specific (combination of time
points {14}+{15}) p-value lists. To obtain the cattle-spe-
cific p-value list, (IV) cattle-specific response, two Poin-
tillist processing steps were required. Firstly, the time
points for each separate bovine microarray experiment,
e.g. 1A (combination of time points {1}+{2}+{3}), 1B
(combination of time points {4}+{5}+{6}), 1C (combina-
tion of time points {7}+{8}), 2 (time point {9}), and 3
(combination of time points {10}+{11}) were analyzed
separately with an initial Pointillist run. Subsequently,
the resulting p-values of each experiment were com-
bined with a second Pointillist run. The final combined
p-values for (I) overall response were obtained by com-
bining with an additional Pointillist run the three spe-
cies-specific p-value lists.
The combined p-value lists for (II) early stage and (III)
late stage responses were obtained by combining the
time points for which respectively no signsor clear
signsof mastitis were observed. In particular, inclusion
of in vivo time points {1}+{6}+{7}+{12} in list (II) and {3}
+{8}+{13} in list (III) (Table 2), was supported by the
absence or the clear presence, respectively, of clinical
signs of acute mastitis such as increased SCC count,
decreased milk yield, leukopenia, fever, and udder swel-
ling (Table 1). The absence of clinical signs in time
points {1}, {6}, and {7} had been confirmed by real-time
PCR of indicators for acute mastitis (TLR2,TLR4,and
b-defensins; [27]). The early time points {10} and {14} of
the in vitro studies were assigned to the early stage
response because minimal or no reaction or cell death
was observed, while the later time points {11} and {15}
were included in the late stage response because clear
reaction or physiological deformation and death of the
cells were observed. Time point {9} was neither included
in the early stage nor in the late stage response because
it was the only available time point for the pathogen S.
uberis.
Overall response to mastitis infection
Because we pooled microarrays of different designs, only
13,162 probes could be analyzed in combination (I)
overall response. Of the 498 probes identified by Pointil-
list as being significantly altered (p 0.05), a total of
298 unique genes were present in the IPA knowledge
database [Additional file 1]. The relative weights
assigned by Pointillist to each species-specific experi-
ment were 0.82 for cattle (experiments 1, 2, and 3), 0.08
for goat (experiments 4 and 6), and 0.09 for sheep
(experiment 5). This indicates that the cattle data had
greater statistical power than the goat- and sheep-speci-
fic data, which were similar in terms of statistical power.
Affected canonical pathways
The 5 canonical pathways identified by IPA as being
most significantly associated to this list of 298 genes
were protein ubiquitination, acute phase response sig-
naling, lipid antigen presentation by CD1, oncostatin M
signaling, and antigen presentation pathway [Additional
file 2].
The protein ubiquitination pathway has a fundamental
role in a myriad of cellular processes, including cell pro-
liferation, antigen presentation, and regulation of both
innate and adaptive immune responses [28,29]). This
pathway was present within the 5 most significant cano-
nical pathways of the other 3 main gene lists [Additional
file 2], confirming its role in defence against pathogens,
including bacteria [30]. The acute phase response is a
rapid, non-specific inflammatory response that provides
protection against microorganisms, and is associated
with the expression of several cytokines [31]. Further-
more, bovine acute phase response has been shown to
be activated by lipopolysaccharide (LPS) [32] and by E.
coli [33], possibly through its LPS. The lipid antigen pre-
sentation by CD1 and the antigen presentation pathways
are important to the development of innate and adaptive
immunity [34]. Finally, oncostatin M signaling is known
to be responsible for the initiation and progression of
inflammation and the acute phase response [35]. These
findings suggest that the alteration of immune response
and lipid metabolism are hallmarks of the response to
infections causing mastitis.
Affected biological functions
[Additional file 3] reports the complete lists of affected
biological functions for all the sub-groups Diseases and
disorders,Physiological system development and
Genini et al.BMC Genomics 2011, 12:225
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Table 1 Summary of the microarray datasets on mastitis infection included in meta-analysis
Experiment
#
(Institution)
Host
species
(# of
biological
replicates)
Pathogen Challenge system Bovine cDNA
microarray
Time
after
infection
{time
point}
Signs of infection References
1A (RI/
RIBFA)
Cattle (4) E. coli Intramammary challenge.
Sampled material: lobulo-
alveolar mammary tissue
(in vivo)
ARK-genomics
20 k
6 h {1} No clinical signs and no alteration of
TLR2,TLR4,
and b-defensins expressions.
[27,63,64]
12 h {2} Mild clinical signs and small changes of
TLR2,TLR4, and b-defensins expressions.
24 h {3} Acute clinical signs (including increased
SCC count, decreased milk yield,
leukopenia, fever, udder swelling) and
up-regulation of TLR2,TLR4, and b-
defensins expressions
1B (RI/
RIBFA)
Cattle (4) S. aureus Intramammary challenge.
Sampled material: lobulo-
alveolar mammary tissue
(in vivo)
ARK-genomics
20 k
6 h {4} No clinical signs and no alteration of
TLR2,TLR4, and b-defensins expressions.
[27,63,64]
12 h {5} No clinical signs and no alteration of
TLR2,TLR4, and b-defensins expressions.
24 h {6} No clinical signs and no alteration of
TLR2,TLR4, and b-defensins expressions.
1C (RI/
RIBFA)
Cattle (4) S. aureus Intramammary challenge.
Sampled material: lobulo-
alveolar mammary tissue
(in vivo)
ARK-genomics
20 k
12 h {7} No clinical signs and no alteration of
TLR2,TLR4, and b-defensins expressions.
[27,63,64]
72 h {8} Acute clinical signs (including increased
SCC count, decreased milk yield,
leukopenia, fever, udder swelling) and
up-regulation of TLR2,TLR4, and b-
defensins expressions
2(CVI-L) Cattle (3) S. uberis Udder samples containing
all layers including
epithelia, muscle tissue
and mammary gland
tissue. In affected samples
neutrophils were also
present (in vivo)
ARK-genomics
20 k
36 h-72 h
{9}
Culling when clear clinical signs were
seen. Sample selection from various
locations of control and infected
mammary gland quarters based on clear
microscopic and macroscopic
observations
-
3(NSVS) Cattle (6) S. aureus Blood derived primary
macrophage cells (in vitro)
ARK-genomics
17 k
2 h {10} Few genes responding, no cell death. -
6 h {11} Many genes responding, beginning
signs of cell deformation and death
4(UNIMI/
PTP/CNR)
Goat (3) S. aureus Leukocytes in milk (in vivo) NBFGC 12 h {12} No clinical signs and no alteration of
milk.
[65,66]
24 h {13} Clear clinical signs (increased SCC count,
decreased milk yield, fever)
5(INRA) Sheep (8) S. aureus Bone marrow derived
primary dendritic cells (in
vitro)
ARK-genomics
17 k
3 h {14} No cell death. -
8 h {15} Clear deformation and death of
dendritic cells
6(UNIMI/
PTP/CNR)
Goat (10) S. aureus Leukocytes in milk (in vivo) Combi-Matrix 24 h {16} Clinical signs (increased SCC count,
decreased milk yield, fever, udder
swelling)
-
The experimental numbers are reported with the names of the institution where they were conducted, host species and number of replicates, pathogens,
challenge systems, microarrays names, time period of observations after infection {in parenthesis the time point #, see also Table 2}, signs of infection, and
corresponding references.
Note: ARK-genomics: centre for comparative & functional genomics, Scotland; CNR: Institut e of Agricultural Biology and Biotechnology, National Research Council,
Italy; CVI-L: Central Veterinary Institute of Wageningen UR, Lelystad, NL; INRA: Institute National de la Recherche Agronomique, France; NBFGC: National Bovine
Functional Genomics Consortium, USA; NSVS: Norwegian School of Veterinary Science, Norway; PTP: Parco Tecnologico Padano (PTP), Italy; RI: Roslin Institute and
R(D)SVS, University of Edinburgh (UEDIN), UK; RIBFA: Research Institute for the Biology of Farm Animals, Germany; UNIMI: Università degli Studi di Milano,
Department of Veterinary Pathology, Hygiene and Public Health, Italy. Microarrays are described in the Materials and Methods section of text.
Genini et al.BMC Genomics 2011, 12:225
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functionand Molecular and cellular functions.The
five most significant molecular and cellular functions
altered during the overall response to mastitis were cell
death, cellular movement, cellular growth and prolifera-
tion, cell-to-cell signaling and interaction, and lipid
metabolism. The first three altered functions were
among the 5 most affected in all 4 main responses [in
bold in Additional file 3].
Perturbation of the lipid metabolism might affect the
lipid antigen presentation by CD1 pathway [Additional
file 2], which consists of a conserved family of MHC-
like glycoproteins specialized in capturing lipid and gly-
colipid antigens for presentation to T lymphocytes [36].
A relevant correlation between lipid metabolism and
mastitis infection caused by S. uberis in mammary tis-
sues has indeed been reported [37]. Furthermore, lipid
metabolism has been identified as one of the most
altered biological functions in cows fed at different
energy balance diets [38] and it has been associated with
differentially regulated proteins detected in cows
infected with E. coli and S. aureus [39]. Consequently,
IPA was used to further dissect the main sub-functions
linked to lipid metabolism. Metabolism of long chain
fatty acids, accumulation of oleic acids, internalization
of lipids, and uptake of fatty acids and arachidonic acid
were the top 5 annotated functions related to lipid
metabolism and altered during the overall response to
mastitis [Additional file 4]. The affected biological func-
tions further confirm a relevant role of the lipid metabo-
lism during response to infections causing mastitis.
Early stage and late stage responses to mastitis infection
Of the 20,527 probes analyzed by Pointillist for the early
and late stage responses, 1,129 and 1,046, respectively,
were significantly altered (p 0.05). Of these, a total of
639 and 631 unique genes, respectively, were present in
the IPA knowledge database [Additional file 1].
Affected canonical pathways
In addition to the protein ubiquitination and polyamine
regulation pathways that were common for both combi-
nations, the early stage response was characterized by
pathways closely related to metabolic regulation, includ-
ing hypoxia signaling, pyruvate metabolism, and endo-
plasmic reticulum (ER) stress [Additional file 2].
Hypoxia inducible factors are known to control innate
immunity and gene expression of pro-inflammatory
molecules [40], and correlations between ER stress,
immune response and apoptosis have been reported
[41]. Also, pyruvate accumulation caused by inhibition
of lipid metabolism has indeed been shown to prompt
hypoxia signaling in mastitis in cattle [37]. The signifi-
cant alterations of these closely linked pathways suggests
that stress signals are launched by the host cells as part
of the activation of the immune response early during
infection, i.e. prior to observation of clear phenotypes
related to mastitis.
On the other hand, the late stage response was specifi-
cally represented by pathways directly involved in the
immune response, i.e. IL-6 signaling, LXR/RXR activa-
tion and IL-10 signaling [Additional file 2]. A close rela-
tionship between polyamine regulation, in particular the
sub-group spermine, and IL-10 signaling has been
reported in macrophages [42]. Other studies reported an
increase of IL-6 and IL-10 expression during mastitis
infection [43,44]. As persistence or over-prolongation of
inflammation is harmful for cells [45], the activation of
the IL-10 signaling might be a beneficial mechanism
Table 2 Combination of experiments and time points to create the 4 main responses to mastitis infection
Time after infection
Experiment # 2 h 3 h 6 h 8 h 12 h 24 h 36 h-72 h 72 h
1A:E. coli in cattle (in vivo) {1}
I, II, IV
{2}
I, IV
{3}
I, III, IV
1B:S. aureus in cattle (in vivo) {4}
I, IV
{5}
I, IV
{6}
I, II, IV
1C:S. aureus in cattle (in vivo) {7}
I, II, IV
{8}
I, III, IV
2:S. uberis in cattle (in vivo) {9}
I, IV
3:S. aureus in cattle macrophages (in vitro) {10}
I, II, IV
{11}
I, III, IV
4:S. aureus in goat (in vivo) {12}
I, II
{13}
I, III
5:S. aureus in sheep dendritic cells (in vitro) {14}
I, II
{15}
I, III
6:S. aureus in goat (in vivo) {16}
I
Combination of microarray data from a total of 6 different experiments and 16 different time points ({in parentheses}, see also Table 1 and text for details) to
analyse 4 different responses to mastitis infection: (I) overall response, (II) early stage response, (III) late stage response, and (IV) cattle-specific response.
Genini et al.BMC Genomics 2011, 12:225
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adopted by the cells during this stage of mastitis infec-
tion to limit and terminate the inflammatory response.
Affected biological functions
Cellular growth and proliferation, cell death and cellular
movement were 3 of the top 5 significant molecular and
cellular functions identified by IPA for both time-depen-
dant responses [Additional file 3]. Two protein-related
functions (post-translational modification and protein
folding) were specific for the early stage response, while
cellular functions (cellular assembly and organization,
cell-to-cell signaling and interaction) were specific for
the late stage response [Additional file 3].
Lipid metabolism was significantly altered during both
early (p = 3.5E-04) and late stage (p = 3.1E-06) infec-
tions, although it was not among the five most signifi-
cant. The altered LXR/RXR signaling pathway
[Additional file 2] is known to be implicated in the reg-
ulation of the lipid metabolism [46]. Since lipid metabo-
lism was among the top 5 affected molecular and
cellular functions in the overall analysis (gene list I), the
main altered sub-functions of the lipid metabolism were
identified by IPA. Hydrolysis of phosphatidylinositol
phosphate, phosphatidylinositol 4,5-diphosphate, and
phosphtidylinositol 5-phosphate, as well as metabolism
of fatty acid and lipids were the most significant affected
sub-functions for the early stage response [Additional
file 4]. For the late stage response on the other hand,
quantity of fatty acid, oleic acid, and lipid, as well as
synthesis of lipid and cholesterol were the identified top
affected sub-functions. These results seem to suggest
that whilst during the early stage response there might
be a generalderegulation of the lipid metabolism, dur-
ing the late stage response the cells might react to the
infection by synthesizing, taking up, or incorporating
lipids and fatty acids.
Relevance of the XBP1 gene during the early stage of
infection
The lists of affected genes during the early and late
stage responses were analyzed with the IPA feature
pathway building, which shows the main relationships
and connections among affected genes belonging to
altered canonical pathways. The two genes X-box bind-
ing protein 1 (XBP1) and sterol regulatory element bind-
ing transcription factor 1 (SREBF1) are of particular
relevance in early and late stage infection, respectively.
Both belong to canonical pathways that were among the
5 most affected (XBP1 to ER stress and SREBF1 to
LXR/RXR activation) [Additional file 2] and, in agree-
ment with their function as transcription factors, they
were directly linked to the highest number of other
affected genes [Additional file 5: Supplemental Figures
S1B and S2].
XBP1 and the additional transcription factors ATF4,as
well as the molecular chaperone DNAJB3 and the heat-
shock protein gene HSPA5, which are key molecules of ER
stress, one of the 5 most significantly affected pathways
[Additional file 2], were altered during the early stage
response. Comparable results have been reported in other
studies in dairy cows where expressions of ATF4,XBP1,
and DNAJB3 were altered in ER stress generated by a
negative energy balance [47]. Hence, XBP1 might have a
central role in launching stress signals in preparation for
an adequate immune response during the early stage of
mastitis infection, as it is also involved in cytokine produc-
tion in different cell types, including macrophages [48,49].
This gene directly regulates the expression of the affected
genes COPZ1,DDOST,KDELR2,KDELR3,RPN1,
SEC23B,SEC24D,SEC61A1,SRPR, as well as genes of the
proteasome and the MHC Class II complex [Additional
file 5: Supplemental Figure S1B]. Indirectly, XBP1 is also
linked to many more affected genes [Additional file 5:
Supplemental Figure S1A]. In line with our results, altera-
tion of several genes that directly interact with XBP1 (e.g.
COPZ1,DDOST,KDELR3,RPN1,SEC23B,SEC24D,
SEC61A1,andSRPR) have also been reported in fibro-
blasts over-expressing XBP1 [50].
Relevance of the SREBF1 gene during the late stage of
infection
Inthelatestageresponse,SREBF1 directly interacts
with several affected lipogenic genes, i.e. TRAF3IP3,
CD36,SCD,SOD1,IDH1,THRB,RETN,PMVK,DBI,
UCP2,HBS1,SC4MOL, and CYP27A1 [Additional file 5:
Supplemental Figure S2]. Among these, expressions of
TRAF3IP3,CD36,andSCD were also reported to be
altered during infection of cattle mammary tissues with
S. uberis [37]. SREBF1 is a component of the LXR/RXR
pathway, one of the 5 most affected pathways, confirm-
ing the relationship between LXR/RXR signalling and
lipid metabolism. This relationship might explain the
observed depression of milk fat synthesis during mastitis
infection in ruminants.
Early and late stage specific responses
In order to better understand the differences between the
two different time-related responses, the (II) early stage
and the (III) late stage responses were subjected to IPA
analysis taking into account only the subset of affected
genes differentiating the two lists. While 375 genes
belonged to list (II) and not list (III) (list V early specific
response), 367 genes belonged to list (III) and not list (II)
(list VI late specific response) [Additional file 1].
Affected canonical pathways
The results of the canonical pathway analysis confirmed
that during early specific response there is
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intensification of cell metabolism (exemplified by the
pyruvate and butanoate metabolism), the protein ubiqui-
tination pathway, as well as the stress signal pathways, e.
g. hypoxia in the cardiovascular system and Ataxia Tel-
angiectasia Mutated (ATM) signaling [Additional file 2].
During the late specific, the top affected pathways (IL-6
signaling, polyamine regulation, acute phase response
signaling, role of macrophages, fibroblasts and endothe-
lial cells in rheumatoid arthritis, and Fc receptor-
mediated phagocytosis in macrophages and monocytes)
indicated an intense activity of the immune response,
with the possible involvement of macrophages.
Affected biological functions
None of the top 5 molecular and cellular functions were
in common between the two time-specific gene lists (V)
and (VI) [Additional file 3]. Similarly to the previous
analysis of gene list (II) early stage response and (III)
late stage response, the early specific response genes
showed molecular and cellular functions related to
metabolism (carbohydrate and lipid), biochemistry and
protein synthesis (post translational modification and
folding), while the late specific response were mainly
involved in cellular functions (movement, growth and
proliferation, assembly and organization, function and
maintenance), as well as cell morphology.
Cattle-specific response to mastitis infection
Pointillist identified 669 out of 19,448 common probes
that were significantly altered (p 0.05) in the cattle-
specific response to mastitis. The weights given by Poin-
tillist to experiments 1A, 1B, 1C, 2, and 3 were 0.27,
0.23, 0.28, 0.17, and 0.04, respectively, showing that the
in vitro data set had a lower statistical power than the
other data sets. Of the 669 probes, a total of 421 unique
genes were present in the IPA knowledge database.
Affected canonical pathways
Besides polyamine regulation and protein ubiquitination,
the top canonical pathways characterizing the cattle-speci-
fic response were acute phase response, lipid antigen pre-
sentation by CD1 (also identified in the overall response),
two highly relevant pathways for immune response, and
the inositol metabolism [Additional file 2], which is
involved in T-cell, B-cell, and neutrophil development and
function [51]. These results indicate a link between masti-
tis and immune response involving T and B cells.
Affected biological functions
In accordance with the top canonical pathway analysis,
the altered molecular and cellular functions identified by
IPA (i.e. antigen presentation, cell death, cell to cell
interaction, and cellular growth, proliferation and move-
ment) reflected an intensification of the immune
response during cattle-specific response to mastitis
infection [Additional file 3].
Alteration of the expression of genes involved in
immune response, antigen presentation, apoptosis, and
acute phase response have been also reported in a simi-
lar study [52].
Lipid metabolism was also significantly affected (p =
4.9E-05), although it was not included among the five
most significant. Sub-functions of lipid metabolism that
were altered during the cattle-specific response included
uptake of arachidonic acid, metabolism of long chain
fatty acid, internalization of cholesterol, transport and
quantity of fatty acid [Additional file 4]. These findings
further underline that lipid metabolism is tightly linked
to immune response and that lipid antigen presentation
might represent an interesting candidate pathway for
future work to gain new insights into the host-pathogen
interplay in mastitis.
Comparison of the host expression profiles in the
different experiments and time points
Next, we compared the different cattle microarray data-
sets, focusing on the impact of the use of different infec-
tive agents (three of the major mastitis-causing
pathogens: E. coli,S. aureus,andS. uberis) and the pat-
terns of gene response that they caused in the host.
When clustering the expression profiles of the cattle-
specific response time points (see heat map in Figure 1)
the first clustering step is primarily based on experiment
number (Tables 1 and 2) (experiment 1A time points
{1-3} clustered together, experiment 1B time points {4-
5} clustered together, experiment 1C time points {7-8}
clustered together along with experiment 1B time point
{6}, and experiment 3 time points {10-11} clustered
together). It is not unexpected that expression profiles
of different time points of the same microarray experi-
ment cluster together. The final clustering steps indi-
cated a pathogen-specific pattern as all S. aureus time
points (along with the S. uberis time point {9}) clustered
together, separately from the E. coli time points. No
inter-laboratory or inter-array clustering was observed.
For instance, the E. coli data (experiment 1A) did not
cluster with the data from the other experiments (1B
and 1C) performed in the same institution (Figure 1).
This provides reassurance that the data were not signifi-
cantly biased towards the experimental conditions used.
Comparison of the strength of the host response to the 3
different pathogens
We also compared the magnitude of fold change differ-
encesingeneexpressioninthecattlehostcausedbyE.
coli,S. aureus,orS. uberis infections with the MaSigPro
package [53]. Figure 2 shows that, in general, the E. coli
infection caused a stronger response in the host than
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the S. aureus and S. uberis infections. High fold change
differences were induced by E. coli, especially at 24 h
post infection (experiment 1A, time point {3}), and to a
lesser extent by S. uberis between 36 and 72 h post
infection (experiment 2, time point {9}).
Although this finding might be related to the specific
experimental conditions used in the different experi-
ments, it reflects previous observations that E. coli infec-
tion is very acute at 24 h, but not yet at 6 h PI [52], and
that it is very acute compared to other pathogens
[39,54]. Furthermore, the results suggest that S. aureus,
but not E. coli, frequently causes subclinical, chronic
infections of the mammary gland and hence elicits an
inadequate mammary immune response [27,55].
Comparison between meta-analysis of (IV) cattle-specific
response and individual experiments
To better quantify the additional power of the proposed
meta-analysis approach, we compared the list of 421
affected genes identified with the meta-analysis of (IV)
Figure 1 Heat map showing cluster analysis of the microarray experiments used in the cattle-specific response to three different
pathogens (E. coli,S. aureus, and S. uberis). The x-axis shows the time points {from 1 to 11} of each different cattle experiment (1A, 1B, 1C, 2,
and 3; Table 2), while the y-axis displays the clustered genes. The map itself contains gene fold changes Z-score normalized over all time points.
They are color coded, with red corresponding to down-regulation and green to up-regulation. White lines in experiment 3 represent missing
genes not present on the microarray. The first clustering step is primarily based on experiment number (Tables 1 and 2) (experiment 1A {1-3}
clustered together, 1B {4-5} clustered together, 1C {7-8} clustered together along with the 1B time point {6}, and 3 {10-11} clustered together).
The final clustering steps indicated a pathogen-specific pattern as all S. aureus time points (along with the S. uberis time point {9}) clustered
together, separately from the E. coli time points.
Figure 2 Magnitude of fold change expression characterizing E. coli,S. aureus,andS. uberis infections in cattle. The x-axis shows the
time points {from 1 to 11} of each different cattle experiment (1A, 1B, 1C, 2, and 3; Table 2), while the y-axis shows the fold changes for each
gene (each line). High differences are observed especially during infection with E. coli (1A {3}), and to a lesser extent with S. uberis (2 {9}).
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cattle-specific response with the lists of affected genes
(using the Benjamini-Hochberg FDR-correction [56], p
0.05) in individual experiments (i.e. experiment 1A
time point {3} and experiment 2 time point {9}). The
results showed that 25 affected genes were in common
between the three lists, while 268, 581, and 15 genes
were specific for (IV) cattle-specific response, experi-
ment 1A time point {3}, and experiment 2 time point
{9}, respectively [Additional file 5: Supplemental Figure
S3 and the corresponding gene lists in Additional file 6].
Next, applying IPA on the lists of affected genes, we
identified the 5 most affected canonical pathways and
molecular and cellular functions of the individual
experiments. The canonical pathways protein ubiquiti-
nation (p = 2.9E-09), ephrin receptor signaling (p =
1.1E-06), regulation of actin-based motility by Rho (p =
5.7E-06), actin cytoskeleton signaling (p = 3.6E-05), and
germ cell-Sertoli cell junction signaling (p = 5.5E-05), as
well as the molecular and cellular functions cell death,
cellular growth and proliferation, cell signaling, cellular
movement, and lipid metabolism were the most affected
within the 745 affected genes of experiment 1A time
point {3}. Canonical pathways and molecular and cellu-
lar functions in common with the five most affected
identified by meta-analysis of the (IV) cattle-specific
response [Additional files 2 and 3] included polyamine
regulation, as well as cell death, cellular growth and pro-
liferation, and cellular movement, respectively.
The IPA canonical pathways iCOS-iCOSL signaling in
T Helper cells (p = 4.7E-04), activation of IRF by cyto-
solic pattern recognition receptors (p = 1.1E-03), dendri-
tic cell maturation (p = 1.8E-03), production of nitric
oxide and reactive oxygen species in macrophages (p =
1.8E-03), and communication between innate and adap-
tive immune cells (p = 3.0E-03), as well as the molecular
and cellular functions cellular growth and proliferation,
cell death, cell-to-cell signaling and interaction, cellular
function and maintenance, and gene expression were
the most affected within the 55 genes of experiment 2
time point {9}. None of the canonical pathways were in
common with the most affected of the meta-analysis of
the (IV) cattle-specific response; whereas cell death, cel-
lular growth and proliferation, and cell-to-cell signaling
and interaction were common molecular and cellular
functions.
The retrieval of common molecular and cellular func-
tions and/or pathways by the two approaches (meta-
analysis vs. individual experiments) confirms the statisti-
cal power of the meta-analysis and its complementary to
the FDR correction with regard to the pruning of false
positives. Furthermore, the identification of novel
affected biological functions and pathways further shows
the added value of the meta-analysis approach.
Comparison between E. coli and S. aureus infections
To better evaluate the pathogen-specific characteristics,
we further compared the responses to infection with E.
coli (experiment 1A) or S. aureus (experiments 1B and
1C) in the cattle host. We excluded the S. uberis data
(experiment 2) as we had only one single time point {9}
available.
We used the PAMR package to identify the genes
which were most dissimilar in terms of their activation
in response to the two different pathogens. Of the
retained 34 most dissimilar genes, 21 were down-regu-
lated by E. coli infection and up-regulated by S. aureus
infection, while 13 showed the opposite trend (Table 3).
This list of dissimilar genes was further analyzed with
IPA to identify altered biological functions and net-
works. The 5 most significant molecular and cellular
functions identified were cellular development, cellular
growth and proliferation, cellular function and mainte-
nance, cell death, and lipid metabolism [Additional file
7]. Both cell death and lipid metabolism were previously
found to be among the 5 most significant molecular
functions altered in proteins of cows infected with either
E. coli or S. aureus [39]. The IPA network called anti-
gen presentation, inflammatory response, cell-to-cell sig-
naling and interactionwas the most significantly
represented by the list of dissimilar genes. Of the 34
genes, 9 are included in this network: BTG1,CD74,
CSDA,FKBP5,IGFBP5,GLUL,HSPD1,LCN2, and PHB.
IGFBP5 and CD74 were up-regulated after E. coli infec-
tion and down-regulated after S. aureus infection, while
the others showed the opposite trend (Table 3).
Pathogen-dependent differences in the time kinetics of
induced receptors and defense molecules (e.g. TLR2,
TLR4,IL-8,TNF,andNFkB), as measured by real-time
PCR, have been reported between E. coli and S. aureus
[27,55]. Although none of these defense genes were in
the list of the 34 most dissimilar genes, our results were
in general agreement with these findings as we found
that the majority of genes with opposed regulation were
associated with immune response and mainly belonged
to the antigen presentation, inflammatory response, cell-
to-cell signaling and interaction network.
These findings suggest that, at least at the transcrip-
tomic level, these two pathogens cause distinct forms of
mastitis infection by the differential modulation of genes
belonging to similar molecular pathways and biological
functions.
Comparisons of the 4 lists (I - IV) of affected genes
In order to have an accurate global view of the lists of
genes belonging to the 4 different responses to mastitis
infection (I to IV), we drew a Venn diagram (Figure 3)
that provides a graphical representation of the number
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of affected genes, as inferred by Pointillist, that are in
common, exclusive, or at the various intersections
between 2 or 3 lists. The corresponding gene lists with
the gene names can be found in [Additional file 8].
Interestingly, we identified a family of antimicrobial
genes (S100A11,S100A12,S100A8,andS100A9)that
were affected in all but the early stage response. This
finding was in line with a recent study in cattle, where
microarray analysis using Affymetrix gene chip revealed
that these genes were differentially expressed after 24 h,
but not 6 h, of E. coli infection [52].
However, the vast majority of the listed genes have
not previously been reported to be implicated in the
mastitis infection process. Of particular interest are
those genes, a total of 92 [Additional file 8], in common
between the 4 (overall, early stage, late stage, and cattle-
specific) responses to mastitis (Figure 3), providing pos-
sible clues for valuable candidate biomarkers.
Altered pathways and biological functions related to the
92 genes in common for all 4 responses
The 3 most affected canonical pathways underlying
these 92 common genes [Additional file 2] were polya-
mine regulation, protein ubiquitination, and molecular
mechanisms of cancer. The pathways LXR/RXR activa-
tion and factors promoting cardiogenesis in vertebrates
Table 3 Dissimilar genes between E. coli and S. aureus infections in cattle
Gene Gene Name E. coli shrunken
centroid
S. aureus shrunken
centroid
ABCG2 ATP-binding cassette, sub-family G WHITE, member 2 -1.007 0.671
IDH1 Isocitrate dehydrogenase 1 NADP+, soluble -0.929 0.619
AGPAT1 1-acylglycerol-3-phosphate O-acyltransferase 1 lysophosphatidic acid acyltransferase, alpha -0.894 0.596
PCGF1 Polycomb group ring finger 1 -0.795 0.53
GALNTL4 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase-like 4 -0.52 0.346
CD74 CD74 molecule, major histocompatibility complex, class II invariant chain -0.496 0.33
TMEM164 Transmembrane protein 164 -0.42 0.28
RHOF Ras homolog gene family, member F -0.391 0.261
MFSD4 Major facilitator superfamily domain containing 4 -0.263 0.175
DGCR2 DiGeorge syndrome critical region gene 2 -0.217 0.145
FEZ1 Fasciculation and elongation protein zeta 1 zygin I -0.204 0.136
PAOX Polyamine oxidase exo-N4-amino -0.154 0.103
PMEPA1 Prostate transmembrane protein, androgen induced 1 -0.106 0.07
HIGD1B HIG1 hypoxia inducible domain family, member 1B -0.134 0.089
DNAJC12 DnaJ Hsp40 homolog, subfamily C, member 12 -0.132 0.088
VWF Von Willebrand factor -0.131 0.088
KIAA1467 KIAA1467 -0.131 0.087
SENP2 SUMO1/sentrin/SMT3 specific peptidase 2 -0.068 0.046
IGFBP5 Insulin-like growth factor binding protein 5 -0.06 0.04
SCP2 Sterol carrier protein 2 -0.018 0.012
NPAL2 NIPA-like domain containing 2 -0.009 0.006
LRRN3 Leucine rich repeat neuronal 3 0.732 -0.488
FKBP5 FK506 binding protein 5 0.7 -0.466
SLC38A7 Solute carrier family 38, member 7 0.641 -0.427
HSPD1 Heat shock 60 kDa protein 1 chaperonin 0.56 -0.373
GLUL Glutamate-ammonia ligase glutamine synthetase 0.352 -0.235
CSDA Cold shock domain protein A 0.174 -0.116
INO80E INO80 complex subunit E 0.142 -0.095
SAT1 Spermidine/spermine N1-acetyltransferase 1 0.118 -0.079
PHB Prohibitin 0.075 -0.05
STAT3 Signal transducer and activator of transcription 3 acute-phase response factor 0.061 -0.04
MAX MYC associated factor X 0.051 -0.034
BTG1 B-cell translocation gene 1, anti-proliferative 0.033 -0.022
LCN2 Lipocalin 2 0.024 -0.016
List of the 34 most dissimilarly regulated genes identified with the PAMR software, showing opposite fold change respo nses during E. coli and S. aureus
infections in cattle in vivo (experiment 1A, B, and C). For each gene, the PAMR shrunken centroid values (using a threshold parameter of 3.77) for the E. coli and
the S. aureus experiments are reported. Twenty-one of the listed genes were up-regulated during infection with S. aureus, while 13 were up-regulated during E.
coli infection.
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only approached statistical significance (0.05 < p < 0.1).
Altered molecular and cellular functions identified by
IPA [Additional file 3] showed general cell related func-
tions (cellular function and maintenance, cellular growth
and proliferation, cellular movement, and nucleic acid
metabolism) as well as, once again, lipid metabolism.
Conclusions
To the best of our knowledge this study is the first that
statistically combines heterogeneous microarray data
realized with different ruminant host species and
infected with different mastitis-causing pathogens. The
results reinforced previous findings but also revealed
several novel themes, including the involvement of
genes and pathways that were not identified in indivi-
dual studies.
Among the 5 most significant molecular and cellular
functions common to all 4 gene lists of differential
responses to mastitis were cell death, cellular movement,
and cellular growth and proliferation, i.e. functions
which are intrinsic to general disease response. This
indicates that the described procedure of meta-analysis
could cope well with the high heterogeneity of the bio-
logical systems and the different microarrays used.
Indeed, this was confirmed by the analysis of the
reduced list of 92 genes in common to all lists that also
identified cellular growth and proliferation and cellular
movement as being altered.
The results show that protein ubiquitination and poly-
amine regulation, two pathways involved in immune
response modulation and represented by different indivi-
dual genes, possibly represent a common biological
manifestation during mastitis infection in different bio-
logical systems. Furthermore, strong complementarities
between the early stage and late stages of infection was
found, showing a prominence of metabolic and stress
signals in the early stage and of the immune response
related to the lipid metabolism in the late stage, Both
mechanisms were apparently triggered by a small num-
ber of genes, including XBP1 and SREBF1.Thecattle-
specific response showed an intensification of the
immune and inflammatory responses through T lym-
phocyte involvement. Furthermore, we found several
strands of evidence suggesting a correlation between
immune response and lipid metabolism as a hallmark of
the response of ruminants against mastitis infection.
Overall, the reported meta-analysis approach success-
fully combined heterogeneous data sets and extracted
information of value from individual microarray studies
of limited size and statistical power. As such, it provides
a global transcriptomic reference which could be useful
for the development of novel therapeutics and vaccines
for mastitis in ruminants. Furthermore, these data and
methodology provide an interesting proof of principle
for future studies combining information from diverse
sources.
Methods
Collection and analysis of microarray data
Microarray data on host responses to infection by masti-
tis-causing pathogens for various challenge systems were
selected to represent contrasting pathogens, hosts, chal-
lenge systems (i.e. host tissues or cells, in vivo and in
vitro), sample sizes, time period of observations, micro-
arrays, and signs of infection (summarized in Table 1
with the corresponding references). The experiments
were performed with the approval of appropriate ethics
committees. Experiment 1 was conducted under the
approval of the ethics committee of the regional govern-
ment in Hannover, Germany (No 509.6-42502-03/678).
Experiment 2 was approved by the ethical committee of
the Central Veterinary Institute of Wageningen UR in
accordance with the Dutch law on animal experiments
(registered under number 870.474.05.00.01). Experiment
3 only involved bleeding bovine heifers for 300 ml
blood. According to Norwegian legislation no special
approval was necessary. The experiments 4 and 6 were,
according to the Italian legislation, successfully notified
and hence approved by the Italian ethics committee. In
experiment 5 ewes were sacrificed in accordance with
local regulations (agreement number 31-2010-67) and
the study was approved by the INRA animal ethics com-
mittee (France).
Spot analysis and quality control of the microarray
data for all experiments were done with BlueFuse ver-
sion 3.1 (BlueGnome, Cambridge, UK; http://www.cam-
bridgebluegnome.com), except for experiment 5
(dendritic cells, DCs) in sheep which was analyzed using
SAS ANOVA. The Bioconductor package Limma (Lin-
ear Models for Microarray Analysis) in R was used for
data normalization and differential expression analyses,
comparing gene expression at given times after infection
Figure 3 Venn diagram showing the number of common and
combination-specific affected genes. Venn diagram illustrating
the number of significantly affected genes in common (92) and
distinct for the four meta-analysis combinations (red: 298 genes of
the overall response, green: 631 genes of the late stage response,
blue: 639 genes of the early stage response, and pink: 421 genes of
the cattle-specific response). The lists of corresponding genes can
be found in [Additional file 8].
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with gene expression in non-infected controls. For each
of these analyses p-values were assigned to all genes,
indicating the probability that the observed difference in
expression occurred by chance. These p-values were
then used for the meta-analysis using the Pointillist soft-
ware. Fold change differences were also calculated and
used for specific analyses, in particular for the cattle-
specific response.
Meta-analysis procedures
The 6 datasets from the three ruminant species were
obtained from different bovine microarrays, including
cDNA arrays (ARK-genomics and National Bovine
Functional Genomics Consortium, NBFGC) and com-
mercial oligonucleotide arrays containing 43,768 unique
probes (CombiMatrix CustomArray
®
,CombiMatrix
Corporation, Seattle, WA, USA) (Table 1).
The preprocessing of the ARK-genomics array data
entailed two noteworthy clone ID mapping steps to
obtain clone ID consistency throughout all ARK-geno-
mics datasets: the mapping of the clone IDs of a 17K
array design onto those of a 20K array design and the
mapping of child clone IDs onto the corresponding
master clone IDs. The p-values of all groups of master
and child clones were averaged, to obtain one value for
each master clone ID. Further, the control probes were
left out of the meta-analysis, as this was also done for
the data stemming from the other microarray platforms.
To compare the probes of the ARK-genomics arrays
to those of goat experiment 4 (NBFGC bovine cDNA
array, [57]) or goat experiment 6 (CombiMatrix array), a
blast comparison between all the spotted sequences was
performed. A contiguous perfect match segment of 100
nucleotides (nt) was considered sufficient for probes to
be similar. This is a conservative threshold, since perfect
matching segments of 30 nt can already cause cross-
hybridization in cDNA microarray experiments [58] and
since according to the Baldino formula [59] 100 nt long
segments under standard conditions can still hybridize
while having a mismatch of 15%. A total of 8,302 and
8,293 probes, respectively, were found to be in common.
After evaluation of different meta-analysis methods
and programs, an appropriate statistical program called
Pointillist (http://magnet.systemsbiology.net/software/
Pointillist; [20,21]) that allowed us to account for the
relevant experimental differences and the heterogeneity
of the datasets, was used to perform meta-analysis.
Pointillist is a general-purpose tool that predicts
whether system elements are affected by a system per-
turbation, by integrating different items of evidence of
that perturbation. The evidence contains p-values for
each addressed element, can address different subsets of
the systems elements and may be derived from any type
of experiment. In our case the elements are the
microarray clones and the items of evidence are the dif-
ferential expression analyses carried out for selected
time points. In a first step Pointillist classifies elements
as affected,ifforanyoftheitemsofevidencethe
quantile value of the elements p-value is below a chosen
threshold alpha (0.05 in our case). Combined effective
significances (CES)are calculated by weighting, normal-
izing, transforming, and combining the elements speci-
fic p-values into one single element significance using a
Fisher-like transformation (with the Pointillist option
called power) and by finally smoothing the distribution
of these significances using a smoothed Gaussian kernel
density function. In each step the overlap between the
combined effective significancedistribution for the
group of affected and for the group of non-affected ele-
ments is iteratively minimized. This process, which is an
alternative method to the FDR-adjustment commonly
usedintheanalysisofsingledatasets,ultimatelymini-
mizes the number of false positives and false negatives.
The weights used during the transforming operation are
also calculated for each item of evidence in each itera-
tion step by comparing the current classification in
affected and non-affected elements with the p-value dis-
tribution of that evidence. Every Pointillist run con-
tained a row for each probe having a p-value in at least
two of the included time points. A special scenario was
followed for the final 3-step Pointillist run of the overall
analysis, in which the probes common to the cattle and
sheep were combined with the probes used in the goat
experiments.
Probe annotation
A probe annotation was performed to transform the
microarray probe IDs into gene IDs recognized by Inge-
nuity Pathways Knowledge Base (IPA, Ingenuity Sys-
tems, Mountain View, CA; http://www.ingenuity.com).
The annotation started from the probesEMBL or Gen-
Bank accession: the ARK-genomic and CombiMatrix
arrays contained probes with references to EMBL acces-
sions in the arraysGAL files, while the NBFGC array
probe names contained references to Genbank acces-
sions. Several probes spotted on the arrays did not have
any accession reference due to the incomplete informa-
tion available at the time of microarray construction. In
case these had protein-like names, they were presented
as such to IPA. Otherwise they had to be discarded
from further analyses. For the probes having an acces-
sion reference, an automated stepwise annotation was
performed with an in-house script based on sigReannot
[60] which took advantage of the recent re-annotation
of the cattle genome [61]. A first step verified whether
the probes were known to be situated within genomic
regions of genes in the Ensembl bovine database (ver-
sion 52). If this was not the case, in a second step the
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extracted EMBL or GenBank sequences were mapped to
the Ensembl bovine transcripts with a blast cutoff
threshold of e-10. In a final step, still unmatched clones
were mapped to the complete RefSeq RNA database at
NCBI http://www.ncbi.nlm.nih.gov/projects/RefSeq with
a blast cutoff threshold of e-5. When the probe coordi-
nates were found to overlap with more than one gene
or when blasting against the Ensembl bovine database
returned multiple blast hits with a difference in nucleo-
tide coverage between the first and second best hit of
<10%, the probe was discarded. For multiple blast hits
against the Ensembl bovine database with higher cover-
age differences, the best covering BLAST hit was never-
theless retained. Next, the Ensembl gene IDs were
themselves mapped onto entries from several other tar-
get gene databases. For a mapped entry to become the
final probe annotation fed to IPA, it obviously had to be
recognized by IPA. An arbitrary preference order of the
target gene databases was used when screening for IPA
recognition: human HGNC, human Entrez, RefSeq Pro-
tein, RefSeq RNA, bovine Unigene and bovine Entrez.
Also, preference was given to one-to-one mappings.
Assignment of affected genes to pathways, networks and
biological functions
Each gene symbol of the affected genes identified with
Pointillist was mapped to its corresponding gene object
in the Ingenuity Pathways Knowledge Base. Feeding the
aforementioned lists of affected genes as input to the
IPA library, significantly associated canonical pathways,
biological functions and networks were identified in
order to gain biological context and understanding.
Affected biological functions included the sub-groups
Diseases and disorders,Physiological system develop-
ment and functionand Molecular and cellular func-
tions. While the two first sub-groups are highly linked
to human diseases and physiology and IPA mainly relies
on human data, the third sub-group is relatively general
and was better suited for our meta-analysis data. In
order to summarize and reduce the vast amount of data
generated, which is reported in [Additional files 2 and
3], we focused and discussed in the text the 5 most
affected pathways and the 5 most affected biological
functions belonging to the sub-group Molecular and
cellular functions.
The found IPA library items were ranked based on
significance of association with the input list of genes.
For the canonical pathways this significance was deter-
mined based on two parameters: (a) ratio of the number
of genes from the input data set that map to the canoni-
cal pathway divided by the total number of genes of that
pathway and (b) p-values calculated using Fischers exact
test determining the probability that the association is
explained by chance alone. For the biological functions
and networks the significance was linked to the p-value
only, calculated by right-tailed Fishers exact test. The p-
values for the network analysis take into account the
number of affected genes in the network and the size of
the network. Identified networks are presented as a
graph indicating the molecular relationships between
genes/gene products. Genes are represented as nodes,
and the biological relationship between two nodes is
represented as an edge (line). All edges are supported by
at least one reference from the literature, from a text-
book, or from information stored in the IPA Knowledge
Base. The intensity of the node color indicates the
degree of up- (red) or down-regulation (green). Genes
in uncolored nodes were not identified as differentially
expressed in the experiment. The intrinsic size of net-
works, functions and pathways, used in the calculation
of the significance of association, depend on the chosen
IPA gene universe. We did not change the IPA default
universe, basically containing all genes and endogen-
ous chemicals of the IPA library.
The additional IPA function called building pathway
was used to graphically show the relationship and inter-
actions between genes belonging to significantly affected
IPA gene networks during the early stage response to
mastitis, and to connect all lipogenic genes identified
during the late stage response.
Venn diagram and heat map building, and visualization
of fold change variations in different cattle experiments
The Venn diagram was built using R script overLapper.
R http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/
My_R_Scripts/overLapper.R.
The heat map was constructed with the heatmap
function of the R package stats. The R package MaSig-
Pro [53] was used to visualize the magnitude of fold
change expressions during the time course of the differ-
ent cattle microarray experiments 1A {time points 1, 2,
3}, 1B {time points 4, 5, 6}, 1C {time points 7, 8}, 2
{time point 9}, and 3 {time points 10, 11}.
Fold change dissimilarities between E. coli and S. aureus
infections in cattle in vivo
The R package PAMR was used to detect dissimilarities
among fold change responses to E. coli and S. aureus
pathogen infections in vivo in cattle (experiment 1A, 1B,
and 1C, Table 1). The PAMR algorithm performs an
expression-profile based sample class prediction [62]. In
a first step, average within-class expression profiles, so
called centroids, are calculated for all sample classes.
In a next step, these centroids are shrunken, shifting the
average within-class expression of each gene towards
the genes overall expression average, and taking a gene
out of the centroid when its within-class expression
average coincides with the overall one. The extent of
Genini et al.BMC Genomics 2011, 12:225
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Page 13 of 17
gene expression shrinkage is proportional to the genes
within-class standard deviation, and is also determined
by the chosen thresholdor shrinkageparameter. The
higher the threshold, the fewer genes that are retained
in the class shrunken centroids and the more dissimilar
they are. Finally, samples can then be classified by map-
ping them to the shrunken centroid that is nearest to
the samples expression profile. Here we used PAMR to
construct shrunken centroids of the two classes of the
E. coli and S. aureus infected samples. For a range of
threshold parameters, PAMR evaluated the classification
accuracy and the size of the resulting shrunken cen-
troids. Out of the threshold parameters yielding the
highest classification accuracy, we selected the lowest
threshold parameter that brought the shrunken cen-
troids size below an arbitrarily chosen limit of 50 dis-
similar genes. In this specific case, a threshold
parameter of 3.77 was selected, and this resulted in the
34 most dissimilar genes being retained in the resulting
shrunken centroids. These dissimilar genes were further
examined with IPA.
Additional material
Additional file 1: Lists of affected genes during different responses
to mastitis infection. Complete lists of affected genes and
corresponding Combined Effective Significances (CES)identified with
pointillist for the 4 main responses to mastitis (I) overall response, (II)
early stage response, (III) late stage response, (IV) cattle-specific response,
as well as the two additional time dependent responses (V) early specific
and (VI) late specific.
Additional file 2: Lists of all affected canonical pathways and
corresponding affected genes. Complete lists of affected canonical
pathways (p < 0.05) and corresponding affected genes identified with
IPA for the meta-analysis combinations (I) overall response, (II) early stage
response, (III) late stage response, (IV) cattle-specific response, (V) early
specific response and (VI) late specific response, as well as for the
common affected genes between the 4 meta-analysis responses (I) to
(IV) (Figure 3, n = 92). The identified canonical pathways are listed from
the lowest to the highest p-value. An asterisk indicates that the pathway
approached statistical significance (0.05<p < 0.1).
Additional file 3: Lists of all affected biological functions and
corresponding affected genes. Complete lists of all affected biological
functions (p < 0.05) and corresponding affected genes identified with
IPA for the meta-analysis combinations (I) overall response, (II) early stage
response, (III) late stage response, (IV) cattle-specific response, (V) early
specific response and (VI) late specific response, as well as for the
common affected genes between the 4 meta-analysis responses (I) to
(IV) (Figure 3, n = 92). The biological functions include all the sub-groups
Diseases and disorders,Physiological system development and
functionand Molecular and cellular functionsand are listed from the
lowest to the highest p-value. The five most affected molecular and
cellular functions, which are discussed in the text, are in bold.
Additional file 4: Affected sub-functions of lipid metabolism during
different responses to mastitis infection. Five most significant sub-
functions of lipid metabolism that are altered during (I) overall, (II) early
stage, (III) late stage, and (IV) cattle-specific responses. The results were
obtained by IPA using the lists of significantly affected genes for each
specific response. The sub-functions of the lipid metabolism are listed
from the lowest to the highest p-value, and are reported with the
involved genes.
Additional file 5: Supplemental Figure S1 - Relationship between
XBP1 and additional affected genes during the early stage response
to mastitis. Gene network showing the connections, as identified with
the IPA option building pathways, between the gene XBP1 and other
affected genes during (II) early stage response to mastitis infection. A.
XBP1 is related and linked to several other affected genes. B.XBP1 is
directly linked to the genes COPZ1,DDOST,KDELR2,KDELR3,RPN1,
SEC23B,SEC24D,SEC61A1, and SRPR, as well as to genes of the
proteasome and the MHC Class II complex. Supplemental Figure S2 -
Relationship between SREBF1 and additional affected genes during the
late stage response to mastitis. Gene network showing the connections,
as identified with the IPA option building pathways, between affected
genes involved in lipid metabolism during (III) late stage response to
mastitis infection. The gene SREBF1 seems to play an important role and
is directly linked to other affected genes (violet colour), i.e. TRAF3IP3,
CD36,SCD,SOD1,IDH1,THRB,RETN,PMVK,DBI,UCP2,HBS1,SC4MOL,and
CYP27A1. Supplemental Figure S3 - Venn diagram showing the number
of common and experiment-specific affected genes between (IV) cattle-
specific response and the individual experiments 1A time point {3} and 2
time point {9}. Venn diagram illustrating the number of significantly
affected genes in common (25) and distinct for the (IV) cattle-specific
response (red: 421 genes), experiment 1A time point {3} (green: 745
genes), and experiment 2 time point {9} (blue: 55 genes). The lists of
corresponding genes can be found in [Additional file 6]. The list of
experiments and time points can be found in Table 1 and the list of
meta-analysis combinations in Table 2.
Additional file 6: Lists of affected genes that are distinct or in
common between (IV) cattle-specific response, experiment 1A time
point {3}, and experiment 2 time point {9}. Complete lists of affected
genes corresponding to the Venn diagram [Additional file 5:
Supplemental Figure S3], including genes that are distinct or in common
at the intersections between (IV) cattle-specific response, experiment 1A
time point {3}, and experiment 2 time point {9}. The list of experiments
and time points can be found in Table 1 and the list of meta-analysis
combinations in Table 2.
Additional file 7: Affected molecular and cellular functions of the
most dissimilar genes between E. coli and S. aureus.Five most
significant molecular and cellular functions identified with IPA using the
34 most dissimilar genes between E. coli and S. aureus infections in cattle
in vivo (experiment 1A, 1B, and 1C), as found with the PAMR software
(Table 3). The identified molecular and cellular functions are listed from
the lowest to the highest p-value, and are reported with the involved
genes.
Additional file 8: Lists of affected genes that are distinct or in
common between the 4 main responses to mastitis infection.
Complete lists of affected genes corresponding to the Venn diagram in
Figure 3, including genes that are distinct or in common at the
intersections between the 4 different responses (I) overall, (II) early stage,
(III) late stage, and (IV) cattle-specific.
Acknowledgements
The authors are grateful to Dr. DJ de Koning, The Roslin Institute and R(D)
SVS, University of Edinburgh, for introducing the Pointillist program and
providing helpful advice and opinions at various stages of the study. This
project was financed by FP6-EADGENE (European Animal Disease Genomics
Network of Excellence, EU Contract No. FOOD-CT-2004-506416). The authors
declare no competing financial interests.
Author details
1
Parco Tecnologico Padano - CERSA, Via Einstein, 26900 Lodi, Italy.
2
The
Roslin Institute and R(D)SVS, Division of Genetics and Genomics, Roslin,
Midlothian, University of Edinburgh, EH25 9RG, UK.
3
INRA/AgroParisTech,
UMR1236 Génétique et Diversité Animales, F-78352 Jouy en Josas, France.
4
INRA, Sigenae UR875 Biométrie et Intelligence Artificielle, BP 52627, F-31326
Castanet-Tolosan Cedex, France.
5
INRA, Sigenae UR83 Recherches Avicoles, F-
37380 Nouzilly, France.
6
Leibniz Institute for Farm Animal Biology (FBN),
Molecular Biology Research Unit, Wilhelm-Stahl-Allee 2, D-18196
Genini et al.BMC Genomics 2011, 12:225
http://www.biomedcentral.com/1471-2164/12/225
Page 14 of 17
Dummerstorf, Germany.
7
Clinic for Ruminants, Ludwig-Maximilians University,
Munich, Germany.
8
Central Veterinary Institute of Wageningen UR, P.O. Box
65, 8200 AB, Lelystad, The Netherlands.
9
Wageningen UR Livestock Research,
Animal Breeding and Genomics Centre, P.O. Box 65, 8200 AB, Lelystad, The
Netherlands.
10
Department of Basic Sciences and Aquatic Medicine,
Norwegian School of Veterinary Science, P.O. Box 8146 Dep, NO-0033 Oslo,
Norway.
11
Università degli Studi di Milano, Department of Veterinary
Pathology, Hygiene and Public Health, via Celoria 10, 20133 Milan, Italy.
12
Istituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle
Ricerche, Milan, Italy.
13
INRA-ENVT, UMR1225, Interactions Hôtes Agents
Pathogènes, F-31300 Toulouse, France.
14
INRA, UR631, Station dAmélioration
Génétique des Animaux, F-31326 Castanet-Tolosan, France.
15
Department of
Clinical Studies, School of Veterinary Medicine, University of Pennsylvania,
Philadelphia, PA 19104, USA.
16
Quality Milk Production Services, Cornell
University, Ithaca, New York, USA.
17
INRA, UMR 1313 de Génétique Animale
et Biologie Intégrative, Jouy-en-Josas, France.
Authorscontributions
SG, BB, and GS wrote the paper, collated the microarray data, and
performed the meta-analysis. SCB helped to conceive the meta-analysis
project, supervised the meta-analysis, helped to write the paper and helped
to collate the microarray data. DW performed analysis of single microarray
experiments and supervised the meta-analysis. MHP coordinated the entire
EADGENE project, helped collecting and sharing the microarray data. CC and
CK developed bioinformatic tools used to produce the gene annotations for
this study. HMS coordinated the work of the EADGENEs groups working on
mastitis and developed microarray experiment 1, in particular arranged for
the animal infection experiments, sample collection, and initial RNA
preparations. WP conducted the animal infections, recorded the
zootechnical/health parameters throughout the experiments, and prepared
all the tissues. KJ did all the hybridizations and data analysis of experiment 1.
EJG developed in conjunction with HMS experiment 1 and helped in
sample preparation and data analysis. AdG performed the experimental
infection with S. uberis and performed microarray experiment 2. HES initiated
the mastitis work at the central veterinary institute, wrote the S. uberis
project, and designed the experimental infection of experiment 2. MAS was
the contact person for EADGENE at the Animals Sciences Group and was
closely involved in the initiation of the operational genomics work package
within the network. IO was involved in the planning of the NSVS microarray
experiment 3, partly participated in the practical work, and helped with the
analysis of the data and the manuscript writing. GMB conducted the
microarray experiment 3 in the laboratory (biological samples preparation
and hybridization). PM, GP, BC, and PC organized the experiments, collected
the samples, performed the experimental infections with S. aureus in goat,
and analyzed the microarray data of experiments 4 and 6. MDC prepared
and edited the data of experiment 6. EF conducted the microarray
experiment 5 in the laboratory (biological samples preparation and
hybridization). GF planned the experimental design of microarray
experiment 5 in sheep. RR developed the design of microarray experiment 5
and did statistical analysis of the data. EG helped to conceive the meta-
analysis project, coordinated the overall project and helped in writing this
manuscript. All authors read and approved the final manuscript.
Received: 3 December 2010 Accepted: 11 May 2011
Published: 11 May 2011
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doi:10.1186/1471-2164-12-225
Cite this article as: Genini et al.: Strengthening insights into host
responses to mastitis infection in ruminants by combining
heterogeneous microarray data sources. BMC Genomics 2011 12:225.
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Page 17 of 17
    • Firstly, there is hardly any literature on the mammary gland transcriptome analysis of mycoplasma mastitis infections, although one report has indirectly evaluated sheep responses by using MFG proteomics [19] . Secondly, unlike for bovine infections , transcriptome analysis of sheep mammary gland, per se, has never been thoroughly described in response to even any bacterial pathogen causing mastitis except for a couple of reports that used milk somatic cell and bone marrow-derived dendritic cell transcriptomics, respectively [45][46][47]. Thirdly, the study utilizes the more powerful deep sequencing strategy to identify DEGs instead of the more commonly used microarray datasets. Lastly, here the spleen transcriptome was also evaluated together with the mammary response to intramammary mycoplasma challenge in order to understand the systemic immune responses in a peripheral tissue.
    [Show abstract] [Hide abstract] ABSTRACT: Mycoplasma agalactiae is a worldwide serious pathogen of small ruminants that usually spreads through the mammary route causing acute to subacute mastitis progressing to chronic persistent disease that is hard to eradicate. Knowledge of mechanisms of its pathogenesis and persistence in the mammary gland are still insufficient, especially the host-pathogen interplay that enables it to reside in a chronic subclinical state. This study reports transcriptome profiling of mammary tissue from udders of sheep experimentally infected with M. agalactiae type strain PG2 in comparison with uninfected control animals using Illumina RNA-sequencing (RNA-Seq). Several differentially expressed genes (DEGs) were observed in the infected udders and RT-qPCR analyses of selected DEGs showed their expression profiles to be in agreement with results from RNA-Seq. Gene Ontology (GO) analysis revealed majority of the DEGs to be associated with mycoplasma defense responses that are directly or indirectly involved in host innate and adaptive immune responses. Similar RNA-Seq analyses were also performed with spleen cells of the same sheep to know the specific systemic transcriptome responses. Spleen cells exhibited a comparatively lower number of DEGs suggesting a less prominent host response in this organ. To our knowledge this is the first study that describes host transcriptomics of M. agalactiae infection and the related immune-inflammatory responses. The data provides useful information to further dissect the molecular genetic mechanisms underlying mycoplasma mastitis, which is a prerequisite for designing effective intervention strategies.
    Full-text · Article · Jan 2017
    • In contrast, IL-17A signaling pathways were downregulated in PMEC at 3 hpc with S. aureus. Genini et al. [31] stated that the comparison of E. coli and S. aureus infections in cattle in vivo reveals affected genes showing opposite regulation with the same altered biological functions and this provides evidence that E. coli can cause a stronger host response. Gilbert et al. [13] suggested that E. coli induces a more intense response associated with strong NF-kappaB stimulation and the recruitment of a wider repertoire of immune cells, whereas S. aureus interferes with cell DNA integrity and may induce a more restricted immune response involving the IL-17A pathway.
    [Show abstract] [Hide abstract] ABSTRACT: Postpartum Dysgalactia Syndrome (PDS) represents a considerable health problem of postpartum sows, primarily indicated by mastitis and lactation failure. The poorly understood etiology of this multifactorial disease necessitates the use of the porcine mammary epithelial cell (PMEC) model to identify how and to what extent molecular pathogen defense mechanisms prevent bacterial infections at the first cellular barrier of the gland. PMEC were isolated from three lactating sows and challenged with heat-inactivated potential mastitis-causing pathogens Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) for 3 h and 24 h, in vitro. We focused on differential gene expression patterns of PMEC after pathogen challenge in comparison with the untreated control by performing microarray analysis. Our results show that a core innate immune response of PMEC is partly shared by E. coli and S. aureus. But E. coli infection induces much faster and stronger inflammatory response than S. aureus infection. An immediate and strong up-regulation of genes encoding cytokines (IL1A and IL8), chemokines (CCL2, CXCL1, CXCL2, CXCL3, and CXCL6) and cell adhesion molecules (VCAM1, ICAM1, and ITGB3) was explicitly obvious post-challenge with E. coli inducing a rapid recruitment and activation of cells of host defense mediated by IL1B and TNF signaling. In contrast, S. aureus infection rather induces the expression of genes encoding monooxygenases (CYP1A1, CYP3A4, and CYP1B1) initiating processes of detoxification and pathogen elimination. The results indicate that the course of PDS depends on the host recognition of different structural and pathogenic profiles first, which critically determines the extent and effectiveness of cellular immune defense after infection.
    Full-text · Article · Dec 2015 · BMC Veterinary Research
    • Another unique feature of this study was that all data were generated from the same individuals and experimental conditions. This contrasts with other studies such as meta-analyses of data obtained from different individuals or collected under different experimental conditions (Biswas et al., 2011; Daves et al., 2011; Genini et al., 2011; Te Pas et al., 2012 ). The current experimental design of using tissues from the same animals reduces variation from non-treatment related sources and therefore helps to refine the clarity of the data analysis.
    [Show abstract] [Hide abstract] ABSTRACT: Avian pathogenic Escherichia coli (APEC), an extraintestinal pathogenic Escherichia coli (ExPEC), constitutes an animal health and a potential zoonotic risk. Most studies focus on the response of a single tissue to APEC infection. Understanding interactions among lymphoid tissues is of importance in controlling APEC infection. Therefore, we studied bone marrow, bursa, and thymus transcriptomes because of these tissues' crucial roles in development of pre-lymphocytes, B cells, and T cells, respectively. Using lesion scores of liver, pericardium, and air sacs, infected birds were classified as either resistant or susceptible. Little difference in gene expression was detected in resistant birds in bone marrow versus bursa or thymus, while there were large differences between tissues in susceptible birds. Phagosome, lysosome and cytokine interactions were strongly enhanced in thymus versus bone marrow in susceptible birds, and T cell receptor (TCR), cell cycle, and p53 signaling were significantly decreased. B cell receptor (BCR) was also significantly suppressed in bursa versus bone marrow in susceptible birds. This research provides novel insights into the complex developmental changes in gene expression occurring across the primary lymphoid organs and, therefore, serves as a foundation to understanding the cellular and molecular basis of host resistance to APEC infection.
    Full-text · Article · Dec 2015
    • was greater in high-SCS ewes than in low-SCS ewes. Selection based on extreme breeding values for SCS was associated with a different metabolic response to energy restriction. This finding is in accordance with previous studies that demonstrated positive genetic correlations between mastitis and ketosis (Zwald et al., 2004;Heringstad et al., 2005).Genini et al. (2011)also showed that pathways related to immune response and lipid metabolism were altered in ruminants infected with mammary gland pathogens. Two hypotheses can be formulated in the light of these results. The first hypothesis is that high-SCS ewes may differ from low-SCS ewes in their genetic background for lipid metabolism, resulting in i
    [Show abstract] [Hide abstract] ABSTRACT: Dairy ruminants experiencing a severe postpartum negative energy balance (NEB) are considered to be more susceptible to mastitis. Although the genetic variability of mastitis resistance is well established, the biological basis of the link between energy metabolism and resistance is mostly unknown. The aim of this study was to characterize the effect of NEB on metabolism and immune response according to the genetic background for mastitis resistance or susceptibility. Forty-eight ewes from high and low somatic cell score (SCS) genetic lines were allocated to 2 homogeneous subgroups 2 wk after lambing: one group (NEB) received an energy-restricted diet to cover 60% of their energy requirements, and the other group received a control (positive energy balance: PEB) diet. Both diets met the protein requirements. After 10 d on either the NEB or PEB diet, all ewes were injected with a Pam3CSK4/MDP solution in one half-udder to induce an inflammatory response. The ewes were monitored for milk production, somatic cell count (SCC), body weight (BW), body condition score (BCS), and blood metabolites. Differential milk cell counts were determined by flow cytometry. Plasma concentrations of glucose, insulin, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHB), and triiodothyronine were determined. Energy restriction resulted in an increased fat:protein ratio in milk and decreased milk yield, BW, and BCS. The NEB ewes had significantly higher NEFA and BHB and lower plasma glucose concentrations than PEB ewes, reflecting a mobilization of body reserves and ketone body synthesis. High-SCS ewes had a higher SCS than low-SCS throughout the experiment, except after the inflammatory challenge, which resulted in similar SCS in all 4 groups. A noteworthy interaction between genetic background and diet was evidenced on metabolic parameters and BW. Indeed, high-SCS ewes subjected to NEB showed greater decrease in BW and increased NEFA and BHB concentrations compared with low-SCS ewes. Thus, NEB in early lactation led to extensive mobilization of body reserves and intense ketone body synthesis in mastitis-susceptible sheep. These results reinforce the hypothesis of a genetic association between mastitis susceptibility and energy metabolism and open the way to further studies on the biological basis for this association.
    Full-text · Article · Nov 2015
    • In contrast, IL-17A signaling pathways were downregulated in PMEC at 3 hpc with S. aureus. Genini et al. [31] stated that the comparison of E. coli and S. aureus infections in cattle in vivo reveals affected genes showing opposite regulation with the same altered biological functions and this provides evidence that E. coli can cause a stronger host response. Gilbert et al. [13] suggested that E. coli induces a more intense response associated with strong NF-kappaB stimulation and the recruitment of a wider repertoire of immune cells, whereas S. aureus interferes with cell DNA integrity and may induce a more restricted immune response involving the IL-17A pathway.
    [Show abstract] [Hide abstract] ABSTRACT: Postpartum Dysgalactia Syndrome (PDS) represents a considerable health problem of postpartum sows, primarily indicated by mastitis and lactation failure. The poorly understood etiology of this multifactorial disease necessitates the use of the porcine mammary epithelial cell (PMEC) model to identify how and to what extent molecular pathogen defense mechanisms prevent bacterial infections at the first cellular barrier of the gland. PMEC were isolated from three lactating sows and challenged with heat-inactivated potential mastitis-causing pathogens Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) for 3 h and 24 h, in vitro. We focused on differential gene expression patterns of PMEC after pathogen challenge in comparison with the untreated control by performing microarray analysis. Our results show that a core innate immune response of PMEC is partly shared by E. coli and S. aureus. But E. coli infection induces much faster and stronger inflammatory response than S. aureus infection. An immediate and strong up-regulation of genes encoding cytokines (IL1A and IL8), chemokines (CCL2, CXCL1, CXCL2, CXCL3, and CXCL6) and cell adhesion molecules (VCAM1, ICAM1, and ITGB3) was explicitly obvious post-challenge with E. coli inducing a rapid recruitment and activation of cells of host defense mediated by IL1B and TNF signaling. In contrast, S. aureus infection rather induces the expression of genes encoding monooxygenases (CYP1A1, CYP3A4, and CYP1B1) initiating processes of detoxification and pathogen elimination. The results indicate that the course of PDS depends on the host recognition of different structural and pathogenic profiles first, which critically determines the extent and effectiveness of cellular immune defense after infection. Electronic supplementary material The online version of this article (doi:10.1186/s13567-015-0178-z) contains supplementary material, which is available to authorized users.
    Full-text · Article · May 2015
    • The Bovine Innate Immune Microarray, which comprised several defensin and cathelicidin genes among 1480 immune related genes, showed no differences in AMP genes expression upon the infection of the bovine mammary gland with Staphylococcus aureus [34] . Metaanalysis done by Genini et al. [35] reveled only cathelicidin antimicrobial peptide (CAMP) which increased upon mastitis infection. Therefore, the aim of the present study was to estimate the in vivo transcript levels of β-defensin and cathelicidin genes in cow mammary gland secretory tissue (parenchyma ) with the chronic, recurrent and incurable mammary gland inflammation caused by coagulase-positive or coagulase-negative Staphyloccoci vs. bacteria-free tissue.
    [Show abstract] [Hide abstract] ABSTRACT: Mastitis is still considered to be the most economically important infectious disease in dairy cattle breeding. The immune response in mammary gland tissues could help in developing support strategies to combat this disease. The role of neutrophils and macrophages in the innate response of mammary gland is well known. However, the immune response in mammary gland tissues, including levels of antimicrobial peptide transcripts, has not been well recognized. Moreover, most studies are conducted in vitro, on cell cultures, or on artificially infected animals, with analysis being done within a several dozen hours after infection. The aim of the study was to examine the in vivo transcript levels of beta-defensin and cathelicidins genes in cow mammary gland secretory tissue (parenchyma) with the chronic, recurrent and incurable mammary gland inflammation induced by coagulase-positive or coagulase-negative Staphyloccoci vs. bacteria-free tissue. The mRNA of DEFB1, BNBD4, BNBD5, BNBD10 and LAP genes, but not of TAP gene, were detected in all investigated samples regardless of the animals’ age and microbiological status of the mammary gland, but at different levels. The expression of most of the beta-defensin genes was shown to be much higher in tissues derived from udders infected with bacteria (CoPS or CoNS) than from bacteria-free udders, regardless of parity. Cathelicidins (CATH4, CATH5 and CATH6) showed expression patterns contrasting those of β-defensins, with the highest expression in tissues derived from bacteria-free udders. Increased expression of genes encoding β-defensins in the infected udder confirms their crucial role in the defense of the cow mammary gland against mastitis. On the other hand, the elevated cathelicidin transcripts in non-infected tissues indicate their role in the maintenance of healthy mammary tissues. The expression levels of investigated genes are likely to depend on the duration of the infection and type of bacteria.
    Full-text · Article · Oct 2014
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