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R E S E A R C H A R T I C L E Open Access
Integrative genomics of the mammalian
alveolar macrophage response to
intracellular mycobacteria
Thomas J. Hall
1
, Michael P. Mullen
2
, Gillian P. McHugo
1
, Kate E. Killick
1,3
, Siobhán C. Ring
4
, Donagh P. Berry
5
,
Carolina N. Correia
1
, John A. Browne
1
, Stephen V. Gordon
6,7
and David E. MacHugh
1,7*
Abstract
Background: Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting
global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar
macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen.
Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host
response to infection with M. bovis at the transcriptome level to identify core infection response pathways and
gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to
enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.
Results: The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM)
infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq
data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes
filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes
obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained
from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated
with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS
summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin
and Holstein-Friesian, respectively.
(Continued on next page)
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* Correspondence: david.machugh@ucd.ie
1
Animal Genomics Laboratory, UCD School of Agriculture and Food Science,
UCD College of Health and Agricultural Sciences, University College Dublin,
Belfield, Dublin D04 V1W8, Ireland
7
UCD Conway Institute of Biomolecular and Biomedical Research, University
College Dublin, Belfield, Dublin D04 V1W8, Ireland
Full list of author information is available at the end of the article
Hall et al. BMC Genomics (2021) 22:343
https://doi.org/10.1186/s12864-021-07643-w
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(Continued from previous page)
Conclusions: The results from the three parallel analyses showed that the three computational approaches could
identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results
indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of
biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets.
These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS
proving most useful for prioritising SNPS through data integration. Because the functional genomics data were
generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may
be more breed-specific than previously assumed.
Keywords: Alveolar macrophage, GWAS, Integrative genomics, Mycobacterium bovis, Network, RNA-seq,
Tuberculosis
Background
Bovine tuberculosis (bTB) is a chronic disease of live-
stock, particularly among domestic dairy and beef cattle,
which has been conservatively estimated to cause more
than $3 billion annual losses to global agriculture [1,2].
The disease can also establish across a large variety of
wildlife species including, for example, American bison
(Bison bison), African buffalo (Syncerus caffer), the
brushtail possum (Trichosurus vulpecula), red deer (Cer-
vus elaphus), wild boar (Sus scrofa), and the European
badger (Meles meles)[3–6]. The aetiological agent of
bTB, Mycobacterium bovis, is a member of the Mycobac-
terium tuberculosis complex (MTBC) and has a genome
sequence 99.95% identical to M. tuberculosis, the pri-
mary cause of human tuberculosis (TB) [7] and the lead-
ing cause of human deaths from a single infectious
agent—approximately 1.25 million in 2018 [8]. In
addition, for several low- and middle-income countries,
the human TB disease burden is increased by zoonotic
TB (zTB) caused by infection with M. bovis [9–12].
Scientific understanding of bTB and human TB has
been synergistically intertwined since the nineteenth
century and the foundational research work of Theobald
Smith and others [5,13,14]. The pathogenesis of bTB
disease in cattle is comparable with human TB disease
and many aspects of M. bovis infection are also charac-
teristic of M. tuberculosis infection [15–19]. Conse-
quently, it is now widely recognised that M. bovis
infection of cattle and bTB disease represent an import-
ant comparative system for understanding human TB
caused by M. tuberculosis [20–24].
Inhalation of aerosolized bacteria is the main route of
transmission for M. bovis in cattle and the primary site of
infection is normally the lungs [17,25,26]. Here the bacilli
are phagocytosed by alveolar macrophages (AM)—key
effector cells of the innate immune system, which provide
surveillance of pulmonary surfaces and can normally des-
troy or restrict inhaled intracellular bacilli [27,28]. M. bovis
and other facultative intracellular MTBC pathogens have
evolved a complex range of mechanisms to evade, subvert,
and exploit innate immune responses, thereby facilitating
colonisation, persistence and replication within host macro-
phages [29–32]. These mechanisms include: recruitment of
cell surface receptors on the host macrophage through mo-
lecular mimicry; restricting phagosome maturation and au-
tophagy; detoxification of reactive oxygen species and
reactive nitrogen intermediates (ROSs and RNIs); modula-
tion of type I interferon (IFN) signalling; suppression of
antigen presentation; rewiring and short-circuiting of
macrophage signal transduction pathways; manipulation of
host macrophage metabolism; egress of bacilli into the
macrophage cytosol; and inhibition of apoptosis with con-
comitant induction of necrosis leading to immunopathol-
ogyandsheddingbythehosttocompletethepathogenic
life cycle [33–39]. Hence after infection, a two-way re-
sponse is triggered between the pathogen and macrophage,
the outcome of which ultimately leads to establishment of
infection or clearance of the pathogen. The latter outcome
of clearance may, or may not, require engagement of the
adaptive immune system. As the detection of M. bovis in-
fection in cattle generally relies on detecting an adaptive
immune response to the pathogen, the outcome of which is
slaughter of positive animals (‘reactors’), identifying genes
that underpin efficacious innate responses promises to
reveal favourable genomic variants for incorporation into
breeding programmes.
Since 2005, substantial efforts have been made to
better understand host-pathogen interaction for bTB
using transcriptomics technologies such as gene expres-
sion microarrays and RNA sequencing (RNA-seq) at the
host cellular level—specifically the bovine alveolar
macrophage (bAM) and initial innate immune responses
to infection by M. bovis [40–46]. These studies have
helped to define a “pathogenic signature”[31,47]ofM.
bovis infection in bAM, which reflects the tension be-
tween macrophage responses to contain and kill intra-
cellular pathogens and evasion and avoidance
mechanisms evolved by these mycobacteria. Using func-
tional genomics data mining of transcriptomics data, it
has also been shown that bAM responses to M. bovis
Hall et al. BMC Genomics (2021) 22:343 Page 2 of 20
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infection can be clearly differentiated from infection with
M. tuberculosis, the primary cause of human TB [45]. In
addition, these studies have been expanded to encom-
pass surveys of the bAM epigenome using methylome
sequencing and chromatin immunoprecipitation sequen-
cing (ChIP-seq). This work has demonstrated that the
transcriptional reprogramming of bAM caused by M.
bovis infection is profoundly shaped by chromatin re-
modelling at gene loci associated with critical compo-
nents of host-pathogen interaction [46,48].
In parallel to functional genomics studies of bTB,
genome-wide association studies (GWAS) have been
performed in Irish and UK cattle populations using
estimated breeding values (EBVs; estimate of genetic
merit of an animal derived from a statistical model)
for several M. bovis infection resistance traits with
heritabilities ranging from 0.04 to 0.37, depending on
the phenotype used [49–55]. These GWAS have used
medium- and high-density single-nucleotide poly-
morphism (SNP) arrays and, more recently, imputed
whole-genome sequence (WGS) data sets for a large
multi-breed GWAS on 7346 bulls, which identified 64
quantitative trait loci (QTLs) associated with resist-
ance to M. bovis infection [55]; the association study
was based on phenotypic data from 781,270 individual
animals.
We have recently shown that integration of bAM
functional genomics data sets—RNA-seq, microRNA-seq
and ChIP-seq—with a GWAS data set for resistance to
M. bovis infection can be used to enhance detection of
genomic regions associated with reduced incidence of
bTB disease [46]. For the present study, we substantially
expand this work by leveraging gene-focused network-
and pathway-based methods under a statistical frame-
work based on a new software tool, gwinteR, to integrate
transcriptomics data from M. bovis-challenged bAM
with WGS-based GWAS results for resistance to M.
bovis infection [55]. Functional genomics data and
downstream data mining (e.g., to generate lists of differ-
entially expressed genes and outputs from network and
pathway analyses) can be used to obtain prioritised sub-
sets of genes that are likely to be important for a specific
biological process or phenomenon [56,57]. The gwinteR
tool can leverage these prioritised gene subsets and com-
bine them with summary statistics from biologically rele-
vant GWAS data sets. Biologically meaningful SNP-
phenotype associations can therefore be identified and
enriched that would otherwise be filtered out because of
stringent multiple test correction for the very large num-
bers of observations in a typical GWAS. The primary
aim of this work was to evaluate whether this approach
can systematically enhance detection of genomic se-
quence variants and genes underpinning bTB disease re-
sistance in cattle populations.
Results
Differential gene expression and pathway analyses of M.
bovis-infected bovine AM
Quality filtering of RNA-seq read pairs yielded a mean
of 22,681,828 ± 3,508,710 reads per individual library
(n= 78 libraries). A mean of 19,582,959 ± 3,021,333 read
pairs (86.17%) were uniquely mapped to locations in the
ARS-UCD1.2 bovine genome assembly. Detailed filtering
and mapping statistics are provided in a data file avail-
able from the Dryad Digital Repository (https://doi.org/
10.5061/dryad.83bk3j9q6) and multivariate PCA analysis
of the individual animal sample expression data using
DESeq2 revealed separation of the control and M. bovis-
infected bAM groups at the 24 and 48 h post infection
(hpi) time points, but not at the 2 and 6 hpi time points
(S1Fig).
Using default criteria for differential expression (FDR
P
adj.
< 0.05; |log
2
FC| > 1), and considering the M. bovis-
infected bAM relative to the control non-infected mac-
rophages, three DE genes were detected at 2 hpi (all
three exhibited increased expression in the M. bovis-in-
fected group); 97 DE genes were detected at 6 hpi (40
increased and 57 decreased); 1345 were detected at 24
hpi (764 increased and 581 decreased); and 2915 were
detected at 48 hpi (1528 increased and 1387 decreased)
(Fig. 2a; Additional file 1). Figure 2b shows that 2982
genes were differentially expressed across the 24 and 48
hpi time points. Table 1shows a breakdown of DE genes
across the infection time course for a range of statistical
thresholds and fold-change cut-offs, including the de-
fault criteria (FDR P
adj.
< 0.05; |log
2
FC| > 1). To ensure
manageable computational loads, the DE gene sets that
were used for GWAS integration with gwinteR were fil-
tered with |log
2
FC| > 2, and P
adj.
< 0.01 and P
adj.
<
0.000001 for 24 and 48 hpi, respectively. With these cri-
teria, there were 378 input genes for GWAS integration
identified at 24 hpi and 390 input genes at 48 hpi. (24
hpi and 48 hpi DEG gene sets). In addition, 210 genes
overlapped between the two time points. The two DEG
gwinteR input gene sets (DEG-24 and DEG-48 –see
Fig. 1) are also detailed in Additional file 1.
To produce gene sets for the IPA Core Analysis within
the recommended range for the number of input entities
[58,59] and to include DE genes with small fold-change
values, gene sets were filtered using only P
adj.
thresholds
of 0.05 and 0.01 at 24 hpi and 48 hpi, respectively. This
resulted in 1957 input genes (1071 upregulated and 886
downregulated) from a background detectable set of 16,
084 at 24 hpi and 2492 input genes (1401 upregulated
and 1091 downregulated) from a background detectable
set of 17,492 genes at 48 hpi. The IPA analysis was fo-
cused on the 24 and 48 hpi time points because a rela-
tively small numbers of DE genes were detected at 2 and
6 hpi (Fig. 2and Table 1).
Hall et al. BMC Genomics (2021) 22:343 Page 3 of 20
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Using the B-H method for multiple test correction in
IPA (P
adj.
< 0.05), there were 68 and 48 statistically sig-
nificant enriched IPA canonical pathways at 24 hpi and
48 hpi, respectively (Additional file 2). Enriched path-
ways at 24 hpi included Role of Pattern Recognition Re-
ceptors in Recognition of Bacteria and Viruses,IL-6
Signalling,TNFR2 Signalling,Role of RIG1-like Receptors
in Antiviral Innate Immunity,Role of Cytokines in Medi-
ating Communication between Immune Cells,Communi-
cation between Innate and Adaptive Immune Cells,IL-
12 Signalling and Production in Macrophages,IL-10 Sig-
nalling,Protein Ubiquitination Pathway,Toll-like Recep-
tor Signalling,NF-κB Signalling,PI3K/AKT Signalling,
and TNFR1 Signalling. The most highly activated path-
way at 24 hpi was PI3K/AKT Signalling. Enriched path-
ways at 48 hpi included Protein Ubiquitination Pathway,
Role of Cytokines in Mediating Communication between
Immune Cells,IL-12 Signalling and Production in Mac-
rophages,Role of RIG1-like Receptors in Antiviral Innate
Immunity,Role of Pattern Recognition Receptors in
Table 1 Differentially expressed genes detected in M. bovis-infected bovine AM relevant to control non-infected bAM
Post-infection time point P
adj.
< 0.05; |log
2
FC| > 0
(increased/decreased)
P
adj.
< 0.05; |log
2
FC| > 1
(increased/decreased)
P
adj.
< 0.01; |log
2
FC| > 0
(increased/decreased)
P
adj.
< 0.01; |log
2
FC| > 1
(increased/decreased)
2 hpi 14 (7/7) 3 (3/0) 8 (4/4) 2 (2/0)
6 hpi 410 (203/207) 97 (40/57) 119 (63/56) 32 (14/18)
24 hpi 3620 (1898/1722) 1345 (764/581) 2059 (1168/891) 933 (577/356)
48 hpi 6442 (3295/3147) 2915 (1528/1387) 4737 (2516/2221) 2386 (1294/1092)
Fig. 1 Schematic showing the experimental and computational workflow use to integrate bAM transcriptomics outputs and M. bovis infection
resistance trait GWAS data (some figure components created with a BioRender.com licence)
Hall et al. BMC Genomics (2021) 22:343 Page 4 of 20
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Fig. 2 Differentially expressed genes in M. bovis-infected bAM at 2, 6, 24, and 48 hpi. aVolcano plots of differentially expressed genes with FDR
P
adj.
value thresholds of 0.05 and absolute log
2
fold-change > 1. bUpSet plot showing the intersection of shared differentially expressed genes
across the four post-infection time points
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Recognition of Bacteria and Viruses,Communication be-
tween Innate and Adaptive Immune Cells,TNFR2 Sig-
nalling,Role of PI3K/AKT Signalling in the Pathogenesis
of Influenza,IL-10 Signalling, and Toll-like Receptor Sig-
nalling. The SIGORA software tool [60] has been previ-
ously used to identify biological pathways associated
with a robust ‘core’bAM response to infection with both
M. bovis and M. tuberculosis [45]. It is therefore reassur-
ing that many of these pathways—including PI3K-Akt
Signalling Pathway,RIG-I-like Receptor Signalling Path-
way,Toll-like Receptor Signalling and Protein Ubiquiti-
nation Pathway—were also enriched using the IPA
methodology at 24 and 48 hpi.
Differential co-expression correlation networks and
identification of functional gene modules
For the generation of bAM differential co-expression
correlation networks, filtering of genes with low
measure of central tendency, which reduces the
number of potential spurious correlations [61], re-
sulted in 11,354 and 11,170 genes at 24 and 48 hpi,
respectively. Following this step, differential correl-
ation analysis using DGCA with an empirical P
adj.
value threshold of 0.10 resulted in 3507 differentially
correlated gene pairs out of 128,913,316 total pair-
wise correlations at 24 hpi; and 1135 from a total of
124,768,900 at 48 hpi (Additional file 3). The correl-
ation networks generated at 24 hpi and 48 hpi
(Fig. 3a) yielded a total of 22 and 14 functional gene
modules, respectively (Fig. 3b and c, and Additional
file 3). After removal of duplicates, consolidated to-
tals of 460 genes and 416 genes were contained in
the functional modules at 24 hpi and 48 hpi, re-
spectively. There were also 26 genes that overlapped
between the functional modules for the two time
points. The two correlation network (CON) gwinteR
input gene sets (CON-24 and CON-48 –see Fig. 1)
are also detailed in Additional file 3.
GO term enrichment was also performed for each
functional module at 24 hpi and 48 hpi, with the top
three GO terms retained for each functional module
(S2Fig and S3Fig). The top five GO terms at 24 hpi
(ranked by P
adj.
)weretranslation (GO:0006412), pep-
tide biosynthetic process (GO:0043043), amide biosyn-
thetic process (GO:0043604), structural constituent of
ribosome (GO:0003735), and cellular amide metabolic
process (GO:0043603) (S2Fig). The top five enriched
GO terms at 48 hpi (ranked by P
adj.
)weresignalling
receptor activity (GO:0038023) and molecular trans-
ducer activity (GO:0060089), transforming growth fac-
tor beta activation (GO:0036363), chemokine activity
(GO:0008009), and signalling receptor binding (GO:
0005102) (S3Fig).
Differential expression network analysis and identification
of activated modular subnetworks
To provide a computationally manageable number of genes
for an InnateDB input data set [62], a GeneCards Relevance
Score (GCRS) threshold was used (GCRS > 2.5). This GCRS
cut-off produced an input list of 258 functionally prioritised
genes for generation of an InnateDB gene interaction net-
work (GIN) and the top ten genes from this list ranked by
GCRS were: interferon gamma receptor 1 (IFNGR1), inter-
leukin 12 receptor subunit beta 1 (IL12RB1), toll like recep-
tor 2 (TLR2), solute carrier family 11 member 1 (SLC11A1),
signal transducer and activator of transcription 1 (STAT1),
interleukin 12B (IL12B), cytochrome b-245 beta chain
(CYBB), tumour necrosis factor (TNF), interferon gamma re-
ceptor 2 (IFNGR2), and interferon gamma (IFNG).
The large GIN produced by InnateDB starting with
the input list of 258 functionally prioritised genes was
visualised using Cytoscape and consisted of 7001 nodes
(individual genes) and 19,713 edges (gene interactions)
(Fig. 4a). Additional file 4provides information for all
gene interactions represented in Fig. 4a. Following visu-
alisation of the large GIN in Cytoscape, the jActivesMo-
dules Cytoscape plugin was used to detect statistically
significant differentially activated subnetworks (modules)
at the 24 hpi and 48 hpi time points. The top five sub-
networks at each time point were retained for down-
stream analyses and consisted of 198 genes in module 1
at 24 hpi (M1–24), 287 genes in M2–24, 272 genes in
M3–24, 53 genes in M4–24, 171 genes in M5–24, 381
genes in M1–48, 330 genes in M2–48, 403 genes in
M3–48, 371 genes in M4–48, and 399 genes in M5–48
(Additional file 4). As an example, Fig. 4b shows the
subnetwork of genes and gene interactions representing
module 5 at 24 hpi (M5–24).
The genes contained in the top five modules at 24 hpi
and 48 hpi were filtered to remove duplicates and con-
solidated into two separate gene sets for GWAS integra-
tion with gwinteR. The consolidated gene sets for the
top five modules at 24 hpi and 48 hpi contained 339 and
495 unique genes, respectively. There were 245 genes
that overlapped between the two subnetwork gene sets
for the two post-infection time points. The two differen-
tial expression network (DEN) gwinteR input gene sets
(DEN-24 and DEN-48 –see Fig. 1) are also detailed in
Additional file 4.
GWAS integration and identification of additional SNP–
trait associations
The six gene sets generated from the three separate
analyses of DE genes in bAM challenged with M. bovis
at 24 hpi and 48 hpi are summarised in Table 2and fur-
ther detailed in Additional files 1,2and 3. Also, S4Fig
and S5Fig show Venn diagrams with the overlaps for
the DEG, CON and DEN input gene sets at 24 hpi and
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Fig. 3 Differential co-expression correlation networks and submodules at 24 and 48 hpi. aThe complete correlation networks for M. bovis-
infected bAM at 24 and 48 hpi. bThe 22 subnetwork modules detected at 24 hpi and the 14 subnetwork modules detected at 48 hpi with
individual example modules highlighted in yellow. cIndividual example subnetwork modules at 24 and 48 hpi
Hall et al. BMC Genomics (2021) 22:343 Page 7 of 20
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Fig. 4 A large gene interaction network (GIN) with superimposed differentially expressed genes. aCytoscape tuberculosis gene interaction
network with superimposed differentially expressed genes at 24 and 48 hpi. bExample subnetwork showing module number 5 detected with
the 24 hpi gene expression data
Hall et al. BMC Genomics (2021) 22:343 Page 8 of 20
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48 hpi, respectively. In addition to these six putative
functionally relevant gene sets, one hundred sets of 250
genes randomly sampled from the bovine genome were
used for statistical context and comparison. These
random gene sets (RAN) are detailed in Additional file 5
(see also Fig. 1). The results from the integrative analyses
using gwinteR with the DEG-24, DEG-48, CON-24,
CON-48, DEN-24, DEN-48, and RAN gene sets are
summarised graphically in Fig. 5and detailed in Add-
itional files 6and 7. We also used DE gene sets at 2 and
6 hpi with the default statistical threshold and fold-
change cut-offs (FDR P
adj.
< 0.05; |log
2
FC| > 1) for
GWAS integration using gwinteR (i.e., DEG-2 and DEG-
6). However, no significant SNP enrichment were ob-
served for these input gene sets, most likely because of
the low numbers of input genes (3 and 97, respectively).
Figure 5a shows circular Manhattan plots with
GWAS results (P
adj.
values) for each of the three
breeds prior to data integration using gwinteR.
Figure 5bshowsthegwinteR permuted P-values
(P
perm.
) for each of the 10 genomic intervals used
and for each of the six input gene sets plus the
RAN gene set with P
adj.
< 0.10. Figure 5cshowscir-
cular Manhattan plots with GWAS results post data
integration using gwinteR. Inspection of Fig. 5b
shows that, in terms of SNP enrichment (P
perm.
<
0.05), the integrative analyses using gwinteR were
most effective for the HOF breed group where the
CON-24, CON-48, and DEG-48 input gene sets pro-
duced enriched SNPs across all 10 genomic ranges.
In addition, the DEG-24 and DEN-24 input gene sets
were effective for the HOF breed across the ±20 to
100 kb and ± 30 to 50 kb genomic ranges, respect-
ively. In the case of the LIM breed, the DEG-48 in-
put gene set produced enriched SNPs across all 10
genomic ranges, the CON-48 between ±10 to 70 kb
and the CON-24 at ±10 kb. For the CHA breed, SNP
enrichment using gwinteR was only observed for the
CON-24 input gene set for the genomic interval be-
tween ±10 to 40 kb.
Figure 6summarises the numbers of statistically
significant SNPs pre- and post-data integration, again with
SNP enrichment being most evident for the HOF breed
with a 24-fold post-gwinteR SNP enrichment at P
adj.
<
0.10 from 40 to 961 SNPs. The SNP enrichments for the
CHA and LIM breed were more modest, although there
was a 2.3-fold enrichment at P
adj.
<0.10from80to182
SNPs for the LIM breed. Inspection of Additional file 7re-
veals notable gene loci associated with enriched GWAS
SNPs for the HOF breed including the allograft inflamma-
tory factor 1 gene (AIF1), which encodes a protein that
promotes macrophage activation and proinflammatory ac-
tivity [63]; the ciliogenesis associated kinase 1 gene (CILK1
aka ICK); IL17A and IL7F, which encode proinflammatory
cytokines and contain polymorphisms in the human
orthologs that have been associated with lower human TB
disease incidence [64,65]; the integrin subunit beta 3 gene
(ITGB3), which encodes a protein that has been shown to
regulate matrix metalloproteinase secretion in pulmonary
human TB [66]; the neuraminidase 1 gene (NEU1), which
encodes a protein that regulates phagocytosis in macro-
phages [67]; and the TNF gene that encodes TNF (aka
TNF-α), a key cytokine for generation and maintenance of
the granuloma, and where gene polymorphisms have been
linked to resistance to M. bovis infection [68]. Gene loci
associated with enriched GWAS SNPs for the CHA breed
group also included the CILK1 gene (aka ICK) and the
Kelch repeat and BTB domain containing 3 gene
(KCNJ15), which has been detected as an expression bio-
marker for human TB [69]; the T cell immune regulator
1, ATPase H+ transporting V0 subunit a3 gene (TCIRG1),
a known antimycobacterial host defence gene that has
been shown to be a key hub gene associated with IFN-γ
stimulation of human macrophages [70]; and the Von
Willebrand factor gene (VWF). Notable gene loci associ-
ated with enriched GWAS SNPs for the Limousin breed
group included the cellular repressor of E1A stimulated
genes 1 gene (CREG1), which encodes a regulator of core
macrophage differentiation genes [71]; the desmoplakin
gene (DSP), which increases in expression during M. tu-
berculosis-derived ESAT6-regulated transition of bone
marrow-derived macrophages (BMDMs) into epithelioid
macrophages [72]; and the SP110 nuclear body protein
gene (SP110), which encodes a protein that modulates
growth of MTBC pathogens in macrophages and has been
successfully exploited for genome editing of cattle to en-
hance resistance to M. bovis infection [73].
Discussion
During the last decade, integrative genomics, multi-
omics analyses and network biology have come to the
fore as powerful strategies for exploring, dissecting
Table 2 Six different input gene sets used for GWAS integration
Post-
infection
time
point
Filtered differentially
expressed genes (DEG)
Correlation networks
analysis (CON)
Differential expression
network analysis (DEN)
Input gene set code No. of genes Input gene set code No. of genes Input gene set code No. of genes
24 hpi DEG-24 378 CON-24 460 DEN-24 338
48 hpi DEG-48 390 CON-48 416 DEN-48 493
Hall et al. BMC Genomics (2021) 22:343 Page 9 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and unpicking the complexities of the vertebrate im-
mune system and immune responses to specific mi-
crobial pathogens [74–77]. In the present study we
have used these approaches to integrate transcripto-
mics data from a pivotal immune effector cell in M.
bovis infection with high-resolution GWAS data for a
bTB resistance trait. We developed a new computa-
tional tool, gwinteR, to enhance detection of QTLs by
leveraging nominal SNP P-values from large GWAS
data sets for resistance to infection by M. bovis in
cattle. Three different integration strategies were
employed with transcriptomics data from bAM
infected with M. bovis across a 48-h time course. The
first and most straightforward method was based on
DE gene sets at 24 and 48 hpi with stringent fold-
change and P-value thresholds as filtering criteria
(DEG-24 and DEG-48). For the second method, a
correlation network approach was used to identify
subnetworks (modules) of co-expressed functional
gene clusters from the bAM transcriptomics data at
the two post-infection time points (CON-24 and
CON-48). The third method was also network-based
but took advantage of the extensive scientific litera-
ture and curated biomolecular data for mycobacterial
Fig. 5 Integration of bAM functional genomics and GWAS data for resistance to M. bovis infection in three cattle breeds. a Circular Manhattan
plots showing GWAS results pre-integration with blue and red data points indicating binned SNP clusters with FDR P
adj.
< 0.10 and < 0.05,
respectively. b Line plots of permuted P-values across different genomic intervals for SNPs from six different input gene sets. c Circular Manhattan
plots showing GWAS results post-integration using gwinteR with blue and red data points indicating binned SNP clusters with FDR P
adj.
< 0.10
and < 0.05, respectively
Hall et al. BMC Genomics (2021) 22:343 Page 10 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
infections and tuberculosis disease. For this approach,
a base GIN was constructed and functional modules
containing overlaid differentially expressed genes were
identified to provide two post-infection input gene
sets (DEN-24 and DEN-48) for downstream data
integration.
Integration of these six input gene sets with bTB
GWAS data from three different cattle breeds (CHA,
LIM and HOF) revealed substantial differences among
the three methods in their capacity to detect additional
QTLs for a M. bovis infection resistance trait in these
particular GWAS data sets. For example, the correlation
network approach was the only method that enriched
SNPs for the CHA breed and that worked for at least
one bAM post-infection time point across all three
breeds (see Fig. 5). Surprisingly, perhaps, the functional
modules obtained using the GIN differential network
(DEN) approach—the most complex method to imple-
ment—produced the least effective input gene sets for
prioritising additional SNPs from the GWAS data set.
This method proved effective only for the DEN-24 input
gene set with the HOF breed and then only for the ±30,
±40, and ± 50 kb genomic intervals. Conversely, the sim-
plest method based on DE genes at 24 and 48 hpi
enriched SNPs for both the LIM and HOF breed groups,
with the DEG-48 input gene set being the most effective
(Fig. 5). The relatively poor performance of the DEN ap-
proach for integration of functional genomics and
GWAS data may be a consequence of the human-
focused GeneCards and InnateDB resources we used to
generate the base GIN [62,78]. Therefore, it would be
instructive to conduct similar integrative analyses for
Fig. 6 Histogram showing numbers of significant GWAS SNPs for the bTB resistance trait, pre- and post-enrichment for the three cattle breeds
Hall et al. BMC Genomics (2021) 22:343 Page 11 of 20
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human TB using appropriate functional genomics and
GWAS data sets.
In summary, across the two different bAM infection
time points and the three breeds, the CON method
enriched 970 SNPs, the DEG method enriched 163 SNPs
and the DEN method enriched only 11 SNPs (Additional
file 7). Although other factors such as linkage disequilib-
rium need to be considered in interpreting these differ-
ences, it is reasonable to hypothesise that GWAS
integration using the correlation network approach is
more sensitive to regulatory genomic variants that alter
expression of co-ordinately regulated protein compo-
nents of the alveolar macrophage pathways and pro-
cesses underpinning host-pathogen interaction for the
early stages of intracellular MTBC infection [27,28,30].
Figure 7illustrates this using the example of SNPs
within and proximal to gene loci associated with PI3K/
AKT signalling, which based on IPA data mining for
bAM DE genes was a highly activated pathway, particu-
larly at 24 hpi. In this regard, previous work has shown
that macrophage PI3K/AKT signalling is key to a range
of cellular processes associated with host-pathogen
interaction in MTBC infections, including modulation of
cell death pathways, manipulation of signalling down-
stream of TLRs, and initiation of granuloma formation
[79–84]. We have also recently demonstrated that genes
encoding protein products embedded in the PI3K/AKT
pathway are primary targets for chromatin modifications
that substantially alter bAM gene expression in response
to M. bovis infection [46].
There were also notable breed differences in the effect-
iveness of the three methods used for multi-omics data in-
tegration and enrichment of GWAS SNPs. The original
GWAS data set for the HOF breed was the least powered
of the three breeds in terms of both sample size (n=1502
sires) and genetic markers (12,740,315 genome-wide
SNPs). This is reflected in the relatively small number of
GWAS SNPs that were detected pre-integration for the
HOF breed: 40 SNPs at P
adj.
<0.10 compared to 475 for
the CHA breed and 80 for the LIM breed (see Fig. 6).
However, the SNP enrichment post-integration was mark-
edly more effective for the HOF breed with a 24-fold in-
crease from 40 to 961 SNPs (P
adj.
<0.10) compared to
1.08-fold (475 to 511 SNPs; P
adj.
< 0.10) and 2.28-fold (80
to 182 SNPs; P
adj.
< 0.10) for the CHA and LIM breeds, re-
spectively (Fig. 6). In addition, a total of 48 genes were
captured by the enriched GWAS SNPs for the HOF breed
compared to 20 and 16 genes for the CHA and LIM
breeds, respectively (Additional file 7).
Regarding the enhanced SNP detection observed for
the HOF breed GWAS after multi-omic integration, it is
noteworthy that the M. bovis infection transcriptomics
data set was generated using bAM from this population
[43]. Most of the SNPs (n= 829, 88%) and genes (n= 36,
67%) obtained post-integration in the HOF breed were
detected using the correlation co-expression network
method, which, again, is likely caused by enrichment of
genomic regulatory variants that modulate expression of
genes associated with alveolar macrophage pathways and
processes critical to early host-pathogen interaction.
This pattern of variation may also reflect the polygenic
architecture of M. bovis infection resistance in domestic
cattle [52,54,55,85].
It is well established that intracellular mycobacterial
pathogens use a host of evolved mechanisms to survive
within, and ultimately disseminate from host macro-
phage cells. Several of these mechanisms are ultimately
linked to regulators of TNF and cognate downstream
signalling pathways [29,30,39]. It is noteworthy, there-
fore, that our analysis prioritised SNPs at the TNF gene
locus, a key nexus for host-pathogen interaction in
mycobacterial infections. In addition, another important
component of host-pathogen interaction and target for
immunoevasion by intracellular mycobacterial infections
is the PI3K/AKT pathway [33,79,83] and our integra-
tive genomics approach was able to prioritise genomic
variation at genes encoding ligands and receptors within
this signalling network, specifically TNXB,GNG11,
ITGB3,andVWF (see Fig. 7).
Conclusions
Elucidation of the mechanisms used by M. bovis to estab-
lish infection in cattle, and ultimately cause disease, re-
quires an intimate knowledge of host-pathogen
interactions, especially at the interface between the bAM
and the invading bacilli. Using a systems biology approach,
we identified and catalogued patterns of gene expression
and gene-gene interactions that occur in bAM during the
initial stages of M. bovis infection, highlighting key re-
sponse pathways and hub genes. Additionally, a new R
package, gwinteR, facilitated integration of these functional
genomics outputs with three large breed based GWAS
data sets for resistance/susceptibility to M. bovis infection.
This revealed genomic variants associated with resistance
in key innate immune genes and supported the hypothesis
that the response to M. bovis infection at the level of the
alveolar macrophage may be more breed-specific than
previously assumed.
Integrative multi-omics approaches to data integra-
tion are now widely used to explore and dissect the
genomic architecture and physiological basis of com-
plex traits in domestic livestock, including network-
based methods to integrate functional genomics and
GWAS data [86–91]. However, to the best of our
knowledge, this study is the first that uses network
biology to systematically combine transcriptomics
data from M. bovis-infected macrophages with
GWAS data for M. bovis infection resistance in
Hall et al. BMC Genomics (2021) 22:343 Page 12 of 20
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Fig. 7 The PI3K/AKT signalling pathway and genomic variants within associated genes. aThe IPA-predicted activation state of the PI3K/AKT
signalling pathway with red and blue colours indicating increased and decreased activity of pathway components, respectively. bSchematic of
enriched bTB disease resistance GWAS SNPs in four genes associated with the PI3K/AKT signalling pathway
Hall et al. BMC Genomics (2021) 22:343 Page 13 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
cattle. Therefore, it provides a novel framework for
integrative genomics studies of complex infectious
disease resistance traits in livestock, particularly
those involving other intracellular bacterial patho-
gens such as Brucella abortus,Mycobacterium avium
subsp. paratuberculosis and Salmonella enterica.The
work is also relevant to development of methods for
integrative analyses of outputs from the Functional
Annotation of Animal Genomics (FAANG) initiative
[92] and for identification and prioritization of tar-
gets for genome editing to enhance resistance to in-
fection in domestic livestock species [93]. The
results from this study may also inform genome-
enabled breeding programmes for resistance to M.
bovis infection in production cattle populations [85,
94]. Finally, this integrative multi-omics approach
could also be used to combine relevant functional
genomics and GWAS data sets to improve know-
ledge of innate immune responses and establishment
of infection in human TB caused by M. tuberculosis.
Methods
Genomics data acquisition and computational and
bioinformatics workflow
Genome-wide RNA-seq transcriptomics data from a
48-h bAM time course challenge experiment using
the sequenced M. bovis AF2122/97 strain have been
previously generated by our group (GEO accession:
GSE62506). The complete laboratory methods used to
isolate, culture and infect bAM with M. bovis
AF2122/9 and generate strand-specific RNA-seq li-
braries using RNA harvested from these cells are de-
scribed in detail elsewhere [42,43,45]. Briefly, these
RNA-seq data were generated using bAM obtained by
lung lavage of ten unrelated age-matched 7 to 12-
week-old male Holstein-Friesian calves. Bovine AM
were either infected in vitro with M. bovis AF2122/97
or incubated with media only. Following total RNA
extraction from M. bovis-infected and control non-
infected alveolar macrophages, 78 strand-specific
RNA-seq libraries were prepared (paired-end 2 × 90
nucleotide reads). These comprised M. bovis-and
non-infected samples from each post-infection time
point (2, 6, 24 and 48 hpi) across 10 animals with the
exception of one animal that did not yield sufficient
alveolar macrophages for in vitro infection at 48 hpi.
GWAS data sets for the present study were obtained
from intra-breed imputed WGS-based GWAS analyses
that used estimated breeding values (EBVs) derived from
a bTB infection phenotype, which were generated for
2039 Charolais, 1964 Limousin and 1502 Holstein-
Friesian sires [55]. The bTB phenotype, the WGS-based
imputed SNP data, and the quantitative genetics
methods are described in detail elsewhere [55]; however,
the following provides a brief summary. The bTB infec-
tion phenotype was defined for every animal present
during each herd-level bTB breakdown when a bTB re-
actor or a slaughterhouse case was identified. Cattle that
yielded a positive single intradermal comparative tuber-
culin test (SICTT), and/or post-mortem lymph node le-
sion, or laboratory culture result/s were coded as bTB =
1 and all other cattle present in the herd during the
bTB-breakdown were coded as bTB = 0; potential expos-
ure of cattle within the bTB breakdown was also consid-
ered in this study [55]. After phenotype data edits, bTB
resistance EBVs were generated for 781,270 phenotyped
cattle (plus their recorded ancestors). After within-breed
SNP filtering using thresholds for minor allele frequency
(MAF ≤0.002) and deviation from Hardy-Weinberg
equilibrium (HWE; P<1×10
−6
), there were 17,250,600,
17,267,260 and 15,017,692 autosomal SNPs for the 2039
Charolais (CHA), 1964 Limousin (LIM) and 1502
Holstein-Friesian (HOF) sire analyses. A single-SNP re-
gression analyses was performed for each breed separ-
ately using weighted (i.e., by an effective record
contribution) sire EBVs for M. bovis infection resistance/
susceptibility and the nominal P-values were used for
downstream integrative genomics analyses.
All data-intensive computational procedures were
performed on a 36-core/72-thread compute server (2×
Intel
®
Xeon
®
CPU E5–2697 v4 processors, 2.30 GHz
with 18 cores each), with 512 GB of RAM, 96 TB SAS
storage (12 × 8 TB at 7200 rpm), 480 GB SSD storage,
and with Ubuntu Linux OS (version 18.04 LTS). The
complete computational and bioinformatics workflow
is available with additional information as a public
GitHub repository (github.com/ThomasHall1688/
Bovine_multi-omic_integration). The individual com-
ponents of the experimental and computational work-
flows are shown in Fig. 1and described in more
detail below.
Differential gene expression analysis of RNA-seq data
A custom Perl script was used to deconvolute barcoded
RNA-seq reads into individual libraries, filter out adapter
sequence reads, and remove poor quality reads [43]. At
each stage of the process, a quality check was performed
on the FASTQ files with FastQC (version 0.11.8) [95].
Paired-end sequence reads were then aligned to the Bos
taurus reference genome (ARS-UCD1.2, GenBank as-
sembly accession: GCA_002263795.2) [96] using the
STAR aligner (version 2.7) [97]. Read counts for each
gene were calculated using featureCounts (version 1.6.4)
[98], set to unambiguously assign uniquely aligned
paired-end reads in a stranded manner to gene exon an-
notation. Using the R statistical programming language
(version 3.5.3) [99], gene annotation was derived from
the NCBI database via a GFF annotation file GCF_
Hall et al. BMC Genomics (2021) 22:343 Page 14 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
002263805.1 with additional descriptions and chromo-
somal locations annotated using GO.db (version 3.8.2)
[100] and biomaRt packages (version 2.40.0) [101]. Dif-
ferential gene expression analysis was performed using
the DESeq2 package (version 1.24.0) [102] with a longi-
tudinal time series design that accounted for time (hpi)
and experimental treatment (M. bovis-infected versus
control). Lowly expressed reads were removed using the
mean of normalized counts as a filter statistic; individual
genes with very low read counts would typically not ex-
hibit significant differential expression due to high dis-
persion [102]. In addition, extreme count outliers were
removed within DESeq2 using the Cook’s distance [103]
as previously described [102]. Multiple testing correction
was performed on each time point using the Benjamini-
Hochberg (B-H) false discovery rate (FDR) method
[104]. The default criteria for differentially expressed
(DE) genes were an FDR-adjusted P-value less than 0.05
(P
adj.
< 0.05).
Ingenuity
®
pathway analysis (IPA) of differentially
expressed genes
Ingenuity
®
Pathway Analysis—IPA
®
(version 1.1, summer
2020 release; Qiagen, Redwood City, CA, USA) was used
to perform a statistical enrichment analysis of DE gene
sets and expression data [58]. This enabled identification
of canonical pathways and functional processes of bio-
logical importance in alveolar macrophages challenged
with M. bovis across the longitudinal infection time
course. The target species selected was Homo sapiens
and the cell type used was Macrophage with the Experi-
mentally Observed and High Predicted confidence set-
tings. Following best practice, the default background
gene set for pathway and functional process enrichment
testing was the set of detectable genes across all RNA-
seq libraries for each time point contrast and not the
complete bovine transcriptome [105].
Identification of functional gene modules using
differential co-expression network analysis
An integrated computational pipeline for differential
co-expression network analysis was implemented
using: 1) the Differential Gene Correlation Analysis
(DGCA) R package (version 1.0.2) [61]; 2) the Cytos-
cape open source Java platform for visualisation and
integration of biomolecular interaction networks (ver-
sion 3.7.0) [106]; and 3) the Multiscale Embedded
Gene Co-expression Network Analysis (MEGENA) R
package (version 1.3.7) [107].
Duetothelowvarianceingeneexpressionob-
served among samples at 2 and 6 hpi (Fig. 2aandb),
only data from the 24 and 48 hpi time points were
used for the differential correlation analysis. The
DGCA R package was used to filter normalised gene
counts such that genes in the lower 30th percentile
of median expression values were removed. Pearson
correlation coefficients were then calculated for each
gene pair between the control non-infected and M.
bovis-infected samples at 24 and 48 hpi. Following
this, for each time point, the infected and control
samples were randomly shuffled, and the analysis was
repeated for a total of ten iterations. Additionally,
using the permutation testing and reference pool dis-
tribution approaches implemented in DGCA, an em-
pirical P-value was calculated for each observed
correlation coefficient and q-values were calculated
based on empirical P-values and the estimated pro-
portion of null hypotheses; gene pairs were then con-
sidered to be differentially correlated with a q-value
threshold of 0.10 [61].
The correlation networks and network parameters
generated by DGCA were initially visualised, examined
and evaluated using the Cytoscape platform. In correl-
ation networks, based on gene co-expression, each gene
acts as a node and each correlation acts as a weighted
edge, depending on the strength of the correlation coef-
ficient [108,109]. The DGCA network data for the 24
and 48 hpi time points were then imported into MEGE
NA for identification of functional subnetworks (mod-
ules) using differentially correlated (q< 0.10) gene pairs
ranked by q-value to a maximum of 3500 gene pairs; this
gave 3500 and 1085 gene pairs for the 24 and 48 hpi
correlation networks, respectively. Each gene pair was
assigned to a class that described the change in correl-
ation depending on infection status, and only those
genes that exhibited a change in expression pattern were
included in the MEGENA analysis to identify functional
modules.
Functional modules in the 24 and 48 hpi correlation
networks were detected as locally coherent subnetwork
clusters with a minimum of 20 unique genes that MEGE
NA classified as statistically significant (P-value < 0.05)
based on analyses of shortest path indices, local path
index, weight of the correlation and overall modularity.
The resulting MEGENA functional modules were
visualised using the Cytoscape platform and genes
embedded in functional modules at 24 and 48 hpi
were combined and annotated for downstream GWAS
integration as described below. The genes contained
in these functional modules were also subject to gene
ontology (GO) term enrichment analyses within the
MEGENA package [107].
Detection of active gene subnetworks using a
tuberculosis and mycobacterial infection gene interaction
network
The GeneCards
®
gene compendium and knowledgebase
(www.genecards.org; version 4.9), which integrates
Hall et al. BMC Genomics (2021) 22:343 Page 15 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
multiple sources of biological information on all anno-
tated and predicted human genes [78], was used to iden-
tify a set of genes that are functionally associated with
the host response to TB and other diseases caused by in-
fection with mycobacteria. The GeneCards search query
generated a total of 2291 gene hits (Additional file 4)
using the search terms: tuberculosis OR mycobac-
terium OR mycobacteria OR mycobacterial.
Genes were ranked by a GeneCards statistic—the Rele-
vance Score—based on the Elasticsearch algorithm [110],
which determines the strength of the relationships be-
tween genes and keyword terms. Gene IDs were con-
verted to human Ensembl gene IDs [111] and retained
for downstream analysis using the InnateDB knowledge-
base and analysis platform for systems level analysis of
the innate immune response (www.innatedb.com; ver-
sion 5.4) [62].
A gene interaction network (GIN) was generated
with the gene list output from GeneCards using Inna-
teDB with default settings and this network was
visualised using Cytoscape. The jActivesModules
Cytoscape plugin (version 3.12.1) [112] was then used
to superimpose the bAM RNA-seq gene expression
data and detect, through a greedy search algorithm,
differentially active subnetworks (modules) of genes at
the 24 and 48 hpi time points. Locally coherent clus-
ters that contain genes that are differentially
expressed were identified using the log
2
FC and P
adj.
values of each differentially expressed gene; the over-
all connectivity of those genes with their immediate
module co-members; and the comparison of that con-
nectivity with a background comprised of randomly
drawn networks using the same genes, but independ-
ent of the base network. Genes embedded in active
modules that were detected as statistically significant
at 24 and 48 hpi were combined and annotated for
downstream GWAS integration as described below.
Integration of M. bovis-infected bovine AM gene
expression data with bTB GWAS data
To facilitate integration of GWAS data with gene sets
generated from functional genomics data analyses, an R
software package was developed—gwinteR (github.com/
ThomasHall1688/gwinteR), which can be used to test
the hypothesis that a specific set of genes is enriched for
signal in a GWAS data set relative to the genomic back-
ground. This gene set, for example, could be an output
from an active gene module network analysis of tran-
scriptomics data from a cell type or tissue relevant to
the GWAS phenotype. To formally test the primary hy-
pothesis, the gwinteR tool was designed to determine if
genomic regions containing GWAS SNPs that are prox-
imal to genes within a gene set are enriched for statis-
tical associations with the trait/s analysed in the GWAS.
The gwinteR tool works as follows: 1) a set of signifi-
cant and non-significant SNPs (named the target SNP
set) is collated across all genes in a specific gene set at
increasing genomic intervals upstream and downstream
from each gene inclusive of the coding sequence (e.g., ±
10 kb, ±20 kb, ±30 kb ……±100 kb); 2) for each gen-
omic region, a null distribution of 1000 SNP sets, each
of which contains the same number of total significant
and non-significant combined SNPs as the target SNP
set, is generated by resampling with replacement from
the search space of the total population of SNPs in the
GWAS data set; 3) the nominal (uncorrected) GWAS P-
values for the target SNP set and the null distribution
SNP sets are converted to local FDR-adjusted P-values
(P
adj.
) using the fdrtool R package (current version
1.2.15) [113]; 4) a permuted P-value (P
perm.
) to the test
the primary hypothesis for each observed genomic inter-
val target SNP set is generated based on the proportion
of permuted random SNP sets where the same or a lar-
ger number of SNPs exhibiting significant q-values (e.g.
q< 0.05 or q< 0.10) are observed; 5) gwinteR generates
data to plot P
perm.
results by genomic interval class and
obtain a graphical representation of the GWAS signal
surrounding genes within the target gene set; and 6) a
summary output file of all SNPs in the observed target
SNP set with genomic locations and q-values is gener-
ated for subsequent investigation.
In the original bTB GWAS data set used for the
present study [55], the WGS-imputed SNPs were
mapped to the UMD3.1 bovine genome assembly [114].
Consequently, prior to GWAS data integration, the im-
puted and previously filtered SNPs for each of the three
breed groups were mapped, using a custom R pipeline
(github.com/ThomasHall1688/Bovine_multi-omic_
integration), to the most recent ARS-UCD1.2 cattle ref-
erence genome assembly [96]. After this step, there were
14,583,567, 14,586,972 and 12,740,315 autosomal SNPs
with nominal GWAS P-values that could be used for in-
tegrative genomics analyses of the CHA, LIM and HOF
breeds, respectively.
For the integrative analyses of bAM functional
genomics outputs with the bTB GWAS data, three
different subsets of genes were used: 1) basic DE
gene sets that were filtered to ensure manageable
computational loads using stringent expression
threshold criteria of |log
2
FC| > 2 and P
adj.
<0.01 and
P
adj.
< 0.000001 for 24 and 48 hpi, respectively; 2)
genes embedded in functional modules at 24 and 48
hpi that were detected using the MEGENA package
in the differential co-expression network analyses;
and 3) genes embedded in active modules at 24 and
48 hpi that were identified using jActiveModules
within the tuberculosis and mycobacterial infection
interaction network.
Hall et al. BMC Genomics (2021) 22:343 Page 16 of 20
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Abbreviations
AM: Alveolar macrophage; bAM: Bovine alveolar macrophage; B-
H: Benjamini-Hochberg; bTB: Bovine tuberculosis; CHA: Charolais; ChIP-
seq: Chromatin immunoprecipitation sequencing; CON: Correlation network;
DEG: Differentially expressed gene; DEN: Differential expression network;
DGCA: Differential Gene Correlation Analysis; EBV: Estimated breeding value;
GCRS: GeneCards Relevance Score; FDR: False discovery rate; GIN: Gene
interaction network; GO: Gene ontology; GWAS: Genome-wide association
study; HOF: Holstein-Friesian; HWE: Hardy-Weinberg equilibrium;
IFN: Interferon; IPA: Ingenuity Pathway Analysis; LIM: Limousin; MEGE
NA: Multiscale Embedded Gene Co-expression Network Analysis; MAF: Minor
allele frequency; MTBC: Mycobacterium tuberculosis complex;
QTL: Quantitative trait locus; RNA-seq: RNA sequencing; RNI: Reactive
nitrogen intermediate; ROS: Reactive oxygen species; SICTT: Single
intradermal comparative tuberculin test; SNP: Single nucleotide
polymorphism; TB: Tuberculosis; WGS: Whole-genome sequence;
zTB: Zoonotic tuberculosis
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12864-021-07643-w.
Additional file 1. Differentially expressed genes across the infection
time course and DEG-24 and DEG-48 input gene sets for GWAS
integration.
Additional file 2. Enriched IPA canonical pathways at 24 and 48 hpi.
Additional file 3. Outputs from correlation network analyses using
DGCA with MEGENA and CON-24 and CON-48 input gene sets for GWAS
integration.
Additional file 4. Outputs from differential network analyses using
GeneCards
®
, InnateDB with Cytoscape/jActiveModules and DEN-24 and
DEN-48 input gene sets for GWAS integration.
Additional file 5. 100 random (RAN) input gene sets used for GWAS
integration.
Additional file 6. Functional genomics and GWAS integration results for
Charolais (CHA), Limousin (LIM) and Holstein-Friesian (HOF).
Additional file 7. GWAS prioritised SNP results for Charolais (CHA),
Limousin (LIM) and Holstein-Friesian (HOF).
Additional file 8: S1 Fig. Principal component analysis (PCA) plots for
individual animal bAM gene expression data at a2 hpi, b6 hpi, c24 hpi,
and d48 hpi. S2 Fig. Gene ontology (GO) enrichment for functional
modules identified from the differential co-expression correlation network
generated from M. bovis-infected bAM gene expression at 24 hpi. S3 Fig.
Gene ontology (GO) enrichment for functional modules identified from
the differential co-expression correlation network generated from M.
bovis-infected bAM gene expression at 48 hpi. S4 Fig. Venn diagram il-
lustrating the overlaps for the three 24 hpi input gene sets used for inte-
gration with cattle GWAS data sets. S5 Fig. Venn diagram illustrating the
overlaps for the three 48 hpi input gene sets used for integration with
cattle GWAS data sets. RNA-seq statistics and results for this study are
available at the Dryad Digital Repository: https://doi.org/10.5061/dryad.
83bk3j9q6.
Acknowledgements
This work was presented at the 37th International Society for Animal
Genetics (ISAG) Conference held in Lleida, Spain; 7–12th July 2019 (www.
isag.us/Docs/Proceedings/ISAG2019_Proceedings.pdf).
Authors’contributions
TJH, MPM, KEK, DPB, SVG and DEM conceived and designed the study. JAB
performed experimental work. SCR and DPB provided cattle genotype and
other genomic data. TJH, MPM, GPM, KEK, SCR and CNC performed
bioinformatics and computational analyses. TJH, CNC and DEM wrote and
prepared the manuscript and figures. All authors read and approved the final
manuscript.
Funding
This study was supported by Science Foundation Ireland (SFI) Investigator
Programme Awards to D.E.M. and S.V.G. (grant nos. SFI/08/IN.1/B2038 and
SFI/15/IA/3154); a Department of Agriculture, Food and the Marine (DAFM)
project award to D.E.M (TARGET-TB; grant no. 17/RD/US-ROI/52); and a
European Union Framework 7 project grant to D.E.M. (no: KBBE-211602-
MACROSYS). The funding agencies had no role in the study design, collec-
tion, analysis, and interpretation of data, and no role in writing the
manuscript.
Availability of data and materials
The RNA-seq data set was generated by the authors and can be obtained
from the NCBI Gene Expression Omnibus (GEO): accession number
GSE62506. GWAS summary statistics data were obtained from a published
study that provides additional information about sequence and genotype
data availability [55]. The source code to reproduce the results of the ana-
lyses is available from a GitHub repository (https://github.com/ThomasHall16
88/Bovine_multi-omic_integration). The source code used to integrate the
outputs from functional genomics and GWAS data sets is available from a
GitHub repository (https://github.com/ThomasHall1688/gwinteR) Detailed
RNA-seq filtering and mapping statistics are provided in a data file available
from the Dryad Digital Repository: https://doi.org/10.5061/dryad.83bk3j9q6.
The computational pipeline and software used for this study is detailed
below.
Complete pipeline: https://github.com/ThomasHall1688/Bovine_multi-omic_
integration
gwinteR: https://github.com/ThomasHall1688/gwinteR
DESeq2: https://github.com/mikelove/DESeq2
DGCA: https://github.com/andymckenzie/DGCA
MEGENA: https://github.com/cran/MEGENA
Cattle genome assembly and annotation: www.ncbi.nlm.nih.gov/datasets/
genomes/?txid=9913
Declarations
Ethics approval and consent to participate
All animal procedures were performed in accordance with EU Directive
2010/63/EU and Irish Statutory Instrument 543/2012, with ethical approval
from the University College Dublin (UCD) Animal Research Ethics Committee
(AREC-13–14-Gordon).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Animal Genomics Laboratory, UCD School of Agriculture and Food Science,
UCD College of Health and Agricultural Sciences, University College Dublin,
Belfield, Dublin D04 V1W8, Ireland.
2
Bioscience Research Institute, Athlone
Institute of Technology, Dublin Road, Athlone, Westmeath N37 HD68, Ireland.
3
Present address: Genuity Science, Cherrywood Business Park. Loughlinstown,
Dublin D18 K7W4, Ireland.
4
Irish Cattle Breeding Federation, Highfield House,
Shinagh, Bandon, Cork P72 X050, Ireland.
5
Teagasc, Animal and Grassland
Research and Innovation Centre, Moorepark, Fermoy, Cork P61 C996, Ireland.
6
UCD School of Veterinary Medicine, UCD College of Health and Agricultural
Sciences, University College Dublin, Belfield, Dublin D04 V1W8, Ireland.
7
UCD
Conway Institute of Biomolecular and Biomedical Research, University
College Dublin, Belfield, Dublin D04 V1W8, Ireland.
Received: 19 November 2020 Accepted: 22 April 2021
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