Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria: RNA-seq statistics and results

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 hours
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.
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.