Pneumonia remains the leading cause of infectious mortality in under-five children,
and the burden is highest in sub-Saharan Africa. To mitigate this burden, further
knowledge is required to accelerate the development of innovative and cost-effective
approaches. To gain a deeper insight into the pathogenesis of pneumonia,
I investigated the central hypothesis that systemic pathway (cellular and molecular)
responses underpin the development of severe pneumonia outcomes.
Mainly, I compared whole blood transcriptomes between severe pneumonia cases
(clinically stratified as mild, severe and very severe) and non-pneumonia community
controls (prospectively matched by age and sex). In total, 803 whole blood RNA
samples were collected from Gambian children (aged 2-59 months) between 2007
and 2010, of which, 518 passed laboratory quality control criteria for the microarray
analysis. After data cleaning, the final database reduced to 503 samples including
the training (n=345) and independent validation (n=158) data sets.
To investigate the cellular responses, I applied computational deconvolution
analysis to assess the variations of immune cell type proportions with pneumonia
severity. To further enhance the computational performance, I applied a data fusion
approach on 3,475 immune marker genes from different resources to derive an
optimal and integrated blood marker list (IBML, m=277) for Neutrophils, Monocytes,
NK, Dendritic, B and T cell types; which robustly performed better than the existing
individual resources. Using the IBML resource, pneumonia severity was significantly
associated with the depletion of B, T, Dendritic and NK cell types, and the elevation
of Monocytes and neutrophil proportions (P-value<0.001).
At the molecular level, pneumonia severity was associated (false discovery
rate<0.05) with a battery of systemic pathway (innate, adaptive and metabolic)
responses in a range of biomedical databases. While the up-regulation of
inflammatory innate responses was also observed in mild cases, severe pneumonia
cases were predominantly associated with the co-inhibition of the cells of the
adaptive immune response (B and T) and Natural killer cells, and the up-regulation
of fatty acid and lipid metabolism. While most of these findings were anticipated, the
involvement of NK cells was unexpected, and potentially presents a novel immune-modulation
target for mitigating the burden of pneumonia. Together, the cellular and
molecular pathways responses consistently support the central hypothesis that
systemic pathway responses contribute significantly to the development of severe
pneumonia outcomes.
Clinically, the identification and appropriate treatment of patients at the higher risk of
developing severe pneumonia outcomes remains the major challenge. To address
that, I applied supervised machine-learning approaches on cellular pathway based
transcriptomic features; and derived a 33-gene classifier (representing the NK, T,
and neutrophils cell types), which accurately detected severe pneumonia cases in
both the training (leave-one-out cross-validated accuracy=99%) and independent
validation (accuracy=98%) datasets. Independently, similar performance (98% in
each dataset) was associated with a subset (m=18) of the validated 52-gene
neonatal sepsis classifier. Conversely, at least 75% of the cellular biomarkers were
differentially expressed (false discovery rate<0.05) in bacterial neonatal sepsis.
Further, very severe pneumonia cases were predominantly associated with
antibacterial responses; and mild pneumonia cases with blood-culture-confirmed
positivity were also associated with an increased frequency of differentially
expressed genes. These findings suggest the significant contribution of bacterial
septicaemia in the development of serious pneumonia outcomes. Together, this
study highlights the future potential of host-derived systemic biomarkers for early
identification and novel treatment modalities of high-risk cases presenting at a
resource-constrained clinic with mild pneumonia. However, further validation studies
are required
