2 research outputs found
Experimental vaccination for onchocerciasis and the identification of early markers of protective immunity
Onchocerciasis, caused by Onchocerca volvulus remains a major public health and
socio-economic problem across the tropics, despite years of mass drug administration
(MDA) with Ivermectin to reduce disease burden. Through modelling, it has been
shown that elimination cannot be achieved with MDA alone and additional tools are
needed, such as vaccination, which remains the most cost-effective tool for long-term
disease control. The feasibility behind vaccination against O. volvulus can be
demonstrated in the Litomosoides sigmodontis mouse model, which shows that
vaccine induced protection can be achieved with immunisation using irradiated L3, the
infective stage of L. sigmodontis and with microfilariae (Mf), the transmission stage
of the parasite. There is further evidence of protective immunity in humans, with
individuals living in endemic areas that show no signs of infection despite being
exposed to the parasite (endemic normal).
The protective efficacy of promising vaccine candidates were evaluated using an
immunisation time course in the L. sigmodontis model, using either DNA plasmid or
peptide vaccines. In immunisation experiments in L. sigmodontis, Mf numbers are
used as a measure of protection and marks the end of an immunisation time course.
However, when changes in gene expression were measured at the end of an
immunisation time course, in attempts to identify gene signatures that could be used
as markers of protection (correlates of protection) in the blood, no gene signatures were
found to be associated with protection. This suggest that at the end of an immunisation
time course, when protection is measured (change in Mf numbers), it is too late in
infection to measure changes in immune pathways being triggered. Changes in gene expression were therefore measured in blood samples collected
throughout an immunisation time course in the L. sigmodontis model, in order to
identify the time point in an immunisation experiment which are the most indicative
of protection. Two independent immunisation time courses were used, either using
irradiated L3 or Mf as vaccine against L. sigmodontis, as these elicit the greatest
protection. This generated a large high dimensional dataset, that was too large and
complex for a differential fold-change analysis. Therefore, an analysis pipeline was
created using machine learning algorithms, to detect changes in gene expression
throughout the time courses to detect markers of protection.
The 6 hour time point following immunisation showed the greatest change in gene
expression, with the analysis pipeline identifying known pathways associated with
vaccine-induced immunity. The pipeline was applied to gene expression data from
human samples obtained from individuals living in endemic areas who were either
infected with O. volvulus or endemic normal (naturally protected), this was to identify
pathways associated with protective immunity in humans. When comparing vaccine
induced immunity seen in mice and natural protective immunity in humans there was
some overlap in pathways being triggered, suggesting that similar pathways are needed
for protection and that if a vaccine can trigger the right pathways in mice, it is likely
to be effective in humans.
Overall the machine learning analysis of the gene expression data, not only shows that
it is feasible to measure change in gene expression in blood during filarial infections,
but that during an immunisation time course it is the early time points following
immunisation that are the most predictive of vaccine efficacy (protection outcome). One of the vaccine candidates, cysteine protease inhibitor-2 (CPI), is a known
immuno-modulator that inhibits MHC-II antigen presentation on antigen presenting
cells such as dendritic cells (DC). This candidate has consistently been shown to
induce protection if its immuno-modulatory active site was modified. In in vitro
studies, it was shown that modification of the active site of CPI rescues antigen
presentation in DC. This shows the importance of DC activation before the onset of
infection, demonstrating the importance of triggering protective responses early in
infection, and provides insight on how one of the vaccine candidates achieves
protection
A Comprehensive Analysis Of Classification Algorithms For Cancer Prediction From Gene Expression
With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions