Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future

Multiomics links global surfactant dysregulation with airflow obstruction and emphysema in COPD

RATIONALE: Pulmonary surfactant is vital for lung homeostasis as it reduces surface tension to prevent alveolar collapse and provides essential immune-regulatory and antipathogenic functions. Previous studies demonstrated dysregulation of some individual surfactant components in COPD. We investigated relationships between COPD disease measures and dysregulation of surfactant components to gain new insights into potential disease mechanisms. METHODS: Bronchoalveolar lavage proteome and lipidome were characterised in ex-smoking mild/moderate COPD subjects (n=26) and healthy ex-smoking (n=20) and never-smoking (n=16) controls using mass spectrometry. Serum surfactant protein analysis was performed. RESULTS: Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, surfactant protein (SP)-B, SP-A and SP-D concentrations were lower in COPD versus controls (log2 fold change (log2FC) -2.0, -2.2, -1.5, -0.5, -0.7 and -0.5 (adjusted p<0.02), respectively) and correlated with lung function. Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, SP-A, SP-B, SP-D, napsin A and CD44 inversely correlated with computed tomography small airways disease measures (expiratory to inspiratory mean lung density) (r= -0.56, r= -0.58, r= -0.45, r= -0.36, r= -0.44, r= -0.37, r= -0.40 and r= -0.39 (adjusted p<0.05)). Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, SP-A, SP-B, SP-D and NAPSA inversely correlated with emphysema (% low-attenuation areas): r= -0.55, r= -0.61, r= -0.48, r= -0.51, r= -0.41, r= -0.31 and r= -0.34, respectively (adjusted p<0.05). Neutrophil elastase, known to degrade SP-A and SP-D, was elevated in COPD versus controls (log2FC 0.40, adjusted p=0.0390), and inversely correlated with SP-A and SP-D. Serum SP-D was increased in COPD versus healthy ex-smoking volunteers, and predicted COPD status (area under the curve 0.85). CONCLUSIONS: Using a multiomics approach, we demonstrate, for the first time, global surfactant dysregulation in COPD that was associated with emphysema, giving new insights into potential mechanisms underlying the cause or consequence of disease

Bioinformatics approaches to vaccine design for bacterial pathogens

This thesis focused on bacterial vaccinology and employed a newly emergent branch of vaccinology; reverse vaccinology (RV). RV is an in silico process that predicts vaccine candidates from an entire bacterial proteome, thus enabling the realisation of a greater number of putative vaccine candidates when compared to conventional vaccinology approaches. A previous RV classifier that utilised the computational field of machine learning (ML) was used to predict bacterial protective antigens (BPAs) (i.e. vaccine candidates) for Mycobacterium tuberculosis (Mtb). Mtb was chosen as the initial focus for RV approaches in this thesis because one third of the world’s population are infected with Mtb and in 2015 Mtb infection killed 1.8 million people. It is also being recognised that the only clinically licensed vaccine against Mtb infection, Bacille Calmette-Guérin (BCG), has varying rates of protection. Predicted BPAs by a published RV classifier were synthesised as DNA vaccines and tested in a mouse model of Mtb infection. However, the predicted BPAs were shown not to generate protection in repeat animal trials. To address the negative result obtained when testing BPAs predicted by a previous RV classifier, enhancements were made to the previously published RV classifier (i.e. nested leave-tenth-out cross-validation, subcellular localisation bias removal, increased size of training dataset and increased type of protein annotation tools used to generate features). Finally, the enhanced RV classifier, developed in this thesis, was assessed using a more biologically revealing metric termed recall in the proteomes of Mtb and Neisseria meningitidis serogroup B (MenB). MenB was chosen to assess the recall metric as it enabled comparisons to the BEXSERO vaccine, which was the first clinically licensed vaccine developed using RV. In summary, this thesis has developed a biologically relevant RV classifier that can now be used to predict BPAs for any bacterial pathogen with a sequenced genome. It is envisaged that these predicted BPAs could then be used to facilitate the rapid formulation of novel subunit vaccines

The promise of reverse vaccinology

Reverse vaccinology (RV) is a computational approach that aims to identify putative vaccine candidates in the protein coding genome (proteome) of pathogens. RV has primarily been applied to bacterial pathogens to identify proteins that can be formulated into subunit vaccines, which consist of one or more protein antigens. An RV approach based on a filtering method has already been used to construct a subunit vaccine against Neisseria meningitidis serogroup B that is now registered in several countries (Bexsero). Recently, machine learning methods have been used to improve the ability of RV approaches to identify vaccine candidates. Further improvements related to the incorporation of epitope-binding annotation and gene expression data are discussed. In the future, it is envisaged that RV approaches will facilitate rapid vaccine design with less reliance on conventional animal testing and clinical trials in order to curb the threat of antibiotic resistance or newly emerged outbreaks of bacterial origin

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction

Previous work has shown that proteins that have the potential to be vaccine candidates can be predicted from features derived from their amino acid sequences. In this work, we make an empirical comparison across various machine learning classifiers on this sequence-based inference problem. Using systematic cross validation on a dataset of 200 known vaccine candidates and 200 negative examples, with a set of 525 features derived from the AA sequences and feature selection applied through a greedy backward elimination approach, we show that simple classification algorithms often perform as well as more complex support vector kernel machines. The work also includes a novel cross validation applied across bacterial species, i.e. the validation proteins all come from a specific species of bacterium not represented in the training set. We termed this type of validation Leave One Bacteria Out Validation (LOBOV)

Transcriptomic analysis of the effect of remote ischaemic conditioning in an animal model of necrotising enterocolitis

Necrotising enterocolitis (NEC) has a complex pathophysiology but the common end-point is ischaemia reperfusion injury (IRI) and intestinal necrosis. We have previously reported that RIC significantly reduces the intestinal injury in a rat model of NEC. Here we describe the changes in intestinal mRNA occurring in the intestine of animals exposed to IRI, both with and without RIC. Related rat-pups were randomly assigned to four groups: SHAM, IRI only, RIC only and RIC + IRI. IRI animals, underwent 40 min of intestinal ischaemia, and 90 min of reperfusion. Animals that underwent RIC had three cycles of 5 min of alternating ischaemia/reperfusion by means of a ligature applied to the hind limb. Samples from the terminal ileum were immediately stored in RNA-preserving media for later next generation sequencing and transciptome analysis using R v 3.6.1. Differential expression testing showed that 868 genes differentially expressed in animals exposed to RIC alone compared to SHAM and 135 in the IRI and RIC group compared to IRI alone. Comparison between these two sets showed that 25 genes were differentially expressed in both groups. Pro-inflammatory molecules: NF-ĸβ2, Cxcl1, SOD2 and Map3k8 all show reduced expression in response to RIC. Targeted gene analysis revealed increased expression in PI3K which is part of the so-called RISK-pathway which is a key part of the protective mechanisms of RIC in the heart. Overall, this transcriptomic analysis shows that RIC provides a protective effect to the intestine via anti-inflammatory pathways. This could be particularly relevant to treating and preventing NEC

NTHi-IAV coinfection of macrophages alters infection outcomes and inflammatory responses

Nontypeable Haemophilus influenzae (NTHi) chronically colonises the airway of individuals with chronic respiratory disease, with persistence suggested to be facilitated by invasion of airway macrophages. Previous data supports an interaction between NTHi colonisation and the risk of viruses exacerbating underlying respiratory diseases. As exacerbations are the main cause of morbidity and mortality of respiratory diseases, the drivers of this increased risk need to be identified.The aim of this work was to investigate whether prior NTHi infection compromises the ability of macrophages to respond to a subsequent viral challenge. A monocyte-derived macrophage (MDM)-NTHi intracellular persistence model was adapted to include coinfection with the influenza A virus (IAV). Compared to pathogen-alone controls, NTHi presence significantly increased by 190% (p&lt;0.05), whereas the percentage of IAV-infected MDM significantly decreased (23.9% to 6.8%, p&lt;0.01) during co-infection. This decreased viral infection was associated with NTHi-mediated transcriptomic upregulation of MDM antiviral responses (FDR p&lt;0.05) and IFN-β release (p&lt;0.05) prior to IAV challenge. Coinfected MDM released higher levels of inflammatory mediators (TNF-α, IL-1β, IL-6, IL-8, IL-15, IL-23 and IL-36β, all p&lt;0.05) compared to IAV infection alone.This work demonstrates that although prior NTHi infection primed MDM to better respond to IAV infection, coinfection resulted in increased NTHi load and MDM pro-inflammatory responses. Considering the interactions of colonising airway bacteria and viral infections on host immune responses may better inform on treatment strategies to reduce exacerbations.<br/

Dual RNASeq unveils NTHi-macrophage transcriptomic changes during intracellular persistence

Nontypeable Haemophilus influenzae (NTHi) is a respiratory tract pathogen associated with severe, neutrophilic asthma. Although macrophages are responsible for orchestrating the immune response and pathogen clearance in the lung, NTHi is able to persist within macrophages. The mechanism of NTHi intracellular persistence is not understood, therefore the aim of this work was to use dual RNASeq to investigate the host-pathogen interactions that allow this persistence. Monocyte-derived macrophages (MDM) were used to model NTHi-macrophage infection. RNA was isolated after 6h and 24h of infection and sequenced using the Illumina NovaSeq 6000 platform. Differential gene expression analysis found expression of 863 MDM genes (FDR p&lt;0.05) conserved across 6h and 24h. Gene set enrichment analysis (GSEA) identified these 863 genes as a core transcriptomic immune response profile, featuring enrichment of defence response and cytokine-mediated signalling pathways. Furthermore, KEGG pathway analysis revealed enriched pathways involved in the response to an intracellular pathogen. In comparison, GSEA of 108 differentially expressed NTHi genes (FDR p&lt;0.05) showed enrichment of ribosome and metabolic pathways, suggesting transcriptomic adaptation of NTHi to intracellular residence within MDM. Taken together, host and pathogen transcriptomic data indicate NTHi intracellular persistence in this model, despite upregulation of macrophage immune response pathways. Ongoing work using lung macrophages from asthma patients will assess whether these gene pathways are detectable and correlate with NTHi persistence in asthma