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Evaluating the Immune Response in Treatment-Naive Hospitalised Patients With Influenza and COVID-19

The worldwide COVID-19 pandemic has claimed millions of lives and has had a profound effect on global life. Understanding the body’s immune response to SARS-CoV-2 infection is crucial in improving patient management and prognosis. In this study we compared influenza and SARS-CoV-2 infected patient cohorts to identify distinct blood transcript abundances and cellular composition to better understand the natural immune response associated with COVID-19, compared to another viral infection being influenza, and identify a prognostic signature of COVID-19 patient outcome. Clinical characteristics and peripheral blood were acquired upon hospital admission from two well characterised cohorts, a cohort of 88 patients infected with influenza and a cohort of 80 patients infected with SARS-CoV-2 during the first wave of the pandemic and prior to availability of COVID-19 treatments and vaccines. Gene transcript abundances, enriched pathways and cellular composition were compared between cohorts using RNA-seq. A genetic signature between COVID-19 survivors and non-survivors was assessed as a prognostic predictor of COVID-19 outcome. Contrasting immune responses were detected with an innate response elevated in influenza and an adaptive response elevated in COVID-19. Additionally ribosomal, mitochondrial oxidative stress and interferon signalling pathways differentiated the cohorts. An adaptive immune response was associated with COVID-19 survival, while an inflammatory response predicted death. A prognostic transcript signature, associated with circulating immunoglobulins, nucleosome assembly, cytokine production and T cell activation, was able to stratify COVID-19 patients likely to survive or die. This study provides a unique insight into the immune responses of treatment naïve patients with influenza or COVID-19. The comparison of immune response between COVID-19 survivors and non-survivors enables prognostication of COVID-19 patients and may suggest potential therapeutic strategies to improve survival.


INTRODUCTION
Previous studies investigating the differences between patients with COVID-19 or influenza on admission to hospital found that both patient groups present with similar systemic inflammation marker levels including C-reactive protein (CRP), white blood cell count, neutrophil count and neutrophil/lymphocyte ratio (1). Once hospitalised, patients with COVID-19 are at a higher risk of developing respiratory distress, pulmonary embolism, septic shock and haemorrhagic strokes, had a longer length of stay in intensive care, and were more likely to require mechanical ventilation compared to patients with influenza (2). The inhospital mortality was found to be roughly three times higher for COVID-19 compared to influenza (2).
The viral immune response against influenza is well characterised (3), it involves the innate immune system [e.g. macrophages, granulocytes and dendritic cells, which release proinflammatory cytokines and type I interferons (IFN)] to inhibit viral replication, recruit other immune cells to the site of infection, and stimulate the adaptive immune response which consists of a humoral and a cellular mediated immunity, initiated principally by virus-specific antibodies and T cells. Our current understanding indicates that COVID-19 severity and duration are due to a total or early innate immune and IFN response evasion by SARS-CoV-2 (4-7). While patients infected with influenza are able to mount an IFN response (1), which correlates with quicker recovery and decreased disease severity and mortality (8,9). Similarly, early administration of IFN-beta for COVID-19 patients results in a lowered in-hospital mortality and quicker recovery (10,11). Pro-inflammatory cytokine expression occurs for a prolonged time in patients with COVID-19 at similar levels with influenza patients (1), with interleukin (IL)-6 and IL-10 (12-14) associated with increased COVID-19 severity, while it has been observed that the presence of antibodies, CD4+ and CD8+ T cells are correlated with a positive patient outcome (15). Therefore, a key question is if an adaptive immune response differs depending on the disease, and whether specific prognostic markers can be identified.
To address this, we first compared a cohort of hospitalised patients infected with influenza virus with an equivalent cohort of SARS-CoV-2 infected patients identified from individuals hospitalised during the first wave of the pandemic and prior to the availability of approved COVID-19 treatments and vaccines. Secondly, we compared individuals who either survived COVID-19 or who succumbed to COVID-19. Both analyses provides us insights to a natural specific antiviral immune response associated with COVID-19, and with COVID-19 survival. Clinical parameters were recorded and peripheral blood, used for RNA sequencing (RNA-seq), were taken at admission to hospital. We aimed to identify distinct patterns of blood transcript abundances and cellular composition to better understand the COVID-19 specific antiviral immune response and to identify a prognostic signature indicative of COVID-19 outcome. The COV-19POC study was a non-randomised interventional trial evaluating the clinical impact of molecular point-of-care testing (mPOCT) for SARS-CoV-2 in adult patients. The trial took place during the first wave of the pandemic, from 20th March to 29th April 2020, and prior to the availability of approved COVID-19 treatments. Patients (≥ 18 years old) were recruited from the Acute Medical Unit (AMU), Emergency Department (ED) or other acute areas of Southampton General Hospital when presenting with acute respiratory illness (ARI), or without ARI but suspected SARS-CoV-2 infection, or without ARI and not a suspected COVID-19 case, according to Public Health England guidelines, but where SARS-CoV-2 testing is considered necessary by the clinical team. ARI is defined as an acute upper or lower respiratory illness or an acute exacerbation of a chronic respiratory illness. Patients were excluded who did not meet the inclusion criteria, declined nasal and/or pharyngeal swabbing, consent declined or whom were already recruited to the study in the last 14 days (16). For this comparative study patients were included who were found to be SARS-CoV-2 positive, according to the QIAGEN QIAstat-Dx PCR testing platform with the QIAstat-Dx Respiratory SARS-CoV-2 Panel (17), in the COV-19POC study.

Recruitment of Patients Positive for SARS-CoV-2 or Influenza Infection
The FluPOC study was a multicentre randomised controlled trial evaluating the clinical impact of mPOCT for influenza in hospitalised adult patients with acute respiratory illness, during influenza season, using the BioFire FilmArray platform with the Respiratory Panel 2.1 (18). The trial took place during influenza seasons over the two winters of 2017/18 and 2018/19. Patients (≥ 18 years old) presenting with ARI, duration less than 10 days prior to admission to hospital, were recruited from the AMU and ED of Southampton General Hospital and Royal Hampshire County Hospital. Patients were excluded when not fulfilling all the inclusion criteria, receiving a purely palliative treatment approach, declining nasal and/or pharyngeal swabbing, consent declined or whom were previously recruited and re-presented after 30 days after hospital discharge (19).
All participants were recruited within the first 24 hours of admission to hospital, and prior to any treatments. Blood samples including whole blood in PAXgene Blood RNA tubes (BRT) (Preanalytix) were collected from 80 SARS-CoV-2 positive patients and 88 influenza positive patients, within 24 hours of enrolment, and stored at -80°C. For both cohorts the demographic and clinical data were collected at enrolment and outcome data from case note and electronic systems. ALEA and BC data management platforms were used for data capture and management.

Comparison of Baseline Clinical Characteristics
Baseline clinical characteristics of the patient groups were assessed using R (20) (v4.0.2) and RStudio (21)

Extraction of RNA From Clinical Samples and Illumina Sequencing
Total RNA was extracted from PAXgene BRT using the PAXgene Blood RNA Kit (PreAnalytix), according to the manufacturer's protocol at Containment Level 3 in a Tripass Class I hood. Extracted RNA was stored at -80°C until further use. Following the manufacturer's protocols, total RNA was used as input material into the QIAseq FastSelect-rRNA/Globin Kit (Qiagen) protocol to remove cytoplasmic and mitochondrial rRNA and globin mRNA with a fragmentation time of 7 or 15 minutes. Subsequently the NEBNext ® Ultra ™ II Directional RNA Library Prep Kit for Illumina ® (New England Biolabs) was used to generate the RNA libraries, followed by 11 or 13 cycles of amplification and purification using AMPure XP beads. Each library was quantified using Qubit and the size distribution assessed using the Agilent 2100 Bioanalyser and the final libraries were pooled in equimolar ratios. Libraries were sequenced using 150 bp paired-end reads on an Illumina ® NovaSeq 6000 (Illumina ® , San Diego, USA). Raw fastq files were trimmed to remove Illumina adapter sequences using Cutadapt v1.2.1 (24). The option "−O 3" was set, so that the 3' end of any reads which matched the adapter sequence with greater than 3 bp was trimmed off. The reads were further trimmed to remove low quality bases, using Sickle v1.200 (25) with a minimum window quality score of 20. After trimming, reads shorter than 10 bp were removed.

Systems Immunology-Based Analysis of Blood Transcript Modules
BTM analysis was performed with molecular signatures derived from 5 vaccine trials (32) as a reference dataset, and BTM activity was calculated using the BTM package (32) (v1.015) in Python (33) (v3.7.2) using the normalized counts as input. Module enrichment significance was calculated using CAMERA (34) (v3.46.0). The significance threshold for the linear model was set at FDR 0.05 for the comparison between patients with COVID-19 or influenza.

Differential Gene Expression Analysis Between Patient Groups
HTSeq (35) (v0.11.2) count was used to assign counts to RNA-seq reads in the SamTools sorted BAM file using GENCODE v34 annotation. Parameters used for HTSeq were -format=bam, -order=pos, -stranded=reverse, -type=exon and the other options were kept at default. EdgeR (36) (v3.30.3) was used for differential gene expression analysis with R (v4.0.2) in RStudio (v1.3.959). Genes with low counts across all libraries were filtered out using the filterByExpr command. Filtered gene counts were normalised using the Trimmed Mean of M -values (TMM) method. A PCA graph was constructed based on all differentially expressed genes to assess sample clustering. Differentially expressed genes were identified, after fitting the negative binomial models and obtaining dispersion estimates, using the exact test and using a threshold criteria of FDR p-value < 0.05 and log2 fold change < -1 and > 1. Genes which were within the threshold criteria were used for ToppGene gene list enrichment analysis, using the default settings, and GO biological process terms.

Unbiased Gene Co-Expression Analysis
Gene co-expression analysis was performed with BioLayout (37) (v3.4) using a correlation value of 0.95, other settings were kept at default. Clusters were manually assessed to determine gene expression differences depending on for example patient cohort. Gene clusters were subsequently analysed with ToppGene (38) gene list enrichment analysis, using the default settings, and Gene Ontology (GO) (39,40) biological process terms. The TMM normalised RNA-seq counts were used, together with the clinical phenotype information, for weighted correlation network analysis (WGCNA) with the R package WGCNA (41), using default settings and a power of 3.

Topological Mapping of Global Gene Patterns
TopMD Pathway Analysis (42) was conducted using the differential transcript abundances identified by differential gene expression analysis, generating a map of the differentially activated pathways between all patients with COVID-19 or influenza. The TopMD pathway algorithm measures the geometrical and topological properties of global differential gene expression embedded on a gene interaction network (43). This enables plotting and measurement of the differentially activated pathways through extrapolation of groups of mechanistically related genes, called TopMD pathways. TopMD pathways possess a natural hierarchical structure and can be analysed for enriched GO terms, by chi-square test.

In Silico Immune Profiling Predicting Immune Cell Levels Between Patient Groups
Relative abundance of 22 immune cell types and their statistical significance was deconvoluted from whole blood using the reference gene signature matrix (LM22) using CIBERSORTx (44). CIBERSORTx analysis was conducted on the CIBERSORTx website (45) using 100 permutations. Immune cell distribution between the groups were compared by Mann-Whitney test.

RESULTS
RNA-seq was undertaken for 80 patients with COVID-19 and 88 patients with influenza. Two patients with COVID-19 were identified as outliers and subsequent assessment revealed elevated white blood cell and lymphocyte counts caused by pre-existing chronic lymphocytic leukaemia (Supplementary

Molecular Differences
The median sequencing depths obtained were: 60.4 million reads for the patients with COVID-19, 58.9 million reads for the patients with influenza ( Supplementary Figure 2A), 55.7 million reads for the COVID-19 non-survivors and 62.6 million reads for the COVID-19 survivors (Supplementary Figure 2B). Clustering of blood transcriptomes revealed homogeneity between patients with COVID-19 or influenza suggesting any variation to be subtle (Supplementary Figure  3A), while a partial separation was found between patients who survived or died of COVID-19 indicative of a larger variation (Supplementary Figure 3B).

Topological Mapping of Global Gene Patterns
Topological analysis was used to define a global map of differentially activated pathways between COVID-19 and influenza. The first differentially activated pathway, with peak gene UBA52, was associated with cytoplasmic ribosomal proteins (FDR 1.55x10 -146 ) and translation factors (FDR 7.90x10 -07 ). This pathway was also found to be enriched for genes expressed by transcription factor Myc against the ChEA 2016 transcription factor database (FDR 7.07x10 -53 ) and of dendritic cells in the ARCHS4 transcription factors' co-expression database (FDR 1.34x10 -36 ). Activated Myc represses IRF7 and a significantly lower abundance of IRF7 was found in patients with COVID-19 (Supplementary Figure 5). The second differentially activated pathway, with peak gene NDUFAB1, was associated with mitochondrial complex I assembly model OXPHOS system (FDR 2.81x10 -66 ). The third differentially activated pathway, with peak gene PSMD14, was associated with proteasome degradation (FDR 1.

Adaptive Immune Response Associates With Patient Survival in COVID-19
After filtering out transcripts with low counts a total of 20,542 gene transcript abundance measures were obtained between patients with COVID-19 or influenza, and 23,850 gene transcript abundance measures between COVID-19 survivors and non-survivors. After further filtering (FDR < 0.05, log2 fold change < -1 or > 1) the following number of transcripts were found at a higher abundance: 71 transcripts in patients with influenza, 126 transcripts in patients with COVID-19 ( Figure 3A  The transcripts with increased abundance in patients with COVID-19 were associated with humoral immune response, complement activation and B cell mediated immunity ( Figure 3C), and the majority of these COVID-19 specific transcripts (83/126) were immunoglobulin genes, associated with an adaptive immune response, and were present at a higher abundance in primarily patients with COVID-19 (Supplementary Figure 8). This adaptive immune response, including complement activation, B cell mediated immunity and a humoral immune response mediated by circulating immunoglobulins, was associated specifically with COVID-19 survivors ( Figure 3D). While the transcripts specific for   Gene clusters were identified with BioLayout (r=0.85, MCL = 1.7). For each cluster the number of genes, predicted cell type and top biological process are given and whether that cluster was enriched in patients with COVID-19 or influenza. COVID-19 non-survivors were associated with an inflammatory response including interleukin signalling, neutrophil activation and neutrophil degranulation ( Figure 3E).

Clinical Covariates and Their Correlation With the Abundance of Different Gene Transcript Clusters
Weighted gene co-expression network analysis (WGCNA) identified 23 modules of co-expressed gene transcripts, and these were assessed with GO analysis to identify the associated biological processes terms. Furthermore, the correlation between these gene transcripts modules and the known clinical covariates was determined to investigate the potential drivers of the differences in gene transcript abundances (Supplementary Figure 9). The gene module which had the highest positive correlation (0.51, p-value 3x10 -12 ) with the type of viral infection, was found to be involved with complement activation via the classical pathway. This gene module was characterised by a weaker positive correlation (0.34, p-value 1x10 -05 ) with the duration of symptoms before hospital admission, lymphocyte count (0.33, p-value 2x10 -05 ), and a negative correlation with the presence of other underlying chronic respiratory disease (-0.37, pvalue 1x10 -06 ). Additionally, B cell activation was negatively correlated with e.g. age (-0.32, p-value 4x10 -05 ) and death within 30 days of hospital admission (-0.25, p-value 1x10 -03 ). While neutrophil degranulation and myeloid leukocyte activation were positively correlated among others with oxygen supplementation (r=0.26, p-value 1x10 -03 ), and death within 30 days of admission (r=0.25, p-value 1x10 -03 ) respectively. The type of viral infection was furthermore the biggest driver for differences in blood coagulation (r=0.39, p-value 2x10 -07 ), cellular response to interleukin-13 (r=0.38, p-value 5x10 -07 ). In contrast, positive regulation of chemokine production was negatively correlated with the type of viral infection (r=-0.28, p-value 3x10 -04 ) ( Table 4).

Immune Signatures as Predictors of COVID-19 Outcome
A distinct immune signature was selected and assessed for prediction accuracy in stratifying patients with COVID-19 for disease outcome. This signature consists of 47 genes ( Figure 4A), representative of the four biggest gene clusters associated with COVID-19 survival or fatality. These gene clusters are associated with humoral immune response mediated by circulating immunoglobulin (FDR p-value 2.23x10 -46 ), nucleosome assembly (FDR p-value 5.46x10 -19 ), regulation of T-helper 1 cell cytokine production (FDR p-value 4.24x10 -03 ) and regulation of T cell activation (FDR p-value 4.51x10 -04 ) (Supplementary Figure 10). This gene signature was highly predictive for outcome, with a maximum specificity of 75% and sensitivity of 93% (Figures 4B, C).

DISCUSSION
This study demonstrated important immune differences between hospitalised adults with COVID-19 and influenza and between COVID-19 survivors and non-survivors, using samples taken from COVID-19 patients obtained in the first SARS-CoV-2 wave, and prior to the use of treatments and vaccines.
Known COVID-19 prognostic mortality and severity variables (49) were compared between patients with COVID-19 or influenza. We found more active smokers and underlying respiratory disease among influenza patients. Among patients with COVID-19 a higher CRP [which has previously been reported to be similar upon admission to hospital between patients with COVID-19 or influenza (1)], and a higher proportion of patients with hypertension, liver disease [which has been classified as a low or very low certainty predictor (49)], and diabetes was found compared to those with influenza. Similar to what has been previously reported (1) upon admission to hospital both patients with COVID-19 or influenza presented with similar white blood cell and neutrophil counts, and although we detected a difference in lymphocytes between patients with COVID-19 or influenza, there was no difference in the neutrophil/lymphocyte ratio. Similar to Piroth et al. (2), we found that the average length of stay was higher for patients with COVID-19 and more patients with COVID-19 needed supplementary oxygen compared to influenza. Piroth et al. (2) previously reported a roughly three times higher relative risk of death for COVID-19 however in our cohort no influenza patients died whilst admitted to hospital and so this could not be assessed. As reported, we found that high certainty prognostic variables for mortality and/or severity of increased age, hypertension, cardiovascular disease, diabetes, underlying respiratory disease and high white blood cell levels (49) in COVID-19 non-survivors. Here we also report the findings of an increased heart rate in COVID-19 survivors, but further research is needed to confirm that this is independently associated with survival. While it has previously been reported that CRP and neutrophil/lymphocyte ratio were elevated in critically ill patients with COVID-19 (1), we detected no difference in CRP, neutrophil count and lymphocyte count between COVID-19 survivors and non-survivors.
Several differentially activated gene pathways were detected between COVID-19 and influenza. One differentially activated pathway was enriched for genes related to ribosomal pathways indicating the possible impact on translational machinery. Furthermore, the pathway was enriched for genes transcribed by Myc. Activated Myc represses IRF7 which regulates type I IFN production (50), and correspondingly a significant lower IRF7 expression and a lower IFN response was detected in patients with COVID-19. This impaired IFN response in COVID-19 may be due to the virus avoiding or delaying an intracellular innate immune response to type I and type III IFNs (4)(5)(6)(7). A pathway involved with the mitochondrial complex I assembly model OXPHOS system was differentially activated supporting reported increased COVID-19 severity due to SARS-CoV-2 being able to highjack and disrupt mitochondrial dynamics of immune cells (51). Cellular ubiquitin-proteasome pathways which are known to play important roles in coronavirus infection cycles were found to be differentially activated (52), these pathways might reflect increased viral replication and suppression of host IFN signalling pathways, including increased degradation of IkBa which suppresses the IFNinduced NF-kB activation pathway. However, PSMD14 the peak marker of this pathway prevents IRF3 autophagic degradation and therefore permits IRF3-mediated type I IFN activation (53).
An impaired immune response to viruses and IFN signalling in patients with COVID-19 was detected, as previously reported (4-7), compared to patients with influenza, which are known to produce strong IFN responses (1). Furthermore, in accordance with evidence of aberrant blood clotting in COVID-19 (54,55), transcripts expressed by megakaryocytes and platelets associated with blood coagulation were at a higher abundance in COVID-19 patients. Innate immune response Weighted correlation network analysis was performed to assess the correlation between different clinical covariates, given are the correlation values and the p-values, and the expression of specific gene transcript clusters. These gene transcript clusters underwent GO analysis which revealed the associated biological process which is given together with the FDR p-value, and the number of genes from the input. related gene pathways were found to be associated with influenza, and an adaptive immune response and an increase of a wide range of immunoglobulin transcripts for patients with COVID-19, which is consistent with previous findings (56). This adaptive immune response was found to have a stronger positive correlation with the type of viral infection as opposed to the difference in duration of symptoms between the patients before admission to hospital. An increase in gene pathways involved with an adaptive immune response and increase in predicted CD4+ and CD8+ T cells and naïve B cells was detected and associated with young COVID-19 survivors, highlighting the importance of an efficient adaptive immune response as previously reported (15). Predicted naïve CD4+ T cells were higher compared to predicted CD8+ T cells indicating an increased CD4+ T cell response to SARS-CoV-2, supporting previous observations (15,57), which has been found to control primary SARS-CoV-2 infection (58). Predicted CD8+ T cells were mostly seen in COVID-19 survivors which has been associated with a positive COVID-19 outcome (58,59).
An enrichment of pathways involved with the negative regulation of lymphocyte activation and increased neutrophil activation and degranulation, a significant decrease in predicted naïve B cells and naïve CD4+ and CD8+ T cells, and an increase of the neutrophil cells was detected in COVID-19 non-survivors. Similar to previous studies reporting elevated neutrophil levels in blood (60) and lungs (61)(62)(63)(64) in severe COVID-19. The activation and degranulation of neutrophils were positively correlated with patients receiving oxygen supplementation and who eventually died within 30 days of hospital admission. Additionally, gene pathways associated with inflammatory response and cytokine signalling, a higher transcript abundance of several IL genes (IL1-RAP, IL-10, IL1-R1, IL1-R2, IL18-R1 and IL18-RAP) and increased levels of TNFa, IL-1b, IL-8, IL-33, IL-6 and IL-10 in blood were detected in COVID-19 non-survivors. This is similar to findings of positive regulation of genes encoding the activation of innate immune system, viral and IFN response (1), increase of proinflammatory macrophages (65) and elevated IL-6 and IL-10 in severe COVID-19 cases (12)(13)(14).
It appears that, and as Sette and Crotty (66) summarised, that COVID-19 severity is largely due to an early virus-driven evasion of innate immune recognition leading to a delayed adaptive immune response with a fatal COVID-19 outcome, as shown by Lucas et al. (67), where the innate immune response is everexpanding due to an absence of a rapid T cell response. In accordance with a delayed T cell response, we noticed a decrease of dendritic cells in patients with COVID-19 potentially leading to impaired T cell priming. A delayed adaptive immune response can occur in the elderly due to a scarcity of naïve T cells caused by aging (68)(69)(70) placing them at an increased risk of death (58). The association of age and COVID-19 severity is already known, for example, as of April 15 th 2021 in the United States 95.4% of COVID-19 deaths occurred in 50-year-olds and older, and 59.3% in 75-year-olds and older (71). In our cohort, patients who survived COVID-19 were younger, had a longer duration of symptoms before admission to hospital and higher levels of predicted naïve CD4+ T cells and naïve B cells. Taken together, in this comparative study we implemented a variety of different bioinformatic analyses on whole blood RNA-seq between a cohort of patients infected with SARS-CoV-2, during the first wave of the COVID-19 pandemic, and patients infected with influenza, with samples taken before treatments for both groups. An increased innate immune response was found to be associated with patients infected with influenza, while an increased adaptive immune response was associated with patients infected with SARS-CoV-2. This early increased adaptive immune response was indicative of patient survival, thus illustrating the importance of an adequate adaptive immune response in successfully countering SARS-CoV-2 infection, while an increased proinflammatory response was seen in COVID-19 non-survivors. Distinct prognostic immune signature genes were identified in whole blood from untreated patients infected with SARS-CoV-2 which can used upon patient admission to hospital to differentiate between COVID-19 patients likely to survive or not.

LIMITATIONS
The authors acknowledge that the inherent characteristics of the dataset being a moderate sample size, sampling time differences between symptom onset and admission to hospital, underlying comorbidities, and the retrospective design could have a direct impact upon the range of immune signature differences observed. However, the gene clusters identified with an adaptive immune response was primarily positively correlated with the type of viral infection, and was weaker correlated to the duration of symptoms before admission to hospital. The comparison between patients with influenza versus patients whom survived COVID-19 was outside the current study's analytical framework and future work could be directed in this direction.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: European Genome-Phenome Archive, EGAS00001005971.

ETHICS STATEMENT
The studies involving human participants were reviewed and approved by South Central Hampshire A Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

ACKNOWLEDGMENTS
The authors would like to acknowledge and gives thanks to the patients who kindly participated in this study and to all the clinical staff at University Hospital Southampton Foundation Trust who cared for them. In addition, we acknowledge the use of the IRIDIS High Performance Computing Facility at the University of Southampton in the completion of this work.