Quantitative host resistance drives the evolution of increased virulence in an emerging pathogen

Emergent infectious diseases can have a devastating impact on host populations. The high selective pressures on both the hosts and the pathogens frequently lead to rapid adaptations not only in pathogen virulence but also host resistance following an initial outbreak. However, it is often unclear whether hosts will evolve to avoid infection‐associated fitness costs by preventing the establishment of infection (here referred to as qualitative resistance ) or by limiting its deleterious effects through immune functioning (here referred to as quantitative resistance ). Equally, the evolutionary repercussions these different resistance mechanisms have for the pathogen are often unknown. Here, we investigate the co‐evolutionary dynamics of pathogen virulence and host resistance following the epizootic outbreak of the highly pathogenic bacterium Mycoplasma gallisepticum in North American house finches (Haemorhous mexicanus ). Using an evolutionary modelling approach and with a specific emphasis on the evolved resistance trait, we demonstrate that the rapid increase in the frequency of resistant birds following the outbreak is indicative of strong selection pressure to reduce infection‐associated mortality. This, in turn, created the ecological conditions that selected for increased bacterial virulence. Our results thus suggest that quantitative host resistance was the key factor underlying the evolutionary interactions in this natural host–pathogen system.Publisher PDFPeer reviewe

Repeated clinical malaria episodes are associated with modification of the immune system in children

The study received funding from the UK Medical Research Council, (MRC Programme grant #: MR/M003906/1). MB and AR are supported by the Wellcome Trust (Grant #: WT 206194).Background There are over 200 million reported cases of malaria each year, and most children living in endemic areas will experience multiple episodes of clinical disease before puberty. We set out to understand how frequent clinical malaria, which elicits a strong inflammatory response, affects the immune system and whether these modifications are observable in the absence of detectable parasitaemia. Methods We used a multi-dimensional approach comprising whole blood transcriptomic, cellular and plasma cytokine analyses on a cohort of children living with endemic malaria, but uninfected at sampling, who had been under active surveillance for malaria for 8 years. Children were categorised into two groups depending on the cumulative number of episodes experienced: high (≥ 8) or low (< 5). Results We observe that multiple episodes of malaria are associated with modification of the immune system. Children who had experienced a large number of episodes demonstrated upregulation of interferon-inducible genes, a clear increase in circulating levels of the immunoregulatory cytokine IL-10 and enhanced activation of neutrophils, B cells and CD8+ T cells. Conclusion Transcriptomic analysis together with cytokine and immune cell profiling of peripheral blood can robustly detect immune differences between children with different numbers of prior malaria episodes. Multiple episodes of malaria are associated with modification of the immune system in children. Such immune modifications may have implications for the initiation of subsequent immune responses and the induction of vaccine-mediated protection.Publisher PDFPeer reviewe

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Identification of immune signatures predictive of clinical protection from malaria

<div><p>Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite <i>Plasmodium falciparum</i> cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual’s malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone.</p></div

Predictive performance of random forests models of the KEN (top) and KTZ (bottom) datasets.

<p>A, C: ROC curves for the random forests models built on antibodies only (red line), exposure proxies only (blue line) and all variables (green line). Area under the curve (AUC) is given in their respective legend, whilst the dashed grey line represents an AUC = 0.5, that is, randomly guessing the individual’s status. B, D: Illustrated confusion matrices derived from models built on all available variables (Ab and Exp). The sizes of the diagonal wedges correspond to the true positive and true negative rates whereas the sizes of the off-diagonal wedges correspond to the false positive and false negative rates.</p

Immunoreactivity heatmap of feature-selected antigens from the MAL dataset when considering all individuals.

<p>Antigens identified as important predictors for clinical immunity show either an increase or decrease in intensity as individuals get older under repeated exposure to the parasite. The columns are the responses from each individual, ordered by age (young to old, from left to right), and separated into children (≤ 10 years) and adults (≥ 18 years). The bar at the top indicate the individual’s protective status (grey: susceptible, black: protected), highlighting the strong correlation between age and clinical protection. The word cloud of the protein product description stratified by whether the response had an increasing or decreasing trend with age, is based on the selected features and any other proteins they are highly correlated with (<i>ρ</i> > 0.8).</p

Immune signatures found in protein microarray data are predictive of clinical immunity but only when considering all individuals.

<p>ROC curves for each cross-validation fold (dashed black line) and the mean ROC curve (solid black line) for random forests models fitted to A: all individuals (2-25 years) B: children only (2-10 years) (the dashed red line represents an AUC = 0.5, that is, randomly guessing the individual’s status).</p

Population-level mean antibody (Ab) responses for the protein microarray data.

<p>A: Mean antibody responses stratified by age (2-10y vs. 18-25y, top) or immune status (protected vs. susceptible, bottom) and sorted by decreasing intensity of one of the strata (old individuals, top, and protected individuals, bottom); each dot represents the mean response to an individual antigen. There is a noticeable shift in the immune profiles of the older and/or protected individuals, with high intensity responses being further elevated and low intensity responses being reduced. B: Beanplots showing the mean and distribution of the immune responses stratified by age class (children and young adults) or immune status for all individuals, or stratified by immune status considering children only. There was no statistically significant difference between the respective means (<i>P</i> > 0.2 in all cases; Welch’s two-sample <i>t</i>-test).</p

Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes

Good blood glucose control is important to people with type 1 diabetes to prevent diabetes-related complications. Too much blood glucose (hyperglycaemia) causes long-term micro-vascular complications, while a severe drop in blood glucose (hypoglycaemia) can cause life-threatening coma. Finding the right balance between quantity and type of food intake, physical activity levels and insulin dosage, is a daily challenge. Increased physical activity levels often cause changes in blood glucose due to increased glucose uptake into tissues such as muscle. To date we have limited knowledge about the minute by minute effects of exercise on blood glucose levels, in part due to the difficulty in measuring glucose and physical activity levels continuously, in a free-living environment. By using a light and user-friendly armband we can record physical activity energy expenditure on a minute-by-minute basis. Simultaneously, by using a continuous glucose monitoring system we can record glucose concentrations. In this paper, Gaussian Processes are used to model the glucose excursions in response to physical activity data, to study its effect on glycaemic contro