Insights into the Evolution and Emergence of a Novel Infectious Disease

Many zoonotic, novel infectious diseases in humans appear as sporadic infections with spatially and temporally restricted outbreaks, as seen with influenza A(H5N1). Adaptation is often a key factor for successfully establishing sustained human-to-human transmission. Here we use simple mathematical models to describe different adaptation scenarios with particular reference to spatial heterogeneity within the human population. We present analytical expressions for the probability of emergence per introduction, as well as the waiting time to a successful emergence event. Furthermore, we derive general analytical results for the statistical properties of emergence events, including the probability distribution of outbreak sizes. We compare our analytical results with a stochastic model, which has previously been studied computationally. Our results suggest that, for typical connection strengths between communities, spatial heterogeneity has only a weak effect on outbreak size distributions, and on the risk of emergence per introduction. For example, if or larger, any village connected to a large city by just ten commuters a day is, effectively, just a part of the city when considering the chances of emergence and the outbreak size distribution. We present empirical data on commuting patterns and show that the vast majority of communities for which such data are available are at least this well interconnected. For plausible parameter ranges, the effects of spatial heterogeneity are likely to be dominated by the evolutionary biology of host adaptation. We conclude by discussing implications for surveillance and control of emerging infections

No evidence for competition between cytotoxic T-lymphocyte responses in HIV-1 infection

Strong competition between cytotoxic T-lymphocytes (CTLs) specific for different epitopes in human immunodeficiency virus (HIV) infection would have important implications for the design of an HIV vaccine. To investigate evidence for this type of competition, we analysed CTL response data from 97 patients with chronic HIV infection who were frequently sampled for up to 96 weeks. For each sample, CTL responses directed against a range of known epitopes in gag, pol and nef were measured using an enzyme-linked immunospot assay. The Lotka–Volterra model of competition was used to predict patterns that would be expected from these data if competitive interactions materially affect CTL numbers. In this application, the model predicts that when hosts make responses to a larger number of epitopes, they would have diminished responses to each epitope and that if one epitope-specific response becomes dramatically smaller, others would increase in size to compensate; conversely if one response grows, others would shrink. Analysis of the experimental data reveals results that are wholly inconsistent with these predictions. In hosts who respond to more epitopes, the average epitope-specific response tends to be larger, not smaller. Furthermore, responses to different epitopes almost always increase in unison or decrease in unison. Our findings are therefore inconsistent with the hypothesis that there is competition between CTL responses directed against different epitopes in HIV infection. This suggests that vaccines that elicit broad responses would be favourable because they would direct a larger total response against the virus, in addition to being more robust to the effects of CTL escape

Inefficient Cytotoxic T Lymphocyte–Mediated Killing of HIV-1–Infected Cells In Vivo

Understanding the role of cytotoxic T lymphocytes (CTLs) in controlling HIV-1 infection is vital for vaccine design. However, it is difficult to assess the importance of CTLs in natural infection. Different human leukocyte antigen (HLA) class I alleles are associated with different rates of progression to AIDS, indicating that CTLs play a protective role. Yet virus clearance rates following antiretroviral therapy are not impaired in individuals with advanced HIV disease, suggesting that weakening of the CTL response is not the major underlying cause of disease progression and that CTLs do not have an important protective role. Here we reconcile these apparently conflicting studies. We estimate the selection pressure exerted by CTL responses that drive the emergence of immune escape variants, thereby directly quantifying the efficiency of HIV-1–specific CTLs in vivo. We estimate that only 2% of productively infected CD4þ cell death is attributable to CTLs recognising a single epitope. We suggest that CTLs kill a large number of infected cells (about 107) per day but are not responsible for the majority of infected cell death

Nine challenges in modelling the emergence of novel pathogens.

Studying the emergence of novel infectious agents involves many processes spanning host species, spatial scales, and scientific disciplines. Mathematical models play an essential role in combining insights from these investigations and drawing robust inferences from field and experimental data. We describe nine challenges in modelling the emergence of novel pathogens, emphasizing the interface between models and data.We acknowledge support from the Research and Policy for Infectious Disease Dynamics (RAPIDD) programme of the Science and Technology Directory, Department of Homeland Security, and Fogarty International Center, National Institutes of Health. JLS was also supported by the National Science Foundation (EF-0928987 and OCE-1335657) and the De Logi Chair in Biological Sciences. SF was supported by a UK Medical Research Council Career Development Award in Biostatistics. SR was supported by: Wellcome Trust Project Award 093488/Z/10/Z; R01 TW008246-01 from Fogarty International Centre; and The Medical Research Council (UK, Project Grant MR/J008761/1). JLNW was also supported by the Alborada Trust and the European Union FP7 project ANTIGONE (contract number 278976).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.epidem.2014.09.00

Quantification of the virus-host interaction in human T lymphotropic virus I infection

BACKGROUND: HTLV-I causes the disabling inflammatory disease HAM/TSP: there is no vaccine, no satisfactory treatment and no means of assessing the risk of disease or prognosis in infected people. Like many immunopathological diseases with a viral etiology the outcome of infection is thought to depend on the virus-host immunology interaction. However the dynamic virus-host interaction is complex and current models of HAM/TSP pathogenesis are conflicting. The CD8+ cell response is thought to be a determinant of both HTLV-I proviral load and disease status but its effects can obscure other factors. RESULTS: We show here that in the absence of CD8+ cells, CD4+ lymphocytes from HAM/TSP patients expressed HTLV-I protein significantly more readily than lymphocytes from asymptomatic carriers of similar proviral load (P = 0.017). A high rate of viral protein expression was significantly associated with a large increase in the prevalence of HAM/TSP (P = 0.031, 89% of cases correctly classified). Additionally, a high rate of Tax expression and a low CD8+ cell efficiency were independently significantly associated with a high proviral load (P = 0.005, P = 0.003 respectively). CONCLUSION: These results disentangle the complex relationship between immune surveillance, proviral load, inflammatory disease and viral protein expression and indicate that increased protein expression may play an important role in HAM/TSP pathogenesis. This has important implications for therapy since it suggests that interventions should aim to reduce Tax expression rather than proviral load per se

Explicit kinetic heterogeneity: mechanistic models for interpretation of labeling data of heterogeneous cell populations

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three models that are most commonly used are in fact mathematically identical and differ only in their interpretation of the estimated parameters. By extending these previous models, we here propose a more mechanistic approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a mechanistic way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length

Identifying alemtuzumab as an anti-myeloid cell antiangiogenic therapy for the treatment of ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Murine studies suggest that myeloid cells such as vascular leukocytes (VLC) and Tie2⁺monocytes play a critical role in tumor angiogenesis and vasculogenesis. Myeloid cells are a primary cause of resistance to anti-VEGF therapy. The elimination of these cells from the tumor microenvironment significantly restricts tumor growth in both spontaneous and xenograft murine tumor models. Thus animal studies indicate that myeloid cells are potential therapeutic targets for solid tumor therapy. Abundant VLC and Tie2⁺monocytes have been reported in human cancer. Unfortunately, the importance of VLC in human cancer growth remains untested as there are no confirmed therapeutics to target human VLC.</p> <p>Methods</p> <p>We used FACS to analyze VLC in ovarian and non-ovarian tumors, and characterize the relationship of VLC and Tie2-monocytes. We performed qRT-PCR and FACS on human VLC to assess the expression of the CD52 antigen, the target of the immunotherapeutic Alemtuzumab. We assessed Alemtuzumab's ability to induce complement-mediated VLC killing in vitro and in human tumor ascites. Finally we assessed the impact of anti-CD52 immuno-toxin therapy on murine ovarian tumor growth.</p> <p>Results</p> <p>Human VLC are present in ovarian and non-ovarian tumors. The majority of VLC appear to be Tie2+ monocytes. VLC and Tie2+ monocytes express high levels of CD52, the target of the immunotherapeutic Alemtuzumab. Alemtuzumab potently induces complement-mediated lysis of VLC in vitro and ex-vivo in ovarian tumor ascites. Anti-CD52 immunotherapy targeting VLC restricts tumor angiogenesis and growth in murine ovarian cancer.</p> <p>Conclusion</p> <p>These studies confirm VLC/myeloid cells as therapeutic targets in ovarian cancer. Our data provide critical pre-clinical evidence supporting the use of Alemtuzumab in clinical trials to test its efficacy as an anti-myeloid cell antiangiogenic therapeutic in ovarian cancer. The identification of an FDA approved anti-VLC agent with a history of clinical use will allow immediate proof-of-principle clinical trials in patients with ovarian cancer.</p

A restatement of recent advances in the natural science evidence base concerning neonicotinoid insecticides and insect pollinators

A summary is provided of recent advances in the natural science evidence base concerning the effects of neonicotinoid insecticides on insect pollinators in a format (a ‘restatement’) intended to be accessible to informed but not expert policymakers and stakeholders. Important new studies have been published since our recent review of this field (Godfray et al. 2014 Proc. R. Soc. B 281,20140558. (doi:10.1098/rspb.2014.0558)) and the subject continues to be an area of very active research and high policy relevance

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

Introduction Glioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge. Methods A total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS). Results The MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p Discussion This study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.The research was supported by the National Institute for Health Research (NIHR) Brain Injury MedTech Cooperative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care (SJP, project reference NIHR/CS/009/011); CRUK core grant C14303/A17197 and A19274 (FM lab); Cambridge Trust and China Scholarship Council (CL & SW); the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung, Taiwan (JLY); the Commonwealth Scholarship Commission and Cambridge Commonwealth Trust (NRB); CRUK & EPSRC Cancer Imaging Centre in Cambridge & Manchester (FM & TT, grant C197/A16465); and NIHR Cambridge Biomedical Research Centre (TM & SJP)