THE PROCESS OF DISCLOSURE OF CHILDHOOD SEXUAL ABUSE: OLDER ADULT WOMEN: A PILOT STUDY

Exeter Conniunity Health Services Trus

Modelling the diversity and persistence of the human T-lymphotropic virus type-1

Human T-lymphotropic virus type-1 (HTLV-1) infects approximately 10-20 million people worldwide. The virus persists within hosts via de novo infection and infected cell proliferation, creating a population structure of multiple clones (infected cell populations with identical genomic proviral integration sites). The number of clones in one host is unknown, and is determined by the rate of de novo infection. Our primary objectives are: i) to estimate HTLV-1 clonal diversity; and ii) to develop a model of HTLV-1 dynamics that can estimate the relative contributions of de novo infection and mitotic replication. We use a combination of mathematical modelling, computer simulation and statistical methods to interpret experimental observation. We develop an estimator (named DivE) to estimate the number of HTLV-1 clones. DivE uses the ecological method of rarefaction, and includes novel model selection criteria. We show that DivE is more accurate than widely-used estimators from population ecology, and we demonstrated that this holds across many systems. Differences between these systems and ecological populations are investigated, and DivE is applied to patients with a range of HTLV-associated diseases. HTLV-1 clonal abundance varies by several orders of magnitude: quantifying within-host HTLV-1 dynamics requires mathematical modelling at multiple scales. Stochastic processes, important for modelling small populations, are introduced, and we explore properties and approximations of a mass-action birth-death process for biologically realistic species extinction scenarios. We combine ordinary differential equations with stochastic processes in a hybrid model and explore its consequences. The estimated HTLV-1 clonal diversity is substantially higher than previously thought, which strongly implies higher rates of de novo infection. The hybrid model captures known behaviour of HTLV-1, and can be used to infer rates of viral persistence. DivE and the hybrid model are applicable to other biological systems, in particular the study of T and B cell receptor repertoires.Open Acces

Genome-wide Determinants of Proviral Targeting, Clonal Abundance and Expression in Natural HTLV-1 Infection

The regulation of proviral latency is a central problem in retrovirology. We postulate that the genomic integration site of human T lymphotropic virus type 1 (HTLV-1) determines the pattern of expression of the provirus, which in turn determines the abundance and pathogenic potential of infected T cell clones in vivo. We recently developed a high-throughput method for the genome-wide amplification, identification and quantification of proviral integration sites. Here, we used this protocol to test two hypotheses. First, that binding sites for transcription factors and chromatin remodelling factors in the genome flanking the proviral integration site of HTLV-1 are associated with integration targeting, spontaneous proviral expression, and in vivo clonal abundance. Second, that the transcriptional orientation of the HTLV-1 provirus relative to that of the nearest host gene determines spontaneous proviral expression and in vivo clonal abundance. Integration targeting was strongly associated with the presence of a binding site for specific host transcription factors, especially STAT1 and p53. The presence of the chromatin remodelling factors BRG1 and INI1 and certain host transcription factors either upstream or downstream of the provirus was associated respectively with silencing or spontaneous expression of the provirus. Cells expressing HTLV-1 Tax protein were significantly more frequent in clones of low abundance in vivo. We conclude that transcriptional interference and chromatin remodelling are critical determinants of proviral latency in natural HTLV-1 infection

The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic

The COVID-19 pandemic has caused over 6.4 million registered deaths to date, and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian vector autoregressive model with both fixed and random effects. We find that increases in disease transmission intensity decreases Gross domestic product (GDP) and increases daily excess deaths, with a longer lasting impact on excess deaths in comparison to GDP, which recovers more rapidly. Broadly, our results reinforce the intuitive phenomenon that significant economic activity arises from diverse person-to-person interactions. We report on the effectiveness of non-pharmaceutical interventions (NPIs) on transmission intensity, excess deaths and changes in GDP, and resulting implications for policy makers. Our results highlight a complex cost-benefit trade off from individual NPIs. For example, banning international travel increases GDP however reduces excess deaths. We consider country random effects and their associations with excess changes in GDP and excess deaths. For example, more developed countries in Europe typically had more cautious approaches to the COVID-19 pandemic, prioritising healthcare and excess deaths over economic performance. Long term economic impairments are not fully captured by our model, as well as long term disease effects (Long Covid). Our results highlight that the impact of disease on a country is complex and multifaceted, and simple heuristic conclusions to extract the best outcome from the economy and disease burden are challenging

Clonality of HTLV-2 in natural infection

Human T-lymphotropic virus type 1 (HTLV-1) and type 2 (HTLV-2) both cause lifelong persistent infections, but differ in their clinical outcomes. HTLV-1 infection causes a chronic or acute T-lymphocytic malignancy in up to 5% of infected individuals whereas HTLV-2 has not been unequivocally linked to a T-cell malignancy. Virus-driven clonal proliferation of infected cells both in vitro and in vivo has been demonstrated in HTLV-1 infection. However, T-cell clonality in HTLV-2 infection has not been rigorously characterized. In this study we used a high-throughput approach in conjunction with flow cytometric sorting to identify and quantify HTLV-2-infected T-cell clones in 28 individuals with natural infection. We show that while genome-wide integration site preferences in vivo were similar to those found in HTLV-1 infection, expansion of HTLV-2-infected clones did not demonstrate the same significant association with the genomic environment of the integrated provirus. The proviral load in HTLV-2 is almost confined to CD8+ T-cells and is composed of a small number of often highly expanded clones. The HTLV-2 load correlated significantly with the degree of dispersion of the clone frequency distribution, which was highly stable over ∼8 years. These results suggest that there are significant differences in the selection forces that control the clonal expansion of virus-infected cells in HTLV-1 and HTLV-2 infection. In addition, our data demonstrate that strong virus-driven proliferation per se does not predispose to malignant transformation in oncoretroviral infections

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Modelling the impact of the tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns.

Funder: Community JameelOBJECTIVE: To measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern. DESIGN: This is a modelling study combining estimates of real-time reproduction number Rt (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers. SETTING: The UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Reduction in real-time reproduction number Rt . RESULTS: Nationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, Rt averaged 1.3 (0.9-1.6) across LTLAs, but declined to an average of 1.1 (0.86-1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%-7%) and 23% (21%-25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality. CONCLUSIONS: The relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed