Clonally diverse T cell homeostasis is maintained by a common program of cell-cycle control

Lymphopenia induces T cells to undergo cell divisions as part of a homeostatic response mechanism. The clonal response to lymphopenia is extremely diverse, and it is unknown whether this heterogeneity represents distinct mechanisms of cell-cycle control or whether a common mechanism can account for the diversity. We addressed this question by combining in vivo and mathematical modeling of lymphopenia-induced proliferation (LIP) of two distinct T cell clonotypes. OT-I T cells undergo rapid LIP accompanied by differentiation that superficially resembles Ag-induced proliferation, whereas F5 T cells divide slowly and remain naive. Both F5 and OT-I LIP responses were most accurately described by a single stochastic division model where the rate of cell division was exponentially decreased with increasing cell numbers. The model successfully identified key biological parameters of the response and accurately predicted the homeostatic set point of each clone. Significantly, the model was successful in predicting interclonal competition between OT-I and F5 T cells, consistent with competition for the same resource(s) required for homeostatic proliferation. Our results show that diverse and heterogenous clonal T cell responses can be accounted for by a single common model of homeostasis

PLoS One

MOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used

J Med Internet Res

BACKGROUND: Many countries around the world have developed mobile phone apps capable of supporting instantaneous contact tracing to control the Covid-19 pandemic. In France, a few people have downloaded and are using the StopCovid contact tracing app. Among them, students in the health domain are especially concerned. Exploring their usage and opinions about the app can inform improvements and diffusion of StopCovid among young people. OBJECTIVE: To investigate health-related students' knowledge, attitudes, beliefs and practices about the StopCovid app. METHODS: A field survey was conducted among 318 students at the health sciences campus of the University of Bordeaux, France, between September 25th and October 16th, 2020. Quota sampling method was used and descriptive statistics and univariate analyses were performed. RESULTS: A total of 77.3% (246/318) students had heard about the app, but only 11.3% (36/318) had downloaded it and 4.7% (15/318) were still using it at the time of the survey. Main reasons for not using the app were: belief that it was not effective given its limited diffusion (17.6%, 37/210), lack of interest (17.6%, 37/210) and distrust in data security and fear to be geo-located (15.7%, 33/210). Among those who had not heard about the app, after a brief description of its functioning and confidentiality policy, 52.7% (38/72) would use it. Participants reported that the main solution for increasing the use of the app would be a better communication (71.4%, 227/318). CONCLUSIONS: Even among health students the contact tracing app was poorly used. Findings suggest that improved communication describing its advantages and simplicity of use as well as clarifying false beliefs about it could help improving significantly its uptake

Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.European Union’s Horizon 2020 research and innovation programm

BMC Nephrol

An amendment to this paper has been published and can be accessed via the original article

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013-2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.Program Initiative d’Excellenc

Br J Haematol

Immune thrombocytopenia (ITP) is defined by a low platelet count that can trigger potentially life-threatening haemorrhages. Three-quarters of adult patients exhibit persistent or chronic disease and require second-line treatments. Among these, rituximab, an anti-CD20 antibody, has yielded valuable results, with global responses in 60% of patients at 6 months and complete responses in 30% at 5 years. Factors predictive of response to ITP therapy would help physicians choose optimal treatments. We retrospectively analysed clinical courses, biological markers and blood lymphocyte subset numbers of 72 patients on rituximab to treat persistent/chronic ITP followed-up in our department between 2007 and 2021, divided into three groups according to the platelet count at 6 months: complete, partial or no response. Among all studied parameters, a low number of CD3 CD16 CD56 circulating NK cells was associated with the complete response to rituximab. We also found that, after rituximab therapy, complete responders exhibited increased NK and decreased activated CD8 T cell percentages. These results emphasize that the role played by NK cells in ITP remains incompletely known but that factors predictive of response to rituximab can be easily derived using blood lymphocyte subset data

Front Immunol

The goal of HIV therapeutic vaccination is to induce HIV-specific immune response able to control HIV replication. We previously reported that vaccination with ex vivo generated Dendritic Cells (DC) loaded with HIV-lipopeptides in HIV-infected patients (n = 19) on antiretroviral therapy (ART) was well-tolerated and immunogenic. Vaccine-elicited HIV-specific T cell responses were associated with improved control of viral replication following antiretroviral interruption (ATI from w24 to w48). We show an inverse relationship between HIV-specific responses (production of IL-2, IL-13, IL-21, IFN-g, CD4 polyfunctionality, i.e., production of at least two cytokines) and the peak of viral load during ATI. Here we have performed an integrative systems vaccinology analysis including: (i) post vaccination (w16) immune responses assessed by cytometry, cytokine secretion, and Interferon-γ ELISPOT assays; (ii) whole blood and cellular gene expression measured during vaccination; and (iii) viral parameters following ATI, with the objective to disentangle the relationships between these markers and to identify vaccine signatures. During vaccination, 69 gene expression modules out of 260 varied significantly including (by order of significance) modules related to inflammation (Chaussabel Modules M3.2, M4.13, M4.6, M5.7, M7.1, M4.2), plasma cells (M4.11) and T cells (M4.1, 4.15). Cellular immune responses were positively correlated to genes belonging to T cell functional modules (M4.1, M4.15) at w16 and negatively correlated to genes belonging to inflammation modules (M7.1, M5.7, M3.2, M4.13, M4.2). More specifically, we show that prolonged increased abundance of inflammatory gene pathways related to toll-like receptor signaling (especially TLR4) are associated with both lower vaccine immune responses and control of viral replication post ATI. Further comparison of DC vaccine gene signatures with previously reported non-HIV vaccine signatures, such as flu and pneumococcal vaccines, revealed common pathways across vaccines. Overall, these results show that too long duration and too high intensity of vaccine inflammatory responses hamper the magnitude of effector responses. [ABSTRACT FROM AUTHOR] Copyright of Frontiers in Immunology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract

An evaluation of HIV elite controller definitions within a large seroconverter cohort collaboration.

BACKGROUND: Understanding the mechanisms underlying viral control is highly relevant to vaccine studies and elite control (EC) of HIV infection. Although numerous definitions of EC exist, it is not clear which, if any, best identify this rare phenotype. METHODS: We assessed a number of EC definitions used in the literature using CASCADE data of 25,692 HIV seroconverters. We estimated proportions maintaining EC of total ART-naïve follow-up time, and disease progression, comparing to non-EC. We also examined HIV-RNA and CD4 values and CD4 slope during EC and beyond (while ART naïve). RESULTS: Most definitions classify ∼ 1% as ECs with median HIV-RNA 43-903 copies/ml and median CD4>500 cells/mm(3). Beyond EC status, median HIV-RNA levels remained low, although often detectable, and CD4 values high but with strong evidence of decline for all definitions. Median % ART-naïve time as EC was ≥ 92% although overlap between definitions was low. EC definitions with consecutive HIV-RNA measurements 90% of measurements <400 copies/ml over ≥ 10 years be used to define this phenotype

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/