Mahdad Noursadeghi

Quantifying T cell response during primary infection in humans is crucial for understanding adaptive immunity. Leveraging a controlled human challenge to severe acute respiratory syndrome coronavirus 2, we characterized antigen-specific T cell responses within and across individuals. Notably, individual clones reached similar maximum frequencies despite differences in the timing of their peak expansion. Mathematical modeling within a coarse-grained framework suggested that variability in precursor frequency between different T cell clones could be one mechanism to explain this relationship. Single-cell profiling revealed distinct temporal programs for CD4 ⁺ and CD8 ⁺ T cells, with CD4 ⁺ cells expanding earlier but contracting to a lower frequency. Clones with similar receptors, likely recognizing the same antigen, expanded at similar times. Together, these findings highlight how clone-intrinsic properties such as precursor frequency and CD4/CD8 subset status shape T cell clonal kinetics. These insights provide a quantitative framework for understanding T cell response in humans, with implications for vaccine design.

Background Tuberculosis (TB) preventive treatment is a key component of TB control in low-incidence settings but uptake remains low. Objective To explore factors influencing patient decisions to accept or decline preventive treatment for TB infection. Design Qualitative study using semi-structured interviews, analysed with the Theoretical Domains Framework (TDF). Setting Routine clinical TB prevention services at two hospitals in London, UK. Participants Adults (≥18 years) diagnosed with TB infection and offered preventive treatment. Methods Semi-structured interviews were audio-recorded and transcribed verbatim. The TDF was applied to analyse transcripts using a combined inductive thematic analysis and deductive framework approach. Themes were also mapped to the capability, opportunity and motivation model of behaviour domains. Results Twenty-five participants (median age 34 years; 64% male) were included; 56% accepted treatment, 28% declined and 16% were undecided. Influences on decision-making mapped to 12 of 14 TDF domains. Facilitators of treatment acceptance included perceived risk of TB (beliefs about consequences), desire to protect others’ health (social influences and goals), confidence in treatment adherence (beliefs about capabilities) and routine integration strategies (behavioural regulation). Barriers to treatment acceptance included low perceived individual risk and doubts about necessity or effectiveness (beliefs about consequences), concerns about side effects and treatment burden (environmental context and resources) and anticipated stigma (social influences). Knowledge had a mixed influence on treatment decision making, primarily shaping perceived necessity, but did not determine decisions alone. Social, environmental and emotional factors further influenced decisions, with participants balancing anticipated benefits against perceived burden and uncertainty across multiple interacting domains. Conclusions Decisions to accept preventive TB treatment are driven by interacting capability, opportunity and motivation factors rather than knowledge alone. Interventions to improve shared decision making should address perceived risk, treatment burden, self-efficacy and social influences. Trial registration number NCT07024836.

SARS-CoV-2 human challenge studies enabled unprecedented high frequency temporal measurements of immune determinants of viral control. We integrated prior knowledge of SARS-CoV-2 replication, innate, cytotoxic T cell and antibody responses into a dynamic model which captured viral-load kinetics in seronegative volunteers challenged with wildtype virus. The model predicted measured innate and adaptive immune dynamics. Perturbation analyses indicated that innate immunity limits peak viral load, whereas antibodies principally mediate clearance while cytotoxic T cells become critical in antibody deficiency. Pre-existing antibodies suppressed both viral replication and subsequent immune activation- a prediction validated in a second challenge study of seropositive participants infected with the SARS-CoV-2 Delta variant. The model explains how specific immunodeficiencies may increase susceptibility to severe or persistent infection, supporting clinical risk stratification. It suggests that T cell-targeted vaccination strategies may provide limited protection without effective neutralising antibodies, and that attenuated boosting after low-dose exposure requires repeated vaccination to reduce breakthrough infections, informing public health policy. One Sentence Summary: A mathematical model of SARS-COV-2 infection predicts innate and adaptive immunity, and informs risk stratification and vaccine policy.

Background: Translation of tuberculosis (TB) biomarker and diagnostic research into tools that improve patient and public health outcomes has been slow, partly because no internationally agreed framework exists defining the use cases that new biomarkers and tests should address. We aimed to identify, validate, and prioritise use cases for TB biomarkers and tests across Mycobacterium tuberculosis ( Mtb ) infection, TB disease, TB treatment, and post-TB care, through an international consensus process. Methods and Findings: We conducted a scoping review of the literature, guidelines, and target product profiles (24 documents; 69 candidate use cases consolidated to 13), followed by a hybrid RAND/UCLA modified Delphi consensus process involving 185 identified interest-holders, including clinicians, researchers, diagnostic developers, industry, funders, civil society, national TB programmes, and policymakers (including WHO representatives). Interest-holders completed an online survey rating agreement with each use case, and attended a consensus meeting to discuss use cases with less than 80% agreement, followed by a further validation meeting. Eleven use cases were retained across four TB care pathway stages: three for Mtb infection, four for disease detection, three for treatment optimisation, and one for post-TB care. Highest-priority use cases were detection of drug-resistant TB, prediction of progression from infection to disease, identification of current Mtb infection, and improved diagnosis of active TB disease. Conclusions: This consensus exercise provides the first comprehensive, prioritised framework of use cases for TB biomarkers and tests, spanning the full care pathway. These eleven priority use cases can guide investment, focus biomarker discovery, and inform future target product profiles, funding calls, and policy development. Applying the framework to the current biomarker pipeline is a key next step to address gaps between innovation and priority needs.

Background: Tuberculosis (TB) preventive treatment is a key component of TB control in low-incidence settings, but uptake remains low. Objective: To explore factors influencing patient decisions to accept or decline preventive treatment for TB infection. Design: Qualitative study using semi-structured interviews, analysed with the Theoretical Domains Framework (TDF). Setting: Routine clinical TB prevention services at two hospitals in London, UK. Participants: Adults (≥18 years) diagnosed with TB infection and offered preventive treatment. Methods: Semi-structured interviews were audio-recorded and transcribed verbatim. The TDF was applied to analyse transcripts using a combined inductive thematic analysis and deductive framework approach. Themes were also mapped to the Capability, Opportunity and Motivation model of Behaviour (COM- B) domains. Results: Twenty-five participants (median age 34 years; 64% male) were included; 56% accepted treatment, 28% declined, and 16% were undecided. Influences on decision-making mapped to 12 of 14 TDF domains. Facilitators of treatment acceptance included perceived risk of TB (beliefs about consequences), desire to protect others’ health (social influences and goals), confidence in treatment adherence (beliefs about capabilities), and routine integration strategies (behavioural regulation). Barriers to treatment acceptance included low perceived individual risk and doubts about necessity or effectiveness (beliefs about consequences), concerns about side effects and treatment burden (environmental context and resources) and anticipated stigma (social influences). Knowledge had a mixed influence, primarily shaping perceived necessity, but did not determine decisions alone. Social, environmental and emotional factors further influenced decisions, with participants balancing anticipated benefits against perceived burden and uncertainty across multiple interacting domains. Conclusions: Decisions to accept preventive TB treatment are driven by interacting capability, opportunity and motivation factors rather than knowledge alone. Interventions to improve shared decision making should address perceived risk, treatment burden, self-efficacy and social influences. Registration: Clinicaltrials.gov ( NCT07024836 ) Strengths and limitations of this study: Use of semi-structured interviews enabled in-depth exploration of patient decision-making about tuberculosis (TB) preventive treatment in routine clinical services. The Theoretical Domains Framework (TDF) provided a systematic behavioural framework for coding and interpreting influences on treatment acceptance, with subsequent mapping to Capability, Opportunity and Motivation model of Behaviour (COM-B) domains. Recruitment included representative adults offered preventive treatment through different screening pathways and included participants who accepted, declined, or remained undecided about treatment. Data were collected from two London hospital sites; future studies could examine generalisability to other settings, including higher TB incidence settings, and those with different models of TB preventive care.

Males incur greater risk of tuberculosis (TB) than females, but the contribution of sex-associated immune differences remains unclear. We addressed this using sex-stratified transcriptomic analyses across four independent studies spanning active pulmonary TB, subclinical TB and latent infection, in peripheral blood, bronchoalveolar lavage (BAL) and by using the tuberculin skin test (TST) as a standardised in vivo antigenic challenge. In blood of active TB patients, expression of TNF- and type I interferon-regulated signatures, genome-wide gene expression, and performance of leading host-response biomarkers of TB were comparable between sexes. Similarly, blood transcriptomic biomarkers showed no meaningful sex-related differences for predicting asymptomatic or incident TB. In the TST of people with latent infection, bulk and single-cell RNA sequencing identified only limited differences, largely restricted to sex chromosome-linked transcripts, with no consistent evidence of dimorphism in immune-regulated pathways. Single-cell RNA sequencing of BAL samples identified reduced abundance of B cells in male TB patients, with gene expression differences again largely restricted to sex chromosome-linked transcripts. These findings suggest that canonical immune responses associated with TB are broadly similar between the sexes, and that increased TB risk among males more likely reflects differential exposure rather than intrinsic immunological susceptibility.

: Background: Bacteraemia is associated with poor outcomes but the diagnostic gold standard, peripheral blood culture, takes up to 24 hours to become clinically actionable, hampering early management decisions in suspected infection. Single predictors and existing sepsis risk scores discriminate poorly, and few multivariable bacteraemia models have been adequately validated in UK populations. Methods: We developed a logistic regression model, using backwards AIC-based selection of pre-defined candidate predictors routinely available within hours of hospital attendance, in a retrospective cohort of 33,874 hospital encounters at University College London Hospitals (UCLH) between 2019 and 2024. Continuous predictors were modelled using restricted cubic splines and missing data handled using multiple imputation. Model performance was assessed via internal-external cross-validation and prediction instability analysis, before temporal validation in held-out 2024 UCLH data and external validation in 53,669 hospital encounters from the Infections in Oxfordshire Research Database (IORD). Results: Bacteraemia occurred in 5.2% of UCLH and 8.9% of IORD encounters, respectively. Twenty predictors were retained, spanning demographics, comorbidities, vital signs and blood tests. Discrimination was stable across development time periods (pooled c-statistic 0.82, 95%CI 0.81–0.84) and was maintained in temporal (0.83, 0.79–0.87) and external validation (0.83, 0.82–0.83), with excellent calibration in external validation (calibration slope 1.08 (1.05-1.11); calibration-in-the-large 0.01 (–0.02-0.04)). The model outperformed single predictors, established risk scores, and a reconstructed comparator model, and showed superior net benefit in decision curve analysis. Performance was consistent across age, sex, ethnicity and socioeconomic subgroups but degraded when blood cultures were sampled more than six hours after attendance and varied by likely infection site. Conclusions: This model accurately predicts bacteraemia using routinely collected data available within hours of hospital attendance, with performance maintained in a large, independent external validation cohort. It offers a generalisable, clinically interpretable tool to support early decision-making in suspected infection, pending further work to establish optimal implementation thresholds.

We investigated host-genetic TB susceptibility by expression quantitative trait loci (eQTL) analysis of the tuberculin skin test (TST) as a standardised challenge model of human in vivo TB immunology. Paired genotyping with 415 RNA-sequencing profiles from day 2 and day 7 TST biopsies in 267 individuals with latent or active TB identified cis-eQTLs affecting 1,719 response genes. The strongest signal mapped to ERAP2 , and colocalisation analysis linked reduced ERAP2 expression to increased TB risk in GWAS data. Heritability was greatest in HLA class II antigen presentation and T-cell activation pathways. HLA-DR haplotypes associated with subsequent expansion of Mtb-reactive T cells, linking host genotype to antigen-specific immunity. Trans-eQTLs also identified a proliferative programme centred on NCAPD3 , implicating genetically regulated cell-cycle control as an antigen-independent determinant of T-cell immunity. These findings provide a functional framework for interpreting TB susceptibility loci and identification of candidate biomarkers for TB risk stratification and vaccine development.

Understanding drivers of tuberculosis (TB) associated lung pathological damage is vital in identifying targets for host directed therapies (HDT). NETosis is a neutrophil specific cell death characterized by release of neutrophil extracellular traps (NETs). The role of NETosis in TB-associated lung damage and disease pathogenesis is still poorly understood. We analysed human lung TB granuloma samples using a proteomics approach, which revealed enrichment of neutrophil-associated proteins in necrotic regions of caseous and cavitary granulomas. Using immunohistochemistry (IHC), we validated the abundance of neutrophil-associated proteins, including myeloperoxidase (MPO), cytochrome b-245 beta chain (CYBB) and neutrophil cytosolic factor 1(NCF1), as well as NETosis markers, neutrophil elastase (NE) and citrullinated H3, in necrotizing caseum of human TB granulomas. MPO protein expression was also more abundant in the plasma of TB patients compared to healthy and latently infected (LTBI) participants. MPO directly correlated with an inflammatory disease marker, IP-10. In addition, MPO and IP-10 colocalized in caseous lesions. In-vitro drug inhibition assays were used to investigate potential drivers of NETosis, with pharmaceutical inhibition of MPO, NE and CYBB resulting in reduction of NETosis induced by Mycobacterium tuberculosis (Mtb). Using RT-qPCR we analysed the expression of 18 neutrophil associated genes in the blood of healthy (n = 20), latent TB infection (LTBI) (n = 20) and TB (n = 30) participants. We found that MPO, NCF1 and NCF2 were upregulated in the TB group. Furthermore, the NETosis-associated genes were induced in a human standardized antigen challenge model. Our data shows evidence of NETosis as an associate of lung pathological damage in TB and identifies key drivers of the neutrophil cell death that can be intercepted as potential HDT targets to reduce neutrophil driven lung pathological damage.

Transcriptomic analyses reveal the status of cells, tissues, or organisms, across states of health and disease. RNA velocity adds a temporal dimension to single cell analyses, predicting future transcriptomic and phenotypic states, based on the current spliced and unspliced mRNA of each cell. We hypothesized that RNA velocity could be adapted to predict future clinical state of individuals with acute and chronic illnesses, using their whole-blood transcriptomes. We developed VeloCD, a method for quantitative prediction of transitions in clinical state from a single time-point RNA sample. This predicts transcriptomic trajectories and future infection status in influenza A and SARS-CoV-2 controlled human infection studies, which are consistent with trajectories in naturally acquired infections. In HIV-TB coinfected individuals, VeloCD predicts the onset of immune reconstitution inflammatory syndrome. In individuals receiving biological therapy for inflammatory bowel disease, whole blood RNA velocity after the first dose of treatment indicates whether remission will be achieved by the end of the treatment course. In a multinational observational study of acutely unwell febrile children, VeloCD predicts those with greatest medical care requirements. Our results demonstrate proof-of-concept for the use of RNA velocity to predict trajectories of human diseases.