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Advancing climate change is an urgent global driver of human disease with some of the world's most marginalized communities being at highest risk. In response to this compound threat, scientific studies exploring the intersections of climate and health are gaining momentum, yet sparse attention is currently paid to models of research that engage and involve those who are most affected. This is a crucial moment for the global health field to take up valuable lessons from the relatively few climate-health studies that have purposefully brought scientists together with communities and members of the wider public, via established approaches such as community-based participatory research and citizen science, to reduce health risks and better understand the deeper determinants of disease. Through a scoping literature review of PubMed and qualitative framework analysis, we investigate scientific, ethical, and social justice rationales that have motivated scientists to involve communities, including Indigenous groups, and other public stakeholders in climate-driven health research to varying degrees in different global contexts. We also examine the benefits of diverse forms of engagement and show that involving communities in research at the climate-health nexus has, for example, generated co-produced adaptation strategies that respond appropriately and effectively to priority health hazards. In addition, our findings demonstrate limitations, challenges, and ethical considerations that can arise when engaging communities in climate-health research. We argue that ethically engaging and involving communities and the wider public in research at the intersection of climate change and health is imperative for identifying relevant research priorities, promoting responsiveness that can appropriately fill gaps in at-risk settings, and advancing social justice. Our paper offers a range of examples of research designs and activities to guide scientists working in this emerging interdisciplinary field, inform funders seeking to support innovative and potentially transformative research, and contribute to global health policy. [ABSTRACT FROM AUTHOR]

Rationale: Contacts of patients with tuberculosis (TB) constitute an important target population for preventive measures because they are at high risk of infection with Mycobacterium tuberculosis and progression to disease.Objectives: We investigated biosignatures with predictive ability for incident TB.Methods: In a case-control study nested within the Grand Challenges 6-74 longitudinal HIV-negative African cohort of exposed household contacts, we employed RNA sequencing, PCR, and the pair ratio algorithm in a training/test set approach. Overall, 79 progressors who developed TB between 3 and 24 months after diagnosis of index case and 328 matched nonprogressors who remained healthy during 24 months of follow-up were investigated.Measurements and Main Results: A four-transcript signature derived from samples in a South African and Gambian training set predicted progression up to two years before onset of disease in blinded test set samples from South Africa, the Gambia, and Ethiopia with little population-associated variability, and it was also validated in an external cohort of South African adolescents with latent M. tuberculosis infection. By contrast, published diagnostic or prognostic TB signatures were predicted in samples from some but not all three countries, indicating site-specific variability. Post hoc meta-analysis identified a single gene pair, C1QC/TRAV27 (complement C1q C-chain / T-cell receptor-α variable gene 27) that would consistently predict TB progression in household contacts from multiple African sites but not in infected adolescents without known recent exposure events.Conclusions: Collectively, we developed a simple whole blood-based PCR test to predict TB in recently exposed household contacts from diverse African populations. This test has potential for implementation in national TB contact investigation programs.

Abstract Background Confirming tuberculosis (TB) disease in suspects in resource limited settings is challenging and calls for the development of more suitable diagnostic tools. Different Mycobacterium tuberculosis (M.tb) infection phase-dependent antigens may be differentially recognized in infected and diseased individuals and therefore useful as diagnostic tools for differentiating between M.tb infection states. In this study, we assessed the diagnostic potential of 118 different M.tb infection phase-dependent antigens in TB patients and household contacts (HHCs) in a high-burden setting. Methods Antigens were evaluated using the 7-day whole blood culture technique in 23 pulmonary TB patients and in 19 to 21 HHCs (total n = 101), who were recruited from a high-TB incidence community in Cape Town, South Africa. Interferon-gamma (IFN-γ) levels in culture supernatants were determined by ELISA. Results Eight classical TB vaccine candidate antigens, 51 DosR regulon encoded antigens, 23 TB reactivation antigens, 5 TB resuscitation promoting factors (rpfs), 6 starvation and 24 other stress response-associated TB antigens were evaluated in the study. The most promising antigens for ascertaining active TB were the rpfs (Rv0867c, Rv2389c, Rv2450c, Rv1009 and Rv1884c), with Areas under the receiver operating characteristics curves (AUCs) between 0.72 and 0.80. A combination of M.tb specific ESAT-6/CFP-10 fusion protein, Rv2624c and Rv0867c accurately predicted 73% of the TB patients and 80% of the non-TB cases after cross validation. Conclusions IFN-γ responses to TB rpfs show promise as TB diagnostic candidates and should be evaluated further for discrimination between M.tb infection states.

Abstract Background For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

Abstract Background Interferon gamma release assays, including the QuantiFERON® TB Gold In Tube (QFT) have been shown to be accurate in diagnosing Mycobacterium tuberculosis infection. These assays however, do not discriminate between latent TB infection (LTBI) and active TB disease. Methods We recruited twenty-three pulmonary TB patients and 34 household contacts from Cape Town, South Africa and performed the QFT test. To investigate the ability of new host markers to differentiate between LTBI and active TB, levels of 29 biomarkers in QFT supernatants were evaluated using a Luminex multiplex cytokine assay. Results Eight out of 29 biomarkers distinguished active TB from LTBI in a pilot study. Baseline levels of epidermal growth factor (EGF) soluble CD40 ligand (sCD40L), antigen stimulated levels of EGF, and the background corrected antigen stimulated levels of EGF and macrophage inflammatory protein (MIP)-1β were the most informative single markers for differentiation between TB disease and LTBI, with AUCs of 0.88, 0.84, 0.87, 0.90 and 0.79 respectively. The combination of EGF and MIP-1β predicted 96% of active TB cases and 92% of LTBIs. Combinations between EGF, sCD40L, VEGF, TGF-α and IL-1α also showed potential to differentiate between TB infection states. EGF, VEGF, TGF-α and sCD40L levels were higher in TB patients. Conclusion These preliminary data suggest that active TB may be accurately differentiated from LTBI utilizing adaptations of the commercial QFT test that includes measurement of EGF, sCD40L, MIP-1β, VEGF, TGF-α or IL-1α in supernatants from QFT assays. This approach holds promise for development as a rapid diagnostic test for active TB.

There is a growing body of literature describing conceptual frameworks for working with participatory visual methods (PVM). Through a global health lens, this paper examines some key themes within these frameworks. We reflect on our experiences of working with with an array of PVM to engage community members in Vietnam, Kenya, the Philippines and South Africa in biomedical research and public health. The participants that we have engaged in these processes live in under-resourced areas with high prevalence of communicable and non-communicable diseases. Our paper describes some of the challenges that we have encountered while using PVM to foster knowledge exchange, build relationships and facilitate change among individuals and families, community members, health workers, biomedical scientists and researchers. We consider multiple ethical situations that have arisen through our work and discuss the ways in which we have navigated and negotiated them. We offer our reflections and learning from facilitating these processes and in doing so we add novel contributions to ethical framework concepts. [ABSTRACT FROM AUTHOR]

Elevated antibody responses to Mycobacterium tuberculosis antigens in individuals with latent infection (LTBI) have previously been linked to an increased risk for progression to active disease. Studies in the field focussed mainly on IgG antibodies. In the present study, IgA and/or IgG responses to the mycobacterial protein antigens AlaDH, NarL, 19 kDa, PstS3, and MPT83 were determined in a blinded fashion in sera from 53 LTBI controls, 14 healthy controls, and 42 active TB subjects. Among controls, we found that elevated IgA levels against all investigated antigens were not randomly distributed but concentrated on a subgroup of <30%—with particular high levels in a small subgroup of ~5% comprising one progressor to active TB. Based on a specificity of 100%, anti-NarL IgA antibodies achieved with 78.6% sensitivity the highest accuracy for the detection of active TB compared to healthy controls. In conclusion, the consistently elevated IgA levels in a subgroup of controls suggest higher mycobacterial load, a risk factor for progression to active TB, and together with high IgG levels may have prognostic potential and should be investigated in future large scale studies. The novel antigen NarL may also be promising for the antibody-based diagnosis of active TB cases. [ABSTRACT FROM AUTHOR]

Although tuberculosis (TB) causes more deaths than any other pathogen, most infected individuals harbor the pathogen without signs of disease. We explored the metabolome of >400 small molecules in serum of uninfected individuals, latently infected healthy individuals and patients with active TB. We identified changes in amino acid, lipid and nucleotide metabolism pathways, providing evidence for anti-inflammatory metabolomic changes in TB. Metabolic profiles indicate increased activity of indoleamine 2,3 dioxygenase 1 (IDO1), decreased phospholipase activity, increased abundance of adenosine metabolism products, as well as indicators of fibrotic lesions in active disease as compared to latent infection. Consistent with our predictions, we experimentally demonstrate TB-induced IDO1 activity. Furthermore, we demonstrate a link between metabolic profiles and cytokine signaling. Finally, we show that 20 metabolites are sufficient for robust discrimination of TB patients from healthy individuals. Our results provide specific insights into the biology of TB and pave the way for the rational development of metabolic biomarkers for TB. [ABSTRACT FROM AUTHOR]

Presents a study which assessed the interferon-gamma responsiveness to purified protein derivative and tuberculin antigens from mycobacteria species among adults in Malawi, where vaccination provides no protection against pulmonary tuberculosis. Information on mycobacteria vaccines; Materials and methods used; Results and discussion.