Dataset Information

Transcriptomics in Human Challenge Models.

ABSTRACT: Human challenge models, in which volunteers are experimentally infected with a pathogen of interest, provide the opportunity to directly identify both natural and vaccine-induced correlates of protection. In this review, we highlight how the application of transcriptomics to human challenge studies allows for the identification of novel correlates and gives insight into the immunological pathways required to develop functional immunity. In malaria challenge trials for example, innate immune pathways appear to play a previously underappreciated role in conferring protective immunity. Transcriptomic analyses of samples obtained in human challenge studies can also deepen our understanding of the immune responses preceding symptom onset, allowing characterization of innate immunity and early gene signatures, which may influence disease outcome. Influenza challenge studies demonstrate that these gene signatures have diagnostic potential in the context of pandemics, in which presymptomatic diagnosis of at-risk individuals could allow early initiation of antiviral treatment and help limit transmission. Furthermore, gene expression analysis facilitates the identification of host factors contributing to disease susceptibility, such as C4BPA expression in enterotoxigenic Escherichia coli infection. Overall, these studies highlight the exceptional value of transcriptional data generated in human challenge trials and illustrate the broad impact molecular data analysis may have on global health through rational vaccine design and biomarker discovery.

SUBMITTER: Barton AJ

PROVIDER: S-EPMC5741696 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Transcriptomics in Human Challenge Models.

Barton Amber J AJ Hill Jennifer J Pollard Andrew J AJ Blohmke Christoph J CJ

Frontiers in immunology 20171218

Human challenge models, in which volunteers are experimentally infected with a pathogen of interest, provide the opportunity to directly identify both natural and vaccine-induced correlates of protection. In this review, we highlight how the application of transcriptomics to human challenge studies allows for the identification of novel correlates and gives insight into the immunological pathways required to develop functional immunity. In malaria challenge trials for example, innate immune path ...[more]

PMID: 29326715

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Project description:The complexity and heterogeneity of ovarian cancer cases are difficult to reproduce in in vitro studies, which cannot adequately elucidate the molecular events involved in tumor initiation and disease metastasis. It has now become clear that, although the multiple histological subtypes of ovarian cancer are being treated with similar surgical and therapeutic approaches, they are in fact characterized by distinct phenotypes, cell of origin, and underlying key genetic and genomic alterations. Consequently, the development of more personalized treatment methodologies, which are aimed at improving patient care and prognosis, will greatly benefit from a better understanding of the key differences between various subtypes. To accomplish this, animal models of all histotypes need to be generated in order to provide accurate in vivo platforms for research and the testing of targeted treatments and immune therapies. Both genetically engineered mouse models (GEMMs) and xenograft models have the ability to further our understanding of key mechanisms facilitating tumorigenesis, and at the same time offer insight into enhanced imaging and treatment modalities. While genetic models may be better suited to examine oncogenic functions and interactions during tumorigenesis, patient-derived xenografts (PDXs) are likely a superior model to assess drug efficacy, especially in concurrent clinical trials, due to their similarity to the tumors from which they are derived. Genetic and avatar models possess great clinical utility and have both benefits and limitations. Additionally, the laying hen model, which spontaneously develops ovarian tumors, has inherent advantages for the study of epithelial ovarian cancer (EOC) and recent work champions this model especially when assessing chemoprevention strategies. While high-grade ovarian serous tumors are the most prevalent form of EOC, rarer ovarian cancer variants, such as small cell ovarian carcinoma of the hypercalcemic type and transitional cell carcinoma, or non-epithelial tumors, including germ cell tumors, will also benefit from the generation of improved models to advance our understanding of tumorigenic mechanisms and the development of selective therapeutic options.

Dataset Information

Transcriptomics in Human Challenge Models.

Publications

Transcriptomics in Human Challenge Models.

Similar Datasets

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