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Charitakis, N. C.

Publications and source records attributed to Charitakis, N. C..

2 recordsLinked to original sources

A BCG Skin Challenge Model for Assessing TB Vaccines

Controlled Human infection models (CHIM) are valuable tools for assessing relevant biological activity in vaccine candidates, with the potential to accelerate Tuberculosis vaccine development into the clinic. Tuberculosis infection poses significant constraints on the design of a CHIM using the causative agent Mycobacterium tuberculosis. As a safer alternative, we propose a challenge model using the attenuated vaccine agent Mycobacterium bovis BCG as a surrogate for Mycobacterium tuberculosis, and intradermal (skin) challenge as an alternative to pulmonary infection. We have developed a unique non-invasive imaging system based on fluorescent reporters to quantitatively measure bacterial load over time, thereby determining a relevant biological vaccine effect. We assessed the utility of this model to measure the effectiveness of two TB vaccines: the currently licenced BCG and a novel subunit vaccine candidate. To assess the efficacy of the skin challenge model a pharmacometric model was built describing the decline of fluorescence over time. The results show that vaccination is a statistically significant factor which reduced the fluorescence readout of both fluorophores. The higher decline in vaccinated mice correlated with bacterial burden in the lungs. This supports the fluorescence output from the skin as a reflection of vaccine induced functional pulmonary immune responses. This novel non-invasive approach allows for repeated measurements from the challenge site, providing a dynamic readout of vaccine induced responses over time. This BCG skin challenge model represents an important contribution to the ongoing development of controlled challenge models for Tuberculosis.

microbiology↗

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics (SRT) data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

bioinformatics↗