Biology subjects

Cameron, J. C.

Publications and source records attributed to Cameron, J. C..

3 recordsLinked to original sources

bioRxiv2020

Meningococcal Deduced Vaccine Antigen Reactivity (MenDeVAR) Index: a Rapid and Accessible Tool that Exploits Genomic Data in Public Health and Clinical Microbiology Applications

As microbial genomics makes increasingly important contributions to clinical and public health microbiology, the interpretation of whole genome sequence data by non-specialists becomes essential. In the absence of capsule-based vaccines, two protein-based vaccines have been used for the prevention of invasive serogroup B meningococcal disease (IMD), since their licensure in 2013/14. These vaccines have different components and different coverage of meningococcal variants. Hence, decisions regarding which vaccine to use in managing serogroup B IMD outbreaks require information about the index case isolate including: (i) the presence of particular vaccine antigen variants; (ii) the expression of vaccine antigens; and (iii) the likely susceptibility of its antigen variants to antibody-dependent bactericidal killing. To obtain this information requires a multitude of laboratory assays, impractical in real-time clinical settings, where the information is most urgently needed. To facilitate assessment for public health and clinical purposes, we synthesised genomic and experimental data from published sources to develop and implement the Meningococcal Deduced Vaccine Antigen Reactivity (MenDeVAR) Index, which is publicly-available on PubMLST (https://pubmlst.org). Using whole genome sequences or individual gene sequences obtained from IMD isolates or clinical specimens, MenDeVAR provides rapid evidence-based information on the presence and possible immunological cross-reactivity of different meningococcal vaccine antigen variants. The MenDeVAR Index enables practitioners who are not genomics specialists to assess the likely reactivity of vaccines for individual cases, outbreak management, or the assessment of public health vaccine programmes. MenDeVAR has been developed in consultation with, but independently of, both vaccine manufacturers.

genomics↗

bioRxiv2020

CyAn: A MATLAB toolbox for image and data analysis of cyanobacteria

Studies of photosynthetic cyanobacteria are often carried out by growing the bacteria in large volume cultures. However, this results in an uneven amount of light received by the cells due to cell-cell shading. Fluorescence microscopy solves this issue as cells can be grown in a single layer, eliminating shading issues. However, microscopy brings its own challenge as hundreds of cells are imaged at the same time, making it difficult to analyze the resulting data by hand. Here we present CyAn, a toolbox to process and analyze microscope images of cyanobacteria. The toolbox automates cyanobacteria identification in microscope images, tracks moving and dividing cells, and computes relevant biological data such as growth rates. As proof-of-principle, we demonstrate that wild type and mutants can be grown on the same slide and identified using the software, allowing a much more accurate probe into photosynthetic processes that are often masked due to cellular heterogeneity or those requiring precise light control to prevent cell-cell shading.

microbiology↗

bioRxiv2020

Proximity-based proteomics reveals the thylakoid lumen proteome in the cyanobacterium Synechococcus sp. PCC 7002

Cyanobacteria possess unique intracellular organization. Many proteomic studies have examined different features of cyanobacteria to learn about the structure-function relationships between the intracellular structures of cyanobacteria and their roles in cells. While these studies have made great progress in understanding cyanobacterial physiology, the previous fractionation methods used to purify cellular structures have limitations; specifically, certain regions of cells cannot be purified with existing fractionation methods. Proximity-based proteomics techniques were developed to overcome the limitations of biochemical fractionation for proteomics. Proximity-based proteomics relies on spatiotemporal protein labeling followed by mass spectrometry of the labeled proteins to determine the proteome of the region of interest. We have performed proximity-based proteomics in the cyanobacterium Synechococcus sp. PCC 7002 with the APEX2 enzyme, an engineered ascorbate peroxidase. We determined the proteome of the thylakoid lumen, a region of the cell that has remained challenging to study with existing methods, using a PsbU-APEX2 gene fusion. This study demonstrates the power of APEX2 as a tool to study the cell biology of intracellular features of cyanobacteria with enhanced spatiotemporal resolution.

biochemistry↗