Something in the air - investigating the impact of air pollution on neurodevelopment in the zebrafish model.

Early-life exposure to particulate matter (PM) may cause neuronal deficits. It is not
known whether PM is directly responsible or rather primes the nervous system to
respond adversely to other environmental challenges. One proposed mechanism of
action is neuroinflammation, mediated by microglial cells, but we are hampered by
lack of real-time in utero imaging to prove this or identify whether additional stressors
are required. Additionally, we lack understanding of how PM impacts upon gene
transcription in the developing nervous system, which may contribute to life-long
neurological impairment. In this project, we will used both the developing zebrafish
embryo model and human fMRI and dMRI datasets to investigate these questions. We
will test the hypothesis that PM activates microglial cells directly resulting in
neuroinflammation but that additional stressors are required to induce
neurodevelopmental deficits. We will test this hypothesis using the following
objectives: Objective 1: Additive impacts of environmental stress on brain
development (RVC) Zebrafish embryos/larvae will be raised in the presence of PM with
or without additional stressors (cortisol/LPS). Outputs of survival, morphology and
behaviour will be evaluated. Objective 2: Cellular contribution to PM-mediated stress
phenotypes (RVC) Immunohistochemistry and/or transgenic lines will be used to
dissect relative contributions of microglia and neurons to the phenotype. Cellular
bioenergetics, metabolic flux, signalling and cell death will also be examined.
Objective 3: Transcriptomic analysis of pathways impacted by PM (RVC) RNAseq
profiles of PM exposed embryos will be analysed to determine which
neurodevelopmental pathways are impacted and if additional stressors change the
transcription profile. Objective 4: Impact of exposure to PM on neurodevelopment in
term born infants (KCL) Neuroinflamation analyses will be performed using fMRI and
dMRI datasets from infants enrolled in the Developing Human Connectome project.
Quantitative connectomic measures will be incorporated into statistical models with
pollution, genetic, clinical and demographic data.