Knight Genetics

Some of the big questions raised by the COVID-19 pandemic are 'Why do we all have such a different reaction to the infection? From the mild cases to the seriously sick?' Our #COMBAT paper in @CellCellPress aims to explore these questions A 🧵by Dr Justin Whalley & @DrCarlaCohen

Our new study shows a genetic variant fine-tunes MTOR and immune crosstalk, improving survival in pneumonia-associated #sepsis - toward personalised treatments. Congrats to @pingzhangneho and everyone involved! @HumanGeneticsOx @CAMS_COI_Ox @NatureComms nature.com/articles/s4146…

Glad to share our study showing how a common genetic variant impacts immune cell-cell communication and patient survival in pneumonia-associated sepsis, providing a framework for stratified therapy. Thanks to our funders and everyone involved. nature.com/articles/s4146…

Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis dlvr.it/Sn0DBD

Our research on the chromatin landscape of ankylosing spondylitis (AS) has just been published in @CellGenomics. An important step towards future drug discovery. Congratulations to @DrCarlaCohen, @HumanGeneticsOx, and and everyone involved!

We are excited to share our recent work mapping the host response to sepsis at single-cell resolution. The study, led by @andrewjkwok and published in @NatImmunol, identifies a subset of immature neutrophils which is increased in severe sepsis. Big thanks to everyone involved!

Congratulations to @KnightGenetics @andrewjkwok @eddiecg and team for their work on immune subsets in sepsis

Our study on AS epigenomics has been covered by @GenomeWeb Watch this space for link to paper and 🧵

A big thank you to the patients, families, clinicians, and research nurses involved in our sepsis studies, without whom this wouldn't have been possible. Congratulations to @eddiecg @ee_davenport @HumanGeneticsOx @sangerinstitute and everyone else involved!

For more information, you can refer to our full publication and the associated R package: R package: bit.ly/3zIRqxs Publication: bit.ly/3FKyhij (14/15)

We are excited to share our new study, out now in @ScienceTM! We developed a machine learning framework (SepstratifieR) to stratify infection patients by risk of severe illness. SepstratifieR is publicly available and applicable to sepsis, influenza and COVID-19. 🧵👇 (1/15)