Back

Novel hemorrhage models of cerebral cavernous malformations

Detter, M. R.; Shenkar, R.; Benavides, C. R.; Neilson, C. A.; Moore, T.; Lightle, R.; Hobson, N.; Shen, L.; Cao, Y.; Girard, R.; Zhang, D.; Griffin, E.; Gallione, C. J.; Awad, I. A.; Marchuk, D. A.

2020-02-13 genetics
10.1101/2020.02.12.944421 bioRxiv
Show abstract

Cerebral cavernous malformations (CCMs) are ectatic capillary-venous malformations that develop in approximately 0.5% of the population. Patients with CCMs may develop headaches, focal neurologic deficits, seizures, and hemorrhages. While symptomatic CCMs, depending upon the anatomic location, can be surgically removed, there is currently no pharmaceutical therapy to treat CCMs. Several mouse models have been developed to better understand CCM pathogenesis and test therapeutics. The most common mouse models induce a large CCM burden that is anatomically restricted to the cerebellum and contributes to lethality in the early days of life. These inducible models thus have a relatively short period for drug administration. We developed an inducible CCM3 mouse model that develops CCMs after weaning and provides a longer period for potential therapeutic intervention. Using this new model, three recently proposed CCM therapies - fasudil, tempol, vitamin D3, and a combination of the three drugs failed to substantially reduce CCM formation when treatment was administered for five weeks, from postnatal day 21 (P21) to P56. We next restricted Ccm3 deletion to the brain vasculature and provided greater time (121 days) for CCMs to develop chronic hemorrhage, recapitulating the human lesions. We also developed the first model of acute CCM hemorrhage by injecting mice harboring CCMs with lipopolysaccharide. These efficient models will enable future drug studies to more precisely target clinically relevant features of CCM disease: CCM formation, chronic hemorrhage, and acute hemorrhage.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.