Gelfand, A. | Search bioRxiv

bioRxiv2023

Cannabinoid Combination Targets NOTCH1-Mutated T-ALL Through the Integrated Stress Response Pathway

In T-cell acute lymphoblastic leukemia (T-ALL) more than 50% of cases display autoactivation of Notch1 signaling, leading to oncogenic transformation. We have previously identified a specific chemovar of Cannabis that induces apoptosis by preventing Notch1 maturation in leukemia cells. Here, we isolated three cannabinoids from this chemovar that synergistically mimic the effects of the whole extract. Two were previously known, Cannabidiol (CBD) and Cannabidivarin (CBDV); whereas the third cannabinoid, which we termed 331-18A, was identified and fully characterized in this study. We demonstrated that these cannabinoids act through Cannabinoid receptor type 2 and TRPV1 to activate the integrated stress response pathway by depleting intracellular Ca2+. This is followed by increased mRNA and protein expression of ATF4, CHOP and CHAC1, which is hindered by inhibiting the upstream initiation factor eIF2. The increased abundance of CHAC1 prevents Notch1 maturation, thereby reducing the levels of the active Notch1 intracellular domain, and consequently decreasing cell viability and increasing apoptosis. Treatment with the three isolated molecules resulted in reduced tumor size and weight in-vivo and slowed leukemia progression in mice models. Altogether, this study elucidated the mechanism of action of three distinct cannabinoids in modulating the Notch1 pathway, and constitutes an important step in the establishment of a new therapy for treating NOTCH1-mutated diseases and cancers such as T-ALL.

cancer biology↗

bioRxiv2018

Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data

Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We present an ambitious agenda with regard to such data. We illuminate the fundamental modeling differences between the two types of data. Most simply, locations are considered as fixed under presence/absence data; locations are random under presence-only data. The definition of \"probability of presence\" is incompatible between the two. So, we take issue with modeling strategies in the literature which ignore this incom-patibility, which assume that presence/absence modeling can be induced from presence-only specifications and therefore, that fusion of presence-only and presence/absence data sources is routine.\n\nWe argue that presence/absence data should be modeled at point level. That is, we need to specify a surface which provides the probability of presence at any location in the region. A realization from this surface is a binary map yielding the results of Bernoulli trials across all locations; this surface is only partially observed. Presence-only data should be modeled as a (partially observed) point pattern, arising from a random number of individuals at random locations, driven by specification of an intensity function. There is no notion of Bernoulli trials; events are associated with areas.\n\nWe further argue that, with just presence/absence data, preferential sampling, using a shared process perspective, can improve our estimated presence/absence surface and prediction of presence. We also argue that preferential sampling can enable a probabilistically coherent fusion of the two data types.\n\nWe illustrate with two real datasets, one presence/absence, one presence-only for invasive species presence in New England in the United States. We demonstrate that potential bias in sampling locations can affect inference with regard to presence/absence and show that in-ference can be improved with preferential sampling ideas. We also provide a probabilistically coherent fusion of the two datasets to again improve inference with regard to presence/absence.\n\nThe importance of our work is to provide more careful modeling when studying species distributions. Ignoring incompatibility between data types and offering incoherent modeling specifications implies invalid inference; the community should benefit from this recognition.

ecology↗