Our new preprint on #COVID19 with Stefan Pöhlmann, Markus Hoffman, and @joans is up. We show that proteases other than TMPRSS2 are capable of promoting SARS-CoV-2 uptake, but camostat (and its active metabolite) can still inhibit their activity.
TMPRSS2 is commonly described as “necessary” for SARS-CoV-2 activation. Many papers look at the expression patterns of ACE2+/TMPRSS2+ double-positive cells, ostensibly to shed light on which cell types are vulnerable to coronavirus infections. But it isn’t that simple!
ACE2 is necessary for viral uptake. TMPRSS2 expression in an ACE2+ cell is sufficient for uptake. But that DOES NOT mean that TMPRSS2 is also necessary! Hoffman et al. show that other TMPRSS-family proteases - particularly TMPRSS13 - are fully capable of activating SARS-CoV-2:
.@joans and I looked at the expression of these TMPRSS genes in tissues targeted by SARS-CoV-2. In the respiratory epithelium, many other TMPRSS’s are expressed in TMPRSS2-negative cells. TMPRSS11D is high in basal cells, TMPRSS13 in the nose, and TMPRSS11E in ionocytes.
21% of ACE2+ cells co-express TMPRSS2. 24% of ACE2+ cells are TMPRSS2-negative but co-express a different TMPRSS capable of viral activation. This likely expands the number of cells and the range of cell types that can be infected by SARS-CoV-2.
Hoffman et al. go on to show that these other TMPRSS proteases can still be inhibited by camostat, a “TMPRSS2 inhibitor” in clinical trials for COVID19.
Additionally, one concern with camostat as a treatment is that it has a very short half-life in vivo. Camostat is rapidly converted into GBPA, which is its major metabolite in the body.
Hoffman et al. found that FOY-251 (a methanesulfonate of GBPA) is about as effective at blocking SARS-CoV-2 pseudoparticle uptake as camostat, suggesting that camostat’s short half-life won’t necessarily be a problem for its use as a COVID19 therapy.
Take-aways from this work: sufficient ≠ necessary, COVID drugs, like cancer drugs, can have off-target activity against their target’s homologs, and a camostat metabolite remains able to block SARS-CoV-2 uptake.
The drug is called PAC-1. It was initially developed to target cancer cells by activating the executioner caspases. But, we generated CASP3/6/7 triple-knockouts and it still eliminated cancer cells, demonstrating that it must have some other target.
We used the PRISM dataset from @corsellos and found that PAC-1 was behaving like an iron-chelation agent. We confirmed that PAC-1 depleted cellular iron, upregulated an iron-starvation transcriptional response, and PAC-1 lethality could be reversed with iron supplementation.
One week from today, the Nobel Prize in Medicine/Physiology will be announced.
Here are the 79 most likely awardees, each of whom has won two or more pre-Nobel “predictor” prizes:
My top picks: Horwich/Hartl for their work on chaperone-mediated protein folding. Their discoveries changed how we think about protein structure and has had significant ramifications for our understanding of neurodegenerative diseases.
Klenerman/Balasubramanian for the development of next-gen sequencing (this could be a Chem prize too). NGS has revolutionized multiple areas of medicine and medical research, and the committee likes recognizing tool/technique development (PCR, CRISPR, monoclonal antibodies, etc).
The new class of HHMI investigators average 3.9 papers as corresponding author in Cell, Nature, or Science. 26 out of 26 members of this group previously trained with a PI who is in the National Academy of Sciences or who was an HHMI investigator themselves.
To back up, I have a longstanding interest in understanding the trajectories of academic careers and uncovering “hidden” factors that influence success. Some of my published work on this topic:
Recently, there has been a push for funding bodies to look more closely at preprints and put less emphasis on journal names. However, if you look at data that I collected from HHMI’s competition in 2018, you can see that the results are pretty similar:
Check out our new study in @ScienceMagazine, where we take on a 100-year-old debate: what’s the role of aneuploidy in cancer?
We discovered that genetically removing extra chromosomes blocks cancer growth - a phenomenon we call “aneuploidy addiction”. science.org/doi/10.1126/sc…
In the 19th century, pathologists observing cancer cells under a microscope noticed that they frequently underwent weird mitoses. The chromosome bodies visible in these cells were not equally divided between daughter nuclei - in other words, they were aneuploid.
Early pathologists like Theodor Boveri proposed that it was this aneuploidy that actually caused cancer. But, there was no way to test it. Eventually, this theory fell out of favor - researchers discovered oncogenes and showed the impact that point mutations could have in cancer.
Very excited to share a new paper from my lab: using a set chromosome-engineering tools, we show that cancers are “addicted” to aneuploidy. If you genetically eliminate single aneuploid chromosomes, cancer cells totally lose their malignant potential! biorxiv.org/content/10.110…
To back up, for many years researchers have used the standard tools of molecular genetics to learn about the function of individual oncogenes and tumor suppressors. We can easily over-express, mutate, or knockout genes like KRAS and TP53 to study their biology.
Chromosome gain events are exceptionally common in cancer, but the genetic tools that allow us to manipulate individual genes don’t work for these chromosome-scale copy number changes. You can’t package a whole chromosome in a lentivirus to over-express it.
If you choose to transfer a manuscript between Nature-family journals, you can consult a web page that lists the acceptance rates for 124 journals published by the Springer Nature Group.
I haven’t seen this data circulated before, so I copied it to share here:
According to this data, "Nature" is not actually the most selective journal. Nature Med, Cancer, and Human Behavior all have lower acceptance rates.
This could be Simpson’s paradox. Maybe a cancer paper has a 2% acceptance rate at Nature and a 4% acceptance rate at Nature Cancer, but Nature also loves to accept ML papers, which increases the overall acceptance rate?