Earlier this week I received the wonderful news that I was awarded an R01 grant together with Zandrea Ambrose and Philana Lin from the university of Pittsburgh!!
Here is a 3 minute video on the kind of work we will be doing (video and paper by Alison Feder, Stanford):
Keith Bowman, Pleuni Pennings, Kadie Williams, Teaster Baird, Blake Riggs.
Danny got accepted in pre-med post-bacc certificate program at UCSF, Christine is studying for the MCAT and Abdul got accepted in a Master’s Program in Biochemistry & Molecular Biology at Georgetown University.
Deshawn was accepted in the MARC program (Maximizing Access to Research Careers) and Gabriella was accepted into the REU program (Research Experience for Undergraduates). They will keep doing research with me.
Dwayne and Olivia are planning to defend their Master’s one year from now. They both have funding for the coming academic year, which allows them to focus on their research.
A new paper that I was lucky enough to be involved in came out this week in PLOS Pathogens. In the paper we follow how drug resistance evolves in space and time in macaques infected with RT-SHIV. The first author of the paper (Alison Feder) made a beautiful short video to explain our main findings. Have a look!
This is the first time I have been involved in work with primates and I know that many of you probably think that primates should not be used for research. I think that this specific type of research is important and may lead to better treatments and less suffering for people.
A spatio-temporal assessment of simian/human immunodeficiency virus (SHIV) evolution reveals a highly dynamic process within the host
Feder AF, Kline C, Polacino P, Cottrell M, Kashuba ADM, et al. (2017) A spatio-temporal assessment of simian/human immunodeficiency virus (SHIV) evolution reveals a highly dynamic process within the host. PLOS Pathogens 13(5): e1006358. https://doi.org/10.1371/journal.ppat.1006358
My friend and former Stanford colleague Anna-Sophie Fiston-Lavier came to visit us at SFSU all the way from Montpellier in France. She met with students from the Code Lab and students from the PINC program and she gave a talk in our seminar series on detecting Transposable Elements (TEs) in genomes. Very exciting stuff!
I (Pleuni Pennings) endorse the following, which was drafted by Graham Coop (UC Davis), Michael Eisen (UC Berkeley) and Molly Przeworski (Columbia):
We are deeply concerned by the Trump administration’s move to gag scientists working at various governmental agencies. The US government employs scientists working on medicine, public health, agriculture, energy, space, clean water and air, weather, the climate and many other important areas. Their job is to produce data to inform decisions by policymakers, businesses and individuals. We are all best served by allowing these scientists to discuss their findings openly and without the intrusion of politics. Any attack on their ability to do so is an attack on our ability to make informed decisions as individuals, as communities and as a nation.
If you are a government scientist who is blocked from discussing their work, we will share it on your behalf, publicly or with the appropriate recipients. You can email us at USScienceFacts@gmail.com.
If you use this email address, here is a PGP public key for PGP encryption: http://pgp.mit.edu/pks/lookup?op=get&search=0x52C7139DE0A3D350
We just published a correction to our 2016 eLife paper.
While we were preparing the code to share it on Github, we discovered an error in calling drug resistance mutations (DRMs) in the protease gene in 140 sequences in our dataset (out of 6,717 sequences). The mistake was related to how R reads in data using read.table(). Even though the error only affected 2% of the sequences, it affected all downstream analysis and multiple figures had to be updated to reflect this correction. The resulting changes are minor and do not substantially change the conclusions and in some cases make them stronger.
When the analysis is updated with new numbers for these 140 sequences, all of our conclusions hold qualitatively, although the points in some figures shift slightly quantitatively. In fact, updating to the correct DRM calling for these sequences results in estimates for two treatments that are more in line with the expectations laid out in our paper.
As an illustration of this effect, we show here a version of Figure 4, which appeared in the paper (although has been slightly altered here for readability), and the shift of the model coefficients after correcting the 140 sequences of DRM calling. As you can see, the shifts are minor in most cases, and only serve to strengthen our conclusions for two of the PI treatments (points in the middle of the figure in light blue).
Figure 4, from the paper (slightly altered here for readability) shows the shift of the model coefficients after correcting the 140 sequences of DRM calling. The shifts are minor in most cases, and only serve to strengthen our conclusions for two of the PI treatments (points in the middle of the figure in light blue).
After finding the original mistake, we spent a lot of time going through all of the code for the paper and found a few other small mistakes in the description of the analysis. In all cases, the mistakes were minor.
The eLife editors and staff were very helpful in the entire process.
Feder, Alison F., et al. “More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1.” Elife 5 (2016): e10670. Link.
