New paper in PLOS Genetics. CpG sites are costly for HIV

28 Jun

We are publishing a new paper today in PLOS Genetics!

Title: Within-patient mutation frequencies reveal fitness costs of CpG dinucleotides and drastic amino acid changes in HIV.

“We” here means 6 coauthors of 5 different nationalities (which shows why travel bans are bad for science)! This is the first published result of our collaboration with Adi Stern in Tel Aviv.

Screenshot 2018-06-28 08.51.05


Result 1: CpG sites are costly for HIV.

The most surprising result of our paper (to me at least) is that mutations that create CpG sites are significantly more costly than mutations that do not create CpG sites. We know that they are more costly because they segregate at lower frequencies. Clearly, CpG sites are not good for HIV!

In this figure (which shows synonymous mutations only) you can see that the light blue dots are at lower frequencies than the green dots – each blue dot is the frequency of a synonymous mutation that creates a CpG site and each green dot is the frequency of a synonymous mutation that does not.

Screenshot 2018-06-28 08.59.05

Why are CpGs bad for HIV? This paper suggests that CpG sites are recognized by our immune system because Zinc-finger Antiviral Protein “binds directly and selectively to RNA sequences containing CG dinucleotides”.

Result 2: It works!

A more technical result is that we show that we can actually use within-patient mutation frequencies to estimate fitness costs of mutations. This means that we can study costs as they occur *now* (as opposed to phylogenetic approaches) and *in vivo* (as opposed to cell-culture based approaches).

This figure shows the single-site frequency spectra for three sites. Mutation frequencies are observed in 160 different patients. The second row shows simulated mutation frequencies using inferred cost estimates from the data. They look very similar to the real site frequency spectra!


Here we show that average in vivo mutation frequencies are lowest for nonsense mutations (black), higher for non-synonymous mutations (pink) and highest for synonymous mutations (yellow). This is exactly what mutation-selection equilibrium predicts.

Screenshot 2018-06-28 08.58.48



Using index cards to get feedback from all students

10 Apr

I do my best to find out what students are thinking and learning in my class. I use iClicker questions every period, I ask students to share ideas with the class, and I am always open for questions. Still, in most classes, I don’t hear from most of my students (I have about 55 students present in each section on a given day).

One solution I love to use is to let them write anonymous feedback on an index card at the end of class. Yesterday my prompt was: “Tell me one thing you learned today and one thing you are confused about.” I receive about 100 index cards (over 2 sections) – which takes about 10 minutes to read. It turned out that many (many!) of my students are confused about SNPs, markers, loci and genes and they are confused about how to read Manhattan plots. So good to know! I’ll spend the first 10 min of class tomorrow on these topics! So happy I found out now, and not next week at the midterm exam!


Summer opportunities for undergraduate students at SFSU

3 Jan

REU programs (paid research for undergrads during the summer)

The National Science Foundation funds a large number of research opportunities for undergraduate students through its REU Sites program. An REU Site consists of a group of around ten undergraduates who work in the research programs of the host institution. The programs usually provide travel money, room and board and some stipend. There is also an REU program at SFSU, but most spots in this program go to non-SFSU students.

The deadlines for these summer programs are early February to early March.

MARC program / SEO office fellowships (paid research for undergrads during the summer and academic year)

Science students at SFSU can apply for fellowships through the SEO office. The MARC fellowship description says:
“Purpose: To prepare students from underrepresented groups (African-American, Native American, Hispanic American and Pacific Islanders) and students with disabilities for biomedical careers by providing academic support and a stimulating research experience. The goal of the program is to prepare each participant for entrance into a competitive graduate program and successful completion of a PhD in the biomedical or physical sciences.”

A MARC fellowship provides mentoring, research experience and financial support ($12,588/year and partial tuition for the junior and senior years). If you are unsure if you are eligible, consider applying nevertheless!

Deadline: March 16th!

PINC summer program (not paid, coding and research)

The exact form of the 2018 PINC summer program is not yet clear. In 2017, 15 undergraduate students were part of the program, and together with 4 graduate students, they learned coding skills in R or python, they read research papers and they performed a small research project. The students spent 8 hours per week on the program for 9 weeks. In terms of coding skills, some students were absolute beginners, whereas others had taken a CS class before.

We will open op the program for applications in April of 2018.

Research (mostly volunteer, possibly for credit / paid)

If you are interested in getting research experience during the summer, you can see if any professor is looking for students to join their lab in the summer. Start with the professors you know or whose research you are particularly interested in. Some professors post opportunities on posters in the hallways of Hensill Hall, but many don’t and so you will just need to go and ask – by email or in person during their office hours. Almost everyone on this list does research and gets help from volunteer undergraduate students. Some students get credit for doing research (fairly common) and some get paid (not so common, but not impossible).

In the biology newsletter that you get by email every week, there are often opportunities posted, for example from labs at UCSF. If you are looking for a volunteer or paid research opportunity, make sure you read the newsletter!

Letters of recommendation

Many times you need a letter of recommendation from a professor when you apply for a fellowship or other opportunity. For us, professors, writing such letters is part of our job. Some professors only write letters for students they know well (for example, because they come to office hours often or because they volunteer in the lab of the professor). Other professors are happy to write a letter for anyone who has taken their class (that’s me!). Either way, don’t be afraid to ask! The worst that can happen is that they say no.
If a professor has agreed to write a letter for you, make sure that you email them all the materials that you also use to apply to the opportunity (e.g., unofficial transcript, essay, resume). Having those materials at hand makes it much easier for us to write a good letter! Plus, if you send them early enough, you may get some useful feedback. Once a professor has written one letter for you, they are usually happy to send that letter to as many programs as necessary. So if you want to apply to 20 different REU sites, go ahead! Our letter can go to all of these places, and really, we don’t mind. It’s our job.


Students in the 2017 PINC summer program.

New plans and new HIV stories

3 Jan

I love making plans and I love the beginning of the new year and the new semester. I actually think that the yearly rhythm of semesters and breaks is a huge benefit of being in academia.

Today I spent some time thinking about the writing I plan to do in the coming semester and the talks I will be giving in the near future. The first talk that’s coming up is an invited talk at Stanford. I am very honored to be an invited speaker at the CEHG symposium alongside Anne Stone (ASU) and Graham Coop (UC Davis). I want to try and use the opportunity of the talk to think about what stories I want to tell and I plan to write the story up for publication in addition to talking about it at Stanford.

So what are the stories I want to tell? There are many! But here are some thoughts:

“The evolution of HIV evolution.”

Recently I’ve given seminars in which the overarching storyline was “The evolution of HIV evolution.” I focused on the evolution of drug resistance within patients and explained how drug resistance evolution used to be very fast, but became slower over time. When people were treated with a single drug (AZT) in the late 80s and early 90s, the virus would evolve drug resistance very quickly and the treatment would quickly become useless. Freddy Mercury probably died because of a very fast evolving virus. Over time, treatments improved (using combinations of drugs and using better drugs) such that it became harder for the virus to evolve and nowadays, drug resistance evolution is so slow in patients on treatment, that it is no longer a big worry, and people can stay on the same drugs for many years.

Screen Shot 2018-01-03 at 12.45.01 PM

A slide from a talk I gave at SMBE and ESEB in the summer of 2017.

“HIV does it all”

Here is another storyline. Any field in science needs some systems that are looked at in detail. In evolutionary genetics, these systems are fruitflies, yeast, humans, mice etc. HIV is a great system as well in part because we know so much about it and data is abundant. One of the things we have learned in recent years, thanks to work by people like Richard Neher and Kathryna Lythgoe, is that HIV evolution can surprise us again and again. For example, HIV evolution, even in absence of drugs, can be fast within patients and slow at the epidemic level. It can be happening with a lot of recombination, or showing clonal interference (unpublished, Kadie-Ann Williams and PSP), and sweeps can be hard or soft. Within host populations can be panmictic or structured. So if everything can happen, how can we make sense of this all?

“Drugs to prevent HIV”

I like the story of how drugs were and are used to prevent HIV infection. In the 90s and well into the 2000s, drugs were used to prevent mother-to-child-transmission of the virus during child birth. In fact, this was one of the big successes in the world of HIV before treatment was really working to keep infected people alive. Nowadays, drugs are available for HIV-negative people who are at increased risk of HIV infection. Pre-exposure-prophylaxis (Prep, marketed as Truvada) is probably contributing to the shrinking of the HIV epidemic in San Francisco as many of the HIV-negative gay men in the city are taking Prep. When drugs were used to save babies, they were uncontroversial, but when they are used to save gay men, they continue to be controversial and there are many places where Prep is not available (for example, in my home country, The Netherlands).

How is this story linked to evolutionary genetics? When someone is taking drugs to prevent HIV, but they end up getting infected anyways, there may be a high risk of drug resistance evolution (this happened in the babies, in their already infected mothers, and it is happening occasionally in Prep users). Also, at an epidemic level, if a large part of the population is on Prep, this may lead to sub-epidemics of drug-resistant viral strains. There is some interesting modeling work by Sally Blower on these questions.


OK, that’s enough brainstorming for today. I’ll develop one of these stories into a presentation for CEHG and for an article to be published somewhere. If you have any questions or suggestions, let me know!



I support the NFL players who kneel for the anthem

26 Sep


Read this essay by Eric Reid in the New York Times.

The links between eugenics and rescinding DACA

7 Sep

This week Trump and Sessions announced that they will end DACA, a program under which certain young immigrants who came to the US as minors and who went to school in the US, can study and work.

The announcement that DACA is rescinded is extremely worrying news for 800,000 people who benefit from DACA plus their families, their friends, their colleagues, and many other people. If we were to lose those 800,000 people, it would be a human tragedy and a great loss for the country.

It should come as no surprise that Trump is doing this, because it fits with his racist ideas and policies.

You may not realize though (and I didn’t realize until recently) that the United States has a long history of using immigration policies to try to keep the country white. So what Trump is doing is not new. And whereas nowadays, California seems to be one of the states that are treating their immigrants better than other states, California was once a hotbed of racist genetics. For example, David Starr Jordan, who was the first president of Stanford University, worked to keep people of color out of the country. Wikipedia writes:

“Jordan promoted the concept of improving human genetics, through removal from the breeding pool of those deemed unworthy to reproduce,[17] in his series of publications titled The Blood of the Nation. He then chaired the first Committee on Eugenics of the American Breeder’s Association, from which the California program of forced deportation and sterilization emerged.[18] Jordan then went on to help found the Human Betterment Foundation as a trustee. The Human Betterment Foundation published “Sterilization for Human Betterment,” a text which formed a cornerstone of the Nazi eugenics program.”

The eugenics movement was strong in the US before it was adopted by Nazi Germany.

Here is a short video from The Rachel Maddow Show about the links between Eugenics and rescinding DACA.



How we elected a new chair for the Biology Department

22 Jun

pennie-chairA few months ago, our department needed to elect a new chair. I was part of the election committee and I couldn’t find much useful information online on how to elect a chair. Now that we have done it, I thought I share how we did it!

Finding candidates to run for chair and volunteers for the election committee

The associate chair for HRTP send out an email to ask who would like to run for chair and to ask who would like to sit on the election committee.

Fairly quickly, four people said they wanted to run for chair, but only few people wanted to be on the election committee. The associate chair for HRTP then sent out a second email to ask for volunteers, letting everyone know that there were not yet enough volunteers. Quickly, people volunteered and we had a committee of 5 (4 women, 1 man, 3 full, 1 associate and 1 assistant prof, one URM).

Setting deadlines

The election committee met a few times. We agreed that we wanted to try to  to make the election as much as possible about the future of the department and not a popularity contest. We also needed to do things quickly because of university deadlines.

The committee quickly decided on some deadlines:

  1. a deadline for people to let us know that they were running for chair
  2. a deadline for the candidates to send us two pages with their vision / plan for the department (to be shared with people in the department)
  3. a deadline for everyone else in the department to send in questions for the candidates (to be asked at the town hall department meeting).
  4. a day for a department meeting devoted to getting to know the plans of the candidates (town hall)
  5. a day for the ballots to be due

Someone from the election committee also talked to the dean of the college about how we could support our new chair. The dean agreed that the department could spend money on hiring a part-time lab tech for the new chair. I think it was useful that this conversation happened before the election, and also that the candidates themselves didn’t have to do this.

Instant-runoff voting

The election committee decided to use the method of instant-runoff voting for the election.

Instant-runoff voting allows everyone to vote for their favorite candidate. Everybody also indicates a second, third etc choice. Initially, only first choices are counted. If one candidate has more than 50% of the first choice votes, then this candidate wins. If no one has more than 50% of the votes, the candidate with the fewest votes is eliminated. For all ballots that had this candidate as their first choice, the second choice now counts. Votes are counted again and if someone has more than 50% of the votes, than that person wins. If not, the candidate with the least votes is eliminated again. The process continues until there is a winner.

Here’s an example of how this could work. Imagine, Hillary, Bernie and Trump all ran against each other. Bernie gets 10%, Hillary 44% and Trump also 46% of the votes in the first round. In the “normal” system, Trump would win and the Hillary voters would blame the Bernie voters for letting a Republican win the election. In the instant-runoff system, however, we would notice that no one has more than 50%, so we go to the second round. Bernie is eliminated because he has the fewest votes, and since most Bernie voters likely ranked Hillary second and Trump third, these votes are transferred to Hillary, allowing her to win in the second round with 54% of the votes.

A town hall meeting with all candidates

Questions from the department were compiled and forwarded anonymously to the candidates to help them prepare for the discussion at the faculty meeting. Specifically, lecturers and faculty were asked to create questions that could be answered by all of the candidates, rather than individual questions for just one candidate.

At the department meeting devoted to the election, our four candidates gave a short presentation (5 minutes each) on how they would run the department and what their plans were for the department. After these presentations, someone from the election committee asked the candidates a selection of the questions that had been sent in by our colleagues (around 45 minutes). Finally, there was time for a spontaneous Q&A session (around 10 minutes). Many colleagues let us know that they found this meeting extremely useful and interesting.

Counting the ballots!

A week after the town hall meeting, the ballots were due. Late that afternoon, three of us spent a few hours counting. Our task was somewhat complicated because of the instant-runoff system and also because many people in our department teach part-time and their votes are weighted by how much they teach.

All tenured and tenure-track faculty will have one vote; .

All four candidates had strong support in the department and we needed all rounds of the instant run-off to determine who had won.

My personal conclusion

I think the election went well. The atmosphere was collegial and I think that the process worked well.

Being on this election committee was a really fun experience!

The others on the committee were Jennifer Breckler, Carmen Domingo, Gretchen LeBuhn and Vance Vredenburg.

Supplementary material: Instant runoff explanation on the ballot

We are electing a new chair for the biology department. The 2017 chair election committee decided to conduct an election using a method called “instant runoff” or “ranked choice voting.” In case you are not familiar with this concept, we include here a short description. This reference is also helpful.  

Each voter will receive only one ballot. The ballot will ask the voter to rank the 4 candidates. (i.e. first choice, second, third, fourth). Voters have the option to rank as many or as few candidates as they wish. Candidates can not share rankings (i.e. you should rank only one person as first choice candidate, one second choice candidate, etc.). First choices are then tabulated. If one candidate obtains the MAJORITY of first-place votes (i.e. greater than 50%) then he/she wins the election. If no one receives a majority, then a “runoff” is necessary. The candidate who received the fewest first choice rankings is eliminated from the ballot. All ballots are then re-tabulated using each voter’s highest ranked candidate who is still in the race. Specifically, voters who had chosen the now-eliminated candidate will now have their ballots added to the totals of their second choice candidate — just as if they were voting in a second election — but all other voters get to continue supporting their first-choice candidate who remains in the race. The person with the majority vote wins. If another runoff is needed, the weakest candidates are successively eliminated and their voters’ ballots are added to the totals of their next choice candidate. Once the field is reduced to ONLY two candidates, the candidate with the majority of votes wins.