Boltz et al 2011 on standing genetic variation and HIV drug resistance

14 Jan

I am re-reading and older but beautiful paper on drug resistance evolution and standing genetic variation by Valerie Boltz and colleagues (Boltz et al 2011, PNAS). I wanted to share this story because it is a nice example of population genetics at work in a relevant system: HIV during antiviral treatment.

Boltz and colleagues look at the risk of virology failure or death in women on first-line HIV therapy who had been previously treated with single-dose Nevirapine (sdNVP) and correlate the risk with the observed frequency of drug resistance mutations prior to first-line treatment.

OK, that’s a long and ugly sentence. I’ll try to explain. So there is a group of women who are all HIV-positive. They have recently given birth to a baby and during childbirth, they were treated with sdNVP. This simple (and cheap) treatment reduces the risk that the baby gets HIV infected during birth (it is not recommended any more as better options are now available). One drawback of the sdNVP treatment is that the women end up with an increased frequency of drug resistance mutations in their viral population (specifically mutations K103N, Y181C and G190A in RT). Now, half-a-year or more later, the women start “normal” triple-drug therapy for their own health. They are in a clinical trial and some of them start NNRTI-based treatment whereas others start PI/r-based treatment. Since sdNVP is an NNRTI, the researchers were (rightfully) worried that the viral populations in these women have standing genetic variation for NNRTI resistance – more than other people who had never been treated with sdNVP. The paper looks at the women in both arms of the trial, but I am only interested in the NNRTI arm.

So what Boltz and colleagues did was: use allele-specific PCR to quantify the amount of standing genetic variation for NNRTI drug resistance in these women’s viral populations before they started their first-line therapy (but after the sdNVP) and then correlate with how well the treatment worked in these women. During treatment, the researchers focus on the occurrence of two bad outcomes: death and virologic failure. Death is not so common in this trial (but does occur, 4 of 241 women die in the three years of the trial) – virologic failure is more common (38 women). Virologic failure means that there is detectable viral replication in the blood even though the person is on treatment. Often, virologic failure is caused by drug resistance.

More SGV -> more virologic failure

Not surprisingly (but very cool nevertheless): more standing genetic variation prior to treatment is associated with a higher probability of virologic failure. Their (and my) interpretation: Standing Genetic Variation matters for drug resistance evolution.

This figure (fig 3 in the original paper) shows that women with >1% of the viral population carrying a resistance mutation have a much higher probability of virologic failure or death. The plot (a Kaplan-Meier plot) shows time in weeks on the x-axis and the fraction of women without failure or death on the y-axis. One way to read such a graph is to focus on one point on the x-axis. After 72 weeks, about 85% of the women with no detected drug resistance mutations are doing well on their treatment. 77% of the women with drug resistance mutations at a frequency <1% are doing well. 53% of the women with drug resistance mutations at a frequency between 1-10% are doing well and 49% of women with drug resistance mutations at a frequency of more than 10% are doing well.




Plotting the number of women in the US congress

10 Nov

I wrote a simple python script to plot the number of women in congress and used it in my intro CS class this week. Two reasons:

1. That jump in 2019 is so good!!! This is helping me get over 2016.

2. We shouldn’t have to choose between activism and science.

Data from wikipedia on Nov 10th (numbers may still change when final races are called).

If anyone has similar data for the number of people of color in congress, I would like to plot that too!


Recursion in real life

7 Oct

This semester, I am teaching a new class: Intro To Programming. I try to find ways to explain stuff so that all of my students (and me) understand it. Recursion is complex, but it reminds me of trying to make lunch plans when several people are involved. And when a function calls itself, it’s like putting a conversation on hold. Recursion

Recursion is when three calls are put on hold before a lunch decision is made.

Asha would like to have lunch with Blake.

They call Blake.

They say: Hi Blake, would you like to go for lunch in the student center?

Blake says to Asha: That’s a cool idea, but I was hoping to meet with Cynthia today.

Let me put you on hold and call them.

They call Cynthia

They say: Hi Cynthia, would you like to go for lunch in the student center?

Cynthia says to Blake: That’s a cool idea, but I was hoping to meet with Danny today.

Let me put you on hold and call them.

They call Danny

They say: Hi Danny, would you like to go for lunch in the student center?

Danny says to Cynthia: That’s a cool idea, but I was hoping to meet with Emilia today.

Let me put you on hold and call them.

They call Emilia

They say: Hi Emilia, would you like to go for lunch in the student center?

Emilia says to Danny: Yes! I’ll meet you at the student center.

Emilia hangs up

Danny says to Cynthia: Yes! I’ll meet you at the student center.

Danny hangs up

Cynthia says to Blake: Yes! I’ll meet you at the student center.

Cynthia hangs up

Blake says to Asha: Yes! I’ll meet you at the student center.

Blake hangs up

Asha is happy. All phone calls are ended and our friends can go to lunch!


My experience with sexism on the academic job market

27 Jul

Earlier this summer I spent a day at Stanford to be on a committee for a PhD defense. As I grabbed a coffee before driving to Stanford I ran in to one of my SF State colleagues. He was surprised and impressed that I was “allowed” to be on a committee at Stanford.

I work at SF State, a Master-granting institution. This means that we don’t have PhD students and most labs have no postdocs. At SF State, we quickly learn that we are not playing in the same league as our colleagues at PhD-granting institutions. I do biomedical research, but my department of 42 professors hadn’t had an R01 grant for years, in part because we’re advised to not apply for them (I did get one in 2017, in part because I had missed the memo about not applying for them). We are not allowed to apply for HHMI funding because, according to HHMI, our institution is not research-intensive enough. And I don’t think SF State Biology has ever had a McArthur Genius award or a Sloan fellowship. We also have a higher teaching load than most faculty in PhD-granting institutions, which makes it harder for us to write grants and do research.

So, when I am “allowed” to be on a committee at Stanford and rub arms with some of the smartest and most influential population geneticists of our time, it feels good! It reminds me that, even though I work at SF State, I am in their league. And it is not just a committee at Stanford, I often get invited to interesting places. I have been invited for talks at ESEB, SMBE, ASM, Biology of Genomes, a Gordon Research Conference, and PopGroup 2019 in Oxford (UK). In 2018 I am flying to Europe for three different invited seminars. These invitations remind me that I am respected in my field. But the invitations also remind me that I don’t work at these places. When I hang out with colleagues at these trips, they are almost always at institutions where they get better salaries, more research support, and fewer teaching duties [at least in the US. Things are sometimes quite different in Europe]. The obvious question therefore arises: How did I end up at SF State?

How I ended up at SF State University

Now, before I talk about how I ended up at SF State, let me make one thing very clear: since I have been here, I’ve learned that SF State is a great place and a great place for me. I love working there, I have amazing colleagues and students and we do important research. I have thriving collaborations within my department, but also with Chemistry/Biochemistry and Computer Science. The quality of teaching is higher than I have seen anywhere else. For these reasons, I love my work. But those reasons are not why I went to SF State.

I took the job at SF State because I had no other way to stay in academia, since no other department was willing to hire me. Stanford, Berkeley, UC Merced, UCSD and about 50 other schools did not invite me for an interview. The University of Arizona, NYU, UC Irvine, the University of Vienna and a few others did invite me for interviews, but they didn’t offer me a job.

Why did I not get a job at a PhD-granting institution?

It is not immediately clear why it was so hard for me to get an academic job. I had 18 papers on my CV in the last job season in which I applied for jobs. Papers in journals like PLOS Genetics, Genetics, MBE and American Naturalist. A few of these papers were very influential in my field (if you are in population genetics or evolutionary genomics, you have probably heard of soft sweeps – this term was coined by my PhD advisor and myself in 2005). Together, the papers from my PhD are now cited more than 1000 times, which is a lot for theoretical population genetics papers. That success early in my career should have made it possible for me to land a job at a PhD-granting university. I also think that I interviewed quite well, at least the last year I was on the market. After my interview at the University of Arizona (a visit that I enjoyed a lot!), one of the people on the committee wrote in an email: “I have to say, in my opinion, you gave one of the best chalk talks I’ve seen.”

So, I don’t think that my publication record or my interviewing skills explain my struggles on the job market. Here are my alternative hypotheses:

  1. My work is too interdisciplinary.
  2. I have a foreign PhD.
  3. I am a woman and implicit bias makes it harder for women to get jobs.

Why do I think that my gender played a role? Well, first of all because of well-known biases in academia as shown by a large body of published work. For example, Knobloch-Westerwick  showed in an experiment that abstracts are rated higher when authored by men (Knobloch-Westerwick, Glynn and Huge, 2013). Moss-Racusin showed in an experiment that faculty judge a male candidate for a lab tech position to be higher quality than the identical female candidate (Moss-Racusin et al., 2012). And Van der Lee showed that women score lower on “quality of researcher” for research grants in The Netherlands, but not “quality of proposal” (van der Lee and Ellemers, 2015). These biases probably exist search committees too.

Secondly, I got insight in the opinions of a committee that was judging one of my job applications, and what they wrote wasn’t pretty. Written reviews for job candidates are uncommon in the US, but one of the jobs I applied for in Europe had a stage where outside reviewers looked at my file and wrote an opinion about me. Three of the reviewers were very positive, but two of them were quite negative. Here are some quotes. Judge for yourself.

A rare look behind the scenes of a committee of reviewers

One reviewer says I am vocal but know nothing about evolution

Reviewer A: “The candidate is an avid organizer (see CV) and can be quite vocal (see the article in PLoS). However, she is not ready for a science leader position.”

This comment suggests that being an avid organizer cannot go together with being a good scientist. Then it says that I am “vocal” in my PLOS Computational Biology paper (2012). Now, I think you’ll agree with me that this is a strange comment about a technical paper in a technical journal. The reviewer may not think it is a good paper, that’s their right, but publishing my results about standing genetic variation and effective population sizes in HIV in a journal like PLOS Computational Biology is what computational biologists do. It is hardly “vocal”.

The reviewer continues: “I feel she needs to acquire more experience in general evolution theory, virus evolution theory, applications to HIV and other important systems, and understand basic principles of data interpretation and parameter handling”

So, this reviewer may think I don’t know theory or data-analysis, but by that time, I had published 16 peer-reviewed papers, 4 of them on new evolutionary theory (soft sweeps and sympatric speciation) and the rest on different experimental systems which all relied on data collection and data interpretation.

Another reviewer suggests I was simply too “light”

Reviewer B: “I do not think that the applicant has shown the ability to build an independent research group at this stage of her career. I hope she will not find my comments too harsh. These are in no way a critique of her abilities but rather the reflection of the fact that her achievements and her project are too light for this position.”

This reviewer thinks I am not ready for a junior group leader position. It is not so clear why he/she thinks this. I had published extensively and my work had appeared in textbooks. I had also successfully applied for research money alone and with others. I had started and run a successful company, coordinated a Master’s program and produced a series of prize-winning videos about evolutionary research. How on earth was I too “light” to run a junior research group?

This reviewer also doubts whether my achievements are actually my achievements.

Reviewer B: “It is difficult to know whether the number of citations the articles from the applicant has attracted is due to her or to her supervisor (in fact, she is not the lead author on her most cited paper, which accounts for 150 citations of over 350). So far, approximately 80% of the citations she has attracted are on articles with her supervisor.”

Well, that’s how academia works. We learn by working with advisors and I have had amazing advisors! It is worth noting here that the paper that got all the citations (Hermisson and Pennings, 2005), is also Joachim Hermisson’s most cited paper.

The same reviewer suggests that my peer-reviewed work in PLoS Computational Biology is scientifically incorrect. 

Reviewer B: “The main aspect that struck me while reading this article is that none of the mathematics are shown in the article (everything is in the online supplementary materials). (…) It makes it very difficult for the reader to assess the solidity of the methods and usually implies that the article rests upon the author’s capacity as a writer rather than the scientific correctness.”

Nowadays, in most biology journals it is quite standard to put most or all math in the supplementary materials. There is nothing special about the lack of math in this paper. Plus, if they were worried about the correctness of the math, they could have just read the supplementary materials, instead of implying that the science is wrong.

When asked about whether the job environment would be good for me, the first reviewer concludes:

Reviewer A: “I have no information regarding environment, but Dr Pennings hardly needs more stimulation. She needs restraint”

I am not even sure what to say about this comment. She needs restraint?!? [I think I need to have that printed on a t-shirt.] I cannot prove it, of course, but I don’t think a man with the same CV would have been judged like this.


My academic job search was extremely frustrating. After a very successful PhD, an HFSP grant to go to Harvard and Stanford, and several papers from my postdoc time, I expected to be able to land a job as an evolutionary biologist at a PhD-granting institution. That wasn’t the case and I think that sexism played a role.

When you want a job in academia, you need to convince a committee, or sometimes an entire department, that you are the right candidate. One or two people on a committee can sow doubt about a candidate. For at least one of my job applications, two reviewers sowed doubt about me. Among other things, they argued that I was (1) “vocal” and (2) “too light” for the position. They also argued (3) that my papers were not really my achievement. These arguments would unlikely be made about a man. The result was that despite very clear quantifiable achievements (papers, citations and funding), their reviews made it sound like I was a newbie who had no idea what I was doing.

Now it is 4 years later. I am happy at SF State. I am still a successful researcher: I am publishing, bringing in grant money, and I get invited for talks. I love it when I get invited to talk about my research at a conference or when I get to sit on a PhD committee at Stanford, but these invitations also remind me of the times where these very same places didn’t invite me for job interviews. And it reminds me of what these lost job opportunities cost me every day in terms of salary and in terms of lost opportunity (HHMI, if you are reading, I would love the opportunity to apply for a grant with you!).

Of course, I acknowledge that I am privileged and my struggles are small compared to what others face. I am white, straight, married, and I never lived in poverty. So many of my colleagues and my students have to fight even harder because they are Black or Latinx or gay or poor or undocumented or a combination of those.

The silver lining of this story is that thanks to certain doors being closed to me, I ended up in an amazing department that has made opening doors for marginalized people almost its core business. Next to my research, I am now in the business of opening the doors of Computer Science to women and minority students. I am picking up a lot of “door-opening” expertise from Drs Letitia Marquez-Magaña, Kimberly Tanner, Blake Riggs, Diana Chu and others. I will try to take that expertise with me on my trips to conferences and departmental seminars and to contribute to some doors being opened at PhD-granting institutions.

PS: I want to thank the soccer player Abby Wambach for making me see the situation more clearly. Worth watching:

Thanks to Diana Chu and Rori Rohlfs for comments on an earlier version of this post.

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.