Why are not all E. coli resistant to antibiotics?

Last summer gave a talk at a Gordon Conference about transmission and evolution of drug resistance in HIV and E. coli. When I was done with my talk, there was time for questions. Dmitri Petrov asked what I thought about why resistant and susceptible strains of bacteria co-exist. I had to admit that I hadn’t really thought about that.

This question of co-existence (why aren’t all bacteria resistant or all of them susceptible?) it not a new one. In fact, about a week after the Gordon Conference, I talked to Marc Lipsitch who has worked on this question for many years. It was just not on my radar. Until last summer.

The question of co-existence marinated in my head for over the summer. I read papers about it. Looked at the data I was analyzing. And then sometime in the fall it suddenly hit me! I saw a solution to the question that was real easy. This is what I think may be happening: Resistance evolves when a bacterial strain finds itself in a person who is treated with antibiotics. But because most of us aren’t on antibiotic treatment most of the time, these resistant strains tend to have lower R0 values than susceptible strains (that is, the resistant strains don’t spread as effectively). Therefore in the human population at large, existing resistant strains are losing against the susceptible strains.

I had been studying the many origins of resistance (resistance in E. coli and other bacteria evolves very often – lots of convergent evolution), and I had been studying the cost of resistance (I think most resistant E. coli strains tend to die out – thought this is not easy to prove). These two ingredients together can explain the co-existence of resistant and susceptible strains.

Early October I emailed Dmitri: “Dmitri, I think I have the answer to your question!”. Dmitri answered: “Exciting! But you forgot to attach the manuscript”. Me: “Oh, I didn’t write it up yet! It is just in my head.”

So I started writing because that’s how academic science works!

The manuscript now lives on Medrxiv. I have submitted it to Nature (desk-rejected) and to Science (reviewed and rejected). Traditionally, after a rejection from a high-profile journal, one would send a manuscript to another journal right away, but one of the reviewers from Science suggested using a particular Norwegian dataset, instead of the Enterobase data I had used for the manuscript. I really liked that idea (as well as some other ideas from the reviewers). So I decided to pause and do more analysis. Some of the new data made their way into my talk for the TAGC conference in Washington DC last week.

If you are curious to hear where I am at with this project, here is the video of my talk:

Scientist spotlight: supervised and unsupervised methods for microbiome data analysis with Dr Nandita Garud

I got to know Nandita Garud when she was a PhD student in the biology department at Stanford and I was a postdoc there. While we were in the same lab, we got to collaborate on two papers: one about population genetics and drug resistance evolution and one about rats in New York City. After finishing her PhD, Nandita worked at UCSF as a postdoc and then took a job as an assistant professor at UCLA. You can read more about her interesting work on the microbiome, fruit flies and other topics on her website. I asked her about a recent paper on using supervised and unsupervised methods to analyze microbiome data. 

Headshot of Dr Nandita Garud, assistant professor UCLA

Pleuni: Hi Nandita! Thanks for taking the time to chat with me! Can you tell me in a few sentences what your job is?

Nandita: Hi Pleuni! Thank you so much for inviting me to chat about my work. I am an assistant professor in the Department of Ecology and Evolutionary Biology at UCLA. My research is on understanding the evolutionary dynamics of natural populations, currently with a focus on the human microbiome, but I also work on Drosophila and other organisms!  My research group (or, ‘lab’) consists of several PhD students that perform computational work to understand how natural populations evolve. 

Pleuni: So, you consider the community of microbes that live in my intestinal tract as a natural population, is that right? And they evolve? 

Nandita: That’s correct. I consider populations that live outside a test tube in the lab to be natural populations. Interestingly, gut microbiota can evolve on even 1-day timescales, even in the absence of a selective pressure like antibiotics!

Pleuni: I saw that you published a paper about supervised and unsupervised methods for background noise correction in human gut microbiome data. Could you explain what the human gut microbiome is? And why you need background noise correction for it?

Nandita: The human gut microbiome is a complex community that is composed of hundreds of microbial species coexisting and interacting with one another. The human microbiome is known to play an essential role in health, and changes in the microbiome are associated with numerous diseases like diabetes, obesity, and inflammatory bowel disease. Being able to predict disease status from the human microbiome is important for helping individuals diagnose any illnesses they may have. One major complication, however, is that technical variables, such as how the DNA was extracted from the sample, can introduce noise in the data, making it harder to predict human phenotypes. So, background noise correction is an important approach for addressing this data heterogeneity so that more reliable predictions can be made. 

Pleuni: Thanks! In the new paper from your lab, you compare supervised methods (which are currently standard for noise correction) and unsupervised methods (which have not been applied to microbiome data). What is the difference here between supervised and unsupervised methods?

Nandita: Supervised methods are ones where a machine is shown labeled data and is trained to understand the differences between data classes. Unsupervised methods are ones where the machine needs to figure out on its own what groupings are present in the data. We use an unsupervised approach because we don’t always know what sources of noise contribute to variation in the data. 

Pleuni: Okay, thanks! So, I imagine something like this: If microbial species A is always 2x as abundant in samples that were sequenced with machine X vs machine Y, then we can correct by changing the abundance of species A so that it matches between the two machines? Is that what’s happening? 

Nandita: Yes, but we aren’t explicitly adjusting the abundances, rather, throwing away variation due to noise. 

Pleuni: Does this mean that you do a dimension reduction method first and then throw away dimensions? 

Nandita: Exactly — we do PCA (principal component analysis) and then throw away the first PCs (principal components) because they usually are correlated with noise. We do run the risk of throwing away signal too, but that’s the tradeoff in an unsupervised approach. But when we compare this unsupervised approach to the standard supervised approaches, it can work just as well in many scenarios! And the good thing is that this way we can correct for unidentified confounders. 

Pleuni: Cool 😎 Thank you for explaining all of this, Nandita! 

I have one more question. What is something you like to do when you are not doing science? 

Nandita: I enjoy taking walks with my family and enjoying the outdoors in Los Angeles! 

Pleuni: Thank you Nandita! 

Here is a link to the paper: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009838

The website of the Garud lab: https://garud.eeb.ucla.edu/

Meet Francisca Catalan, SFSU PINC alum and research associate at UCSF (spotlight)

Francisca Catalan, SFSU PINC alum and research associate at UCSF

1. How did you get into coding? 

I took a regular CS class my second year at SF state. I thought it would be a good skill to have as an aspiring researcher and saw that it fulfilled one of my major requirements. It was a PowerPoint-heavy 8 am class three times a week. I didn’t talk to anyone else in the class and by the end of the semester I found it very difficult to show up. I passed the class but was really devastated about my experience. I thought I could never learn to program, though I never gave up completely. A couple semesters went by and I saw a friendly flier announcing PINC, SFSU’s program that promotes inclusivity in computing for biologist and other non-computer science majors. I eagerly signed up and started the “Intro to Python” class soon after. Then, with some more programming under my belt, I joined Dr. Rohlfs’ lab and began doing research in the dry lab for the remainder of my undergraduate career.

2. What kind of work do you do now? 

I currently work at UCSF as a dry lab research associate. Our lab focuses on an aggressive form of brain cancer, glioblastoma. We try to find gene targets for new drug treatments and research the cell type of these cancerous cells in order to fight drug resistance. My main duties now include creating pipelines for our single cell, RNA-Seq, and Whole Genome Sequencing data. You can read about our lab’s latest study in our new publication on cancer discovery! DOI: 10.1158/2159-8290.

https://cancerdiscovery.aacrjournals.org/content/candisc/early/2019/09/25/2159-8290.CD-19-0329.full.pdf

3. How did learning coding skills impact your career?

Coding has opened so many pathways for me. I was able to find a great job at UCSF soon after graduating with my Bachelor’s of Science in cell and molecular biology and minor in Computing Applications. It has also given be a giant boost of confidence! As a woman of color in STEM, I often felt underrepresented and out of place, but those feelings now quickly subside when I can help my colleagues answer coding questions! It’s motivating to feel like a necessary component of your community when often time you feel pushed out. It’s also impacted my career choices! I know now I want to be a professor in the future, I want to provide access to programming to others in hopes it will open pathways like it did for me!

4. Do you have any advice for students who are just starting? 

Yes! Don’t give up! It can be really difficult to learn coding, but know that it’s not you, talking to a computer can just be hard sometimes! Continue practicing and ask questions, google your heart out. Take breaks when necessary, remember to breathe, and keep in mind all the amazing science you will be able to do once you have these skills under your belt!

The Code Lab visits Tel Aviv!

In February, 10 of us from the Code Lab visited Adi Stern’s lab in Tel Aviv, Israel. It was stressful to organize the trip (filling out so many forms, waiting for permission, finding reasonably priced tickets…), but now that we’ve done it I think it was totally worth it. The trip was really good for the lab and for our research. We are grateful to NSF for funding the trip (as part of a collaborative grant between the two labs), to everyone in Adi Stern’s group for hosting us and to the folks at SFSU who helped us (and are still helping us) with the administrative side of this trip!

Here are the best things from the trip:

1. We bonded as a lab!

Emily: One of the things I enjoyed the most about our trip was getting to know and meet people. There are many different individuals in our lab who I don’t know very well and this trip was a great opportunity to spend time with them!

Anjani: We were mostly together as a group during the trip and it made our bonding stronger than what we had when we left SFO.

Jasmeen, Anjani, Caroline, Geo, Stuart, Ryan (all SF)

2. We got to know our current and future collaborators from Tel Aviv and learned about their research.

Caroline: One thing I really liked about the trip was how welcoming and organized the Stern lab was even though we arrived sooner than initially planned. I got really excited seeing the HeLa cells and hearing about the different projects revolving around them. Olivia: I really enjoyed talking to Talia about her research on the Tilapia virus. This virus is found in both wild and farmed tilapia. I found the questions she was asking interesting because it affects people all over the world.

Ryan: Everyone was very cool and super nice.  

Caroline Solis (SF), Talia Kustin (Tel Aviv), Moran Meir (Tel Aviv), Anjani Pradhananga (SF), Jasmeen Kaur (SF).

3. We presented our work and got useful feedback.

Olivia: Sometimes it feels like I haven’t been able to finish or see a product, but I used my old poster that I had created for the COSE showcase (…) and found that I actually accomplished the aims/next steps. It was a nice feeling to learn that I have done something as I was updating my poster.

Kaho: I really enjoyed talking to Adi, Maoz and Sherry about research. The discussion we had was helpful and gave me clearer directions for the next steps of my research, and it was great to find out we have such cool collaborators!

Ryan: I also loved meeting the Tel Aviv lab because it made me see out research, not only from the dry lab perspective, but also from the wet lab side too.

Oded Kushnir (Tel Aviv) and Ryan Winstead (SF) look at a poster.

4. Visiting new cities and a foreign country

Emily: I was glad to see that Tel Aviv had a vibrant and openly queer community.

Getting to see the historical sites was equally important to me. Though I am not a fan or organized religion, (…), I learned many of the stories from the old and new testament, so seeing the actual places where they occurred was really cool. My grandma would have been so happy to see my pictures and hear about everything, and though she is gone now, she was in my heart the whole time.

Ryan: At first, I was very nervous about traveling so far from home. However, as soon as I was on board the plane, I felt excited about it. Tel Aviv was an amazing city and seeing it and the university was rewarding.

The Code Lab with our Tel Aviv guides Danielle Miller and Omer Tirosh from the Stern Lab. Clockwise from Anjani (taking the selfie), Caroline, Olivia, Geo, Emily, Danielle Miller, Omer Tirosh, Ryan, Stuart, Jasmeen, Kaho.

5. We enjoyed the food!

Stuart: Trying out new foods and deserts were a blast at dinner.

Shakshouka is a dish of eggs poached in a sauce of tomatoes, chili peppers, and garlic, commonly spiced with cumin, paprika, cayenne pepper, and nutmeg.

What we didn’t like as much

There were only a few things we didn’t like. Some of the students had never had a jet lag before and they hated it. We also didn’t like when we felt cheated by taxi-drivers or bartenders and we were sad to see and feel the tension between different groups in Jerusalem. One thing that we’ll keep in mind for our next trip is to schedule more time for the posters.

But all in all it was a fun, interesting and memorable trip!

The Code Lab and the Stern Lab. Front: Pleuni Pennings(SF), Yaara Ben Ari (Tel Aviv), Gal Goldman (Tel Aviv), Jasmeen Kaur (SF), Kaho Tisthammer (SF), Adi Stern (Tel Aviv), Tal Zinger (Tel Aviv), Olivia Pham (SF). Standing: Danielle Miller (Tel Aviv), Sherry Harari (Tel Aviv), Talia Kustin (Tel Aviv), Maoz Gelbart (Tel Aviv), Oded Kushnir (Tel Aviv), Caroline Solis (SF), Ryan Winstead (SF), Stuart Castaneda (SF), Anjani Pradhananga (SF), Geo Pineda (SF), Emily Fryer (SF)

 

Scientist spotlight : Jazlyn Mooney, PhD student UCLA

Jazlyn Mooney grew up in Albuquerque New Mexico. She went to high school and college there too (Eldorado High School and University of New Mexico).

Sketching science created a lasting interest

“I became interested in science in middle school. I had a science teacher, Mr. Pecknik, who made us draw everything we learned about (from central dogma to phylogenies) for class. So we kept a sketch book for our science class and I thought it was super cool.”

Not “cut out for MD/PhD” ?

Becoming a researcher didn’t always seem possible for Jazlyn. One summer, when she was an undergrad, she participated in an MD/PhD prep program. At the end of the summer, her summer advisor told her that she wasn’t cut out to be MD or PhD! Fortunately, she didn’t listen to him but instead listened to her other undergrad advisor, her family and herself and decided to continue her path to become a scientist! She did research as an undergraduate and then applied to PhD programs.

The history of Latin American populations

Jazlyn is now a PhD student at UCLA in the lab of Dr. Kirk Lohmueller and works to better understand the history of human populations using genetic data. She recently published a paper entitled: “Understanding the Hidden Complexity of Latin American Population Isolates.” In this paper she showed how Costa Rican and Colombian people are descended mostly from European males and Amerindian females, and a small number of African individuals.

The field that uses genetic data to understand the history of populations is called “population genetics”. Jazlyn got interested in population genetics when she was an undergrad and got an opportunity to do research with Dr Jeff Long.

Learning new things and presenting at meetings

Jazlyn loves learning new things and her favorite part of being a researcher is that it allows her to learn new things and create new knowledge. Jazlyn has presented her work at many conferences including : University of Chicago Research Forum, the meeting of the American Society for Human Genetics, the Bay Area Population Genomics meeting at UC Santa Cruz in 2018.

Links

Link to paper about the history of people in Costa Rica and Colombia

Link to a free “preprint” version of the same paper

Tacos, R and Twitter

Jazlyn’s favorite coding language: R

Jazlyn’s favorite food: Tacos

Jazlyn’s Twitter handle: @Jazlyn_Mooney

My experience with sexism on the academic job market

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”.

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002527

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. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002527

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.

Conclusion

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: https://barnard.edu/commencement/archives/2018/abby-wambach-remarks

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

Five Reasons why you should attend the Annual SACNAS National Conference

Guest post by: Bridget Hansen, SFSU undergraduate researcher

First, who am I? What is SACNAS?

My name is Bridget Hansen and I am an undergraduate in Microbiology at San Francisco State University, doing research at the Romberg Tiburon Center for Environmental Studies. Over the summer, I participated in the Howard Hughes Medical Institute Excellent Research Opportunity Program (HHMI-ExROP) summer research and the AMGEN program at the University of California, Berkeley. I worked on a project that I then presented at the SACNAS Conference this past October.

SACNAS stands for the Society for Advancing Chicanos/Hispanics and Native Americans in Science. This society, made up of many successful Chicanos and Native Americans in science related careers, puts on a national conference once a year. The conference has opportunities for scientists at all levels, from undergraduates to professors and researchers. Many graduate school recruiters and other professional organizations come to this conference to recruit, providing a great platform for networking.

I used this opportunity to network for graduate schools! I will be attending a PhD program in the fall, in part, thanks to my interactions that I had at SACNAS.

What happens at the SACNAS National Conference?

Students from all over the country submit abstracts for the opportunity to present their work, either in the form of a poster or an oral presentation. The students had a scheduled time and room to present. Other than presentations, the meat of the conference was geared towards guest speakers and networking. The whole goal of the conference was to introduce young students to the world of research and science related careers! The best part is the graduate student recruiter booths where you have the opportunity to chat with recruiters, professors, and students from that university.

Five reasons why I recommend SACNAS

1. The networking

There were hundreds of booths set up, all stocked with professors, recruiters, graduate students and pamphlets listing the reasons why you should come to their school. Nearly every research institution was in attendance, looking for the next round of graduate students to apply to their programs. They want you to apply to their programs but most importantly, they want to make sure their school lines up with your research interests. You can ask them about the programs, the application process, what it is like to live in that part of the United States and any funding opportunities. Exchanging business cards or information is very common and the badge that you are given upon arriving even has a scanner square that the recruiters can use to keep in touch with you (they scan your badge and your e-mail is logged with them).

I spoke to over a dozen booths about their programs and had all my questions answered. I was even recruited during my poster session presentation! Which brings me to my next point.

2. The presentations

The presentations are great for two reasons: 1. You have an opportunity to talk about your work and receive feedback on your presentation skills and 2. Other schools can come by your presentation and see you as a researcher. This is fantastic! I am not the best on paper in some ways, so having other schools approach me based on my science, reassures me that I am more than just my GPA or my GRE scores. Not only that, I received written evaluations based on my presentation skills and my poster, which were all constructive and positive!

3. The seminars

The guest speakers focus on their journeys as minorities in the sciences and how their transforming experiences have brought them to where they are today. They inspire us to continue to pursue our passions and create a sense of community, which I will get to in a minute. The seminars are also great opportunities for junior scientists, like myself, because they offer an opportunity to check out new areas of research, hear about different paths in science outside of academia and get insights into how to be successful. There are workshops on how to give a compelling interview, what to expect in graduate school and how to master networking. All of these skills are important ones that give you a competitive edge.

4. The experience

The experience itself was wonderful. Surrounded by 3,600 other students, mentors and researchers, the conference felt grand. I say grand because the conference center was massive, the sheer number of attendees was at times, a bit overwhelming, and the hotel that we were assigned to left me in awe. The Gaylord National Conference Center in Washington D.C. was an incredible place to hold this conference this year. As apart of the conference fees, we were fed in a large hall, which also created a sense of community.

5. The sense of community

The SACNAS conference creates a sense of community for young scientists; a community that they can be a part of throughout their careers in the sciences. The idea of having a supportive community that I can be part of is a great feeling, especially coming from a background that does not have any college graduates. It can be lonely sometimes, walking into a completely new field that no one you grew up around, has any experience in. So, when I attended the conference with other San Francisco State students who were also presenting, they immediately considered me one of the group, even though we had just met. Similarly, other students from other places also welcomed conversation with open arms. The inclusion that occurs at SACNAS is excellent.

Overall, I highly recommend attending a SACNAS national conference. It looks great on your CV, it is great for your future scientific career and definitely gives you an edge when applying for graduate school. Bring your own business cards!

If you have any questions about SACNAS, please refer to the SACNAS website: http://sacnas.org .

Hope to see you there next year! I will be attending as a graduate student!

Feel free to contact me with questions at: blhansen “at” mail.sfsu.edu or missbridgette4 “at” aol.com. and indicate you read this blog so I know where the questions are coming from!

 

How a collaboration on imperfect drug penetration got started

Almost three years ago, in early 2012, I attended a talk by Martin Nowak. He talked about cancer and one of the things he said was that treatment with multiple drugs at the same time is a good idea because it helps prevent the evolution of drug resistance. Specifically, he explained, when treatment is with multiple drugs, the pathogen (tumor cells in the case of cancer) needs to acquire multiple resistance mutations at the same time in order to escape drug pressure.

As I listened to Martin Nowak’s talk, I was thinking of HIV, not cancer. At that time, I had already spent about two years working on drug resistance in HIV. Treatment of HIV is always with multiple drugs, for the same reason that Martin Nowak highlighted in his talk: it helps prevent the evolution of drug resistance.

However, as I read the HIV drug resistance literature and analyzed sequence data from HIV patients, I found evidence that drug resistance mutations in HIV tend to accumulate one at a time. This is contrary to the generally accepted idea that the pathogen must acquire resistance mutations simultaneously.

There seemed to be a clear mismatch between data and theory. Data show mutations are acquired one at a time, and theory says mutations must be acquired simultaneously. One of the two must be wrong, and it can’t be the data![1]

Interesting!

After Martin Nowak’s talk, I went up to him and told him how I thought data didn’t fit the theory. Martin’s response: “Oh, that is interesting!” (Imagine this being said with an Austrian accent). “Let’s meet and talk about it.”

So, we met. Logically, Alison Hill and Daniel Rosenbloom, then grad students in Martin’s group, were there too. I had already met with Alison and Daniel many times, since they were also working on drug resistance in HIV.  John Wakeley (my advisor at Harvard) came to the meeting too.

Between the five of us, we brainstormed and fairly quickly realized that one solution to the conundrum was to assume that a body’s patient consisted of different compartments and that each drug may not penetrate into each compartment. Maybe we found this solution quickly because Alison and Daniel had already been thinking of the issue of drug penetration in the context of another project. A body compartment that has only one drug instead of two or three would allow a pathogen that has acquired one drug resistance mutation to replicate. If a pathogen with just one mutation has a place to replicate, this makes it possible for the pathogen to acquire resistance mutations one at a time.

We decided to start a collaboration to analyze a formal model to see whether our intuition was correct. Over the following three years, there were some personnel changes and several moves, graduations and new jobs. Stefany Moreno joined the project as a student from the European MEME Master’s program when she spent a semester in Martin’s group. When I moved to Stanford, Dmitri Petrov became involved in the project. Next, Alison and Daniel each got their PhD and started postdocs (Alison at Harvard, Daniel at Columbia), Stefany got her MSc and started a PhD in Groningen, I had a baby and became an assistant professor at SFSU. No one would have been surprised if the project would never have been finished! But we stuck with it and after many hours of work, especially by the first authors Alison and Stefany, and uncountable Google Hangout meetings, we can now confidently say that our initial intuition from that meeting in 2012 was correct. Compartments with imperfect drug penetration indeed allow pathogens to acquire drug resistance one mutation at a time. And, importantly, the evolution of multi-drug resistance can happen fast if mutations can be acquired one at a time, much faster than when simultaneous mutations are needed.

Our manuscript can be found on the BioRxiv (link). It is entitled “Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance.” We hope you find it useful!

[1]Of course, it could be my interpretation of the data!

Stefany Moreno (in large window), Alison Hill, Daniel Rosenbloom and myself in one of the many Google Hangout meetings we had.

Collaborative science in the 1850s

I just finished reading a nice book about John Snow and London in the mid-19th century. The book tells the story of Snow’s idea that Cholera is transmitted through water (and not air). The book has a website here.

The book is from 2006, so chances are that you have already read it.

I was reading the book in the hope to learn more about diseases and epidemiology, but I think I ended up learning more about research in general. I also learned that London was a smelly place in the 1850s.
The book is full of wonderful lessons for a young researcher. For example, the book shows that

1. Research is hardly ever done by one person alone. John Snow was probably a great researcher, but he couldn’t have done his important work on Cholera without the help of statistician Farr and reverend Whitehead. Even in the 1850s, science was a collaborative enterprise.
2. It is OK if people don’t believe you. In fact, a lot of the evidence for the waterborne transmission route of Cholera came from Whitehead, who was on a mission to disprove Snow’s theory.
3. Changing opinions takes time. It took many years, lots of data and papers before people started to believe Snow’s idea, even though Snow was well known and a respected physician.

The map that figures in the title of the book didn’t reproduce well on my Kindle, so I had to look it up on Wikipedia later.

John Snow Cholera Map

Honestly, I find the map not very impressive. Sure, it may be important in the history of epidemiology and the history of data visualization, but I was slightly disappointed that the map wasn’t clearer. It is supposed to show that all the deaths occurred near one pump. But The locations of the pumps are not very clear at all. It also doesn’t show the surprising pockets of Cholera absence that are described in the book and that were important for Snow’s inference.

The book is highly recommended!

On the current situation of HIV drug resistance

I recently published a review paper on HIV drug resistance. It was the first time I wrote a review paper and I enjoyed it thoroughly! It is also my first paper with Stanford as my affiliation: I walked the beautiful Stanford campus and enjoyed the sun in the breaks from writing.

Many of you probably know that the majority of the world’s HIV patients live in poor countries, especially in southern Africa. You may not know that many of these people are now receiving very good treatment. I had the unique opportunity to see the effects of improved access to treatment “on the ground” in Africa.

Last summer I was in Ethiopia for a few weeks and stayed in the Medhen social center in one of the slums in Addis Ababa. I knew that, for many years, much of the efforts of the Medhen center had focused on HIV/AIDS, and I was expecting to see the devastating effects of HIV/AIDS during my visit. However, the situation had changed a lot. The Ethiopian government now provides HIV drugs free of cost and most HIV infected people in the slum were doing well. In fact, there was no way for us to know who was infected, unless one of the nurses told us. The social center still supports some HIV patients, such as infected orphans who need financial and social support, but most of the center now focuses on “normal” poverty relief: housing, education, employment etc.

The World Health Organization also reports that access to treatment has improved dramatically in low- and middle income countries: from 300 000 people in 2002 to 9.7 million in 2012 (see here).
HIV treatment almost always consists of a combination of three drugs, often in a single pill. However, treatment only works well as long as the virus is not resistant against the drugs used. Fortunately, there is good news about drug resistance too: HIV treatments have become better and better at slowing down the evolution of drug resistance. Thanks to powerful drugs and close monitoring, many patients are now treated for many years without having any resistance problems.

In my review paper I describe that drug resistance is virtually solved in rich countries, but still a problem in poor countries. One reason for this is that poor countries often lack the possibilities to monitor the viral load of patients and to sequence the patient’s virus.
The paper also describes what is know about the relevance of pre-existing mutations for the evolution of drug resistance in a patient (also known as standing genetic variation or minority variants, depending on which field you’re from). Finally, I write about pre-exposure prophylaxis (taking HIV drugs to prevent infection) and how this is related to drug resistance.

I hope you enjoy reading the review paper as much as I enjoyed writing it!

You can download the paper here:  2013PenningsHIVReviewIDR