New plans and new HIV stories

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.

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 It can be happening with a lot of recombination, or showing clonal interference (unpublished, Kadie-Ann Williams and PSP), and sweeps can be hard or soft.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.

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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!

 

 

The acknowledgement section of our NSF proposal

A few weeks ago two colleagues and I submitted an NSF proposal. We submitted on a Friday afternoon even though the deadline wasn’t until Tuesday! I am proud that we managed this almost without any deadline stress!

I had fun and we wrote a great proposal

I know that we may not end up getting funded by NSF, but until we get that message, I plan to be very optimistic. We wrote a really neat proposal for a great project. I can’t wait to get started! The ambitious goal of the project is to determine the fitness cost of every possible point mutation in the HIV genome in vivo.

I think nobody likes to write proposals when the success rate is only 5%, but I actually enjoyed working on this proposal and I learned a lot while writing it: both about the biology of our project and about the art of proposal writing. It’s important for me to commit that to paper (OK, screen) so that if NSF decides not to fund us, I will remember that writing the proposal was actually a good experience.

Writing with a newborn

In addition the many scientists and administrators who contributed to the proposal, I also want to mention how I could write a proposal with a newborn. We started working on the proposal two weeks before I gave birth and we submitted the proposal when our baby was just shy of seven weeks old. The hours that I spent on the proposal were made possible by my mom who flew in to help and by the fact that Facebook gives new parents four months paid paternity leave so that my husband was also at home during my maternity leave. It was fun to be home together with my husband and we took shifts working and taking care of Maya. Most days I worked on the proposal just two or three hours, so a large part of the work was done by others.

Me in my home office with baby, changing table, a laptop and a grant writing handbook.

It was a huge team effort

Many people were involved in writing the proposal. Many more than I ever expected to be. I want to list them here so that I remember who helped out and also to show that being a researcher doesn’t have to be a lonely affair.

Note that these people are only the people I am aware off. Others certainly helped my co-PI Adi Stern.

The main team that wrote the proposal consisted of four people:

• co-PI Adi Stern (Tel Aviv)
• postdoc Marion Hartl (SFSU)
• professional grant writer Kristin Harper
• myself

At SFSU, people from the Office for Research and Sponsored Programs helped:

• Rowena Manalo
• Raman Paul
• Michael Scott
• Jessica Mankus
• Uschi Simonis (vice-dean for Research)

At Stanford there were

• co-PI Bob Shafer
• collaborator David Katzenstein
• Elizabeth White (Katzenstein lab)
• Holly Osborne (Office for Sponsored Research)

In Tel Aviv

• Office for Sponsored Research
• Adi Stern’s lab members brainstormed ideas
• Maoz Gelbart help with ideas and figures

Colleagues who read earlier versions of the proposal

• Sarah Cobey (U Chicago)
• Sarah Cohen (SFSU)
• Alison Feder (Stanford)
• Nandita Garud (UCSF)
• Arbel Harpak (Stanford)
• Joachim Hermisson (U Vienna)
• Claus Wilke (U Texas Austin)

A huge thank you to all these amazing people! I am lucky to be part of such a supportive community.

Reading in the lab

The winter break is a great opportunity to spend time in the lab with my students. One of the things we do, is read papers. Last week, we spent a morning reading the following paper:

Triple-Antiretroviral Prophylaxis to Prevent Mother-To-Child HIV Transmission through Breastfeeding—The Kisumu Breastfeeding Study, Kenya: A Clinical Trial. PLoS Medicine, 2011. Thomas , Masaba, Borkowf, et al. 

The paper shows that antiretroviral drugs taken by an HIV-infected mother help prevent transmission to the baby through breastfeeding. The reported rates of HIV infection of the infants during breastfeeding were less than half the previously reported rates from untreated women.

After everyone read the paper, and we all discussed it together, two students worked together to write an abstract and three students worked together to draw an abstract. Here are the results:

Abstract (by Kadie and Melissa)

The Kisumu Breastfeeding Study was a single-arm trial conducted with 522 HIV–infected pregnant women who took a triple antiretroviral regimen from 34 weeks of pregnancy to 6 months after delivery. The triple-ARV regimen consisted of zidovudine and lamivudine and either nevirapine or the protease inhibitor nelfinavir. The purpose of the study was to investigate how various ARV regimens given to mother and/or their infants affect mother to child transmission of HIV.

Data collected showed that between 0 and 24 months, the cumulative HIV transmission rate rose from 2.5% to 7.0%. The cumulative HIV transmission or death rate was 15.7%. Three percent of babies born to mothers with a low viral load were HIV-positive compared to 8.7% of babies born to mothers with a high viral load. Similarly, 8.4% of babies born to mothers with low baseline CD4 cell counts were HIV positive compared to 4.1% of babies born to mothers with high baseline CD4 cell counts. Although these findings are limited by the single-arm design, this study supports the idea that a simple triple-ARV regimen given to HIV-positive pregnant women regardless of their baseline CD4 cell count can reduce MTCT during pregnancy and breastfeeding in a resource-limited setting.

Graphical abstract (by Olivia, Patricia and Dasha)

 

No programming background? No problem! Learn R

Guest post by Rosana Callejas

Rosana Callejas

Can someone with no programming knowledge learn “R”? The answer is yes! My name is Rosana Callejas. I am a Physiology major, and recent graduate from San Francisco State University. I began to learn the programming language “R” at the beginning of February of this year. Despite not having any previous programming experience , I analyzed my first data set of more than 20,000 data points in only a couple of months. Would you like to learn how I did it? Stay tuned.

The power of “R”

So what exactly is “R”? It is a programming language used by many data analysts, scientists, and statisticians, to analyze data, and perform statistical analysis with graphs and figures. “R” is a great tool when analyzing large data sets. It has many additional packages that can be downloaded, which allow the user to expand or simplify commands when analyzing data.

How R coded its way into my heart

Dr. Pleuni Pennings, an evolutionary biologist, and Professor at SFSU, introduced me to this wonderful tool. “I do all my research on my computer,” Dr. Pennings said, as she showed me the open program. At first, the idea puzzled me. In all my years as a biology student, I had never met a biologist like Dr. Pennings, who has made many discoveries from analyzing HIV DNA sequences using R. She explained to me that there is an accumulation of data collected by scientists everyday waiting to be analyzed. Therefore, there is a need for scientists with the skills to interpret, and draw conclusions from such large data sets. This interested me as biologist. I imagined all the new findings that could be made if all the data collected was analyzed. It would definitely contribute to the advancement of science. With this in mind, I embarked myself in the adventure of learning R.

One command at a time

I began by taking the online course “Exploratory Data Analysis with R” on Udacity.com. The course is composed of 6 lessons, in which I first learned the basics of R, a few basic commands, followed by the analysis of one variable, and how to make simple plots. In my learning, I used R, and R studio, which can be downloaded free online. I also used data sets provided by Udacity to analyze. In addition, R comes with other data sets I practiced with. My first graphing assignment was a simple bar plot (Figure 1), that represented friend count for Facebook users of different ages. This task required the package “ggplot2”, which allows graphing.

Figure 1. Friend count as function of age.

As I learned more, I began to work with different packages, new commands, and to make better graphs. I discovered how to add color to the graphs. I learned how to order variables, make subsets, group variables, add a new columns to my data sets, work with multiple variables, run correlation tests, and much more. The following are some figures that followed that first one, and show the progress of my learning as I added more detail to that first plot throughout the course.

Figure 2. Median friend count as function of age by gender.

Figure 3. Friend count as function of age.  In the green graph each point represents 20 data points in the data set. The black line represents the mean friend count. The blue line represents with the 50th quantile. The dotted lines represent the 90th and 10th quantiles.

 

Figure 4. The top graph represents friend count as function of age in months, with the blue line representing the mean. The middle graph represents friend count as a function of age with blue line represents the mean. The bottom graph represents friend count vs. age in moths rounded, multiplied, and divided by 5.

Patience is the mother of all virtues

Learning R was definitely a challenge. Commands that in theory should work, sometimes did not work. As a new user, it was difficult to know exactly what had gone wrong. Fortunately, I had the guidance of Dr. Pennings who helped me through the process. I also looked for resources outside of Udacity. One great package to use along with R is “swirl,” which is a teaching package. With swirl, I learned commands not taught in the Udacity course. It has multiple lessons that give the user immediate feedback. Patience and persistence are key to learning R. Now I have seen what R can do, I know it was worth learning.

The possibilities are endless

My favorite feature of R is that the code used in a previous analysis can be saved, and reused. R users can also share pieces of code with one another, which helps expand the knowledge among users. If changes need to be made in the middle of analysis, this is rather simple, and there is no need to reanalyze the data. R can be used to study many different types of data of any size or background. Scientists such a Dr. Pennings make major findings in Biology using R.

Although new to R, I was able to begin the analysis of my own data set [1] within only a few months of learning about it. Below is a figure which resulted from the question: Which HIV regimens are most common and in what years? In order to answer this question, many hours of work were invested in preparing the data set, excluding undesired data points, sub setting, color coding, etc., ending up with 6255 HIV data points, which included only the 26 most common unique regimens as a function of time. The graph represents the most common regimens of HIV treatments taken by patients in different years. It is also organized in order of increasing number of drugs per regimen. Each regimen was color coded to include a NNRTI drug, a PI drug, or consist of nRTIs.

Figure 5. The graph represents the most common regimens of HIV treatments taken by patients in different years belonging either to NNRTI, nRTI, or PI.

As the graph shows in 1989, and early 1990s, the HIV treatment consisted of the single drug AZT, and later in 1997, NVP. As the years progressed, regimens composed of two drugs became more common. It isn’t until 1996 that we begin to see regimens composed of three drugs. Regimens composed of three drugs are the most abundant and continue to be taken by patients up to 2013, while the single drug treatments seemed to have ceased in 2008. In 2002, we first observe regimens composed of four drugs (although RTV is often not counted as a drug, so these regimens may be considered 3-drug regimens as well), which also continue to be used along with the three drugs regimens.

R is a great program for data analysis. I believe that anyone who would like to learn it, with persistence can definitely do it. I will continue learning R, and analyzing my data set. I hope to use it as a useful tool for future investigations in my career.

[1] Thanks to Dr Robert Shafer from Stanford University for sharing the data with us!

15 papers on contemporary evolution in human viruses

In the fall semester of 2014 I taught a reading seminar for master students at SF State on contemporary evolution in human viruses. This blog post contains a list of the papers we read in the seminar.

I posted about this seminar previously here (about the seminar format) and here (no powerpoint allowed), and here (about being nervous for a talk).

The students’ work can be read and seen here (about H1N5), here (polio outbreak), here (Dengue), here (Ebola), here (HIV in court), here (doing my own homework), here (the origin of HIV), here (on bad small things) and here (Hep B).

These are the papers we read:

1. Fast evolution of drug resistance in HIV patient the 1980s

Resumption of HIV antigen production during continuous zidovudine treatment. Lancet. 1988 Feb 20;1(8582):421.
Reiss P, Lange JM, Boucher CA, Danner SA, Goudsmit J.

2. HIV: Doctor infects his ex-girlfriend, phylogenetic evidence in court

Metzker, Michael L., et al. “Molecular evidence of HIV-1 transmission in a criminal case.” Proceedings of the National Academy of Sciences 99.22 (2002): 14292-14297.

3. Very contemporary: the genomics of the West-African Ebola epidemic

Gire, Stephen K., et al. “Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.” Science 345.6202 (2014): 1369-1372.

4. Using phylogenetics to determine origin of Dengue-3 outbreak in Australia

An explosive epidemic of DENV-3 in Cairns, Australia. PLoS One. 2013 Jul 16;8(7):e68137. doi: 10.1371/journal.pone.0068137. Print 2013. Ritchie SA1, Pyke AT, Hall-Mendelin S, Day A, Mores CN, Christofferson RC, Gubler DJ, Bennett SN, van den Hurk AF.

5. Classic paper from Beatrice Hahn’s lab on origin of HIV-1

Gao, Feng, et al. “Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.” Nature 397.6718 (1999): 436-441.

6. Timing the start of the HIV-1 pandemic

Korber, Bette, et al. “Timing the ancestor of the HIV-1 pandemic strains.”Science 288.5472 (2000): 1789-1796.

7. Where did the polio outbreak in Dominican Republic and Haiti come from?

Kew, Olen, et al. “Outbreak of poliomyelitis in Hispaniola associated with circulating type 1 vaccine-derived poliovirus.” Science 296.5566 (2002): 356-359.

8. Within-patient evolution of vaccine-derived polio virus

Martín, Javier, et al. “Evolution of the Sabin strain of type 3 poliovirus in an immunodeficient patient during the entire 637-day period of virus excretion.”Journal of Virology 74.7 (2000): 3001-3010.

 9. Hepatitis B within-patient evolution

Lim, Seng Gee, et al. “Viral quasi-species evolution during hepatitis Be antigen seroconversion.” Gastroenterology 133.3 (2007): 951-958.

10. Permissive mutations and the evolution of drug resistance in Influenza

Bloom JD, Gong LI, Baltimore D. Permissive Secondary Mutations Enable the Evolution of Influenza Oseltamivir Resistance. Science (New York, NY). 2010;328(5983):1272-1275. doi:10.1126/science.1187816.

11. Controversial experiments on H5N1 Influenza

Airborne transmission of influenza A/H5N1 virus between ferrets. Science. 2012 Jun 22;336(6088):1534-41. doi: 10.1126/science.1213362.
Herfst S1, Schrauwen EJ, Linster M, Chutinimitkul S, de Wit E, Munster VJ, Sorrell EM, Bestebroer TM, Burke DF, Smith DJ, Rimmelzwaan GF, Osterhaus AD, Fouchier RA.

12. Influential study on treatment to prevent HIV

Grant, Robert M., et al. “Preexposure chemoprophylaxis for HIV prevention in men who have sex with men.” New England Journal of Medicine 363.27 (2010): 2587-2599.

 13. HIV drug resistance in women in Africa who were treated to prevent mother-to-child transmission

Eshleman, Susan H., et al. “Nevirapine (NVP) resistance in women with HIV-1 subtype C, compared with subtypes A and D, after the administration of single-dose NVP.” Journal of Infectious Diseases 192.1 (2005): 30-36.

 14. Evolution of Acyclovir resistance in Varicalla-Zoster Virus

Morfin, Florence, et al. “Phenotypic and genetic characterization of thymidine kinase from clinical strains of varicella-zoster virus resistant to acyclovir.”Antimicrobial agents and chemotherapy 43.10 (1999): 2412-2416.

 

15. Soft and hard sweeps during evolution of drug resistance in HIV

Loss and recovery of genetic diversity in adapting populations of HIV. PLoS Genet. 2014 Jan;10(1):e1004000. doi: 10.1371/journal.pgen.1004000. Epub 2014 Jan 23.
Pennings PS1, Kryazhimskiy S2, Wakeley J3.

Using deep sequencing data to estimate selection coefficients in HIV

Messer, P. W., & Neher, R. (2011). Estimating the strength of selective sweeps from haplotype diversity data. Genetics.

I recently reread this paper by my colleagues Philipp Messer (used to be my office mate at Stanford) and Richard Neher (who works on the population genetics of HIV, just like I do). I thought it’d be worth writing a short blog post about this paper because it has some really nice ideas but it is quite technical and you may not have read it.

Selective sweeps in HIV

Selective sweeps happen in HIV when the virus fixes immune escape mutations or drug resistance mutations. Often, we don’t have good enough time series data to determine the frequency path of the beneficial mutation (i.e., how fast does the beneficial mutation increase in frequency in the viral population). Without frequency path it is hard to quantify the selection coefficient of the beneficial mutation; how much fitter are they than the virus they replace?

The authors of the paper present a new method to estimate the selection coefficient of a beneficial mutation. The method requires deep sequencing data from a population in which a beneficial mutation has recently gone to fixation. The method is applied to HIV sequences from patients in which a drug resistance mutation or an immune escape mutation has just gone to fixation. It seems to me that the method may be especially useful for drug resistance mutations because they may go to fixation rapidly and at unpredictable times, so that it is hard to follow their frequency path. The proposed method just requires a sample after fixation has happened.

The idea

The method is based on the following idea: If the selection coefficient of a beneficial mutation is very high, then the selected allele will quickly reach a high frequency without accumulating many new mutations. But if the selection coefficient is not so high, then it will take more time for the selected allele to reach a high frequency, during this time it will accumulate new mutations.

New, neutral, mutations that occur on the background of the beneficial mutation, will be dragged to a higher frequency by the beneficial mutation. If a new mutation occurs on the background of the beneficial mutation very early when there is only one copy of the beneficial mutation, then the frequency of the new mutation will always be the same as the frequency of the beneficial mutation. They likely fix in the population together. If, however, the new mutation occurs when there are already 8 copies of the beneficial mutation, then the new mutation will likely reach approximately 12% frequency (like the red fraction of the population in the figure).

This figure shows how earlier mutations on the background of the beneficial mutation reach higher frequencies. (Fig 1 A in the paper)

In a fast sweep, the “5 copy moment” goes by quickly

For a new, neutral, mutation on the background of the beneficial mutation to ultimately reach frequency 20% in the population, it needs to occur when the beneficial mutation is present at approximately 5 copies. The new mutation then occurs on one of the 5 copies, and is thus present on 20% of the viruses with the beneficial mutation. If the beneficial mutation fixes, the new mutation will have a population frequency of around 20%. In a slow sweep, the beneficial mutation may spend several generations at around 5 copies, whereas in a fast sweep, the “5 copy moment” goes by quickly. A mutation that happens when there are 10 copies may reach 10% freq, at 100 copies 1%. If we have many sequences from the population (say, 1000), we can look at all the new mutations and their frequencies and determine how fast the sweep went, or what the frequency path of the beneficial mutation was. If we know the frequency path, we can estimate the selection coefficient of the beneficial mutation.

Richard and Philipp used their method on HIV data because these data are deep enough to do this.

This is a sweep of a drug resistance mutation. The inset shows the genetic distances between the most common haplotypes in the dataset. All haplotypes have just one new mutation, except haplotype 13 which has 2. The main figure shows the ranks of the haplotypes on the x-axis vs their abundance (relative to the haplotype that had no new mutations) on the y-axis. Haplotype 1 (with 1 new mutation) has approximately frequency 0.05, so it must have occurred when there were around 20 copies of the beneficial mutation. The estimated selection coefficient is 0.07. This is figure 6 A in the paper.

Use the method to study new infections?

I wonder whether this method can be used to see how quickly a new HIV infection is growing in a person if we’d have deep sequence data from a newly infected person.

World AIDS Day at SF State

Yesterday it was the 1st of December, and I almost forgot that it was world AIDS day! However walking around on the campus of SF State University (where I work) reminded me of the day it as. On the central lawn of the campus red and blue flags were planted in the shape of an aids ribbon. Surrounding the lawn along the path, there were white labels with names of people who died of AIDS. Some of these people were students or staff of SF State University. Others were well known people, such as Nelson Mandela’s son, Makgatho Mandela. In addition, in the student union the SF State AIDS memorial quilt is on display, with names of people from SF State who died of AIDS. The sign next to the quilt explains that Cleve Jones, who started the AIDS quilt was also an SF State student.

Finally, the university health services offered HIV testing yesterday. Testing is extremely important, because many people in the US do not know they are infected with HIV and these people may unknowingly infect others. If you’re looking for a place to get tested in San Francisco, check out the website of the San Francisco AIDS Foundation.

I attach some pictures to this blog.

Doing my own homework

This week I decided to do some of my own homework. Just for fun.

It’s a graphical abstract of a classic paper we read in class.

Turns out, making a graphical abstract is no easy task! Next week, there’ll be students’ work here again.

What I found most surprising about this paper is that they had to sequence the chimps’ MtDNA to find out what subspecies they were. I would have expected that experts could simply look at a chimp and know what subspecies it is.

Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.
Gao F, Bailes E, Robertson DL, Chen Y, Rodenburg CM, Michael SF, Cummins LB, Arthur LO, Peeters M, Shaw GM, Sharp PM, Hahn BH. Nature. 1999 Feb 4;397(6718):436-41.

Reading about using phylogenetics in court

In my new job at SFSU, I am teaching a seminar on the evolution of human viruses. We are reading one paper every week and every student gets a different assignment for each paper. We’ve done one week now and I am very happy with the results. The paper we read was Metzker et al (PNAS, 2002), it is about using phylogenetic methods in an HIV infection case that went to court (thanks to Graham Coop for suggesting the paper).

I asked the students if I could publish some of their work. Here we go:

Describe the context and main question of the paper

The Metzker et al. study details the first instance of the admission of phylogenetic analysis as forensic evidence in a criminal case. It sought to determine whether scientific support existed for the proposed viral transmission event between the suspect (via injection of blood from an HIV-positive patient) and the victim by inferring phylogenies of the patient, victim, and HIV-infected control strains from the same geographic region using two loci under different selective pressures. In trees generated from both loci, the isolates from the victim clustered with the patient’s, supporting a close relationship between victim and patient HIV strains. Phylogenetic analysis has previously been used in inferring HIV transmission events, notably in the “Florida dentist case”. Five individuals were inferred to have contracted HIV-1 from their dentist based on the distinct clustering of their strains with the dentist’s relative to geographically similar HIV-positive controls.

Roxanne Bantay

Who are the (main) authors of the paper?

Dr. Michael Metzker, the primary author of Molecular evidence of HIV-1 transmission in a criminal case (2012), is an associate professor at Baylor college of Medicine and Rice University where he teaches human genetics. Additionally, he is president & CEO at RedVault Biosciences, a technology company that aims to advance personalized genomic medicine. Metzker is also an active researcher in the field of bioinformatics and next-generation sequencing.

The last author of the preceding publication is Dr. David Hillis, who is a current evolutionary biology professor and former director of the biology and bioinformatics department at the University of Texas (Austin). Hillis’ research focuses on experimental laboratory evolution; he believes that by studying this process we can ultimately gain insight into the underlying mechanisms that drive evolution.

Eduardo Lujan

Explain the main results of the paper using only the 1000 most common English words

This paper is about a doctor who tried to kill his girlfriend by using blood from a sick person. The doctor got the blood from their work and stuck their girlfriend during a fight. The important part of this case is the way that they showed that it really was the doctor who made the woman sick. For this case, tiny changes that happened in the thing that made the woman sick were found. These changes can show which person made the other people sick and show the relationships between all of the sick people.   By looking at these changes and the relationships, they showed that the doctor was the one who was at fault for making the woman sick.

Bradley Bowser

(see http://splasho.com/upgoer5/)

Make a graphical abstract of the paper

Peter Manzo

 

 

Joep Lange

[I accidentally published an earlier version of this post on August 23rd]

Even though I am Dutch and I work on HIV, I knew almost nothing about Joep Lange when he died a couple of weeks ago in the M17 flight.

For me, his name was mainly associated with one of his early papers, where he was not even the first author (Reiss, Lange et al 1988, Lancet). I frequently used a figure from that paper in my presentations (see below). The short paper, published in the Lancet in 1988, shows very clearly that the HIV drug AZT works initially, but after a few months HIV levels increase again. Later it became clear that this happens because HIV becomes resistant to AZT, which I am sure the authors suspected, but the word resistance doesn’t occur in the paper. The paper is important because it is one of the first that used quantification of HIV load to study treatment response. It also shows that world-class and important HIV research was coming out of the Amsterdam Medical Center.

Later, of course, in the 1990s, it was discovered that combining three drugs in a “cocktail” was a good way to reduce the probability that resistance evolved in HIV. This discovery marked a turning point in the HIV/AIDS epidemic, and Joep Lange was involved in it too.

Figure 1A of Reiss, Lange et al, 1988: Serum HIV antigen levels in two AIDS patients on zidovudine.

After the M17 crash, Joep Lange’s name was mentioned very often and I became curious to learn a little more about him. It turns out that he had done quite a few interviews for Dutch radio. I listened to two interviews which he gave in 2013 on Dutch public radio, and very much enjoyed them. Three things I found most interesting to learn:

1. Joep Lange was a very successful scientist, but when asked about his career, he explained that he owed a lot to luck. For example, he was a young specialist in internal medicine, with an interest for infectious disease in the Amsterdam Medical Center (AMC) right when in 1982 the first cases of HIV/AIDS occurred in Amsterdam, so just by being in the right place at the right time, he could start a career working on HIV/AIDS.

2. I am slightly embarrassed to say that I had no idea – but learned from the radio interviews – that Joep Lange, and not David Ho,  was the first to suggest that triple-drug cocktails (later called HAART, highly active anti-retroviral therapy, or simply combination therapy) would be the key to preventing the evolution of drug resistance in HIV and therefore sustained treatment success.

3. Joep Lange, like many others, was wrong about the “Mississippi baby” who was thought to be cured of HIV, but now turns out to still be HIV positive.
In the interviews, Lange talks about four people who were thought to be cured: the “Berlin patient” (he got a bone marrow transplant from an HIV-resistant donor and is still considered cured), the “Mississippi baby” (who was given treatment from a few hours after birth, and seemed HIV negative for quite a while, but is now known to be HIV positive), and two patients in Boston (they got a bone marrow transplant and were negative for a while, but now turn out to be positive).