Quick Q&A with the Statistical Computational Biology Group

In Quick Q&A, NLM staff share a bit about why they’re motivated and inspired. We also ask them to add something surprising about themselves.

For our third Quick Q&A, we reached three scientists in the National Center for Biotechnology Information (NCBI) who work in the Statistical Computational Biology Group.

Meet John Spouge, Mileidy Gonzalez, and Amirhossein Manzourolajdad.

Quick Q&A with John Spouge, Mileidy Gonzalez, and Amirhossein Manzourolajdad
Question John Spouge, MD, PhD Mileidy Gonzalez, PhD Amirhossein Manzourolajdad, PhD
casual headshot of John Spouge Headshot of Mileidy Gonzalez Casual headshot of Amirhossein Manzourolajdad
What is the focus of your NLM research? I am interested in the statistics of sequence alignment. In practical terms, I work on ways to determine whether two similar DNA or protein sequences really have the same biological functionality. I also work with HIV virologists to understand hidden factors influencing their infectivity assays and interpret data from their animal trials. I use large-scale sequence analysis, bioinformatics, pattern/motif discovery, algorithm development, and statistics to study HIV transmission and host-virus interactions.

Recently, most of my focus has been on identifying molecular signatures characterizing the transmitted/founder viruses that initiate productive HIV infections in new hosts. HIV infection is generally established by only one virus. We would like to know what makes those founder viruses so infectious. The goal is to use this information to inform the design of an HIV vaccine.

I started as a visiting post doc at NIH. Coming in with a degree in bioinformatics from the University of Georgia, I focused on RNA structure prediction for projects involving evolutionary studies here at NLM. In a collaborative effort, we studied changes that occurred in HIV-1 Nef RNA over the past few decades in North America.
Why is it significant, in your opinion? My work in alignment statistics helps experimenters understand the unknown functions of biological sequences. My work in HIV helps experimenters improve their interpretation of assay results and helps them find targets for vaccines and other HIV therapies. When you create a vaccine, ideally, you don’t want one that works only part of the time or for only a portion of the population. An ideal vaccine is one whose efficacy is universal. This has been an elusive goal in HIV because the virus mutates so rapidly that mutants have escaped the HIV vaccines created to date.

One of my goals has been to identify universal features (signatures) characterizing the founder viruses establishing HIV infections. I recently discovered several molecular signatures common to all HIV founder viruses and conserved regardless of subtype, gender, age, or route of transmission. These findings are significant because they point to universal sites of vulnerability in HIV, which, if targeted, could provide a means to combat the HIV epidemic. We are working closely with experimental collaborators to evaluate the feasibility of the signatures as vaccine targets, which has brought us a step closer to designing a universal vaccine.

One of the less explored areas of HIV research, particularly when it comes to the accessory Nef gene, is the study of Nef RNA (as opposed to the Nef protein) and its possible impact on viral fitness. This approach may be rewarding in antiretroviral drug therapy.
What or who inspired you to pursue your career? I had two superlative mathematics teachers, Anthony Parker-Jervis in high school and Alex Melzak in university, both unflinchingly demanding. They gave me their intense appreciation of mathematics, which at its best is subtle, beautiful, and practical. I knew I wanted to do something related to human health, but I also like numbers, and I’m very analytical. I’m grateful I had a few teachers in high school and early college who loved what they did and shared that enthusiasm by making science, math, and programming a fun, intellectual challenge. During my electrical engineering master’s studies in my home country of Iran, my information theory instructor, Dr. Golestani, had a great influence on my career path. His intuitive approach inspired me to learn.
How did you get started in your career? When I was 13, I taught myself calculus during a spring break. In retrospect, everything between then and my arrival at NLM just seems to have flowed naturally. Given that I was interested in human health, I thought my only option was medicine. I took the pre-medical route by getting undergraduate degrees in molecular biology and chemistry. As an undergrad, I shadowed medical doctors but also participated in research projects. In my first summer internship I was introduced to bioinformatics and fell in love! I discovered that my mind was created to write code: I learn and store information in diagrams and subroutines. I found my path. I came back to my home institution and supplemented my curriculum with computer classes. Once I finished college, I enrolled in a doctoral program with an emphasis in bioinformatics. I was fascinated by the ubiquity of the notion of digital information shared across fields of engineering and science. That is why I pursued my PhD in bioinformatics and eventually pursued biological sciences.
What really gets you jazzed about science and research? Understanding something new. My work involves close collaboration with biologists, computer scientists, mathematicians, and clinicians to translate knowledge discovered in silico to the experimental and clinical platforms. I thrive when interacting with collaborators and moving research forward through an interdisciplinary approach. The never-ending, evolving biological entities, such as RNAs. The ability to capture, quantify, and track these changes, and the possible impact of such analyses on the well-being of our fellow humans.
If you weren’t doing this work, what other profession might you have pursued? I have a brother and two sisters, all three practicing physicians. I have an MD.

Is this a trick question?

I might have pursued a career in medicine, but I think I would have found my way back to research. Probably doing the 9-to-5, writing computer codes for a company whose mission I wouldn’t be passionate about.
Tell us something surprising about yourself. In singing, I have a three-octave range (F2 to F5). I grew up in the Amazon jungle and am the first person in my family to go to college. I went to six (elementary and high) schools. Then I enrolled in and transferred between a total of five (undergraduate and graduate) universities in three different countries.

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