In this chapter of Career Stories, we bring you the story of Dr Mahita Jarjapu, who is a Bioinformatics Researcher & a postdoctoral fellow at La Jolla Institute for Immunology, San Diego, California, USA.
Being a researcher with profound bilateral sensorineural hearing loss, only fuels her sheer grit and determination to continue thriving and building a Career in Academia. She also vouches for empowering other researchers who have disabilities, to make a significant mark in STEM fields.
In this candid discussion, she speaks at length about her interdisciplinary research journey, sheds light on how she overcame the challenges faced due to her disability, while suggesting some simple, and practical measures that society at large can adopt in order to practice inclusivity.
What inspired your transition from a masters in chemistry to a PhD in biology?
I did my BSc in Biology (Botany, Zoology, Chemistry). I have always been interested in biology from my school days. During my BSc, I started developing an interest in Chemistry. So, during my MSc, my interest in the interface of biology and chemistry grew. Basically, I have always been interested in this because I wanted to understand how biology works at the molecular level. Molecular Biology involves protein molecules and their interactions. These interactions come under the domain of chemistry. Hence, this is what I wanted to do for my PhD.
We have divided our understanding of the universe into different disciplines (physics, chemistry, biology, and mathematics). In reality, all these principles come into play simultaneously. Presently, even in science, when we are trying to solve a problem or investigate a research question, we tend to look at it from only one point of view; we do not consider other factors that are outside our area. For example, you take the interactions between two cells. From the perspective of biology, you will observe the proteins involved in both the protein and cellular interactions. If observed through physics, cells are dynamic and not static. All these factors need to be considered. If you want a complete picture of the problem for solving it effectively, you need to look at it through the lenses of different disciplines.
Your current research journey has been filled with immunology, omics, computational protein biology, structural biology, and bioinformatics. What influenced you to choose interdisciplinary research for your career?
As I explained earlier, it is important to look at the context or the bigger picture. So, I understand that it can be difficult because if you have done MSc in chemistry and you go and do a PhD in Biology, then you have to revise many concepts. I have to admit that in the beginning there will always be a steep learning curve but if you can overcome that, then it is worth it. I have benefitted from this. After my MSc in Chemistry, when I started my PhD, my knowledge of biology was very poor. So, when you do a PhD, you have to clear a qualifying exam after two years of enrolling into the PhD program. This qualifying exam is tough because the question paper is designed to apply your knowledge instead of being fact-based. For that, you need to have a very good understanding of biology concepts. Hence, for the first two years, I had to spend a lot of time catching up with my knowledge of biology. After I gained that knowledge, I could put together a lot of my chemistry knowledge gained from my MSc. Having these two subjects together helped me to look at the question from multiple perspectives. I think that is very important when you are approaching your problem. It will teach you how to design an experiment and will also tell you whether the experiment is feasible; or what would be the drawbacks of the experiment. The experiment will solve one question but it will also lead to another question. Hence, you have to keep all these factors in your mind. This is the reason why I wanted to work across disciplines.
My PhD was mainly in Bioinformatics but I did not have any working knowledge about coding. I had to teach myself to practice, which is not very difficult. You can learn it yourself through online courses. From personal experience, it is better to learn anything involving wet lab research in real time. You need practical experience with a wet lab. These practical experiences are not possible to obtain when learning from online courses. But, coding languages can be learnt online for dry lab work. Initially, if you want a career in wet lab and then want to switch to dry lab, you need to have more practical experiences first. Later, you can teach yourself coding online.
For my first postdoctoral research at Dartmouth College in New Hampshire, USA, even though the focus of the lab was in computational biology, it was located in the Computer Science department. Most of my friends were computer science graduates. In my lab, we had people who were working towards a PhD degree in computer science and their research involved developing different algorithms that could be applied to address problems in biology. If you are a PhD in Biology, you would have to apply it in computer science. That was something new for me because I had to learn computer science. I had to write code and use it to generate data. The computational time and memory usage taken by the algorithm needs to be kept in mind while writing a code for it.
Now, in my current postdoctoral research, I deal majorly with immunology. So, whatever I learnt in computer science, I apply and develop it to investigate questions in immunology.
How did your research interests lead you to your current Post-doctoral fellowship at Peters Lab, La Jolla Institute for Immunology?
When I was pursuing my PhD, it was on protein-protein interactions, in the context of an innate immunity pathway, which is the TLR signaling pathway. I studied how mutations in the TLR proteins affected their ability to interact with downstream signaling proteins. So, this led me to be interested in molecular recognition.
Antibody-antigen interactions are a type of protein-protein interaction which are much more diverse due to millions of antibodies produced by each individual. Each of these antibodies has a different specificity. So, I am curious to know how these specificities arise, and what mutations in the antibodies cause them to be specific to one antigen but not another. It is not feasible to study millions of antibodies experimentally. The Peters lab is a pioneer in the development of computational tools for addressing these types of questions in immunology.
My current project is wholly based on COVID, I have been studying antibodies that bind to the SARS-CoV-2 virus. I have also been studying the mutations of different variants of this virus: alpha, beta, gamma, and delta. These variants have their differences because of their mutations. I have been trying to understand how these mutations in these variants affect the ability of the antibodies to bind or interact with them. That is the question I am currently working on. Another question I am dealing with is basically understanding what makes antibodies specific to a particular antigen. Again, it all comes down to the molecular level. It is all about chemistry. Because protein-protein interactions are nothing but various amino acids interacting with each other. So, that is how I got interested to work here at Peters Lab. It is a fantastic place. My colleagues come from all over the world: Europe, South America, Africa, Australia, Asia. They all have their diverse perspectives on life and research. So, it is a very good learning experience here.
Your current PostDoc research lab has majorly focussed on immunoinformatics i.e. immunology and bioinformatics. How is bioinformatics applicable to research in immunology?
There are multiple examples of this.
One example from our institute is single-cell RNA sequencing. Blood samples taken from volunteers who have been vaccinated or have a specific disease can be used for extracting white blood cells/leukocytes. Single-cell sequencing of these cells, taken from blood extracted at different time intervals, produces millions of gene sequences. Bioinformatic algorithms help to cluster/align these sequences together and help identify differences in the transcription/expression of certain genes and proteins in these cells. Under-expression or over-expression of these genes signifies that they play a role in a given disease.
Another example is BCR (B cell receptor) sequencing. Antibodies are secreted forms of BCRs. Antigen-specific B cells can be isolated from different blood samples and sorted. Sequencing of these B cells provides the sequence of antibody that the particular B cell is expressing. Millions of such sequences from different individuals help researchers identify what sequences are important for an antibody to be effective against a specific antigen. The unique features of these sequences are understood. Different individuals produce different antibodies but sometimes there are commonalities in the sequences between two individuals. Such examples are types of convergent evolution – i.e. the sequence is favorable for the antibody which is why multiple individuals produce such antibody sequences. All these findings are due to the application of bioinformatics algorithms to immunology. In India, this has a lot of scope for such studies owing to its diverse population.
Depending on where humans live, whether in the North or South hemisphere, we have been exposed to different types of pathogens that influence our immunity and determine how we respond to them. In the case of India which is a tropical country, we see more cases of malaria or dengue. If Indians are infected with malaria, they have antibodies against the respective antigens. Sometimes, these antibodies have been found to interact with dengue proteins. This is an example of cross-reactivity which can be explored more in India.
Another example is understanding the cross-reactivity of T cells and antibodies to different pathogens. T cells are usually specific enough to bind/interact with a peptide from a particular antigen. The same T cell can recognize a peptide from another antigen if there are similarities between these two antigens. Bioinformatics can help here to understand the similarity in peptide sequences between these two antigens and its extent to understand cross-reactivity. So that is another way of applying bioinformatics to immunology.
As data generation increases, there is also a need to develop databases and repositories to store and access this immunological data. There are several databases for the immunology community – Immune Epitope Database, Coronavirus Antibody Database, and IMGT database, to name a few.
Many students consider the terms “bioinformatics” and “computational biology” to be the same. Please clear this misconception.
Computational biology is a broader field and encompasses bioinformatics as one of its subjects. Basically, computational biology is using computers to investigate any biological question.
Bioinformatics is mostly related to data generation, handling, development of softwares, algorithms and databases to deal with biological data.
Another group that comes under computational biology is systems biology, which is the mathematical and theoretical calculation-based study of the dynamics of large biological systems. For example, understanding the fusion of protein molecules within a signaling pathway; understanding the dynamics of the various proteins involved, so on and so forth. So, for time 0, if you have, say x number of proteins, after time t, their number is estimated and their effect on the signaling pathway is explored. These are questions dealt in computational biology and not bioinformatics because there is not much scope for involvement of ‘data handing’ here.
However, Bioinformatics has become increasingly important now because of the improvement in technology which has led to the generation of more data; especially proteomic data. Hence, to handle that data and to make sense of it, you need numerous innovative bioinformatic algorithms. I would recommend checking out The Human Cell Atlas as a great example.
Machine learning, on the other hand is a completely different concept. On a fundamental level, if you look at it, it is all about modeling the data. Basically, if you have the data, you will try to derive a mathematical equation from it. For example, if you have two groups: one control group and another group with diabetics, machine learning will try to develop a probability model to predict diabetes in a person. For that, you will have a dataset which will have the information of the sample population: age, gender, eating habits. Each of them will be labeled as “diabetic” or “non-diabetic.” Based on the machine learning algorithms, you will be able to derive an equation from this dataset to separate this data into these two categories. This model will be used on a new dataset to predict the risk of diabetes. This is a very simple example of machine learning.
You have a stellar record of numerous scientific publications and you have also held the position of scientific writer for “The STEM Times” newsletter for the past 4 years. How did you discover your passion for science communication?
Writing is something I have been very good at since I was a child. I think you might be aware that I was trained at Balavidyalaya. For my training, I had to do a lot of writing and reading and liked it a lot. I caught the habit from a very young age of 4-5 years. Writing was one way of expressing myself, because talking for me was limited by the choice of words that I could use. But when I write, I can express myself much more beautifully. I have been interested in writing for a very long time. In fact, when I was in school, my teacher used to tell me not to become a scientist. She always suggested I become a journalist. However, I was interested in science.
I did not find it difficult. I enjoyed the process of writing. Even during my MSc, PhD students would request me for help in editing their thesis and I used to enjoy it. During my PhD also, I liked writing, making stories, and explaining concepts. During this time, I realized that there is a disconnect between the public and what we scientists do. I felt that it is important to make them understand what we are doing. It is very important to be able to explain to others in a very simple way so that they understand the importance of what we are doing. So that’s how I ended up working for “The STEM Times.” The focus of STEM Times was to basically convey scientific concepts to people in a simplified format.
During my bachelors and masters, I used to write a blog. I used to write about my experiences at NCBS through poems. People would tell me how they liked my writing and then it struck me how I could use my fondness for writing to communicate science and research.
Your previous interviews that outline what you are and how you grew up, are an inspiration to many, especially for women in STEM. Please tell us one or two pieces of specific advice you wish family members, mentors, or colleagues of children with any form of disability to adopt.
I think parents should not underestimate their children just because they have a disability. Each of us has flaws. For some of us, it is visible while for others it is hidden. I feel that humans have the ability to somehow find solutions or alternatives to an issue, quite unknowingly, if the issue does not seem big to them. It is all in the mind. The bigger we imagine our issues to be, the harder it becomes to overcome that. This can be solved by the approach of breaking down the problem into multiple steps.
Everyone has their own shortcomings. No one is perfect. However, the society we live in does not see a person with a disability as capable of doing anything. But that is not true. This mindset is similar to people noticing a single black spot on a white paper. People should be open-minded, supportive as well as encouraging towards people with disabilities, especially disabled children. This instills self-confidence in them. Above all, please do not discourage them. I understand that well-wishers and parents of these children would have a feeling of wanting to protect them but it only hurts them in the long run.
Small actions can make a lot of difference. In my own experience, my parents never discouraged me from doing whatever I wanted to do; especially when I wanted to move to a new place for my MSc. They never asked questions or doubted my ability to live on my own, far from home. They just encouraged me to study. But other people were very surprised when my parents decided to send me to IIT Madras. Such actions make a huge impression on children because it conveys that you are confident in them and their qualities. It makes them believe in themselves instead of depending upon others for their confidence.
What measures would you suggest to increase the inclusivity of researchers who are deaf, in STEM fields in India?
You should ask what they want. Researchers who are deaf should be asked what they need as it can vary from person to person. And others need to take some minimum effort to accommodate these needs. This goes a long way in helping them. Something that might seem small to you actually makes a big difference to us. The best example is – closed captions and the service of live captioning.
For example, during my education, I did not have many facilities that could have helped me. For me, it is very important to look at a person’s face while they are speaking. If they turn away and speak, I cannot understand. So it was really difficult following classes, since we did not have any captioning service. So, I had to depend a lot on my classmates for notes. During the class, I would have to look at the notes from the classmates sitting besides me and copy them down. Basically, in college you know how they teach. The professor will speak and tell us to hear and write about it simultaneously. For me, both were not possible. Either, I can look or I can write- only one thing at a time. Hence, I would listen to the professor and then once the lecture was over, I would copy the notes from my classmates. Unfortunately, you miss out on a lot of information; you do not know 100% of what was discussed in the lecture. So, I used to go to the library to catch up on whatever I missed out on. Hence, live captions are very important.
The experience during my PhD was very different from my MSc, because instead of classes, we had research, conferences, and seminars. Seminars and conferences usually consist of questions between the audience and the speaker. For me, I would miss out on most of the questions. I did not like that. All that changed when I came to the US and started using Zoom. I saw these closed captions and then finally understood what goes on in Q&A sessions. When you are asked a question, it makes you think about how to address it. For people like me who are deaf who communicate using spoken language, we need closed captions to follow meetings and seminars.
I have attended conferences in India as well as abroad. Whenever I was in India, I was saddened to see that no one would care to ask or arrange real-time captioning for me. But when I visited conferences in Canada, they asked about my needs and made arrangements for me to have live captions. Everyone was given a device and a URL link. The link had to be opened. Then the microphone in the device would hear any speech and convey it to a third person who would immediately start typing these captions and it would be visible to a person who is deaf. This system should be implemented in Indian conferences, as in Canada I could follow everything that was being spoken and it made a huge difference to me. I could understand questions which enabled me to think more about the problem or the topic. This facility is not offered either at Indian conferences or in Indian universities. I hope this facility is provided at least in top-tier institutions in India, like IIT, NCBS, TIFR, etc. – I know that it is provided in IIMs.
Another important aspect is to increase awareness and not judge a researcher because they are deaf. I hope my experiences are able to help others like me who are also pursuing their research in India.
These days, there are softwares to help people who are deaf: Live transcribe by Google. It converts speech into text. MS Windows 11 also has an inbuilt feature for converting audio into text. This feature was designed by another person who is deaf, and who was trained in the same school as mine and is currently working at Microsoft in Seattle, USA.
