The inherent complexity of multifactorial diseases such as aberrant immunity, chronic inflammation, and cancer makes them incredibly challenging to study. Using systems biology, translational researchers like Dr. Diana Mechtcheriakova put pieces of data from multi-omic approaches together to decipher the pathomechanisms underlying complex diseases.
Dr. Mechtcheriakova is an Associate Professor at the Medical University of Vienna in Austria and heads the Molecular Systems Biology and Pathophysiology research group. She is also the Head of Systems Medicine subdivision at the Austrian Platform for Personalized Medicine. Her lab focuses on the characterization of cellular checkpoints involved in the development and progression of multifactorial diseases. Our Senior Marketing Manager, Dr. Deepak Tripathi, sat down with her to understand how her group leverages the power of systems biology to uncover mechanisms underlying these diseases.
You are a theoretical physicist turned biologist. What led you to this transition?
Already at school, I got interested in physics and mathematics, and I studied theoretical physics and biophysics at the University. I was fascinated by multi-sightedness of ultimate reality, in physics, biology, and medicine. This is what brought me to science.
Why from physics to biomedical studies? During my studies, I realized how important it is to be closer to the biomedical sciences. So, I continued my PhD in antiviral immunity at the Institute of Immunology. I apply the elements of broad analytical thinking from physics in understanding and dissecting the complexity of disease pathobiology of multifactorial diseases.
What were the technical challenges that existed when you first started this work? How have these obstacles evolved with new scientific technologies?
Before the era of high-throughput technologies, research was often focused on the doctrine – one molecule, one gene, one outcome. The advent of multi-omic technologies allowed and enabled the implementation of systems-based integrative approaches.
What is the central question you are trying to answer with your research?
Our long-term research goal is to understand on a deeper level how cellular checkpoints lead to the development and progression of multifactorial diseases. We focus on a unique molecule in B cells called activation-induced cytidine deaminase (AID).
AID, encoded by the AICDA gene, is an ultimate marker of the germinal center reaction. Germinal centers are beautiful, highly-organized cellular structures where the various subtypes of immune cells interact. I call these structures the “eyes of adaptive immunity.”
In my group, we dissect the molecular mechanisms of germinal center reactions at tumor sites, which contribute significantly to the onsite immune response. We, and other groups, have shown that the appearance and functionality of these lymphoid structures at the tumor site are associated with favorable patient prognosis in more than ten cancer types.
Apart from cancer, you are also studying COVID-19 immunity. Could you elaborate?
Lymphoid structures have gained attention in recent years due to the COVID-19 pandemic. They are the antibody factories responsible for vaccine-mediated antibody response. We are studying the interrelations between the patient-specific AID/APOBEC gene signature and the pathobiology of COVID-19. AID is a member of the AID/APOBEC gene family. APOBECs are important host intrinsic antiviral factors that protect us from various DNA and RNA viruses. We believe that APOBECs play a significant role in protecting and fighting against SARS-CoV-2.
Utilizing Systems Biology
How do you apply systems biology to study complex multifactorial diseases?
Our research approach is best described by this saying from Aristotle – the whole is more than the sum of its parts. Deciphering the complexity of multifactorial diseases demands a holistic approach where you consider individual molecules, gene networks, pathways and pathomechanisms, and clinical relevance to patients.
To address this, we developed and successfully applied the systems biology-based integrative algorithm – MuSiCO, short for Multigene Signature to Patient-Oriented Clinical Outcome. MuSiCO bridges systems biology and systems medicine to maximize the benefit for the patient. MuSiCO implements consecutive analytical models, integrating advanced gene expression profiling, prognostic predictive modeling, clinical outcomes and digital pathology, and systems biology, including the integrative analysis of transcriptomic datasets.
Could you elaborate with an example how you applied your systems biology-based algorithm to uncover novel disease mechanisms?
The starting point of MuSiCO is the multigene signature, an expert-designed set of genes used for real-time PCR-based gene expression profiling. To give an example, in one of our projects we created a 41-gene sphingolipid-associated gene signature, which covers the entire network of sphingolipid-modifying enzymes, including lipid-specific kinases, lipid-specific phosphatases, specific receptors for the bioactive lipid mediators, and lipid transporters. We then combined this sphingolipid-associated signature with a 23-gene signature covering the process of epithelial-mesenchymal transition (EMT). This helped us uncover a completely novel role of sphingolipid machinery in the progression and mestatasis of lung cancer.
You have integrated tissue imaging into your systems biology-based algorithm. What additional insights do you gain from this approach?
We started by asking ourselves – how can we define the cell type which contributes the most to the gene expression value? This became the starting point of our second integrative approach called DIICO, short for Digital Immune Imaging to Clinical Outcome. In this approach, we combined next-generation tissue image cytometry with the MuSiCO algorithm to define the cell type and the tissue localization of potential candidate molecules identified by gene expression profiling.
We applied this strategy to characterize the immune phenotype of lymphoid structures at the tumor site. We discovered a strong prognostic effect of lymphoid structures for patients with metastatic colorectal cancer, not only at the metastatic site, but surprisingly also in the normal, non-tumoral, colonic mucosa.
Do you use Applied Biosystems technologies in your research?
I have been using Applied Biosystems technologies for a long time. My research group at Novartis Institute for Biomedical Research in Vienna was among the pioneers in implementing Applied Biosystems-based gene expression profiling for translational research.
Later, in the Medical University of Vienna, my group was involved in validating [the Applied Biosystems] QuantStudio 12[K Flex Real-Time PCR System], a highly flexible, real-time PCR-system from Applied Biosystems. We were genuinely amazed by the flexibility it offered to perform gene expression profiling in three different formats – the classic 96-well format, the 384-well format for advanced users, and a microfluidic card for 384 genes.
To give an example of the scientific outcome in terms of translational research, we are happy to share that our research in the field of sphingolipids contributed to the development of the FDA-approved sphingolipid mimetic, FTY720, also named fingolimod, trade name Gilenya™, for the treatment of patients with relapsing forms of multiple sclerosis, was carried out with the help of Applied Biosystems technologies.
Looking Towards the Future
What inspires you to continue your work?
There are still many gaps in our understanding of the pathway mechanisms of multifactorial diseases. Besides, COVID-19 continues to challenge us. Seeing the research community come together to tackle this challenge was inspiring. I view the situation as an opportunity to find and develop the most efficient ways to understand and tackle SARS-CoV-2.
What advice would you give to a young researcher just starting in this field?
As a scientific supervisor and mentor, I apply a forward-thinking approach to teaching. I believe that qualities like broad thinking and open-mindedness are necessary to comprehensively evaluate complex research questions in biology and medicine. Particularly for young scientists, these abilities would empower them to interpret, analyze, and judge the information to gain valuable insights. And I am also very proud that my daughter Anastasia, a postdoc at the Medical University of Vienna, is among such extraordinary next-generation scientists.
What are your hopes for the future of your research field over the next ten years?
It is difficult to predict what will happen in the next ten years in this area. Our research group is excited about artificial intelligence-based algorithms in diagnostics and prognostic models. We have started to develop an AI-based approach focused on lymphoid structures in diseased tissues.
What’s the best thing about being a scientist?
To be a scientist is not a job, but a calling, a mission, a gift. To see the shiny eyes of young researchers – after their first successful experiment, first successful results, first own conclusions, or first own proven hypothesis – is one of the best things for me about being a scientist.
FOOTNOTE: This interview has been edited for length and clarity.
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