Trends in Immunology
Volume 41, Issue 7, July 2020, Pages 549-555
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Special Issue: Molecular Mechanisms of Immunity
A Future Outlook on Molecular Mechanisms of Immunity

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What do you see as the new frontiers in molecular mechanisms of immunity research? What are the areas and technologies that we should be watching closely in the near future? What are you most excited about?

Amy Weinmann (AW): A lot of recent research has pointed towards a need to incorporate thinking about genetic backgrounds in molecular immunology research. This includes understanding how DNA content in different mouse strains impacts events ranging from transcription factor association, open chromatin states, DNA methylation, to genome topology. Studies are demonstrating that relatively small differences in the genetics of inbred mouse strains can have big consequences. This leads to an area

What do you see as the distinction between potentially reprogramming and resetting the immune system? How close are we to realistically achieving this goal – perhaps for therapeutic purposes?

AW: Gene programs are defined by a series of events, and I like to think about the concept of how ‘fixed’ a gene program is in this context. For example, if ten events are needed to reach the endpoint of a programming decision, then, how difficult it is to reprogram the cell will depend on where in the spectrum of those ten events, the program is at the time of an intervention. If only two events are in place, it might not be too difficult to reverse these, whereas if eight events are in place,

From your perspective, what are the biggest limitations and challenges in molecular immunology research?

AW: I think we need to be careful to acknowledge the limitations of technologies and not over-interpret datasets. Single-cell approaches are informative and represent one of the technologies that will have a profound impact on molecular immunology research in years ahead. There are limitations associated with the depth of single-cell-sequencing approaches, and what can be realistically sampled in each cell. Therefore, we need to be somewhat careful about interpreting the absence of a transcript

In terms of epigenetics and molecular immunology, what should the scientific community be most cautious about?

AW: We need to be careful in how we process genomics datasets to define mechanistic principles in gene regulation. There is a tendency in studies to try to identify one common mechanism that broadly regulates a gene program for an aspect of immune cell activity or function. In this case, oftentimes, all statistically significant differences in genomics datasets are treated the same, without accounting for the fold change of the differences being monitored. When considering the approach of

What conceptions should be corrected regarding past perspectives in your field?

AW: I am not a fan of the term 'master regulator', or assuming that the activity of one transcription factor can explain an entire gene program. Many steps are needed for a defined differentiation decision, and as a field, I think it is more important to tease apart how different factors contribute to unique aspects of cellular programs, rather than trying to focus on identifying one key factor to explain all aspects of a program. I think the research from Andrea Schietinger’s group defining

Overall, what are the key points that we should be teaching students about molecular immunology?

(AW): Whenever I teach a class, I always discuss two sides of the coin for each experimental strategy, and stress that each side is equally important; that is, ‘what we can learn and what we cannot learn’ from each experimental strategy. I think critically analyzing data and being cognizant of limitations has always been important in research, but it is perhaps even more so with big data science approaches. To this goal, I think we all need to conceptually understand the principles of

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