Diving into the complex world of microbiome research, I had the privilege of speaking with Ivan Kairatov, a renowned biopharma expert with extensive experience in technology and innovation within the industry. With a strong background in research and development, Ivan has been instrumental in advancing data standardization efforts like STREAMS, a groundbreaking set of guidelines for environmental and non-human microbiome studies. In our conversation, we explored the intricate process of creating these standards, the challenges of adapting frameworks for diverse ecosystems, the integration of cutting-edge technology, and the importance of collaboration across global research communities. Ivan also shared personal insights into balancing demanding roles and how unexpected paths can lead to transformative contributions in science.
How did the idea for STREAMS come about, and what was the driving force behind expanding beyond human microbiomes to include environments like soil and water?
Well, the idea for STREAMS really stemmed from a growing recognition in the scientific community that we needed a unified way to report data across incredibly varied microbiome studies. While frameworks like STORMS did wonders for human microbiome research, we saw a glaring gap when it came to environmental and non-human host-associated microbiomes—think soil, water, air, and even synthetic systems. The driving force was the sheer frustration of trying to compare studies or replicate findings when critical details were missing or inconsistently reported. I remember sitting in a workshop with 50 diverse participants—researchers, editors, and data reps—and feeling this collective urgency to create something comprehensive. We knew that without standard guidelines, the field risked stalling, and I personally felt a deep responsibility to ensure that future researchers, especially students, wouldn’t face the same roadblocks we did.
What were some of the toughest challenges in developing the 67 checklist items for STREAMS, and can you recall a specific moment during the collaborative process that shaped the final framework?
Developing the 67 checklist items was like piecing together a puzzle with pieces that didn’t always fit. One of the toughest challenges was ensuring the guidelines were detailed enough to cover the nuances of environmental microbiomes yet practical for researchers to implement without feeling overwhelmed. We had to account for things like permit information and sample collection differences that just don’t come up in human studies. I vividly recall a moment during our workshop when an early-career researcher passionately argued for including metadata collection challenges specific to remote field sites—like how weather or limited equipment impacts data quality. Their raw honesty about struggling in muddy, unpredictable conditions struck a chord with everyone in the room, and it directly influenced items on contextual data reporting. That discussion reminded me how vital diverse voices are; without that perspective, we might have missed a critical piece of the puzzle.
With over 1,100 pieces of feedback to review, how did you manage such a massive volume of input, and what was one piece of critique that really pushed you to rethink an aspect of STREAMS?
Reviewing over 1,100 pieces of feedback was, frankly, a grueling task, but it was also incredibly rewarding. My approach was to create a systematic workflow—categorizing comments by manuscript section or theme and compiling a 100-page response document to ensure nothing slipped through the cracks. I’d block out hours each day, often late at night with a strong cup of coffee, to read every single comment with fresh eyes. One critique that hit hard was about the accessibility of our guidelines for non-native English speakers; someone pointed out that the language in early drafts was too jargon-heavy. I remember feeling a pang of disappointment in myself for not catching that earlier, but it spurred us to rewrite sections with clearer terms and build tutorials and acronym lists. That feedback didn’t just tweak a checklist item—it reshaped our entire philosophy to prioritize inclusivity.
STREAMS tackles unique challenges in non-human microbiomes compared to human studies. Can you dive into one specific caveat you encountered and how the team addressed it?
Absolutely, non-human microbiomes come with a whole different set of hurdles compared to human studies. One major caveat we wrestled with was the variability in sample collection across environments like soil or water. Unlike human samples, where protocols are more standardized, environmental samples can be influenced by factors like depth, temperature, or seasonal shifts—details that drastically affect microbial composition. For instance, we had to address how to report soil sampling when a researcher might be digging in a rainforest versus a desert; the conditions are worlds apart. We tackled this by including specific checklist items for environmental context, ensuring researchers document those variables meticulously. I recall heated debates in our team about balancing specificity with flexibility, but seeing the final guideline felt like a small victory—it was a moment of relief knowing we’d built a tool to navigate such complexity.
Making STREAMS machine-readable for data analysis is a forward-thinking move. How did you ensure compatibility with existing metadata standards, and can you share an example of its impact during development?
Making STREAMS machine-readable was a priority from the get-go because we knew it could revolutionize how data is analyzed and shared. To ensure compatibility with existing metadata standards, we aligned our framework with established public database formats and consulted with data repository experts during our workshop. We mapped out every checklist item to ensure it could be parsed by computational tools without losing meaning, which involved countless iterations and testing phases. I remember one testing round where a colleague uploaded a dataset using STREAMS guidelines into a database, and the system instantly flagged inconsistencies in sample metadata that would’ve taken hours to spot manually. That moment, watching the screen light up with real-time feedback, gave me a rush of excitement—it confirmed we were on the right track to make research not just standardized but smarter.
STREAMS is described as a ‘living’ resource with plans for regular updates. How do you plan to keep it evolving with community input, and what’s one future direction you’re particularly excited about?
We envisioned STREAMS as a ‘living’ resource because microbiome research is constantly evolving, and static guidelines just won’t cut it. Our plan is to establish an ongoing feedback loop—think online portals for submissions, periodic surveys, and workshops at major conferences to hear directly from the community. We’re also exploring ways to leverage technology, like using large language models to help parse and summarize input for faster integration. I’m personally thrilled about the potential to expand STREAMS into emerging areas like synthetic microbiomes, where engineered microbial communities are being studied for industrial applications. I can still feel the buzz from a recent chat with a colleague about how this could transform biopharma innovation, and I can’t wait to see how community insights shape that frontier in future updates.
Collaborating with nearly 250 researchers from 28 countries must have been a logistical challenge. What strategies helped keep everyone aligned, and can you share a specific hurdle you faced and how you overcame it?
Coordinating nearly 250 researchers across 28 countries was indeed a monumental task, akin to herding cats on a global scale. We leaned heavily on digital tools—regular virtual meetings with carefully planned time zones, shared documents for real-time updates, and breakout groups to tackle specific guideline sections. One strategy that worked wonders was assigning regional leads to bridge cultural and logistical gaps, ensuring no voice was lost in the noise. A specific hurdle I recall was a misunderstanding about terminology during early drafts; a term common in one region had a completely different meaning elsewhere, causing confusion in feedback. I felt the tension rising during a call, but we resolved it by hosting a dedicated discussion to clarify definitions and embedding a glossary in STREAMS. That solution not only fixed the issue but also deepened our mutual respect as a team.
Incorporating AI usage reporting in STREAMS is a progressive step. What led to this decision, and how do you see AI shaping the future of microbiome research?
Including AI usage reporting in STREAMS came from a realization that artificial intelligence is becoming a powerful tool in research, yet transparency around its application is often lacking. We decided to include it after discussions with journal editors and tech experts who highlighted that not all publications require such disclosures yet, and we wanted STREAMS to be ahead of the curve. The thought process was simple: if AI is used to analyze data or generate hypotheses, researchers should document it just as they would any other method. I can envision AI dramatically accelerating microbiome research—imagine algorithms sifting through massive datasets to predict microbial interactions in soil or water, something that would take humans years. I’ve felt a mix of awe and caution thinking about this during late-night brainstorming sessions; it’s thrilling, but we must ensure ethical guidelines keep pace with the tech.
Your background in biopharma and innovation is fascinating. How have your past experiences influenced your approach to leading initiatives like STREAMS?
My journey in biopharma and tech innovation has profoundly shaped how I approach projects like STREAMS. Having worked on R&D teams where data inconsistencies derailed promising drug development, I’ve developed a near-obsessive focus on standardization and clarity—something I brought to every discussion about STREAMS. My experience with cross-functional teams also taught me the value of diverse perspectives, which is why I pushed so hard for inclusivity in our workshop, especially for early-career voices. I remember a particularly tough project years ago where a lack of shared protocols cost us months; that memory still stings and fuels my drive to ensure tools like STREAMS prevent such setbacks. Leading this initiative, I’ve felt a renewed sense of purpose, knowing we’re building something that can streamline innovation across the field.
What is your forecast for the future of microbiome data standardization, and how do you see initiatives like STREAMS evolving over the next decade?
Looking ahead, I believe microbiome data standardization will become the backbone of transformative discoveries, not just in health but in agriculture, climate science, and beyond. I foresee a future where frameworks like STREAMS are seamlessly integrated into research pipelines, with machine-readable formats and AI-driven tools making data sharing instantaneous and intuitive across disciplines. Over the next decade, I think we’ll see STREAMS evolve into a dynamic, global platform—perhaps with real-time community updates and plugins for emerging tech. I can almost picture a world where a student in a remote lab uploads data using STREAMS and instantly connects with a collaborator halfway across the globe; that vision keeps me energized despite the challenges. I’m optimistic, but I also know it’ll take relentless collaboration to get there, and I’m eager to see how the community shapes this journey.
