Mendelspod Podcast

What is the value of someone’s genome over their life? Is a genome today what it was 10 years ago? How does the adoption of genomic testing compare to other areas in medicine, such as imaging or electronic health records?Today we take a pretty comprehensive look at genomic testing in practice with Damon Hostin, Head of Market Access, Clinical Solutions at Illumina. Damon brings a rare perspective to this conversation. He’s been in the field since the Celera era, when sequencing was helping define modern genomics, and he’s also worked on the front lines in a large community health system, CommonSpirit Health. At Illumina, he speaks regularly with payers and other stakeholders.Across oncology, rare disease, reproductive health, and pharmacogenomics, Damon describes a field that has clearly moved into standard of care in key areas—but is still very much in the phase of identifying the “eligible but under-tested.” Adoption is real, but it’s incomplete.Chapters:0:00 Genomic medicine arrives4:51 Genomics, imaging, and the EMR11:23 Oncology—from diagnostics to decision-making18:16 Rare disease and reproductive genetics28:51 The lifetime value of a genome36:03 Cost, quality, and what a genome isA central idea running through the podcast is that the genome is no longer a one-time diagnostic. Its value compounds over time as databases grow, variants are reinterpreted, and new therapies emerge. At the same time, even the basic notion of what a “genome” is, is beginning to shift. With the rise of multi-omic data—transcriptomics, proteomics, methylation—the question is no longer just cost per genome, but what kind of biological insight we’re actually measuring. “A genome isn’t a genome isn’t a genome,” Damon says.He ends with a line that neatly reframes the entire debate around cost: “When you look at the cost of healthcare . . . the cost of the genomics is almost nothing.”Genomic medicine is here. We’re now wrestling with how to scale it, how to use it earlier, and how to make it part of the everyday infrastructure of care. Note: For more discussion and analysis on this topic, check out this upcoming Virtual Roundtable Discussion at GenomeWeb. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Peptides are having a moment. But beneath the market excitement and the GLP-1 headlines, something more interesting is going on. A field that for years seemed technically promising but perpetually constrained is becoming wide open.To see into that open terrain, we’re joined by Charlie Johannes, founder of EPOC Scientific and president of the Peptide Drug Hunting Consortium, along with Tomi Sawyer, a founder of the Consortium and founder of Maestro Therapeutics. We asked them for a high-level look at a field being reshaped by advances in chemistry, screening, delivery, and by a growing sense that peptides may be uniquely positioned to open up biology that other modalities have only partly been able to reach.And yet both are clear: the field is not mature. AI is accelerating biology, which still depends on existing knowledge. Prediction remains limited, especially with non-natural chemistry. And the core challenge may now be human—how to turn an overwhelming amount of data into real innovation. As Johannes puts it, “Turning knowledge into innovation is the real challenge.”Chapters:1:31 Why peptides are suddenly hot again6:10 Between small molecules and biologics10:14 Oral delivery, screening15:45 AI, automation, and the limits of prediction32:17 The Consortium and where the field is headingThis is not a finished revolution—it’s a launch. The field, Sawyer says, is “in the Artemis II rocket right now heading towards the moon.” The peptide story is now much bigger than obesity drugs. Where does the field stand today? What has changed, and what remains difficult? This episode is the first in a new partnership between Mendelspod and Peptide and Protein News, a media platform covering peptide and protein drug development. You can see what they’re up to at peptideandprotein.com. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Why do some animals live ten times longer than others?That question opens today’s interview with Steve Austad, Distinguished Professor at the University of Alabama at Birmingham and one of the leading thinkers in the biology of aging. It quickly becomes clear why he’s been such an important voice in bringing aging research from the margins into the center of science. As he puts it, the field was once “where scientists went to die,” but with modern genetic and molecular tools, it has become one of the most active areas in biomedicine.Steve’s approach, laid out in his book for the empiricist (I’m an amateur), Methuselah’s Zoo, is deceptively simple: look at the animals. From birds and bats to clams that live for centuries, he shows that lifespan follows a clear evolutionary logic. Safer, more stable environments favor slower aging. “If it’s unstable and unsafe… it makes sense… to reproduce fast,” he explains, while protected environments allow organisms to invest in long-term maintenance. It’s a framework that turns curiosity into theory—and theory into something testable.Chapters:1:31 Where scientists went to die4:11 The opossum problem8:00 Air, land, sea14:23 The longevity quotient33:30 Not forever, just longerWhat makes Steve such a compelling guide is his tone. He’s low-key, almost amused at times, but unwavering on the science. Aging, he reminds us, isn’t programmed for our benefit—“evolution does not care how long you live.” That doesn’t mean we can’t intervene. The field is now moving into human trials, even if key tools like aging clocks are still imperfect. He has little patience for talk of immortality—calling it “completely delusional.” Still, he’s optimistic. Adding a decade or two of healthy life—not forever—is the goal today. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

This is a free preview of a paid episode. To hear more, visit www.mendelspod.comFor two decades, tests like Oncotype DX have helped oncologists decide which early-stage breast cancer patients should receive chemotherapy. But those tools were designed mainly to predict early recurrence, leaving physicians with far less clarity about the risk that cancer might return years later.For today’s program, George Sledge, Chief Medical Officer at Caris Life Sciences, discusses new findings from the TAILORx trial showing how multimodal AI—combining molecular sequencing, digital pathology, and clinical data—can improve long-term prediction of breast cancer recurrence.Sledge explains that breast cancer recurrence may actually reflect two different biological processes unfolding over time. Molecular signals captured through RNA analysis appear most informative for predicting recurrence in the first five years, while computational analysis of digital pathology images becomes especially powerful for predicting recurrence later in the disease course.“The best results come from looking at multiple omic levels,” Sledge says, describing a shift away from single biomarker tests toward integrated biological analysis.

“We have been talking now for 15, 20 years about the diagnostic odyssey. That shouldn’t exist anymore. The new odyssey is the therapeutic odyssey.”That’s Stephen Kingsmore, president and CEO of Rady Children’s Hospital (he just announced his retirement), explaining the impact of a new genome mapping technology from Illumina.Whole-genome sequencing has transformed diagnosis, but some of the hardest pediatric cases persist because the structure of the genome has remained difficult to resolve. Today on Mendelspod, we cover Illumina’s newly launched proximity mapped reads, showing how long-range genomic context can be captured directly on existing Illumina sequencers and integrated into the lab workflow. The conversation traces how this added structural clarity is already improving diagnostic confidence and, critically, enabling highly precise n-of-1 therapies such as antisense oligonucleotides (ASOs).Olivia Kim-MacManus, a pediatric neurologist and ASO trial leader, shows how the new diagnostic precision directly feeds therapeutic design. “All of these genetic therapy approaches hinge on precise diagnostics,” she notes, emphasizing that allele-specific and haplotype-aware targeting is essential for ASOs and other emerging gene-based interventions.From the product and workflow side, Ali Crawford joins us as Senior Director of Science Research at Illumina, detailing how the technology works without requiring new instruments or complex workflows, eliminating the need for separate library preparation steps.“You just order the kit and go,” she says, highlighting how preserving spatial information on the flow cell unlocks variant calls and structural insight that were previously inaccessible with their standard short-read sequencing.When genome structure comes into better focus, treatments are no longer theoretical. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Large-scale genomics is back — and this time, it’s global by design.In this episode of Mendelspod, we return to the kind of ambitious, shared genomics project that helped define the field a decade ago. The Global Parkinson’s Genetics Program (GP2) has now genotyped more than 100,000 participants worldwide, with roughly one third of samples coming from historically underrepresented populations. That scale and diversity are already reshaping how Parkinson’s disease is studied — and how it may eventually be treated.My guests are Andrew Singleton, co-lead of GP2, and Ignacio (Nacho) Mata, a geneticist at Cleveland Clinic and founder of the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD). Together, they describe how globally representative datasets are not a political aspiration, but a scientific necessity — especially in an era of precision medicine.Singleton explains that studying Parkinson’s across populations doesn’t just broaden participation; it increases scientific power. “The more we learn about individual populations, the more we understand about disease as a whole — and the more chances we have to come up with treatments for disease as a whole,” he says. Mata brings a complementary perspective from years of building Parkinson’s genetics infrastructure in Latin America. He emphasizes that without inclusion in genetic and biomarker research, entire populations risk being excluded from the next generation of molecularly targeted therapies. “If we don’t have our patients studied for genetics or biomarkers, then those patients will not have access to the new treatments,” he notes, adding that GP2 is designed to narrow rather than widen existing health disparities.We explores how GP2’s open-science structure has been key to its success and could serve as a model for other global research projects. GP2 has invested heavily in training and infrastructure so that researchers around the world can lead analyses locally, rather than simply contributing samples.As both guests make clear, this is only the beginning. With hundreds of thousands of samples committed and a new generation of globally distributed investigators, GP2 is laying the groundwork for biologically defined subtypes of Parkinson’s and for more precise diagnostics and disease-modifying therapies.When genomics gets big enough — and inclusive enough — scale itself becomes a discovery. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

For today’s show, we return to discussing the exciting new Cellanome platform. Joining Theral are Pier Federico Gherardini, VP of Computational Biology at Cellanome, and Matthew Spitzer, Associate Professor at University of California, San Francisco, whose lab is using Cellanome’s CellCage technology to study immune cells in dynamic, interactive contexts.0:00 From static snapshots to observing cell function in real time4:45 Pairing phenotype with function like we never could before7:30 Can see cell-cell interaction19:40 Early applicationsRather than relying on static single-cell snapshots, the Cellanome platform enables longitudinal observation of live cells—tracking division, interaction, and function over time—before pairing those behaviors with transcriptomic and molecular readouts. As Gherardini explains, “This creates essentially a new data type where you observe cells over time… and then you can pair all of that functional information with the molecular readouts that you get from sequencing.”For Spitzer, that shift fundamentally changes what can be known. Traditional approaches often force scientists to infer function indirectly, correlating phenotype measured in one experiment with behavior measured in another. With CellCage, his lab can finally measure both in the same individual cell. “Now we have measured the function of the cell and the phenotype for the same exact individual cell,” Spitzer says, “and this allows us to really understand how those core characteristics are linked in a much more detailed way.”For Spitzer, a major advance comes from observing cell–cell interactions as they unfold. Where previous methods could show proximity in a tissue section, they could not reveal outcomes. Using Cellanome, Spitzer’s team can now watch whether a T cell activated by a dendritic cell actually proliferates, produces effector molecules, or kills a tumor cell—and then trace those outcomes back to specific molecular programs. This has already revealed surprising heterogeneity within supposedly uniform cell populations, identifying rare but highly potent immune cells that would have been invisible in bulk assays.Looking ahead, both guests see immediate applications in cell therapy development, target discovery, and functional CRISPR screening—areas where measuring what cells actually do matters more than what they merely express. We close with a sense that cell biology is entering a new phase—one where function, interaction, and time are no longer inferred, but directly observed, measured, and modeled. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Aging may be the last great frontier of precision medicine—not a single disease, but the slow re-patterning of immunity, metabolism, and resilience that determines how well and how long we live.In this wide-ranging and genuinely mind-bending conversation, Alan Landay and Tom Blackwell make a compelling case that aging itself is finally becoming a legitimate—and testable—target of medicine.For Landay, the path into aging biology began decades ago through HIV. Antiretroviral therapy transformed HIV from a fatal disease into a chronic one—but something didn’t add up. Patients were surviving, yet developing cardiovascular disease, neurocognitive decline, and metabolic disorders years earlier than expected. The immune system recovered on paper, but inflammation never fully resolved. That realization led Landay to view HIV as a model of accelerated aging, and to ask whether the same inflammatory processes drive aging in the broader population. As he explains, “we realized that persistent inflammation was the driver—pushing comorbidities forward in time. That’s when HIV stopped being just an infectious disease and became a window into aging itself.”Over the past decade, Landay has brought the full toolkit of systems biology to that question—proteomics, metabolomics, glycomics, microbiome analysis, and epigenetic clocks—to understand why some bodies grow frail while others remain resilient. A central theme is the gut: age-related changes in the microbiome weaken the intestinal barrier, allowing inflammatory signals to leak into circulation and quietly accelerate biological aging.Blackwell approaches the same problem from the clinic. As a geriatrician, he sees that most people ultimately die from one of three conditions—heart disease, cancer, or dementia—and that aging is the common denominator behind them all. His bold question is not whether we can treat these diseases individually, but whether we can slow the biological aging process that gives rise to them. That question underpins his ongoing clinical trial testing tirzepatide, a GLP-1–based therapy, not for weight loss, but for its potential to slow aging itself. ““There is no drug in the world proven to slow aging,” Blackwell says. “We haven’t proven this one either—but we’re finally running the experiment that can give us a real answer.”At the heart of the discussion is a shared fascination—and healthy skepticism—around aging clocks and biomarkers. Both researchers are using advanced epigenetic and proteomic clocks, including the DunedinPACE measure, to track whether interventions truly change the rate at which people age biologically. The clocks are powerful, but not yet definitive. The episode also explores how geroscience has moved from the fringe to the mainstream: NIH-wide initiatives, ARPA-H funding, repurposed drugs, and growing FDA openness to aging as a trial framework. Rather than chasing immortality, both guests emphasize healthspan—more years of mobility, cognition, and social engagement. “Our vision isn’t to live longer in a nursing home. It’s having a lot more 98-year-olds who drive themselves to clinic, go on dates, and still love their lives,” says Blackwell. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Perhaps more than in any other field, AI is impacting drug discovery and development. To begin the year we’re joined by two AI software-as-service companies, one on the target discovery side and the other built for new compound identification for those targets.Theral speaks with Aqib Hasnain, Product Lead at Mithrl, and Cheng Hu, co-founder and CEO of Technetium Therapeutics, about how scientists can go from AI generated insights to AI generated assets, from AI-driven fast science, to AI-driven fast drug discovery.Aqib describes Mithrl as a virtual lab partner focused on shrinking the time between experiments by letting scientists interrogate their own data directly. One of the biggest lessons in building Mithrl, he says, was how much transparency matters. Biologists need to understand the methodology through and through, and this translates directly to how Mithrl works.“Scientists need to be able to scrutinize and trace everything—because it’s their responsibility to make the next decision.”Cheng explains Technetium’s vision of an “AI-driven hatchery of novel medicines,” using design-based, physics-guided approaches to move from target discovery to small-molecule hits in weeks rather than years as has been the case screening libraries of millions of compounds. Reflecting on the promise of AI co-scientists, he points to the industry’s biggest unmet need. “There’s a very serious deficit of novel therapeutic targets and also a very serious deficit of novel chemicals.”Together, the conversation explores how these two AI tools for target discovery and hit generation are beginning to reshape drug discovery workflows—and how a new ecosystem of services is developing that is redefining the field. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

As sequencing continues to become cheaper, more attention is being paid to sample prep. Today we’re following up with the company, Volta Labs, a genomics applications company transforming sample prep for NGS by increasing robustness and precision, and lowering operating costs. CEO Udayan Umapathi reflects on what has been a breakout first commercial year for Callisto, the company’s sequencer-agnostic, digital-fluidics platform for sample prep. When he was last on the show, Callisto had just launched. One year later, it is deployed across North America, Europe, and Asia, with rapid uptake in clinical labs, pediatric oncology centers, and high-throughput sequencing sites.Udayan says the scale of adoption surprised even the team. “We said we wanted to be the front end of every sequencing technology. We’ve actually done that,” he notes, adding that more than ten applications now support short- and long-read sequencing.What’s driving the momentum? Three things keep coming up from customers: true walk-away automation, the ability to run any chemistry on any sequencer, and major improvements in quality and cost. Labs without automation engineers can now “simply buy a kit and run software…without having to learn sample prep,” Udayan explains.A standout story this year has been pediatric oncology, where whole-genome sequencing and hybrid-capture workflows have shown strong performance on Callisto. Customers such as Prinses Máxima Center and UMC Utrecht are using the platform across Illumina, Oxford Nanopore, Ultima, and other chemistries, achieving the sequencer-agnostic vision Volta set out from the start.Looking ahead, Udayan sees sequencing as still early in its evolution and believes sample prep has vast room for innovation. “One platform to do Illumina, one platform to do Oxford Nanopore, one platform to do Ultima… long read, short read—we do it all,” he says. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

At the end of each year we look for a guest who in many ways defines the year. Today we sit down former NHGRI director Eric Green to reflect on the most turbulent year in his 31-year career at NIH. After leading the National Human Genome Research Institute for more than 15 years, Green’s appointment was abruptly non-renewed—a decision he learned about with “two or three days notice that I was going to have to retire from federal service.” What followed, he says, was a wave of terminations and forced retirements across NIH that left NHGRI “in trauma” as entire communications, education, and policy groups disappeared overnight.Yet alongside this institutional upheaval, Green describes a scientific landscape moving at astonishing speed—from the maturation of genome editing and long-read sequencing to the rise of multi-omics and the accelerating push toward routine healthy newborn genome sequencing. He believes widespread newborn sequencing is no longer a distant vision but “within striking distance,” driven by global studies, new U.S. programs, and rapidly falling costs.The conversation also explores the political pressures shaping genomics today, especially around the collection of heterogeneous genomic data and the cultivation of a diverse workforce. Green argues that scientists must learn to explain their work in human terms—as stories about patients and cures, not grants and budgets. He says it might also be a good idea to not use the “d” word (for example, “assortment” rather than “diversity”) in grants for now, silly as that is.Despite the personal and institutional losses of the past year, Green remains committed to the future of U.S. biomedical science which continues to surge in the headlines each day. In a reference to Dickens, he says it is literally the best and worst of times.Now entering what he calls “version 3.0,” Green sees his role as genomics evangelist, educator, and advocate—helping ensure that the momentum of genomic medicine continues even as the nation’s scientific infrastructure undergoes profound stress.“I am officially on call to help rebuild the NIH… It’s very easy to destroy a place, and very hard to rebuild it.” This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

This week on Mendelspod, we speak with Petter Brodin, Professor of Pediatric Immunology at the Karolinska Institutet and Director of Systems Immunology at Imperial College London, about his pioneering work in childhood immune development and his new spatial-proteomics investigations into lupus.Petter shares how a single lecture on natural killer cells pulled him into immunology, and how early twin studies convinced him that “our immune systems are shaped predominantly by non-heritable factors.” That insight drove him to study the earliest stages of immune development—when newborns leave a sterile environment for a microbial world that imprints their immune trajectories for life.A major theme of the conversation is Petter’s insistence that immune responses cannot be understood by looking at cells one by one. As he puts it: “Cells don’t ever work in isolation, but historically we’ve always been studying them in isolation—and I think that’s fundamentally problematic.”This systems view is now being partly enabled by Pixelgen’s spatial interactomics. Using their Proximity Network Assay, Petter’s group is finding that lupus B cells don’t just differ in protein expression—they differ in protein distribution, revealing organization patterns that classical flow cytometry cannot capture.These spatial signatures may point directly to new, more precise therapies. Petter explains: “If there is a difference in protein–protein interaction or protein distribution that characterizes disease, then surely that indicates a dysregulation—and that is something we can target.” Instead of broad immunosuppression or depleting whole cell populations, future treatments could focus on the exact cell states driving autoimmunity.Petter ends on an optimistic note: spatial interactomics won’t just help treat autoimmune disease—it may allow us to intervene earlier, even preventatively, as we learn how early-life immune disturbances set the stage for disease decades later. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe