Umber Dube

Physician-scientist @UCSDHealth Dermatology | Building the | Patient-inspired. Patient-informed.

[Umber Dube @UmberDube]

@anthonystaj You should test it on our live clinical trial outcome prediction challenge (https://t.co/BMRHPVndk5):
https://t.co/lQgSeIepzP

[Sarah Urbut @tigerstatdoc]

One of my first consults as a cardiology fellow: a 34-year-old, textbook MI. A day earlier, no risk model would have flagged him for prevention. That paradox has driven my work ever since — our models miss how disease actually evolves, dynamically, on top of a genetic background. @Nature https://t.co/PHDOjmGxE3

[Umber Dube @UmberDube]

Proud of our UCSD Laboratory for Emerging Intelligence team! Our clinical trial prediction benchmark—completely free of data leakage and contamination—has been accepted to COLM 2026.

Quick Intro to CT Open: https://t.co/lQgSeIepzP

More details in the thread👇

@InnovationUCSDH

[Youze "Hargen" Zheng @hgznnn___]

Thrilled to share 𝗖𝗧 𝗢𝗽𝗲𝗻, accepted to @COLM_conf 2026! 🎉🎉🎉

𝗖𝗮𝗻 𝗔𝗜 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗮𝗿𝗲 𝗽𝘂𝗯𝗹𝗶𝗰 -- 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗼𝗻𝗹𝗶𝗻𝗲?

We built a live, open-access, contamination-resistant benchmark + platform to find out.

🧵 1/9
📄 https://t.co/FHQmtxTM3L
🌐 https://t.co/W3ebZIrW3m

[Peter Koo @pkoo562]

Excited to share new work on interpreting and steering pathology foundation models!

A fun collaboration led by @ChanwooKim_ and @suinleelab, with support from our pathologist collaborators Zhen Zhao and Deepika Savant, and Jakub Kaczmarzyk.

Paper: https://t.co/QBxBLNCpk4

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[Matthew Taliaferro @JMTali]

New paper! How do RNAs "know" where to go inside a cell? We dug into the sequence elements that route RNAs to the right place. It turns out that, in mammals, they're surprisingly massive (>200 nt), multipartite, and wonderfully complicated. 🧵

[IdoAmitLab @IdoAmitLab]

For those of you who think macrophages do almost everything, rethink “almost.”
They are programmable cells, recruited into whatever new function evolution needs.
https://t.co/tz6VrVQWpw

[Peter Ly @PeterLyLab]

Excited to share our latest paper, out today @CellCellPress. We found that large pieces of the human genome can transfer between cells upon direct contact, endowing recipient cells with heritable phenotypic changes. (1/7)

https://t.co/SbshGhofN0

[Patrick Turley @patrickaturley]

Very excited about this preprint that we just posted! It introduces the Genomic-Relatedness Matched Association (GRMA) study. It’s an extension to family-based GWAS that uses extended relatives beyond only siblings in diverse-ancestry data with very little bias.

[Jonathan Sebat @sebatlab]

I'm pleased to share our latest preprint: Combinatorial effects of gene dosage, polygenic background and environment on complex traits 🧵https://t.co/KBpAJXm6nn

[Simon Myers @simon_r_myers]

New preprint led by Hrushikesh Loya, Leo Speidel, and I where we introduce GhostBuster! https://t.co/MgfPEVeHQJ
Our method uses genealogies to find "ghost" ancestries hidden within DNA. We find both modern humans, Neanderthals formed as mixtures of two ancient hominin groups

[Veera Rajagopal  @doctorveera]

A new paper in @Nature from David Reich, @aliakbari23 and colleagues breaks the conventional understanding of recent human evolution. The field believed that strong selection in the recent past (~10,000 years) was rare, with few exceptions like the lactase persistence locus. In this paper, the authors challenge that belief, showing that we weren't looking at the problem right.

Previous studies that looked for evidence of selection using ancient DNA addressed the problem cross-sectionally, asking if allele frequencies differed across populations more than what one would expect based on genetic drift and migration. Most arrived at the conclusion that population structure primarily explained the observed differences. Here, the authors addressed the problem longitudinally, accounting for when ancient individuals lived by explicitly modeling time as a variable in the analysis. It turns out doing it this way dramatically increases power, increasing the number of genome-wide significant selection signals by 20-fold!

Looking at why accounting for the time variable led to such dramatic changes in results, the authors find that previous studies missed so much because selection often happened not on new variants leading to dramatic sweeps (the conventional model: new variant -> selection -> increase in frequency) but on already existing variants driven by transient environmental pressures. Many of these variants underwent reversals, selected up when a pressure existed, then purged when it disappeared or the trade-off cost became dominant. A great example is the TYK2 variant, where an allele boosting immunity was selected for thousands of years because it protected against TB, then got purged as TB endemicity declined and the autoimmune cost took over.

The scale of what they found is striking: hundreds of loci showing strong selection in the past 10,000 years with a median selection coefficient of ~0.86%. This number is pretty big in evolutionary terms, meaning allele frequencies have been shifting by ~1% per generation in a consistent direction. Previous selection scans found a maximum of 20 loci, and this one finds hundreds. That isn't an incremental change. It fundamentally reframes our understanding of how common strong selection has been in recent human history.

Some of the most striking findings come from polygenic selection, where hundreds of small-effect alleles were pushed in the same direction simultaneously. Polygenic scores based on large-scale GWAS of today predict recent negative selection for traits like body fat, waist circumference and schizophrenia, and positive selection for others like cognitive traits. One important caveat is that GWAS phenotypes are measured in industrialized societies today, and how well they capture what was actually being selected in ancient environments is debatable.

For me personally, these findings have direct implications for drug discovery. When using human genetics to find drug targets, we often fixate on the benefit and risk profiles of variants visible today. But we need to be aware that a variant's benefit:harm ratio might be environmentally contingent, and could reverse when the wrong environment manifests. An evolutionary understanding of a variant's association with traits is therefore essential.

The same logic applies, perhaps even more urgently, to embryo selection. Selecting embryos based on polygenic traits is humans making permanent, heritable decisions for their offspring with a narrow view of today's environment. The ancient DNA record now shows that cost-benefit landscapes flip over time. So, an embryo carrying man-made selections is carrying those changes into an unpredictable future environment.

The broader takeaway is that human evolution didn't freeze in the last 10,000 years. We just lacked the tools and datasets to see its movement. The current findings are based on European populations. I am curious to see these analyses extended to other populations too, like South Asian, East Asian and African populations, which might be holding more surprises to blow our minds.

Akbari et al. Nature 2026
https://t.co/3WWjpTiVgA

[Veera Rajagopal  @doctorveera]

A new preprint reports that genetic loss of JAK1 increases susceptibility to HPV infection and subsequent non-melanoma skin cancer (NMSC) risk.

This human genetic evidence arrives at a critical moment. Selective JAK1 inhibitors like upadacitinib and abrocitinib are a major drug class in modern medicine, approved for atopic dermatitis, rheumatoid arthritis, psoriatic arthritis, ulcerative colitis, and Crohn's disease. Millions of patients, many of them young, take these drugs for years or decades. The new genetic data hints HPV susceptibility and NMSC risk as potential long-term adverse effects of these drugs.

The story gets more interesting when you look at how JAK inhibitors evolved. Early versions like tofacitinib were non-selective; they are effective, but caused hematological toxicities (from JAK2 inhibition), and lymphocyte depletion (from JAK3 inhibition). Researchers eventually recognized a useful dissociation: the therapeutic benefit in autoimmune disease flows primarily through JAK1-dependent pathways (IL-6, IL-4, IL-13 signaling), while the most visible toxicities came from the other isoforms. That insight drove the field toward JAK1-selective agents, which are now the dominant class.

The logic was sound for the toxicities the field could measure. What nobody systematically evaluated was JAK1's specific role in antiviral skin immunity. JAK1 GoF causing autoimmunity was well characterized, but JAK1 LoF in humans wasn't, except for one patient with biallelic mutations and broad immunodeficiency. Interestingly this patient also presented with warts, which didn't stand out at that time. There were logical reasons to worry about interferon signaling and viral susceptibility in JAK1 inhibition, but no human genetic evidence was there to back that concern.

Fan et al. (medRxiv, April 2026) now fill that gap. Across four independent pedigrees with epidermodysplasia verruciformis (EV), a Mendelian disorder linking HPV to skin cancer, they identify heterozygous JAK1 loss-of-function variants as causative, establishing that JAK1 haploinsufficiency permits persistent β-HPV infection and NMSC development.

Scattered case reports of warts and NMSC in patients on JAK1 inhibitors exist in the literature, and NMSC signals seem to exist in trial data, though without HPV genotyping info. These signals weren't strong enough to motivate anyone to look harder. Fan et al. data now will motivate drug developers to retrospectively analyze their trial data to quantify this important adverse outcome.

This is another beautiful example of rare disease patients informing on possible adverse effects of drugs used for common diseases.

Fan et al. medRxiv 2026
https://t.co/g4KxfxTclg

[Veera Rajagopal  @doctorveera]

Happy to see our work on repeat expansions led by my colleagues--Sahar Gelfman & Vijay Kumar--at RGC published in @Nature.

Below is a thread highlighting what we learned from systematically studying repeat mutations at population-scale in ~1 million individuals

[Adam Auton @adamauton]

Delighted to share our latest research from the @23andMeResearch Team, just published in @Nature
! We looked at data from >27,000 participants to uncover how human genetics influences weight loss efficacy and side effects of GLP-1 medications like semaglutide. A thread 🧵👇

[Kotaro Fujii @KotaroFujii1]

Protein synthesis is not equally accurate across organs.

Excited to share our new preprint:
https://t.co/SKoTVsPspZ

We developed a new mouse model to quantitatively monitor translation errors and uncovered the spatiotemporal dynamics of the “quality” of protein synthesis.

[Zuzana Kadlecova @ZuzanaKadlecov6]

🧵How do cells coordinate what goes out with what comes in? We built Shape2Fate - a deep learning pipeline that tracks individual exocytic & endocytic events at ~100nm in live cells. What we found surprised us. 📄bioRxiv preprint: https://t.co/H6LPFegV4W

[Mo Lotfollahi @mo_lotfollahi]

Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery.

Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology?

To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease.

Led by Lloyd Steele, an MD/PhD student working between @HaniffaLab and my lab at @sangerinstitute and @Cambridge_Uni . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of @humancellatlas.

A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale.

We used:
• scArches to build and map into a reference scRNA-seq atlas of human skin: https://t.co/c5TgcG7PU4
• NicheCompass to identify and characterize spatial niches: https://t.co/c5TgcG7PU4
• MINT-Flow to extract microenvironment-induced cell states and gene programs: https://t.co/sfE47AnF3c
Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding.
At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E.

Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence.

Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale.

More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them.

This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane.

Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned.

The data and pre-trained models will be released soon.

Preprint: https://t.co/LeWxOKkgMt

[Danny Saban @SabanLab]

Resident tissue #macrophages (RTM) are now recognized as integral regulators of tissue physiology, not transient immune sentinels. But one fundamental question remains unresolved: why do some populations achieve lifelong self-maintenance, while others are progressively replaced under steady-state conditions? 🧵

[Andrew Ji @andrewlji]

Thrilled to share our new @NatureGenet paper! We mapped human skin at single-cell spatial resolution and found that anatomy is encoded in cell states and neighborhoods, led by PhD student @paularstrpo. Link: https://t.co/mBTaLUix3A
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[Aaron Ring @aaronmring]

How specific are therapeutic monoclonal antibodies, really?

In our new paper, @Yile_Dai led a collaboration with Adimab to profile 174 FDA-approved and clinical-stage mAbs against 6,172 human extracellular proteins.

What we found surprised us.🧵

https://t.co/ONTSF60B2g