Very cool and important paper on bat evolution and genomics. nature.com/articles/s41586-0…

1/ Why can you encounter an allergen repeatedly before becoming allergic to it? In our new @ImmunityCP paper, led by the wonderful @XuepingZhu, we asked whether the sensory neurons that first detect allergens might carry a record of earlier exposures. 🧵 cell.com/immunity/fulltext/S…

Today, the Toll-like Receptors reveal a new mechanism of signal transduction: Receptor-Free Signal Transduction. nature.com/articles/s41586-0…

ʙɪɴᴅᴄʀᴀꜰᴛ2 is out, and we're not waiting for the paper. The full code drops today, free for academic and industry use. We're releasing it early so you can start designing right now, and bring its full power to the current Adaptyv competition. github.com/PacesaLab/BindCra…

Among infants with a family history of allergy, a maternal diet high in eggs and peanuts during pregnancy and lactation did not result in a lower risk of egg or peanut allergy at 1 year than a standard diet. Full PrEggNut trial results: nej.md/4yE7qNg Editorial: Maternal Allergen Consumption and Infant Food Allergy — Reassurance, Not Prescription nej.md/46llW0e

Exciting!!! Dr. Erica Herzog is the new Chief of Pulmonary, Critical Care and Sleep Medicine at Yale!! Congratulations from all of us in @YalePCCSM & #CurePF4All community!! #AcademicBliss #BestInPulm #ThisIsYalePCCSM click.message.yale.edu/?qs=A…

This is a "detective story" paper that shows how setting up the right negative controls can lead to real discoveries. Basically, the authors wanted to reproduce a published result showing that a specific CRISPR tool, called Csm, could chop up RNA molecules and lower their levels in a cell. They measured this with RT-qPCR, a common experiment that quantifies how much of a given RNA is present before and after the CRISPR knockdown. When they went to reproduce this result, they found that Csm did reduce RNA levels. But a negative control, a mutated version of Csm that shouldn't be able to cut anything, also knocked down RNA by roughly the same amount. Weird! So the authors started varying the experimental parameters to solve this dilemma. First, they tried different RNA extraction methods and tested several more CRISPR tools. They confirmed, too, that the mutant Csm couldn't cut RNA. The RT-qPCR results persisted anyway. Second, they built an mCherry reporter to measure protein levels directly. Normal Csm reduced protein levels, which makes sense since it's cutting the RNA. Mutant Csm did not change protein levels at all. This was another good sign that the mutant Csm was producing some kind of artifact. To figure out why, we need to talk about RT-qPCR. This is a method that, first, converts RNA into DNA using reverse transcriptase, an enzyme that builds a matching DNA copy. qPCR then counts how many DNA copies got made from a short stretch of that RNA, called an amplicon. More copies of DNA = more RNA was present at the start. Normally, scientists place this amplicon so that it overlaps with the guide RNA's binding site, since that's the region expected to get cut. But with mutant Csm, an amplicon located at the guide RNA site showed false knockdowns. When the amplicon was moved to *before* the binding site, the artifact disappeared and RNA levels appeared unchanged. Why was this happening? The reason, oddly, was that the guide RNAs (without any protein!) were blocking reverse transcription, making it look like mutant Csm was reducing RNA levels. The researchers tested this by spiking synthetic guide RNA, made with no CRISPR protein at all, directly into the reverse transcription reaction. This reproduced the false knockdown. If a guide RNA can bind a target without a CRISPR protein at all, then we should also see similar gene silencing in cells. Right? Well, no; it turns out guide RNAs break down quickly in cells unless a CRISPR protein binds and stabilizes them. The proteins protect the guide RNA. So TL;DR: guide RNAs can bind a target and block reverse transcription in test tubes. This causes weird experimental artifacts and likely means dozens of CRISPR papers that used RT-qPCR are wrong, in either a minor or possibly major way.

We are on the cusp of a wave of new therapies for some of the worst diseases. But the world won’t benefit unless the US fixes its drug regulatory system. My new essay for @nytimes, on how slow clinical trials are now the biggest obstacle to curing cancer. nytimes.com/2026/09/04/opini… - I interviewed dozens of researchers, especially oncologists at leading U.S. centers. A striking consensus emerged: science is no longer the main bottleneck to new cancer drugs. It is our ability to test discoveries in patients through clinical trials. - The cost of starting a Phase 1 trial in America has roughly doubled over the past decade. As a result, companies increasingly take early trials abroad: Australia’s Phase 1 trial volume has nearly doubled in a decade, driven mainly by U.S. companies. - Unfortunately, the underlying incentives are badly asymmetric: Institutions can be blamed for harms caused by moving too fast, but almost no one is blamed when patients deteriorate during avoidable delays. One doctor called the emerging system “ritualized safety over actual risk assessment.” Or as, @DavidHongMD put it: "We often forget that the biggest risk is the cancer itself." - This problem is becoming more urgent because medicine itself is changing. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations. - @sytse, the co-founder of GitLab, shows what personalized medicine can achieve: after relapsed osteosarcoma and being told there were no options left, he pursued a highly individualized approach and has now been cancer-free for a year. But doing so required extraordinary resources and regulatory expertise. - Pierce Ogden’s father was less lucky. After molecular analysis identified a drug that might target his glioblastoma, the manufacturer agreed to provide it. But administrative barriers delayed access until it was too late. “My dad was ready to try anything,” Pierce told me. “But the system is paternalistic.” - The A.I. revolution is making this bottleneck more important, not less. A.I. relies on relevant data. Information from early-stage trials could compound with A.I. tools to achieve truly revolutionary medicines. Without the data, this is far less likely to happen. - Another important shift is that innovation increasingly comes from academic labs and small biotech companies rather than Big Pharma. These small companies find it far harder to unable to absorb delays and regulatory barriers. - Apart from cancer, China is the biggest winner from America's outdated medical regulations. China has a much faster trial system, with testing often starting a full year earlier. This allows Chinese pharmaceutical companies to experiment and improve medicines while American companies play with mice. Today, half of all drugs licensed by major pharmaceutical companies originate there, up from less than 5 percent only a decade ago. - But we don't need to copy China. The best model is Australia: lots of on-site scientific and ethics reviews, and requirements that are proportionate to small, early-stage trials. Phase 1 studies there begin roughly 6–12 months faster, without any notable increases in adverse safety events. - Operation TrialBlazer, a 2026 HHS initiative is a good start in this direction, but we need legislative action by Congress to truly make Phase I trials faster and more efficient! I want to thank everyone who helped me with this article: everyone I interviewed and the amazing editors at the Times. This is the result of a months long journey of extensive interviews and research. Special thanks go to those who came on the record. One of the features of the system is an atmosphere of fear, where practitioners are afraid to publicly come out and explain these issues. So anyone who does is a hero in my book!

Excited to share new work from my lab showing that early-life colonization with C. difficile reshapes the developing infant gut, published today in @ScienceMagazine! This study was co-led by two truly amazing scientists: @HaiderSManzer and @_alexafluor. science.org/doi/10.1126/scie…

Change the textbooks. Trans-splicing as a means to diversify the proteome. Many congrats to Ruaidhri Jackson and Olivia Venezia on this remarkable discovery. nature.com/articles/s41586-0…

Great work from Stef van der Krieken with @miranda_graaf trying to understand what drives norovirus epidemics. pubmed.ncbi.nlm.nih.gov/4262… Like other RNA viruses, NOV evolve, may escape antibodies and change the way they bind to host cells for successful maintenance. Fascinating!

With nice lay summary here: news.yale.edu/2026/08/19/new…

Today in @ScienceMagazine, we report the first functional AI-generated genomes. 🧵 science.org/doi/10.1126/scie…

A nice python package for creating Publication-Ready Scientific Plots github.com/faridrashidi/cnsp… if you use R, take a look at rpkgs.datanovia.com/ggpubr/

Lab account is official! go follow @AntigenEvo 👇👇👇 research.pasteur.fr/en/team/…

Report calls for focused support to stem the loss of physician-scientists via @VUMChealth news.vumc.org/2026/07/02/rep…

Congratulations to the 2026 CAMS award recipients! These exceptional physician-scientists are advancing groundbreaking research & shaping the future of medicine. Learn more about this year’s awardees and their work: buff.ly/8PGyqJh #bwfcams #BiomedicalResearch