Chinese scientists find genetic switch for organ regeneration in mammals

Breakthrough in regenerative medicine from Chinese researchers at NIBS and BGI Research. Publication in Science showed how to restore healing ability in mammals.

The key is the Aldh1a2 gene, responsible for vitamin A metabolism. In fish and salamanders, it is active, but in mammals, it is blocked by evolution.

Scientists "switched on" this gene in mice and rats, leading to complete regeneration of soft and joint tissues. The mechanism works through the production of retinoic acid.

This discovery could simplify the treatment of bone, limb, skin, nerve, and lung injuries in humans.

#regeneration #Aldh1a2 #biotechnology #China #Science

🧬 Harvard Built a Silicon Chip That Can Write DNA Using Electricity and Water

Writing custom DNA has barely changed in decades. Today’s DNA synthesis relies on phosphoramidite chemistry—a highly effective but solvent-intensive process that depends on hazardous chemicals and specialized manufacturing facilities.

Researchers at Harvard have now demonstrated a completely different approach: parallel enzymatic DNA synthesis on a semiconductor chip, controlled simply by electricity in water.

The CMOS chip contains 64 programmable synthesis sites, each surrounded by two concentric ring electrodes. When current flows through the inner ring, it generates protons that create a tiny acidic region—exactly what’s needed to remove the temporary blocking group from a growing DNA strand. At the same time, the outer ring consumes escaping protons, preventing neighboring reactions from interfering with one another.

Using this approach, the team synthesized 64 different DNA sequences, each 38–39 nucleotides long, entirely in an aqueous enzymatic process. Previous demonstrations of parallel enzymatic DNA synthesis were limited to roughly a dozen sequences, making this the largest demonstration of its kind so far.

The hardware has an unusual history. It was originally developed for recording electrical activity from thousands of neurons. The researchers later realized that the same ability to precisely control microscopic electrical currents could also be used to control chemical reactions needed for DNA synthesis.

Key points:

• 64 unique DNA sequences synthesized in parallel
• Water-based enzymatic process instead of traditional solvent-heavy chemistry
• Precise local pH control using dual concentric electrodes
• DNA strands up to 39 nucleotides long
• Demonstrated storage of a 169-byte text message in the synthesized DNA

The technology is still at an early stage. DNA strands of 39 nucleotides are far shorter than real genes, which typically contain thousands of bases. According to the researchers, the main limitation is now the chemistry used to remove temporary protecting groups—not the chip itself—suggesting that future advances may come from improved chemistry rather than new electronics.

If the method can be scaled, it could eventually make DNA manufacturing cleaner, cheaper, and more accessible for synthetic biology, diagnostics, gene therapies, and even DNA-based data storage, one of the highest-density storage media ever proposed.

📄 Nature Electronics: https://www.nature.com/articles/s41928-026-01662-9
🏛 Harvard SEAS: https://seas.harvard.edu/news/making-dna-semiconductor-chip

#DNA #SyntheticBiology #Biotechnology #Semiconductors #DataStorage

🧬 A Personalized mRNA Cancer Vaccine Has Passed Its Biggest Test Yet

For the first time, a personalized mRNA cancer therapy has succeeded in a Phase 3 clinical trial — the final major testing stage before regulators can consider approval.

The treatment, called intismeran autogene, is not an off-the-shelf vaccine. Doctors sequence each patient’s tumour, identify mutations unique to their cancer, and manufacture an individual mRNA therapy encoding up to 34 of those tumour-specific targets. The goal is essentially to give the immune system a personalized wanted poster for the cancer.

In the trial, 1,137 people with stage IIB–IV melanoma whose tumours had been completely removed received either the personalized mRNA therapy plus the immunotherapy drug pembrolizumab (Keytruda), or pembrolizumab alone. The combination produced statistically significant and clinically meaningful improvements in both recurrence-free survival and distant-metastasis-free survival. No new safety signal was reported.

There is an important caveat: the companies have so far announced only top-line Phase 3 results. They have not yet released the detailed numbers, and it is not yet known whether the treatment ultimately extends overall survival. The therapy also remains investigational and has not been approved.

But the principle is now much harder to dismiss. Instead of finding one cancer vaccine that works for everyone, medicine may be able to manufacture a different vaccine for each individual tumour.

Cancer is personal.

The vaccine might have to be too.

#Cancer #mRNA #Melanoma #Immunotherapy #Biotechnology #Medicine #Science

🧬 Scientists Just Found a Hidden Layer of the Mammalian Genome

Our genes may be considerably more combinatorial than we realized.

Researchers have discovered that mammalian cells can take pieces of RNA copied from completely different genes — sometimes located on different chromosomes — and splice them together into a single messenger RNA. That hybrid RNA can then be used to manufacture a new protein containing parts encoded by both genes.

Using direct RNA sequencing, the team detected more than 30,000 candidate chimeric RNAs in mouse immune cells and more than 900 in human macrophages. Crucially, they then showed that at least one of these strange hybrids is not biological noise.

In mice, an inflammatory response causes two genes called Gsdmd and Tmem106a to combine into a chimeric RNA. Its resulting hybrid protein helps accelerate pyroptosis — the dramatic process in which infected immune cells rupture and release inflammatory signals. When researchers specifically blocked the hybrid protein, the mice became worse at controlling Salmonella infection. But they were also dramatically more resistant to lethal inflammation in a sepsis model.

Even stranger, inflammation appears to physically reorganize the genome: normally distant chromosome regions move closer together inside the nucleus, helping their RNA transcripts combine.

There is a major caveat. The detailed functional experiments were performed in mice. Similar chimeric RNAs exist in human immune cells, but scientists have not yet established what most of them do. The researchers themselves say it could take years to map their biological importance.

Still, if the phenomenon is widespread, our current catalogue of proteins may be missing an entire class of molecules hiding in combinations of genes we already thought we understood.

We finished sequencing the human genome more than two decades ago.

Apparently, reading the instruction manual was another problem entirely.

#Genetics #Biology #RNA #Genome #Immunology #Biotechnology #Science

https://www.nature.com/articles/s41586-026-10982-x

🧬 Life Uses 4 DNA Letters. Scientists Just Made 8 Work.

Every known organism on Earth writes its genetic instructions using the same four DNA letters: A, T, C and G.

Scientists have now shown that one of biology’s most fundamental molecular machines can read an alphabet containing eight.

Researchers tested E. coli RNA polymerase — the enzyme that reads DNA and turns its information into RNA — with synthetic DNA containing four additional chemical letters known as P, Z, B and S. Remarkably, the enzyme successfully recognized and transcribed the artificial base pairs using much of the same molecular machinery it employs for natural DNA.

Using cryo-electron microscopy at resolutions down to about 2.4 ångströms, the team could watch how the synthetic letters fit inside the polymerase. The artificial pairs adopted almost the same geometry as ordinary Watson–Crick DNA pairs, allowing the enzyme’s catalytic machinery to close around them and continue transcription. Researchers also engineered a modified version of one synthetic letter to reduce copying errors.

The implications are potentially enormous. A larger genetic alphabet could eventually produce RNA molecules with chemical capabilities unavailable to natural biology and might help scientists design new diagnostics, drugs and engineered biological systems. Expanded genetic alphabets have already been used experimentally to create molecules that recognize cancer cells.

But there is an important boundary: scientists have not created an eight-letter living organism here. The experiment demonstrates transcription by bacterial RNA polymerase; reliably replicating a full eight-letter genome and translating that expanded information into proteins inside living cells remain much harder problems.

For four billion years, life on Earth has been writing with four letters.

Apparently, biology’s machinery can read a bigger alphabet than evolution ever gave it.

#Genetics #DNA #SyntheticBiology #Biotechnology #RNA #Science

Primary paper — Nature Communications⁠

🧬 Google DeepMind Just Precomputed 9 Billion Possible Human DNA Mutations

This may be one of DeepMind’s most ambitious biology releases since AlphaFold.

AlphaGenome Atlas contains AI predictions for the molecular effects of essentially every possible single-letter substitution in the human genome — around 9 billion variants.

The resulting dataset is about 1 petabyte, more than 30 times larger than the AlphaFold Database.

Why does this matter?

Only around 2% of our genome directly encodes proteins. Much of the remaining 98% regulates when, where and how strongly genes are switched on — and contains huge numbers of variants associated with human traits and disease.

AlphaGenome predicts how mutations may alter processes including gene expression, RNA splicing, chromatin accessibility and regulatory activity. DeepMind then combines these predictions with AlphaMissense into a single AlphaGenome Variant Impact — AVI — score, allowing researchers to rapidly rank variants across both coding and non-coding DNA.

In an analysis of whole-genome data from more than 54,000 UK Biobank participants, the approach uncovered 22% more associations involving rare non-coding variants that had previously been buried in statistical noise.

And there is another important shift happening alongside it.

DeepMind has released Science Skills — an open collection of agent tools connecting AI workflows to resources including AlphaGenome, AlphaFold DB, UniProt, ClinVar and dozens of other scientific databases.

This does not turn an AI agent into a doctor or make consumer DNA tests clinically diagnostic.

But it does move genomics toward something fundamentally new:

A human genome is becoming a dataset an AI agent can systematically interrogate, prioritize and explain.

We sequenced the human genome 25 years ago.

Now we are starting to make it searchable.

#AlphaGenome #DeepMind #Genetics #AI #Bioinformatics #Biotechnology #Science

Atlas:
https://alphagenome.google/atlas

🧠 Scientists Replaced Most of a Mouse’s Cortex With Human Brain Tissue

This sounds like science fiction, but the experiment is real.

Stanford researchers genetically engineered mice so that most of their cerebral cortex and hippocampus never developed. The adult animals were left with only about 2% of the normal amount of corresponding cortical tissue.

Then, shortly after birth, scientists implanted tiny human cortical organoids — brain-like structures grown from human stem cells — into the empty space.

Three months later, more than 90% of the cortical tissue by volume was human-derived.

And it did not simply sit there.

Human neurons became integrated into the mouse nervous system, developed organized electrical activity and sent long-range projections — some extending as far as the spinal cord. The mice retained broadly normal movement, although researchers found specific differences in coordination and spontaneous behavior.

Then came an unexpected discovery.

Inside the transplanted human tissue, researchers found cells resembling von Economo neurons — extremely rare, large neurons associated with brain regions involved in social awareness and decision-making. They occur in humans, great apes, elephants, dolphins and whales, but scientists had never previously succeeded in generating them in laboratory brain cultures.

The team also demonstrated why the model could matter medically. When the animals experienced several hours of reduced oxygen, the human cortical tissue was severely damaged while comparable mouse tissue was largely spared — potentially giving researchers a living model for studying why the developing human brain is particularly vulnerable to oxygen deprivation.

An important distinction: these are not mice with human intelligence or a human brain. The transplanted tissue remained developmentally immature, and the experiment provides no evidence of human-like cognition or consciousness. It is a new animal model for studying human neural development and disease.

But the boundary scientists have crossed is remarkable.

We can now grow substantial amounts of developing human neural tissue not just in a dish —

but inside a living brain, connected to a living nervous system.

Where should the ethical boundary for experiments like this be?

#Neuroscience #Brain #Organoids #StemCells #Biotechnology #Stanford #Science

https://www.nature.com/articles/s41586-026-11032-2

🧬 An AI Just Made a Biological Discovery

Not summarized a paper.
Not predicted a protein structure.

Found something in nature that scientists apparently hadn’t noticed before.

Researchers gave Claude AI agents a relatively broad task: search enormous DNA databases for unusual reverse transcriptases — enzymes that copy RNA into DNA.

Then they mostly stepped aside.

About 950 AI agents spent 21 hours analyzing more than 200,000 enzymes, narrowing them to 3,500 candidate systems and eventually 20 especially interesting ones.

One agent noticed something strange.

Next to a reverse transcriptase gene in a bacteriophage — a virus that infects bacteria — it found a long, organized array of repeating DNA sequences.

The pattern looked oddly familiar.

It resembled the repeat architecture of CRISPR.

Claude investigated the sequence, measured the repeats, compared them with known systems and searched the scientific literature. It concluded that the combination appeared to represent a previously uncharacterized biological system.

Human scientists then took over in the laboratory.

Their initial experiments supported a key prediction: the mysterious DNA array is actually expressed into multiple short RNA molecules.

The researchers named the system ART — array-associated reverse transcriptases.

And this is where the story gets interesting.

CRISPR also contains arrays that generate short RNAs, which ultimately help make the system programmable. Several other recently discovered molecular systems with similar combinations of features can cut, copy or insert genetic material.

But an important warning: nobody yet knows what ART actually does.

This is an early preprint, not a peer-reviewed discovery, and there is currently no evidence that ART is a new gene-editing system. Experiments are still underway to determine its biological function.

The bigger story may therefore be Claude itself.

For decades, biological discovery depended partly on humans noticing something strange hidden inside enormous datasets.

Now we may have machines capable of doing the noticing.

What happens when thousands — or millions — of AI scientists start searching nature simultaneously?

#AI #Biology #CRISPR #Genetics #Biotechnology #Claude #Science

https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

🔬 An AI Scientist Just Made a Discovery by Running Its Own Lab Experiments

We’ve seen AI write scientific papers.

We’ve seen it predict proteins and search enormous biological databases.

This is different.

Researchers built a closed-loop AI scientist connected to a physical laboratory. It can generate a hypothesis, design an experiment, turn that experiment into instructions for laboratory automation, analyze the resulting data — and then decide what to investigate next.

The system was given knowledge about Saccharomyces cerevisiae — ordinary baker’s yeast — including roughly 60,000 known biological relationships involving its metabolism, physiology and phenotype.

From these, it generated 1,933 testable hypotheses about how different compounds might affect yeast growth under stress.

Then came the important part:

the hypotheses met reality.

The system selected experiments and controls, converted them into machine-readable laboratory procedures and analyzed the resulting biological data. Some predictions worked.

Others failed.

And one failure produced the most interesting result.

The AI initially predicted that glutamate might protect yeast from formic-acid stress.

The experiment contradicted it.

Instead of simply recording “wrong,” the system analyzed the new metabolomic data, searched for another explanation and identified aminoadipate, a molecule involved in lysine metabolism, as a candidate.

It formulated a new hypothesis.

The lab tested it.

And aminoadipate did improve yeast growth under formic-acid stress — by about 7% for each millimolar increase in the experiment. The researchers report this as a previously unknown protective interaction.

There is an important caveat.

This was not a completely autonomous robot scientist. Humans defined the research domain and safety boundaries, moved some physical samples between instruments and supplied the overall experimental infrastructure. The biological questions were also relatively narrow yeast-metabolism problems — not Nobel-level discoveries.

But something important has happened.

AI has already become very good at generating hypotheses from existing information.

Now the loop can close:

Hypothesis → physical experiment → unexpected result → new hypothesis → new experiment.

That is no longer just AI analyzing science.

It is AI participating in the scientific method.

What happens when systems like this can run 10,000 experiments while a human scientist sleeps?

#AI #Science #Biology #Robotics #Biotechnology #Automation #Research

https://doi.org/10.1098/rsif.2026.0043