There have been dozens of studies on the possible harmful effects of glyphosate. There are many well-funded organizations and tons of lawyers who would like nothing better than to sue Monsanto/Bayer into bankruptcy for promoting Roundup® and there are many environmental and health organizations who claim that glyphosate is harmful to humans.
The claims that glyphosate causes cancer and other ill effects in humans fall into three main categories.
I'll follow up with links to all the articles showing that glyphosate is safe for humans. This is important because there's a lot of misinformation out there and the news media are not doing a very good job of countering the hype against glyphosate by presenting the consensus views of the scientific community. It's time for scientists to push back and make sure the the media are doing the job they're supposed to do; informing the public.
Glyphosate is a relatively simple chemical called N-(phosphonomethyl) glycine. It is a potent inhibitor of one of the key enzymes in the pathway for synthesis of the aromatic amino acids, tryptophan, phenylalanine, and tyrosine [How Cells Make Tryptophan, Phenyalanine, and Tyrosine].
Specifically, the herbicide blocks the activity of EPSP synthase, the enzyme that catalyzes one of the steps leading to chorismate. Chorismate is the precursor of all three aromatic amino acids so by blocking this enzyme, the synthesis of three plant amino acids is prevented.
Plants need to synthesize all 20 amino acids so this blockage causes plants to die.
The glyphosate mechanism is well known from studies of the homologous bacterial versions of EPSP synthase. An example of glyphosate bound to the active site of the E. coli enzyme is shown on the right. When glyphosate is bound, the enzyme can't catalyze any reaction.
Animals have lost the ability to synthesize chorismate and the aromatic amino acids so they require tryptophan, phenyalanine, and tyrosine in their diet. What this means is that the potent herbicide, glyphosate, has no effect on animals since they have already dispensed with the EPSP synthase enzyme. That's one of the reasons why Roundup® is so safe for humans.
Those of you who have used Roundup® on your driveways and walkways know that it kills all plants indiscriminately. You'd better not get it on your wife's favorite roses (... not that I'm admitting anything, mind you).
You can't spray it on crops, such as soybeans, corn, cotton, granola, and wheat to get rid of weeds because it kills the crops as well as the weeds. Wouldn't it be nice to have Roundup® resistant crops so you could spray them to control weeds?
Monsanto makes Roundup® and and they thought so too. That's why their scientists searched for, and found, bacteria that were resistant to Roundup®. Then they transferred the gene for the resistant EPSP enzyme to various crop plants in order to make them resistant to Roundup®. [Roundup Ready® Transgenic Plants]
These Roundup Ready® crops are now found everywhere in Canada and the United States. Farmers routinely spray these crops with Roundup® in order to kill all the weeds in a field while saving the crop.
Since these farmers are handling tons of Roundup® every year, they would make a good group to examine for any adverse health effects, don't you think?
I've been reading Mathew Cobb's biography of Francis Crick and a sentence in the Prologue caught my eye.
Cricks' scientific writings and ideas are described in a way that should be easy for the general reader to understand, but if you find yourself struggling, follow Crick's advice to readers of his own books and skip the hard bits.
I meet regularly with a group of retired professors to discuss a wide range of topics. Yesterday we talked about misinformation and how to deal with it but the discussion brought out the differences between dealing with misinformation in the humanities and in medicine or the natural sciences. This led to a diversion that focused on The Two Cultures.
We may not know a lot about how artificial intelligence (AI) algorithms work but the one thing we do know is that they are only as good as their databases. If you ask an AI program to tell you when Charles Darwin was born then chances are good it's going to give you the correct answer because that information is in Wikipedia and lots of other reliable online sources.
However, if you ask it to tell you how many genes are in the human genome it will not give you the correct answer. The correct answer is that we don't know for sure because it depends on how you define a gene and how many non-coding genes there are using various definitions. That's not the answer you will get. (I personally believe that there are only about 1000 non-coding genes but I don't expect a good "intelligence" program to favor my view over others. I DO expect it to not favor other opinions over mine.)
I just asked ChatGPT and it told me that there are tens of thousands of non-coding genes based on the Human Genome Project plus GENCODE and Ensemble annotations. This is correct ... and misleading. It's giving the best answer it can based on the databases it searches. However, many of us are skeptical of the GENCODE and Ensemble annotations and for good reason. They tend to err on the side of inclusion in order to avoid false negatives. In other words, they don't want to risk ignoring a real biologically relevant feature for lack of evidence so they deliberately risk including a lot of false positives. This is why those databases include a lot of questionable features such as non-coding genes, multiple transcription start sites, multiple splice variants, and tons of potential regulatory elements.
Along comes AlphaGenome. It's an AI program designed to scan those GENCODE and Ensemble databases to identify important features that might play a role in genetic diseases. What could possibly go wrong? [How intelligent is artificial intelligence?] [Will AlphaGenome from Google DeepMind help us understand the human genome?]
The average science writer jumped all over the original announcement of AlphGenome to let us all know that artificial intelligence was going to solve the problem of the mysterious genome. Apparently the complexity of the human genome has astonished scientists ever since the first human genome sequence was published 25 years ago.1 The typical article on AlphaGenome fits nicely into the common theme that AI is soon going to rule the world.
That's why I was excited to pick up my copy of the New York Times yesterday and see that Carl Zimmer had written about AlphaGenome. Finally, an intelligent, highly respected, science writer was going to give us the truth. Here's the article that I saw in my version of the paper. (It was originally published several weeks ago on January 28, 2026.)
What a disappointment! Zimmer goes with the hype about AlphaGenome and repeats some of the tropes that he has avoided in the past. For example, he writes about how alternative splicing can create hundreds of different proteins from a single gene and how regulatory sequences can lie thousands or million of base pairs away from a gene. (There's no question that this is true for a small number of transcription factor binding sites but the vast majority are close to the promoter.)
I checked the online version of Carl Zimmer's article in order to prepare for this blog post. I was surprised to see that there were lots of things in the online version that weren't in the newspaper article. For example, Zimmer quotes my colleague Alex Palazzo saying that everybody uses AlphaFold to study proteins then later on in the article Zimmer notes that, "But the more scientists studied the human genome, the more complicated and messy it turned out to be." The newspaper article left out the words "and messy" and that's significant because junk DNA supporters like Alex Palazzo often refer to the human genome as "messy" and full of junk DNA and that's a very different perspective than opponents of junk DNA who emphasize things like "complicated" and "mysterious."2
Zimmer has an even more revealing section that's in the online version but not the newspaper version.
Peter Koo, a computational biologist at Cold Spring Harbor Laboratory in New York who was not involved in the project, said that AlphaGenome represented an important step forward in applying artificial intelligence to the genome. “It’s an engineering marvel,” he said.
But Dr. Koo and other outside experts cautioned that it represented just one step on a long road ahead. “This is not AlphaFold, and it’s not going to win the Nobel Prize,” said Mark Gerstein, a computational biologist at Yale.
AlphaGenome will be useful. Dr. Gerstein said that he would probably add it to his toolbox for exploring DNA, and others expect to follow suit. But not all scientists trust A.I. programs like AlphaGenome to help them understand the genome.
“I see no value in them at all right now,” said Steven Salzberg, a computational biologist at Johns Hopkins University. “I think there are a lot of smart people wasting their time.”
The end of the online article is quite different from the final paragraphs of the newspaper article. In the newspaper article, Zimmer describes the TAL1 result then ends it with the paragraph starting with "In reality." I've highlighted that paragraph in the quotations below from the online version.
The AlphaGenome researchers shared their TAL1 predictions with Dr. Marc Mansour, a hematologist at University College London who spent years uncovering the leukemia-driving mutations with lab experiments.
“It was quite mind-blowing,” Dr. Mansour said. “It really showed how powerful this is.”
But, Dr. Mansour noted, AlphaGenome’s predictive powers fade the farther its gaze strays from a particular gene. He is now using AlphaGenome in his cancer research but does not blindly accept its results.
“These prediction tools are still prediction tools,” he said. “We still need to go to the lab.”
Dr. Salzberg of Johns Hopkins is less sanguine about AlphaGenome, in part because he thinks its creators put too much trust in the data they trained it on. Scientists who study splice sites don’t agree on which sites are real and which are genetic mirages. As a result, they have created databases that contain different catalogs of splice sites.
“The community has been working for 25 years to try to figure out what are all the splice sites in the human genome, and we’re still not really there,” Dr. Salzberg said. “We don’t have an agreed-upon gold-standard set.”
Dr. Pollard also cautioned that AlphaGenome was a long way from being a tool that doctors could use to scan the genomes of patients for threats to their health. It predicts only the effects of a single mutation on one standard human genome.
In reality, any two people have millions of genetic differences in their DNA. Assessing the effects of all those variations throughout a patient’s body remains far beyond AlphaGenome’s industrial-strength power.
“It is a much, much harder problem — and yet that’s the problem we need to solve if we want to use a model like this for health care,” Dr. Pollard said.
The net effect of these differences is to transform the article from one that promotes AlphaGenome in the newspaper version to one that's far more skeptical in the online version. I believe that the online version is far more accurate and reflects the high standard that I expect from Carl Zimmer. I'm assuming that the newspaper article was edited for the New York Times supplement that I read and I'm assuming that Zimmer did not approve of that edit.
Note: The cartoon was generated by ChatGPT in response to the request, "draw a cartoon illustrating GIGO - garbage in garbage out."
Note: The photo is from 10 years ago when Carl was in Toronto working on his junk DNA article for The New York Times [Is Most of Our DNA Garbage?]. That's Alex Palazzo on the left, then me, Ryan Gregory, and Carl Zimmer on the right.
1. Most knowledgeable scientists were not astonished to learn that 90% of our genome really is junk and there are fewer than 30,000 genes.
2. See the last chapter of my book: "Chapter 11: Zen and the Art of Coping with a Sloppy Genome."
The amount of a given protein in Escherichia coli depends on a number of factors such as the amount of mRNA and the rate of translation. The standard model of regulation is based on decades of study of individual genes and it reveals that the amount of protein is mostly dependent on the amount of mRNA that was translated. This, in turn, indicates that most regulation occurs at the level of transcription initiation.
It's now possible to look simultaneously at the characteristics of large numbers of protein-coding genes to see whether this generality holds. That's what Balakrishan et al. (2022) reported in a Science paper a few years ago. They looked at the characteristics of 1900 protein-coding genes in E. coli to see how protein concentration was regulated.
We're interested in the last universal common ancestor of all life (LUCA). In theory, this is a species that gave rise to both Bacteria and Archaea. The general assumption is that this is a single species with a well-defined set of genes that can now be found in all, or almost all, living species.
There are some scientists who question that assumption because they see massive transfers of genes between "species" during the early history of life. This gives rise to a web of life and not a well-defined tree. [The Three Domain Hypothesis: RIP] [The Web of Life] If that model is correct, then the ancestor of all living species could be a group of species that contributed different genes to a pool of organisms that lived billions of years ago. Early Bacterial and Archaeal ancestors could have independently acquired some genes by horizontal gene transfer.
Over the past few years I've been assessing AI algorithms to see if they can answer difficult questions about junk DNA, alternative splicing, evolution, epigenetics and a number of other topics. As a general rule, these AI algorithms are good at searching the internet and returning a consensus view of what's out there. Unfortunately, the popular view on some of these topics is wrong and most AI algorithms are incapable of sorting the wheat from the chaff.
In most cases, they aren't even capable of recognizing that there's a controversy and that their preferred answer might not be correct. They are quite capable of getting their answer from known kooks and unreliable, non-scientific, websites, [The scary future of AI is revealed by how it deals with junk DNA].
Others have now recognized that there's a problem with AI so they devised a set of expert questions that have definitive, correct, answers but the answers cannot be retrieved by simple internet searches. The idea is to test whether AI algorithms are actually intelligent or just very fast search engines that can summarize the data they retrieve and create an intelligent-sounding output.
It seems reasonable to assume that there's a genetic component to aging and the human life span. For example, it's clear that if you inherit bad genes (alleles) from your parents then your chances of living a long life will be diminished. It's also clear that a lot of deaths (short life span) are not due to alleles you inherit from your parents but to extrinsic factors such as accident, war or disease. It's true that some diseases, such as cancer, have a heritable component but even people with "good genes" can die of cancer.
What's not clear is how much of the underlying, intrinsic, component of life span is due to alleles you inherit from your parents. If you look at the average life expectancy of men and women in different countries you can see that the average life expectancy of an American is about 80 years but people in Japan can expect to live five years longer. Is that difference due to genetics, or better health care, or something else?
New Scientist has produced a special issue on the best ideas of the 21st century. Here's the complete list.
