dna mouse experiment

Microbe Notes

DNA Experiments (Griffith & Avery, McCarty, MacLeod & Hershey, Chase)

DNA, deoxyribonucleic acid, is the carrier of all genetic information. It codes genetic information passed on from one generation to another and determines individual attributes like eye color, facial features, etc. Although DNA was first isolated in 1869 by a Swiss scientist, Friedrich Miescher, from nuclei of pus-rich white blood cells (which he called nuclein ), its role in the inheritance of traits wasn’t realized until 1943. Miescher thought that the nuclein, which was slightly acidic and contained a high percentage of phosphorus, lacked the variability to account for its hereditary significance for diversity among organisms. Most of the scientists of his period were convinced by the idea that proteins could be promising candidates for heredity as they were abundant, diverse, and complex molecules, while DNA was supposed to be a boring, repetitive polymer. This notion was put forward as the scientists were aware that genetic information was contained within organic molecules.

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Griffith’s Transformation Experiment

In 1928, a young scientist Frederick Griffith discovered the transforming principle. In 1918, millions of people were killed by the terrible Spanish influenza epidemic, and pneumococcal infections were a common cause of death among influenza-infected patients. This triggered him to study the bacteria Streptococcus pneumoniae and work on designing a vaccine against it . It became evident that bacterial pneumonia was caused by multiple strains of S. pneumoniae, and patients developed antibodies against the particular strain with which they were infected. Hence, serum samples and bacterial isolates used in experiments helped to identify DNA as the hereditary material.

He used two related strains of S. pneumoniae and mice and conducted a series of experiments using them.

When type II R-strain bacteria were grown on a culture plate, they produced rough colonies. They were non-virulent as they lacked an outer polysaccharide coat. Thus, when RII strain bacteria were injected into a mouse, they did not cause any disease and survived.
When type I S-strain bacteria were grown on a culture plate, they produced smooth, glistening, and white colonies. The smooth appearance was apparent due to a polysaccharide coat around them that provided resistance to the host’s immune system. It was virulent and thus, when injected into a mouse, resulted in pneumonia and death.
In 1929, Griffith experimented by injecting mice with heat-killed SI strain (i.e., SI strain bacteria exposed to high temperature ensuing their death). But, this failed to harm the mice, and they survived.
Surprisingly, when he mixed heat-treated SI cells with live RII cells and injected the mixture into the mice, the mice died because of pneumonia. Additionally, when he collected a blood sample from the dead mouse, he found that sample to contain live S-strain bacteria.

Conclusion of Griffith’s Transformation Experiment

Based on the above results, he inferred that something must have been transferred from the heat-treated S strain into non-virulent R strain bacteria that transformed them into smooth coated and virulent bacteria. Thus, the material was referred to as the transforming principle.

Following this, he continued with his research through the 1930s, although he couldn’t make much progress. In 1941, he was hit by a German bomb, and he died.

Avery, McCarty, and MacLeod Experiment

During World War II, in 1943, Oswald Avery, Maclyn McCarty, and Colin MacLeod working at Rockefeller University in New York, dedicated themselves to continuing the work of Griffith in order to determine the biochemical nature of Griffith’s transforming principle in an in vitro system. They used the phenotype of S. pneumoniae cells expressed on blood agar in order to figure out whether transformation had taken place or not, rather than working with mice. The transforming principle was partially purified from the cell extract (i.e., cell-free extract of heat-killed type III S cells) to determine which macromolecule of S cell transformed type II R-strain into the type III S-strain. They demonstrated DNA to be that particular transforming principle.

Initially, type III S cells were heat-killed, and lipids and carbohydrates were removed from the solution.
Secondly, they treated heat-killed S cells with digestive enzymes such as RNases and proteases to degrade RNA and proteins. Subsequently, they also treated it with DNases to digest DNA, each added separately in different tubes.
Eventually, they introduced living type IIR cells mixed with heat-killed IIIS cells onto the culture medium containing antibodies for IIR cells. Antibodies for IIR cells were used to inactivate some IIR cells such that their number doesn’t exceed the count of IIIS cells. that help to provide the distinct phenotypic differences in culture media that contained transformed S strain bacteria.

Observation of Avery, McCarty, and MacLeod Experiment

The culture treated with DNase did not yield transformed type III S strain bacteria which indicated that DNA was the hereditary material responsible for transformation.

Conclusion of Avery, McCarty, and MacLeod Experiment

DNA was found to be the genetic material that was being transferred between cells, not proteins.

Hershey and Chase Experiment

Although Avery and his fellows found that DNA was the hereditary material, the scientists were reluctant to accept the finding. But, not that long afterward, eight years after in 1952, Alfred Hershey and Martha Chase concluded that DNA is the genetic material. Their experimental tool was bacteriophages-viruses that attack bacteria which specifically involved the infection of Escherichia coli with T2 bacteriophage.

T2 virus depends on the host body for its reproduction process. When they find bacteria as a host cell, they adhere to its surface and inject its genetic material into the bacteria. The injected hereditary material hijacks the host’s machinery such that a large number of viral particles are released from them. T2 phage consists of only proteins (on the outer protein coat) and DNA (core) that could be potential genetic material to instruct E. coli to develop its progeny. They experimented to determine whether protein or DNA from the virus entered into the bacteria.

Bacteriophage was allowed to grow on two of the medium: one containing a radioactive isotope of phosphorus( 32 P) and the other containing a radioactive isotope of sulfur ( 35 S).
Phages grown on radioactive phosphorus( 32 P) contained radioactive P labeled DNA (not radioactive protein) as DNA contains phosphorus but not sulfur.
Similarly, the viruses grown in the medium containing radioactive sulfur ( 35 S) contained radioactive 35 S labeled protein (but not radioactive DNA) because sulfur is found in many proteins but is absent from DNA.
E. coli were introduced to be infected by the radioactive phages.
After the progression of infection, the blender was used to remove the remains of phage and phage parts from the outside of the bacteria, followed by centrifugation in order to separate the bacteria from the phage debris.
Centrifugation results in the settling down of heavier particles like bacteria in the form of pellet while those light particles such as medium, phage, and phage parts, etc., float near the top of the tube, called supernatant.

Observation of Hershey and Chase Experiment

On measuring radioactivity in the pellet and supernatant in both media, 32 P was found in large amount in the pellet while 35 S in the supernatant that is pellet contained radioactively P labeled infected bacterial cells and supernatant was enriched with radioactively S labeled phage and phage parts.

Conclusion of Hershey and Chase Experiment

Hershey and Chase deduced that it was DNA, not protein which got injected into host cells, and thus, DNA is the hereditary material that is passed from virus to bacteria.

Fry, M. (2016). Landmark Experiments in Molecular Biology. Academic Press.
https://bio.libretexts.org/Bookshelves/Introductory_and_General_Biology/Book%3A_Introductory_Biology_(CK-12)/04%3A_Molecular_Biology/4.02%3A_DNA_the_Genetic_Material
https://byjus.com/biology/dna-genetic-material/
https://bio.libretexts.org/Bookshelves/Genetics/Book%3A_Online_Open_Genetics_(Nickle_and_Barrette-Ng)/01%3A_Overview_DNA_and_Genes/1.02%3A_DNA_is_the_Genetic_Material
https://www.toppr.com/guides/biology/the-molecular-basis-of-inheritance/the-genetic-material/
https://www.nature.com/scitable/topicpage/discovery-of-dna-as-the-hereditary-material-340/
https://www.biologydiscussion.com/genetics/dna-as-a-genetic-material-biology/56216
https://www.nature.com/scitable/topicpage/discovery-of-the-function-of-dna-resulted-6494318/
https://www.ndsu.edu/pubweb/~mcclean/plsc411/DNA%20replication%20sequencing%20revision%202017.pdf
https://www.britannica.com/biography/Frederick-Griffith
https://ib.bioninja.com.au/higher-level/topic-7-nucleic-acids/71-dna-structure-and-replic/dna-experiments.html
https://biolearnspot.blogspot.com/2017/11/experiments-of-avery-macleod-and.html
https://www.khanacademy.org/science/biology/dna-as-the-genetic-material/dna-discovery-and-structure/a/classic-experiments-dna-as-the-genetic-material

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Prakriti Karki

Fearful Memories Passed Down to Mouse Descendants

Genetic imprint from traumatic experiences carries through at least two generations

By Ewen Callaway & Nature magazine

From Nature magazine

Certain fears can be inherited through the generations, a provocative study of mice reports. The authors suggest that a similar phenomenon could influence anxiety and addiction in humans. But some researchers are sceptical of the findings because a biological mechanism that explains the phenomenon has not been identified.

According to convention, the genetic sequences contained in DNA are the only way to transmit biological information across generations. Random DNA mutations, when beneficial, enable organisms to adapt to changing conditions, but this process typically occurs slowly over many generations.

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Yet some studies have hinted that environmental factors can influence biology more rapidly through 'epigenetic' modifications, which alter the expression of genes, but not their actual nucleotide sequence. For instance, children who were conceived during a harsh wartime famine in the Netherlands in the 1940s are at increased risk of diabetes, heart disease and other conditions — possibly because of epigenetic alterations to genes involved in these diseases. Yet although epigenetic modifications are known to be important for processes such as development and the inactivation of one copy of the X-chromsome in females, their role in the inheritance of behaviour is still controversial.

Kerry Ressler, a neurobiologist and psychiatrist at Emory University in Atlanta, Georgia, and a co-author of the latest study, became interested in epigenetic inheritance after working with poor people living in inner cities, where cycles of drug addiction, neuropsychiatric illness and other problems often seem to recur in parents and their children. “There are a lot of anecdotes to suggest that there’s intergenerational transfer of risk, and that it’s hard to break that cycle,” he says.

Heritable traits

Studying the biological basis for those effects in humans would be difficult. So Ressler and his colleague Brian Dias opted to study epigenetic inheritance in laboratory mice trained to fear the smell of acetophenone, a chemical the scent of which has been compared to those of cherries and almonds. He and Dias wafted the scent around a small chamber, while giving small electric shocks to male mice. The animals eventually learned to associate the scent with pain, shuddering in the presence of acetophenone even without a shock.

This reaction was passed on to their pups, Dias and Ressler report today in Nature Neuroscience 1. Despite never having encountered acetophenone in their lives, the offspring exhibited increased sensitivity when introduced to its smell, shuddering more markedly in its presence compared with the descendants of mice that had been conditioned to be startled by a different smell or that had gone through no such conditioning. A third generation of mice — the 'grandchildren' — also inherited this reaction, as did mice conceived through in vitro fertilization with sperm from males sensitized to acetophenone. Similar experiments showed that the response can also be transmitted down from the mother.

These responses were paired with changes to the brain structures that process odours. The mice sensitized to acetophenone, as well as their descendants, had more neurons that produce a receptor protein known to detect the odour compared with control mice and their progeny. Structures that receive signals from the acetophenone-detecting neurons and send smell signals to other parts of the brain (such as those involved in processing fear) were also bigger.

The researchers propose that DNA methylation — a reversible chemical modification to DNA that typically blocks transcription of a gene without altering its sequence — explains the inherited effect. In the fearful mice, the acetophenone-sensing gene of sperm cells had fewer methylation marks, which could have led to greater expression of the odorant-receptor gene during development.

But how the association of smell with pain influences sperm remains a mystery. Ressler notes that sperm cells themselves express odorant receptor proteins, and that some odorants find their way into the bloodstream, offering a potential mechanism, as do small, blood-borne fragments of RNA known as microRNAs, that control gene expression.

Contentious findings

Predictably, the study has divided researchers. “The overwhelming response has been 'Wow! But how the hell is it happening?'" says Dias. David Sweatt, a neurobiologist at the University of Alabama at Birmingham who was not involved in the work, calls it “the most rigorous and convincing set of studies published to date demonstrating acquired transgenerational epigenetic effects in a laboratory model".

However, Timothy Bestor, a molecular biologist at Columbia University in New York who studies epigenetic modifications, is incredulous. DNA methylation is unlikely to influence the production of the protein that detects acetophenone, he says. Most genes known to be controlled by methylation have these modifications in a region called the promoter, which precedes the gene in the DNA sequence. But the acetophenone-detecting gene does not contain nucleotides in this region that can be methylated, Bestor says. "The claims they make are so extreme they kind of violate the principle that extraordinary claims require extraordinary proof,” he adds.

Tracy Bale, a neuroscientist at the University of Pennsylvania in Philadelphia, says that researchers need to “determine the piece that links Dad's experience with specific signals capable of producing changes in epigenetic marks in the germ cell, and how these are maintained”.

“It's pretty unnerving to think that our germ cells could be so plastic and dynamic in response to changes in the environment,” she says.

Humans inherit epigenetic alterations that influence behaviour, too, Ressler suspects. A parent’s anxiety, he speculates, could influence later generations through epigenetic modifications to receptors for stress hormones. But Ressler and Dias are not sure how to prove the case, and they plan to focus on lab animals for the time being.

The researchers now want to determine for how many generations the sensitivity to acetophenone lasts, and whether that response can be eliminated. Scepticism that the inheritance mechanism is real will likely persist, Ressler says, “until someone can really explain it in a molecular way”, says Ressler. “Unfortunately, it’s probably going to be complicated and it’s probably going to take a while.”

This article is reprinted with permission from Nature magazine. It was first published on December 1, 2013.

Oswald Avery and the Avery-McLeod-McCarthy Experiment

Oswald Avery and his colleagues showed that DNA was the key component of Griffith’s experiment , in which mice are injected with dead bacteria of one strain and live bacteria of another, and develop an infection of the dead strain’s type

On February 1 , 1944 , physician and medical researcher Oswald Avery together with his colleagues Colin MacLeod and Maclyn McCarty announced that DNA is the hereditary agent in a virus that would transform a virus from a harmless to a pathogenic version. This study was a key work in modern bacteriology .

Prelude – The Griffith Experiment

The achievement by the scientists Avery, MacLeod, and McCarty were based on Frederick Griffith’s studies on bacteria, believing that bacteria types were not changeable from one to another generation. His also famous attempt is called the Griffith experiment, and was published in 1928. In it, the medical officer Griffith identified a principle in pneumococcal bacteria, in which they could transform from one to another type. After several years of research on the disease pneumonia , he found out that types changed into another rather than multiple types being present at the same time. His later research proved, that the transformation occurred, when dead bacteria of a virulent and live bacteria of a non-virulent type were injected in mice, they would suffer an infection and die shortly after. The other case proved, that the injected virulent bacteria was to be isolated from an infected mouse, depicted in the picture above. The German bacteriologist Fred Neufeld was the first to prove Griffith’s findings right and soon, renowned institutes, like the Koch Institute or the Rockefeller Institute took over the case, doing further research on Griffith’s great accomplishments.

Oswald Avery (1877-1955)

The Avery-MacLeod-McCarty Experiment

Avery and his collaborators Colin MacLeod and Maclyn McCarty at Rockefeller University (then Rockefeller Institute) in New York wanted to elucidate the chemical nature of the transforming substance. They refined the purification process until the result was a cell extract whose amounts of carbon, hydrogen, nitrogen and phosphorus corresponded to those of DNA.[ 4 ] To ensure that the transformation was not induced by residues of RNA or proteins, they treated the cell extract with different enzymes prior to the transformation. One of these enzymes had a deoxyribonucleode polymerase activity described by Greenstein in 1940. Only this neutralized the transformation activity of the extract, while trypsin, chymotrypsin (two protein cleaving enzymes), ribonuclease, protein phosphatases and esterase had no effect on transformation activity. They were also able to show that all offspring inherited the S-properties and that the repetition of the experiment with extracts from these offspring led to the same results.

This experiment shows that the genetic information must lie on the DNA, since the R-cells needed information from the S-cells to form a mucus capsule, i. e. to become S-cells. And only the DNA made it possible to transform R cells into S cells. In the counterexample with an enzyme, it became even clearer that the genetic information must lie in the DNA, since only R cells develop when a DNAse is added, because the DNA was broken down by the enzyme.

The achievements of the Avery-MacLeod-McCarty experiment quickly spread out into the scientific community and it was proven right just as fast. However, only very few scientists would accept the thought that genetics were to be applied to bacteria, but as Joshua Lederberg , himself an American molecular biologist suggested, the three scientists paved the early way for molecular genetics. The experiment opened up new possibilities and research fields for following biologists. Avery was awarded the Copley Medal for his bacterial transformations, but neglected by many scientists and organizations for his work.

References and Further Reading:

[1] Lederberg J (February 1994). “The transformation of genetics by DNA: an anniversary celebration of Avery, MacLeod and McCarty (1944)” . Genetics 136 (2): 423–6
[2] History of DNA Researc h Timeline
[3] Isolating Hereditary Material: Frederick Griffith, Oswald Avery, Alfred Hershey, and Martha Chase at Nature
[4] Crick and Watson Decipher the DNA , SciHi Blog, February 28, 2013
[4] Louis Pasteur – The Father of Medical Microbiology , SciHi Blog, December 27, 2012
[5] National Academy of Sciences Biographical Memoir
[6] Oswald T. Avery Collection (1912-2005) – National Library of Medicine finding aid
[7] DNA: The Search for the Genetic Material Avery, MacLeod and McCarty’s Experiment for the Advanced Science Hobbyist.
[8] Oswald T. Avery at Wikidata
[9] How 3 Scientists Found that DNA is the Molecule of Heredity – Avery, MacLeod, and McCarty , 2020, YourekaScience @ youtube
[10] Timeline of famous Biology Experiments , via DBpedia and Wikidata

Harald Sack

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How Did Scientists Prove That DNA Is Our Genetic Material?

Griffith experiment, avery, macleod and mccarty experiment, hershey and chase experiment.

Three seminal experiments proved, without doubt, that DNA was the genetic material, and not proteins. These experiments were the Griffith experiment, Avery, MacLeod, and McCarthy Experiment, and finally the Hershey-Chase Experiment.

DNA is the fundamental component of our being. The human body is merely the carrier for this genetic material, passing it down from generation to generation. Our purpose is to ensure the survival of the species. Humans are to DNA like a fruit is to a seed. We are just an outer covering to ensure the safe passage and protection of the source code of our existence through time. Makes you feel pretty useless, doesn’t it?

However, that’s not what I want you to focus on. The main focus is, how did we discover that DNA is the carrier of information? How did we determine that it wasn’t something else, like proteins? After all, proteins are also present in every cell.

For a long time this debate had been going on. Even after Gregor Mendel formed the 3 laws of inheritance , it wasn’t accepted by the scientific community for 45 years. The reason? There was no concept of DNA or genes being the information carriers! The whole debate was finally put to rest by 3 main experiments carried out by independent researchers, which formed the basis of all our evolutionary and molecular biology studies.

What Is DNA And How Does It Work?

3d rendering Virus bacteria cells background - Illustration( ranjith ravindran)s

Where Did Viruses Come From?

flat genetic science people illustration(Tasty_Cat)S

What Does RNA Do In A Cell?

Chromatin strand and isolated nucleosome( Juan Gaertner)s

Chromatin: Structure And Function Within A Cell

Is 98% Of Our DNA Really Junk?

Mendel’s Laws Of Inheritance: The Law Of Segregation

Why Do Some People Look More Like Their Grandparents Than Their Parents?

Photosynthesis: The Biochemistry Behind How Plants Make Their Food

What are Mutations and what are the different types of Mutations?

What are GMOs (Genetically Modified Organisms)?

What is Evolution: A REALLY SIMPLE and Brief Explanation

Can We Bring Back Dinosaurs (Just Like In Jurassic Park)?

Biology Article
Griffith Experiment Genetic Material

Griffith Experiment and Search of Genetic Material

The search for Genetic material started during the mid-nineteenth century. The principle of inheritance was discovered by Mendel. Based on his investigation, Mendel concluded that some ‘factors’ are transferred from one generation to another. Mendel’s Law of Inheritance was the basis for the researchers on genetic material. Keeping his conclusions in mind, scientists who came after him, focused on chromosomes in search of genetic material. Even though the chromosomal components were identified, the material which is responsible for inheritance remained unanswered. It took a long time for the acceptance of DNA as the genetic transformation. Let’s go through a brief account of the discovery of genetic material and Griffith experiment.

Griffith Experiment & Transforming Principle

Griffith experiment was a stepping stone for the discovery of genetic material. Frederick Griffith experiments were conducted with Streptococcus pneumoniae.

During the experiment, Griffith cultured Streptococcus pneumoniae bacteria which showed two patterns of growth. One culture plate consisted of smooth shiny colonies (S) while other consisted of rough colonies (R). The difference was due to the presence of mucous coat in S strain bacteria, whereas the R strain bacteria lacked them.

Experiment: Griffith injected both S and R strains to mice. The one which was infected with the S strain developed pneumonia and died while that infected with the R strain stayed alive.

In the second stage, Griffith heat-killed the S strain bacteria and injected into mice, but the mice stayed alive. Then, he mixed the heat-killed S and live R strains. This mixture was injected into mice and they died. In addition, he found living S strain bacteria in dead mice.

Conclusion: Based on the observation, Griffith concluded that R strain bacteria had been transformed by S strain bacteria. The R strain inherited some ‘transforming principle’ from the heat-killed S strain bacteria which made them virulent. And he assumed this transforming principle as genetic material.

DNA as Genetic Material

Griffith experiment was a turning point towards the discovery of hereditary material. However, it failed to explain the biochemistry of genetic material. Hence, a group of scientists, Oswald Avery, Colin MacLeod and Maclyn McCarty continued the Griffith experiment in search of biochemical nature of the hereditary material. Their discovery revised the concept of protein as genetic material to DNA as genetic material .

Avery and his team extracted and purified proteins, DNA, RNA and other biomolecules from the heat-killed S strain bacteria. They discovered that DNA is the genetic material and it is alone responsible for the transformation of the R strain bacteria. They observed that protein-digesting enzymes (proteases) and RNA-digesting enzymes (RNases) didn’t inhibit transformation but DNase did. Although it was not accepted by all, they concluded DNA as genetic material.

Frequently Asked Questions on Griffith Experiment

What was griffith’s experiment and why was it important.

Griffith’s experiment was the first experiment which suggested that bacteria can transfer genetic information through a process called transformation.

What is the conclusion of Griffith experiment?

The experiment concluded that bacteria are capable of transfering genetic information through transformation.

What was the most significant conclusion of Griffith’s experiments with pneumonia in mice?

The experiment conducted by Griffith found that bacteria are capable of transfering genetic information through transformation.

What did Frederick Griffith want to learn about bacteria?

Frederick Griffith wanted to learn if bacterial transformation was possible.

How did the two types of bacteria used by Griffith differ?

Griffith used two strains of pneumococcus (Streptococcus pneumoniae) bacteria: a type III-S and a type II-R.

Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

Select the correct answer and click on the “Finish” button Check your score and answers at the end of the quiz

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1944: DNA is \"Transforming Principle\"

1944: dna is "transforming principle".

Avery, MacLeod and McCarty identified DNA as the "transforming principle" while studying Streptococcus pneumoniae , bacteria that can cause pneumonia. The bacteriologists were interested in the difference between two strains of Streptococci that Frederick Griffith had identified in 1923: one, the S (smooth) strain, has a polysaccharide coat and produces smooth, shiny colonies on a lab plate; the other, the R (rough) strain, lacks the coat and produces colonies that look rough and irregular. The relatively harmless R strain lacks an enzyme needed to make the capsule found in the virulent S strain.

Griffith had discovered that he could convert the R strain into the virulent S strain. After he injected mice with R strain cells and, simultaneously, with heat-killed cells of the S strain, the mice developed pneumonia and died. In their blood, Griffith found live bacteria of the deadly S type. The S strain extract somehow had "transformed" the R strain bacteria to S form. Avery and members of his lab studied transformation in fits and starts over the next 15 years. In the early 1940s, they began a concerted effort to purify the "transforming principle" and understand its chemical nature.

Bacteriologists suspected the transforming factor was some kind of protein. The transforming principle could be precipitated with alcohol, which showed that it was not a carbohydrate like the polysaccharide coat itself. But Avery and McCarty observed that proteases - enzymes that degrade proteins - did not destroy the transforming principle. Neither did lipases - enzymes that digest lipids. They found that the transforming substance was rich in nucleic acids, but ribonuclease, which digests RNA, did not inactivate the substance. They also found that the transforming principle had a high molecular weight. They had isolated DNA. This was the agent that could produce an enduring, heritable change in an organism.

Until then, biochemists had assumed that deoxyribonucleic acid was a relatively unimportant, structural chemical in chromosomes and that proteins, with their greater chemical complexity, transmitted genetic traits.

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Animation 17: A gene is made of DNA.

Description

Oswald Avery explains Fred Griffith's and his own work with Pneumococcus bacteria.

How do you do? I'm Oswald Avery. My colleagues and I did a series of experiments using strains of Pneumococcus bacteria, which cause pneumonia. Pneumococcus grows in the body of the host, but, like other types of bacteria, also can be grown on solid or liquid cultures. In 1928, Fred Griffith published a study on the different strains of Pneumococcus. Two in particular, S and R, look different. The S colonies have a smooth surface, and the R colonies look rough. The S colonies look smooth because each bacterium has a capsule-like coat made of sugars. This coat protects the S bacteria from the host's immune system, and so the S strain is infectious. The coat-less R strain is not. Griffith found that mice injected with the S strain develop pneumonia and die within days. Mice injected with the R strain do not get pneumonia. Griffith noticed that different strains of Pneumococcus could be cultured from one patient. He began to wonder if one strain could change into another. To test this idea, he did a series of experiments using the R and S strains. First, Griffith heated the S strain culture to kill the bacteria. As predicted, when injected into mice, the heat-killed bacteria did not produce an infection. Griffith co-injected the heat-killed S with live R into mice, and, much to his surprise, the mice developed pneumonia and died. Even more astonishing, Griffith was able to isolate live S strain from the blood of infected mice. These cultures could infect other mice. S strain cultured from infected mice remained active â€” showing that the change was stable and inherited. Griffith concluded that some "principle" was transferred from the heat-killed S to the R strain. The principle transformed the R into the infective S strain with a smooth coat. When I read about Griffith's results, I became very interested in the identity of this transforming "principle." Colin MacLeod, Maclyn McCarty, and I began experimenting using a test tube assay instead of mice. We used detergent to lyse the heat-killed S cells. Then we used this lysate for transformation assays. The test tube assays worked well, and showed us that the heat-killed S lysate could change R to S. The transforming principle was something in the lysate. We tested each of the lysate components for the transforming activity. First, we incubated the heat-killed S lysate with an enzyme, SIII, that completely chewed up the sugar coat. We tested the transforming ability of the sugar coat-less S lysates. The sugar coat-less S lysate was still able to transform. This told us that the R strain was not just assembling a new S sugar coat from the pieces. Next, we incubated the coat-less S extract with protein digesting enzymes â€” trypsin and chymotrypsin. Next, we incubated the coat-less S extract with protein digesting enzymes â€” trypsin and chymotrypsin. We tested this lysate's ability to transform. This protein-less lysate was still able to transform. So, the transforming principle is not protein. While we were testing and purifying the lysate, we precipitated nucleic acids â€” DNA and RNA â€” with alcohol. We were the first to isolate nucleic acids from Pneumococcus. While we were testing and purifying the lysate, we precipitated nucleic acids â€” DNA and RNA â€” with alcohol. We were the first to isolate nucleic acids from Pneumococcus. Since the transforming principle was not the sugar coat, and not protein, we suspected that it may be one of the nucleic acids. We dissolved the precipitate in water, and tested the transforming ability of the solution. Since the transforming prinicple was not the sugar coat, and not protein, we suspected that it may be one of the nucleic acids. We dissolved the precipitate in water, and tested the transforming ability of the solution. First, we destroyed the RNA using the RNase enzyme. We tested this solution for its ability to transform. The solution still had the ability to transform. Therefore, RNA could not be the transforming principle. What we had left was virtually pure DNA. As a final test, we incubated the solution with the DNA-digesting enzyme, DNase. We used this solution to test for transforming ability. This solution was unable to transform. My colleagues and I concluded that DNA is the transforming principle, and we published these results in 1944.

oswald avery, pneumococcus bacteria, liquid cultures, fred griffith, smooth coat, transforming principle, rough coat

Source: DNALC.DNAFTB

15674. Oswald Avery (c.1930)

Oswald Avery, circa 1930.

Source: DNAi

16374. Concept 17: A gene is made of DNA.

Oswald Avery's team proves that DNA, not protein, is the genetic molecule.

Source: DNAFTB

16395. Animation18: Bacteria and viruses have DNA too.

Joshua Lederberg worked with bacterial genetics while Alfred Hershey showed that DNA is responsible for the reproduction of new viruses in a cell.

16391. Biography 17: Oswald Theodore Avery (1877-1955)

In 1944, Oswald Avery and his colleagues, Colin MacLeod and Maclyn McCarty published their landmark paper on the transforming ability of DNA.

16381. Gallery 17: Oswald Avery's letter to his brother, 1943

A page from the May 15, 1943 letter from Oswald Avery to his brother Roy. In the letter Avery speculated on how transformation could happen. Avery never publicly connected genes with DNA and his transformation experiments.

16705. Animation 34: Genes can be moved between species.

Stanley Cohen and Herbert Boyer transform bacteria with a recombinant plasmid, and Doug Hanahan studies induced transformation.

16392. Biography 17: Maclyn McCarty (1911- 2005)

In 1944, Maclyn McCarty and his colleagues, Colin MacLeod and Oswald Avery published their landmark paper on the transforming ability of DNA.

16378. Gallery 17: Oswald Avery, around 1930

Oswald Avery at work in the laboratory, around 1930.

16390. Video 17: Maclyn McCarty, clip 6

How the bacterial transformation experiments provided the first real opportunity to study the chemical nature of the gene.

Structural Biochemistry/Nucleic Acid/DNA/Avery-MacLeod-McCarty Experiment

The Avery-MacLeod-McCarty Experiment was presented by Oswald Avery, Colin MacLeod, and Maclyn McCarty in 1944. During the 1930s and early 1940s, Avery and MacLeod performed this experiment at Rockefeller Institute for Medical Research, after the departure of MacLeoirulency (measure of deadly potency). This experiment would allow them to determine if rough bacteria could be transformed into smooth bacteria, hence passing along the genetic information causing the transformation. By isolating and purifying this chemical component, they could deduce if it had characteristics of a protein or DNA molecule.

2.1 Simpler Experimental
3 References

The purpose behind this experiment was to better understand the chemical component that carries the genetic information and transforms one molecule to the next.

Bacteria grown in petri dishes can grow spots or colonies inside the dish multiplying under certain conditions. Virulent (deadly) colonies look smooth or like tiny droplets, where as non-deadly bacteria formed rigid, uneven edges, basically rough colonies. While analyzing a certain kind of pneumonia caused by bacteria in mice, they were able to isolate a "variant" (mutant) strain that did not kill the mice. During the experiments, Avery and MacLeod injected a mouse simultaneously with "boiled" or dead smooth bacteria and live rough bacteria. Thereafter a short while they were surprised to see that the mouse died. When they took samples from the dead mice, and cultured the samples in a petri dish, Avery and MacLeod found that what grew inside the culture was in fact the smooth deadly bacteria. This suggested that something from the "dead" bacteria somehow converted the rough bacteria into smooth bacteria. The rough bacteria had been permanently converted or transformed into the smooth dangerous bacteria. They had confirmed that they could not grow smooth bacteria from the boiled culture and cause disease if the dead smooth bacteria were injected alone. This all implied that a chemical component in the smooth bacteria survived and transformed the rough bacteria into smooth. Isolating and purifying that chemical component had shown that is was DNA, NOT proteins that transferred the genetic code from the smooth to the rough.

Simpler Experimental

Here is a simpler demonstration for this experiment by Oswald Avery, Colin MacLeod, and Maclyn McCarty. There are two sets of bacteria – one is smooth (virulent), one is rough (nonvirulent).

1) They first inject deadly encapsulated bacteria into the mouse – the mouse dies at the end.

2) They then inject non-encapsulated, nonvirulent bacteria into the mouse – the mouse lives.

3) Next, they heated the virulent bacteria at a temperature that kills them and injected these bacteria into the mouse – the mouse lives.

4) After that, they then have the denatured fatal bacteria mix into the living non-encapsulated, nonfatal bacteria. The mixture was then injected into the mouse – the mouse dies.

5) Finally, they mix the live, non-encapsulated harmless bacteria with the DNA that was extracted from the heated, lethal bacteria. These “harmless” bacteria injected to the mouse after being mixed – the mouse dies.

From these experiments, Avery and his group showed that nonvirulent bacteria become deadly after mixing with the DNA of the virulent bacteria . Such a demonstration shows that nonvirulent bacteria became virulent because of the genetic information that originally came from the virulent bacteria. The protein from the virulent bacteria was already denatured during Step 3. Thus, it was DNA and not protein that transferred the genetic information to the nonvirulent bacteria.

Griffith Experiment

In 1928, Frederick Griffith performed a DNA experiment using pneumonia bacteria and mice. This experiment provided evidence that some particular chemical within cells is genetic material. The objective of the experiment was to find the material within the cells responsible for the genetic codes.

For the experiment, Griffith used Streptococcus pneumoniae , known as pneumonia. Pneumonia contains two strains - a smooth and a rough strain. The smooth strain causes pneumonia and contains a polysaccharide coating around it. The rough strain does not cause pneumonia and also lacks a polysaccharide coating. For his first experiment, Griffith took the S strain (smooth strain) and injected it into the mice. He found that the mice contracted pneumonia and ended up dying. He then took the R strain (rough strain) and injected it into the mice and found that they did not contract the pneumonia illness and survived the insertion of the strain. Through these first two experiments Griffith concluded that the polysaccharide coating on the bacteria somehow caused the pneumonia illness, so he used heat to kill the bacteria (polysaccharides are prone to heat) of the S strain and injected the dead bacteria into the mice. He found that the mice lived, which indicated that the polysaccharide coating was not what caused the disease, but rather something living inside the cell. Then he hypothesized that the heat used to kill the bacteria denatured a protein within the living cells, which caused the disease. He then injected the mice with a heat killed S strain and a live R strain, which resulted in the mice dying.

Griffith performed a necropsy on the dead mice and isolated the S strain bacteria from the corpses. He concluded that the live R strain bacteria must have absorbed the genetic material from the dead S strain bacteria, which is called transformation, a process where one strain of a bacterium absorbs genetic material from another strain of bacteria and turns into the type of bacterium whose genetic material it absorbed. Since heat denatures proteins, the protein in the bacterial chromosomes was not the genetic material. However, evidence pointed to DNA. This experiment that Griffith performed was a precursor to the Avery experiment. Avery, Macleod and McCarty followed up on the experiment because they wanted a more definitive experiment and answer.

Avery, MacLeod and McCarty used heat to kill the virulent Streptococcus pneumonia bacteria and extracted RNA, DNA, carbohydrates, lipids and proteins - which were considered possible candidates for the carriers of genetic information - from the dead cells. Each molecule was added to a culture of live non-virulent bacteria to determine which was responsible for changing them into virulent bacteria. DNA was the only molecule that turned the non-virulent cells into virulent cells, which they concluded was the genetic material within cells.

Lockshin, Richard A., The Joy of Science 2007.

Book:Structural Biochemistry

DNA Is Not Destiny: The New Science of Epigenetics

Discoveries in epigenetics are rewriting the rules of disease, heredity, and identity..

Back in 2000, Randy Jirtle, a professor of radiation oncology at Duke University, and his postdoctoral student Robert Waterland designed a groundbreaking genetic experiment that was simplicity itself. They started with pairs of fat yellow mice known to scientists as agouti mice, so called because they carry a particular gene—the agouti gene—that in addition to making the rodents ravenous and yellow renders them prone to cancer and diabetes. Jirtle and Waterland set about to see if they could change the unfortunate genetic legacy of these little creatures. Typically, when agouti mice breed, most of the offspring are identical to the parents: just as yellow, fat as pincushions, and susceptible to life-shortening disease. The parent mice in Jirtle and Waterland's experiment, however, produced a majority of offspring that looked altogether different. These young mice were slender and mousy brown. Moreover, they did not display their parents' susceptibility to cancer and diabetes and lived to a spry old age. The effects of the agouti gene had been virtually erased. Remarkably, the researchers effected this transformation without altering a single letter of the mouse's DNA. Their approach instead was radically straightforward—they changed the moms' diet. Starting just before conception, Jirtle and Waterland fed a test group of mother mice a diet rich in methyl donors, small chemical clusters that can attach to a gene and turn it off. These molecules are common in the environment and are found in many foods, including onions, garlic, beets, and in the food supplements often given to pregnant women. After being consumed by the mothers, the methyl donors worked their way into the developing embryos' chromosomes and onto the critical agouti gene. The mothers passed along the agouti gene to their children intact, but thanks to their methyl-rich pregnancy diet, they had added to the gene a chemical switch that dimmed the gene's deleterious effects. "It was a little eerie and a little scary to see how something as subtle as a nutritional change in the pregnant mother rat could have such a dramatic impact on the gene expression of the baby," Jirtle says. "The results showed how important epigenetic changes could be." Our DNA—specifically the 25,000 genes identified by the Human Genome Project—is now widely regarded as the instruction book for the human body. But genes themselves need instructions for what to do, and where and when to do it. A human liver cell contains the same DNA as a brain cell, yet somehow it knows to code only those proteins needed for the functioning of the liver. Those instructions are found not in the letters of the DNA itself but on it, in an array of chemical markers and switches, known collectively as the epigenome, that lie along the length of the double helix. These epigenetic switches and markers in turn help switch on or off the expression of particular genes. Think of the epigenome as a complex software code, capable of inducing the DNA hardware to manufacture an impressive variety of proteins, cell types, and individuals. The even greater surprise is the recent discovery that epigenetic signals from the environment can be passed on from one generation to the next, sometimes for several generations, without changing a single gene sequence. It's well established, of course, that environmental effects like radiation, which alter the genetic sequences in a sex cell's DNA, can leave a mark on subsequent generations. Likewise, it's known that the environment in a mother's womb can alter the development of a fetus. What's eye-opening is a growing body of evidence suggesting that the epigenetic changes wrought by one's diet, behavior, or surroundings can work their way into the germ line and echo far into the future. Put simply, and as bizarre as it may sound, what you eat or smoke today could affect the health and behavior of your great-grandchildren.In recent years, epigenetics researchers have made great strides in understanding the many molecular sequences and patterns that determine which genes can be turned on and off. Their work has made it increasingly clear that for all the popular attention devoted to genome-sequencing projects, the epigenome is just as critical as DNA to the healthy development of organisms, humans included. Jirtle and Waterland's experiment was a benchmark demonstration that the epigenome is sensitive to cues from the environment. More and more, researchers are finding that an extra bit of a vitamin, a brief exposure to a toxin, even an added dose of mothering can tweak the epigenome—and thereby alter the software of our genes—in ways that affect an individual's body and brain for life. All of these discoveries are shaking the modern biological and social certainties about genetics and identity. We commonly accept the notion that through our DNA we are destined to have particular body shapes, personalities, and diseases. Some scholars even contend that the genetic code predetermines intelligence and is the root cause of many social ills, including poverty, crime, and violence. "Gene as fate" has become conventional wisdom. Through the study of epigenetics, that notion at last may be proved outdated. Suddenly, for better or worse, we appear to have a measure of control over our genetic legacy. "Epigenetics is proving we have some responsibility for the integrity of our genome," Jirtle says. "Before, genes predetermined outcomes. Now everything we do—everything we eat or smoke—can affect our gene expression and that of future generations. Epigenetics introduces the concept of free will into our idea of genetics." Scientists are still coming to understand the many ways that epigenetic changes unfold at the biochemical level. One form of epigenetic change physically blocks access to the genes by altering what is called the histone code. The DNA in every cell is tightly wound around proteins known as histones and must be unwound to be transcribed. Alterations to this packaging cause certain genes to be more or less available to the cell's chemical machinery and so determine whether those genes are expressed or silenced. A second, well-understood form of epigenetic signaling, called DNA methylation, involves the addition of a methyl group—a carbon atom plus three hydrogen atoms—to particular bases in the DNA sequence. This interferes with the chemical signals that would put the gene into action and thus effectively silences the gene. Until recently, the pattern of an individual's epigenome was thought to be firmly established during early fetal development. Although that is still seen as a critical period, scientists have lately discovered that the epigenome can change in response to the environment throughout an individual's lifetime. Epigenetic Changes "People used to think that once your epigenetic code was laid down in early development, that was it for life," says Moshe Szyf, a pharmacologist with a bustling lab at McGill University in Montreal. "But life is changing all the time, and the epigenetic code that controls your DNA is turning out to be the mechanism through which we change along with it. Epigenetics tells us that little things in life can have an effect of great magnitude." Szyf has been a pioneer in linking epigenetic changes to the development of diseases. He long ago championed the idea that epigenetic patterns can shift through life and that those changes are important in the establishment and spread of cancer. For 15 years, however, he had little luck convincing his colleagues. One of his papers was dismissed by a reviewer as a "misguided attempt at scientific humor." On another occasion, a prominent scientist took him aside and told him bluntly, "Let me be clear: Cancer is genetic in origin, not epigenetic." Despite such opposition, Szyf and other researchers have persevered. Through numerous studies, Szyf has found that common signaling pathways known to lead to cancerous tumors also activate the DNA-methylation machinery; knocking out one of the enzymes in that pathway prevents the tumors from developing. When genes that typically act to suppress tumors are methylated, the tumors metastasize. Likewise, when genes that typically promote tumor growth are demethylated—that is, the dimmer switches that are normally present are removed—those genes kick into action and cause tumors to grow. Szyf is now far from alone in the field. Other researchers have identified dozens of genes, all related to the growth and spread of cancer, that become over- or undermethylated when the disease gets under way. The bacteria Helicobacter, believed to be a cause of stomach cancer, has been shown to trigger potentially cancer-inducing epigenetic changes in gut cells. Abnormal methylation patterns have been found in many cancers of the colon, stomach, cervix, prostate, thyroid, and breast. Szyf views the link between epigenetics and cancer with a hopeful eye. Unlike genetic mutations, epigenetic changes are potentially reversible. A mutated gene is unlikely to mutate back to normal; the only recourse is to kill or cut out all the cells carrying the defective code. But a gene with a defective methylation pattern might very well be encouraged to reestablish a healthy pattern and continue to function. Already one epigenetic drug, 5-azacytidine, has been approved by the Food and Drug Administration for use against myelodysplastic syndrome, also known as preleukemia or smoldering leukemia. At least eight other epigenetic drugs are currently in different stages of development or human trials. Methylation patterns also hold promise as diagnostic tools, potentially yielding critical information about the odds that a cancer will respond to treatment. A Berlin-based company called Epigenomics, in partnership with Roche Pharmaceuticals, expects to bring an epigenetic screening test for colon cancer to market by 2008. They are working on similar diagnostic tools for breast cancer and prostate cancer. Szyf has cofounded a company, MethylGene, that so far has developed two epigenetic cancer drugs with promising results in human trials. Others have published data on animal subjects suggesting an epigenetic component to inflammatory diseases like rheumatoid arthritis, neurodegenerative diseases, and diabetes. Other researchers are focusing on how people might maintain the integrity of their epigenomes through diet. Baylor College of Medicine obstetrician and geneticist Ignatia Van den Veyver suggests that once we understand the connection between our epigenome and diseases like cancer, lifelong "methylation diets" may be the trick to staying healthy. Such diets, she says, could be tailored to an individual's genetic makeup, as well as to their exposure to toxins or cancer-causing agents. In 2003 biologist Ming Zhu Fang and her colleagues at Rutgers University published a paper in the journal Cancer Research on the epigenetic effects of green tea. In animal studies, green tea prevented the growth of cancers in several organs. Fang found that epigallocatechin-3-gallate (EGCG), the major polyphenol from green tea, can prevent deleterious methylation dimmer switches from landing on (and shutting down) certain cancer-fighting genes. The researchers described the study as the first to demonstrate that a consumer product can inhibit DNA methylation. Fang and her colleagues have since gone on to show that genistein and other compounds in soy show similar epigenetic effects. Meanwhile, epigenetic researchers around the globe are rallying behind the idea of a human epigenome project, which would aim to map our entire epigenome. The Human Genome Project, which sequenced the 3 billion pairs of nucleotide bases in human DNA, was a piece of cake in comparison: Epigenetic markers and patterns are different in every tissue type in the human body and also change over time. "The epigenome project is much more difficult than the Human Genome Project," Jirtle says. "A single individual doesn't have one epigenome but a multitude of them." Research centers in Japan, Europe, and the United States have all begun individual pilot studies to assess the difficulty of such a project. The early signs are encouraging. In June, the European Human Epigenome Project released its data on epigenetic patterns of three human chromosomes. A recent flurry of conferences have forwarded the idea of creating an international epigenome project that could centralize the data, set goals for different groups, and standardize the technology for decoding epigenetic patterns. Until recently, the idea that your environment might change your heredity without changing a gene sequence was scientific heresy. Everyday influences—the weights Dad lifts to make himself muscle-bound, the diet regimen Mom follows to lose pounds—don't produce stronger or slimmer progeny, because those changes don't affect the germ cells involved in making children. Even after the principles of epigenetics came to light, it was believed that methylation marks and other epigenetic changes to a parent's DNA were lost during the process of cell division that generates eggs and sperm and that only the gene sequence remained. In effect, it was thought, germ cells wiped the slate clean for the next generation. That turns out not to be the case. In 1999 biologist Emma Whitelaw, now at the Queensland Institute of Medical Research in Australia, demonstrated that epigenetic marks could be passed from one generation of mammals to the next. (The phenomenon had already been demonstrated in plants and yeast.) Like Jirtle and Waterland in 2003, Whitelaw focused on the agouti gene in mice, but the implications of her experiment span the animal kingdoms.

"It changes the way we think about information transfer across generations," Whitelaw says. "The mind-set at the moment is that the information we inherit from our parents is in the form of DNA. Our experiment demonstrates that it's more than just DNA you inherit. In a sense that's obvious, because what we inherit from our parents are chromosomes, and chromosomes are only 50 percent DNA. The other 50 percent is made up of protein molecules, and these proteins carry the epigenetic marks and information."

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Concept 17 A gene is made of DNA.

It's hard to imagine now the impact that Avery's experiments must have had. Until Avery's experiments, scientists weren't even sure that bacteria had genes.

Avery's experiments showed that DNA is the tranforming principle, but he didn't try to figure out how transformation works. How do you think transformation works?

CLASSICAL GENETICS

GENETIC ORGANIZATION AND CONTROL

Baby Mice Can Inherit Fear of Certain Smells From Their Parents

But researchers are far from pinning down the mechanism by which this may be possible, or what specific roles epigenetics plays in human disease

Rachel Nuwer

Epigenetics has become something of a buzzword these days. Researchers have long studied how changes in an organism's DNA sequence affect how genes behave, but epigenetics looks at how environmental factors, like diet or lifestyle, can change gene activity in a way that passes from generation to generation. There's interest in how epigenetics might be connected to conditions ranging from cancer to kidney disease to autism . Yet scientists struggle to pin down the specifics of this phenomenon. As the New Scientist explains :

Previous studies have hinted that stressful events can affect the emotional behaviour or metabolism of future generations, possibly through chemical changes to the DNA that can turn genes off and on – a mechanism known as epigenetic inheritance. However, although epigenetic changes have been observed, identifying which ones are relevant is a bit like searching for a needle in a haystack. That's because many genes control behaviours or metabolic diseases like obesity.

Now, a new study published in Nature Neuroscience provides "some of the best evidence yet" that behaviors can indeed be passed from one generation to another, the New Scientist says.

In an experiment reminiscent of A Clockwork Orange , researchers trained male mice to fear a cherry blossom-like scent called acetophenone by inducing slight electric shocks every time the smell wafted into the animals' cages. After ten days of this treatment, whenever cherry blossoms were in the air, they report, the mice trained to fear it went on edge. The researchers found that those mice developed more smell receptors associated with that particular scent, which allowed them to detect it at lower concentrations. Additionally, when researchers examined those males' sperm they found that the gene responsible for acetophenone detection was packaged differently compared to the same gene in control mice.

After imprinting those males with a fear of acetophenone, the researchers inseminated females with the scared mice's sperm. The baby mice never met their father, but those sired by a blossom-hating dad had more acetophenone smell receptors. Compared to pups born of other dads, most were also agitated when acetophenone filled the air. This same finding held true for those original males' grandpups.

Information transfer from one generation to another, outside experts told the New Scientist , may play a role in human diseases such as obesity, diabetes and psychiatric disorders. But researchers are far from pinning down the mechanism by which this may be possible, how long these sensitivities may last or whether these seemingly inherited behaviors affect anything more than smell in mice.

In other words, epigenetics is a field still largely obscured by unanswered questions. As Virginia Hughes summarizes at National Geographic , about all we can know for certain is this: "Our bodies are constantly adapting to a changing world. We have many ways of helping our children make that unpredictable world slightly more predictable, and some of those ways seem to be hidden in our genome."

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Timothy Blake, a postdoctoral fellow in the Waymouth lab, was hard at work on a fantastical interdisciplinary experiment. He and his fellow researchers were refining compounds that would carry instructions for assembling the protein that makes fireflies light up and deliver them into the cells of an anesthetized mouse. If their technique worked, the mouse would glow in the dark.

Colin McKinlay and Jessica Vargas in the lab

Colin McKinlay and Jessica Vargas are co-lead authors of research that could mark a significant step forward for gene therapy by providing a new way of inserting therapeutic proteins into diseased cells. (Image credit: L.A. Cicero)

Not only did the mouse glow, but it also later woke up and ran around, completely unaware of the complex series of events that had just taken place within its body. Blake said it was the most exciting day of his life.

This success, the topic of a recent paper in Proceedings of the National Academy of Sciences , could mark a significant step forward for gene therapy. It’s hard enough getting these protein instructions, called messenger RNA (mRNA), physically into a cell. It’s another hurdle altogether for the cell to actually use them to make a protein. If the technique works in people, it could provide a new way of inserting therapeutic proteins into diseased cells.

“It’s almost a childlike enthusiasm we have for this,” said chemistry Professor Robert Waymouth , co-senior author of the study. “The code for an insect protein is put into an animal and that protein is not only synthesized in the cells but it’s folded and it becomes fully functional, capable of emitting light.”

Although the results are impressive, this technique is remarkably simple and fast. And unlike traditional gene therapy that permanently alters the genetic makeup of the cell, mRNA is short-lived and its effects are temporary. The transient nature of mRNA transmission opens up special opportunities, such as using these compounds for vaccination or cancer immunotherapy.

Making a protein

Gene therapy is a decades-old field of research that usually focuses on modifying DNA, the fundamental genetic code. That modified DNA then produces a modified mRNA, which directs the creation of a modified protein. The current work skips the DNA and instead just delivers the protein’s instructions.

Previous work has been successful at delivering a different form of RNA – called short interfering RNA, or siRNA – but sending mRNA through a cell membrane is a much bigger problem. While both siRNA and mRNA have many negative charges – so-called polyanions – mRNA is considerably more negatively charged, and therefore more difficult to sneak through the positively charged cell membrane.

What the researchers needed was a positively charged delivery method – a polycation – to complex, protect and shuttle the polyanions. However, this alone would only assure that the mRNA made it through the cell membrane. Once inside, the mRNA needed to detach from the transporter compound in order to make proteins.

The researchers addressed this twofold challenge with a novel, deceptively straightforward creation, which they call charge-altering releasable transporters (CARTs).

“What distinguishes this polycation approach from the others, which often fail, is the others don’t change from polycations to anything else,” said chemistry Professor Paul Wender , co-senior author of the study. “Whereas, the ones that we’re working with will change from polycations to neutral small molecules. That mechanism is really unprecedented.”

As part of their change from polycations to polyneutrals, CARTs biodegrade and are eventually excreted from the body.

The power of collaboration

This research was made possible through coordination between the chemists and experts in imaging molecules in live animals, who rarely work together directly. With this partnership, the synthesis, characterization and testing of compounds could take as little as a week.

“We are so fortunate to engage in this kind of collaborative project between chemistry and our clinical colleagues. It allowed us to see our compounds go from very basic building blocks – all the way from chemicals we buy in a bottle – to putting a firefly gene into a mouse,” said Colin McKinlay, a graduate student in the Wender lab and co-lead author of the study.

Not only did this enhanced ability to test and re-test new molecules lead to the discovery of their charge-altering behavior, it allowed for quick optimization of their properties and applications. As different challenges arise in the future, the researchers believe they will be able to respond with the same rapid flexibility.

After showing that the CARTs could deliver a glowing jellyfish protein to cells in a lab dish, the group wanted to find out if they worked in living mice, which was made possible through the expertise of the Contag lab , run by Christopher Contag , professor of pediatrics and of microbiology and immunology and co-senior author of the study. Together, the multidisciplinary team showed that the CARTs could effectively deliver mRNA that produced glowing proteins in the thigh muscle or in the spleen and liver, depending on where the injection was made.

A bright future ahead

The researchers said CARTs could move the field of gene therapy forward dramatically in several directions.

“Gene therapy has been held up as a silver bullet because the idea that you could pick any gene you want is so alluring,” said Jessica Vargas, co-lead author of the study, who was a PhD student in the Wender lab during this research. “With mRNA, there are more limitations because the protein expression is transient, but that opens up other applications where you wouldn’t use other types of gene therapy.”

One especially appropriate application of this technology is vaccination. At present, vaccines require introducing part of a virus or an inactive virus into the body in order to elicit an immune response. CARTs could potentially cut out the middleman, directly instructing the body to produce its own antigens. Once the CART dissolves, the immunity remains without any leftover foreign material present.

The team is also working on applying their technique to another genetic messenger that would produce permanent effects, making it a complementary option to the temporary mRNA therapies. With the progress already made using mRNA and the potential of their ongoing research, they and others could be closer than ever to making individualized therapeutics using a person’s own cells. “Creating a firefly protein in a mouse is amazing but, more than that, this research is part of a new era in medicine,” said Wender.

Additional co-authors of this study, “Charge-altering releasable transporters (CARTs) for the delivery and release of mRNA in living animals,” include Timothy Blake, Jonathan Hardy, Masamitsu Kanada and Christopher Contag. Waymouth is also a professor, by courtesy, of chemical engineering, a member of Stanford Bio-X , a faculty fellow of Stanford ChEM-H and an affiliate of the Stanford Woods Institute for the Environment . Wender is also a professor, by courtesy, of chemical and systems biology, a member of Stanford Bio-X, a member of the Stanford Cancer Institute and a faculty fellow of Stanford ChEM-H. Contag is also a professor, by courtesy, of radiology and of bioengineering, a member of Stanford Bio-X, a member of the Child Health Research Institute and a member of the Stanford Cancer Institute.

This work was funded by the Department of Energy, the National Science Foundation, the National Institutes of Health, the Chambers Family Foundation for Excellence in Pediatric Research, the Child Health Research Institute, the Stanford Center for Molecular Analysis and Design and the National Center for Research Resources.

Mice Inherit Specific Memories, Because Epigenetics?

Two weeks ago I wrote about some tantalizing research coming out of the Society for Neuroscience meeting in San Diego. Brian Dias , a postdoctoral fellow in Kerry Ressler’s lab at Emory University, had reported that mice inherit specific smell memories from their fathers — even when the offspring have never experienced that smell before, and even when they’ve never met their father. What’s more, their children are born with the same specific memory.

This was a big, surprising claim, causing many genetics experts to do a double-take, as I discovered from a subsequent flurry of Tweets . “Crazy Lamarkian shit,” quipped Laura Hercher (@laurahercher), referring to Lamarckian inheritance, the largely discredited theory that says an organism can pass down learned behaviors or traits to its offspring. “My instinct is deep skepticism, but will have to wait for paper to come out,” wrote Kevin Mitchell (@WiringTheBrain). “If true, would be revolutionary.”

The paper is out today in Nature Neuroscience , showing what I reported before as well as the beginnings of an epigenetic explanation. (Epigenetics usually refers to chemical changes that affect gene expression without altering the DNA code).

Having the data in hand allowed me to fill in the backstory of the research, as well as gather more informed reactions from experts in neuroscience and in genetics. I’ve gone into a lot of detail below, but here’s the bottom line: The behavioral results are surprising, solid, and will certainly inspire further studies by many other research groups. The epigenetic data seems gauzy by comparison, with some experts saying it’s thin-but-useful and others finding it full of holes.

So what is the surprising part, again?

If you’ve followed science news over the past decade then you’ve probably heard about epigenetics, a field that’s caught fire in the minds of scientists and the public, and understandably so. Epigenetic studies have shown that changes in an organism’s external environment — its life experiences and even its choices, if you want to get hyperbolic — can influence the expression of its otherwise inflexible DNA code. Epigenetics, in other words, is enticing because it offers a resolution to the tedious, perennial debates of nurture versus nature.

But some scientists dispute the notion that epigenetic changes have much influence on behavior (see this Nature feature this Nature feature this Nature feature for a great overview of the debate). Even more controversial is the idea that epigenetic changes can be passed down from one generation to the next, effectively giving parents a way to prime their children for a specific environment. The key question isn’t whether this so-called ‘transgenerational epigenetic inheritance’ happens — it does — but rather how it happens (and how frequently, and in what contexts and species).

That’s what Dias and Ressler wanted to investigate. Trouble is, environmental influences such as stress are notoriously difficult to measure. So the researchers focused on the mouse olfactory system, the oft-studied and well-mapped brain circuits that process smell. “We thought it would give us a molecular foothold into how transgenerational inheritance might occur,” Dias says.

The researchers made mice afraid of a fruity odor, called acetophenone, by pairing it with a mild shock to the foot. In a study published a few years ago, Ressler had shown that this type of fear learning is specific: Mice trained to fear one particular smell show an increased startle to that odor but not others. What’s more, this fear learning changes the organization of neurons in the animal’s nose, leading to more cells that are sensitive to that particular smell.

Ten days after this fear training, Dias allowed the animals to mate. And that’s where the crazy begins. The offspring (known as the F1 generation) show an increased startle to the fruity smell even when they have never encountered the smell before, and thus have no obvious reason to be sensitive to it. And their reaction is specific: They do not startle to another odor called propanol. Craziest of all, their offspring (the F2 generation) show the same increased sensitivity to acetophenone.

The scientists then looked at the F1 and F2 animals’ brains. When the grandparent generation is trained to fear acetophenone, the F1 and F2 generations’ noses end up with more “M71 neurons,” which contain a receptor that detects acetophenone. Their brains also have larger “M71 glomeruli,” a region of the olfactory bulb that responds to this smell.

“When Brian came in with the first set of data, we both just couldn’t believe it,” Ressler recalls. “I was like, ‘Well, it must just be random, let’s do it again.’ And then it just kept working. We do a lot of behavior [experiments], but being able to see structural change that correlates with behavior is really pretty astounding.”

Still, those experiments couldn’t rule out some kind of social, rather than biological transmission. Perhaps fathers exposed to the fear training treated their children differently. Or maybe mothers, sensing something odd in their mate’s behavior, treated their children differently.

To control for these possibilities, the researchers performed an in vitro fertilization (IVF) experiment in which they trained male animals to fear acetophenone and then 10 days later harvested the animals’ sperm. They sent the sperm to another lab across campus where it was used to artificially inseminate female mice. Then the researchers looked at the brains of the offspring. They had larger M71 glomeruli, just as before. (The researchers couldn’t perform behavioral tests on these animals because of laboratory regulations about animal quarantine.)

“For me it clicked when we did the IVF,” Dias says. “When the brain anatomy persisted, that to me emphasized that it’s not really a social transmission. It’s inherited.”

Other researchers also seem convinced. “It is high time public health researchers took human transgenerational responses seriously,” says Marcus Pembrey, emeritus professor of paediatric genetics at University College London, who has been championing the idea of epigenetic inheritance for over a decade. “I suspect we will not understand the rise in neuropsychiatric disorders or obesity, diabetes and metabolic disruptions generally without taking a multigenerational approach,” he says.

In an interesting historical aside, Pembrey also notes that the new study echoes an experiment that Ivan Pavlov did * 90 years ago, in which he trained mice to associate food with the sound of a bell. Pavlov “reported that successive generations took fewer and fewer training sessions before they would search for food on hearing a bell even when food was absent,” Pembrey says. Nevertheless, the idea that experience could be biologically inherited fell out of favor in the 20th century. “If alive today, Pavlov would have been delighted by the Dias and Ressler paper, first as a vindication of his own experiment and results, and second by the amazing experimental tools available to the modern scientist.”

Neuroscientists, too, are enthusiastic about what these results might mean for understanding the brain.

“To my knowledge this is the first example, in any animal, of epigenetic transmission of a simple memory for a specific perceptual stimulus,” says Tomás Ryan , a research fellow at MIT who studies how memories form in the brain. “The broader implications for the neuroscience of memory and to evolutionary biology in general could be paradigm shifting and unprecedented.”

There are still some unanswered questions, Ryan notes. For example, the researchers didn’t do a control experiment where the F0 animals are exposed to the fruity odor without the shock. So it’s unclear whether the “memory” they’re transmitting to their offspring is a fear memory, per se, or rather an increased sensitivity to an odor. This is an important distinction, because the brain uses many brain circuits outside of the olfactory bulb to encode fear memories. It’s difficult to imagine how that kind of complicated brain imprint might get passed down to the next generation.

Ressler and Dias agree, and for that reason were careful not to refer to the transmitted information as a fear memory. “I don’t know if it’s a memory,” Dias says. “It’s a sensitivity, for now.”

What’s that got to do with epigenetics?

So let’s call it a sensitivity. How could a smell sensitivity, formed in an adult animal’s olfactory bulb in its brain, possibly be transmitted to its gonads and passed on to future generations?

The researchers are nowhere near being able to answer that question, but they have some data that points to epigenetics.

There are several types of epigenetic modifications. One of the best understood is DNA methylation. There are millions of spots along the mouse genome (and the human genome), called CpG sites, where methyl groups can attach and affect the expression of nearby genes. Typically, methylation dials down gene expression.

Dias and Ressler sent sperm samples of mice that had been fear-conditioned to either acetophenone or propanol to a private company, called Active Motif , which specializes in methylation analyses. The company’s researchers (who were blinded to which samples were which) mapped out the sperm methylation patterns near two olfactory genes: Olfr151, which codes for the M71 receptor that’s sensitive to acetophenone, and Olfr6, which codes for another odor receptor that is not sensitive to either odor.

It turns out that Olfr151, but not the other gene, is significantly less methylated in sperm from animals trained to fear acetophenone than in sperm from those trained to fear propanol. Because less methylation usually means a boost in gene expression, this could plausibly explain why these animals have more M71 receptors in their brains, the researchers say.

What’s more, the same under-methylation shows up in the sperm of F1 animals whose fathers had been trained to fear acetophenone.

“It’s a very precise signal,” Ressler says. “The convergence of this data, we think, shows that this is a really profound and robust phenomenon.”

Others, though, find a number of flaws in this epigenetic explanation.

Timothy Bestor , professor of genetics and development at Columbia University, points out that methylated CpG sites only affect gene expression when they are located in the so-called gene promoter, a region about 500 bases upstream of the gene. But the Olfr151 gene doesn’t have any CpG sites in its promoter.

That means the differences in methylation reported in the paper must have occurred within the body of the gene itself. “And methylation in the gene body is common to all genes whether they’re expressed or not,” Bestor says. “I don’t see any way by which that gene could be directly regulated by methylation.”

But what would explain the methylation differences between the trained animals and controls? They’re pretty subtle, he says, and “could easily be a statistical fluke.”

Bestor was skeptical from the outset, based on the mechanics of the reproductive system alone. “There’s a real problem in how the signal could reach the germ cells,” he says.

For one thing, the seminiferous tubules, where sperm is made inside of the testes, don’t have any nerves. “So there’s no way the central nervous system could affect germ cell development.” What’s more, he says it’s not likely that acetophenone would be able to cross the blood-testis barrier , the sheet of cells that separates the seminiferous tubules from the blood.

By this point in my conversation with Bestor, I was starting to feel a bit defensive on behalf of epigenetics and all of its wonder. “Are you saying you think epigenetic inheritance is a bunch of bologna?” I asked helplessly.

“No,” he said, laughing. “It’s just not as dynamic as people think.”

What’s next?

A good next step in resolving these pesky mechanistic questions would be to use chromatography to see whether odorant molecules like acetophenone actually get into the animals’ bloodstream, Dias says. “The technology is surely there, and I think we are going to go down those routes.”

First, though, Dias and Ressler are working on another behavioral experiment. They want to know: If the F0 mice un-learn the fear of acetophenone (which can be done by repeated exposures to the smell without a shock) and then reproduce, will their children still have an increased sensitivity to it?

“We have no idea yet,” says Ressler, a practicing psychiatrist who has long been interested in the effects of post-traumatic stress disorder (PTSD). “But we think this would have tremendous implications for the treatment of adults [with PTSD] before they have children.”

It will take a lot more work before scientists come close to understanding how these data relate to human anxiety disorders. So what, after all of these words, should we take away from this study now?

Hell if I know. Here’s the most rational and conservative appraisal I can muster: Our bodies are constantly adapting to a changing world. We have many ways of helping our children make that unpredictable world slightly more predictable, and some of those ways seem to be hidden in our genome.

Anne Ferguson-Smith , a geneticist at the University of Cambridge, put it more succinctly. The study, she says, “potentially adds to the growing list of compelling models telling us that something is going on that facilitates transmission of environmentally induced traits.”

Scientists, I have to assume, will be furiously working on what that something is for many decades to come. And I’ll be following along, or trying to, with awe.

*Update, 12/1/13, 2:35pm: It seems that that Pavlov experiment may have been retracted in 1927, though I don’t know anything about that beyond what is stated here .

Style note: A few paragraphs of this post were adapted from my earlier post on this research, published November 15 .

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Open access
Published: 19 November 2014

A comparative encyclopedia of DNA elements in the mouse genome

Feng Yue 1 , 2 na1 nAff43 ,
Yong Cheng 3 na1 ,
Alessandra Breschi 4 na1 ,
Jeff Vierstra 5 na1 ,
Weisheng Wu 6 na1 nAff43 ,
Tyrone Ryba 7 na1 nAff43 ,
Richard Sandstrom 5 na1 ,
Zhihai Ma 3 na1 ,
Carrie Davis 8 na1 ,
Benjamin D. Pope 7 na1 ,
Yin Shen 1 na1 ,
Dmitri D. Pervouchine 4 ,
Sarah Djebali 4 ,
Robert E. Thurman 5 ,
Rajinder Kaul 5 ,
Eric Rynes 5 ,
Anthony Kirilusha 9 ,
Georgi K. Marinov 9 ,
Brian A. Williams 9 ,
Diane Trout 9 ,
Henry Amrhein 9 ,
Katherine Fisher-Aylor 9 ,
Igor Antoshechkin 9 ,
Gilberto DeSalvo 9 ,
Lei-Hoon See 8 ,
Meagan Fastuca 8 ,
Jorg Drenkow 8 ,
Chris Zaleski 8 ,
Alex Dobin 8 ,
Pablo Prieto 4 ,
Julien Lagarde 4 ,
Giovanni Bussotti 4 ,
Andrea Tanzer 4 , 10 ,
Olgert Denas 11 ,
Kanwei Li 11 ,
M. A. Bender 12 , 13 ,
Miaohua Zhang 14 ,
Rachel Byron 14 ,
Mark T. Groudine 14 , 15 ,
David McCleary 1 ,
Long Pham 1 ,
Zhen Ye 1 ,
Samantha Kuan 1 ,
Lee Edsall 1 ,
Yi-Chieh Wu 16 ,
Matthew D. Rasmussen 16 ,
Mukul S. Bansal 16 ,
Manolis Kellis 16 , 17 ,
Cheryl A. Keller 6 ,
Christapher S. Morrissey 6 ,
Tejaswini Mishra 6 ,
Deepti Jain 6 ,
Nergiz Dogan 6 ,
Robert S. Harris 6 ,
Philip Cayting 3 ,
Trupti Kawli 3 ,
Alan P. Boyle 3 nAff43 ,
Ghia Euskirchen 3 ,
Anshul Kundaje 3 ,
Shin Lin 3 ,
Yiing Lin 3 ,
Camden Jansen 18 ,
Venkat S. Malladi 3 ,
Melissa S. Cline 19 ,
Drew T. Erickson 3 ,
Vanessa M. Kirkup 19 ,
Katrina Learned 19 ,
Cricket A. Sloan 3 ,
Kate R. Rosenbloom 19 ,
Beatriz Lacerda de Sousa 20 ,
Kathryn Beal 21 ,
Miguel Pignatelli 21 ,
Paul Flicek 21 ,
Jin Lian 22 ,
Tamer Kahveci 23 ,
Dongwon Lee 24 ,
W. James Kent 19 ,
Miguel Ramalho Santos 20 ,
Javier Herrero 21 , 25 ,
Cedric Notredame 4 ,
Audra Johnson 5 ,
Shinny Vong 5 ,
Kristen Lee 5 ,
Daniel Bates 5 ,
Fidencio Neri 5 ,
Morgan Diegel 5 ,
Theresa Canfield 5 ,
Peter J. Sabo 5 ,
Matthew S. Wilken 26 ,
Thomas A. Reh 26 ,
Erika Giste 5 ,
Anthony Shafer 5 ,
Tanya Kutyavin 5 ,
Eric Haugen 5 ,
Douglas Dunn 5 ,
Alex P. Reynolds 5 ,
Shane Neph 5 ,
Richard Humbert 5 ,
R. Scott Hansen 5 ,
Marella De Bruijn 27 ,
Licia Selleri 28 ,
Alexander Rudensky 29 ,
Steven Josefowicz 29 ,
Robert Samstein 29 ,
Evan E. Eichler 5 ,
Stuart H. Orkin 30 ,
Dana Levasseur 31 ,
Thalia Papayannopoulou 32 ,
Kai-Hsin Chang 31 ,
Arthur Skoultchi 33 ,
Srikanta Gosh 33 ,
Christine Disteche 34 ,
Piper Treuting 35 ,
Yanli Wang 36 ,
Mitchell J. Weiss 37 ,
Gerd A. Blobel 38 , 39 ,
Xiaoyi Cao 40 ,
Sheng Zhong 40 ,
Ting Wang 41 ,
Peter J. Good 42 ,
Rebecca F. Lowdon 42 nAff43 ,
Leslie B. Adams 42 nAff43 ,
Xiao-Qiao Zhou 42 ,
Michael J. Pazin 42 ,
Elise A. Feingold 42 ,
Barbara Wold 9 ,
James Taylor 11 ,
Ali Mortazavi 18 ,
Sherman M. Weissman 22 ,
John A. Stamatoyannopoulos 5 ,
Michael P. Snyder 3 ,
Roderic Guigo 4 ,
Thomas R. Gingeras 8 ,
David M. Gilbert 7 ,
Ross C. Hardison 6 ,
Michael A. Beer 24 na1 ,
Bing Ren 1 &

The Mouse ENCODE Consortium

Nature volume 515 , pages 355–364 ( 2014 ) Cite this article

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Epigenomics

The laboratory mouse shares the majority of its protein-coding genes with humans, making it the premier model organism in biomedical research, yet the two mammals differ in significant ways. To gain greater insights into both shared and species-specific transcriptional and cellular regulatory programs in the mouse, the Mouse ENCODE Consortium has mapped transcription, DNase I hypersensitivity, transcription factor binding, chromatin modifications and replication domains throughout the mouse genome in diverse cell and tissue types. By comparing with the human genome, we not only confirm substantial conservation in the newly annotated potential functional sequences, but also find a large degree of divergence of sequences involved in transcriptional regulation, chromatin state and higher order chromatin organization. Our results illuminate the wide range of evolutionary forces acting on genes and their regulatory regions, and provide a general resource for research into mammalian biology and mechanisms of human diseases.

Perspectives on ENCODE

Expanded encyclopaedias of DNA elements in the human and mouse genomes

Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals

Despite the widespread use of mouse models in biomedical research 1 , the genetic and genomic differences between mice and humans remain to be fully characterized. At the sequence level, the two species have diverged substantially: approximately one half of human genomic DNA can be aligned to mouse genomic DNA, and only a small fraction (3–8%) is estimated to be under purifying selection across mammals 2 . At the cellular level, a systematic comparison is still lacking. Recent studies have revealed divergent DNA binding patterns for a limited number of transcription factors across multiple related mammals 3 , 4 , 5 , 6 , 7 , 8 , suggesting potentially wide-ranging differences in cellular functions and regulatory mechanisms 9 , 10 . To fully understand how DNA sequences contribute to the unique molecular and cellular traits in mouse, it is crucial to have a comprehensive catalogue of the genes and non-coding functional sequences in the mouse genome.

Advances in DNA sequencing technologies have led to the development of RNA-seq (RNA sequencing), DNase-seq (DNase I hypersensitive sites sequencing), ChIP-seq (chromatin immunoprecipitation followed by DNA sequencing), and other methods that allow rapid and genome-wide analysis of transcription, replication, chromatin accessibility, chromatin modifications and transcription factor binding in cells 11 . Using these large-scale approaches, the ENCODE consortium has produced a catalogue of potential functional elements in the human genome 12 . Notably, 62% of the human genome is transcribed in one or more cell types 13 , and 20% of human DNA is associated with biochemical signatures typical of functional elements, including transcription factor binding, chromatin modification and DNase hypersensitivity. The results support the notion that nucleotides outside the mammalian-conserved genomic regions could contribute to species-specific traits 6 , 12 , 14 .

We have applied the same high-throughput approaches to over 100 mouse cell types and tissues 15 , producing a coordinated group of data sets for annotating the mouse genome. Integrative analyses of these data sets uncovered widespread transcriptional activities, dynamic gene expression and chromatin modification patterns, abundant cis -regulatory elements, and remarkably stable chromosome domains in the mouse genome. The generation of these data sets also allowed an unprecedented level of comparison of genomic features of mouse and human. Described in the current manuscript and companion works, these comparisons revealed both conserved sequence features and widespread divergence in transcription and regulation. Some of the key findings are:

Although much conservation exists, the expression profiles of many mouse genes involved in distinct biological pathways show considerable divergence from their human orthologues.

A large portion of the cis -regulatory landscape has diverged between mouse and human, although the magnitude of regulatory DNA divergence varies widely between different classes of elements active in different tissue contexts.

Mouse and human transcription factor networks are substantially more conserved than cis -regulatory DNA.

Species-specific candidate regulatory sequences are significantly enriched for particular classes of repetitive DNA elements.

Chromatin state landscape in a cell lineage is relatively stable in both human and mouse.

Chromatin domains, interrogated through genome-wide analysis of DNA replication timing, are developmentally stable and evolutionarily conserved.

Overview of data production and initial processing

To annotate potential functional sequences in the mouse genome, we used ChIP-seq, RNA-seq and DNase-seq to profile transcription factor binding, chromatin modification, transcriptome and chromatin accessibility in a collection of 123 mouse cell types and primary tissues ( Fig. 1a , Supplementary Tables 1–3 ). Additionally, to interrogate large-scale chromatin organization across different cell types, we also used a microarray-based technique to generate replication-timing profiles in 18 mouse tissues and cell types ( Supplementary Table 3 ) 16 . Altogether, we produced over 1,000 data sets. The list of the data sets and all the supporting material for this manuscript are also available at website http://mouseencode.org . Below we briefly outline the experimental approach and initial data processing for each class of sequence features.

a , A genome browser snapshot shows the primary data and annotated sequence features in the mouse CH12 cells (Methods). b , Chart shows that much of the human and mouse genomes is transcribed in one or more cell and tissue samples. c , A bar chart shows the percentages of the mouse genome annotated as various types of cis -regulatory elements (Methods). DHS, DNase hypersensitive sites; TF, transcription factor. d , Pie charts show the fraction of the entire genome that is covered by each of the seven states in the mouse embryonic stem cells (mESC) and adult heart. e , Charts showing the number of replication timing (RT) boundaries in specific mouse and human cell types, and the total number of boundaries from all cell types combined. ESC, embryonic stem cell; endomeso, endomesoderm; NPC, neural precursor; GM06990, B lymphocyte; HeLa-S3, cervical carcinoma; IMR90, fetal lung fibroblast; EPL, early primitive ectoderm-like cell; EBM6/EpiSC, epiblast stem cell; piPSC, partially induced pluripotent stem cell; MEF, mouse embryonic fibroblast; MEL, murine erythroleukemia; CH12, B-cell lymphoma.

PowerPoint slide

RNA transcriptome

To comprehensively identify the genic regions that produce transcripts in the mouse genome, we performed RNA-seq experiments in 69 different mouse tissues and cell types with two biological replicates each ( Supplementary Table 3 , Supplementary Information ) and uncovered 436,410 contigs ( Supplementary Table 4 ). Confirming previous reports 13 , 17 , 18 and similar to the human genome, the mouse genome is pervasively transcribed ( Fig. 1b ), with 46% capable of producing polyadenylated messenger RNAs (mRNA). By comparison, 39% of the human genome is devoted to making mRNAs. In both species, the vast majority (87–93%) of exonic nucleotides were detected as transcribed, confirming the sensitivity of the approach. However, a higher percentage of intronic sequences were detected as transcribed in the mouse, and this might be owing to a greater sequencing depth and broader spectrum of biological samples analysed in mouse ( Fig. 1b ).

Candidate cis -regulatory sequences

To identify potential cis -regulatory regions in the mouse genome, we used three complementary approaches that involved mapping of chromatin accessibility, specific transcription factor occupancy sites and histone modification patterns. All of these approaches have previously been shown to uncover cis regulatory elements with high accuracy and sensitivity 19 , 20 .

By mapping DNase I hypersensitive sites (DHSs) in 55 mouse cell and tissue types 21 , we identified a combined total of ∼ 1.5 million distinct DHSs at a false discovery rate (FDR) of 1% ( Supplementary Table 5 ) 22 . Genomic footprinting analysis in a subset (25) of these cell types further delineated 8.9 million distinct transcription factor footprints. De novo derivation of a cis -regulatory lexicon from mouse transcription factor footprints revealed a recognition repertoire nearly identical with that of the human, including both known and novel recognition motifs 25 .

We used ChIP-seq to determine the binding sites for a total of 37 transcription factors in various subsets of 33 cell/tissue types. Of these 37 transcription factors, 24 were also extensively mapped in the murine and human erythroid cell models (MEL and K562) and B-lymphoid cell lines (CH12 and GM12878) 23 . In total we defined 2,107,950 discrete ChIP-seq peaks, representing differential cell/tissue occupancy patterns of 280,396 distinct transcription factor binding sites ( Supplementary Methods and Supplementary Table 6 ).

We also performed ChIP-seq for as many as nine histone H3 modifications (H3K4me1, H3K4me2, H3K4me3, H3K9me3, H3K27ac, H3K27me3, H3K36me3, H3K79me2 and H3K79me3) in up to 23 mouse tissues and cell types per mark. We applied a supervised machine learning technique, random-forest based enhancer prediction from chromatin state (RFECS), to three histone modifications (H3K4me1, H3K4me3 and H3K27ac) 24 , identifying a total of 82,853 candidate promoters and 291,200 candidate enhancers in the mouse genome ( Supplementary Tables 7 and 8 ). To functionally validate the predictions, we randomly selected 76 candidate promoter elements (average size 1,000 bp, Supplementary Table 9 ) and 183 candidate enhancer elements (average size 1,000 bp, Supplementary Table 10 ). For candidate promoter elements, we cloned these previously unannotated sequences into reporter constructs, and performed luciferase reporter assays via transient transfection in pertinent mouse cell lines . For candidate enhancer elements, we performed functional validation assay using a high throughput method (see Supplementary Methods ). Overall, 66/76 (87%) candidate promoters and 129/183 (70.5%) candidate enhancers showed significant activity in these assays, compared to 2/30 randomly selected negative controls ( Supplementary Fig. 1c ).

Collectively, our studies assigned potential regulatory function to 12.6% of the mouse genome ( Fig. 1c ).

Transcription factor networks

We explored the transcription factor networks and combinatorial transcription factor binding patterns in the mouse samples in two companion papers, and compared these networks to regulatory circuitry models generated for the human genome 23 , 25 . From genomic footprints, we constructed transcription-factor-to-transcription-factor cross-regulatory network in each of 25 cell/tissue types for a total of ∼ 500 transcription factors with known recognition sequences. Analyses of these networks revealed regulatory relationships between transcription factor genes that are strongly preserved in human and mouse, in spite of the extensive plasticity of the cis -regulatory landscape (detailed below). Whereas only 22% of transcription factor footprints are conserved, nearly 50% of cross-regulatory connections between mouse transcription factors are conserved in human through the innovation of novel binding sites. Moreover, analysis of network motifs shows that larger-scale architectural features of mouse and human transcription factor networks are strikingly similar 25 .

Chromatin states

We produced integrative maps of chromatin states in 15 mouse tissue and cell types and six human cell lines ( Supplementary Table 11 ), using a hidden Markov model (chromHMM) 26 , 27 that allowed us to segment the genome in each cell type into seven distinct combination of chromatin modification marks (or chromatin states). One state is characterized by the absence of any chromatin marks, while every other state features either predominantly one modification or a combination of two modifications ( Extended Data Table 1 , Supplementary Information ). The portion of the genome in each chromatin state varied with cell type ( Fig. 1d , Supplementary Fig. 2 ). Similar proportions of the genome are found in the active states in each cell type, for both mouse and human. Interestingly, excluding the ‘unmarked’ state, the fraction of each genome that is in the H3K27me3-dominated, transcriptionally repressed state is the most variable, suggesting a profound role of transcriptional repression in shaping the cis -regulatory landscape during mammalian development.

Replication domains

Replication-timing, the temporal order in which megabase-sized genomic regions replicate during S-phase, is linked to the spatial organization of chromatin in the nucleus 28 , 29 , 30 , 31 , serving as a useful proxy for tracking differences in genome architecture between cell types 32 , 33 . Since different types of chromatin are assembled at different times during the S phase 34 , changes in replication timing during differentiation could elicit changes in chromatin structure across large domains. We obtained 36 mouse and 31 human replication-timing profiles covering 11 and 9 distinct stages of development, respectively ( Supplementary Table 12 ). We defined ‘replication boundaries’ as the sites where replication profiles change slope from synchronously replicating segments (discussed later). A total of 64,535 and 50,194 boundaries identified across all mouse and human data sets, respectively, were mapped to 4,322 and 4,675 positions, with each cell type displaying replication-timing transitions at 50–80% of these positions ( Fig. 1e ).

Annotation of orthologous coding and non-coding genes

To facilitate a systematic comparison of the transcriptome, cis -regulatory elements and chromatin landscape between the human and mouse genomes, we built a high-quality set of human–mouse orthologues of protein coding and non-coding genes 35 . The list of protein-coding orthologues, based on phylogenetic reconstruction, contains a total of 15,736 one-to-one and a smaller set of one-to-many and many-to-many orthologue pairs ( Supplementary Tables 13–15 ). We also inferred orthologous relationships among short non-coding RNA genes using a similar phylogenetic approach. We established one-to-one human–mouse orthologues for 151,257 internal exon pairs ( Supplementary Table 16 ) and 204,887 intron pairs ( Supplementary Table 17 ), and predicted 2,717 (3,446) novel human (respectively, mouse) exons ( Supplementary Table 18 ). Additionally, we mapped the 17,547 human long non-coding RNA (lncRNA) transcripts annotated in Gencode v10 onto the mouse genome. We found 2,327 (13.26%) human lncRNA transcripts (corresponding to 1,679, or 15.48%, of the lncRNA genes) homologous to 5,067 putative mouse transcripts (corresponding to 3,887 putative genes) ( Supplementary Fig. 3 , Supplementary Table 19 ). Consistent with previous observations, only a small fraction of lncRNAs are constrained at the primary sequence level, with rapid evolutionary turnover 36 . Other comparisons of human and mouse transcriptomes, covering areas including pre-mRNA splicing, antisense and intergenic RNA transcription, are detailed in an associated paper 37 .

Divergent and conserved gene expression patterns

Previous studies have revealed remarkable examples of species-specific gene expression patterns that underlie phenotypic changes during evolution 38 , 39 , 40 , 41 , 42 . In these cases changes in expression of a single gene between closely related species led to adaptive changes. However, it is not clear how extensive the changes in expression patterns are between more distantly related species, such as mouse and human, with some studies emphasizing similarities in transcriptome patterns of orthologous tissues 43 , 44 , 45 and others emphasizing substantial interspecies differences 46 . Our initial analyses revealed that gene expression patterns tended to cluster more by species rather than by tissue ( Fig. 2a ). To resolve the sets of genes contributing to different components in the clustering, we employed variance decomposition (see Methods) to estimate, for each orthologous human–mouse gene pair, the proportion of the variance in expression that is contributed by tissue and by species ( Fig. 2b ). This analysis revealed the sets of genes whose expression varies more across tissues than between species, and those whose expression varies more between species than across tissues. As expected, the clustering of the RNA-seq samples is dominated either by species or tissues, depending on the gene set employed ( Extended Data Fig. 1a, b ). Furthermore, removal of the ∼ 4,800 genes that drive the species-specific clustering (see ref. 47 , Supplementary Fig. 1d therein) or normalization methods that reduce the species effects reveal tissue-specific patterns of expression in the same samples ( Extended Data Fig. 1c ). Categorizing orthologous gene pairs into these groups should enable more informative translation of research results between mouse and human. In particular, for gene pairs whose variance in expression is largest between tissues (and less between species), mouse should be a particularly informative model for human biology. In contrast, interpretation of studies involving genes whose variance in expression is larger between species needs to take into account the species variation. The relative contributions of species-specific and tissue-specific factors to each gene’s expression are further explored in two associated papers 37 , 47 .

a , Principal component analysis (PCA) was performed for RNA-seq data for 10 human and mouse matching tissues. The expression values are normalized across the entire data set. Solid squares denote human tissues. Open squares denote mouse tissues. Each category of tissue is represented by a different colour. b , Gene expression variance decomposition (see Methods) estimates the relative contribution of tissue and species to the observed variance in gene expression for each orthologous human–mouse gene pair. Green dots indicate genes with higher between-tissue contribution and red dots genes with higher between-species contributions. c , Neighbourhood analysis of conserved co-expression (NACC) in human and mouse samples. The distribution of NACC scores for each gene is shown. d , A scatter plot shows the average of NACC score over the set of genes in each functional gene ontology category. Highlighted are those biological processes that tend to be more conserved between human and mouse and those processes that have been less conserved (see Supplementary Table 21 for list of genes).

To further identify genes with conserved expression patterns and those that have diverged between humans and mice, we developed a novel method, referred to as neighbourhood analysis of conserved co-expression (NACC), to compare the transcriptional programs of orthologous genes in a way that did not require precisely matched cell lines, tissues or developmental stages, as long as a sufficiently diverse panel of samples is used in each species ( Supplementary Methods ). Observing that the orthologues of most sets of co-expressed genes in one species remained significantly correlated across samples in the other species, we use the mean of these small correlated sets of orthologous genes as a reference expression pattern in the other species. We compute Euclidean distance to the reference pattern in the multi-dimensional tissue/gene expression space as a relative measure of conservation of expression of each gene. Specifically, for each human gene (the test gene), we defined the most similarly expressed set of genes ( n = 20) across all the human samples as that gene’s co-expression neighbourhood. We then quantify the average distance between the transcript levels of the mouse orthologue of the test gene and the transcript levels of each mouse orthologue of the neighbourhood genes across the mouse samples. We then invert the analysis, and choose a mouse test gene and define a similar gene co-expression neighbourhood in the mouse samples, and calculate the average distance between the expression of orthologues of the test gene and expression of neighbourhood genes across the human samples. The average change in the human-to-mouse and mouse-to-human distances, referred herein as a NACC score, is a symmetric measure of the degree of conservation of co-expression for each gene. The distribution of this quantity for each gene is shown in Fig. 2c , showing that genes in one species show a strong tendency to be co-expressed with orthologues of similarly expressed genes in the other species compared to random genes (also see Supplementary Information ). We quantify the degree to which a specific biological process diverges between human and mouse as the average NACC scores of genes in each gene ontology category by calculating a z -score using random sampling of equal size sets of genes. Figure 2d shows that genes coding for proteins in the nuclear and intracellular organelle compartments, and involved in RNA processing, nucleic acid metabolic processes, chromatin organization and other intracellular metabolic processes, tend to exhibit more similar gene expression patterns between human and mouse. On the other hand, genes involved in extracellular matrix, cellular adhesion, signalling receptors, immune responses and other cell-membrane-related processes are more diverged (for a complete list of all GO categories and conservation analysis, see Supplementary Table 21 ). As a control, when we applied the NACC analysis to two different replicates of RNA-seq data sets from the same species, no difference in biological processes can be detected ( Supplementary Fig. 5 ).

Several lines of evidence indicate that NACC is a sensitive and robust method to detect conserved as well as diverged gene expression patterns from a panel of imperfectly matched tissue samples. First, when we applied NACC to a set of simulated data sets, we found that NACC is robust for the diversity and conservation of the mouse–human sample panel (in Supplementary Fig. 6 ). Second, we randomly sampled subsets of the full panel of samples and demonstrated that the categories of human–mouse divergence shown in Fig. 2d are robust to the particular sets of samples we selected ( Supplementary Fig. 7 ). Third, when we repeated NACC on a limited collection of more closely matched tissues and primary cell types (see Supplementary Methods ), the biological processes detected as conserved and species-specific in the larger panel of mismatched human–mouse samples are largely recapitulated, although some pathways are detected with somewhat less significance, probably owing to the smaller number of data sets used ( Supplementary Fig. 8 ). In summary, the NACC results support and extend the principal component analysis, showing that while large differences between mouse and human transcriptome profiles can be observed (revealed in PC1), genes involved in distinct cellular pathways or functional groups exhibit different degrees of conservation of expression patterns between human and mouse, with some strongly preserved and others changing markedly.

Prevalent species-specific regulatory sequences along with a core of conserved regulatory sequences

To better understand how divergence of cis -regulatory sequences is linked to the range of conservation patterns detected in comparisons of gene expression programs between species, we examined evolutionary patterns in our predicted regulatory sequences. Previous studies have identified a wide range of evolutionary patterns and rates for cis -regulatory regions in mammals 5 , 8 , but there are still questions regarding the overall degree of similarity and divergence between the cis -regulatory landscapes in the mouse and human. The variety of assays and breadth of tissue and cell-type coverage in the mouse ENCODE data therefore provide an opportunity to address this problem more comprehensively.

We first determined sequence homology of the predicted cis -elements in the mouse and human genomes. We established one-to-one and one-to-many mapping of human and mouse bases derived from reciprocal chained blastz alignments 48 and identified conserved cis -regulatory sequences 49 . This analysis showed that 79.3% of chromatin-based enhancer predictions, 79.6% of chromatin-based promoter predictions, 67.1% of the DHS, and 66.7% of the transcription factor binding sites in the mouse genome have homologues in the human genome with at least 10% overlapping nucleotides, while by random chance one expects 51.2%, 52.3%, 44.3% and 39.3%, respectively ( Fig. 3a , Supplementary Information for details). With a more stringent cutoff that requires 50% alignment of nucleotides, we found that 56.4% of the enhancer predictions, 62.4% of promoter predictions, 61.5% of DHS, and 53.3% of the transcription factor binding sites have homologues, compared with an expected frequency of 34%, 33.8%, 33.6% and 33.7% by random chance ( Supplementary Fig. 9 ). The candidate mouse regulatory regions with human homologues are listed in Supplementary Tables 22–25 . Thus, between half and two-thirds of candidate regulatory regions demonstrate a significant enrichment in sequence conservation between human and mouse. The remaining half to one-third have no identifiable orthologous sequence.

a , Chart shows the fractions of the predicted mouse cis -regulatory elements with homologous sequences in the human genome (Methods). TFBS, transcription factor binding site. b , A bar chart shows the fraction of the DNA fragments tested positive in the reporter assays performed either using mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEF). c , A chart shows the gene ontology (GO) categories enriched near the predicted mouse-specific enhancers. d , A bar chart shows the percentage of the predicted mouse-specific enhancers containing various subclasses of LTR and SINE elements. As control, the predicted mouse cis elements with homologous sequences in the human genome or random genomic regions are included.

The candidate regulatory regions in mouse with no orthologue in human could arise either because they were generated by lineage-specific events, such as transposition, or because the orthologue in the other species was lost. Species-specific cis -regulatory sequences have been reported before 3 , 14 , but the fraction of regulatory sequences in this category remains debatable and may vary with different roles in regulation. We find that 15% (12,387 out of 82,853) of candidate mouse promoters and 16.6% (48,245 out of 291,200) of candidate enhancers (both predicted by patterns of histone modifications) have no sequence orthologue in humans ( Supplementary Tables 26, 28 , for details please refer to Supplementary Methods section). However, the question remains as to whether these species-specific elements are truly functional elements or simply correspond to false-positive predictions due to measurement errors or biological noise. Supporting the function of mouse-specific cis elements, 18 out of 20 randomly selected candidate mouse-specific promoters tested positive using reporter assays in mouse embryonic stem cells, where they were initially identified ( Fig. 3b , Supplementary Table 27 ). Further, when these 18 mouse-specific promoters were tested using reporter assays in the human embryonic stem cells, all of them also exhibited significant promoter activities ( Extended Data Fig. 2a , Supplementary Table 27 ), indicating that the majority of candidate mouse-specific promoters are indeed functional sequences, which are either gained in the mouse lineage or lost in the human lineage. Similarly, a majority of the candidate mouse-specific enhancers discovered in embryonic stem cells are also likely bona fide cis elements, as 70.2% (26 out of 37) candidate enhancers randomly selected from this group were found to exhibit enhancer activities in reporter assays ( Fig. 3b , Supplementary Table 29 ). Like the candidate mouse-specific promoters, 61.5% (16 out of 26) of the candidate mouse-specific enhancers also show enhancer activities in human embryonic stem cells ( Extended Data Fig. 2a ).

We next tested whether the rapidly diverged cis -regulatory elements would correspond to the same cellular pathways shown to be less conserved by the NACC analysis of gene expression programs. Indeed, gene ontology analysis revealed that the mouse-specific regulatory elements are significantly enriched near genes involved in immune function ( Fig. 3c ), in agreement with the divergent transcription patterns for these genes reported earlier and a previous report based on a smaller number of primate-specific candidate regulatory regions 50 . This suggests that regulation of genes involved in immune function tends to be species-specific 50 , just as the protein-coding sequences coding for immunity, pheromones and other environmental genes are frequent targets for adaptive selection in each species 2 , 51 . The target genes for mouse-specific transcription factor binding sites ( Supplementary Table 30 ) are enriched in molecular functions such as histone acetyltransferase activity and high-density lipoprotein particle receptor activity, in addition to immune function (IgG binding).

We next investigated the mechanisms generating mouse-specific cis -regulatory sequences: loss in human, gain in mouse, or both. 89% (42,947 out of 48,245) of mouse-specific enhancers and 85% (10,535 out of 12,387) of mouse-specific promoters overlap with at least one class of repeat elements (compared to 78% by random chance). Confirming earlier reports 52 , 53 , 54 , we found that mouse-specific candidate promoters and enhancers are significantly enriched for repetitive DNA sequences, with several classes of repeat DNA highly represented ( Fig. 3d and Extended Data Fig. 2b ). Furthermore, mouse-specific transcription factor binding sites are highly enriched in mobile elements such as short interspersed elements (SINEs) and long terminal repeats (LTRs) 55 .

The 50% to 60% of candidate regulatory regions with sequences conserved between mouse and human are a mixture of (1) sequences whose function has been preserved via strong constraint since these species diverged, (2) sequences that have been co-opted (or exapted) to perform different functions in the other species, and (3) sequences whose orthologue in the other species no longer has a discernable function, but divergence by evolutionary drift has not been sufficient to prevent sequence alignment between mouse and human. Several companion papers delve deeply into these issues 22 , 23 , 49 . In particular, ref. 23 shows that the conservation of transcription factor binding at orthologous positions (falling in category (1)) is associated with pleiotropic roles of enhancers, as evidenced by activity in multiple tissues. References 22 , 49 describe the exaptation of conserved regulatory sequences for other functions.

We surveyed the conservation of function in the subset of mouse candidate cis elements that have sequence counterparts in the human genome. Of the 51,661 chromatin-based promoter predictions that have human orthologues, 44% (22,655) of them are still predicted as promoters in human on the basis of the same analysis of histone modifications ( Supplementary Table 31 , see Supplementary Methods for details). Of the 164,428 chromatin-based enhancer predictions that have human orthologues, 40% (64,962) of them are predicted as an enhancer in human ( Supplementary Table 32 ). The remaining 56–60% of candidate mouse regulatory regions with a human orthologue fall into category (2) or (3) (see earlier), that is, the orthologous sequence in human either performs a different function or does not maintain a detectable function.

One caveat of the above observation is that the tissues or cell samples used in the survey were not perfectly matched. To better examine the conservation of biochemical activities among these predicted cis -regulatory elements with orthologues between mouse and human, we analysed the chromatin modifications at the promoter or enhancer predictions in a broad set of 23 mouse tissue and cell types with the neighbourhood co-expression association analysis (NACC) method described above. Instead of gene expression levels, we selected the histone modification H3K27ac as an indicator of promoter or enhancer activity as previously reported 56 . As shown in Fig. 4a , the promoter predictions (blue) show a significantly higher correlation in the level of H3K27ac in human and mouse than the random controls (red). Similarly, most chromatin-based enhancer predictions in the mouse genome exhibit conserved chromatin modification patterns in the human, albeit to a lesser degree than the promoters ( Fig. 4b ). NACC analysis on DNase-seq signal resulted in very similar distributions of conserved chromatin accessibility patterns at promoters ( Fig. 4c ) and enhancers ( Fig. 4d ). Thus many sequence-conserved candidate cis -regulatory elements appeared to have conserved patterns of activities in mice and humans.

a , b , Histograms show the distribution of the NACC score for the chromatin modification H3K27ac signal at the predicted mouse promoters ( a ) or enhancers ( b ). c , d , Histograms show the distributions of NACC scores for DNase I signal at the promoter proximal ( c ) and distal ( d ) DNase I hypersensitive sites (DHS).

Taken together, these analyses show that the mammalian cis -regulatory landscapes in the human and mouse genomes are substantially different, driven primarily by gain or loss of sequence elements during evolution. These species-specific candidate regulatory elements are enriched near genes involved in stress response, immunity and certain metabolic processes, and contain elevated levels of repeated DNA elements. On the other hand, a core set of candidate regulatory sequences are conserved and display similar activity profiles in humans and mice.

Chromatin state landscape reflects tissue and cell identities

We examined gene-centred chromatin state maps in the mouse and human cell types (see Supplementary Methods ) ( Fig. 5a , Supplementary Fig. 10 ). In all cell types, the low-expressed genes were almost uniformly in chromatin states with the repressive H3K27me3 mark or in the state unmarked by these histone modifications. In contrast, expressed genes showed the canonical pattern of H3K4me3 at the transcription start site surrounded by H3K4me1, followed by H3K36me3-dominated states in the remainder of the transcription unit. A similar pattern was seen for all the active genes, regardless of the level of expression; the only exception was a tendency for the H3K4me3 to spread further into the transcription unit for the most highly expressed genes. The same binary relationship between chromatin state maps and expression levels of genes was observed in mouse and human cell types ( Supplementary Fig. 10 ).

a , Map displaying the distribution of chromatin states over the neighbourhoods of human–mouse one-to-one orthologue genes in CH12 cells. The gene neighbourhood intervals were sorted by the transcription level of each gene, shown by white dots. TSS, transcription start site. b , c , Distribution of chromatin states in human–mouse one-to-one orthologues that are differentially expressed genes between erythroid progenitor and erythroblasts models ( b ) and between erythroblast and megakaryocyte ( c ).

For both mouse and human cells, the majority of the genome was in the unmarked state in each cell type, consistent with previous observations in Drosophila 57 and human cell lines 12 ( Supplementary Fig. 2 ). About 55% of the mouse genome was in an unmarked state in all the 15 cell types examined, while 65% is unmarked in all six human cell types. For genes that were in the unmarked state in mouse, their orthologues in human also tended to be in the unmarked state, and vice versa, leading to a positive correlation for the amount of gene neighbourhoods in unmarked states ( Supplementary Fig. 11 ). Strong correlations were also observed in profiles of other chromatin marks averaged over cell lines and tissues 37 . The genes in the unmarked zones were depleted of transcribed nucleotides relative to the number expected based on fraction of the genome included, and the levels of the transcripts mapped there were lower than those seen in the active chromatin states ( Supplementary Fig. 12 ).

Previous studies revealed limited changes of the chromatin states in lineage-restricted cells as they undergo large-scale changes in gene expression during maturation 58 , 59 , 60 . The chromatin state maps recapitulated this result, showing very similar patterns of chromatin modification in a cell line model for proliferating erythroid progenitor cells (G1E) and in maturing erythroblasts (G1E-ER4 cells treated with oestradiol) across genes whose expression level changed significantly during maturation ( Fig. 5b , Supplementary Fig. 10b ). This limited change raised the possibility that the chromatin landscape, once established during lineage commitment, dictates a permissive (or restrictive) environment for the gene regulatory programs in each cell lineage 60 , and that the chromatin states may differ between cell lineages. We tested this by examining the chromatin state maps for genes that were differentially expressed between haematopoietic cell lineages (erythroblasts versus megakaryocytes), and we found marked differences between the two cell types ( Fig. 5c and Supplementary Fig. 10b ). Genes expressed at a higher level in megakaryocytes than in erythroblasts were all in active chromatin states in megakaryocytes, but many were in inactive chromatin states in erythroblasts ( Fig. 5c ). In the converse situation, genes expressed at a higher level in erythroblasts than in megakaryocytes showed more inactive states in the cells in which they were repressed ( Supplementary Fig. 10b ). These greater differences in chromatin states correlating with differential expression of genes between, but not within, cell lineages support the model that chromatin states are established during the process of lineage commitment. The clustering of cell types together by lineage based on chromatin state maps ( Supplementary Fig. 10c ) also supports the model that the landscape of active and repressed chromatin is established no later than lineage commitment, and that this landscape is a defining feature of each cell type. Greater differences in chromatin states correlating with differences in gene expression were also observed when comparing average chromatin profiles in human and mouse 37 .

Mouse chromatin states inform interpretation of human disease-associated sequence variants

To investigate whether the mouse chromatin states were informative on sequence variants linked to human diseases by genome-wide association studies (GWAS), we combined the chromatin state segmentations of the fifteen mouse samples into a refined segmentation, which we used to train a self-organizing map (SOM) 61 on four histone modification ChIP-seq data sets (H3K4me3, H3K4me1, H3K36me3 and H3K27me3) for each mouse sample. We mapped 4,265 single nucleotide polymorphisms (SNPs) from the human GWAS studies uniquely onto the mouse genome and scored these SNPs onto the trained SOM to determine whether SNP subsets were enriched in specific areas of the map. As shown in Fig. 6a , the highest enriched H3K4me1 unit in the kidney contains five GWAS hits ( P value < 3.95 × 10 −14 ) on different chromosomes related to blood characteristics such as platelet counts ( Fig. 6a , Extended Data Table 2a ). Similarly, the second highest enriched unit in liver H3K36me3 contained six GWAS hits ( P value < 7.54 × 10 −31 ) related to cholesterol and alcohol dependence out of twelve in that unit ( Fig. 6b , Extended Data Table 2b ). In contrast, one of the highest units in brain H3K27me3 has five GWAS hits ( P value < 4.93 × 10 −33 ) on different chromosomes associated with brain disorders/response to addictive substances ( Fig. 6c , Extended Data Table 2c ). This unit is different from the other examples in that it is enriched for H3K27me3 signal in multiple tissues, with brain being the highest. 801 out of the 1,350 units of the map showed statistical enrichment of SNPs of 0.05 after Holm–Bonferroni correction for multiple hypothesis testing, 55% of which (accounting for 1,750 GWAS hits) had signal for at least one histone mark that ranked within the top 100 units on the map ( Fig. 6d ). The best histone marks for enriched GWAS units were primarily H3K4me1 (23%), H3K36me3 (18%) and H3K27me3 (12%), with H3K4me3 accounting for less than 2% of the remainder. Together these results suggest that the chromatin state maps can be used to identify potential sites for functional characterization in mouse for human GWAS hits. Indeed, ref. 23 shows that conserved DNA segments bound by orthologous transcription factors in human and mouse are enriched for trait-associated SNPs mapped by GWAS.

a , A self-organization map of histone modification H3K4me1 shows association between kidney H3K4me1 state and specific GWAS hits associated with urate levels (Methods). b , Liver-specific H3K36me3 unit shows enrichment in GWAS hits related to cholesterol, alcohol dependence and triglyceride levels. c , Brain-specific H3K27me3 high unit shows enrichment in GWAS SNPs associated with neurological disorders. d , Characterization of every unit with statistically significant GWAS enrichments in terms of highest histone modification signal in at least one sample. Units with no signal in top 100 map units for every histone modification are listed as none. RPKM, reads per kilobase per million reads mapped.

Large-scale chromatin domains are developmentally stable and evolutionarily conserved

We mapped the positions of early and late replication timing boundaries in each of 36 mouse and 31 human profiles ( Fig. 7a ). Significantly clustered boundary positions (above the 95th percentile of re-sampled positions) were identified and peaks in boundary density were aligned between cell types using a common heuristic ( Extended Data Fig. 3a, b , Supplementary Fig. 13 ). After alignment, consensus boundaries were further classified by orientation and amount of replication timing separation, resulting in a more stringent filtering of boundaries ( Supplementary Figs 14, 15 ). Overall, we found that 88% of boundary positions (versus 20% expected for random alignment; Fisher exact test P < 2 × 10 −16 ) aligned position and orientation between two or more cell types in both mouse and human (that is, 12% were cell-type-specific, Fig. 7b , Extended Data Fig. 3 ). Pair-wise comparisons of boundaries were consistent with developmental similarity between cell types ( Supplementary Fig. 16 ). The earliest and latest replicating boundaries were most well preserved between cell types, while those of mid-S replicating boundaries were highly variable ( Extended Data Fig. 3e, f ).

a , Depiction of a timing transition region (TTR) between the early and late replication domains. Early and late boundaries are defined as slope changes at either end of TTRs. b , Boundaries conserved between species for matched mouse and human cell types as a function of preservation among mouse cell types. c , Percentage of boundaries conserved between species (bar graph) and overall conservation of boundaries between comparable mouse and human cell types (CH12 versus GM06990, mESC versus hESC, mouse epiblast stem cells (mEpiSC) versus hESC) as a function of preservation among mouse cell types. d , A Venn diagram compares the replication timing boundaries identified in the mouse and human genome.

Interestingly, the greatest number of boundaries was detected in embryonic stem cells in both species, with significant reduction in boundary numbers during differentiation ( Supplementary Fig. 16 ), consistent with consolidation of domains and by proxy large-scale chromatin organization into larger ‘constant timing regions’ during differentiation 62 . Given that over half of the mouse and human genomes exhibit significant replication timing changes during development 16 , 63 , these observations support the model that developmental plasticity in replication timing is derived from differential regulation of replication timing within constant timing regions whose boundaries are preserved during development.

Although conservation of replication timing between mouse and human has been reported 29 , 30 , the conservation of replicating timing boundaries has not been examined. We converted boundary coordinates ± 100 kb across boundary positions between species, revealing significant overlap ( Fig. 7c, d ; P < 2.2 × 10 −16 by Fisher’s exact test relative to a randomized boundary list). The level of conservation of the positions of boundaries improved from a median of 27% for cell-type-specific boundaries to 70% for boundaries preserved in nine or more cell types ( Fig. 7c ), demonstrating that boundaries most highly preserved during development were the most conserved across species. This was consistent with results for transcription ( Fig. 2 ), as well as the previous observation that suggests that an increased plasticity of replication timing during development is associated with increased plasticity of replication timing during evolution 64 . Together, these findings identify evolutionarily labile versus constrained domains of the mammalian genome at the megabase scale.

Given the link between replication and chromatin assembly, we compared replication timing and levels of other chromatin properties in 200-kb windows across the genome ( Supplementary Fig. 17 ). Features associated with active enhancers (H3K4me1, H3K27ac, DNase I sensitivity) were more closely correlated to replication timing than features associated with active transcription (RNA polymerase II, H3K4me3, H3K36me3, H3K79me2). By contrast, the correlation of replication timing to repressive features, such as H3K9me3, was poor and cell-type-specific, consistent with prior results. A more stringent comparison of differences in chromatin to differences in replication timing between cell types ( Extended Data Fig. 3c, g , Supplementary Fig. 17 ) again revealed that marks of enhancers, including p300, H3K4me1 and H3K27ac, and DNase I sensitivity were more strongly correlated to replication timing than marks of active transcription.

By comparing the transcriptional activities, chromatin accessibilities, transcription factor binding, chromatin landscapes and replication timing throughout the mouse genome in a wide spectrum of tissues and cell types, we have made significant progress towards a comprehensive catalogue of potential functional elements in the mouse genome. The catalogue described in the current study should provide a valuable reference to guide researchers to formulate new hypotheses and develop new mouse models, in the same way as the recent human ENCODE studies have impacted the research community 12 .

We provide multiple lines of evidence that gene expression and their underlying regulatory programs have substantially diverged between the human and mouse lineages although a subset of core regulatory programs are largely conserved. The divergence of regulatory programs between mouse and human is manifested not only in the gain or loss of cis -regulatory sequences in the mouse genome, but also in the lack of conservation in regulatory activities across different tissues and cell types. This finding is in line with previous observations of rapidly evolving transcription factor binding in mammals, flies and yeasts, and highlights the dynamic nature of gene regulatory programs in different species 3 , 4 , 7 , 65 . Furthermore, by comprehensively delineating the potential cis -regulatory elements we demonstrated that specific groups of genes and regulatory elements have undergone more rapid evolution than others. Of particular interest is the finding that cis -regulatory sequences next to immune-system-related genes are more divergent. The finding of species-specific cis -elements near genes involved in immune function suggests rapid evolution of regulatory mechanisms related to the immune system. Indeed, previous studies have uncovered extensive differences in the immune systems among different mouse strains and between humans and mice 66 , ranging from relative makeup of the innate immune and adaptive immune cells 66 , to gene expression patterns in various immune cell types 67 , and transcriptional responses to acute inflammatory insults 68 , 69 . At least some of these differences may be attributed to distinct regulatory mechanisms 67 , and our finding that many predicted mouse cis elements near genes with immune function lack sequence conservation supports the model that evolution of cis -regulatory sequences contributes to differences in the immune systems between humans and mice. More generally, our findings are consistent with the view that changes in transcriptional regulatory sequences are a source for phenotypic differences in species evolution.

How can species-specific gains or loss of cis -regulatory elements during evolution be compatible with their putative regulatory function? The finding of different rates of divergence associated with regulatory programs of distinct biological pathways suggests complex forces driving the evolution of the cis -regulatory landscape in mammals. We discovered that specific classes of endogenous retroviral elements are enriched at the species-specific putative cis -regulatory elements, implicating transposition of DNA as a potential mechanism leading to divergence of gene regulatory programs during evolution. Previous studies have shown that endogenous retroviral elements can be transcribed in a tissue-specific manner 70 , 71 , with a fraction of them derived from enhancers and necessary for transcription of genes involved in pluripotency 72 , 73 . Future studies will be necessary to determine whether retroviral elements at or near enhancers are generally involved in driving tissue-specific gene expression programs in different mammalian species.

Despite the divergence of the regulatory landscape between mouse and human, the pattern of chromatin states (defined by histone modifications) and the large-scale chromatin domains are highly similar between the two species. Half of the genome is well conserved in replication timing (and by proxy, chromatin interaction compartment) with the other half highly plastic both between cell types and between species. It will be interesting to investigate the significance of these conserved and divergent classes of DNA elements at different scales, both with regard to the forces driving evolution and for implications of the use of the laboratory mouse as a model for human disease.

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Acknowledgements

This work is funded by grants R01HG003991 (B.R.), 1U54HG007004 (T.R.G.), 3RC2HG005602 (M.P.S.), GM083337 and GM085354 (D.M.G.), F31CA165863 (B.D.P.), RC2HG005573 and R01DK065806 (R.C.H.) from the National Institutes of Health, and BIO2011-26205 from the Spanish Plan Nacional and ERC 294653 (to R.G.). J.V. is supported by a National Science Foundation Graduate Research Fellowship under grant no. DGE-071824. K.B., M.P., J.H. and P.F. acknowledge the Wellcome Trust (grant number 095908), the NHGRI (grant number U01HG004695) and the European Molecular Biology Laboratory. We thank G. Hon for helping the analysis of high-throughput enhancer validation. L.S. is supported by R01HD043997-09. S.L. was supported by grants F32HL110473 and K99HL119617.

Author information

Feng Yue, Weisheng Wu, Tyrone Ryba, Alan P. Boyle, Rebecca F. Lowdon & Leslie B. Adams

Present address: Present addresses: Department of Biochemistry and Molecular Biology, School of Medicine, The Pennsylvania State University, Hershey, Pennsylvania 17033, USA (F.Y.); BRCF Bioinformatics Core, University of Michigan, Ann Arbor, Michigan 48105, USA (W.W.); Division of Natural Sciences, New College of Florida, Sarasota, Florida 34243, USA (T.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA (A.P.B.); Washington University in St Louis, St Louis, Missouri 63108, USA (R.L.); University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina 27599, USA (L.B.A.),

Feng Yue, Yong Cheng, Alessandra Breschi, Jeff Vierstra, Weisheng Wu, Tyrone Ryba, Richard Sandstrom, Zhihai Ma, Carrie Davis, Benjamin D. Pope, Yin Shen and Michael A. Beer: These authors contributed equally to this work.

Authors and Affiliations

Ludwig Institute for Cancer Research and University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA.,

Feng Yue, Yin Shen, David McCleary, Long Pham, Zhen Ye, Samantha Kuan, Lee Edsall & Bing Ren

Department of Biochemistry and Molecular Biology, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania 17033, USA.,

Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477 Stanford, California 94305, USA.,

Yong Cheng, Zhihai Ma, Philip Cayting, Trupti Kawli, Alan P. Boyle, Ghia Euskirchen, Anshul Kundaje, Shin Lin, Yiing Lin, Venkat S. Malladi, Drew T. Erickson, Cricket A. Sloan & Michael P. Snyder

Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, 08003 Barcelona, Catalonia, Spain.,

Alessandra Breschi, Dmitri D. Pervouchine, Sarah Djebali, Pablo Prieto, Julien Lagarde, Giovanni Bussotti, Andrea Tanzer, Cedric Notredame & Roderic Guigo

Department of Genome Sciences, University of Washington, Seattle, 98195, Washington, USA

Jeff Vierstra, Richard Sandstrom, Robert E. Thurman, Rajinder Kaul, Eric Rynes, Audra Johnson, Shinny Vong, Kristen Lee, Daniel Bates, Fidencio Neri, Morgan Diegel, Theresa Canfield, Peter J. Sabo, Erika Giste, Anthony Shafer, Tanya Kutyavin, Eric Haugen, Douglas Dunn, Alex P. Reynolds, Shane Neph, Richard Humbert, R. Scott Hansen, Evan E. Eichler & John A. Stamatoyannopoulos

Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA

Weisheng Wu, Cheryl A. Keller, Christapher S. Morrissey, Tejaswini Mishra, Deepti Jain, Nergiz Dogan, Robert S. Harris & Ross C. Hardison

Department of Biological Science, 319 Stadium Drive, Florida State University, Tallahassee, 32306-4295, Florida, USA

Tyrone Ryba, Benjamin D. Pope & David M. Gilbert

Functional Genomics, Cold Spring Harbor Laboratory, Bungtown Road, Cold Spring Harbor, New York 11724, USA.,

Carrie Davis, Lei-Hoon See, Meagan Fastuca, Jorg Drenkow, Chris Zaleski, Alex Dobin & Thomas R. Gingeras

Division of Biology, California Institute of Technology, Pasadena, 91125, California, USA

Anthony Kirilusha, Georgi K. Marinov, Brian A. Williams, Diane Trout, Henry Amrhein, Katherine Fisher-Aylor, Igor Antoshechkin, Gilberto DeSalvo & Barbara Wold

Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Waehringerstrasse 17/3/303, A-1090 Vienna, Austria.,

Andrea Tanzer

Departments of Biology and Mathematics and Computer Science, Emory University, O. Wayne Rollins Research Center, 1510 Clifton Road NE, Atlanta, Georgia 30322, USA.,

Olgert Denas, Kanwei Li & James Taylor

Department of Pediatrics, University of Washington, Seattle, 98195, Washington, USA

M. A. Bender

Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, Washington, USA

Basic Science Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, Washington, USA

Miaohua Zhang, Rachel Byron & Mark T. Groudine

Department of Radiation Oncology, University of Washington, Seattle, 98195, Washington, USA

Mark T. Groudine

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, 02139, Massachusetts, USA

Yi-Chieh Wu, Matthew D. Rasmussen, Mukul S. Bansal & Manolis Kellis

Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

Manolis Kellis

Department of Developmental and Cell Biology, University of California, Irvine, Irvine, California 92697, USA.,

Camden Jansen & Ali Mortazavi

Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, 95064, California, USA

Melissa S. Cline, Vanessa M. Kirkup, Katrina Learned, Kate R. Rosenbloom & W. James Kent

Departments of Obstetrics/Gynecology and Pathology, and Center for Reproductive Sciences, University of California San Francisco, San Francisco, 94143, California, USA

Beatriz Lacerda de Sousa & Miguel Ramalho Santos

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.,

Kathryn Beal, Miguel Pignatelli, Paul Flicek & Javier Herrero

Department of Genetics, Yale University, PO Box 208005, 333 Cedar Street, New Haven, Connecticut 06520-8005, USA.,

Jin Lian & Sherman M. Weissman

Computer & Information Sciences & Engineering, University of Florida, Gainesville, 32611, Florida, USA

Tamer Kahveci

McKusick-Nathans Institute of Genetic Medicine and Department of Biomedical Engineering, Johns Hopkins University, 733 N. Broadway, BRB 573 Baltimore, Maryland 21205, USA.,

Dongwon Lee & Michael A. Beer

Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London WC1E 6DD, UK.,

Javier Herrero

Department of Biological Structure, University of Washington, HSB I-516, 1959 NE Pacific Street, Seattle, Washington 98195, USA.,

Matthew S. Wilken & Thomas A. Reh

MRC Molecular Haemotology Unit, University of Oxford, Oxford OX3 9DS, UK.,

Marella De Bruijn

Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, 10065, New York, USA

Licia Selleri

HHMI and Ludwig Center at Memorial Sloan Kettering Cancer Center, Immunology Program, Memorial Sloan Kettering Cancer Canter, New York, 10065, New York, USA

Alexander Rudensky, Steven Josefowicz & Robert Samstein

Dana Farber Cancer Institute, Harvard Medical School, Cambridge, 02138, Massachusetts, USA

Stuart H. Orkin

Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, 52242, Iowa, USA

Dana Levasseur & Kai-Hsin Chang

Division of Hematology, Department of Medicine, University of Washington, Seattle, 98195, Washington, USA

Thalia Papayannopoulou

Department of Cell Biology, Albert Einstein College of Medicine, Bronx, 10461, New York, USA

Arthur Skoultchi & Srikanta Gosh

Department of Pathology, University of Washington, Seattle, 98195, Washington, USA

Christine Disteche

Department of Comparative Medicine, University of Washington, Seattle, 98195, Washington, USA

Piper Treuting

Bioinformatics and Genomics program, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA

Department of Hematology, St Jude Children’s Research Hospital, Memphis, 38105, Tennessee, USA

Mitchell J. Weiss

Division of Hematology, The Children’s Hospital of Philadelphia, Philadelphia, 19104, Pennsylvania, USA

Gerd A. Blobel

Perelman School of Medicine at the University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.,

Xiaoyi Cao & Sheng Zhong

Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, 63108, Missouri, USA

NHGRI, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA.,

Peter J. Good, Rebecca F. Lowdon, Leslie B. Adams, Xiao-Qiao Zhou, Michael J. Pazin & Elise A. Feingold

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Contributions

F.Y., Y.C., A.B., J.V., W.W., T.R., M.A.Beer, R.C.H., J.A.S., M.P.S., R.G., T.R.G., D.M.G. and B.R. led the data analysis effort, R.Sandstrom, Z.M., C.D., B.D.P., Y.S., R.C.H., J.A.S., M.P.S., R.G., T.R.G., D.M.G. and B.R. led the data production. F.Y., M.A.Beer, L.E., Y.C., P.C., A.B., A.K., S.L., Y.L., J.V., R.Sandstrom, R.E.T., E.R., E.H., A.P.R., S.N., R.H., W.W., T.M., R.S.H., C.J., A.M., B.D.P., T.R., T.K., D.Lee, O.D., J.T., C.Z., A.D., D.D.P., S.D., P.P., J.Lagarde, G.B., A.T., K.B., M.P., P.F. and J.H. analysed data. Y.S., D.M., L.P., Z.Y., S.K., Z.M., T.K., G.E., J.Lian, S.M.W., R.K., M.A.Bender, S.L., Y.L., M.Z., R.B., M.T.G., A.J., S.V., K.L., D.B., F.N., M.D., T.C., R.S.H., P.J.S., M.S.W., T.A.R., E.G., A.S., T.K., E.H., D.D., M.D.B., L.S., A.R., S.J., R.Samstein, E.E.E., S.H.O., D.Levasseur, T.P., K.-H.C., A.S., C.D., P.T., W.W., C.A.K., C.S.M., T.M., D.J., N.D., B.D.P., T.R., C.D., L.-H.S., M.F., J.D. produced data. F.Y., Y.C., W.W., T.R., B.D.P., S.L., Y.L., C.J., C.D., A.D., A.B., D.D.P., S.D., C.N., A.M., J.A.S., M.P.S., R.G., T.R.G., D.M.G., R.C.H., M.A.Beer., B.R. wrote the manuscript. The role of the NHGRI Project Management Group (P.J.G., R.F.L., L.B.A., X.-Q.Z., M.J.P., E.A.F.) in the preparation of this paper was limited to coordination and scientific management of the Mouse ENCODE consortium.

Corresponding authors

Correspondence to John A. Stamatoyannopoulos , Michael P. Snyder , Roderic Guigo , Thomas R. Gingeras , David M. Gilbert , Ross C. Hardison , Michael A. Beer or Bing Ren .

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The authors declare no competing financial interests.

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Lists of participants and their affiliations appear in the Supplementary Information .

Extended data figures and tables

Extended data figure 1 clustering analysis of human and mouse tissue samples..

a , RNA-seq data from Ilumina Body Map (adipose, adrenal, brain, colon, heart, kidney, liver, lung, ovary and testis) were analysed together with that from the matched mouse samples using clustering analysis. Genes with high variance across tissues were used, resulting in cell samples clustering by tissues, not by species. b , Clustering employing genes with high variance between species shows clustering by species instead of tissues. c , Principal Component Analysis (PCA) was performed for RNA-seq data for 10 human and mouse matching tissues. The expression values are normalized within each species and we observed the clustering of samples by tissue types.

Extended Data Figure 2 Comparative analysis of sequence conservation in the cis elements predicted in the human and mouse genome.

a , The predicted mouse-specific promoters and enhancers can function in human embryonic stem cells (hESCs). Percentages of predicted enhancers or promoters that test positive are shown in a bar chart. b , A bar chart shows the percentage of the predicted mouse-specific promoters containing various subclasses of LTR and SINE elements. As control, the predicted mouse cis elements with homologous sequences in the human genome or random genomic regions are included.

Extended Data Figure 3 Replication timing boundaries preserved among tissues are conserved during evolution.

a , Heat map of TTR overlap with positive (yellow) or negative (blue) slope. Replication timing (RT) boundaries were identified as clustered TTR endpoints (grey) above the 95th percentile (dashed line) of randomly resampled positions (black). b , Examples of constitutive boundaries (blue regions) and regulated boundaries (grey regions) highlighted. c , Spearman correlations between differences in chromatin feature enrichment and differences in RT in non-overlapping 200-kb windows. d , Percentage of boundaries preserved between the indicated number of human cell types. e , f , Distribution of boundary replication timing in mouse ( e ) and human ( f ) as a function of preservation level between cell types. g , Comparison of changes in replication timing versus various histone marks across a segment of mouse chromosome 6.

Supplementary information

Supplementary information.

This file contains Supplementary Methods and Materials, Supplementary Figures 1-22, Supplementary Tables 1-3, 9, 10, 14, 15, 20, 27, 29, 33 and Supplementary References. Supplementary Tables 4-8, 11-13, 16-19, 21-26, 28, 30-32 are available at http://mouse.encodedcc.org . (PDF 28647 kb)

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Yue, F., Cheng, Y., Breschi, A. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515 , 355–364 (2014). https://doi.org/10.1038/nature13992

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Accepted : 24 October 2014

Published : 19 November 2014

Issue Date : 20 November 2014

DOI : https://doi.org/10.1038/nature13992

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In a possible step forward for gene therapy, Stanford researchers made mice glow like fireflies. A collaboration between chemists and gene therapy experts produced a new way of inserting the code ...
Mice Inherit Specific Memories, Because Epigenetics?
There are millions of spots along the mouse genome (and the human genome), called CpG sites, where methyl groups can attach and affect the expression of nearby genes. Typically, methylation dials ...
A comparative encyclopedia of DNA elements in the mouse genome
To comprehensively identify the genic regions that produce transcripts in the mouse genome, we performed RNA-seq experiments in 69 different mouse tissues and cell types with two biological ...