the open window summary essay

A Summary and Analysis of Saki’s ‘The Open Window’

By Dr Oliver Tearle (Loughborough University)

‘The Open Window’ is one of Saki’s shortest stories, and that’s saying something. Few of his perfectly crafted and deliciously written tales exceed four or five pages in length, but ‘The Open Window’, at barely three pages, outstrips even ‘ The Lumber-Room ’ or ‘ Tobermory ’ for verbal economy.

It is so brief it has almost the air of a parable about it, except that it’s far from clear what the ‘moral’ of the story is, or even if there is one. Saki uses language so deftly and to such effect, that it is worth unpicking and analysing ‘The Open Window’ (which can be read in full here ) a little.

Although on first glance it seems different from some of Saki’s better-known stories, such as his classic werewolf tale ‘Gabriel-Ernest ’ and his story about a polecat worshipped as a god, ‘Sredni Vashtar’ , ‘The Open Window’ follows the same essential setup as many of Saki’s other stories, in having an adolescent character whose supposed innocence (supposed by the adult character, that is) turns out to be guile, cunning, and the mischief in disguise.

But whereas Nicholas in ‘The Lumber-Room’, Conradin in ‘Sredni Vashtar’, or Gabriel-Ernest actively seek to cause harm to their adult antagonists (or, in the case of Nicholas, to refuse to help an aunt who has got herself trapped in the water tank), Vera’s only weapon is her imagination. Yet this alone suggests that she shares some kinship with Conradin in ‘Sredni Vashtar’, whose cousin and guardian dislikes her ward’s imaginative streak.

‘The Open Window’: plot summary

What happens in ‘The Open Window’, in summary, is this: a man, who has the glorious name of Framton Nuttel, has newly arrived in a ‘rural retreat’, to help him settle his nerves. His sister, worried that he will hide himself away there and ‘mope’, thus making his nerves worse, has given him the names and addresses of all the people she knows in the area, and told him to go and introduce himself to them. (His sister had stayed at the rectory four years earlier.)

‘The Open Window’ takes place at the house of one of Framton’s sister’s contacts, a woman named Mrs Sappleton and her 15-year-old niece, Vera, whom Framton has gone round to visit so he might introduce himself.

While Mrs Sappleton is upstairs making herself ready to meet their new guest, Vera entertains Framton. After she learns that Framton knows barely anything about her aunt, Vera tells him that three years ago Mrs Sappleton’s husband and her two brothers went out through the French window on a shooting trip, and never returned. They drowned in a ‘treacherous piece of bog’ and their bodies were never recovered. The spaniel they took with them was lost, too.

Vera tells Framton that her aunt has kept the French window open ever since, in the belief that her husband and brothers are going to walk back through the open window any moment, alive and well.

Mrs Sappleton then arrives from upstairs and apologises for being late coming down. She mentions the open window and explains that her husband and brothers are out shooting but will be back any minute. They exchange small talk about shooting and birds, and Framton iterates that he has been told to have complete rest and avoid ‘mental excitement’, when Mrs Sappleton announces that her husband and brothers are returning home.

Framton looks with horror at the sight of three men and a ‘tired brown spaniel’ approaching the open window – he sees that Vera shares his look of shock. Believing he is seeing three ghosts (four if you include the dog!), he picks up his hat and coat and runs from the house as fast as he can.

Back at the house, Mrs Sappleton remarks that Mr Nuttel was an odd man – all he could do was talk about his ailments, and then he ‘dashed off’ as soon as the men arrived. Vera suggests that he was scared of dogs, and the sight of the spaniel caused him to run off. The final sentence of the story refers to Vera: ‘Romance at short notice was her speciality.’

‘The Open Window’: analysis

‘The Open Window’ is an amusing little story; but is it more than this? Closer analysis of Saki’s tale reveals that the devil is in the detail. Note that Framton is not presented as a gullible fool, and if he is, we as readers are encouraged to be gulled, too, for we are supposed to be taken in by Vera’s lie about the dead husband and brothers as well.

But as Framton is wondering whether Mrs Sappleton is married or widowed, he senses a male presence in the house: ‘An undefinable something about the room seemed to suggest masculine habitation.’ His first instinct is correct, but Vera’s entirely fabricated narrative leads him to believe he was mistaken about the ‘masculine’ atmosphere.

And she convinces him that she should be believed by a number of subtle details: the spaniel that accompanied the men on their apparently ill-fated trip, for instance, and the white waterproof coat which the husband was carrying over his arm when they left. Vera obviously saw the men leaving with the dog and coat, and weaves them into the narrative she feeds to Framton, so that when the men return – with the dog and the coat, as described – the idea that Framton is seeing dead men walking is all the more powerful.

Vera’s look of horror when they see the men returning to the house is also a nice touch. Of course, being still technically a child, female, and named Vera (meaning literally ‘truth’), all help, too. But you can never trust children in Saki, those ‘feral ephebes’ in Sandie Byrne’s memorable phrase.

But does ‘The Open Window’ mean anything else beyond itself? That is, can it be analysed as a commentary on anything other than lying teenage girls? Well, the story does raise questions which, we might argue, prefigure the concerns of the modernist writers who were active a few years after Saki, in the immediate post-WWI period.

There is no absolute truth or absolute reality, writers such as James Joyce and Virginia Woolf suggest, because everything is mediated through personal human experience, and we cannot know everything. Virginia Woolf’s first great novel, Jacob’s Room (1922), is a good example of this: no one character fully knows or understands the title character, and everyone gets a slightly different glimpse of who he is. Framton has only Vera’s word to go on about Mrs Sappleton’s husband and brothers.

But, conversely, Mrs Sappleton, unaware that her niece has been spinning their guest a web of lies, has a different perception of him, too, believing him to be an odd man who has an excessive reaction to the sight of her male relatives. Vera, the fiction-master (and thus the author-surrogate in the story), is the only one who knows both sides and can enjoy playing these two characters, with their partial glimpses of the whole story, off each other.

Although Saki’s style and approach are very different from someone like Virginia Woolf, the preoccupation with ‘fiction’ and ‘perception’ is the same – only Saki’s take on this issue is funnier.

Vera’s lie in ‘The Open Window’ about three members of one family – all of them male – going off together on a shooting trip and never returning, leaving the female characters at home to grieve for them, seems eerily to prefigure the events of a few years later, when hundreds of thousands of Englishmen – including, in many cases, every single man in a particular family – would go off to fight in the First World War and never come back. (When we consider that, in Vera’s fictional account, the three men meet their end by drowning in boggy mud, and their bodies are never recovered, the foreshadowing of the Western Front becomes downright spooky.)

Saki himself would be one of them, killed in action in 1916. With him, and many like him, the Edwardian way of life that Saki so ruthlessly skewers in his stories would die, too. But ‘The Open Window’ remains more than a window (to reach for the inevitable metaphor) onto a vanished world. It is a timeless tale about truth and fiction, and, yes, a parable without a moral. For that reason, it deserves to be revisited, analysed and studied, discussed, and celebrated.

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1 thought on “A Summary and Analysis of Saki’s ‘The Open Window’”

I love the ending to this story – the irony and surprise packed into a single sentence reminds me a lot of O. Henry’s works…

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The Open Window Summary & Analysis

One of Saki’s best-known short stories, “The Open Window”, originally published in  1911 , describes an encounter of Framton Nuttel, with the fifteen-year-old niece of Mrs. Sappleton, Nuttel’s hostess for the duration of his temporary rural retreat. The story is narrated by an omniscient, third-person narrator. 

British author Hector Hugh Munro, better known by his pen name, Saki , is one of the greatest writers of short stories in Britain, often compared to the likes of O. Henry. 

The Open Window | Summary

The story begins  in medias res  with Mrs. Sappleton’s niece,  Vera, who is “a very self-possessed young lady of fifteen ”, who explains to Framton that her aunt would meet him shortly. As Framton waits for Mrs. Sappleton, he is unable to carry on a conversation with the young girl, being naturally shy and introverted. He doubts if living with total strangers will cure his nerve symptoms, but his sister insists on introducing him to the people that she knows in the village. Vera asks him if he knows anything about her aunt, and Frampton replies that all he knows about her is her name and address. He tries to figure out if Mrs. Sappleton is married or widowed, and by observing the room, he finds subtle signs of masculine habitation. 

Vera suddenly mentions the ‘ great tragedy ’ that befell her aunt three years ago, to attract his curiosity. It works, and Framton curiously enquires about the tragedy. She draws his attention to the large French window in the room, asking if he wonders why the window is open on a late October afternoon. He asks if it is somehow connected to the tragedy. She explains that  three years ago, her aunt’s husband and two brothers had gone shooting along with their dog, a little brown spaniel. In that “dreadfully wet summer”, they drowned in an inobtrusive piece of bog, and none of their bodies could be recovered.  Not being able to deal with the tragic demise of her husband and brothers, her aunt still hopes for their return,  keeping the window open every day in the hope that they will return , as she still narrates the story of their departure that day, her husband with a white coat over his shoulders, her youngest brother singing “Bertie, why do you bound?” to deliberately annoy his sister. 

At this point, Mrs. Sappleton herself comes down to greet her guest, hoping that Vera had kept him amused in the meantime. She hopes that Framton will excuse the open window, as her husband and brothers will return from their snipe hunt in the marshes. Thinking that she is delusional, Framton attempts to change the subject by narrating the details of his sickness. Suddenly, Mrs. Sappleton cries, “Here they are at last!”, and Framton looks at her niece to nod in sympathetic comprehension. However, seeing Vera looking out the window with “dazed horror in her eyes”, he looks up at the window and sees three figures walking towards the window in the deepening twilight, armed with guns and a white coat flung over one of their shoulders, a tired brown spaniel at their heels. Thinking that he is seeing ghosts, Framton flees the spot terrified. 

As Mr. Sappleton and his brothers-in-law enter the house, they ask who it was who bolted out the door. A surprised Mrs. Sappleton is herself puzzled, unable to comprehend Framton’s sudden, unexplained departure. Her niece, Vera, however, explains that it must have been because of his fear of dogs, as he was telling her that he was once chased by a pack of pariah dogs in India, being forced to spend the night inside a dug grave which the dogs snarling and drooling over him.

The last line explains that “ Romance at short notice was her specialty .”

The Open Window | Analysis

The  humorous and ironic short story  explores the  outstanding creativity of a fifteen-year-old girl who is able to come up with thrilling, fictitious explanations behind real events in a matter of seconds,  fooling all adults around her. While ethically, Vera’s actions constitute lying, Saki is not condemning the little girl’s habit, but taking an indulgent, paternalistic attitude to her marvelous ability and creative genius. Even her  acting skills  are so convincing that she changes her voice tone and expression according to the demands of the situation, her body language shifting from her natural sense of self-possession to an expression of ‘dazed horror’ at will, making Framton Nuttel completely forget about his own observations of the rectory that would have helped him uncover Vera’s prank. Naturally shy and timid, he completely forgets that he had seen signs of ‘masculine habitation’ in the house, or that there is a perfectly rational explanation for keeping the window open in an exceptionally hot October. Instead of reacting in a rational manner and enquiring about Mr. Sappleton from her wife instead of relying solely on the words of a child, he is so convinced by Vera’s story that he runs away in horror the moment he sees the men return, not even providing an explanation to his hostess, or waiting for one from her side. The comicality of the situation is strikingly in contrast to the horrifying quality of Vera’s stories, and the audience, aware of the reality, is able to enjoy the irony and humor of the story. 

The last line “Romance at short notice was her speciality”, is an exceptional use of a  plot twist  by Saki, successfully explaining the entire story in one brief line.

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The Open Window

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Welcome to the LitCharts study guide on Saki's The Open Window . Created by the original team behind SparkNotes, LitCharts are the world's best literature guides.

The Open Window: Introduction

The open window: plot summary, the open window: detailed summary & analysis, the open window: themes, the open window: quotes, the open window: characters, the open window: symbols, the open window: literary devices, the open window: theme wheel, brief biography of saki.

Historical Context of The Open Window

Other books related to the open window.

• Full Title: The Open Window
• When Written: 1914
• Where Written: England
• When Published: 1914
• Literary Period: Edwardian
• Genre: Short story
• Setting: An English country house in the early twentieth century
• Climax: Thinking he is seeing ghosts enter the Sappleton home through an open window, Framton Nuttel runs away in horror, much to the confusion of his host.
• Point of View: Third person

Extra Credit for The Open Window

Nom de Plume. The exact origin of the pen name Saki remains up for debate. It may be in reference to an ancient Persian poem, or a South American monkey of same name.

Private Life. Saki never married and is believed to have been gay. Because homosexuality was considered a crime in Britain at the time, he was forced to keep this part of his identity hidden.

Analysis of "The Open Window" by Saki

Twist Ending in a Classic Tale

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• Ph.D., English, State University of New York at Albany
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Saki is the pen name of the British writer Hector Hugh Munro, also known as H. H. Munro (1870-1916). In " The Open Window ," possibly his most famous story, social conventions and proper etiquette provide cover for a mischievous teenager to wreak havoc on the nerves of an unsuspecting guest.

Framton Nuttel, seeking a "nerve cure" prescribed by his doctor, visits a rural area where he knows no one. His sister provides letters of introduction so he can meet people there.

He pays a visit to Mrs. Sappleton. While he waits for her, her 15-year-old niece keeps him company in the parlor. When she realizes Nuttel has never met her aunt and knows nothing about her, she explains that it has been three years since Mrs. Sappleton's "great tragedy," when her husband and brothers went hunting and never returned, presumably engulfed by a bog (which is similar to sinking in quicksand). Mrs. Sappleton keeps the large French window open every day, hoping for their return.

When Mrs. Sappleton appears she is inattentive to Nuttel, talking instead about her husband's hunting trip and how she expects him home any minute. Her delusional manner and constant glances at the window make Nuttel uneasy.

Then the hunters appear in the distance, and Nuttel, horrified, grabs his walking stick and exits abruptly. When the Sappletons exclaim over his sudden, rude departure, the niece calmly explains that he was probably frightened by the hunters' dog. She claims that Nuttel told her he was once chased into a cemetery in India and held at bay by a pack of aggressive dogs.

Social Conventions Provide "Cover" for Mischief

The niece uses social decorum very much to her favor. First, she presents herself as inconsequential, telling Nuttel that her aunt will be down soon, but "[i]n the meantime, you must put up with me." It's meant to sound like a self-effacing pleasantry, suggesting that she isn't particularly interesting or entertaining. And it provides perfect cover for her mischief.

Her next questions to Nuttel sound like boring small talk. She asks whether he knows anyone in the area and whether he knows anything about her aunt. But as the reader eventually understands, these questions are reconnaissance to see whether Nuttel will make a suitable target for a fabricated story.

Smooth Storytelling

The niece's prank is impressively underhanded and hurtful. She takes the ordinary events of the day and deftly transforms them into a ghost story. She includes all the details needed to create a sense of realism: the open window, the brown spaniel, the white coat, and even the mud of the supposed bog. Seen through the ghostly lens of tragedy, all of the ordinary details, including the aunt's comments and behavior, take on an eerie tone .

The reader understands that the niece won't get caught in her lies because she's clearly mastered a lying lifestyle. She immediately puts the Sappletons' confusion to rest with her explanation about Nuttel's fear of dogs. Her calm manner and detached tone ("enough to make anyone lose his nerve") add an air of plausibility to her outrageous tale.

The Duped Reader

One of the most engaging aspects of this story is that the reader is initially duped, too, just like Nuttel. The reader has no reason to disbelieve the niece's "cover story"—that she's just a demure, polite girl making conversation.

Like Nuttel, the reader is surprised and chilled when the hunting party shows up. But unlike Nuttel, the reader finally learns the truth of the situation and enjoys Mrs. Sappleton's amusingly ironic observation: "One would think he had seen a ghost."

Finally, the reader experiences the niece's calm, detached explanation. By the time she says, "He told me he had a horror of dogs," the reader understands that the real sensation here is not a ghost story, but rather a girl who effortlessly spins sinister stories.

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The Open Window Summary, Characters and Themes

“Hector Hugh Munro, known by his penname Saki, masterfully satirizes Edwardian society in his frequently anthologized short story ‘The Open Window.’ 

First appearing in the Westminster Gazette on November 18, 1911, and later included in his 1914 collection ‘Beasts and Super-Beasts,’ this tale is a classic example of Saki’s wit and insight.

The story unfolds with a clever use of the middle-of-things storytelling strategy, introducing us to the protagonist, Framton Nuttel, in the midst of an unexpected situation. 

Nuttel, a man besieged by anxiety, arrives in the countryside seeking a cure for his nerves. Armed with letters of introduction from his sister, he visits Mrs. Sappleton, only to be greeted by her 15-year-old niece.

This precocious young girl, known simply as ‘the niece,’ quickly discerns Nuttel’s unfamiliarity with local affairs. 

Seizing the opportunity, she weaves a tragic tale: Three years ago, she recounts, Mrs. Sappleton’s husband, two brothers, and their spaniel left through the large French window to hunt snipe in the marshes. Tragically, she claims, they were swallowed by a bog, their bodies never recovered. 

The niece poignantly describes Mrs. Sappleton’s unwavering belief that they will return through the open window, a symbol of her hope and denial.

The story takes a turn with Mrs. Sappleton’s entrance. She introduces herself and Vera, her niece, to Nuttel. 

Oblivious to the story her niece has just spun, Mrs. Sappleton talks about the open window and her anticipation of her husband’s return. This conversation, coupled with Nuttel’s heightened nerves, sets the stage for the story’s climax.

In a dramatic twist, Mrs. Sappleton suddenly exclaims that the hunting party is returning. Nuttel, seeking a sympathetic look from Vera, is instead met with feigned horror as she gazes toward the open window. 

His anxiety reaching a breaking point, Nuttel witnesses what seems to be the ghostly return of the hunting party, prompting him to flee the house in terror.

However, the twist in Saki’s tale is that the hunting party is very much alive. As the adults wonder about Nuttel’s abrupt departure, Vera concocts another tale: Nuttel, she says, was terrified of dogs due to a traumatic experience involving a pack of dogs and an overnight entrapment in a cemetery.

Saki concludes the story with a line that perfectly encapsulates Vera’s talent for improvisation: ‘ Romance at short notice was her specialty.’ 

This ending serves as a clever denouement, untangling the story’s threads while highlighting the themes of absurdity, escapism, control, and the contrast between appearance and reality that pervade Saki’s work.

Framton Nuttel

Framton Nuttel is the central character in “The Open Window.” Portrayed as a nervous, anxious man, he visits the countryside to seek relief for his nerve issues. Nuttel is an outsider to the area and lacks knowledge about the local people and their histories. 

His naivety and anxious disposition make him a prime target for Vera’s mischievous story.

Vera, Mrs. Sappleton’s 15-year-old niece, is a pivotal character known for her inventiveness and storytelling ability. 

She is astute and quickly gauges Nuttel’s ignorance about local events. 

Vera fabricates a tragic and ghostly story about her aunt’s family, demonstrating her skill in improvisation and her penchant for creating dramatic narratives.

Mrs. Sappleton

Mrs. Sappleton is the hostess to whom Nuttel was sent with a letter of introduction. She is unaware of the fictitious story Vera has told Nuttel. 

Mrs. Sappleton’s conversation about her husband and brothers returning through the open window inadvertently reinforces the fabricated story, contributing to Nuttel’s mounting anxiety.

Mrs. Sappleton’s Husband and Brothers

Mrs. Sappleton’s husband and brothers are indirectly involved in the story. 

They are the subjects of Vera’s fictional tale, believed to have been lost in a bog while hunting. Their unexpected return towards the end of the story, very much alive and well, triggers Framton Nuttel’s panicked exit.

The Spaniel

The family’s spaniel is a minor but significant character in the story. The dog accompanies the hunting party and is mentioned in Vera’s fabricated tale. Its presence with the returning hunters serves as a crucial detail that lends authenticity to Vera’s story in Nuttel’s eyes.

1. The Absurdity of Social Etiquette and Class Norms

Saki’s story masterfully dissects the often rigid and absurd nature of Edwardian social etiquette. 

Through the character interactions and the unfolding of events, Saki portrays how societal expectations can lead to awkward, and even bizarre, situations. The story highlights the discomfort and misunderstandings that arise from the strict adherence to social norms, as seen in Framton Nuttel’s visit to Mrs. Sappleton. 

Nuttel, adhering to the social protocol of his time, finds himself entangled in an increasingly strange scenario due to his expectations of proper conduct. 

This theme underscores Saki’s critique of the superficial aspects of social life, where appearances and manners often overshadow sincerity and understanding.

2. Escapism and the Power of Storytelling

Escapism is a central theme in the story, exemplified through the character of Vera and the stories she concocts. 

Vera’s vivid imagination and ability to create elaborate tales serve as a form of escape from the mundanities of her everyday life. 

For Nuttel, the encounter with Vera and her fantastical story becomes an unintentional escape from reality, albeit a terrifying one. Saki uses this theme to explore how storytelling can be a powerful tool for manipulation, distraction, and transformation of reality. 

The story within a story structure not only serves as an escape for the characters but also invites the reader to question the line between fiction and reality, blurring the boundaries of what is believable and what is not.

3. Appearance Versus Reality

Saki skillfully plays with the theme of appearance versus reality throughout the narrative. 

The story challenges the perceptions and expectations of both the characters and the readers. Vera’s fabricated story about the tragic hunting accident and the perpetually open window creates a false reality for Nuttel, leading him to misinterpret the events that unfold. 

This theme is further reinforced by the twist ending, where the presumed supernatural occurrence is revealed to be a misunderstanding fueled by Vera’s deception. 

Through this theme, Saki comments on the ease with which reality can be distorted, and the fine line that often exists between what is real and what is perceived to be real. 

The story ultimately serves as a commentary on the deceptive nature of appearances and the human propensity to be influenced by them.

Final Thoughts

“The Open Window” is a masterful example of Saki’s sharp wit and ability to weave complex themes into a short narrative. The story is not just an entertaining read but also a clever commentary on human nature and society. 

Saki’s use of irony and the unexpected twist at the end not only amuse but also provoke thought about how easily reality can be manipulated by a skilled storyteller. 

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The Open Window by Saki Plot Summary

The open window summary.

The open window summary offers a great way of learning about the story in brief. It follows the life of Framton, who moves into a new town. He wishes to cure his nerves and his sister helps him as she lived there. She arranges a meeting with one of her acquaintances, Mrs Sappleton. On reaching her house, he encounters her niece, Vera. She points to an open window and tells him about the reason behind it. She tells Framton that her aunt’s husband and his two brothers got killed in a tragic hunting accident.

Moreover, she warns him that Mrs Sappleton anticipates their return so she keeps the window open. Finally, Mrs Sappleton comes to meet him and tells him that she is waiting for her husband. This disturbs Framton and he gets horrified when he sees three male approaching him in hunting gear. Thus, he flees the house.

The Open Window Summary in English

The open window summary takes us through Framton Nuttel’s arrival at a new town. The story is written by Saki. Framton is not a social man so his sister has set up him to know her acquaintances.

He arrives at her acquaintance’s house, Mrs Sappleton. Her niece, Vera, greets him. She entertains him till the aunt arrives to greet him. During this encounter, we learn a few things about the Sappletons.

When talking to Vera, Framton reveals to her about his lack of social skills. Thus, she starts telling him about the story of the open window. She says it was a great tragedy which took place in her aunt’s life.

The open window summary explains the great tragedy that took place three years ago. Vera points at the large French window which was open even in the chilly weather. Thus, she begins to tell all about it.

Mrs Sappleton’s husband and her two brothers left through the same window for hunting. However, she says the earth swallowed them up as they never returned. Thus, her aunt still in grief keeps the window open waiting for them to return.

She describes the same exact way in which they left. Her husband was carrying a white coat. At this point, her aunt comes in to greet Framton. She apologizes for being late and hopes Vera amused him meanwhile.

After that, to Framton’s shock, Mrs Sappleton makes a remark on the open window. She tells him it is open as she is waiting for her husband and brothers to return from a hunting trip.

This convinces Framton even more of Vera’s story. He is shocked to see her still fixated on their death. Moreover, Mrs Sappleton continues looking at the open window anticipating their return.

But, what shocks Framton the most is the arrival of three men in hunting attire. They approach the house and Framton is convinced they are ghosts. Thus, he runs away frantically.

Finally, we see Vera has a story for this as well. Framton’s reaction confuses Mrs Sappleton. But she assures her that Framton got scared of the hunting dog because he has a phobia of dogs.

Thus, we look at how Vera is so good at spinning tales. She utilizes the situation at hand and is quick to whip tales back to back without any hesitation.

C onclusion of The Open Window Summary

The Open Window summary tells us about the ability of clever people weaving deceptive stories to be manipulative to others. Moreover, it also shows how tough it can be to determine the truth in a story.

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3 responses to “My Greatest Olympic Prize Summary”

The wonderful summary thank you for this.

They did not belong to the family of gorden cook and you also didn’t write the spelling correct it’s James cook 😶😑

What’s funny is that Miss Fairchild said the line- “Money isn’t everything. But people always misunderstand things and remain stupid-” when she herself misunderstood the situation.

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The Open Window

By saki (h.h. munro), the open window essay questions.

Describe how the title of the story relates to the themes of the story itself.

“The Open Window” is about the capacity of storytelling, particularly short stories, to entertain through humor and trickery. The story itself is therefore an imagined world that inverts the normal power between adult and children, and casts Vera as the holder of truth and power (through her trickery) and Framton as the powerless, gullible adult. The reader looks through the ‘open window’ by reading the story and becomes a character herself, subject to the same foibles as Framton as a result of Saki’s diction and Vera’s character.

How are adults characterized in the short story?

From the muddied hunting troupe to the obsessive Mrs. Sappleton and the unstable Framton Nuttel, adults in “the Open Window” are characterized as a motley crew of dullness and daftness. This is especially true when their humdrum characters are contrasted with the quick wit and exciting world Vera creates all on her own. Even the adult character names, ‘ Sap pleton’ and ‘ Nut tel’, emphasize a negative almost mocking view of them.

Explain how gender roles contribute to the meaning of the story.

With the exception of Framton, the characters that remain at home are all women and those that go hunting are all men. Framton’s nerves may align him more with traditional and stereotypical portrayals of women in Saki’s day. However, Saki also inverts a traditionally male-aligned role as trickster by attributing it to a young woman. Gender in “The Open Window” is thus an interesting reversal of traditional gender roles presented by Saki’s contemporaries.

What is the role of nature's influence in the story?

Nature is frequently presented as at odds with the human characters in the story. For example, the hunting party meets its supposed demise on account of poor weather conditions and a muddy bog. In Vera’s final tale, Framton is also harassed by a pack of wild dogs. Finally, Saki uses the landscape of the rural countryside as a setting for his macabre tale, showing that nature is not always peaceful and serene: it also has a darker side.

Is Vera an antagonist or a protagonist? Explain.

Though Vera plays a cruel trick on Framton, she does not perfectly fit into the role of antagonist. Conversely, though her trick is based on a morbid joke, Vera is presented as the hero of the tale. She saves the reader from another boring rendering of an adult house visit. Furthermore, she quickly disproves Framton’s doctor, who suggested a change in scenery would cure him of his anxiety.

What does the window symbolize to the various characters of the story?

To Vera, the window is a blank canvas. She uses it to create a world separate from the dull adult world she is forced to inhabit. Mrs. Sappleton views the window as the vessel that will bring back her male companion and brothers. Though she complains about all the dirt they will drag in, she also seems to wait on bated breath for the window to bring back the only company she truly cares to keep. For Framton the window symbolizes the failure of his plan to find a respite in the rural countryside, which is why he seeks to put so much distance between himself and the window at the story’s end.

How does the omniscient narrator shape the short story?

The omniscient narrator functions almost as another character. Through him, Saki provides clues to the readers, thereby suggesting that Vera is not such a trustworthy storyteller. Were the story instead told from the perspective of Framton or Vera herself, it might have been harder to dupe the reader. Moreover, the omniscient narrator in some ways is another way in which Saki inserts himself into the story, a co-conspirator of sorts, laying the path for Vera’s trickery.

The Open Window Questions and Answers

The Question and Answer section for The Open Window is a great resource to ask questions, find answers, and discuss the novel.

The Open Window by Saki

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The open window

Girl Trickster (Situational Irony)

In Saki’s time girls were frequently portrayed as trustworthy and honest people. It is thus ironic that he chooses a female character to play the role of trickster and storyteller in “The Open Window.”

Do you mean literary element? Please specify the literary element that you mean.

Study Guide for The Open Window

The Open Window study guide contains a biography of Saki (Hector Hugh Munro), literature essays, quiz questions, major themes, characters, and a full summary and analysis.

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Lesson Plan for The Open Window

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• Relationship to Other Books
• Bringing in Technology
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Wikipedia Entries for The Open Window

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• Published: 01 May 2024

Temporal dynamics of the multi-omic response to endurance exercise training

• MoTrPAC Study Group ,
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Nature volume  629 ,  pages 174–183 ( 2024 ) Cite this article

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• Epigenetics
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Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood 1 , 2 , 3 . Here, the Molecular Transducers of Physical Activity Consortium 4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

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Regular exercise provides wide-ranging health benefits, including reduced risks of all-cause mortality 1 , 5 , cardiometabolic and neurological diseases, cancer and other pathologies 2 , 6 , 7 . Exercise affects nearly all organ systems in either improving health or reducing disease risk 2 , 3 , 6 , 7 , with beneficial effects resulting from cellular and molecular adaptations within and across many tissues and organ systems 3 . Various ‘omic’ platforms (‘omes’) including transcriptomics, epigenomics, proteomics and metabolomics, have been used to study these events. However, work to date typically covers one or two omes at a single time point, is biased towards one sex, and often focuses on a single tissue, most often skeletal muscle, heart or blood 8 , 9 , 10 , 11 , 12 , with few studies considering other tissues 13 . Accordingly, a comprehensive, organism-wide, multi-omic map of the effects of exercise is needed to understand the molecular underpinnings of exercise training-induced adaptations. To address this need, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established with the goal of building a molecular map of the exercise response across a broad range of tissues in animal models and in skeletal muscle, adipose and blood in humans 4 . Here we present the first whole-organism molecular map of the temporal effects of endurance exercise training in male and female rats and provide multiple insights enabled by this MoTrPAC multi-omic data resource.

Multi-omic analysis of exercise training

Six-month-old male and female Fischer 344 rats were subjected to progressive treadmill endurance exercise training (hereafter referred to as endurance training) for 1, 2, 4 or 8 weeks, with tissues collected 48 h after the last exercise bout (Fig. 1a ). Sex-matched sedentary, untrained rats were used as controls. Training resulted in robust phenotypic changes (Extended Data Fig. 1a–d ), including increased aerobic capacity (VO 2 max) by 18% and 16% at 8 weeks in males and females, respectively (Extended Data Fig. 1a ). The percentage of body fat decreased by 5% in males at 8 weeks (Extended Data Fig. 1b ), without a significant change in lean mass (Extended Data Fig. 1c ). In females, the body fat percentage did not change after 4 or 8 weeks of training, whereas it increased by 4% in sedentary controls (Extended Data Fig. 1b ). Body weight of females increased in all intervention groups, with no change for males (Extended Data Fig. 1d ).

a , Experimental design and tissue sample processing. Inbred Fischer 344 rats were subjected to a progressive treadmill training protocol. Tissues were collected from male and female animals that remained sedentary or completed 1, 2, 4 or 8 weeks of endurance exercise training. For trained animals, samples were collected 48 h after their last exercise bout (red pins). b , Summary of molecular datasets included in this study. Up to nine data types (omes) were generated for blood, plasma, and 18 solid tissues, per animal: ACETYL: acetylproteomics; protein site acetylation; ATAC, chromatin accessibility, ATAC-seq data; IMMUNO, multiplexed immunoassays; METAB, metabolomics and lipidomics; METHYL, DNA methylation, RRBS data; PHOSPHO, phosphoproteomics; protein site phosphorylation; PROT, global proteomics; protein abundance; TRNSCRPT, transcriptomics, RNA-seq data; UBIQ, ubiquitylome, protein site ubiquitination. Tissue labels indicate the location, colour code, and abbreviation for each tissue used throughout this study: ADRNL, adrenal gland; BAT, brown adipose tissue; BLOOD, whole blood, blood RNA; COLON, colon; CORTEX, cerebral cortex; HEART, heart; HIPPOC, hippocampus; HYPOTH, hypothalamus; KIDNEY, kidney; LIVER, liver; LUNG, lung; OVARY, ovaries; PLASMA, plasma; SKM-GN, gastrocnemius (skeletal muscle); SKM-VL, vastus lateralis (skeletal muscle); SMLINT, small intestine; SPLEEN, spleen; TESTES, testes; VENACV, vena cava; WAT-SC, subcutaneous white adipose tissue. Icons next to each tissue label indicate the data types generated for that tissue. c , Number of training-regulated features at 5% FDR. Each cell represents results for a single tissue and data type. Colours indicate the proportion of measured features that are differential.

Whole blood, plasma and 18 solid tissues were analysed using genomics, proteomics, metabolomics and protein immunoassay technologies, with most assays performed in a subset of these tissues (Fig. 1b and Extended Data Fig. 1e,f ). Specific details for each omic analysis are provided in Extended Data Fig. 2 , Methods, Supplementary Discussion and Supplementary Table 1 . Molecular assays were prioritized on the basis of available tissue quantity and biological relevance, with the gastrocnemius, heart, liver and white adipose tissue having the most diverse set of molecular assays performed, followed by the kidney, lung, brown adipose tissue and hippocampus (Extended Data Fig. 1e ). Altogether, datasets were generated from 9,466 assays across 211 combinations of tissues and molecular platforms, resulting in 681,256 non-epigenetic and 14,334,496 epigenetic (reduced-representation bisulfite sequencing (RRBS) and assay for transposase-accessible chromatin using sequencing (ATAC-seq)) measurements, corresponding to 213,689 and 2,799,307 unique non-epigenetic and epigenetic features, respectively.

Differential analysis was used to characterize the molecular responses to endurance training (Methods). We computed the overall significance of the training response for each feature, denoted as the training P value, where 35,439 features at 5% false discovery rate (FDR) comprise the training-regulated differential features (Fig. 1c and Supplementary Table 2 ). Timewise summary statistics quantify the exercise training effects for each sex and time point. Training-regulated molecules were observed in the vast majority of tissues for all omes, including a relatively large proportion of transcriptomics, proteomics, metabolomics and immunoassay features (Fig. 1c ). The observed timewise effects were modest: 56% of the per-feature maximum fold changes were between 0.67 and 1.5. Permutation testing showed that permuting the group or sex labels resulted in a significant reduction in the number of selected analytes in most tissues (Extended Data Fig. 3a–d and Supplementary Discussion ). For transcriptomics, the hypothalamus, cortex, testes and vena cava had the smallest proportion of training-regulated genes, whereas the blood, brown and white adipose tissues, adrenal gland and colon showed more extensive effects (Fig. 1c ). For proteomics, the gastrocnemius, heart and liver showed substantial differential regulation in both protein abundance and post-translational modifications (PTMs), with more restricted results in white adipose tissue, lung and kidney protein abundance. For metabolomics, a large proportion of differential metabolites were consistently observed across all tissues, although the absolute numbers were related to the number of metabolomic platforms used (Extended Data Fig. 1e ). The vast number of differential features over the training time course across tissues and omes highlights the multi-faceted, organism-wide nature of molecular adaptations to endurance training.

Multi-tissue response to training

To identify tissue-specific and multi-tissue training-responsive gene expression, we considered the six tissues with the deepest molecular profiling: gastrocnemius, heart, liver, white adipose tissue, lung and kidney. In sum, 11,407 differential features from these datasets were mapped to their cognate gene, for a total of 7,115 unique genes across the tissues (Fig. 2a , Extended Data Fig. 4a and Supplementary Table 3 ). Most of the genes with at least one training-responsive feature were tissue-specific (67%), with the greatest number appearing in white adipose tissue (Fig. 2a ). We identified pathways enriched by these tissue-specific training-responsive genes (Extended Data Fig. 4b ) and tabulated a subset of highly specific genes to gain insight into tissue-specific training adaptation (Supplementary Table 4 ). Focusing on sexually conserved responses revealed tissue-dependent adaptations. These included changes related to immune cell recruitment and tissue remodelling in the lung, cofactor and cholesterol biosynthesis in the liver, ion flux in the heart, and metabolic processes and striated muscle contraction in the gastrocnemius ( Supplementary Discussion ). A detailed analysis of white adipose tissue adaptations to exercise training is provided elsewhere 14 . We also observed ‘ome’-specific responses, with unique transcript and protein responses at the gene and pathway levels (Extended Data Fig. 4c,d , Supplementary Discussion and Supplementary Tables 5 and 6 ).

a , UpSet plot of the training-regulated gene sets associated with each tissue. Bars and dots indicating tissue-specific differential genes are coloured by tissue. Pathway enrichment analysis is shown for selected sets of genes in b , c as indicated by the arrows. b , c , Significantly enriched pathways (10% FDR) corresponding to genes that are differential in both LUNG and WAT-SC datasets ( b ) and the 22 genes that are training-regulated in all six tissues considered in a ( c ). Redundant pathways (those with an overlap of 80% or greater with an existing pathway) were removed. ESR, oestrogen receptor; T H 17, T helper 17.

2,359 genes had differential features in at least two tissues (Fig. 2a ). Lung and white adipose tissue had the largest set of uniquely shared genes ( n  = 249), with predominantly immune-related pathway enrichments (Fig. 2b ); expression patterns suggested decreased inflammation in the lung and increased immune cell recruitment in white adipose tissue (Supplementary Tables 2 and 3 ). Heart and gastrocnemius had the second-largest group of uniquely shared genes, with enrichment of mitochondrial metabolism pathways including the mitochondria fusion genes Opa1 and Mfn1 (Supplementary Table 3 ).

Twenty-two genes were training-regulated in all six tissues, with particular enrichment in heat shock response pathways (Fig. 2c ). Exercise induces the expression of heat shock proteins (HSPs) in various rodent and human tissues 15 . A focused analysis of our transcriptomics and proteomics data revealed HSPs as prominent outliers (Extended Data Fig. 5a and Supplementary Discussion ). Specifically, there was a marked, proteomics-driven up-regulation in the abundance of HSPs, including the major HSPs HSPA1B and HSP90AA1 (Extended Data Fig. 5b,c ). Another ubiquitous endurance training response involved regulation of the kininogenases KNG1 and KNG2 (Supplementary Table 3 ). These enzymes are part of the kallikrein–kininogen system and have been implicated in the hypotensive and insulin-sensitizing effects of exercise 16 , 17 .

Transcription factors and phosphosignalling

We used proteomics and transcriptomics data to infer changes in transcription factor and phosphosignalling activities in response to endurance training through transcription factor and PTM enrichment analyses (Methods). We compared the most significantly enriched transcription factors across tissues (Fig. 3a , Extended Data Fig. 6a and Supplementary Table 7 ). In the blood, we observed enrichment of the haematopoietic-associated transcription factors GABPA, ETS1, KLF3 and ZNF143; haematopoietic progenitors are proposed to be transducers of the health benefits of exercise 18 . In the heart and skeletal muscle, we observed a cluster of enriched Mef2 family transcription factor motifs (Fig. 3a ). MEF2C is a muscle-associated transcription factor involved in skeletal, cardiac and smooth muscle cell differentiation and has been implicated in vascular development, formation of the cardiac loop and neuron differentiation 19 .

a , Transcription factor motif enrichment analysis of the training-regulated transcripts in each tissue. The heat map shows enrichment z -scores across the differential genes for the 13 tissues that had at least 300 genes after mapping transcript IDs to gene symbols. Transcription factors were hierarchically clustered by their enrichment across tissues. CRE, cAMP response element. b , Estimate of activity changes in selected kinases and signalling pathways using PTM signature enrichment analysis on phosphoproteomics data. Only kinases or pathways with a significant difference in at least one tissue, sex or time point ( q value < 0.05) are shown. The heat map shows normalized enrichment score (NES) as colour; tissue, sex and time point combinations as columns, and either kinases or pathways as rows. Kinases are grouped by family; rows are hierarchically clustered within each group. FSH, follicle-stimulating hormone; TSH, thyroid-stimulating hormone.

Phosphorylation signatures of key kinases were altered across many tissues (Fig. 3b and Supplementary Table 8 ). This included AKT1 across heart, kidney and lung, mTOR across heart, kidney and white adipose tissue, and MAPK across heart and kidney. The liver showed an increase in the phosphosignature related to regulators of hepatic regeneration, including EGFR1, IGF and HGF (Extended Data Fig. 6b , Supplementary Discussion ). Increased phosphorylation of STAT3 and PXN, HGF targets involved in cell proliferation, suggest a mechanism for liver regeneration in response to exercise (Extended Data Fig. 6c ). In the heart, kinases showed bidirectional changes in their predicted basal activity in response to endurance training (Extended Data Fig. 6d and Supplementary Discussion ). Several AGC protein kinases showed a decrease in predicted activity, including AKT1, whereas tyrosine kinases, including SRC and mTOR, were predicted to have increased activity. The known SRC target phosphorylation sites GJA1 pY265 and CDH2 pY820 showed significantly increased phosphorylation in response to training (Extended Data Fig. 6e ). Notably, phosphorylation of GJA1 Y265 has previously been shown to disrupt gap junctions, key transducers of cardiac electrical conductivity 20 . This suggests that SRC signalling may regulate extracellular structural remodelling of the heart to promote physiologically beneficial adaptations. In agreement with this hypothesis, gene set enrichment analysis (GSEA) of extracellular matrix proteins revealed a negative enrichment in response to endurance training, showing decreased abundance of proteins such as basement membrane proteins (Extended Data Fig. 6f–h and Supplementary Table 9 ).

Molecular hubs of exercise adaptation

To compare the dynamic multi-omic responses to endurance training across tissues, we clustered the 34,244 differential features with complete timewise summary statistics using an empirical Bayes graphical clustering approach (Methods). By integrating these results onto a graph, we summarize the dynamics of the molecular training response and identify groups of features with similar responses (Extended Data Fig. 7 and Supplementary Table 10 ). We performed pathway enrichment analysis for many graphically defined clusters to characterize putative underlying biology (Supplementary Table 11 ).

We examined biological processes associated with training using the pathway enrichment results for up-regulated features at 8 weeks of training (Extended Data Fig. 8 , Supplementary Table 12 and Supplementary Discussion ). Compared with other tissues, the liver showed substantial regulation of chromatin accessibility, including in the nuclear receptor signalling and cellular senescence pathways. In the gastrocnemius, terms related to peroxisome proliferator-activated receptors (PPAR) signalling and lipid synthesis and degradation were enriched at the protein level, driven by proteins including the lipid droplet features PLIN2, PLIN4 and PLIN5. At the metabolomic level, terms related to ether lipid and glycerophospholipid metabolism were enriched. Together, these enrichments highlight the well-known ability of endurance training to modulate skeletal muscle lipid composition, storage, synthesis and metabolism. The blood displayed pathway enrichments related to translation and organelle biogenesis and maintenance. Paired with the transcription factor analysis (Fig. 3a ), this suggests increased haematopoietic cellular mobilization in the blood. Less studied tissues in the context of exercise training, including the adrenal gland, spleen, cortex, hippocampus and colon, also showed regulation of diverse pathways ( Supplementary Discussion ).

To identify the main temporal or sex-associated responses in each tissue, we summarized the graphical cluster sizes by tissue and time (Extended Data Fig. 7a ). We observed that the small intestine and plasma had more changes at weeks 1 and 2 of training. Conversely, many up-regulated features in brown adipose tissue and down-regulated features in white adipose tissue were observed only at week 8. The largest proportion of opposite effects between males and females was observed at week 1 in the adrenal gland. Other tissues, including the blood, heart, lung, kidney and skeletal muscle (gastrocnemius and vastus lateralis), had relatively consistent numbers of up-regulated and down-regulated features.

We next focused on characterizing shared molecular responses in the three striated muscles (gastrocnemius, vastus lateralis and heart). The three largest graphical clustering paths of differential features in each muscle tissue converged to a sex-consistent response by week 8 (Fig. 4a ). Because of the large number of muscle features that were up-regulated in both sexes at week 8, we further examined the corresponding multi-omic set of analytes (Fig. 4b ). Pathway enrichment analysis of the genes associated with these differential features demonstrated a sex- and muscle-consistent endurance training response that reflected up-regulation of mitochondrial metabolism, biogenesis and translation, and cellular response to heat stress (Fig. 4c and Supplementary Table 11 ).

a , Graphical representation of training-differential features in the three muscle tissues: gastrocnemius (SKM-GN), vastus lateralis (SKM-VL) and heart. Each node represents one of nine possible states (rows) at each of the four training time points (columns). Triangles to the left of row labels map states to symbols used in Fig. 5a . Edges represent the path of differential features over the training time course (see Extended Data Fig. 7 for a detailed explanation). Each graph includes the three largest paths of differential features in that tissue, with edges split by data type. Both node and edge size are proportional to the number of features represented. The node corresponding to features that are up-regulated in both sexes at 8 weeks of training (8w_F1_M1) is circled in each graph. b , Line plots of standardized abundances of all 8w_F1_M1 muscle features. The black line represents the average value across all features. c , Network view of significant pathway enrichment results (10% FDR) corresponding to the features in b . Nodes represent pathways; edges represent functionally similar node pairs (set similarity ≥ 0.3). Nodes are included only if they are significantly enriched in at least two of the muscle tissues, as indicated by node colour. Node size is proportional to the number of differential feature sets (for example, gastrocnemius transcripts) for which the pathway is significantly enriched. High-level biological themes were defined using Louvain community detection of the nodes. d , A subnetwork of a larger cluster identified by network clustering 8w_F1_M1 features from SKM-GN. Mech., mechanical.

We used a network connectivity analysis to study up-regulated features in the gastrocnemius at week 8 (Extended Data Fig. 9a,b , Methods and Supplementary Discussion ). Mapping features to genes revealed overlaps between transcriptomic, chromatin accessibility, and proteomic assays, but no overlaps with methylation. Three molecular interaction networks were compared (Methods), and BioGRID 21 was used for further clustering analysis, which identified three clusters (Extended Data Fig. 9c and Supplementary Table 13 ). The largest cluster was significantly enriched for multiple muscle adaptation processes (Fig. 4d and Supplementary Table 14 ). This analysis illustrates the direct linkage among pathways and putative central regulators, emphasizing the importance of multi-omic data in identifying interconnected networks and understanding skeletal muscle remodelling.

Connection to human diseases and traits

To systematically evaluate the translational value of our data, we integrated our results with extant exercise studies and disease ontology (DO) annotations (Methods). First, we compared our vastus lateralis transcriptomics results to a meta-analysis of long-term training gene-expression changes in human skeletal muscle tissue 8 , demonstrating a significant and direction-consistent overlap (Extended Data Fig. 9d–g and Supplementary Discussion ). We also identified a significant overlap between differential transcripts in the gastrocnemius of female rats trained for 8 weeks and differentially expressed genes identified in the soleus in a study of sedentary and exercise-trained female rats selectively bred for high or low exercise capacity 22 (Extended Data Fig. 9h ). Similarly, adaptations from high-intensity interval training in humans 23 significantly overlapped with the proteomics response in rats (Extended Data Fig. 9i ), particularly for female rats trained for 8 weeks (Extended Data Fig. 9j ). Finally, we performed DO enrichment analysis using the DOSE R package 24 (Supplementary Table 15 and Methods). Down-regulated genes from white adipose tissue, kidney and liver were enriched for several disease terms, suggesting a link between the exercise response and type 2 diabetes, cardiovascular disease, obesity and kidney disease (5% FDR; Extended Data Fig. 9k and Supplementary Discussion ), which are all epidemiologically related co-occurring diseases 25 . Overall, these results support a high concordance of our data from rats with human studies and their relevance to human disease.

Sex-specific responses to exercise

Many tissues showed sex differences in their training responses (Extended Data Fig. 10 ), with 58% of the 8-week training-regulated features demonstrating sex-differentiated responses. Opposite responses between the sexes were observed in adrenal gland transcripts, lung phosphosites and chromatin accessibility features, white adipose tissue transcripts and liver acetylsites. In addition, proinflammatory cytokines exhibited sex-associated changes across tissues (Extended Data Fig. 11a,b and Supplementary Table 16 ). Most female-specific cytokines were differentially regulated between weeks 1 and 2 of training, whereas most male-specific cytokines were differentially regulated between weeks 4 and 8 (Extended Data Fig. 11c ).

We observed extensive transcriptional remodelling of the adrenal gland, with more than 4,000 differential genes. Notably, the largest graphical path of training-regulated features was negatively correlated between males and females, with sustained down-regulation in females and transient up-regulation at 1 week in males (Extended Data Fig. 11d ). The genes in this path were also associated with steroid hormone synthesis pathways and metabolism, particularly those pertaining to mitochondrial function (Supplementary Table 11 ). Further, transcription factor motif enrichment analysis of the transcripts in this path showed enrichment of 14 transcription factors (5% FDR; Supplementary Table 17 ), including the metabolism-regulating factors PPARγ, PPARα and oestrogen-related receptor gamma (ERRγ). The gene-expression levels of several significantly enriched transcription factors themselves followed the same trajectory as this path (Extended Data Fig. 11e ).

In the rat lung, we observed decreased phosphosignalling activity with training primarily in males (Fig. 3b ). Among these, the PRKACA phosphorylation signature showed the largest sex difference at 1 and 2 weeks (Extended Data Fig. 11f–h and Supplementary Table 8 ). PRKACA is a kinase that is involved in signalling within multiple cellular pathways. However, four PRKACA substrates followed this pattern and were associated with cellular structures (such as cytoskeleton and cell–cell junctions): DSP, MYLK, STMN1 and SYNE1 (Extended Data Fig. 11i ). The phosphorylation of these proteins suggests a sex-dependent role of PRKACA in mediating changes in lung structure or mechanical function with training. This is supported as DSP and MYLK have essential roles in alveolar and epithelial cell remodelling in the lung 26 , 27 .

Immune pathway enrichment analysis of training-regulated transcripts at 8 weeks showed limited enrichment in muscle (heart, gastrocnemius and vastus lateralis) and brain (cortex, hippocampus, hypothalamus), down-regulation in the lung and small intestine, and strong up-regulation in brown and white adipose tissue in males only (Fig. 5a , Extended Data Fig. 12a and Supplementary Table 11 ). Many of the same immune pathways (Supplementary Table 18 ) and immune-related transcription factors (Supplementary Table 19 ) were enriched in both adipose tissues in males. Furthermore, correlation between the transcript expression profiles of male-specific up-regulated features in the adipose tissues and immune cell markers from external cell-typing assays revealed a strong positive correlation for many immune cell types, including B, T and natural killer cells, and low correlation with platelets, erythrocytes and lymphatic tissue (Fig. 5b,c , Methods and Supplementary Table 20 ). These patterns suggest recruitment of peripheral immune cells or proliferation of tissue-resident immune cells as opposed to non-biological variation in blood or lymph content. Correlations at the protein level were not as marked (Extended Data Fig. 12b,c ). Complementary analyses using CIBERTSORTx produced similar results (Extended Data Fig. 12d,e ). In summary, our data suggest an important role of immune cell activity in the adaptation of male adipose tissue to endurance training.

a , Enrichment analysis results of the training-differential transcripts at 8 weeks in Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways (10% FDR). NK, natural killer. b , Line plots of standardized abundances of selected training-differential transcripts. Brown and white adipose tissue show male-specific up-regulation at week 8 (8w_F0_M1). The small intestine (SMLINT) shows down-regulation in females and partial down-regulation in males at week 8 (8w_F-1_M0 or 8w_F-1_M-1). c , Box plots of the sample-level Pearson correlation between markers of immune cell types, lymphatic tissue or cell proliferation and the average value of features in b at the transcript level. A pink dot indicates that the marker is also one of the differential features plotted in b . A pound sign indicates that the distribution of Pearson correlations for a set of at least two markers is significantly different from 0 (two-sided one-sample t -test, 5% FDR). When only one marker is used to define a category on the y axis, the gene name is provided in parentheses. In box plots, the centre line represents median, box bounds represent 25th and 75th percentiles, whiskers represent minimum and maximum excluding outliers and blue dots represent outliers.

The small intestine was among the tissues with the highest enrichment in immune-related pathways (Extended Data Fig. 12a ), with down-regulation of transcripts at 8 weeks, and a more robust response in females (Fig. 5b ). This transcript set was significantly enriched with pathways related to gut inflammation (Supplementary Table 11 ). We observed positive associations between these transcripts and markers of several immune cell types, including B, T, natural killer and dendritic cells, suggesting decreased abundance (Fig. 5c and Supplementary Discussion ). Endurance training also decreased the expression of transcripts with genetic risk loci for inflammatory bowel disease (IBD), including major histocompatability complex class II 28 , a finding that also emerged through the DO enrichment analysis (Supplementary Table 15 ). Endurance training is suggested to reduce systemic inflammation, in part by increasing gut microbial diversity and gut barrier integrity 29 . In accordance, we observed decreases in Cxcr3 and Il1a with training (Extended Data Fig. 12f ), both of which are implicated in the pathogenesis of IBD 30 , 31 . Together, these data suggest that endurance training improves gut homeostasis, potentially conferring systemic anti-inflammatory effects.

Multi-tissue changes in mitochondria and lipids

We summarized the organism-wide metabolic changes for metabolomic datasets using RefMet metabolite classes (Fig. 6a and Supplementary Table 21 ) and for non-metabolomics datasets using metabolic subcategories of KEGG pathways (10% FDR; Extended Data Fig. 13a and Supplementary Table 11 ). The liver showed the greatest number of significantly enriched metabolite classes, followed by the heart, lung and hippocampus (Fig. 6a and Supplementary Discussion ). Inspection of individual metabolites and acylcarnitine groups revealed changes associated with functional alterations in response to training (Extended Data Fig. 13b–d and Supplementary Discussion ). Of particular interest, trimethylamine- N -oxide has been associated with cardiovascular disease 32 . We observed up-regulation of 1-methylhistidine, a marker of muscle protein turnover, in the kidney at 1, 2 and 4 weeks, which may indicate muscle breakdown and clearance through the kidney during early training time points. Cortisol levels were increased as expected from the physiological stress of training, and we observed a substantial increase in the kidney, again probably owing to renal clearance 33 . The liver showed up-regulation of 1-methylnicotinamide, which may have a role in inflammation 34 , at 8 weeks.

a , RefMet metabolite class enrichment calculated using GSEA with the −log 10 training P value. Significant chemical class enrichments (5% FDR) are shown as black circles with size is proportional to FDR. Small grey circles are chemical class enrichments that were not significant, and blank cells were not tested owing to low numbers of detected metabolites. TCA, tricarboxylic acid cycle. b , GSEA results using the MitoCarta MitoPathways gene set database and proteomics (PROT) or acetylome (ACETYL) timewise summary statistics for training. NESs are shown for significant pathways (10% FDR). Mitochondrial pathways shown as rows are grouped using the parental group in the MitoPathways hierarchy. OXPHOS, oxidative phosphorylation. c , Line plots of standardized abundances of liver training-differential features across all data types that are up-regulated in both sexes, with a later response in females (LIVER: 1w_F0_M1 − >2w_F0_M1 − >4w_F0_M1 − >8w_F1_M1). The black line represents the average value across all features. d , Network view of pathway enrichment results corresponding to features in c . Nodes indicate significantly enriched pathways (10% FDR); edges connect nodes if there is a similarity score of at least 0.375 between the gene sets driving each pathway enrichment. Node colours indicate omes in which the enrichment was observed. e , log 2 fold changes (logFC) relative to sedentary controls for metabolites within the ‘Lipids and lipid related compounds’ category in the 8-week liver. Heat map colour represents fold change (red, positive; blue, negative). Compounds are grouped into columns based on category (coloured bars).

The heart showed enrichment of various carbohydrate metabolism subcategories across many omes (Extended Data Fig. 13a ), and remarkably, all enzymes within the glycolysis–gluconeogenesis pathway showed a consistent increase in abundance, except for GPI, FBP2 and DLAT (Extended Data Fig. 13e ). Oxidative phosphorylation was enriched in most tissues and is consistent with the joint analyses of the muscle tissues (Fig. 4c ), suggesting potential changes in mitochondria biogenesis. We estimated proportional mitochondrial changes to endurance training using mitochondrial RNA-sequencing (RNA-seq) reads (Extended Data Fig. 14a–c ) and changes of mitochondrial functions through GSEA using gene expression, protein abundance and protein PTMs (Fig. 6b , Extended Data Fig. 14d and Supplementary Tables 22 – 25 ). Increased mitochondrial biogenesis was observed in skeletal muscle, heart and liver across these analyses. Moreover, sex-specific mitochondrial changes were observed in the adrenal gland, as described above, and in the colon, lung and kidney. These results highlight a highly adaptive and pervasive mitochondrial response to endurance training; a more in-depth analysis of this response is provided elsewhere 35 .

In the liver, we observed substantial regulation of metabolic pathways across the proteome, acetylome and lipidome (Fig. 6a,b and Extended Data Fig. 13a ). For example, there was significant enrichment in 12 metabolite classes belonging to ‘lipids and lipid-related compounds’ (Fig. 6a and Supplementary Table 26 ). We therefore focused on the large group of features that increased in abundance over time for both sexes (Fig. 6c ). Most of these liver features corresponded to protein abundance and protein acetylation changes in the mitochondrial, amino acid and lipid metabolic pathways (Fig. 6d and Supplementary Table 27 ). We also observed an increase in phosphatidylcholines and a concomitant decrease in triacylglycerols (Fig. 6e ). Finally, there was increased abundance and acetylation of proteins from the peroxisome, an organelle with key functions in lipid metabolism (Extended Data Fig. 14e ). To our knowledge, these extensive changes in protein acetylation in response to endurance training have not been described previously. Together, these molecular adaptations may constitute part of the mechanisms underlying exercise-mediated improvements in liver health, particularly protection against excessive intrahepatic lipid storage and steatosis 36 .

Mapping the molecular exercise responses across a whole organism is critical for understanding the beneficial effects of exercise. Previous studies are limited to a few tissues, a narrow temporal range, or a single sex. Substantially expanding on the current work in the field, we used 25 distinct molecular platforms in as many as 19 tissues to study the temporal changes to endurance exercise training in male and female rats. Accordingly, we identified thousands of training-induced changes within and across tissues, including temporal and sex-biased responses, in mRNA transcripts, proteins, post-translational modifications and metabolites. Each omic dataset provides unique insights into exercise adaptation, where a holistic understanding requires multi-omic analysis. This work illustrates how mining our data resource can both recapitulate expected mechanisms and provide novel biological insights.

This work can be leveraged to deepen our understanding of exercise-related improvement of health and disease management. The global heat shock response to exercise may confer cytoprotective effects, including in pathologies related to tissue damage and injury recovery 37 . Increased acetylation of liver mitochondrial enzymes and regulation of lipid metabolism may link exercise to protection against non-alcoholic fatty liver disease and steatohepatitis 36 . Similarly, exercise-mediated modulation of cytokines, receptors and transcripts linked to intestinal inflammation or IBD may be associated with improved gut health. These examples highlight unique training responses illuminated by a multi-omics approach that can be leveraged for future hypothesis-driven research on how exercise improves whole-body and tissue-specific health.

We note limitations in our experimental design, datasets and analyses ( Supplementary Discussion ). In short, samples were collected 48 h after the last exercise bout to capture sustained alterations, thereby excluding acute responses. Our assays were performed on bulk tissue and do not cover single-cell platforms. Our resource has limited omic characterization for certain tissues, and additional platforms with emerging biological relevance were not utilized, including microbiome profiling. Moreover, our results are hypothesis-generating and require biological validation; supporting this, we have established a publicly accessible tissue bank from this study.

This MoTrPAC resource provides future opportunities to enhance and refine the molecular map of the endurance training response. We expect that this dataset will remain an ongoing platform to translate tissue- and sex-specific molecular changes in rats to humans. MoTrPAC has made extensive efforts to facilitate access, exploration and interpretation of this resource. We developed the MoTrPAC Data Hub to easily explore and download data ( https://motrpac-data.org/ ), software packages to provide reproducible source code and facilitate data retrieval and analysis in R (MotrpacRatTraining6mo and MotrpacRatTraining6moData 38 , 39 ), and visualization tools for data exploration ( https://data-viz.motrpac-data.org ). Altogether, this multi-omic resource serves as a broadly useful reference for studying the milieu of molecular changes in endurance training adaptation and provides new opportunities to understand the effects of exercise on health and disease.

All methods are included in the  Supplementary Information .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

MoTrPAC data are publicly available via http://motrpac-data.org/data-access . Data access inquiries should be sent to [email protected]. Additional resources can be found at http://motrpac.org and https://motrpac-data.org/ . Interactive data visualizations are provided through a website ( https://data-viz.motrpac-data.org ) and HTML reports summarizing the multi-omic graphical analysis results in each tissue 40 . Processed data and analysis results are additionally available in the MotrpacRatTraining6moData R package 39 ( https://github.com/MoTrPAC/MotrpacRatTraining6moData ). Raw and processed data for were deposited in the appropriate public repositories as follows. RNA-seq, ATAC-seq and RRBS data were deposited at the Sequence Read Archive under accession PRJNA908279 and at the Gene Expression Omnibus under accession GSE242358 ; multiplexed immunoassays were deposited at IMMPORT under accession SDY2193 ; metabolomics data were deposited at Metabolomics Workbench under project ID PR001020 ; and proteomics data were deposited at MassIVE under accessions MSV000092911 , MSV000092922 , MSV000092923 , MSV000092924 , MSV000092925 and MSV000092931 . We used the following external datasets: release 96 of the Ensembl R. norvegicus (rn6) genome ( https://ftp.ensembl.org/pub/release-96/fasta/rattus_norvegicus/dna/ ) and gene annotation ( https://ftp.ensembl.org/pub/release-96/gtf/rattus_norvegicus/Rattus_norvegicus.Rnor_6.0.96.gtf.gz ); RefSeq protein database ( https://ftp.ncbi.nlm.nih.gov/refseq/R_norvegicus/ , downloaded 11/2018); the NCBI gene2refseq mapping files ( https://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2refseq.gz , accessed 18 December 2020); RGD rat gene annotation ( https://download.rgd.mcw.edu/data_release/RAT/GENES_RAT.txt , accessed 12 November 2021); BioGRID v4.2.193 ( https://downloads.thebiogrid.org/File/BioGRID/Release-Archive/BIOGRID-4.2.193/BIOGRID-ORGANISM-4.2.193.tab3.zip ); STRING v11.5 ( https://stringdb-downloads.org/download/protein.physical.links.v11.5/10116.protein.physical.links.v11.5.txt.gz ); GENCODE release 39 metadata and annotation files ( https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_39/ , accessed 20 January 2022); MatrisomeDB ( https://doi.org/10.1093/nar/gkac1009 ); MitoPathways database available through MitoCarta ( https://personal.broadinstitute.org/scalvo/MitoCarta3.0/ ); PTMSigDB v1.9.0 PTM set database ( https://doi.org/10.1074/mcp.TIR118.000943 ); UniProt human proteome FASTA for canonical protein sequences (UniProtKB query “reviewed:true AND proteome:up000005640”, download date 3 March 2021); the CIBERSORT LM22 leukocyte gene signature matrix ( https://doi.org/10.1007/978-1-4939-7493-1_12 ); published results from Amar et al. 8 , Bye et al. 22 and Hostrup et al. 23 ; and GTEx v8 gene-expression data (dbGaP Accession phs000424.v8.p2). Details are provided in the Supplementary Information , Methods.

Code availability

Code for reproducing the main analyses is provided in the MotrpacRatTraining6mo R package 38 ( https://motrpac.github.io/MotrpacRatTraining6mo/ ). MoTrPAC data processing pipelines for RNA-seq, ATAC-seq, RRBS and proteomics are available in the following Github repositories: https://github.com/MoTrPAC/motrpac-rna-seq-pipeline 41 , https://github.com/MoTrPAC/motrpac-atac-seq-pipeline 42 , https://github.com/MoTrPAC/motrpac-rrbs-pipeline 43 and https://github.com/MoTrPAC/motrpac-proteomics-pipeline 44 . Normalization and quality control scripts are available at https://github.com/MoTrPAC/MotrpacRatTraining6moQCRep 45 .

Blair, S. N. et al. Physical fitness and all-cause mortality. A prospective study of healthy men and women. JAMA 262 , 2395–2401 (1989).

Article   CAS   PubMed   Google Scholar  

Booth, F. W., Roberts, C. K. & Laye, M. J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2 , 1143–1211 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Neufer, P. D. et al. Understanding the cellular and molecular mechanisms of physical activity-induced health benefits. Cell Metab. 22 , 4–11 (2015).

Sanford, J. A. et al. Molecular Transducers of Physical Activity Consortium (MoTrPAC): mapping the dynamic responses to exercise. Cell 181 , 1464–1474 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Nocon, M. et al. Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis. Eur. J. Cardiovasc. Prev. Rehabil. 15 , 239–246 (2008).

Article   PubMed   Google Scholar  

Moore, S. C. et al. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA . Intern. Med. 176 , 816–825 (2016).

Google Scholar  

Pedersen, B. K. & Saltin, B. Exercise as medicine — evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand. J. Med. Sci. Sports 25 , 1–72 (2015).

Amar, D. et al. Time trajectories in the transcriptomic response to exercise - a meta-analysis. Nat. Commun. 12 , 3471 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Gibb, A. A. et al. Exercise-induced changes in glucose metabolism promote physiological cardiac growth. Circulation 136 , 2144–2157 (2017).

Lindholm, M. E. et al. An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training. Epigenetics 9 , 1557–1569 (2014).

Overmyer, K. A. et al. Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation. Cell Metab. 21 , 468–478 (2015).

Pillon, N. J. et al. Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity. Nat. Commun. 11 , 470 (2020).

Sato, S. et al. Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis. Cell Metab. 34 , 329–345.e8 (2022).

Many, G. M. Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue. Nat. Metab. https://doi.org/10.1038/s42255-023-00959-9 (2024).

Henstridge, D. C., Febbraio, M. A. & Hargreaves, M. Heat shock proteins and exercise adaptations. Our knowledge thus far and the road still ahead. J. Appl. Physiol. 120 , 683–691 (2016).

Dumke, C. L., Kim, J., Arias, E. B. & Cartee, G. D. Role of kallikrein–kininogen system in insulin-stimulated glucose transport after muscle contractions. J. Appl. Physiol. 92 , 657–664 (2002).

Vettor, R. et al. Effect of exercise on plasma kallikrein and muscular phospholipase A2 activity in rats. Mol. Cell. Endocrinol. 45 , 65–70 (1986).

De Lisio, M. & Parise, G. Exercise and hematopoietic stem and progenitor cells: protection, quantity, and function. Exerc. Sport Sci. Rev. 41 , 116–122 (2013).

Cho, E.-G. et al. MEF2C enhances dopaminergic neuron differentiation of human embryonic stem cells in a parkinsonian rat model. PLoS ONE 6 , e24027 (2011).

Lin, R., Warn-Cramer, B. J., Kurata, W. E. & Lau, A. F. v-Src phosphorylation of connexin 43 on Tyr247 and Tyr265 disrupts gap junctional communication. J. Cell Biol. 154 , 815–827 (2001).

Oughtred, R. et al. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30 , 187–200 (2021).

Bye, A. et al. Gene expression profiling of skeletal muscle in exercise-trained and sedentary rats with inborn high and low VO 2max . Physiol. Genomics 35 , 213–221 (2008).

Hostrup, M. et al. High-intensity interval training remodels the proteome and acetylome of human skeletal muscle. eLife 11 , e69802 (2022).

Yu, G., Wang, L.-G., Yan, G.-R. & He, Q.-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31 , 608–609 (2015).

Aguilar, D. Heart failure, diabetes mellitus, and chronic kidney disease: a clinical conundrum. Circ. Heart Fail. 9 , e003316 (2016).

van Moorsel, C. H. M. Desmoplakin: an important player in aging lung disease. Am. J. Respir. Crit. Care Med. 202 , 1201–1202 (2020).

Wang, T. et al. Myosin light chain kinase (MYLK) coding polymorphisms modulate human lung endothelial cell barrier responses via altered tyrosine phosphorylation, spatial localization, and lamellipodial protrusions. Pulm. Circ. 8 , 2045894018764171 (2018).

Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491 , 119–124 (2012).

Clarke, S. F. et al. Exercise and associated dietary extremes impact on gut microbial diversity. Gut 63 , 1913–1920 (2014).

Lammers, K. M. et al. Gliadin induces an increase in intestinal permeability and zonulin release by binding to the chemokine receptor CXCR3. Gastroenterology 135 , 194–204.e3 (2008).

Scarpa, M. et al. The epithelial danger signal IL-1α is a potent activator of fibroblasts and reactivator of intestinal inflammation. Am. J. Pathol. 185 , 1624–1637 (2015).

Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472 , 57–63 (2011).

Daly, W., Seegers, C., Timmerman, S. & Hackney, A. C. Peak cortisol response to exhausting exercise: effect of blood sampling schedule. Med. Sportiva 8 , 17–20 (2004).

CAS   Google Scholar  

Zhang, W. et al. Nicotinamide N- methyltransferase ameliorates renal fibrosis by its metabolite 1-methylnicotinamide inhibiting the TGF-β1/Smad3 pathway. FASEB J. 36 , e22084 (2022).

CAS   PubMed   Google Scholar  

Amar, D. et al. The mitochondrial multi-omic response to exercise training across tissues. Prepint at BioRxiv https://doi.org/10.1101/2023.01.13.523698 (2023).

Thyfault, J. P. & Rector, R. S. Exercise combats hepatic steatosis: potential mechanisms and clinical implications. Diabetes 69 , 517–524 (2020).

Dornbos, D. et al. Preischemic exercise reduces brain damage by ameliorating metabolic disorder in ischemia/reperfusion injury. J. Neurosci. Res. 91 , 818–827 (2013).

Gay, N. R., Amar, D., Beltran, P. M. J. & MoTrPAC Study Group. MotrpacRatTraining6mo: Analysis of the MoTrPAC endurance exercise training study in 6-month-old. R package version 1.6.3 https://motrpac.github.io/MotrpacRatTraining6mo/ (2023).

Gay, N. R. & MoTrPAC Study Group. MotrpacRatTraining6moData: Data for analysis of the MoTrPAC endurance exercise training study in 6-month-old rats. R package version 1.9.0 https://motrpac.github.io/MotrpacRatTraining6moData/ (2023).

Gay, N. R., Amar, D. & MoTrPAC Study Group. Visualization of graphical analysis results: Temporal dynamics of the multi-omic response to endurance exercise training across tissues. Zenodo https://doi.org/10.5281/zenodo.7703294 (2023).

Raja, A. et al. MoTrPAC/motrpac-rna-seq-pipeline. GitHub https://github.com/MoTrPAC/motrpac-rna-seq-pipeline (2023).

Gay, N. R., Raja, A. & MoTrPAC Study Group. MoTrPAC/motrpac-atac-seq-pipeline. GitHub https://github.com/MoTrPAC/motrpac-atac-seq-pipeline (2023).

Akre, S., Raja, A., Samdarshi, M. & MoTrPAC Study Group. MoTrPAC/motrpac-rrbs-pipeline. GitHub https://github.com/MoTrPAC/motrpac-rrbs-pipeline (2023).

Jimenez-Morales, D., Samdarshi, M., Hershman, S. & MoTrPAC Study Group. MoTrPAC/motrpac-proteomics-pipeline. GitHub https://github.com/MoTrPAC/motrpac-proteomics-pipeline (2023).

Amar, D., Samdarshi, M., Raja, A. & Gay, N. R. MoTrPAC/MotrpacRatTraining6moQCRep. GitHub https://github.com/MoTrPAC/MotrpacRatTraining6moQCRep (2023).

McCarron, A. et al. Phenotypic characterization and comparison of cystic fibrosis rat models generated using CRISPR/Cas9 gene editing. Am. J. Pathol. 190 , 977–993 (2020).

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Acknowledgements

Funding: The MoTrPAC Study is supported by NIH grants U24OD026629 (Bioinformatics Center), U24DK112349, U24DK112342, U24DK112340, U24DK112341, U24DK112326, U24DK112331, U24DK112348 (Chemical Analysis Sites), U01AR071133, U01AR071130, U01AR071124, U01AR071128, U01AR071150, U01AR071160, U01AR071158 (Clinical Centers), U24AR071113 (Consortium Coordinating Center), U01AG055133, U01AG055137 and U01AG055135 (PASS/Animal Sites). This work was also supported by other funding sources: NHGRI Institutional Training Grant in Genome Science 5T32HG000044 (N.R.G.), National Science Foundation Graduate Research Fellowship Grant No. NSF 1445197 (N.R.G.), National Heart, Lung, and Blood Institute of the National Institute of Health F32 postdoctoral fellowship award F32HL154711 (P.M.J.B.), the Knut and Alice Wallenberg Foundation (M.E.L.), National Science Foundation Major Research Instrumentation (MRI) CHE-1726528 (F.M.F.), National Institute on Aging P30AG044271 and P30AG003319 (N.M.), and NORC at the University of Chicago grant no. P30DK07247 (E.R.). Parts of this work were performed in the Environmental Molecular Science Laboratory, a US Department of Energy national scientific user facility at Pacific Northwest National Laboratory in Richland, WA. The views expressed are those of the authors and do not necessarily reflect those of the NIH or the US Department of Health and Human Services. Some figures were created using Biorender.com. Fig. 1b was modified with permission from ref. 46 .

Author information

These authors contributed equally: David Amar, Nicole R. Gay, Pierre M. Jean-Beltran

These authors jointly supervised this work: Sue C. Bodine, Steven A. Carr, Karyn A. Esser, Stephen B. Montgomery, Simon Schenk, Michael P. Snyder, Matthew T. Wheeler

Authors and Affiliations

Department of Medicine, Stanford University, Stanford, CA, USA

David Amar, David Jimenez-Morales, Malene E. Lindholm, Shruti Marwaha, Archana Natarajan Raja, Jimmy Zhen, Euan Ashley, Matthew T. Wheeler, Karen P. Dalton, Steven G. Hershman, Mihir Samdarshi & Christopher Teng

Department of Genetics, Stanford University, Stanford, CA, USA

Nicole R. Gay, Bingqing Zhao, Jose J. Almagro Armenteros, Nasim Bararpour, Si Wu, Stephen B. Montgomery, Michael P. Snyder, Clarisa Chavez, Roxanne Chiu, Krista M. Hennig, Chia-Jui Hung, Christopher A. Jin & Navid Zebarjadi

Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Pierre M. Jean-Beltran, Hasmik Keshishian, Natalie M. Clark, Steven A. Carr, D. R. Mani, Charles C. Mundorff & Cadence Pearce

Department of Internal Medicine, University of Iowa, Iowa City, IA, USA

Dam Bae, Ana C. Lira, Sue C. Bodine, Michael Cicha, Luis Gustavo Oliveira De Sousa, Bailey E. Jackson, Kyle S. Kramer, Andrea G. Marshall & Collyn Z-T. Richards

Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

Surendra Dasari

Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Courtney Dennis, Julian Avila-Pacheco & Clary B. Clish

Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

Charles R. Evans & Charles F. Burant

School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA

David A. Gaul, Evan M. Savage & Facundo M. Fernández

Department of Medicine, Duke University, Durham, NC, USA

Olga Ilkayeva, William E. Kraus & Kim M. Huffman

Duke Molecular Physiology Institute, Duke University, Durham, NC, USA

Olga Ilkayeva, Michael J. Muehlbauer, William E. Kraus, Christopher Newgard, Kim M. Huffman & Megan E. Ramaker

Emory Integrated Metabolomics and Lipidomics Core, Emory University, Atlanta, GA, USA

Anna A. Ivanova, Xueyun Liu & Kristal M. Maner-Smith

BRCF Metabolomics Core, University of Michigan, Ann Arbor, MI, USA

Maureen T. Kachman, Alexander (Sasha) Raskind & Tanu Soni

Division of Endocrinology, Nutrition, and Metabolism, Mayo Clinic, Rochester, MN, USA

Ian R. Lanza

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Venugopalan D. Nair, Gregory R. Smith, Yongchao Ge, Stuart C. Sealfon, Mary Anne S. Amper, Kristy Guevara, Nada Marjanovic, German Nudelman, Hanna Pincas, Irene Ramos, Stas Rirak, Aliza B. Rubenstein, Frederique Ruf-Zamojski, Nitish Seenarine, Sindhu Vangeti, Mital Vasoya, Alexandria Vornholt, Xuechen Yu & Elena Zaslavsky

Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

Paul D. Piehowski

Department of Pathology and Laboratory Medicine, University of Vermont, Burlington, VT, USA

Jessica L. Rooney, Russell Tracy, Elaine Cornell, Nicole Gagne & Sandy May

Department of Pathology, Stanford University, Stanford, CA, USA

Kevin S. Smith, Nikolai G. Vetr, Stephen B. Montgomery & Daniel Nachun

Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA

Cynthia L. Stowe, Fang-Chi Hsu, Scott Rushing & Michael P. Walkup

Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

Gina M. Many, James A. Sanford, Joshua N. Adkins, Wei-Jun Qian, Marina A. Gritsenko, Joshua R. Hansen, Chelsea Hutchinson-Bunch, Matthew E. Monroe, Ronald J. Moore, Michael D. Nestor, Vladislav A. Petyuk & Tyler J. Sagendorf

Department of Biochemistry, Emory University, Atlanta, GA, USA

Tiantian Zhang, Zhenxin Hou & Eric A. Ortlund

Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Boston, MA, USA

David M. Presby, Laurie J. Goodyear, Brent G. Albertson, Tiziana Caputo, Michael F. Hirshman, Nathan S. Makarewicz, Pasquale Nigro & Krithika Ramachandran

Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

Alec Steep & Jun Z. Li

Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Yifei Sun & Martin J. Walsh

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

• Sue C. Bodine

Department of Physiology and Aging, University of Florida, Gainesville, FL, USA

Karyn A. Esser & Marco Pahor

Department of Orthopaedic Surgery, School of Medicine, University of California, San Diego, La Jolla, CA, USA

Simon Schenk

Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

Stephen B. Montgomery

Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA

Gary Cutter

Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

Robert E. Gerszten & Jeremy M. Robbins

Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA

Michael E. Miller

Department of Medicine, Mayo Clinic, Rochester, MN, USA

K. Sreekumaran Nair

Department of Statistics, Stanford University, Stanford, CA, USA

Trevor Hastie & Rob Tibshirani

Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA

Rob Tibshirani

Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA

Brian Bouverat, Christiaan Leeuwenburgh & Ching-ju Lu

Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA

• Barbara Nicklas

Department of Health and Exercise Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA

W. Jack Rejeski

National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

• John P. Williams

National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA

Elisabeth R. Barton

Department of Biomedical Sciences, University of Missouri, Columbia, MO, USA

Frank W. Booth

Department of Medical Pharmacology and Physiology, University of Missouri, Columbia, MO, USA

Department of Nutrition and Exercise Physiology, University of Missouri, Columbia, MO, USA

Frank W. Booth & R. Scott Rector

Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, USA

Department of Kinesiology and Health Education, University of Texas, Austin, TX, USA

Roger Farrar

Department of Medicine, Division of Endocrinology and Diabetes, University of California, Los Angeles, CA, USA

Andrea L. Hevener

Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA

Benjamin G. Ke & Chongzhi Zang

Section on Clinical, Behavioral, and Outcomes Research, Joslin Diabetes Center, Boston, MA, USA

Sarah J. Lessard

Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA

Andrea G. Marshall

Department of Health Sciences, Stetson University, Deland, FL, USA

Scott Powers

Department of Medicine, University of Missouri, Columbia, MO, USA

R. Scott Rector

NextGen Precision Health, University of Missouri, Columbia, MO, USA

Cell Biology and Physiology, Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA

John Thyfault

Center for Skeletal Muscle Research at Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA, USA

Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA

Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA, USA

Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine, Charlottesville, VA, USA

Fralin Biomedical Research Institute, Center for Exercise Medicine Research at Virginia Tech Carilion, Roanoke, VA, USA

Department of Human Nutrition, Foods, and Exercise, College of Agriculture and Life Sciences, Virginia Tech, Blacksburg, VA, USA

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA

Ali Tugrul Balci & Maria Chikina

Petit Institute of Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA

Samuel G. Moore

Department of Medicine, Emory University, Atlanta, GA, USA

Karan Uppal

Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA

Marcas Bamman & Anna Thalacker-Mercer

Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Bryan C. Bergman, Daniel H. Bessesen, Wendy M. Kohrt, Edward L. Melanson, Kerrie L. Moreau, Irene E. Schauer & Robert S. Schwartz

Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

Thomas W. Buford

Human Performance Laboratory, Ball State University, Muncie, IN, USA

Toby L. Chambers, Bridget Lester, Scott Trappe & Todd A. Trappe

Translational Research Institute, AdventHealth, Orlando, FL, USA

Paul M. Coen, Bret H. Goodpaster & Lauren M. Sparks

Department of Pediatrics, University of California, Irvine, CA, USA

Dan Cooper, Fadia Haddad & Shlomit Radom-Aizik

Pennington Biomedical Research Center, Baton Rouge, LA, USA

Kishore Gadde, Melissa Harris, Neil M. Johannsen, Tuomo Rankinen & Eric Ravussin

College of Nursing, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Catherine M. Jankowski

Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Nicolas Musi

Population and Public Health, Pennington Biomedical Research Center, Baton Rouge, LA, USA

Robert L. Newton Jr

Biochemistry and Structural Biology, Center for Metabolic Health, Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, San Antonio, TX, USA

Blake B. Rasmussen

Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, San Antonio, TX, USA

Elena Volpi

MoTrPAC Study Group

• Primary authors

Lead Analysts

• , Nicole R. Gay
• , Pierre M. Jean-Beltran

Lead Data Generators

• , Surendra Dasari
• , Courtney Dennis
• , Charles R. Evans
• , David A. Gaul
• , Olga Ilkayeva
• , Anna A. Ivanova
• , Maureen T. Kachman
• , Hasmik Keshishian
• , Ian R. Lanza
• , Ana C. Lira
• , Michael J. Muehlbauer
• , Venugopalan D. Nair
• , Paul D. Piehowski
• , Jessica L. Rooney
• , Kevin S. Smith
• , Cynthia L. Stowe
•  & Bingqing Zhao
• Natalie M. Clark
• , David Jimenez-Morales
• , Malene E. Lindholm
• , Gina M. Many
• , James A. Sanford
• , Gregory R. Smith
• , Nikolai G. Vetr
• , Tiantian Zhang
• , Bingqing Zhao
• , Jose J. Almagro Armenteros
• , Julian Avila-Pacheco
• , Nasim Bararpour
• , Yongchao Ge
• , Zhenxin Hou
• , Shruti Marwaha
• , David M. Presby
• , Archana Natarajan Raja
• , Evan M. Savage
• , Alec Steep
• , Yifei Sun
• , Si Wu
•  & Jimmy Zhen

Animal Study Leadership

• , Karyn A. Esser
• , Laurie J. Goodyear
•  & Simon Schenk

Manuscript Writing Group Leads

• Nicole R. Gay
•  & David Amar

Manuscript Writing Group

• Malene E. Lindholm
• , Simon Schenk
• , Stephen B. Montgomery
• , Sue C. Bodine
• , Facundo M. Fernández
• , Stuart C. Sealfon
• , Michael P. Snyder
•  & Tiantian Zhang

Senior Leadership

• Joshua N. Adkins
• , Euan Ashley
• , Charles F. Burant
• , Steven A. Carr
• , Clary B. Clish
• , Gary Cutter
• , Robert E. Gerszten
• , William E. Kraus
• , Jun Z. Li
• , Michael E. Miller
• , K. Sreekumaran Nair
• , Christopher Newgard
• , Eric A. Ortlund
• , Wei-Jun Qian
• , Russell Tracy
• , Martin J. Walsh
•  & Matthew T. Wheeler

Co-corresponding Authors

Bioinformatics center.

• , Karen P. Dalton
• , Trevor Hastie
• , Steven G. Hershman
• , Mihir Samdarshi
• , Christopher Teng
• , Rob Tibshirani
• , Matthew T. Wheeler

Biospecimens Repository

• Elaine Cornell
• , Nicole Gagne
• , Sandy May
•  & Russell Tracy

Administrative Coordinating Center

• Brian Bouverat
• , Christiaan Leeuwenburgh
• , Ching-ju Lu
•  & Marco Pahor

Data Management, Analysis, and Quality Control Center

• Fang-Chi Hsu
• , Scott Rushing
•  & Michael P. Walkup

Exercise Intervention Core

•  & W. Jack Rejeski
•  & Ashley Xia

Preclinical Animal Study Sites

• Brent G. Albertson
• , Dam Bae
• , Elisabeth R. Barton
• , Frank W. Booth
• , Tiziana Caputo
• , Michael Cicha
• , Luis Gustavo Oliveira De Sousa
• , Roger Farrar
• , Andrea L. Hevener
• , Michael F. Hirshman
• , Bailey E. Jackson
• , Benjamin G. Ke
• , Kyle S. Kramer
• , Sarah J. Lessard
• , Nathan S. Makarewicz
• , Andrea G. Marshall
• , Pasquale Nigro
• , Scott Powers
• , Krithika Ramachandran
• , R. Scott Rector
• , Collyn Z-T. Richards
• , John Thyfault
• , Zhen Yan
•  & Chongzhi Zang

Chemical Analysis Sites

• , Mary Anne S. Amper
• , Ali Tugrul Balci
• , Clarisa Chavez
• , Maria Chikina
• , Roxanne Chiu
• , Natalie M. Clark
• , Marina A. Gritsenko
• , Kristy Guevara
• , Joshua R. Hansen
• , Krista M. Hennig
• , Chia-Jui Hung
• , Chelsea Hutchinson-Bunch
• , Christopher A. Jin
• , Xueyun Liu
• , Kristal M. Maner-Smith
• , D. R. Mani
• , Nada Marjanovic
• , Matthew E. Monroe
• , Ronald J. Moore
• , Samuel G. Moore
• , Charles C. Mundorff
• , Daniel Nachun
• , Michael D. Nestor
• , German Nudelman
• , Cadence Pearce
• , Vladislav A. Petyuk
• , Hanna Pincas
• , Irene Ramos
• , Alexander (Sasha) Raskind
• , Stas Rirak
• , Jeremy M. Robbins
• , Aliza B. Rubenstein
• , Frederique Ruf-Zamojski
• , Tyler J. Sagendorf
• , Nitish Seenarine
• , Tanu Soni
• , Karan Uppal
• , Sindhu Vangeti
• , Mital Vasoya
• , Alexandria Vornholt
• , Xuechen Yu
• , Elena Zaslavsky
• , Navid Zebarjadi

Clinical Sites

• Marcas Bamman
• , Bryan C. Bergman
• , Daniel H. Bessesen
• , Thomas W. Buford
• , Toby L. Chambers
• , Paul M. Coen
• , Dan Cooper
• , Fadia Haddad
• , Kishore Gadde
• , Bret H. Goodpaster
• , Melissa Harris
• , Kim M. Huffman
• , Catherine M. Jankowski
• , Neil M. Johannsen
• , Wendy M. Kohrt
• , Bridget Lester
• , Edward L. Melanson
• , Kerrie L. Moreau
• , Nicolas Musi
• , Robert L. Newton Jr
• , Shlomit Radom-Aizik
• , Megan E. Ramaker
• , Tuomo Rankinen
• , Blake B. Rasmussen
• , Eric Ravussin
• , Irene E. Schauer
• , Robert S. Schwartz
• , Lauren M. Sparks
• , Anna Thalacker-Mercer
• , Scott Trappe
• , Todd A. Trappe
•  & Elena Volpi

Contributions

All authors reviewed and revised the manuscript. Detailed author contributions are provided in the  Supplementary Information .

Corresponding authors

Correspondence to Sue C. Bodine , Karyn A. Esser , Simon Schenk , Stephen B. Montgomery , Michael P. Snyder , Steven A. Carr or Matthew T. Wheeler .

Ethics declarations

Competing interests.

S.C.B. has equity in Emmyon, Inc. G.R.C. sits on data and safety monitoring boards for AI Therapeutics, AMO Pharma, Astra-Zeneca, Avexis Pharmaceuticals, Biolinerx, Brainstorm Cell Therapeutics, Bristol Meyers Squibb/Celgene, CSL Behring, Galmed Pharmaceuticals, Green Valley Pharma, Horizon Pharmaceuticals, Immunic, Mapi Pharmaceuticals, Merck, Mitsubishi Tanabe Pharma Holdings, Opko Biologics, Prothena Biosciences, Novartis, Regeneron, Sanofi-Aventis, Reata Pharmaceuticals, NHLBI (protocol review committee), University of Texas Southwestern, University of Pennsylvania, Visioneering Technologies, Inc.; serves on consulting or advisory boards for Alexion, Antisense Therapeutics, Biogen, Clinical Trial Solutions LLC, Genzyme, Genentech, GW Pharmaceuticals, Immunic, Klein-Buendel Incorporated, Merck/Serono, Novartis, Osmotica Pharmaceuticals, Perception Neurosciences, Protalix Biotherapeutics, Recursion/Cerexis Pharmaceuticals, Regeneron, Roche, SAB Biotherapeutics; and is the president of Pythagoras Inc., a private consulting company. S.A.C. is a member of the scientific advisory boards of Kymera, PrognomiQ, PTM BioLabs, and Seer. M.P.S. is a cofounder and scientific advisor to Personalis, Qbio, January AI, Filtricine, SensOmics, Protos, Fodsel, Rthm, Marble and scientific advisor to Genapsys, Swaz, Jupiter. S.B.M. is a consultant for BioMarin, MyOme and Tenaya Therapeutics. D.A. is currently employed at Insitro, South San Francisco, CA. N.R.G. is currently employed at 23andMe, Sunnyvale, CA. P.M.J.B. is currently employed at Pfizer, Cambridge, MA. Insitro, 23andMe and Pfizer had no involvement in the work presented here.

Peer review

Peer review information.

Nature thanks Atul Deshmukh, Jorge Ruas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended data fig. 1 animal phenotyping and data availability..

a-d) Clinical measurements before and after the training intervention in untrained control rats (SED), 4-week trained rats (4w), and 8-week trained rats (8w). Data are displayed pre and post for each individual rat (connected by a line), with males in blue and females in pink. Filled symbols (n = 5 per sex and time point) represent rats used for all omics analyses, whereas the rat utilized for proteomics only (n = 1 per sex and time point) is represented by a non-filled symbol. Significant results by ANOVA of the overall group effect (#, p < 0.05; ##, p < 0.01) and interaction between group and time (§, p < 0.05; §§ p < 0.01) are indicated. Significant within-group differential responses from a Bonferroni post hoc test are indicated (*, q-value < 0.05; **, q-value < 0.01). a) Aerobic capacity through a VO 2 max test until exhaustion. Data are reported in ml/(kg.min) for all individual rats and time points. b) Body fat percentage. c) Percent lean mass. ( b-c ) were assessed through nuclear magnetic resonance spectroscopy. d) Body weight (in grams). e) Description of available datasets. Colored cells indicate that data are available for that tissue and assay. Individual panels and platforms are shown for metabolomics and the multiplexed immunoassays. f) Detailed availability of sample-level data across assays. Each column represents an individual animal, ordered by training group and colored by sex. Gray cells indicate that data were generated for that animal and assay; black cells indicate that data were not generated. Rows are ordered by ome and colored by assay and tissue.

Extended Data Fig. 2 Quality control metrics for omics data.

a) Proteomics multiplexing design using TMT11 reagents for isobaric tagging and a pooled reference sample. The diagram describes processing of a single tissue. Following multiplexing, peptides were used for protein abundance analysis, serial PTM enriched for phosphosite and optional acetylsite quantification, or ubiquitylsite quantification through enrichment of lysine-diglycine ubiquitin remnants. b) Total number of fully quantified proteins per plex in each global proteome dataset. c-e) The total number of fully quantified phosphosites (c) , acetylsites (d) , and ubiquitylsites (e) per plex in each dataset. f) Distributions of coefficients of variation (CVs) calculated from metabolomics features identified in pooled samples and analyzed periodically throughout liquid chromatography-mass spectrometry runs. CVs were aggregated and plotted separately for named and unnamed metabolites. g) Transcription start site (TSS) enrichment (top) and fraction of reads in peaks (FRiP, bottom) across ATAC-seq samples per tissue. h) Distributions of RNA integrity numbers (RIN, top) and median 5′ to 3′ bias (bottom) across samples in each tissue in the RNA-Seq data. i) Percent methylation of CpG, CHG and CHH sites in the RRBS data. For boxplots in (h,i) : center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; filled dots represent outliers. j) Number of wells across multiplexed immunoassays with fewer than 20 beads. Measurements from these 182 wells were excluded from downstream analysis. k) 2D density plot of targeted analytes’ mean fluorescence intensity (MFI) versus corresponding CHEX4 MFI from the same well for each multiplexed immunoassay measurement, where CHEX4 is a measure of non-specific binding.

Extended Data Fig. 3 Permutation tests.

a-b) Permutation tests of groups within males (a) and females (b) . For each sex, the original group labels were shuffled to minimize the number of animal pairs that remain in the same group. Only the group labels were shuffled and all other covariates remained as in the original data. For each permuted dataset, the differential abundance pipeline was rerun and the number of transcripts that were selected at 5% FDR adjustment were re-counted. c-d) Permutation tests of sex within groups. For each group and each sex, half of the animals were selected randomly and their sex was swapped. Only the sex labels were shuffled and all other covariates remained as in the original data. For each permutation the differential analysis pipeline was rerun and the timewise summary statistics were extracted. A gene was considered sexually dimorphic if for at least one time point the z-score (absolute) difference between males and females was greater than 3. c) Counts of sexually dimorphic genes among the IHW-selected genes of the original data. d) Counts of sexually dimorphic genes among the 5% FDR selected genes within each permuted dataset. Each boxplot in (a-d) represents the differential abundance analysis results over 100 permutations of the transcriptomics data in a specific tissue. Center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; open circles represent outliers. Added points represent the results of the true data labels, and their shape corresponds to the empirical p-value ( ● : p > 0.05; ×: 0.01 < p < 0.05; *: p ≤ 0.01).

Extended Data Fig. 4 Correlations between proteins and transcripts throughout endurance training.

a) Number of tissues in which each gene, including features mapped to genes from all omes, is training-regulated. Only differential features from the subset of tissues with deep molecular profiling (lung, gastrocnemius, subcutaneous white adipose, kidney, liver, and heart) and the subset of omes that were profiled in all six of these tissues (DNA methylation, chromatin accessibility, transcriptomics, global proteomics, phosphoproteomics, multiplexed immunoassays) were considered. Numbers above each bar indicate the number of genes that are differential in exactly the number of tissues indicated on the x-axis. b) Pathways significantly enriched by tissue-specific training-regulated genes represented in Fig. 2a (q-value < 0.1). KEGG and Reactome pathways were queried, and redundant pathways were removed (i.e., those with an overlap of 80% or greater with an existing pathway). c) Heatmaps showing the Pearson correlation between the TRNSCRPT and PROT timewise summary statistics (z- and t-scores, respectively) (top, gene-level) and pathway-level enrichment results (Gene Set Enrichment Analysis normalized enrichment scores) (bottom, pathway-level). d) Scatter plots of pathway GSEA NES of the TRNSCRPT and PROT datasets in the seven tissues for which these data were acquired. Pathways showing high discordance or agreement across TRNSCRPT and PROT and with functional relevance or general interest were highlighted.

Extended Data Fig. 5 Heat shock response.

a) Scatter plots of the protein t-scores (PROT) versus the transcript z-scores (TRNSCRPT) by gene at 8 weeks of training (8 W) relative to sedentary controls. Data are shown for the seven tissues for which both proteomics and transcriptomics was acquired. Red points indicate genes associated with the heat shock response, and the labeled points indicate those with a large differential response at the protein level. b-c) Line plots showing protein b) and transcript (c) log 2 fold-changes relative to the untrained controls for a subset of heat shock proteins with increased abundance during exercise training. Each line represents a protein in a single tissue.

Extended Data Fig. 6 Regulatory signaling pathways modulated by endurance training.

a) Heatmap of differences in TF motif enrichment in training-regulated genes across tissues. Each value reflects the average difference in motif enrichment for shared transcription factors. Tissues are clustered with complete linkage hierarchical clustering. b) (left) Filtered PTM-SEA results for the liver showing kinases and signaling pathways with increased activity. (right) Heatmap showing t-scores for phosphosites within the HGF signaling pathway. c) Hypothetical model of HGF signaling effects during exercise training. Phosphorylation of STAT3 and PXN is known to modulate cell growth and cell migration, respectively. Error bars=SEM. d) Filtered PTM-SEA results for the heart showing selected kinases with significant enrichments in at least one time point. Heatmap shows the NES as color and enrichment p-value as dot size. Kinases are grouped by kinase family and sorted by hierarchical clustering. e) (top) Log 2 fold-change of GJA1 and CDH2 protein abundance in the heart. No significant response to exercise training was observed for these proteins (F-test; q-value > 0.05). (bottom) Log 2 fold-changes for selected Src kinase phosphosite targets, GJA1 pY265 and CDH2 pY820, in the heart. These phosphosites show a significant response to exercise training (F-test, 5% FDR). Error bars=SEM. f) Gene Set Enrichment Analysis (GSEA) results from the heart global proteome dataset using the matrisome gene set database. Heatmap shows NES as color and enrichment p-value as dot size. Rows are clustered using hierarchical clustering. g) Log 2 fold-change for basement membrane proteins in heart. Proteins showing a significant response to exercise training are highlighted in orange (F-test; 5% FDR). Error bars=SEM. h) Log 2 protein fold-change of NTN1 protein abundance in heart. A significant response to exercise training was observed for these proteins (F-test; 5% FDR). Error bars=SEM.

Extended Data Fig. 7 Graphical representation of differential results.

a) Number of training-regulated features assigned to groups of graphical states across tissues and time. Red points indicate features that are up-regulated in at least one sex (e.g., only in males: F0_M1; only in females: F1_M0; in both sexes: F1_M1), and blue points indicate features down-regulated in at least one sex (only in males: F0_M-1; only in females: F-1_M0; in both sexes: F-1_M-1). Green points indicate features that are up-regulated in males and down-regulated in females or vice versa (F-1_M1 and F1_M-1, respectively). Point size is proportional to the number of features. Point opacity is proportional to the within-tissue fraction of features represented by that point. Features can be represented in multiple points. The number of omes profiled in each tissue is provided in parentheses next to the tissue abbreviation. b) A schematic example of the graphical representation of the differential analysis results. Top: the z-scores of four features. A positive score corresponds to up-regulation (red), and a negative score corresponds to down regulation (blue). Bottom: the assignment of features to node sets and full path sets (edge sets are not shown for conciseness but can be easily inferred from the full paths). Node labels follow the [time]_F[x]_M[y] format where [time] shows the animal sacrifice week and can take one of (1w, 2w, 4w, or 8w), and [x] and [y] are one of (−1,0,1), corresponding to down-regulation, no effect, and up-regulation, respectively. c) Graphical representation of the feature sets. Columns are training time points, and rows are the differential abundance states. Node and edge sizes are proportional to the number of features that are assigned to each set.

Extended Data Fig. 8 Key pathway enrichments per tissue.

Key pathway enrichments for features that are up-regulated in both sexes at 8 weeks of training in each tissue. For display purposes, enrichment q-values were floored to 1e-10 (Enrichment FDR (−log10) = 10). Bars are colored by the number of omes for which the pathway was significantly enriched (q-value < 0.01) (lighter gray: 1 ome; darker gray: 2 omes; black: 3 omes). Pathways were selected from Supplementary Table 10 .

Extended Data Fig. 9 Associations with signatures of human health and complex traits.

a) Jaccard coefficients between gene sets identified by different omes in 8-week gastrocnemius up-regulated features (“X” marks overlap p > 0.05). b) Network connectivity p-values (Pathways, Biogrid, and string) among the gastrocnemius week-8 multi-omic genes and with the single-omic genes. c) Proportion of features from each ome represented in the gastrocnemius response clusters, identified by the network clustering analysis. d-g) Overlap between our rat vastus lateralis differential expression results and the meta-analysis of human long-term exercise studies by Amar et al. d-e) Spearman correlation (d) and its significance (e) between the meta-analysis fold-changes and the log 2 fold-changes foreach sex and time point. f) GSEA results. Genes were ranked by meta-analysis (−log 10 p-value*log 2 fold-change) and the rat training-differential, sex-consistent gene sets were tested for enrichment at the bottom of the ranking (negative scores) or the top (positive scores). g) Overlap between the rat gene sets from (f) and the high-heterogeneity human meta-analysis genes (I 2  > 75%). h) -log 10 overlap p-values (Fisher’s exact test), comparing rat female gastrocnemius and vastus lateralis week-8 differential transcripts from this study (p < 0.01) and the differential genes from the rat female soleus data of Bye et al. (p < 0.01). HCR: high capacity runners, LCR: low capacity runners. i) A comparison of rat gastrocnemius differential proteins from this study (p < 0.01) and the human endurance training proteomics results of Hostrup et al. (p < 0.01) using Fisher’s exact test. Left: -log 10 overlap p-values. Right: -log 10 sex concordance p-values. j) Statistics of the overlapping proteins from ( i ), week-8 female comparison (y: rat z-scores, x: human t-scores). k) DOSE disease enrichment results of the white adipose, kidney, and liver gene sets. DOSE was applied only on diseases that are relevant for each tissue. The network shows the results for the sex-consistent down-regulated features at week-8.

Extended Data Fig. 10 Characterization of the extent of sex difference in the endurance training response.

The extent of sex differences in the training response were characterized in two ways: first, by correlating log 2 fold-changes between males and females for each training-differential feature; second, by calculating the difference between the area under the log 2 fold-change curve for each training-differential feature, including a (0,0) point (Δ AUC , males - females). The first approach characterizes differences in direction of effect while the second approach characterizes differences in magnitude. Left plot for each tissue: density line plots of correlations from the first approach. Densities or correlations corresponding to features in each ome are plotted separately, with a label that provides the ome and the number of differential features represented. Right plot for each tissue: 2D density plot of Δ AUC against the correlation between the male and female log 2 fold-changes for each training-differential feature used to simultaneously evaluate sex differences in the direction and magnitude of the training response. Points at the top-center of these 2D density plots represent features with high similarity between males and females in terms of both direction and magnitude; features on the right and left sides of the plots represent features with greater magnitudes of response in males and females, respectively.

Extended Data Fig. 11 Sex differences in the endurance training response.

a) Heatmap of the training response of immunoassay analytes across tissues. Gray indicates no data. Bars indicate the number of training-regulated analytes in each tissue (top) and the number of tissues in which the analyte is training-regulated (right, 5% FDR). b) Training-differential cytokines across tissues. 5, 24, and 9 cytokines were annotated as anti-, pro-, and pro/anti- inflammatory, respectively. Bars indicate the number of annotated cytokines in each category that are differential (5% FDR). c) Counts of early vs. (1- or 2-week) vs. late (4- or 8-week) differential cytokines, according to states assigned by the graphical analysis, including all tissues. Cytokines with both early and late responses in the same tissue were excluded. d) Line plots of standardized abundances of training-differential features that follow the largest graphical path in the adrenal gland (i.e., 1w_F-1_M1 − >2w_F-1_M0 − >4w_F-1_M0 − >8w_F-1_M0 according to our graphical analysis notation). The black line represents the average value across all features. The closer a colored line is to this average, the darker it is (distance calculated using sum of squares). e) Line plots of transcript-level log 2 fold-changes corresponding to six transcription factors (TFs) whose motifs are significantly enriched by transcripts in (d) . TF motif enrichment q-values are provided in the legend (error bars = SEM). f) Male versus female NES from PTM-SEA in the lung. Anticorrelated points corresponding to PRKACA NES are in dark red. g) Line plots of standardized abundances of training-differential phosphosites that follow the largest graphical edges of phosphosites in the lung (1w_F1_M-1 − >2w_F1_M-1 − >4w_F0_M-1). h) Top ten kinases with the greatest over-representation of substrates (proteins) corresponding to training-differential phosphosites in (g) . MeanRank scores by library are shown, as reported by KEA3. i) Line plots showing phosphosite-level log 2 fold-changes of PRKACA phosphosite substrates identified in the lung as differential with disparate sex responses (error bars = SEM).

Extended Data Fig. 12 Assessment of immune responses to endurance training.

a) Heatmap of the number and percent of KEGG and Reactome immune pathways significantly enriched by training-regulated features at 8 weeks. b) Line plots of standardized abundances of training-differential proteins in white adipose tissue up-regulated only in males at 8 weeks. Black line shows average across all features. c) Boxplots of the sample-level Pearson correlation between markers of immune cell types, lymphatic tissue, or cell proliferation and the average value of features in (b) at the protein level. Center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; filled dots represent outliers. A pink point indicates that the marker is also one of the differential features plotted in (b) . # indicates when the distribution of Pearson correlations for a set of at least two markers is significantly different from 0 (two-sided one-sample t-test, 5% BY FDR). When only one marker is used to define a category on the y-axis, the gene name is provided in parentheses. d) Trajectories of mean absolute signal of various immune cell types in BAT or WAT-SC following deconvolution of bulk RNA-Seq with CIBERSORTx (error bars = SEM). e) Immune cell type enrichment analysis results of training-differentially expressed transcripts. Points represent significant enrichments (5% FDR, one-sided Mann-Whitney U test). f) Line plots showing the log 2 fold-changes for Cxcr3 and Il1a transcripts in the small intestine (error bars = SEM).

Extended Data Fig. 13 Metabolic effects of endurance training.

a) Significant enrichments for relevant categories of KEGG metabolism pathways from features that are up- or down- regulated in both sexes at 8 weeks (8w_F1_M1 and 8w_F-1_M-1 nodes, respectively). Triangles point in the direction of the response (up or down). Points are colored by ome. b) Log 2 fold-change of metabolites regulated across many tissues (F-Test, 5% FDR, error bars=SEM). c) Log 2 fold-change of training-regulated metabolites: 1-methylhistidine in the kidney, cortisol in the kidney, and 1-methylnicotinamide in the liver (F-Test, 5% FDR, error bars = SEM). d) Volcano plots showing abundance changes (log 2 fold-changes; logFC) and significance (-log 10 nominal p-values) for acyl-carnitines. Features are colored based on the carnitine chain length. e) Protein abundance changes in the glycolysis and gluconeogenesis pathway in the heart tissue after 8 weeks of training. Line plots show the log 2 fold-changes over the training time course (error bars = SEM). Red and blue boxes indicate a statistically significant (F-test, 5% FDR) increase and decrease in abundance, respectively, for both males and females at 8 weeks.

Extended Data Fig. 14 Mitochondria and peroxisome adaptations to endurance training.

a) Boxplots showing the percent of mitochondrial genome reads across samples in each tissue that map to the mitochondrial genome (% MT reads). b) Comparison of % MT reads between untrained controls and animals trained for 8 weeks. Plot shows tissues with a statistically significant change after 8 weeks in at least one sex (red asterisk, two-sided Dunnett’s test, 10% FDR). For boxplots in (b,c) : center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; filled dots represent outliers. c) Boxplots showing the percent of mitochondrial genome reads across tissue, sex, and time points. Center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; open circles represent outliers. Red asterisks indicate a significant change throughout the training time course (F-test, 5% FDR). Center line represents median; box bounds represent 25th and 75th percentiles; whiskers represent minimum and maximum excluding outliers; blue dots represent outliers. d) GSEA using the MitoCarta MitoPathways gene set database and transcriptome (TRNSCRPT) or phosphoproteome (PHOSPHO) differential analysis results. NES are shown for significant pathways (10% FDR) for all tissues, sexes, and time points within the heatmap. Mitochondria pathways (rows) are grouped using the parental group in the MitoPathways hierarchy. e) Protein abundance and protein acetylation level changes in the peroxisome KEGG pathway in the liver tissue after 8 weeks of training. Red boxes indicate an increase in abundance for both males and females, while red circles indicate an increase in at least one acetylsite within the protein (8w_F1_M1 cluster).

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MoTrPAC Study Group., Lead Analysts. & MoTrPAC Study Group. Temporal dynamics of the multi-omic response to endurance exercise training. Nature 629 , 174–183 (2024). https://doi.org/10.1038/s41586-023-06877-w

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Received : 21 September 2022

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Issue Date : 02 May 2024

DOI : https://doi.org/10.1038/s41586-023-06877-w

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