essay on blood group

• Biology Article
• Blood Groups

Blood Groups-ABO Blood Group and Rh Group System

Blood is a fluid connective tissue and the most crucial component of the circulatory system. In a healthy person, approximately 5 liters (12 pints) of blood circulates throughout their body. In this article, blood groups and their types are explained in detail.

Composition of blood is rather interesting. It consists of erythrocytes, leukocytes and platelets suspended in plasma along with the millions of different molecules with its own specific roles and functions.

Even though components of blood are the same for all humans, there are various blood types. In fact, there are more than 40 blood groups, but all of them are not clinically significant. The discovery of the ABO blood group created great excitement as until then, all blood had been assumed to be the same.

Blood Group System

Karl Landsteiner, an Austrian scientist discovered the ABO blood group system in the year 1900. In his experiments, he mixed different blood types and noted that the plasma from certain blood type produced agglutinates or formed clusters which were caused by the absence of molecules on red blood cells and resulting in antibodies to defeat that molecule. He then made a note of the agglutination and divided the blood types into 4 different groups. For the discovery of ABO blood group, he was awarded the Nobel Prize.

The blood grouping system is pivotal in blood transfusion. Our immune system recognizes another blood type as foreign and attacks it if introduced in the body causing a transfusion reaction .  Any inappropriate match with the Rh and ABO blood types, causes the most serious and life-threatening transfusion reactions. Therefore, before blood transfusion, it is suggested to have a blood group checked.

What are ABO and Rh blood groups?

During the blood transfusion, the two most important group systems examined are the ABO-system and the Rhesus system .

The ABO blood group system consists of 4 types of blood group – A, B, AB, and O and is mainly based on the antigens and antibodies on red blood cells and in the plasma. Both antigens and antibodies are protein molecules in which antigens are present on the surface of Red Blood Cells and antibodies are present in the plasma which is involved in defending mechanisms.

On the other hand, the Rh blood group system consists of 50 defined blood group antigens. In the Rh system, the most important antigens are D, C, c, E, and e. The ABO and Rh blood systems are discussed in detail below.

1. ABO blood Group system

The basis of ABO grouping is of two antigens- Antigen A and Antigen B. The ABO grouping system is classified into four types based on the presence or absence of antigens on the red blood cells surface and plasma antibodies.

• Group A – contains antigen A and antibody B.
• Group B –contains antigen B and antibody A.
• Group AB –contains both A and B antigen and no antibodies (neither A nor B).
• Group O – contains neither A nor B antigen and both antibodies A and B.

The ABO group system is important during blood donation or blood transfusion as mismatching of blood group can lead to clumping of red blood cells with various disorders. It is important for the blood cells to match while transfusing i.e. donor-recipient compatibility is necessary. For example, a person of blood group A can receive blood either from group A or O as there are no antibodies for A and O in blood group A.

As shown in the above table, individuals of blood group O are called as universal donors , whereas individuals of blood group AB are universal recipients .

2. Rh Blood Group System

In addition to the ABO blood grouping system, the other prominent one is the Rh blood group system. About two-thirds of the population contains the third antigen on the surface of their red blood cells known as Rh factor or Rh antigen ; this decides whether the blood group is positive or negative. If the Rh factor is present, an individual is rhesus positive (Rh+ve); if an Rh factor is absent individual is rhesus negative (Rh-ve) as they produce Rh antibodies. Therefore, compatibility between donor and individual is crucial in this case as well.

Frequently Asked Questions

What are blood group antigens and antibodies.

The ABO system divides blood into four major blood groups:

• Blood type A contains anti-B antibodies and A antigens in the plasma.
• Blood group B contains anti-A antibodies and B antigens in the plasma.
• Blood type O has both anti-A and anti-B antibodies in the plasma but no antigens.
• Blood type AB lacks antibodies but possesses both A and B antigens.

How are blood antibodies formed?

The immune system uses antibodies white blood cells produce to recognise and combat foreign elements in the body. Red blood cells have blood type antigens on their surface, but the immune system does not recognise them. However, antibodies will recognise the antigens of a different blood type as foreign and attack them.

To learn more about Blood groups with interactive video lessons, keep visiting BYJU’S.

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

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

Explore BYJU’S for more concepts on Biology

Your result is as below

Request OTP on Voice Call

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Post My Comment

Very helpful thanks Byju’s

• Share Share

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

ABO Blood Group System

ABO blood grouping is based on the principle of an agglutination reaction . It is the popular method for blood group identification to determine the presence and absence of cellular antigens and their relative antibodies in the blood. In blood typing, the detection of antigen in the donor’s RBCs is called forward typing. In contrast, the  detection of antibodies in the donor’s plasma or serum is known as reverse typing .

According to the ABO-blood group system, A , B , AB and O are the four phenotypes. Type A and B are the two antigens associated with the RBC membrane, while anti-A and anti-B are the two antibodies naturally present in the blood plasma. The blood typing or the blood group identification is performed by the blood-test kit that contains anti-A, anti-B and anti-D antisera.

The antigens of the ABO blood group system are glycolipid in nature, and the antibodies are predominantly of IgM type. In this context, we will study the principle, method and result interpretation of the ABO blood typing. Besides, we will also discuss some of the discoveries, facts and the overview of the ABO blood group system.

Content: ABO Blood Group System

Interpretation of result, abo compatibility, meaning of abo blood group system.

The ABO blood group system has type A, B, AB and O phenotypes and it is used to identify the type of surface antigens and antibodies present in the donor’s blood. If agglutination occurs in the RBCs, then the corresponding antibody must be absent in the blood plasma. The antigens if absent on the RBCs membrane, then the corresponding antibodies must be present in the blood plasma. Individuals above 3-6 month have naturally occurring antibodies which arise without any antigenic stimulation from the maternal placenta. These antibodies belong to the IgM class. ABO blood group is also present in some other animals like gorillas, chimpanzees etc.

History of ABO Blood Grouping

According to the ABO blood group system, there are four blood groups, namely A , B , AB and O .

Method of ABO Blood Group System

• First, scrub the middle finger with cotton saturated with 70% of alcohol .
• Then, prick the middle finger by sterilized needle or lancet.
• After that, place three drops on a clean glass slide.
• Then after this, add antisera in a sequence of anti- A in a first drop, anti- B in a second drop and anti- D to the third drop, respectively.
• Mix the blood with the antisera separately by using a sterilized toothpick.
• Allow the slide to stand for 2-3 minutes and then note down the results based on clump formation or agglutination reaction.

• If the agglutination occurs in the RBCs, to which anti-A is added, then the blood group is ‘ A ’.
• When agglutination occurs in the RBCs, to which anti-B is added, then the blood group is ‘ B ’.
• If the agglutination occurs in the RBCs, to which both anti-A and B are added, then the blood group is ‘ AB ’.
• When there is no agglutination occurs in the RBCs, then the blood group is ‘ O ’.

In addition, there is also another antiserum that is anti-D , which determines the positive and negative blood type .

• If the agglutination occurs in the RBCs to which anti-D is added, then the blood type is positive (+) and if no agglutination occurs in the RBCs mixed anti-D, then the blood type is negative (-).

Theory of blood transfusion states that before transfusion, the ABO compatibility of blood type must be checked, as any carelessness can affect the immune system.

• A person with blood group A can receive blood from the person with blood types A and O.
• The person with blood group B can receive blood from the person with blood types B and O.
• A person with blood group AB can receive blood from the person with all blood types A, B, AB and O and called as Universal recipient .
• The person with blood group O can receive blood from only the person with blood type O.
• A person with blood group A can donate blood to the person with blood types A and AB.
• The person with blood group B can donate blood to the person with blood types B and AB.
• A person with blood group AB can donate blood to only the person with blood type AB.
• A person with blood group O can donate blood to the person with all blood types A, B, AB and O and hence called Universal donor .

Therefore, the ABO blood group system is one of the popular technique to classify human blood. ABO blood group system majorly classifies the blood into four types, i.e. A, B, AB and O. Agglutination reaction determines the ABO blood type, which is determined by the clump formation in the blood. ABO blood group system was accepted by the National Research Council and popularly known as Landsteiner classification .  Karl Landsteiner won the Nobel prize in the year 1930 for his contribution in this.

Related Topics:

• Difference Between Passive and Active Transport
• Complement Fixation Test
• Fluorescence Microscopy

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Start typing and press enter to search

The ABO Blood Group System Essay

Introduction, reference list.

There are more than 30 blood grouping system in use in the world to day; the most popular and of great use to medical applications and related researches is ABO grouping system. This blood grouping was discovered by an Australian by the name Karl Landsteiner in early 20 th century. Works of Landsteiner that led to this noble prize winning development were observations that Red Blood Cells of some individuals were agglutinated by serums of some other individuals. This agglutination whose literal meaning is clumping together of blood cells; He associated with antigens on the Red Blood Cells and Antibodies in the serum (Yamamoto, 1995, p.5). Antigens are distinct macromolecules on the Red Blood Cells surface that trigger formation of antibodies; an immunogenic response to presence of foreign matter in blood compartment.

Antigens In broader sense are in themselves foreign to the body cells, but are introduced for the purpose of antibody formation to protect the body from future invasion by disease causing organisms ; a classical example being vaccination with attenuated life vaccine. Landsteiner’s works involved separation of these antigens and classifying them as either B or A, depending on the kind of antibody response they elicited. This reaction formed the bases of ABO blood grouping system (Hisao et al, 2000, p. 4).

There are four antigens to the ABO blood group that is A, B, AB and A1; there is a sequence of oligosaccharides (a form of stored body sugars) that determines whether the antigen is A, B, or A1. The antigens attach themselves on oligosaccharides protruding above the red blood cells surface (Olsson and Chester, 1996, p.30). At a molecular level the ABO gene indirectly encodes the blood group antigen through ABO locus which present three allelic forms of A, B and O. A and B alleles separately encode an enzyme called glycosyltransferase that catalyses in finality synthesis of A and B antigen. “The A and B polymorphism is as result of several base changes on the DNA strand of the ABO gene resulting into A and B transferases distinct by four amino acids. O allele encodes an inactive glycosyltransferase that leaves the ABO antigen precursor unchanged” (Lung-Chih et al, 2000, p. 6). Blood group ABO antigens have antibodies produced against them; antibody A is present in people with blood groups O and B. Antibody B is found in people with blood group O and A.

In an event of introduction of contra antigen in the serum through transfusion, respective antibodies bind to Red Blood Cells and activate the compliment cascade which breaks down red blood cells in circulation leading to intravascular hemolysis. This is the main cause of death resulting from ABO incompatible blood transfusion (Lung-Chih et al, 2000, p. 6). Another medical condition arising from such incompatibilities is the Hemolytic Disease of The new born, where a mother possessing blood group O gets more than one pregnancy with a child possessing either A, B or AB blood groups. Bearing in mind that mother’s blood does not mix with the fetus’, she is able to bear the first pregnancy to delivery; during which traces of child’s blood antigen gets introduced to mother’s through blood contacts (Kobata et al, 1968, p. 272). These elicit formation of respective antibodies in the mother’s serum which later come to haunt subsequent pregnancies leading to spontaneous abortions.

Despite the fact that the ABO blood group antigens exist in a variety of human tissues they mostly prevail in the blood tissues as well as the endothelial and epithelial cells. A single red blood cell exhibit about two million ABO antigens. Other blood cells exhibit less ABO antigens and are mainly taken up from the serum. “A soluble form of ABO blood group antigen is found in all bodily fluids except the cerebrospinal fluid” (Lung-Chih et al, 2000, p. 6).

Person’s ABO phenotype can be changed by a number of causes, of most importance being diseases. Bacteria in a necrotizing infection produce an enzyme which change A1 antigen into B-like antigen. In this time of necrotizing infection, patients who receive blood products with antigen B will risk suffering from intravascular hemolysis. Patient’s blood group turns back to normal on healing. “Another cause of alteration in expression of ABO blood group system is diseases that increase demand of red blood cells, and cancers that lend use of A and B antigens as tumor markers of acute leukemia and in other blood disorders like myelodysplasia and myeloproliferative disorders” (Nakamura et al, 2003, p. 926). Individuals lacking ABO blood group antigens are healthy, portraying that ABO antigens are not beneficial to the body.

ABO antibodies may not have great benefits to the human body but are significant clinically due to the fact that they are very reactive and occur naturally. “In blood transfusions, reactions can be prevented in cross matching blood products” (Nakamura et al, 2003, p. 926). Hemolytic disease of the newborn is possible to prevent with advent of modern technologies where immune-suppressors are applied during the course of pregnancy. ABO blood group antigens are encoded to by one genetic locus with three allelic forms A, B and O (Trepicchio and Krontiris, 1992, 2430). An offspring receives one of the three alleles from each parent making possible six genotypes and four phenotypes as illustrated below.

An individual’s immune systems form antibodies against antigens absent from his red blood cells. Phenotype A will exhibit antibodies B, phenotype B will exhibit antibodies A, phenotype AB will have no antibodies will phenol type O will have both A and B antibodies as illustrated below.

Formation of ABO antibodies is triggered by absence of ABO blood group antigens in foods (certain sugars) or in micro organisms inhabiting the body e.g. the bacterial E-coli. Formation of these antibodies takes place at an early age because babies start feeding on carbohydrates identical ABO blood group antigens early in life. “ABO locus with its three allelic forms encodes a glycosyltransferase, i.e. an enzyme that produces the antigen A, an N-acetylgalactosamine immunodominant sugar while the B allele encodes same enzyme creating antigen B which is D-galactose immunodominant sugar” (Yamamoto, 1995, p.5).

ABO grouping system finds great application in modern medicine, in medical sciences and in their related researches.

Hisao, T. Oshihiko, K. and Ichiro S. (2000). Progress in the study of Al30 blood group System. Department of Legal Medicine, Faculty of Medicine, Toyama Medical and Pharmaceutical University, Toyama 930-01 94, Japan.

Lung-Chih Yu. Ching-Yi Chang, Yuh-Ching Twu, and Marie Lin. (2000). Human Histo- blood Group ABO Glycosyltransferase Genes: Different Enhancer Structures with Different Transcriptional Activities. Web.

Kobata A, Grollman E, and Ginsburg V. (1968). An enzymatic basis for blood type B in humans. Biochem Biophys Res Comm.

Nakamura, S. Matsushita, H. Nagai, T. Sugie, H. Furukawa, M. and Kurihara, K. (2003). DNA analysis of ABO blood group system detected by single-base nucleotide substitutions in a paternity case . International Congress Series.

Olsson, M. and Chester, M. (1996) Frequent occurrence of a variant O1 gene at the blood group ABO locus. Vox Sang.

Trepicchio, W. and Krontiris, T. (1992) Members of the rel /NF-kB family of transcriptional regulatory proteins bind the HRAS1 minisatellite DNA sequence. Nucleic Acids Res.

Yamamoto F. (1995). Molecular genetics of the ABO histblood group system. VOX Sang 69.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, April 8). The ABO Blood Group System. https://ivypanda.com/essays/the-abo-blood-group-system/

"The ABO Blood Group System." IvyPanda , 8 Apr. 2024, ivypanda.com/essays/the-abo-blood-group-system/.

IvyPanda . (2024) 'The ABO Blood Group System'. 8 April.

IvyPanda . 2024. "The ABO Blood Group System." April 8, 2024. https://ivypanda.com/essays/the-abo-blood-group-system/.

1. IvyPanda . "The ABO Blood Group System." April 8, 2024. https://ivypanda.com/essays/the-abo-blood-group-system/.

Bibliography

IvyPanda . "The ABO Blood Group System." April 8, 2024. https://ivypanda.com/essays/the-abo-blood-group-system/.

• The Role of ABO Blood Typing in Organ Transplantation
• Hemolytic Disease of the Newborn
• Rh Factor: The Effect on Pregnancy
• Human Inheritance and Disorders Prevention
• Biology. Erythrocytes or Red Blood Cells
• A1 Sauce versus Lawry’s Sauce
• Development of Serological Techniques for Identifying Weak Antigens
• Immunology: Antigens vs. Antibodies
• Antibody Detection and Identification
• The Issue of Small Populations
• Functional Health Assessment of the Community Alamosa
• Analysis and Conceptualisation of Reflection Models
• Gibb's Reflective Cycle
• Participating in a Practice-Based Research Network
• A Critique of the Study: Childbearing

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

17.6: Blood Types

• Last updated
• Save as PDF
• Page ID 16828

• Suzanne Wakim & Mandeep Grewal
• Butte College

Giving the Gift of Life

Did you ever donate blood as the individual in Figure \(\PageIndex{1}\) is doing? If you did, then you probably know that your blood type is an important factor in blood transfusions. People vary in the type of blood they inherit, and this determines which type(s) of blood they can safely receive in a transfusion. Do you know your blood type?

What Are Blood Types?

Blood type (or blood group) is a genetic characteristic associated with the presence or absence of certain molecules, called antigens, on the surface of red blood cells. These molecules may help maintain the integrity of the cell membrane, act as receptors, or have other biological functions. A blood group system refers to all of the gene(s), alleles, and possible genotypes and phenotypes that exist for a particular set of blood type antigens. Human blood group systems include the well-known ABO and Rhesus (Rh) systems, as well as at least 33 others that are less well known.

Antigens and Antibodies

Antigens such as those on the red blood cells are molecules that the immune system identifies as either self (produced by your own body) or non-self (not produced by your own body). Blood group antigens may be proteins, carbohydrates, glycoproteins (proteins attached to chains of sugars), or glycolipids (lipids attached to chains of sugars), depending on the particular blood group system. If antigens are identified as nonself, the immune system responds by forming antibodies that are specific to the non-self antigens. Antibodies are large, Y-shaped proteins produced by the immune system that recognize and bind to non-self antigens. The analogy of a lock and key is often used to represent how an antibody and antigen fit together, as shown in Figure \(\PageIndex{2}\). When antibodies bind to antigens, it marks them for destruction by other immune system cells. Nonself antigens may enter your body on pathogens such as bacteria or viruses, on foods, or on red blood cells in a blood transfusion from someone with a different blood type than your own. The last way is virtually impossible nowadays because of effective blood typing and screening protocols.

Genetics of Blood Type

An individual’s blood type depends on which alleles for a blood group system were inherited from their parents. Generally, blood type is controlled by alleles for a single gene or for two or more very closely linked genes. Closely linked genes are almost always inherited together because there is little or no recombination between them. Like other genetic traits, a person’s blood type is generally fixed for life, but there are rare instances in which blood type can change. This could happen, for example, if an individual receives a bone marrow transplant to treat a disease such as leukemia. If the bone marrow comes from a donor who has a different blood type, the patient’s blood type may eventually convert to the donor’s blood type because red blood cells are produced in the bone marrow.

ABO Blood Group System

The ABO blood group system is the best known human blood group system. Antigens in this system are glycoproteins. These antigen compounds are shown in Figure \(\PageIndex{3}\). There are four common blood types for the ABO system:

• Type A, in which only the A antigen is present
• Type B, in which only the B antigen is present
• Type AB, in which both the A and B antigens are present
• Type O, in which neither the A nor the B antigen is present

Genetics of the ABO System

The ABO blood group system is controlled by a single gene on chromosome 9. There are three common alleles for the gene, often represented by the letters I A (or A), I B (or B), and i (or O). With three alleles, there are six possible genotypes for the ABO blood group. However, alleles I A and I B are both dominant to allele i and codominant to each other. This results in just four possible phenotypes (blood types) for the ABO system. These genotypes and phenotypes are shown in Table \(\PageIndex{1}\).

Table \(\PageIndex{1}\): ABO Blood Group System

The diagram in Figure \(\PageIndex{4}\) shows an example of how ABO blood type is inherited. In this particular example, the father has blood type A (genotype AO) and the mother has blood type B (genotype BO). This mating type can produce children with each of the four possible ABO phenotypes, although in any given family not all phenotypes may be present in the children.

Medical Significance of ABO Blood Type

The ABO system is the most important blood group system in blood transfusions. If red blood cells containing a particular ABO antigen are transfused into a person who lacks that antigen, the person’s immune system will recognize the antigen on the red blood cells as non-self. Antibodies specific to that antigen will attack the red blood cells, causing them to agglutinate, or clump and break apart. If a unit of incompatible blood were to be accidentally transfused into a patient, a severe reaction (called acute hemolytic transfusion reaction) is likely to occur in which many red blood cells are destroyed. This may result in kidney failure, shock, and even death. Fortunately, such medical accidents virtually never occur today.

ABO antibodies are likely to already be present in a recipient’s blood for antigens that the person lacks. These antibodies are produced in the first years of life by sensitization to similar antigens commonly occurring in the environment. Anti-A antibodies are thought to originate from an immune response to an antigen on the influenza virus, and anti-B antibodies are thought to originate from an immune response to an antigen found on bacteria such as E. coli . Once the antibodies have been produced, they circulate in the plasma. The relationship between ABO red blood cell antigens and plasma antibodies is shown in Table \(\PageIndex{2}\).

Which blood types are compatible and which are not? Type O blood contains both anti-A and anti-B antibodies, so people with type O blood can only receive type O blood. However, they can donate blood to people of any ABO blood type. That’s why individuals with type O blood are called universal donors. Type AB blood contains neither anti-A nor anti-B antibodies, so people with type AB blood can receive blood from people of any ABO blood type. That’s why individuals with type AB blood are called universal recipients. However, they can donate blood only to people who also have type AB blood. These and other relationships between blood types of donors and recipients are summarized in Figure \(\PageIndex{5}\).

ABO blood type antigens are found not only on red blood cells but also on platelets, in other body fluids such as tears and urine, and on cells of other types of tissues. Blood type compatibility is important to consider for successful organ transplantation. If a transplanted organ has nonself antigens for ABO, it may be attacked by antibodies and rejected by the body.

Rhesus Blood Group System

Another well-known blood group system is the Rhesus (Rh) blood group system . The Rhesus system has dozens of different antigens but only five main antigens (named D, C, c, E, and e). The major Rhesus antigen is the D antigen. People with the D antigen are called Rh-positive (Rh+), and people who lack the D antigen are called Rh-negative (Rh-). Rhesus antigens are thought to play a role in transporting ions across cell membranes by acting as channel proteins.

The Rhesus blood group system is controlled by two linked genes on chromosome 1. One gene, called RHD, produces a single antigen, antigen D. The other gene, called RHCE, produces the other four relatively common Rhesus antigens (C, c, E, and e), depending on which alleles for this gene are inherited.

Rhesus Blood Group and Transfusions

After the ABO system, the Rhesus system is the second most important blood group system in blood transfusions. The D antigen is the one most likely to provoke an immune response in people who lack the antigen. People who have the D antigen (Rh+) can be safely transfused with either Rh+ or Rh- blood, whereas people who lack the D antigen (Rh-) can be safely transfused only with Rh- blood.

Unlike anti-A and anti-B antibodies to ABO antigens, anti-D antibodies for the Rhesus system are not usually produced by sensitization to environmental substances. However, people who lack the D antigen (Rh-) may produce anti-D antibodies if exposed to Rh+ blood. This may happen accidentally in a blood transfusion, although this is extremely unlikely today. It may also happen during pregnancy with an Rh+ fetus if some of the fetal blood cells pass into the mother’s blood circulation.

Hemolytic Disease of the Newborn

If a woman who is Rh- is carrying an Rh+ fetus, the fetus may be at risk. This is especially likely if the mother has formed anti-D antibodies during a prior pregnancy because of a mixing of maternal and fetal blood during childbirth. Unlike antibodies against ABO antigens, antibodies against the Rhesus D antigen can cross the placenta and enter the blood of the fetus. This may cause hemolytic disease of the newborn (HDN) , also called erythroblastosis fetalis, an illness in which fetal red blood cells are destroyed by maternal antibodies, causing anemia. This illness may range from mild to severe. If it is severe, it may cause brain damage and is sometimes fatal for the fetus or newborn. Fortunately, HDN can be prevented by preventing the formation of anti-D antibodies in the Rh- mother. This is achieved through an injection into the mother of a medication called Rho(D) immune globulin.

Feature: Myth vs. Reality

Myth: Your nutritional needs can be determined by your ABO blood type. Knowing your blood type allows you to choose the appropriate foods that will help you lose weight, increase your energy, and live a longer, healthier life.

Reality: This idea was proposed in 1996 in a New York Times bestseller Eat Right for Your Type , by Peter D’Adamo, a naturopath. Naturopathy is a method of treating disorders that involve the use of herbs, sunlight, fresh air, and other natural substances. Some medical doctors consider naturopathy a pseudoscience. A major scientific review of the blood type diet could find no evidence to support it. In one study, adults eating the diet designed for blood type A showed improved health, but this occurred in everyone regardless of their blood type. Because the blood type diet is based solely on blood type, it fails to account for other factors that might require dietary adjustments or restrictions. For example, people with diabetes but different blood types would follow different diets, and one or both of the diets might conflict with standard diabetes dietary recommendations and be dangerous.

Myth: ABO blood type is associated with certain personality traits. For example, people with blood type A are patient and responsible but may also be stubborn and tense, whereas people with blood type B are energetic and creative but may also be irresponsible and unforgiving. In selecting a spouse, both your own and your potential mate’s blood type should be taken into account to ensure the compatibility of your personality.

Reality: The belief that blood type is correlated with personality is widely held in Japan and other East Asian countries (the Japanese booth pictured below offers fortunes based on blood type). The idea was originally introduced in the 1920s in a study commissioned by the Japanese government but later shown to have no scientific support. The idea was revived in the 1970s by a Japanese broadcaster who wrote popular books about it. There is no scientific basis for the idea, and it is generally dismissed as pseudoscience by the scientific community. Nonetheless, it remains popular in East Asian countries, like astrology in many other countries.

• Define blood type and blood group system.
• Explain the relationship between antigens and antibodies.
• Identify the alleles, genotypes, and phenotypes in the ABO blood group system.
• Discuss the medical significance of the ABO blood group system.
• Give examples of how different ABO blood types vary in their susceptibility to diseases.
• Describe the Rhesus blood group system.
• Relate Rhesus blood groups to blood transfusions.
• What causes hemolytic disease of the newborn?

a. What are the possible genotypes of their offspring in terms of ABO blood group?

b. What are the possible phenotypes of their offspring in terms of ABO blood group?

c. Can the woman donate blood to her husband? Explain your answer.

d. Can the man donate blood to his wife? Explain your answer.

• True or False. The D antigen is part of the ABO blood group system.
• Explain why hemolytic disease of the newborn may be more likely to occur in a second pregnancy than in a first.

Explore More

Is malaria the reason so many people have type O blood? Listen to this fascinating National Public Radio interview with Dr. Christine Cserti-Gazdewich, a blood specialist (hematologist) at the University of Toronto, who discusses an emerging theory of universal blood. If you click on the icon of the podcast, you will find the transcript on their site.

Attributions

• Offutt blood drive by Charles Haymond; public domain via Wikimedia Commons
• Antibody by Fvasconcellos; public domain via Wikimedia Commons
• ABO blood group diagram by InvictaHOG; public domain via Wikimedia Commons
• ABO system codominance by NIH; public domain via Wikimedia Commons
• Blood compatibility by InvictaHOG; public domain via Wikimedia Commons
• Text adapted from Human Biology by CK-12 licensed CC BY-NC 3.0

• Type 2 Diabetes
• Heart Disease
• Digestive Health
• Multiple Sclerosis
• Diet & Nutrition
• Supplements
• Health Insurance
• Public Health
• Patient Rights
• Caregivers & Loved Ones
• End of Life Concerns
• Health News
• Thyroid Test Analyzer
• Doctor Discussion Guides
• Hemoglobin A1c Test Analyzer
• Lipid Test Analyzer
• Complete Blood Count (CBC) Analyzer
• What to Buy
• Editorial Process
• Meet Our Medical Expert Board

Blood Types: What Letters, Positive, and Negative Signs Mean

What to Know About Your Blood Type

• Blood Types
• Why to Know Your Type
• Compatibility
• Significance in Pregnancy
• Finding Your Blood Type

Your blood type is a combination of letters and signs identifying antigens present or absent on the surface of your red blood cells. Antigens are substances that can trigger the immune system to produce antibodies. Antibodies are proteins that lead an attack on substances perceived as foreign "invaders."

Blood typing is essential if you need to receive a blood transfusion . Your antibodies can attack transfused red blood cells of incompatible types. Mixing certain blood types can have dangerous health consequences. Some types of blood are more common than others, and they vary in compatibility.

This article will discuss how blood types are identified, how rare or common they are, and what that means for you.

Illustration by Lara Antal for Verywell Health

How Many Blood Types Are There?

The ABO system has four major blood types: A, B, AB, and O. Blood types are further categorized by the presence (positive or +) or absence (negative or -) of the Rh(D) antigen on the surface of their red blood cells, also known as the Rh factor . This produces the eight major blood types.

A and B antigens are sugars. The type of sugar antigens a person has determines whether they have A, B, or a mix of A and B (AB). If they lack both A and B, they are type O.

Protein antigens identify if you have a negative or positive Rh factor. A plus (+) or minus (-) sign indicates the presence or absence of the Rh factor. The plus indicates the presence of the antigen, while the minus means it is not widely present. About 85% of the population is Rh positive.

The International Society of Blood Transfusion further divides blood types into blood group systems by other types of antigens that may be present. They have identified 45 different blood group systems with hundreds of different antigens.

Some blood types are found in a limited number of people. In the United States, the blood types each found in less than 5% of the population are:

• AB- : 0.6% of the population
• B- : 1.5% of the population
• AB+ : 3.4% of the population

Most Common

More than 70% of the people in the United States have one of these two common blood types:

• O+ : 37.4% of the population
• A+ : 35.7% of the population

What Is Golden Blood?

Golden blood is the rarest known type of blood in the world. It has no Rh antigens at all, known as Rh null . It is dubbed "golden blood" because it can be donated to people with almost any Rh blood type, including those with rare types of Rh antigens.

However, if people with golden blood need blood, they can only receive the same type of blood. Experts estimate that only about 50 people are known to have golden blood, which was first detected in Australian aboriginal people.

Reasons to Know Your Blood Type

If you need blood during surgery or due to an injury or illness, it's essential to receive blood of a type that is compatible with your own. The hospital laboratory will type your blood and match it to donor units to ensure you only receive compatible blood.

Otherwise, you may have a hemolytic transfusion reaction when your immune system detects foreign proteins on the cells of an incompatible blood type and attempts to destroy them. Transfusion reactions range from mild to life-threatening. They can appear right after a transfusion or up to weeks later.

You can also help others by knowing your blood type in case you are in a position to donate to another individual in need or because blood bank supplies of your type of blood are low.

Different blood types also appear to make people more or less likely to develop certain conditions, including kidney stones, high blood pressure during pregnancy, and bleeding disorders. One study found people with blood group A have a higher likelihood of infection with COVID-19 than those in blood group O.

Compatibility of Different Blood Types

Compatible blood types are based on whether the recipient has antibodies to the donor blood antigens or may develop them.

Early in life, your immune system forms antibodies against A or B antigens  not  present on your red blood cells. People with blood type A will have anti-B antibodies, and those with type B blood will have anti-A antibodies. Type O blood has both anti-A and anti-B antibodies. Type AB blood has neither A nor B antibodies.

Antibodies only form against the Rh factor if an Rh negative person is exposed to Rh positive blood due to transfusion or pregnancy. The following chart shows what types of blood are compatible with each other.

Universal Donors and Recipients

Type O negative blood is called a universal donor , meaning that it can be safely given to people with most other blood types and has a low risk of a transfusion reaction. People with type AB positive blood are known as universal recipients, meaning they can be given almost any type of blood safely.

Unless blood is needed immediately to save a person's life, the hospital laboratory will type the person's blood and perform compatibility testing with the donor blood units (crossmatching) to ensure the safety of the transfusion.

Testing Blood Types in Pregnancy

If you are pregnant, it's important to identify your Rh blood type so you and your healthcare providers can prevent the consequences of Rh incompatibility. This affects only pregnant people who are Rh negative.

If the pregnant person is Rh negative and the other parent is Rh positive, the fetus may be Rh positive. This is called Rh incompatibility.

This incompatibility will not affect a child born during a first incompatible pregnancy. During birth, however, the blood of the pregnant person and fetus mixes. The Rh negative pregnant person can develop antibodies to the Rh factor.

Those antibodies could harm subsequent fetuses that are Rh positive. The pregnant person's anti-Rh(D) antibodies will identify fetal Rh proteins as foreign and attack them. Fetal red blood cells can swell and tear in response, known as hemolytic disease of the fetus and newborn .

This can lower the fetus's or newborn's red blood cell count and lead to serious consequences, such as brain damage, pregnancy loss, or death of the newborn.

An Rh negative pregnant person who has not developed anti-Rh(D) antibodies should given  RhoGAM, or intravenous WinRho, a Rho(D) immune globulin to prevent the development of the antibodies.

How to Find Out Your Blood Type

A blood test can determine your blood type . If you donate blood or plasma , blood typing will be performed at no charge. You can learn your blood type from the report of the donor service.

Blood typing is not a part of routine blood tests. It's commonly ordered if you are having surgery, need a blood transfusion or organ transplant, or are pregnant.

You could request a blood type test from your healthcare provider, but it may not be covered by health insurance if it isn't medically necessary. At a healthcare facility, a small amount of blood will be drawn and sent to a lab for testing.

Check your medical record to see if a blood type test was done in the past and is reported there. If you are unsure how to access your medical record, ask your healthcare provider.

Home blood type tests are available in most states. They are generally accurate if performed correctly. Saliva tests are another option, but they may be more costly and less accurate.

While your blood type doesn't change, a blood type test will be performed each time you need a transfusion. An incompatible transfusion can be fatal, so extreme care is taken to ensure you receive only compatible units.

Blood typing is reported using the ABO blood system and the presence or absence of the Rh(D) antigen known as the Rh factor, resulting in eight major blood types. Some blood types are much more common than others.

If someone needs a blood transfusion, it is essential to use the same or a compatible type of blood to avoid potentially serious reactions to a transfusion. Pregnant people and their healthcare providers must know their Rh factor status to avoid hemolytic disease of the fetus and newborn.

Professional laboratory blood typing is more reliable than home tests, though home blood type tests are available.

Stanford Blood Center. Blood types.

International Society of Blood Transfusion. Red cell immunogenetics and blood group terminology.

Stanford Blood Center. Blood types .

Australian Academy of Science. Rare blood types .

MedlinePlus. Hemolytic transfusion reaction.

Dahlén T, Clements M, Zhao J, Olsson ML, Edgren G. An agnostic study of associations between ABO and RhD blood group and phenome-wide disease risk . Ginsburg D, Wittkopp PJ, Desch KC, eds. eLife. 2021;10:e65658. doi:10.7554/eLife.65658.

Wu SC, Arthur CM, Jan HM, et al. Blood group A enhances SARS-CoV-2 Infection . Blood . 2023;142(8):742-747. doi:10.1182/blood.2022018903

National Library of Medicine. The ABO blood group.

American College of Obstetricians and Gynecologists. The Rh factor: how it can affect your pregnancy.

Myle AK, Al-Khattabi GH.  Hemolytic disease of the newborn: a review of current trends and prospects .  Pediatric Health Med Ther.  2021;12:491-498. doi:10.2147/PHMT.S327032

Eldon Biologicals A/S. Eldoncard: home blood type testing kit .

Velani PR, Shah P, Lakade L.  Determination of ABO blood groups and Rh typing from dry salivary samples.   Int J Clin Pediatr Dent . 2018;11(2):100-104. doi:10.5005/jp-journals-10005-1493

By Nancy LeBrun LeBrun is a Maryland-based freelance writer and award-winning documentary producer with a bachelor's degree in communications.

Home — Essay Samples — Nursing & Health — Blood — Abo Blood Group: Definition, Features and Principles

Abo Blood Group: Definition, Features and Principles

• Categories: Blood

About this sample

Words: 2624 |

14 min read

Published: Jan 4, 2019

Words: 2624 | Pages: 6 | 14 min read

Cite this Essay

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Verified writer

• Expert in: Nursing & Health

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

2 pages / 752 words

4 pages / 1884 words

3 pages / 1416 words

2 pages / 1119 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

High blood pressure is a significant health concern affecting adults worldwide. This quantitative essay employs a research investigation approach to examine the effects of physical activity on blood pressure levels in adults. [...]

In the world of Shakespearean literature, one play stands out as a timeless classic that continues to captivate audiences with its intricate plot and profound themes. "Macbeth," a tragedy written by the legendary playwright [...]

Iron deficiency anemia occurs when there is a deficiency in the hemoglobin levels due to a lack of iron in their body. Iron deficiency anemia is a very common type of anemia. The body requires a certain amount of iron and with [...]

All humans have blood, but what is blood? What is blood’s composition? What is blood’s purpose? How is blood produced? How does the blood circulatory system work? What are the types of blood? Do all animals have blood? Is blood [...]

Although the heart plays one of the biggest roles in the circulatory system, the rest of the system is also very important as it is what transports blood to and from the heart to keep the body alive. Blood is transported [...]

Sickle cell is hereditary, cures are difficult to discover. By the time a person is diagnosed with the disease it’s too late, the harm has already been done. The complicated task at hand required even more depth research [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

Essay on Blood

Students are often asked to write an essay on Blood in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Blood

What is blood.

Blood is a body fluid in humans and animals that delivers necessary substances to cells and carries waste products away. It is made up of two main components, namely, plasma and blood cells. Plasma, the liquid part, makes up about 55% of the blood’s volume.

Blood Cells

Blood cells are of three types: red blood cells, white blood cells, and platelets. Red blood cells carry oxygen from our lungs to the rest of our body. White blood cells defend our body against infections. Platelets help in blood clotting when we have a wound.

Importance of Blood

Blood plays a vital role in our body. It transports nutrients and oxygen to our body cells. It also helps in removing waste materials like carbon dioxide. Blood carries hormones and signals from one part of the body to another. It also helps in fighting against diseases.

Blood Groups

There are four main blood groups: A, B, AB, and O. Each group is either Rh positive or Rh negative. Blood group is important during blood transfusion. A person can only receive blood from a compatible blood group.

Blood Donation

Blood donation is a lifesaving act. Donated blood is used in various medical treatments and emergencies. It’s a safe process and a healthy person can donate blood every three months. It’s important to donate blood as it can save someone’s life.

250 Words Essay on Blood

Blood is a body fluid in humans and other animals that delivers necessary substances to the body’s cells. It’s like a delivery service inside our bodies. It carries food, water, and oxygen to our body parts. It also takes away waste, like carbon dioxide, from our cells.

Parts of Blood

Blood has four main parts. These are red blood cells, white blood cells, platelets, and plasma. Red blood cells carry oxygen. They are like trucks that move oxygen from our lungs to all parts of our body. White blood cells are our body’s defense team. They fight germs and help us stay healthy. Platelets are like band-aids. They help our body heal when we get a cut by making clots to stop bleeding. Plasma is a yellowish liquid that carries all these parts and more.

Blood is very important for our body. It helps keep us alive by carrying oxygen and nutrients to our cells, fighting infections, and healing wounds. Without blood, our body would not be able to function properly.

Blood Types

There are four main blood types: A, B, AB, and O. Each type can be either positive or negative. These types are important when it comes to blood transfusions. This is when blood is given from one person to another. For example, if a person with type A blood is given type B blood, it can make them very sick.

In conclusion, blood is a vital part of our body that performs many important functions. It’s like a transportation system and a defense team all in one. Understanding blood can help us appreciate how our bodies work and stay healthy.

500 Words Essay on Blood

Blood is a body fluid that delivers necessary substances like nutrients and oxygen to the cells and carries waste products away from those same cells. In simple words, blood is like a transport system inside our bodies. It is red in color and is slightly thicker than water.

Components of Blood

Blood is made up of four main components. These are red blood cells, white blood cells, platelets, and plasma.

Red blood cells, also known as RBCs, carry oxygen from our lungs to the rest of our body. They also bring back carbon dioxide from the body to the lungs, which we breathe out. This makes RBCs a very important part of our blood.

White blood cells, or WBCs, are the body’s defense system. They fight off germs, bacteria, and viruses that enter the body and help to keep us healthy.

Platelets are tiny blood cells that help the body form clots to stop bleeding. If one of your blood vessels gets damaged, it sends out signals that are picked up by platelets. The platelets then rush to the site of damage and form a clot to repair the vessel.

Plasma is the liquid part of the blood. It carries the blood cells and platelets around the body. It also carries other important things like hormones, which control many things in the body, and nutrients.

Types of Blood

There are four main types of blood: A, B, AB, and O. Each type is determined by the presence or absence of certain substances on the surface of the red blood cells. People with type A blood have A antigens, those with type B have B antigens, those with type AB have both, and those with type O have neither.

Blood is very important because it keeps us alive by carrying out many important jobs. It delivers oxygen and nutrients to our cells, takes away waste products, fights infections, and helps to heal wounds. Without blood, our bodies would not be able to function.

Blood donation is a simple, safe process where a person voluntarily agrees to have blood drawn from them to be used in medical treatments. Donated blood can be life-saving for people who have lost large amounts of blood due to accidents or surgery, and for people with certain diseases. It’s a great way to help others and make a big difference in someone’s life.

In conclusion, blood is a vital part of our body that performs many important functions. It’s a fascinating substance that’s much more than just a red liquid flowing in our veins.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on Blockchain Technology
• Essay on Blindness
• Essay on Black Power Movement

Apart from these, you can look at all the essays by clicking here .

Happy studying!

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 01 May 2024

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

• MoTrPAC Study Group ,
• Lead Analysts &

MoTrPAC Study Group

Nature volume  629 ,  pages 174–183 ( 2024 ) Cite this article

38k Accesses

652 Altmetric

Metrics details

• Epigenetics
• Metabolomics
• Transcriptomics

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/ ).

Similar content being viewed by others

Multimodal cell atlas of the ageing human skeletal muscle

Small extracellular vesicles from young plasma reverse age-related functional declines by improving mitochondrial energy metabolism

Genome-wide association studies

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).

Download references

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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).

Supplementary information

Supplementary information.

Supplementary Methods, Tables, discussion, author contributions and references.

Reporting Summary

Supplementary tables.

Supplementary Tables 1–27. See ‘README’ tab for details.

Peer Review File

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 21 September 2022

Accepted : 16 November 2023

Published : 01 May 2024

Issue Date : 02 May 2024

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

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Gossamer Bio and Chiesi Group to collaborate in therapy for lung condition

• Medium Text

• Company Chiesi Farmaceutici SpA Follow
• Company Gossamer Bio Inc Follow
• Company Merck & Co Inc Follow

Sign up here.

Reporting by Christy Santhosh; Editing by Vijay Kishore

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

Business Chevron

Global shares rally on rate cut hopes, yen weakens

A gauge of global stock markets rallied on Monday on optimism that major central banks will cut interest rates this year, while the yen weakened against the dollar after a surge last week from Japan's suspected currency intervention.

You are using an outdated browser. Please upgrade your browser to improve your experience.

• AGRICULTURE

The Capitol Punishment Podcast

Listen Here »

Gossamer Bio and Chiesi Group to collaborate for blood pressure treatment

(Reuters) – Gossamer Bio said on Monday it will collaborate with Chiesi Group to develop and commercialize its drug seralutinib in multiple indications related to blood pressure conditions.

Gossamer is to receive $160 million development reimbursement payment and is eligible to receive up to $146 million in regulatory and $180 million in sales milestones as part of the deal.

(Reporting by Christy Santhosh; Editing by Vijay Kishore)

Leave a Reply Cancel reply

You must be logged in to post a comment.

Current Weather »

More From Forbes

Florida a&m receives record $237 million gift during commencement.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

Florida A&M University has received a $237 million gift, the largest donation in its history. (Photo ... [+] by: Jeffrey Greenberg/Universal Images Group via Getty Images)

Florida A&M University has received a $237.75 million gift from the Issac Batterson 7th Family Trust and Gregory Gerami, the founder and CEO of Batterson Farms Corp, a leading industrial hemp business.

The gift, the largest in Florida A&M’s 136-year history, was presented to FAMU President Larry Robinson at one of the university’s commencement ceremonies on May 4. You can watch the moment the gift was made here .

FAMU will add the money to its endowment to support various academic initiatives, student success programs and its athletics program. It will almost double the size of the university’s current endowment, bringing its value to nearly $400 million.

With an enrollment of about 10,000 students, Florida A&M, located in Tallahassee, Florida, is one of the nation’s leading Historically Black Colleges and Universities (HBCUs). The new gift would appear to also be the largest donation ever made to an HBCU, eclipsing the prior record established in January when Spelman College announced receiving a $100 million gift.

Sony Is Making A Truly Terrible Mistake With Helldivers 2 Update Sony Reverses Course

Google tests much needed google photos feature upgrade, ‘baby reindeer’: stephen king writes essay praising netflix stalker series.

“This gift is breathtaking in its generosity and its scope,” said Robinson in a news release. “It changes the narrative about what is possible for FAMU. I cannot thank Gregory Gerami and the Issac Batterson 7th Family Trust enough. Their names are now etched into the annals of Florida A&M University in perpetuity.”

According to the university, Gerami reached out to FAMU officials last fall to discuss the possibility of making a sizable donation. Gerami believed that FAMU’s mission and research capabilities, especially in the area of hemp production, were strongly aligned with his own company’s emphasis and direction.

“FAMU has become like a family to our Trust, our company and to me. Our morals and our mission are in line with FAMU and FAMU’s mission,” said Gerami, in the press release. He emphasized his commitment to the university’s sustainability and growth. “It’s also about making sure that we set FAMU on the path to being the top HBCU in this country.”

Gregory Gerami achieved his business success despite a childhood of adversity, including being born with opiate addiction. After starting a small lawn care business, he eventually branched out and founded the Batterson Farms Corp , a hydroponic farming and hemp plastic company, in 2021.

In recognition of Gerami’s history of physical and health challenges, which are detailed in a biography on the Batterson Farms website, Florida A&M will use a portion of his gift to enhance its Center for Disability Access and Resources, which serves about 700 students. Center director Deborah Sullivan said the donation would increase the center’s ability to offer more programs and services on campus.

“The impact is just tremendous because we are not a department that often gets gifts like this. Alumni tend to give to their school or to athletics, but the Office of Disability Services doesn’t necessarily get recognized with monetary gifts like this and, especially, this magnitude,” said Sullivan.

• Editorial Standards
• Reprints & Permissions

• Share full article

Advertisement

Supported by

8 Daily Newspapers Sue OpenAI and Microsoft Over A.I.

The suit, which accuses the tech companies of copyright infringement, adds to the fight over the online data used to power artificial intelligence.

By Katie Robertson

Eight daily newspapers owned by Alden Global Capital sued OpenAI and Microsoft on Tuesday, accusing the tech companies of illegally using news articles to power their A.I. chatbots.

The publications — The New York Daily News, The Chicago Tribune, The Orlando Sentinel, The Sun Sentinel of Florida, The San Jose Mercury News, The Denver Post, The Orange County Register and The St. Paul Pioneer Press — filed the complaint in federal court in the U.S. Southern District of New York. All are owned by MediaNews Group or Tribune Publishing, subsidiaries of Alden, the country’s second-largest newspaper operator.

In the complaint, the publications accuse OpenAI and Microsoft of using millions of copyrighted articles without permission to train and feed their generative A.I. products, including ChatGPT and Microsoft Copilot. The lawsuit does not demand specific monetary damages, but it asks for a jury trial and said the publishers were owed compensation from the use of the content.

The complaint said the chatbots regularly surfaced the entire text of articles behind subscription paywalls for users and often did not prominently link back to the source. This, it said, reduced the need for readers to pay subscriptions to support local newspapers and deprived the publishers of revenue both from subscriptions and from licensing their content elsewhere.

“We’ve spent billions of dollars gathering information and reporting news at our publications, and we can’t allow OpenAI and Microsoft to expand the Big Tech playbook of stealing our work to build their own businesses at our expense,” Frank Pine, the executive editor overseeing Alden’s newspapers, said in a statement.

An OpenAI spokeswoman said in a statement that the company was “not previously aware” of Alden’s concerns but was engaged in partnerships and conversations with many news organizations to explore opportunities.

“Along with our news partners, we see immense potential for A.I. tools like ChatGPT to deepen publishers’ relationships with readers and enhance the news experience,” she said.

A Microsoft spokesman declined to comment.

The lawsuit adds to a fight over the use of data to power generative A.I. Online information, including articles, Wikipedia posts and other data, has increasingly become the lifeblood of the booming industry. A recent investigation by The New York Times found that numerous tech companies, in their push to keep pace, had ignored policies and debated skirting copyright law in an effort to obtain as much data as possible to train chatbots.

Publishers have paid attention to the use of their content. In December, The Times sued OpenAI and Microsoft, accusing them of using copyrighted articles to train chatbots that then competed with the paper as a source of news and information. Microsoft has sought to have parts of that lawsuit dismissed . It also argued that The Times had not shown actual harm and that the large language models that drive chatbots had not replaced the market for news articles. OpenAI has filed a similar argument.

Other publications have sought to make deals with the tech companies for compensation. The Financial Times, which is owned by the Japanese company Nikkei, said on Monday that it had reached a deal with OpenAI to allow it to use Financial Times content to train its AI chatbots. The Financial Times did not disclose the terms of the deal.

OpenAI has also struck agreements with Axel Springer, the German publishing giant that owns Business Insider and Politico; The Associated Press ; and Le Monde, the French news outlet.

The lawsuit from the Alden newspapers, filed by the law firm Rothwell, Figg, Ernst & Manbeck, accuses OpenAI and Microsoft of copyright infringement, unfair competition by misappropriation and trademark dilution. The newspapers say the chatbots falsely credited the publications for inaccurate or misleading reporting, “tarnishing the newspapers’ reputations and spreading dangerous information.”

One example included ChatGPT’s response to a query about which infant lounger The Chicago Tribune recommended. ChatGPT, according to the complaint, responded that The Tribune recommended the Boppy Newborn Lounger, a product that was recalled after it was linked to infant deaths and that the newspaper had never recommended.

In a separate incident, an A.I. chatbot claimed that The Denver Post had published research indicating that smoking could potentially cure asthma, a complete fabrication, the complaint said.

“This issue is not just a business problem for a handful of newspapers or the newspaper industry at large,” the lawsuit said. “It is a critical issue for civic life in America.”

Katie Robertson covers the media industry for The Times. Email:  [email protected]   More about Katie Robertson

• Newsletters
• Account Activating this button will toggle the display of additional content Account Sign out

Navel Gazing

John dickerson’s notebooks: remembering early 1990s new york.

Getting used to a new city, work advice, passing on wisdom, and more are explored in this week’s audio essay from John Dickerson.

Listen & Subscribe

Choose your preferred player:

• Apple Podcasts
• Amazon Music

Please enable javascript to get your Slate Plus feeds.

Get Your Slate Plus Podcast

If you can't access your feeds, please contact customer support.

Thanks! Check your phone for a link to finish setting up your feed.

Please enter a 10-digit phone number.

Listen on your phone: RECOMMENDED

Enter your phone number and we'll text you a link to set up the podcast in your app:

We'll only text you about setting up this podcast, no spam.

Listen on your computer:

Apple Podcasts will only work on MacOS operating systems since Catalina . We do not support Android apps on desktop at this time.

Listen on your device: RECOMMENDED

These links will only work if you're on the device you listen to podcasts on.

Set up manually:

How does this work?

We're sorry, but something went wrong while fetching your podcast feeds. Please contact us at [email protected] for help.

Episode Notes

In this week’s essay, John discusses an onboarding memo for his assistant Laura, and recounts his early days living and working in New York City.

Notebook Entries:

Notebook 75

Onboard memo for Laura

Notebook 3, page 44. May 1991

June 17 start job. Good stuff

Notebook 3, page 46. May 1991

Tips on buying renting in NYC

Ask about broker

20s and 30s East side. Murry Hill

Live on no major avenue

Interest bearing account for security deposit

Medeco locks

Notebook 4, page 15

Scared standing on 34th and Broadway

$6 cab fare

Notebook 4, page 42

Getting lost in the village

References:

The Little Brown Book of Anecdotes by Clifton Fadiman

Medeco Locks

“ Here is New York ” by E.B. White

“ Silly Job Interview ” - Monty Python

John Cleese on Creativity in Management

Herbie Hancock: Miles Davis’ Essential Lesson On Mistakes

Want to listen to Navel Gazing uninterrupted? Subscribe to Slate Plus to immediately unlock ad-free listening to Navel Gazing and all your other favorite Slate podcasts. Subscribe now on Apple Podcasts by clicking “Try Free” at the top of our show page. Or, visit slate.com/navelgazingplus to get access wherever you listen.

Podcast production by Cheyna Roth.

Email us at [email protected]

• New York City

About the Show

Political Gabfest host John Dickerson has been a journalist for more than three decades, reporting about presidential campaigns, political scandals, and the evolving state of our democracy. Along the way, he’s also been recording his observations in notebooks he has carried in his back pocket. He has captured his thoughts about life, parenthood, death, friendship, writing, God, to-do lists, and more. On the Navel Gazing podcast, John Dickerson invites you to join him in figuring out what these 30 years of notebooks mean: sorting out what makes a life—or a day in a life—noteworthy.

John Dickerson is host of CBS News Prime Time With John Dickerson , co-host of the Slate Political Gabfest, host of the Whistlestop podcast, and author of The Hardest Job in the World .