Trait#109: MC4R and obesity

Monday, October 25, 2021. Author FitnessGenes

Trait#109: MC4R and obesity

What is the melanocortin 4 receptor?

The melanocortin 4 receptor (MC4R) is a receptor found on specialised neurons in the brain that control food intake and energy balance. MC4Rs therefore play a key role in the regulation of appetite, eating behaviour, and bodyweight.  

Activation of MC4Rs in the brain typically leads to the suppression of food intake and an increase in energy expenditure. By contrast, inhibition of MC4Rs leads to higher food intake and reduced energy expenditure.

As we’ll find out in the following sections, genetic mutations that affect MC4R function can affect eating behaviour and risk of obesity. On this note, rare mutations that lead to a complete loss of MC4R function can cause MC4R deficiency – a rare form of severe obesity characterised by excessive hunger and appetite (hyperphagia) that begins in the first year of life.

 

How does the melanocortin 4 receptor regulate food intake and weight?

As explained in the PCSK1 and overeating and Leptin Resistance trait articles, we have a small structure in the centre of the brain, known as the hypothalamus, which is plays a key role in regulating energy balance – i.e. the balancing of energy taken in from eating food against energy spent on physical activity and metabolic processes by the body.

More specifically, a part of the hypothalamus called the arcuate nucleus (ARC), contains two distinct populations of neurons that either stimulate or suppress food-intake:

  • POMC neurons – which suppress appetite
  • AgRP/NPY neurons – which stimulate appetite

When POMC neurons in the hypothalamus are stimulated - for example by gut hormones such as PYY and GLP-1, or leptin, a hormone produced by fat cells - the POMC neurons produce an appetite-suppressing or “anorexigenic” hormone called α-MSH (alpha-melanocyte stimulating hormone).

α-MSH then binds to melanocortin 4 receptors (MC4Rs) on different (“second-order”) neurons in another part of the hypothalamus, the paraventricular nucleus (PVN). When α-MSH binds to and activates MC4R neurons, they send signals to wider brain circuits that act to reduce food intake and effect metabolic changes that increase energy expenditure.

Source: Ramos-Molina, B., Martin, M. G., & Lindberg, I. (2016). PCSK1 variants and human obesity. Progress in molecular biology and translational science, 140, 47-74.

 

By contrast, when AgRP/NPY neurons in the hypothalamus are stimulated, for example by the gut hormone ghrelin, they release an appetite-stimulating or “orexigenic” hormone called AgRP (Agouti-related peptide). AgRP is what is known as an “inverse agonist” of the melanocortin 4 receptor (MC4R). When it binds to the MC4R, it inhibits signalling by the MC4R neuron. As the suppressive effect of MC4R on food intake is reduced, this leads to increased appetite and food intake.

 

Source: Kühnen, P., Krude, H., & Biebermann, H. (2019). Melanocortin-4 receptor signalling: importance for weight regulation and obesity treatment. Trends in molecular medicine, 25(2), 136-148.

 

Overall, the MC4R allows the brain to integrate hormonal signals from peripheral tissues, e.g. the stomach, intestines, pancreas, adipose (fat) tissue, and then adjust appetite, metabolism, and food intake accordingly.

Phrased slightly differently, we can say that the MC4R is crucial to energy homeostasis. As explained in PCSK1 and overeating trait article, homeostasis refers to the maintenance of a stable, internal environment. With regards to energy, homeostasis involves maintaining an equilibrium between energy intake (through consuming calories in food), energy storage (by storing chemical energy in fat tissues), and energy expenditure (by burning calories for various chemical reactions that sustain life and movement).

Due in part to these homeostatic mechanisms based on MC4R signalling, we tend to feel full and stop eating after consuming a certain amount of food. When we store energy in fat deposits, our fat cells secrete leptin which, by activating MC4Rs, typically acts to suppress food intake and curb excessive weight gain. In the long term, this means our bodyweight and fat mass tends to hover around a stable set point.

Impaired MC4R signalling, however, can disrupt normal energy homeostasis and regulation of bodyweight. For example, as we’ve briefly mentioned earlier, rare mutations that cause of a loss of MC4R function cause severe childhood obesity, excessive appetite, and higher fat mass. More subtle changes in MC4R function can also have small effects on energy homeostasis, bodyweight and obesity risk.

 

Source: Shen, W. J., Yao, T., Kong, X., Williams, K. W., & Liu, T. (2017). Melanocortin neurons: Multiple routes to regulation of metabolism. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1863(10), 2477-2485.

 

In addition to regulating energy balance, MC4R signalling also plays a role in the control of heart rate and blood pressure, glucose and lipid metabolism, and reproductive function.

 

What is the MC4R gene?

The MC4R gene encodes the melanocortin 4 receptor (MC4R protein).

In the last twenty or so years, there has been considerable interest in the MC4R gene as a candidate gene for obesity. In 1997, researchers inactivated or “knocked out” the MC4R gene in mice, and found that it caused them to become obese, with excessive food intake, high insulin levels and high blood glucose levels.

Shortly afterwards, in 1998, mutations of the MC4R gene were identified in human children with severe childhood-onset obesity. These mutations were found to cause a complete loss of function of the MC4R protein (known as MC4R deficiency) thereby severely impairing MC4R signalling in the brain. The graph below shows the bodyweight trajectory for a 4 year old male who carried a complete loss-of-function MC4R mutation in the aforementioned study.

 

Source: Yeo, G. S., Farooqi, I. S., Aminian, S., Halsall, D. J., Stanhope, R. G., & O'Rahilly, S. (1998). A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nature genetics, 20(2), 111-112.

 

Since then, over 200 different mutations within the MC4R gene have been identified in obese subjects. It’s estimated that these mutations are found in roughly 2-5% of severely obese children and 1% of severely obese adults (severe obesity is typically defined as a BMI > 35 kg/m2). As such, MC4R deficiency is deemed to be the most common form of monogenic obesity (i.e. obesity caused by mutations within a single gene).

Not all MC4R mutations give rise to MC4R deficiency and severe, monogenic obesity, however. Some MC4R mutations may reduce the expression of the MC4R protein and slightly increase risk of common obesity (i.e. the main type of obesity caused by multiple genetic and lifestyle factors). Other MC4R mutations can actually be protective against common obesity, by enhancing MC4R signalling in the brain. We’ll discuss these different types off mutations in more detail in the following section.

 

What are the different types of MC4R mutations?

Mutations are simply changes in our DNA sequence. Single Nucleotide Polymorphisms (SNPs), which we analyse at FitnessGenes, are a particular type of mutation, involving single-letter (nucleotide) changes in the DNA code.

In order to simplify things, we’ll use the terms “SNP” and “mutation” interchangeably. Strictly speaking however, SNPs only involve single-letter (single-nucleotide) changes and are present in more than 1% of the population.

Before describing the different types of MC4R mutations, it’s worth going over some basics of genetics.

Genes are stretches of DNA that code for proteins. Proteins, which includes enzymes, structural proteins, and receptors like the melanocortin 4 receptor (MC4R), are made up of chains of individual amino acids linked together.

The diagram below shows the chain of amino acids that make up the MC4R protein.

Source: Farooqi, I. S., Yeo, G. S., Keogh, J. M., Aminian, S., Jebb, S. A., Butler, G., ... & O’Rahilly, S. (2000). Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. The Journal of clinical investigation, 106(2), 271-279.

 

Missense mutations

Each amino acid in a protein is coded for by a sequence of three nucleotides (letters) in a gene’s DNA sequence. These three-letter sequences are called codons. For example, the codon G, T, A in the DNA sequence of the MC4R gene codes for the amino acid valine (V).

The exact sequence of letters/codons in a gene’s DNA code (i.e. the exact sequence of the letters A, C, G, T) therefore dictates the precise sequence of amino acids that make up a protein.

Consequently, changes in the DNA code (in other words, mutations) can alter the sequence of amino acids making up a protein. Some mutations, known as missense mutations, cause one amino acid to be substituted with another.

 

Source: Kasper, C. K., & Buzin, C. H. (2008). Genetics of Hemophilia A and B: An Introduction for Clinicians, 2009. Southland Publications.

 

The image above, for example, shows how a T --> A change in the DNA code (in the second from left codon) causes a change in the amino acid from cysteine to serine.

With regards to the MC4R gene, there have been over a hundred missense mutations identified. For example a G-->C change in the DNA code leads to the amino acid valine (V) being replaced with another amino acid, isoleucine (I). This occurs at a particular position (103) in the amino acid sequence that makes up the MC4R protein. This missense mutation is therefore denoted V103I and is highlighted in an oval in the earlier MC4R amino acid sequence diagram.

 

Loss-of-function vs Gain-of-function mutations

Some amino acid changes caused by missense mutations can impair the function of the protein or cause less of it to be produced (“expressed”). These mutations are known as loss-of-function (LoF) mutations.

On this note, various missense mutations in the MC4R gene are shown to impair the function of the MC4R protein and thereby impair MC4R signalling. For example, the S136P missense mutation, which causes a serine amino acid to be substituted with proline, has been shown to significantly hamper MC4R function.

Not all missense mutations and amino acid changes are deleterious to protein function, however. Some amino acid substitutions can actually enhance the function and activity of proteins. These mutations are known as gain-of-function (GoF) mutations.

Certain missense mutations of the MC4R gene are shown to enhance MC4R protein function and activity. For example, the V103I missense mutation is shown to enhance MC4R signalling when the receptor is stimulated.

It’s worth pointing out that some missense mutations in the MC4R gene do not seem to directly affect MC4 receptor activity. These are sometimes called wildtype (WT)-like mutations.  

 

Nonsense mutations

Not all three letter sequences of nucleotides (codons) in a stretch of DNA code for amino acids. Some three-letter sequences are known as stop codons – they mark the end of a protein chain and instruct a cell to stop reading the DNA instructions and terminate production of a protein.

Some mutations, known as nonsense mutations, can introduce a stop codon into a DNA sequence prematurely. This results in a shorter protein chain being produced. A shorter-than-normal protein chain typically cannot function properly. As such, nonsense mutations tend to be loss-of-function mutations.

In line with this, various nonsense mutations have been found in the MC4R gene and are shown to impair MC4R function and signalling. Some rare MC4R nonsense mutations can cause a complete loss of function of the MC4 receptor, thereby causing MC4R deficiency, which is characterised by severe childhood-onset obesity, excessive hunger and higher fat mass.

 

Frameshift mutations

Some types of mutations involve the insertion or deletion of nucleotides (letters) into the DNA code. These insertions or deletions can go on to affect the ‘reading frame’ of a gene- i.e. how the DNA sequence is read and converted into amino acids by cellular machinery that manufacture proteins. Such mutations are known as frameshift mutations.

For example, consider the following DNA sequence:

G, T, A, G, T, A…

As explained earlier, amino acids are coded for by 3-letter sequences of DNA (called codons). As we’ve also mentioned before, the G,T,A sequence/codon codes for the amino acid valine. Reading from left to right, the full sequence above therefore codes for two valines in a protein chain.

If a frameshift mutation inserts the letter ‘T’ into the above gene, the new DNA sequence will read:

T, G, T, A, G, T, A…

However, as cell machinery reads DNA in 3-letter sections, the new DNA sequence will now code for completely different amino acids. Reading from the left to right, the new sequence codes for cysteine (T,G,T), followed by serine (A,G,T).

 

Source: Shaw, H., Hussein, S., & Helgert, H. (2011). Genomics-based Security Protocols: From Plaintext to Cipherprotein. International Journal on Advances in Security, 4(1).

 

The image above is another example of a frameshift mutation, with the insertion of an additional 'G' nucleotide causing a completely different string of amino acids to be produced. 

By changing the amino acid sequence and/or by introducing stop codons that terminate protein production, frameshift mutations can significantly alter the structure and function of proteins. In several cases, the encoded protein is completely non-functional.

In line with this, rare frameshift mutations in the MC4R gene cause a complete loss of function of the MC4 receptor, giving rise to MC4R deficiency. For example, the CTCT deletion, illustrated in the above diagram, was one of the earliest mutations found in subjects with severe, childhood-onset, monogenic obesity.

Hundreds of different mutations in the MC4R gene have been discovered. Broadly speaking, these can be classified as:

  • missense gain-of-function mutations
  • missense loss-of-function mutations
  • nonsense loss-of-function mutations
  • frameshift loss-of-function mutations
  • missense wildtype-like mutations

These different MC4R mutations are illustrated in the diagram below:  

 

Source: Lotta, L. A., Mokrosiński, J., de Oliveira, E. M., Li, C., Sharp, S. J., Luan, J. A., ... & Farooqi, I. S. (2019). Human gain-of-function MC4R variants show signaling bias and protect against obesity. Cell, 177(3), 597-607.

 

Rare vs common mutations

We can also classify mutations by how common they are in the population. There have been over 200 different mutations identified in the MC4R gene, and these vary widely in their frequency in the population.

When a mutation occurs in the MC4R gene, it creates a variant of the gene or ‘allele’. The term ‘variant allele frequency’ or ‘minor allele frequency’ denotes what percentage of the population carries at least one copy of the mutant gene variant.

A study of 61 MC4R mutations in a sample population of 0.5 million people from the UK Biobank database found that their variant allele frequency ranged from 0.0001% - 2%.

This means that some of the mutations are extremely rare, occurring in 1 in 10,000 people, whereas others, such as the V103L mutation that is present in 1 in 50 people, are relatively common.

The exact cut-off between “rare” and “common” is largely arbitrary and varies according to different studies and researchers. Many researchers have used a threshold of 1%, such that:

  • Rare MC4R mutations – present in < 1% of the population.
  • Common MC4R mutations – present in ≥ 1% of the population.

Your MC4R and obesity trait will also use this classification.

The MC4R mutations that cause a complete loss of function of the MC4 receptor and lead to MC4R deficiency and severe childhood-onset, monogenic obesity are all rare – and thought to be present in 0.2% of the population.

 

MC4R mutations analysed in this trait

Your MC4R and obesity trait looks at several different mutations in and around the MC4R gene. To simplify matters, we have ignored the distinction between missense, nonsense and frameshift mutations and sorted the MC4R mutations into “gain-of-function vs loss-of-function” and “rare vs common”.

Your results will therefore tell you whether you carry the following:

  • Rare loss-of-function mutations – that are present in <1% of the population and severely impair MC4 receptor signalling. These are linked to MC4R deficiency and monogenic obesity.
  • Rare gain-of-function mutations – that are present in <1% of the population and enhance MC4 receptor signalling. These are linked to a lower risk of common (i.e. not caused by mutations in a single gene) obesity (explained in the next section).
  • Common loss-of-function mutations – that are present in ≥1% of the population and impair MC4 receptor signalling. These do not cause MC4R deficiency but are associated with a higher risk of common obesity.
  • Common gain-of-function mutations - that are present in ≥1% of the population and enhance MC4 receptor signalling. These are linked to a lower risk of common obesity.

 

How do MC4R gene variants affect risk of obesity?

Variants of the MC4R gene have been linked to both an increased and decreased risk of obesity, depending on whether the variants are due to gain-of-function or loss-of-function mutations.

Broadly speaking, the following trend has been observed:

  • Gain-of-function mutations – enhance MC4R signalling and lower obesity risk.
  • Loss-of-function mutations – impair MC4R signalling and increase obesity risk.

On this note, a study of 0.5 million subjects from the UK Biobank database analysed 61 MC4R variants and explored how they were related to BMI, obesity, and severe obesity.

As illustrated in the Forest plot below, the researchers found that gain-of-function MC4R mutations were protective against obesity, being significantly associated with lower BMI and a reduced odds of obesity (BMI > 30kg/m2) and severe obesity (BMI > 40kg/m2).

By contrast, loss-of-function MC4R mutations were associated with higher BMI and a greater odds of obesity and severe obesity.

 

Source: Lotta, L. A., Mokrosiński, J., de Oliveira, E. M., Li, C., Sharp, S. J., Luan, J. A., ... & Farooqi, I. S. (2019). Human gain-of-function MC4R variants show signaling bias and protect against obesity. Cell, 177(3), 597-607.

 

These findings are understandable given the role of the MC4 receptor in the control of food intake and energy balance. As discussed in the ‘How does the melanocortin 4 receptor regulate food intake?’ section, MC4R neurons act to suppress appetite and food intake, while increasing energy expenditure. Gain-of-function mutations that enhance MC4R activity and MC4R signalling in the brain are therefore expected to strengthen suppression of food intake, which would be protective against obesity.

Conversely, loss-of-function mutations that reduce or abolish MC4R activity are expected to weaken the suppressive effect of MC4R neurons on food intake and appetite. These mutations are therefore predicted to promote food intake and increase obesity risk.  

Not all gain-of-function and loss-of-function mutations will have the same effect on the MC4 receptor and on obesity risk. Loss-of-function mutations that completely disrupt the function of the MC4 receptor, therefore leading to MC4R deficiency, are highly likely to cause severe, monogenic obesity.

Other loss-of-function mutations that have a less severe effect on MC4 receptor function, for example by mildly reducing expression of the receptor, are likely to slightly increase the risk of common obesity. The precise effect on obesity risk therefore varies according to the particular mutation and its impact on the MC4 receptor and other biological pathways.

Adopting scientific terms, we say that the MC4R mutations / gene variants vary in their penetrance. Penetrance is defined as the proportion of individuals who carry a specific gene variant and also express the trait related to that gene. (Trait, in this sense, refers to a set of observable characteristics).

With regards to the trait of obesity (BMI >30kg/m2): if a particular MC4R gene variant has 75% penetrance, then, of a group of 100 individuals carrying this variant, we would expect 75 of them to be obese.

 

Source: Chami, N., Preuss, M., Walker, R. W., Moscati, A., & Loos, R. J. (2020). The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population. PLoS medicine, 17(7), e1003196.

 

On this note, another study using data from UK Biobank database looked at the penetrance of various MC4R mutations and their effect on obesity risk. As shown in the graph above, there is considerable variation in both the penetrance and effect on obesity risk. The effect (OR) refers to the odds ratio of having obesity i.e. the odds of a person carrying the mutation to be obese compared to being normal weight.

 

Rare loss-of-function mutations and obesity risk

The rare loss-of-function mutations analysed in this trait typically cause a complete loss of MC4R function (MC4R deficiency) and are linked to severe, childhood-onset, monogenic obesity.

Generally speaking, these rare mutations are highly penetrant: - if you inherit the mutation, there’s a high probability you will be obese. In line with this, the aforementioned UK Biobank study found that of individuals carrying at least one of eleven highly penetrant loss-of-function MC4R mutations, 85% were overweight or obese.

Another large study of European subjects and their relatives found a penetrance of 40% in MC4R-deficient adults aged over 52 years, 60% in 18 to 52-year-old adults, and 79% in children. The fact that loss-of-function MC4R mutations are less penetrant with increasing age points to the role of environmental factors in the development of obesity. Similarly, it accords with clinical observations that some symptoms of MC4R deficiency, such as extreme hunger (hyperphagia), become less severe with age.

 

Rare gain-of-function mutations and obesity risk

As explained earlier, gain-of-function MC4R mutations have been linked to a reduced risk of common obesity. Rare gain-of-function mutations, defined as those occurring in less than 1% of the population, have been linked to up to a 50% decreased risk of obesity in some studies.

The exact impact of these gain-of-function mutations on obesity risk will depend on what and how many mutant alleles you inherit. In this respect, it has been shown that inheriting two gain-of-function alleles has a greater protective effect on obesity than inheriting one.

 

Common loss-of-function mutations and obesity risk

Common loss-of-function mutations tend to be missense mutations that reduce expression of the MC4 receptor rather than completely abolish its function. These common mutations, present in 1% or more of the population, have been linked to an increased risk of common obesity.

For example, carrying two copies of a common MC4R loss-of-function mutation, rs17782313, has been linked to a 64% higher risk of obesity. Of course, the exact impact of common loss-of-function mutations on obesity risk will depend on what and how many mutant alleles you inherit.  

 

Common gain-of-function mutations and obesity risk

Common gain-of-function mutations, which are present in 1% or more of the population, have been linked to a reduced risk of obesity.

In an analysis of UK Biobank data, one common gain-of-function mutation, V103I, present in 2% of the study population, was linked to a 23% and 31% lower risk of obesity (BMI > 30kg/m2) and severe obesity (BMI > 40kg/m2), respectively.  

Again, the number and type of common gain-of-function alleles inherited will influence overall obesity risk.

 

Impact of other genetic factors

It is important to note, for this and indeed every other FitnessGenes trait, that individual genes do not work in isolation and that the impact of single gene variants on an outcome (e.g. obesity risk) will be strongly affected by other genes.

Reflecting this sentiment, one study looked at normal-weight carriers of rare loss-of-function MC4R mutations. These subjects had seemingly “defied” their MC4R variants linked to severe obesity and had a healthy BMI (18-25 kg/m2).

The researchers compiled the data on 351,597 other (i.e. not MC4R) genetic variants associated with BMI to calculate a polygenic risk score for obesity. Higher polygenic risk scores suggest someone has multiple gene variants that contribute to higher obesity risk. Conversely, lower polygenic risk scores suggest a person has fewer of these risk variants and/or more gene variants associated with a lower risk of obesity.

 

Source: Chami, N., Preuss, M., Walker, R. W., Moscati, A., & Loos, R. J. (2020). The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population. PLoS medicine, 17(7), e1003196.

 

As demonstrated in the graph above, the researchers found that normal weight subjects who carried MC4R mutations had significantly lower polygenic risk scores for obesity (PRSBMI). Compared to obese MC4R mutation carriers, normal weight carriers had polygenic risk scores that were more than 1 standard deviation score lower.

This finding suggests that, although loss-of-function MC4R mutations can have a significant impact on obesity risk, this can be counteracted or offset by other gene variants in a person’s genome.

Although not explicitly tested in this study, it is also likely to be the case that the protective effect of gain-of-function MC4R mutations on obesity risk can also be offset by other gene variants that promote obesity.  

 

Your MC4R and obesity trait

Your MC4R and obesity trait looks at several mutations within the MC4R gene, including both rare and common loss-of-function and gain-of-function mutations that impair and enhance MC4 receptor signalling, respectively.

Depending on what variants you carry, you will be classified into one of the following groups:

  • Increased MC4R function – you carry rare gain-of-function mutations associated with lower obesity risk.
  • Reduced MC4R function – you carry rare loss-of-function mutations associated with MC4R deficiency and severe, childhood-onset, monogenic obesity.
  • Complex MC4R function – you carry both rare loss-of-function and gain-of-function mutations. The combined impact of these mutations on obesity risk is difficult to predict.
  • Moderately increased MC4R function – you carry common gain-of-function mutations associated with a lower obesity risk.
  • Moderately reduced MC4R function – you carry common loss-of-function mutations associated with an increased obesity risk.
  • Mildly reduced MC4R function – you carry common loss-of-function mutations associated with slightly higher obesity risk (i.e. you carry fewer risk alleles than the “moderately reduced MC4R function” group).
  • Complex MC4R function – you carry both common loss-of-function and gain-of-function mutations. The combined impact of these mutations on obesity risk is difficult to predict.
  • Average MC4R function – you do not carry either loss-of-function or gain-of-function mutations (of those mutations tested in this trait).

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