importance of receptors biology essay

9.1 Signaling Molecules and Cellular Receptors

Learning objectives.

By the end of this section, you will be able to do the following:

• Describe four types of signaling mechanisms found in multicellular organisms
• Compare internal receptors with cell-surface receptors
• Recognize the relationship between a ligand’s structure and its mechanism of action

There are two kinds of communication in the world of living cells. Communication between cells is called intercellular signaling , and communication within a cell is called intracellular signaling . An easy way to remember the distinction is by understanding the Latin origin of the prefixes: inter- means "between" (for example, intersecting lines are those that cross each other) and intra- means "inside" (as in intravenous).

Chemical signals are released by signaling cells in the form of small, usually volatile or soluble molecules called ligands. A ligand is a molecule that binds another specific molecule, in some cases, delivering a signal in the process. Ligands can thus be thought of as signaling molecules. Ligands interact with proteins in target cells , which are cells that are affected by chemical signals; these proteins are also called receptors . Ligands and receptors exist in several varieties; however, a specific ligand will have a specific receptor that typically binds only that ligand.

Forms of Signaling

There are four categories of chemical signaling found in multicellular organisms: paracrine signaling, endocrine signaling, autocrine signaling, and direct signaling across gap junctions ( Figure 9.2 ). The main difference between the different categories of signaling is the distance that the signal travels through the organism to reach the target cell. We should note here that not all cells are affected by the same signals.

Paracrine Signaling

Signals that act locally between cells that are close together are called paracrine signals . Paracrine signals move by diffusion through the extracellular matrix. These types of signals usually elicit quick responses that last only a short period of time. In order to keep the response localized, paracrine ligand molecules are normally quickly degraded by enzymes or removed by neighboring cells. Removing the signals will reestablish the concentration gradient for the signal, allowing them to quickly diffuse through the intracellular space if released again.

One example of paracrine signaling is the transfer of signals across synapses between nerve cells. A nerve cell consists of a cell body, several short, branched extensions called dendrites that receive stimuli, and a long extension called an axon, which transmits signals to other nerve cells or muscle cells. The junction between nerve cells where signal transmission occurs is called a synapse. A synaptic signal is a chemical signal that travels between nerve cells. Signals within the nerve cells are propagated by fast-moving electrical impulses. When these impulses reach the end of the axon, the signal continues on to a dendrite of the next cell by the release of chemical ligands called neurotransmitters from the presynaptic cell (the cell emitting the signal). The neurotransmitters are transported across the very small distances (20–40 nanometers) between nerve cells, which are called chemical synapses ( Figure 9.3 ). The small distance between nerve cells allows the signal to travel quickly; this enables an immediate response, such as, "Take your hand off the stove!"

When the neurotransmitter binds the receptor on the surface of the postsynaptic cell, the electrochemical potential of the target cell changes, and the next electrical impulse is launched. The neurotransmitters that are released into the chemical synapse are degraded quickly or get reabsorbed by the presynaptic cell so that the recipient nerve cell can recover quickly and be prepared to respond rapidly to the next synaptic signal.

Endocrine Signaling

Signals from distant cells are called endocrine signals , and they originate from endocrine cells . (In the body, many endocrine cells are located in endocrine glands, such as the thyroid gland, the hypothalamus, and the pituitary gland.) These types of signals usually produce a slower response but have a longer-lasting effect. The ligands released in endocrine signaling are called hormones, signaling molecules that are produced in one part of the body but affect other body regions some distance away.

Hormones travel the large distances between endocrine cells and their target cells via the bloodstream, which is a relatively slow way to move throughout the body. Because of their form of transport, hormones become diluted and are present in low concentrations when they act on their target cells. This is different from paracrine signaling, in which local concentrations of ligands can be very high.

Autocrine Signaling

Autocrine signals are produced by signaling cells that can also bind to the ligand that is released. This means the signaling cell and the target cell can be the same or a similar cell (the prefix auto- means self, a reminder that the signaling cell sends a signal to itself). This type of signaling often occurs during the early development of an organism to ensure that cells develop into the correct tissues and take on the proper function. Autocrine signaling also regulates pain sensation and inflammatory responses. Further, if a cell is infected with a virus, the cell can signal itself to undergo programmed cell death, killing the virus in the process. In some cases, neighboring cells of the same type are also influenced by the released ligand. In embryological development, this process of stimulating a group of neighboring cells may help to direct the differentiation of identical cells into the same cell type, thus ensuring the proper developmental outcome.

Direct Signaling Across Gap Junctions

Gap junctions in animals and plasmodesmata in plants are connections between the plasma membranes of neighboring cells. These fluid-filled channels allow small signaling molecules, called intracellular mediators , to diffuse between the two cells. Small molecules or ions, such as calcium ions (Ca 2+ ), are able to move between cells, but large molecules like proteins and DNA cannot fit through the channels. The specificity of the channels ensures that the cells remain independent but can quickly and easily transmit signals. The transfer of signaling molecules communicates the current state of the cell that is directly next to the target cell; this allows a group of cells to coordinate their response to a signal that only one of them may have received. In plants, plasmodesmata are ubiquitous, making the entire plant into a giant communication network.

Types of Receptors

Receptors are protein molecules in the target cell or on its surface that bind ligand. There are two types of receptors, internal receptors and cell-surface receptors.

Internal receptors

Internal receptors , also known as intracellular or cytoplasmic receptors, are found in the cytoplasm of the cell and respond to hydrophobic ligand molecules that are able to travel across the plasma membrane. Once inside the cell, many of these molecules bind to proteins that act as regulators of mRNA synthesis (transcription) to mediate gene expression. Gene expression is the cellular process of transforming the information in a cell's DNA into a sequence of amino acids, which ultimately forms a protein. When the ligand binds to the internal receptor, a conformational change is triggered that exposes a DNA-binding site on the protein. The ligand-receptor complex moves into the nucleus, then binds to specific regulatory regions of the chromosomal DNA and promotes the initiation of transcription ( Figure 9.4 ). Transcription is the process of copying the information in a cell's DNA into a special form of RNA called messenger RNA (mRNA); the cell uses information in the mRNA (which moves out into the cytoplasm and associates with ribosomes) to link specific amino acids in the correct order, producing a protein. Internal receptors can directly influence gene expression without having to pass the signal on to other receptors or messengers.

Cell-Surface Receptors

Cell-surface receptors , also known as transmembrane receptors, are cell surface, membrane-anchored (integral) proteins that bind to external ligand molecules. This type of receptor spans the plasma membrane and performs signal transduction, through which an extracellular signal is converted into an intracellular signal. Ligands that interact with cell-surface receptors do not have to enter the cell that they affect. Cell-surface receptors are also called cell-specific proteins or markers because they are specific to individual cell types.

Because cell-surface receptor proteins are fundamental to normal cell functioning, it should come as no surprise that a malfunction in any one of these proteins could have severe consequences. Errors in the protein structures of certain receptor molecules have been shown to play a role in hypertension (high blood pressure), asthma, heart disease, and cancer.

Each cell-surface receptor has three main components: an external ligand-binding domain called the extracellular domain , a hydrophobic membrane-spanning region called a transmembrane domain, and an intracellular domain inside the cell. The size and extent of each of these domains vary widely, depending on the type of receptor.

Evolution Connection

How viruses recognize a host.

Unlike living cells, many viruses do not have a plasma membrane or any of the structures necessary to sustain metabolic life. Some viruses are simply composed of an inert protein shell enclosing DNA or RNA. To reproduce, viruses must invade a living cell, which serves as a host, and then take over the hosts cellular apparatus. But how does a virus recognize its host?

Viruses often bind to cell-surface receptors on the host cell. For example, the virus that causes human influenza (flu) binds specifically to receptors on membranes of cells of the respiratory system. Chemical differences in the cell-surface receptors among hosts mean that a virus that infects a specific species (for example, humans) often cannot infect another species (for example, chickens).

However, viruses have very small amounts of DNA or RNA compared to humans, and, as a result, viral reproduction can occur rapidly. Viral reproduction invariably produces errors that can lead to changes in newly produced viruses; these changes mean that the viral proteins that interact with cell-surface receptors may evolve in such a way that they can bind to receptors in a new host. Such changes happen randomly and quite often in the reproductive cycle of a virus, but the changes only matter if a virus with new binding properties comes into contact with a suitable host. In the case of influenza, this situation can occur in settings where animals and people are in close contact, such as poultry and swine farms. 1 Once a virus jumps the former "species barrier" to a new host, it can spread quickly. Scientists watch newly appearing viruses (called emerging viruses) closely in the hope that such monitoring can reduce the likelihood of global viral epidemics.

Cell-surface receptors are involved in most of the signaling in multicellular organisms. There are three general categories of cell-surface receptors: ion channel-linked receptors, G-protein-linked receptors, and enzyme-linked receptors.

Ion channel-linked receptors bind a ligand and open a channel through the membrane that allows specific ions to pass through. To form a channel, this type of cell-surface receptor has an extensive membrane-spanning region. In order to interact with the double layer of phospholipid fatty acid tails that form the center of the plasma membrane, many of the amino acids in the membrane-spanning region are hydrophobic in nature. Conversely, the amino acids that line the inside of the channel are hydrophilic to allow for the passage of water or ions. When a ligand binds to the extracellular region of the channel, there is a conformational change in the protein's structure that allows ions such as sodium, calcium, magnesium, and hydrogen to pass through ( Figure 9.5 ).

G-protein-linked receptors bind a ligand and activate a membrane protein called a G-protein. The activated G-protein then interacts with either an ion channel or an enzyme in the membrane ( Figure 9.6 ). All G-protein-linked receptors have seven transmembrane domains, but each receptor has its own specific extracellular domain and G-protein-binding site.

Cell signaling using G-protein-linked receptors occurs as a cyclic series of events. Before the ligand binds, the inactive G-protein can bind to a site on the receptor specific for its binding. Once the ligand binds to the receptor, the resulting change in shape activates the G-protein, which releases guanosine diphosphate (GDP) and picks up guanosine 3-phosphate (GTP). The subunits of the G-protein then split into the α subunit and the βγ subunit. One or both of these G-protein fragments may be able to activate other proteins as a result. After awhile, the GTP on the active α subunit of the G-protein is hydrolyzed to GDP and the βγ subunit is deactivated. The subunits reassociate to form the inactive G-protein and the cycle begins anew.

G-protein-linked receptors have been extensively studied and much has been learned about their roles in maintaining health. Bacteria that are pathogenic to humans can release poisons that interrupt specific G-protein-linked receptor function, leading to illnesses such as pertussis, botulism, and cholera. In cholera ( Figure 9.7 ), for example, the water-borne bacterium Vibrio cholerae produces a toxin, choleragen, that binds to cells lining the small intestine. The toxin then enters these intestinal cells, where it modifies a G-protein that controls the opening of a chloride channel and causes it to remain continuously active, resulting in large losses of fluids from the body and potentially fatal dehydration as a result.

Enzyme-linked receptors are cell-surface receptors with intracellular domains that are associated with an enzyme. In some cases, the intracellular domain of the receptor itself is an enzyme. Other enzyme-linked receptors have a small intracellular domain that interacts directly with an enzyme. The enzyme-linked receptors normally have large extracellular and intracellular domains, but the membrane-spanning region consists of a single alpha-helical region of the peptide strand. When a ligand binds to the extracellular domain, a signal is transferred through the membrane, activating the enzyme. Activation of the enzyme sets off a chain of events within the cell that eventually leads to a response. One example of this type of enzyme-linked receptor is the tyrosine kinase receptor ( Figure 9.8 ). A kinase is an enzyme that transfers phosphate groups from ATP to another protein. The tyrosine kinase receptor transfers phosphate groups to tyrosine molecules (tyrosine residues). First, signaling molecules bind to the extracellular domain of two nearby tyrosine kinase receptors. The two neighboring receptors then bond together, or dimerize. Phosphates are then added to tyrosine residues on the intracellular domain of the receptors (phosphorylation). The phosphorylated residues can then transmit the signal to the next messenger within the cytoplasm.

Visual Connection

HER2 is a receptor tyrosine kinase. In 30 percent of human breast cancers, HER2 is permanently activated, resulting in unregulated cell division. Lapatinib, a drug used to treat breast cancer, inhibits HER2 receptor tyrosine kinase autophosphorylation (the process by which the receptor adds phosphates onto itself), thus reducing tumor growth by 50 percent. Besides autophosphorylation, which of the following steps would be inhibited by Lapatinib?

• Signaling molecule binding, dimerization, and the downstream cellular response
• Dimerization, and the downstream cellular response
• The downstream cellular response
• Phosphatase activity, dimerization, and the downsteam cellular response

Signaling Molecules

Produced by signaling cells and the subsequent binding to receptors in target cells, ligands act as chemical signals that travel to the target cells to coordinate responses. The types of molecules that serve as ligands are incredibly varied and range from small proteins to small ions like calcium (Ca 2+ ).

Small Hydrophobic Ligands

Small hydrophobic ligands can directly diffuse through the plasma membrane and interact with internal receptors. Important members of this class of ligands are the steroid hormones. Steroids are lipids that have a hydrocarbon skeleton with four fused rings; different steroids have different functional groups attached to the carbon skeleton. Steroid hormones include the female sex hormone, estradiol, which is a type of estrogen; the male sex hormone, testosterone; and cholesterol, which is an important structural component of biological membranes and a precursor of steroid hormones ( Figure 9.9 ). Other hydrophobic hormones include thyroid hormones and vitamin D. In order to be soluble in blood, hydrophobic ligands must bind to carrier proteins while they are being transported through the bloodstream.

Water-Soluble Ligands

Water-soluble ligands are polar and, therefore, cannot pass through the plasma membrane unaided; sometimes, they are too large to pass through the membrane at all. Instead, most water-soluble ligands bind to the extracellular domain of cell-surface receptors. This group of ligands is quite diverse and includes small molecules, peptides, and proteins.

Other Ligands

Nitric oxide (NO) is a gas that also acts as a ligand. It is able to diffuse directly across the plasma membrane, and one of its roles is to interact with receptors in smooth muscle and induce relaxation of the tissue. NO has a very short half-life and, therefore, only functions over short distances. Nitroglycerin, a treatment for heart disease, acts by triggering the release of NO, which causes blood vessels to dilate (expand), thus restoring blood flow to the heart. NO has become better known recently because the pathway that it affects is targeted by prescription medications for erectile dysfunction, such as Viagra (erection involves dilated blood vessels).

• 1 A. B. Sigalov, The School of Nature. IV. Learning from Viruses, Self/Nonself 1, no. 4 (2010): 282-298. Y. Cao, X. Koh, L. Dong, X. Du, A. Wu, X. Ding, H. Deng, Y. Shu, J. Chen, T. Jiang, Rapid Estimation of Binding Activity of Influenza Virus Hemagglutinin to Human and Avian Receptors, PLoS One 6, no. 4 (2011): e18664.

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Receptor – Definition, Structure, Types, Mechanism, Importance, Examples

What is receptor, definition of receptor.

A receptor is a specialized protein molecule that selectively recognizes and binds to specific ligands, leading to cellular responses or signal transduction within the cell.

Types of Receptors

In essence, receptors are the molecular gatekeepers of cells, facilitating precise responses to diverse external and internal signals, ensuring the cell’s proper function and survival.

Structure of Receptors

To study these receptors, they can be isolated from cell membranes using intricate extraction techniques involving solvents, detergents, and affinity purification. Biophysical methodologies, including X-ray crystallography, NMR, circular dichroism, and dual polarisation interferometry, offer insights into receptor structures and functions. Additionally, computer simulations provide a dynamic perspective on receptor mechanisms.

Binding and activation

Here, L represents the ligand, R denotes the receptor, and LR signifies the ligand-receptor complex.

It’s imperative to understand that the terms agonism and antagonism pertain solely to ligand-receptor interactions and not their broader biological effects.

Constitutive Activity : Some receptors exhibit constitutive activity, meaning they can produce a biological response even in the absence of a bound ligand. Inverse agonists can block this activity. For instance, the GABAA receptor, which displays constitutive activity, can be inhibited by beta carboline, an inverse agonist. Mutations leading to heightened constitutive activity can result in diseases, such as precocious puberty or hyperthyroidism, due to altered hormone receptor functions.

Theories of drug-receptor interaction

Regulation of receptor.

In the intricate cellular environment, the regulation of receptors plays a pivotal role in ensuring appropriate cellular responses to external stimuli. This regulation is multifaceted and encompasses several mechanisms:

In summary, the regulation of receptor dynamics is a complex interplay of various mechanisms, ensuring that cells maintain a balanced and appropriate response to external cues. This regulation is crucial for maintaining cellular homeostasis and ensuring precise cellular signaling.

Examples of ligands

2. Enzyme-Linked Receptors : Enzyme-linked receptors encompass a broad category, including Receptor Tyrosine Kinases (RTKs). Notable examples include:

Advantages of Receptor

Receptors are specialized protein molecules that play a central role in cellular communication and response to external and internal stimuli. Their presence and function confer several advantages to organisms:

Disadvantages of Receptor

While receptors play crucial roles in cellular communication and response, they also have inherent limitations and can be associated with certain disadvantages:

In summary, while receptors are fundamental to many physiological processes, their inherent characteristics and the complexities of their regulation can also lead to vulnerabilities, challenges, and potential disadvantages in certain contexts.

Importance of Receptor

Receptors play a pivotal role in the physiology and biochemistry of organisms, acting as the primary mediators of cellular responses to external and internal stimuli. Their significance spans a wide range of biological processes:

Examples of a Receptor

Receptors play a pivotal role in various physiological processes, serving as the primary interface for cellular responses to external stimuli. Here are two illustrative examples of receptors and their intricate functions:

Practice Quiz

Which of the following best describes a receptor? a) A type of enzyme b) A protein that binds to a specific ligand c) A type of neurotransmitter d) A type of hormone

Which receptor type is primarily responsible for fast synaptic transmission in the nervous system? a) G-protein coupled receptors b) Enzyme-linked receptors c) Ligand-gated ion channels d) Intracellular receptors

Which of the following is NOT a function of receptors? a) Facilitate cell-to-cell communication b) Initiate cellular responses c) Synthesize new ligands d) Transduce external signals into cellular actions

Which receptors alter cell function by activating intracellular signal transduction pathways? a) Intracellular receptors b) Ligand-gated ion channels c) G-protein coupled receptors d) Enzyme-linked receptors

Which type of receptor is primarily found inside the cell and binds to lipid-soluble ligands? a) G-protein coupled receptors b) Enzyme-linked receptors c) Ligand-gated ion channels d) Intracellular receptors

Which of the following ligands binds to the GABAA receptor? a) Glutamate b) Dopamine c) GABA d) Serotonin

Which receptor type has intrinsic enzymatic activity? a) Ligand-gated ion channels b) G-protein coupled receptors c) Enzyme-linked receptors d) Intracellular receptors

Which process describes the reduction in the number of receptors on the cell surface after prolonged exposure to high levels of a ligand? a) Desensitization b) Downregulation c) Cross-reactivity d) Overcompensation

Which of the following is a characteristic of spare receptors? a) They are always active b) They are only found in the brain c) They allow maximal cellular response even with low receptor occupancy d) They are synthetic receptors

Which receptor type is targeted by the drug Ketamine? a) Dopamine receptors b) GABAA receptors c) NMDA receptors d) Serotonin receptors

What is a receptor?

A receptor is a protein molecule that receives and responds to specific signals or stimuli, often in the form of molecules called ligands.

How do receptors work?

What are the different types of receptors, why are receptors important in pharmacology.

Receptors are the primary targets for many drugs. By binding to and modulating the activity of receptors, drugs can produce therapeutic effects or cause side effects.

What is receptor specificity?

How do cells regulate the number of receptors on their surface, what is the difference between an agonist and an antagonist.

An agonist is a molecule that activates a receptor, while an antagonist is a molecule that blocks or inhibits receptor activation.

What are spare receptors?

Can receptors be found inside cells, why do some drugs lose their effectiveness over time.

One reason is receptor downregulation or desensitization, where prolonged exposure to a drug leads to a decrease in the number or sensitivity of its target receptors.

Related Biology Study Notes

Classification of protein on the basis of structure, composition, functions, proteins – structure, properties, type, denaturation, functions, protein synthesis inhibitors – definition, mechanism, examples, four types of protein structure with diagram – primary, secondary, tertiary and quaternary, g protein coupled receptors – structure, functions, and mechanism, glycoprotein – definition, structure, functions, examples, active transport – definition, types, process, functions, examples, protein purification methods, latest questions, leave a comment cancel reply.

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Receptors have two important features:

Specificity

• Temperature.
• This means that a receptor that responds to light will not respond to temperature or pressure.

Generator potentials

• Receptors connect with sensory neurones. When stimulated, the receptor creates a generator potential in the sensory neurone.
• An example where stimulation of the receptor creates a generator potential is in the Pacinian corpuscle.

The Pacinian Corpuscle

The Pacinian corpuscle is a mechanoreceptor found in the skin. Mechanoreceptors respond to changes in pressure to establish a generator potential. The receptor reacts in the following way:

Resting potential

• The Pacinian corpuscle consists of concentric rings of connective tissue that surround a sensory neurone.
• When the corpuscle is not being stimulated it is at resting state.
• At resting state, the charge inside the neurone is more negative than outside (-70mV). This is because there are more Na + ions outside the neurone than inside.
• A difference in charge across the cell membrane is called the potential difference.

Stimulation of the receptor

• When pressure is applied to the Pacinian corpuscle, the rings of connective tissue apply pressure on the sensory neurone.
• The sensory neurone has stretch mediated Na + channels, these channels normally restrict the movement of Na + ions.
• Applied pressure causes the stretch-mediated Na + channels to open.

Generator potential

• Na + ions flood into the sensory neurone through the open Na + channels.
• There are now more Na + ions inside the neurone than outside.
• The charge inside the neurone becomes more positive than outside, so the potential difference has changed.
• The generator potential has been established.

Action potential

• If the generator potential reaches the threshold level (about -50mV) then an action potential is produced in the sensory neurone.

1 Biological Molecules

1.1 Monomers & Polymers

1.1.1 Monomers & Polymers

1.1.2 Condensation & Hydrolysis Reactions

1.2 Carbohydrates

1.2.1 Structure of Carbohydrates

1.2.2 Types of Polysaccharides

1.2.3 End of Topic Test - Monomers, Polymers and Carbs

1.2.4 Exam-Style Question - Carbohydrates

1.2.5 A-A* (AO3/4) - Carbohydrates

1.3.1 Triglycerides & Phospholipids

1.3.2 Types of Fatty Acids

1.3.3 Testing for Lipids

1.3.4 Exam-Style Question - Fats

1.3.5 A-A* (AO3/4) - Lipids

1.4 Proteins

1.4.1 The Peptide Chain

1.4.2 Investigating Proteins

1.4.3 Primary & Secondary Protein Structure

1.4.4 Tertiary & Quaternary Protein Structure

1.4.5 Enzymes

1.4.6 Factors Affecting Enzyme Activity

1.4.7 Enzyme-Controlled Reactions

1.4.8 End of Topic Test - Lipids & Proteins

1.4.9 A-A* (AO3/4) - Enzymes

1.4.10 A-A* (AO3/4) - Proteins

1.5 Nucleic Acids

1.5.1 DNA & RNA

1.5.2 Nucleotides

1.5.3 Polynucleotides

1.5.4 DNA Replication

1.5.5 Exam-Style Question - Nucleic Acids

1.5.6 A-A* (AO3/4) - Nucleic Acids

1.6.1 Structure of ATP

1.6.2 Hydrolysis of ATP

1.6.3 Resynthesis of ATP

1.6.4 End of Topic Test - Nucleic Acids & ATP

1.7.1 Importance of Water

1.7.2 Structure of Water

1.7.3 Properties of Water

1.7.4 A-A* (AO3/4) - Water

1.8 Inorganic Ions

1.8.1 Inorganic Ions

1.8.2 End of Topic Test - Water & Inorganic Ions

2.1 Cell Structure

2.1.1 Introduction to Cells

2.1.2 Eukaryotic Cells & Organelles

2.1.3 Eukaryotic Cells & Organelles 2

2.1.4 Prokaryotes

2.1.5 A-A* (AO3/4) - Organelles

2.1.6 Methods of Studying Cells

2.1.7 Microscopes

2.1.8 End of Topic Test - Cell Structure

2.1.9 Exam-Style Question - Cells

2.1.10 A-A* (AO3/4) - Cells

2.2 Mitosis & Cancer

2.2.1 Mitosis

2.2.2 Stages of Mitosis

2.2.3 Investigating Mitosis

2.2.4 Cancer

2.2.5 A-A* (AO3/4) - The Cell Cycle

2.3 Transport Across Cell Membrane

2.3.1 Cell Membrane Structure

2.3.2 A-A* (AO3/4) - Membrane Structure

2.3.3 Diffusion

2.3.4 Osmosis

2.3.5 Active Transport

2.3.6 End of Topic Test - Mitosis, Cancer & Transport

2.3.7 Exam-Style Question - Membranes

2.3.8 A-A* (AO3/4) - Membranes & Transport

2.3.9 A-A*- Mitosis, Cancer & Transport

2.4 Cell Recognition & the Immune System

2.4.1 Immune System

2.4.2 Phagocytosis

2.4.3 T Lymphocytes

2.4.4 B Lymphocytes

2.4.5 Antibodies

2.4.6 Primary & Secondary Response

2.4.7 Vaccines

2.4.9 Ethical Issues

2.4.10 End of Topic Test - Immune System

2.4.11 Exam-Style Question - Immune System

2.4.12 A-A* (AO3/4) - Immune System

3 Substance Exchange

3.1 Surface Area to Volume Ratio

3.1.1 Size & Surface Area

3.1.2 A-A* (AO3/4) - Cell Size

3.2 Gas Exchange

3.2.1 Single-Celled Organisms

3.2.2 Multicellular Organisms

3.2.3 Control of Water Loss

3.2.4 Human Gas Exchange

3.2.5 Ventilation

3.2.6 Dissection

3.2.7 Measuring Gas Exchange

3.2.8 Lung Disease

3.2.9 Lung Disease Data

3.2.10 End of Topic Test - Gas Exchange

3.2.11 A-A* (AO3/4) - Gas Exchange

3.3 Digestion & Absorption

3.3.1 Overview of Digestion

3.3.2 Digestion in Mammals

3.3.3 Absorption

3.3.4 End of Topic Test - Substance Exchange & Digestion

3.3.5 A-A* (AO3/4) - Substance Ex & Digestion

3.4 Mass Transport

3.4.1 Haemoglobin

3.4.2 Oxygen Transport

3.4.3 The Circulatory System

3.4.4 The Heart

3.4.5 Blood Vessels

3.4.6 Cardiovascular Disease

3.4.7 Heart Dissection

3.4.8 Xylem

3.4.9 Phloem

3.4.10 Investigating Plant Transport

3.4.11 End of Topic Test - Mass Transport

3.4.12 A-A* (AO3/4) - Mass Transport

4 Genetic Information & Variation

4.1 DNA, Genes & Chromosomes

4.1.2 Genes

4.1.3 Non-Coding Genes

4.1.4 The Genetic Code

4.1.5 A-A* (AO3/4) - DNA

4.2 DNA & Protein Synthesis

4.2.1 Protein Synthesis

4.2.2 Transcription & Translation

4.2.3 End of Topic Test - DNA, Genes & Protein Synthesis

4.2.4 Exam-Style Question - Protein Synthesis

4.2.5 A-A* (AO3/4) - Coronavirus Translation

4.2.6 A-A* (AO3/4) - Transcription

4.2.7 A-A* (AO3/4) - Translation

4.3 Mutations & Meiosis

4.3.1 Mutations

4.3.2 Meiosis

4.3.3 A-A* (AO3/4) - Meiosis

4.3.4 Meiosis vs Mitosis

4.3.5 End of Topic Test - Mutations, Meiosis

4.3.6 A-A* (AO3/4) - DNA,Genes, CellDiv & ProtSynth

4.4 Genetic Diversity & Adaptation

4.4.1 Genetic Diversity

4.4.2 Natural Selection

4.4.3 A-A* (AO3/4) - Natural Selection

4.4.4 Adaptations

4.4.5 Investigating Natural Selection

4.4.6 End of Topic Test - Genetic Diversity & Adaptation

4.4.7 A-A* (AO3/4) - Genetic Diversity & Adaptation

4.5 Species & Taxonomy

4.5.1 Courtship Behaviour

4.5.2 Phylogeny

4.5.3 Classification

4.5.4 DNA Technology

4.5.5 A-A* (AO3/4) - Species & Taxonomy

4.6 Biodiversity Within a Community

4.6.1 Biodiversity

4.6.2 Index of diversity

4.6.3 Agriculture

4.6.4 End of Topic Test - Species,Taxonomy& Biodiversity

4.6.5 A-A* (AO3/4) - Species,Taxon&Biodiversity

4.7 Investigating Diversity

4.7.1 Genetic Diversity

4.7.2 Quantitative Investigation

5 Energy Transfers (A2 only)

5.1 Photosynthesis

5.1.1 Overview of Photosynthesis

5.1.2 Photoionisation of Chlorophyll

5.1.3 Production of ATP & Reduced NADP

5.1.4 Cyclic Photophosphorylation

5.1.5 Light-Independent Reaction

5.1.6 A-A* (AO3/4) - Photosynthesis Reactions

5.1.7 Limiting Factors

5.1.8 Photosynthesis Experiments

5.1.9 End of Topic Test - Photosynthesis

5.1.10 A-A* (AO3/4) - Photosynthesis

5.2 Respiration

5.2.1 Overview of Respiration

5.2.2 Anaerobic Respiration

5.2.3 A-A* (AO3/4) - Anaerobic Respiration

5.2.4 The Link Reaction

5.2.5 The Krebs Cycle

5.2.6 Oxidative Phosphorylation

5.2.7 Respiration Experiments

5.2.8 End of Topic Test - Respiration

5.2.9 A-A* (AO3/4) - Respiration

5.3 Energy & Ecosystems

5.3.1 Biomass

5.3.2 Production & Productivity

5.3.3 Agricultural Practices

5.4 Nutrient Cycles

5.4.1 Nitrogen Cycle

5.4.2 Phosphorous Cycle

5.4.3 Fertilisers & Eutrophication

5.4.4 End of Topic Test - Nutrient Cycles

5.4.5 A-A* (AO3/4) - Energy,Ecosystems&NutrientCycles

6 Responding to Change (A2 only)

6.1 Nervous Communication

6.1.1 Survival

6.1.2 Plant Responses

6.1.3 Animal Responses

6.1.4 Reflexes

6.1.5 End of Topic Test - Reflexes, Responses & Survival

6.1.6 Receptors

6.1.7 The Human Retina

6.1.8 Control of Heart Rate

6.1.9 End of Topic Test - Receptors, Retina & Heart Rate

6.2 Nervous Coordination

6.2.1 Neurones

6.2.2 Action Potentials

6.2.3 Speed of Transmission

6.2.4 End of Topic Test - Neurones & Action Potentials

6.2.5 Synapses

6.2.6 Types of Synapse

6.2.7 Medical Application

6.2.8 End of Topic Test - Synapses

6.2.9 A-A* (AO3/4) - Nervous Comm&Coord

6.3 Muscle Contraction

6.3.1 Skeletal Muscle

6.3.2 Sliding Filament Theory

6.3.3 Contraction

6.3.4 Slow & Fast Twitch Fibres

6.3.5 End of Topic Test - Muscles

6.3.6 A-A* (AO3/4) - Muscle Contraction

6.4 Homeostasis

6.4.1 Overview of Homeostasis

6.4.2 Blood Glucose Concentration

6.4.3 Controlling Blood Glucose Concentration

6.4.4 End of Topic Test - Blood Glucose

6.4.5 Primary & Secondary Messengers

6.4.6 Diabetes Mellitus

6.4.7 Measuring Glucose Concentration

6.4.8 Osmoregulation

6.4.9 Controlling Blood Water Potential

6.4.11 End of Topic Test - Diabetes & Osmoregulation

6.4.12 A-A* (AO3/4) - Homeostasis

7 Genetics & Ecosystems (A2 only)

7.1 Genetics

7.1.1 Key Terms in Genetics

7.1.2 Inheritance

7.1.3 Linkage

7.1.4 Multiple Alleles & Epistasis

7.1.5 Chi-Squared Test

7.1.6 End of Topic Test - Genetics

7.1.7 A-A* (AO3/4) - Genetics

7.2 Populations

7.2.1 Populations

7.2.2 Hardy-Weinberg Principle

7.3 Evolution

7.3.1 Variation

7.3.2 Natural Selection & Evolution

7.3.3 End of Topic Test - Populations & Evolution

7.3.4 Types of Selection

7.3.5 Types of Selection Summary

7.3.6 Overview of Speciation

7.3.7 Causes of Speciation

7.3.8 Diversity

7.3.9 End of Topic Test - Selection & Speciation

7.3.10 A-A* (AO3/4) - Populations & Evolution

7.4 Populations in Ecosystems

7.4.1 Overview of Ecosystems

7.4.2 Niche

7.4.3 Population Size

7.4.4 Investigating Population Size

7.4.5 End of Topic Test - Ecosystems & Population Size

7.4.6 Succession

7.4.7 Conservation

7.4.8 End of Topic Test - Succession & Conservation

7.4.9 A-A* (AO3/4) - Ecosystems

8 The Control of Gene Expression (A2 only)

8.1 Mutation

8.1.1 Mutations

8.1.2 Effects of Mutations

8.1.3 Causes of Mutations

8.2 Gene Expression

8.2.1 Stem Cells

8.2.2 Stem Cells in Disease

8.2.3 End of Topic Test - Mutation & Gene Epression

8.2.4 A-A* (AO3/4) - Mutation & Stem Cells

8.2.5 Regulating Transcription

8.2.6 Epigenetics

8.2.7 Epigenetics & Disease

8.2.8 Regulating Translation

8.2.9 Experimental Data

8.2.10 End of Topic Test - Transcription & Translation

8.2.11 Tumours

8.2.12 Correlations & Causes

8.2.13 Prevention & Treatment

8.2.14 End of Topic Test - Cancer

8.2.15 A-A* (AO3/4) - Gene Expression & Cancer

8.3 Genome Projects

8.3.1 Using Genome Projects

8.4 Gene Technology

8.4.1 Recombinant DNA

8.4.2 Producing Fragments

8.4.3 Amplification

8.4.4 End of Topic Test - Genome Project & Amplification

8.4.5 Using Recombinant DNA

8.4.6 Medical Diagnosis

8.4.7 Genetic Fingerprinting

8.4.8 End of Topic Test - Gene Technologies

8.4.9 A-A* (AO3/4) - Gene Technology

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End of Topic Test - Reflexes, Responses & Survival

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Stimuli and Responses: Receptors

• Understand the role of receptors in the Pacinian corpuscle and retina
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• v.147(4); 2018 Apr

Receptor biology

Sandhya s. visweswariah.

Department of Molecular Reproduction, Development & Genetics, Indian Institute of Science, Bengaluru 560 012, Karnataka, India ni.tenre.csii.gdrm@ayhdnas

M. F Roberts, A.E Krutchen, editors. Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany. 2016. 264. US$ 95.00. ISBN 978-3-527-33726-2.

The ability of living organisms to communicate and modify their behaviour is an essential requirement for their survival. This process, which is called signal transduction, begins with the recognition of the ‘signal’ by the organism. At the molecular level, this process usually involves the detection of the signaling molecule by a ‘receptor’. An understanding of the behavior of receptors, their characterization, and appreciation of their functioning, is integral to the development of new drugs and therapeutics. This book attempts to provide a basic appreciation of receptor biology, suitable for undergraduates, as well as researchers who have not been exposed to the molecular basis of signaling mechanisms earlier.

The introductory chapter provides a basic overview of definitions and terms that are used throughout the text. An overview of the material that will be covered is also presented, to orient the reader to the contents of the book. A welcome inclusion in this chapter is a brief overview of the history of receptor theory and enunciation of concepts of pharmacology. Too often, one does not appreciate the fundamental insight that early researchers provided, when basic aspects of protein structure and function were so little understood.

Since many receptors are present on the cell membrane, the following chapters discuss the structure of lipid membranes and proteins. The rather brief overview of protein structure and function is perhaps redundant when the book is clearly directed to those with some knowledge of basic biology. The introduction to ‘ first messengers’, such as hormones and polypeptides is well prepared, as is the brief overview of various types of receptors, including receptor tyrosine kinases and G-protein coupled receptors. What is especially welcome is a chapter devoted to receptor theory. Too often, students use the terms K d , IC 50 or B max without a clear understanding of what they mean, or how these are derived from experiments. These concepts are explained in a clear and simple manner here, so that it is accessible to undergraduates and graduates alike.

The third part of the book deals with various signaling systems including ion channels, G-protein coupled receptors, receptor tyrosine kinases and nuclear receptors. The summaries of these complex systems are well written, and clearly illustrated. Almost all receptor families, including the transforming growth factor beta (TGFβ) serine-threonine kinase receptors, and guanylyl cyclase receptors, are briefly described. These chapters would serve as excellent primers in introductory classes on signaling systems. There is a wealth of information that has been obtained in the past decade of the structure and signaling outputs following activation of G-protein coupled receptors, and this has been nicely summarized.

The final sections in this book are directed towards describing biological systems where receptor function has been found to play an important role. This is an interesting section, since it provides relevance in terms of biology to the reader. Important signaling pathways such as the MAPK (mitogen-activated protein kinase) pathway, second messenger-mediated signaling via cAMP, cGMP, gaseous molecules, calcium and inositol phosphates, are all covered with sufficient detail for the reader to appreciate the complexity and cross-talk in cellular signaling. Aspects of receptor-mediated signaling during metazoan fertilization and development, and importantly, aberrations in receptor signaling caused by receptor mutations in diverse diseases such as cancer, cholera, cystic fibrosis, cardiovascular diseases, obesity, depression and diabetes, provide a meaningful and application-oriented turn to the discussion. Finally, given the current interest in neuroscience and functioning of the brain, the last chapter is devoted to the role of receptors in the mind. Here, memory, schizophrenia, addiction and the function of opioid receptors are all presented in a concise and understandable manner.

Overall, this book is a useful addition to presenting fundamental aspects of receptor biology to undergraduate students, as well as graduate students who propose to think about receptors in their research activities. It would also be a good addition to the textbooks recommended for medical students. Most drugs mediate their action by binding to, and regulating the activity of, various receptors. A greater understanding of the molecular basis for drug action would no doubt lead to an appreciation of reasons for side effects of these drugs, and spur medical professionals to direct and maybe pursue research in aspects of receptor biology in future.

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• Published: 13 September 2024

Ligand requirements for immunoreceptor triggering

• Michael I. Barton 1 ,
• Rachel L. Paterson   ORCID: orcid.org/0000-0001-6268-5947 1   nAff2 ,
• Eleanor M. Denham 1   nAff3 ,
• Jesse Goyette   ORCID: orcid.org/0000-0002-1008-1890 1   na1   nAff4 &
• Philip Anton van der Merwe   ORCID: orcid.org/0000-0001-9902-6590 1   na1  

Communications Biology volume  7 , Article number:  1138 ( 2024 ) Cite this article

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• Cell signalling
• Signal transduction

Leukocytes interact with other cells using cell surface receptors. The largest group of such receptors are non-catalytic tyrosine phosphorylated receptors (NTRs), also called immunoreceptors. NTR signalling requires phosphorylation of cytoplasmic tyrosine residues by SRC-family tyrosine kinases. How ligand binding to NTRs induces this phosphorylation, also called NTR triggering, remains controversial, with roles suggested for size-based segregation, clustering, and mechanical force. Here we exploit a recently developed cell-surface generic ligand system to explore the ligand requirements for NTR triggering. We examine the effect of varying the ligand’s length, mobility and valency on the activation of representative members of four NTR families: SIRPβ1, Siglec 14, NKp44 and TREM-1. Increasing the ligand length impairs activation via NTRs, despite enhancing cell-cell conjugation, while varying ligand mobility has little effect on either conjugation or activation. Increasing the valency of the ligand, while enhancing cell-cell conjugation, does not enhance activation at equivalent levels of conjugation. These findings are more consistent with a role for size-based segregation, rather than mechanical force or clustering, in NTR triggering, suggesting a role for the kinetic-segregation model.

Introduction

The cell surface of an immune cell or leucocyte presents many different receptors, which sense their environment through ligand binding 1 , 2 , 3 . Many leucocyte receptors bind to ligands on the surface of other cells to mediate adhesion and/or transduce signals which regulate leucocyte function. These signals determine whether the leucocyte ignores or responds to the cell and influence the nature of the response. The largest class of such receptors are n on-catalytic t yrosine-phosphorylated r eceptors (NTRs), which are also called immunoreceptors 4 . More than one hundred leucocyte receptors, in more than 20 families, can be classified as NTRs 4 . Because they regulate immune cell function, NTRs have roles in a wide range of diseases, and they are being exploited for therapeutic purposes. For example, synthetic NTRs (e.g. chimeric antigen receptors) and antibodies targeting NTRs or their ligands (e.g. checkpoint inhibitors) have become standard therapies for several forms of cancer 5 , 6 .

While NTRs have structurally diverse extracellular regions they all have, or are associated with signalling subunits that have, conserved tyrosine containing motifs in their cytoplasmic domains, such as the immunoreceptor tyrosine-based activation motif (ITAM) and immunoreceptor tyrosine-based switch motif (ITSM) 4 . These motifs are phosphorylated by SRC-family tyrosine kinases, which are tethered via acyl groups to the inner leaflet of the plasma membrane. This phosphorylation is regulated by the receptor tyrosine phosphatases CD45 and CD148, which act on both SRC-kinases and their substrates. While the extracellular regions of NTRs are structurally diverse, they are typically smaller (4-10 nm) than many abundant cell surface molecules such as CD43, CD44, CD45, CD148 and integrins, which range in size from 21-50 nm. When these size differences were first noted for the TCR and its peptide-MHC (pMHC) ligand it was predicted that, when T cells contacted other cells, there would be segregation of the TCR/pMHC complex from larger molecules like CD45 1 . This observation, together with evidence that constitutive tyrosine phosphatase activity suppresses TCR triggering in resting cells 7 , 8 , led to the proposal that TCR binding to pMHC induced tyrosine phosphorylation of the TCR by trapping it in small regions of close contact which exclude large receptor tyrosine phosphatases CD45 and CD148 but not the SRC-kinases 9 . This mechanism was subsequently termed the kinetic segregation (KS) model 10 . Subsequent studies from multiple laboratories using a wide range of techniques have demonstrated that the KS mechanism plays a key role in TCR triggering 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 . Recently it has been shown that synthetic receptors based on the TCR, namely chimeric antigen receptors (CARs), also appear to trigger by the KS mechanism, which has important implications for the design of these receptors and selection of their target antigens 22 . Other mechanisms that been proposed to contribute to TCR triggering are aggregation 23 or conformational change, with conformational change being either allosteric 24 or induced by mechanical force 25 , 26 .

The similarities in signalling between the TCR and other NTRs have led to the hypothesis that the KS mechanism may contribute to triggering by other NTRs 4 , 27 . Indeed, evidence for this has been reported for NKG2D in NK cells 28 , Dectin-1 29 and FcyRs 30 in macrophages, and CD28 in T cells 31 . However, the diversity of NTRs and their ligands, and the fact that many NTR ligands have yet to be identified, has hampered investigation of the triggering mechanism in a wider range of NTRs. We have recently developed a generic ligand system based on the SpyTag/SpyCatcher split protein, which enables cell-surface Streptactin to be used to engage any NTR incorporating a membrane-distal StrepTagII peptide 32 . Importantly, this generic ligand stimulated TCRs at the same surface density as the native TCR ligand, validating it as a suitable model system for investigating NTR triggering 32 .

In the present study we used this generic ligand system to explore the ligand requirement for triggering by representative members of 4 distinct NTR families (Fig.  1A ): Signal regulatory protein β1 (SIRPβ1), Sialic acid-binding immunoglobulin-type lectin 14 (Siglec 14), Natural killer receptor 44 (NKp44) and Triggering receptor expressed on myeloid cells 1 (TREM-1). SIRPβ1 is highly homologous to the inhibitory receptor and therapeutic target SIRPα (Kharitonenkov et al. 33 ). SIRPβ1 and SIRPα are examples of paired activatory/inhibitory NTRs 4 , with conserved extracellular regions but distinct transmembrane and cytoplasmic domains. The native ligand of SIRPβ1 is unknown but it is thought to promote phagocytosis in macrophages (Hayashi et al. 34 ). Siglec 14 is a member of the sialic acid-binding Siglec family of receptors 35 , 36 . Siglec 14 and Siglec 5 are paired activatory and inhibitory NTRs, respectively (Angata et al. 36 ). NKp44 is important for the activation and cytotoxic activity of natural killer cells and reportedly binds a wide range of ligands 37 . Finally, TREM-1 is an activatory member of a family of NTRs 38 that has been reported to bind peptidoglycan recognition protein 1 (PGLYRP1) (Read et al. 39 ) and extracellular actin (Fu et al. 40 ). All four receptors associate with, and presumably signal using, the ITAM-containing adaptor protein DAP12 (Lanier and Bakker 41 ).

A Schematic depiction of the components of the generic ligand system used to test the effect of increasing ligand length and valency. The shared DAP-12 signalling homodimer is grey. With tetravalent streptactin (not shown) all four of the binding sites are able to bind Steptag II. B Representative flow cytometry data from a conjugation assay between SIRPβ1 expressing THP1 receptor cells and CHO cells expressing short ligands coupled with the indicated concentration of monovalent Strep-Tactin SpyCatcher. Receptor and ligand cells were stained with CellTrace Violet and Far Red, respectively, and events in the upper right quadrant were presumed to be conjugates. C Conjugation of (left panel) and IL-8 secretion by (middle panel) SIRPβ1 expressing THP1 cells incubated with CHO cells expressing the indicated number of short (blue) or long (red) monovalent ligands, measured as described in the “Materials and methods” section using parameters determined in sFig.  1 . IL-8 secretion is plotted against conjugation in the right panel. These are representative results from three independent experiments, which are combined in Fig.  2A for statistical analysis. Due to experimental constraints the stimulation and conjugation assays in this (and subsequent) experiment(s) were performed on successive days. The same result was obtained when performed on the same day (sFig.  2 ).

To investigate the triggering mechanism used by these NTRs we examined the effect of changing ligand size, mobility and valency on activation. In addition to using the same generic ligand, we used the same cell type and readouts for all NTRs, to facilitate comparison. Elongating the ligand inhibited activation via these receptors despite enhancing receptor/ligand mediated cell-cell conjugation. In contrast, changing the ligand mobility had little effect on conjugation or activation. Finally, while increasing the ligand valency increased cell-cell conjugation as well as activation, it decreased the level of activation at equivalent levels of cell-cell conjugation. Taken together, these results are more consistent with a role for the KS mechanism in triggering by these NTRs, and do not support a role for mechanical force or clustering.

Varying ligand length

In the SpyTag-SpyCatcher system a covalent (isopeptide) bond spontaneously forms between Spytag and SpyCatcher when they are mixed together (Zakeri et al. 42 ). In our previously described generic ligand system 32 , SpyTag is fused to the N-terminus of a transmembrane protein expressed on ligand-presenting CHO cells, forming the ligand anchor, while SpyCatcher is fused to a Strep-Tactin tetramer, which can have from one to four active binding sites, as required (Fig.  1A ). The receptor is modified at its membrane-distal N-terminus to contain a Strep-tag II peptide, which binds monovalent Strep-Tactin with a K D of 43 µM 32 . This is within the affinity range typical of leucocyte cell-cell interactions 3 , 43 .

To test the effect of ligand length on activation via NTRs, we produced CHO cells expressing either a short or a long ligand anchor, where the latter includes a ~ 11 nm spacer comprising the four Ig domains of CD4 (Fig.  1A ) 44 . By titrating the monovalent Strep-Tactin SpyCatcher we produced a panel of CHO cells with a range of binding sites. We used a previously described method to accurately quantify the number of Strep-Tactin binding sites presented by these cells 32 . This involved measuring the maximum number of biotin binding sites and the K D for Strep-Tactin SpyCatcher coupling to cells (sFig.  1 ) and using these parameters to calculate the number of binding sites, as described in the “Materials and methods” section.

We first measured the impact of ligand elongation on the receptor engagement using a cell-cell conjugation assay as a readout. For receptor cells we used THP-1 cells expressing the SIRPβ1 receptor with an N-terminal Strep-tag II peptide. THP-1 cells are a human monocytic cell line widely used as a model for studying monocyte/macrophage function 45 . They are a suitable model as SIRPβ, TREM-1, Siglec-14 and NKp44 are all known to be expressed on monocytes. The conjugation assay involves mixing receptor and ligand cells stained with different fluorescent dyes and measuring double positive events by flow cytometry (Fig.  1B , upper right quadrants). As expected, reducing the number of binding sites resulted in a decrease in the percentage of receptor cells in conjugates (Fig.  1B ).

We then compared the conjugation efficacy of SIRPβ1 THP-1 cells mixed with short or long CHO cells presenting different numbers of binding sites. When plotting conjugates against binding sites, cells presenting the long ligand produced more conjugates (Fig.  1C , left panel). This suggests that increasing the ligand length promotes conjugate formation, which is consistent with other studies suggesting that increasing the length of short (< 8 nm) cell surface ligands improves receptor engagement 11 , 31 , 46 .

We next examined the effect of ligand length on SIRPβ1 mediated interleukin 8 (IL-8) production. IL-8 is inflammatory chemokine produced monocytes in response to a wide variety of activation signals 47 . It is widely used a convenient and reliable readout of THP-1 activation, with low levels of basal release and a rapid increase following stimulation 48 . Interestingly the long ligand stimulated less IL-8 production than the short ligand (Fig.  1C , middle panel), despite mediating improved conjugate formation. To normalise for differences in conjugate formation we plotted the functional IL-8 response against the percentage of receptor cells in conjugates (Fig.  1C , right panel). This confirmed that, at equivalent levels of conjugate formation, the long ligand induced lower levels of IL-8 production.

We performed conjugation and stimulation assays on other NTRs (Siglec 14, NKp44 and TREM-1) using the short or long generic ligand (Fig.  2 ). As in the case of SIRPβ1, the results for NKp44 and TREM-1 show an increase in conjugation efficacy when binding to the long ligand compared to the short ligand (Fig.  2 , left data panels). This was not the case for Siglec 14, where no difference was seen (Fig.  2B , left data panel). Elongation of the ligand impaired activation of IL-8 release via all four NTRs, both before and after normalising for conjugate formation (Fig.  2 , centre and right data panels, respectively).

THP-1 cells expressing ( A ) SIRPβ1, ( B ) Siglec 14, ( C ) NKp44 or ( D ) TREM-1 with N-terminal StrepTagII peptides were incubated with CHO cells expressing the indicated numbers of short (blue) or long (red) monovalent generic ligand binding sites and conjugate formation (left data panel) and IL-8 release (middle data panel) measured. Ligand binding sites were determined as described in the Materials and Methods using parameters determined in sFig.  1 . The IL-8 release versus conjugation level is plotted in the right panels. The data from three biological replicates (including one SIRPβ1 replicate shown in Fig.  1 ) are plotted with the data normalised to the level of conjugation or stimulation achieved with the short ligand within each replicate. The data were fitted as described in the “Materials and methods” section and an F test was used to test the significance of differences between the fits.

One likely explanation for the effect of ligand length on activation efficiency is that increasing the ligand length increases the length of the NTR/ligand complex. If this is the case, activation should also be reduced by increasing the length of the NTRs. The extracellular regions of SIRPβ1 and Siglec 14 each contain 3 immunoglobulin superfamily (IgSF) domains, whereas the extracellular regions of NKp44 and TREM-1 only have 1 IgSF domain (Fig.  1A ). To examine whether this size difference had a measurable impact we reanalysed the data in Fig.  2 to enable comparison of conjugation and activation via NTRs exposed to the same ligand (sFig.  3 ). Short NTRs (NKp44 and TREM-1) mediated lower levels of conjugation (sFig.  3 , left panels) but high levels of stimulation when normalised for conjugation (sFig.  3 , right panels), and this was observed with both short (sFig.  3A ) and long (sFig.  3B ) ligands. Taken together, these data show that elongation of these NTR/ligand complexes attentuates activation via these four NTRs.

Varying ligand mobility

We next examined the effect of varying the ligand anchor on NTR activation. We compared ligand anchors based on the transmembrane and cytoplasmic domains of CD80, CD43 and the glycosylphosphatidylinositol (GPI) anchor of CD52. The CD43 cytoplasmic domain interacts with the actin cytoskeleton though Ezrin/Radixin/Moesin (ERM) proteins (Yonemura et al. 49 ), and so is likely to be more firmly anchored. In contrast, CD52 is a GPI anchored protein and thus less firmly anchored, and presumably more mobile (Fig.  3A ). To examine mobility, SpyCatcher-GFP was coupled to ligand anchors and fluorescence recovery after photobleaching (FRAP) performed (sFig. 4A ). As expected, the CD52 anchor conferred greater mobility than the CD80 or CD43 anchor, which were similar (sFig.  4A and Fig.  3B ).

A Schematic depiction of the components of the generic ligand system used to test the effect of varying the ligand anchor. B The diffusion coefficients of the different ligand anchors were measured by FRAP after coupling GFP-Spycatcher. The mean and SD from three independent experiments were compared by ANOVA. C THP-1 cells expressing SIRPβ1 with an N-terminal StrepTagII peptide were incubated with CHO cells expressing the indicated number of ligand binding sites with CD80 (blue), CD43 (orange) or CD52 (green) anchors, and conjugate formation (top panel) and IL-8 release (middle panel) measured. Ligand binding sites were determined as described in the “Materials and methods” section using parameters determined in sFig.  4 . The IL-8 release versus conjugation level is plotted in the bottom panel. The data from three biological replicates are plotted with the data normalised to the level of conjugation or stimulation achieved with the CD52 ligand anchor within each replicate. These data were fitted as described in the “Materials and methods” section and an F test was used to test the significance of differences between the fits collectively and pairwise (Tables).

We next compared the effect of changing the ligand mobility on conjugation with and stimulation of SIRPβ1 expressing THP-1 cells. It was not possible to attain as high a level of binding sites on the CD43 and CD52 ligand anchor cells but comparison was possible over a reasonable range. CD52 and CD43 anchored ligands induced similar levels of conjugate formation (Fig.  3C , top panel) and IL-8 release (Fig.  3C , middle panel) at comparable levels of binding sites. CD52 anchored ligand induced slightly less IL-8 release than CD43 anchored ligands when plotted against levels of conjugation (Fig.  3C , bottom panel). However this difference was small and could be the result of faster turnover of the CD52 anchored ligand (sFig.  5 ), which would reduce engagement during the 20 h stimulation. While the CD80 anchored ligand was less potent at mediating conjugation and IL-8 release, this was not a consequence of differences in lateral mobility which was similar to the lateral mobility of the CD43 anchored ligand (Fig.  3B ). Taken together, these results suggest that changes in the ligand mobility do not affect SIRPβ1 mediated conjugation or triggering.

Varying ligand valency

Since ligand-induced clustering of receptors is often assumed to be the mechanism of receptor-activation, we next examined the effect of increasing the valency of the ligand from 1 to 4 by using a tetravalent form of Strep-Tactin Spycatcher. As expected, conjugation with tetravalent instead of monovalent Strep-Tactin Spycatcher resulted in a four-fold increase in the number of binding sites (sFig.  6 ). We then compared the ability of monovalent and tetravalent ligand to mediate conjugation and induce IL-8 secretion from THP-1 cells expressing the 4 different NTRs. Tetravalent ligand induced conjugation via all 4 NTRs at lower ligand binding site numbers than monovalent ligand (Fig.  4 , left data panels), indicating that increasing the ligand valency increases NTR binding, presumably by increasing avidity. Increasing the valency enabled activation of all four receptors, as measured by IL-8 release, at much lower ligand binding sites (Fig.  4 , middle data panels). However, after controlling for increased conjugate formation, tetravalent ligand was actually less effective than monovalent ligand at stimulating IL-8 release at equivalent levels of conjugation (Fig.  4 , right panels). The same result was observed with the CD80, CD43 and CD52 ligand anchors (sFig.  7 and 8 ). These results indicate that, while increasing the valency of a cell surface-associated ligand enhances binding to NTRs, it does not increase activation via NTRs.

THP-1 cells expressing ( A ) SIRPβ1, ( B ) Siglec 14, ( C ) NKp44 or ( D ) TREM-1 with N-terminal StrepTagII peptides were incubated with CHO cells expressing the indicated numbers of monovalent (filled circles) or tetravalent (open circles) short generic ligand binding sites and conjugate formation (left data panel) and IL-8 release (middle data panel) measured. Ligand binding sites were determined as described in the Materials and Methods using parameters determined in sFig.  6 . The IL-8 release versus conjugation level is plotted in the right panels. The data from three biological replicates are plotted with the data normalised to the level of conjugation or stimulation achieved with the short tetravalent ligand within each replicate. These data were fitted as described in the “Materials and methods” section and an F test was used to test the significance of differences between the fits.

We next investigated whether activation of NTR by the high avidity tetravalent Strep-Tactin SpyCatcher was sensitive to ligand length. THP-1 cells expressing four representative NTRs were exposed to CHO cells presenting tetravalent Strep-Tactin SpyCatcher on either short or long CD80 anchors (Fig.  5 ). The short ligands were less effective at mediating conjugation than the long ligand for two (SIRPβ1 & NKp44) of the four NTRs (Fig.  5 , left panels), but more effective at stimulating IL-8 production for three of the four NTRs (SIRPβ1, NKp44 and TREM-1), both before (Fig.  5 , centre panels) and after (Fig.  5 , right panels) controlling for conjugate formation. These results show that even high avidity NTR/ligand interactions remain sensitive to ligand length for three of the four NTRs studied here.

THP-1 cells expressing ( A ) SIRPβ1, ( B ) Siglec 14, ( C ) NKp44 or ( D ) TREM-1 with N-terminal StrepTagII peptides were incubated with CHO cells expressing the indicated numbers of short (blue) or long (red) tetravalent ligand binding sites and conjugate formation (left panel) and IL-8 release (middle panel) measured. Ligand binding sites were determined as described in the Materials and Methods using parameters determined in sFig.  9 . The IL-8 release versus conjugation level is plotted in the right panels. The data from three biological replicates are plotted with the data normalised to the level of conjugation or stimulation achieved with the short tetravalent ligand within each replicate. These data were fitted as described in the “Materials and methods” section and an F test was used to test the significance of differences between the fits.

We have exploited our previously described generic cell surface ligand system 32 to explore the effects of varying ligand length, mobility, and valency on activation of four NTRs, SIRPβ1, Siglec-14, NKp44 and TREM-1. One advantage of this system is that it enables titration of ligand surface density, enabling detection of quantitative differences in the ability of cell-surface ligands to mediate conjugation and stimulation. A second advantage is that it enables multiple NTRs to be assessed using the same set of ligands, increasing throughput and facilitating comparisons between NTRs. Comparison was also enabled by expressing them in the same cell type and using the same functional readout. A third advantage is that it enables analysis of orphan NTRs, such as SIRPβ1, whose ligands have yet to be identified.

Our first key finding is that elongation of generic ligands abrogated activation of all four NTRs. This was not a consequence of decreased binding as elongated ligands mediate enhanced cell-cell conjugation. While this contrasted with results in a supported lipid bilayer (SLB) system, in which elongation of CD48 abrogated CD2 binding 50 , it is consistent with results obtained with cell surface expressed ligands, including CD48 11 , 28 , 31 , 46 . A likely explanation for this is that ligands on cell surfaces, unlike ligands on SLBs, are crowded by the larger molecules present at high densities. Our finding that long NTRs were less effectively activated than short NTRs suggest that the increased NTR/ligand length abrogates NTR signalling. These data are most consistent with the KS mechanism of NTR triggering 27 , since increasing the NTR/ligand length would be expected to increase the intermembrane distance and thus reduce segregation of inhibitory receptor tyrosine phosphatases such as CD45 from the engaged NTR 11 . Numerous studies have confirmed that increasing receptor/ligand length abrogates CD45 segregation from engaged NTRs 11 , 22 , 30 , 31 , 51 , 52 . In one of these studies an elongated high affinity TCR ligand, derived from the OKT3 monoclonal antibody, was able to activate TCRs despite less efficient exclusion of CD45 51 . However, no titration of the ligand number was performed, and a lower affinity variant of the same ligand was unable to activate T cells 51 .

An alternative explanation for our finding that elongation abrogates activation through NTRs is that this could decrease the level of force experienced by the NTR upon ligand engagement 53 . These data are therefore also consistent with models of NTR triggering postulating that a mechanical force imposed upon ligand binding alters the conformation of the NTR 25 , 26 . However, whether changing ligand length affects the force experienced by an NTR has yet to be confirmed, and it remains unclear how such a conformational change of NTRs could be transmitted through the membrane to enhance phosphorylation of their cytoplasmic domains. Such a mechanism is difficult to reconcile with enormous structural variability of NTRs 4 and the fact that chimeric NTRs such as CARs tolerate extensive variation in the regions (hinge, transmembrane and cytoplasmic domains) that couple their ligand binding domains with their tyrosine-containing signalling motifs 54 , 55 .

A second key finding is that changing the mobility of the ligand anchor had little impact on its ability to mediate activation via NTRs. The CD52 anchor comprises a lipid (GPI) which we show confers greater lateral mobility. While there is only a ~ 2 fold change in mobility, it should be noted that membrane diffusion in cells is far slower than in model membranes, likely because it is impaired by membrane proteins that associate with the cortical actin cytoskeleton, which have been termed ‘picket fences’ 56 , 57 . A lipid anchor also allows a ligand to be more easily extracted from the plasma membrane by force. It follows that the amount of force that can be exerted on an NTR, both tangential and perpendicular to the membrane, should be lower with lipid-anchored than a transmembrane-anchored ligand, such as CD43, able to bind to the actin cytoskeleton through ERM proteins. Thus, our finding that CD43 and CD52 anchors were similarly effective at activating an NTR does not support a role for mechanical force in triggering for these NTRs. We note, however, that we did not directly measured force. Much stronger evidence against a role for force was reported by Göhring et al. 58 , who showed that changing the lateral mobility of the TCR ligand in an SLB system by ~1000 fold had no effect on TCR triggering despite substantially changing the force experienced by the TCR 58 .

The third key finding is that increasing the valency of the NTR ligand did not increase activation of the NTR after controlling for enhanced NTR binding, as assessed by cell-cell conjugation. This result contrasts with the findings obtained with soluble NTR ligands such as cross-linked antibodies and natural ligands engineered to be multivalent, where increasing the valency is required for NTR triggering 59 , 60 . One possible explanation for this discrepancy between the effect of valency with soluble and surface associated ligand is that a soluble multivalent ligand could, by forming clusters of NTRs, exclude molecules with bulky ectodomain, such as CD45, from clustered NTRs, whereas soluble monovalent ligands are unable to do this. In contrast, even a monovalent surface-associated ligand can trap the NTR in zones of a close intermembrane contact from which CD45 and CD148 are excluded. While our finding that tetravalent ligands are less effective at activating NTRs than monovalent ligands at equivalent levels of NTR binding requires confirmation and further analysis, the fact that increased valency does not enhance NTR triggering argues against aggregation as the primary mechanism of physiological NTR triggering. Further evidence against this model is that almost all cell surface NTR ligands that have been identified to date are monovalent 4 . In contrast, cell surface receptors known to signal by binding induced multimerisation, such as class III tyrosine kinase receptors 61 and TNF receptor superfamily members 62 , typically have multivalent ligands.

Our finding that elongation of a tetravalent generic ligand did not impair activation via Siglec 14, while it did impair activation via the three other NTRs, suggests that, for some NTRs, increasing ligand valency can bypass the need for the KS mechanism. It is noteworthy that some Siglec family members, including the Siglec-14 paired receptor Siglec-5 63 , are able to form disulfide linked dimers. Dimeric Siglec-14 would allow formation of large ‘zipper-like’ aggregates with tetravalent generic ligand, enabling exclusion of CD45 and CD148 without needing the KS mechanism.

Taken together, our finding that activation via NTRs is abrogated by ligand elongation and aggregation and unaffected by the mobility of the ligand anchor supports our hypothesis that NTRs signal by the KS mechanism. While our results focus only on four representative NTRs, these results are likely to apply to a larger number of NTRs, including other members of the Siglec and TREM families and NTRs from other families that signal via the DAP-12 adaptor 64 . As reviewed in the Introduction, there is already evidence that many NTRs that do not associate with DAP12, including the TCR, NKG2D, CD28, and FcγR, exploit the KS mechanism for triggering. Taken together with the results presented in this paper, this suggests that the KS mechanism is used by NTRs which signal through intrinsic tyrosine motifs (CD28, FcγRI and FcγRII) and a variety of signalling subunits (DAP12, DAP10, CD3δεγ, CD247, and FcRγ).

One limitation of the present work is that we used artificial ligands rather than native ligands. While we have previously validated the generic ligand system for the T cell receptor 32 , this is more challenging for other NTRs, as their ligands cannot easily be titrated. Our recent development of Combicells 65 , which exploit the SpyCatcher/SpyTag system to present native ligand on antigen-presenting cells, should enable our key results to be confirmed with native ligands, where known. A second limitation, which is a consequence of the system used, is lack of imaging data of the interface between THP-1 cells and CHO cells. Advanced microscopy, beyond the scope of this study, is likely needed to image the molecular events in the microvilli-like structures and close contact areas involved in NTR triggering at cell-cell interfaces 18 , 66 , 67 . A third limitation is that we only test one prediction of the KS model, that elongation of the receptor/ligand complex will abrogate NTR triggering. Other predictions of the KS model should be tested in future work. A final limitation is that we did not examine early signalling steps, such as tyrosine phosphorylation of DAP-12. Such studies are very difficult to perform in the high-throughtput manner required for our titration-based analysis, and are unlikely to provide further insights into the the ligand requirements for activation of these NTRs.

Receptor sequences for SIRPβ1, Siglec 14, NKp44 and TREM-1 were inserted into the pHR-SIN-BX-Strep-tag II plasmid as described by (Denham et al., 32 ). The same constructs containing the DAP12 adaptor protein was also used.

For the ligand anchors DNA encoding the Igk leader sequence (bold), HA tag (italics), SpyTag (underlined) bracketed by GGS linkers, and the hinge, transmembrane and intracellular regions of mouse CD80, mouse CD43, or human CD52 (italics underlined) was inserted into the vector pEE14. To express the long ligand anchor DNA encoding human CD4 (italics bold) was inserted between the SpyTag and CD80 hinge. This included an R to D point mutation (underlined) to prevent CD4 binding MHC class II. After expression CD52 anchor is cleaved and linked to GPI anchor at the serine residue marked in bold.

CD80 anchor (short)

M E T D T L L L W V L L L W V P G S T G D Y P Y D V P D Y A T G G S A H I V M V D A Y K P T K G G S G G S H V S E D F T W E K P P E D P P D S K N T L V L F G A G F G A V I T V V V I V V I I K C F C K H R S C F R R N E A S R E T N N S L T F G P E E A L A E Q T V F L

CD80 anchor (long)

M E T D T L L L W V L L L W V P G S T G D Y P Y D V P D Y A T G G S A H I V M V D A Y K P T K G G S G G S K V V L G K K G D T V E L T C T A S Q K K S I Q F H W K N S N Q I K I L G N Q G S F L T K G P S K L N D D A D S R R S L W D Q G N F P L I I K N L K I E D S D T Y I C E V E D Q K E E V Q L L V F G L T A N S D T H L L Q G Q S L T L T L E S P P G S S P S V Q C R S P R G K N I Q G G K T L S V S Q L E L Q D S G T W T C T V L Q N Q K K V E F K I D I V V L A F Q K A S S I V Y K K E G E Q V E F S F P L A F T V E K L T G S G E L W W Q A E R A S S S K S W I T F D L K N K E V S V K R V T Q D P K L Q M G K K L P L H L T L P Q A L P Q Y A G S G N L T L A L E A K T G K L H Q E V N L V V M R A T Q L Q K N L T C E V W G P T S P K L M L S L K L E N K E A K V S K R E K A V W V L N P E A G M W Q C L L S D S G Q V L L E S N I K V L P T R S H V S E D F T W E K P P E D P P D S K N T L V L F G A G F G A V I T V V V I V V I I K C F C K H R S C F R R N E A S R E T N N S L T F G P E E A L A E Q T V F L

CD43 anchor

M E T D T L L L W V L L L W V P G S T G D Y P Y D V P D Y A T G G S A H I V M V D A Y K P T K G G S G G S Q E S S G M L L V P M L I A L V V V L A L V A L L L L W R Q R Q K R R T G A L T L S G G G K R N G V V D A W A G P A R V P D E E A T T T S G A G G N K G S E V L E T E G S G Q R P T L T T F F S R R K S R Q G S L V L E E L K P G S G P N L K G E E E P L V G S E D E A V E T P T S D G P Q A K D E A A P Q S L

CD52 anchor

M E T D T L L L W V L L L W V P G S T G D Y P Y D V P D Y A T G G S A H I V M V D A Y K P T K G G S G G S D T S Q T S S P S A S S N I S G G I F L F F V A N A I I H L F C F S

Strep-Tactin-SpyCatcher sequence

Strep-Tactin is underlined, SpyCatcher is in italics and the polyaspartate sequence is in bold.

M A E A G I T G T W Y N Q L G S T F I V T A G A D G A L T G T Y V T A R G N A E S R Y V L T G R Y D S A P A T D G S G T A L G W T V A W K N N Y R N A H S A T T W S G Q Y V G G A E A R I N T Q W L L T S G T T E A N A W K S T L V G H D T F T K V K P S A A S D D D G D D D G D D D D S A T H I K F S K R D E D G K E L A G A T M E L R D S S G K T I S T W I S D G Q V K D F Y L Y P G K Y T F V E T A A P D G Y E V A T A I T F T V N E Q G Q V T V N G K A T K G D A H I

Strep-Tactin sequence

M A E A G I T G T W Y N Q L G S T F I V T A G A D G A L T G T Y V T A R G N A E S R Y V L T G R Y D S A P A T D G S G T A L G W T V A W K N N Y R N A H S A T T W S G Q Y V G G A E A R I N T Q W L L T S G T T E A N A W K S T L V G H D T F T K V K P S A A S

Dead Streptavidin sequence

Bold amino acids mark substitutions in order to prevent binding to Strep tag II or biotin.

M A E A G I T G T W Y A Q L G D T F I V T A G A D G A L T G T Y E A A V G A E S R Y V L T G R Y D S A P A T D G S G T A L G W T V A W K N N Y R N A H S A T T W S G Q Y V G G A E A R I N T Q W L L T S G T T E A N A W K S T L V G H D T F T K V K P S A A S

The GFP-SpyCatcher construct was described in (Denham et al., 32 ).

THP-1 cell lines

THP-1 cells (ATCC #TIB 202) were maintained in RPMI-1640 media (Sigma-Aldrich #R8758) supplemented with 10% foetal bovine serum (FBS) and 1 in 100 penicillin/streptomycin (Thermo Fisher Scientific #15140122) at 37 °C in a 5% CO2 containing incubator.

CHO cell lines

Chinese Hamster Ovary (CHO) mock cells were maintained in DMEM (Sigma-Aldrich #D6429) supplemented with 5% FBS and 1 in 100 penicillin/streptomycin. CHO ligand anchor cells (short and long) were maintained in L-Glutamine-free DMEM (Sigma-Aldrich #D6546) supplemented with 10% dialysed FBS (dialysed three times against 10 L PBS), 1 in 100 penicillin/streptomycin, 1x GSEM supplement (Sigma-Aldrich #G9785) and 50 μM L-Methionine sulfoximine (Sigma-Aldrich #M5379).

Lentiviral transduction of THP-1 cells

Either receptor-expressing lentivector alone, or with the DAP12 adaptor-expressing lentivector, was co-transfected with the lentiviral packaging plasmids pRSV-Rev (Addgene plasmd #12253), pMDLg/pRRE (Addgene plasmd #12251) and pMD2.g (Addgene plasmd #12259) into HEK293T cells using X-tremeGENETM HP (Sigma-Aldrich 6366546001) as per the manufacturer’s instructions. Two days after transfection, viral supernatants were harvested, filtered (0.45 µM syringe filter) and used for the transduction of THP-1 cells in the presence of 5 µg mL −1 Polybrene.

Analysing receptor and adaptor expression using flow cytometry and cell sorting by fluorescence-activated cell sorting

Cells were analysed for receptor surface expression by flow cytometry using anti-Strep-tag II antibody StrepMAB™ directly conjugated to Oyster 645 (IBA Lifesciences #2-1555-050), or unconjugated StrepMAB™ (IBA Lifesciences #2-1507-001) with anti-mouse IgG1 antibody Alexa Fluor 647 (Thermo Fisher Scientific #A-21240), on a Cytek DxP8 . Introduced adaptor expression was tested via expression of EmGFP encoded on the adaptor lentivector. Cells were sorted for matched high expression of receptor plus introduced adaptor by fluorescence-activated cell sorting (FACS) (MoFlo Astrios, Beckman Coulter).

Transfection of CHO cells with various ligand anchors

CHO cells were transfected with either pEE14 (CHO mock) or pEE14-ligand anchor (CHO ligand anchor) using Xtreme-GENE 9 TM as per the manufacturer’s instructions. Transfected lines were cultured in the appropriate selection media after 48 h.

Checking ligand anchor expression by flow cytometry

CHO Cells were analysed for ligand anchor surface expression by flow cytometry using anti-HA-Tag antibody Alexa Fluor 647 (6E2; Cell Signalling Technology). Alternatively, cells were coupled with saturating concentration of monovalent StrepTactin SpyCatcher. Biotin ATTO 647 was then added at 2 μM for 30 min. The cells were washed 3 times in PBS 1%BSA before they were fixed in PBS 1% formaldehyde and analysed via flow cytometry.

Expression and purification of monovalent and tetravalent Strep-Tactin-SpyCatcher

Strep-Tactin SpyCatcher and dead streptavidin (monovalent) or Strep-Tactin SpyCatcher and Strep-Tactin (tetravalent) expressed in Escherichia coli BL21-CodonPlus (DE3)-RIPL cells (Agilent Technologies #230280) were combined and refolded from inclusion bodies. Inclusion bodies were washed in BugBuster (Merck Millipore #70921) supplemented with lysozyme, protease inhibitors, DNase I and magnesium sulfate as per the manufacturers’ instructions. Subunits were then mixed at a 3:1 molar ratio to improve the yield of the desired tetramer. The subunits were refolded by rapid dilution in cold PBS and contaminates removed via precipitation using ammonium sulfate before additional ammonium sulfate was added to precipitate the desired tetramer. Precipitated protein was resuspended in 20 mM Tris pH 8.0, filtered (0.22 µm syringe filter), and loaded onto a Mono Q HR 5/5 column (GE Healthcare Life Sciences). Desired tetramers were eluted using a linear gradient of 0-0.5 M NaCl in 20 mM Tris pH 8.0, concentrated, and buffer exchanged into 20 mM MES, 140 mM NaCl pH 6.0 (Denham et al., 32 ).

Coupling CHO generic ligand cells

Ligand anchor expressing or mock transduced CHO cells were incubated with various concentrations of monovalent or tetravalent Strep-Tactin SpyCatcher in 20 mM MES, 140 mM NaCl, pH 6.0 and 1% BSA for 10 min at RT. Unbound Strep-Tactin SpyCatcher was removed by washing three times with PBS 1% BSA.

FACS sorting of ligand anchor expressing CHO cells

CHO short and long cells were coupled with a saturating concentration of monovalent Strep-Tactin SpyCatcher. Biotin ATTO 647 (ATTO-TEC #AD 647-71) was then added at 2 µM for one hour and the excess washed off with PBS 1% BSA. The short and long CHO cells were then sorted for matched expression of atto 647 signal corresponding to the expression level of ligand anchor using FACS (MoFlo Astrios, Beckman Coulter).

Biotin-4-fluorescein quenching assay

The valency of purified monovalent and tetravalent Strep-Tactin SpyCatcher was measured using biotin-4-fluorescein (Sigma-Aldrich #B9431-5MG) which when bound to Strep-Tactin become quenched. Monovalent and tetravalent Strep-Tactin SpyCatcher was incubated with a titration of biotin-4-fluorescein in black, opaque plates for 30 min at RT in PBS 1% BSA. Fluorescence was measured (λ ex 485 nm, λ ex 520 nm) using a plate reader. Fluorescence values were corrected for background fluorescence before analysis. Negative control (buffer alone) data were fitted with the linear regression. Sample data was fitted with a segmental linear regression, equation below, where X is the biotin-4-fluorescein concentration, Y is fluorescence (AU), X0 is the biotin-4-fluorescein concentration at which the line segments intersect. Slope1 was constrained to zero and is the gradient of the first line segment, slope2 is the gradient of the second line segment. Intercept1 was constrained to zero and is the Y value at which the first line segment intersects the Y axis. Slope2 was constrained to the gradient given by the linear regression of the negative control.

The X0 value was converted into an estimate of the number of biotin-binding sites per tetramer using the concentration of Strep-Tactin SpyCatcher added and assuming complete binding of biotin-4-fluorescein.

Measurement of ligand binding sites on cells

Ligand-anchor expressing or mock transduced CHO cells (3 × 10 6 ) were pre-incubated with a saturating concentration of monovalent or tetravalent Strep-Tactin SpyCatcher. The above biotin-4-fluorescein quenching assay was performed in the same manner but with a known number of cells. The X0 term (calculated from the curve fit using equations above) was converted to the average number of binding sites per cell using the equation below, where N is the average number of ligands per cell, X0 is the saturation concentration of biotin-4-fluorescein extracted (M), V is the sample volume (L), N A is Avogadro’s constant and C is the number of cells in the sample.

To measure relative levels of coupled SpyCatcher per cell, ligand cells were pre-incubated with a range of concentrations of monovalent or tetravalent Strep-Tactin SpyCatcher or buffer alone (as a negative control) before being incubated with 2 μM biotin ATTO 488 (ATTO-TEC #AD 488-71) pre-mixed with 40 μM biotin for 30 min at RT. The presence of biotin minimises the self-quenching activity of ATTO dye observed with tetravalent Strep-Tactin. Cells were analysed by flow cytometry and the gMFI when cells were incubated with buffer alone instead of Strep-Tactin SpyCatcher was subtracted from all corresponding sample gMFI values. These values were then fitted with the single site binding model, equation below, where Y is the gMFI (AU), Bmax is the maximum specific SpyCatcher binding indicated by gMFI in AU, X is the [Streptactin-SpyCatcher] added (M) and K D is the [SpyCatcher] that yields 50% maximal binding to CHO cells (M).

To convert Y values into the average number of binding sites per ligand cell, the number of binding sites per cell at saturating monovalent/tetravalent Strep-Tactin SpyCatcher concentration calculated in the biotin-4-fluorescein assay was substituted into the equation above as Bmax. Y values were then re-calculated following this adjustment. This method was followed in each independent experiment and then an average value for the K D and Bmax was used when combining replicates.

Fluorescent recovery after photobleaching

CHO cells (1 × 10 5 ) expressing the three ligand anchors (CD80, CD43 and CD52) were transferred to a 35 mm glass bottom dish one day before imaging. To prepare cells for imaging each dish was washed 3 times with coupling buffer plus 10% FBS. SpyCatcher-GFP was then added in excess (approximately 10 μM) and left for 10 min. Before washing the cells three times in PBS 1% BSA 10% FBS. Cells were transferred to the Olympus FV1200 laser scanning microscope with 37 °C chamber for equilibration. The 60X magnification lens was used to locate cells spread over the glass coverslip. A small area of the cell (approximately 10%) was selected a few control images were taken before a 20 s bleach performed. A time lapse series of images was then taken to track the recovery of the GFP signal.

Time lapse image series were imported into ImageJ for analysis. For each time lapse the bleach area was selected (bleach) along with a control area which was taken to be the rest of the cell contact with the glass (control area) and a negative control area around the outside of the cell (negative). Firstly, the intensity from the negative area was subtracted from the bleach and control area. The bleach area was then divided by the control area for each time frame and these values were normalised to the control image before the bleach was performed. These values were then plotted against time and the equation below used to find the half time for each ligand anchor.

Where Y0 is the value when X (time) is zero, Plateau is the Y value at infinite times and K is the rate constant from which the half time is derived.

The half time value could then be converted into the diffusion coefficient using the equation below (Soumpasis, 1983).

Where 0.224 is a constant for a circular bleach area, r is the radius of the bleach area, and the half time is the derived from fitting the one phase association equation to the bleach recovery above.

Ligand turnover

CHO ligand anchor cells were incubated with 15 μM monovalent Strep-Tactin SpyCatcher (or buffer alone) as described above and incubated at 37 °C to match stimulation assays for the time points indicated. Cells were analysed for generic ligand surface expression using ATTO 488 biotin as above and normalised to the geometric fluorescence intensity value at time 0. To calculate the decay, the gMFI values were fitted with the equation below where Y0 is the Y value when X = 0, Plateau is the Y value at which the curve reaches a plateau, X is time in minutes, and K is the rate constant in inverse minutes.

IL-8 production

CHO ligand anchor cells (2 ×10 5 ) coupled with either monovalent/tetravalent Strep-Tactin SpyCatcher or buffer alone were mixed with single Strep-tag II tagged receptor and adaptor expressing THP-1 or untransduced THP-1 cells (1 ×10 5 ) in DMEM 5% FBS, 1 in 100 penicillin/streptomycin, 2 μg mL −1 avidin. Cells were incubated in a 37 °C 10% CO2 containing incubator for 20 h. Supernatants were harvested and assayed for IL-8 by ELISA following manufacturer’s instructions (Thermo Fisher Scientific #88808688).

Conjugation assays

Ligand cells were stained with CellTrace Far Red (Thermo Fisher Scientific #C34564) at a final concentration of 1 μM in PBS at a density of 1 × 10 6 cells per ml. THP-1 receptor cells were stained with CellTrace Violet (Thermo Fisher Scientific #C34557) at a final concentration of 1 μM in PBS for 20 min. CHO Ligand cells (4 ×10 5 ) coupled with either monovalent/tetravalent Strep-Tactin SpyCatcher were mixed with (2 ×10 5 ) THP-1 cells in PBS 1% BSA on Ice for 1 h. Conjugation efficacy was analysed by flow cytometry.

Due to experimental constraints stimulation and conjugation assays were completed on successive days. However, we confirmed that the same result was observed when the stimulation and conjugation assays were performed in parallel on the same day by splitting the cells in half for each assay (Supplementary Fig.  2 ).

Data analysis, statistics and reproducibility

For receptor stimulation assays, IL-8 concentrations in negative controls (where CHO cells were pre-incubated with buffer alone instead of Strep-Tactin SpyCatcher) were subtracted from corresponding sample IL-8 concentrations to correct for background levels. Dose-response curves were then fitted with the below equation where Y is the measured cell response (pg mL −1 ), Bottom and Top are the minimum and maximum cell response respectively (pg mL −1 ), EC50 is the number of binding sites per cell that yields a half maximal response, X is the number of binding sites per cell and Hill slope relates to the steepness of the curve.

For conjugate assays the percentage of THP-1 cells forming conjugates was calculated using the formula below. The data was then fit using the same dose response equation above except with X being the percentage of THP-1 cells in conjugates, normalised where indicated.

To plot stimulation as a function of conjugation the average number of binding sites used in the stimulation assay were interpolated from the fit of the conjugation data using the four parameter dose response model. The IL-8 values from the stimulation were then plotted against the interpolated values of conjugate formation.

To enable the results from multiple experiments to be included in single plots data were normalised. This increased the statistical power of the experiments by increasing the number of data points for each fit. For statistical analysis F tests, t tests ANOVA were performed as appropriate and results presented with the following symbols: ns, not significant ; * p  < 0.05; ** p   < 0.01; *** p  < 0.001; **** p  < 0.0001.

Reporting summary

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

Data availability

The source data underlying the graphs in the study can be found in  Supplementary Data .

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Acknowledgements

We acknowledge Mark Howarth for providing Strep-Tactin, streptavidin, SpyTag and SpyCatcher constructs and for helpful discussions and advice. We thank Marion H Brown and Omer Dushek and their groups for helpful discussion. This work was supported by a Wellcome Trust Senior Investigator Award (P.A.v.d.M., grant reference101799/Z/13:/Z) and a Nuffield Medical Fellowship from the Australian Academy of Science (J.G., grant reference: #1016848).

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Rachel L. Paterson

Present address: Stemmatters, Biotecnologia e Medicina Regenerativa SA, Parque de Ciência e Tecnologia Avepark, Zona Industrial da Gandra, Barco, Portugal

Eleanor M. Denham

Present address: Enara Bio, The Magdalen Centre, Oxford Science Park, 1 Robert Robinson Avenue, Oxford, UK

Jesse Goyette

Present address: Department of Molecular Medicine, School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia

These authors contributed equally: Jesse Goyette, Philip Anton van der Merwe.

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Sir William Dunn School of Pathology, University of Oxford, Oxford, UK

Michael I. Barton, Rachel L. Paterson, Eleanor M. Denham, Jesse Goyette & Philip Anton van der Merwe

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The study was conceived by P.A.v.d.M. and J.G., the experiments were performed by M.I.B. with reagents prepared by R.L.P. and E.M.D., the paper was drafted by M.I.B. and P.A.v.d.M. and finalised by all authors.

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Barton, M.I., Paterson, R.L., Denham, E.M. et al. Ligand requirements for immunoreceptor triggering. Commun Biol 7 , 1138 (2024). https://doi.org/10.1038/s42003-024-06817-y

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DOI : https://doi.org/10.1038/s42003-024-06817-y

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