Rate-determining step

From Wikipedia, the free encyclopedia

 

In chemical kinetics, the overall rate of a reaction is often approximately determined by the slowest step, known as the rate-determining step (RDS) or rate-limiting step. For a given reaction mechanism, the prediction of the corresponding rate equation (for comparison with the experimental rate law) is often simplified by using this approximation of the rate-determining step.

In principle, the time evolution of the reactant and product concentrations can be determined from the set of simultaneous rate equations for the individual steps of the mechanism, one for each step. However, the analytical solution of these differential equations is not always easy, and in some cases numerical integration may even be required. The hypothesis of a single rate-determining step can greatly simplify the mathematics. In the simplest case the initial step is the slowest, and the overall rate is just the rate of the first step.

Also, the rate equations for mechanisms with a single rate-determining step are usually in a simple mathematical form, whose relation to the mechanism and choice of rate-determining step is clear. The correct rate-determining step can be identified by predicting the rate law for each possible choice and comparing the different predictions with the experimental law, as for the example of NO
2 and CO below.

The concept of the rate-determining step is very important to the optimization and understanding of many chemical processes such as catalysis and combustion.

Example reaction: NO
2 + CO

As an example, consider the gas-phase reaction NO
2 + CO → NO + CO
2. If this reaction occurred in a single step, its reaction rate (r) would be proportional to the rate of collisions between NO
2 and CO molecules: r = k[NO
2 ][CO], where k is the reaction rate constant, and square brackets indicate a molar concentration. Another typical example is the Zel'dovich mechanism.

First step rate-determining

In fact, however, the observed reaction rate is second-order in NO
2 and zero-order in CO, with rate equation r = k[NO
2 ]². This suggests that the rate is determined by a step in which two NO
2 molecules react, with the CO molecule entering at another, faster, step. A possible mechanism in two elementary steps that explains the rate equation is:

1. NO
   2 + NO
   2 → NO + NO
   3 (slow step, rate-determining)
2. NO
   3 + CO → NO
   2 + CO
   2 (fast step)

In this mechanism the reactive intermediate species NO
3 is formed in the first step with rate r₁ and reacts with CO in the second step with rate r₂. However NO
3 can also react with NO if the first step occurs in the reverse direction (NO + NO
3 → 2 NO
2 ) with rate r₋₁, where the minus sign indicates the rate of a reverse reaction. 

The concentration of a reactive intermediate such as [NO
3 ] remains low and almost constant. It may therefore be estimated by the steady-state approximation, which specifies that the rate at which it is formed equals the (total) rate at which it is consumed. In this example NO
3 is formed in one step and reacts in two, so that

${\displaystyle {\frac {d{\ce {[NO3]}}}{dt}}=r_{1}-r_{2}-r_{-1}\approx 0.}$

The statement that the first step is the slow step actually means that the first step in the reverse direction is slower than the second step in the forward direction, so that almost all NO
3 is consumed by reaction with CO and not with NO. That is, r₋₁ ≪ r₂, so that r₁ − r₂ ≈ 0. But the overall rate of reaction is the rate of formation of final product (here CO
2), so that r = r₂ ≈ r₁. That is, the overall rate is determined by the rate of the first step, and (almost) all molecules that react at the first step continue to the fast second step.

Pre-equilibrium: if the second step were rate-determining

The other possible case would be that the second step is slow and rate-determining, meaning that it is slower than the first step in the reverse direction: r₂ ≪ r₋₁. In this hypothesis, r₁ − r₋₁ ≈ 0, so that the first step is (almost) at equilibrium. The overall rate is determined by the second step: r = r₂ ≪ r₁, as very few molecules that react at the first step continue to the second step, which is much slower. Such a situation in which an intermediate (here NO
3 ) forms an equilibrium with reactants prior to the rate-determining step is described as a pre-equilibrium For the reaction of NO
2 and CO, this hypothesis can be rejected, since it implies a rate equation that disagrees with experiment.

If the first step were at equilibrium, then its equilibrium constant expression permits calculation of the concentration of the intermediate NO
3 in terms of more stable (and more easily measured) reactant and product species:

${\displaystyle K_{1}={\frac {{\ce {[NO][NO3]}}}{{\ce {[NO2]^2}}}},}$

${\displaystyle [{\ce {NO3}}]=K_{1}{\frac {{\ce {[NO2]^2}}}{{\ce {[NO]}}}}.}$

The overall reaction rate would then be

${\displaystyle r=r_{2}=k_{2}{\ce {[NO3][CO]}}=k_{2}K_{1}{\frac {{\ce {[NO2]^2 [CO]}}}{{\ce {[NO]}}}},}$

which disagrees with the experimental rate law given above, and so disproves the hypothesis that the second step is rate-determining for this reaction. However, some other reactions are believed to involve rapid pre-equilibria prior to the rate-determining step, as shown below.

Nucleophilic substitution

Another example is the unimolecular nucleophilic substitution (S_N1) reaction in organic chemistry, where it is the first, rate-determining step that is unimolecular. A specific case is the basic hydrolysis of tert-butyl bromide (t-C
₄H
₉Br) by aqueous sodium hydroxide. The mechanism has two steps (where R denotes the tert-butyl radical t-C
₄H
₉):

1. Formation of a carbocation R−Br → R⁺ + Br⁻.
2. Nucleophilic attack by one water molecule R⁺ + OH⁻ → ROH.

This reaction is found to be first-order with r = k[R−Br], which indicates that the first step is slow and determines the rate. The second step with OH⁻ is much faster, so the overall rate is independent of the concentration of OH⁻. 

In contrast, the alkaline hydrolysis of methyl bromide (CH
₃Br) is a bimolecular nucleophilic substitution (S_N2) reaction in a single bimolecular step. Its rate law is second-order: r = k[R−Br][OH⁻].

Composition of the transition state

A useful rule in the determination of mechanism is that the concentration factors in the rate law indicate the composition and charge of the activated complex or transition state. For the NO
2 –CO reaction above, the rate depends on [NO
2 ]², so that the activated complex has composition N
₂O
₄, with 2 NO
2 entering the reaction before the transition state, and CO reacting after the transition state.

A multistep example is the reaction between oxalic acid and chlorine in aqueous solution: H
₂C
₂O
₄ + Cl
₂ → 2 CO
2 + 2 H⁺ + 2 Cl⁻. The observed rate law is

${\displaystyle v=k{\frac {{\ce {[Cl2][H2C2O4]}}}{[{\ce {H+}}]^{2}[{\ce {Cl^-}}]}},}$

which implies an activated complex in which the reactants lose 2H⁺ + Cl⁻ before the rate-determining step. The formula of the activated complex is Cl
₂ + H
₂C
₂O
₄ − 2 H⁺ − Cl⁻ + x H
2O, or C
₂O
₄Cl(H
₂O)^–
_x (an unknown number of water molecules are added because the possible dependence of the reaction rate on H
2O was not studied, since the data were obtained in water solvent at a large and essentially unvarying concentration).

One possible mechanism in which the preliminary steps are assumed to be rapid pre-equilibria occurring prior to the transition state is

Cl
₂ + H
2O ⇌ HOCl + Cl⁻ + H⁺

H
₂C
₂O
₄ ⇌ H⁺ + HC
₂O⁻
₄

HOCl + HC
₂O⁻
₄ → H
2O + Cl⁻ + 2 CO
2

Reaction coordinate diagram

In a multistep reaction, the rate-determining step does not necessarily correspond to the highest Gibbs energy on the reaction coordinate diagram. If there is a reaction intermediate whose energy is lower than the initial reactants, then the activation energy needed to pass through any subsequent transition state depends on the Gibbs energy of that state relative to the lower-energy intermediate. The rate-determining step is then the step with the largest Gibbs energy difference relative either to the starting material or to any previous intermediate on the diagram.

Also, for reaction steps that are not first-order, concentration terms must be considered in choosing the rate-determining step.

Chain reactions

Not all reactions have a single rate-determining step. In particular, the rate of a chain reaction is usually not controlled by any single step.

Diffusion control

In the previous examples the rate determining step was one of the sequential chemical reactions leading to a product. The rate-determining step can also be the transport of reactants to where they can interact and form the product. This case is referred to as diffusion control and, in general, occurs when the formation of product from the activated complex is very rapid and thus the provision of the supply of reactants is rate-determining.

Gene expression profiling

From Wikipedia, the free encyclopedia

 

Heat maps of gene expression values show how experimental conditions influenced production (expression) of mRNA for a set of genes. Green indicates reduced expression. Cluster analysis has placed a group of down regulated genes in the upper left corner.

 

In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. 

Several transcriptomics technologies can be used to generate the necessary data to analyse. DNA microarrays measure the relative activity of previously identified target genes. Sequence based techniques, like RNA-Seq, provide information on the sequences of genes in addition to their expression level.

Background

Expression profiling is a logical next step after sequencing a genome: the sequence tells us what the cell could possibly do, while the expression profile tells us what it is actually doing at a point in time. Genes contain the instructions for making messenger RNA (mRNA), but at any moment each cell makes mRNA from only a fraction of the genes it carries. If a gene is used to produce mRNA, it is considered "on", otherwise "off". Many factors determine whether a gene is on or off, such as the time of day, whether or not the cell is actively dividing, its local environment, and chemical signals from other cells. For instance, skin cells, liver cells and nerve cells turn on (express) somewhat different genes and that is in large part what makes them different. Therefore, an expression profile allows one to deduce a cell's type, state, environment, and so forth.

Expression profiling experiments often involve measuring the relative amount of mRNA expressed in two or more experimental conditions. This is because altered levels of a specific sequence of mRNA suggest a changed need for the protein coded by the mRNA, perhaps indicating a homeostatic response or a pathological condition. For example, higher levels of mRNA coding for alcohol dehydrogenase suggest that the cells or tissues under study are responding to increased levels of ethanol in their environment. Similarly, if breast cancer cells express higher levels of mRNA associated with a particular transmembrane receptor than normal cells do, it might be that this receptor plays a role in breast cancer. A drug that interferes with this receptor may prevent or treat breast cancer. In developing a drug, one may perform gene expression profiling experiments to help assess the drug's toxicity, perhaps by looking for changing levels in the expression of cytochrome P450 genes, which may be a biomarker of drug metabolism. Gene expression profiling may become an important diagnostic test.

Comparison to proteomics

The human genome contains on the order of 25,000 genes which work in concert to produce on the order of 1,000,000 distinct proteins. This is due to alternative splicing, and also because cells make important changes to proteins through posttranslational modification after they first construct them, so a given gene serves as the basis for many possible versions of a particular protein. In any case, a single mass spectrometry experiment can identify about 2,000 proteins or 0.2% of the total. While knowledge of the precise proteins a cell makes (proteomics) is more relevant than knowing how much messenger RNA is made from each gene, gene expression profiling provides the most global picture possible in a single experiment. However, proteomics methodology is improving. In other species, such as yeast, it is possible to identify over 4,000 proteins in just over one hour.

Use in hypothesis generation and testing

Sometimes, a scientist already has an idea of what is going on, a hypothesis, and he or she performs an expression profiling experiment with the idea of potentially disproving this hypothesis. In other words, the scientist is making a specific prediction about levels of expression that could turn out to be false.

More commonly, expression profiling takes place before enough is known about how genes interact with experimental conditions for a testable hypothesis to exist. With no hypothesis, there is nothing to disprove, but expression profiling can help to identify a candidate hypothesis for future experiments. Most early expression profiling experiments, and many current ones, have this form which is known as class discovery. A popular approach to class discovery involves grouping similar genes or samples together using one of the many existing clustering methods such the traditional k-means or hierarchical clustering, or the more recent MCL. Apart from selecting a clustering algorithm, user usually has to choose an appropriate proximity measure (distance or similarity) between data objects. The figure above represents the output of a two dimensional cluster, in which similar samples (rows, above) and similar gene probes (columns) were organized so that they would lie close together. The simplest form of class discovery would be to list all the genes that changed by more than a certain amount between two experimental conditions. 

Class prediction is more difficult than class discovery, but it allows one to answer questions of direct clinical significance such as, given this profile, what is the probability that this patient will respond to this drug? This requires many examples of profiles that responded and did not respond, as well as cross-validation techniques to discriminate between them.

Limitations

In general, expression profiling studies report those genes that showed statistically significant differences under changed experimental conditions. This is typically a small fraction of the genome for several reasons. First, different cells and tissues express a subset of genes as a direct consequence of cellular differentiation so many genes are turned off. Second, many of the genes code for proteins that are required for survival in very specific amounts so many genes do not change. Third, cells use many other mechanisms to regulate proteins in addition to altering the amount of mRNA, so these genes may stay consistently expressed even when protein concentrations are rising and falling. Fourth, financial constraints limit expression profiling experiments to a small number of observations of the same gene under identical conditions, reducing the statistical power of the experiment, making it impossible for the experiment to identify important but subtle changes. Finally, it takes a great amount of effort to discuss the biological significance of each regulated gene, so scientists often limit their discussion to a subset. Newer microarray analysis techniques automate certain aspects of attaching biological significance to expression profiling results, but this remains a very difficult problem. 

The relatively short length of gene lists published from expression profiling experiments limits the extent to which experiments performed in different laboratories appear to agree. Placing expression profiling results in a publicly accessible microarray database makes it possible for researchers to assess expression patterns beyond the scope of published results, perhaps identifying similarity with their own work.

Validation of high throughput measurements

Both DNA microarrays and quantitative PCR exploit the preferential binding or "base pairing" of complementary nucleic acid sequences, and both are used in gene expression profiling, often in a serial fashion. While high throughput DNA microarrays lack the quantitative accuracy of qPCR, it takes about the same time to measure the gene expression of a few dozen genes via qPCR as it would to measure an entire genome using DNA microarrays. So it often makes sense to perform semi-quantitative DNA microarray analysis experiments to identify candidate genes, then perform qPCR on some of the most interesting candidate genes to validate the microarray results. Other experiments, such as a Western blot of some of the protein products of differentially expressed genes, make conclusions based on the expression profile more persuasive, since the mRNA levels do not necessarily correlate to the amount of expressed protein.

Statistical analysis

Data analysis of microarrays has become an area of intense research. Simply stating that a group of genes were regulated by at least twofold, once a common practice, lacks a solid statistical footing. With five or fewer replicates in each group, typical for microarrays, a single outlier observation can create an apparent difference greater than two-fold. In addition, arbitrarily setting the bar at two-fold is not biologically sound, as it eliminates from consideration many genes with obvious biological significance. 

Rather than identify differentially expressed genes using a fold change cutoff, one can use a variety of statistical tests or omnibus tests such as ANOVA, all of which consider both fold change and variability to create a p-value, an estimate of how often we would observe the data by chance alone. Applying p-values to microarrays is complicated by the large number of multiple comparisons (genes) involved. For example, a p-value of 0.05 is typically thought to indicate significance, since it estimates a 5% probability of observing the data by chance. But with 10,000 genes on a microarray, 500 genes would be identified as significant at p < 0.05 even if there were no difference between the experimental groups. One obvious solution is to consider significant only those genes meeting a much more stringent p value criterion, e.g., one could perform a Bonferroni correction on the p-values, or use a false discovery rate calculation to adjust p-values in proportion to the number of parallel tests involved. Unfortunately, these approaches may reduce the number of significant genes to zero, even when genes are in fact differentially expressed. Current statistics such as Rank products aim to strike a balance between false discovery of genes due to chance variation and non-discovery of differentially expressed genes. Commonly cited methods include the Significance Analysis of Microarrays (SAM) and a wide variety of methods are available from Bioconductor and a variety of analysis packages from bioinformatics companies. 

Selecting a different test usually identifies a different list of significant genes since each test operates under a specific set of assumptions, and places a different emphasis on certain features in the data. Many tests begin with the assumption of a normal distribution in the data, because that seems like a sensible starting point and often produces results that appear more significant. Some tests consider the joint distribution of all gene observations to estimate general variability in measurements, while others look at each gene in isolation. Many modern microarray analysis techniques involve bootstrapping (statistics), machine learning or Monte Carlo methods.

As the number of replicate measurements in a microarray experiment increases, various statistical approaches yield increasingly similar results, but lack of concordance between different statistical methods makes array results appear less trustworthy. The MAQC Project makes recommendations to guide researchers in selecting more standard methods (e.g. using p-value and fold-change together for selecting the differentially expressed genes) so that experiments performed in different laboratories will agree better. 

Different from the analysis on differentially expressed individual genes, another type of analysis focuses on differential expression or perturbation of pre-defined gene sets and is called gene set analysis. Gene set analysis demonstrated several major advantages over individual gene differential expression analysis. Gene sets are groups of genes that are functionally related according to current knowledge. Therefore, gene set analysis is considered a knowledge based analysis approach. Commonly used gene sets include those derived from KEGG pathways, Gene Ontology terms, gene groups that share some other functional annotations, such as common transcriptional regulators etc. Representative gene set analysis methods include Gene Set Enrichment Analysis (GSEA), which estimates significance of gene sets based on permutation of sample labels, and Generally Applicable Gene-set Enrichment (GAGE), which tests the significance of gene sets based on permutation of gene labels or a parametric distribution.

Gene annotation

While the statistics may identify which gene products change under experimental conditions, making biological sense of expression profiling rests on knowing which protein each gene product makes and what function this protein performs. Gene annotation provides functional and other information, for example the location of each gene within a particular chromosome. Some functional annotations are more reliable than others; some are absent. Gene annotation databases change regularly, and various databases refer to the same protein by different names, reflecting a changing understanding of protein function. Use of standardized gene nomenclature helps address the naming aspect of the problem, but exact matching of transcripts to genes remains an important consideration.

Categorizing regulated genes

Having identified some set of regulated genes, the next step in expression profiling involves looking for patterns within the regulated set. Do the proteins made from these genes perform similar functions? Are they chemically similar? Do they reside in similar parts of the cell? Gene ontology analysis provides a standard way to define these relationships. Gene ontologies start with very broad categories, e.g., "metabolic process" and break them down into smaller categories, e.g., "carbohydrate metabolic process" and finally into quite restrictive categories like "inositol and derivative phosphorylation".

Genes have other attributes beside biological function, chemical properties and cellular location. One can compose sets of genes based on proximity to other genes, association with a disease, and relationships with drugs or toxins. The Molecular Signatures Database and the Comparative Toxicogenomics Database are examples of resources to categorize genes in numerous ways.

Finding patterns among regulated genes

Ingenuity Gene Network Diagram which dynamically assembles genes with known relationships. Green indicates reduced expression, red indicates increased expression. The algorithm has included unregulated genes, white, to improve connectivity.

 

Regulated genes are categorized in terms of what they are and what they do, important relationships between genes may emerge. For example, we might see evidence that a certain gene creates a protein to make an enzyme that activates a protein to turn on a second gene on our list. This second gene may be a transcription factor that regulates yet another gene from our list. Observing these links we may begin to suspect that they represent much more than chance associations in the results, and that they are all on our list because of an underlying biological process. On the other hand, it could be that if one selected genes at random, one might find many that seem to have something in common. In this sense, we need rigorous statistical procedures to test whether the emerging biological themes is significant or not. That is where gene set analysis comes in.

Cause and effect relationships

Fairly straightforward statistics provide estimates of whether associations between genes on lists are greater than what one would expect by chance. These statistics are interesting, even if they represent a substantial oversimplification of what is really going on. Here is an example. Suppose there are 10,000 genes in an experiment, only 50 (0.5%) of which play a known role in making cholesterol. The experiment identifies 200 regulated genes. Of those, 40 (20%) turn out to be on a list of cholesterol genes as well. Based on the overall prevalence of the cholesterol genes (0.5%) one expects an average of 1 cholesterol gene for every 200 regulated genes, that is, 0.005 times 200. This expectation is an average, so one expects to see more than one some of the time. The question becomes how often we would see 40 instead of 1 due to pure chance. 

According to the hypergeometric distribution, one would expect to try about 10^57 times (10 followed by 56 zeroes) before picking 39 or more of the cholesterol genes from a pool of 10,000 by drawing 200 genes at random. Whether one pays much attention to how infinitesimally small the probability of observing this by chance is, one would conclude that the regulated gene list is enriched in genes with a known cholesterol association.

One might further hypothesize that the experimental treatment regulates cholesterol, because the treatment seems to selectively regulate genes associated with cholesterol. While this may be true, there are a number of reasons why making this a firm conclusion based on enrichment alone represents an unwarranted leap of faith. One previously mentioned issue has to do with the observation that gene regulation may have no direct impact on protein regulation: even if the proteins coded for by these genes do nothing other than make cholesterol, showing that their mRNA is altered does not directly tell us what is happening at the protein level. It is quite possible that the amount of these cholesterol-related proteins remains constant under the experimental conditions. Second, even if protein levels do change, perhaps there is always enough of them around to make cholesterol as fast as it can be possibly made, that is, another protein, not on our list, is the rate determining step in the process of making cholesterol. Finally, proteins typically play many roles, so these genes may be regulated not because of their shared association with making cholesterol but because of a shared role in a completely independent process.

Bearing the foregoing caveats in mind, while gene profiles do not in themselves prove causal relationships between treatments and biological effects, they do offer unique biological insights that would often be very difficult to arrive at in other ways.

Using patterns to find regulated genes

As described above, one can identify significantly regulated genes first and then find patterns by comparing the list of significant genes to sets of genes known to share certain associations. One can also work the problem in reverse order. Here is a very simple example. Suppose there are 40 genes associated with a known process, for example, a predisposition to diabetes. Looking at two groups of expression profiles, one for mice fed a high carbohydrate diet and one for mice fed a low carbohydrate diet, one observes that all 40 diabetes genes are expressed at a higher level in the high carbohydrate group than the low carbohydrate group. Regardless of whether any of these genes would have made it to a list of significantly altered genes, observing all 40 up, and none down appears unlikely to be the result of pure chance: flipping 40 heads in a row is predicted to occur about one time in a trillion attempts using a fair coin. 

For a type of cell, the group of genes whose combined expression pattern is uniquely characteristic to a given condition constitutes the gene signature of this condition. Ideally, the gene signature can be used to select a group of patients at a specific state of a disease with accuracy that facilitates selection of treatments. Gene Set Enrichment Analysis (GSEA) and similar methods take advantage of this kind of logic but uses more sophisticated statistics, because component genes in real processes display more complex behavior than simply moving up or down as a group, and the amount the genes move up and down is meaningful, not just the direction. In any case, these statistics measure how different the behavior of some small set of genes is compared to genes not in that small set.

GSEA uses a Kolmogorov Smirnov style statistic to see whether any previously defined gene sets exhibited unusual behavior in the current expression profile. This leads to a multiple hypothesis testing challenge, but reasonable methods exist to address it.

Conclusions

Expression profiling provides new information about what genes do under various conditions. Overall, microarray technology produces reliable expression profiles. From this information one can generate new hypotheses about biology or test existing ones. However, the size and complexity of these experiments often results in a wide variety of possible interpretations. In many cases, analyzing expression profiling results takes far more effort than performing the initial experiments.

Most researchers use multiple statistical methods and exploratory data analysis before publishing their expression profiling results, coordinating their efforts with a bioinformatician or other expert in DNA microarrays. Good experimental design, adequate biological replication and follow up experiments play key roles in successful expression profiling experiments.

Amylin

From Wikipedia, the free encyclopedia

 

IAPP
Available structures PDB Ortholog search: PDBe RCSB

List of PDB id codes
Identifiers
Aliases	IAPP, DAP, IAP, islet amyloid polypeptide
External IDs	OMIM: 147940 MGI: 96382 HomoloGene: 36024 GeneCards: IAPP

Gene location (Human) Chr. Chromosome 12 (human) Band 12p12.1 Start 21,354,959 bp End 21,379,980 bp

RNA expression pattern

Orthologs
Species	Human	Mouse
Entrez	3375	15874
Ensembl	ENSG00000121351	ENSMUSG00000041681
UniProt	P10997	P12968
RefSeq (mRNA)	NM_000415 NM_001329201	NM_010491
RefSeq (protein)	NP_000406 NP_001316130	NP_034621
Location (UCSC)	Chr 12: 21.35 – 21.38 Mb	Chr 6: 142.3 – 142.3 Mb
PubMed search

Amino acid sequence of amylin with disulfide bridge and cleavage sites of insulin degrading enzyme indicated with arrows

Amylin, or islet amyloid polypeptide (IAPP), is a 37-residue peptide hormone. It is cosecreted with insulin from the pancreatic β-cells in the ratio of approximately 100:1 (insulin:amylin). Amylin plays a role in glycemic regulation by slowing gastric emptying and promoting satiety, thereby preventing post-prandial spikes in blood glucose levels.

IAPP is processed from an 89-residue coding sequence. Proislet amyloid polypeptide (proIAPP, proamylin, proislet protein) is produced in the pancreatic beta cells (β-cells) as a 67 amino acid, 7404 Dalton pro-peptide and undergoes post-translational modifications including protease cleavage to produce amylin. 

Synthesis

ProIAPP consists of 67 amino acids, which follow a 22 amino acid signal peptide which is rapidly cleaved after translation of the 89 amino acid coding sequence. The human sequence (from N-terminus to C-terminus) is: 

(MGILKLQVFLIVLSVALNHLKA) TPIESHQVEKR^ KCNTATCATQRLANFLVHSSNNFGAILSSTNVGSNTYG^ KR^ NAVEVLKREPLNYLPL. The signal peptide is removed during translation of the protein and transport into the endoplasmic reticulum. Once inside the endoplasmic reticulum, a disulfide bond is formed between cysteine residues numbers 2 and 7. Later in the secretory pathway, the precursor undergoes additional proteolysis and posttranslational modification (indicated by ^). 11 amino acids are removed from the N-terminus by the enzyme proprotein convertase 2 (PC2) while 16 are removed from the C-terminus of the proIAPP molecule by proprotein convertase 1/3 (PC1/3). At the C-terminus Carboxypeptidase E then removes the terminal lysine and arginine residues. The terminal glycine amino acid that results from this cleavage allows the enzyme peptidylglycine alpha-amidating monooxygenase (PAM) to add an amine group. After this the transformation from the precursor protein proIAPP to the biologically active IAPP is complete (IAPP sequence: KCNTATCATQRLANFLVHSSNNFGAILSSTNVGSNTY)

Regulation

Insulin and IAPP are regulated by similar factors since they share a common regulatory promoter motif. The IAPP promoter is also activated by stimuli which do not affect insulin, such as tumor necrosis factor alpha and fatty acids. One of the defining features of Type 2 diabetes is insulin resistance. This is a condition wherein the body is unable to utilize insulin effectively, resulting in increased insulin production; since proinsulin and proIAPP are cosecreted, this results in an increase in the production of proIAPP as well. 

Although little is known about IAPP regulation, its connection to insulin indicates that regulatory mechanisms that affect insulin also affect IAPP. Thus blood glucose levels play an important role in regulation of proIAPP synthesis.

Function

Amylin functions as part of the endocrine pancreas and contributes to glycemic control. The peptide is secreted from the pancreatic islets into the blood circulation and is cleared by peptidases in the kidney. It is not found in the urine. 

Amylin's metabolic function is well-characterized as an inhibitor of the appearance of nutrient [especially glucose] in the plasma. It thus functions as a synergistic partner to insulin, with which it is cosecreted from pancreatic beta cells in response to meals. The overall effect is to slow the rate of appearance (Ra) of glucose in the blood after eating; this is accomplished via coordinate slowing down gastric emptying, inhibition of digestive secretion [gastric acid, pancreatic enzymes, and bile ejection], and a resulting reduction in food intake. Appearance of new glucose in the blood is reduced by inhibiting secretion of the gluconeogenic hormone glucagon. These actions, which are mostly carried out via a glucose-sensitive part of the brain stem, the area postrema, may be over-ridden during hypoglycemia. They collectively reduce the total insulin demand.

Amylin also acts in bone metabolism, along with the related peptides calcitonin and calcitonin gene related peptide.

Rodent amylin knockouts do not have a normal reduction of appetite following food consumption. Because it is an amidated peptide, like many neuropeptides, it is believed to be responsible for the effect on appetite.

Structure

The human form of IAPP has the amino acid sequence KCNTATCATQRLANFLVHSSNNFGAILSSTNVGSNTY, with a disulfide bridge between cysteine residues 2 and 7. Both the amidated C-terminus and the disulfide bridge are necessary for the full biological activity of amylin. IAPP is capable of forming amyloid fibrils in vitro. Within the fibrillization reaction, the early prefibrillar structures are extremely toxic to beta-cell and insuloma cell cultures. Later amyloid fiber structures also seem to have some cytotoxic effect on cell cultures. Studies have shown that fibrils are the end product and not necessarily the most toxic form of amyloid proteins/peptides in general. A non-fibril forming peptide (1–19 residues of human amylin) is toxic like the full-length peptide but the respective segment of rat amylin is not. It was also demonstrated by solid-state NMR spectroscopy that the fragment 20-29 of the human-amylin fragments membranes. Rats and mice have six substitutions (three of which are proline substitions at positions 25, 28 and 29) that are believed to prevent the formation of amyloid fibrils, although not completely as seen by its propensity to form amyloid fibrils in vitro. Rat IAPP is nontoxic to beta-cells when overexpressed in transgenic rodents.

History

IAPP was identified independently by two groups as the major component of diabetes-associated islet amyloid deposits in 1987.

The difference in nomenclature is largely geographical; European researchers tend to prefer IAPP whereas American researchers tend to prefer amylin. Some researchers discourage the use of "amylin" on the grounds that it may be confused with the pharmaceutical company.

Clinical significance

ProIAPP has been linked to Type 2 diabetes and the loss of islet β-cells. Islet amyloid formation, initiated by the aggregation of proIAPP, may contribute to this progressive loss of islet β-cells. It is thought that proIAPP forms the first granules that allow for IAPP to aggregate and form amyloid which may lead to amyloid-induced apoptosis of β-cells.

IAPP is cosecreted with insulin. Insulin resistance in Type 2 diabetes produces a greater demand for insulin production which results in the secretion of proinsulin. ProIAPP is secreted simultaneously, however, the enzymes that convert these precursor molecules into insulin and IAPP, respectively, are not able to keep up with the high levels of secretion, ultimately leading to the accumulation of proIAPP.

In particular, the impaired processing of proIAPP that occurs at the N-terminal cleavage site is a key factor in the initiation of amyloid. Post-translational modification of proIAPP occurs at both the carboxy terminus and the amino terminus, however, the processing of the amino terminus occurs later in the secretory pathway. This might be one reason why it is more susceptible to impaired processing under conditions where secretion is in high demand. Thus, the conditions of Type 2 diabetes—high glucose concentrations and increased secretory demand for insulin and IAPP—could lead to the impaired N-terminal processing of proIAPP. The unprocessed proIAPP can then serve as the nidus upon which IAPP can accumulate and form amyloid.

The amyloid formation might be a major mediator of apoptosis, or programmed cell death, in the islet β-cells. Initially, the proIAPP aggregates within secretory vesicles inside the cell. The proIAPP acts as a seed, collecting matured IAPP within the vesicles, forming intracellular amyloid. When the vesicles are released, the amyloid grows as it collects even more IAPP outside the cell. The overall effect is an apoptosis cascade initiated by the influx of ions into the β-cells.

General Scheme for Amyloid Formation

 

In summary, impaired N-terminal processing of proIAPP is an important factor initiating amyloid formation and β-cell death. These amyloid deposits are pathological characteristics of the pancreas in Type 2 diabetes. However, it is still unclear as to whether amyloid formation is involved in or merely a consequence of type 2 diabetes. Nevertheless, it is clear that amyloid formation reduces working β-cells in patients with Type 2 diabetes. This suggests that repairing proIAPP processing may help to prevent β-cell death, thereby offering hope as a potential therapeutic approach for Type 2 diabetes. 

Amyloid deposits deriving from islet amyloid polypeptide (IAPP, or amylin) are commonly found in pancreatic islets of patients suffering diabetes mellitus type 2, or containing an insulinoma cancer. While the association of amylin with the development of type 2 diabetes has been known for some time, its direct role as the cause has been harder to establish. Recent results suggest that amylin, like the related beta-amyloid (Abeta) associated with Alzheimer's disease, can induce apoptotic cell-death in insulin-producing beta cells, an effect that may be relevant to the development of type 2 diabetes.

A 2008 study reported a synergistic effect for weight loss with leptin and amylin coadministration in diet-induced obese rats by restoring hypothalamic sensitivity to leptin. However, in clinical trials, the study was halted at Phase 2 in 2011 when a problem involving antibody activity that might have neutralized the weight-loss effect of metreleptin in two patients who took the drug in a previously completed clinical study. The study combined metreleptin, a version of the human hormone leptin, and pramlintide, which is Amylin’s diabetes drug Symlin, into a single obesity therapy. Finally, a recent proteomics study showed that human amylin shares common toxicity targets with beta-amyloid (Abeta), providing evidence that type 2 diabetes and Alzheimer's disease share common toxicity mechanisms.

Pharmacology

A synthetic analog of human amylin with proline substitutions in positions 25, 26 and 29, or pramlintide (brand name Symlin), was approved in 2005 for adult use in patients with both diabetes mellitus type 1 and diabetes mellitus type 2. Insulin and pramlintide, injected separately but both before a meal, work together to control the post-prandial glucose excursion.

Amylin is degraded in part by insulin-degrading enzyme.

Receptors

There appear to be at least three distinct receptor complexes that amylin binds to with high affinity. All three complexes contain the calcitonin receptor at the core, plus one of three receptor activity-modifying proteins, RAMP1, RAMP2, or RAMP3.