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let's suppose the following reaction:
At equilibrium we can calculate the Kd (Dissociation constant) using the following formula:
Kd = [P][L] / [PL] or kd / ka
We can then use the Hill equation to calculate the fraction of binding sites which are occupied:
occupied binding sites = [L] / [L] + Kd
When [L] euals Kd, half of the ligand binding sites are occupied (Lehninger principles of biochemistry)
Why is this true? This would mean that at equilibrium half of the binding sites are occupied?
As the other answer says, it is true for a simplified model. The model you describe has only one ligand binding site per protein, which makes it the most simple model there is.
It doesn't mean that this is the case at equilibrium as you asked. This is because Kd is not necessarily equal to [L] at equilibrium. Hopefully this simple derivation will help:
As you said, we know the formula for the dissociation constant: Kd = [P][L]/[PL]
Suppose [L] = Kd. If we plug back into the equation above we get: [L] = [P][L]/[PL]
Doing some algebra we can derive: [P] = [PL]
Therefore, at equilibrium, when [L] = Kd, the amount of free protein ([P]) and the amount of ligand-bound protein ([PL]) are equal. In other words, half of the protein is bound to ligand, which is exactly what you were asking about.
But now let's suppose that [L] =/= Kd. The reaction will still come to equilibrium, finding concentrations of [P] and [PL] that satisfy the equation Kd = [P][L]/[PL]. However, these values will not be equal. Either more or less than half of the protein will be bound to ligand.
Only in the specific case of [L] = Kd will half of the protein be bound to ligand when equilibrium is reached.
The Hill equation in that link uses the simplifying assumption that all ligands bind simultaneously and that the binding sites are identical. The reaction is then P + nL = PL, and Kd = ( [P][L]^n )/[PL] Then by rearranging you get the expression in the link.
The equation "fraction occupied binding sites = [L] / [L] + Kd" Is not the Hill equation. It is called the binding isotherm. You can see that if Kd = [L] then the fraction of occupied binding sites (let's call it theta) = 1/2. Thus half of the binding sites are occupied.
Protein – Which is Best?
Protein intake that exceeds the recommended daily allowance is widely accepted for both endurance and power athletes. However, considering the variety of proteins that are available much less is known concerning the benefits of consuming one protein versus another. The purpose of this paper is to identify and analyze key factors in order to make responsible recommendations to both the general and athletic populations. Evaluation of a protein is fundamental in determining its appropriateness in the human diet. Proteins that are of inferior content and digestibility are important to recognize and restrict or limit in the diet. Similarly, such knowledge will provide an ability to identify proteins that provide the greatest benefit and should be consumed. The various techniques utilized to rate protein will be discussed. Traditionally, sources of dietary protein are seen as either being of animal or vegetable origin. Animal sources provide a complete source of protein (i.e. containing all essential amino acids), whereas vegetable sources generally lack one or more of the essential amino acids. Animal sources of dietary protein, despite providing a complete protein and numerous vitamins and minerals, have some health professionals concerned about the amount of saturated fat common in these foods compared to vegetable sources. The advent of processing techniques has shifted some of this attention and ignited the sports supplement marketplace with derivative products such as whey, casein and soy. Individually, these products vary in quality and applicability to certain populations. The benefits that these particular proteins possess are discussed. In addition, the impact that elevated protein consumption has on health and safety issues (i.e. bone health, renal function) are also reviewed.
Higher protein needs are seen in athletic populations.
Animal proteins is an important source of protein, however potential health concerns do exist from a diet of protein consumed from primarily animal sources.
With a proper combination of sources, vegetable proteins may provide similar benefits as protein from animal sources.
Casein protein supplementation may provide the greatest benefit for increases in protein synthesis for a prolonged duration.
The American Heart Association recommends aiming for a dietary pattern that achieves 5% to 6% of calories from saturated fat.
For example, if you need about 2,000 calories a day, no more than 120 of them should come from saturated fat.
That&rsquos about 13 grams of saturated fat per day.
What are saturated fats?
From a chemical standpoint, saturated fats are simply fat molecules that have no double bonds between carbon molecules because they are saturated with hydrogen molecules. Saturated fats are typically solid at room temperature.
How do saturated fats affect my health?
Replacing foods that are high in saturated fat with healthier options can lower blood cholesterol levels and improve lipid profiles
What foods contain saturated fat?
Saturated fats occur naturally in many foods. The majority come mainly from animal sources, including meat and dairy products.
Examples of foods with saturated fat are:
- fatty beef,
- poultry with skin,
- beef fat (tallow),
- lard and cream,
- cheese and
- other dairy products made from whole or reduced-fat (2 percent) milk.
In addition, many baked goods and fried foods can contain high levels of saturated fats. Some plant-based oils, such as palm oil, palm kernel oil and coconut oil, also contain primarily saturated fats, but do not contain cholesterol.
What are alternatives to replace saturated fats in the foods I eat?
Choose lean meats and poultry without skin and prepare them without added saturated and trans fat.
You should replace foods high in saturated fats with foods high in monounsaturated and/or polyunsaturate fats. This means eating foods made with liquid vegetable oil but not tropical oils. It also means eating fish and nuts. You also might try to replace some of the meat you eat with beans or legumes.
There&rsquos a lot of conflicting information about saturated fats. Should I eat them or not?
The American Heart Association recommends limiting saturated fats &ndash which are found in butter, cheese, red meat and other animal-based foods. Decades of sound science has proven it can raise your &ldquobad&rdquo cholesterol and put you at higher risk for heart disease.
The more important thing to remember is the overall dietary picture. Saturated fats are just one piece of the puzzle. In general, you can&rsquot go wrong eating more fruits, vegetables, whole grains and fewer calories.
When you hear about the latest &ldquodiet of the day&rdquo or a new or odd-sounding theory about food, consider the source. The American Heart Association makes dietary recommendations only after carefully considering the latest scientific evidence.
Written by American Heart Association editorial staff and reviewed by science and medicine advisers. See our editorial policies and staff.
The epigenetics of pluripotent stem cells
Stephanie L. Battle , R. David Hawkins , in Stem Cell Epigenetics , 2020
Heterochromatin, H3K9me2 and H3K9me3
The H3K9me3 methyltransferase Setdb1 is required for embryogenesis as null mice embryos die shortly after implantation  . The pluripotency factor Oct4 complexes with the SUMOylated form of Setdb1/Eset to help maintain pluripotency in serum + Lif mESCs  . Setdb1 binds and represses lineage-specific genes and most imprinted genes through H3K9me3  . When Setdb1 is KD in mESC, they lose their pluripotent phenotype downregulate OSN, Klf4, and Esrrb and begin expressing differentiation factors like Cdx2, which is normally repressed by H3K9me3 at its promoter [135, 136] . Over half of Setdb1, target genes lose H3K9me3 when Setdb1 is KD in mESCs and KD of Pou5f1 (Oct4) causes loss of Setdb1 binding at trophoblasts lineage genes Cdx2 and Tfap2a/Tcfap2a but not the imprinted gene H19  . KD Setdb1 mESCs differentiate to trophectoderm cells easier than WT  . When injected into mouse embryos, KD Setdb1 mESCs incorporate into the trophectoderm of blastocysts, due to upregulation of trophoblasts lineage genes like Cdx2 and Tfap2a  .
Other K9 methyltransferases have also shown to be essential for development and pluripotency. Enzyme G9a/EHMT2, a K9 and K27 methyltransferase, is required for proper embryonic development. Null mutations of the enzyme are embryonic lethal by
E9.5  . Lack of G9a results in a dramatic loss of H3K9me2, except at constitutive heterochromatic regions, and an increase in active modifications H3K9ac and H3K4me2  . KD of H3K9 demethylases Jmjd2b and Jmjd2c in serum + Lif mESCs causes loss of pluripotency phenotype and downregulation of stemness genes like Nanog, Klf4, Tbx3, and Esrrb  .
Array- and sequencing-based methods have provided more detail into the features of heterochromatin. H3K9me2 can form broad domains in the genome, up to 2.9 Mb long, and cover > 20% of the genome in a differentiated cell  . Many of these regions are flanked by the insulator CTCF  to prevent heterochromatin spreading. However in ESCs, which overall have less dense compacted chromatin, only
4% of the genome contains H3K9me2, and the domains are much smaller in size compared with differentiated cells  . Wen et al. compared genes found within H3K9me2 blocks in brain and liver cells and found that H3K9me2 marked genes in one cell type but not the other were silenced when marked but had variable expression when unmarked  . From this, they concluded that H3K9me2 contributes to cell type-specific gene expression. Interestingly in this study, they found little overlap of H3K9me2 with H3K9me3, which did not show the broad domain structure like its dimethyl counterpart  . However, in differentiated cells, H3K9me3 does form broad repressive domains, which can be over a megabase in structure  . Some hESC master regulatory genes are also repressed by H3K9me3 in differentiated cells.
As mentioned previously, naïve ESCs have less repressive chromatin structure, with fewer regions of their genome being marked by repressive histone modifications like H3K9me3. When comparing naïve 2iL mESCs with metastable serum + Lif mESCs, there is little difference observed in where H3K9me3 is deposited in the genome  . Many of the same regions are marked in naïve and metastable mESCs, mostly including satellite regions and imprinted genes  . Mouse ESCs grown in 2iL have H3K9me3 at DNA methylated regions, even in its hypomethylated genome  . Methylated regions in 2iL also cover many IAP elements [63, 64] . Thus there is a dual mechanism of repression of these retroviral elements via DNA methylation and histone modification.
Unlike H3K27me3, H3K9me3 regions identified in primed hESCs do not show the same DNA methylation levels in human preimplantation ICM as in primed hESCs. H3K9me3 regions are hypomethylated in human ICM (
25% DNA methylation) but have higher methylation levels in postimplantation embryos and primed hESCs (> 50%)  . Since preimplantation embryonic cells are overall hypomethylated, this finding may seem to fall in line with previous data. However, it also begs the question, do histone modifications direct DNA methylation in the preimplantation epigenome? Perhaps, future research will be able to tease apart the cause and effect relationship between histone modifications and DNA methylation in the developing embryo.
RESULTS AND DISCUSSION
The results of an EMSA to measure the affinity of the interaction between GAL4-p53 and DNA are shown in Fig. 1. Lane 1 of the gel shows the migration of the unbound DNA and Lanes 2–11 show the shift in migration that occurred upon titrating GAL4-p53 into the binding reactions. Approximately, 50% of the DNA was in a complex with GAL4-p53 in reactions containing 2 nM protein, which provided an estimate of the KD for the interaction. Shown in Figs. 2a and 2b are plots of the data from Fig. 1. The fluorescent signal in each bound and unbound band was quantitated, and the fraction of DNA bound in each reaction was plotted versus GAL4-p53 concentration (in nM). The data were fit with a binding equation to obtain values for the KD and maximum fraction bound (Bmax) of 2.0 ± 0.8 nM and 1.05 ± 0.05, respectively. The errors are the 95% confidence interval obtained from the curve fit the R 2 is 0.978. In Fig. 2a the data are plotted with a linear X-axis, and in Fig. 2b with a logarithmic the X-axis. In the latter case the binding curve becomes sigmoidal, which allows the concentration of GAL4-p53 at which 50% of the DNA is bound (e.g. KD) to be more easily visualized.
GAL4-p53 binds to DNA with high affinity. (a) The plot shows the fraction of DNA bound as GAL4-p53 was titrated. The values for the KD and Bmax obtained from the curve-fit are 2.0 ± 0.8 nM and 1.05 ± 0.05, respectively. (b) The data in panel (a) were plotted with a logarithmic X-axis.
This experiment is designed such that the DNA should be fully bound at the highest concentrations of GAL4-p53 in Table I. For various experimental reasons, however, complete binding may not occur. For example, if a given protein preparation is only partially active then it may take significantly higher concentrations of the protein preparation to saturate the DNA. Such a result can stimulate useful discussions about quantitating the fractional activity of a protein preparation or predicting other experimental reasons why binding may not have been complete. Importantly, even if the DNA is not fully saturated, students can usually still detect changes in the fraction bound as the experimental conditions are varied (i.e. using mutant templates or changing salt concentrations), therefore, still making the experiment useful as an assay to monitor a protein/DNA interaction.
Lastly, a student's success in performing this experiment is typically related to his/her ability to accurately assemble and manipulate small reaction volumes. Similarly, the quality of the curve-fit (a good R 2 regardless of the value of the KD) largely depends on a student's pipetting skills, which can vary quite a bit between students. If necessary, simple tests of pipetting can help students practice prior to the experiment. For example, pipetting water onto a microbalance, or even adding small volumes of dyes to water, are helpful exercises. In addition, tips to help avoid common experimental pitfalls are listed in Table II.
|Mix well after each addition by finger tapping or a light, short vortex Buffer B can be difficult to mix in due to the glycerol.|
|Thaw the GAL4-p53 immediately prior to adding it to reactions.|
|Ensure the reactions are in the bottoms of the tubes, using a short spin in a microfuge if necessary.|
|Clean the gel plates prior to use with either ethanol or water.|
|When quantitating the fluorescence in gels make sure the regions used for background subtraction do not contain stray spots of fluorescence.|
|If necessary practice pipetting small reaction volumes|
|Dyes can be loaded in the native gel next to the sample lanes in order to visually monitor the progress of the electrophoresis.|
|Keep the lids to all tubes closed unless a reagent is being added or removed.|
|If DNA that has been frozen for a long period of time is not shifting well, a new annealing and purification is recommended|
C3. Mathematical Analysis of Cooperative Binding - Hill Plot
- Contributed by Henry Jakubowski
- Professor (Chemistry) at College of St. Benedict/St. John's University
Previously we have shown that the binding of oxygen to Mb, which can be described by the equilibrium,
M + L <=> ML, can be described mathematically by
This is the equation of a hyperbola. Remember, that this hyperbolic plot can be transformed in a variety of ways, as summarized in the graphs below for Mb.
Figure: 4 Ways to plot Mb and O2 plots
How does the sigmoidal binding curve for Hb arise. At least three models (Hill, MWC, and KNF) can be developed that give rise to sigmoidal binding curves. Remember, sigmoidal curves imply cooperative binding of oxygen to Hb: As oxygen binds, the next oxygen seems to bind with higher affinity (lower Kd)
Hill Model: In this model, we base our mathematical analysis on the fact that the stoichiometry of binding is not one to one, but rather 4 to 1: Perhaps a more useful equation to express the equilibrium would be M + 4L <=> ML4. For this equilibrium, we can derive an equation analogous to the equation 1 above. This equation is:
2) Y = L4/[Kd + L4].
For any given L and Kd, a corresponding Y can be calculated. Using this equation, the plot of Y vs L is not hyperbolic but sigmoidal (see next link below). Hence we're getting closer to modeling that actual data. However, there is one problem. This sigmoidal curve does not give a great fit to the actual oxygen binding curve for Hb. Maybe a better fit can be achieved by altering the exponents in equation 2. A more general equation for binding might be M + nL <=> MLn, which gives the following equation:
If n is set to 2.8, the theoretical curve of Y vs L gives the best but still not perfect fit to the experimental data. It must seem arbitrary to change the exponent which seems to reflect the stoichiometry of binding. What molecular interpretation could you give to 2.8Consider another meaning of the equilbrium described above:
M + 4L <=> ML4. One interpretation of this is that all 4 oxygens bind at once to Hb. Or, alternatively, the first one binds with some low affinity, which through associated conformational changes changes the remaining 3 sites to very high affinity sites which immediately bind oxygen if the oxygen concentration is high enough. This model implies what is described as infinitely cooperative binding of oxygen.
(Notice that this equation becomes: Y = L/[Kd + L], when n =1 (as in the case with myoglobin, and in any equilbrium expression of the form: M + L <==> ML. Remember plots of ML vs L or Y vs L gives hyperbolas, with Kd = L at Y = 0.5.)
Does Kd = L at Y = 0.5? The oxygen concentration at which Y = 0.5 is defined as P50. We can substitute this value into equation 3 which gives an operational definition of Kd in terms of P50.
Y = 0.5 = P50n/[Kd + P50n] - multiple both sides by 2
1 = 2P50n/[Kd + P50n]
Kd + P50n = 2P50n
4) Kd = P50n
Note that for equation 3, Kd is not the ligand concentration at half-saturation as we saw in the case with hyperbolic binding curves.
Wolfram Mathematica CDF Player - Hill Model (free plugin required)
Now consider another model:
M + L <=> ML + L <=> ML2 + L <=> ML3 + L <=> ML4 where the binding of each oxygen to the unligated or increasing ligated Hb has the same Kd. That is, the affinity of each binding site for oxygen does not increase as more sites are bound to oxygen. In this model, n in equation 3 is 1, and the resulting graph is completely hyperbolic. The fact that the experimental data fits the equilbrium M + 2.8L <=> ML2.8 implies that the binding is cooperative but not infinitely cooperative. Graphs of Y vs L showing these three cases (n=1, 2.8, and 4) are shown below:
Figure: Plots of Y vs L for Hb with varying degrees of cooperativity: n = 1, 2. 8, and 4
The general equation 3), Y = Ln/[Kd + Ln] can be rearranged as shown below:
1 - Y = [Kd + Ln]/[Kd + Ln] - Ln/[Kd + Ln] =
where 1 - Y is the fraction not bound. Solving for Y/[1-Y] by using equations 3 and 5 gives:
Taking the log of both sides gives:
7) log (Y/1-Y) = nlog L - log Kd
A plot of log (Y/1-Y) vs log L is called a Hill plot, where n is the Hill coefficient. This equation is of the form:
y = mx + b which is a straight line with slope n and y intercept of - log Kd. When n = 1, as it would be with Mb or Hb when oxygen binds to each site with the same affinity irrespective of the number of other oxygens bound to other sites, the Hill plot is linear with a slope of 1. Solving for the x intercept (when the y axis variable is 0) in equations 7 gives:
8) 0 = nlog L -log Kd, or nlogL = log Kd, or log L = (logKd)/n.
The X intercept is when the dependent variable "y" value is 0. This occurs when Y/(1-Y) = 1, which occurs at half fractional saturation. (Remember log 1 = log 100 = 0)
Substituting equation 4 (Kd = P50n) into (7) and (8) gives
(9) log (Y/1-Y) = nlog L - n log P50 - the Hill Equation with P50 instead of Kd,
(10) 0 = nlog L -nlog P50, or nlogL = nlog P50, or log L = logP50.
Even when n does not equal 1, the Hill plot is linear, since it has the form y=mx+b. If n = 2.8 or 4, the plot is linear, but has a slope of 2.8 and 4, respectively. This can be seen in the graph below which shows HIll plots with n = 1, 2.8, and 4.
Figure: Hill Plot for Mb (n =1)
However, the affinity of dixoygen for Hb changes, so that there must be more than one effective Kd. Hence, the actual Hill plot of Hb, log (Y/1-Y) vs log L, can not be linear over all ranges of dioxygen. A linear plot, such as for Mb, crosses the x axis at one point, with a value of (logKd)/n = logKd since n = 1. In contrast for Hb, since the Kd seems to change with L concentration, there can not be just 1 value of Kd, as given by the x intercept. The Hill plot of actual Hb binding data is curvilinear, and cross the x axis only once. Howver, the ends of the curve (at low and high dioxygen) approach straight lines with slopes of 1 (i.e. n=1). If extrapolated through the x axis, these lines would give the Kd for the binding of the first and last dioxygens, which bind noncooperatvely. LogL values near the region of the curve that crosses the x axis approximate a straight line with slope of 2.8. This implies there is maximal cooperativity in the middle of the binding curve. The graphs shows that the Kd for the first oxygen binding is much higher than the Kd for the last oxygen binding. Hence the Hill Plots supports our ideas than cooperativity is caused by conformational changes in Hb which occur on oxygen binding such that as progressively more oxygen is bound, the affinity for the remaining sites increases.
Figure: Hill Plots For Hb Showing straight lines for n=2.8 and for n's=1 which model the low and high affinity sites.
9.2 Can p53 Act as a Biomarker in Cancer Management and Therapy?
A cancer biomarker should play a key role in guiding clinical practice. It can be used as a marker for early diagnosis or as a prognositic marker for survival. As p53 is expressed at low levels in normal cells in healthy individuals, immune system tolerance of wild-type p53 is low. However, cancer cells often contain mutant forms of p53. An important effect of p53 mutation is often the production of a more stable p53 mutant protein with concomitant higher expression. Consequently, mutant p53 is regarded as a “foreign” antigen by the immune system and triggers an immune response that results in the production of auto-anti-p53 antibodies. Detection of mutant p53 in patient DNA, or auto-anti-p53 antibodies in patient serum, therefore could be used for early diagnosis for a number of tumors types in which p53 mutation occurs at an early stage of tumor development.
Perhaps most importantly, p53 mutations may have a predictive value for cancer treatment, such as radiotherapy and most chemotherapies, which damage DNA and induce p53-mediated apoptosis or senescence. As mutations in p53 often impair its ability to induce apoptosis in response to DNA damage, resulting in cellular resistance to cancer therapy, the detection of p53 mutation would have a predictive value for cancer therapy however, challenges remain in translating this knowledge into a clinical setting.
9.2.1 p53 Mutation Status and Cancer Management
p53 is one of the most frequently mutated genes in human cancer and is mutated in the early stages of lung, skin, head and neck, and esophageal cancers. Can this information be used for early cancer diagnosis? To address this question, we need to review the spectrum of p53 mutations in human cancer. Of the 25,000 mutations registered in the p53 mutation database, around 30% of them fall into six hotspot codons (175, 245, 248, 249, 273, and 282). Based on the p53/DNA co-crystal structure, p53 mutants can be classified into two main groups: DNA contact defective mutants and mutants with altered conformations. Of the six mutation hotspots, amino acids 248 and 273 contact DNA, whereas amino acids 175, 245, 249, and 282 are involved in maintaining the structural integrity of the DNA-binding surface. Thus, all of these mutations result in the loss of the tumor suppressive function of p53.
Aside from the hotspots described earlier, the mutation frequency of other p53 codons varies dramatically. Some are more common in certain types of cancer and correspond to carcinogen fingerprints. One such example is squamous cell carcinoma (SCC) of the skin, where codons 177, 178, 179, 196, and 278 have been identified as mutation hotspots. As the majority of cases of SCC are induced by excessive exposure to UV radiation, mutations at codons 177, 196, and 278 might be selected because repair at these codons occurs more slowly than at other codons. Similarly, it is well established that preferential selection of a p53 mutation at codon 249 in hepatocellular carcinoma (HCC) is tightly linked to exposure to a high dose of the carcinogen aflatoxin B1. Smoking also results in signature p53 mutations. Phytohemagglutinins (PHA) metabolites, the carcinogens derived from tobacco, cause p53 mutations at codons 156 and 157, as well as at 245, 248, and 273. The latter three hotspots are also frequently mutated in nontobacco linked tumors of the breast, colon, and brain. Therefore, in theory, one should be able to use the mutation signature of p53 together with an individual’s history of smoking, exposure to UV, or aflatoxin levels to provide an early diagnosis of cancer in the lung, skin, or liver ( http://p53.free.fr http://www-p53.iarc.fr ). 17 Unfortunately, this approach is not used yet in regular practice mainly due to technical limitations, such as poor detection (less than 5% of cases) of mutant p53 in the material obtained from a patient’s urine, stool, or bronchial lavage. 19 Nevertheless, with the current rate of improvement in sequencing technology, the detection of mutant p53 in such samples should become easier and more efficient in the near future. The most challenging issue facing us will be to accurately predict the effect of defined mutations on the tumor suppressive activity of p53.
Over 100 studies have been carried out with the aim of determining the prognostic and predictive value of p53 mutations. One of the underlying reasons for extensive work in this area is that not all p53 mutations are inactivating. The exceptionally high percentage of missense mutations in p53 argues strongly that p53 mutations often lead to a gain of function, such as the drug resistance conferred by p53 missense mutations but not by p53 null mutations. Mutation of p53 at codon 175 from an arginine to a histidine confers resistance to chemotherapeutic agents such as cisplatinum and etoposide. 18,20 In agreement with this observation, patients with mutated p53 tend to respond less well to cancer therapy. Hence, mutant p53 is often associated with a poor prognosis for cancers of the breast (23/27 cohorts), bladder (6/8 cohorts), head and neck (8/9 cohorts), and those of hematological origin (14/14 cohorts) ( http://p53.free.fr http://www-p53.iarc.fr ). Nevertheless, p53 mutation status is not always predictive of poor prognosis. In a number of colon, lung, and esophageal cancer studies, the picture is mixed. Here, p53 mutation status is associated with poor prognosis in some cohorts (13/19, 8/18, 3/5, respectively) but not in others, such as an esophageal cohort in which p53 mutations were associated with a good prognosis. This type of positive association has also been observed in a number of other tumor types, such as ovarian, pancreas, and brain tumors. In addition, the prognostic value of p53 mutation status in brain tumor cohort studies has been inconclusive and all possible combinations have been reported 1/8 cohorts exhibited p53 mutations linked to a poor prognosis 2/8 cohorts showed p53 mutations linked to a good prognosis and 5/8 cohorts showed no association between p53 mutations and prognosis ( http://p53.free.fr http://www-p53.iarc.fr ). 21,22
Although the mutation status of p53 is not uniformly associated with prognosis, 65 out of 93 tumor cohorts consisting of 16 different tumor types showed a tight association between mutant p53 and poor prognosis, which argues strongly for the importance of p53 mutation status in cancer management. 22 Thus, the question that perhaps needs to be addressed is why, in some tumor types, p53 mutation status is not linked to poor prognosis. The complexity of p53 regulation and the biological functions of a defined p53 mutant in different tumor types resemble a complex barcode. 3 The true prognostic and predictive values of individual p53 mutations will only be accurately evaluated once we can decipher the regulatory barcodes of both wild-type and mutant p53.
9.2.2 Clinical Implications of Serological Analysis of Auto-Anti-p53 Antibodies
One of the most attractive methods for early diagnosis is serological analysis. A unique feature of the p53 mutation is its tight association with an increase in p53 protein stability, which results in the generation of an anti-p53 humoral response in vivo. In fact, it was this property that led Dr. Old’s group to use the auto-anti-p53 antibody produced in tumor barring mice to identify the mutant p53 as a tumor antigen. 23 Subsequent studies showed that auto-anti-p53 antibodies can often be detected in the sera of patients with tumors that express mutant p53, whereas levels of circulating anti-p53 antibodies remain very low in normal individuals. 24 Based on published data, it is estimated that around 30–40% of patients that have mutant p53-expressing tumors produce anti-p53 antibodies. Most anti-p53 antibody producing patients have a mutation that alters the expression level of p53. The antibody epitopes are predominantly located at the N- and C-termini of p53 and are not linked to p53 mutation sites. Auto-anti-p53 antibodies are produced through self-immunization in response to an in vivo increase in p53 expression in the tumor cells, because the level of p53 in healthy individuals is insufficient to elicit a humoral response. This feature makes the development of a p53 antibody assay attractive, as it would also detect elevated wild-type p53 levels resulting from defects in p53 regulatory pathways. However, while the specificity of the existing serological assay is very high, with around 90% of patients with detectable auto-anti-p53 antibodies having mutant p53-expressing tumors, it is not yet suitable for clinical practice as the sensitivity of the assay is too low. 19
Auto-anti-p53 antibody production has also been associated with poor prognosis in breast 25 and oral cancers. 26,27 Studies of lung, 28–30 ovarian, 31 colon, 32–34 and esophageal 32,35 cancer patients have also shown that a reduction in p53 antibody production is closely linked to a patient’s response to treatment. These data suggest that serological analysis of auto-p53 antibody levels may have a future in guiding clinical practice and could in theory serve as a prognostic and predictive tool in cancer management. However, several features have prevented this assay from entering clinical practice. For instance, circulating auto-anti-p53 antibodies are not always associated with poor survival. In one study, auto-anti-p53 antibody levels increased relative to stomach tumor size, 36 whereas in another study p53 antibodies were associated with good survival of gastric carcinoma patients. 37 In ovarian 38 and colon 39 cancers, auto-anti-p53 antibody is not linked to prognosis. The lack of a reliable and sensitive means to detect auto-anti-p53 antibodies is perhaps one of the biggest obstacles in this area of research. As a result, the percentage of patients with detectable auto-anti-p53 antibodies varies dramatically between studies. Even in patients with the same cancer type, levels vary widely, ranging from just over 16% 32 to over 47% 34 in colon cancers. Nonetheless, the clinical potential of this serological test remains high, largely due to its ability to specifically detect patients harboring mutant p53-expressing tumors. Furthermore, many studies have shown a very tight link between auto-anti-p53 antibody detection and high level p53 expression. A significant improvement in auto-anti-p53 antibody detection and a better understanding of the factors that affect auto-anti-p53 antibody production are needed desperately if this assay is to be used in clinical practice.
Insulin Resistance and the Metabolic Syndrome
Edward (Lev) Linkner MD, ABHM , in Integrative Medicine (Second Edition) , 2007
A high-protein diet (not to be interpreted as the Atkins Diet ) is recommended. The rationale is that proteins are more slowly absorbed and may contain soluble fiber, which slows down the absorption of glucose from the gut, thus reducing the insulin response. Also, high-quality proteins are needed to manufacture amino acids and body tissue. Non-animal sources of protein, such as nuts, legumes, and soy, are preferable. Soy protein contains the isoflavones genistein and daidzein, which influence beta-signaling pathways and help glucose transport. Soy foods also contain omega-3 fatty acids. Lastly, proteins stimulate the production of glucagon, which opposes insulin and promotes burning of stored fats and glycogen. Jeff Bland, PhD, 18 founder of the Institute for Functional Medicine, states, “It is not just the ratios of carbohydrate, protein, and fat one consumes that determine insulin response. It is the combination of the ratio with the total amount of carbohydrate and the type of carbohydrate, protein and fat consumed.”
The nuclear envelope (NE) is a shared and essential feature of the endomembrane systems in all eukaryotes. It has long been appreciated that the NE is a defining structure for cellular organization as it is responsible for the separation of nucleoplasm from cytoplasm (Figure 1A) and the respective biological processes including transcription and translation that are operative therein. However, the NE is anything but a simple barrier. Instead, the inner nuclear membrane (INM) is endowed with a specialized proteome that is dedicated to a plethora of critical cellular functions (Korfali et al., 2012 Garapati and Mishra, 2018 Gerace and Tapia, 2018). These include the sensation and buffering of mechanical forces (Burke, 2018 Kirby and Lammerding, 2018), genome organization and regulation (Buchwalter et al., 2019a), and lipid metabolism (Bahmanyar et al., 2014 Bahmanyar, 2015 Barbosa et al., 2015, 2019 Haider et al., 2018 Romanauska and Kohler, 2018). From a toplogical perspective, the NE is a physical extension of the ER that encases chromatin via inner and outer nuclear membranes (INM/ONM) that have distinct protein compositions and are linked at sites of fusion where nuclear pore complexes (NPCs) reside (Ungricht and Kutay, 2017) (Figure 1A).
FIGURE 1: Features of the nuclear envelope and ER and regulation of and functions for lipid asymmetry at the inner nuclear membrane (INM). (A) Schematic of the continuous NE and ER membranes. The inner nuclear membrane (INM) facing the nucleoplasm and outer nuclear membrane (ONM) physically linked to the ER at NE-ER junctions are separated by a lumen designated perinuclear space (PNS). A nuclear pore complex (NPC) is located at a fusion point between the INM/ONM to generate the pore membrane. Highlighted in different shades of red are proteins that may regulate lipid trafficking between the NE and ER as well as enzymes and proteins that are regulated by or sense bilayer lipid composition (PKC and Nup133), or that regulate de novo lipid synthesis (CTDNEP1/lipin and CCTα). The curvature of the membrane bilayers may also play a role in restricting diffusion of lipid species past NE-ER junctions (negative curvature) or the pore membrane (positive curvature). Schematic of a membrane fusion reaction (middle) highlights membrane bending at each intermediate step. (B) The de novo glycerolipid synthesis pathway. Mol% for lipid species specific to ER/NE membranes is shown (van Meer et al., 2008).
Given the diverse functionality of the NE, it is not surprising that a steadily growing and diverse list of human pathologies are caused by mutations in NE or INM-associated nuclear lamina proteins. These pathologies include movement disorders and myopathies (Dauer and Worman, 2009 Meinke and Schirmer, 2016), cases of severely reduced life span and progeria (Kubben and Misteli, 2017 Fichtman et al., 2019), embryonic lethality (Turner and Schlieker, 2016), and lipodystrophies (Shackleton et al., 2000). Disruption of NE stability is also common in cancer cells causing DNA damage, cancer-relevant chromosomal rearrangements, and the intiation of proinflammatory pathways (Lim et al., 2016 Umbreit and Pellman, 2017 Hatch, 2018).
Studies tackling NE pathologies, together with investigations centered on NE proteins in model organisms or tissue culture systems, revised the view of the NE to a dynamic membrane system that undergoes significant membrane remodeling even outside of open mitosis. This raises the question of what mechanisms are put to work to maintain or reestablish the NE permeabilty barrier, especially when NE integrity is perturbed. ESCRT (endosomal sorting complexes required for transport)-dependent processes are involved in the sealing of NE holes and have recently been discussed elsewhere (Campsteijn et al., 2016 Webster and Lusk, 2016 Gatta and Carlton, 2019 Vietri et al., 2020). We will discuss findings that connect lipid regulation and de novo lipid synthesis (see Figure 1B and more details below) to NE sealing (Kinugasa et al., 2019 Lee et al., 2020 Penfield et al., 2020). Another emerging principle underlying NE dynamics pertains to the sculpting of its proteome by quality control mechanisms. These serve to degrade unwanted or misfolded proteins to maintain INM identity and safeguard protein homeostasis (Smoyer and Jaspersen, 2019). Being situated close to the nuclear transcriptional machinery, proteolytic mechanisms at the INM would also be ideally positioned to relay a perceived physiological demand to a transcriptional output, for example in the context of lipid homeostasis.
Finally, the best established facet of NE dynamics is the phenomenon of nuclear transport. NPCs traverse the NE and create a selective passageway through the INM and ONM and the enclosed perinuclear space (PNS) (Figure 1A). Regulated transport relies on nuclear transport receptors that enable cargo to passage through a meshwork of phenylalanine–glycine repeat nucleoporins (FG-nups) that establish a permeablity barrier between cytosolic and nuclear compartments. However, it is not known how NPC assembly is coordinated. In the following, we argue that the selective permeability barrier function relies on the fidelity and timing of membrane fusion between the INM and ONM during nuclear pore biogenesis, because an uncoordinated process could create a NE breach (Ungricht and Kutay, 2017).
In this Perspective, we discuss mechanisms critical for maintaining the identity and genome barrier function of the NE, with a focus on emerging roles of lipid metabolism and regulation of NPC biogenesis. Connected to these processes is the discovery of proteolytic systems that survey the NE proteome and may additionally play roles in the sharpening of compartmental identity and regulation of activities involved in lipid metabolism.
How to Reverse Stage 3 Kidney Disease Using a Plant-Based Diet
Diagnosed with type 1 diabetes in 2007 and stage 3 kidney disease 10 years later, Sanna was told by her doctors that there was nothing she could do to improve her kidney health. Kidney specialists told her that living with stage 3 kidney disease would eventually require dialysis and a kidney transplant.
Imagine you were diagnosed with a serious health condition and told that there was nothing you could do reverse or even slow down the progression of the illness. How would you feel?
Sanna refused to accept such a grim future. She believed that there was something she could do to improve her kidney health, so she began searching online for answers.
Eventually she found the Mastering Diabetes Program and joined the coaching program in January of 2018. At this point, she was living with stage 3 kidney disease, evidenced by a decreased glomerular filtration rate (GFR), an increased albumin/creatinine ratio, elevated urine protein, and high blood pressure.
At that time, Sanna also experienced a fasting glucose of 126 mg/dL, a total cholesterol of 267 mg/dL, and elevated triglycerides of 106 mg/dL. She had eaten a low-carbohydrate diet for the past six years, believing that was the healthiest choice for her body.
Sanna’s dietician had recommended eating a low-carbohydrate diet because of her diagnosis with type 1 diabetes, so she ate approximately 25 grams of carbohydrate per day. Each meal consisted of foods like eggs, dairy, beef, chicken, and fish, combined with either non-starchy vegetables or berries.
Although her low-carbohydrate diet didn’t contain processed foods, added sugars, or refined flour, her blood glucose was difficult to control and her kidneys had become progressively more inflamed over time.
Evidence-based research shows that a low-carbohydrate diet tends to be high in dietary components that are known to be nephrotoxic , including saturated fat, choline, and carnitine, while also being low in components that are necessary for healthy kidney function, such as vitamin c, polyphenols, and antioxidants.
Foods high in choline and/or carnitine, such as eggs, fish, meat, and poultry, cause the bacteria in your gastrointestinal tract to produce a metabolic byproduct called trimethylamine-N-oxide (TMAO).
TMAO is a dangerous metabolite that has long been implicated in the development of atherosclerosis, and more current research now shows that it plays a leading role in the development of chronic kidney disease and renal failure (1).
Research also demonstrates that high circulating levels of HDL and LDL cholesterol – which tend to result from eating a diet high in animal protein, saturated fat, and cholesterol – leads to chronic inflammation in your kidneys, which causes glomerulosclerosis, a form of kidney atherosclerosis (2).
As expected, cholesterol-lowering therapies (either through medications or lifestyle changes) have been demonstrated to improve renal function.
The best way to lower your cholesterol and improve renal function as well as overall health is to minimize or eliminate animal protein and animal fat, and consume a high-fiber, low-fat, plant-based, whole-food diet.
Unfortunately, dietary guidelines provided by the National Kidney Foundation currently emphasize the importance of eating red meat, pork, poultry, seafood, fish, and eggs in addition to vegetables and grains in order to get obtain “the right amount of protein to help your kidneys.”
The guidelines further state: “Some people may be told to eat more calories. They may need to eat extra sweets like sugar, jam, jelly, hard candy, honey, and syrup. Other good sources of calories come from fats such as soft (tub) margarine and oils like canola or olive oil.”
The guidelines also provide confusing information to patients, including that they should get enough (but not too much) sodium, potassium, calcium, and phosphorus. Exactly how much is enough, but not too much? That’s anyone’s guess!
Sanna decided to eat a low-fat, plant-based, whole-food diet to reverse stage 3 kidney disease. After only 6 months of diligently following this lifestyle, her kidney function improved so much that her nephrologist told her to not come back!
“I was told in February [by my doctor] that this is not curable. I would end up on dialysis and need a kidney transplant. The kidney doctor was absolutely amazed about these numbers. Actually, I'm now in remission so I don't have to see her anymore except for diabetes check-ups twice a year.”
Take a look at Sanna’s bloodwork between January and September of 2018 below:
24-hour urine protein
We applaud Sanna’s bravery and her ability to keep seeking nutritional excellence, even after being told that it was impossible to reverse stage 3 kidney disease.
Needless to say, her doctors were astonished by her recovery, and we are extremely proud of her progress.
1. Tang W.H. Wilson, Wang Zeneng, Kennedy David J., Wu Yuping, Buffa Jennifer A., Agatisa-Boyle Brendan, et al. Gut Microbiota-Dependent Trimethylamine N-Oxide (TMAO) Pathway Contributes to Both Development of Renal Insufficiency and Mortality Risk in Chronic Kidney Disease. Circulation Research. 2015 Jan 30116(3):448–55.
2. Gyebi L, Soltani Z, Reisin E. Lipid Nephrotoxicity: New Concept for an Old Disease. Curr Hypertens Rep. 2012 Apr 114(2):177–81.
3. Gardner CD, Coulston A, Chatterjee L, Rigby A, Spiller G, Farquhar JW. The effect of a plant-based diet on plasma lipids in hypercholesterolemic adults: a randomized trial. Ann Intern Med. 2005 May 3142(9):725–33.
4. Chainani-Wu N, Weidner G, Purnell DM, Frenda S, Merritt-Worden T, Pischke C, et al. Changes in emerging cardiac biomarkers after an intensive lifestyle intervention. Am J Cardiol. 2011 Aug 15108(4):498–507.