Hill's function for translational regulation

Hill's function for translational regulation

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Transcriptional regulation is generally modeled as a Hill's function (similar to Michaelis-Menten Kinetics):

$$frac{dm_X}{dt}=alpha _{m_X}.frac{R}{K+R} -eta _{m_X}.m_X$$

Where $m_X$ is the mRNA for some gene-$X$, $R$ is a Regulator $alpha$ and $eta$ are formation and degradation rate constants respectively. This equation denotes a saturation kinetics; increasing activator wont cause indefinite increase in transcription. Sounds logical because all promoter sites will be occupied at some point.

In case of a repression the equation looks like:

$$frac{dm_X}{dt}=alpha _{m_X}.frac{K}{K+R} -eta _{m_X}.m_X$$

I want to model repression of translation using a similar equation. However, the issue is that even though a single mRNA can be saturated by a regulator, increasing mRNA will require more regulators. So effectively the regulator available for a single mRNA molecule will be total regulator ÷ total mRNA.

My question is that whether in such a case the following equation is logical:

$$frac{dX}{dt}=alpha _{X}.m_X.frac{K}{K+R/m_X} -eta _{X}.X$$

Where $X$ is the protein.

Which means we are taking into consideration the effective concentration of a regulator per mRNA. In other words the Hill's constant $K'$ should scale with mRNA concentration. ($K'=K imes m_X$)


  • Well mixed system
  • System at thermodynamic limit
  • Amino-acid pool infinite
  • Ribosomes infinite

Your logic looks correct to me. Essentially, what you are doing is uniformly distributing the regulator among the available mRNA.

Note that even when using Hill functions to model transcription, the ratio of transcription factor (TF) concentration to the number of TF binding sites must be large - otherwise, you would have to consider binding ratios even at the level of transcription modeling, similarly to what you are doing now for translation. E.g. consider a hypothetical situation where 10 TF molecules compete for binding to 5 different promoter regions, each having 10 binding site repeats - you need to somehow distribute the available 10 TF molecules among 50 target binding sites. Clearly, this is not something accounted for by the standard Hill equation, which would in such case wrongly assume that 10 TF molecules are regulating each construct.

In your specific case, introducing the ratio of the regulator per each mRNA may not even be necessary if this ratio is large, effectively leading to saturation levels anyway. Note that the maximum level that $m_x$ can reach (at steady state) is equal to $max(m_X) = frac{alpha_{mX}}{eta_{mX}}$. If you can ensure this value to be much smaller than your translational regulator concentration, you will get similar results even if you use simply $frac{K}{K+R}$ for translation modeling.

If the latter assumption cannot be made, then your reasoning makes sense. For each individual mRNA molecule:

$$ ext{X produced per mRNA} = alpha_{X} .frac{K}{K+R'}$$

where $R'$ is the amount of mRNA bound to this molecule. If $R$ is the total available regulator concentration and uniform binding affinity is assumed, this means that $R'=frac{R}{m_X}$. Summing this over a total of $m_X$ mRNA and considering degradation yields exactly your final ODE.

Note that another important assumption you are making is that regulator binding/unbinding to/from mRNA is fast compared to translation, and that no cooperative interactions are present.

If you want to verify things further, you could establish system reactions, then derive Hill-free ODE equations according to the law of mass action. You could then compare this model to the one you propose. Furthermore, performing a stochastic simulation might make sense. If you want to go down this road but don't want to implement Gillespie's algorithm on your own, you can use e.g. SGNSim or COPASI.

Translational Regulation

W hen MCB Assistant Professor Nicholas Ingolia was a post-doc, he developed a long-sought technique — called ribosome profiling — that gives a snapshot of mRNA translation into proteins.

“You immobilize the ribosomes, use nucleases to digest the exposed mRNA, and sequence the protected areas,” he explains. “It’s a great way to measure what and how much is being translated.” The ribosome-protected mRNA footprints are long enough to be matched to specific genes in most cases.

Now, Ingolia uses ribosome profiling to find out how cells regulate mRNA translation, which is thought to affect protein levels as much as the transcription of genes into mRNAs. In people, for example, there’s a 20 to 30-fold difference in the number of protein molecules made from a given mRNA. “Protein synthesis is the true end point of gene expression,” he says.

His lab is asking three main questions about translational control of gene expression, using yeast and human cells. One is how do cells control where translation starts? This wasn’t even in question until recently because translation had generally been thought to start at AUG codons. But ribosomal profiling showed otherwise. “More than half of potential starting places are unconventional,” Ingolia says.

These alternate start sites can affect how much of a protein is synthesized, and can also lead to production of forms of a protein with different functions. “We believe start codon selection is an underappreciated control point for gene expression,” Ingolia says.

Another question is how do drugs stop translation of some mRNAs but not others? Take rocaglamide, a plant-derived compound used in traditional Chinese medicine that kills cancer cells preferentially. “Rocaglamide targets an RNA helicase, which unwinds mRNA so ribosomes can load on,” Ingolia says. “But why does this drug affect only some mRNAs?” Rocaglamide binds the helicase near this enzyme’s mRNA binding site, so the drug’s proximity to mRNA could affect translation.

Ingolia also wants to know why ribosomes load onto some mRNAs better than others. The answer might be in the hundreds of proteins that bind mRNAs. During transcription, sequence-specific proteins bind and regulate gene expression, and translational regulation may be similar. This has been hard to study until now but “with ribosome profiling, we can ask how proteins affect translation,” he says.

In recognition of his work, Ingolia was selected as a 2014 Rose Hills Innovator, a new UC Berkeley program for up to five early-career faculty with exceptionally high scientific promise. Ingolia also recently received an NIH New Innovator Award.

(A) Ribosomes protect mRNA footprints from digestion.
(B) Aligning the footprints to the genome shows which proteins are being synthesized, and at what levels.

Biology 171

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

  • Describe how changes to gene expression can cause cancer
  • Explain how changes to gene expression at different levels can disrupt the cell cycle
  • Discuss how understanding regulation of gene expression can lead to better drug design

Cancer is not a single disease but includes many different diseases. In cancer cells, mutations modify cell-cycle control and cells don’t stop growing as they normally would. Mutations can also alter the growth rate or the progression of the cell through the cell cycle. One example of a gene modification that alters the growth rate is increased phosphorylation of cyclin B, a protein that controls the progression of a cell through the cell cycle and serves as a cell-cycle checkpoint protein.

For cells to move through each phase of the cell cycle, the cell must pass through checkpoints. This ensures that the cell has properly completed the step and has not encountered any mutation that will alter its function. Many proteins, including cyclin B, control these checkpoints. The phosphorylation of cyclin B, a post-translational event, alters its function. As a result, cells can progress through the cell cycle unimpeded, even if mutations exist in the cell and its growth should be terminated. This post-translational change of cyclin B prevents it from controlling the cell cycle and contributes to the development of cancer.

Cancer: Disease of Altered Gene Expression

Cancer can be described as a disease of altered gene expression. There are many proteins that are turned on or off (gene activation or gene silencing) that dramatically alter the overall activity of the cell. A gene that is not normally expressed in that cell can be switched on and expressed at high levels. This can be the result of gene mutation or changes in gene regulation (epigenetic, transcription, post-transcription, translation, or post-translation).

Changes in epigenetic regulation, transcription, RNA stability, protein translation, and post-translational control can be detected in cancer. While these changes don’t occur simultaneously in one cancer, changes at each of these levels can be detected when observing cancer at different sites in different individuals. Therefore, changes in histone acetylation (epigenetic modification that leads to gene silencing), activation of transcription factors by phosphorylation, increased RNA stability, increased translational control, and protein modification can all be detected at some point in various cancer cells. Scientists are working to understand the common changes that give rise to certain types of cancer or how a modification might be exploited to destroy a tumor cell.

Tumor Suppressor Genes, Oncogenes, and Cancer

In normal cells, some genes function to prevent excess, inappropriate cell growth. These are tumor-suppressor genes, which are active in normal cells to prevent uncontrolled cell growth. There are many tumor-suppressor genes in cells. The most studied tumor-suppressor gene is p53, which is mutated in over 50 percent of all cancer types. The p53 protein itself functions as a transcription factor. It can bind to sites in the promoters of genes to initiate transcription. Therefore, the mutation of p53 in cancer will dramatically alter the transcriptional activity of its target genes.

Watch Using p53 to Fight Cancer (webpage, video) to learn more.

Proto-oncogenes are positive cell-cycle regulators. When mutated, proto-oncogenes can become oncogenes and cause cancer. Overexpression of the oncogene can lead to uncontrolled cell growth. This is because oncogenes can alter transcriptional activity, stability, or protein translation of another gene that directly or indirectly controls cell growth. An example of an oncogene involved in cancer is a protein called myc. Myc is a transcription factor that is aberrantly activated in Burkett’s Lymphoma, a cancer of the lymph system. Overexpression of myc transforms normal B cells into cancerous cells that continue to grow uncontrollably. High B-cell numbers can result in tumors that can interfere with normal bodily function. Patients with Burkett’s lymphoma can develop tumors on their jaw or in their mouth that interfere with the ability to eat.

Cancer and Epigenetic Alterations

Silencing genes through epigenetic mechanisms is also very common in cancer cells. There are characteristic modifications to histone proteins and DNA that are associated with silenced genes. In cancer cells, the DNA in the promoter region of silenced genes is methylated on cytosine DNA residues in CpG islands. Histone proteins that surround that region lack the acetylation modification that is present when the genes are expressed in normal cells. This combination of DNA methylation and histone deacetylation (epigenetic modifications that lead to gene silencing) is commonly found in cancer. When these modifications occur, the gene present in that chromosomal region is silenced. Increasingly, scientists understand how epigenetic changes are altered in cancer. Because these changes are temporary and can be reversed—for example, by preventing the action of the histone deacetylase protein that removes acetyl groups, or by DNA methyl transferase enzymes that add methyl groups to cytosines in DNA—it is possible to design new drugs and new therapies to take advantage of the reversible nature of these processes. Indeed, many researchers are testing how a silenced gene can be switched back on in a cancer cell to help re-establish normal growth patterns.

Genes involved in the development of many other illnesses, ranging from allergies to inflammation to autism, are thought to be regulated by epigenetic mechanisms. As our knowledge of how genes are controlled deepens, new ways to treat diseases like cancer will emerge.

Cancer and Transcriptional Control

Alterations in cells that give rise to cancer can affect the transcriptional control of gene expression. Mutations that activate transcription factors, such as increased phosphorylation, can increase the binding of a transcription factor to its binding site in a promoter. This could lead to increased transcriptional activation of that gene that results in modified cell growth. Alternatively, a mutation in the DNA of a promoter or enhancer region can increase the binding ability of a transcription factor. This could also lead to the increased transcription and aberrant gene expression that is seen in cancer cells.

Researchers have been investigating how to control the transcriptional activation of gene expression in cancer. Identifying how a transcription factor binds, or a pathway that activates where a gene can be turned off, has led to new drugs and new ways to treat cancer. In breast cancer, for example, many proteins are overexpressed. This can lead to increased phosphorylation of key transcription factors that increase transcription. One such example is the overexpression of the epidermal growth-factor receptor (EGFR) in a subset of breast cancers. The EGFR pathway activates many protein kinases that, in turn, activate many transcription factors which control genes involved in cell growth. New drugs that prevent the activation of EGFR have been developed and are used to treat these cancers.

Cancer and Post-transcriptional Control

Changes in the post-transcriptional control of a gene can also result in cancer. Recently, several groups of researchers have shown that specific cancers have altered expression of miRNAs. Because miRNAs bind to the 3′ UTR of RNA molecules to degrade them, overexpression of these miRNAs could be detrimental to normal cellular activity. Too many miRNAs could dramatically decrease the RNA population, leading to a decrease in protein expression. Several studies have demonstrated a change in the miRNA population in specific cancer types. It appears that the subset of miRNAs expressed in breast cancer cells is quite different from the subset expressed in lung cancer cells or even from normal breast cells. This suggests that alterations in miRNA activity can contribute to the growth of breast cancer cells. These types of studies also suggest that if some miRNAs are specifically expressed only in cancer cells, they could be potential drug targets. It would, therefore, be conceivable that new drugs that turn off miRNA expression in cancer could be an effective method to treat cancer.

Cancer and Translational/Post-translational Control

There are many examples of how translational or post-translational modifications of proteins arise in cancer. Modifications are found in cancer cells from the increased translation of a protein to changes in protein phosphorylation to alternative splice variants of a protein. An example of how the expression of an alternative form of a protein can have dramatically different outcomes is seen in colon cancer cells. The c-Flip protein, a protein involved in mediating the cell-death pathway, comes in two forms: long (c-FLIPL) and short (c-FLIPS). Both forms appear to be involved in initiating controlled cell-death mechanisms in normal cells. However, in colon cancer cells, expression of the long form results in increased cell growth instead of cell death. Clearly, the expression of the wrong protein dramatically alters cell function and contributes to the development of cancer.

New Drugs to Combat Cancer: Targeted Therapies

Scientists are using what is known about the regulation of gene expression in disease states, including cancer, to develop new ways to treat and prevent disease development. Many scientists are designing drugs on the basis of the gene expression patterns within individual tumors. This idea, that therapy and medicines can be tailored to an individual, has given rise to the field of personalized medicine. With an increased understanding of gene regulation and gene function, medicines can be designed to specifically target diseased cells without harming healthy cells. Some new medicines, called targeted therapies, have exploited the overexpression of a specific protein or the mutation of a gene to develop a new medication to treat disease. One such example is the use of anti-EGF receptor medications to treat the subset of breast cancer tumors that have very high levels of the EGF protein. Undoubtedly, more targeted therapies will be developed as scientists learn more about how gene expression changes can cause cancer.

Clinical Trial Coordinator A clinical trial coordinator is the person managing the proceedings of the clinical trial. This job includes coordinating patient schedules and appointments, maintaining detailed notes, building the database to track patients (especially for long-term follow-up studies), ensuring proper documentation has been acquired and accepted, and working with the nurses and doctors to facilitate the trial and publication of the results. A clinical trial coordinator may have a science background, like a nursing degree, or other certification. People who have worked in science labs or in clinical offices are also qualified to become a clinical trial coordinator. These jobs are generally in hospitals however, some clinics and doctor’s offices also conduct clinical trials and may hire a coordinator.

Section Summary

Cancer can be described as a disease of altered gene expression. Changes at every level of eukaryotic gene expression can be detected in some form of cancer at some point in time. In order to understand how changes to gene expression can cause cancer, it is critical to understand how each stage of gene regulation works in normal cells. By understanding the mechanisms of control in normal, non-diseased cells, it will be easier for scientists to understand what goes wrong in disease states including complex ones like cancer.

Free Response

New drugs are being developed that decrease DNA methylation and prevent the removal of acetyl groups from histone proteins. Explain how these drugs could affect gene expression to help kill tumor cells.

These drugs will keep the histone proteins and the DNA methylation patterns in the open chromosomal configuration so that transcription is feasible. If a gene is silenced, these drugs could reverse the epigenetic configuration to re-express the gene.

How can understanding the gene expression pattern in a cancer cell tell you something about that specific form of cancer?

Understanding which genes are expressed in a cancer cell can help diagnose the specific form of cancer. It can also help identify treatment options for that patient. For example, if a breast cancer tumor expresses the EGFR in high numbers, it might respond to specific anti-EGFR therapy. If that receptor is not expressed, it would not respond to that therapy.



Exosomes are nanoscale extracellular lipid bilayer vesicles of endocytic origin, and they are secreted by nearly all cell types in physiological and pathological conditions. Initial studies regarded exosomes as a simple means for the disposal of unwanted cellular components [1]. They have now been shown to play a crucial role in intercellular communication through the intercellular transfer of nucleic acids and specific repertoires of proteins and lipids, which is important for protein and lipid homeostasis [2]. During these processes, exosomes can regulate the properties of target cells, which can be beneficial or detrimental [3]. Exosomes contribute to fundamental physiological processes, such as neuronal communication [4], antigen presentation [5], immune responses [6], organ development [7], and reproductive performances [8]. They also participate in some pathological disorders, including cancer progression [9], cardiovascular disease [10], and inflammation [11], and they even favor viral infection [12] and prion dissemination [13]. Given that exosomes can carry toxic damaged forms of aggregated proteins that are fated for destruction, they are also relevant to the progression of neurodegenerative diseases [14].

Exosome secretion occurs naturally, and cellular stress and activation signals can modulate the involved processes [15]. They can be found in multiple types of extracellular fluids, such as blood, urine, amniotic fluid, saliva, cerebrospinal fluid, and even breast milk [16-19]. The heterogeneity of exosome size and cargo reflect the state and types of the cells of origin. Thus, exosomes can be used as biomarkers for disease diagnostics and even fetal sex determination [20]. Since surface-bound proteins on exosomes stem from the plasma membranes of the cells from which they originated, exosomes released by antigen-presenting cells (APCs), dendritic cells (DCs) and tumor cells are promising for use in vaccines development. Moreover, exosomes can protect their cargoes from clearance or damage by the complement fixation or macrophages due to their double-layered membrane and nanoscale size, thus prolonging their circulation half-life and improving their biological activity. Hence, exosomes could potentially be used as drug delivery vesicles for treating disease. Furthermore, exosome engineering, i.e., the chemical or biological modification of these nanoscale extracellular lipid bilayer vesicles, may provide opportunities to enhance or broaden the innate therapeutic capability of exosomes.

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The DELLA ‘hub’ is a convergence point for cross-talk

Gibberellins are another important group of hormones that are required for growth, and mediate adaptation to various stresses ( Colebrook et al., 2014). The gibberellin receptor, gibberellin INSENSITIVE DWARF 1 (GID1), is a soluble hormone-sensitive lipase-like protein ( Ueguchi-Tanaka et al., 2005 Griffiths et al., 2006). Gibberellin promotes the interaction between GID1 and the DELLA proteins which are negative regulators of gibberellin signalling. SLEEPY 1 (SLY1), an F-box component of the SCF SLY1 E3 ubiquitin ligase, is subsequently recruited to the GID1–DELLA complex and targets the DELLA for 26S proteasome-mediated degradation (reviewed by Xu et al., 2014) ( Fig. 1E, F). The Arabidopsis genome encodes five DELLA proteins, gibberellic ACID INSENSITIVE (GAI), REPRESSOR OF ga1-3 (RGA), REPRESSOR OF ga1-3-LIKE 1 (RGL1), RGL2, and RGL3, and genetic analysis has shown that some of these proteins mediate specific gibberellin responses ( Daviere and Achard, 2013).

Meta-analysis of transcriptome data demonstrated the role of gibberellins as central nodes in multiple networks that connect environmental inputs to growth. These networks are tissue-specific and feature dynamic and direct regulation of transcriptional effectors ( Claeys et al., 2014). DELLA is a key player in these responses. Protein–protein interactions involving DELLAs have been shown to occur on ( Zentella et al., 2007 Yoshida et al., 2014) and off the promoter of DELLA-regulated genes ( de Lucas et al., 2008 Feng et al., 2008 Oh et al., 2014a).

DELLA proteins serve as a direct convergence point for cross-talk between gibberellin and brassinosteroid, and gibberellin and auxin ( Fig. 2). DELLA directly interacts with BZR1 and supresses its transcriptional activity by holding it away from the promoter of target genes ( Gallego-Bartolome et al., 2012 Li et al., 2012). Gibberellin promotes the degradation of DELLA, thus enhancing the transcription of brassinosteroid-responsive genes. A similar mechanism appears to account for the positive effect of gibberellin on the transcription of auxin-responsive genes. The DELLA protein RGA interacts with ARF6, ARF7, and ARF8, but not the ‘repressor’ ARF1 and, in ChIP assays, the presence of DELLA decreases ARF6-binding to the promoters of target genes ( Oh et al., 2014a). Unlike mechanisms that promote transcription factor degradation, DELLA’s antagonistic actions are rapidly reversed by gibberellin-mediated degradation of DELLA, or perhaps, as discussed shortly, via post-translational modifications.

Direct cross-talk involving DELLA proteins. The effects of DELLA protein on auxin (Aux left) and brassinosteroid (BR right) signalling pathways under low (A) and high gibberellin (B) concentrations. PM, plasma membrane P, phosphorylated SUMO, SUMO conjugate BSU1, BRI1 SUPPRESSOR 1 BIN2, BRASSINOSTEROID INSENSITIVE 2 BZR, BRASSINAZOLE RESISTANT 1 (BZR1) and BRI1-EMS SUPPRESSOR 1 (BES1/BZR2) IAA, Auxin/INDOLE-3-ACETIC ACID co-repressors, ARF, ‘activator’ AUXIN RESPONSE TRANSCRIPTION FACTORs (ARF5–8, 19) HDAC, HISTONE DEACETYLASE complex GID1, GA INSENSITIVE DWARF 1 DELLA, GA INSENSITIVE (GAI), REPRESSOR OF ga1-3 (RGA), REPRESSOR OF ga1-3-LIKE 1 (RGL1), RGL2, and RGL3 TFs, transcription factors that interact with DELLA proteins IDD, INDETERMINATE DOMAIN (IDD) transcription factors.

Direct cross-talk involving DELLA proteins. The effects of DELLA protein on auxin (Aux left) and brassinosteroid (BR right) signalling pathways under low (A) and high gibberellin (B) concentrations. PM, plasma membrane P, phosphorylated SUMO, SUMO conjugate BSU1, BRI1 SUPPRESSOR 1 BIN2, BRASSINOSTEROID INSENSITIVE 2 BZR, BRASSINAZOLE RESISTANT 1 (BZR1) and BRI1-EMS SUPPRESSOR 1 (BES1/BZR2) IAA, Auxin/INDOLE-3-ACETIC ACID co-repressors, ARF, ‘activator’ AUXIN RESPONSE TRANSCRIPTION FACTORs (ARF5–8, 19) HDAC, HISTONE DEACETYLASE complex GID1, GA INSENSITIVE DWARF 1 DELLA, GA INSENSITIVE (GAI), REPRESSOR OF ga1-3 (RGA), REPRESSOR OF ga1-3-LIKE 1 (RGL1), RGL2, and RGL3 TFs, transcription factors that interact with DELLA proteins IDD, INDETERMINATE DOMAIN (IDD) transcription factors.

Chromatin immunoprecipitation experiments showed that DELLA proteins associate with the promoter regions of gibberellin-responsive genes in vivo ( Zentella et al., 2007), and the amino-terminal DELLA and TVHYNP motifs of a rice DELLA protein (SLENDER RICE 1, SLR1) are required for transactivation of target genes ( Hirano et al., 2012). Because DELLAs do not possess a DNA-binding region, transcriptional activity is assumed to be conferred via recruitment to the promoters by DNA-bound transcription factors. SCARECROW-LIKE 3 (SCL3) was first reported as a DELLA-direct target by Zentella et al. (2007) and, more recently, Yoshida et al. (2014) demonstrated that it was recruited to the promoter through interactions with the INDETERMINATE DOMAIN (IDD) family of transcription factors. SCL3 is itself a positive regulator of gibberellin signalling, a repressor of its own transcription, and also able to interact with IDDs. Therefore, DELLA and SCL3 were hypothesized to interact competitively with IDD proteins, via their C-terminal GRAS domains, to regulate downstream gene expression ( Yoshida et al., 2014 Yoshida and Ueguchi-Tanaka, 2014) ( Fig. 2).


Any step of gene expression may be modulated, from the DNA-RNA transcription step to post-translational modification of a protein. The following is a list of stages where gene expression is regulated, the most extensively utilised point is Transcription Initiation:

In eukaryotes, the accessibility of large regions of DNA can depend on its chromatin structure, which can be altered as a result of histone modifications directed by DNA methylation, ncRNA, or DNA-binding protein. Hence these modifications may up or down regulate the expression of a gene. Some of these modifications that regulate gene expression are inheritable and are referred to as epigenetic regulation.

Structural Edit

Transcription of DNA is dictated by its structure. In general, the density of its packing is indicative of the frequency of transcription. Octameric protein complexes called histones together with a segment of DNA wound around the eight histone proteins (together referred to as a nucleosome) are responsible for the amount of supercoiling of DNA, and these complexes can be temporarily modified by processes such as phosphorylation or more permanently modified by processes such as methylation. Such modifications are considered to be responsible for more or less permanent changes in gene expression levels. [2]

Chemical Edit

Methylation of DNA is a common method of gene silencing. DNA is typically methylated by methyltransferase enzymes on cytosine nucleotides in a CpG dinucleotide sequence (also called "CpG islands" when densely clustered). Analysis of the pattern of methylation in a given region of DNA (which can be a promoter) can be achieved through a method called bisulfite mapping. Methylated cytosine residues are unchanged by the treatment, whereas unmethylated ones are changed to uracil. The differences are analyzed by DNA sequencing or by methods developed to quantify SNPs, such as Pyrosequencing (Biotage) or MassArray (Sequenom), measuring the relative amounts of C/T at the CG dinucleotide. Abnormal methylation patterns are thought to be involved in oncogenesis. [3]

Histone acetylation is also an important process in transcription. Histone acetyltransferase enzymes (HATs) such as CREB-binding protein also dissociate the DNA from the histone complex, allowing transcription to proceed. Often, DNA methylation and histone deacetylation work together in gene silencing. The combination of the two seems to be a signal for DNA to be packed more densely, lowering gene expression. [ citation needed ]

Regulation of transcription thus controls when transcription occurs and how much RNA is created. Transcription of a gene by RNA polymerase can be regulated by several mechanisms. Specificity factors alter the specificity of RNA polymerase for a given promoter or set of promoters, making it more or less likely to bind to them (i.e., sigma factors used in prokaryotic transcription). Repressors bind to the Operator, coding sequences on the DNA strand that are close to or overlapping the promoter region, impeding RNA polymerase's progress along the strand, thus impeding the expression of the gene. The image to the right demonstrates regulation by a repressor in the lac operon. General transcription factors position RNA polymerase at the start of a protein-coding sequence and then release the polymerase to transcribe the mRNA. Activators enhance the interaction between RNA polymerase and a particular promoter, encouraging the expression of the gene. Activators do this by increasing the attraction of RNA polymerase for the promoter, through interactions with subunits of the RNA polymerase or indirectly by changing the structure of the DNA. Enhancers are sites on the DNA helix that are bound by activators in order to loop the DNA bringing a specific promoter to the initiation complex. Enhancers are much more common in eukaryotes than prokaryotes, where only a few examples exist (to date). [4] Silencers are regions of DNA sequences that, when bound by particular transcription factors, can silence expression of the gene.

In vertebrates, the majority of gene promoters contain a CpG island with numerous CpG sites. [5] When many of a gene's promoter CpG sites are methylated the gene becomes silenced. [6] Colorectal cancers typically have 3 to 6 driver mutations and 33 to 66 hitchhiker or passenger mutations. [7] However, transcriptional silencing may be of more importance than mutation in causing progression to cancer. For example, in colorectal cancers about 600 to 800 genes are transcriptionally silenced by CpG island methylation (see regulation of transcription in cancer). Transcriptional repression in cancer can also occur by other epigenetic mechanisms, such as altered expression of microRNAs. [8] In breast cancer, transcriptional repression of BRCA1 may occur more frequently by over-expressed microRNA-182 than by hypermethylation of the BRCA1 promoter (see Low expression of BRCA1 in breast and ovarian cancers).

One of the cardinal features of addiction is its persistence. The persistent behavioral changes appear to be due to long-lasting changes, resulting from epigenetic alterations affecting gene expression, within particular regions of the brain. [9] Drugs of abuse cause three types of epigenetic alteration in the brain. These are (1) histone acetylations and histone methylations, (2) DNA methylation at CpG sites, and (3) epigenetic downregulation or upregulation of microRNAs. [9] [10] (See Epigenetics of cocaine addiction for some details.)

Chronic nicotine intake in mice alters brain cell epigenetic control of gene expression through acetylation of histones. This increases expression in the brain of the protein FosB, important in addiction. [11] Cigarette addiction was also studied in about 16,000 humans, including never smokers, current smokers, and those who had quit smoking for up to 30 years. [12] In blood cells, more than 18,000 CpG sites (of the roughly 450,000 analyzed CpG sites in the genome) had frequently altered methylation among current smokers. These CpG sites occurred in over 7,000 genes, or roughly a third of known human genes. The majority of the differentially methylated CpG sites returned to the level of never-smokers within five years of smoking cessation. However, 2,568 CpGs among 942 genes remained differentially methylated in former versus never smokers. Such remaining epigenetic changes can be viewed as “molecular scars” [10] that may affect gene expression.

In rodent models, drugs of abuse, including cocaine, [13] methampheamine, [14] [15] alcohol [16] and tobacco smoke products, [17] all cause DNA damage in the brain. During repair of DNA damages some individual repair events can alter the methylation of DNA and/or the acetylations or methylations of histones at the sites of damage, and thus can contribute to leaving an epigenetic scar on chromatin. [18]

Such epigenetic scars likely contribute to the persistent epigenetic changes found in addiction.

In mammals, methylation of cytosine (see Figure) in DNA is a major regulatory mediator. Methylated cytosines primarily occur in dinucleotide sequences where cytosine is followed by a guanine, a CpG site. The total number of CpG sites in the human genome is approximately 28 million. [19] and generally about 70% of all CpG sites have a methylated cytosine. [20]

In a rat, a painful learning experience, contextual fear conditioning, can result in a life-long fearful memory after a single training event. [21] Cytosine methylation is altered in the promoter regions of about 9.17% of all genes in the hippocampus neuron DNA of a rat that has been subjected to a brief fear conditioning experience. [22] The hippocampus is where new memories are initially stored.

Methylation of CpGs in a promoter region of a gene represses transcription [23] while methylation of CpGs in the body of a gene increases expression. [24] TET enzymes play a central role in demethylation of methylated cytosines. Demethylation of CpGs in a gene promoter by TET enzyme activity increases transcription of the gene. [25]

When contextual fear conditioning is applied to a rat, more than 5,000 differentially methylated regions (DMRs) (of 500 nucleotides each) occur in the rat hippocampus neural genome both one hour and 24 hours after the conditioning in the hippocampus. [22] This causes about 500 genes to be up-regulated (often due to demethylation of CpG sites in a promoter region) and about 1,000 genes to be down-regulated (often due to newly formed 5-methylcytosine at CpG sites in a promoter region). The pattern of induced and repressed genes within neurons appears to provide a molecular basis for forming the first transient memory of this training event in the hippocampus of the rat brain. [22]

After the DNA is transcribed and mRNA is formed, there must be some sort of regulation on how much the mRNA is translated into proteins. Cells do this by modulating the capping, splicing, addition of a Poly(A) Tail, the sequence-specific nuclear export rates, and, in several contexts, sequestration of the RNA transcript. These processes occur in eukaryotes but not in prokaryotes. This modulation is a result of a protein or transcript that, in turn, is regulated and may have an affinity for certain sequences.

Three prime untranslated regions (3'-UTRs) of messenger RNAs (mRNAs) often contain regulatory sequences that post-transcriptionally influence gene expression. [26] Such 3'-UTRs often contain both binding sites for microRNAs (miRNAs) as well as for regulatory proteins. By binding to specific sites within the 3'-UTR, miRNAs can decrease gene expression of various mRNAs by either inhibiting translation or directly causing degradation of the transcript. The 3'-UTR also may have silencer regions that bind repressor proteins that inhibit the expression of a mRNA.

The 3'-UTR often contains miRNA response elements (MREs). MREs are sequences to which miRNAs bind. These are prevalent motifs within 3'-UTRs. Among all regulatory motifs within the 3'-UTRs (e.g. including silencer regions), MREs make up about half of the motifs.

As of 2014, the miRBase web site, [27] an archive of miRNA sequences and annotations, listed 28,645 entries in 233 biologic species. Of these, 1,881 miRNAs were in annotated human miRNA loci. miRNAs were predicted to have an average of about four hundred target mRNAs (affecting expression of several hundred genes). [28] Freidman et al. [28] estimate that >45,000 miRNA target sites within human mRNA 3'-UTRs are conserved above background levels, and >60% of human protein-coding genes have been under selective pressure to maintain pairing to miRNAs.

Direct experiments show that a single miRNA can reduce the stability of hundreds of unique mRNAs. [29] Other experiments show that a single miRNA may repress the production of hundreds of proteins, but that this repression often is relatively mild (less than 2-fold). [30] [31]

The effects of miRNA dysregulation of gene expression seem to be important in cancer. [32] For instance, in gastrointestinal cancers, a 2015 paper identified nine miRNAs as epigenetically altered and effective in down-regulating DNA repair enzymes. [33]

The effects of miRNA dysregulation of gene expression also seem to be important in neuropsychiatric disorders, such as schizophrenia, bipolar disorder, major depressive disorder, Parkinson's disease, Alzheimer's disease and autism spectrum disorders. [34] [35] [36]

The translation of mRNA can also be controlled by a number of mechanisms, mostly at the level of initiation. Recruitment of the small ribosomal subunit can indeed be modulated by mRNA secondary structure, antisense RNA binding, or protein binding. In both prokaryotes and eukaryotes, a large number of RNA binding proteins exist, which often are directed to their target sequence by the secondary structure of the transcript, which may change depending on certain conditions, such as temperature or presence of a ligand (aptamer). Some transcripts act as ribozymes and self-regulate their expression.

    is a process in which a molecule (e.g., a drug) induces (i.e., initiates or enhances) the expression of an enzyme.
  • The induction of heat shock proteins in the fruit fly Drosophila melanogaster.
  • The Lac operon is an interesting example of how gene expression can be regulated.
  • Viruses, despite having only a few genes, possess mechanisms to regulate their gene expression, typically into an early and late phase, using collinear systems regulated by anti-terminators (lambda phage) or splicing modulators (HIV).
  • Gal4 is a transcriptional activator that controls the expression of GAL1, GAL7, and GAL10 (all of which code for the metabolic of galactose in yeast). The GAL4/UAS system has been used in a variety of organisms across various phyla to study gene expression. [37]

Developmental biology Edit

A large number of studied regulatory systems come from developmental biology. Examples include:

  • The colinearity of the Hox gene cluster with their nested antero-posterior patterning
  • Pattern generation of the hand (digits - interdigits): the gradient of sonic hedgehog (secreted inducing factor) from the zone of polarizing activity in the limb, which creates a gradient of active Gli3, which activates Gremlin, which inhibits BMPs also secreted in the limb, results in the formation of an alternating pattern of activity as a result of this reaction-diffusion system.
  • Somitogenesis is the creation of segments (somites) from a uniform tissue (Pre-somitic Mesoderm). They are formed sequentially from anterior to posterior. This is achieved in amniotes possibly by means of two opposing gradients, Retinoic acid in the anterior (wavefront) and Wnt and Fgf in the posterior, coupled to an oscillating pattern (segmentation clock) composed of FGF + Notch and Wnt in antiphase. [38]
  • Sex determination in the soma of a Drosophila requires the sensing of the ratio of autosomal genes to sex chromosome-encoded genes, which results in the production of sexless splicing factor in females, resulting in the female isoform of doublesex. [39]

Up-regulation and down-regulation Edit

Up-regulation is a process that occurs within a cell triggered by a signal (originating internal or external to the cell), which results in increased expression of one or more genes and as a result the protein(s) encoded by those genes. Conversely, down-regulation is a process resulting in decreased gene and corresponding protein expression.

    occurs, for example, when a cell is deficient in some kind of receptor. In this case, more receptor protein is synthesized and transported to the membrane of the cell and, thus, the sensitivity of the cell is brought back to normal, reestablishing homeostasis. occurs, for example, when a cell is overstimulated by a neurotransmitter, hormone, or drug for a prolonged period of time, and the expression of the receptor protein is decreased in order to protect the cell (see also tachyphylaxis).

Inducible vs. repressible systems Edit

Gene Regulation can be summarized by the response of the respective system:

  • Inducible systems - An inducible system is off unless there is the presence of some molecule (called an inducer) that allows for gene expression. The molecule is said to "induce expression". The manner by which this happens is dependent on the control mechanisms as well as differences between prokaryotic and eukaryotic cells.
  • Repressible systems - A repressible system is on except in the presence of some molecule (called a corepressor) that suppresses gene expression. The molecule is said to "repress expression". The manner by which this happens is dependent on the control mechanisms as well as differences between prokaryotic and eukaryotic cells.

The GAL4/UAS system is an example of both an inducible and repressible system. Gal4 binds an upstream activation sequence (UAS) to activate the transcription of the GAL1/GAL7/GAL10 cassette. On the other hand, a MIG1 response to the presence of glucose can inhibit GAL4 and therefore stop the expression of the GAL1/GAL7/GAL10 cassette. [40]

Theoretical circuits Edit

  • Repressor/Inducer: an activation of a sensor results in the change of expression of a gene
  • negative feedback: the gene product downregulates its own production directly or indirectly, which can result in
    • keeping transcript levels constant/proportional to a factor
    • inhibition of run-away reactions when coupled with a positive feedback loop
    • creating an oscillator by taking advantage in the time delay of transcription and translation, given that the mRNA and protein half-life is shorter
    • signal amplification
    • bistable switches when two genes inhibit each other and both have positive feedback
    • pattern generation

    In general, most experiments investigating differential expression used whole cell extracts of RNA, called steady-state levels, to determine which genes changed and by how much. These are, however, not informative of where the regulation has occurred and may mask conflicting regulatory processes (see post-transcriptional regulation), but it is still the most commonly analysed (quantitative PCR and DNA microarray).

    When studying gene expression, there are several methods to look at the various stages. In eukaryotes these include:

    Expression of Genes

    For a cell to function properly, necessary proteins must be synthesized at the proper time. All cells control or regulate the synthesis of proteins from information encoded in their DNA. The process of turning on a gene to produce RNA and protein is called gene expression. Whether in a simple unicellular organism or a complex multi-cellular organism, each cell controls when and how its genes are expressed. For this to occur, there must be a mechanism to control when a gene is expressed to make RNA and protein, how much of the protein is made, and when it is time to stop making that protein because it is no longer needed.

    The regulation of gene expression conserves energy and space. It would require a significant amount of energy for an organism to express every gene at all times, so it is more energy efficient to turn on the genes only when they are required. In addition, only expressing a subset of genes in each cell saves space because DNA must be unwound from its tightly coiled structure to transcribe and translate the DNA. Cells would have to be enormous if every protein were expressed in every cell all the time.

    The control of gene expression is extremely complex. Malfunctions in this process are detrimental to the cell and can lead to the development of many diseases, including cancer.

    Gene regulation makes cells different

    Gene regulation is how a cell controls which genes, out of the many genes in its genome, are “turned on” (expressed). Thanks to gene regulation, each cell type in your body has a different set of active genes—despite the fact that almost all the cells of your body contain the exact same DNA. These different patterns of gene expression cause your various cell types to have different sets of proteins, making each cell type uniquely specialized to do its job.

    For example, one of the jobs of the liver is to remove toxic substances like alcohol from the bloodstream. To do this, liver cells express genes encoding subunits (pieces) of an enzyme called alcohol dehydrogenase. This enzyme breaks alcohol down into a non-toxic molecule. The neurons in a person’s brain don’t remove toxins from the body, so they keep these genes unexpressed, or “turned off.” Similarly, the cells of the liver don’t send signals using neurotransmitters, so they keep neurotransmitter genes turned off (Figure 1).

    Figure 1. Different cells have different genes “turned on.”

    There are many other genes that are expressed differently between liver cells and neurons (or any two cell types in a multicellular organism like yourself).

    How do cells “decide” which genes to turn on?

    Now there’s a tricky question! Many factors that can affect which genes a cell expresses. Different cell types express different sets of genes, as we saw above. However, two different cells of the same type may also have different gene expression patterns depending on their environment and internal state.

    Broadly speaking, we can say that a cell’s gene expression pattern is determined by information from both inside and outside the cell.

    • Examples of information from inside the cell: the proteins it inherited from its mother cell, whether its DNA is damaged, and how much ATP it has.
    • Examples of information from outside the cell: chemical signals from other cells, mechanical signals from the extracellular matrix, and nutrient levels.

    How do these cues help a cell “decide” what genes to express? Cells don’t make decisions in the sense that you or I would. Instead, they have molecular pathways that convert information—such as the binding of a chemical signal to its receptor—into a change in gene expression.

    As an example, let’s consider how cells respond to growth factors. A growth factor is a chemical signal from a neighboring cell that instructs a target cell to grow and divide. We could say that the cell “notices” the growth factor and “decides” to divide, but how do these processes actually occur?

    Figure 2. Growth factor prompting cell division

    • The cell detects the growth factor through physical binding of the growth factor to a receptor protein on the cell surface.
    • Binding of the growth factor causes the receptor to change shape, triggering a series of chemical events in the cell that activate proteins called transcription factors.
    • The transcription factors bind to certain sequences of DNA in the nucleus and cause transcription of cell division-related genes.
    • The products of these genes are various types of proteins that make the cell divide (drive cell growth and/or push the cell forward in the cell cycle).

    This is just one example of how a cell can convert a source of information into a change in gene expression. There are many others, and understanding the logic of gene regulation is an area of ongoing research in biology today.

    Growth factor signaling is complex and involves the activation of a variety of targets, including both transcription factors and non-transcription factor proteins.

    In Summary: Expression of Genes

    • Gene regulation is the process of controlling which genes in a cell’s DNA are expressed (used to make a functional product such as a protein).
    • Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA.
    • The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties.
    • In eukaryotes like humans, gene expression involves many steps, and gene regulation can occur at any of these steps. However, many genes are regulated primarily at the level of transcription.

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    Multifaceted functions of post-translational enzyme modifications in the control of plant glycolysis

    PTM’s and allosteric effectors interact to control glycolytic enzyme activities.

    Reversible phosphorylation, redox-sensitive thiol modifications, and monoubiquitination are the most prevalent regulatory PTMs of plant glycolytic enzymes.

    PEP carboxylase provides one of the most comprehensive examples of the post-translational control of plant glycolysis by allosteric effectors, PTMs, and protein:protein interactions.

    Evaluating the impact and mechanisms of enzyme PTMs in the regulation and functions of plant glycolysis is a key area for future research.

    Glycolysis is a central feature of metabolism and its regulation plays important roles during plant developmental and stress responses. Recent advances in proteomics and mass spectrometry have documented extensive and dynamic post-translational modifications (PTMs) of most glycolytic enzymes in diverse plant tissues. Protein PTMs represent fundamental regulatory events that integrate signalling and gene expression with cellular metabolic networks, and can regulate glycolytic enzyme activity, localization, protein:protein interactions, moonlighting functions, and turnover. Serine/threonine phosphorylation and redox PTMs of cysteine thiol groups appear to be the most prevalent forms of reversible covalent modification involved in plant glycolytic control. Additional PTMs including monoubiquitination also have important functions. However, the molecular functions and mechanisms of most glycolytic enzyme PTMs remain unknown, and represent important objectives for future studies.


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    Keywords: Foxp3, Treg cells, epigenetic regulation, Foxp3 stability, autoimmunity

    Citation: Colamatteo A, Carbone F, Bruzzaniti S, Galgani M, Fusco C, Maniscalco GT, Di Rella F, de Candia P and De Rosa V (2020) Molecular Mechanisms Controlling Foxp3 Expression in Health and Autoimmunity: From Epigenetic to Post-translational Regulation. Front. Immunol. 10:3136. doi: 10.3389/fimmu.2019.03136

    Received: 30 September 2019 Accepted: 23 December 2019
    Published: 03 February 2020.

    Lucy S. K. Walker, University College London, United Kingdom

    Masahiro Ono, Imperial College London, United Kingdom
    Bhalchandra Mirlekar, School of Medicine, University of North Carolina at Chapel Hill, United States

    Copyright © 2020 Colamatteo, Carbone, Bruzzaniti, Galgani, Fusco, Maniscalco, Di Rella, de Candia and De Rosa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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