View source for Regulation of gene expression

{{Short description|Biological cell regulation mechanisms}}
{{Redirect|Gene modulation|information on therapeutic regulation of gene expression|therapeutic gene modulation}}
{{For|vocabulary|Glossary of gene expression terms}}
[[File:Regulation of gene expression by steroid hormone receptor.svg|thumb|Regulation of gene expression by a hormone receptor]]
[[File:Gene expression control.png|thumb|Diagram showing at which stages in the DNA-mRNA-protein pathway expression can be controlled]]

'''Regulation of gene expression''', or '''gene regulation''',<ref name="genetics">{{cite web |title=Can genes be turned on and off in cells? |url=https://ghr.nlm.nih.gov/primer/howgeneswork/geneonoff |archive-url=https://archive.today/20160429134213/https://ghr.nlm.nih.gov/primer/howgeneswork/geneonoff |archive-date=April 29, 2016 |website=Genetics Home Reference |language=en}}</ref> includes a wide range of mechanisms that are used by cells to increase or decrease the production of specific [[gene product]]s ([[protein]] or [[RNA]]). Sophisticated programs of [[gene expression]] are widely observed in biology, for example to trigger developmental pathways, respond to environmental stimuli, or adapt to new food sources. Virtually any step of gene expression can be modulated, from [[Transcriptional regulation|transcriptional initiation]], to [[RNA processing]], and to the [[post-translational modification]] of a protein. Often, one gene regulator controls another, and so on, in a [[gene regulatory network]].

Gene regulation is essential for [[viruses]], [[prokaryote]]s and [[eukaryote]]s as it increases the versatility and adaptability of an [[organism]] by allowing the cell to express protein when needed. Although as early as 1951, [[Barbara McClintock]] showed interaction between two genetic loci, Activator (''Ac'') and Dissociator (''Ds''), in the color formation of maize seeds, the first discovery of a gene regulation system is widely considered to be the identification in 1961 of the [[lac operon|''lac'' operon]], discovered by [[François Jacob]] and [[Jacques Monod]], in which some enzymes involved in [[lactose]] metabolism are expressed by ''[[Escherichia coli|E. coli]]'' only in the presence of lactose and absence of glucose.

In multicellular organisms, gene regulation drives [[cellular differentiation]] and [[morphogenesis]] in the embryo, leading to the creation of different cell types that possess different gene expression profiles from the same [[genome]] sequence. Although this does not explain how gene regulation originated, evolutionary biologists include it as a partial explanation of how [[evolution]] works at a [[molecular biology|molecular level]], and it is central to the science of [[evolutionary developmental biology]] ("evo-devo").

== Regulated stages of gene expression ==
Any step of gene expression may be modulated, from [[Signal transduction|signaling]] to [[transcription (genetics)|transcription]] to [[post-translational modification]] of a protein. The following is a list of stages where gene expression is regulated, where the most extensively utilized point is transcription initiation, the first stage in transcription:{{cn|date=April 2023}}
* [[Signal transduction]]
* [[Chromatin]], [[chromatin remodeling]], [[Epigenome|chromatin domains]]
* [[Transcription (genetics)|Transcription]]
* [[Post-transcriptional modification]]
* RNA transport
* [[translation (genetics)|Translation]]
* [[mRNA#Degradation|mRNA degradation]]

== Modification of DNA ==
[[File:Histone tails and their function in chromatin formation.svg|thumb|Histone tails and their function in chromatin formation]]

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]], [[Non-coding RNA|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]].{{cn|date=April 2023}}

=== Structural ===

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.<ref name=MethylGeneExpression>{{cite journal | vauthors = Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, Gilad Y, Pritchard JK | display-authors = 6 | title = DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines | journal = Genome Biology | volume = 12 | issue = 1 | article-number = R10 | year = 2011 | pmid = 21251332 | pmc = 3091299 | doi = 10.1186/gb-2011-12-1-r10 | doi-access = free }}</ref>

=== Chemical ===

[[DNA methylation|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 island]]s" 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.<ref name="Vertino_1993">{{cite journal | vauthors = Vertino PM, Spillare EA, Harris CC, Baylin SB | title = Altered chromosomal methylation patterns accompany oncogene-induced transformation of human bronchial epithelial cells | journal = Cancer Research | volume = 53 | issue = 7 | pages = 1684–9 | date = April 1993 | pmid = 8453642 | url = http://cancerres.aacrjournals.org/content/53/7/1684.full.pdf }}</ref>

[[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|date=January 2011}}

== Regulation of transcription ==
{{main|Transcriptional regulation}}
[[File:Lac Operon.svg|thumb|250px|'''''1'': RNA Polymerase, ''2'': Repressor, ''3'': Promoter, ''4'': Operator, ''5'': Lactose, ''6'': lacZ, ''7'': lacY, ''8'': lacA.
''' '''Top''': The gene is essentially turned off. There is no lactose to inhibit the repressor, so the repressor binds to the operator, which obstructs the RNA polymerase from binding to the promoter and making lactase.
'''Bottom''': The gene is turned on. Lactose is inhibiting the repressor, allowing the RNA polymerase to bind with the promoter, and express the genes, which synthesize lactase. Eventually, the lactase will digest all of the lactose, until there is none to bind to the repressor. The repressor will then bind to the operator, stopping the manufacture of lactase.]]

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 factor]]s alter the specificity of RNA polymerase for a given [[promoter (biology)|promoter]] or set of promoters, making it more or less likely to bind to them (i.e., [[sigma factor]]s used in [[Bacterial transcription|prokaryotic transcription]]).
[[Repressor]]s bind to the [[Operator (biology)|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 factor]]s position RNA polymerase at the start of a protein-coding sequence and then release the polymerase to transcribe the mRNA.
[[Activator (genetics)|Activators]] enhance the interaction between RNA polymerase and a particular [[promoter (biology)|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.
[[Enhancer (genetics)|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).<ref name="pmid1534752">{{cite journal | vauthors = Austin S, Dixon R | title = The prokaryotic enhancer binding protein NTRC has an ATPase activity which is phosphorylation and DNA dependent | journal = The EMBO Journal | volume = 11 | issue = 6 | pages = 2219–28 | date = June 1992 | pmid = 1534752 | pmc = 556689 | doi = 10.1002/j.1460-2075.1992.tb05281.x }}</ref>
[[Silencer (DNA)|Silencers]] are regions of DNA sequences that, when bound by particular transcription factors, can silence expression of the gene.

== Regulation by RNA ==
RNA can be an important regulator of gene activity, e.g. by [[microRNA]] (miRNA), [[Antisense RNA|antisense-RNA]], or [[long non-coding RNA]] (lncRNA). LncRNAs differ from [[Messenger RNA|mRNAs]] in the sense that they have specified subcellular locations and functions. They were first discovered to be located in the [[Cell nucleus|nucleus]] and [[chromatin]], and the localizations and functions are highly diverse now. Some still reside in chromatin where they interact with proteins. While this lncRNA ultimately affects gene expression in neuronal disorders such as [[Parkinson's disease|Parkinson]], [[Huntington's disease|Huntington]], and [[Alzheimer's disease|Alzheimer disease]], others, such as, PNCTR(pyrimidine-rich non-coding transcriptors), play a role in [[lung cancer]]. Given their role in disease, lncRNAs are potential [[biomarker]]s and may be useful targets for drugs or [[gene therapy]], although there are no approved drugs that target lncRNAs yet. The number of lncRNAs in the human genome remains poorly defined, but some estimates range from 16,000 to 100,000 lnc genes.<ref>{{cite journal | vauthors = Statello L, Guo CJ, Chen LL, Huarte M | title = Gene regulation by long non-coding RNAs and its biological functions | journal = Nature Reviews. Molecular Cell Biology | volume = 22 | issue = 2 | pages = 96–118 | date = February 2021 | pmid = 33353982 | pmc = 7754182 | doi = 10.1038/s41580-020-00315-9|issn=1471-0072  }}</ref>

== Epigenetic gene regulation ==
[[File:Epigenetic mechanisms.jpg|thumb|Overview of Epigenetic mechanisms.]]
[[Epigenetics]] refers to the modification of genes that is not changing the DNA or RNA sequence. Epigenetic modifications are also a key factor in influencing [[gene expression]]. They occur on genomic DNA and [[histone]]s and their chemical modifications regulate gene expression in a more efficient manner. There are several modifications of DNA (usually [[methylation]]) and more than 100 modifications of RNA in mammalian cells." Those modifications result in altered protein binding to DNA and a change in [[RNA stability]] and [[translation efficiency]].<ref>{{cite journal | vauthors = Kan RL, Chen J, Sallam T | title = Crosstalk between epitranscriptomic and epigenetic mechanisms in gene regulation | language = English | journal = Trends in Genetics | date = July 2021 | volume = 38 | issue = 2 | pages = 182–193 | pmid = 34294427 | doi = 10.1016/j.tig.2021.06.014 | pmc = 9093201 | s2cid = 236200223 }}</ref>

== Special cases in human biology and disease ==

===Regulation of transcription in cancer===
{{main|Regulation of transcription in cancer}}
In vertebrates, the majority of gene [[Promoter (genetics)#CpG islands in promoters|promoters]] contain a [[CpG site#CpG islands|CpG island]] with numerous [[CpG site]]s.<ref name="pmid16432200">{{cite journal | vauthors = Saxonov S, Berg P, Brutlag DL | title = A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 103 | issue = 5 | pages = 1412–7 | date = January 2006 | pmid = 16432200 | pmc = 1345710 | doi = 10.1073/pnas.0510310103 | bibcode = 2006PNAS..103.1412S | doi-access = free }}</ref>  When many of a gene's promoter CpG sites are [[DNA methylation|methylated]] the gene becomes silenced.<ref name=Bird>{{cite journal | vauthors = Bird A | title = DNA methylation patterns and epigenetic memory | journal = Genes & Development | volume = 16 | issue = 1 | pages = 6–21 | date = January 2002 | pmid = 11782440 | doi = 10.1101/gad.947102 | doi-access = free }}</ref>  Colorectal cancers typically have 3 to 6 [[Somatic evolution in cancer#Glossary|driver]] mutations and 33 to 66 [[Genetic hitchhiking|hitchhiker]] or passenger mutations.<ref name="pmid23539594">{{cite journal | vauthors = Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW | title = Cancer genome landscapes | journal = Science | volume = 339 | issue = 6127 | pages = 1546–58 | date = March 2013 | pmid = 23539594 | pmc = 3749880 | doi = 10.1126/science.1235122 | bibcode = 2013Sci...339.1546V }}</ref>   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 [[Cancer epigenetics|epigenetic]] mechanisms, such as altered expression of [[MicroRNA#DNA repair and cancer|microRNAs]].<ref name="pmid24616890">{{cite journal | vauthors = Tessitore A, Cicciarelli G, Del Vecchio F, Gaggiano A, Verzella D, Fischietti M, Vecchiotti D, Capece D, Zazzeroni F, Alesse E | display-authors = 6 | title = MicroRNAs in the DNA Damage/Repair Network and Cancer | journal = International Journal of Genomics | volume = 2014 | article-number = 820248 | year = 2014 | pmid = 24616890 | pmc = 3926391 | doi = 10.1155/2014/820248 | doi-access = free }}</ref>  In breast cancer, transcriptional repression of [[BRCA1]] may occur more frequently by over-expressed microRNA-182 than by hypermethylation of the BRCA1 promoter (see [[BRCA1#Low expression of BRCA1 in breast and ovarian cancers|Low expression of BRCA1 in breast and ovarian cancers]]).

===Regulation of transcription in addiction===

One of the cardinal features of addiction is its persistence.  The persistent behavioral changes appear to be due to long-lasting changes, resulting from [[Epigenetics|epigenetic]] alterations affecting gene expression, within particular regions of the brain.<ref name="pmid23643695">{{cite journal | vauthors = Nestler EJ | title = Epigenetic mechanisms of drug addiction | journal = Neuropharmacology | volume = 76 Pt B | pages = 259–68 | date = January 2014 | pmid = 23643695 | pmc = 3766384 | doi = 10.1016/j.neuropharm.2013.04.004 }}</ref>  Drugs of abuse cause three types of epigenetic alteration in the brain.  These are (1) [[Histone acetylation and deacetylation|histone]] acetylations and [[histone methylation]]s, (2) DNA methylation at [[CpG site]]s, and (3) epigenetic [[Downregulation and upregulation|downregulation or upregulation]] of [[microRNA]]s.<ref name="pmid23643695"/><ref name=Nestler2011>{{cite journal | vauthors = Robison AJ, Nestler EJ | title = Transcriptional and epigenetic mechanisms of addiction | journal = Nature Reviews. Neuroscience | volume = 12 | issue = 11 | pages = 623–37 | date = October 2011 | pmid = 21989194 | pmc = 3272277 | doi = 10.1038/nrn3111 }}</ref>  (See [[Epigenetics of cocaine addiction]] for some details.)

Chronic nicotine intake in mice alters brain cell epigenetic control of gene expression through [[Histone acetylation and deacetylation|acetylation of histones]].  This increases expression in the brain of the protein FosB, important in addiction.<ref name="pmid22049069">{{cite journal | vauthors = Levine A, Huang Y, Drisaldi B, Griffin EA, Pollak DD, Xu S, Yin D, Schaffran C, Kandel DB, Kandel ER | display-authors = 6 | title = Molecular mechanism for a gateway drug: epigenetic changes initiated by nicotine prime gene expression by cocaine | journal = Science Translational Medicine | volume = 3 | issue = 107 | pages = 107ra109 | date = November 2011 | pmid = 22049069 | pmc = 4042673 | doi = 10.1126/scitranslmed.3003062 }}</ref>   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.<ref name="pmid27651444">{{cite journal | vauthors = Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, Guan W, Xu T, Elks CE, Aslibekyan S, Moreno-Macias H, Smith JA, Brody JA, Dhingra R, Yousefi P, Pankow JS, Kunze S, Shah SH, McRae AF, Lohman K, Sha J, Absher DM, Ferrucci L, Zhao W, Demerath EW, Bressler J, Grove ML, Huan T, Liu C, Mendelson MM, Yao C, Kiel DP, Peters A, Wang-Sattler R, Visscher PM, Wray NR, Starr JM, Ding J, Rodriguez CJ, Wareham NJ, Irvin MR, Zhi D, Barrdahl M, Vineis P, Ambatipudi S, Uitterlinden AG, Hofman A, Schwartz J, Colicino E, Hou L, Vokonas PS, Hernandez DG, Singleton AB, Bandinelli S, Turner ST, Ware EB, Smith AK, Klengel T, Binder EB, Psaty BM, Taylor KD, Gharib SA, Swenson BR, Liang L, DeMeo DL, O'Connor GT, Herceg Z, Ressler KJ, Conneely KN, Sotoodehnia N, Kardia SL, Melzer D, Baccarelli AA, van Meurs JB, Romieu I, Arnett DK, Ong KK, Liu Y, Waldenberger M, Deary IJ, Fornage M, Levy D, London SJ | display-authors = 6 | title = Epigenetic Signatures of Cigarette Smoking | journal = Circulation: Cardiovascular Genetics | volume = 9 | issue = 5 | pages = 436–447 | date = October 2016 | pmid = 27651444 | pmc = 5267325 | doi = 10.1161/CIRCGENETICS.116.001506 }}</ref>  In blood cells, more than 18,000 [[CpG site]]s (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 site]]s 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"<ref name=Nestler2011 /> that may affect gene expression.

In rodent models, drugs of abuse, including cocaine,<ref name="pmid24552452">{{cite journal | vauthors = de Souza MF, Gonçales TA, Steinmetz A, Moura DJ, Saffi J, Gomez R, Barros HM | title = Cocaine induces DNA damage in distinct brain areas of female rats under different hormonal conditions | journal = Clinical and Experimental Pharmacology & Physiology | volume = 41 | issue = 4 | pages = 265–9 | date = April 2014 | pmid = 24552452 | doi = 10.1111/1440-1681.12218 | s2cid = 20849951 }}</ref> methamphetamine,<ref name="pmid25867833">{{cite journal | vauthors = Johnson Z, Venters J, Guarraci FA, Zewail-Foote M | title = Methamphetamine induces DNA damage in specific regions of the female rat brain | journal = Clinical and Experimental Pharmacology & Physiology | volume = 42 | issue = 6 | pages = 570–5 | date = June 2015 | pmid = 25867833 | doi = 10.1111/1440-1681.12404 | s2cid = 24182756 }}</ref><ref name="pmid18797138">{{cite journal | vauthors = Tokunaga I, Ishigami A, Kubo S, Gotohda T, Kitamura O | title = The peroxidative DNA damage and apoptosis in methamphetamine-treated rat brain | journal = The Journal of Medical Investigation | volume = 55 | issue = 3–4 | pages = 241–5 | date = August 2008 | pmid = 18797138 | doi = 10.2152/jmi.55.241 | doi-access = free }}</ref> alcohol<ref name="pmid18482162">{{cite journal | vauthors = Rulten SL, Hodder E, Ripley TL, Stephens DN, Mayne LV | title = Alcohol induces DNA damage and the Fanconi anemia D2 protein implicating FANCD2 in the DNA damage response pathways in brain | journal = Alcoholism: Clinical and Experimental Research | volume = 32 | issue = 7 | pages = 1186–96 | date = July 2008 | pmid = 18482162 | doi = 10.1111/j.1530-0277.2008.00673.x | doi-access = free }}</ref> and tobacco smoke products,<ref name="pmid28912356">{{cite journal | vauthors = Adhami N, Chen Y, Martins-Green M | title = Biomarkers of disease can be detected in mice as early as 4 weeks after initiation of exposure to third-hand smoke levels equivalent to those found in homes of smokers | journal = Clinical Science | volume = 131 | issue = 19 | pages = 2409–2426 | date = October 2017 | pmid = 28912356 | doi = 10.1042/CS20171053 | doi-access = free }}</ref> 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.<ref name="pmid27259203">{{cite journal | vauthors = Dabin J, Fortuny A, Polo SE | title = Epigenome Maintenance in Response to DNA Damage | journal = Molecular Cell | volume = 62 | issue = 5 | pages = 712–27 | date = June 2016 | pmid = 27259203 | pmc = 5476208 | doi = 10.1016/j.molcel.2016.04.006 }}</ref>

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

===Regulation of transcription in learning and memory===

[[File:DNA methylation.svg|thumb|300px|DNA methylation is the addition of a [[methyl]] group to the DNA that happens at [[cytosine]]. The image shows a cytosine single ring base and a methyl group added on to the 5 carbon. In mammals, DNA methylation occurs almost exclusively at a cytosine that is followed by a [[guanine]].]]

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 site]]s in the human genome is approximately 28 million.<ref name="pmid26932361">{{cite journal | vauthors = Lövkvist C, Dodd IB, Sneppen K, Haerter JO | title = DNA methylation in human epigenomes depends on local topology of CpG sites | journal = Nucleic Acids Research | volume = 44 | issue = 11 | pages = 5123–32 | date = June 2016 | pmid = 26932361 | pmc = 4914085 | doi = 10.1093/nar/gkw124 }}</ref> and generally about 70% of all CpG sites have a methylated cytosine.<ref name="Jabbari2004">{{cite journal |vauthors=Jabbari K, Bernardi G |title=Cytosine methylation and CpG, TpG (CpA) and TpA frequencies |journal=Gene |volume=333 |pages=143–9 |date=May 2004 |pmid=15177689 |doi=10.1016/j.gene.2004.02.043 }}</ref>

[[File:Brain regions involved in memory formation.jpg|thumb|400 px|The identified areas of the human brain are involved in memory formation.]]

In a rat, a painful learning experience, contextual [[fear conditioning]], can result in a life-long fearful memory after a single training event.<ref name=KimJung>{{cite journal | vauthors = Kim JJ, Jung MW | title = Neural circuits and mechanisms involved in Pavlovian fear conditioning: a critical review | journal = Neuroscience and Biobehavioral Reviews | volume = 30 | issue = 2 | pages = 188–202 | date = 2006 | pmid = 16120461 | pmc = 4342048 | doi = 10.1016/j.neubiorev.2005.06.005 }}</ref>  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.<ref name=DukeSweatt>{{cite journal | vauthors = Duke CG, Kennedy AJ, Gavin CF, Day JJ, Sweatt JD | title = Experience-dependent epigenomic reorganization in the hippocampus | journal = Learning & Memory | volume = 24 | issue = 7 | pages = 278–288 | date = July 2017 | pmid = 28620075 | pmc = 5473107 | doi = 10.1101/lm.045112.117 }}</ref>  The [[hippocampus]] is where new memories are initially stored.

Methylation of CpGs in a promoter region of a gene represses transcription<ref name="pmid17334365">{{cite journal |vauthors=Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S, Rebhan M, Schübeler D |title=Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome |journal=Nat. Genet. |volume=39 |issue=4 |pages=457–66 |date=April 2007 |pmid=17334365 |doi=10.1038/ng1990 |s2cid=22446734 }}</ref> while methylation of CpGs in the body of a gene increases expression.<ref name="pmid25263941">{{cite journal |vauthors=Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G |title=Gene body methylation can alter gene expression and is a therapeutic target in cancer |journal=Cancer Cell |volume=26 |issue=4 |pages=577–90 |date=October 2014 |pmid=25263941 |pmc=4224113 |doi=10.1016/j.ccr.2014.07.028 }}</ref>  [[TET enzymes]] play a central role in demethylation of methylated cytosines.  Demethylation of CpGs in a gene promoter by [[TET enzymes|TET enzyme]] activity increases transcription of the gene.<ref name="pmid24108092">{{cite journal |vauthors=Maeder ML, Angstman JF, Richardson ME, Linder SJ, Cascio VM, Tsai SQ, Ho QH, Sander JD, Reyon D, Bernstein BE, Costello JF, Wilkinson MF, Joung JK |title=Targeted DNA demethylation and activation of endogenous genes using programmable TALE-TET1 fusion proteins |journal=Nat. Biotechnol. |volume=31 |issue=12 |pages=1137–42 |date=December 2013 |pmid=24108092 |pmc=3858462 |doi=10.1038/nbt.2726 }}</ref>

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.<ref name=DukeSweatt />  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.<ref name=DukeSweatt />

== Post-transcriptional regulation ==
{{main|Post-transcriptional regulation}}

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 and microRNAs ==
{{main|Three prime untranslated region}}
{{main|MicroRNA}}
[[Three prime untranslated region]]s (3'-UTRs) of [[messenger RNA]]s (mRNAs) often contain regulatory sequences that post-transcriptionally influence gene expression.<ref name=Ogorodnikov>{{cite journal | vauthors = Ogorodnikov A, Kargapolova Y, Danckwardt S | title = Processing and transcriptome expansion at the mRNA 3' end in health and disease: finding the right end | journal = Pflügers Archiv | volume = 468 | issue = 6 | pages = 993–1012 | date = June 2016 | pmid = 27220521 | pmc = 4893057 | doi = 10.1007/s00424-016-1828-3 }}</ref> Such 3'-UTRs often contain both binding sites for [[microRNA]]s (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 [[Three prime untranslated region#MicroRNA response elements|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,<ref>miRBase.org</ref> an archive of miRNA [[Sequence (biology)|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).<ref name=Freidman>{{cite journal | vauthors = Friedman RC, Farh KK, Burge CB, Bartel DP | title = Most mammalian mRNAs are conserved targets of microRNAs | journal = Genome Research | volume = 19 | issue = 1 | pages = 92–105 | date = January 2009 | pmid = 18955434 | pmc = 2612969 | doi = 10.1101/gr.082701.108 }}</ref>  Freidman et al.<ref name=Freidman /> 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.<ref name="pmid15685193">{{cite journal | vauthors = Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM | display-authors = 6 | title = Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs | journal = Nature | volume = 433 | issue = 7027 | pages = 769–73 | date = February 2005 | pmid = 15685193 | doi = 10.1038/nature03315 | bibcode = 2005Natur.433..769L | s2cid = 4430576 }}</ref> 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).<ref>{{cite journal | vauthors = Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N | title = Widespread changes in protein synthesis induced by microRNAs | journal = Nature | volume = 455 | issue = 7209 | pages = 58–63 | date = September 2008 | pmid = 18668040 | doi = 10.1038/nature07228 | bibcode = 2008Natur.455...58S | s2cid = 4429008 }}</ref><ref>{{cite journal | vauthors = Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP | title = The impact of microRNAs on protein output | journal = Nature | volume = 455 | issue = 7209 | pages = 64–71 | date = September 2008 | pmid = 18668037 | pmc = 2745094 | doi = 10.1038/nature07242 | bibcode = 2008Natur.455...64B }}</ref>

The effects of miRNA dysregulation of gene expression seem to be important in cancer.<ref name="pmid21931505">{{cite journal | vauthors = Palmero EI, de Campos SG, Campos M, de Souza NC, Guerreiro ID, Carvalho AL, Marques MM | title = Mechanisms and role of microRNA deregulation in cancer onset and progression | journal = Genetics and Molecular Biology | volume = 34 | issue = 3 | pages = 363–70 | date = July 2011 | pmid = 21931505 | pmc = 3168173 | doi = 10.1590/S1415-47572011000300001 }}</ref> For instance, in gastrointestinal cancers, a 2015 paper identified nine miRNAs as [[Epigenetics|epigenetically]] altered and effective in down-regulating DNA repair enzymes.<ref name="pmid25987950">{{cite journal | vauthors = Bernstein C, Bernstein H | title = Epigenetic reduction of DNA repair in progression to gastrointestinal cancer | journal = World Journal of Gastrointestinal Oncology | volume = 7 | issue = 5 | pages = 30–46 | date = May 2015 | pmid = 25987950 | pmc = 4434036 | doi = 10.4251/wjgo.v7.i5.30 | doi-access = free }}</ref>

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.<ref name="pmid24653674">{{cite journal | vauthors = Maffioletti E, Tardito D, Gennarelli M, Bocchio-Chiavetto L | title = Micro spies from the brain to the periphery: new clues from studies on microRNAs in neuropsychiatric disorders | journal = Frontiers in Cellular Neuroscience | volume = 8 | page = 75 | year = 2014 | pmid = 24653674 | pmc = 3949217 | doi = 10.3389/fncel.2014.00075 | doi-access = free }}</ref><ref name="pmid22539927">{{cite journal | vauthors = Mellios N, Sur M | title = The Emerging Role of microRNAs in Schizophrenia and Autism Spectrum Disorders | journal = Frontiers in Psychiatry | volume = 3 | page = 39 | year = 2012 | pmid = 22539927 | pmc = 3336189 | doi = 10.3389/fpsyt.2012.00039 | doi-access = free }}</ref><ref name="pmid25636176">{{cite journal | vauthors = Geaghan M, Cairns MJ | title = MicroRNA and Posttranscriptional Dysregulation in Psychiatry | journal = Biological Psychiatry | volume = 78 | issue = 4 | pages = 231–9 | date = August 2015 | pmid = 25636176 | doi = 10.1016/j.biopsych.2014.12.009 | doi-access = free | hdl = 1959.13/1335073 | hdl-access = free }}</ref>

== Regulation of translation ==
{{main|Translational regulation}}

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 [[ribozyme]]s and self-regulate their expression.

== Examples of gene regulation ==
* [[Enzyme induction and inhibition|Enzyme induction]] 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 protein]]s 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 transcription factor|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.<ref>{{cite journal | vauthors = Barnett JA | title = A history of research on yeasts 7: enzymic adaptation and regulation | journal = Yeast | volume = 21 | issue = 9 | pages = 703–46 | date = July 2004 | pmid = 15282797 | doi = 10.1002/yea.1113 | s2cid = 36606279 | doi-access = free }}</ref>

=== Developmental biology ===
{{main|Evolutionary developmental biology}}

A large number of studied regulatory systems come from [[developmental biology]]. Examples include:<!--needs connected prose, not example list-->
* 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 (protein)|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.<ref>{{cite journal | vauthors = Dequéant ML, Pourquié O | title = Segmental patterning of the vertebrate embryonic axis | journal = Nature Reviews. Genetics | volume = 9 | issue = 5 | pages = 370–82 | date = May 2008 | pmid = 18414404 | doi = 10.1038/nrg2320 | s2cid = 2526914 }}</ref>
* 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.<ref>Gilbert SF (2003). Developmental biology, 7th ed., Sunderland, Mass: Sinauer Associates, 65–6. {{ISBN|0-87893-258-5}}.</ref>

== Circuitry ==
{{main|Gene regulatory network}}

=== Up-regulation and down-regulation ===
Up-regulation is a process which 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 proteins encoded by those genes. Conversely, down-regulation is a process resulting in decreased gene and corresponding protein expression.
* [[Up-regulation]] 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]].
* [[Down-regulation]] 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 ===
[[File:InduciblevsRepressibleSystems.png|thumb|440x440px|Gene regulation works using operators and repressors in bacteria.]]
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 transcription factor|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.<ref name="pmid1915298">{{cite journal | vauthors = Nehlin JO, Carlberg M, Ronne H | title = Control of yeast GAL genes by MIG1 repressor: a transcriptional cascade in the glucose response | journal = The EMBO Journal | volume = 10 | issue = 11 | pages = 3373–7 | date = November 1991 | pmid = 1915298 | pmc = 453065 | doi = 10.1002/j.1460-2075.1991.tb04901.x }}</ref>

=== Theoretical circuits ===
* 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
* positive feedback: the gene product upregulates its own production directly or indirectly, which can result in
** signal amplification
** bistable switches when two genes inhibit each other and both have positive feedback
** pattern generation

== Study methods ==
[[File:Human karyotype with bands and sub-bands.png|thumb|Schematic [[karyotype|karyogram]] of a human, showing an overview of the [[human genome]] on [[G banding]], which is a method that includes [[Giemsa stain]]ing, wherein the lighter staining regions are generally more [[Transcription (biology)|transcriptionally]] active, whereas darker regions are more inactive.{{further|Karyotype}}]]
{{For|DNA and RNA methods|nucleic acid methods}}

{{For|protein methods|protein methods}}

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:
* The local chromatin environment of the region can be determined by [[ChIP-chip]] analysis by pulling down [[RNA Polymerase II]], [[Histone 3]] modifications, [[Trithorax-group protein]], [[Polycomb-group protein]], or any other DNA-binding element to which a good antibody is available.
* [[Epistatic]] interactions can be investigated by [[synthetic genetic array]] analysis
* Due to post-transcriptional regulation, transcription rates and total RNA levels differ significantly. To measure the transcription rates [[nuclear run-on]] assays can be done and newer high-throughput methods are being developed, using [[thiol]] labelling instead of [[radioactivity in biology|radioactivity]].<ref name="pmid15907206">{{cite journal | vauthors = Cheadle C, Fan J, Cho-Chung YS, Werner T, Ray J, Do L, Gorospe M, Becker KG | display-authors = 6 | title = Control of gene expression during T cell activation: alternate regulation of mRNA transcription and mRNA stability | journal = BMC Genomics | volume = 6 | article-number = 75 | date = May 2005 | pmid = 15907206 | pmc = 1156890 | doi = 10.1186/1471-2164-6-75 | doi-access = free }}</ref>
* Only 5% of the RNA polymerised in the nucleus exits,<ref name="pmid10657981">{{cite journal | vauthors = Jackson DA, Pombo A, Iborra F | title = The balance sheet for transcription: an analysis of nuclear RNA metabolism in mammalian cells | journal = FASEB Journal | volume = 14 | issue = 2 | pages = 242–54 | date = February 2000 | pmid = 10657981 | doi = 10.1096/fasebj.14.2.242 | doi-access = free | s2cid = 23518786 }}</ref> and not only introns, [[Abortive initiation|abortive products]], and non-sense transcripts are degradated.  Therefore, the differences in nuclear and cytoplasmic levels can be seen by separating the two fractions by gentle lysis.<ref name="pmid16962184">{{cite journal | vauthors = Schwanekamp JA, Sartor MA, Karyala S, Halbleib D, Medvedovic M, Tomlinson CR | title = Genome-wide analyses show that nuclear and cytoplasmic RNA levels are differentially affected by dioxin | journal = Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression | volume = 1759 | issue = 8–9 | pages = 388–402 | year = 2006 | pmid = 16962184 | doi = 10.1016/j.bbaexp.2006.07.005 }}</ref>
* Alternative splicing can be analysed with a splicing array or with a tiling array (''see [[DNA microarray]]'').
* All [[in vivo]] RNA is complexed as [[Ribonucleoprotein Particle|RNPs]]. The quantity of transcripts bound to specific protein can be also analysed by [[RIP-Chip]]. For example, [[DCP2]] will give an indication of sequestered protein; [[ribosome]]-bound gives and indication of transcripts active in transcription (although a more dated method, called [[polysome]] fractionation, is still popular in some labs)
* Protein levels can be analysed by [[Mass spectrometry]], which can be compared only to [[quantitative PCR]] data, as [[DNA microarray|microarray]] data is relative and not absolute.
* RNA and protein degradation rates are measured by means of transcription inhibitors ([[actinomycin D]] or [[α-Amanitin]]) or translation inhibitors ([[Cycloheximide]]), respectively.

== See also ==
* [[Artificial transcription factor]]s (small molecules that mimic transcription factor protein)
* [[Cellular model]]
* [[Conserved non-coding sequence|Conserved non-coding DNA sequence]]
* [[Enhancer (genetics)]]
* [[Gene structure]]
* [[Spatiotemporal gene expression]]
*[[Regulator gene glucosyltransferases (Rgg/SHP) systems]]

== Notes and references ==
{{Reflist|2}}

==Bibliography==
* {{cite book|author=Latchman, David S.|title=Gene regulation: a eukaryotic perspective|publisher=Psychology Press|year=2005|isbn=978-0-415-36510-9|url=https://books.google.com/books?id=4x3ZzLNyfDsC}}

== External links ==
* [http://plantregmap.cbi.pku.edu.cn/ Plant Transcription Factor Database and Plant Transcriptional Regulation Data and Analysis Platform]
* {{MeshName|Regulation+of+Gene+Expression|3=Regulation of Gene Expression (MeSH)}}
* [http://rna.sysu.edu.cn/chipbase/ ChIPBase] An open database for decoding the transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

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[[Category:Gene expression]]
[[Category:DNA]]
[[Category:RNA]]
[[Category:Post-translational modification]]
[[Category:Evolutionary developmental biology]]