Which patterns do I have to avoid when modifying the 3'-UTR?

Which patterns do I have to avoid when modifying the 3'-UTR?

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I want to change a pre-miRNA sequence (in my case the pre-miRNA is encoding in a 3'UTR of a gene) and then put it in a lentivirus to see if it is still processed.

After modification (permutation of ten regions of ~20 nt) which kind of newly appeared pattern I've to be careful about? I do not want to disturb the gene in which the miRNA is encoded

If your goal is to avoid regulatory elements, I would not believe any prediction program, since new regulatory elements are found every day. The best way would be empirical and just clone and infect with a mutated 3'UTR and see if your gene's regulation is perturbed. You could at least swap the entire 3'UTR with that from another gene to see if there are even any gene regulatory elements in the 3'UTR.

How I can design a skirt pattern without taking measurements?

I am thinking of making a skirt as a birthday gift for my sister. Therefore taking measurements seems kinda impossible.

So is it recommended to use size charts in order to make the pattern instead, such as this one:


In the central nervous system, myelin provides metabolic support and increases conduction velocity along axons. Myelin is produced by oligodendrocytes, glial cells that extend multiple long processes, and wrap layers of membrane around axons. Myelin sheaths originating from a single oligodendrocyte can vary considerably in length and thickness, suggesting that sheath growth is locally regulated [1–3]. In line with this model, the myelin transcriptome is distinct compared to the cell body [4]. For example, proteolipid protein (PLP) and myelin basic protein (MBP) are the most abundant proteins in myelin. Yet the underlying mechanisms driving their protein expression in the myelin are entirely different. Plp mRNA is retained in the cell body and translated at the endoplasmic reticulum and the protein is transported to myelin [5,6]. By contrast, Mbp mRNA is trafficked to nascent sheaths and locally translated [5–10]. This evidence supports the model that mRNAs are selectively targeted to nascent sheaths and locally translated during growth and maturation.

Transport and local translation of mRNAs are broadly utilized mechanisms for controlling subcellular gene expression. In neurons, mRNAs are subcellularly localized to axons [11–13], dendrites [14], and growth cones [15], and local translation is required for axon growth and synaptogenesis [16–19]. Frequently, mRNA localization in neurons is determined by elements within the 3′ UTR [20,21]. For instance, the 3′ UTR of β-actin contains a sequence that is recognized by the RNA binding protein ZBP1 for localization to cellular projections including growth cones, axons, and dendrites [22–26]. Neurons localize hundreds of mRNAs to different subcellular compartments, but the underlying localization elements within the transcripts are largely unknown.

Similar to neurons, oligodendrocytes localize hundreds of mRNAs to distal myelin sheaths [27], but the localization signals necessary for myelin enrichment are limited to a few mRNAs. To date, the most extensively investigated transcript in oligodendrocytes is Mbp mRNA. The Mbp 3′ UTR is required for mRNA localization to myelin sheaths [28,29] and contains 2 minimal sequences including a 21-nt conserved sequence that is necessary for localization to processes in cultured mouse oligodendrocytes [30,31]. However, the minimal sequence is not required for localization in vivo, indicating that the Mbp 3′ UTR contains clandestine localization signals [29]. The investigations into Mbp mRNA localization have provided significant insights into the molecular mechanisms underlying mRNA localization in oligodendrocytes. However, we know very little about the mechanisms that promote localization of the other hundreds of myelin transcripts. How are mRNAs selected for localization to myelin sheaths? Do myelin-localized transcripts share similar cis-regulatory elements?

Here we bioinformatically identified myelin-enriched transcripts and investigated the ability of their 3′ UTR sequences to promote mRNA localization to nascent sheaths in living zebrafish. The 3′ UTRs that promote myelin localization contain shared cis-regulatory motifs necessary for mRNA localization. Moreover, we found that the motifs are sufficient to promote localization in some, but not all, contexts. Furthermore, we identified a sequence motif that is highly enriched in the myelin transcriptome, implicating the motif as a global regulator of mRNA localization in myelinating oligodendrocytes. Together, our data support a model whereby motifs in 3′ UTRs promote mRNA localization to nascent myelin sheaths.


APA is a crucial post-transcriptional regulatory mechanism that could generate various mRNA isoforms from a single gene. Each mRNA isoform eventually translates into a protein product with unique biological functions. APA could regulate almost every major step of molecular cell biological process, such as cellular genomic stability, proliferation capability, and transformation feasibility. Our journal of understanding APA is just beginning. However, the emerging evidences indicate that APA, at least, could be potentially a biomarker for disease diagnosis, severity stratification, and prognostic forecast and possibly a novel therapy target.

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 that control genes involved in cell growth. New drugs that prevent the activation of EGFR have been developed and are used to treat these cancers.

Mortality & Population Declines

When animals cross roads, mortality is often the result. In fact, road mortality is the leading source of mortality to many wildlife populations and an estimated 1 million vertebrates die on roads every day in the United States. 2 This rate of mortality can severely threaten animals and has been identified as a leading cause of decline in some populations.

While the consequences of road mortality can be severe, many factors influence the degree to which roads impact particular animal populations. When a road crosses through an animal's preferred habitat, the chances increase for road mortality. For example, Highway 27 in Florida that passes over a lake inhabited by many turtles has been shown to have very high turtle mortality rates and be one of the most dangerous roads for wildlife in the country. 3 Particular behaviors also put some animals more at risk. Chimney swifts eat insects and fly close to the ground as they follow prey. When these birds follow prey that fly over roads, it increases their chances of being struck by a car. 4 Groups of animals like amphibians that have regular mass migrations are also particularly vulnerable. 4

Homosexuality may be caused by chemical modifications to DNA

“Baby, I was born this way,” Lady Gaga sang in a 2011 hit that quickly became a gay anthem. Indeed, over the past 2 decades, researchers have turned up considerable evidence that homosexuality isn't a lifestyle choice, but is rooted in a person's biology and at least in part determined by genetics. Yet actual “gay genes” have been elusive.

A new study of male twins, scheduled for presentation at the annual meeting of the American Society of Human Genetics (ASHG) in Baltimore, Maryland, today, could help explain that paradox. It finds that epigenetic effects, chemical modifications of the human genome that alter gene activity without changing the DNA sequence, may have a major influence on sexual orientation.

The new work, from Eric Vilain's lab at the University of California (UC), Los Angeles, is “exciting” and “long overdue,” says William Rice, an evolutionary geneticist at UC Santa Barbara, who proposed in 2012 that epigenetics plays a role in sexual orientation. But Rice and others caution that the research is still preliminary and based on a small sample.

Researchers thought they were hot on the trail of “gay genes” in 1993, when a team led by geneticist Dean Hamer of the National Cancer Institute reported in Science that one or more genes for homosexuality had to reside on Xq28, a large region on the X chromosome. The discovery generated worldwide headlines, but some teams were unable to replicate the findings and the actual genes have not been found—not even by a team that vindicated Hamer's identification of Xq28 in a sample size 10 times larger than his last year. Twin studies suggested, moreover, that gene sequences can't be the full explanation. For example, the identical twin of a gay man, despite having the same genome, only has a 20% to 50% chance of being gay himself.

That's why some have suggested that epigenetics—instead of or in addition to traditional genetics—might be involved. During development, chromosomes are subject to chemical changes that don't affect the nucleotide sequence but can turn genes on or off the best known example is methylation, in which a methyl group is attached to specific DNA regions. Such “epi-marks” can remain in place for a lifetime, but most are erased when eggs and sperm are produced, so that a fetus starts with a blank slate. Recent studies, however, have shown that some marks are passed on to the next generation.

In a 2012 paper, Rice and his colleagues suggested that such unerased epi-marks might lead to homosexuality when they are passed on from father to daughter or from mother to son. Specifically, they argued that inherited marks that influence a fetus's sensitivity to testosterone in the womb might “masculinize” the brains of girls and “feminize” those of boys, leading to same-sex attraction.

Such ideas inspired Tuck Ngun, a postdoc in Vilain's lab, to study the methylation patterns at 140,000 regions in the DNA of 37 pairs of male identical twins who were discordant—meaning that one was gay and the other straight—and 10 pairs who were both gay. After several rounds of analysis—with the help of a specially developed machine-learning algorithm—the team identified five regions in the genome where the methylation pattern appears very closely linked to sexual orientation. One gene is important for nerve conduction, whereas another has been implicated in immune functions.

To test how important the five regions are, the team divided the discordant twin pairs into two groups. They looked at the associations between specific epi-marks and sexual orientation in one group, then tested how well those results could predict sexual orientation in the second group. They were able to reach almost 70% accuracy, although the presentation makes clear that—in contrast to what a provocative ASHG press release about the study suggested—this predictive ability applies only to the study sample and not to the wider population.

Just why identical twins sometimes end up with different methylation patterns isn't clear. If Rice's hypothesis is right, their mothers' epi-marks might have been erased in one son, but not the other or perhaps neither inherited any marks but one of them picked them up in the womb. In an earlier review, Ngun and Vilain cited evidence that methylation may be determined by subtle differences in the environment each fetus experiences during gestation, such as their exact locations within the womb and how much of the maternal blood supply each receives.

Such subtle influences are “where the action is,” says psychologist J. Michael Bailey of Northwestern University in Evanston, Illinois. “Discordant [identical] twins comprise the best way to study this.” But he and Rice caution that the study must be replicated with more twins to be fully credible. Sergey Gavrilets, an evolutionary biologist at the University of Tennessee, Knoxville, and a co-author of Rice's epigenetics model, adds that the study would also be “more convincing” if the team could link the regions showing epigenetic differences to testosterone sensitivity in the womb.

Vilain's team stresses that the findings shouldn't be used to produce tests for homosexuality or a misguided “cure.” Bailey says he's not worried about such misuse. “We will not have the potential to manipulate sexual orientation anytime soon,” he says. And in any case, he adds, “we should not restrict research on the origins of sexual orientation on the basis of hypothetical or real implications.

Bluher M, Kahn BB, Kahn CR. Extended longevity in mice lacking the insulin receptor in adipose tissue. Science. 2003299(5606):572–4.

Kimura KD, Tissenbaum HA, Liu Y, Ruvkun G. daf-2, an insulin receptor-like gene that regulates longevity and diapause in Caenorhabditis elegans. Science. 1997277(5328):942–6.

Tatar M, Kopelman A, Epstein D, Tu MP, Yin CM, Garofalo RS. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science. 2001292(5514):107–10.

Arantes-Oliveira N. Regulation of life-span by germ-line stem cells in Caenorhabditis elegans. Science. 2002295(5554):502–5.

Mattison JA, Roth GS, Beasley TM, Tilmont EM, Handy AM, Herbert RL, et al. Impact of caloric restriction on health and survival in rhesus monkeys from the NIA study. Nature. 2012489(7415):318–21.

Booth LN, Brunet A. The aging epigenome. Mol Cell. 201662(5):728–44.

Villeponteau B. The heterochromatin loss model of aging. Exp Gerontol. 199732(4–5):383–94.

Imai S, Kitano H. Heterochromatin islands and their dynamic reorganization: a hypothesis for three distinctive features of cellular aging. Exp Gerontol. 199833(6):555–70.

De Sandre-Giovannoli A, Bernard R, Cau P, Navarro C, Amiel J, Boccaccio I, et al. Lamin a truncation in Hutchinson-Gilford progeria. Science. 2003300(5628):2055–5.

Eriksson M, Brown WT, Gordon LB, Glynn MW, Singer J, Scott L, et al. Recurrent de novo point mutations in lamin A cause Hutchinson-Gilford progeria syndrome. Nature. 2003423(6937):293–8.

Hu Z, Chen K, Xia Z, Chavez M, Pal S, Seol J-H, et al. Nucleosome loss leads to global transcriptional up-regulation and genomic instability during yeast aging. Genes Dev. 201428(4):396–408.

Dang W, Steffen KK, Perry R, Dorsey JA, Johnson FB, Shilatifard A, et al. Histone H4 lysine 16 acetylation regulates cellular lifespan. Nature. 2009459(7248):802–7.

Feser J, Truong D, Das C, Carson JJ, Kieft J, Harkness T, et al. Elevated histone expression promotes life span extension. Mol Cell. 201039(5):724–35.

Jin C, Li J, Green CD, Yu X, Tang X, Han D, et al. Histone demethylase UTX-1 regulates C. elegans life span by targeting the insulin/IGF-1 signaling pathway. Cell Metab. 201114(2):161–72.

Maures TJ, Greer EL, Hauswirth AG, Brunet A. The H3K27 demethylase UTX-1 regulates C. elegans lifespan in a germline-independent, insulin-dependent manner. Aging Cell. 201110(6):980–90.

Shumaker DK, Dechat T, Kohlmaier A, Adam SA, Bozovsky MR, Erdos MR, et al. Mutant nuclear lamin A leads to progressive alterations of epigenetic control in premature aging. Proc Natl Acad Sci U S A. 2006103(23):8703–8.

Scaffidi P, Misteli T. Lamin A-dependent nuclear defects in human aging. Science. 2006312(5776):1059–63.

Ni Z, Ebata A, Alipanahiramandi E, Lee SS. Two SET domain containing genes link epigenetic changes and aging in Caenorhabditis elegans. Aging Cell. 201211(2):315–25.

Vandamme J, Lettier G, Sidoli S, Di Schiavi E, Nørregaard Jensen O, Salcini AE. The C. elegans H3K27 demethylase UTX-1 is essential for normal development, independent of its enzymatic activity. Plos Genet. 20128(5):e1002647.

Greer EL, Maures TJ, Hauswirth AG, Green EM, Leeman DS, Maro GS, et al. Members of the H3K4 trimethylation complex regulate lifespan in a germline-dependent manner in C. elegans. Nature. 2010466(7304):383–7.

Andersen EC, Lu X, Horvitz HR. C. elegans ISWI and NURF301 antagonize an Rb-like pathway in the determination of multiple cell fates. Development. 2006133(14):2695–704.

Bannister AJ, Kouzarides T. The CBP co-activator is a histone acetyltransferase. Nature. 1996384(6610):641–3.

Ogryzko VV, Schiltz RL, Russanova V, Howard BH, Nakatani Y. The transcriptional coactivators p300 and CBP are histone acetyltransferases. Cell. 199687(5):953–9.

Dillin A, Hsu A-L, Arantes-Oliveira N, Lehrer-Graiwer J, Hsin H, Fraser AG, et al. Rates of behavior and aging specified by mitochondrial function during development. Science. 2002298(5602):2398–401.

Feng J, Bussière F, Hekimi S. Mitochondrial electron transport is a key determinant of life span in Caenorhabditis elegans. Dev Cell. 20011(5):633–44.

Kenyon C, Hsin H. Signals from the reproductive system regulate the lifespan of: C. elegans: abstract: nature. Nature. 1999399(6734):362–6.

Lin K, Dorman JB, Rodan A, Kenyon C. daf-16: an HNF-3/forkhead family member that can function to double the life-span of Caenorhabditis elegans. Science. 1997278(5341):1319–22.

Ogg S, Paradis S, Gottlieb S, Patterson GI, Lee L, Tissenbaum HA, et al. The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature. 1997389(6654):994–9.

Henderson ST, Johnson TE. daf-16 integrates developmental and environmental inputs to mediate aging in the nematode Caenorhabditis elegans. Curr Biol. 200111(24):1975–80.

Qadota H, Inoue M, Hikita T, Köppen M, Hardin JD, Amano M, et al. Establishment of a tissue-specific RNAi system in C. elegans. Gene. 2007400(1–2):166–73.

Calixto A, Chelur D, Topalidou I, Chen X, Chalfie M. Enhanced neuronal RNAi in C. elegans using SID-1. Nature Methods. 20107(7):554–9.

Wolkow CA, Kimura KD, Lee M-S, Ruvkun G. Regulation of C. elegans life-span by insulinlike signaling in the nervous system. Science. 2000290(5489):147–50.

Hamilton B, Dong Y, Shindo M, Liu W, Odell I, Ruvkun G, et al. A systematic RNAi screen for longevity genes in C. elegans. Genes Dev. 200519(13):1544–55.

Ni Z, Lee SS. RNAi screens to identify components of gene networks that modulate aging in Caenorhabditis elegans. Brief Funct Genomics. 20109(1):53–64.

Curran SP, Ruvkun G. Lifespan regulation by evolutionarily conserved genes essential for viability. Plos Genet. 20073(4):e56.

Hansen M, Taubert S, Crawford D, Libina N, Lee S-J, Kenyon C. Lifespan extension by conditions that inhibit translation in Caenorhabditis elegans. Aging Cell. 20076(1):95–110.

Siebold AP, Banerjee R, Tie F, Kiss DL, Moskowitz J, Harte PJ. Polycomb repressive cComplex 2 and trithorax modulate Drosophila longevity and stress resistance. Proc Natl Acad Sci U S A. 2010107(1):169–74.

Ketel CS, Andersen EF, Vargas ML, Suh J, Strome S, Simon JA. Subunit contributions to histone methyltransferase sctivities of fly and worm polycomb group complexes. Mol Cell Biol. 200525(16):6857–68.

Bender LB, Cao R, Zhang Y, Strome S. The MES-2/MES-3/MES-6 complex and regulation of histone H3 methylation in C. elegans. Curr Biol. 200414(18):1639–43.

Gaydos LJ, Rechtsteiner A, Egelhofer TA, Carroll CR, Strome S. Antagonism between MES-4 and polycomb repressive complex 2 promotes appropriate gene expression in C. elegans germ cells. Cell Rep. 20122(5):1169–77.

Agger K, Cloos PAC, Christensen J, Pasini D, Rose S, Rappsilber J, et al. UTX and JMJD3 are histone H3K27 demethylases involved in HOX gene regulation and development. Nature. 2007449(7163):731–4.

Miller SA, Mohn SE, Weinmann AS. Jmjd3 and UTX play a demethylase-independent role in chromatin remodeling to regulate T-box family member-dependent gene expression. Mol Cell. 201040(4):594–605.

Zhao L, Zhao Y, Liu R, Zheng X, Zhang M, Guo H, et al. The transcription factor DAF-16 is essential for increased longevity in C. elegans exposed to Bifidobacterium longum BB68. Sci Rep. 20177(1):1–7.

Vellai T, Takacs-Vellai K, Zhang Y, Kovacs AL, Orosz L, Müller F. Influence of TOR kinase on lifespan in C. elegans. Nature 2003 426(6967):620–0.

Apfeld J, Kenyon C. Cell nonautonomy of C. elegans daf-2 function in the regulation of diapause and life span. Cell. 199895(2):199–210.

Libina N, Berman JR, Kenyon C. Tissue-specific activities of C. elegans DAF-16 in the regulation of lifespan. Cell. 2003115(4):489–502.

Kaletsky R, Yao V, Williams A, Runnels AM, Tadych A, Zhou S, et al. Transcriptome analysis of adult Caenorhabditis elegans cells reveals tissue-specific gene and isoform expression. Barsh GS, editor. Plos Genet. 201814(8):e1007559.

Brenner S. The genetics of Caenorhabditis elegans. Genetics. 197477(1):71–94.

Sulston J, Hodgkin J. Methods. Cold Spring Harbor Monograph Archive Volume 17 (1988): The Nematode <em>Caenorhabditis elegans</em>. 1988.

Garvin C, Holdeman R, Strome S. The phenotype of mes-2, mes-3, mes-4 and mes-6, maternal-effect genes required for survival of the germline in Caenorhabditis elegans, is sensitive to chromosome dosage. Genetics. 1998148(1):167–85.

Kamath RS, Fraser AG, Dong Y, Poulin G, Durbin R, Gotta M, et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature. 2003421(6920):231EP–237.

Timmons L, Court DL, Fire A. Ingestion of bacterially expressed dsRNAs can produce specific and potent genetic interference in Caenorhabditis elegans. Gene. 2001263(1–2):103–12.

Mello CC, Kramer JM, Stinchcomb D, Ambros V. Efficient gene transfer in C.elegans: extrachromosomal maintenance and integration of transforming sequences. EMBO J. 199110(12):3959–70.

Mello C, Fire A. DNA transformation. Methods Cell Biol. 199548:451–82.

Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2 −ΔΔCT method. Methods. 200125(4):402–8.

Which patterns do I have to avoid when modifying the 3'-UTR? - Biology

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

  • Understand the process of translation and discuss its key factors
  • Describe how the initiation complex controls translation
  • Explain the different ways in which the post-translational control of gene expression takes place

After RNA has been transported to the cytoplasm, it is translated into protein. Control of this process is largely dependent on the RNA molecule. As previously discussed, the stability of the RNA will have a large impact on its translation into a protein. As the stability changes, the amount of time that it is available for translation also changes.

The Initiation Complex and Translation Rate

Like transcription, translation is controlled by proteins that bind and initiate the process. In translation, the complex that assembles to start the process is referred to as the translation initiation complex. In eukaryotes, translation is initiated by binding the initiating met-tRNAi to the 40S ribosome. This tRNA is brought to the 40S ribosome by a protein initiation factor, eukaryotic initiation factor-2 (eIF-2). The eIF-2 protein binds to the high-energy molecule guanosine triphosphate (GTP). The tRNA-eIF2-GTP complex then binds to the 40S ribosome. A second complex forms on the mRNA. Several different initiation factors recognize the 5′ cap of the mRNA and proteins bound to the poly-A tail of the same mRNA, forming the mRNA into a loop. The cap-binding protein eIF4F brings the mRNA complex together with the 40S ribosome complex. The ribosome then scans along the mRNA until it finds a start codon AUG. When the anticodon of the initiator tRNA and the start codon are aligned, the GTP is hydrolyzed, the initiation factors are released, and the large 60S ribosomal subunit binds to form the translation complex. The binding of eIF-2 to the RNA is controlled by phosphorylation. If eIF-2 is phosphorylated, it undergoes a conformational change and cannot bind to GTP. Therefore, the initiation complex cannot form properly and translation is impeded ((Figure)). When eIF-2 remains unphosphorylated, the initiation complex can form normally and translation can proceed.

Art Connection

Figure 1. Gene expression can be controlled by factors that bind the translation initiation complex.

An increase in phosphorylation levels of eIF-2 has been observed in patients with neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s. What impact do you think this might have on protein synthesis?

Protein synthesis would be inhibited.

Chemical Modifications, Protein Activity, and Longevity

Proteins can be chemically modified with the addition of groups including methyl, phosphate, acetyl, and ubiquitin groups. The addition or removal of these groups from proteins regulates their activity or the length of time they exist in the cell. Sometimes these modifications can regulate where a protein is found in the cell—for example, in the nucleus, in the cytoplasm, or attached to the plasma membrane.

Chemical modifications occur in response to external stimuli such as stress, the lack of nutrients, heat, or ultraviolet light exposure. These changes can alter epigenetic accessibility, transcription, mRNA stability, or translation—all resulting in changes in expression of various genes. This is an efficient way for the cell to rapidly change the levels of specific proteins in response to the environment. Because proteins are involved in every stage of gene regulation, the phosphorylation of a protein (depending on the protein that is modified) can alter accessibility to the chromosome, can alter translation (by altering transcription factor binding or function), can change nuclear shuttling (by influencing modifications to the nuclear pore complex), can alter RNA stability (by binding or not binding to the RNA to regulate its stability), can modify translation (increase or decrease), or can change post-translational modifications (add or remove phosphates or other chemical modifications).

The addition of an ubiquitin group to a protein marks that protein for degradation. Ubiquitin acts like a flag indicating that the protein lifespan is complete. These proteins are moved to the proteasome, an organelle that functions to remove proteins, to be degraded ((Figure)). One way to control gene expression, therefore, is to alter the longevity of the protein.

Figure 2. Proteins with ubiquitin tags are marked for degradation within the proteasome.

Section Summary

Changing the status of the RNA or the protein itself can affect the amount of protein, the function of the protein, or how long it is found in the cell. To translate the protein, a protein initiator complex must assemble on the RNA. Modifications (such as phosphorylation) of proteins in this complex can prevent proper translation from occurring. Once a protein has been synthesized, it can be modified (phosphorylated, acetylated, methylated, or ubiquitinated). These post-translational modifications can greatly impact the stability, degradation, or function of the protein.

Art Connections

(Figure) An increase in phosphorylation levels of eIF-2 has been observed in patients with neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s. What impact do you think this might have on protein synthesis?


In this study, we carried out an analysis of the genetic variability of the 5′ URR, coding, and 3′ UTR of the HLA-G locus in a randomly selected series of bone marrow donors from southeastern Brazil. Although several reports have demonstrated the importance of both 5′ URR and 3′ UTR for the expression profile of the HLA-G locus, a complete LD and haplotype evaluation among these 5′ URR, coding, and 3′ UTR variation sites has not been simultaneously performed, and the same pattern of variation observed in the 3′ UTR could be present in the promoter region or even in the entire gene. Because Brazilians represent one of the most heterogeneous populations in the world ( Parra et al. 2003) and show the largest HLA-G variability already detected ( Castelli, Mendes-Junior et al. 2007 Castelli, Mendes-Junior, Wiezel, et al. 2007 Castelli, Mendes-Junior, et al. 2008) among the populations studied at a high resolution level, including North India, Polish, German, and Chinese Han ( Donadi et al. 2011), a high level of variability and haplotype diversity would be expected in a Brazilian sample.

The promoter haplotype frequency distribution in the Brazilian population resembles those described for both African American and European American populations ( Tan et al. 2005), which is compatible with the Brazilian ancestry background ( Parra et al. 2003). The association between promoter haplotypes and HLA-G alleles presented by Ober’s group was confirmed by the present data, exception made for the association of the PROMO-G010102a haplotype and the G*01:01:08 allele.

Although almost all promoter haplotypes found in the present study are shared by European Americans, African Americans, Chinese, Danes ( Ober et al. 2003 Hviid et al. 2004 Tan et al. 2005), and Brazilians, some inconsistencies regarding the HLA-G promoter diversity and haplotypes were observed in recent studies. Berger et al. have addressed the variability of the HLA-G promoter region and compared the frequency of all the variation sites and haplotypes detected in European American (EA) women with recurrent spontaneous abortion and healthy EA fertile women ( Berger et al. 2010). Although three of seven frequent promoter haplotypes found in Berger’s study are shared by the Brazilian population, most of the haplotypes with a frequency higher than 0.5% found in EA were not detected among Brazilians. In Berger’s study, the haplotypes were obtained using 12 variation sites and the three most frequent haplotypes found in EA were compatible with the PROMO-G010101, PROMO-G010102, PROMO-G0103, and PROMO-G0104 promoter lineages described here. Given the background of the Brazilian population, the larger number of haplotypes described by Berger et al. is quite unexpected and may be explained by the different sample sizes in the two studies and the large number of promoter region haplotypes exhibiting very low frequencies.

Another study addressed the variability of the HLA-G promoter region regarding its influence on the expression level of soluble HLA-G ( Hviid et al. 2006), evaluating the 5′ URR between −762 and −400 nucleotides as well as the 14-bp polymorphism in 61 Caucasian healthy individuals, but only the frequent haplotypes were shared between samples.

Recently, Rizzo et al. have addressed the variability of the promoter and coding region in patients with systemic lupus erythematosus and healthy controls. These authors also used the PHASE method to infer promoter and coding haplotypes in 14-bp insertion homozygous individuals. With the exception of a promoter haplotype that is compatible with both PROMO-G0102 and PROMO-G0104 lineages, considering only 13 promoter SNPs, none of the haplotypes found in this particular Italian population is compatible with the ones observed in the present series, including the haplotypes associated with the guanine insertion at −540 and the adenine deletion at −533( Rizzo et al. 2008).

The discrepancies observed between the above mentioned studies may reflect the features and the size of the populations used in each work [Hviid: 61 individuals ( Hviid et al. 2006) and Rizzo: 36 individuals ( Rizzo et al. 2008)], which may directly influence haplotyping accuracy ( Bettencourt et al. 2008).

HLA-G Extended Haplotypes

The haplotype analysis using 55 segregating sites, 25 in the promoter region, 22 in the coding region, and 8 in the 3′ UTR did reveal 28 different haplotypes. The pattern of the haplotypes is quite concise, giving rise to six HLA-G haplotype lineages, that is, HG010101, HG010102, HG010103, HG0103, HG0104, and HG010108 ( fig. 2). The most divergent of these lineages was HG010101, which may be subdivided into three sublineages, each associated with a specific 3′ UTR haplotype. In addition, it should be noticed that all other 3′ UTR haplotypes only occur associated with a specific HLA-G lineage ( table 2). The HG010108 lineage do not follow the same pattern observed in the remaining lineages because it has a promoter of the HG010102 or HG010103 lineage and a 3′ UTR of the HG0103 lineage, but a coding sequence that apparently do not have an origin in the HG010102 or HG010103 lineages. Curiously, the HG010102, HG010103, and HG010108 promoter is the most compatible with primate sequences ( Tan et al. 2005). Moreover, a small sample of 3′ UTR sequence data from nonhuman primates ( Castro et al. 2000) revealed that Old World Monkeys and Great Apes present exclusively UTR-5 and UTR-3, respectively, which are not so frequent in humans ( Castelli et al. 2010). Given that the promoter and the 3′ UTR haplotypes of this HG010108 lineage are compatible with primates, it is possible that this haplotype is in fact much older than the other ones, resembling an ancestral haplotype that has been lost by genetic drift or selection. In the present series, three chromosomes could not be properly assigned to a specific lineage because they probably resulted from crossing-over between frequent haplotypes. One such example is represented by the H07 and H27 haplotypes ( fig. 3 table 2), which seem to be a product of crossing-over between haplotypes from the HG010101a and HG010101b lineages in the first case and from HG0104 and HG010102 in the second case ( table 2).

The six HLA-G haplotype lineages do present functional variation mainly in their regulatory regions. Considering the promoter region (5′ URR), the minor allele frequency in 24 of 25 variation sites is higher than 2%, with a nucleotide diversity higher than the coding region ( table 3) and an average of one variation site per 52 nucleotides. The 3′ UTR presents the minor allele frequency higher than 2% in all eight variation sites, as well as the highest nucleotide diversity, with an average of one variation site per 45 nucleotides. Although the coding region presented the lowest nucleotide diversity, with an average of one variation site per 62 nucleotides and with only 18 of 22 variation sites presenting minor allele frequencies higher than 2%, it did reveal the highest haplotype diversity. However, it should be emphasized that most of the polymorphic sites in the coding region are in fact synonymous substitutions. Therefore, it is plausible to propose that the regulatory regions (5′ URR and 3′ UTR) are more functionally variable than the coding region.

Genetic Diversity and Functional Aspects of HLA-G Extended Haplotypes

The mRNA level of a particular gene is usually regulated by its rate of synthesis, mainly driven by its 5′ URR, transcription factors that are produced and microenvironmental agents, as well as by the rate of mRNA decay, specially driven by the 3′ UTR influencing mRNA stability and degradation ( Kuersten and Goodwin 2003). Although many factors may affect transcriptional and post-transcriptional control of HLA-G production ( Moreau et al. 2009), the reasons for HLA-G expression in some but not in other tissues have not been fully elucidated. Several lines of evidence indicate that numerous nucleotide variations at 5′ URR and 3′ UTR of the HLA-G locus may influence HLA-G expression and consequently tissue distribution in physiological and pathological conditions. In addition, variation sites observed in introns may be involved in HLA-G regulation processes, such as alternative splicing.

The HLA-G 5′ URR is unique among the HLA genes ( Solier et al. 2001). Due to the presence of a modified enhancer A (enhA) and a deleted interferon-stimulated response element (ISRE), the proximal HLA-G promoter is unresponsive to NF-κB ( Gobin et al. 1998) and IFN-γ ( Gobin et al. 1999). Among the regulatory elements known to stimulate HLA-G, a 244-bp region located −1.2 kb from exon 1 has been shown to be important for its spatiotemporal expression in transgenic mice and was proposed to have a locus control region (LCR) function ( Schmidt et al. 1993 Yelavarthi et al. 1993). This LCR exhibits a binding site for CREB1 factor −1380/−1370), which also binds to two additional cAMP response elements at −934 and −770 positions from the ATG. CREB1 allows promoter transactivation with the coactivators CREB-binding protein (CBP)/P300 ( Gobin et al. 2002). In addition, a binding site (Interferon Sequence Responsive Element/ISRE) for IFN response factor-1 (IRF-1) is located at the −744 bp position ( Lefebvre et al. 2001), beside a nonfunctional GAS-like element (−734) ( Chu et al. 1999) and is involved in HLA-G transactivation following IFN-β treatment ( Lefebvre et al. 2001). The HLA-G promoter also contains a heat shock element at the −459/−454 position that binds heat shock factor-1 (HSF-1) ( Ibrahim et al. 2000) and a progesterone receptor binding site at −37 bp from ATG ( Yie et al. 2006). On the other hand, the ras-responsive element binding 1 factor (RREB-1) downregulates HLA-G promoter activity through three ras response elements located at positions −1356, −142, and−53 ( Flajollet et al. 2009) and is likely to act through C-terminal–binding protein (CtBP) implicated in chromatin remodeling ( Shi et al. 2003).

Many of the promoter region polymorphisms ( table 1 fig. 4) either coincide with or are close to known or putative regulatory elements and thus may affect the binding of HLA-G regulatory factors. For instance, this phenomenon was demonstrated by Ober’s group regarding the −725 G/C/T SNP, a variation site that is very close to ISRE, in which the −725G allele was associated with a significantly higher expression level compared with the others ( Ober et al. 2006). In addition, besides the variation sites influencing the binding of transcription factors per se, the methylation status of the HLA-G gene promoter is crucial to the transcriptional activity of the gene ( Moreau et al. 2003 Mouillot et al. 2005) and the promoter methylation status may also be affected by polymorphisms located at CpG sites ( Ober et al. 2006).

Nucleotide differences between the two most frequent HLA-G lineages, G010101a and HG010102.

Nucleotide differences between the two most frequent HLA-G lineages, G010101a and HG010102.

The HLA-G 3′ UTR contains several regulatory elements ( Kuersten and Goodwin 2003) including polyadenylation signals and AU-rich elements ( Yie et al. 2008 Alvarez et al. 2009), and polymorphic sites that may potentially influence HLA-G transcription, translation, or both by several different mechanisms, particularly targets for microRNAs (miRNAs) ( Castelli et al. 2009). Among them, it is worth mentioning the 14-bp polymorphism, which has been associated with the magnitude of HLA-G production ( Rebmann et al. 2001), particularly by modulating HLA-G mRNA stability ( Hiby et al. 1999 O'Brien et al. 2001 Hviid et al. 2003 Rousseau et al. 2003). Although the mechanisms implicated have not been elucidated, HLA-G alleles presenting the 14-bp (5′-ATTTGTTCATGCCT-3′) sequence ( Harrison et al. 1993) have been associated with lower mRNA production for most membrane bound and soluble isoforms in trophoblast samples ( Hviid et al. 2003 Hviid 2006). On the other hand, a fraction of HLA-G mRNA transcripts presenting the 14-base insertion can be further processed (alternatively spliced) by the removal of 92 bases from the mature HLA-G mRNA ( Hiby et al. 1999 Hviid et al. 2003), yielding smaller HLA-G transcripts, reported to be more stable than the complete mRNA forms ( Rousseau et al. 2003). Besides the 14-bp polymorphism, two variation sites at the 3′ UTR have been reported to influence HLA-G expression. The presence of an adenine at the +3187 position, which is 4-bp upstream of an AU-rich motif, mediates mRNA degradation, leading to a decreased HLA-G expression ( Yie et al. 2008). The presence of a guanine at the +3142 position may increase the affinity of the miR-148a, miR-148b, and miR-152 microRNAs for the HLA-G mRNA, therefore decreasing mRNA availability by mRNA degradation and translation suppression ( Tan et al. 2007). In addition to the +3142 SNP, a recent in silico analysis revealed that several human miRNAs have the potential to bind to the HLA-G mRNA 3′ UTR and may influence HLA-G expression however, the binding affinity might be influenced by polymorphisms present in their target regions, emphasizing the role of the 14-bp polymorphism and the +3003, +3010, +3027, and +3035 SNPs ( fig. 3), which encompass a region of only 32 nucleotides ( Castelli et al. 2009 Donadi et al. 2011).

Alleles from these three major 3′ UTR polymorphic sites associated with HLA-G production are in strong LD with each other, illustrating a scenario in which their influence may not be mutually exclusive ( Castelli et al. 2010). It is noteworthy that the 14-bp insertion is always accompanied by the +3142G and +3187A alleles ( fig. 3), both previously associated with low mRNA availability, indicating that the low mRNA production associated with the 14-bp insertion ( Hviid et al. 2003) may also be a consequence of the presence of these polymorphisms associated with the 14-bp polymorphism ( Castelli et al. 2010). Besides, these three polymorphisms present a high LD with promoter variation sites ( fig. 3 and table 2), which indicates that, in an in vivo scenario, given a proper microenvironment stimulus for HLA-G expression, the rate of transcription, and translation may be influenced both by promoter and by 3′ UTR polymorphisms, that is, each variation site exerts its influence in a coordinated and dependent manner.

Genetic Diversity and Evolutionary Aspects of HLA-G Extended Haplotypes

Given the immune tolerance property of HLA-G and its benign or harmful presence depending on the situation, the expression of this gene must be under very tight control ( Donadi et al. 2011). It was previously shown that the pattern of variation at the HLA-G promoter region is characterized by two divergent lineages of promoter haplotypes that are maintained by balancing selection in worldwide human populations. These two divergent lineages may have different promoter activity and might be involved in a fine balance between high-expressing and low-expressing HLA-G haplotypes ( Tan et al. 2005). Our data do corroborate the presence of these two main HLA-G lineages, in which the extended HLA-G lineages HG010102, HG010103, HG010108, and HG0104 (left part of fig. 2) correspond to the first one of Ober’s lineage, whereas extended lineages HG010101 and HG0103 correspond to the second Ober’s lineage ( table 1).

Regarding the HLA-G locus as a whole, the two most frequent lineages, that is, sublineage HG010101a (H01 and H05) and lineage HG010102 (H10 to H15), which are equally frequent (about 26%) and account for more than 52% of the HLA-G haplotypes, are very different from each other. In fact, they differ in more than 50% of the 55 segregating sites analyzed, especially in the 5′ URR and 3′ UTR, with 11 and 4 fixed differences, respectively ( fig. 4). Most of these nucleotide differences coincide with or are close to known or putative transcription factor–binding sites at the 5′ URR or are even present at the 3′ UTR sites that have been reported to influence HLA-G mRNA availability (14-bp polymorphism, +3142 and +3187 SNPS). Additionally, both lineages do present several differences in the coding region, but the same G*01:01 HLA-G protein is encoded in approximately 79.8% of cases.

The coding region suffers a strong selective pressure for invariance (purifying selection), that is, preservation of nucleotide and amino acid sequences, reduced variability, and lower than expected nonsynonymous mutation rate ( Castro et al. 2000). In fact, strong evidence of purifying selection at the coding region was disclosed by the performance of a synonymous and nonsynonymous nucleotide substitution test considering all HLA-G alleles available at the IMGT database, which revealed an excess of synonymous changes in all exons (1—5), which is consistent with purifying selection (Mendes-Junior CT et al., unpublished data). Because HLA-G presents several biological effects related to the control of immune response ( Lee et al. 1995 Diehl et al. 1996 Ishitani et al. 2003), one may expect that an invariable mechanism of maternal tolerance should be more effective, conferring a higher reproductive fitness. It is possible that such purifying selection would result from this tolerogenic feature of the HLA-G molecule. For example, LILRB1 and LILRB2 are receptors expressed on the surface of several leukocytes and they bind to the α3 domain of the HLA-G molecule. Given that LILRBs are considered to be the major HLA-G receptors, it is noteworthy that only one worldwide HLA-G allele do present a nonsynonymous polymorphic site at exon 4 (G*01:06), which codes the α3 domain of the molecule. One might expect that polymorphic residues observed in this domain may negatively influence LILRB interactions, modulating the inhibitory intracellular signaling. It is interesting to observe that the only frequent allele with a nonsynonymous mutation at exon 4 (G*01:06) has been associated with preeclampsia in several populations ( Donadi et al. 2011).

In contrast to the coding region, the regulatory regions (5′ URR and 3′ UTR) suffer selection toward heterozygosis (balancing selection) ( Aldrich et al. 2002 Tan et al. 2005 Mendes-Junior et al. 2007 Castelli et al. 2010). It could be possible that the balancing selection signature at the 3′ UTR region results from a hitchhiking effect of balancing selection at the 5′ URR. However, this scenario is not straightforward, given that an in silico study revealed that most of the polymorphic sites of the 3′ UTR region are miRNA-binding sites and their alleles presumably affect the miRNA-binding affinity ( Castelli et al. 2009). These results suggest that these miRNAs might play a relevant role on the HLA-G expression pattern and, hence, the 3′ UTR region would be a direct target for balancing selection.

The coexistence of different selective pressures over a given gene would be favored by intragenic recombination. In fact, the existence of three recombining haplotypes in the present sample support the existence of crossing-over events throughout the HLA-G gene, particularly inside the HLA-G coding-region. It should be emphasized that different patterns of natural selection shaping variability of different parts of a same gene have been previously observed. Various class I and II MHC genes have provided evidence consistent with both balancing and purifying selection in humans, mice, and elephants. Although HLA class I antigen-binding sites are suffering overdominant selection, coding regions that are not involved in antigen presentation appear to have experienced purifying selection ( Hughes and Nei 1989 Archie et al. 2010). Outside the MHC, there is strong evidence that balancing selection has shaped the pattern of variation of the 5′ URR region of the CCR5 gene ( Bamshad et al. 2002 Ramalho et al. 2010), whereas its coding region has been subject to positive selection or neutral evolution ( Sabeti et al. 2005).

In order to better evaluate which polymorphic sites are in fact driven by these balancing selection signatures, windows of 150-bp in the promoter, coding, and 3′ UTR regions were established and the Tajima’s D were calculated in each one of them. The promoter region did present three windows with significantly positive Tajima’s D values: one window with the variation −1306, another window with variations −762, −725, −616, −689, and −666, and a third window with the variation −201. The 3′ UTR region did present significantly positive Tajima’s D only in the window with the variations +3142, +3187, and +3196. The coding region presented two windows with significantly positive Tajima’s D values, one with the variations +15 and +36 at exon 1 and +99, +126, +130, and +147 at intron 1, and the other with the variation +372. Interestingly, these windows are not adjacent and may indicate polymorphisms with a greater functional relevance. For example, the position −1306 is in the LCR of the HLA-G gene and may influence the binding of several transcriptional factors, including the RREB-1. The positions −762, −725, and −716 are close to the ISRE motif present around position −744, a binding site for IRF-1. The position −201 is in the nonfunctional enhancer A, which may influence its functionality. In addition, positions −201, +15, and +36 are in complete LD with position −1306, which may also explain such high Tajima’s D value by a hitchhiking effect. However, the window with the polymorphic sites in intron 1 (+99 to +147) was in fact intriguing. It is not possible to evaluate the impact of such polymorphisms in the HLA-G function, unless they influence HLA-G splicing patterns, the mRNA secondary structure, or its stability. The polymorphic site +372 may be not relevant because it is a synonymous exchange, but it is in elevated LD with all promoter variations discussed above. The 3′ UTR region presented balancing selection signature only in the window with polymorphic sites that were evaluated regarding their functional relevance (+3142 influencing miRNA binding and +3187 influencing mRNA stability). Taking these evidences, we believe that indeed balancing selection may act primarily in the regulatory regions in the most functionally relevant polymorphic sites.

The HLA-G trend toward heterozygosity may assure a fine balance between high-expressing and low-expressing HLA-G haplotypes, that is, during pregnancy, high-expressing haplotypes would be favored in the absence of infection, whereas low-expressing haplotypes would be favored in the presence of infection. Apparently, the presence of both a high-expressing and a low-expressing haplotypes in an individual may have been an advantage during evolution, proning individuals to face situations when a high or low HLA-G expression is profitable. This is strongly reinforced by all the neutrality tests performed (Ewens–Watterson, Tajima’s D and Fu and Li’s F and D), which revealed evidences of balancing selection acting only on the regulatory regions (5′ URR and 3′ UTR) and on the HLA-G locus as whole, probably by a hitchhiking effect due to the regulatory regions surrounding the coding region. These results may be, however, obscured by the population history (admixture) that characterizes this Brazilian urban population. Population stratification, for example, may add to selection, overestimating the balancing selection signatures obtained here. However, these same evidences have been found in other populations, such as Han Chinese, African American, and European American ( Tan et al. 2005). Nevertheless, analyses of other worldwide autochthonous population samples should be carried out to reinforce this hypothesis.

Due to the observation of very divergent HLA-G lineages, as illustrated in figure 4, accounting for more than 52% of the HLA-G haplotypes, one may argue that the six main HLA-G lineages (HG010101 and their sublineages, HG010102, HG010103, HG010108, HG0103, and HG0104) as well as the very divergent HLA-G lineages ( fig. 2, sides A and B) are also suffering balancing selection toward heterozygosis and that these very divergent HLA-G lineages might be associated with different HLA-G expression profiles. The Ewens–Watterson neutrality test was used to evaluate this matter, and three additional tests were performed considering 1) the main HLA-G lineages of each sample, with all the HG010101 lineages stratified into their sublineages and each crossing considered to involve different haplotypes 2) the main HLA-G lineages of each sample, with all HG010101 lineages considered as a whole, the possible crossing-overs between sequences of the same lineage considered as an allele of this lineage and the possible crossing-overs between different lineages considered as different haplotypes and 3) the side (A or B) of figure 2 in which each HLA-G haplotype of each sample was placed, in order to evaluate the heterozygosis between very divergent HLA-G lineages. All these tests revealed negative normalized F values, but only the last test (divergent lineages) did reveal a significant negative normalized F value (F = −1.9637, P = 0.0205). A closer evaluation of the HLA-G lineages from sides A and B ( fig. 2) reveals that each side is composed exclusively by HLA-G haplotypes harboring one of the promoter lineages proposed by Tan et al. (2005). Therefore, although the inclusion of both the 3′ UTR and the coding regions enhance the resolution of the HLA-G network, the present results corroborate previous findings ( Tan et al. 2005), that is, the existence of two highly divergent lineages of haplotypes(each one with sublineages) in diverse human populations, which may have very different transcriptional activity (determined by both the promoter and the 3′ UTR variability) and might result in precise and adequate protein levels.

In conclusion, the HLA-G locus seems to present six different HLA-G lineages showing functional variations mainly in nucleotides of the regulatory regions. These include differences in the 5′ URR at positions that either coincide with or are close to known transcription factor–binding sites and differences in the 3′ UTR mainly at positions that have already been reported to influence HLA-G mRNA stability and degradation rate. The evidence of balancing selection acting on the regulatory regions indicates that these HLA-G lineages are probably related to different expression profiles, depending on microenvironmental factors and on physiological or pathological conditions of the individual.