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Posts Tagged ‘Histone’


DNA Replication

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

Decades Old DNA Replication Models Called into Question

http://www.genengnews.com/gen-news-highlights/decades-old-dna-replication-models-called-into-question/81251929/

 

Decades Old DNA Replication Models Called into Question

http://www.genengnews.com/media/images/GENHighlight/102252_web8123122217.jpg

A series of electron micrographs show the barrel-shaped helicase, which is the enzyme that separates the two DNA strands, along with other components of the replisome, including polymerase-epsilon (green).[Brookhaven National Laboratory]

  • It may be time to update biology texts to reflect newly published data from a collaborative team of scientists at Rockefeller University, Stony Brook University, and the U.S. Department of Energy’s Brookhaven National Laboratory. Using cutting-edge electron microscopy (EM) techniques, the investigators gathered the first ever images of the fully assembled replisome, providing new insight into the molecular mechanisms of replication.

    “Our finding goes against decades of textbook drawings of what people thought the replisome should look like,” remarked co-senior author Michael O’Donnell, Ph.D., professor and head of Rockefeller’s Laboratory of DNA Replication. “However, it’s a recurring theme in science that nature does not always turn out to work the way you thought it did.”

    “Our finding goes against decades of textbook drawings of what people thought the replisome should look like,” remarked co-senior author Michael O’Donnell, Ph.D., professor and head of Rockefeller’s Laboratory of DNA Replication. “However, it’s a recurring theme in science that nature does not always turn out to work the way you thought it did.”

http://www.genengnews.com/Media/images/GENHighlight/102254_web2322915422.jpg

Previously (left), the replisome’s two polymerases (green) were assumed to be below the helicase (tan), the enzyme that splits the DNA strands. The new images reveal one polymerase is located at the front of the helicase, causing one strand to loop backward as it is copied (right). [Brookhaven National Laboratory]

The researcher’s findings focused on the replisome found in eukaryotic organisms, a category that includes a broad swath of living things, including humans and other multicellular organisms. Over the past several decades, there has been an array of data describing the individual components comprising the complex nature of replisome. Yet, until now no pictures existed to show just how everything fit together.

“This work is a continuation of our long-standing research using electron microscopy to understand the mechanism of DNA replication, an essential function for every living cell,” explained co-senior author Huilin Li, Ph.D., biologist with joint appointments at Brookhaven Lab and Stony Brook University. “These new images show the fully assembled and fully activated ‘helicase’ protein complex—which encircles and separates the two strands of the DNA double helix as it passes through a central pore in the structure—and how the helicase coordinates with the two ‘polymerase’ enzymes that duplicate each strand to copy the genome.”

The image and implications from this study were described in a paper entitled “The architecture of a eukaryotic replisome,” published recently through Nature Structural & Molecular Biology.

Traditional models of DNA replication show the helicase enzyme moving along the DNA, separating the two strands of the double helix, with two polymerases located at the back where the DNA strand is split. In this configuration, the polymerases would add nucleotides to the side-by-side split ends as they move out of the helicase to form two new complete double helix DNA strands. However, the images that the researchers collected of intact replisomes revealed that only one of the polymerases is located at the back of the helicase. The other is on the front side of the helicase, where the helicase first encounters the double-stranded helix. This means that while one of the two split DNA strands is acted on by the polymerase at the back end, the other has to thread itself back through or around the helicase to reach the front-side polymerase before having its new complementary strand assembled.

“DNA replication is one of the most fundamental processes of life, so it is every biochemist’s dream to see what a replisome looks like,” stated lead author Jingchuan Sun, EM biologist in Dr. Li’s laboratory. “Our lab has expertise and a decade of experience using electron microscopy to study DNA replication, which has prepared us well to tackle the highly mobile therefore very challenging replisome structure. Working together with the O’Donnell lab, which has done beautiful, functional studies on the yeast replisome, our two groups brought perfectly complementary expertise to this project.”

The positioning of one polymerase at the front of the helicase suggests that it may have an unforeseen function—the possibilities of which the collaborative group of scientists is continuing to study. Whatever the function the offset polymerase ends up having, Drs. Li and O’Donnell hope that it will not only provide them better insight into the replication machinery but that they may uncover useful information that can be exploited for disease intervention.

“Clearly, further studies will be required to understand the functional implications of the unexpected replisome architecture reported here,” the scientists concluded.

 

RELATED CONTENT

 

Fifth Histone Found to Recruit Proteins for DNA Repair   

http://www.genengnews.com/gen-news-highlights/fifth-histone-found-to-recruit-proteins-for-dna-repair/81251895/

Scientists at the University of Copenhagen say they have located a previously unknown function for histones, which allows for an improved understanding of how cells protect and repair DNA damages. This new discovery may be of great importance to the treatment of diseases caused by cellular changes such as cancer and immune deficiency syndrome.

The study (“Histone H1 couples initiation and amplification of ubiquitin signaling after DNA damage”) is published in Nature.

“I believe that there’s a lot of work ahead. It’s like opening a door onto a previously undiscovered territory filled with lots of exciting knowledge. The histones are incredibly important to many of the cells’ processes as well as their overall wellbeing,” said Niels Mailand, Ph.D., from the Novo Nordisk Foundation Center for Protein Research at the Faculty of Health and Medical Science.

Histones enable the tight packaging of DNA strands within cells. The strands are two meters in length and the cells usually about 100,000 times smaller. Generally speaking, there are five types of histones. Four of them are core histones and they are placed like beads on the DNA strands, which are curled up like a ball of wool within the cells. The role of the histones is already well described in research, and in addition to enabling the packaging of the DNA strands they also play a central part in practically every process related to the DNA-code, including repairing possibly damaged DNA.

The four core histones have tails and, among other things, they signal damage to the DNA and thus attract the proteins that help repair the damage. Between the histone “yarn balls” we find the fifth histone, Histone H1, but up until now its function has not been thoroughly examined.

Using a mass spectrometer, Dr. Mailand and his team have discovered that, surprisingly, the H1 histone also helps summon repair proteins.

“In international research, the primary focus has been on the core histones and their functionality, whereas little attention has been paid to the H1 histone, simply because we weren’t aware that it too influenced the repair process. Having discovered this function in the H1 constitutes an important piece of the puzzle of how cells protect their DNA, and it opens a door onto hitherto unknown and highly interesting territory,” noted Dr. Mailand.

He expects the discovery to lead to increased research into Histone H1 worldwide, which will lead to increased knowledge of cells’ abilities to repair possible damage to their DNA and thus increase our knowledge of the basis for diseases caused by cellular changes. It will also generate more knowledge about the treatment of these diseases.

“By mapping the function of the H1 histone, we will also learn more about the repair of DNA damages on a molecular level. In order to provide the most efficient treatment, we need to know how the cells prevent and repair these damages,” point out Dr. Mailand.

 

Cover All the Bases for Oligonucleotide Analysis

Stephen Luke

Synthetic oligonucleotides have emerged as promising therapeutic agents for the treatment of a variety of diseases, including viral infections and cancer. Researchers are looking at several classes of nucleic acids, such as antisense oligonucleotides, small interfering RNAs (siRNAs), and aptamers, for therapeutic applications.

However, various impurities – product-related, in the starting materials, and arising from incomplete capping of coupling reactions – must be identified and removed and postsynthesis processing must be monitored. Thus, a key challenge in the development and manufacture of oligonucleotide therapeutics is to establish analytical methods that are capable of separating and identifying impurities.

Exploring Better Options for Oligonucleotide LC Separations

 

 

Table 1. Options for oligonucleotide LC separations

Ion-pair, reversed-phase separation of the trityl-on oligos and is relatively simple to perform. This method separates the full-length target oligo, which still has the dMT group attached, from the deprotected failure sequences. The analytical information obtained is limited, so this is generally considered a purification method.

An alternate method, ion-exchange separations of the trityl-off, deprotected oligos uses the negative charge on the backbone of the oligo to facilitate the separation. Resolution is good for the shorter oligos but decreases with increasing chain length. Aqueous eluents are used but oligos are highly charged, and high concentrations of salt are needed to achieve elution from the column, making the technique unsuitable for use with LC/MS.

Finally, ion-pair, reversed-phase separation of the trityl-off, deprotected oligos makes use of organic solvents and mobile phase additives such as TEAA (triethylammonium acetate) or TEA-HFIP (triethylamine and hexafluoroisopropanol) to ion-pair with the negatively charged phosphodiester backbone of the oligonucleotide. High-performance columns deliver excellent resolution. What’s more, methods with volatile mobile phase constituents such as TEA-HFIP are suitable for use with LC/MS, providing useful information to help characterize oligonucleotide structures and sequences.

In Table 1 we summarize some of the options for oligonucleotide analysis by liquid chromatography.

Designed for ion-pair, reversed-phase separation of the trityl-off, deprotected oligos using either TEAA or TEA-HFIP mobile phases –Agilent AdvanceBio Oligonucleotide columns meet these challenges.

more….   http://www.genengnews.com/gen-articles/cover-all-the-bases-for-oligonucleotide-analysis/5594/

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The role and importance of transcription factors

Larry H. Bernstein, MD, FCAP, Writer and Curator

https://pharmaceuticalintelligence.com/2014/8/05/The-role-and-importance-of-transcripton-factors

The following is a second in the 2nd series that is focused on the topic of the impact of genomics and transcriptomics in the evolution of 21st century of medicine, which shall have to be more efficient and more effective by the end of this decade, if the prediction for the funding of Medicare is expected to run out. Even so, Social Security was devised by none other than the Otto von Bismarck, who unified Germany, and United Kingdom has had a charity hospital care system begun to protect the widows of the ravages of war, and nursing was developed by Florence Nightengale as a result of the experience of war. It can only be concluded that the care for the elderly, the infirm, and those who have little resources to live on has a long history in western civilization, and it will not cease to exist as a public social obligation anytime soon. The 20th century saw an explosive development of physics; organic, inorganic, biochemistry, and medicinal chemistry, and the elucidation of the genetic code and its mechanism of translation in plants, microorganisms, and eukaryotes.  All of which occurred irrespective of the most horrendous wars that have reshaped the world map.

The following are the second portions of a puzzle in construction that is intended to move into deeper complexities introduced by proteomics, cell metabolism, metabolomics, and signaling.  This is the only manner by which I can begin to appreciate what a wonder it is to view and live in this world with all its imperfections.

We have already visited the transcription process, by which an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. We shall examine this further.

  1. RNA and the transcription the genetic code

Larry H. Bernstein, MD, FCAP, Writer and Curator
https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/

  1. The role and importance of transcription factors?
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    https://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs
  2. What is the meaning of so many RNAs?

Larry H. Bernstein, MD, FCAP, Writer and Curator
https://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs

  1. Pathology Emergence in the 21st Century
    Larry Bernstein, MD, FCAP, Author and Curator
    https://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  2. The Arnold Relman Challenge: US HealthCare Costs vs US HealthCare Outcomes

Larry H. Bernstein, MD, FCAP, Reviewer and Curator; and
Aviva Lev-Ari, PhD, RN, Curator
https://pharmaceuticalintelligence.com/2014/08/05/the-relman-challenge/

 

 

 

Quantifying transcription factor kinetics: At work or at play?

Posted online on September 11, 2013. (doi:10.3109/10409238.2013.833891)

Florian Mueller1,2, Timothy J. Stasevich3, Davide Mazza4, and James G. McNally5
1Institut Pasteur, Computational Imaging and Modeling Unit, CNRS, Paris, Fr
2Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure, Paris, Fr
3Graduate School of Frontier Biosciences, Osaka University, Osaka, Jp
4Istituto Scientifico Ospedale San Raffaele, Centro di Imaging Sperimentale e Università Vita-Salute
San Raffaele, Milano, It, and
5Fluorescence Imaging Group, National Cancer Institute, NIH, Bethesda, MD, USA

Read More: http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

Abstract

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Here we summarize and compare the four different techniques that are currently used to measure these kinetics in live cells, namely fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single molecule tracking (SMT) and competition ChIP (CC). We highlight the principles underlying each of these approaches as well as their advantages and disadvantages. A comparison of data from each of these techniques raises an important question: do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or do they reflect non-specific interactions (i.e. playing TFs)? To help resolve this dilemma we discuss five key unresolved biological questions related to the functionality of transient and prolonged binding events at both specific promoter response elements as well as non-specific sites. In support of functionality, we review data suggesting that TF residence times are tightly regulated, and that this regulation modulates transcriptional output at single genes. We argue that in addition to this site-specific regulatory role, TF residence times also determine the fraction of promoter targets occupied within a cell thereby impacting the functional status of cellular gene networks. Thus, TF residence times are key parameters that could influence transcription in multiple ways.

Keywords: Competition-ChIP, kinetic modeling, live-cell imaging, non-specific binding, specific binding, transcription, transcription factor dynamics http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

The Transcription Factor Titration Effect Dictates Level of Gene ExpressionCalifornia Institute of Technology

Robert C. Brewster, Franz M. Weinert, Hernan G. Garcia, Dan Song, Mattias Rydenfelt, and Rob Phillips  CalTech
 Cell Mar 13, 2014; 156:1312–1323,.

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number—in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle.

 

 

Optimal reference genes for normalization of qRT-PCR data from archival formalin-fixed, paraffin-embedded breast tumors controlling for tumor cell content and decay of mRNA.

Tramm TSørensen BSOvergaard JAlsner J.

Diagn Mol Pathol. 2013 Sep;22(3):181-7. http://dx.doi.org:/10.1097/PDM.0b013e318285651e

Gene-expression analysis is increasingly performed on degraded mRNA from formalin-fixed, paraffin-embedded tissue (FFPE), giving the option of examining retrospective cohorts. The aim of this study was to select robust reference genes showing stable expression over time in FFPE, controlling for various content of tumor tissue and decay of mRNA because of variable length of storage of the tissue.

Sixteen reference genes were quantified by qRT-PCR in 40 FFPE breast tumor samples, stored for 1 to 29 years. Samples included 2 benign lesions and 38 carcinomas with varying tumor content. Stability of the reference genes were determined by the geNorm algorithm. mRNA was successfully extracted from all samples, and the 16 genes quantified in the majority of samples.

Results showed 14% loss of amplifiable mRNA per year, corresponding to a half-life of 4.6 years. The 4 most stable expressed genes were CALM2, RPL37A, ACTB, and RPLP0. Several of the other examined genes showed considerably instability over time (GAPDH, PSMC4, OAZ1, IPO8).

In conclusion, we identified 4 genes robustly expressed over time and independent of neoplastic tissue content in the FFPE block.   PMID:23846446

 

Structures of Cas9 Endonucleases Reveal RNA-Mediated Conformational Activation

Martin Jinek1,*,Fuguo Jiang2,*David W. Taylor3,4,*Samuel H. Sternberg5,*Emine Kaya2, et al.

 

1Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland. 2Department of Molecular and Cell Biology,3Howard Hughes Medical Institute, 4California Institute for Quantitative Biosciences, 5Department of Chemistry, 6Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,. 7The Laboratory for Molecular Infection Medicine Sweden, Umeå University, Umeå S-90187, Sweden. 8Helmholtz Centre for Infection Research, Department of Regulation in Infection Biology, D-38124 Braunschweig, Germany. 9Hannover Medical School, D-30625 Hannover, Germany. 10Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720.

‡ Present address: Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66 CH-4058 Basel, Switzerland.

§ Present address: Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA.

 

Science  http://dx.doi.org:/10.1126/science.1247997

 

Type II CRISPR-Cas systems use an RNA-guided DNA endonuclease, Cas9,

  • to generate double-strand breaks in invasive DNA during an adaptive bacterial immune response.

Cas9 has been harnessed as a powerful tool for genome editing and gene regulation in many eukaryotic organisms.

Here, we report 2.6 and 2.2 Å resolution crystal structures of two major Cas9 enzymes subtypes,

  • revealing the structural core shared by all Cas9 family members.

The architectures of Cas9 enzymes define nucleic acid binding clefts, and

single-particle electron microscopy reconstructions show that the two structural lobes harboring these clefts undergo guide

  • RNA-induced reorientation to form a central channel where DNA substrates are bound.

The observation that extensive structural rearrangements occur before target DNA duplex binding

  • implicates guide RNA loading as a key step in Cas9 activation.

MicroRNA function in endothelial cells
Dr. Virginie Mattot
Angiogenesis, endothelium activation
Solving the mystery of an unknown target gene using microRNA Target Site Blockers

Dr. Virgine Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets which functions are yet unknown.

What is the main focus of the research conducted in your lab?

We are studying endothelial cell functions with a particular interest in angiogenesis and endothelium activation during physiological and tumoral vascular development.

How did your research lead to the study of microRNAs?

A few years ago, we identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

We have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

What is the aim of your current project?

While we were searching for the functions of the intronic microRNA,

  • we identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and if regulation of this gene by the microRNA was involved in endothelial cell function. We had already characterized the endothelial cell phenotype associated with the inhibition of our intronic microRNA. We then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

How did you perform the experiments and analyze the results?

LNA™ enhanced target site blockers (TSB) for our microRNA were designed by Exiqon. We

  • transfected the TSBs into endothelial cells using our standard procedure and
  • analysed the induced phenotype.

As a control for these experiments, a mutated version of the TSB was designed by Exiqon and transfected into endothelial cells. We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

What do you find to be the main benefits/advantage of the LNA™ microRNA target site blockers from Exiqon?

Target Site Blockers are efficient tools to demonstrate the specific involvement of

  • putative microRNA targets in the function played by this microRNA.

What would be your advice to colleagues about getting started with microRNA functional analysis?

  • it is essential to perform both gain and loss of functions experiments.

 Changing the core of transcription

Different members of the TAF family of proteins work in differentiated cells, such as motor neurons or brown fat cells, to control the expression of genes that are specific to each cell type.

Katherine A Jones
Jones. eLife 2014;3:e03575. http://dx.doi.org:/10.7554/eLife.03575

 

Related research articles: Herrera FJ, Yamaguchi T, Roelink H, Tjian R. 2014. Core promoter factor TAF9B regulates neuronal gene expression. eLife 3:e02559. http://dx.doi.org:/10.7554eLife.02559

Zhou H, Wan B, Grubisic I, Kaplan T, Tjian R. 2014. TAF7L modulates brown adipose tissue formation. eLife 3:e02811. Http://dx.doi.org:/10.7554/eLife.02811

 

Motor neurons (green) being grown in vitro

Motor neurons (green) being grown in vitro

Image Motor neurons (green) being grown in vitro

 

In a developing organism, different genes are expressed at different times

 

  • the pattern of gene expression can often change abruptly.

 

Expressing a gene involves multiple steps:

 

  • the DNA must be transcribed into a molecule of messenger RNA,
  • which is then trans­lated into a protein.

 

The mechanisms that start the transcription of protein-coding genes in rap­idly growing cells are reasonably well understood: two types of proteins—

 

  • DNA-binding activators and general transcription factors—

 

cooperate to recruit an enzyme called RNA polymerase, which then transcribes the gene (Kadonaga, 2012).

 

These proteins bind to a region of the gene called the promoter, which is

 

  • upstream from the protein-coding region of the gene.

 

TATA-binding protein is a general transcrip­tion factor that

  • binds to certain sequences of DNA bases found within promoters

14 TATA-binding protein associated factors (TAFs) are included into two different protein complexes called TFIID and SAGA (Müller et al., 2010). which, in budding yeast, can recruit TATA-binding protein to gene promoters (Basehoar et al., 2004), but not all genes require all of the general transcription factors, and some genes require both TFIID and SAGA complexes.

Although the steps that are required to switch on genes when cells are rapidly dividing are fairly well known,

  • the same is not true for cells that are differentiating into specialised cell types.

In these cells, many transcription factors are downregulated and

  • the entire pattern of gene expression changes dramatically.

Moreover, certain TAFs are strongly up-regulated during differentiation. The core transcriptional machinery is essentially rebuilt at the genes that are expressed in differentiated cells.

Over the years Robert Tjian of the University of California Berkeley and co-workers have illu­minated how individual TAFs can affect how a cell differentiates in different contexts (Figure 1). Now, in eLife, Francisco Herrera of UC Berkeley and co-workers—including Teppei Yamaguchi, Henk Roelink and Tjian—have identified a critical role for a TAF called TAF9B in the expression of genes in motor neurons (Herrera et al., 2014).

Herrera et al. found that TAF9B predominantly associates with the SAGA complex, rather than the TFIID complex, in the motor neuron cells. Mice in which the gene for TAF9B had been deleted had less neuronal tissue in the developing spinal cord. Moreover, the genes that are involved in forming the branches of neurons were not properly regu¬lated in these mice.

Recently, in another eLife paper, Tjian and co-workers at Berkeley, Fudan University and the Hebrew University of Jerusalem—including Haiying Zhou as first author, Bo Wan, Ivan Grubisic and Tommy Kaplan—reported that another TAF protein, called TAF7L, works as part of the TFIID complex to up-regulate genes that direct cells to become brown adipose tissue (Zhou et al., 2014).

 

TATA-binding protein associated factors

TATA-binding protein associated factors

Figure 1. TATA-binding protein associated factors (TAFs) regulate transcription in specific cell types. TAF3, for example, works with another transcription factor to regulate the expression of genes that are critical for the differentiation of the endoderm in the early embryo (Liu et al., 2011). TAF3 also forms a complex with the TATA-related factor, TRF3, to regulate Myogenin and other muscle-specific genes to form myotubes (Deato et al., 2008). TAF7L interacts with another transcription factor to activate genes involved in the formation of adipocytes (‘fat cells’) and adipose tissue (Zhou et al., 2013; Zhou et al., 2014). Finally, TAF9B is a key regulator of transcription in motor neurons (Herrera et al., 2014). The names of some of the genes regulated by the TAFs are shown in brackets.

TAF9B

Deleting the gene for TAF9B in mouse embryonic stem cells revealed that this TAF

  • is not needed for the growth of stem cells, or
  • required for the expression of genes that prevent differentiation:

both of these processes are known to be highly-dependent upon the TFIID complex
(Pijnappel et al., 2013). However,

  • genes that would normally be expressed specifically in neurons were not
  • up-regulated when cells without the TAF9B gene started to specialise.

Herrera et al. identified numerous genes that can only be switched on when the TAF9B protein is present, which means that it joins a growing list of TAF proteins that are dedicated to controllingthe expression of genes in specialised cell types.

TAF9B activates neuron-specific genes by binding to sites that

  • reside outside of these genes’ core promoters.

Further, many of these sites were also bound by a master regulator of motor neuron-specific genes.

TAF7L

 

Whilst most of the fat tissue in humans is white adipose tissue, which contains cells that store fatty molecules, some is brown adipose tissue, or ‘brown fat’, that instead generates heat. When TAF7L promotes the differentiation of brown fat, it up-regulates genes that are targeted by a tran­scription

factor called PPAR-γ; last year it was shown that this transcription factor also promotes the differentiation of white adipose tissue (Zhou et al., 2013).
Mice without the TAF7L gene had 40% less brown fat than wild-type mice, and also grew too much skeletal muscle tissue. TAF7L was specifi­cally required to activate genes that control how brown fat develops and functions. Thus TAF7L expression appears to shift the fate of a stem cell towards brown adipose tissue, potentially at the expense of skeletal muscle, as both cell types develop from the same group of stem cells.

When stem cells with less TAF7L than normal are differentiated in vitro, they yield more muscle than fat cells. Conversely, cells with an excess of TAF7L express brown fat-specific genes and switch off muscle-specific genes.

The work of Herrera et al. and Zhou et al. reinforces the idea that different TAFs

  • provide the flexibility needed to control gene expression in a tissue-specific manner, and
  • enable differenti­ating cells to change which genes they express rapidly.

However many interesting questions remain:

Which signals lead to the destruction of core transcription factors?
Are core promoter ele­ments at tissue-specific genes designed to rec­ognise variant TAFs?
What determines whether variant TAFs are incorporated within TFIID, SAGA, or other complexes?

Shortly after RNA polymerase II starts to tran­scribe a gene, it briefly pauses. Interestingly, a DNA sequence associated with this pausing, called the pause button, closely matches the sequences that bind to two subunits of TFIID (TAF6 and TAF9; Kadonaga, 2012). Consequently, TAF6 and TAF9 might be involved in pausing transcription, and if so, the variant TAF9B could play a similar role at motor neuron genes.

Molecular basis of transcription pausing

Jeffrey W. Roberts
Science 344, 1226 (2014);  http://dx.doi.org:/10.1126/science.1255712
http://www.sciencemag.org/content/344/6189/1226.full.html

During RNA synthesis, RNA polymerase moves erratically along DNA, frequently
resting as it produces an RNA copy of the DNA sequence. Such pausing helps coordinate the appearance of a transcript with its utilization by cellular processes; to this end,

  • the movement of RNA polymerase is modulated by mechanisms that determine its rate. For example,
  • pausing is critical to regulatory activities of the enzyme such as the termination of transcription. It is also
  • essential during early modifications of eukaryotic RNA polymerase II that activate the enzyme for elongation.

 

Two reports analyzing transcription pausing on a global scale in Escherichia coli, by Larson et al. ( 1) and by Vvedenskaya et al. ( 2) on page 1285 of this issue, suggest

 

  • new functions of pausing and important aspects of its molecular basis.

 

The studies of Larson et al. and Vvedenskaya et al. follow decades of analysis of

bacterial transcription that has illuminated the molecular basis of polymerase pausing

events that serve critical regulatory functions.

 

A transcription pause specified by the DNA sequence synchronizes the translation of RNA into protein

 

  • with the transcription of leader regions of operons (groups of genes transcribed together) for amino acid biosynthesis;

 

  • this coordination controls amino acid synthesis in response to amino acid availability ( 3).

A protein induced pause occurs when the E. coli initiation factor σ70 restrains RNA polymerase by binding a second occurrence of the “–10” promoter element.

 

This paused polymerase provides a structure for engaging a transcription antiterminator (the bacteriophage λ Q protein) ( 4) that, in turn, inhibits transcription

pauses, including those essential for transcription termination.

 

Biochemical and structural analyses have identified an endpoint of the pausing process called the “elemental pause” in which the catalytic structure in the active site is distorted,

 

  • preventing further nucleotide addition ( 7).

 

The elemental paused state also involves distinct

 

  • conformational changes in the polymerase that may favor transcription termination
  • and allow the his and related pauses to be stabilized by RNA hairpins ( 8).

A consensus sequence for ubiquitous pauses was identified, with two important elements:

 

  • a preference for pyrimidine [mostly cytosine (C)] at the newly formed RNA end
  • followed by G to be incorporated next—just as found for the his pause; and a preference for G at position –10 of the RNA (10 nucleotides before the 3’ end)

 

 

Polymerase, paused

Polymerase, paused

Polymerase, paused. During transcription, RNA exists in two states as RNA polymerase progresses: pretranslocated, just after the addition of the last nucleotide [here, cytosine (C)];

and posttranslocated, after all nucleic acids have shifted in register by one nucleotide relative

to the enzyme, exposing the active site for binding of the next substrate molecule [here, guanine (G)]. The pretranslocated state is dominant in the pause. The critical G-C base (RNA-DNA) pair at position –10 in the pretranslocated state and the nontemplate DNA strand G bound in the

polymerase in the posttranslocated state are marked with an asterisk.
Binding of G at position 􀀀1 to CRE only occurs in the posttranslocated state, which would thus

be favored over the pretranslocated state. Hence, if G binding inhibits pausing, then the rate-limiting paused structure must be in the pretranslocated state (a conclusion also made by Larson et al. from biochemical experiments).
This is an important insight into the sequence of protein–nucleic acid interactions that occur in pausing. Vvedenskaya et al. suggest that the actual role of the G binding site is to promote translocation and thus

inhibit pausing, to smooth out adventitious pauses in genomic DNA.
The studies by Larson et al. and Vvedenskaya et al. provide a refined and detailed analysis of DNA sequence–induced transcription pausing.
Processive Antitermination

Robert A. Weisberg1* and Max E. Gottesman2

Section on Microbial Genetics, Laboratory of Molecular Genetics, National Institute of Child Health and

Human Development, National Institutes of Health, Bethesda, Maryland 20892-2785,1 and

Institute of Cancer Research, Columbia University, New York, New York 100322

Journal Of Bacteriology, Jan. 1999; 181(2): 359–367.
After initiating synthesis of RNA at a promoter, RNA polymerase (RNAP) normally continues to elongate the transcript until it reaches a termination site. Important elements of termination sites are transcribed before polymerase translocation stops, and the resulting RNA is an active element of the termination pathway. Nascent transcripts of intrinsic sites can halt transcription without the assistance of additional factors, and

those of Rho-dependent sites recruit the Rho termination protein to the elongation complex. In both cases, RNAP, the transcript, and the template dissociate (reviewed in references 76 and 80).

 

Termination is rarely, if ever, completely efficient, and the expression of downstream genes can be controlled by altering the efficiency of terminator readthrough. Two distinct mechanisms of elongation control have been reported for bacterial RNA polymerases. In one, exemplified by attenuation of the his and trp operons of Salmonella typhimurium and Escherichia coli, respectively,

  • a single terminator is inactivated by interaction with an upstream sequence in the transcript, with a terminator-specific protein, or with a translating ribosome that follows closely behind RNAP (reviewed in references 35 and 104).

In a second, whose prototype is antitermination of phage l early transcription,

  • polymerase is stably modified to a terminator-resistant form after it leaves the promoter.

In this case, the modified enzyme not only transcribes through sequential downstream terminators,

  • but also it is less sensitive to the pause sites that normally delay transcript elongation.

Both pathways are widespread in nature, but in this minireview we consider only the second,

  • known as processive antitermination
    (for previous reviews, see references 22, 23, 27, and 32).

The recent explosive growth in our understanding of transcription elongation (reviewed in references 57, 96, and 99) make this an especially appropriate time to survey regulatory elements that target the transcription elongation complex.

Antitermination in l is induced by two quite distinct mechanisms.

  • the result of interaction between l N protein and its targets in the early phage transcripts,
  • an interaction between the l Q protein and its target in the late phage promoter.

We describe the N mechanism first. Lambda N, a small basic protein of the arginine- rich motif (ARM) (Fig. 1) family of RNA binding proteins, binds to a 15-nucleotide (nt) stem-loop called BOXB (17) (Fig. 2).

 

FIG. 1. [not shown] (A) Alignment of phage N proteins and the HK022 Nun protein. The color groupings reflect the frequency of amino acid substitutions in evolutionarily related protein domains: an amino acid is more likely to be replaced by one in the same color group than by one in a different color group in related proteins (34).

The amino-proximal ARM regions were aligned by eye and according to the structures of the P22 and l ARMs complexed to their cognate nut sites (see text and Fig. 2), and the remainder of the proteins was aligned by ClustalW (38). The dots indicate gaps introduced to improve the alignment. Aside from the ARM regions, the

proteins fall into three very distantly related (or unrelated) families: (i) l and phage 21; (ii) P22, phage L, and HK97; and (iii) HK022 Nun.

 

FIG. 2. [not shown] BOXA and BOXB RNAs and their interaction with the ARM of their cognate N proteins. The amino acid-nucleotide interactions are shown to the left except for BOXB of phage 21, for which the structure of the complex is unknown. The sequences of BOXA and BOXA-BOXB spacer are shown to the right. The dots

to the left and right of the spacer sequences are for alignment. (A) l N-ARM-BOXB complex (adapted from reference 48 with permission of the publisher). Open circles, pentagons, and rectangles represent phosphates, riboses, and bases, respectively. Watson-Crick base pairs (????) are indicated. The zigzag line denotes a sheared

G z A base pair. Open circles, open rectangles, and arrowheads depict ionic, hydrophobic, and hydrogen-bonding interactions, respectively. Guanine-11, indicated by a bold rectangle, is extruded from the BOXB loop (see text). (B) P22 N-ARM-BOXB complex (adapted from reference 15 with permission of the publisher). Open

circles, pentagons, rectangles, and ovals represent phosphates, riboses, bases, and amino acids, respectively. The solid pentagons indicate riboses with a C29-endo pucker.

Base stacking ( ), intermolecular hydrogen bonding or electrostatic interactions (,—–), intermolecular hydrophobic or van der Waals interactions (4), intramolecular hydrogen bonds (– – – –) and Watson-Crick base pairs (?????) are indicated. Cytosine-11 is extruded from the loop (see text). Note that the amino-terminal amino acid

residue in the complex corresponds to Asn-14 in the complete protein (Fig. 1), and the displayed amino acids are numbered accordingly. (C) NUTL site of phage 21. The arrows indicate the inverted sequence repeats of BOXB.

 

FIG. 3. [not skown] HK022 put sites and folded PUT RNAs. (A) Alignment of putL and putR (43). The numbers give distances from the start sites of the PL and PR promoters, respectively, and the pairs of arrows indicate inverted sequence repeats. (B) Folded PUTL and PUTR RNAs. The structures, which were generated by energy

minimization as described (43), have been partially confirmed by genetic and biochemical studies (7, 43).
The active bacterial elongation complex consists of

  • core RNAP,
  • template, and
  • RNA product.

The 39 end of the RNA

  • is engaged in the active site of the enzyme,
  • The following ;8 nt are hybridized to the template strand of the DNA, and
  • the next ;9 nt remain closely associated with RNAP (64).
  • About 17 nt of the nontemplate DNA strand are separated from the template strand in the transcription bubble.

Elongation complexes can also contain NusA and/or NusG. These proteins, which

  • increase the stability of the N-mediated antitermination complex (see above),
  • have different effects on elongation.
  • NusA decreases and NusG increases the elongation rate, and
  • both proteins alter termination efficiency in a terminator-specific manner (13, 14, 86; see reference 76).

An elongation complex, unless located at a terminator, is extraordinarily stable,

  • even when translocation is prevented by removal of substrates.

Recent observations suggest that this stability depends mainly on

  • interactions between RNAP and the RNA-DNA hybrid as well as
  • between polymerase and the downstream duplex DNA template (63, 87).

Nascent RNA emerging from the hybrid region and upstream duplex DNA

  • do not appear to be required.

The strength of the RNA-DNA hybrid is believed to

  • assure the lateral stability of the complex.

 

Reducing the strength of the RNA-DNA bonds, for example

  • by incorporation of nucleotide analogs,
  • favors backsliding of RNAP on the template, with consequent
  • disengagement of the 39 RNA end from the active site, and
  • concerted retreat of the RNA-DNA hybrid region from the 39 end (65).

Such a disengaged complex retains its resistance to dissociation and

  • is capable of resuming elongation if the original or a newly created 39 end reengages with the active site (10, 44, 45, 65, 71, 95).

Intrinsic terminators consist of a guanine- and cytosine-rich RNA hairpin stem

  • immediately followed by a short uracil-rich segment
  • within which termination can occur.

 

If termination does not occur at this point,

  • polymerase continues to elongate the transcript with normal processivity
  • until it reaches the next terminator.

Neither the stem nor the uracil-rich segment

  • is sufficient for termination, although
  • either can transiently slow elongation.

The weakness of base pairing between rU and dA

  • destabilizes the RNA-DNA hybrid in the uracil-rich segment, and
  • this probably contributes to termination.

Formation of the hairpin stem as nascent terminator RNA emerges from polymerase

  • destabilizes the RNA-DNA hybrid and
  • interrupts contacts between the emerging nascent RNA and RNAP (62a).

It might also interfere with the stabilizing interactions between

  • RNAP and the hybrid or those between RNAP and
  • the downstream region of the template.

Cross-linking of nucleic acid to RNAP suggests that

  • both the downstream DNA and the nascent RNA
  • that emerges from the hybrid region, and
  • within which the terminator hairpin might form,
  • are located close to the same regions of the enzyme (64).

Conversely, modifications that render RNAP termination resistant

  • could prevent the terminator stem from destabilizing one or more of these targets,
  • at least while the 39 end of the RNA is within the uracil rich segment of the terminator.

The l N and Q proteins and HK022 PUT RNA

  • also suppress Rho-dependent terminators (43a, 79, 103) which,
  • in contrast to intrinsic terminators, lack a precisely determined termination point.

Rho is an RNA-dependent ATPase that binds to cytosine-rich, unstructured regions in nascent RNA and acts preferentially

  • to terminate elongation complexes that are paused at nearby downstream sites
    (19, 29, 46, 47, 59, 60).

Rho possesses RNA-DNA helicase activity, and this activity is directional,

  • unwinding DNA paired to the 39 end of the RNA molecule (11, 90).
  • This corresponds to the location of the hybrid and of RNAP
    in an active ternary elongation complex.

The ability of antiterminators to suppress Rho-dependent and -independent terminators

  • suggests that they prevent a step that is common to both classes.

Given the helicase activity of Rho, a likely candidate for this step is disruption of the RNA-DNA

hybrid. However, other candidates, such as destabilization of RNAP-template or RNAP-hybrid interactions, are also plausible.

Alternatively, the ability of N, Q, and PUT to suppress RNAP pausing (31, 43, 54, 74)

  • suggests that they prevent Rho-dependent termination
  • by accelerating polymerase away from Rho bound at upstream RNA sites.

This explanation raises the problem of why NusG,

  • which also accelerates polymerase,
  • enhances rather than suppresses Rho-dependent termination (see above).

Clearly, the molecular details of processive antitermination remain poorly understood despite the 30 years that have elapsed since its discovery.

 

 

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

OPEN ACCESS

Jingyi Jessica Li1, 2, Peter J Bickel1 and Mark D Biggin3

1Department of Statistics, University of California, Berkeley, CA, USA

2Departments of Statistics and Human Genetics, University of California, Los Angeles, CA, USA

3Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Academic editor – Barbara Engelhardt   http://dx.doi.org:/10.7717/peerj.270

Distributed under Creative-Commons CC-0

ABSTRACT

Large scale surveys in mammalian tissue culture cells suggest that the protein ex-

pressed at the median abundance is present at 8,000_16,000 molecules per cell and

that differences in mRNA expression between genes explain only 10_40% of the dif-

ferences in protein levels. We find, however, that these surveys have significantly un-

derestimated protein abundances and the relative importance of transcription.

Using individual measurements for 61 housekeeping proteins to rescale whole proteome

data from Schwanhausser et al. (2011), we find that the median protein detected is

expressed at 170,000 molecules per cell and that our corrected protein abundance

estimates show a higher correlation with mRNA abundances than do the uncorrected

protein data. In addition, we estimated the impact of further errors in mRNA and

protein abundances using direct experimental measurements of these errors.

The resulting analysis suggests that mRNA levels explain at least

  • 56% of the differences in protein abundance for the 4,212 genes

detected by Schwanhausser et al. (2011), though because one major source of error

could not be estimated the true percent contribution should be higher.
We also employed a second, independent strategy to

  • determine the contribution of mRNA levels to protein expression.

The variance in translation rates directly measured by ribosome profiling is only 12%

of that inferred by Schwanhausser et al. (2011), and

  • the measured and inferred translation rates correlate poorly (R2 D 13).

Based on this, our second strategy suggests that

  • mRNA levels explain _81% of the variance in protein levels.

We also determined the percent contributions of

  • transcription,
  • RNA degradation,
  • translation
  • and protein degradation

to the variance in protein abundances using both of our strategies.

While the magnitudes of the two estimates vary, they both suggest that

  • transcription plays a more important role than the earlier studies implied and
  • translation a much smaller role.

Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately

  • 40% of genes in a given cell within a population express no mRNA.

Since there can be no translation in the absence of mRNA, we argue that

  • differences in translation rates can play no role in determining the expression levels for the _40% of genes that are non-expressed.

Subjects Bioinformatics, Computational Biology

Keywords Transcription, Translation, Mass spectrometry, Gene expression, Protein abundance

How to cite this article Li et al. (2014), System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2:e270; 

http://dx.doi.org:/10.7717/peerj.270

 

 

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

Evgeny Shmelkov1,2, Zuojian Tang2, Iannis Aifantis3, Alexander Statnikov2,4

Shmelkov et al. Biology Direct 2011, 6:15  http://www.biology-direct.com/content/6/1/15

 

Background: Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The employed benchmarking methodology first

  • involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors.
  • Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that

  • the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation.

 

Illustration of statistical methodology

Illustration of statistical methodology

 

Figure 2 Illustration of statistical methodology for comparison

between a gold-standard and a pathway database

 

Additional material

Additional file 1: Supplementary Information. Table S1: Functional gene expression data. Table 2: Transcription factor-DNA binding data. Table S3: Most confident direct transcriptional targets of each of the four transcription factors. These targets were obtained by overlapping several gold-standards obtained with different datasets for the same transcription factor. Table S4: Genes directly regulated by two or more of the three transcription factors: MYC, NOTCH1, and RELA. Figure S1: Comparison of gene sets of transcriptional targets derived from ten different pathway databases by Jaccard index. In case, where Jaccard index of an overlap could not be determined due to comparison of two empty gene lists, we assigned value 0. Cells are colored according to the Jaccard index, from white (Jaccard index equal to 0) to dark-orange (Jaccard index equal to 1). Each sub-figure gives results for a different transcription factor: (a) AR, (b) BCL6, (c) MYC, (d) NOTCH1, (e) RELA, (f) STAT1, (g) TP53

 

http://dx.doi.org:/10.1186/1745-6150-6-15

 

Cite this article as: Shmelkov et al.: Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale. Biology Direct 2011 6:15

 

 

The Functional Consequences of Variation in Transcription Factor Binding
Darren A. Cusanovich1, Bryan Pavlovic1,2, Jonathan K. Pritchard1,2,3*, Yoav Gilad1*

1 Department of Human Genetics, University of Chicago, 2 Howard Hughes Medical Institute, University of Chicago, Chicago,

Illinois, 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, California,

 

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output.

To evaluate the context of functional TF binding we knocked down

  • 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line.
  • We identified genes whose expression was affected by the knockdowns.
  • We intersected the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites

This combination of data allowed us to infer functional TF binding.

  • we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that
  • most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes.
  • functional TF binding is enriched in regulatory elements that harbor
    • a large number of TF binding sites,
    • at sites with predicted higher binding affinity, and
    • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

Author Summary

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to

study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might

influence what binding is functional, but they also suggest that a simple model of functional vs. non-functional binding may not suffice.

Citation: Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y (2014) The Functional Consequences of Variation in Transcription Factor Binding. PLoS Genet 10(3):e1004226. http://dx.doi.org:/10.1371/journal.pgen.1004226

Editor: Yitzhak Pilpel, Weizmann Institute of Science, Israel

 

 

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

Figure 2. Effect sizes for differentially expressed genes.

Boxplots of absolute Log2(fold-change) between knockdown arrays

and control arrays for all genes identified as differentially expressed in

each experiment. Outliers are not plotted. The gray bar indicates the

interquartile range across all genes differentially expressed in all

knockdowns. Boxplots are ordered by the number of genes differentially

expressed in each experiment. Outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

 

 

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

 

 

 

 

 

Figure 3. Intersecting binding data and expression data for each knockdown. (a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1). (b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors. (c) Boxplot summarizing the distribution of the fraction of

bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

 

Degree of binding correlated with function

Degree of binding correlated with function

 

Figure 4. Degree of binding correlated with function. Boxplots comparing (a) the number of sites bound, and (b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown. (c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knock-down experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

 

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

 

Figure 5. Distribution of functional binding about the TSS. (a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming

the distances and outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g005

 

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

 

 

Figure 6. Magnitude and direction of differential expression after knockdown. (a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor. (b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

 

To test whether the number of paralogs or the degree of similarity with the closest paralog for each transcription factor knocked down might influence the number of genes differentially expressed in our experiments, we obtained definitions of paralogy and the calculations of percent identity for 29 different factors from Ensembl’s BioMart (http://useast.ensembl.org/biomart/martview/) [31]. We used genome build GRCh37.p13.

For each gene, we counted the number of paralogs classified as a ‘‘within_species_paralog’’. After selecting only genes considered a ‘‘within_species_paralog’’, we also assigned the maximum percent identity as the closest paralog.

To evaluate the effect that an independent assignment of target genes to regulatory regions might have on our analyses, we used the definition of target genes defined by Thurman et al. (ftp://ftp.ebi.ac.uk/pub/databases/…)

which use correlations in DNase hypersensitivity between distal and proximal regulatory regions across different cell types to link distal elements to putative target genes [38].

We intersected the midpoints of our called binding events (defined above) with these regulatory elements in order to assign our binding events to specific target genes and then re-analyzed the overlap between

binding and differential expression in our experiments.

PLOS Genetics 6 Mar 2014; 10 (3), e1004226

 

 

 

The essential biology of the endoplasmic reticulum stress response

for structural and computational biologists

Sadao Wakabayashia, Hiderou Yoshidaa,*

aDepartment of Molecular Biochemistry, Graduate School of Life Science,

University of Hyogo, Hyogo 678-1297, Japan

CSBJ Mar 2013; 6(7), e201303010, http://dx.doi.org/10.5936/csbj.201303010

 

Abstract: The endoplasmic reticulum (ER) stress response is a cytoprotective mechanism that maintains homeostasis of the ER by

  • upregulating the capacity of the ER in accordance with cellular demands.

If the ER stress response cannot function correctly, because of reasons such as aging, genetic mutation or environmental stress,

  • unfolded proteins accumulate in the ER and cause ER stress-induced apoptosis,
  • resulting in the onset of folding diseases,
    • including Alzheimer’s disease and diabetes mellitus.

Although the mechanism of the ER stress response has been analyzed extensively by biochemists, cell biologists and molecular biologists, many aspects remain to be elucidated. For example,

  • it is unclear how sensor molecules detect ER stress, or
  • how cells choose the two opposite cell fates
    (survival or apoptosis) during the ER stress response.

To resolve these critical issues, structural and computational approaches will be indispensable, although the mechanism of the ER stress response is complicated and difficult to understand holistically at a glance. Here, we provide a concise introduction to the mammalian ER stress response for structural and computational biologists.

The basic mechanism of the mammalian ER stress response

The mammalian ER stress response consists of three pathways: the ATF6, IRE1 and PERK pathways, of which the main functions are

  • augmentation of folding and ERAD capacity, and
  • translational attenuation, respectively.

Although these response pathways cross-talk with each other and have several branched subpathways, we focus on the main pathways in this section.

  • The ATF6 pathway regulates the transcriptional induction of ER chaperone genes
  • pATF6(P) is a sensor molecule comprising a type II transmembrane protein residing on the ER membrane (Figure 2).

When pATF6(P) detects ER stress,

  • the protein is transported to the Golgi apparatus through vesicular transport in a COP-II vesicle
  • and is sequentially cleaved by two proteases residing in the Golgi,
    • namely site 1 protease (S1P) and site 2 protease (S2P)

The cytoplasmic portion of pATF6(P) (pATF6(N)) is

  1. released from the Golgi membrane,
  2. translocates into the nucleus,
  3. binds to an enhancer element called the ER stress response element (ERSE),
  4. and activates the transcription of ER chaperone genes,
  • including BiP, GRP94, calreticulin and protein disulfide isomerase (PDI)

The consensus nucleotide sequence of ERSE is CCAAT(N9)CCACG, and pATF6(N) recognizes both the CCACG portion and another transcription factor NF-Y,

  • which binds to the CCAAT portion

NF-Y is a general transcription factor required for

  • the transcription of various human genes

 

Figure 2. The ATF6 pathway. The sensor molecule pATF6(P) located on the ER membrane is transported to the Golgi apparatus by transport vesicles in response to ER stress. In the Golgi apparatus, pATF6(P) is sequentially cleaved by two proteases, S1P and S2P, resulting in release of the cytoplasmic portion pATF6(N) from the ER membrane. pATF6(N) translocates into the nucleus and activates transcription of ER chaperone genes through binding to the cis-acting enhancer ERSE.

 

Figure 3. The IRE1 pathway. In normal growth conditions, the sensor molecule IRE1 is an inactive monomer, whereas IRE1 forms an active oligomer in response to ER stress. Activated IRE1 converts unspliced XBP1 mRNA to mature mRNA by the cytoplasmic mRNA splicing. From mature XBP1 mRNA, an active transcription factor pXBP1(S) is translated and activates the transcription of ERAD genes through binding to the enhancer UPRE.

 

Figure 4. The PERK pathway. When PERK detects unfolded proteins in the ER, PERK phosphorylates eIF2α, resulting in translational attenuation and translational induction of ATF4. ATF4 activates the transcription of target genes encoding translation factors, anti-oxidation factors and a transcription factor CHOP. Other kinases such as PKR, GCN2 and HRI also phosphorylate eIF2α, and phosphorylated eIF2α is dephosphorylated by CReP, PP1C-GADD34 and p58IPK

 

Figure 7. Three functions of pXBP1(U). pXBP1(U) translated from XBP1(U) mRNA binds to pXBP1(S) and enhances its degradation. The CTR region of pXBP1(U) interacts with the ribosome tunnel and slows translation, while the HR2 region anchors XBP1(U) mRNA to the ER membrane, in order to enhance splicing of XBP1(U) mRNA by IRE1.

 

Figure 8. Major pathways of ER stress-induced apoptosis. ER stress induces apoptosis through various pathways, including transcriptional induction of CHOP by the PERK and ATF6 pathways, the IRE1-TRAF2 pathway and the caspase-12 pathway.

If cells are damaged by strong and sustained ER stress that they cannot deal with and ER stress still persists and hampers the survival of the organism, the ER stress response activates the apoptotic pathways and disposes of damaged cells from the body.

Computational simulation of response pathways to analyze the decision mechanism that determines cell fate (survival or apoptosis) provides a valuable analysis tool, although there have been few such studies to date.

Read Full Post »


Larry H Bernstein, MD, FCAP, Reporter and Curator

http://pharmaceyticalinnovation.com/7/10/2014/A new relationship identified in preterm stress and development of autism or schizophrenia/

 

This is a fascinating study.  It is of considerable interest because it deals with several items that need to be addressed with respect to neurodevelopmental disruptive disorders.  It leaves open some aspects that are known, but not subject to investigation in the experiments.  Then there is also no reporting of some associations that are known at the time of deveopment of these disorders – autism spectrum, and schizophrenia.  Of course, I don’t know how it would be possible to also look at prediction of a possible relationship to later development of mood disorders.

  1. The placenta functions as an endocrine organ in the conversion of androsteinedione to testosterone during pregnancy, which is delivered to the fetus.
  2. The conversion is by a known enzymatic pathway – and there is a sex difference in the depression of testosterone in males, females not affected.
  3. There is a greater susceptibility of males to autism and schizophrenia than of females, which I as reader, had not known, but if this is true, it would lend some credence to a biological advantage to protect the females of animal species, and might raise some interest into what relationship it has to protecting multitasking for females.
  4. It is well known that the twin studies that have been carried out determined that in identical twins, there is discordance as a rule.  Those studies are old, and they did not examine whether the other identical twin might be anywhere on the autism spectrum disorder (not then termed “spectrum”.
  5. However, there is a clear effect of stress on “gene expression”, and in this case we are looking at enzymation suppression at the placental level affecting trascriptional activity in the male fetus.  The same genetic signature exists in the male genetic profile, so we are not looking at a clear somatic mutation in this study.
  6. There is also much less specific an association with the MTHFR gene mutation at either one or two loci. This would have to be looked at as a possible separate post translational somatic mutation.
  7. Whether there is another component expressed later in the function of the zinc metalloproteinase under stress in the affected subject is worth considering, but can’t be commented on with respect to the study.

Penn Team Links Placental Marker of Prenatal Stress to Neurodevelopmental Problems 

By Ilene Schneider          July 8, 2014

When a woman experiences a stressful event early in pregnancy, the risk that her child will develop autism spectrum disorders or schizophrenia increases. The way in which maternal stress is transmitted to the brain of the developing fetus, leading to these problems in neurodevelopment, is poorly understood.

New findings by University of Pennsylvania School of Veterinary Medicine scientists suggest that an enzyme found in the placenta is likely playing an important role. This enzyme, O-linked-N-acetylglucosamine transferase, or OGT, translates maternal stress into a reprogramming signal for the brain before birth. The study was supported by the National Institute of Mental Health.

“By manipulating this one gene, we were able to recapitulate many aspects of early prenatal stress,” said Tracy L. Bale, senior author on the paper and a professor in the Department of Animal Biology at Penn Vet. “OGT seems to be serving a role as the ‘canary in the coal mine,’ offering a readout of mom’s stress to change the baby’s developing brain. Bale, who also holds an appointment in the Department of Psychiatry, co-authored tha paper with postdoctoral researcher Christopher L. Howerton, for PNAS.

OGT is known to play a role in gene expression through chromatin remodeling, a process that makes some genes more or less available to be converted into proteins. In a study published last year in PNAS, Bale’s lab found that placentas from male mice pups had lower levels of OGT than those from female pups, and placentas from mothers that had been exposed to stress early in gestation had lower overall levels of OGT than placentas from the mothers’ unstressed counterparts.

“People think that the placenta only serves to promote blood flow between a mom and her baby, but that’s really not all it’s doing,” Bale said. “It’s a very dynamic endocrine tissue and it’s sex-specific, and we’ve shown that tampering with it can dramatically affect a baby’s developing brain.”

To elucidate how reduced levels of OGT might be transmitting signals through the placenta to a fetus, Bale and Howerton bred mice that partially or fully lacked OGT in the placenta. They then compared these transgenic mice to animals that had been subjected to mild stressors during early gestation, such as predator odor, unfamiliar objects or unusual noises, during the first week of their pregnancies.

The researchers performed a genome-wide search for genes that were affected by the altered levels of OGT and were also affected by exposure to early prenatal stress using a specific activational histone mark and found a broad swath of common gene expression patterns.

They chose to focus on one particular differentially regulated gene called Hsd17b3, which encodes an enzyme that converts androstenedione, a steroid hormone, to testosterone. The researchers found this gene to be particularly interesting in part because neurodevelopmental disorders such as autism and schizophrenia have strong gender biases, where they either predominantly affect males or present earlier in males.

Placentas associated with male mice pups born to stressed mothers had reduced levels of the enzyme Hsd17b3, and, as a result, had higher levels of androstenedione and lower levels of testosterone than normal mice.

“This could mean that, with early prenatal stress, males have less masculinization,” Bale said. “This is important because autism tends to be thought of as the brain in a hypermasculinized state, and schizophrenia is thought of as a hypomasculinized state. It makes sense that there is something about this process of testosterone synthesis that is being disrupted.”

Furthermore, the mice born to mothers with disrupted OGT looked like the offspring of stressed mothers in other ways. Although they were born at a normal weight, their growth slowed at weaning. Their body weight as adults was 10 to 20 percent lower than control mice.

Because of the key role that that the hypothalamus plays in controlling growth and many other critical survival functions, the Penn Vet researchers then screened the mouse genome for genes with differential expression in the hypothalamus, comparing normal mice, mice with reduced OGT and mice born to stressed mothers.

They identified several gene sets related to the structure and function of mitochrondria, the powerhouses of cells that are responsible for producing energy. And indeed, when compared by an enzymatic assay that examines mitochondria biogenesis, both the mice born to stressed mothers and mice born to mothers with reduced OGT had dramatically reduced mitochondrial function in their hypothalamus compared to normal mice. These studies were done in collaboration with Narayan Avadhani’s lab at Penn Vet. Such reduced function could explain why the growth patterns of mice appeared similar until weaning, at which point energy demands go up.

“If you have a really bad furnace you might be okay if temperatures are mild,” Bale said. “But, if it’s very cold, it can’t meet demand. It could be the same for these mice. If you’re in a litter close to your siblings and mom, you don’t need to produce a lot of heat, but once you wean you have an extra demand for producing heat. They’re just not keeping up.”

Bale points out that mitochondrial dysfunction in the brain has been reported in both schizophrenia and autism patients. In future work, Bale hopes to identify a suite of maternal plasma stress biomarkers that could signal an increased risk of neurodevelopmental disease for the baby.

“With that kind of a signature, we’d have a way to detect at-risk pregnancies and think about ways to intervene much earlier than waiting to look at the term placenta,” she said.

 

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Differentiation Therapy – Epigenetics Tackles Solid Tumors

Author-Writer: Stephen J. Williams, Ph.D.

Genetic and epigenetic events within a cell which promote a block in normal development or differentiation coupled with unregulated proliferation are hallmarks of neoplastic transformation.  Differentiation therapy is a chemotherapeutic strategy directed at re-activating endogenous cellular differentiation programs in a tumor cell therefore driving the cancerous cell to a state closer resembling the normal or preneoplastic cell and therefore incurring loss of the tumorigenic phenotype.

This post will deal with:

  • Agents such as histone deacetylase inhibitors (HDACi), retinoids, and PPARϒ agonists which have been shown to reactivate terminal differentiation programs in solid tumors
  • Clinical trials in solid tumors
  • Issues regarding the use of differentiation therapy in solid tumors

This post is a follow-up post to Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition in Prostate Cancer Cells

To put the need for alternate chemotherapeutic strategies in perspective, one is referred to the National Cancer Statistics from http://www.cancer.gov show that 33% of cancer patients, treated with standard cytolytic chemotherapy, will still die within five years (i.e. one in three will die within 5 years).  However the addition of the differentiation agent retinoic acid to standard chemotherapy regimen for treatment of acute promyelocytic leukemia (APML) had improved 5 year survival rates from a range of 50-80% up to near 90% complete remission rates while 75% become disease free, an astonishing success story.  For a review of APML please be referred to http://en.wikipedia.org/wiki/Acute_promyelocytic_leukemia.  Briefly, APML is predominantly a result of the chromosomal translocation producing a fusion gene between the promyelocytic leukemia (PML) and RARα receptor genes.  The PML-RARα fusion protein recruits transcriptional repressors, histone deacetylases (HDACs), and DNA methyltransferases.  Treatment with pharmacologic doses of retinoic acid dissociates the PML-RARα from HDACs and results in degradation of PML-RARα, eventually resulting in the differentiation of the myeloid cells in APML.

Dr. Igor Matushansky of Columbia University believes such differentiation therapy could be useful in soft tissue sarcomas, due to the existence of a connective tissue (mesenchymal) stem cell,  in vitro methods which can differentiate these cells into mature tissues, and, from a gene clustering analysis his group had performed, correlation of expression signatures of each liposarcoma subtype throughout the adipocytic differentiation spectrum, including early differentiated to more mature differentiated cells(1).   A parallel study by Riester and colleagues had been able to classify breast tumors and liposarcomas along a phylogenetic tree showing solid tumors can be reclassified based on cell of origin via expression patterns(2).  In addition, other solid tumors, such as ovarian cancer are easily classified, based both on pathologic, histologic, and expression analysis into well and poorly differentiated tumors, correlating differentiation status with prognosis.

Compound Classes which have potential in

differentiation therapy for solid tumors

A. Histone Deacetylase Inhibitors (HDACi)

In eukaryotes, epigenetic post-translational modification of histones is critical for regulation of chromatin structure and gene expression.  Histone deacetylation leads to chromatin compaction and is associated with transcriptional repression of tumor suppressors, cell growth and differentiation.  Therefore, HDACi are promising anti-tumor agents as they may affect the cell cycle, inhibit proliferation, stimulate differentiation and induce apoptotic cell death (3). In a review by Kniptein and Gore, entinostat was found to be a well-tolerated HDACi that demonstrates promising therapeutic potential in both solid and hematologic malignancies(4). The path to the discovery of suberoylanilide hydroxamic acid (SAHA, vorinostat) began over three decades ago with our studies designed to understand why dimethylsulfoxide causes terminal differentiation of the virus-transformed cells, murine erythroleukemia cells. SAHA can cause growth arrest and death of a broad variety of transformed cells both in vitro and in vivo at concentrations that have little or no toxic effects on normal cells (for references see (5). In fact, treatment of MCF-7 breast carcinoma cells with SAHA resulted in morphologic changes resembling epithelial mammary differentiation(6).

HDAC inhibitors

Figure.  Structures of some HDACi used in clinical trials for cancer (see section below)

hdacwithsaha

Figure.  HDAC with SAHA

B. Retinoids

Vitamin A and retinoids play significant roles in basic physiological processes such as vision, reproduction, growth, development, hematopoiesis and immunity (7). Retinoids are the natural derivatives and synthetic analogs of vitamin A. They have been shown to prevent mammary carcinogenesis in rodents (8), to inhibit the growth of human cancer cells in vitro  (9,10) and be effective chemopreventive and chemotherapeutic agents in a variety of human epithelial and hematopoietic tumors (11-14).

Retinoids cannot be synthesized de novo by higher animals and consequently must be consumed in the diet. The two sources of retinoids are animal products that contain retinol and retinyl esters, and plant-derived carotenoids (provitamin A). b-carotene is the most potent vitamin A precursor and has been shown to be an active inhibitor of both tumor initiation and promotion (15).

A major function of retinol, relevant to cancer, is its function as an antioxidant. The antioxidant properties of vitamin A have been shown both in vitro and in vivo (16,17). Retinol deficiency causes oxidative damage to liver mitochondria in rats that can be reversed by vitamin A supplementation (18). A caveat to this is in vitro and in vivo evidence of chronic hypervitaminosis A inducing oxidative DNA damage, as well (19-21). Therefore, it is evident that maintaining the vitamin A concentration within a physiological range is critical to normal cell function because either a deficiency or an excess of vitamin A induces oxidative stress (22). Retinoic acids (RA) (all-trans, 9-cis and 13-cis) are the major biologically active retinoids and exert their effects by regulation of gene expression by binding two families of ligand-activated nuclear retinoid receptors (23). Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) regulate the transcription of a large number of target genes that contain retinoic acid response elements (RAREs) in their promoters. Many of these genes are involved in cancer (13,24) and differentiation (24-26).

Several lines of evidence suggest involvement of defects in retinol signaling in cancer, from the observation that a vitamin A-deficient (VAD) diet leads to an increase in the number of spontaneous and chemically induced tumors in animals (27-29) to the observation that RA itself can induce  differentiation and inhibit the growth of many tumor cells (30-32), as well as the identification that components of the RA signaling pathway are absent in cancer cells (33). Vitamin A and its metabolites have been proposed to have a dual effect in cancer prevention, as antioxidants (16,17,19,34) and differentiating agents (35-37). as it is well accepted that retinoid signaling is integral in maintaining the differentiated state of many cell types (13,38). Additionally, current rationale for chemoprevention with retinoids is based, in part, on the hypothesis that some tumors, may arise due to loss of normal somatic differentiation during tissue repair.

C. PPARϒ Agonists

Peroxisome proliferator-activated receptor ϒ (PPARϒ) is a member of the steroid hormone receptor superfamily that responds to changes in lipid and glucose homeostasis but has increasing roles in differentiation and tumorigenesis. The first PPAR (PPARα) was discovered during the search of a molecular target for a group of agents then referred to as peroxisome proliferators, as they increased peroxisomal numbers in rodent liver tissue, apart from improving insulin sensitivity.  One of the first agents, developed in the early 80’s for treatment of hyperlipidemia and hperlipoproteinemia, was clofibrate.  All PPAR subtypes heterodimerize with the retinoid-x-receptor (RXR) and, upon binding of ATRA, activate target genes.

PPARϒ agonists have shown potential as a therapeutic in a variety of cancer types including bladder cancer (39), colon cancer(40),  breast cancer(41), prostate cancer(42).  There are numerous studies showing that PPARϒ agonists have anti-tumorigenic activity via anti-proliferative, pro-differentiation and anti-angiogenic mechanisms of action(43). For example, Papi et al. observed that agonists for the retinoid X receptor (6-OH-11-O-hydroxyphenanthrene), retinoic acid receptor (all-trans retinoic acid (RA)) and peroxisome proliferator-activated receptor (PPAR)-γ (pioglitazone (PGZ)), reduce the survival of MS generated from breast cancer tissues and MCF7 cells, but not from normal mammary gland or MCF10 cells(44) with concomitant upregulation of differentiation markers.

A great website for further information on PPAR is Dr. Jack Vanden Heuvel, Professor of Toxicology at Penn State University at http://ppar.cas.psu.edu/general_information.html.

D. Trabectedin

Trabectedin (ecteinascidin-743 (ET-743); Yondelis) is derived from the Caribbean tunicate Ecteinascidia turbinacta has antitumor activity by binding to the DNA minor groove thus disrupting binding of transcription factors and inhibiting DNA synthesis.  However, it has also been shown, in myxoid liposarcoma (MLS) cells, to cause dissociation of transcription factor TLS-CHOP from promoter sequences resulting in downregulation of target genes such as CHOP, PTX3 and FN1 and induces an adipogenic differentiation program by enhancing activation of CAAT/enhancer binding protein (C/EBP) family of genes.  In MLS, TLS-CHOP sequesters C/EBPβ resulting in block of differentiation programs while trabectedin disrupts this association freeing up C/EBPβ to act as transcriptional activator of genes related to differentiation.

Ongoing Cancer Clinical Trials with HDAC Inhibitors

The following is a listing of some clinical trials using histone deacetylase inhibitors in combination with approved chemotherapeutics in various tumors.  This data was taken from the New Medicine Oncology Knowledge Base ( at http://www.nmok.net).

hdactrial1 hdactrial2

Issues and Future of Differentiation-based Therapy

In the review by Filemon Dela Cruz and Igor Matushansky(1), the authors suggest that, like days of old of cytotoxic monotherapy, differentiation therapy would not evolve as a simplistic one-size-fits –all but mirror an extremely complicated process.  Therefore they suggest three theoretical mechanisms in which differentiation therapy may occur:

  1. Cancer directed differentiation: differentiation pathways are activated without correcting the underlying oncogenic mechanisms which produced the initial differentiation block
  2. Cancer reverted differentiation: correction of the underlying oncogenic mechanism results in restoration of endogenous differentiation pathways
  3. Cancer diverted differentiation: cancer cell is redirected to an earlier stage of differentiation

Finally the authors suggest that “the potential for reversion of the malignant cancer phenotype to a more benign, or at the very least a lower grade of biological aggressiveness, may serve as a critical clinical and biologic transition of a uniformly fatal cancer into one more amenable to management or to treatment using conventional therapeutic approaches.”

References:

1.            Cruz, F. D., and Matushansky, I. (2012) Oncotarget 3, 559-567

2.            Riester, M., Stephan-Otto Attolini, C., Downey, R. J., Singer, S., and Michor, F. (2010) PLoS computational biology 6, e1000777

3.            Seidel, C., Schnekenburger, M., Dicato, M., and Diederich, M. (2012) Genes & nutrition 7, 357-367

4.            Knipstein, J., and Gore, L. (2011) Expert opinion on investigational drugs 20, 1455-1467

5.            Marks, P. A. (2007) Oncogene 26, 1351-1356

6.            Munster, P. N., Troso-Sandoval, T., Rosen, N., Rifkind, R., Marks, P. A., and Richon, V. M. (2001) Cancer research 61, 8492-8497

7.            Napoli, J. L. (1999) Biochim Biophys Acta 1440, 139-162

8.            Moon, R., Metha, R., and Rao, K. (1994) Retinoids and cancer in experimental animals. in The Retinoids: Biology, Chemistry, and Medicine (Sporn, M., Roberts, A., and Goodman, D. eds.), 2 Ed., Raven Press, New York. pp 573-596

9.            De Luca, L. M. (1991) Faseb J 5, 2924-2933

10.          Gudas, L. J. (1992) Cell Growth Differ 3, 655-662

11.          Degos, L., and Parkinson, D. (1995) Retinoids in Oncology, Springer-Verlag, Berlin

12.          Lotan, R. (1996) Faseb J 10, 1031-1039

13.          Zhang, D., Holmes, W. F., Wu, S., Soprano, D. R., and Soprano, K. J. (2000) J Cell Physiol 185, 1-20

14.          Fontana, J. A., and Rishi, A. K. (2002) Leukemia 16, 463-472

15.          Suda, D., Schwartz, J., and Shklar, G. (1986) Carcinogenesis 7, 711-715

16.          Ciaccio, M., Valenza, M., Tesoriere, L., Bongiorno, A., Albiero, R., and Livrea, M. A. (1993) Arch Biochem Biophys 302, 103-108

17.          Palacios, A., Piergiacomi, V. A., and Catala, A. (1996) Mol Cell Biochem 154, 77-82

18.          Barber, T., Borras, E., Torres, L., Garcia, C., Cabezuelo, F., Lloret, A., Pallardo, F. V., and Vina, J. R. (2000) Free Radic Biol Med 29, 1-7

19.          Borras, E., Zaragoza, R., Morante, M., Garcia, C., Gimeno, A., Lopez-Rodas, G., Barber, T., Miralles, V. J., Vina, J. R., and Torres, L. (2003) Eur J Biochem 270, 1493-1501

20.          Omenn, G. S., Goodman, G. E., Thornquist, M. D., Balmes, J., Cullen, M. R., Glass, A., Keogh, J. P., Meyskens, F. L., Jr., Valanis, B., Williams, J. H., Jr., Barnhart, S., Cherniack, M. G., Brodkin, C. A., and Hammar, S. (1996) J Natl Cancer Inst 88, 1550-1559

21.          Murata, M., and Kawanishi, S. (2000) J Biol Chem 275, 2003-2008

22.          Schwartz, J. L. (1996) J Nutr 126, 1221S-1227S

23.          Chambon, P. (1996) Faseb J 10, 940-954

24.          Freemantle, S. J., Kerley, J. S., Olsen, S. L., Gross, R. H., and Spinella, M. J. (2002) Oncogene 21, 2880-2889

25.          Collins, S. J., Robertson, K. A., and Mueller, L. (1990) Mol Cell Biol 10, 2154-2163

26.          Grunt, T. W., Somay, C., Oeller, H., Dittrich, E., and Dittrich, C. (1992) J Cell Sci 103 ( Pt 2), 501-509

27.          Lasnitzki, I. (1955) Br J Cancer 9, 434-441

28.          Moore, T. (1965) Proc Nutr Soc 24, 129-135

29.          Saffiotti, U., Montesano, R., Sellakumar, A. R., and Borg, S. A. (1967) Cancer 20, 857-864

30.          Strickland, S., and Mahdavi, V. (1978) Cell 15, 393-403

31.          Breitman, T. R., Selonick, S. E., and Collins, S. J. (1980) Proc Natl Acad Sci U S A 77, 2936-2940

32.          Breitman, T. R., Collins, S. J., and Keene, B. R. (1981) Blood 57, 1000-1004

33.          Niles, R. M. (2000) Nutrition 16, 573-576

34.          Monagham, B., and Schmitt, F. (1932) J Biol Chem 96, 387-395

35.          Miller, W. H., Jr. (1998) Cancer 83, 1471-1482

36.          Miyauchi, J. (1999) Leuk Lymphoma 33, 267-280

37.          Reynolds, C. P. (2000) Curr Oncol Rep 2, 511-518

38.          Ortiz, M. A., Bayon, Y., Lopez-Hernandez, F. J., and Piedrafita, F. J. (2002) Drug Resist Updat 5, 162-175

39.          Mansure, J. J., Nassim, R., and Kassouf, W. (2009) Cancer biology & therapy 8, 6-15

40.          Osawa, E., Nakajima, A., Wada, K., Ishimine, S., Fujisawa, N., Kawamori, T., Matsuhashi, N., Kadowaki, T., Ochiai, M., Sekihara, H., and Nakagama, H. (2003) Gastroenterology 124, 361-367

41.          Stoll, B. A. (2002) Eur J Cancer Prev 11, 319-325

42.          Smith, M. R., and Kantoff, P. W. (2002) Investigational new drugs 20, 195-200

43.          Rumi, M. A., Ishihara, S., Kazumori, H., Kadowaki, Y., and Kinoshita, Y. (2004) Current medicinal chemistry. Anti-cancer agents 4, 465-477

44.          Papi, A., Guarnieri, T., Storci, G., Santini, D., Ceccarelli, C., Taffurelli, M., De Carolis, S., Avenia, N., Sanguinetti, A., Sidoni, A., Orlandi, M., and Bonafe, M. (2012) Cell death and differentiation 19, 1208-1219

Other research papers on Cancer and Cancer Therapeutics were published on this Scientific Web site as follows:

Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition in Prostate Cancer Cells

PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Nanotechnology Tackles Brain Cancer

Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling: a MED12 Control

Personalized medicine-based cure for cancer might not be far away

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

Lung Cancer (NSCLC), drug administration and nanotechnology

Non-small Cell Lung Cancer drugs – where does the Future lie?

Cancer Innovations from across the Web

arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

mRNA interference with cancer expression

Search Results for ‘cancer’ on this web site

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology

mRNA interference with cancer expression

Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?

Crucial role of Nitric Oxide in Cancer

Targeting Glucose Deprived Network Along with Targeted Cancer Therapy Can be a Possible Method of Treatment

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Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition in Prostate Cancer Cells(1)

Authors: Dejuan Kong, Aamir Ahmad, Bin Bao, Yiwei Li, Sanjeev Banarjee, Fazlul H. Sarkar, Wayne State University School of Medicine

Reporter-Curator: Stephen J. Williams, Ph.D.

Clinically, there has not been much success in treating solid tumors with histone deacetylase inhibitors (HDACi). Histone acetylation and deacetylation play an important role in transcriptional regulation of genes and increased activity is associated with many cancers, therefore it was thought that HDAC inhibition might be fruitful as a therapy.  There have been several phase I and II clinical trials using HDACi for treatment of various malignancies, including hematological and solid malignancies(2), with most success seen in hematologic malignancies such as cutaneous T-cell lymphoma and peripheral T-cell lymphoma and little or no positive outcome with solid tumors.  Many mechanisms of resistance to HDACi in solid tumors have been described, most of which are seen with other chemotherapeutics such as increased multidrug resistance gene MDR1, increased anti-apoptotic proteins and activation of cell survival pathways(3).

A report in PLOS One by Dr. Dejuan Kong, Dr. Fazlul Sarkar, and colleagues from Wayne State University School of Medicine, demonstrate another possible mechanism of resistance to HDACi in prostate cancer, by induction of the epithelial-to-mesenchymal transition (EMT), which has been associated with the development of resistance to chemotherapies in other malignancies of epithelial origin(4,5).

EMT is an important differentiation process in embryogenesis and felt to be important in progression of cancer.  Epithelial cells will acquire a mesenchymal morphology (on plastic this looks like a cuboidal epithelial cell gaining a more flattened, elongated, tri-corner morphology; see paper Figure 1) and down-regulate epithelial markers such as cytokeratin, up-regulation of mesenchymal markers, increased migration and invasiveness in standard assays, and increased resistance to chemotherapeutics, and similarity to cancer stem cells(6-10).

ImageFigure 1. HDACis led to the induction of EMT phemotype. (A and B) PC3 cells treated with TSA and SAHA for 24 h at indicated doses.  The photomicrographs of PC3 cells treated with TSA and SAHA exhibited a fibroblastic-type phenotype, while cells treated with DMAO control displayed rounded epithelial cell morphology (original magnification, x 100). (C) Treated PC3 cells show increased mesenchymal markers vimentin and ZEB1 and F-actin reorganization.  Figure taken from Kong, D., Ahmad, A., Bao, B., Li, Y., Banerjee, S., and Sarkar, F. H. (2012) PloS one 7, e45045

In this study the authors found that treatment of prostate carcinoma cells with two different HDACis (trichostatin A (TSA) and suberoylanilide hydroxamic acid (SAHA)) induced EMT phenotype mediated through up-regulation of transcription factors ZEB1, ZEB2 and Slug, increased expression of mesenchymal markers vimentin, N-cadherin and fibronectin by promoting histone 3 acetylation on gene promoters.  In addition TSA increased the stem cell markers Sox2 and Nanog with concomitant EMT morphology and increased cell motility.

Below is the abstract of this paper(1):

ABSTRACT

Clinical experience of histone deacetylase inhibitors (HDACIs) in patients with solid tumors has been disappointing; however, the molecular mechanism of treatment failure is not known. Therefore, we sought to investigate the molecular mechanism of treatment failure of HDACIs in the present study. We found that HDACIs Trichostatin A (TSA) and Suberoylanilide hydroxamic acid (SAHA) could induce epithelial-to-mesenchymal transition (EMT) phenotype in prostate cancer (PCa) cells, which was associated with changes in cellular morphology consistent with increased expression of transcription factors ZEB1, ZEB2 and Slug, and mesenchymal markers such as vimentin, N-cadherin and Fibronectin. CHIP assay showed acetylation of histone 3 on proximal promoters of selected genes, which was in part responsible for increased expression of EMT markers. Moreover, TSA treatment led to further increase in the expression of Sox2 and Nanog in PCa cells with EMT phenotype, which was associated with cancer stem-like cell (CSLC) characteristics consistent with increased cell motility. Our results suggest that HDACIs alone would lead to tumor aggressiveness, and thus strategies for reverting EMT-phenotype to mesenchymal-to-epithelial transition (MET) phenotype or the reversal of CSLC characteristics prior to the use of HDACIs would be beneficial to realize the value of HDACIs for the treatment of solid tumors especially PCa.

Highlights of the research include:

  • TSA and SAHA induce morphologic changes  in prostate carcinoma LNCaP and PC3 cells related to EMT by microscopy as well as accumulation of mesenchymal markers ZEB1, vimentin, and F-actin reorganization shown by immunofluorescence microscopy and increased expression of these markers shown by real-time PCR
  • Western blotting showed TSA treatment resulted in hyperacetyulation of histone 3 whi8le CHIP analysis revealed increased histone 3 acetylation on the promoters of vimentin, ZEB2, Slug, and MMP2
  • Western analysis revealed that HDACi not only induced EMT but increased the expression of cancer stem cell markers associated with increased motility such as Sox2 and Nanog.  Increased cell migration was measured by Transwell migration assays and increased cell motility was measured via cell detachment assays

1.            Kong, D., Ahmad, A., Bao, B., Li, Y., Banerjee, S., and Sarkar, F. H. (2012) PloS one 7, e45045

2.            Bertino, E. M., and Otterson, G. A. (2011) Expert opinion on investigational drugs 20, 1151-1158

3.            Robey, R. W., Chakraborty, A. R., Basseville, A., Luchenko, V., Bahr, J., Zhan, Z., and Bates, S. E. (2011) Molecular pharmaceutics 8, 2021-2031

4.            Wang, Z., Li, Y., Kong, D., Banerjee, S., Ahmad, A., Azmi, A. S., Ali, S., Abbruzzese, J. L., Gallick, G. E., and Sarkar, F. H. (2009) Cancer research 69, 2400-2407

5.            Wang, Z., Li, Y., Ahmad, A., Azmi, A. S., Kong, D., Banerjee, S., and Sarkar, F. H. (2010) Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 13, 109-118

6.            Hugo, H., Ackland, M. L., Blick, T., Lawrence, M. G., Clements, J. A., Williams, E. D., and Thompson, E. W. (2007) Journal of cellular physiology 213, 374-383

7.            Thiery, J. P. (2002) Nature reviews. Cancer 2, 442-454

8.            Kong, D., Banerjee, S., Ahmad, A., Li, Y., Wang, Z., Sethi, S., and Sarkar, F. H. (2010) PloS one 5, e12445

9.            Kong, D., Li, Y., Wang, Z., and Sarkar, F. H. (2011) Cancers 3, 716-729

10.          Bao, B., Wang, Z., Ali, S., Kong, D., Li, Y., Ahmad, A., Banerjee, S., Azmi, A. S., Miele, L., and Sarkar, F. H. (2011) Cancer letters 307, 26-36

Other research papers on Cancer and Cancer Therapeutics were published on this Scientific Web site as follows:

PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Nanotechnology Tackles Brain Cancer

Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling: a MED12 Control

Personalized medicine-based cure for cancer might not be far away

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

Lung Cancer (NSCLC), drug administration and nanotechnology

Non-small Cell Lung Cancer drugs – where does the Future lie?

Cancer Innovations from across the Web

arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis.

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

mRNA interference with cancer expression

Search Results for ‘cancer’ on this web site

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Closing the gap towards real-time, imaging-guided treatment of cancer patients.

Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology

mRNA interference with cancer expression

Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?

Crucial role of Nitric Oxide in Cancer

Targeting Glucose Deprived Network Along with Targeted Cancer Therapy Can be a Possible Method of Treatment

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ENCODE data reveals important information from Genome Wide Association Studies relevant to understanding complex genetic diseases

Author: Ritu Saxena, Ph.D.

Introduction

“The depth, quality, and diversity of the ENCODE data are unprecedented” is what was stated by John Stamatoyannopoulos, professor of genomic sciences at the University of Washington and one of the many principle investigators of ENCODE project. ENCODE (Encyclopedia of DNA elements), indeed, was an ambitious project launched as a pilot in 2003 and then expanded in 2007 for the whole genome analysis and identification of all the functional elements of the human genome. The findings were striking as they challenged the definition of “gene” and ‘the central dogma of genetics (Gene-mRNA-protein). Infact, the non-coding part that constitutes about 80% of the genome or the so-called “junk DNA” was found to contain elements crucial for gene regulation. The elements, in large part, include RNA transcripts that are not transcribed into proteins but might have a regulatory role. For detailed reading, refer to the findings published in the issue of Nature, The ENCODE Project Consortium Nature 489, 57–74 (2012) An integrated encyclopedia of DNA elements in the human genome

Key features of the data, as explained in the National Human Genome Research Institute website (National Human Genome Research Institute News feature), include comprehensive mapping of:

  • Protein-coding genes — Proteins are molecules made of amino acids linked together in a specific sequence; the amino acid sequence is encoded by the sequence of DNA subunits called nucleotides that make up genes.
  • Non-coding genes — Stretches of DNA that are read by the cell as if they were genes but do not encode proteins. These appear to help regulate the activity of the genome.
  • Chromatin structure features — Complex physical structures made from a combination of DNA and binding proteins that make up the contents of the nucleus and affects genome function.
  • Histone modifications — Histones are the proteins that make up the chromatin structures that help shape and control the genome. In addition, histone proteins can be physically modified by adding chemical groups, such as a methyl molecule, that further regulates genomic activity.
  • DNA methylation — Just like histones, methyl groups can be added to DNA itself in a process called DNA methylation. Chemically attaching methyl groups to DNA physically changes the ability of enzymes to reach the DNA and thus alters the gene expression pattern in cells. Methylation helps cells “remember what they are doing” or alter levels of gene expression, and it is a crucial part of normal development and cellular differentiation in higher organisms.
  • Transcription factor binding sites — Transcription factors are proteins that bind to specific DNA sequences, controlling the flow (or transcription) of genetic information from DNA to mRNA. Mapping the binding sites can help researchers understand how genomic activity is controlled.

How could ENCODE be helpful in the study of complex human diseases?

Complex diseases and Genome wide association studies (GWAS)

Coronary artery disease, type 2 diabetes and many forms of cancer are complex human diseases that have a significant genetic component. Unlike mendelian disorders that have defined loci, the genetic component of complex disorders lies in the form of genetic variations in the genome making an individual susceptible to these complex diseases.

Researchers have performed Genome-wide association studies (GWAS) of the human genome, leading to the identification of thousands of DNA variants that could be linked with complex traits and diseases. However, identifying the variants, referred to as SNPs (Single Nucleotide Polymorphisms), that actually contribute to the disease, and understanding how they exert influence on a disease has been more of a mystery.

How would ENCODE solve the puzzle?

The puzzle lies in interpreting how the SNPs found in the genome affect a person’s susceptibility to a particular trait or disease and what is the mechanism behind it. As identified in the GWAS, most variants that are associated with the phenotype of the trait or disease lie in the non-coding region of the genome. Infact, in more than 400 studies compiled in the GWAS catalog only a small minority of the trait/disease-associated SNPs occur in protein-coding regions; the large majority (89%) are in noncoding regions. These variants fall in the gene deserts that lie far from protein-coding region, similar to those where cis-regulatory modules (CRMs) are found. CRMs such as promoters and enhancers are a group of binding sites for transcription factors, and the presence of transcription factors bound to these sites is a good indicator of the potential regulatory regions.

The integrative analysis of ENCODE data has give important insights to the results of GWAS studies. Investigators have employed ENCODE data as an initial guide to discover regulatory regions in which genetic variation is affecting a complex trait. Additionally, ENCODE study when examined the SNPs from GWAS that were associated with the phenotype of the trait, found that these regions are enriched in DNase-sensitive regions i.e, lie in the function-associated DNA region of the genome as it could be bound by transcription factors affecting the regulation of gene expression. Thus, the project demonstrates that non-coding regions must be considered when interpreting GWAS results, and it provides a strong motivation for reinterpreting previous GWAS findings.

Using ENCODE Data to Interpret GWAS Results

ENCODE and predisposition to CANCER:

C-Myc, a proto-oncogene, codes for a transcripton factor, when expressed constitutively leads to uninhibited cell proliferation resulting in cancer. It has been observed that common variants within a ~1 Mb region upstream of c-Myc gene have been associated with cancers of the colon, prostate, and breast. Several SNPs have been reported in this region, that although affect the phenotype, lie in the distal cis-region of the MYC gene. Alignment of the ENCODE data in this region with the significant variants from the GWAS also reveals that key variants are found in the transcription factor occupied DNA segments mapped by this consortium. One variant rs698327, lies within a DNase hypersensitive site that is bound by several transcription factors, enhancer-associated protein p300, and contains histone modifications relative to enhancers (high H3K4me1, low H3K4me3). ENCODE data indicates that non-coding regions in the human chromosome 8q24 loci are associated with cancer and as observed in the case of c-myc gene, similar studies on cancer-related genes could help explain predisposition to cancer.

ENCODE and fetal hemoglobin expression:

Another example of the use of ENCODE data is that of gene regulation of fetal hemoglobin. Several regions were predicted via ENCODE that were involved in the regulation of fetal hemoglobin. It was found that these predicted regions are close to the SNPs in the BLC11A gene that is associated with persistent expression of fetal hemoglobin.

Future perspective

As evident from the above examples, the ENCODE data shows that genetic variants do affect regulated expression of a target gene. Recently, several research groups in the UK performed a large-scale GWAS study to determine the genetic predisposition to fracture risk. The collaborative effort, published in a recent issue of the PLoS journal, was made to identify genetic variants associated with cortical bone thickness (CBT) and bone mineral density (BMD) with data from more than 10,000 subjects. http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002745 The study generated a wealth of data including the result – identification of SNPs in the WNT16 and its adjacent gene, FAM3C were found to be relevant to CBT and BMD. ENCODE data, in this case, could be helpful in interpreting more detailed information including determining additional SNPs, the regulatory information of the genes involved and much more. Thus, it could be concluded that ENCODE data could be immensely useful in interpreting associations between disease and DNA sequences that can vary from person to person.

Sources:

Research articles

An integrated encyclopedia of DNA elements in the human genome

A User’s Guide to the Encyclopedia of DNA Elements (ENCODE)

What does our genome encode?

Genome-wide Epigenetic Data Facilitate Understanding of Disease Susceptibility Association Studies

Genomics: ENCODE explained

ENCODE Project Writes Eulogy For Junk DNA

WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk

 News articles

ENCODE project: In massive genome analysis new data suggests ‘gene’ redefinition

National Human Genome Research Institute News feature

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ENCODE Findings as Consortium

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Reporter: Aviva Lev-Ari, PhD, RN
Blood. 2012 Aug 24. [Epub ahead of print]

Chromatin accessibility, p300 and histone acetylation define PML-RARα and AML1-ETO binding sites in acute myeloid leukemia.

Source

Radboud University, Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Nijmegen, Netherlands;

Abstract

Chromatin accessibility plays a key role in regulating cell type specific gene expression during hematopoiesis, but has also been suggested to be aberrantly regulated during leukemogenesis. To understand the leukemogenic chromatin signature we analyzed acute promyelocytic leukemia (APL), a subtype of leukemia characterized by the expression of RARα-fusion proteins such as PML-RARα. We used nuclease accessibility sequencing in cell lines as well as patient blasts to identify accessible DNA elements and identified over 100,000 accessible regions in each case. Using ChIP-seq we identified H2A.Z as a histone modification generally associated with these accessible regions while unsupervised clustering analysis of other chromatin features including DNA methylation, H2A.Zac, H3ac, H3K9me3, H3K27me3 and the regulatory factor p300 distinguished six distinct clusters of accessible sites, each with a characteristic functional make-up. Of these, PML-RARα binding was found specifically at accessible chromatin regions characterized by p300 binding and hypoacetylated histones. Identifying regions with a similar epigenetic make up in t(8;21) AML cells, another subtype of AMLs, revealed that these regions are occupied by the oncofusion protein AML1-ETO. Together our results suggest that oncofusion proteins localize to accessible regions and that chromatin accessibility together with p300 binding and histone acetylation characterize AML1-ETO and PML-RARα binding sites.

 

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