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Posts Tagged ‘mRNA’


Reporter and Curator: Irina Robu, PhD

Monitoring cancer patients and evaluating their response to treatment can sometimes involve invasive procedures, including surgery.

The liquid biopsies have become something of a Holy Grail in cancer treatment among physicians, researchers and companies gambling big on the technology. Liquid biopsies, unlike traditional biopsies involving invasive surgery — rely on an ordinary blood draw. Developments in sequencing the human genome, permitting researchers to detect genetic mutations of cancers, have made the tests conceivable. Some 38 companies in the US alone are working on liquid biopsies by trying to analyze blood for fragments of DNA shed by dying tumor cells.

Premature research on the liquid biopsy has concentrated profoundly on patients with later-stage cancers who have suffered treatments, including chemotherapy, radiation, surgery, immunotherapy or drugs that target molecules involved in the growth, progression and spread of cancer. For cancer patients undergoing treatment, liquid biopsies could spare them some of the painful, expensive and risky tissue tumor biopsies and reduce reliance on CT scans. The tests can rapidly evaluate the efficacy of surgery or other treatment, while old-style biopsies and CT scans can still remain inconclusive as a result of scar tissue near the tumor site.

As recently as a few years ago, the liquid biopsies were hardly used except in research. At the moment, thousands of the tests are being used in clinical practices in the United States and abroad, including at the M.D. Anderson Cancer Center in Houston; the University of California, San Diego; the University of California, San Francisco; the Duke Cancer Institute and several other cancer centers.

With patients for whom physicians cannot get a tissue biopsy, the liquid biopsy could prove a safe and effective alternative that could help determine whether treatment is helping eradicate the cancer. A startup, Miroculus developed a cheap, open source device that can test blood for several types of cancer at once. The platform, called Miriam finds cancer by extracting RNA from blood and spreading it across plates that look at specific type of mRNA. The technology is then hooked up at a smartphone which sends the information to an online database and compares the microRNA found in the patient’s blood to known patterns indicating different type of cancers in the early stage and can reduce unnecessary cancer screenings.

Nevertheless, experts warn that more studies are essential to regulate the accuracy of the test, exactly which cancers it can detect, at what stages and whether it improves care or survival rates.

SOURCE

https://www.fastcompany.com/3037117/a-new-device-can-detect-multiple-types-of-cancer-with-a-single-blood-test

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4356857/

Other related articles published in this Open Access Online Scientific Publishing Journal include the following:

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/28/liquid-biopsy-chip-detects-an-array-of-metastatic-cancer-cell-markers-in-blood-rd-worcester-polytechnic-institute-micro-and-nanotechnology-lab/

Liquid Biopsy Assay May Predict Drug Resistance

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/06/liquid-biopsy-assay-may-predict-drug-resistance/

One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI

Curator: Marzan Khan, B.Sc

https://pharmaceuticalintelligence.com/2017/01/05/one-blood-sample-can-be-tested-for-a-comprehensive-array-of-cancer-cell-biomarkers-rd-wpi

 

 

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Moderna Therapeutics Deal with Merck: Are Personalized Vaccines here?

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

Take aways:

  • RNA based vaccines are a cost-effective method of developing and manufacturing a personalized cancer vaccine strategy; traditional vaccine methodology has not been met with much success as a cancer therapeutic
  • Most of the older RNA vaccine technology depended on isolated dendritic cells or T cell populations and ex-vivo treatment with RNA vaccine, HOWEVER, Moderna has developed a technology that circumvents the need for ex-vivo vaccination
  • There are multiple companies involved in this new RNA strategy (Moderna, Caperna {now Moderna}, CureVac, Biontech)

From BusinessWire at http://www.businesswire.com/news/home/20160629005446/en/Merck-Moderna-Announce-Strategic-Collaboration-Advance-mRNA-Based

Merck and Moderna Announce Strategic Collaboration to Advance Novel mRNA-Based Personalized Cancer Vaccines with KEYTRUDA®(pembrolizumab) for the Treatment of Multiple Types of Cancer

Collaboration Combines Merck’s Leadership in Immuno-Oncology with Moderna’s Pioneering mRNA Vaccine Technology and Rapid Cycle Time, Small-Batch GMP Manufacturing Capabilities

KENILWORTH, N.J. & CAMBRIDGE, Mass.–(BUSINESS WIRE)–Merck (NYSE:MRK), known as MSD outside the United States and Canada, and Moderna Therapeutics today announced a strategic collaboration and license agreement to develop and commercialize novel messenger RNA (mRNA)-based personalized cancer vaccines. The collaboration will combine Merck’s established leadership in immuno-oncology with Moderna’s pioneering mRNA vaccine technology and GMP manufacturing capabilities to advance individually tailored cancer vaccines for patients across a spectrum of cancers.

“Combining immunotherapy with vaccine technology may be a new path toward improving outcomes for patients”

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Moderna and Merck will develop personalized cancer vaccines that utilize Moderna’s mRNA vaccine technology to encode a patient’s specific neoantigens, unique mutations present in that specific patient’s tumor. When injected into a patient, the vaccine will be designed to elicit a specific immune response that will recognize and destroy cancer cells. The companies believe that the mRNA-based personalized cancer vaccines’ ability to specifically activate an individual patient’s immune system has the potential to be synergistic with checkpoint inhibitor therapies, including Merck’s anti-PD-1 therapy, KEYTRUDA® (pembrolizumab). In addition, Moderna has developed a rapid cycle time, small-batch manufacturing technique that will uniquely allow the company to supply vaccines tailored to individual patients within weeks.

Under the terms of the agreement, Merck will make an upfront cash payment to Moderna of $200 million, which Moderna will use to lead all research and development efforts through proof of concept. The development program will entail multiple studies in several types of cancer and include the evaluation of mRNA-based personalized cancer vaccines in combination with Merck’s KEYTRUDA® (pembrolizumab). Moderna will also utilize the upfront payment to fund a portion of the build-out of a GMP manufacturing facility in suburban Boston for the purpose of personalized cancer vaccine manufacturing.

Following human proof of concept studies, Merck has the right to elect to make an additional undisclosed payment to Moderna. If exercised, the two companies will then equally share cost and profits under a worldwide collaboration for the development of personalized cancer vaccines. Moderna will have the right to elect to co-promote the personalized cancer vaccines in the U.S. The agreement entails exclusivity around combinations with KEYTRUDA. Moderna and Merck will each have the ability to combine mRNA-based personalized cancer vaccines with other (non-PD-1) agents.

Combining immunotherapy with vaccine technology may be a new path toward improving outcomes for patients,” said Dr. Roger Perlmutter, President, Merck Research Laboratories. “While the area of personalized cancer vaccine research has faced challenges in the past, there have been many recent advances, and we believe that working with Moderna to combine an immuno-oncology approach, using KEYTRUDA, with mRNA-based personalized cancer vaccines may have the potential to transform the treatment of cancer.”

“Our team has made significant progress since beginning our work in personalized cancer vaccines just last year. Through this collaboration with Merck, we are now well-positioned to accelerate research and development with a goal of entering the clinic in 2017, as well as to apply our unique GMP manufacturing capabilities to support the rapid production of these highly individualized vaccines,” said Stéphane Bancel, chief executive officer of Moderna. “We value our continued collaboration with Merck, and we look forward to working together to harness the potential of personalized cancer vaccines and immuno-oncology to bring a new treatment paradigm to patients.”

Merck and Moderna have an existing collaboration and license agreement focused on the discovery and development of mRNA-based infectious disease vaccines and passive immunity treatments. Moderna is also advancing its own pipeline of infectious disease vaccine candidates and currently has two phase 1 studies underway in Europe and the U.S.

About Moderna Therapeutics

Moderna is a clinical stage pioneer of messenger RNA Therapeutics™, an entirely new in vivo drug technology that produces human proteins, antibodies and entirely novel protein constructs inside patient cells, which are in turn secreted or active intracellularly. This breakthrough platform addresses currently undruggable targets and offers a potentially superior alternative to existing drug modalities for a wide range of diseases and conditions. Moderna is developing and plans to commercialize its innovative mRNA drugs through its own ventures and its strategic relationships with established pharmaceutical and biotech companies. Its current ventures are:

  • Onkaido, focused on oncology,
  • Valera, focused on infectious diseases,
  • Elpidera, focused on rare diseases, and
  • Caperna, focused on personalized cancer vaccines.

Cambridge-based Moderna is privately held and currently has strategic agreements with AstraZeneca, Alexion Pharmaceuticals and Merck. To learn more, visit www.modernatx.com.

From the Moderna Therapeutics Website

Our mRNA Platform

At Moderna, we are pioneering the development of a new class of drugs made of messenger RNA (mRNA). This novel drug platform builds on the discovery that modified mRNA can direct the body’s cellular machinery to produce nearly any protein of interest, from native proteins to antibodies and other entirely novel protein constructs that can have therapeutic activity inside and outside of cells.

Our efforts are helping Moderna and the industry to flatten the mRNA learning curve across the full breadth of competencies needed to drive the platform forward, including chemistry, mRNA biology, formulation, process development, automation and high-throughput production, quality, and Good Manufacturing Practice (GMP) manufacturing.

Drug Modalities

Building from our mRNA core expression platform, we have created a new scale of drug discovery and development that enables a series of new drug modalities. Each modality represents a distinct approach to using the mRNA platform to encode proteins that achieve a therapeutic benefit, enabling us to develop numerous drug candidates across a wide array of therapeutic areas.

Vaccines

Vaccines are substances that teach the immune system to rapidly recognize and destroy invading pathogens such as bacteria or viruses, preparing the body’s adaptive immunity for future exposure to the pathogen. Historically, vaccines have introduced immune-activating markers from pathogens into the body. Conversely, Moderna is developing mRNA-based vaccines that enable the body to produce and present immunogenic proteins to the immune system.

Moderna is also developing mRNA-based personalized cancer vaccines to prime the immune system to recognize cancer cells and mount a strong, tailored response to each individual patient’s cancer. Moderna’s technology allows for a rapid turn-around time in production of these unique mRNA vaccines.

Intracellular/Transmembrane

Many diseases are caused by defects in proteins that function inside cells. Existing methods of protein-based therapy do not allow for proteins to reach the intracellular space, and as such are unable to replace the defective, disease-causing proteins within cells. Moderna’s platform allows for the development of mRNA therapies that can stimulate production of intracellular proteins as well as transmembrane proteins. This could potentially lead to a novel approach to treating a vast array of rare genetic and other diseases caused by intracellular protein defects.

Intratumoral

Many targets for the treatment of cancer have been identified but their therapeutic potential has been limited by either the inability to access these targets, or by systemic toxicities. Moderna’s platform allows for localized expression of therapeutic proteins within the tumor microenvironment.

Secreted antibodies

Antibodies are secreted proteins that bind to and inhibit specific targets. Moderna’s platform has the potential to stimulate the body’s own cells to produce specific antibodies that can bind to cellular targets.

Secreted proteins

Proteins are large, complex molecules that have many critical functions both inside and outside of cells. Moderna’s platform stimulates cells to produce and secrete proteins that can have a therapeutic benefit through systemic exposure.

Moderna is comprised of four smaller companies, the following three are involved in their personalized immunotherapy and cancer vaccine strategy

Caperna LLC

Caperna

Caperna LLC is the fourth Moderna venture company — formed, funded and wholly-owned by Moderna — and focused exclusively on the advancement of personalized cancer vaccines.

Caperna will apply Moderna’s mRNA vaccine technology to the field of cancer vaccines, building on advances in recent years in cancer immunotherapy. Utilizing Moderna’s demonstrated engineering and process capability to synthesize over 1,000 unique novel mRNA’s per month in Moderna’s, automated, in-house productions systems. This provides the basis for a vision of rapid turnaround times that will allow Caperna’s personalized cancer vaccine, customized after tumor biopsy and sequencing to code for specific neoantigens in patients’ tumors, to be used to treat patients with aggressive tumors and high unmet need (rather than those with less aggressive tumors which can’t wait for prolonged turnaround times). Caperna will develop its personalized cancer vaccines in combination with checkpoint inhibitors that unleash the immune system and other cancer immunotherapies.

Corporate Facts

  • President: Tal Zaks, M.D., Ph.D.
  • Head of Research: Nicholas Valiante, Ph.D.
  • Head of Operations: Ted Ashburn, M.D., Ph.D.
  • Headquarters: 500 Technology Square, Cambridge, Mass.
  • Phone: 617-714-6500
  • Website: Caperna.com

 

Onkaido

Onkaido

Onkaido Therapeutics is the first Moderna venture company – formed, funded and wholly-owned by Moderna. Onkaido is focused exclusively on developing mRNA-based oncology treatments for currently undruggable targets or as a superior alternative to existing drug modalities. Onkaido is leveraging all of the tools and modalities developed at Moderna, with plans to rapidly turn mRNA science into truly novel cancer therapies that can make a real difference for patients.

Onkaido is currently focused on three therapeutic areas of oncology drug discovery and development: immuno-oncology, hepatocellular carcinoma (liver cancer) and myeloid malignancies – with programs investigating multiple targets and therapies simultaneously. Onkaido scientists are also exploring the power of mRNA technology in precision cancer pharmacology – researching areas such as tumor biology, targeting and gene silencing, driving the science toward the delivery of truly personalized cancer treatment.

Corporate Facts

  • President: Stephen Kelsey, M.D.
  • Headquarters: 500 Technology Square, Cambridge, Mass.
  • Phone: 617-714-6500
  • Website: Onkaido.com

 

Valera

Valera

Valera LLC is the second Moderna venture company — formed, funded and wholly-owned by Moderna — and focused exclusively on the advancement of vaccines and therapeutics for the prevention and treatment of viral, bacterial and parasitic infectious diseases.

The vaccines work of Valera builds on a body of preclinical research at Moderna showing the ability of modified mRNA to express viral antigens in vivo and to induce robust immune responses. Valera’s therapeutic passive immunity programs will expand on Moderna’s research using mRNA to express antibodies that bind to viral and other targets. The robust data from these programs across a range of preclinical infectious disease models, together with the inherent, rapid turn-around time in creating novel mRNA constructs, provide Valera with a potentially powerful and versatile new platform for the creation of a broad array of vaccines and passive immunity therapies.

Corporate Facts

  • President: Michael Watson, MB ChB, MRCP, AFPM
  • Chief Scientific OfficerGiuseppe Ciaramella, Ph.D.
  • Interim Chief Medical OfficerTal Zaks, M.D., Ph.D.
  • Headquarters: 500 Technology Square, Cambridge, Mass.
  • Phone: 617-714-6500
  • Website: Valeratx.com

And from http://endpts.com/neoantigens-beckon-merck-into-a-200m-cancer-collaboration-with-moderna/

Neoantigens beckon Merck into a $200M cancer collaboration with Moderna


Now that Galena has added fresh evidence that first-gen cancer vaccines make for a poor R&D program, Merck is betting $200 million upfront that the next-gen neoantigen approach to personalized cancer vaccines can succeed where all else has failed.

Merck is tying up with the mRNA specialists at Cambridge, MA-based Moderna, which has inked a long lineup of marquee partnerships. The big idea here is that each person’s cancer cells present unique “neoantigens” that can be used to tailor a cancer vaccine for each patient.

That’s a radical idea that has gained considerable steam in recent months, with Gritstone and Neon Therapeutics — paired now with Bristol-Myers on Opdivo — rounding up significant venture cash. Biotech billionaire Patrick Soon-Shiong has also jumped into the game, including it in its growing slate of cancer R&D work in a group of startups.

Moderna says it has already set up a manufacturing system that can be used to create these personalized vaccines in a matter of weeks. And Merck will use the partnership to advance new combination therapies that include its checkpoint inhibitor Keytruda.

The way the deal works, Moderna notes in its statement, is that Merck can step up after it sees some evidence in humans that the tech is working as planned. After human proof-of-concept, if Merck wants to opt in they can pay a significant milestone and then both companies can share the cost on Phase III and commercializations, profiting equally.Moderna CEO Stéphane Bancel says they can jump into the clinic next year.

The deal marks another rare pact by Merck R&D chief Roger Perlmutter, who’s been carefully focused on making Keytruda a foundation franchise that can sustain the company for years to come. While Merck has been a couple of steps behind Bristol-Myers in gaining market share, Perlmutter’s not settling for a second place finish.

“Combining immunotherapy with vaccine technology may be a new path toward improving outcomes for patients,” said Perlmutter, president, Merck Research Laboratories. “While the area of personalized cancer vaccine research has faced challenges in the past, there have been many recent advances, and we believe that working with Moderna to combine an immuno-oncology approach, using KEYTRUDA, with mRNA-based personalized cancer vaccines may have the potential to transform the treatment of cancer.”

From FierceBiotech on failure of Galena’s breast cancer vaccine trial

Galena plummets into microcap territory on Phase III breast cancer vaccine trial halt

Immunother Cancer. 2015; 3: 26.
Published online 2015 Jun 16. doi:  10.1186/s40425-015-0068-y
PMCID: PMC4468959

Self-adjuvanted mRNA vaccination in advanced prostate cancer patients: a first-in-man phase I/IIa study

Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
CureVac GmbH, Paul-Ehrlich-Str. 15, Tuebingen, 72076 Germany
Charité University Hospital Berlin, Berlin, Germany
University Hospital Freiburg, Freiburg, Germany
Universitäty Hospital Essen, Essen, Germany
San Raffaele Scientific Institute, Milan, Italy
University Hospital of the Johannes-Gutenberg-University Mainz, Mainz, Germany
Ortenau Klinikum Offenburg-Gengenbach, Offenburg, Germany
University Hospital Göttingen, Göttingen/University Hospital Mannheim, Mannheim, Germany
University Hospital Schleswig-Holstein Campus Luebeck, Luebeck, Germany
Rippin-Consulting, Solingen, Germany
University Hospital Tuebingen, Tuebingen, Germany
Hubert Kübler, ed.nehcneum-ut.zrl@relbeuK.H.
corresponding authorCorresponding author.
#Contributed equally.

Abstract

Background

CV9103 is a prostate-cancer vaccine containing self-adjuvanted mRNA (RNActive®) encoding the antigens PSA, PSCA, PSMA, and STEAP1. This phase I/IIa study evaluated safety and immunogenicity of CV9103 in patients with advanced castration-resistant prostate-cancer.

Methods

44 Patients received up to 5 intra-dermal vaccinations. Three dose levels of total mRNA were tested in Phase I in cohorts of 3–6 patients to determine a recommended dose. In phase II, 32 additional patients were treated at the recommended dose. The primary endpoint was safety and tolerability, the secondary endpoint was induction of antigen specific immune responses monitored at baseline and at weeks 5, 9 and 17.

Results

The most frequent adverse events were grade 1/2 injection site erythema, injection site reactions, fatigue, pyrexia, chills and influenza-like illness. Possibly treatment related urinary retention occurred in 3 patients. The recommended dose was 1280 μg. A total of 26/33 evaluable patients treated at 1280 μg developed an immune response, directed against multiple antigens in 15 out of 33 patients. One patient showed a confirmed PSA response. In the subgroup of 36 metastatic patients, the Kaplan-Meier estimate of median overall survival was 31.4 months [95 % CI: 21.2; n.a].

Conclusions

The self-adjuvanted RNActive® vaccine CV9103 was well tolerated and immunogenic.

The technology is a versatile, fast and cost-effective platform allowing for creation of vaccines. The follow-up vaccine CV9104 including the additional antigens prostatic acid phosphatase (PAP) and Muc1 is currently being tested in a randomized phase IIb trial to assess the clinical benefit induced by this new vaccination approach.

SOURCE

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468959/

Other articles in the Open Access Journal on Cancer Vaccines Include:

Cancer Vaccines: Targeting Cancer Genes for Immunotherapy – A Conference by Keystone Symposia on Molecular and Cellular Biology

AACR2016 – Cancer immunotherapy

Aduro Biotech Phase II Pancreatic Cancer Trial CRS-207 plus cancer vaccine GVAX Fails

Cases in Biotech Entrepreneurship: Selective Start Ups in 2016

at #JPM16 – Moderna Therapeutics turns away an extra $200 million: with AstraZeneca (collaboration) & with Merck ($100 million investment)

 

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Topology of Protein Complexes

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Periodic Table of Protein Complexes Unveiled

http://www.genengnews.com/gen-news-highlights/periodic-table-of-protein-complexes-unveiled/81252091/

 

 

http://www.genengnews.com/Media/images/GENHighlight/thumb_Dec11_2015_EMBLEBI_ProteinComplexesPeriodicTable1692141643.jpg

A new periodic table presents a systematic, ordered view of protein assembly, providing a visual tool for understanding biological function. [EMBL-EBI / Spencer Phillips]

 

Move over Mendeleev, there’s a new periodic table in science. Unlike the original periodic table, which organized the chemical elements, the new periodic table organizes protein complexes, or more precisely, quaternary structure topologies. Though there are other differences between the old and new periodic tables, they share at least one important feature—predictive power.

When Mendeleev introduced his periodic table, he predicted that when new chemical elements were discovered, they would fill his table’s blank spots. Analogous predictions are being ventured by the scientific team that assembled the new periodic table. This team, consisting of scientists from the Wellcome Genome Campus and the University of Cambridge, asserts that its periodic table reveals the regions of quaternary structure space that remain to be populated.

The periodic table of protein complexes not only offers a new way of looking at the enormous variety of structures that proteins can build in nature, it also indicates which structures might be discovered next. Moreover, it could point protein engineers toward entirely novel structures that never occurred in nature, but could be engineered.

The new table appeared December 11 in the journal Science, in an article entitled, “Principles of assembly reveal a periodic table of protein complexes.” The “principles of assembly” referenced in this title amount to three basic assembly types: dimerization, cyclization, and heteromeric subunit addition. In dimerization, one protein complex subunit doubles, and becomes two; in cyclization, protein complex subunits from a ring of three or more; and in heteromeric subunit addition, two different proteins bind to each other.

These steps, repeated in different combinations, gives rise to enormous number of proteins of different kinds. “Evolution has given rise to a huge variety of protein complexes, and it can seem a bit chaotic,” explained Joe Marsh, Ph.D., formerly of the Wellcome Genome Campus and now of the MRC Human Genetics Unit at the University of Edinburgh. “But if you break down the steps proteins take to become complexes, there are some basic rules that can explain almost all of the assemblies people have observed so far.”

The authors of the Science article noted that many protein complexes assemble spontaneously via ordered pathways in vitro, and these pathways have a strong tendency to be evolutionarily conserved. “[There] are strong similarities,” the authors added, “between protein complex assembly and evolutionary pathways, with assembly pathways often being reflective of evolutionary histories, and vice versa. This suggests that it may be useful to consider the types of protein complexes that have evolved from the perspective of what assembly pathways are possible.”

To explore this rationale, the authors examined the fundamental steps by which protein complexes can assemble, using electrospray mass spectrometry experiments, literature-curated assembly data, and a large-scale analysis of protein complex structures. Ultimately, they derived their approach to explaining the observed distribution of known protein complexes in quaternary structure space. This approach, they insist, provides a framework for understanding their evolution.

“In addition, it can contribute considerably to the prediction and modeling of quaternary structures by specifying which topologies are most likely to be adopted by a complex with a given stoichiometry, potentially providing constraints for multi-subunit docking and hybrid methods,” the authors concluded. “Lastly, it could help in the bioengineering of protein complexes by identifying which topologies are most likely to be stable, and thus which types of essential interfaces need to be engineered.”

The rows and columns of the periodic table of the elements, called periods and groups, were originally determined by each element’s atomic mass and chemical properties, later by atomic number and electron configuration. In contrast, the rows and columns of the periodic table of protein complexes correspond to the number of different subunit types and the number of times these subunits are repeated. The new table is not, it should be noted, periodic in the same sense as the periodic table of the elements. It is in principle open-ended.

Although there are no theoretical limitations to quaternary structure topology space in either dimension, the abridged version of the table presented in the Science article can accommodate the vast majority of known structures. Moreover, when the table’s creators compared the large variety of countenanced topologies to observed structures, they found that about 92% of known protein complex structures were compatible with their model.

“Despite its strong predictive power, the basic periodic table model does not account for about 8% of known protein complex structures,” the authors conceded. “More than half of these exceptions arise as a result of quaternary structure assignment errors.

“A benefit of this approach is that it highlights likely quaternary structure misassignments, particularly by identifying nonbijective complexes with even subunit stoichiometry. However, this still leaves about 4% of known structures that are correct but are not compatible with the periodic table.” The authors added that the exceptions to their model are interesting in their own right, and are the subject of ongoing studies.

 

 

http://phys.org/news/2015-12-periodic-table-protein-complexes.html

The Periodic Table of Protein Complexes, published today in Science, offers a new way of looking at the enormous variety of structures that proteins can build in nature, which ones might be discovered next, and predicting how entirely novel structures could be engineered. Created by an interdisciplinary team led by researchers at the Wellcome Genome Campus and the University of Cambridge, the Table provides a valuable tool for research into evolution and protein engineering.

Different ballroom dances can be seen as an endless combination of a small number of basic steps. Similarly, the ‘dance’ of assembly can be seen as endless variations on dimerization (one doubles, and becomes two), cyclisation (one forms a ring of three or more) and subunit addition (two different proteins bind to each other). Because these happen in a fairly predictable way, it’s not as hard as you might think to predict how a novel protein would form.

“We’re bringing a lot of order into the messy world of protein complexes,” explains Sebastian Ahnert of the Cavendish Laboratory at the University of Cambridge, a physicist who regularly tangles with biological problems. “Proteins can keep go through several iterations of these simple steps, , adding more and more levels of complexity and resulting in a huge variety of structures. What we’ve made is a classification based on these underlying principles that helps people get a handle on the complexity.”

The exceptions to the rule are interesting in their own right, adds Sebastian, as are the subject of on-going studies.

“By analysing the tens of thousands of protein complexes for which three-dimensional structures have already been experimentally determined, we could see repeating patterns in the assembly transitions that occur – and with new data from we could start to see the bigger picture,” says Joe.

“The core work for this study is in theoretical physics and computational biology, but it couldn’t have been done without the mass spectrometry work by our colleagues at Oxford University,” adds Sarah Teichmann, Research Group Leader at the European Bioinformatics Institute (EMBL-EBI) and the Wellcome Trust Sanger Institute. “This is yet another excellent example of how extremely valuable interdisciplinary research can be.”

Read more at: http://phys.org/news/2015-12-periodic-table-protein-complexes.html#jCp

 

More information: “Principles of assembly reveal a periodic table of protein complexes” www.sciencemag.org/lookup/doi/10.1126/science.aaa2245

PRINCIPLES OF ASSEMBLY REVEAL A PERIODIC TABLE OF PROTEIN COMPLEXES

Sebastian E. Ahnert1,*Joseph A. Marsh2,3,*Helena Hernández4Carol V. Robinson4Sarah A. Teichmann1,3,5,
Science 11 Dec 2015; 350(6266): aaa2245         DOI:http://dx.doi.org:/10.1126/science.aaa2245      

INTRODUCTION

The assembly of proteins into complexes is crucial for most biological processes. The three-dimensional structures of many thousands of homomeric and heteromeric protein complexes have now been determined, and this has had a broad impact on our understanding of biological function and evolution. Despite this, the organizing principles that underlie the great diversity of protein quaternary structures observed in nature remain poorly understood, particularly in comparison with protein folds, which have been extensively classified in terms of their architecture and evolutionary relationships.

RATIONALE

In this work, we sought a comprehensive understanding of the general principles underlying quaternary structure organization. Our approach was to consider protein complexes in terms of their assembly. Many protein complexes assemble spontaneously via ordered pathways in vitro, and these pathways have a strong tendency to be evolutionarily conserved. Furthermore, there are strong similarities between protein complex assembly and evolutionary pathways, with assembly pathways often being reflective of evolutionary histories, and vice versa. This suggests that it may be useful to consider the types of protein complexes that have evolved from the perspective of what assembly pathways are possible.

RESULTS

We first examined the fundamental steps by which protein complexes can assemble, using electrospray mass spectrometry experiments, literature-curated assembly data, and a large-scale analysis of protein complex structures. We found that most assembly steps can be classified into three basic types: dimerization, cyclization, and heteromeric subunit addition. By systematically combining different assembly steps in different ways, we were able to enumerate a large set of possible quaternary structure topologies, or patterns of key interfaces between the proteins within a complex. The vast majority of real protein complex structures lie within these topologies. This enables a natural organization of protein complexes into a “periodic table,” because each heteromer can be related to a simpler symmetric homomer topology. Exceptions are mostly the result of quaternary structure assignment errors, or cases where sequence-identical subunits can have different interactions and thus introduce asymmetry. Many of these asymmetric complexes fit the paradigm of a periodic table when their assembly role is considered. Finally, we implemented a model based on the periodic table, which predicts the expected frequencies of each quaternary structure topology, including those not yet observed. Our model correctly predicts quaternary structure topologies of recent crystal and electron microscopy structures that are not included in our original data set.

CONCLUSION

This work explains much of the observed distribution of known protein complexes in quaternary structure space and provides a framework for understanding their evolution. In addition, it can contribute considerably to the prediction and modeling of quaternary structures by specifying which topologies are most likely to be adopted by a complex with a given stoichiometry, potentially providing constraints for multi-subunit docking and hybrid methods. Lastly, it could help in the bioengineering of protein complexes by identifying which topologies are most likely to be stable, and thus which types of essential interfaces need to be engineered.

http://www.sciencemag.org/content/350/6266/aaa2245/F1.small.gif

Protein assembly steps lead to a periodic table of protein complexes and can predict likely quaternary structure topologies.

Three main assembly steps are possible: cyclization, dimerization, and subunit addition. By combining these in different ways, a large set of possible quaternary structure topologies can be generated. These can be arranged on a periodic table that describes most known complexes and that can predict previously unobserved topologies.

Ahnert SE, et. al. ‘Principles of assembly reveal a periodic table of protein complexes.’
Science (2015).   DOI: http://dx.doi.org:/10.1126/science.aaa2245    http://www.cam.ac.uk/research/news/the-periodic-table-of-proteins

 

Evolution, classification and dynamics of protein complexes

This talk is included in these lists:

This talk is part of the Biological and Statistical Physics discussion group (BSDG) series.

Classification of protein structure has had a broad impact on our understanding of biological function and evolution, yet this work has largely focused on individual protein domains and their pairwise interactions. In contrast, the assembly of individual polypeptides into protein complexes, which are ubiquitous in cells, has received comparatively little attention. The periodic table of protein complexes is a new framework for analysis of complexes based on the principles of self-assembly. This reveals that sequence-identical subunits almost always have identical assembly roles within a complex and allows us to unify the vast majority of complexes of known structure (~32,000) into about 120 topologies. This facilitates the exhaustive enumeration of unobserved protein complex topologies and has significant practical applications for quaternary structure prediction, modelling and engineering.

http://talks.cam.ac.uk/talk/index/61632

 

 

Genome-wide analysis of thylakoid-bound ribosomes in maize reveals principles of cotranslational targeting to the thylakoid membrane

Reimo Zoschke1 and Alice Barkan2
http://www.pnas.org/content/112/13/E1678.full.pdf

Chloroplast genomes encode ∼37 proteins that integrate into the thylakoid membrane. The mechanisms that target these proteins to the membrane are largely unexplored. We used ribosome profiling to provide a comprehensive, high-resolution map of ribosome positions on chloroplast mRNAs in separated membrane and soluble fractions in maize seedlings. The results show that translation invariably initiates off the thylakoid membrane and that ribosomes synthesizing a subset of membrane proteins subsequently become attached to the membrane in a nucleaseresistant fashion. The transition from soluble to membraneattached ribosomes occurs shortly after the first transmembrane segment in the nascent peptide has emerged from the ribosome. Membrane proteins whose translation terminates before emergence of a transmembrane segment are translated in the stroma and targeted to the membrane posttranslationally. These results indicate that the first transmembrane segment generally comprises the signal that links ribosomes to thylakoid membranes for cotranslational integration. The sole exception is cytochrome f, whose cleavable N-terminal cpSecA-dependent signal sequence engages the thylakoid membrane cotranslationally. The distinct behavior of ribosomes synthesizing the inner envelope protein CemA indicates that sorting signals for the thylakoid and envelope membranes are distinguished cotranslationally. In addition, the fractionation behavior of ribosomes in polycistronic transcription units encoding both membrane and soluble proteins adds to the evidence that the removal of upstream ORFs by RNA processing is not typically required for the translation of internal genes in polycistronic chloroplast mRNAs.

 

Significance Proteins in the chloroplast thylakoid membrane system are derived from both the nuclear and plastid genomes. Mechanisms that localize nucleus-encoded proteins to the thylakoid membrane have been studied intensively, but little is known about the analogous issues for plastid-encoded proteins. This genome-wide, high-resolution analysis of the partitioning of chloroplast ribosomes between membrane and soluble fractions revealed that approximately half of the chloroplast encoded thylakoid proteins integrate cotranslationally and half integrate posttranslationally. Features in the nascent peptide that underlie these distinct behaviors were revealed by analysis of the position on each mRNA at which elongating ribosomes first become attached to the membrane.

 

 

Structures of the HIN Domain:DNA Complexes Reveal Ligand Binding and Activation Mechanisms of the AIM2 Inflammasome and IFI16 Receptor

Tengchuan Jin, Andrew Perry, Jiansheng Jiang, Patrick Smith, James A. Curry, et al.
Immunity 20 Apr 2012; 36(4):561–571    http://dx.doi.org/10.1016/j.immuni.2012.02.014

Figure thumbnail fx1
Highlights
  • Electrostatic attraction underlies innate dsDNA recognition by the HIN domains
  • Both OB folds and the linker between them engage the dsDNA backbone
  • An autoinhibited state of AIM2 is activated by DNA that liberates the PYD domain
  • DNA serves as an oligomerization platform for the inflammasome assembly

 

Summary

Recognition of DNA by the innate immune system is central to antiviral and antibacterial defenses, as well as an important contributor to autoimmune diseases involving self DNA. AIM2 (absent in melanoma 2) and IFI16 (interferon-inducible protein 16) have been identified as DNA receptors that induce inflammasome formation and interferon production, respectively. Here we present the crystal structures of their HIN domains in complex with double-stranded (ds) DNA. Non-sequence-specific DNA recognition is accomplished through electrostatic attraction between the positively charged HIN domain residues and the dsDNA sugar-phosphate backbone. An intramolecular complex of the AIM2 Pyrin and HIN domains in an autoinhibited state is liberated by DNA binding, which may facilitate the assembly of inflammasomes along the DNA staircase. These findings provide mechanistic insights into dsDNA as the activation trigger and oligomerization platform for the assembly of large innate signaling complexes such as the inflammasomes.

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long noncoding RNAs

Larry H. Bernstein, MD, FCAP, Curator

LPBI

UPDATED 3/17/2020

What are lncRNAs?

Advances in RNA sequencing technologies have revealed the complexity of our genome. Non-coding RNAs make up the majority (98%) of the transcriptome, and several different classes of regulatory RNA with important functions are being discovered. Understanding the significance of this RNA world is one of the most important challenges facing biology today, and the non-coding RNAs within it represent a gold mine of potential new biomarkers and drug targets.
lncRNA sequences

Long non-coding RNAs (lncRNAs) are a large and diverse class of transcribed RNA molecules with a length of more than 200 nucleotides that do not encode proteins (or lack > 100 amino acid open reading frame). lncRNAs are thought to encompass nearly 30,000 different transcripts in humans, hence lncRNA transcripts account for the major part of the non-coding transcriptome. lncRNA discovery is still at a preliminary stage. There are many specialized lncRNA databases, which are organized and centralized throughRNAcentral.

lncRNAs can be transcribed as whole or partial natural antisense transcripts (NAT) to coding genes, or located between genes or within introns. Some lncRNAs originate from pseudogenes (Milligan & Lipovich, 2015). lncRNAs may be classified into different subtypes (Antisense, Intergenic, Overlapping, Intronic, Bidirectional, and Processed) according to the position and direction of transcription in relation to other genes (Peschansky & Wahlestedt, 2014, Mattick & Rinn, 2015).
lncRNA expression

Gene expression profiling and in situ hybridization studies have revealed that lncRNA expression is developmentally regulated, can be tissue- and cell-type specific, and can vary spatially, temporally, or in response to stimuli. Many lncRNAs are expressed in a more tissue-specific fashion and with greater variation between tissues compared to protein-coding genes (Derrien et al., 2012).

In general, the expression level of lncRNA is at least one order of magnitude below that of mRNA. Many lncRNAs are located exclusively in the nucleus, but some are cytoplasmic or are located in both nucleus and cytoplasm.
lncRNA functions

To date, very few lncRNAs have been characterized in detail. However, it is clear that lncRNAs are important regulators of gene expression, and lncRNAs are thought to have a wide range of functions in cellular and developmental processes. lncRNAs may carry out both gene inhibition and gene activation through a range of diverse mechanisms, adding yet another layer of complexity to our understanding of genomic regulation. It is estimated that 25 – 40% of coding genes have overlapping antisense transcription, so the impact of lncRNAs on gene regulation is not to be underestimated.

Figure 1

https://www.exiqon.com/ls/PublishingImages/Figures/lncRNA-s.gif

Overview of some of the functions of long non-coding RNA. (Click for a larger image) lncRNAs are involved in gene regulation through a variety of mechanisms. The process of transcription of the lncRNA itself can be a marker of transcription and the resulting lncRNA can function in transcriptional regulation or in chromatin modification (usually via DNA and protein interactions) both in cis and in trans. lncRNAs can bind to complementary RNA and affect RNA processing, turnover or localization. The interaction of lncRNA with proteins can affect protein function and localization as well as facilitate formation of riboprotein complexes. Some lncRNAs are actually precursors for smaller regulatory RNAs such as microRNAs or piwi RNAs. Figure modified from Wilusz et al. Genes Dev. 2009. 23: 1494-1504. PMID: 19571179.

 

lncRNA mechanisms of gene regulation

lncRNAs are not defined by a common mode of action, and can regulate gene expression and protein synthesis in a number of different ways (Figure 1). Some lncRNAs are relatively highly expressed, and appear to function as scaffolds for specialized subnuclear domains. lncRNA possess secondary structures which facilitate their interactions with DNA, RNA and proteins. lncRNA may also bind to DNA or RNA in a sequence-specific manner. Gene regulation may occur in cis (e.g. in close proximity to the transcribed lncRNA) or in trans (at a distance from the transcription site). In the case of chromatin modulation, the effect of lncRNA is typically gene-specific, exerted at a local level (in cis) however regulation of chromatin can also occur in trans.

A few lncRNAs have had their functions experimentally defined and have been shown to be involved in fundamental processes of gene regulation including:

  • Chromatin modification and structure
  • Direct transcriptional regulation
  • Regulation of RNA processing events such as splicing, editing, localization, translation and turnover/degradation
  • Post-translational regulation of protein activity and localization
  • Facilitation of ribonucleoprotein (RNP) complex formation
  • Modulation of microRNA regulation
  • Gene silencing through production of endogenous siRNA (endo-siRNA)
  • Regulation of genomic imprinting

It has recently been attempted to categorize the various types of molecular mechanisms that may be involved in lncRNA function. lncRNAs may be defined as one or more of the following five archetypes:

  • The Signal archetype: functions as a molecular signal or indicator of transcriptional activity.
  • The Decoy archetype: binds to and titrates away other regulatory RNAs (e.g. microRNAs) or proteins (e.g. transcription factors).
  • The Guide archetype: directs the localization of ribonucleoprotein complexes to specific targets (e.g. chromatin modification enzymes are recruited to DNA).
  • The Scaffold archetype: has a structural role as platform upon which relevant molecular components (proteins and or RNA) can be assembled into a complex or spatial proximity.
  • The Enhancer archetype: controls higher order chromosomal looping in an enhancer-like model.

lncRNA and disease

With such a wide range of functions, it is not surprising that lncRNA play a role in the development and pathophysiology of disease. Interestingly, genome wide association studies have demonstrated that most disease variants are located outside of protein-coding genes.

lncRNAs have been found to be differentially expressed in various types of cancer including leukemia, breast cancer, hepatocellular carcinoma, colon cancer, and prostate cancer. Key oncogenes and tumor suppressors including PTEN and KRAS are now known to be regulated by corresponding lncRNA pseudogenes which also act as competing endogenous RNAs (ceRNAs) or microRNA sponges (Poliseno et al., 2010, Johnsson et al., 2013). This highlights the important role that lncRNAs play in oncogenesis.

Other diseases where lncRNAs are dysregulated include cardiovascular diseases, neurological disorders and immune-mediated diseases and genetic disorders. One of the first lncRNA to be discovered was the Xist lncRNA which plays an important role in X chromosome inactivation (Penny et al., 1996), an extreme case of genomic imprinting. lncRNAs are present at almost all imprinted loci, arguing for an important role for lncRNAs in this form of epigenetic regulation.

lncRNAs represent a gold mine of potential new biomarkers and drug targets, as well as a step change in the way we understand mechanisms of disease.
The challenges of studying lncRNA

Only a relatively small proportion of lncRNAs have so far been investigated and although we can start to classify different types of lncRNA functions, we are still far from being able to predict the function of new lncRNAs. This is mainly due to the fact that unlike protein-coding genes whose sequence motifs are indicative of their function, lncRNA sequences are not usually conserved and they don’t tend to contain conserved motifs. Other differences between lncRNA and mRNA are summarized in Table 1.

The main challenges of working with lncRNA are the fact that they can be present in very low amounts (typically an order of magnitude lower than mRNA expression levels), can overlap with coding transcripts on both strands and are often restricted to the nucleus.

Table 1

mRNA lncRNA
Tissue-specific expression Tissue-specific expression
Form secondary structure Form secondary structure
Undergo post-transcriptional processing, i.e. 5’cap, polyadenylation, splicing Undergo post-transcriptional processing, i.e. 5’cap, polyadenylation, splicing
Important roles in diseases and development Important roles in diseases and development
Protein coding transcript Non-protein coding, regulatory functions
Well conserved between species Poorly conserved between species
Present in both nucleus and cytoplasm Many predominantly nuclear, others nuclear and/or cytoplasmic
Total 20-24,000 mRNAs Currently ~30,000 lncRNA transcripts, predicted 3-100 fold of mRNA in number
Expression level: low to high Expression level: very low to moderate

Similarities and differences (dark) between mRNA and lncRNA

 

ncRNA discovery and profiling using Next Generation Sequencing

Expression profiling is one way to start to uncover the function of lncRNA. Identifying lncRNAs that are differentially expressed during development or in particular situations can shed light on their potential functions. Alternatively, looking for lncRNAs and protein-coding genes whose expression is correlated, can perhaps indicate co-regulation or related functions.

Whole transcriptome RNA sequencing is the method of choice for comprehensive lncRNA expression profiling, including the discovery of novel lncRNAs. Whole transcriptome sequencing enables the characterization of all RNA transcripts, including both the coding mRNA and non-coding RNA larger than 170 nucleotides in length, regardless of whether they are polyadenylated or not.

Exiqon offers a comprehensive whole transcriptome NGS Service including everything from RNA isolation to the final report including advanced data analysis and interpretation.
Advantages of LNA™-enhanced research tools for lncRNA

Exiqon offer a broad range of sensitive and specific tools specifically designed to address the challenges faced when investigating lncRNA expression and function. Exiqon’s tools are based on the Locked nucleic acid (LNA™) technology. LNA™ is a class of high-affinity RNA analogs that exhibit unprecedented thermal stability when hybridized to a complementary DNA or RNA strand. Hence, LNA™ enables superior sensitivity and specificity in any hybridization-based approach. We continue to use the LNA™ technology to develop new and innovative ways to improve our understanding of lncRNAs in this rapidly developing field.

Functional analysis of lncRNAs has been revolutionized by the development of Antisense LNA™ GapmeRs which enable efficient silencing of lncRNA both in vitro and in vivo. Exiqon also offer tools to investigate lncRNA function in other ways, for example using LNA™ oligos to block interactions between lncRNA and DNA, RNA or proteins. ExiLERATE LNA™ qPCR assays have been developed to enable robust detection of even low abundance and challenging lncRNAs by qPCR. Precise subcellular localization of lncRNAs can be studied using LNA™ probes for in situ hybridization.
Silencing lncRNA to disrupt their function

One strategy to study the function of lncRNA is to silence them using specific and potent antisense oligonucleotides (Antisense LNA™ GapmeRs).

The nuclear localization of many lncRNAs has meant that siRNA approaches to knockdown lncRNA, have met with limited success. The double-stranded siRNA duplex has difficulty crossing the nuclear membrane and the passenger strand (non-targeting sequence) of the duplex can often elicit its own effect, confounding interpretation of results.

Antisense LNA™ GapmeRs overcome this challenge by enabling highly efficient RNase H mediated silencing of all lncRNA. RNase H is present both in the cytoplasm and in the nucleus and it has been shown that LNA™ Gapmers offer significantly better knockdown of nuclear targets than siRNA mediated silencing. In addition, the single stranded LNA™ GapmeRs are an advantage for lncRNAs that are transcribed as antisense transcripts to coding genes because there is no second strand that could compromise specificity.

Antisense LNA™ GapmeRs show high potency in a broad range of tissues in vivo when administered systemically without formulation in animal models. This makes LNA™ GapmeRs very promising antisense drugs for lncRNA targets in the future.
Studying lncRNA interactions with DNA, RNA and proteins

The fact that the molecular mechanism of lncRNAs often relies on sequence specific interaction with DNA, RNA or proteins means that it is possible to design highly specific LNA™-oligonucleotides that can be used to inhibit these interactions and thereby reveal the details of how lncRNAs function. Please contact us and our experts can help you with the design of custom LNA™ oligonucleotides for studying lncRNA interactions.
lncRNA analysis by qPCR

Short, high affinity, LNA™-enhanced qPCR primers offer an advantage for the detection of low abundance targets. In addition, the use of LNA™ to adjust primer melting temperature provides greater flexibility in primer design which is important for qPCR analysis of overlapping transcripts. The ExiLERATE LNA™ qPCR System offers a sophisticated primer design tool combined with highly sensitive and specific qPCR assays for any RNA target.

ExiLERATE LNA™ qPCR assays are ideal to monitor the efficiency of LNA™ GapmeR-mediated RNA knockdown. Validated LNA™ qPCR primer sets are available to detect the lncRNA targeted by Antisense LNA™ GapmeR positive controls.

ExiLERATE LNA™ qPCR primer sets also provide a convenient way to validate RNA sequencing data. Our advanced online design algorithm can design LNA™ qPCR assays for novel lncRNA transcripts, isoforms or splice variants. LNA™qPCR assays for multiple lncRNAs can easily be designed using the batch mode function in our online design algorithm.
Subcellular localization of lncRNA expression by in situ hybridization

Understanding the subcellular localization of a lncRNA is important information when starting to hypothesize the potential functions that the lncRNA may be performing. LNA™-enhanced probes for in situ hybridization have increased affinity for their target sequence and offer increased sensitivity and increased signal to noise ratio, which is important for detection of rare targets such as lncRNA.

 

UPDATED 3/17/2020

From the journal Science :

Coding function of “noncoding” RNAs

Lian-Huan WeiJunjie U. Guo

Science  06 Mar 2020:
Vol. 367, Issue 6482, pp. 1074-1075
DOI: 10.1126/science.aba6117

 

Summary

High-throughput RNA sequencing studies have revealed pervasive transcription of the human genome, which generates a variety of long noncoding RNAs (lncRNAs) that have no apparent protein-coding functions (1). Subsequent studies that globally monitor translation have similarly identified numerous translation events outside of canonical protein-coding sequences (24), suggesting pervasive translation of the transcriptome. However, only a few examples of functional peptides encoded by RNA regions previously thought to be noncoding have been reported to regulate distinct biological processes (59). On page 1140 of this issue, Chen et al. (10) provide evidence for an expanded repertoire of functional peptides encoded by lncRNAs and other “untranslated” RNA regions.

The researchers sequenced ribosome-protected messenger RNA fragments (RFPs) to identify global translated open reading frames (ORFs).  RFPs are considered noncanonical ORFs and found in many lncRNAs and untranslated regions of RNA.  Therefore Chen et al. used genome-wide loss of function screens to assess noncanonical ORFs affect on cell growth.  They filtered RFPs obtained from several human cell types and identified over 5000 previously unannotated ORFs which also include many variants if canonical ORFs, upstream ORFs in 5′ UTRs, and ORFs within transcripts that were annotated as lncRNAs.

Using CRISPR-Cas9, Chen et al. disrupted 2353 unannotated ORFs and identified over 400 RFPs that promoted cell growth in human  leukemic cells and stem cells.  However there were only a few lncRNAs, when disrupted, showed consistent effects on growth suggesting noncoding functions of the remaining lncRNA loci.  Many of the lncRNA ORFs encoded for small peptides.  These microproteins present a challenge to identify such proteins that don’t have much evolutionary conservation.

This noncanonical translation has been linked to many neurological diseases such as short tandem repeats diseases such as fragile X sydrome and other polyglutamate diseases such as Huntington’s Chorea.

References cited within this paper include

1. I. Ulitsky, D. P. Bartel, Cell154, 26 (2013).
2. A. A. Bazzini et al., EMBO J. 33, 981 (2014).
3. N.T. Ingolia et al., Cell Rep. 8, 1365 (2014).
4. Z. Ji, R. Song, A. Regev, K. Struhl, eLife 4, e08890 (2015).
5. D. M. Anderson et al., Cell160, 595 (2015).
6. T. Kondo et al., Nat. Cell Biol. 9, 660 (2007).
7. A. Pauli et al., Science 343, 1248636 (2014).
8. E. G. Magny et al., Science 341, 1116 (2013).
9. S. R. Starck et al., Science 351, aad3867 (2016).
10. J. Chen et al., Science 367, 1140 (2020).
11. M. Guttman, P. Russell, N. T. Ingolia, J. S. Weissman, E. S. Lander, Cell154, 240 (2013).
12. T.G. Johnstone, A. A. Bazzini, A. J. Giraldez, EMBO J. 35, 706 (2016).
13. W. F. Doolittle, T. D. Brunet, S. Linquist, T. R. Gregory,
Genome Biol.Evol. 6, 1234 (2014).
14. F. B. Gao, J. D. Richter, D. W. Cleveland, Cell171, 994 (2017).
15. M. G. Kearse et al., Mol. Cell 62, 314 (2016).

Other articles of note on lncRNAs on this Online Open Access Journal Include

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Pathology Insights

Larry H Bernstein, MD, FCAP, Curator

LPBI

 

Predicting the Prognosis of Lung Cancer: The Evolution of Tumor, Node and Metastasis in the Molecular Age—Challenges and Opportunities

Ramón Rami-Porta; Hisao Asamura; Peter Goldstraw

Transl Lung Cancer Res. 2015;4(4):415-423.

http://www.medscape.com/viewarticle/852315

 

 

The tumor, node and metastasis (TNM) classification of malignant tumors was proposed by Pierre Denoit in the mid-20th century to code the anatomic extent of tumors. Soon after, it was accepted by the Union for International Cancer Control and by the American Joint Committee on Cancer, and published in their respective staging manuals. Till 2002, the revisions of the TNM classification were based on the analyses of a database that included over 5,000 patients, and that was managed by Clifton Mountain. These patients originated from North America and almost all of them had undergone surgical treatment. To overcome these limitations, the International Association for the Study of Lung Cancer proposed the creation of an international database of lung cancer patients treated with a wider range of therapeutic modalities. The changes introduced in the 7th edition of the TNM classification of lung cancer, published in 2009, derived from the analysis of an international retrospective database of 81,495 patients. The revisions for the 8th edition, to be published in 2016, will be based on a new retrospective and prospective international database of 77,156 patients, and will mainly concern tumor size, extrathoracic metastatic disease, and stage grouping. These revisions will improve our capacity to indicate prognosis and will make the TNM classification more robust. In the future the TNM classification will be combined with non-anatomic parameters to define prognostic groups to further refine personalized prognosis.

Introduction

Obvious as it may seem, it is important that the readers of this article keep in mind that the tumor, node and metastasis (TNM) classification of lung cancer is no more and no less than a system to code the anatomic extent of the disease. Therefore, by definition, the TNM classification does not include other elements that, while they can help improve our capacity to prognosticate the disease for a given patient, are unrelated to the anatomy of the tumor, i.e., parameters from blood analysis, tumor markers, genetic signatures, comorbidity index, environmental factors, etc. Prognostic indexes combining the TNM classification and other non-anatomic parameters are called, by consensus between the Union for International Cancer Control (UICC) and the American Joint Committee on Cancer (AJCC), prognostic groups to differentiate them from the anatomic stage groupings.

The TNM classification of lung cancer is applied to all histopathological subtypes of non-small cell carcinoma, to small cell carcinoma and to typical and atypical carcinoids. It is governed by general rules[1–3] (Table 1) that apply to all malignancies classified with this system, and by site-specific rules applicable to lung cancer exclusively.[4] There also are recommendations and requirements issued with the objective to classify tumors in a uniform way when their particular characteristics do not fit in the basic rules.[4]

The three components of the classification have several categories that are defined by different descriptors. For lung cancer, those for the T component are based on tumor size, tumor location and involved structures; those for the N, on the absence, presence and location of lymph node metastasis; and those for the M, on the absence, presence and location of distant metastasis. There are optional descriptors that add information on the local aggressiveness of the tumor (differentiation grade, perineural invasion, vascular invasion and lymphatic permeation) all of which have prognostic relevance;[5–8] assess the intensity of the investigation to determine the stage (certainty factor); and assess the residual tumor after therapy (residual tumor).

The TNM classification was developed by Pierre Denoit in a series of articles published from 1943 to 1952. It was soon adopted by the UICC that published brochures covering several anatomical sites, the lung being included in 1966. Two years later, the UICC published the first edition of the TNM Classification of Malignant Tumors and agreements were reached with the AJCC, created in 1959 as the American Joint Committee for Cancer Staging and End Results Reporting, to consult each other to avoid publication of differing classifications. Since then, the UICC and the AJCC have been responsible for updating and revising the TNM classifications of malignant tumors with the participation of national TNM committees of several countries and taking into account the published reports on the topic. The second to sixth editions of the UICC manual on the TNM Classification of Malignant Tumors and the first to sixth editions of the AJCC Staging Manual included classifications for lung cancer that had been informed by a progressively enlarging database initially collected by Mountain, Carr and Anderson, and subsequently managed by Mountain. Their database originally contained a little over 2,000 patients, but it had grown to more than 5,000 by the time the fifth edition of the TNM classification for lung cancer was published in 1997. The sixth edition was published in 2002 with no modifications.[9]

While the fifth edition of the classification was being printed, the International Workshop on Intrathoracic Staging took place in London, United Kingdom, in October 1996, sponsored by the International Association for the Study of Lung Cancer (IASLC).[10] At that meeting, in the presence of Dr. Mountain, the limitations of the database that had been used to revise the TNM classification for lung cancer were openly discussed. In essence, it was considered that, while the database consisted of a relatively large number of patients, all of them originated from the United States of America, and, therefore, the staging system could not really be called ‘international’, as it was called at that time; and, although all tumors had clinical and pathological classifications, the majority had been treated surgically. So, the database was thought not to be representative of the international community, as there were no patients from other countries; or of the current clinical practice, as there were no patients treated with other therapies. Therefore, an agreement was reached to issue a worldwide call to build a really international database of lung cancer patients treated by all therapeutic modalities. This required the constitution of an International Staging Committee that was approved and given a small amount of funding, to pump-prime, by the IASLC Board in 1998. Subsequently substantial financial support was secured by an unrestricted grant from Eli-Lilly. Cancer Research And Biostatistics (CRAB), a not-for-profit biosciences statistical center in Seattle, was appointed to collect, manage and analyze the new database. The proprietors and managers of known databases were subsequently summoned to attend a series of preparatory meetings to identify potential contributors to the IASLC international database for the purpose of revising the TNM classification of lung cancer.

The Future of the TNM Classification

The TNM classification of lung cancer is the most consistent and solid prognosticator of the disease, but it does not explain the whole prognosis because prognosis is multifactorial. In addition to the anatomic extent of the tumor, patient and environmental factors also count. Prognosis also is dynamic, as it may be different at the time of diagnosis, after treatment or at recurrence.[71] In the TNM classification, tumor resection plays an important role as it defines pathological staging and may modify the prognostic assessment based on clinical staging. Other than that, the TNM classification does not include blood analyses, tumor markers, genetic characteristic of the tumor or environmental factors that may account for the differences in survival among similar tumors in different geographic areas.

In order to make progress to indicate a more personalized prognosis, instead of a prognosis based on cohorts of patients with tumors of similar anatomic extent, the IASLC Staging and Prognosis Factors Committee decided to expand its activities to the study of non-anatomic prognostic factors. Therefore, in the third phase of the IASLC Lung Cancer Staging Project, the activities of the committee will be directed to further refine the TNM classification and to find available factors that can be combined with tumor staging to define prognostic groups. To some extent, this already was done with the analyses of the database used for the 7th edition. Prognostic groups with statistically significant differences were defined by combining anatomic tumor extent and very simple clinical variables, such as performance status, gender, and age. These prognostic groups were defined for clinically and pathologically staged tumors, and for small-cell and non-small cell lung cancers.[22,23]

The database used for the 8th edition includes several non-anatomical elements related to the patient, the tumor and the environment that may help refine prognosis at clinical and pathological staging.[69]Due to the limitations of the previous databases, future revisions of the TNM classification will need to be more balanced in terms of therapeutic modalities, and better populated with patients from underrepresented geographical areas, such as Africa, India, Indonesia, North, Central and South America, and South East Asia. The data contributed in the future will have to be complete regarding the TNM descriptors, and preferably prospective. The more robust the TNM, the more important its contribution to the prognostic groups.

To achieve all of the above, international collaboration is essential. Those interested in participating in this project should send an email expressing their interest to information@crab.org, stating ‘IASLC staging project’ in the subject of the email. The IASLC Staging and Prognostic Factors Committee has been very touched by the overwhelming generosity of colleagues around the world who have contributed cases to inform the 7th and the 8th editions of the TNM classification of lung cancer. We continue to count on their collaboration to further revise future editions and to define prognostic groups that will eventually allow a more personalized indication of prognosis.

MicroRNAs in the Pathobiology of Sarcomas

Anne E Sarver; Subbaya Subramanian

Lab Invest. 2015;95(9):987-984

http://www.medscape.com/viewarticle/852145

 

Sarcomas are a rare and heterogeneous group of tumors. The last decade has witnessed extensive efforts to understand the pathobiology of many aggressive sarcoma types. In parallel, we have also begun to unravel the complex gene regulation processes mediated by microRNAs (miRNAs) in sarcomas and other cancers, discovering that microRNAs have critical roles in the majority of both oncogenic and tumor suppressor signaling networks. Expression profiles and a greater understanding of the biologic roles of microRNAs and other noncoding RNAs have considerably expanded our current knowledge and provided key pathobiological insights into many sarcomas, and helped identify novel therapeutic targets. The limited number of sarcoma patients in each sarcoma type and their heterogeneity pose distinct challenges in translating this knowledge into the clinic. It will be critical to prioritize these novel targets and choose those that have a broad applicability. A small group of microRNAs have conserved roles across many types of sarcomas and other cancers. Therapies that target these key microRNA-gene signaling and regulatory networks, in combination with standard of care treatment, may be the pivotal component in significantly improving treatment outcomes in patients with sarcoma or other cancers.

Sarcomas are a heterogenous group of tumors that account for ~200 000 cancers worldwide each year (~1% of all human malignant tumors); however, they represent a disproportionately high 15% of all pediatric malignant tumors.[1,2] Sarcomas comprise over 50 subtypes that can broadly be classified into bone and soft-tissue sarcomas that are generally based on the cell and/or tissue type.[3] The vast majority of sarcomas fall into the soft-tissue group, primarily affecting connective tissues such as muscle (smooth and skeletal), fat, and blood vessels. Bone sarcomas are relatively rare, representing only ~20% of all diagnosed sarcomas (~0.2% of all cancers). Even within a specific subtype, sarcomas are highly heterogenous making them diagnostically and therapeutically challenging. Several sarcoma types are genetically characterized by chromosomal translocations or DNA copy number alterations, both of which are used as diagnostic markers.[2,4,5]

The four main types of bone sarcomas are defined by their histology, cell of origin (when known), clinical features, and site distribution—osteosarcoma, Ewing’s sarcoma, chondrosarcoma, and chordoma. The most common primary bone malignancy, osteosarcoma, predominantly affects children and young adults and is characterized by undifferentiated bone-forming proliferating cells.[6] Ewing’s sarcoma, another aggressive pediatric malignancy, usually arises in growing bone and is genetically characterized by a fusion of EWS–FLI1 oncoproteins that act as gain-of-function transcriptional regulators.[7] Chondrosarcoma is itself a heterogenous group of malignant bone tumors arising from the malignant transformation of cartilage-producing cells, frequently with mutations in IDH1/2 and COL2A1.[8,9] Chordoma is an aggressive, locally invasive cancer that typically arises from bones in the base of the skull and along the spine. It is characterized, in part, by its abnormal expression of transcription factor T, which is normally only expressed during embryonic development or in the testes.[10]

Soft-tissue sarcomas are also primarily defined by their histology, cell of origin, and, in some cases, by characteristic genetic translocation events. Rhabdomyosarcoma is a malignant skeletal-muscle derived tumor comprised of two main histological subtypes, embryonal and alveolar, is one of the most common childhood soft-tissue sarcomas, accounting for 6–8% of all pediatric tumors.[11] Liposarcoma is the most common soft-tissue cancer overall, accounting for 20% of adult sarcoma cases. It originates in deep-tissue fat cells and is characterized primarily by amplification of the 12q chromosomal region.[12] Other common soft-tissue sarcomas include angiosarcomas, fibrosarcomas, gastrointestinal stromal tumors, and synovial sarcomas, each with their own unique genetic signature.

Ever since the discovery of oncogenes, the primary emphasis in cancer research has been on understanding the role of proteins and protein-coding genes. However, the percent of the genome dedicated to coding genes is small compared with noncoding regions. The last decade has seen a surge of interest in these noncoding regions with small noncoding RNAs such as microRNAs (miRNAs) gaining particular prominence. These small RNAs have critical roles in tumor formation and progression. Understanding their roles in sarcoma will lead to new therapeutic targets and diagnostic biomarkers, opening the door to a greater understanding of the molecular mechanisms of all cancers.

miRNAs are evolutionarily conserved, small, noncoding RNA molecules of 18–24 nucleotides in length at maturity that can control gene function through mRNA degradation, translational inhibition, or chromatin-based silencing mechanisms.[13] Each miRNA can potentially regulate hundreds of targets via a ‘seed’ sequence of ~5–8 nucleotides at the 5′ end of the mature miRNA. miRNAs bind to complementary sequences in the 3′-untranslated regions (3′-UTRs) of target mRNA molecules, leading to either translational repression or transcriptional degradation.[14] The short seed sequence length and relatively low stringency requirement for these miRNA–3′-UTR interactions allow a single miRNA to potentially regulate hundreds of genes.[15] Small changes in the expression level of a few miRNAs can therefore have a dramatic biological impact, particularly when dysregulated. miRNA expression profiles can be used to distinguish between closely related soft-tissue sarcoma subtypes and may provide a more consistent diagnosis than histological inspection.[16–18]

miRNAs have critical roles in the majority of canonical cellular signaling networks and their dysregulation is implicated in many cancers including breast cancer, colon cancer, gastric cancer, lung cancer, and sarcomas.[19,20] Dysregulation of miRNA expression may result from a variety of factors, including abnormal cellular stimuli, genetic mutations, epigenetic alterations, copy number variations, and chromosomal fusions. Because miRNAs act as critical regulator molecules in a variety of signaling pathways and regulatory networks, their dysregulation can be amplified across the entire signaling network.[21–24] Selected miRNAs and targets that have critical regulatory roles in sarcoma and other cancers are summarized in Table 1 .
The p53 signaling pathway is one of the most highly studied cellular signaling networks. It actively induces apoptosis in response to DNA damage and oncogene activation and is therefore a key tumor suppressor pathway.[25] Germline mutations in TP53 are strongly associated with the development of soft-tissue sarcomas, osteosarcoma, and are the underlying cause of Li–Fraumeni Syndrome, a familial clustering of early-onset tumors including sarcomas.[26,27] It is estimated that over 50% of human tumors harbor a TP53 mutation but over 80% of tumors have dysfunctional p53 signaling.[28,29] It is only within the last 10 years that researchers have started uncovering the roles of miRNAs in mediating p53’s activity and resulting pro-apoptotic signals (Figure 1). miRNA dysregulation could be a key factor in the ~30% of tumors with dysfunctional p53 signaling that lack an apparent TP53 mutation.


Figure 1.

http://img.medscape.com/article/852/145/852145-fig1.jpg

p53–miRNA interaction network. p53 interacts with the Drosha complex and promotes the processing of pri-miRNA to pre-miRNA. Although p53 directly or indirectly regulates hundreds of miRNAs, for clarity, only selected cancer-relevant miRNAs are shown. miRNAs and proteins in red are upregulated by p53. miRNAs and proteins in green are downregulated by p53. miRNAs in gray are not known to be directly regulated by p53, they are included because they target p53 regulators MDM2 and/or MDM4. miRNA, microRNA.

Like other transcription factors, p53 exerts its function primarily through transcriptional regulation of target genes that contain p53 response elements in their promoters. p53 also regulates the post-transcriptional maturation of miRNAs by interacting with the Drosha processing complex, promoting the processing of primary miRNAs to precursor miRNAs.[30] In addition to protein-coding genes, many miRNA genes also contain p53 regulatory sites in their promoter regions. Large-scale screens have revealed many different miRNAs directly regulated by p53 including miR-22-3p, miR-34a, miR-125a/b, miR-182, and miR-199a-3p.[31] Some of these miRNAs, such as miR-34a and miR-199a-3p, function themselves as tumor suppressors via the regulation of genes involved in cell cycle, cell proliferation, and even of itself.[32–34] Although some p53-targeted miRNAs form a feedback loop, translationally and transcriptionally inhibiting the TP53 gene (e.g., miR-22-3p, miR-34a, and miR-125b), others target, or are predicted to target, p53 repressors such as MDM2 and/or MDM4 (miR-199a-3p, miR-661).[31,33,35,36] It is impossible to fully understand the regulation of the p53 signaling network without considering the role of these miRNAs.

miR-34a has emerged as a critical and conserved member of the p53 signaling pathway. miR-34a is downregulated in osteosarcoma tumor samples and, in conjunction with other miRNAs, regulates p53-mediated apoptosis in human osteosarcoma cell lines.[32,33,37] The gene encoding miR-34a contains a conserved p53-binding site and is upregulated in response to cellular damage in a p53-dependent manner.[37,38] Protein-coding members of the p53 signaling pathway are well-liked targets for anticancer therapeutic development efforts and miRNAs may prove equally effective. In a preclinical model of lung cancer, therapeutic delivery of a miR-34a mimic specifically downregulated miR-34a-target genes and resulted in slower tumor growth. When combined with a siRNA targeting Kras, this small RNA combination therapy resulted in tumor regression.[39] miRNAs such as miR-34a, miR-125b, and miR-199a-3p also mediate p53’s regulation of other key signaling pathways such as the IGF-1/PI3K/AKT/mTOR signaling network. Activation of the AKT network due to downregulation of PTEN (a negative regulator of AKT) by miR-21 or miR-221 or by alternate activation of AKT is a common mechanism underlying many different types of cancer.[40–43] The induction of cell growth, migration, invasion, and metastasis resulting from the upregulation of either miR-21 or miR-221 is seen across different tumor types.[41,44–50] Dysregulation of these miRNAs is a common factor in sarcomas and other tumors. Understanding their mechanisms of action in sarcoma could lead to broadly useful cancer therapeutics.

In prospective analyses that could be models for other sarcoma studies with sufficient numbers of patient samples, Thayanithy et al[19] and Maire et al[23] each analyzed collections of osteosarcoma tissues and compared them with either normal bone or osteoblasts. They each found a set of consistently downregulated miRNAs localized to the 14q32 region.[19,23] Targeting predictions performed by Thayanithy et al[19] identified a subset of four miRNAs as potential regulators of cMYC. One of the many roles of cMYC is to promote the expression of the miR-17–92 family, a known oncogenic cluster that has been observed to be highly expressed in many cancer types including osteosarcoma, leiomyosarcoma, and alveolar rhabdomyosarcoma.[51–57] Restoring the expression of the four 14q32 miRNAs increased apoptosis of SAOS-2 cells, an effect that was attenuated either by overexpression of a cMYC construct lacking the 3′UTR or by ectopic expression of the miR-17–92 cluster.[19] Although the 14q32 region is dysregulated across many different cancer types, this pattern of dysregulation appears to be a hallmark of osteosarcoma, which is particularly interesting due to the heterogenous nature of osteosarcomas and provides an extremely attractive common therapeutic target.

One particular challenge with these types of expression profiling studies is that the cell-of-origin for a particular sarcoma subtype may not be definitely established. Another challenge is the scarcity of patient samples, particularly for the rare sarcoma subtypes. As a result, there have only been a limited number of studies designed to comprehensively profile miRNA expression in various sarcoma subtypes and to compare those expression profiles with the corresponding normal tissues or cell lines. These studies were reviewed recently in Drury et al[20] and Subramanian and Kartha.[58]

Owing to the scarcity of frozen sarcoma tissue samples, it is tempting to study sarcoma cells in vitro, using either primary or immortalized cell cultures. Studies performed in culture are less expensive and more accessible; however, the cell lines used must be chosen with care and may not truly represent the tumors. Any results derived from cultured cells must be interpreted with caution and validated in vivo when possible. A tumor cell’s microenvironment has a profound effect on gene expression and cell metabolism and culturing for even short periods of time can result in large changes in gene/miRNA expression.[59] Three-dimensional cultures can provide more physiological relevant in vitro models of individual tumors (eg, spheroid cultures) or multi-layered epithelial tissues (eg, organotypic cultures using extracellular matrix proteins, fibroblasts, and/or artificial matrix components) vs the previous standard two dimensional culture model.[60,61]

Complicating the analysis of these miRNA expression changes is the fact that many miRNAs showing differential expression in multiple different studies do not have a consistent direction of change and/or a consistent role (tumor suppressor vs tumor promoter). This likely reflects both random chance observational differences and different tissue biology reflected in different regulatory networks. Elucidation of the regulatory roles played by miRNAs in these networks in their appropriate biological contexts may provide suitable upstream targets for more effective treatment of sarcomas. Recent advances in sequencing and downstream bioinformatics techniques provide the tools to efficiently examine these questions.

For two decades, microarray gene chips containing synthetic oligonucleotides whose sequences are designed to be representative of thousands of genes have allowed researchers to perform simultaneous expression analysis of thousands of RNA transcripts in a single reaction.[62–65] Gene expression profiling has been used to characterize and classify a wide range of sarcomas, in some cases providing a diagnostic resolution more accurate than histological examination.[66–72] With the advent of high-throughput RNA-Seq, sarcoma researchers are now able to prospectively analyze the differential expression of small RNAs, such as miRNAs, without prior knowledge of their sequence.[73,74] RNA-Seq also allows for the prospective identification of novel genomic rearrangements resulting from gene fusions or premature truncations that may be of particular interest to cancer researchers.[75,76] These data are highly quantitative and digital in nature, allowing for a dynamic range that is theoretically only limited by the sequencing depth and approaches the estimated range within the cell itself.[77] Marguerat and Bähler[78] provide a basic overview of the different RNA-Seq technologies and their differences from array-based technologies.[78]

Several groups have taken advantage of these technologies to create miRNA expression profiles for a number of different sarcomas in an effort to find both common sarcoma oncomirs and to discover unique miRNA signatures that could be used in diagnosis, prognosis, and novel therapeutic development. Renner et al[18] used a microarray-based miRNA screen, followed by qRT-PCR verification, to analyze the expression of 1146 known miRNAs across a collection of 76 primary soft-tissue sarcoma samples representing eight different subtypes and across a panel of 15 sarcoma cell lines. In addition to identifying overrepresented miRNAs synovial sarcomas (miR-200 family) and liposarcomas (miR-9) compared with other sarcomas and adipose tissue, respectively, their results revealed a high degree of co-expression of 63 miRNAs clustering in the chromosomal region 14q32.[18] The most comprehensive sarcoma miRNA data set has been published by Sarver et al[79] who profiled miRNA expression in over 300 sarcoma primary tumor tissue samples representing 22 different sarcoma types. These data form the basis for the web-accessible comprehensive Sarcoma microRNA Expression Database (SMED) database, which has tools that allows users to query specific sarcoma types and/or specific miRNAs.[79]

Integrative miRNA–mRNA analysis using a tool such as Ingenuity Pathway Analysis (Qiagen) or GeneSpring (Agilent) allows for more biologically relevant results by highlighting miRNA–mRNA pairs that are linked not only by predicted targeting interactions but whose expression levels are inversely correlated (i.e., as miRNA expression increases one would expect the target mRNA levels to decrease). For example, out of 177 differentially expressed miRNAs in osteosarcoma cell lines vs normal bone, an integrated miRNA–mRNA analysis highlighted two particularly interesting miRNA/mRNA pairs (miR-9/TGFBR2 and miR-29/p85α regulatory subunit of PI3K) that were dysregulated.[44]

It is important to note that the general consensus is that there is often no single ‘correct’ method to analyze miRNA expression data. Different experimental and bioinformatics techniques may reveal different aspects in the data that can be further investigated and experimentally validated. All of these experiments, whether performed at the bench or systems biology, contribute to our greater understanding of sarcoma biology and the central role of dysregulated miRNA–gene networks as drivers of tumor formation and progression.

miRNAs are part of a larger family of noncoding RNAs including long noncoding RNAs (lncRNAs) and competing endogenous RNAs (ceRNAs) that deserve to be evaluated for therapeutic potential in sarcomas with broader applicability to other cancer types. Just like miRNAs, lncRNAs are widely expressed in tissue-specific patterns that are highly disrupted in cancer.[80] As their name implies, ceRNAs compete for their common miRNA targets and influence their expression, which has an indirect effect on the protein-coding genes, such as PTEN, regulated by those miRNAs.[81,82] We have just begun to unravel the role of lncRNAs and ceRNAs in cancer development and progression but recent results hint at yet another layer of complexity and genetic control in tumor biology.

The lessons learned from carcinomas, leukemias, and lymphomas will be helpful in understanding the pathobiology of sarcomas and the insights gained from sarcoma biology may form the foundation for therapeutics to treat a wide range of other cancers. Recent studies have shown miRNAs are very stable in blood serum and plasma, and extensive efforts are underway to develop circulating miRNA-based diagnostic and prognostic markers. Major technical challenges in developing circulating miRNA-based markers still need to be addressed, including standardization of pre-analytical, analytical, and post-analytical methods for effective reproducibility. For example, miR-16, which is used in the normalization of miRNA expression in serum/plasma is also found in red blood cells; thus, any hemolysis during sample collection could significantly affect the downstream expression data analysis.

Cancers do not exist in isolation inside the body and extensive research has been performed on how tumor-derived proteins adapt their microenvironment to provide more favorable conditions for tumor growth and development. Recent studies have shown that miRNAs also have a major role in modulating tumor microenvironment. Although most miRNAs are found inside the cell, a significant number of miRNAs are encapsulated in exosomes that can be used as a delivery system to send miRNAs from one cell to another, allowing tumor cells to modulate gene expression in surrounding tissues.[83,84] Exosome and miRNA-mediated cross talk between sarcoma tumor cells and the surrounding stromal cells is a new and exciting avenue of research and the potential for novel therapeutics is high.

Sarcomas are a diverse collection of rare cancers with proportionally limited resources for research and development of novel treatments. It is therefore crucial that potential therapeutic targets are prioritized and novel therapeutic agents carefully selected for clinical trials to succeed. Extensive studies in preclinical models will be required; however, there are also challenges in the development of appropriate in vitro and in vivo model systems that accurately reflect the different sarcoma types. Sarcomas, such as osteosarcoma, leiomyosarcoma, and angiosarcoma are very heterogeneous in nature, making it unlikely that therapies targeting specific genomic mutations will be successful. Even if specific targets were to be identified it would still be a challenge to develop clinical trials based on the small number of patients harboring those specific mutations. Coordinated efforts such as the Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/) and its associated preclinical and clinical trial consortiums will help unravel novel miRNA–mRNA interactions and their significance as potential therapeutic targets.

Targeting common miRNA–gene oncogenic or tumor suppressor networks goes after the common denominator underlying many of these cancers. Key regulatory molecules in sarcoma are highly likely to have similar roles in leukemias and lymphomas, for instance, and vice versa. For example, oncogenic activation of STAT3 strongly promotes the expression of miR-135b in lymphoma, resulting in increased angiogenesis and tumor growth.[85] miR-135b is widely overexpressed in sarcomas and STAT3 may be having a similar transcriptional regulatory role, indicating that STAT3 inhibitors could be an effective supplemental therapy in sarcomas.[86] Interestingly, p53 promotes the transcription of miR-125b, which can directly target both STAT3 and p53 transcription. This finely balanced regulatory network is frequently dysregulated in osteosarcoma and Ewing’s sarcoma.[87,88] In retinoblastoma, STAT3 activation is associated with upregulation of the miR-17-92 cluster via a positive feedback loop and inhibition of STAT3-suppressed retinoblastoma proliferation, providing further evidence that STAT3 may be an attractive therapeutic target in many cancers.[89] The dysregulation of key signaling molecules such as the p53 and STAT3 along with their associated signaling networks are a common feature across most cancer types implying that advances in understanding of sarcoma biology may be highly impactful in more frequently occurring solid tumors and lymphomas.

Certain miRNAs appear to be common players across many types of sarcomas and other cancers and their dysregulation contributes to the development of the hallmarks of cancer (Figure 2). miR-210, a key modulator of many downstream pathways involved in the hypoxic response, is upregulated under hypoxic conditions in most solid tumors, including soft-tissue sarcomas, osteosarcoma, renal cancer, and breast cancer.[90] A recent meta-analysis demonstrated that the elevated expression of miR-210 is a prognostic indicator for disease-free, progression-free, and relapse-free survival in a variety of cancer patients.[91] Perhaps the most consistently upregulated miRNA across all tumor types is the anti-apoptotic miR-21, which directly targets the tumor suppressor PDCD4.[92] Levels of miR-21 correlate with cancer progression and patient prognosis.[93]
Figure 2.

http://img.medscape.com/article/852/145/852145-thumb2.png

Conserved miRNA-tumor suppressor signaling networks in cancer. These miRNAs and tumor suppressors are involved in other network and signaling pathway interactions, such as the p53 signaling network; this figure highlights selected critical conserved pathways.

 

Human Papillomavirus Oncogenic mRNA Testing for Cervical Cancer Screening

Jennifer L. Reid, PhD; Thomas C. Wright Jr, MD; Mark H. Stoler, MD; Jack Cuzick, PhD; Philip E. Castle, PhD; Janel Dockter; Damon Getman, PhD; Cristina Giachetti, PhD

Am J Clin Pathol. 2015;144(3):473-483.

http://www.medscape.com/viewarticle/850740

 

Objectives: This study determined the longitudinal clinical performance of a high-risk human papillomavirus (HR-HPV) E6/E7 RNA assay (Aptima HPV [AHPV]; Hologic, San Diego, CA) compared with an HR-HPV DNA assay (Hybrid Capture 2 [HC2]; Qiagen, Gaithersburg, MD) as an adjunctive method for cervical cancer screening.

Methods: Women 30 years or older with a negative result for intraepithelial lesions or malignancy cytology (n = 10,860) positive by AHPV and/or HC2 assays and randomly selected women negative by both assays were referred to colposcopy at baseline. Women without baseline cervical intraepithelial neoplasia (CIN) grade 2 or higher (CIN2+) continued into the 3-year follow-up.

Results: The specificity of AHPV for CIN2 or lower was significantly greater at 96.3% compared with HC2 specificity of 94.8% (P < .001). Estimated sensitivities and risks for detection of CIN2+ were similar between the two assays. After 3 years of follow-up, women negative by either human papillomavirus test had a very low risk of CIN2+ (<0.3%) compared with CIN2+ risk in women with positive AHPV results (6.3%) or positive HC2 results (5.1%).

Conclusions: These results support the use of AHPV as a safe and effective adjunctive cervical cancer screening method.

Introduction

Cervical cancer is one of the most frequent cancers in women worldwide, accounting for approximately 530,000 new cases and 275,000 deaths annually.[1] Countries with well-organized screening programs using conventional Papanicolaou (Pap) stain cytology have experienced substantially reduced mortality from the disease in the past 5 decades.[2–4] Despite this advance, the relatively low sensitivity and reproducibility of both conventional Pap smear and liquid-based cytology screening methods have prompted investigation into identifying adjunctive methods with Pap cytology for improving detection of cervical neoplasia.[5–9]

Infection with 14 high-risk human papillomavirus (HR-HPV) genotypes (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68) is associated with almost all cases of cervical precancer, defined as cervical intraepithelial neoplasia (CIN) grade 2 (CIN2), grade 3 (CIN3), and cancer.[10] Addition of HR-HPV nucleic acid testing to a cervical cytology screening regimen offers higher sensitivity and negative predictive value (NPV) for detection of cervical precancer and cancer compared with cytology alone, especially in older women.[11–15] For this reason, HR-HPV nucleic acid testing is recommended as an adjunctive test to cytology to assess the presence of HR-HPV types in women 30 years of age or older.[16] In this context, HR-HPV testing guides patient management by identifying women at elevated risk for CIN2 or higher (CIN2+) but, importantly, also reassures women who are negative for HR-HPV of their extremely low cancer risk.[17–19]
First-generation HR-HPV molecular tests used for adjunctive cervical cancer screening function by detecting viral genomic DNA in cellular samples from the uterine cervix. However, because the presence of HR-HPV in the female genital tract is common and often transient in nature,[20,21] and most cervical HPV infections resolve without becoming cancerous,[22,23] HR-HPV DNA-based test methods yield only moderate specificity for detection of high-grade cervical disease.[12,24] This leads to unnecessary follow-up and referral of patients to colposcopy, increasing the physical and emotional burdens on patients and elevating health care costs.

A test approved by the US Food and Drug Administration (FDA) for detection of HR-HPV E6/E7 messenger RNA (mRNA) (Aptima HPV [AHPV]; Hologic, San Diego, CA) has shown higher specificity with similar sensitivity for detection of CIN2+ compared with HPV DNA-based tests in patients referred for colposcopy due to an abnormal Pap smear result as well as in a screening setting.[25–30] Expression of mRNA from viral E6 and E7 oncogenes is highly associated with the development of CIN,[31,32] and extensive investigation into the role of E6 and E7 oncoproteins in the human papillomavirus (HPV) life cycle has revealed that the expression of the corresponding oncogenes is necessary and sufficient for cell immortalization, neoplastic transformation, and the development of invasive cancer.[33–35]

To confirm and extend the previous evidence on the clinical utility of HR-HPV oncogenic mRNA testing in a US population-based setting, the clinical performance of AHPV was evaluated as an adjunctive method for cervical cancer screening in women aged 30 years or older with negative for intraepithelial lesions or malignancy (NILM) cytology results from routine Pap testing in a pivotal, prospective, multicenter US clinical study including 3 years of follow-up (the Clinical Evaluation of Aptima mRNA [CLEAR] study). We report herein the results from this study.

1 of 4

Figure 1.

Clinical evaluation of Aptima mRNA study participant disposition. aReasons for withdrawal: did not meet eligibility criteria (70); Pap volume insufficient for AHPV testing (117); specimen expired or unsuitable for testing (190); specimen lost (58); noncompliant site (320); other reasons (26). bReasons for withdrawal: collection site did not participate in follow-up (243); subject terminated participation (37); participant had hysterectomy (22); participant not eligible (17); participant treated prior to CIN2+ diagnosis (8); other reasons (4). AHPV, Aptima HPV (Hologic, San Diego, CA); ASC-US, atypical squamous cells of unknown significance; CIN2+, cervical intraepithelial neoplasia grade 2 or higher; HC2, Hybrid Capture 2 (Qiagen, Gaithersburg, MD); HPV, human papillomavirus; NILM, negative for intraepithelial lesions or malignancy; Pap, Papanicolaou test.

HPV Testing

Baseline PreservCyt specimens (1-mL aliquot) were tested with the AHPV (Hologic) on both the automated Tigris DTS System and Panther System. Results from the two systems were similar; Panther System results are presented here. AHPV is a target amplification assay that uses transcription-mediated amplification to detect the E6/E7 oncogene mRNA of 14 HR-HPV genotypes (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68).

 

HPV and Disease Prevalence

Cervical disease and HPV status are shown in Table 2 for the baseline evaluation and cumulatively after 3 years of follow-up. Of the 10,860 evaluable participants with NILM cytology at baseline, 512 were positive for AHPV, yielding a prevalence of 4.7% for HR-HPV E6/E7 oncogenic mRNA, whereas prevalence of HR-HPV DNA was 6.5% among 10,229 women with HC2 results. A total of 845 HPV RNA-positive or DNA-positive women and 556 randomly selected HPV-negative women were referred to colposcopy at baseline (Figure 1).

At baseline, the percentage of colposcopy attendance was similar between HPV-positive (62%, n = 526) and randomly selected HPV-negative (61%, n = 339) women with 29 cases of CIN1, nine cases of CIN2, eight cases of CIN3, and three cases of adenocarcinoma in situ (AIS) identified (Table 2). Four of the CIN2 cases and two of the AIS cases were identified based on an ECC biopsy specimen only.

In total, 6,271 women completed the 3-year follow-up with a known disease status (Table 2). Of these, 6,098 (97.2%) women had normal (negative) disease status, and 56 (0.9%) had low-grade lesions (CIN1). In addition to the 20 women with CIN2+ identified at baseline, 15 (0.2%) women had CIN2 and 12 (0.2%) women had CIN3 identified during follow-up, with two cases identified from an ECC biopsy specimen only.

Of the 27 women with CIN2+ identified during follow-up, two had CIN1 at baseline, with CIN3 identified during year 1. Ten women had no disease found at baseline, with five cases of CIN2+ identified during year 1, one case of CIN2+ identified during year 2, and four cases of CIN2+ identified during year 3. The remaining 15 women with CIN2+ identified during follow-up did not have a baseline colposcopy; among them, two cases of CIN2+ were identified during year 1, six cases of CIN2+ during year 2, and seven cases of CIN2+ during year 3.

AHPV Assay Performance

Baseline risk and prevalence estimates adjusted for verification bias are provided in Table 3. The prevalence of CIN2+ was 0.9% in the overall population. CIN2+ occurred in 4.5% (95% CI, 2.7%-7.4%) of women with positive AHPV results and in 0.6% (95% CI, 0.2%–1.9%) of women with negative AHPV results, yielding a relative risk of 7.5 (95% CI, 2.1–26.3). This indicates that women with a positive AHPV result are at significantly greater risk of CIN2+ than women with a negative AHPV result. The CIN2+ relative risk obtained for the HC2 test at baseline was similar (7.3; 95% CI, 1.6–33.5). For CIN3+ diagnosis, the overall prevalence was 0.4%. The AHPV relative risk was 24.9 (95% CI, 2.0–307.0), again with a similar relative risk for HC2 (21.0; 95% CI, 1.0–423.8).

Cumulative absolute and relative risks for AHPV and HC2 over the 3-year follow-up period for HPV-positive and HPV-negative women are shown in Table 4. Women with an HPV-negative result with either test had very low cervical disease risk after 3 years of follow-up (<0.3%). Comparatively, 5% to 6% of women with an HPV-positive result had CIN2+ and 3% to 4% had CIN3+, with overall cumulative absolute and relative risks slightly higher for the AHPV assay than for HC2. Younger women aged 30 to 39 years who were HPV positive had twice the prevalence of disease but a similar increase in relative risk of cervical disease compared with HPV-positive women 40 years and older (Table 4). Risk of cervical disease in HPV-negative women did not vary by age group.

Figure 2 and Figure 3 show the cumulative absolute risk of CIN2+ and CIN3+, respectively, by year according to AHPV or HC2 positivity status at baseline. Both assays show a similar trend, with consistent slightly higher risk for the AHPV assay each year.

Figure 2.

Cumulative absolute risk of cervical intraepithelial neoplasia grade 2 or higher (CIN2+) by year. AHPV, Aptima HPV (Hologic, San Diego, CA); HC2, Hybrid Capture 2 (Qiagen, Gaithersburg, MD).

Figure 3.

Cumulative absolute risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) by year. AHPV, Aptima HPV (Hologic, San Diego, CA); HC2, Hybrid Capture 2 (Qiagen, Gaithersburg, MD).

After 3 years of follow-up, the specificity of AHPV for CIN2 or lower was 96.3% (95% CI, 95.8%-96.7%), significantly greater (P < .001) compared with HC2 specificity of 94.8% (95% CI, 94.3%-95.4%) Table 5. AHPV specificity for CIN3 or lower (96.2%; 95% CI, 95.5%–96.5%) was also significantly greater (P < .001) than HC2 specificity (94.7%; 95% CI, 94.1%-95.2%). Estimated sensitivities for detection of CIN2+ and CIN3+ were similar between the two assays (P = .219 and P = 1.0, respectively). For detection of CIN2+, AHPV sensitivity was 55.3% (95% CI, 41.2%-68.6%), and HC2 sensitivity was 63.6% (95% CI, 48.9%-76.2%). For CIN3+ detection, AHPV sensitivity was 78.3% (95% CI, 58.1%-90.3%), and HC2 sensitivity was 81.8% (95% CI, 61.5%-92.7%) (Table 5).

Discussion

This study presents the results of a 3-year longitudinal evaluation of the AHPV assay as an adjunctive method for screening women 30 years and older who have NILM Pap cytology results. Consistent with previously published data,[28,29] these results demonstrate that HR-HPV oncogenic E6/E7 mRNA testing has a sensitivity similar to an HR-HPV DNA-based test for detection of CIN2+ and CIN3+ and slightly, but significantly, improved specificity compared with HR-HPV DNA testing for both end points. We found that use of AHPV as an adjunctive method for HPV-induced cervical disease screening provided disease detection capability similar to HC2 while reducing the false-positive rate (from 5.2% to 3.7%) relative to the HPV DNA-based test. Reduction of HPV detection in women without cervical disease minimizes the anxiety and burden associated with spurious positive HPV molecular test results in women with NILM cytology, decreases health care costs, and reduces unnecessary follow-up procedures, thereby improving the safety of cervical cancer screening (unnecessary colposcopy is considered a significant “harm” in the recent American Cancer Society guidelines[16]).

Importantly, we show that women with a NILM cytology result who also had a positive AHPV result are approximately 24 times more likely to have CIN2+ disease after 3 years than women with a negative AHPV result. This risk increased to approximately 68-fold for detection of CIN3+ disease. Similar but slightly lower risk estimates were obtained with HC2, demonstrating comparable accuracy of the AHPV and HC2 for identifying participants with CIN2+ and CIN3+ in this respect.

After 3 years of follow-up, women in this study who were HPV negative at baseline using any test method had very low risk for CIN2+ (<0.3%), a result similar to previously published studies with HC2.[42,43] These findings reinforce evidence from previous studies showing that HR-HPV nucleic acid testing should be performed as an adjunctive test to routine Pap for cervical cancer screening of women 30 years or older to increase sensitivity of disease detection.[28] Correspondingly, compared with annual cytology-only screening, this study supports longer screening intervals for women negative for both abnormal cytology and HPV E6/E7 mRNA, due to the high NPV and low risk of disease afforded by this screening algorithm for 3 years following a test-negative baseline visit. Extension of cervical cancer screening intervals following negative HPV and cytology test results in women 30 years or older is a key recommendation of current US screening guidelines from both the American Cancer Society and the US Preventive Services Task Force.[16]

Conversely, since the positive predictive value of any HPV test in women with NILM cytology is low, additional AHPV testing to detect persistent HR-HPV infection during follow-up care in women with an initial AHPV-positive result is likely a better option than direct referral to colposcopy. Alternatively, genotyping with referral for HPV 16– or HPV 18–positive women can optimize referral and minimize loss to follow-up.[44]

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RNAi – On Transcription and Metabolic Control

Writer and Curator: Larry H Bernstein, MD, FCAP

 

RNAi

This is the third contribution to a series on transcription and metabolic control. It reveals the enormous complexity in this emerging research.

 

mRNA, small RNAs, long RNAs, RNAi and DicAR

Aberrant mRNA translation in cancer pathogenesis
Pier Paolo Pandolfi
Oncogene (2004) 23, 3134–3137
http://dx.doi.org:/10.1038/sj.onc.1207618

As the molecular processes that control mRNA translation and ribosome biogenesis in the eukaryotic cell are extremely complex and multilayered, their deregulation can in principle occur at multiple levels, leading to both disease and cancer pathogenesis. For a long time, it was speculated that disruption of these processes may participate in tumorigenesis, but this notion was, until recently, solely supported by correlative studies. Strong genetic support is now being accrued, while new molecular links between tumor-suppressive and oncogenic pathways and the control of protein synthetic machinery are being unraveled. The importance of aberrant protein synthesis in tumorigenesis is further underscored by the discovery that compounds such as Rapamycin, known to modulate signaling pathways regulatory of this process, are effective anticancer drugs. A number of fundamental questions remain to be addressed and a number of novel ones emerge as this exciting field evolves.

 

mRNA Translation and Energy Metabolism in Cancer
I. Topisirovic and N. Sonenberg
Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXVI
http://dx.doi.org:/10.1101/sqb.2011.76.010785

A prominent feature of cancer cells is the use of aerobic glycolysis under conditions in which oxygen levels are sufficient to support energy production in the mitochondria (Jones and Thompson 2009; Cairns et al. 2010). This phenomenon, named the “Warburg effect,” after its discoverer Otto Warburg, is thought to fuel the biosynthetic requirements of the neoplastic growth (Warburg 1956; Koppenol et al. 2011) and has recently been acknowledged as one of the hallmarks of cancer (Hanahan and Weinberg 2011). mRNA translation is the most energy-demanding process in the cell (Buttgereit and Brand 1995).In mammalian cells it consumes >20% of cellular ATP, not considering the energy that is required for the biosynthesis of the components of the translational machinery (e.g., ribosome biogenesis; Buttgereit and Brand 1995). Control of mRNA translation plays a pivotal role in the regulation of gene expression (Sonenberg and Hinnebusch 2009). In fact, a recent study demonstrated that mammalian proteome is mostly governed at the mRNA translation level (Schwanhausser et al. 2011). Malfunction of mRNA translation critically contributes to human disease, including diabetes, heart disease, blood disorders, and, most notably, cancer (Fig. 1; Crozier et al. 2006; Narla and Ebert 2010; Silvera et al. 2010; Spriggs et al. 2010). The first account of changes in the translational apparatus in cancer dates back to 1896, showing enlarged and irregularly shaped nucleoli that are the site of ribosome biogenesis (Pianese 1896). Rapidly proliferating cancer cells have more ribosomes than normal cells.

Figure 1. Dysregulated mRNA translation plays a pivotal role in cancer. Malignant cells are characterized by enlarged nucleoli and a larger number of ribosomes than their normal counterparts. Mutations and/or altered expression of ribosomal proteins (e.g., RPS19, RPS 24), rRNA-modifying enzymes (e.g., dyskerin), translation initiation factors (e.g., eIF4E), or the initiator tRNA (tRNAiMet) result in malignant transformation. Signaling pathways whose dysfunction is frequent in cancer (e.g., MAPK, PI3K/AKT) affect mRNA translation. Perturbations in the translatome result in aberrant cellular growth, proliferation, and survival characteristic of tumorigenesis.

 

In stark contrast to normal cells, in cancer cells ribosomal biogenesis is uncoupled from cell proliferation (Stanners et al. 1979). Accordingly, cancer cells exhibit abnormally high rates of protein synthesis (Silvera et al. 2010). That ribosomal dysfunction plays a central role in cancer is further corroborated by the findings that genetic alterations, which encompass the components of the ribosome machinery (i.e., “ribosomopathies”), are characterized by elevated cancer risk (Narla and Ebert 2010).

mRNA translation is the most energy-consuming process in the cell and strongly correlates with cellular metabolic activity. Translation and energy metabolism play important roles in homeostatic cell growth and proliferation, and when dysregulated lead to cancer. eIF4E is a key regulator of translation, which promotes oncogenesis by selectively enhancing translation of a subset of tumor-promoting mRNAs (e.g., cyclins and c-myc). PI3K/AKT and mitogen-activated protein kinase (MAPK) pathways, which are strongly implicated in cancer etiology, exert a number of their biological effects by modulating translation. The PI3K/AKT pathway regulates eIF4E function by inactivating the inhibitory 4E-BPs via mTORC1, whereas MAPKs activate MAP kinase signal-integrating kinases 1 and 2, which phosphorylate eIF4E. In addition, AMP-activated protein kinase, which is a central sensor of the cellular energy balance, impairs translation by inhibiting mTORC1. Thus, eIF4E plays a major role in mediating the effects of PI3K/AKT, MAPK, and cellular energetics on mRNA translation.Figure 2. eIF4E is regulated by multiple mechanisms. The expression of eIF4E is regulated by several transcription factors (e.g., c-myc, hnRNPK, p53) and adenine-uracil-rich element binding proteins (i.e., HuR and AUF1). eIF4E is suppressed by 4E-BPs, which are regulated by mTORC1. MAP kinase signal integrating kinases 1 and 2 (MNKs) phosphorylate eIF4E.

 

Figure 3. Ras/MAPK and PI3K/AKT/mTORC1 regulate the activity of eIF4E. Various stimuli activate phosphoinositide-3-kinase (PI3K) through the receptor tyrosine kinases (RTKs). Upon activation, PI3K converts phosphatidylinositol 4,5-bisphosphate (PIP2) into phosphatidylinositol-3,4,5-triphosphate (PIP3). This reaction is reversed by PTEN. Phosphoinositide-dependent protein kinase 1 (PDK1) and AKT bind to PIP3 via their pleckstrin homology domains, which allows for the phosphorylation and activation of AKT by PDK1. In addition, the mammalian target of rapamycin complex 2 (mTORC2) modulates the activity of AKT by phosphorylating its hydrophobic motif. AKT phosphorylates tuberous sclerosis complex 2 (TSC2) at multiple sites, which results in its inhibition and consequent activation of Ras homolog enriched in brain (Rheb), which is a small GTPase that activates mTORC1. mTORC1 phosphorylates 4E-BPs leading to their dissociation from eIF4E. In addition to the PI3K/AKT pathway, the activity of mTORC1 is regulated by the serine/threonine kinase 11/LKB1/AMP-kinase (LKB1/AMPK) pathway, regulated in development and DNA damage response 1 (REDD1) and Rag GTPases in response to the changes in cellular energy balance, oxygen and amino acid availability, respectively. Ras and the MAPK pathways are activated by various stimuli through receptor tyrosine kinases (RTKs). In addition the MAPK pathway isactivatedthrough theGprotein–coupled receptors(GPCRs) and byproteinkinaseC (PKC;notshown).TheMAPK pathways encompass an initial GTPase-regulated kinase (MAPKKK), which activates an effector kinase (MAPK) via an intermediate kinase (MAPKK). In response to stimuli such as growth factors, hormones, and phorbol-esters, Ras GTPase stimulates Raf kinase (MAPKKK), which activates extracellular signal-regulated kinases 1 and 2 (ERK 1 and 2) via extracellular signal-regulated kinase activator kinases MEK1 and 2 (MAPKK). Cellular stresses, including osmotic shock, inflammatory cytokines, and UV light, activate p38 MAPKs via multiple mechanisms including Rac kinase (MAPKKK) and MKK3 and 6 (MAPKK). p38 MAPK and ERK activate the MAPK signal–integrating kinases 1 and 2 (MNK1/2), which phosphorylate eIF4E. Additional abbreviations are provided in the text.

 

Cancer Exosomes Perform Cell-Independent MicroRNA Biogenesis and Promote Tumorigenesis
Cancer Cell Nov, 2014; 26: 707–721.
http://dx.doi.org/10.1016/j.ccell.2014.09.005

Breast cancer cells secrete exosomes with specific capacity for cell-independent miRNA biogenesis, while normal cellderivedexosomes lack thisability. Exosomes derivedfrom cancer cellsand serum frompatients withbreast cancer contain the RISC loading complex proteins, Dicer, TRBP, and AGO2, which process pre-miRNAs into mature miRNAs. Cancer exosomes alter the transcriptome of target cells in a Dicer-dependent manner, which stimulate nontumorigenic epithelial cells to form tumors.This study identifies a mechanism whereby cancer cells impart an oncogenic field effect by manipulating the surrounding cells via exosomes. Presence of Dicer in exosomes may serve as biomarker for detection of cancer.


Dicers at RISC. The Mechanism of RNAi

Marcel Tijsterman and Ronald H.A. Plasterk
Cell, Apr 2014; 117:1–4

Figure 1. Model for RNA Silencing in Drosophila In an ordered biochemical pathway, miRNAs (left panel) and siRNAs (right panel) are processed from double-stranded precursor molecules by Dcr-1and Dcr-2, respectively, and stay attached to Dicer-containing complexes, which assemble into RISC. The degree of complementarity between the RNA silencing molecule (in red) and its cognate target determines the fate of the mRNA: blocked translation or immediate destruction.

Argonaute2 Cleaves the Anti-Guide Strand of siRNA during RISC Activation
Cell 2005; 123:621-629
http://www.cell.com/cgi/content/full/123/4/621/DC1/
Dicing and slicing- The core machinery of the RNA interference pathway
Scott C Hammond
FEBS Letters 579 (2005) 5822–5829
http://dx.doi.org:/10.1016/j.febslet.2005.08.079

Fig. 1. Domain organization of RNaseIII gene family. Three classes of RNaseIII genes are shown. The PAZ domain in Dm-Dicer-2 contains mutations in several residues required for RNA binding and may not be functional.

Fig. 2. Model for Dicer catalysis. The PAZ domain binds the 2 nt 30 overhang of a dsRNA terminus. The RNaseIII domains form a pseudo-dimer. Each domain hydrolyzes one strand of the substrate. The binding site of the dsRBD is not defined. The function of the helicase domain is not known.

Fig. 3. Biogenesis pathway of microRNAs. MicroRNA genes are transcribed by RNA polymerase II. The primary transcript is referred to as ‘‘primicroRNA’’. Drosha processing occurs in the nucleus. The resulting precursor, ‘‘pre-microRNA’’, is exported to the cytoplasm for Dicer processing. In a coordinated manner, the mature microRNA is transferred to RISC and unwound by a helicase. mRNA targets that duplex in the Slicer scissile site are cleaved and degraded, if the microRNA is loaded into an Ago2 RISC. Mismatched targets are translationally suppressed. All Ago family members are believed to function in translational suppression.

Fig. 4. Model for Slicer catalysis. The siRNA guide strand is bound at the 50 end by the PIWI domain and at the 30 end by the PAZ domain. The 50 phosphate is coordinated by conserved basic residues. mRNA targets are initially bound by the seed region of the siRNA and pairing is extended to the 30 end. The RNaseH fold hydrolyzes the target in a cation dependent manner. Slicer cleavage is measured from the 50 end of the siRNA. Product is released by an unknown mechanism and the enzyme recycles.

 

 

RNA interference (RNAi) is a biological process in which RNA molecules inhibit gene expression, typically by causing the destruction of specific mRNA molecules. Historically, it was known by other names, including co-suppression, post transcriptional gene silencing (PTGS), and quelling. Only after these apparently unrelated processes were fully understood did it become clear that they all described the RNAi phenomenon. Andrew Fire and Craig C. Mello shared the 2006 Nobel Prize in Physiology or Medicine for their work on RNA interference in the nematode worm Caenorhabditis elegans, which they published in 1998.

 

Two types of small ribonucleic acid (RNA) molecules – microRNA (miRNA) and small interfering RNA (siRNA) – are central to RNA interference. RNAs are the direct products of genes, and these small RNAs can bind to other specific messenger RNA (mRNA) molecules and either increase or decrease their activity, for example by preventing an mRNA from producing a protein. RNA interference has an important role in defending cells against parasitic nucleotide sequences – viruses and transposons. It also influences development.

 

The RNAi pathway is found in many eukaryotes, including animals, and is initiated by the enzyme Dicer, which cleaves long double-stranded RNA (dsRNA) molecules into short double stranded fragments of ~20 nucleotide siRNAs. Each siRNA is unwound into two single-stranded RNAs (ssRNAs), the passenger strand and the guide strand. The passenger strand is degraded and the guide strand is incorporated into the RNA-induced silencing complex (RISC). The most well-studied outcome is post-transcriptional gene silencing, which occurs when the guide strand pairs with a complementary sequence in a messenger RNA molecule and induces cleavage by Argonaute, the catalytic component of the RISC complex. In some organisms, this process spreads systemically, despite the initially limited molar concentrations of siRNA.
http://en.wikipedia.org/wiki/RNA_interference

 

http://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/ShRNA_Lentivirus.svg/481px-ShRNA_Lentivirus.svg.png

 

http://www.frontiersin.org/files/Articles/66078/fnmol-06-00040-HTML/image_m/fnmol-06-00040-g001.jpg
http://dx.doi.org:/10.3389/fnmol.2013.00040

The enzyme dicer trims double stranded RNA, to form small interfering RNA or microRNA. These processed RNAs are incorporated into the RNA-induced silencing.
MiRNA biogenesis and function. (A) The canonical miRNA biogenesis pathway is Drosha- and Dicer-dependent. It begins with RNA Pol II-mediated transcription..

 

Dicer Promotes Transcription Termination

Dicer Promotes Transcription Termination

Dicer Promotes Transcription Termination at Sites of Replication Stress to Maintain Genome Stability
Cell Oct 2014; 159(3): 572–583
http://dx.doi.org/10.1016/j.cell.2014.09.031

http://www.cell.com/cms/attachment/2019646604/2039684570/fx1.jpg

 

18-13 miRNA- protein complex ap-chap-18-pp-42-728

18-13 miRNA- protein complex ap-chap-18-pp-42-728

18-13 miRNA- protein complex (a) Primary miRNA transcript Translation blocked Hydrogen bond (b) Generation and function of miRNAs Hairpin miRNA miRNA Dicer …

http://image.slidesharecdn.com/ap-chap-18-pp-1229097198123780-1/95/ap-chap-18-pp-42-728.jpg?cb=1229090143

 

 

Identification and characterization of small RNAs involved in RNA silencing
FEBS Letters 579 (2005) 5830–5840
http://dx.doi.org:/10.1016/j.febslet.2005.08.009

Fig. 1. Small RNA cloning procedure. Outline of the small RNA cloning procedure. RNA is dephosphorylated (step 1) for joining the 30 adapter by T4 RNA ligase 1 in the presence of ATP (step 2). The use of a chemically adenylated adapter and truncated form of T4 RNA ligase 2 (Rnl2) allows eliminating the dephosphorylation step (step 4). If the RNA was dephosphorylated, it is re-phosphorylated (step 3) prior to 50 adapter ligation with T4 RNA ligase 1 and ATP (step 5). After 50 adapter ligation, a standard reverse transcription is performed (step 6). Alternatively, after 30 adapter ligation, the RNA is used directly for reverse transcription simultaneously with 50 adaptor joining (step 7). In this case, the property of reverse transcriptase to add non-templated cytidine residues at the 50 end of synthesized DNA is used to facilitate template switch of the reverse transcriptase to the 30 guanosine residues of the 50 adapter (SMART technology, Invitrogen). Abbreviations: P and OH indicate phosphate and hydroxyl ends of the RNA; App indicates 50 chemically adenylated adapter; L, 30 blocking group; CIP, calf alkaline phosphatase and PNK, polynucleotide kinase.

 

Transcriptional regulatory functions of nuclear long noncoding RNAs
Trends in Genetics, Aug 2014; 30(8):348-356
http://dx.doi.org/10.1016/j.tig.2014.06.001

Cis-acting lncRNAEnhancer-associated lncRNAIntergenic lncRNA

lncRNA

Promoter-associated lncRNA

Proximity transfer

Trans-acting lncRNA

 

Functional interactions among microRNAs and long noncoding RNAs
Sem Cell Dev Biol 2014; 34:9-14
http://dx.doi.org/10.1016/j.semcdb.2014.05.015
Genome-wide application of RNAi to the discovery of potential drug targets
FEBS Letters 579 (2005) 5988–599
http://dx.doi.org://10.1016/j.febslet.2005.08.015

Fig. 1. Schematic representation of gene silencing by an shRNA-expression vector. The shRNA is processed by Dicer. The processed siRNA enters the RNA-induced silencing complex (RISC), where it targets mRNA for degradation.

Fig. 2. Schematic representation of a transcription system for production of siRNA

Fig. 3. (A) Schematic representation of the proposed siRNA-expression system. Three or four C to U or A to G mutations are introduced into the sense strand. (B) Schematic representation of the discovery of a novel gene using an siRNA library.

 

Imperfect centered miRNA binding sites are common and can mediate repression of target mRNAs
Martin et al. Genome Biology 2014, 15:R51 http://genomebiology.com/2014/15/3/R51

 

 

 

 

Table 1 Number of inferred targets for each miRNA tested

miRNA Probes Transcripts Genes
miR-10a 2,206 5,963 1,887
miR-10a-iso 1,648 1,468 4,211
miR-10b 1,588 3,940 1,365
miR-10b-iso 963 2,235 889
miR-17-5p 1,223 2,862 1,137
miR-17-5p-iso 1,656 3,731 1,461
miR-182 2,261 6,423 2,008
miR-182-iso 1,569 4,316 1,444
miR-23b 2,248 5,383 1,990
miR-27a 2,334 5,310 2,069

Probes: number of probes significantly enriched in pull-downs compared to controls (5% FDR). Transcripts: number of transcripts to which those probes map exactly. Genes: number of genes from which those transcripts originate

Figure 2 Biotin pull-downs identify bone fide miRNA targets. (A) Volcano plot showing the significance of the difference in expression between the miR-17-5p pull-down and the mock-transfected control, for all transcripts expressed in HEK293T cells. Both targets predicted by TargetScan or validated previously via luciferase assay were significantly enriched in the pull-down compared to the controls. (B) Results from luciferase assays on previously untested targets predicted using TargetScan and uncovered using the biotin pull-down. The plot indicates mean luciferase activity from either the empty plasmid or from pMIR containing a miRNA binding site in the 3′ UTR, relative to a negative control. Asterisks indicate a significant reduction in luciferase activity (one-sided t-test; P<0.05) and error bars the standard error of the mean over three replicates. (C-E) Targets identified through PAR-CLIP or through miRNA over-expression studies show greater enrichment in the pull-down. Cumulative distribution of log fold-change in the pull-down for transcripts identified as targets by the indicated miRNA over-expression study or not. Red, canonical transcripts found to be miR-17-5p targets in the indicated study (Table S5 in Additional file 1); black, all other canonical transcripts; p, one-sided P-value from Kolmogorov-Smirnov test for a difference in distributions. (F) To confirm that our results were dependent on RISC association, cells were transfected with either single or double-stranded synthetic miRNAs, then subjected to AGO2 immunoprecipitation. The biotin pull-down was performed in the AGO2-enriched and AGO2-depleted fractions. (G-H) Quantitative RT-PCR revealed that, with double-stranded (ds) miRNA (G), four out of five known targets were enriched relative to input mRNA (*P≤0.05, **P<0.01, ***P<0.001) in the AGO2-enriched but not in the AGO2-depleted fractions, but this enrichment was not seen for the cells transfected with a single-stranded (ss) miRNA (H). The numbers on the x-axis correspond to those in Figure 2F. Error bars represent the standard error of mean (sem).

Figure 5 IsomiRs and canonical miRNAs target many of the same transcripts.

Hammerhead ribozymes in therapeutic target discovery and validation
Drug Disc Today 2009; 14(15/16): 776-783
http://dx.doi.org/10.1016/j.drudis.2009.05.003

Figure 1. Features of hammerhead ribozymes. A generic diagram of a hammerhead ribozyme bound to its target substrate: NUH is the cleavage triplet on target sequence, stems I and III are sites of the specific interactions between ribozyme and target, stem II is the structural element connecting separate parts of the catalytic core. Arrows represent the cleavage site, numbering system according to Hertel et al. [60].

hammerhead ribozyme

hammerhead ribozyme

https://www-ssrl.slac.stanford.edu/research/highlights_archive/ribozyme_fig1.jpg

 

Figure 1  Schematic (A) and ribbon (B) diagrams depicting the crystal structure of the full-length hammerhead ribozyme. The sequence and secondary structure

 

TABLE 1 Typical examples of successful applications of hammerhead ribozymes. Most of the data are derived from [10] and [11], the others are expressly specified.

  • Growth factors, receptors, transduction elements
  • Oncogenes, protoncogenes, fusion genes
  • Apoptosis, survival factors, drug resistance
  • Transcription factors
  • Extracellular matrix, matrix modulating factors
  • Circulating factors
  • Viral genome, viral genes

Figure 2.Target–ribozyme interactions. (a) As cheme of ribozyme binding to full substrate. The calculated energy of this binding ensures the formation of a stable complex. At the denaturating temperature, Tm, will allow this complex to survive to biological conditions. Conversely, after cleavage, binding energies calculated on single, (b) and (c), ribozyme arms are very low and no longer stable. These properties will ensure both the efficient release of cleavage fragments and the prevention of binding to unrelated targets. RNAs complementary to one binding arm only will not be bound or cleaved by the hammerhead catalytic sequence.

Figure 3. ‘Chemical omics’ approach. According to this target discovery strategy: (1) a first round of ‘omic’ study (proteomic, genomic, metabolomic, …) will enable the discovery of a set of (2) putative markers. A series of hammerhead ribozymes will then be prepared in order to target each marker. (4) A second ‘omic’ study round will be performed on (3) knocked down samples obtained after ribozymes administration. (5) A new series of markers will then be produced. An expanding analytical process of this type may be further repeated. Finally, a robust bioinformatic algorithm will make it possible to connect the different markers and draw new hypothetical links and pathways.

 

miRNA

ADAR Enzyme and miRNA Story
Sara Tomaselli, Barbara Bonamassa, Anna Alisi, et al.
Int. J. Mol. Sci. 2013, 14, 22796-22816;
http://dx.doi.org:/10.3390/ijms141122796

Adenosine deaminase acting on RNA (ADAR) enzymes convert adenosine (A) to inosine (I) in double-stranded (ds) RNAs. Since Inosine is read as Guanosine, the biological consequence of ADAR enzyme activity is an A/G conversion within RNA molecules. A-to-I editing events can occur on both coding and non-coding RNAs, including microRNAs (miRNAs), which are small regulatory RNAs of ~20–23 nucleotides that regulate several cell processes by annealing to target mRNAs and inhibiting their translation. Both miRNA precursors and mature miRNAs undergo A-to-I RNA editing, affecting the miRNA maturation process and activity. ADARs can also edit 3′ UTR of mRNAs, further increasing the interplay between mRNA targets and miRNAs. In this review, we provide a general overview of the ADAR enzymes and their mechanisms of action as well as miRNA processing and function. We then review the more recent findings about the impact of ADAR-mediated activity on the miRNA pathway in terms of biogenesis, target recognition, and gene expression regulation.

Figure 1. Structure of ADAR family proteins: ADAR1, ADAR2, and ADAR3. The ADAR enzymes contain a C-terminal conserved catalytic deaminase domain (DM), two or three dsRBDs in the N-terminal portion. ADAR1 full-length protein also contains a N-terminal Zα domain with a nuclear export signal (NES) and a Zβ domain, while ADAR3 has a  R-domain. A nuclear localization signal is also indicated.

 

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
Doron Betel, Anjali Koppal, Phaedra Agius, Chris Sander, Christina Leslie
Genome Biology 2010, 11:R90 http://genomebiology.com/2010/11/8/R90

microRNAs are a class of small regulatory RNAs that are involved in post-transcriptional gene silencing. These small (approximately 22 nucleotide) single-strand RNAs guide a gene silencing complex to an mRNA by complementary base pairing, mostly at the 3′ untranslated region (3′ UTR). The association of the RNAinduced silencing complex (RISC) to the conjugate mRNA results in silencing the gene either by translational repression or by degradation of the mRNA. Reliable microRNA target prediction is an important and still unsolved computational challenge, hampered both by insufficient knowledge of microRNA biology as well as the limited number of experimentally validated targets.

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
Human RISC – MicroRNA Biogenesis and Posttranscriptional Gene Silencing
Cell 2005; 123:631-640
http://dx.doi.org:/10.1016/j.cell.2005.10.022
Development of microRNA therapeutics
Eva van Rooij & Sakari Kauppinen
EMBO Mol Med (2014) 6: 851–864
http://dx.doi.org:/10.15252/emmm.20110089

MicroRNAs (miRNAs) play key regulatory roles in diverse biological processes and are frequently dysregulated in human diseases. Thus, miRNAs have emerged as a class of promising targets for therapeutic intervention. Here, we describe the current strategies for therapeutic modulation of miRNAs and provide an update on the development of miRNA-based therapeutics for the treatment of cancer, cardiovascular disease and hepatitis C virus (HCV) infection.

Figure 1. miRNA biogenesis and modulation of miRNA activity by miRNA mimics and antimiR oligonucleotides. MiRNA genes are transcribed by RNA polymerase II from intergenic, intronic or polycistronic loci to long primary miRNA transcripts (pri-miRNAs) and processed in the nucleus by the Drosha–DGCR8 complex to approximately 70 nt pre-miRNA hairpin structures. The most common alternative miRNA biogenesis pathway involves short intronic hairpins, termed mirtrons, that are spliced and debranched to form pre-miRNA hairpins. Pre-miRNAs are exported into the cytoplasm and then cleaved by the Dicer–TRBP complex to imperfect miRNA: miRNA* duplexes about 22 nucleotides in length. In the cytoplasm, miRNA duplexes are incorporated into Argonaute-containing miRNA induced silencing complex (miRISC), followed by unwinding of the duplex and retention of the mature miRNA strand in miRISC, while the complementary strand is released and degraded. The mature miRNA functions as a guide molecule for miRISC by directing it to partially complementary sites in the target mRNAs, resulting in translational repression and/or mRNA degradation. Currently, two strategies are employed to modulate miRNA activity: restoring the function of a miRNA using double-stranded miRNA mimics, and inhibition of miRNA function using single-stranded anti-miR oligonucleotides.

Figure 2. Design of chemically modified miRNA modulators. (A) Structures of chemical modifications used in miRNA modulators. A number of different sugar modifications are used to increase the duplex melting temperature (Tm) of anti-miR oligonucleotides. The20-O-methyl(20-O-Me), 20-O-methoxyethyl(20-MOE )and 20-fluoro(20-F) nucleotides are modified at the 20 position of the sugar moiety, whereas locked nucleic acid (LNA) is a bicyclic RNA analogue in which the ribose is locked in a C30-endo conformation by introduction of a 20-O,40-C methylene bridge. To increase nuclease resistance and enhance the pharmacokinetic properties, most anti-miR oligonucleotides harbor phosphorothioate (PS) backbone linkages, in which sulfur replaces one of the non-bridging oxygen atoms in the phosphate group. In morpholino oligomers, a six-membered morpholine ring replaces the sugar moiety. Morpholinos are uncharged and exhibit a slight increase in binding affinity to their cognate miRNAs. PNA oligomers are uncharged oligonucleotide analogues, in which the sugar–phosphate backbone has been replaced by a peptide-like backbone consisting of N-(2-aminoethyl)-glycine units. (B) An example of a synthetic double-stranded miRNA mimic described in this review. One way to therapeutically mimic a miRNA is by using synthetic RNA duplexes that harbor chemical modifications for improved stability and cellular uptake. In such constructs, the antisense (guide) strand is identical to the miRNA of interest, while the sense (passenger) strand is modified and can be linked to a molecule, such as cholesterol, for enhanced cellular uptake. The sense strand contains chemical modifications to prevent mi-RISC loading. Several mismatches can be introduced to prevent this strand from functioning as an anti-miR, while it is further left unmodified to ensure rapid degradation.The20-F modification helps to protect the antisense strand against exonucleases, hence making the guide strand more stable, while it does not interfere with mi-RISC loading. (C) Design of chemically modified anti-miR oligonucleotides described in this review. Antagomirs are30 cholesterol-conjugated,20-O-Me oligonucleotides fully complementary to the mature miRNA sequence with several PS moieties to increase their in vivo stability. The use of unconjugated 20-F/MOE-, 20-MOE- or LNA-modified anti-miR oligonucleotides harboring a complete PS backbone represents another approach for inhibition of miRNA function in vivo. The high duplex melting temperature of LNA-modified oligonucleotides allows efficient miRNA inhibition using truncated, high-affinity 15–16-nucleotide LNA/DNA anti-miR oligonucleotides targeting the 50 region of the mature miRNA. Furthermore, the high binding affinity of fully LNA-modified 8-mer PS oligonucleotides, designated as tiny LNAs, facilitates simultaneous inhibition of entire miRNA seed families by targeting the shared seed sequence.

Human MicroRNA Targets
Bino John, Anton J. Enright, Alexei Aravin, Thomas Tuschl,.., Debora S. Mark
PLoS Biol 2004; 2(11): e363  http://www.plosbiology.org

More than ten years after the discovery of the first miRNA gene, lin-4 (Chalfie et al. 1981; Lee et al. 1993), we know that miRNA genes constitute about 1%–2% of the known genes in eukaryotes. Investigation of miRNA expression combined with genetic and molecular studies in Caenorhabditis elegans, Drosophila melanogaster, and Arabidopsis thaliana have identified the biological functions of several miRNAs (recent review, Bartel 2004). In C. elegans, lin-4 and let-7 were first discovered as key regulators of developmental timing in early larval developmental transitions (Ambros 2000; Abrahante et al. 2003; Lin et al. 2003; Vella et al. 2004). More recently lsy-6 was shown to determine the left–right asymmetry of chemoreceptor expression (Johnston and Hobert 2003). In D. melanogaster, miR-14 has a role in apoptosis and fat metabolism (Xu et al. 2003) and the bantam miRNA targets the gene hid involved in apoptosis and growth control (Brennecke et al. 2003).

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 39 untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.

Figure 1. Target Prediction Pipeline for miRNA Targets in Vertebrates The mammalian (human, mouse, and rat) and fish (zebra and fugu) 39 UTRs were first scanned for miRNA target sites using position specific rules of sequence complementarity. Next, aligned UTRs of orthologous genes were used to check for conservation of miRNA– target relationships (‘‘target conservation’’) between mammalian genomes and, separately, between fish genomes. The main results (bottom) are the conserved mammalian and conserved fish targets, for each miRNA,as well as a smaller set of super-conserved vertebrate targets.   http://dx.doi.org:/10.1371/journal.pbio.0020363.g00
Figure 2. Distribution of Transcripts with Cooperativity of Target Sites and Estimated Number of False Positives Each bar reflects the number of human transcripts with a given number of target sites on their UTR. Estimated rate of false positives(e.g., 39%for2 targets) is given by the number of target sites predicted using shuffled miRNAs processed in a way identical to real miRNAs, including the use of interspecies conservation filter. http://dx.doi.org:/10.1371/journal.pbio.0020363.g002

Conserved Seed Pairing, Often improved an-Flanked by Adenosines, Indicates Thousands of Human Genes are MicroRNA Targets
Cell, Jan 2005; 120: 15–20
http://dx.doi.org:/10.1016/j.cell.2004.12.035

Integrated analysis of microRNA and mRNA expression. adding biological significance to microRNA target predictions.
Maarten van Iterson, Sander Bervoets, Emile J. de Meijer, et al.
Nucleic Acids Research, 2013; 41(15), e146
http://dx.doi.org:/10.1093/nar/gkt525

Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA–mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

Long non-coding RNA and microRNAs might act in regulating the expression of BARD1 mRNAs
Int J Biol & Cell Biol 2014; 54:356-367
http://dx.doi.org/10.1016/j.biocel.2014.06.018

 

Passenger-Strand Cleavage Facilitates Assembly of siRNA into Ago2-Containing RNAi Enzyme Complexes
Cell 2006; 123:607-620
http://dx.doi.org:/10.1016/j.cell.2006.08.044

 

RNAi- RISC Gets Loaded
Cell 2005; 123:543-553
http://dx.doi.org:/10.1016/j.cell.2005.11.006
RNAi- The Nuts and Bolts of the RISC Machine
Cell 2005; 122:17-20
http://dx.doi.org:/10.1016/j.cell.2005.06.023
Structural domains in RNAi
FEBS Letters 579 (2005) 5841–5849
http://dx.doi.org:/10.1016/j.febslet.2005.07.072

Fig. 1. A ‘‘Domain-centric’’ view of RNAi. (A) The conserved pathways of RNA silencing. The domain structure of each protein in (hypothetical) interaction with its RNA is shown. For clarity, the second column lists domains in order N- to C-terminal. Figures are not to scale. In brief, Drosha, an RNase III enzyme, and its obligate binding partner, Pasha recognize pri-mRNA loops, and cut these into 70 nt hairpin pre-miRNAs. Dicer utilizes a PAZ domain to sense the 30 2-nt overhang created, and further processes these, and dsRNAs into miRNAs and siRNAs. Argonaute binds the 50 end of guide RNAs via its PIWI domain, and the 30 end via a PAZ domain, yielding RISCs that effect RNA silencing through several mechanisms. A Viral protein, VP19 can suppress RNA silencing by sequestering siRNAs. (B) A summary of known siRNA structural biology. Listed by domain are solved structures, their protein/organism of origin, and ligands, where applicable. Also shown are PDB codes.

Fig. 2. Novel modes of RNA recognition. (A) A typical dsRBD: Xenopus binding protein A (1DI2). A RNA helix is modeled pink, and the protein is rendered in transparent electrostatic contours (blue is basic, red acidic). Note the interaction of helices along the major groove, and the position of helix 1. A second dsRBD protein is visible, in the lower right. (B) A dsRBD, Saccharomyces Rnt1P (1T4L), recognizes hairpin loops. A novel third helix (top) pushes helix one into the loop of a hairpin RNA. (C) 30-OH recognition by PAZ. Human Eif2c1 (1SI3) bound to RNA (pink) is shown. PAZ is green, with transparent electrostatic surface plot. The OB-fold (nucleotide binding fold) and the insertion domain are labeled. Note the glove-and-thumb like cleft they form, that the 30-OH is inserted into. A basic groove (blue) the RNA binds along outside the cleft is visible. (D) A close-up view of PAZ, as in C (surface not-transparent, slightly rotated). See white arrows for orientation, and location of 30-OH binding site. RNA is shown red in sticks. The terminal –OH is barely visible, buried in a cleft. It and the carbon it bonds have been colored yellow for clarity. (E) The PIWI domain (2BGG). Note the insertion of the 50P red (labeled) into the binding site. Its complimentary strand (pink) is not annealed to it, and the 30 overhang and first complimentary bases sit on the protein surface. (F) An enlarged view of (E), with protein in slate and RNA modeled as red sticks. The coordinated magnesium is a grey sphere, which is coordinated by the terminal carboxylate of the protein, protein side chains, and RNA phosphate oxygens. The 50 base stacks against a conserved Tyr. Several other sidechain contacts are shown.

Fig. 3. Argonaute/RISC. (A) P. furiosus Argonaute (PDB 1Z26). A color-guided key to the domains is presented. PAZ sits over the PIWI/N/MID bowl and active site. The liganding atoms for the catalytic metal are depicted as yellow balls for clarity. The tungstate binding site (50P surrogate) is shown as tan spheres. (B) A guide strand channel. Looking down from the PAZ domain towards the active site, Z-sections are clipped off. Colors of domains are as in the key in (A). Wrapping down along a basic cleft from the PAZ 30OH binding site (approximate position labeled), a RNA binding groove passes the active site (yellow), and runs down to the 50P binding site (tan balls). A second cleft running perpendicular to this one at its entry may accommodate target strand RNA. For more detail, and models of siRNA placed into the grooves, see [27,29].

Fig. 4. VP19 sequestration of siRNA. (A) CIRV VP19 (1RPU, RNA removed). Two monomers (blue and cyan) form an 8 strand, concave b-sheet with bracketing helices at the ends. (B) Tombus viral VP19 bound to siRNA (1 monomer shown). RNA strands are modeled as sticks, with one strand pink and one red. The bracketing helix places two tryptophans in position to stack over the terminal RNA bases. On the b-sheet surface, and Arg and a Lys interact with the phosphate backbone, and at the center of the RNA binding surface, a number of Ser and Thr mediate an extensive hydrogen bond network. Both the Trp brackets and RNA binding by an extended b-sheet are unique.

 

Small RNA asymmetry in RNAi- Function in RISC assembly and gene regulation
FEBS Letters 579 (2005) 5850–5857
http://dx.doi.org:/10.1016/j.febslet.2005.08.071

 

The role of the oncofetal IGF2 mRNA-binding protein 3 (IGF2BP3) in cancer
Seminars in Cancer Biol 2014; 29:3-12
http://dx.doi.org/10.1016/j.semcancer.2014.07.006

Table 1 – Target mRNAs of IGF2BP3.

Target cis-Element Regulation
CD44 3’ -utr Control of mRNA stability
IGF2 5’ -utr Translational control
H19 ncRNA Unknown
ACTB 3’ -utr Unknown
MYC CRD Unknown
CD164 Unknown Control of mRNA stability
MMP9 Unknown Control of mRNA stability
ABCG2 Unknown Unknown
PDPN 3’ -utr Control of mRNA stability
HMGA2 3’ -utr Protection from miR directed degradation
CCND1 3’ -utr translational control
CCND3 3’ -utr translational control
CCNG1 3’ -utr translationalcontrol

 

Targeting glucose uptake with siRNA-based nanomedicine for cancer therapy
Biomaterials 2015; 51:1-11
http://dx.doi.org/10.1016/j.biomaterials.2015.01.068
The therapeutic potential of RNA interference
FEBS Letters 579 (2005) 5996–6007
http://dx.doi.og:/10.1016/j.febslet.2005.08.004

Table 1 Companies developing RNAi therapeutics that includes cancer

Company name Primary areas of interest
Atugen AG Metabolic disease; cancer ocular disease; skin disease
Benitec Australia Limited Hepatitis C virus; HIV/AIDS; cancer; diabetes/obesity
Calando Pharmaceuticals Nanoparticle technology
Genta Incorporated Cancer
Intradigm Corporation Cancer; SARS; arthritis
Sirna Therapeutics, Inc. AMD; Hepatitis C virus; asthma; diabetes; cancer; Huntington s disease; hearing loss

 

The Noncoding RNA Revolution—Trashing Old Rules to Forge New Ones
Cell 2014; 157:77-94
http://dx.doi.org/10.1016/j.cell.2014.03.008

Figure 1. Noncoding RNAs Function in Diverse Contexts Noncoding RNAs function in all domains of life, regulating gene expression from transcription to splicing to translation and contributing to genome organization and stability. Self-splicing RNAs, ribosomes, and riboswitches function in both eukaryotes and bacteria. Archaea (not shown) also utilize ncRNA systems including ribosomes, riboswitches, snoRNPs, and CRISPR. Orange strands, ncRNA performing the action indicated; red strands, the RNA acted upon by the ncRNA. Blue strands, DNA. Triangle, small-molecule metabolite bound by a riboswitch. Ovals indicate protein components of an RNP, such as the spliceosome (white oval), ribosome (two purple subunits), or other RNPs (yellow ovals). Because of the importance of RNA structure in these ncRNAs, some structures are shown but they are not meant to be realistic.

 

miRNAs and cancer targeting

Table 1 of targets

miRNA Cancer type reference
NA GI cancer Current status of miRNA-targeting therapeutics and preclinical studies against gastroenterological carcinoma
NA Renal cell Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis
NA urothelial
cancer
A microRNA expression ratio defining the invasive phenotype in bladder tumors
miR-31 breast A Pleiotropically Acting MicroRNA, miR-31, inhibits breast cancer growth
miR-512-3p NSCLC Inhibition of RAC1-GEF DOCK3 by miR-512-3p contributes to suppression of metastasis in non-small cell lung cancer
miR-495 gastric Methylation-associated silencing of miR-495 inhibit the migration and invasion of human gastric cancer cells
microRNA-218 prostate microRNA-218 inhibits prostate cancer cell growth and promotes apoptosis by repressing TPD52 expression
MicroRNA-373 cervical cancer MicroRNA-373 functions as an oncogene and targets YOD1 gene in cervical cancer
miR-25 NSCLC miR-25 modulates NSCLC cell radio-sensitivity – inhibiting BTG2 expression
miR-92a cervical cancer miR-92a. upregulated in cervical cancer & promotes cell proliferation and invasion by targeting FBXW7
MiR-153 NSCLC MiR-153 inhibits migration and invasion of human non-small-cell lung cancer by targeting ADAM19
miR-203 melanoma miR-203 inhibits melanoma invasive and proliferative abilities by targeting the polycomb group gene BMI1
miR-204-5p Papillary thyroid miR-204-5p suppresses cell proliferation by inhibiting IGFBP5 in papillary thyroid carcinoma
miR-342-3p Hepato-cellular miR-342-3p affects hepatocellular carcinoma cell proliferation via regulating NF-κB pathway
miR-1271 NSCLC miR-1271 promotes non-small-cell lung cancer cell proliferation and invasion via targeting HOXA5
miR-203 pancreas Pancreatic cancer derived exosomes regulate the expression of TLR4 in dendritic cells via miR-203
miR-203 metastatic SCC Rewiring of an Epithelial Differentiation Factor, miR-203, to Inhibit Human SCC Metastasis
miR-204 RCC TRPM3 and miR-204 Establish a Regulatory Circuit that Controls Oncogenic Autophagy in Clear Cell Renal Cell Carcinoma
NA urologic MicroRNAs and cancer. Current and future perspectives in urologic oncology
NA RCC MicroRNAs and their target gene networks in renal cell carcinoma
NA osteoSA MicroRNAs in osteosarcoma
NA urologic MicroRNA in Prostate, Bladder, and Kidney Cancer
NA urologic Micro-RNA profiling in kidney and bladder cancers

 

Current status of miRNA-targeting therapeutics and preclinical studies against gastroenterological carcinoma
Shibata et al. Molecular and Cellular Therapies 2013, 1:5 http://www.molcelltherapies.com/content/1/1/5

Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis
Clinical Biochemistry 43 (2010) 150–158
http://dx.doi.org:/10.1016/j.clinbiochem.2009.07.020

A microRNA expression ratio defining the invasive phenotype in bladder tumors
Urologic Oncology: Seminars and Original Investigations 28 (2010) 39–48
http://dx.doi.org:/10.1016/j.urolonc.2008.06.006

A Pleiotropically Acting MicroRNA, miR-31, inhibits breast cancer growth
Cell 137, 1032–1046, June 12, 2009
http://dx.doi.org:/10.1016/j.cell.2009.03.047

Inhibition of RAC1-GEF DOCK3 by miR-512-3p contributes to suppression of metastasis in non-small cell lung cancer
Intl JBiochem & Cell Biol 2015; 61:103-114
http://dx.doi.org/10.1016/j.biocel.2015.02.005

Methylation-associated silencing of miR-495 inhibit the migration and invasion of human gastric cancer cells by directly targeting PRL-3
Biochem Biochem Res Commun 2014; 456:344-350
http://dx.doi.org/10.1016/j.bbrc.2014.11.083

microRNA-218 inhibits prostate cancer cell growth and promotes apoptosis by repressing TPD52 expression
Biochem Biophys Res Commun 2015; 456:804-809
http://dx.doi.org/10.1016/j.bbrc.2014.12.026

MicroRNA-373 functions as an oncogene and targets YOD1 gene in cervical cancer
BBRC 2015; xx:1-6
http://dx.doi.org/10.1016/j.bbrc.2015.02.138

miR-25 modulates NSCLC cell radio-sensitivity – inhibiting BTG2 expression
BBRC 2015; 457:235-241
http://dx.doi.org/10.1016/j.bbrc.2014.12.094

miR-92a. upregulated in cervical cancer & promotes cell proliferation and invasion by targeting FBXW7
BBRC 2015; 458:63-69
http://dx.doi.org/10.1016/j.bbrc.2015.01.066

MiR-153 inhibits migration and invasion of human non-small-cell lung cancer by targeting ADAM19
BBRC 2015; 456:381-385
http://dx.doi.org/10.1016/j.bbrc.2014.11.093

miR-203 inhibits melanoma invasive and proliferative abilities by targeting the polycomb group gene BMI1
BBMC 2015; 456: 361-366
http://dx.doi.org/10.1016/j.bbrc.2014.11.087

miR-204-5p suppresses cell proliferation by inhibiting IGFBP5 in papillary thyroid carcinoma
BBRC 2015; 457:621-627
http://dx.doi.org/10.1016/j.bbrc.2015.01.037

miR-342-3p affects hepatocellular carcinoma cell proliferation via regulating NF-κB pathway
BBRC 2015; 457:370-377
http://dx.doi.org/10.1016/j.bbrc.2014.12.119

miR-1271 promotes non-small-cell lung cancer cell proliferation and invasion via targeting HOXA5
BBRC 2015; 458:714-719
http://dx.doi.org/10.1016/j.bbrc.2015.02.033

Pancreatic cancer derived exosomes regulate the expression of TLR4 in dendritic cells via miR-203
Cell Immunol 2014; 292:65-69
http://dx.doi.org/10.1016/j.cellimm.2014.09.004

Rewiring of an Epithelial Differentiation Factor, miR-203, to Inhibit Human Squamous Cell Carcinoma Metastasis
Cell Reports 2014; 9:104-117
http://dx.doi.org/10.1016/j.celrep.2014.08.062

TRPM3 and miR-204 Establish a Regulatory Circuit that Controls Oncogenic Autophagy in Clear Cell Renal Cell Carcinoma
Cancer Cell Nov 10, 2014; 26: 738–753
http://dx.doi.org/10.1016/j.ccell.2014.09.015

MicroRNA in Prostate, Bladder, and Kidney Cancer
Eur Urol 2011; 59:671-681
http://dx.doi.org/10.1016/j.eururo.2011.01.044

Micro-RNA profiling in kidney and bladder cancers
Urologic Oncology: Seminars and Original Investigations 2007; 25:387–392
http://dx.doi.org:/10.1016/j.urolonc.2007.01.019

MicroRNAs and cancer. Current and future perspectives in urologic oncology
Urologic Oncology: Seminars and Original Investigations 2010; 28:4–13
http://dx.doi.org:/10.1016/j.urolonc.2008.10.021

MicroRNAs and their target gene networks in renal cell carcinoma
BBRC 2011; 405:153-156
http://dx.doi.org/10.1016/j.bbrc.2011.01.019

MicroRNAs in osteosarcoma
Clin Chim Acta 2015; 444:9-17
http://dx.doi.org/10.1016/j.cca.2015.01.025

 

Table 2. miRNA cancer therapeutics

 

 

  • miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients
    | PNAS | Nov 19, 2013; 110(47): 19160–19165
    http://www.pnas.org/cgi/doi/10.1073/pnas.1316991110The study of mRNA and microRNA (miRNA) expression profiles of cells and tissue has become a major tool for therapeutic development. The results of such experiments are expected to change the methods used in the diagnosis and prognosis of disease. We introduce surprisal analysis, an information-theoretic approach grounded in thermodynamics, to compactly transform the information acquired from microarray studies into applicable knowledge about the cancer phenotypic state. The analysis of mRNA and miRNA expression data from ovarian serous carcinoma, prostate adenocarcinoma, breast invasive carcinoma, and lung adenocarcinoma cancer patients and organ specific control patients identifies cancer-specific signatures. We experimentally examine these signatures and their respective networks as possible therapeutic targets for cancer in single cell experiments.

 

 

RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently,RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA  editing may be a potential target for therapeutic natural products

 

Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific.

 

We describe a novel approach to infer Probabilistic Mi RNA–mRNA  Interaction Signature (‘ProMISe’) from a single pair of miRNA–mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone.

Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related interactions. For example, ProMISe– BRCA network involves miR-155/183/21, which exhibits higher ProMISe coupled with coherently higher miRNA expression and lower target expression; oncomirs miR-221/222 in the ProMISe–THCA network engage with many downregulated target genes. Together, our probabilistic approach of integrating expression and sequence scores establishes a functional link between the aberrant miRNA and mRNA expression, which was previously under-appreciated due to the methodological differences.

 

 

 

 

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Introduction to Protein Synthesis and Degradation

Curator: Larry H. Bernstein, MD, FCAP

Updated 8/31/2019

 

Introduction to Protein Synthesis and Degradation

This chapter I made to follow signaling, rather than to precede it. I had already written much of the content before reorganizing the contents. The previous chapters on carbohydrate and on lipid metabolism have already provided much material on proteins and protein function, which was persuasive of the need to introduce signaling, which entails a substantial introduction to conformational changes in proteins that direct the trafficking of metabolic pathways, but more subtly uncovers an important role for microRNAs, not divorced from transcription, but involved in a non-transcriptional role.  This is where the classic model of molecular biology lacked any integration with emerging metabolic concepts concerning regulation. Consequently, the science was bereft of understanding the ties between the multiple convergence of transcripts, the selective inhibition of transcriptions, and the relative balance of aerobic and anaerobic metabolism, the weight of the pentose phosphate shunt, and the utilization of available energy source for synthetic and catabolic adaptive responses.

The first subchapter serves to introduce the importance of transcription in translational science.  The several subtitles that follow are intended to lay out the scope of the transcriptional activity, and also to direct attention toward the huge role of proteomics in the cell construct.  As we have already seen, proteins engage with carbohydrates and with lipids in important structural and signaling processes.  They are integrasl to the composition of the cytoskeleton, and also to the extracellular matrix.  Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest.  They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide.

The amino acids that go into protein synthesis include “indispensable” nutrients that are not made for use, but must be derived from animal protein, although the need is partially satisfied by plant sources. The essential amino acids are classified into well established groups. There are 20 amino acids commonly found in proteins.  They are classified into the following groups based on the chemical and/or structural properties of their side chains :

  1. Aliphatic Amino Acids
  2. Cyclic Amino Acid
  3. AAs with Hydroxyl or Sulfur-containing side chains
  4. Aromatic Amino Acids
  5. Basic Amino Acids
  6. Acidic Amino Acids and their Amides

Examples include:

Alanine                  aliphatic hydrophobic neutral
Arginine                 polar hydrophilic charged (+)
Cysteine                polar hydrophobic neutral
Glutamine             polar hydrophilic neutral
Histidine                aromatic polar hydrophilic charged (+)
Lysine                   polar hydrophilic charged (+)
Methionine            hydrophobic neutral
Serine                   polar hydrophilic neutral
Tyrosine                aromatic polar hydrophobic

Transcribe and Translate a Gene

  1. For each RNA base there is a corresponding DNA base
  2. Cells use the two-step process of transcription and translation to read each gene and produce the string of amino acids that makes up a protein.
  3. mRNA is produced in the nucleus, and is transferred to the ribosome
  4. mRNA uses uracil instead of thymine
  5. the ribosome reads the RNA sequence and makes protein
  6. There is a sequence combination to fit each amino acid to a three letter RNA code
  7. The ribosome starts at AUG (start), and it reads each codon three letters at a time
  8. Stop codons are UAA, UAG and UGA

 

protein synthesis

protein synthesis

http://learn.genetics.utah.edu/content/molecules/transcribe/images/TandT.png

mcell-transcription-translation

mcell-transcription-translation

http://www.vcbio.science.ru.nl/images/cellcycle/mcell-transcription-translation_eng_zoom.gif

transcription_translation

transcription_translation

 

http://www.biologycorner.com/resources/transcription_translation.JPG

 

What about the purine inosine?

Inosine triphosphate pyrophosphatase – Pyrophosphatase that hydrolyzes the non-canonical purine nucleotides inosine triphosphate (ITP), deoxyinosine triphosphate (dITP) as well as 2′-deoxy-N-6-hydroxylaminopurine triposphate (dHAPTP) and xanthosine 5′-triphosphate (XTP) to their respective monophosphate derivatives. The enzyme does not distinguish between the deoxy- and ribose forms. Probably excludes non-canonical purines from RNA and DNA precursor pools, thus preventing their incorporation into RNA and DNA and avoiding chromosomal lesions.

Gastroenterology. 2011 Apr;140(4):1314-21.  http://dx.doi.org:/10.1053/j.gastro.2010.12.038. Epub 2011 Jan 1.

Inosine triphosphate protects against ribavirin-induced adenosine triphosphate loss by adenylosuccinate synthase function.

Hitomi Y1, Cirulli ET, Fellay J, McHutchison JG, Thompson AJ, Gumbs CE, Shianna KV, Urban TJ, Goldstein DB.

Genetic variation of inosine triphosphatase (ITPA) causing an accumulation of inosine triphosphate (ITP) has been shown to protect patients against ribavirin (RBV)-induced anemia during treatment for chronic hepatitis C infection by genome-wide association study (GWAS). However, the biologic mechanism by which this occurs is unknown.

Although ITP is not used directly by human erythrocyte ATPase, it can be used for ATP biosynthesis via ADSS in place of guanosine triphosphate (GTP). With RBV challenge, erythrocyte ATP reduction was more severe in the wild-type ITPA genotype than in the hemolysis protective ITPA genotype. This difference also remains after inhibiting adenosine uptake using nitrobenzylmercaptopurine riboside (NBMPR).

ITP confers protection against RBV-induced ATP reduction by substituting for erythrocyte GTP, which is depleted by RBV, in the biosynthesis of ATP. Because patients with excess ITP appear largely protected against anemia, these results confirm that RBV-induced anemia is due primarily to the effect of the drug on GTP and consequently ATP levels in erythrocytes.

Ther Drug Monit. 2012 Aug;34(4):477-80.  http://dx.doi.org:/10.1097/FTD.0b013e31825c2703.

Determination of inosine triphosphate pyrophosphatase phenotype in human red blood cells using HPLC.

Citterio-Quentin A1, Salvi JP, Boulieu R.

Thiopurine drugs, widely used in cancer chemotherapy, inflammatory bowel disease, and autoimmune hepatitis, are responsible for common adverse events. Only some of these may be explained by genetic polymorphism of thiopurine S-methyltransferase. Recent articles have reported that inosine triphosphate pyrophosphatase (ITPase) deficiency was associated with adverse drug reactions toward thiopurine drug therapy. Here, we report a weak anion exchange high-performance liquid chromatography method to determine ITPase activity in red blood cells and to investigate the relationship with the occurrence of adverse events during azathioprine therapy.

The chromatographic method reported allows the analysis of IMP, inosine diphosphate, and ITP in a single run in <12.5 minutes. The method was linear in the range 5-1500 μmole/L of IMP. Intraassay and interassay precisions were <5% for red blood cell lysates supplemented with 50, 500, and 1000 μmole/L IMP. Km and Vmax evaluated by Lineweaver-Burk plot were 677.4 μmole/L and 19.6 μmole·L·min, respectively. The frequency distribution of ITPase from 73 patients was investigated.

The method described is useful to determine the ITPase phenotype from patients on thiopurine therapy and to investigate the potential relation between ITPase deficiency and the occurrence of adverse events.

 

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

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

PeerJ 2:e270; http://dx.doi.org:/10.7717/peerj.270

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.We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 D 0.13). 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 estimat that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the ab-sence 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.

 

Related studies that reveal issues that are not part of this chapter:

  1. Ubiquitylation in relationship to tissue remodeling
  2. Post-translational modification of proteins
    1. Glycosylation
    2. Phosphorylation
    3. Methylation
    4. Nitrosylation
    5. Sulfation – sulfotransferases
      cell-matrix communication
    6. Acetylation and histone deacetylation (HDAC)
      Connecting Protein Phosphatase to 1α (PP1α)
      Acetylation complexes (such as CBP/p300 and PCAF)
      Sirtuins
      Rel/NF-kB Signal Transduction
      Homologous Recombination Pathway of Double-Strand DNA Repair
    7. Glycination
    8. cyclin dependent kinases (CDKs)
    9. lyase
    10. transferase

 

This year, the Lasker award for basic medical research went to Kazutoshi Mori (Kyoto University) and Peter Walter (University of California, San Francisco) for their “discoveries concerning the unfolded protein response (UPR) — an intracellular quality control system that

detects harmful misfolded proteins in the endoplasmic reticulum and signals the nucleus to carry out corrective measures.”

About UPR: Approximately a third of cellular proteins pass through the Endoplasmic Reticulum (ER) which performs stringent quality control of these proteins. All proteins need to assume the proper 3-dimensional shape in order to function properly in the harsh cellular environment. Related to this is the fact that cells are under constant stress and have to make rapid, real time decisions about survival or death.

A major indicator of stress is the accumulation of unfolded proteins within the Endoplasmic Reticulum (ER), which triggers a transcriptional cascade in order to increase the folding capacity of the ER. If the metabolic burden is too great and homeostasis cannot be achieved, the response shifts from

damage control to the induction of pro-apoptotic pathways that would ultimately cause cell death.

This response to unfolded proteins or the UPR is conserved among all eukaryotes, and dysfunction in this pathway underlies many human diseases, including Alzheimer’s, Parkinson’s, Diabetes and Cancer.

 

The discovery of a new class of human proteins with previously unidentified activities

In a landmark study conducted by scientists at the Scripps Research Institute, The Hong Kong University of Science and Technology, aTyr Pharma and their collaborators, a new class of human proteins has been discovered. These proteins [nearly 250], called Physiocrines belong to the aminoacyl tRNA synthetase gene family and carry out novel, diverse and distinct biological functions.

The aminoacyl tRNA synthetase gene family codes for a group of 20 ubiquitous enzymes almost all of which are part of the protein synthesis machinery. Using recombinant protein purification, deep sequencing technique, mass spectroscopy and cell based assays, the team made this discovery. The finding is significant, also because it highlights the alternate use of a gene family whose protein product normally performs catalytic activities for non-catalytic regulation of basic and complex physiological processes spanning metabolism, vascularization, stem cell biology and immunology

 

Muscle maintenance and regeneration – key player identified

Muscle tissue suffers from atrophy with age and its regenerative capacity also declines over time. Most molecules discovered thus far to boost tissue regeneration are also implicated in cancers.  During a quest to find safer alternatives that can regenerate tissue, scientists reported that the hormone Oxytocin is required for proper muscle tissue regeneration and homeostasis and that its levels decline with age.

Oxytocin could be an alternative to hormone replacement therapy as a way to combat aging and other organ related degeneration.

Oxytocin is an age-specific circulating hormone that is necessary for muscle maintenance and regeneration (June 2014)

 

Proc Natl Acad Sci U S A. 2014 Sep 30;111(39):14289-94.   http://dx.doi.org:/10.1073/pnas.1407640111. Epub 2014 Sep 15.

Role of forkhead box protein A3 in age-associated metabolic decline.

Ma X1, Xu L1, Gavrilova O2, Mueller E3.

Aging is associated with increased adiposity and diminished thermogenesis, but the critical transcription factors influencing these metabolic changes late in life are poorly understood. We recently demonstrated that the winged helix factor forkhead box protein A3 (Foxa3) regulates the expansion of visceral adipose tissue in high-fat diet regimens; however, whether Foxa3 also contributes to the increase in adiposity and the decrease in brown fat activity observed during the normal aging process is currently unknown. Here we report that during aging, levels of Foxa3 are significantly and selectively up-regulated in brown and inguinal white fat depots, and that midage Foxa3-null mice have increased white fat browning and thermogenic capacity, decreased adipose tissue expansion, improved insulin sensitivity, and increased longevity. Foxa3 gain-of-function and loss-of-function studies in inguinal adipose depots demonstrated a cell-autonomous function for Foxa3 in white fat tissue browning. Furthermore, our analysis revealed that the mechanisms of Foxa3 modulation of brown fat gene programs involve the suppression of peroxisome proliferator activated receptor γ coactivtor 1 α (PGC1α) levels through interference with cAMP responsive element binding protein 1-mediated transcriptional regulation of the PGC1α promoter.

 

Asymmetric mRNA localization contributes to fidelity and sensitivity of spatially localized systems

RJ Weatheritt, TJ Gibson & MM Babu
Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839 http://dx.do.orgi:/10.1038/nsmb.2876

Although many proteins are localized after translation, asymmetric protein distribution is also achieved by translation after mRNA localization. Why are certain mRNA transported to a distal location and translated on-site? Here we undertake a systematic, genome-scale study of asymmetrically distributed protein and mRNA in mammalian cells. Our findings suggest that asymmetric protein distribution by mRNA localization enhances interaction fidelity and signaling sensitivity. Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies. Our observations are consistent across multiple mammalian species, cell types and developmental stages, suggesting that localized translation is a recurring feature of cell signaling and regulation.

 

An overview of the potential advantages conferred by distal-site protein synthesis, inferred from our analysis.

 

An overview of the potential advantages conferred by distal-site protein synthesis

An overview of the potential advantages conferred by distal-site protein synthesis

 

Turquoise and red filled circle represents off-target and correct interaction partners, respectively. Wavy lines represent a disordered region within a distal site synthesis protein. Grey and red line in graphs represents profiles of t…

http://www.nature.com/nsmb/journal/v21/n9/carousel/nsmb.2876-F5.jpg

 

Tweaking transcriptional programming for high quality recombinant protein production

Since overexpression of recombinant proteins in E. coli often leads to the formation of inclusion bodies, producing properly folded, soluble proteins is undoubtedly the most important end goal in a protein expression campaign. Various approaches have been devised to bypass the insolubility issues during E. coli expression and in a recent report a group of researchers discuss reprogramming the E. coli proteostasis [protein homeostasis] network to achieve high yields of soluble, functional protein. The premise of their studies is that the basal E. coli proteostasis network is insufficient, and often unable, to fold overexpressed proteins, thus clogging the folding machinery.

By overexpressing a mutant, negative-feedback deficient heat shock transcription factor [σ32 I54N] before and during overexpression of the protein of interest, reprogramming can be achieved, resulting in high yields of soluble and functional recombinant target protein. The authors explain that this method is better than simply co-expressing/over-expressing chaperones, co-chaperones, foldases or other components of the proteostasis network because reprogramming readies the folding machinery and up regulates the essential folding components beforehand thus  maintaining system capability of the folding machinery.

The Heat-Shock Response Transcriptional Program Enables High-Yield and High-Quality Recombinant Protein Production in Escherichia coli (July 2014)

 

 Unfolded proteins collapse when exposed to heat and crowded environments

Proteins are important molecules in our body and they fulfil a broad range of functions. For instance as enzymes they help to release energy from food and as muscle proteins they assist with motion. As antibodies they are involved in immune defence and as hormone receptors in signal transduction in cells. Until only recently it was assumed that all proteins take on a clearly defined three-dimensional structure – i.e. they fold in order to be able to assume these functions. Surprisingly, it has been shown that many important proteins occur as unfolded coils. Researchers seek to establish how these disordered proteins are capable at all of assuming highly complex functions.

Ben Schuler’s research group from the Institute of Biochemistry of the University of Zurich has now established that an increase in temperature leads to folded proteins collapsing and becoming smaller. Other environmental factors can trigger the same effect.

Measurements using the “molecular ruler”

“The fact that unfolded proteins shrink at higher temperatures is an indication that cell water does indeed play an important role as to the spatial organisation eventually adopted by the molecules”, comments Schuler with regard to the impact of temperature on protein structure. For their studies the biophysicists use what is known as single-molecule spectroscopy. Small colour probes in the protein enable the observation of changes with an accuracy of more than one millionth of a millimetre. With this “molecular yardstick” it is possible to measure how molecular forces impact protein structure.

With computer simulations the researchers have mimicked the behaviour of disordered proteins.
(Courtesy of Jose EDS Roselino, PhD.

 

MLKL compromises plasma membrane integrity

Necroptosis is implicated in many diseases and understanding this process is essential in the search for new therapies. While mixed lineage kinase domain-like (MLKL) protein has been known to be a critical component of necroptosis induction, how MLKL transduces the death signal was not clear. In a recent finding, scientists demonstrated that the full four-helical bundle domain (4HBD) in the N-terminal region of MLKL is required and sufficient to induce its oligomerization and trigger cell death.

They also found a patch of positively charged amino acids on the surface of the 4HBD that bound to phosphatidylinositol phosphates (PIPs) and allowed the recruitment of MLKL to the plasma membrane that resulted in the formation of pores consisting of MLKL proteins, due to which cells absorbed excess water causing them to explode. Detailed knowledge about how MLKL proteins create pores offers possibilities for the development of new therapeutic interventions for tolerating or preventing cell death.

MLKL compromises plasma membrane integrity by binding to phosphatidylinositol phosphates (May 2014)

 

Mitochondrial and ER proteins implicated in dementia

Mitochondria and the endoplasmic reticulum (ER) form tight structural associations that facilitate a number of cellular functions. However, the molecular mechanisms of these interactions aren’t properly understood.

A group of researchers showed that the ER protein VAPB interacted with mitochondrial protein PTPIP51 to regulate ER-mitochondria associations and that TDP-43, a protein implicated in dementia, disturbs this interaction to regulate cellular Ca2+ homeostasis. These studies point to a new pathogenic mechanism for TDP-43 and may also provide a potential new target for the development of new treatments for devastating neurological conditions like dementia.

ER-mitochondria associations are regulated by the VAPB-PTPIP51 interaction and are disrupted by ALS/FTD-associated TDP-43. Nature (June 2014)

 

A novel strategy to improve membrane protein expression in Yeast

Membrane proteins play indispensable roles in the physiology of an organism. However, recombinant production of membrane proteins is one of the biggest hurdles facing protein biochemists today. A group of scientists in Belgium showed that,

by increasing the intracellular membrane production by interfering with a key enzymatic step of lipid synthesis,

enhanced expression of recombinant membrane proteins in yeast is achieved.

Specifically, they engineered the oleotrophic yeast, Yarrowia lipolytica, by

deleting the phosphatidic acid phosphatase, PAH1 gene,

which led to massive proliferation of endoplasmic reticulum (ER) membranes.

For all 8 tested representatives of different integral membrane protein families, they obtained enhanced protein accumulation.

 

An unconventional method to boost recombinant protein levels

MazF is an mRNA interferase enzyme in E.coli that functions as and degrades cellular mRNA in a targeted fashion, at the “ACA” sequence. This degradation of cellular mRNA causes a precipitous drop in cellular protein synthesis. A group of scientists at the Robert Wood Johnson Medical School in New Jersey, exploited the degeneracy of the genetic code to modify all “ACA” triplets within their gene of interest in a way that the corresponding amino acid (Threonine) remained unchanged. Consequently, induction of MazF toxin caused degradation of E.coli cellular mRNA but the recombinant gene transcription and protein synthesis continued, causing significant accumulation of high quality target protein. This expression system enables unparalleled signal to noise ratios that could dramatically simplify structural and functional studies of difficult-to-purify, biologically important proteins.

 

Tandem fusions and bacterial strain evolution for enhanced functional membrane protein production

Membrane protein production remains a significant challenge in its characterization and structure determination. Despite the fact that there are a variety of host cell types, E.coli remains the popular choice for producing recombinant membrane proteins. A group of scientists in Netherlands devised a robust strategy to increase the probability of functional membrane protein overexpression in E.coli.

By fusing Green Fluorescent Protein (GFP) and the Erythromycin Resistance protein (ErmC) to the C-terminus of a target membrane protein they wer e able to track the folding state of their target protein while using Erythromycin to select for increased expression. By increasing erythromycin concentration in the growth media and testing different membrane targets, they were able to identify four evolved E.coli strains, all of which carried a mutation in the hns gene, whose product is implicated in genome organization and transcriptional silencing. Through their experiments the group showed that partial removal of the transcriptional silencing mechanism was related to production of proteins that were essential for functional overexpression of membrane proteins.

 

The role of an anti-apoptotic factor in recombinant protein production

In a recent study, scientists at the Johns Hopkins University and Frederick National Laboratory for Cancer Research examined an alternative method of utilizing the benefits of anti-apoptotic gene expression to enhance the transient expression of biotherapeutics, specifically, through the co-transfection of Bcl-xL along with the product-coding target gene.

Chinese Hamster Ovary(CHO) cells were co-transfected with the product-coding gene and a vector containing Bcl-xL, using Polyethylenimine (PEI) reagent. They found that the cells co-transfected with Bcl-xL demonstrated reduced apoptosis, increased specific productivity, and an overall increase in product yield.

B-cell lymphoma-extra-large (Bcl-xL) is a mitochondrial transmembrane protein and a member of the Bcl-2 family of proteins which are known to act as either pro- or anti-apoptotic proteins. Bcl-xL itself acts as an anti-apoptotic molecule by preventing the release of mitochondrial contents such as cytochrome c, which would lead to caspase activation. Higher levels of Bcl-xL push a cell toward survival mode by making the membranes pores less permeable and leaky.

Introduction to Protein Synthesis and Degradation Updated 8/31/2019

N-Terminal Degradation of Proteins: The N-End Rule and N-degrons

In both prokaryotes and eukaryotes mitochondria and chloroplasts, the ribosomal synthesis of proteins is initiated with the addition of the N-formyl methionine residue.  However in eukaryotic cytosolic ribosomes, the N terminal was assumed to be devoid of the N-formyl group.  The unformylated N-terminal methionine residues of eukaryotes is then  often N-acetylated (Ac) and creates specific degradation signals, the Ac N-end rule.  These N-end rule pathways are proteolytic systems which recognize these N-degrons resulting in proteosomal degradation or autophagy.  In prokaryotes this system is stimulated by certain amino acid deficiencies and in eukaryotes is dependent on the Psh1 E3 ligase.

Two papers in the journal Science describe this N-degron in more detail.

Structured Abstract
INTRODUCTION

In both bacteria and eukaryotic mitochondria and chloroplasts, the ribosomal synthesis of proteins is initiated with the N-terminal (Nt) formyl-methionine (fMet) residue. Nt-fMet is produced pretranslationally by formyltransferases, which use 10-formyltetrahydrofolate as a cosubstrate. By contrast, proteins synthesized by cytosolic ribosomes of eukaryotes were always presumed to bear unformylated N-terminal Met (Nt-Met). The unformylated Nt-Met residue of eukaryotic proteins is often cotranslationally Nt-acetylated, a modification that creates specific degradation signals, Ac/N-degrons, which are targeted by the Ac/N-end rule pathway. The N-end rule pathways are a set of proteolytic systems whose unifying feature is their ability to recognize proteins containing N-degrons, thereby causing the degradation of these proteins by the proteasome or autophagy in eukaryotes and by the proteasome-like ClpAP protease in bacteria. The main determinant of an N‑degron is a destabilizing Nt-residue of a protein. Studies over the past three decades have shown that all 20 amino acids of the genetic code can act, in cognate sequence contexts, as destabilizing Nt‑residues. The previously known eukaryotic N-end rule pathways are the Arg/N-end rule pathway, the Ac/N-end rule pathway, and the Pro/N-end rule pathway. Regulated degradation of proteins and their natural fragments by the N-end rule pathways has been shown to mediate a broad range of biological processes.

RATIONALE

The chemical similarity of the formyl and acetyl groups and their identical locations in, respectively, Nt‑formylated and Nt-acetylated proteins led us to suggest, and later to show, that the Nt-fMet residues of nascent bacterial proteins can act as bacterial N-degrons, termed fMet/N-degrons. Here we wished to determine whether Nt-formylated proteins might also form in the cytosol of a eukaryote such as the yeast Saccharomyces cerevisiae and to determine the metabolic fates of Nt-formylated proteins if they could be produced outside mitochondria. Our approaches included molecular genetic techniques, mass spectrometric analyses of proteins’ N termini, and affinity-purified antibodies that selectively recognized Nt-formylated reporter proteins.

RESULTS

We discovered that the yeast formyltransferase Fmt1, which is imported from the cytosol into the mitochondria inner matrix, can generate Nt-formylated proteins in the cytosol, because the translocation of Fmt1 into mitochondria is not as efficacious, even under unstressful conditions, as had previously been assumed. We also found that Nt‑formylated proteins are greatly up-regulated in stationary phase or upon starvation for specific amino acids. The massive increase of Nt-formylated proteins strictly requires the Gcn2 kinase, which phosphorylates Fmt1 and mediates its retention in the cytosol. Notably, the ability of Gcn2 to retain a large fraction of Fmt1 in the cytosol of nutritionally stressed cells is confined to Fmt1, inasmuch as the Gcn2 kinase does not have such an effect, under the same conditions, on other examined nuclear DNA–encoded mitochondrial matrix proteins. The Gcn2-Fmt1 protein localization circuit is a previously unknown signal transduction pathway. A down-regulation of cytosolic Nt‑formylation was found to increase the sensitivity of cells to undernutrition stresses, to a prolonged cold stress, and to a toxic compound. We also discovered that the Nt-fMet residues of Nt‑formylated cytosolic proteins act as eukaryotic fMet/N-degrons and identified the Psh1 E3 ubiquitin ligase as the recognition component (fMet/N-recognin) of the previously unknown eukaryotic fMet/N-end rule pathway, which destroys Nt‑formylated proteins.

CONCLUSION

The Nt-formylation of proteins, a long-known pretranslational protein modification, is mediated by formyltransferases. Nt-formylation was thought to be confined to bacteria and bacteria-descended eukaryotic organelles but was found here to also occur at the start of translation by the cytosolic ribosomes of a eukaryote. The levels of Nt‑formylated eukaryotic proteins are greatly increased upon specific stresses, including undernutrition, and appear to be important for adaptation to these stresses. We also discovered that Nt-formylated cytosolic proteins are selectively destroyed by the eukaryotic fMet/N-end rule pathway, mediated by the Psh1 E3 ubiquitin ligase. This previously unknown proteolytic system is likely to be universal among eukaryotes, given strongly conserved mechanisms that mediate Nt‑formylation and degron recognition.

The eukaryotic fMet/N-end rule pathway.

(Top) Under undernutrition conditions, the Gcn2 kinase augments the cytosolic localization of the Fmt1 formyltransferase, and possibly also its enzymatic activity. Consequently, Fmt1 up-regulates the cytosolic fMet–tRNAi (initiator transfer RNA), and thereby increases the levels of cytosolic Nt-formylated proteins, which are required for the adaptation of cells to specific stressors. (Bottom) The Psh1 E3 ubiquitin ligase targets the N-terminal fMet-residues of eukaryotic cytosolic proteins, such as Cse4, Pgd1, and Rps22a, for the polyubiquitylation-mediated, proteasome-dependent degradation.

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The eukaryotic fMet/N-end rule pathway.

(Top) Under undernutrition conditions, the Gcn2 kinase augments the cytosolic localization of the Fmt1 formyltransferase, and possibly also its enzymatic activity. Consequently, Fmt1 up-regulates the cytosolic fMet–tRNAi (initiator transfer RNA), and thereby increases the levels of cytosolic Nt-formylated proteins, which are required for the adaptation of cells to specific stressors. (Bottom) The Psh1 E3 ubiquitin ligase targets the N-terminal fMet-residues of eukaryotic cytosolic proteins, such as Cse4, Pgd1, and Rps22a, for the polyubiquitylation-mediated, proteasome-dependent degradation.

 

A glycine-specific N-degron pathway mediates the quality control of protein N-myristoylation. Richard T. Timms1,2Zhiqian Zhang1,2David Y. Rhee3J. Wade Harper3Itay Koren1,2,*,Stephen J. Elledge1,2

Science  05 Jul 2019: Vol. 365, Issue 6448

The second paper describes a glycine specific N-degron pathway in humans.  Specifically the authors set up a screen to identify specific N-terminal degron motifs in the human.  Findings included an expanded repertoire for the UBR E3 ligases to include substrates with arginine and lysine following an intact initiator methionine and a glycine at the extreme N-terminus, which is a potent degron.

Glycine N-degron regulation revealed

For more than 30 years, N-terminal sequences have been known to influence protein stability, but additional features of these N-end rule, or N-degron, pathways continue to be uncovered. Timms et al. used a global protein stability (GPS) technology to take a broader look at these pathways in human cells. Unexpectedly, glycine exposed at the N terminus could act as a potent degron; proteins bearing N-terminal glycine were targeted for proteasomal degradation by two Cullin-RING E3 ubiquitin ligases through the substrate adaptors ZYG11B and ZER1. This pathway may be important, for example, to degrade proteins that fail to localize properly to cellular membranes and to destroy protein fragments generated during cell death.

Science, this issue p. eaaw4912

Structured Abstract

INTRODUCTION

The ubiquitin-proteasome system is the major route through which the cell achieves selective protein degradation. The E3 ubiquitin ligases are the major determinants of specificity in this system, which is thought to be achieved through their selective recognition of specific degron motifs in substrate proteins. However, our ability to identify these degrons and match them to their cognate E3 ligase remains a major challenge.

RATIONALE

It has long been known that the stability of proteins is influenced by their N-terminal residue, and a large body of work over the past three decades has characterized a collection of N-end rule pathways that target proteins for degradation through N-terminal degron motifs. Recently, we developed Global Protein Stability (GPS)–peptidome technology and used it to delineate a suite of degrons that lie at the extreme C terminus of proteins. We adapted this approach to examine the stability of the human N terminome, allowing us to reevaluate our understanding of N-degron pathways in an unbiased manner.

RESULTS

Stability profiling of the human N terminome identified two major findings: an expanded repertoire for UBR family E3 ligases to include substrates that begin with arginine and lysine following an intact initiator methionine and, more notably, that glycine positioned at the extreme N terminus can act as a potent degron. We established human embryonic kidney 293T reporter cell lines in which unstable peptides that bear N-terminal glycine degrons were fused to green fluorescent protein, and we performed CRISPR screens to identify the degradative machinery involved. These screens identified two Cul2 Cullin-RING E3 ligase complexes, defined by the related substrate adaptors ZYG11B and ZER1, that act redundantly to target substrates bearing N-terminal glycine degrons for proteasomal degradation. Moreover, through the saturation mutagenesis of example substrates, we defined the composition of preferred N-terminal glycine degrons specifically recognized by ZYG11B and ZER1.

We found that preferred glycine degrons are depleted from the native N termini of metazoan proteomes, suggesting that proteins have evolved to avoid degradation through this pathway, but are strongly enriched at annotated caspase cleavage sites. Stability profiling of N-terminal peptides lying downstream of all known caspase cleavages sites confirmed that Cul2ZYG11Band Cul2ZER1 could make a substantial contribution to the removal of proteolytic cleavage products during apoptosis. Last, we identified a role for ZYG11B and ZER1 in the quality control of N-myristoylated proteins. N-myristoylation is an important posttranslational modification that occurs exclusively on N-terminal glycine. By profiling the stability of the human N-terminome in the absence of the N-myristoyltransferases NMT1 and NMT2, we found that a failure to undergo N-myristoylation exposes N-terminal glycine degrons that are otherwise obscured. Thus, conditional exposure of glycine degrons to ZYG11B and ZER1 permits the selective proteasomal degradation of aberrant proteins that have escaped N-terminal myristoylation.

CONCLUSION

These data demonstrate that an additional N-degron pathway centered on N-terminal glycine regulates the stability of metazoan proteomes. Cul2ZYG11B– and Cul2ZER1-mediated protein degradation through N-terminal glycine degrons may be particularly important in the clearance of proteolytic fragments generated by caspase cleavage during apoptosis and in the quality control of protein N-myristoylation.

The glycine N-degron pathway.

Stability profiling of the human N-terminome revealed that N-terminal glycine acts as a potent degron. CRISPR screening revealed two Cul2 complexes, defined by the related substrate adaptors ZYG11B and ZER1, that recognize N-terminal glycine degrons. This pathway may be particularly important for the degradation of caspase cleavage products during apoptosis and the removal of proteins that fail to undergo N-myristoylation.

” data-icon-position=”” data-hide-link-title=”0″>

The glycine N-degron pathway.

Stability profiling of the human N-terminome revealed that N-terminal glycine acts as a potent degron. CRISPR screening revealed two Cul2 complexes, defined by the related substrate adaptors ZYG11B and ZER1, that recognize N-terminal glycine degrons. This pathway may be particularly important for the degradation of caspase cleavage products during apoptosis and the removal of proteins that fail to undergo N-myristoylation.

 

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What is the meaning of so many RNAs?

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

 

The following is a third 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, 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.

  • 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/

  • 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

  • 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

  • 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/

  • 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/

Exploring the Roles of Enhancer RNAs Scientists have recently discovered that enhancers are often transcribed into RNAs. But they’re still not sure what, if anything, these eRNAs do.

By Ashley P. Taylor | May 7, 2014

http://www.the-scientist.com/?articles.view/articleNo/39906/title/Exploring-the-Roles-of-Enhancer-RNAs/

Four mechanisms by which eRNAs can function

Four mechanisms by which eRNAs can function

Four mechanisms by which eRNAs can function Wikimedia, PClermont There’s a lot that scientists don’t yet know about enhancers, genetic elements first described almost 35 years ago that, unlike promoters,

  • can upregulate genes from some distance.

That distance, while generally under 100 kilobases, can vary greatly. Usually,

  • enhancers regulate the genes closest to them,

but not always; the enhancer for the developmental gene Sonic hedgehog is a megabase away from its promoter in the human genome. What scientists do know is that enhancers seem to play key roles in human biology. One recently published atlas of enhancer expression in the human genome suggested that

  • enhancers, which are expressed differently across cell types,

could help explain how one genome encodes so many different kinds of cells. The same paper reported that

  • single-nucleotide changes associated with human diseases are
  • over-represented in enhancers and promoters relative to exons.

In a 2010 Nature paper, researchers in the lab of neurobiologist Michael Greenberg at Harvard Medical School reported that enhancers can produce RNA. Working with cultured mouse neurons, the scientists found that

  • enhancers activated by neuron depolarization were transcribed all over the genome
  • and that levels of enhancer RNAs (eRNAs)
  • correlated with the production of messenger RNA (mRNA)
  • from genes near the enhancers.

Researchers had observed enhancer RNAs before, but this was the first evidence of widespread enhancer transcription. In the years since, several other groups have reported finding eRNAs in various biological systems. While eRNAs promise to help researchers understand how enhancers work, they also raise many questions of their own. ERNAs are fairly short, ranging in length from 500 basepairs to 5 kilobases. Most of the time, although not always,

  • enhancer RNAs are transcribed from both DNA strands, producing what are called bidirectional transcripts.

As the Greenberg lab originally found,

  • eRNA production correlates with the production of mRNA from the genes that enhancers regulate.

“Perhaps the best mark of an active enhancer is the induction of an enhancer RNA,” said M. Geoffrey Rosenfeld from the University of California, San Diego, whose group studies genome-wide regulation of gene expression and has been probing eRNA function. Of course, correlation does not equal causation, he warned. “The next question is whether the enhancer RNA is just a mark of an active enhancer or if it could have function, per se.” What might those functions be? Scientists have crafted a few hypotheses, although most agree that the mechanisms by which eRNAs function remain a mystery. One thought is that eRNAs represent transcriptional noise. That’s a view held by Albin Sandelin at the Bioinformatics Centre of Copenhagen University, who led the aforementioned enhancer atlas project. “Something that most people believe is that the enhancer, when it’s active, it loops in to the promoter, and then the concentration of RNA polymerase II will be a lot higher,” Sandelin told The Scientist. He referred to the idea that

  • enhancers upregulate genes by physically interacting with target promoters;
  • as enhancers and the promoters they regulate are usually on the same chromosome,
  • this interaction creates a loop of DNA between the two genetic elements.

“I think that most of the eRNAs are due to this: you have piece of open DNA, which is the enhancer, near a promoter with high concentrations of RNA polymerase II, and

  • then you will get transcription of the enhancer.”

This idea is supported by the fact that, because they are so short, eRNAs tend to degrade rather quickly. “There are a few cases where [enhancer RNAs] are proven to be functional; I just personally don’t think that the majority work that way,” Sandelin said. “[But] it seems to me that this [supposed function] is a byproduct of proximity to some sites that have a lot of polymerase II.” Another hypothesis suggests that

  • the act of transcription trumps the importance of the transcripts themselves.

Experiments in macrophages, led by the University of California, San Diego’s Chris Glass, support this idea. “I don’t think that transcription at enhancers is noise,” said Vittorio Sartorelli, a researcher at the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) who has studied eRNAs in the context of myogenesis. “Whether eRNAs are absolutely required in every situation or the act of transcription at enhancers is the major determinant, I think that it’s still open for debate,” he said. Several studies have presented evidence to suggest that eRNAs do play a key role. As described in a 2013 Nature paper, the Rosenfeld lab found that estrogen bound to its receptor in breast cancer cells increases expression of particular genes by binding to enhancers and inducing the transcription of eRNAs. Eliminating the eRNAs using both RNA interference (RNAi) and antisense oligonucleotides—

  • which bind to the RNA and lead to its degradation by an RNase—
  • reduced the ability of enhancers to upregulate gene expression.

Depletion of eRNAs in this system also

  • reduced looping between enhancers and promoters.

“Using two different strategies, there [was] evidence that

  • the presence of the interrogated eRNA is important for activation of the target gene
  • and for the interaction between the enhancer and the cognate regulated promoter,” said Rosenfeld.

Taking the reverse approach, the researchers also tested whether eRNAs could upregulate transcription independent of the enhancer itself. Rosenfeld and his colleagues experimentally

  • tethered eRNAs to a promoter driving expression of a luciferase reporter gene in a plasmid construct. When this construct was
  • transfected into cultured breast cancer cells,
    • luciferase expression was upregulated about 2.5-fold.

“These data indicate that

  • the interrogated eRNA plays a functional role, at least in this system,
  • in activation of the coding target gene,”

said Rosenfeld. “But it does not distinguish between the alternative possibilities that this is because of a specific sequence in the eRNA that might interact with a regulatory factor or that some other function of the eRNA …” Scientists, such as Reuven Agami from the Netherlands Cancer Institute, the NIAMS’s Sartorelli, and San Diego’s Glass, have reported similar results in other systems. Greenberg said that some eRNAs could have functions unrelated to the promoters activated by their associated enhancers while others could play direct roles in gene expression. “I think that we need to keep an open mind as to what the functions are; there are likely to be multiple functions.” Tags: transcriptional       regulationtranscriptionRNAipromotersmRNAEnhancers and DNA   SiRNA-Mediated Down-Regulation of Livin Expression in Breast Cancer Cells Hussein Sabit, Mohamed M.M. Ibrahim and Nabil S. Awad 1College of Biotechnology, Misr University for Science and Technology, Egypt 2Scientific Research Deanship, Taif University, KSA Academic Journal of Cancer Research 6 (2): 69-73, 2013  http://dx.doi.org:/10.5829/idosi.ajcr.2013.6.2.76211   Livin, also called melanoma inhibitor of apoptosis protein (IAP) or kidney IAP, is an anti-apoptotic protein belonging to the IAP family which consists of eight members. The genes of this family render cancer cells insensitive to apoptotic stimulation. The aim of the present study was to investigate and assess the role of siRNA

  • in the regulation of livin gene expression in two breast cancer cell lines (4Ti and MCF-7).

Lipofection was carried out to introduce the livin-specific small interference RNA (siRNA) segment (19 mer) into the cancerous cells and the livin expression was determined using RT-PCR. Trypan blue assay was conducted to assess the integrity of the cell membranes after being transfected. 3-(4, 5-dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide (MTT) assay was also implemented to assess the cell viability through the mitochondrial reductase enzymes activity. The obtained results concluded that

  • transfecting the cancerous cells with livin-specific siRNA have
  • led to the down regulation of livin expression.

RNA interference (RNAi) targeting the anti-apoptotic genes such as livin is a promising approach and may help as a future therapeutic tool for breast cancer. Key words: SiRNA Livin Down-regulation Breast cancer     Endogenous RNA interference is driven by copy number Cristina Cruz, Jonathan Houseley* Epigenetics Programme, The Babraham Institute, Cambridge, United Kingdom Cell biology | Genomics and evolutionary biology cruz and Houseley. eLife 2014;3:e01581  http://dx.doi.org:/10.7554/elife.01581   A plethora of non-protein coding RNAs are produced throughout eukaryotic genomes, many of which are transcribed antisense to protein-coding genes and could potentially instigate

  • RNA interference (RNAi) responses.

Here we have used a synthetic RNAi system to show that

  • gene copy number is a key factor controlling RNAi for transcripts from endogenous loci,

since transcripts from multi-copy loci form double stranded RNA more efficiently than transcripts from equivalently expressed single-copy loci.

  • Selectivity towards transcripts from high-copy DNA

is therefore an emergent property of a minimal RNAi system. The ability of RNAi to

  • selectively degrade transcripts from high-copy loci
  • would allow suppression of newly emerging transposable elements,

but such a surveillance system requires transcription. We show that

  • low-level genome-wide pervasive transcription
  • is sufficient to instigate RNAi, and propose that
  • pervasive transcription is part of a defense mechanism capable of directing a
  • sequence-independent RNAi response against
  • transposable elements amplifying within the genome.

http://dx.doi.org:/10.7554/eLife.01581.001     Over the past decade, our understanding of the complexity of the eukaryotic transcriptome has been revolutionized. Genome-wide sequencing studies in many organisms have revealed that protein-coding mRNAs are augmented by a multitude of non-protein coding RNAs (ncRNAs), many produced from regions of the genome traditionally considered to be transcriptionally silent (Brummelkamp et al., 2002; Bertone et al., 2004; Cheng et al., 2005; David et al., 2006; Birney et al., 2007). Functional data for the vast majority of ncRNAs are currently lacking, with only a few examples characterized in any detail; however, the diversity of mechanisms by which these act suggests that ncRNAs have a rich and varied biology that is largely still to be sampled. Long ncRNAs which overlap protein-coding genes have the potential to modulate the expression of their cognate coding RNA. Early characterized examples in yeast were thought to work by directly disrupting transcription factor or polymerase binding to the promoter of the coding RNA (Martens et al., 2004; Hongay et al., 2006); however, more recent data implicate specific chromatin structure changes in repression (Gelfand et al., 2011; Hainer et al., 2011), and many other cases of ncRNAs that alter chromatin modifications have been described (Camblong et al., 2007; Berretta et al., 2008; Houseley et al., 2008; Pinskaya et al., 2009; van Werven et al., 2012). Chromatin modifications are not necessarily repressive, and ncRNAs that enhance expression of their overlapping coding gene have also been described (Uhler et al., 2007; Hirota et al., 2008).

Frequency of annotated antisense non-protein coding RNAs

Frequency of annotated antisense non-protein coding RNAs

Figure 1. Frequency of annotated antisense non-protein coding RNAs (ncRNAs) and effects on mRNA abundance. (A) Schematic of an example sense mRNA-antisense (ncRNA) system. (B) Number of annotated open reading frames (ORFs) with antisense transcripts. Positions of CUTs, SUTs, and XUTs were collated with expressed ORFs (Xu et al., 2009; van Dijk et al., 2011), SUTs were later re-classified as XUTs were removed. Overlaps between ORFs expressed in glucose media (total 5171, Xu et al., 2009) and other RNAs were calculated and summed for increasing minimum overlaps of 50–500 bp. ORF–ORF overlaps and ORF–ncRNA overlaps were analyzed separately as ORF–ORF overlaps are consistently smaller. Detailed figures are given in Table 1. (C) Abundance of short interfering RNAs (siRNAs) in RNA interference (RNAi)+ strain produced from expressed ORFs with and without an annotated overlapping antisense ncRNA, based on read counts from published high-throughput sequencing data (Drinnenberg et al., 2011). Minimum antisense overlap with ORF was set at 250 bp; only ORFs with >100 reads in the wild-type poly(A)+ library were assessed to remove noise. Stated p value calculated by Student’s t test. (D) Abundance of mRNA in RNAi+ cells relative to wild-type; data source and categories as in C, differences were not significant. http://dx.doi.org:/10.7554/eLife.01581.003

Multi-copy loci are preferentially targeted by RNA interference (RNAi).

Multi-copy loci are preferentially targeted by RNA interference (RNAi).

Figure 4. Multi-copy loci are preferentially targeted by RNA interference (RNAi). (A) Short interfering RNA (siRNA) (Drinnenberg et al., 2011) and total RNA (Silva et al., 2002) abundance for loci with copy number <2 (left, single-copy) or ≥2 (right, multi-copy). (B) Quantification of data from A binned into categories of increasing total RNA level, with p values for pairwise comparisons of siRNA abundance in single-copy and multi-copy datasets using the Wilcoxon Rank Sum test. (C) Copy number distribution of the 1% of loci with the highest siRNA:total RNA ratio compared with other loci; difference is significant by Wilcoxon Rank Sum test, p<2.2 × 10−16, loci scoring below noise threshold (0–2 category in B) were removed. n values for tests in B and C are given in Table 2. (D) Comparison of copy number with siRNA:total RNA ratio across chromosome I. Cruz and Houseley. eLife 2014;3:e01581. http://dx.doi.org:/10.7554/elife.01581   eLife digest Genes contain the codes that are needed to make the proteins used by cells. This code is transcribed to make a messenger RNA molecule that is then translated to make a protein. However, other types of RNA called

  • non-coding RNA molecules can disrupt this process
  • by binding to messenger RNA molecules,
  • with matching sequences, before translation begins.

RNA interference involves enzymes called Dicer and Argonaute. Many cells contain large numbers of non-coding RNA molecules—

  • so called because they are not translated to produce proteins—
  • and many of these are capable of starting the process of RNA interference.

However, most do not, and the reasons for this are not understood. Now, work by Cruz and Houseley has provided new insight into this phenomenon by showing that

  • it is related to the number of copies of the gene encoding such RNAs in the genome.

Yeast cells normally do not have the genes for RNA interference, but Cruz and Houseley used

  • genetically engineered yeast cells containing Dicer and Argonaute.

Although most of the messenger RNA molecules in these cells showed no change,

  • the expression of some genes with high ‘copy numbers’ was reduced.

Further experiments that involved adding more and more copies of other genes showed that

  • RNA interference could selectively target messenger RNA molecules produced from genes with an increased copy number—
  • particularly if the copies of the genes were clustered in one location in the genome.

RNA interference is also used to defend against DNA sequences that invade and multiply within a genome, such as viruses and other ‘genetic parasites’. As such, the effect observed by Cruz and Houseley could explain why entire genomes are often continuously copied to RNA at low levels. This activity would allow the monitoring of the genome for the invasion of any genetic parasites that had multiplied to high numbers. Following on from this work, the next challenge will be to understand how gene copy number and location are balanced to achieve a selective RNA interference system.   RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts Sarah Geisler1,2 and Jeff Coller1 1Center for RNA Molecular Biology, Case Western Reserve, Cleveland , OH 2Present address: Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, 4058 Basel, Switzerland. NATURE REVIEWS | MOLECULAR CELL BIOLOGY

  1. nature.com/reviews/molcellbio http://dx.doi.org:/10.1038/nrm3679

Abstract | The increased application of transcriptome-wide profiling approaches has led to an explosion in the number of documented long non-coding RNAs (lncRNAs). While these new and enigmatic players in the complex transcriptional milieu are encoded by a significant proportion of the genome, their functions are mostly unknown. Early discoveries support a paradigm in which   lncRNAs regulate transcription via chromatin modulation, but

  • new functions are steadily emerging.

Given the biochemical versatility of RNA, lncRNAs may be used for various tasks, including

  • post-transcriptional regulation,
  • organization of protein complexes,
  • cell-cell signalling and
  • allosteric regulation of proteins.

Nature Reviews – Molecular Cell Biology | 9 Oct 2013; http://dx.doi.org:/10.1038/nrm3679   In this Review, we focus on the functional attrib­utes of RNA and highlight the unconventional, and perhaps underappreciated, biological contributions of lncRNAs, including the diverse mechanisms through which lncRNAs participate in transcriptional regu­lation. We touch briefly on the roles of lncRNAs in regulating chromatin states, as this has been explored in several recent reviews (see REFS 8,9,13–15). In addi­tion, we highlight roles beyond transcription whereby lncRNAs function in various cellular contexts, includ­ing

  1. post-transcriptional regulation,
  2. post-translational regulation of protein activity,
  3. organization of protein complexes,
  4. cell–cell signalling, as well as
  5. recombination

Transcription activator-like effectors (TALEs). Naturally found in some bacteria, TALEs are proteins that bind DNA through repeat domains, and their code for sequence specificity has been elucidated allowing sequence specific TALEs to be engineered.

  • PUF proteins

A family of sequence-specific RNA-binding proteins, which bind 3ʹ untranslated regions within mRNAs to repress target mRNA translation.

  • Pseudogenes

Dysfunctional relatives of normal genes thought to arise from duplication or retrotransposition.

  • Chromatin-modifying complexes

Protein complexes that catalyse the covalent chemical modification of chromatin

  • Adaptive immune system

A system of specialized cells that create immunological memory via specific antibodies after an initial response to a pathogen.   A biochemically versatile polymer   Figure 1 | RNA is a biochemically versatile polymer. a | RNA is particularly well suited for sequence-specific nucleic acid targeting through base pairing interactions over a short region (for example, eight nucleotides). By contrast, proteins require repeat motifs comprising 35–39 amino acids (105–117 base pairs of genomic sequence) to recognize a single RNA base with specificity. Therefore, to recognize eight nucleotides, 280–312 amino acids (840–936 base pairs of genomic sequence) would be required. Compared to the eight base pairs required for an RNA, protein-based nucleic acid recognition requires substantially more genomic sequence17. b | RNA can fold into complex three-dimensional structures that can specifically bind various ligands, including small molecules and peptides18. c | RNA is suitable for transient expression, because a fully functional RNA can be generated immediately following transcription and processing but can also be rapidly degraded. Together, this allows RNA effectors to be produced in quick pulses. Proteins, however, require additional steps, including mRNA export and translation, to produce a functional peptide. Likewise, both the mRNA and the protein need to be degraded to turn off expression. d | RNA is malleable and, therefore, more tolerant of mutations. Although some mutations in protein-coding genes are silent, many are deleterious such as nonsense mutations that generate truncated polypeptides. RNA, however, can tolerate mutations even within the regions responsible for target recognition. e | RNA-dependent events can be heritable. For instance, processed pseudogenes were once RNA transcripts that have been genomically integrated. In addition, telomerase uses an RNA template to add telomeric repeats to the ends of chromosomes. ORF, open reading frame; Pol II, RNA polymerase II.   lncRNAs as regulators of transcription   Figure 2 | lncRNAs regulate transcription through several mechanisms. ac | Long non-coding RNAs (lncRNAs) can modulate chromatin through transcription-independent (part a) and transcription-dependent mechanisms (parts b and c). lncRNAs can bind one or more chromatin-modifying complexes and target their activities to specific DNA loci (part a). Depending on the nature of the enzymes bound, lncRNA-mediated chromatin modifications can activate or repress gene expression22,23,26,27,120. Chromatin-modifying complexes bound to the RNA polymerase II (Pol II) carboxy-terminal domain (CTD) can modify chromatin during transcription of lncRNAs33–35 (part b). Transcription of lncRNAs can also result in chromatin remodelling that can either favour or inhibit the binding of regulatory factors (part c). Depending on the nature of the factors that bind during remodelling, gene expression is activated or repressed 37–40. dg | lncRNAs can modulate both the general transcription machinery (parts d and e) as well as specific regulatory factors (parts f and g). lncRNAs can bind Pol II directly to inhibit transcription47 (part d). Formation of lncRNA–DNA triplex structures can also inhibit the assembly of the pre-initiation complex48 (part e). lncRNAs can fold into structures that mimic DNA-binding sites (left) or that generally inhibit or enhance the activity of specific transcription factors (right)50–53 (part f). lncRNAs can also regulate gene expression by binding specific transport factors to inhibit the nuclear localization of specific transcription factors54 (part g). Regulators of mRNA processing Modulators of post-transcriptional control   Figure 3 | lncRNAs influence mRNA processing and post-transcriptional regulation. a,b | Long non-coding RNAs (lncRNAs) can modulate mRNA processing. Splicing patterns can be influenced by lncRNAs that associate with the pre-mRNA (part a). For example, splicing of the first intron of neuroblastoma MYC mRNA is prevented by a natural antisense transcript61. Antisense lncRNAs that associate with an mRNA could direct mRNA editing, perhaps through association of the duplex with ADAR (adenosine deaminase acting on RNA) enzymes that catalyse adenosine to inosine conversion in double-stranded RNA63,66 (part b). cf | lncRNAs modulate post-transcriptional regulatory events. lncRNAs containing SINEB2 repeat elements can upregulate translation through association with the 5ʹ region of an mRNA68 (part c). lncRNAs containing Alu repeat elements associate with the Alu elements in the 3ʹ untranslated region (UTR) of an mRNA, and this double-stranded structure can direct Staufen-mediated decay through a pathway that is molecularly similar to nonsense-mediated decay70 (part d). lncRNAs can mask miRNA-binding sites on a target mRNA to block miRNA-induced silencing through the RNA-induced silencing complex (RISC)72 (part e). Linear or circular lncRNAs can function as miRNA decoys to sequester miRNAs from their target mRNAs74,75 (part f).                   Regulators of protein activity                 Scaffolds for higher-order complexes                 Signaling molecules   Figure 4 | lncRNAs are involved in various cellular contexts. Long non-coding RNAs (lncRNAs) modulate protein activity by post-translational mechanisms (parts ac). a | Small nucleolar lncRNAs (sno-lncRNAs) generated from the 15q11‑q13 locus bind and modulate the activity of the FOX2 alternative splicing factor, and this can inhibit FOX2‑mediated splicing80. b | The highly structured rncs‑1 lncRNA binds Dicer to inhibit the processing of small RNAs81. c | The gadd7 lncRNA binds and modulates the ability of TDP43 (TAR DNA-binding protein 43) to target and process specific mRNAs84. d | lncRNAs can act as scaffolds to organize several complexes24. e | As the cargo of exosomes that mediate transfer of material between cells, exosomal shuttle RNAs (exRNAs) may act as signalling molecules during cell–cell communication; exosomal cargo includes mRNAs, microRNAs (miRNAs) and lncRNAs102. f | lncRNAs expressed from the switch region of genes encoding antibodies form R‑loops to direct class switch recombination via activation-induced deaminase (AID) recruitment111                   Vehicles for increasing genetic diversity                 Conclusions and perspectives                 lncRNAs have now been demonstrated to regulate all aspects of gene expression, including transcription (FIG. 2), processing and post-transcriptional control path­ways (FIG. 3). Likewise, lncRNAs have also been shown to regulate protein function and organize multiprotein com­plex assembly. Now with hints that lncRNAs might par­ticipate in cell–cell communication and recombination, the possible reach of lncRNA functions seems endless (FIG. 4).   Targeting Noncoding RNAs in Disease: Challenges and Opportunities Science/AAAS technology webinar   4 Sept 2013  

  • Noncoding RNAs serve a wide range of functions in cellular and developmental processes and are therefore likely involved in the development and pathophysiology of many diseases.

Thanks to the effective inhibition of micro RNAs in vivo, scientists have already made groundbreaking discoveries about the contribution of short regulating RNAs in human diseases in areas such as cancer, heart disease, and diabetes. Dr. David Corey from the Department of Pharmacology at the University of Texas Southwestern Medical Campus in Dallas, Texas; Dr. Stefanie Dimmeler from the Institute of Cardiovascular Regeneration at Goethe‐University in Frankfurt, Germany; Dr. Jan‐Wilhelm Kornfeld from the Department of Mouse Genetics and Metabolism, University of Cologne in Germany.   Dr. Corey’s group is interested in antigene oligonucleotides, antisense oligonucleotides, nucleic acids, RNAi, and telomerase.   I have two goals for my presentation today.

  • give a brief introduction to the concept of using nucleic acids as dugs.
  • show the boundaries of using nucleic acids to affect gene expression

 

  • a search for new methods to develop drugs includes nucleic acids that can bind to RNA and affect gene expression. The advantages of this approach are that one can identify an active oligomer, a lead compound very quickly in weeks rather than years.
  • the medicinal chemistry and pharmacology of all of these nucleic acids is similar
  • by affecting gene expression, one has the ability to treat almost any disease

the two main strategies for using nucleic acid to affect gene expression are

  • use single cell stranded oligonucleotides to bind directly to an RNA target and block their action.
  • use double stranded RNA. Double stranded RNA then goes through the RNA silencing process. That machinery
  • helps it to find a messenger RNA target and efficiently inhibit gene expression.

What kind of cellular RNAs can be targeted by nucleic acids?

  • they could be the RNA domains of ribonucleoproteins and the classic example of that is telomerase.
  • One could also target messenger RNA.
  • You could block translation or you could affect splicing so for example upregulate an isoform that might be useful in treating a disease.
  • Today, we focus mainly on targetingnoncodingRNAs and one of thosenoncodingRNAs ismicroRNAs and by
    • blocking the microRNA you can affect its action.

I’m going to discuss targeting long noncoding RNAs, which can be used to either up or down regulate gene transcription. Earlier this year Kynamro, an antisense oligonucleotide that targets Ap‐B1 messenger RNA, was approved by the food and drug administration. This is a systemically administered oligonucleotide that’s been shown to reduce LDL cholesterol. So it’s the strongest proof to date that synthetic oligonucleotides can be made on the scale that’s large enough to be used as drugs and be administered to patients and get through the FDA approval process.

  • Now I’d like to show you just how the boundaries of regulation can be pushed by using noncoding RNAs to regulate transcription of an operon within the icosanoid signaling pathway. Messenger RNAs are often overlapped by long RNAs at both their 3’ and the 5’ termini as well as within the gene providing a new realm of potential targets for addressing gene expression.

 

  • examples of the important ones include XIST and HOTAIR
  • These are genes that are known to regulate x chromosome inactivation or transcriptional multigene regulation programs. one might think that with RNA that RNAi factors that are so successful in regulating messenger RNA might be involved. But today they haven’t really been strongly implicated in mammalian cells.

 

  •  we know that microRNAs are in the nucleus.
  • we also know that RNAi factors like argonaute 2 are in the nucleus.
  • we also know that noncoding RNAs are in the nucleus.

The hypothesis that we’ve built up over that time is that these RNAi factors can interact with small RNAs

  • to form what are essentially ribonucleoprotein complexes that can act to control either gene transcription or gene splicing.
  • The RNA domain protects the RNA and promotes binding to the target. The RNA domain directs specificity to a particular RNA target inside the cell, for example a long noncoding RNA.

in about 2010 working with my colleague Bethany Janowski, we decided to go very deeply into an important physiology pathway — the eicosanoid production pathway and cyclooxygenase‐2 and PLA2G4a

  • we began by asking whether or not there was noncoding expression at the COX‐2 promoter
  • we characterized this expression by RNA sequencing by quantitative PCI and 5’ RACE and
  • we discovered that there were transcripts overlapping the COX-promoter in both the antisense and the sense direction –
  • we now have the noncoding RNA raw material that might allow recognition to control gene expression of COX‐2 messenger RNA.

 

  • there were a substantial number of microRNAs with complementarity to the COX‐2 promoter.
  • small RNA sequencing to identify microRNAs in the nucleus and
  • microRNAs that were both in the nucleus and complementary to the

COX‐2 promoter became candidates for regulating COX‐2.

  • The most promising of these microRNAs was microRNA‐
  • It had strong complementarity to two adjacent sequences within the COX‐2

promoter. So that resembles how micro‐RNAs recognize typically 3’ untranslated regions.

  • it became our prime candidate for investigating for potential regulation of COX‐2 expression through regulating its transcription by binding a noncoding RNA.
  •  we used a microRNA inhibitor
  • When we add these inhibitors into cells COX‐2 expression goes down.
  • consistent with a microRNA binding to the noncoding RNA and activating COX‐2 expression

This is as far as this reviewer wishes to prodeed in the presentation(s)   Explore microRNA as therapeutic targets Efficient [in vivo] silencing using LNA™-enhanced inhibitors exiqon.com/in-vivo-mirna-inhibitors   Nature Reprint Collection MicroRNAs from bench to clinic   Progress in the microRNA fi eld over the last 12 years has been nothing but remarkable. MicroRNAs were only discovered in humans in 2001, but since then they have revolutionized cell biology and completely changed the way we view the regulation of gene expression. They are now known to be involved, at some level, in all cellular and developmental pathways and all major types of disease, including all cancers, as well as metabolic, cardiovascular, neuronal and immune-related disorders. Exiqon’s LNA™- based microRNA research tools have been instrumental in many of the groundbreaking discoveries in the field. In this collection, we are thrilled to present some of the recent advances in moving microRNAs from basic research into the clinic both as biomarkers and therapeutic targets. Since the discovery of circulating or extracellular microRNAs, their potential as minimally invasive diagnostic and prognostic markers for disease has been actively investigated. Here we feature two articles where qPCR profiling of microRNAs in biofluids have been shown to have diagnostic potential. Another promising area with clinical prospects is microRNA in situ hybridization (ISH) in FFPE samples. We have included an article detailing the prognostic potential of microRNA ISH in this collection. Due to their extensive involvement in human disease, microRNAs are naturally interesting targets for therapeutic intervention. One of the most advanced areas in this respect is the potential of microRNAs as therapeutic targets in cardiovascular disease and we have included a review of this area. In addition, two very recent and groundbreaking studies that have shown the exciting potential for microRNA inhibition in diabetes and epilepsy are also included.   Identification of serum microRNA profiles in colon cancer E Hofsli*,1,2,7, W Sjursen3,4,7, W S Prestvik5, J Johansen2, M Rye2, G Tranø6, et al. 1Department of Oncology, St Olavs Hospital, Trondheim University Hospital, 2Faculty of Medicine, Department of Cancer and Molecular Medicine, Norwegian University of Science and Technology,  3Department of Laboratory Medicine Children’s and Women’s Health, Norwegian University of Science and Technology, 4Department of Pathology and Medical Genetics, St Olavs Hospital, Trondheim University Hospital, 5 Faculty of Technology, Sør-Trøndelag University College, and 6Department of Gastrointestinal Surgery, St Olavs Hospital, Trondheim University Hospital, Olav Kyrresgt 17, Trondheim 7006, Norway British Journal of Cancer (2013) 108, 1712–1719 |  http://dx.doi.org/10.1038/bjc.2013.121 Background: microRNAs (miRNAs) exist in blood in an apparently stable form. We have explored whether serum miRNAs can be used as non-invasive early biomarkers of colon cancer. Methods: Serum samples from 30 patients with colon cancer stage IV and 10 healthy controls were examined for the expression of 375 cancer-relevant miRNAs. Based on the miRNA profile in this study, 34 selected miRNAs were measured in serum from 40 patients with stage I–II colon cancer and from 10 additional controls. Results: Twenty miRNAs were differentially expressed in serum from stage IV patients compared with controls (Po0.01). Unsupervised clustering revealed four subgroups; one corresponding mostly to the control group and the three others to the patient groups. Of the 34 miRNAs measured in the follow-up study of stage I–II patients, 21 showed concordant expression between stage IV and stage I–II patient. Based on the profiles of these 21 miRNAs, a supervised linear regression analysis (Partial Least Squares Regression) was performed. Using this model we correctly assigned stage I–II colon cancer patients based on miRNA profiles of stage IV patients. Conclusion: Serum miRNA expression profiling may be utilised in early detection of colon cancer. MicroRNAs from bench to clinic   Figure 2. Differentially expressed miRNAs in stage IV (red bars) vs stage I–II (blue bars) colon cancer. The expression of 34 miRNAs was compared, and 26 miRNAs were detected. In all, 21 of 26 detected miRNAs showed the same expression profile in early-stage I–II vs metastatic stage IV colon cancer.     Figure 3. Prediction analysis of early-stage colon cancer patients. Controls are shown in red and cancer samples in blue. 9 out of 10 healthy controls were correctly predicted as true negatives and 35 out of 40 patients with cancer as true positives.     MicroRNA profiling of diagnostic needle aspirates from patients with pancreatic cancer S Ali1, H Saleh2,3, S Sethi2, FH Sarkar1,2 and PA Philip*,1 1Department of Oncology; 2Department of Pathology; 3Karmanos Cancer Institute, Detroit Medical Center, Wayne State University School of Medicine, Detroit, MI   BACKGROUND: A major challenge to the development of biomarkers for pancreatic cancer (PC) is the small amount of tissue obtained at the time of diagnosis. Single-gene analyses may not reliably predict biology of PC because of its complex molecular makeup.MicroRNA (miRNA) profiling may provide a more informative molecular interrogation of tumours. The primary objective of this study was to determine the feasibility of performing miRNA arrays and quantitative real-time PCR (qRT– PCR) from archival formalin fixed paraffin-embedded (FFPE) cell blocks obtained from fine-needle aspirates (FNAs) that is the commonest diagnostic procedure for suspected PC. METHODS: MicroRNA expression profiling was performed on FFPE from FNA of suspicious pancreatic masses. Subjects included those who had a pathological diagnosis of pancreatic adenocarcinoma and others with a non-malignant pancreatic histology. Exiqon assay was used to quantify miRNA levels and qRT–PCR was used to validate abnormal expression of selected miRNAs. RESULTS: A total of 29 and 15 subjects had pancreatic adenocarcinoma and no evidence of cancer, respectively. The RNA yields per patient varied from 25 to 100 ng. Profiling demonstrated deregulation of over 228 miRNAs in pancreatic adenocarcinoma of which the top 7 were further validated by qRT–PCR. The expression of let-7c, let-7 f, and miR-200c were significantly reduced in most patients whereas the expression of miR-486-5p and miR-451 were significantly elevated in all pancreas cancer patients. MicroRNAs let-7d and miR-423-5p was either downregulated or upregulated with a significant inter-individual variation in their expression. CONCLUSION: This study demonstrated the feasibility of using archival FFPE cell blocks from FNAs to establish RNA-based molecular signatures unique to pancreatic adenocarcinoma with potential applications in clinical trials for risk stratification, patient selection, and target validation. British Journal of Cancer (2012) 107, 1354–1360.  http://dx.doi,org:/10.1038/bjc.2012.383 Comparative expression of seven miRNAs tested in FNA samples   Figure 1 Ingenuity network analysis showing up (red) and downregulation (green) of miRNAs analysed by miRNA profiling in PC and their targeted genes.

miRNAs analysed by miRNA profiling in PC and their targeted genes

miRNAs analysed by miRNA profiling in PC and their targeted genes

Figure 2 Ingenuity network analysis showing up (red) and downregulation (green) of miRNAs involved in PC and their target genes (A). The solid lines connecting genes represent a direct relation and dotted lines indirect relation. We also observed 15 bio functional network groups that included cancer, genetic disorder, and gastrointestinal disease (B).

Ingenuity network analysis showing up (red) and downregulation (green) of miRNAs involved in PC and their target genes

Ingenuity network analysis showing up (red) and downregulation (green) of miRNAs involved in PC and their target genes

Figure 6 Box plot representing the expression of 7 miRNAs as assessed by qRT–PCR in 29 FNA cell blocks from PC patients analysed individually compared with FNA cell blocks obtained from 15 normal controls by using qRT–PCR. The graph is presented in log2 values and 1.0 represents average of normal subjects (n¼15). [not shown]   The prognostic importance of miR-21 in stage II colon cancer:a population-based study S Kjaer-Frifeldt*,1,2, TF Hansen1, BS Nielsen3, S Joergensen3, J Lindebjerg4, …on behalf of Danish Colorectal Cancer Group 1Department of Oncology, Danish Colorectal Cancer Group South, Vejle Hospital; 2University of Southern Denmark, Odense, Denmark; 3Diagnostic Product Development, Exiqon A/S, Vedbæk 2950, Denmark; 4Department of Clinical Pathology, Vejle Hospital, Vejle, Denmark British Journal of Cancer (2012) 107, 1169–1174   BACKGROUND: Despite several years of research and attempts to develop prognostic models a considerable fraction of stage II colon cancer patients will experience relapse within few years from their operation. The aim of the present study was to investigate the prognostic importance of miRNA-21 (miR-21), quantified by in situ hybridisation, in a unique, large population-based cohort. PATIENTS AND METHODS: The study included 764 patients diagnosed with stage II colon cancer in Denmark in the year 2003. One section from a representative paraffin-embedded tumour tissue specimen from each patient was processed for analysis of miR-21 and quantitatively assessed by image analysis. RESULTS: The miR-21 signal was predominantly observed in fibroblast-like cells located in the stromal compartment of the tumours. We found that patients expressing high levels of miR-21 had significantly inferior recurrence-free cancer-specific survival (RF-CSS): HR¼1.26; 95% CI: 1.15–1.60; Po0.001. In Cox regression analysis, a high level of miR-21 retained its prognostic importance and was found to be significantly related to poor RF-CSS: HR¼1.41; 95% CI: 1.19–1.67; Po0.001. CONCLUSION: The present study showed that increasing miR-21 expression levels were significantly correlated to decreasing RF-CSS. Further investigations of the clinical importance of miR-21 in the selection of high-risk stage II colon cancer patients are merited. British Journal of Cancer (2012) 107, 1169–1174. http://dx.doi.org:/10.1038/bjc.2012.365

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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.

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Larry H Bernstein, MD, FCAP, Repost
Leaders in Pharmaceutical  Intelligence
https://pharmaceuticalintelligence.com/2013/04/29/francois-jacob…-lwoff/

Dr. Francois Jacob dies at 92; Nobel-winning biologist
A biologist who shared a Nobel Prize in 1965, Dr. Francois Jacob helped unlock the mysteries of RNA.
April 24, 2013, 7:21 p.m.
When James Watson and Francis Crick deciphered the structure of DNA in 1953, their discovery answered a crucial question in biology: How is genetic information passed down from parent to child?
Their work also created conundrums, however. They and others showed that every cell of an organism contains all of its genetic material. How, then, does an individual cell know which genes to use and when? And how does information from DNA get to the cell’s protein-making machinery?
The seminal insight into those questions came from three biologists at the Pasteur Institute in Paris — Dr. Francois Jacob, Jacques Monod and Andre Lwoff. They identified messenger RNA which, as the name implies, carries the blueprint for a protein from cellular DNA to the ribosome, where proteins are built. They also identified the complex system of regulatory genes that turn protein-making genes on and off.
Their achievement ushered in the modern age of molecular biology. It also won them the 1965 Nobel Prize in Medicine or Physiology, only five years after Watson and Crick received theirs.
Jacob died Friday in Paris at 92. His death was announced by the French government, but no details were released.
The inspiration for Jacob’s achievements came in the early 1950s, while he was working in Lwoff’s laboratory studying bacteriophages, viruses that infect only bacteria. They studied a bacteriophage, or phage, that infected the common bacterium Escherichia coli. They observed that the phage could infect bacterial cells and lie dormant in its genes until something triggered explosive replication that caused the cell to split open.
In related experiments with male and female bacteria, they found that male DNA that was infected with a dormant phage could be transferred to a female cell, but not vice versa. They concluded that something in the cells was suppressing the activity of the phage genes.
Nobel-winner Dr. Francois Jacob dies
That led Jacob and Monod to study E. coli that normally live on the sugar glucose. But if the bacteria are deprived of glucose and placed in a medium containing the more complicated sugar lactose, they suddenly begin producing three enzymes that: 1) take lactose into the cell; 2) break it down into its constituents glucose and galactose; and 3) break galactose down into glucose.
Through an elegant series of experiments, the researchers showed that the genes that serve as the blueprints for those three enzymes are each accompanied by another gene called the operator. In this system, glucose acts as a repressor, binding to the operator and physically preventing the blueprint gene from being copied into messenger RNA.
In other words, when the gene is not needed, it is shut off.
But when lactose is present, it binds more strongly to the operator than does glucose, pushing out the latter and allowing the structural gene to be copied. The researchers called this system of two genes an “operon” and the specific system for lactose the lactose or lac operon. They submitted their findings to the Journal of Molecular Biology on Christmas Eve in 1960 and it was published the next year.
In a review in the journal Science, molecular biologist Gunther S. Stent called it “one of the monuments in the literature of molecular biology.” Introducing the three biologists at the Nobel awards ceremony, Sven Gard of the Royal Karolinska Institute proclaimed that the French workers “opened up a field of research which in the truest sense of the word can be described as molecular biology.”
Francois Jacob was born June 17, 1920, in Nancy, France, the son of a merchant. He began studying medicine at the University of Paris with the intention of becoming a surgeon, but the war intervened after his first two years. At the age of 20, he caught one of the last vessels to England, where he joined the Free French forces.
With his two years of medical training, he served as a doctor with the Free French armored forces throughout North Africa. After the Allies’ 1944 Normandy invasion, as his armored brigade was nearing Paris, Jacob was severely wounded in a German attack when he used his own body to protect his lieutenant. He spent seven months in a hospital, missing the Free French forces’ grand reentry into the city. Damage to his hand ended his hopes of becoming a surgeon.
For his service, he received the Companion of the Liberation, the country’s highest World War II decoration for valor. He was also awarded the Legion d’Honneur and the Croix de Guerre.
After he received his medical degree in 1947, he joined a company that was attempting to make a French version of penicillin and helped in the development of a related antibiotic called tyrothrycin. In 1950, at the age of 30, he decided he was interested in cellular genetics and obtained a fellowship at the Pasteur Institute, receiving his doctor of science degree in 1954.
He later studied mechanisms of cell division and the early development of the mouse embryo.
In addition to his many research papers, he authored four books, including the 1988 autobiography “The Statue Within.”
He married the pianist Lysiane “Lise” Bloch in 1947, and they had four children. After her death, he married Genevieve Barrier in 1999. Information about survivors was unavailable.
news.obits@latimes.com

http://pgabiram.scientificlegacies.org/doc/7_nobelist_abs_1991.pdf

Stained glass window in the dining hall of Gon...

Stained glass window in the dining hall of Gonville and Caius College, in Cambridge (UK), commemorating Francis Crick, who co-discovered the molecular structure of DNA, received a Nobel Prize and was an honorary fellow of the college. The window represents a double helix; the text on the windows reads: F.H.C. CRICK, HONORARY FELLOW 1976. (Photo credit: Wikipedia)

Diagram of a eukaryotic gene

Diagram of a eukaryotic gene (Photo credit: Wikipedia)

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