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Irreconciliable Dissonance in Physical Space and Cellular Metabolic Conception

Irreconciliable Dissonance in Physical Space and Cellular Metabolic Conception

Curator: Larry H. Bernstein, MD, FCAP

Pasteur Effect – Warburg Effect – What its history can teach us today. 

José Eduardo de Salles Roselino

The Warburg effect, in reality the “Pasteur-effect” was the first example of metabolic regulation described. A decrease in the carbon flux originated at the sugar molecule towards the end of the catabolic pathway, with ethanol and carbon dioxide observed when yeast cells were transferred from an anaerobic environmental condition to an aerobic one. In Pasteur´s studies, sugar metabolism was measured mainly by the decrease of sugar concentration in the yeast growth media observed after a measured period of time. The decrease of the sugar concentration in the media occurs at great speed in yeast grown in anaerobiosis (oxygen deficient) and its speed was greatly reduced by the transfer of the yeast culture to an aerobic condition. This finding was very important for the wine industry of France in Pasteur’s time, since most of the undesirable outcomes in the industrial use of yeast were perceived when yeasts cells took a very long time to create, a rather selective anaerobic condition. This selective culture media was characterized by the higher carbon dioxide levels produced by fast growing yeast cells and by a higher alcohol content in the yeast culture media.

However, in biochemical terms, this finding was required to understand Lavoisier’s results indicating that chemical and biological oxidation of sugars produced the same calorimetric (heat generation) results. This observation requires a control mechanism (metabolic regulation) to avoid burning living cells by fast heat released by the sugar biological oxidative processes (metabolism). In addition, Lavoisier´s results were the first indications that both processes happened inside similar thermodynamics limits. In much resumed form, these observations indicate the major reasons that led Warburg to test failure in control mechanisms in cancer cells in comparison with the ones observed in normal cells.

[It might be added that the availability of O2 and CO2 and climatic conditions over 750 million years that included volcanic activity, tectonic movements of the earth crust, and glaciation, and more recently the use of carbon fuels and the extensive deforestation of our land masses have had a large role in determining the biological speciation over time, in sea and on land. O2 is generated by plants utilizing energy from the sun and conversion of CO2. Remove the plants and we tip the balance. A large source of CO2 is from beneath the earth’s surface.]

Biology inside classical thermodynamics places some challenges to scientists. For instance, all classical thermodynamics must be measured in reversible thermodynamic conditions. In an isolated system, increase in P (pressure) leads to increase in V (volume), all this occurring in a condition in which infinitesimal changes in one affects in the same way the other, a continuum response. Not even a quantic amount of energy will stand beyond those parameters.

In a reversible system, a decrease in V, under same condition, will led to an increase in P. In biochemistry, reversible usually indicates a reaction that easily goes either from A to B or B to A. For instance, when it was required to search for an anti-ischemic effect of Chlorpromazine in an extra hepatic obstructed liver, it was necessary to use an adequate system of increased biliary system pressure in a reversible manner to exclude a direct effect of this drug over the biological system pressure inducer (bile secretion) in Braz. J. Med. Biol. Res 1989; 22: 889-893. Frequently, these details are jumped over by those who read biology in ATGC letters.

Very important observations can be made in this regard, when neutral mutations are taken into consideration since, after several mutations (not affecting previous activity and function), a last mutant may provide a new transcript RNA for a protein and elicit a new function. For an example, consider a Prion C from lamb getting similar to bovine Prion C while preserving  its normal role in the lamb when its ability to change Human Prion C is considered (Stanley Prusiner).

This observation is good enough, to confirm one of the most important contributions of Erwin Schrodinger in his What is Life:

“This little book arose from a course of public lectures, delivered by a theoretical physicist to an audience of about four hundred which did not substantially dwindle, though warned at the outset that the subject matter was a difficult one and that the lectures could not be termed popular, even though the physicist’s most dreaded weapon, mathematical deduction, would hardly be utilized. The reason for this was not that the subject was simple enough to be explained without mathematics, but rather that it was much too involved to be fully accessible to mathematics.”

After Hans Krebs, description of the cyclic nature of the citrate metabolism and after its followers described its requirement for aerobic catabolism two major lines of research started the search for the understanding of the mechanism of energy transfer that explains how ADP is converted into ATP. One followed the organic chemistry line of reasoning and therefore, searched for a mechanism that could explain how the breakdown of carbon-carbon link could have its energy transferred to ATP synthesis. One of the major leaders of this research line was Britton Chance. He took into account that relatively earlier in the series of Krebs cycle reactions, two carbon atoms of acetyl were released as carbon dioxide ( In fact, not the real acetyl carbons but those on the opposite side of citrate molecule). In stoichiometric terms, it was not important whether the released carbons were or were not exactly those originated from glucose carbons. His research aimed at to find out an intermediate proteinaceous intermediary that could act as an energy reservoir. The intermediary could store in a phosphorylated amino acid the energy of carbon-carbon bond breakdown. This activated amino acid could transfer its phosphate group to ADP producing ATP. A key intermediate involved in the transfer was identified by Kaplan and Lipmann at John Hopkins as acetyl coenzyme A, for which Fritz Lipmann received a Nobel Prize.

Alternatively, under possible influence of the excellent results of Hodgkin and Huxley a second line of research appears. The work of Hodgkin & Huxley indicated that the storage of electrical potential energy in transmembrane ionic asymmetries and presented the explanation for the change from resting to action potential in excitable cells. This second line of research, under the leadership of Peter Mitchell postulated a mechanism for the transfer of oxide/reductive power of organic molecules oxidation through electron transfer as the key for the energetic transfer mechanism required for ATP synthesis.
This diverted the attention from high energy (~P) phosphate bond to the transfer of electrons. During most of the time the harsh period of the two confronting points of view, Paul Boyer and followers attempted to act as a conciliatory third party, without getting good results, according to personal accounts (in L. A. or Latin America) heard from those few of our scientists who were able to follow the major scientific events held in USA, and who could present to us later. Paul  Boyer could present how the energy was transduced by a molecular machine that changes in conformation in a series of 3 steps while rotating in one direction in order to produce ATP and in opposite direction in order to produce ADP plus Pi from ATP (reversibility).

However, earlier, a victorious Peter Mitchell obtained the result in the conceptual dispute, over the Britton Chance point of view, after he used E. Coli mutants to show H+ gradients in the cell membrane and its use as energy source, for which he received a Nobel Prize. Somehow, this outcome represents such a blow to Chance’s previous work that somehow it seems to have cast a shadow over very important findings obtained during his earlier career that should not be affected by one or another form of energy transfer mechanism.  For instance, Britton Chance got the simple and rapid polarographic assay method of oxidative phosphorylation and the idea of control of energy metabolism that brings us back to Pasteur.

This metabolic alternative result seems to have been neglected in the recent years of obesity epidemics, which led to a search for a single molecular mechanism required for the understanding of the accumulation of chemical (adipose tissue) reserve in our body. It does not mean that here the role of central nervous system is neglected. In short, in respiring mitochondria the rate of electron transport linked to the rate of ATP production is determined primarily by the relative concentrations of ADP, ATP and phosphate in the external media (cytosol) and not by the concentration of respiratory substrate as pyruvate. Therefore, when the yield of ATP is high as it is in aerobiosis and the cellular use of ATP is not changed, the oxidation of pyruvate and therefore of glycolysis is quickly (without change in gene expression), throttled down to the resting state. The dependence of respiratory rate on ADP concentration is also seen in intact cells. A muscle at rest and using no ATP has a very low respiratory rate.   [When skeletal muscle is stressed by high exertion, lactic acid produced is released into the circulation and is metabolized aerobically by the heart at the end of the activity].

This respiratory control of metabolism will lead to preservation of body carbon reserves and in case of high caloric intake in a diet, also shows increase in fat reserves essential for our biological ancestors survival (Today for our obesity epidemics). No matter how important this observation is, it is only one focal point of metabolic control. We cannot reduce the problem of obesity to the existence of metabolic control. There are numerous other factors but on the other hand, we cannot neglect or remove this vital process in order to correct obesity. However, we cannot explain obesity ignoring this metabolic control. This topic is so neglected in modern times that we cannot follow major research lines of the past that were interrupted by the emerging molecular biology techniques and the vain belief that a dogmatic vision of biology could replace all previous knowledge by a new one based upon ATGC readings. For instance, in order to display bad consequences derived from the ignorance of these old scientific facts, we can take into account, for instance, how ion movements across membranes affects membrane protein conformation and therefore contradicts the wrong central dogma of molecular biology. This change in protein conformation (with unchanged amino acid sequence) and/or the lack of change in protein conformation is linked to the factors that affect vital processes as the heart beats. This modern ignorance could also explain some major pitfalls seen in new drugs clinical trials and in a small scale on bad medical practices.

The work of Britton Chance and of Peter Mitchell have deep and sound scientific roots that were made with excellent scientific techniques, supported by excellent scientific reasoning and that were produced in a large series of very important intermediary scientific results. Their sole difference was to aim at very different scientific explanations as their goals (They have different Teleology in their minds made by their previous experiences). When, with the use of mutants obtained in microorganisms P Mitchell´s goal was found to survive and B Chance to succumb to the experimental evidence, all those excellent findings of B Chance and followers were directed to the dustbin of scientific history as an example of lack of scientific consideration.  [On the one hand, the Mitchell model used a unicellular organism; on the other, Chance’s work was with eukaryotic cells, quite relevant to the discussion.]

We can resume the challenge faced by these two great scientists in the following form: The first conceptual unification in bioenergetics, achieved in the 1940s, is inextricably bound up with the name of Fritz Lipmann. Its central feature was the recognition that adenosine triphosphate, ATP, serves as a universal energy  “currency” much as money serves as economic currency. In a nutshell, the purpose of metabolism is to support the synthesis of ATP. In microorganisms, this is perfect! In humans or mammals, or vertebrates, by the same reason that we cannot consider that gene expression is equivalent to protein function (an acceptable error in the case of microorganisms) this oversimplifies the metabolic requirement with a huge error. However, in case our concern is ATP chemistry only, the metabolism produces ATP and the hydrolysis of ATP pays for the performance of almost, all kinds of works. It is possible to presume that to find out how the flow of metabolism (carbon flow) led to ATP production must be considered a major focal point of research of the two contenders. Consequently, what could be a minor fall of one of the contenders, in case we take into account all that was found during their entire life of research, the real failure in B Chance’s final goal was amplified far beyond what may be considered by reason!

Another aspect that must be taken into account: Both contenders have in the scientific past a very sound root. Metabolism may produce two forms of energy currency (I personally don´t like this expression*) and I use it here because it was used by both groups in order to express their findings. Together with simplistic thermodynamics, this expression conveys wrong ideas): The second kind of energy currency is the current of ions passing from one side of a membrane to the other. The P. Mitchell scientific root undoubtedly have the work of Hodgkin & Huxley, Huxley &  Huxley, Huxley & Simmons

*ATP is produced under the guidance of cell needs and not by its yield. When glucose yields only 2 ATPs per molecule it is oxidized at very high speed (anaerobiosis) as is required to match cellular needs. On the other hand, when it may yield (thermodynamic terms) 38 ATP the same molecule is oxidized at low speed. It would be similar to an investor choice its least money yield form for its investment (1940s to 1972) as a solid support. B. Chance had the enzymologists involved in clarifying how ATP could be produced directly from NADH + H+ oxidative reductive metabolic reactions or from the hydrolysis of an enolpyruvate intermediary. Both competitors had their work supported by different but, sound scientific roots and have produced very important scientific results while trying to present their hypothetical point of view.

Before the winning results of P. Mitchell were displayed, one line of defense used by B. Chance followers was to create a conflict between what would be expected by a restrictive role of proteins through its specificity ionic interactions and the general ability of ionic asymmetries that could be associated with mitochondrial ATP production. Chemical catalyzed protein activities do not have perfect specificity but an outstanding degree of selective interaction was presented by the lock and key model of enzyme interaction. A large group of outstanding “mitochondriologists” were able to show ATP synthesis associated with Na+, K+, Ca2+… asymmetries on mitochondrial membranes and any time they did this, P. Mitchell have to display the existence of antiporters that exchange X for hydrogen as the final common source of chemiosmotic energy used by mitochondria for ATP synthesis.

This conceptual battle has generated an enormous knowledge that was laid to rest, somehow discontinued in the form of scientific research, when the final E. Coli mutant studies presented the convincing final evidence in favor of P. Mitchell point of view.

Not surprisingly, a “wise anonymous” later, pointed out: “No matter what you are doing, you will always be better off in case you have a mutant”

(Principles of Medical Genetics T D Gelehrter & F.S. Collins chapter 7, 1990).

However, let’s take the example of a mechanical wristwatch. It clearly indicates when the watch is working in an acceptable way, that its normal functioning condition is not the result of one of its isolated components – or something that can be shown by a reductionist molecular view.  Usually it will be considered that it is working in an acceptable way, in case it is found that its accuracy falls inside a normal functional range, for instance, one or two standard deviations bellow or above the mean value for normal function, what depends upon the rigor wisely adopted. While, only when it has a faulty component (a genetic inborn error) we can indicate a single isolated piece as the cause of its failure (a reductionist molecular view).

We need to teach in medicine, first the major reasons why the watch works fine (not saying it is “automatic”). The functions may cross the reversible to irreversible regulatory limit change, faster than what we can imagine. Latter, when these ideas about normal are held very clear in the mind set of medical doctors (not medical technicians) we may address the inborn errors and what we may have learn from it. A modern medical technician may cause admiration when he uses an “innocent” virus to correct for a faulty gene (a rather impressive technological advance). However, in case the virus, later shows signals that indicate that it was not so innocent, a real medical doctor will be called upon to put things in correct place again.

Among the missing parts of normal evolution in biochemistry a lot about ion fluxes can be found. Even those oscillatory changes in Ca2+ that were shown to affect gene expression (C. De Duve) were laid to rest since, they clearly indicate a source of biological information that despite the fact that it does not change nucleotides order in the DNA, it shows an opposing flux of biological information against the dogma (DNA to RNA to proteins). Another, line has shown a hierarchy, on the use of mitochondrial membrane potential: First the potential is used for Ca2+ uptake and only afterwards, the potential is used for ADP conversion into ATP (A. L. Lehninger). In fact, the real idea of A. L. Lehninger was by far, more complex since according to him, mitochondria works like a buffer for intracellular calcium releasing it to outside in case of a deep decrease in cytosol levels or capturing it from cytosol when facing transient increase in Ca2+ load. As some of Krebs cycle dehydrogenases were activated by Ca2+, this finding was used to propose a new control factor in addition to the one of ADP (B. Chance). All this was discontinued with the wrong use of calculus (today we could indicate bioinformatics in a similar role) in biochemistry that has established less importance to a mitochondrial role after comparative kinetics that today are seen as faulty.

It is important to combat dogmatic reasoning and restore sound scientific foundations in basic medical courses that must urgently reverse the faulty trend that tries to impose a view that goes from the detail towards generalization instead of the correct form that goes from the general finding well understood towards its molecular details. The view that led to curious subjects as bioinformatics in medical courses as training in sequence finding activities can only be explained by its commercial value. The usual form of scientific thinking respects the limits of our ability to grasp new knowledge and relies on reproducibility of scientific results as a form to surpass lack of mathematical equation that defines relationship of variables and the determination of its functional domains. It also uses old scientific roots, as its sound support never replaces existing knowledge by dogmatic and/or wishful thinking. When the sequence of DNA was found as a technical advance to find amino acid sequence in proteins it was just a technical advance. This technical advance by no means could be considered a scientific result presented as an indication that DNA sequences alone have replaced the need to study protein chemistry, its responses to microenvironmental changes in order to understand its multiple conformations, changes in activities and function. As E. Schrodinger correctly describes the chemical structure responsible for the coded form stored of genetic information must have minimal interaction with its microenvironment in order to endure hundreds and hundreds years as seen in Hapsburg’s lips. Only magical reasoning assumes that it is possible to find out in non-reactive chemical structures the properties of the reactive ones.

For instance, knowledge of the reactions of the Krebs cycle clearly indicate a role for solvent that no longer could be considered to be an inert bath for catalytic activity of the enzymes when the transfer of energy include a role for hydrogen transport. The great increase in understanding this change on chemical reaction arrived from conformational energy.

Again, even a rather simplistic view of this atomic property (Conformational energy) is enough to confirm once more, one of the most important contribution of E. Schrodinger in his What is Life:

“This little book arose from a course of public lectures, delivered by a theoretical physicist to an audience of about four hundred which did not substantially dwindle, though warned at the outset that the subject matter was a difficult one and that the lectures could not be termed popular, even though the physicist’s most dreaded weapon, mathematical deduction, would hardly be utilized. The reason for this was not that the subject was simple enough to be explained without mathematics, but rather that it was much too involved to be fully accessible to mathematics.”

In a very simplistic view, while energy manifests itself by the ability to perform work conformational energy as a property derived from our atomic structure can be neutral, positive or negative (no effect, increased or decreased reactivity upon any chemistry reactivity measured as work)

Also:

“I mean the fact that we, whose total being is entirely based on a marvelous interplay of this very kind, yet if all possess the power of acquiring considerable knowledge about it. I think it possible that this knowledge may advance to little just a short of a complete understanding -of the first marvel. The second may well be beyond human understanding.”

In fact, scientific knowledge allows us to understand how biological evolution may have occurred or have not occurred and yet does not present a proof about how it would have being occurred. It will be always be an indication of possible against highly unlike and never a scientific proven fact about the real form of its occurrence.

As was the case of B. Chance in its bioenergetics findings, we may get very important findings that indicates wrong directions in the future as was his case, or directed toward our past.

The Skeleton of Physical Time – Quantum Energies in Relative Space of S-labs

By Radoslav S. Bozov  Independent Researcher

WSEAS, Biology and BioSystems of Biomedicine

Space does not equate to distance, displacement of an object by classically defined forces – electromagnetic, gravity or inertia. In perceiving quantum open systems, a quanta, a package of energy, displaces properties of wave interference and statistical outcomes of sums of paths of particles detected by a design of S-labs.

The notion of S-labs, space labs, deals with inherent problems of operational module, R(i+1), where an imagination number ‘struggles’ to work under roots of a negative sign, a reflection of an observable set of sums reaching out of the limits of the human being organ, an eye or other foundational signal processing system.

While heavenly bodies, planets, star systems, and other exotic forms of light reflecting and/or emitting objects, observable via naked eye have been deduced to operate under numerical systems that calculate a periodic displacement of one relative to another, atomic clocks of nanospace open our eyes to ever expanding energy spaces, where matrices of interactive variables point to the problem of infinity of variations in scalar spaces, however, defining properties of minute universes as a mirror image of an astronomical system. The first and furthermost problem is essentially the same as those mathematical methodologies deduced by Isaac Newton and Albert Einstein for processing a surface. I will introduce you to a surface interference method by describing undetermined objective space in terms of determined subjective time.

Therefore, the moment will be an outcome of statistical sums of a numerical system extending from near zero to near one. Three strings hold down a dual system entangled via interference of two waves, where a single wave is a product of three particles (today named accordingly to either weak or strong interactions) momentum.

The above described system emerges from duality into trinity the objective space value of physical realities. The triangle of physical observables – charge, gravity and electromagnetism, is an outcome of interference of particles, strings and waves, where particles are not particles, or are strings strings, or  are waves waves of an infinite character in an open system which we attempt to define to predict outcomes of tomorrow’s parameters, either dependent or independent as well as both subjective to time simulations.

We now know that aging of a biological organism cannot be defined within singularity. Thereafter, clocks are subjective to apparatuses measuring oscillation of defined parameters which enable us to calculate both amplitude and a period, which we know to be dependent on phase transitions.

The problem of phase was solved by the applicability of carbon relative systems. A piece of diamond does not get wet, yet it holds water’s light entangled property. Water is the dark force of light. To formulate such statement, we have been searching truth by examining cooling objects where the Maxwell demon is translated into information, a data complex system.

Modern perspectives in computing quantum based matrices, 0+1 =1 and/or 0+0=1, and/or 1+1 =0, will be reduced by applying a conceptual frame of Aladdin’s flying anti-gravity carpet, unwrapping both past and future by sending a photon to both, placing present always near zero. Thus, each parallel quantum computation of a natural system approaching the limit of a vibration of a string defining 0 does not equal 0, and 1 does not equal 1. In any case, if our method 1+1 = 1, yet, 1 is not 1 at time i+1. This will set the fundamentals of an operational module, called labris operator or in simplicity S-labs. Note, that 1 as a result is an event predictable to future, while interacting parameters of addition 1+1 may be both, 1 as an observable past, and 1 as an imaginary system, or 1+1 displaced interactive parameters of past observable events. This is the foundation of Future Quantum Relative Systems Interference (QRSI), taking analytical technologies of future as a result of data matrices compressing principle relative to carbon as a reference matter rational to water based properties.

Goedel’s concept of loops exist therefore only upon discrete relative space uniting to parallel absolute continuity of time ‘lags’. ( Goedel, Escher and Bach: An Eternal Golden Braid. A Metaphorical Fugue on Minds and Machines in the Spirit of Lewis Carroll. D Hofstadter.  Chapter XX: Strange Loops, Or Tangled Hierarchies. A grand windup of many of the ideas about hierarchical systems and self-reference. It is concerned with the snarls which arise when systems turn back on themselves-for example, science probing science, government investigating governmental wrongdoing, art violating the rules of art, and finally, humans thinking about their own brains and minds. Does Gödel’s Theorem have anything to say about this last “snarl”? Are free will and the sensation of consciousness connected to Gödel’s Theorem? The Chapter ends by tying Gödel, Escher, and Bach together once again.)  The fight struggle in-between time creates dark spaces within which strings manage to obey light properties – entangled bozons of information carrying future outcomes of a systems processing consciousness. Therefore, Albert Einstein was correct in his quantum time realities by rejecting a resolving cube of sugar within a cup of tea (Henri Bergson 19th century philosopher. Bergson’s concept of multiplicity attempts to unify in a consistent way two contradictory features: heterogeneity and continuity. Many philosophers today think that this concept of multiplicity, despite its difficulty, is revolutionary.) However, the unity of time and space could not be achieved by deducing time to charge, gravity and electromagnetic properties of energy and mass.

Charge is further deduced to interference of particles/strings/waves, contrary to the Hawking idea of irreducibility of chemical energy carrying ‘units’, and gravity is accounted for by intrinsic properties of   anti-gravity carbon systems processing light, an electromagnetic force, that I have deduced towards ever expanding discrete energy space-energies rational to compressing mass/time. The role of loops seems to operate to control formalities where boundaries of space fluctuate as a result of what we called above – dark time-spaces.

Indeed, the concept of horizon is a constant due to ever expanding observables. Thus, it fails to acquire a rational approach towards space-time issues.

Richard Feynman has touched on issues of touching of space, sums of paths of particle traveling through time. In a way he has resolved an important paradigm, storing information and possibly studying it by opening a black box. Schroedinger’s cat is alive again, but incapable of climbing a tree when chased by a dog. Every time a cat climbs a garden tree, a fruit falls on hedgehogs carried away parallel to living wormholes whose purpose of generating information lies upon carbon units resolving light.

In order to deal with such a paradigm, we will introduce i+1 under square root in relativity, therefore taking negative one ( -1 = sqrt (i+1), an operational module R dealing with Wheelers foam squeezed by light, releasing water – dark spaces. Thousand words down!

What is a number? Is that a name or some kind of language or both? Is the issue of number theory possibly accountable to the value of the concept of entropic timing? Light penetrating a pyramid holding bean seeds on a piece of paper and a piece of slice of bread, a triple set, where a church mouse has taken a drop of tear, but a blood drop. What an amazing physics! The magic of biology lies above egoism, above pride, and below Saints.

We will set up the twelve parameters seen through 3+1 in classic realities:

–              discrete absolute energies/forces – no contradiction for now between Newtonian and Albert Einstein mechanics

–              mass absolute continuity – conservational law of physics in accordance to weak and strong forces

–              quantum relative spaces – issuing a paradox of Albert Einstein’s space-time resolved by the uncertainty principle

–              parallel continuity of multiple time/universes – resolving uncertainty of united space and energy through evolving statistical concepts of scalar relative space expansion and vector quantum energies by compressing relative continuity of matter in it, ever compressing flat surfaces – finding the inverse link between deterministic mechanics of displacement and imaginary space, where spheres fit within surface of triangles as time unwraps past by pulling strings from future.

To us, common human beings, with an extra curiosity overloaded by real dreams, value happens to play in the intricate foundation of life – the garden of love, its carbon management in mind, collecting pieces of squeezed cooling time.

The infinite interference of each operational module to another composing ever emerging time constrains unified by the Solar system, objective to humanity, perhaps answers that a drop of blood and a drop of tear is united by a droplet of a substance separating negative entropy to time courses of a physical realities as defined by an open algorithm where chasing power subdue to space becomes an issue of time.

Jose Eduardo de Salles Roselino

Some small errors: For intance an increase i P leads to a decrease in V ( not an increase in V)..

 

Radoslav S. Bozov  Independent Researcher

If we were to use a preventative measures of medical science, instruments of medical science must predict future outcomes based on observable parameters of history….. There are several key issues arising: 1. Despite pinning a difference on genomic scale , say pieces of information, we do not know how to have changed that – that is shift methylome occupying genome surfaces , in a precise manner.. 2. Living systems operational quo DO NOT work as by vector gravity physics of ‘building blocks. That is projecting a delusional concept of a masonry trick, who has not worked by corner stones and ever shifting momenta … Assuming genomic assembling worked, that is dealing with inferences through data mining and annotation, we are not in a position to read future in real time, and we will never be, because of the rtPCR technology self restriction into data -time processing .. We know of existing post translational modalities… 3. We don’t know what we don’t know, and that foundational to future medicine – that is dealing with biological clocks, behavior, and various daily life inputs ranging from radiation to water systems, food quality, drugs…

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Size Matters

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

MinION Sequencing Untangles RNA Transcripts in a Difficult Gene

By Aaron Krol

http://www.bio-itworld.com/2015/11/3/minion-sequencing-untangles-rna-transcripts-difficult-gene.html

 

RNA isoforms are distinct versions of the same isoforms quotegene. Through a process called alternative splicing, the different subunits, or “exons,” that make up a gene can be reshuffled in new combinations. Many genes have two or more mutually exclusive exons, and which ones are actually expressed as RNA and protein can have big effects on cellular behavior ― in effect, expanding the protein arsenal of the genome.

 

November 3, 2015 | Brenton Graveley received his first MinION shipment in April 2014, at his lab at the University of Connecticut’s Institute of Systems Genomics. His lab was among the first to unwrap one of the candy bar-sized DNA sequencers made by Oxford Nanopore Technologies, and although its accuracy was shaky and its throughput low, right away Graveley and his colleagues could see it was producing real DNA data.

“I’m still amazed to this day that it works at all,” Graveley says. “It’s like Star Trek.”

A lot of buzz around the MinION has focused on its tiny size: early adopters have plotted to take MinIONs into outbreak zones and species-hunting tromps through the rainforest, working with bare-bones labs and laptop computers. But for Graveley, the size of the DNA strands the MinION reads is just as exciting as the size of the sequencer itself. That’s because most other sequencers rely on picking up chemical reactions that become more error-prone over time, meaning DNA can only be read in short fragments. The MinION, which reads genetic material by observing single molecules of DNA as they pass through extremely narrow “nanopores,” keeps producing data for as long as DNA is moving through the pore.

“You get the read length of whatever fragment you put into the MinION,” he says. “We’ve gotten reads that are over 100 kilobases,” hundreds or even thousands of times longer than researchers can expect with most other technologies.

Now, in a paper published in Genome Biology, Graveley and two of his lab members, post-doc Mohan Bolisetty and PhD student Gopinath Rajadinakaran, have shown how these read lengths can help explain the cellular behavior of Dscam1, one of the most difficult-to-study genes known to science. Related to a gene in humans that has been linked to Down syndrome ― the name stands for “Down Syndrome Cell Adhesion Molecule” ―Dscam1 plays a fundamental role in forming the architecture of insect brains. This single gene can produce thousands of subtly different proteins, an ability that makes it both a fascinating subject of research, and almost impossible to understand using standard sequencing technology.

 

Determining exon connectivity in complex mRNAs by nanopore sequencing

Mohan T. Bolisetty12, Gopinath Rajadinakaran1 and Brenton R. Graveley1*
Genome Biology 2015, 16:204       http://dx.doi.org:/10.1186/s13059-015-0777-z                    http://genomebiology.com/2015/16/1/204

Short-read high-throughput RNA sequencing, though powerful, is limited in its ability to directly measure exon connectivity in mRNAs that contain multiple alternative exons located farther apart than the maximum read length. Here, we use the Oxford Nanopore MinION sequencer to identify 7,899 ‘full-length’ isoforms expressed from four Drosophila genes, Dscam1, MRP, Mhc, and Rdl. These results demonstrate that nanopore sequencing can be used to deconvolute individual isoforms and that it has the potential to be a powerful method for comprehensive transcriptome characterization.

High throughput RNA sequencing has revolutionized genomics and our understanding of the transcriptomes of many organisms. Most eukaryotic genes encode pre-mRNAs that are alternatively spliced [1]. In many genes, alternative splicing occurs at multiple places in the transcribed pre-mRNAs that are often located farther apart than the read lengths of most current high throughput sequencing platforms. As a result, several transcript assembly and quantitation software tools have been developed to address this [2], [3]. While these computational approaches do well with many transcripts, they generally have difficulty assembling transcripts of genes that express many isoforms. In fact, we have been unable to successfully assemble transcripts of complex alternatively spliced genes such as Dscam1 or Mhc using any transcript assembly software (data not shown). These software tools also have difficulty quantitating transcripts that have many isoforms, and for genes with distantly located alternatively spliced regions, they can only infer, and not directly measure, which isoforms may have been present in the original RNA sample [4]. For example, consider a gene containing two alternatively spliced exons located 2 kbp away from one another in the mRNA. If each exon is observed to be included at a frequency of 50 % from short read sequence data, it is impossible to determine whether there are two equally abundant isoforms that each contain or lack both exons, or four equally abundant isoforms that contain both, neither, or only one or the other exon.

Pacific Bioscience sequencing can generate read lengths sufficient to sequence full length cDNA isoforms and several groups have recently reported the use of this approach to characterize the transcriptome [5]. However, the large capital expense of this platform can be a prohibitive barrier for some users. Thus, it remains difficult to accurately and directly determine the connectivity of exons within the same transcript. The MinION nanopore sequencer from Oxford Nanopore requires a small initial financial investment, can generate extremely long reads, and has the potential to revolutionize transcriptome characterization, as well as other areas of genomics.

Several eukaryotic genes can encode hundreds to thousands of isoforms. For example, inDrosophila, 47 genes encode over 1,000 isoforms each [6]. Of these, Dscam1 is the most extensively alternatively spliced gene known and contains 115 exons, 95 of which are alternatively spliced and organized into four clusters [7]. The exon 4, 6, 9, and 17 clusters contain 12, 48, 33, and 2 exons, respectively. The exons within each cluster are spliced in a mutually exclusive manner and Dscam1 therefore has the potential to generate 38,016 different mRNA and protein isoforms. The variable exon clusters are also located far from one another in the mRNA and the exons within each cluster are up to 80 % identical to one another at the nucleotide level. Together, these characteristics present numerous challenges to characterize exon connectivity within full-length Dscam1 transcripts for any sequencing platform. Furthermore, though no other gene is as complex as Dscam1, many other genes have similar issues that confound the determination of exon connectivity.

We are interested in developing methods to perform simple and robust long-read sequencing of individual isoforms of Dscam1 and other complex alternatively spliced genes. Here, we use the Oxford Nanopore MinION to sequence ‘full-length’ cDNAs from four Drosophila genes – Rdl, MRP,Mhc, and Dscam1 – and identify a total of 7,899 distinct isoforms expressed by these four genes.

 

Similarity between alternative exons

We were interested in determining the feasibility of using the MinION nanopore sequencer to characterize the connectivity of distantly located exons in the mRNAs expressed from genes with complex splicing patterns. For the purposes of these experiments, we have focused on fourDrosophila genes with increasingly complex patterns of alternative splicing (Fig. 1). Resistant to dieldrin (Rdl) contains two clusters, each containing two mutually exclusive exons and therefore has the potential to generate four different isoforms (Fig. 1a). Multidrug-Resistance like Protein 1(MRP) contains two mutually exclusive exons in cluster 1 and eight mutually exclusive exons in cluster 2, and can generate 16 possible isoforms (Fig. 1b). Myosin heavy chain (Mhc) can potentially generate 180 isoforms due to five clusters of mutually exclusive exons – clusters 1 and 5 contain two exons, clusters 2 and 3 each contain three exons, and cluster 4 contains five exons. Finally, Dscam1 contains 12 exon 4 variants, 48 exon 6 variants, 33 exon 9 variants (Fig. 1d), and two exon 17 variants (not shown) and can potentially express 38,016 isoforms. For this study, however, we have focused only on the exon 3 through exon 10 region of Dscam1, which encompasses the 93 exon 4, 6, and 9 variants, and 19,008 potential isoforms (Fig. 1d).

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Fig. 1. Schematic of the exon-intron structures of the genes examined in this study. a The Rdl gene contains two clusters (cluster one and two) which each contain two mutually exclusive exons. b The MRP gene contains contains two and eight mutually exclusive exons in clusters 1 and 2, respectively. Mhc contains two mutually exclusive exons in clusters 1 and 5, three mutually exclusive exons in clusters 2 and 3, and five mutually exclusive exons in cluster 4. The Dscam1 gene contains 12, 48, and 33 mutually exclusive exons in the exon 4, 6, and 9 clusters, respectively. For each gene, the constitutive exons are colored blue, while the variable exons are colored yellow, red, orange, green, or light blue

Because our nanopore sequence analysis pipeline uses LAST to perform alignments [8], we aligned all of the Rdl, MRP, Mhc, and Dscam1 exons within each cluster to one another using LAST to determine the extent of discrimination needed to accurately assign nanopore reads to a specific exon variant. For Rdl, each variable exon was only aligned to itself, and not to the other exon in the same cluster (data not shown). For MRP, the two exons within cluster 1 only align to themselves, and though the eight variable exons in cluster 2 do align to other exons, there is sufficient specificity to accurately assign nanopore reads to individual exons (Fig. 2a). For Mhc, the variable exons in cluster 1 and cluster 5 do not align to other exons, and the variable exons in cluster 2, cluster 3, and cluster 4 again align with sufficient discrimination to identify the precise exon present in the nanopore reads (Fig. 2b). Finally, for Dscam1, the difference in the LAST alignment scores between the best alignment (each exon to itself) and the second, third, and fourth best alignments are sufficient to identify the Dscam1 exon variant (Fig. 2c). This analysis indicates that for each gene in this study, LAST alignment scores are sufficiently distinct to identify the variable exons present in each nanopore read.

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Fig. 2. Similarity distance between the variable alternative exons of MRP,Mhc, and Dscam1. a Violin plots of the LAST alignment scores of each variable exon within MRP cluster 1 and MRP cluster 2 to themselves and the second (2nd) best alignments. b Violin plots of the LAST alignment scores of each variable exon within each Mhc cluster to themselves and the second (2nd) best alignments. c Violin plots of the LAST alignment scores of each variable exon within each Dscam1 cluster to themselves (1st), and to the exons with the second (2nd), third (3rd) and fourth (4th) best alignments

Optimizing template switching in Dscam1 cDNA libraries

Template switching can occur frequently when libraries are prepared by PCR and can confound the interpretation of results [9], [10]. For example, CAM-Seq [11] and a similar method we independently developed called Triple-Read sequencing [12] to characterize Dscam1 isoforms, were found to have excessive template switching due to amplification during the library prep protocols. To assess template switching in our current study, we generated a spike-in mixture of in vitro transcribed RNAs representing six unique Dscam1 isoforms – Dscam1 4.2,6.32,9.31 , Dscam14.1,6.46,9.30 , Dscam1 4.3,6.33,9.9 , Dscam1 4.12,6.44,9.32 , Dscam1 4.7,6.8,9.15 , and Dscam1 4.5,6.4,9.4. We used 10 pg of this control spike-in mixture and prepared libraries for MinION sequencing by amplifying the exon 3 through exon 10 region for 20, 25, or 30 cycles of RT-PCR. We then end-repaired and dA-tailed the fragments, ligated adapters, and sequenced the samples on a MinION (7.3) for 12 h each. We obtained 33,736, 8,961, and 7,511 base-called reads from the 20, 25, and 30 cycle libraries, respectively. Consistent with the size of the exon 3 to 10 cDNA fragment being 1,806–1,860 bp in length, depending on the precise combination of exons it contains, most reads we observed were in this size range (Fig. 3a). We used Poretools [13] to convert the raw output files into fasta format and then used LAST to align the reads to a LAST database containing each variable exon. From these alignments, we identified reads that mapped to all three exon clusters, as well as the exon with the best alignment score within each cluster. When examining the alignments to each cluster independently, we found that for these spike-in libraries, all reads mapped uniquely to the exons present in the input isoforms. Therefore, any observed isoforms that were not present in the input pool were a result of template switching during the RT-PCR and library prep protocol and not due to false alignments or sequencing errors.

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Fig. 3. Optimized RT-PCR minimizes template-switching for MinION sequencing. a Histogram of read lengths from MinION sequencing ofDscam1 spike-ins from the library generated using 25 cycles of PCR. bBar plot indicating the extent of template switching in Dscam1 spike-ins at different PCR cycles (left). The blue portions indicate the fraction of reads corresponding to input isoforms while the red portions correspond to the fraction of reads corresponding to template-switched isoforms. On the right, plots of the rank order versus number of reads (log10) for the 20, 25, and 30 cycle libraries. The blue dots indicate input isoforms while the red portions correspond to template-switched isoforms

When comparing the combinations of exons within each read to the input isoforms, we observed that 32 % of the reads from the 30 cycle library corresponded to isoforms generated by template switching (Fig. 3b). The template-switched isoforms observed by the greatest number of reads in the 30 cycle library were due to template switching between the two most frequently sequenced input isoforms. In most cases, template switching occurred somewhere within exon 7 or 8 and resulted in a change in exon 9. However, the extent of template switching was reduced to only 1 % in the libraries prepared using 25 cycles, and to 0.2 % in the libraries prepared using 20 cycles of PCR (Fig. 3b). Again, for these two libraries the most frequently sequenced template-switched isoforms involved the input isoforms that were also the most frequently sequenced. These experiments demonstrate that the MinION nanopore sequencer can be used to sequence ‘full length’ Dscam1 cDNAs with sufficient accuracy to identify isoforms and that the cDNA libraries can be prepared in a manner that results in a very small amount of template switching.

Dscam1 isoforms observed in adult heads

To explore the diversity of Dscam1 isoforms expressed in a biological sample, we prepared aDscam1 library from RNA isolated from D. melanogaster heads prepared from mixed male and female adults using 25 cycles of PCR and sequenced it for 12 h on the MinION nanopore sequencer obtaining a total of 159,948 reads of which 78,097 were template reads, 48,474 were complement reads, and 33,377 were 2D reads (Fig. 4a). We aligned the reads individually to the exon 4, 6, and 9 variants using LAST. A total of 28,971 reads could be uniquely or preferentially aligned to a single variant in all three clusters. For further analysis, we used all 16,419 2D read alignments and 31 1D reads when both template and complement aligned to same variant exons (not all reads with both a template and complement yield a 2D read). The remaining 12,521 aligned reads were 1D reads where there was either only a template or complement read, or when the template and complement reads disagreed with one another and were therefore not used further. We observed 92 of the 93 potential exon 4, 6, or 9 variants – only exon 6.11 was not observed in any read (Fig. 4f). To assess the accuracy of the results we performed RT-PCR using primers in the flanking constitutive exons that contained Illumina sequencing primers to separately amplify the Dscam1exon 4, 6, and 9 clusters from the same RNA used to prepare the MinION libraries, and sequenced the amplicons on an Illumina MiSeq. The frequency of variable exon use in each cluster was extremely consistent between the two methods (R 2  = 0.95, Fig. 5a).

Fig. 4. MinION sequencing of Dscam1 identified 7,874 isoforms. aHistogram of read length distribution for Drosophila head samples. b The total number of Dscam1 isoforms identified from MinION sequencing. cCumulative distribution of Dscam1 isoforms with respect to expression. dViolin plot of the number of isoforms identified using 100 random pools of the indicated number of reads. e Plot of the estimated number of total isoforms present in the library using the capture-recapture method with two random pools of the indicated number of reads. The shaded blue area indicates the 95 % confidence interval. f Deconvoluted expression of Dscam1 exon cluster variants (top) and the isoform connectivity of two highly expressed Dscam1 isoforms (bottom)

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Fig. 5. Accuracy of Dscam1 sequencing results. a Comparison of the frequency of variable exon inclusion for the Dscam1 exon 4 (yellow), 6 (red), and 9 (orange) clusters as determined by nanopore sequencing or by amplicon sequencing using an Illumina MiSeq. b Percent identities (left) or LAST alignment scores (right) of full-length template, complement, and two directions (sequencing both template and complements) nanopore read alignments

Over their entire lengths, the 2D reads that map specifically to one exon 4, 6, and 9 variants map with an average 90.37 % identity and an average LAST score of approximately 1,200 (Fig. 5b). The 16,450 full length reads correspond to 7,874 unique isoforms, or 42 % of the 18,612 possible isoforms given the exon 4, 6, and 9 variants observed. We note, however, that while 4,385 isoforms were represented by more than one read, 3,516 of isoforms were represented by only one read indicating that the depth of sequencing has not reached saturation (Fig. 4b and c). This was further confirmed by performing a bootstrapped subsampling analysis (Fig. 4d) and by using the capture-recapture method to attempt to assess the complexity of isoforms present in the library (Fig. 4e), which suggests that over 11,000 isoforms are likely to be present, though even this analysis has not yet reached saturation. The most frequently observed isoforms were Dscam14.1,6.12,9.30 and Dscam1 4.1,6.1,9.30 which were observed with 30 and 25 reads, respectively (Fig. 4e). In conclusion, these results demonstrate the practical application of using the MinION nanopore sequencer to identify thousands of distinct Dscam1 isoforms in a single biological sample.

Nanopore sequencing of ‘full-length’ Rdl, MRP, and Mhc isoforms

To extend this approach to other genes with complex splicing patterns, we focused on Rdl, MRP, and Mhc which have the potential to generate four, 16, and 180 isoforms, respectively. We prepared libraries for each of these genes by RT-PCR using primers in the constitutive exons flanking the most distal alternative exons using 25 cycles of PCR, pooled the three libraries and sequenced them together on the MinION nanopore sequencer for 12 h obtaining a total of 22,962 reads. The input libraries for Rdl, MRP, and Mhc were 567 bp, 1,769-1,772 bp, and 3,824 bp, respectively. The raw reads were aligned independently to LAST indexes of each cluster of variable exons. The alignment results were then used to assign reads to their respective libraries, identify reads that mapped to all variable exon clusters for each gene, and the exon with the best alignment score within each cluster. In total, we obtained 301, 337, and 112 full length reads forRdl (Fig. 6), MRP (Fig. 7), and Mhc (Fig. 8), respectively. For Rdl, both variable exons in each cluster was observed, and accordingly all four possible isoforms were observed, though in each case the first exon was observed at a much higher frequency than the second exon (Fig. 6d). Interestingly, the ratio of isoforms containing the first versus second exon in the second cluster is similar for isoforms containing either the first exon or the second exon in the first cluster indicating that the splicing of these two clusters may be independent. For MRP, both exons in the first cluster were observed and all but one of the exons in the second cluster (exon B) were observed, though the frequency at which the exons in both clusters were used varied dramatically (Fig. 7d). For example, within the first cluster, exon B was observed 333 times while exon A was observed only four times. Similarly, in the second cluster, exon A was observed 157 times whereas exons B, E, F, and G were observed 0 times, thrice, once, and twice, respectively, and exons D, E, and H were observed between 40 and 76 times. As a result, we observed only nine MRP isoforms. For Mhc, we again observed strong biases in the exons observed in each of the five clusters (Fig. 8d). In the first cluster, exon B was observed more frequently than exon A. In the second cluster, 109 of the reads corresponded to exon A, while exons B and C were observed by only two and one read, respectively. In the third cluster, exon A was not observed at all while exons B and C were observed in roughly 80 % and 20 % of reads, respectively. In the fourth cluster, exon A was observed only once, exons B and C were not observed at all, exon E was observed 13 times while exon D was present in all of the remaining reads. Finally, in the fifth cluster, only exon B was observed. As with MRP, these strong biases and near or complete absences of exons in some of the clusters severely reduces the number of possible isoforms that can be observed. In fact, of the 180 potential isoforms encoded by Mhc, we observed only 12 isoforms. Various Mhc isoforms are known to be expressed in striking spatial and temporally restricted patterns [14] and thus it is likely that other Mhc isoforms that we did not observe, could be observed by sequencing other tissue samples.

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Fig. 6. MinION sequencing of Rdl identified four isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

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Fig. 7. MinION sequencing of MRP identified nine isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

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Fig. 8. MinION sequencing of Mhc identified 12 isoforms. a Histogram of read lengths. b The number of reads per isoform. c Cumulative distribution of isoforms with respect to expression. d The number of reads per alternative exon (top) and per isoform (below)

Conclusions

Here we have demonstrated that nanopore sequencing with the Oxford Nanopore MinION can be used to easily determine the connectivity of exons in a single transcript, including Dscam1, the most complicated alternatively spliced gene known in nature. This is an important advance for several reasons. First, because short-read sequence data cannot be used to conclusively determine which exons are present in the same RNA molecule, especially for complex alternatively spliced genes, long-read sequence data are necessary to fully characterize the transcript structure and exon connectivity of eukaryotic transcriptomes. Second, although the Pacific Bioscience platform can perform long-read sequencing, there are several differences between it and the Oxford Nanopore MinION that could cause users to choose one platform over the other. In general, the quality of the sequence generated by the Pacific Bioscience is higher than that currently generated by the Oxford Nanopore MinION. This is largely due to the fact that each molecule is sequenced multiple times on the Pacific Bioscience platform yielding a high quality consensus sequence whereas on the Oxford Nanopore MinION, each molecule is sequenced at most twice (in the template and complement). We have previously used the Pacific Bioscience platform to characterize Dscam1 isoforms and found that it works well, though due to the large amount of cDNA needed to generate the libraries, many cycles of PCR are necessary and we observed an extensive amount of template switching, making it impractical to use for these experiments (BRG, unpublished data). However, over the past year that we have been involved in the MAP, the quality of sequence has steadily increased. As this trend is likely to continue, the difference in sequence quality between these two platforms is almost certain to shrink. Nonetheless, as we demonstrate, the current quality of the data is more than sufficient to allow us to accurately distinguish between highly similar alternatively spliced isoforms of the most complex gene in nature. Third, the ability to accurately characterize alternatively spliced transcripts with the Oxford Nanopore MinION makes this technology accessible to a much broader range of researchers than was previously possible. This is in part due to the fact that, in contrast to all other sequencing platforms, very little capital expense is needed to acquire the sequencer. Moreover, the MinION is truly a portable sequencer that could literally be used in the field (provided one has access to an Internet connection), and due to its size, almost no laboratory space is required for its use.

Although nanopore sequencing has many exciting and potentially disruptive advantages, there are several areas in which improvement is needed. First, although we were able to accurately identify over 7,000 Dscam1 isoforms with an average identity of full-length alignments >90 %, there are several situations in which this level of accuracy will be insufficient to determine transcript structure. For instance, there are many micro-exons in the human genome [15], and these exons would be difficult to identify if they overlapped a portion of a read that contained errors. Additionally, small unannotated exons could be difficult to identify for similar reasons. Second, the current number of usable reads is lower than that which will be required to perform whole transcriptome analysis. One issue that plagues transcriptome studies is that the majority of the sequence generated comes from the most abundant transcripts. Thus, with the current throughput, numerous runs would be needed to generate a sufficient number of reads necessary to sample transcripts expressed at a low level. In fact, this is one reason that we chose in this study, to begin by targeting specific genes rather than attempting to sequence the entire transcriptome. We do note, however, that over the past year of our participation in the MAP, the throughput of the Oxford Nanopore MinION has increased, and it is reasonable to expect additional improvements in throughput that should make it possible to generate a sufficient number of long reads to deeply interrogate even the most complex transcriptome.

In conclusion, we anticipate that nanopore sequencing of whole transcriptomes, rather than targeted genes as we have performed here, will be a rapid and powerful approach for characterizing isoforms, especially with improvements in the throughput and accuracy of the technology, and the simplification and/or elimination of the time-consuming library preparations.

 

The Tangled Transcriptome

Graveley’s lab studies the transcriptome, the mass of RNA molecules in living cells whose job is to translate DNA into proteins. The transcriptome is a sort of snapshot of which parts of the genome are active at a given time and place. Which genes are transcribed into RNA, and in what quantities, changes from organ to organ and even cell to cell, and can vary over an organism’s lifetime or in response to environmental changes.

Of particular interest to Graveley are those RNA molecules than can take different shapes, or “isoforms,” depending on random chance or what the cell needs at a particular time. RNA isoforms are distinct versions of the same isoforms quotegene. Through a process called alternative splicing, the different subunits, or “exons,” that make up a gene can be reshuffled in new combinations. Many genes have two or more mutually exclusive exons, and which ones are actually expressed as RNA and protein can have big effects on cellular behavior ― in effect, expanding the protein arsenal of the genome.

“For the entire field of transcriptomics and gene function, knowing what isoforms are expressed is critical,” says Graveley. “Most genes are complicated, especially in humans, and have alternative splicing that occurs at multiple places.”

That brings us to the challenge of Dscam1, the world record holder for alternative splicing. In fruit flies, a particularly well-studied model organism, Dscam1 is made up of 115 exons, only 20 of which are always transcribed into RNA. The other 95 exist in four “clusters” of mutually exclusive exons, and as a result, over 38,000 possible isoforms of Dscam1 have been predicted.

“This is by far, an order of magnitude, more than any other gene,” Graveley explains. This flexibility makes sense in light of Dscam1’s function. The protein it makes helps to “identify” single neurons in the insect brain, making them distinct enough from their neighbors for these cells to assemble a neural circuit on principles of like avoiding like. In experiments where Dscam1 has been altered to make fewer RNA isoforms, the neural wiring breaks down during development, sometimes severely enough to kill the flies.

Dscam1 also plays a role in the insect immune system, another reason for it to produce a huge variety of isoforms. Each of these molecules might be more or less effective at fighting certain pathogens.

It’s frustratingly hard, however, to figure out exactly which isoforms are in a specific sample. Graveley has been working on Dscam1 in fruit flies for more than a decade, but very basic questions remain unanswered: are some isoforms more common, or more important, than others? Are all the theoretical isoforms expressed? Do the isoforms have different behaviors, or are they just arbitrary ways of tagging neurons?

Size Matters

The trouble is the current state of the art in sequencing technology, which reads just a couple of hundred DNA bases at a time. That works great for identifying which exons are present in the transcriptome, but it’s no good for saying which mix of exons any specific strand of RNA is carrying. Different exons can lie thousands of bases apart on the RNA molecule, and there’s no way to bridge the gap between reads.

Graveley has tried a lot of solutions. He’s used the outdated Sanger sequencing method, which is much slower and more labor-intensive than modern sequencers, but does span longer reads. His lab also worked out a roundabout way of reconstructing RNA transcripts with contemporary Illumina sequencers, through a combination of chemistry and computational approaches.

“It worked,” he says, “but it was complicated by a lot of library preparation artifacts, and you basically had to jury-rig a genome analyzer to do something it was not supposed to do.”

Graveley’s preferred method is to use a sequencer produced by Pacific Biosciences, which, like the MinION, is built on long-read, single-molecule technology. PacBio sequencing is much better established than nanopores, and its results are known to be reliable; it also has the high throughput typical of modern instruments. For researchers working on alternative splicing, it’s clearly the technology to beat.

Unfortunately, it’s also very expensive. So Graveley’s team set out to learn whether the MinION, a low-throughput but extremely cheap alternative, could be an adequate substitute.

For the Genome Biology paper, the team focused on a 1.8-kilobase region of Dscam1 RNA that covers 93 of the gene’s 95 alternatively spliced exons. To get their samples, they crushed fruit fly heads, isolated Dscam1 RNA from the sample using a polymerase, and reverse-transcribed it into cDNA for sequencing. They also sequenced transcripts of three other alternatively spliced genes, Rdl, MRP, and Mhc.

splicing quote

The biggest concern for new applications of the MinION is its shaky accuracy. While most sequencers can achieve comfortably over 99% consensus with reference sequences, Graveley’s group has seen only about 90% identity with the MinION. That’s actually a little better than most MinION users have managed, although the device’s accuracy has been steadily improving. Users have had to pick their projects carefully to account for this: the device is pretty reliable in resequencing studies that map DNA reads to known references, but it’s still a dubious choice for sequencing unknown genetic material from scratch (although it’s been tried).

To accurately pin down the exact isoforms in the transcriptome, the MinION didn’t have to read every RNA molecule perfectly, but it did have to come close enough to decisively tell one exon from another ― and inDscam1, those exons could be as much as 80% identical.

In fact, Graveley and his co-authors found that the MinION was very capable of this. Out of around 33,000 high-quality Dscam1 reads pulled off the sequencer, almost 29,000 were a strong match for one and only one combination of exons. To further check their accuracy, the team also sequenced the same sample on Illumina technology. While the Illumina sequencer could not give whole isoforms, it did show the same proportions of different exons, suggesting that the MinION gave a complete and unbiased picture of the sample.

“Alternative splicing, it turns out, is probably one of the ideal applications for this platform,” Graveley says. “Even with a gene as complicated as this one, we’re able to accurately distinguish the isoforms from one another. Unless you have very, very small exons, or two exons that are almost identical to each other, the accuracy is good enough.”

Make Way for PromethION

The results are good news for researchers studying the transcriptome, but the MinION probably won’t push out other methods for dealing with alternative splicing just yet. Its low throughput means that at best it can cover a very small portion of the transcriptome with each run ― and that means isolating targeted RNA transcripts, a process that can introduce new biases into the data.

“You need a lot of reads to get the whole transcriptome, and what happens is you end up sequencing boring genes like actin and tubulin, the really abundantly expressed things,” Graveley explains. Still, his data from this experiment was good enough to replicate a few earlier findings: for instance, that Dscam1 does appear to make every predicted isoform. In this experiment, his lab observed almost half the possible isoforms, containing 92 of 93 possible exons.

Meanwhile, Oxford Nanopore Technologies is working on a new instrument, the PromethION, which will contain 48 MinION-style flow cells in a battery. Graveley has already signed on to be one of the first recipients, in an access program that is likely to start in the winter.

Judging by studies like this one, the PromethION stands a good chance of becoming the instrument of choice for large-scale RNA sequencing. With Dscam1, Graveley hopes to reach high enough throughput to do functional studies, seeking to learn whether different combinations of isoforms give rise to physical or behavioral differences. He also wants to look at human genes with high levels of alternative splicing, and to test whether the MinION can accurately count total numbers of RNA isoforms.

“The fact that you can use this technology to characterize whole isoforms is very exciting,” Graveley says. “It’s going to help us start characterizing the transcriptome in ways that have been very difficult.”

 

 

 

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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

Read Full Post »

Brain Science

Larry H Bernstein, MD, FCAP, Curator

LPBI

 

A Protein Atlas of the Brain

http://www.biosciencetechnology.com/news/2015/11/protein-atlas-brain

What looks like an island is actually a schematic representation of a mouse brain. Researchers have now analyzed the mouse brain proteome and summarized the data in an atlas. (Image:  MPI of Biochemistry/ K. Scharma )

http://www.biosciencetechnology.com/sites/biosciencetechnology.com/files/bt1511_mpi_proteome.jpg

What looks like an island is actually a schematic representation of a mouse brain. Researchers have now analyzed the mouse brain proteome and summarized the data in an atlas. (Image: MPI of Biochemistry/ K. Scharma )

 

Just as in the Middle Ages when there were still many uncharted areas on Earth, researchers today are aware that there is still a great deal to learn about cells in our microcosm. But instead of sextants and compasses, researchers nowadays use modern methods such as mass spectrometry to look into the world of protein molecules. Neuroscientists are focussed particularly on resolving brain complexity with its billions of specialized cells. To understand the brain’s functions, scientists from the Max Planck Institutes of Biochemistry in Martinsried and Experimental Medicine in Göttingen have for the first time quantified the entire set of proteins ‒ the proteome ‒ in the adult mouse brain. The information about which proteins and how many of them are found in the various cell types and regions has been summarized in a protein atlas.

The brain consists of hundreds of billions of interconnected cells which communicate with one another. Different cell types specialize in different functions. Nerve cells transmit and process stimuli from outside; distinct glial cells supply them with nutrients, regulate the flow of blood in the brain, help in isolating nerve fibres and perform tasks in the immune system.

Cells are comprised of proteins which are the functional building blocks. They act as small molecular machines and give the cell its structure. The information for synthesis of protein molecules is encoded in DNA and RNA; biomolecules which have been extensively examined in the brain. “Up to now, however, it was not known which and how many proteins are produced in the different, highly specialized cells or even how the numbers of proteins in the individual regions differ”, explains neuroscientist Mikael Simons. “To examine this, we needed modern measuring and analysis methods in order to be able to record and evaluate these enormous numbers of proteins.” Together with protein research specialists, a team headed by Matthias Mann in Martinsried, the scientists further developed the mass spectrometry technology for in-depth profiling of brain proteins in a rapid, reproducible and a quantitative fashion.

They were able to show that there are around 13,000 different proteins in the adult mouse brain. The quantity of proteins in the different cell types and brain regions, and how they differ from one another can now be found in the recently established protein atlas at http://www.mousebrainproteome.com. The protein data presented there from five different cell types and ten regions in the mouse brain constitute the most comprehensive collection to date.

This deep proteome investigation should serve as a rich resource for analyses of brain development and function. “Surprisingly, only 10 per cent of all proteins are cell type-specific”, explains Kirti Sharma, lead author of the study. “These cell-specific proteins are mostly found on the surface of the cell.” The large majority – 90 per cent of all proteins – are found in all cell types. As in a satellite view of previously uncharted landscapes, the researchers have created a protein atlas based on the most comprehensive data collection that should help in the development of new treatments for alleviating brain diseases.

Source: Max Planck Institute

 

 

New Computational Strategy Finds Brain Tumor-shrinking Molecules

http://www.biosciencetechnology.com/news/2015/11/new-computational-strategy-finds-brain-tumor-shrinking-molecules

 

These are MRI renderings of mouse brain tumors. Tumors treated with SKOG102 (lower panels) shrank by about half compared to tumors treated with a control (upper panels). (Credit: UC San Diego Health)

http://www.biosciencetechnology.com/sites/biosciencetechnology.com/files/bt1511_ucsandiego_braintumor.jpg

These are MRI renderings of mouse brain tumors. Tumors treated with SKOG102 (lower panels) shrank by about half compared to tumors treated with a control (upper panels). (Credit: UC San Diego Health)

 

Patients with glioblastoma, a type of malignant brain tumor, usually survive fewer than 15 months following diagnosis. Since there are no effective treatments for the deadly disease, University of California, San Diego researchers developed a new computational strategy to search for molecules that could be developed into glioblastoma drugs. In mouse models of human glioblastoma, one molecule they found shrank the average tumor size by half. The study is published October 30 byOncotarget.

The newly discovered molecule works against glioblastoma by wedging itself in the temporary interface between two proteins whose binding is essential for the tumor’s survival and growth. This study is the first to demonstrate successful inhibition of this type of protein, known as a transcription factor.

“Most drugs target stable pockets within proteins, so when we started out, people thought it would be impossible to inhibit the transient interface between two transcription factors,” said first author Igor Tsigelny, Ph.D., research scientist at UC San Diego Moores Cancer Center, as well as the San Diego Supercomputer Center and Department of Neurosciences at UC San Diego. “But we addressed this challenge and created a new strategy for drug design — one that we expect many other researchers will immediately begin implementing in the development of drugs that target similar proteins, for the treatment of a variety of diseases.”

Transcription factors control which genes are turned “on” or “off” at any given time. For most people, transcription factors labor ceaselessly in a highly orchestrated system. In glioblastoma, one misfiring transcription factor called OLIG2 keeps cell growth and survival genes “on” when they shouldn’t be, leading to quick-growing tumors.

In order to work, transcription factors must buddy up, with two binding to each other and to DNA at same time. If any of these associations are disrupted, the transcription factor is inhibited.

In this study, Tsigelny and team aimed to disrupt the OLIG2 buddy system as a potential treatment for glioblastoma. Based on the known structure of related transcription factors, study co-author Valentina Kouznetsova, Ph.D., associate project scientist at UC San Diego, developed a computational strategy to search databases of 3D molecular structures for those small molecules that might engage the hotspot between two OLIG2 transcription factors. The team used the Molecular Operation Environment (MOE) program produced by the Chemical Computing Group in Montreal, Canada and high-performance workstations at the San Diego Supercomputer Center to run the search.

With this approach, the researchers identified a few molecules that would likely fit the OLIG2 interaction. They then tested the molecules for their ability to kill glioblastoma tumors in the Moores Cancer Center lab of the study’s senior author, Santosh Kesari, M.D., Ph.D.. The most effective of these candidate drug molecules, called SKOG102, shrank human glioblastoma tumors grown in mouse models by an average of 50 percent.

“While the initial pre-clinical findings are promising,” Kesari cautioned, “it will be several years before a potential glioblastoma therapy can be tested in humans. SKOG102 must first undergo detailed pharmacodynamic, biophysical and mechanistic studies in order to better understand its efficacy and possible toxicity.”

To this end, SKOG102 has been licensed to Curtana Pharmaceuticals, which is currently developing the inhibitor for clinical applications. Kesari is a co-founder, has an equity interest in and is chair of the scientific advisory board for Curtana Pharmaceuticals. Co-authors Rajesh Mukthavaram, Ph.D., and Wolfgang Wrasidlo, Ph.D., also own stock in Curtana Pharmaceuticals.

his research was funded, in part, by the National Institutes of Health, Voices Against Brain Cancer Foundation, Christopher and Bronwen Gleeson Family Trust and American Brain Tumor Association Drug Discovery Grant.

Source: University of California San Diego

Musical Rhythms in the Brain

http://www.biosciencetechnology.com/news/2015/10/musical-rhythms-brain

 

Researchers at Max Planck Institute for Empirical Aesthetics in Frankfurt and of New York University have identified how brain rhythms are used to process music, a finding that also contributes to a better understanding of the auditory system. Furthermore, the study suggests that musical training can enhance the functional role of brain rhythms.

The paper, which appears in the journal Proceedings of the National Academy of Sciences, points to a newfound role the brain’s cortical oscillations play in the detection of musical sequences. The term “cortical oscillations” refers to the rhythmic electrical activity generated spontaneously and in response to stimuli by neural tissue in the central nervous system. The importance of brain oscillations in sensory-cognitive processes has become increasingly evident.

“We’ve isolated the rhythms in the brain that match rhythms in music,” explains Keith Doelling, lead author. “Specifically, our findings show that the presence of these rhythms enhances our perception of music and of pitch changes.”

 Headbanging is most common in the rock, punk and heavy metal music genres. Scientist have now identified how rhythms inside the human brain are used to process music. (Image: Wikimedia / Małgorzata Miłaszewska)

http://www.biosciencetechnology.com/sites/biosciencetechnology.com/files/bt1510_maxplanck_music.jpg

Headbanging is most common in the rock, punk and heavy metal music genres. Scientist have now identified how rhythms inside the human brain are used to process music. (Image: Wikimedia / Małgorzata Miłaszewska)

 

Previous research has shown that brain rhythms very precisely synchronize with speech, enabling us to parse continuous streams of speech — in other words, how we can isolate syllables, words, and phrases from speech, which is not, when we hear it, marked by spaces or punctuation.

However, it has not been clear what role such cortical brain rhythms, or oscillations, play in processing other types of complex sounds, such as music.

To address these questions, the researchers conducted three experiments using magnetoencephalography (MEG), which allows the tiny magnetic fields generated by brain activity to be measured. The study’s subjects were asked to detect short pitch distortions in 13-second clips of classical piano music (by Bach, Beethoven, Brahms) that varied in tempo — from half a note to eight notes per second. The study’s authors divided the subjects into musicians (those with at least six years of musical training and who were currently practicing music) and non-musicians (those with two or fewer years of musical training and who were no longer involved in it).

Not surprisingly, the study found that musicians have more potent oscillatory mechanisms than non-musicians do. “What this shows is we can be trained, in effect, to make more efficient use of our auditory-detection systems,” observes study co-author David Poeppel, director of the Max Planck Institute for Empirical Aesthetics. “Musicians, through their experience, are simply better at this type of processing.”

For music that is faster than one note per second, both musicians and non-musicians showed cortical oscillations that synchronized with the note rate of the clips. The researchers therefore conclude that these oscillations were effectively employed by everyone to process the sounds they heard, although musicians’ brains synchronized more to the musical rhythms. Only musicians, however, showed oscillations that synchronized with unusually slow clips. This difference, the researchers say, may suggest that non-musicians are less able to process the music as a continuous melody rather than as individual notes. Moreover, musicians are able to detect pitch distortions much more accurately — as evidenced by corresponding cortical oscillations.

Thus, brain rhythms appear to play a role in parsing and grouping sound streams into ‘chunks’ that are then analyzed as speech or music, the scientists add.

Source: Max Planck Institute

 

 

New Three-Minute Test Detects Lewy Body Disease

http://www.biosciencetechnology.com/news/2015/10/new-three-minute-test-detects-lewy-body-disease

Lewy Body disease is the second most common type of progressive dementia, according to the Mayo Clinic, and affects approximately 1.3 million Americans.

The Lewy Body Dementia Association says the disease is widely accepted to be highly underdiagnosed and is the most frequently misdiagnosed form of dementia.

The new test, called the “Lewy Body Composite Risk Score” (LBCRS), developed by James E. Galvin, M.D., M.P.H., professor of clinical biomedical science at FAU, is a simple, one page-survey, that includes yes or no questions for a clinician to complete.  The structured questions look at six non-motor features that are present in patients with LBD, but are much less common in other forms of dementia.  The tool helps clinician assess whether a patient has rest tremor, postural instability, rigidity, or bradykensia, without having to grade each extremity.

Bioscience Bulletin: Potential Alzheimer’s Test, and Stress Linked to Stroke

http://www.biosciencetechnology.com/news/2015/10/bioscience-bulletin-potential-alzheimers-test-cancer-drug-overestimated-and-stress-linked-stroke

N.J. Researchers Closing in on Alzheimer’s, Parkinson’s Tests
Researchers from the Rowan University School of Osteopathic Medicine, have developed a test that could detect Alzheimer’s before symptoms start by identifying a series of obscure antibodies. Scientists narrowed down the auto-antibodies he was looking at from a sample of nearly 10,000 to just 10.

Role Found for Critical Gene in 95% of ALS
Cynthia Fox interviewed experts about a recent Science study that offered new insight surrounding a protein called TDP-43 in relation to amyotrophic lateral sclerosis.  The study found that in 95 percent of ALS cases the protein leaves its home – the nucleus of motor neuron cells – resulting in the creation of improper “cryptic” exons.

Like a bad teenager, in 95 percent of all amyotrophic lateral sclerosis (ALS) cases, a protein called TDP-43 leaves its home— the nucleus of motor neuron cells—to congregate, in suspect fashion, in the cytoplasm.

In a study published in Science this summer, the Johns Hopkins University (JHU) team of pathologist Phillip Wong, Ph.D., offered new insight into this molecular rebellion. It confirmed a function of normal TDP-43 in the nucleus: orchestrating proper RNA splicing and exon formation. It confirmed what lack of nuclear TDP-43 does: creates improper “cryptic” exons. And it identified proteins that mitigate effects of nuclear TDP-43 loss: potential drug leads.

“The recently published work in Science very clearly demonstrates that in the absence of TDP-43, RNA is misspliced, and in many cases targeted for degradation,” University of Michigan neurologist Sami Barmada, M.D., Ph.D., told Bioscience Technology. Barmada was uninvolved with the research. “A very real consequence of such dysfunctional RNA splicing and degradation is an inability to maintain cell health, ultimately resulting in neuron loss. The authors assembled an intriguing story that tells us quite a bit about how TDP-43 functions, and what happens to those functions in diseases such as ALS, and fronto-temporal dementia (FTD). If the mechanism identified in this manuscript does indeed underlie toxicity due to TDP-43 mislocalization, then it may very well contribute to neuron loss in the vast majority of ALS.”

High Stress Jobs May be Linked to Increased Stroke Risk
The final story in our round up this week takes a look at a new Neurology study, which found there may be a link between high stress jobs and an increased risk for stroke. After analyzing six studies, comprised of a total of 138,782 participants, researchers concluded that people with high stress jobs (such as waitresses and nursing aides) were 58 percent more likely to have an ischemic stroke than those in low stress jobs (such as natural scientists and architects).

New Scanner to Help Uncover Causes of Dementia

http://www.biosciencetechnology.com/news/2015/11/new-scanner-help-uncover-causes-dementia

The funding from the Medical Research Council will allow the SIGNA PET/MR scanner, made by GE Healthcare, to be installed in Central Manchester University Hospitals NHS Foundation Trust.  Currently there are only two of these scanners in the UK, but following the Manchester funding and money to other university centers in the UK, this number will increase to seven, from a number of manufacturers.

The new scanner will help scientists and clinicians understand the causes and progression of dementia, and provide ways to test the effects of new treatments.  Molecular changes in the brain are believed to be responsible for dementia and the scanners have the potential to link these with the brain changes that they cause – leading to new understanding and new treatments.

Professor Nigel Hooper is the University’s Director for Dementia Research.  He said: “Dementia is a condition that is poorly understood and difficult to treat effectively.  It’s going to become more of a problem in the coming decades, so our research response needs to pick up as well.

“This scanner and the wider network will give us that ability to understand dementia better and to develop more treatments.”

The scanner is expected to be operational from July 2016 and work is currently underway to refurbish a shelled space adjacent to the Nuclear Medicine Centre to house the scanner suite which will have three treatment rooms, a research office and a radiopharmacy room. CMFT was chosen as the ideal location for the scanner due to its central Manchester location and close proximity to the main University campus, the co-location of the suite adjacent to the clinical PET/CT scanner at CMFT and the opportunity to take research into clinical practice.

Christine Tonge, the Director of Medical Physics at Central Manchester said: “We are excited by this opportunity to contribute to this important area of research. This scanner will put Manchester at the forefront of dementia research and we look forward to collaborating not only with our colleagues from the University, but also  from other hospitals in Greater Manchester and beyond.”

The scanner is being funded as part of the Dementias Platform UK – a multi-million pound public-private partnership, developed by the Medical Research Council, to accelerate progress in dementias research. DPUK’s aims are early detection, improved treatment and, ultimately, prevention of dementias. It is the world’s largest study group for use in dementias research, pulling together two million well-characterized participants from over 30 national population studies.

The Manchester scanner will be supervised by Professor Alan Jackson, who is the director of the University’s Wolfson Molecular Imaging Centre, which hosts two PET scanners and one MR scanner and produces radiotracers for use in PET scanning.  He said: “Manchester now has a range of scanning facilities which mean that clinicians and scientists can produce high quality research across a range of conditions.

“With the growing urgency of developing treatments for dementia, this new equipment is vital in addressing a major growing health concern.

“Most importantly, being linked with the other four universities which are also purchasing PET-MR scanners will mean Manchester has the ability to become involved in multi-centre trials and research grants and contracts – increasing the effectiveness of the UK’s research in this area.”

Source: University of Manchester

 

Trends Mol Med. 2014 Feb;20(2):66-71. doi: 10.1016/j.molmed.2013.11.003. Epub 2013 Dec 16.
TDP-43-mediated neurodegeneration: towards a loss-of-function hypothesis?
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are clinically distinct fatal neurodegenerative disorders. Increasing molecular evidence indicates that both disorders are linked in a continuous spectrum (ALS-FTD spectrum). Neuronal cytoplasmic inclusions consisting of the nuclear TAR DNA-binding protein 43 (TDP-43) are found in the large majority of patients in the ALS-FTD spectrum and dominant mutations in the TDP-43 gene cause ALS. A major unresolved question is whether TDP-43-mediated neuronal loss is caused by toxic gain of function of cytoplasmic aggregates, or by a loss of its normal function in the nucleus. Here we argue that based on recent genetic studies in worms, flies, fish, and rodents, loss of function of TDP-43, rather than toxic aggregates, is the key factor in TDP-43-related proteinopathies.
Gains or losses: molecular mechanisms of TDP43-mediated neurodegeneration

Edward B. Lee, Virginia M.-Y. Lee & John Q. Trojanowski

Nature Reviews Neuroscience 13, 38-50 (January 2012) |   http://dx.doi.org:/10.1038/nrn3121

RNA-binding proteins, and in particular TAR DNA-binding protein 43 (TDP43), are central to the pathogenesis of motor neuron diseases and related neurodegenerative disorders. Studies on human tissue have implicated several possible mechanisms of disease and experimental studies are now attempting to determine whether TDP43-mediated neurodegeneration results from a gain or a loss of function of the protein. In addition, the distinct possibility of pleotropic or combined effects — in which gains of toxic properties and losses of normal TDP43 functions act together — needs to be considered.

 

Transposable Elements in TDP-43-Mediated Neurodegenerative Disorders

PLOS

Published: September 5, 2012   DOI: http://dx.doi.org:/10.1371/journal.pone.0044099

Elevated expression of specific transposable elements (TEs) has been observed in several neurodegenerative disorders. TEs also can be active during normal neurogenesis. By mining a series of deep sequencing datasets of protein-RNA interactions and of gene expression profiles, we uncovered extensive binding of TE transcripts to TDP-43, an RNA-binding protein central to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Second, we find that association between TDP-43 and many of its TE targets is reduced in FTLD patients. Third, we discovered that a large fraction of the TEs to which TDP-43 binds become de-repressed in mouse TDP-43 disease models. We propose the hypothesis that TE mis-regulation contributes to TDP-43 related neurodegenerative diseases.

Citation: Li W, Jin Y, Prazak L, Hammell M, Dubnau J (2012) Transposable Elements in TDP-43-Mediated Neurodegenerative Disorders. PLoS ONE 7(9): e44099. doi:10.1371/journal.pone.0044099

Editor: Koichi M. Iijima, Thomas Jefferson University, United States of America

 

Accumulation of TAR DNA-binding protein 43 (TDP-43) containing cytoplasmic inclusions is a shared pathological hallmark in a broad spectrum of neurodegenerative disorders, including ALS, FTLD and Alzheimer’s disease [1]. Mutations in this multifunctional RNA binding protein are also known to underlie some familial and sporadic cases of ALS [1]. Despite considerable progress, the mechanisms that link TDP-43 to neurodegeneration still are unclear. We conducted a meta-analysis of TDP-43 protein:RNA target binding datasets and of mRNA expression datasets. All previous analyses of such data focused on sequence reads that uniquely map to the reference genome, thereby excluding transcripts derived from transposable elements (TEs). In contrast, we included sequences that map to multiple locations and examined reads that align to TEs. Our analyses lead to the striking hypothesis that TE over-expression may contribute to TDP-43 mediated neurodegeneration.

Transposable elements (TEs) are highly abundant mobile genetic elements that constitute a large fraction of most eukaryotic genomes. Retrotransposons, which copy themselves through an RNA intermediate, represent approximately 40% of the human genome [2], [3]. Although the majority of TE copies are nonfunctional, a subset have retained the ability to mobilize and even the immobile copies can be expressed [4]. Because of their potential to copy themselves and insert into new genomic locations as well as to generate enormous levels of expression, transposable elements present a massive endogenous reservoir of genomic instability and cellular toxicity [3]. The impacts of these parasitic genetic elements normally are stifled by potent cellular mechanisms involving small interfering RNAs that act via the RNA induced silencing complex (RISC) to inhibit transposon expression ([5] for review). Although most investigations have naturally focused on the germline, where new insertions are heritable and thus favored by transposon evolution, somatic tissues also have an active transposon silencing mechanism whose functional significance is less understood. An emerging literature has established that certain TEs are normally active in brain [6], [7], [8], [9] and elevated expression of some LINE, SINE (which are non-LTR retrotransposons) and LTR elements have been correlated with several neurodegenerative disorders [10], [11], [12], [13], [14], [15], [16]. We therefore investigated whether the RNA targets of TDP-43 include transposon-derived transcripts.

Several recent studies used deep sequencing to profile the RNA targets that co-purify with immunoprecipitated mouse, rat or human TDP-43 and also to profile gene expression changes in mouse after knockdown or over-expression of TDP-43 [17], [18], [19], [20]. In each case, however, these studies analyzed annotated protein coding sequences and excluded TE-derived transcripts and other repetitive elements due to the difficulties inherent in working with ambiguously mapped reads from short read technologies [e.g. [21]]. Despite efforts to develop new algorithms for analyzing multiple alignments of short reads [22], these algorithms have not been applied systematically for analyzing TE-derived transcripts in any neurodegenerative disease. Because each of the above mentioned TDP-43 related studies provided public access to their raw data, we were able to use this resource to search for TDP-43 targets and for transcript mis-expression when we included sequence reads that map to multiple genomic locations, the majority of which are TE derived transcripts in these datasets. Our meta-analysis supports three main conclusions. First, TDP-43 broadly targets TE-derived transcripts, including many SINE, LINE and LTR classes as well as some DNA elements. Second, the association between TDP-43 and TE-derived RNA targets is reduced in FTLD patients relative to healthy subjects, consistent with the idea that loss of TE control might be part of the disease pathology. Third, we observe broad over-expression of TE derived transcripts in each of two different mouse models with TDP-43 dysfunction. Finally there is a striking overlap between the TE transcripts identified as targets and those that are over-expressed with TDP-43 misexpression.

 

Results

We first re-analyzed raw data from the rat TDP-43 RNA immunoprecipitation sequencing (RIP-seq) dataset [17] and the mouse and human TDP-43 in vivo crosslinking-immunoprecipitation sequencing (CLIP-seq) datasets [18], [19]. We tested three different analysis methods to examine effects on TEs (Fig. 1A–C; Methods and Figs. S1 and Tables S1, S2, S3). Because reads could potentially map to many regions, we first used an analysis in which each location was weighted based on the number of alignments (Figs. 1A,B) see methods). This analysis method (MULTI), which included both unique and multi mapped reads, assigns an enrichment level for each element, but does not distinguish contributions of individual instances of each element. Although this method can potentially include effects from TEs that are difficult to map with short read sequence, a disadvantage is that it does not distinguish which instances of a given TE are detected. In addition, because many TE copies are present within introns of genes, the MULTI method does not distinguish whether the TE sequences are co-expressed with genes or expressed from TEs per se. To address these issues, and to test the robustness of our observations, we also tested two additional mapping methods for the rat and human datasets (Figs. 1C and S1E,F; Methods). First, we examined only the subset of reads that map uniquely to the genome (UNIQ). This method does bias the results to the fraction of TEs that have diverged enough to have unique sequences, but provides confidence that signal derives from unique chromosomal locations. As a third mapping strategy (UNIQ+SameEle), we examined the effects of including both uniquely mapped sequences and those that map to multiple locations so long as they map to the same element (weighted for their contribution to each instance as above – see Methods).

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Figure 1. TDP-43 binds broadly to transposable element (TE)-derived transcripts.

Magnitude (log2-fold) of enrichments (up) or depletions (down) are shown (A, rat; B, mouse) for significantly bound repeat elements grouped by class. MULTI method (see text) was used for A and B. (C) The majority of rat TE targets identified with MULTI also are identified (Left Panel, Rat) when analysis is restricted to reads that map uniquely (UNIQ) or when both uniquely mapped and multi-mapped reads that map to the same TE were included (UNIQ+SameEle). These conclusions also hold for TE targets whose binding is reduced in FTLD samples from human tissue relative to healthy controls (Left panel, Human). Most rat TE targets and differentially bound human TE targets identified from uniquely mapped reads are intergenic (Right panel). (D) For TDP-43, peaks (UNIQ+SameEle) over TE targets are tall and sharp with mean peak height of 158 counts/peak. In contrast, peak heights are lower for FUS (mean peak height of 17).

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With all three mapping strategies we find a dramatic enrichment of sequences that derive from each major class of TE (Figs. 1A–C; S1; Table S3). With the MULTI method, we find 271 significantly enriched or depleted (most were enriched) repeat element sub-families in the rat TDP-43-IP samples versus control (Fig. 1A), of which 245 correspond to TEs. In the mouse dataset (Fig. 1B), MULTI detects significant enrichment of 352 repeat element sub-families of which 334 correspond to TEs (Table S3). These comprise all major classes of TEs, including LINE, SINE, LTR and some DNA elements [3]. For instance, 85 out of the 122 known mouse LINE elements and 6 out of the 7 known rat LINE elements are identified as TDP-43 targets. Similarly 26 out of 41 mouse SINE elements and 36 out of 37 rat SINE elements also were detected as TDP-43 targets. One caveat to the mouse clip-seq analysis was the lack of a control IP to use in estimating background counts for this single dataset, which could potentially lead to a larger false positive rate in the detected peaks (see Methods); however, the similarity in the results obtained for this dataset as compared to the well-controlled studies for rat (Fig. 1A) and human datasets (see below) argues for the inclusion of this dataset despite its caveats.

Overall, we detect the most extensive binding to TEs with the MULTI method, and these findings are not an artifact of the way we assigned weights with the MULTI method because even with the more restricted UNIQ analysis, we identify ∼80% of the rat elements that are differentially enriched when all mappable reads are included (Figs. 1C, S1F). Moreover, among the uniquely mapped subset of TE instances that we identify as TDP-43 targets, greater than 80% map to intergenic regions rather than to elements contained within genes (Fig. 1C). When we include both unique mappers and multi mappers from the same element (UNIQ+SameEle), we detect enrichment for 95% of the TE sub-families that were identified as TDP-43 targets with the MULTI method (Figs. 1C, S1F). The concordant results from these three different mapping strategies provide confidence that identification of TE derived transcripts as TDP-43 targets is a robust effect that is detected with a variety of methods for dealing with multi-copy elements.

As a test of the biological specificity of our finding that TDP-43 selectively binds to TE derived transcripts, we applied the UNIQ mapping method to a CLIP-seq dataset for an unrelated RNA binding protein. For this purpose we chose fused in sarcoma (FUS), which like TDP-43, is an hnRNP RNA binding protein that plays diverse roles in RNA biology, including splicing [23]. FUS is a relevant control for specificity because like TDP-43, it is implicated in neurodegenerative disorders including ALS [24]. The result with FUS is in stark contrast with TDP-43 (Fig. 1D). For TDP-43, peaks (defined within a 500 bp window) that map to TEs are relatively large, with a mean peak height of 158 counts. In contrast, with FUS we only see small peaks over TEs with a height of just a few counts (mean peak height of 17; Fig. 1D for distribution). Peaks that map over RefGene annotations, on the other hand, are similarly distributed for both FUS and TDP-43 (Mean height of 32 and 68 respectively, Fig. S1H). The distributions of mean peak heights (see histogram, Fig. 1D) shows a clear separation between TDP-43 peaks and those obtained with FUS and this separation between peak heights is statistically significant (Wilcoxon rank sum p-value<2.2e−16). Thus our findings show specificity for TDP-43 and are not a byproduct of inherent biases in library construction or analysis.

Because TDP-43 has a known binding motif among its mRNA targets, we used MEME ([25]and see Methods) to identify enriched motifs among both the RefGene and repetitive targets. We identify a UGUGU pentamer motif that is equivalently enriched in uniquely mapped and repetitive targets (Fig. S1C; Methods). This motif is consistent with the binding specificity of TDP-43 that has previously been observed for uniquely mapped sequences [17], [18], [19], [20]. Thus TDP-43 binds TE derived transcripts via a similar sequence motif as identified for RefGene targets.

Because the human dataset [18] includes samples from healthy and FTLD patients (which exhibit TDP-43 positive cytoplasmic inclusions), it also provided an opportunity to identify differences in the TDP-43 targets between FTLD and healthy controls. As in rat and mouse, we observe in human samples a dramatic and significant enrichment in target sequences that derive from many classes of TEs. As with the mouse and rat data, the distribution of peak heights for TE and RefGene targets of TDP-43 are similar (Fig. S1I), indicating that the targeting of TE transcripts is as robust as it is for RefGene targets. More striking, however, is the comparison between healthy subjects and FTLD patients. When we examine the relative enrichment for each repeat element within healthy vs. FTLD samples, we detect a dramatic difference in binding to TE derived RNAs (Fig. 1E–H). Overall, the association between TDP-43 and TE transcripts is significantly reduced in FTLD patients, which leads to a relative enrichment of 38 repeat elements in healthy versus FTLD, 28 of which correspond to transcripts derived from TEs (Fig. 2 and Table S3; See Methods for statistical analyses). We see reduced binding of TDP-43 to transcripts from all major classes of TE including SINE, LINE, LTR and a few DNA elements. Here too, we observe that the majority of the TE targets whose binding to TDP-43 was reduced in FTLD are consistently identified with all three methods (Fig. 1C). Most of the TE targets that show reduced binding to TDP-43 in FTLD samples are intergenic rather than contained within genes (Fig. 1C). Example peaks are shown for one RefGene control (Fig. 1F) as well as two differentially targeted TEs (Figs. 1G,H).

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Figure 2. TDP-43 binding to TEs is selectively lost in FTLD patients.

(A) In the human CLIP-seq data from FTLD versus healthy control, 38 repeat elements showed significant (p-value< = 1e-5 and fold changes> = 2) differential binding. Log2 fold binding differences are shown for significantly enriched/depleted elements. (B,C,D) Peaks are shown in genome browser for one RefGene control (B) and two differentially targeted TEs (C,D) in Healthy (top) versus FTLD (bottom). (E) Enrichment for the UGUGU motif relative to its prevalence in the genome is shown across a 51-nt window surrounding binding sites (−25 nt, 25 nt). Healthy samples (Blue) show similar enrichment for the UGUGU pentamer motif among RefGene (solid) and repeat (dashed) sequences (RefGene/repeat motif enrichment ratio ≈1.3). In contrast, motif enrichment in FTLD samples (Red) is significantly reduced among repeat (dashed) annotations relative to RefGene (solid; p-value< = 0.01; RefGene/repeat motif enrichment ratio ≈2.0).

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This reduced binding in FTLD patients of TDP-43 to TE-derived transcripts also is apparent when we examine over-all enrichment for the UGUGU pentamer motif (Figs. 2E and S1) relative to the genome. In the rat and mouse samples as well as in the dataset from healthy human brain samples, we observe equivalent enrichment of UGUGU binding motifs among uniquely mapped (RefGene) versus repetitively mapped (repeat) TDP-43 targets (RefGene/repeat enrichment ratio near 1.0; Fig. S1D; see Methods). In the FTLD-TDP-43-CLIP samples, we also see enrichment for the UGUGU motif among RefGene targets that is equivalent to that seen in healthy subjects (Fig. 2E), but the level of enrichment for this UGUGU motif is significantly lower among the sequences that map to repeat elements. In the FTLD samples, the RefGene/repeat enrichment ratio is increased to 2.0 (Fig. 2E; p-value< = 0.01, p-values were assigned with 100 iterations on randomly chosen sets containing 50% of original data; see Methods). In other words, FTLD samples exhibit a selective reduction of binding to TE transcripts and also exhibit reduced UGUGU motif enrichment among the remaining repetitive sequences that still co-purify with TDP-43. This difference in motif enrichment between FTLD and control samples is only manifested among repeat annotations.

The reduced binding of TE transcripts in FTLD patients suggested that TDP-43 pathology might include a loss of TE regulation. We investigated this possibility in two ways. First, we analyzed the repetitive sequence reads from two different mRNA-seq datasets from mouse models of TDP-43 pathology.

The first mRNA-seq study that we analyzed [20] used over-expression of human TDP-43 in transgenic mice. Overexpression of this aggregation prone protein is associated with toxic TDP-43 pathological effects and is thought to act as a dominant-negative, causing reduction in the normal functions of TDP-43. The second mRNA-seq study [19] used antisense oligonucleotide-mediated depletion of TDP-43 in mouse striatum to test the effects of TDP-43 loss of function. Both studies identified transcripts that are differentially expressed or spliced in response to these TDP-43 manipulations. To ask if the above TDP-43 depletion and over-expression/dominant-negative impacted TE derived transcripts, we again analyzed sequence reads including those that map to multiple locations. We found broad elevations of TE derived transcripts in both the over-expression transgenic mouse model and in the striatal depletion of TDP-43 (Figs. 3A,B). TDP-43 over-expression was associated with elevated expression of 86 repetitive elements (Fig. 3A), whereas TDP-43 depletion results in increased expression levels of 223 repetitive element species (Fig. 3B). In both cases, most of these correspond to LINE, SINE and LTR elements. Overall, the affected TE transcripts are expressed at comparable levels to those of the differentially expressed RefGene transcripts (Fig. S1J), suggesting that these are robust effects on transcripts whose expression levels are not at the limit of detection. More importantly, when TDP-43 function is compromised, we observe a striking degree of concordance between the TE transcripts that are elevated and the ones that we identified as RNA targets of TDP-43 in normal tissue (Red in Fig. 3; See Table S3). Indeed the majority of elevated TE transcripts in both mouse mRNA-seq datasets also were detected as TDP-43 targets in the iCLIP-seq binding dataset (Fig. 3; Table S3). This remarkable concordance between the transcripts that are targeted by TDP-43 and those that are elevated in response to TDP-43 misexpression is unique to the repetitive elements in the genome. In contrast, CLIP targets identified from the RefGene fraction of the transcriptome have little overlap with those that show over-expression when TDP-43 function is compromised suggesting that the coding gene expression increases are largely indirect effects [19]. RefGene transcripts whose expression is reduced show good concordance with direct target identification.

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Figure 3. Concordance between mis-regulated TE transcripts upon TDP-43 manipulation and TDP-43 bound TE transcripts.

(A,B) Over-expression [20] of TDP-43 in transgenic mice and depletion [19] of TDP-43 in mouse striatum each result in elevated expression of many TE derived transcripts. The majority of over-expressed TEs also were detected (Table S3) as binding targets by CLIP-seq (RED). A few showed elevated expression but were not detected as binding targets (BLUE).

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Discussion

TDP-43 aggregation and neuropathology plays a fundamental role in a broad spectrum of neurodegenerative disorders [1], [26], [27]. This hnRNP-like RNA binding protein already has been implicated in a remarkable number of cellular functions including repression of HIV-1, alternative splicing, regulation of mRNA stability and microRNA biogenesis [26], [27]. Importantly, a large number of cellular targets of TDP-43 have been characterized, leading to the hypothesis that one key role of this multi-functional protein is to regulate alternative splicing of mRNA targets with a preference for those with large UG rich introns [17], [18], [19], [26], [28]. Our findings support the novel hypothesis that TDP-43 also targets the mobile element derived transcriptome. This association is defective in FTLD patients and the TE transcriptome is broadly over-expressed in mouse models of TDP-43 pathology.

A large fraction of the genetic material of multicellular organisms is made up of mobile elements as well as inactivated TEs. A fraction of these TEs retain the capacity to copy themselves and insert at new genomic locations. During the co-evolution of TEs with their host genomes, organisms have evolved elaborate and efficient mechanisms to prevent or at least regulate such transposition events. As a result, even the potentially active TE copies rarely mobilize within the germline and are also largely constrained in somatic tissue. Several recent studies demonstrate, however, that LINE-1 elements are normally active and mobile during neurogenesis in both rodent and human tissue [7], [8], [9]. Somatic mobilization of Alu and SVA elements as well as LINEs also has recently been detected in several different human brain regions [6]. This raises the intriguing hypothesis that active mobilization of some TEs plays a role in normal brain development or physiology. On the other hand, there also is emerging evidence that unregulated activation of TEs is associated with neuropathology. TE activation in brain has been observed in macular degeneration [14], Rett syndrome [11], Prion diseases [13],[29], Fragile-X associated tremor/ataxia syndrome (FXTAS) [15] and ALS [12]. Moreover, for the cases of macular degeneration and FXTAS, there is evidence that activation of SINEs and an LTR-retrotransposon respectively may contribute to the observed pathology [14], [15].

Our findings support three conclusions. First, that TDP-43 broadly targets TE-derived transcripts, including many SINE, LINE and LTR classes as well as some DNA elements. This conclusion is replicated in three independent datasets from rat, mouse and human. Second, the association between TDP-43 and TE-derived RNA targets is reduced in FTLD patients relative to healthy subjects, consistent with the idea that loss of TE control might be part of the disease pathology. Third, we observe broad over-expression of TE derived transcripts in each of two different mouse models with TDP-43 dysfunction. And there is a striking overlap between the TE targets identified in the CLIP study and those that are over-expressed with TDP-43 misexpression. Taken together, our findings raise the hypothesis that TDP-43 normally functions to silence or regulate TE expression. When TDP-43 protein function is compromised, TEs become over-expressed. Unregulated TE expression can have a number of detrimental impacts including genome instability, activation of DNA-damage stress response or toxic effects from accumulation of TE-derived RNAs or proteins. Such toxicity from activation of mobile genetic elements may contribute to TDP-43-mediated neurodegenerative disorders.

 

Does a loss of TDP-43 function cause neurodegeneration?

Zuo-Shang Xu

Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, 364 Plantation St, 817 LRB, Worcester, MA, 01605, USA

Molecular Neurodegeneration 2012, 7:27  doi:10.1186/1750-1326-7-27     http://www.molecularneurodegeneration.com/content/7/1/27

In 2006, TAR-DNA binding protein 43 kDa (TDP-43) was discovered to be in the intracellular aggregates in the degenerating cells in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), two fatal neurodegenerative diseases [1,2]. ALS causes motor neuron degeneration leading to paralysis [3,4]. FTLD causes neuronal degeneration in the frontal and temporal cortices leading to personality changes and a loss of executive function [5]. The discovery triggered a flurry of research activity that led to the discovery of TDP-43 mutations in ALS patients and the widespread presence of TDP-43 aggregates in numerous neurodegenerative diseases. A key question regarding the role of TDP-43 is whether it causes neurotoxicity by a gain of function or a loss of function. The gain-of-function hypothesis has received much attention primarily based on the striking neurodegenerative phenotypes in numerous TDP-43-overexpression models. In this review, I will draw attention to the loss-of-function hypothesis, which postulates that mutant TDP-43 causes neurodegeneration by a loss of function, and in addition, by exerting a dominant-negative effect on the wild-type TDP-43 allele. Furthermore, I will discuss how a loss of function can cause neurodegeneration in patients where TDP-43 is not mutated, review the literature in model systems to discuss how the current data support the loss-of-function mechanism and highlight some key questions for testing this hypothesis in the future.

 

Amyotrophic lateral sclerosis (ALS) is a disorder where progressive degeneration of large motor neurons in the spinal cord and cerebral cortex leads to paralysis and death [3,4]. Frontotemporal lobar degeneration (FTLD) causes degeneration of neurons in frontal and temporal cortices, leading to deterioration of executive, cognitive and social functions, as well as loss of emotional control[5]. Although clinically distinct, a significant overlap exists between these two diseases in the patient population, resulting in a continuous spectrum ranging from patients with one disease at either end and patients with varying degrees of both diseases in the middle [6,7]. Recent genetic data has reaffirmed the connection between these two diseases. Some genetic mutations cause one disease but rarely the other, e.g. SOD1, FUS and TDP-43 for ALS, and tau, progranulin and CHMP2B for FTLD. Other mutations cause either or both diseases in the same patient or family, e.g. ubiquilin 2 and C9ORF72. In a significant population of patients (~95 % ALS and ~50 % FTLD), TDP-43 positive intracellular inclusions are present in the CNS even though the TDP-43 gene is not mutated [811], raising the question of how wild-type TDP-43 is involved in the pathogenesis of these cases.

TDP-43 is a RNA binding protein containing two RNA-recognition motifs (RRM), a nuclear localization signal (NLS) and a nuclear export signal (NES) [12]. The protein is normally concentrated in the nucleus but also shuttles back and forth between the nucleus and cytoplasm[13]. TDP-43 is a global regulator of gene expression and is involved in regulation of transcription and multiple aspects of RNA processing and functioning, including splicing, stability, transport, translation and microRNA maturation [1417]. TDP-43 interacts with many proteins and RNAs and functions in multi-protein/RNA complexes [1821]. TDP-43 maintains its protein expression at a constant level within a tight range by auto-feedback mechanisms, which involve TDP-43 binding to its own 3’ untranslated region [15,22]. Overexpression of TDP-43 leads to down-regulation of the endogenous TDP-43 [23,24], and blocking expression of one allele leads to a compensatory increase in the expression of the other allele [2527]. The tight regulation of TDP-43 levels is suggestive of its crucial role in the functioning of multi-protein/RNA complexes, where maintaining a certain stoichiometry between TDP-43 and the other components may be critical.

Because mutations in TDP-43 lead to ALS, a causal role of TDP-43 for neurodegeneration is firmly established [12,28,29]. Therefore, understanding how the mutants cause neurodegeneration offers a convenient entry point for exploring how TDP-43 plays this role. The first question is whether a gain, a loss of function or a dominant-negative effect mediates neurotoxicity. A resolution to this question is of critical importance because it sets the direction of further research on the disease mechanism and on the design of therapeutic strategies. To answer this question, model systems of both gain or loss of function must be employed (Table 1). Gain-of-function models are usually achieved by gene overexpression and loss-of-function models by gene knockout or knockdown. Based on the phenotypic readouts, the mechanism whereby the mutants cause neurodegeneration can be deduced (Table 1).

Table 1.Assay for disease mechanism using transgenic animals

A gain-of-function (Table 1, GF column) mechanism includes two scenarios: first, the mutant gene gains a novel toxic activity that is independent of the normal function of the gene, and second, the mutant becomes hyperactive in one of its normal functions leading to toxicity. In the first scenario, overexpression of the mutant gene, but not the wild type, will cause the disease phenotype. In the second scenario, overexpression of either the mutant or wild-type gene will cause the disease phenotype. In both gain-of-function scenarios, knockout or knockdown of the gene is not expected to cause the disease phenotype.

A loss of function (haploinsufficiency; Table 1, LF column) means that the mutant gene has no function or a reduced function but does not interfere with the function of the wild-type allele. In this scenario, neither overexpression of the mutant nor the wild type is expected to cause the disease phenotype. But knockout or knockdown reproduces the loss of function, and therefore, is expected to generate the disease phenotype.

A dominant-negative mechanism (Table 1, DN column) denotes the condition where the mutant allele is dysfunctional and inhibitory to the function of the wild-type allele. In this scenario, overexpression of the mutant gene is expected to cause the disease phenotype because it dominant-negatively inhibits the function of the endogenous wild-type protein. On the other hand, overexpression of the wild type is generally not expected to generate the disease phenotype because the wild-type gene can function normally and does not inhibit the function of the normal endogenous allele. However, there are exceptions under certain circumstances, for example, if the protein functions in a multi-protein complex (see details below). Knockout or knockdown of the gene is expected to reproduce the disease phenotype because this reduces the function of the wild-type gene. Thus, in model systems, the dominant-negative mechanism can display characteristics of both a gain and a loss of function—it is a loss of function in essence, yet its effect can dominate over the endogenous wild-type allele.

In the case of TDP-43, an abundance of gain-of-function models have been generated in various species, including worm, fly, fish and rodents [12]. In all models with rare exceptions, a consistent finding is that overexpression of both mutant and wild type TDP-43 can cause a neurodegenerative phenotype (Table 1, TDP-43 columns), thus supporting a gain-of-function mechanism and a potential overactivation of TDP-43 in the mutants [12]. Loss-of-function models have also been generated in non-mammalian species and all except the worm showed neurological and neurodegenerative phenotypes [3033,35,44]. The difference between worm and the other species may reflect some species difference, since TDP-43 is dispensable for survival in the worm but not so in other species. In general, the degenerative phenotypes in the loss-of-function models appear less overwhelming than the overexpression models and are often difficult to separate from the developmental effects stemming from a lack of TDP-43 function. Importantly, there is a lack of evidence in mammalian models that a loss of TDP-43 function causes neurodegeneration. This is largely due to the failure in generating such a model using a gene knockout approach [2527,36]. As a result, the current literature leans towards a gain-of-function mechanism as far as the role of TDP-43 in neurodegeneration is concerned.

Yet despite the preponderance of evidence for the gain-of-function mechanism, it has not been sufficient to rule out the loss-of-function mechanism, because the gain-of-function mechanism does not explain well a phenomenon that is consistently observed in numerous pathological studies, i.e. the nuclear clearance of TDP-43 that accompanies the presence of TDP-43 intracellular aggregates[1,2,45]. The question whether the depletion of TDP-43 in the nucleus is consequential in the pathogenesis remains unanswered. In addition, although the aggregates in the cytoplasm may generate gain-of-function type of toxicity, it is also conceivable that the aggregation of TDP-43 renders TDP-43 non-functional, and as such, causes TDP-43 dysfunction. In this review, I propose a model that is centered on the loss-of-function mechanism whereby TDP-43 plays its role in neurodegeneration. I will highlight the evidence in the current literature that is consistent with this model and the evidence that is still needed from future experiments to test this model.

A model for the loss of TDP-43 function as a central mechanism of pathogenesis in human disease

The TDP-43 protein is normally expressed through transcription and translation, and once produced, it regulates its own expression by a feedback mechanism, i.e., upregulating its own expression when the protein level is too low and inhibiting its expression when the protein level is too high [15,2227]. By this auto-regulatory mechanism, the intracellular level of TDP-43 is maintained within a narrow range (Figure 1, #1 normal). This tightly maintained TDP-43 level may be important because TDP-43 functions in multiprotein/RNA complexes [1821], where a proper structure and function of the complex requires a certain stoichiometric ratio between TDP-43 and its protein and RNA partners (Figure 1, #1 normal). Such a requirement is not unique to TDP-43 complexes as it has been demonstrated in other protein-RNA or protein complexes. For example, in the primary micro RNA (pri-miRNA) processing Drosha complex, overexpression of one subunit DGCR8 leads to an inhibition in the processing activity [46]. As another example, in the kinesin-2 heterotrimeric complex that drives the antegrade transport of late endosomes and lysosomes, overexpression of one subunit KAP3 inhibited the transport similar to the KAP3 knockdown [47].

Figure 1 .
Mechanisms that can cause TDP-43 dysfunction in ALS, FTLD and other neurodegenerative conditions. AD means Alzheimer’s disease, PD Parkinson’s disease, HD Huntington’s disease, LBD Lewy body dementia, DS Down syndrome, HSD hippocampal sclerosis dementia, FBD familial British dementia, and SCA spinal cerebellar ataxia. See the section subtitled “A model for the loss of TDP-43 function as a central mechanism of pathogenesis in human disease” for a detailed description of this diagram.

Xu Molecular Neurodegeneration 2012 7:27   doi:10.1186/1750-1326-7-27   Download authors’ original image

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In the disease situation, conditions in patients’ cells become conducive for TDP-43 aggregation. For example, TDP-43 mutants and its C-terminal fragments associated with ALS and FTLD have enhanced aggregation propensity [4851], and therefore, can drive TDP-43 aggregation. The aggregation can lead to a reduction in the pool of TDP-43 that can be incorporated into the TDP-43 protein/RNA complexes (Figure 1, #2 aggregation), thereby reducing the complex function and leading to neurodegeneration.

In model systems where TDP-43 is overexpressed (Figure 1, #3), the function of TDP-43 can be inhibited because an oversupply of exogenous TDP-43 mismatches with a limited supply of its endogenous interacting protein/RNA partners, resulting in the formation of incomplete and dysfunctional complexes. Below I highlight the evidence in the current literature that is consistent with this model and the future experiments that are need to test this model.

TDP-43 performs functions of vital importance, but the consequence of its dysfunction in neurodegeneration remains unclear

A crucial piece of evidence for a loss-of-function mechanism would be demonstration that a loss of TDP-43 function can cause neurodegeneration. This has not yet been experimentally achieved in a convincing manner, particularly in mammalian species. Knockouts in rodents cause early embryonic lethality [2527,36]. Inducible knockout in adult mice causes a rapid loss of fat tissue and lethality [36]. These results have not been informative as to the consequences of TDP-43 dysfunction in the nervous system. Nevertheless, the severity of the phenotype in the knockout models suggests a critical functional importance of TDP-43 in the health and survival of mammalian cells. Indeed, the conditional knockout of TDP-43 in mouse embryonic stem cells causes cell death [36]. Therefore, it is conceivable that TDP-43 function may also be vital for the survival and function of neurons. Supporting this notion are the experiments where TDP-43 knockdown causes morphological abnormalities and cell death in cultured neurons [50,52,53] and a large change in gene expression in cells of the CNS [15,16].

Experimental data from non-mammalian species have also been consistent with the critical functional importance of TDP-43. In C. Elegans, TDP-43 deletion mutants are viable, but show low fertility, slow growth and locomotor defects [44]. In Drosophila, TDP-43 knockout causes abortive embryonic development and lethality [30,31]. Although some escape the lethality and develop to adults, they display severe locomotor defects, premature death and abnormal neuronal morphology [30,31]. Evidence for progressive axonal degeneration and locomotor defects has also been reported in adult TDP-43 knockdown flies [32]. In zebrafish, TDP-43 knockdown during embryonic development causes selective defects in motor axonal growth and results in motor behavioral abnormalities [35]. These results do not conclusively demonstrate a role of TDP-43 dysfunction in neurodegeneration in ALS and FTLD, but do indicate that TDP-43 is important in the development and functioning of the nervous system, thus leaving open the possibility that TDP-43 dysfunction could play a role in neurodegeneration.

How a loss of TDP-43 function explains the pathogenic mechanism of TDP-43 mutants

Mutations in TDP-43 cause motor neuron degeneration and ALS [28,29]. The overwhelming majority of the mutations are located in the C-terminal glycine-rich domain [12], which is unstructured and responsible for interactions with other proteins [17,21,54]. How mutant TDP-43 causes neurodegeneration is not known. Overexpression models support a gain of function, but the reliance of overexpression to elicit neurodegenerative phenotypes risks over-interpretation. A lack of convincing evidence that TDP-43 levels are elevated in human disease leaves open the question of whether the results from the overexpression models are relevant for the human disease.

While there is room for doubt for the gain-of-function mechanism, evidence for the loss-of-function mechanism is also weak, primarily because few experiments have generated data directly relevant to this question, especially in mammalian systems. Nevertheless, reasonable scenarios for this mechanism can be formulated based on the current, albeit fragmented and incomplete, experimental literature. First, wild-type TDP-43 is an aggregation-prone protein and mutant TDP-43 is even more so [48,51,55]. Therefore, TDP-43 mutants can initiate and drive protein aggregation, leading to TDP-43 depletion from the cell nucleus, as has been observed in patients [1,2,56]. In addition, mutant TDP-43 may have an enhanced susceptibility for polypeptide fragmentation, which generates the patient-specific 25-kDa fragments [29,57]. These fragments have a high propensity for aggregation [50,55,58] and can coaggregate with wild-type TDP-43, thereby sequestering wild-type TDP-43 into the aggregates and depleting TDP-43 from the nucleus [50].

Second, the mutant may be functionally less active or inactive but may still retain its autoregulation capability. As a result, the overall TDP-43 level would be maintained but the function of TDP-43 would be reduced because the protein expressed from the mutant allele is dysfunctional. Some experimental data support this scenario. In mice, overexpression of mutant TDP-43 inhibited the expression of the endogenous TDP-43 to the same extent as wild type overexpression [23,37,38], suggesting that the disease-causing mutants retain their autoregulatory function. In Drosophila, wild-type TDP-43 is capable of promoting growth of dendrites and increasing the size of synaptic terminals at the neuromuscular junction. However, these activities are lost in the ALS-causing mutants [31,34], suggesting that the mutants have lost some of the wild-type functions.

Third, mutant TDP-43 may form defective TDP-43 protein/RNA complexes, thereby poisoning the function of the complex. In this capacity, the mutant TDP-43 can act dominant-negatively to inhibit the function of the wild-type allele. There is evidence that TDP-43 forms a homodimer [59] and that multiple TDP-43 molecules are incorporated into each complex [19]. Therefore, if a mutant TDP-43 molecule were capable of rendering dysfunction to the whole complex that contains both mutant and wild-type TDP-43 molecules, then the function of the wild-type allele would be inhibited.

These scenarios are consistent with a model where TDP-43 mutants cause a loss of TDP-43 function by a dominant negative mechanism. Notably, while the first scenario requires the formation of aggregates for cellular toxicity, the second and third scenarios make such a requirement unnecessary. Indeed, in both cellular and animal models, toxicity induced by mutant TDP-43 does not require its aggregation [33,37,39,60].

How TDP-43 dysfunction could contribute to neurotoxicity from overexpression of either mutant or wild-type TDP-43 in model systems

The prevailing interpretation for the observation that overexpression of mutant TDP-43 causes neurodegeneration is that mutant TDP-43 exert its toxicity by a gain of function. However, these results are also consistent with a dominant-negative mechanism, as discussed above (also see Table 1). The dominant-negative model predicts that overexpression of the mutant in sufficient quantities will inhibit the function of the two endogenous wild-type alleles in the model systems.

A puzzling observation is that overexpression of wild-type TDP-43 causes similar neurotoxic phenotypes in model systems [23,33,35,37,38,4043,60,61]. Because of the autoregulatory mechanism, overexpression of human wild-type TDP-43 leads to a suppression of the endogenous TDP-43 [23,24]. This has led to a proposal that a loss of the endogenous TDP-43 caused neurotoxicity [24]. While this proposal can reasonably explain the toxicity of the mutants on the premise that they are dysfunctional, the toxicity from the wild-type TDP-43 poses a problem because several studies have shown that the human wild-type TDP-43 gene can substitute the function of its homologue in species as distant as Drosophila and C. Elegans[30,44]. A more plausible explanation can be derived from the fact that TDP-43 functions in multiprotein/RNA complexes, whose function may depend on a certain stoichiometric composition of the different protein/RNA components. Overexpression of wild-type TDP-43 provides an amount of TDP-43 in excess of the other components that form the complexes, thereby sequestering those components into incomplete and dysfunctional complexes (Figure 1, #3 overexpression). Therefore, both overexpression of the mutants and the wild-type TDP-43 can cause neurodegeneration by dominant-negatively inhibiting the normal function of TDP-43 complexes so long as it interacts with two or more components in the complexes simultaneously and with near equal binding affinities.

While the above interpretation of the literature remains to be confirmed by further experimentation, some of the predictions from this loss-of-function/dominant-negative hypothesis are supported by observations in the current literature. First, overexpression of mutant should be more potent in causing neurodegeneration than overexpression of the wild type, which has been the case in several overexpression models [35,40,60,61]. Although this finding is not inconsistent with the gain-of-function mechanism, the result can also be explained readily by the dominant-negative mechanism outlined above. Overexpression of mutants can inhibit normal TDP-43 function by three mechanisms: (1) displacing the endogenous TDP-43 through the autoregulation mechanism, (2) inserting itself into the TDP-43 complexes in the place of the wild-type protein, and (3) forming dysfunctional complexes by disruption of the stoichiometry between TDP-43 and other protein/RNA components. In contrast, overexpression of the wild-type TDP-43 can inhibit TDP-43 function only through the third mechanism because unlike the mutant protein, it has full function. Therefore, to inhibit TDP-43 function to the same degree, a higher level of expression will be required for the wild-type TDP-43 than the mutant.

Second, if the dominant-negative hypothesis is correct, overexpression and knockout or knockdown of the gene can cause similar phenotypes. Currently, data from mammalian species is lacking to address this point. However, evidence can be drawn from other species. For example, overexpression of either mutant or the wild-type TDP-43 in Drosophila motor neurons causes progressive locomotor defects and a shortening of lifespan [33]. These phenotypes are similar to those caused by TDP-43 knockdown [33]. As another example, expression of human TDP-43 mutants but not the wild type in zebrafish embryos compromised motor axonal growth and caused locomotor defects. Similarly as in flies, knocking down the endogenous TDP-43 caused the same phenotypes [35]. Importantly, the phenotypes in the knockdown fish are rescued by the expression of human wild-type TDP-43 but not the mutants. These results are consistent with the view that the ALS-relevant TDP-43 mutants are dysfunctional and are capable of inhibiting TDP-43 function in a dominant negative manner.

Third, the loss-of-function/dominant-negative hypothesis predicts that ALS-causing mutants should be loss-of-function alleles. As discussed above, the observations that the mutants lost their ability to stimulate the growth of dendrites and axons in flies [31,34,35] and their inability to rescue phenotypes from TDP-43 knockdown in zebrafish [35] supports the loss-of-function proposition. However, key evidence from mammalian species remains to be produced.

While the case for a loss of function by a dominant-negative mechanism can be argued for, it may be overly simplistic to argue that a gain of function does not contribute to the phenotypes caused by TDP-43 overexpression in the model systems. Some evidence indicate that TDP-43 is capable of causing cellular toxicity by a gain of function under ectopic and overexpressed conditions. For example, TDP-43 causes toxicity in yeast, which does not possess an endogenous TDP-43 homologue [62]. Similarly, TDP-43 is not essential in C. Elegans, yet overexpression of human TDP-43 can still cause toxicity that is not observed in knockouts [44,61,63,64]. Therefore, in model systems where TDP-43 performs vital functions, phenotypes caused by TDP-43 overexpression are likely derived from both an interference of endogenous TDP-43 function and a gain of function. Given the complexity in the protein/RNA interaction networks of TDP-43, perhaps this would not be surprising. Overexpression is likely to generate new aberrant interactions as well as to disrupt the authentic interactions that are vital for the cell. Therefore, disentangling these effects will be complex in the overexpression models.

What is the role of wild type TDP-43 in human neurodegeneration

While the case for a loss of function in the TDP-43 mutants and in the overexpression model systems can be made, can the loss-of-function mechanism play a role in patients where TDP-43 is not mutated and not overexpressed? This is an important question because the vast majority of patients with ALS and FTLD-TDP do not have TDP-43 mutations. The answer to this question is yes because even though the primary trigger of the degenerative process lies not in TDP-43 but elsewhere, the same kind of TDP-43 aggregation and nuclear clearance is observed in the CNS of these patients [1,2,45] (Figure 1). The loss-of-function/dominant-negative model will predict that the nuclear clearance and the cytoplasmic aggregation of TDP-43 are probably a significant contributor to neurodegeneration by causing a loss of TDP-43 function. However, the experimental data for testing this prediction is scarce. In Drosophila and zebrafish, knockout or knockdown of TDP-43 produced similar neurodegenerative phenotypes [33,35]. However, further analysis is needed to differentiate the effects of TDP-43 dysfunction on neurodegeneration from those on neurodevelopment, and the relevance of these observations to human neurodegeneration remains to be established. A mammalian model with TDP-43 dysfunction in the mature CNS is urgently needed to understand the effects from a loss of TDP-43-function.

Based on the loss-of-function/dominant-negative hypothesis outlined above, what triggers TDP-43 aggregation will be one of the most intriguing and important questions in understanding the pathogenic mechanisms in ALS and FTLD. Recent investigations have shown that multiple causes can trigger secondary TDP-43 aggregation and nuclear clearance. These causes can be classified into several categories: (1) Gene mutations that enhance the mutant protein aggregation propensity and cause ALS-FTLD with TDP-43 aggregation. Examples in this category include VCP, optineurin, dynactin, ataxin 2 and ubiquilin 2. All the mutant proteins form aggregates and some form coaggregates with wild-type TDP-43 [9,6569]. The mechanism whereby these mutants cause TDP-43 aggregation is not understood. One possibility is that the aggregation of these proteins weakens the capacity of cellular proteostasis [70], which creates an environment conducive for aggregation-prone proteins such as TDP-43 to aggregate. Some of the proteins such as VCP and ubiquilin may be involved in TDP-43 degradation [71,72]. Therefore, mutations in these proteins may directly alter the TDP-43 economy and cause TDP-43 aggregation. (2) Gene mutations that cause ALS and FTLD with TDP-43 aggregation, but the mutant proteins are not involved in protein aggregation themselves. Examples in this category include progranulin, angiogenin and C9ORF72[1,11,73,74]. At present, it is not known how these mutations lead to TDP-43 aggregation. (3) Traumatic brain injury that lead to ALS-FTLD without gene mutations. Repetitive traumatic brain injury has been shown to be associated with ALS and FTLD with intracellular TDP-43 aggregation[75,76]. (4) Other neurodegenerative diseases that are not ALS-FTLD but trigger secondary TDP-43 aggregation. Examples of this category include some of the most common neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease and numerous others [8,77,78] (Figure1). Aggregation of TDP-43 in these cases may also be attributed to a disruption of proteostasis environment due to the aggregation of other proteins, although direct experimental evidence for this hypothesis is not yet in existence. (5) Unknown causes in sporadic ALS and FTLD cases. Some of the speculated causes include genetic predisposition in combination with environmental stress, e.g. environmental toxins, trauma and high physical activity [7982].

Recent studies have suggested that a redistribution of TDP-43 to the cytoplasm may be a precursor to TDP-43 aggregation. In ALS and FTLD patients, some neurons show an increase in cytoplasmic TDP-43 immunoreactivity with diffused or granular appearance, which may represent an early stage of TDP-43 aggregation [8386]. The cause for the cytoplasmic redistribution is not clear. However, a recent study demonstrate that a single traumatic brain injury can be followed by a persistent increase in the cytoplasmic levels of TDP-43 [87], suggesting that injuries to the CNS can be an initial trigger for increased levels of cytoplasmic TDP-43. In model systems, the redistribution of TDP-43 can be triggered by various stresses, including neuronal injury [8890], overexpression of disease-associated mutant TDP-43 and VCP [9193], oxidative stress [93,94] and proteasome inhibition [53]. The functional consequence of the cytoplasmic localization of TDP-43 will require further characterization. Nevertheless, some studies suggest that the cytoplasm-localized TDP-43 is recruited to stress granules before being transformed into aggregates that can persist independent of stress granules [9395]. Another study demonstrated that a modest knockdown of TDP-43 exacerbated, rather than alleviated, cell death that is induced by proteasome inhibition and associated with TDP-43 cytoplasmic translocation [53], suggesting that any toxicity that might be associated with TDP-43 cytoplasmic translocation is derived from a loss of TDP-43 function. These data are consistent with the hypothesis that an increased cytoplasmic level of TDP-43, which follows the initial cellular stress, can lead to TDP-43 aggregation and nuclear depletion.

Therapeutic implications from the dominant-negative model

Discussion on therapeutic implication based on the loss-of-function hypothesis may be premature since the hypothesis remains to be tested. However, such an exercise may be helpful for illustration of the critical importance for a resolution of this question. In the case of a gain of function, strategies that reduce the function should be effective. This may be achieved by lowering the protein levels through an inhibition of its synthesis or a stimulation of its degradation. If the toxic activity is known, strategies that inhibit the specific toxic activity may also be effective. In the case of a loss of function, on the other hand, strategies that increase the function should be effective. This may be achieved by increasing expression and stability of the protein, or stimulating its activity.

The therapeutic strategy for the dominant negative mechanism differs from both purely gain- or loss-of-function mechanisms and will be most challenging. We cannot simply increase the level of TDP-43 because uncontrolled increase of TDP-43 may inhibit the function of TDP-43 rather than improving it. High levels of TDP-43 could also further accelerate its aggregation and produce aberrant interactions with other proteins and RNA. Moreover, we do not understand why TDP-43 stays in the cytoplasm and becomes depleted from the nucleus in the disease. Therefore, it is not clear whether a simple increase of TDP-43 will replenish its level in the nucleus. In the case of mutant TDP-43, allele-specific inhibition of the mutant TDP-43 may be helpful but may not be sufficient to compensate for the lost function of the mutant allele. If the hypothesis that TDP-43 aggregation drives nuclear depletion of the TDP-43 is correct, preventing or reversing the aggregation may be a rational and safe approach to mitigate the loss of TDP-43 function. To achieve this, we need to understand how TDP-43 aggregation is triggered and propagated. We also need to understand the TDP-43 aggregation process at molecular and structural levels. Alternatively, strategies that enhance the function of TDP-43 without resorting to increase the protein level, or retain TDP-43 in the nucleus may also be effective.

Conclusions

TDP-43 aggregation and nuclear depletion have been observed widely in neurodegenerative diseases. The role of TDP-43 in neurodegeneration remains to be defined. Chief among the questions is whether a gain of function, a loss of function or a dominant-negative mechanism is responsible for neurotoxicity. The answer to this question is of critical importance because it guides the future direction of research and sets the foundation for therapeutic strategies. Current experimental data from model systems has been predominantly invoked to support the gain-of-function mechanism. However, a careful review of the data suggests that a loss of TDP-43 function caused by its mutations, its aggregation and nuclear depletion, and the inhibition of TDP-43 function by a dominant-negative mechanism in the overexpression models, are at least as plausible as the gain-of-function theory, if not more so. Therefore, in our future research, we need to gain a more detailed understanding of the normal function of TDP-43, particularly in the cells of the CNS. We need models of loss of TDP-43 function in the CNS, particularly in mammalian species, to understand the consequence of TDP-43 dysfunction. In such a pursuit, models with a partial loss of TDP-43 function may be especially desirable because in humans, it is unlikely that the TDP-43 function is totally lost. We need evidence from human diseases to determine whether the conditions are more in tune with a gain or a loss of TDP-43 function. Lastly, we need to design strategies to address the difficult problem of how to restore the normal levels of TDP-43 function as a therapy.

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GEN Tech Focus: Rethinking Gene Expression Analysis, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

GEN Tech Focus: Rethinking Gene Expression Analysis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Quantitating gene expression is essential for researchers to answer important biological questions about basic cellular functions, as well as disease states. In the following articles you will discover the multitude of advances investigators have made to accurately measure and quantitate genetic transcripts within the cell.

Diverse Pathways to Drug Targets

A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression. With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.

 

The Gene-Expression Undergrowth Have Been Well Trodden, but RNA Paths Want Wear, Too

  • Hepatitis C virus depends on a functional interaction between its genome and miR-122 for viral stability and replication. Researchers recently used an antisense oligonucleotide that targets the liver-specific microRNA miR-122, blocking its function. [Bluebay2014/Fotolia]

    A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression.

    With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.

    This knowledge—combined with the available technology and strategies to decipher RNA pathways and link alterations in the levels or activity of miRNAs or lncRNAs to gene expression, epigenetic mechanisms, and protein activity in normal and disease phenotypes—is driving the development and clinical testing of novel drug targets and therapeutics that target regulatory RNAs.

    For example, a microRNA was targeted in a Phase II clinical study that assessed the effect of miravirsen, an antisense oligonucleotide, in patients with hepatitis C. The study, which was described in 2013 in the New England Journal of Medicine, indicated that miravirsen sequesters the liver-specific microRNA miR-122 in a highly stable heteroduplex, thereby inhibiting its function.

    Hepatitis C virus (HCV) depends on a functional interaction between its genome and miR-122 for viral stability and replication. According to the study, inhibition of miR-122 in HCV-infected patients was associated with decreased levels of HCV RNA that continued beyond the treatment period, without evidence of viral resistance.

    The therapeutic potential of regulatory RNAs is also being assessed in other conditions such as cancer. Specifically, miRNAs and other ncRNAs in cancer initiation, progression, and metastasis are being studied by George Calin, M.D., Ph.D., a professor of experimental therapeutics, MD Anderson Cancer Center, University of Texas. Dr. Calin’s group is scouring the “microRNAome” to identify miRNAs of about 21–22 nucleotides that can serve as reliable biomarkers for cancer diagnosis and to guide decision-making in patient management, including as predictors of survival and response to drug therapy.

    miRNAs are involved in every aspect of tumorigenesis, cancer progression, and dissemination. Not only are they expressed in tumor cells, they are also stably expressed in exosomes and are present in various bodily fluids, where they can act like hormones and signaling molecules. Comparative profiling of these fluids for differences in miRNA levels between patients with and without cancer could identify relevant biomarkers.

     

    Analyzing RNA Pathways

  • Using Qiagen’s Ingenuity Pathway Analysis, researchers can analyze relationships between molecules and diseases of interest by modeling how gene expression patterns affect functional outcomes or disease processes.

     

     

     

     

     

     

     

     

     

    Dr. Calin and colleagues have described the significance of miRNA signatures obtained in recent studies involving miRNA profiling of human tumors. An overview appeared 2014 in CA: A Cancer Journal for Clinicians (“MicroRNAome genome: a treasure for cancer diagnosis and therapy”). Also, last February, Dr. Calin gave an account of his group’s work at the Molecular Med Tri Conference in San Francisco.

    Technology is not holding back advances in the field of RNA pathway analysis according to Dr. Calin. The main bottleneck at present is in the design of prospective studies needed to confirm the predictive value of miRNA-based biomarkers.

    Dr. Calin points to two other key challenges that scientists currently face in translating research findings into diagnostic, prognostic, and therapeutic tools. One is the difficulty in selecting an miRNA target, mainly because an individual miRNA could have a role in regulating tens, hundreds, or even thousands of protein-coding genes. For drug discovery, the aim is to identify miRNAs that affect a single pathway of interest to help limit off-target effects. The need for novel delivery systems for RNA-targeted drugs is another key challenge.

    At the Molecular Med Tri Conference, Jean-Noel Billaud, Ph.D., principal scientist at Qiagen Bioinformatics, presented a case study demonstrating how the company’s Ingenuity Pathway Analysis technology can be used to conduct a systems biology analysis to identify the pathways, potential upstream regulators, and downstream outcomes involved in the host response to West Nile Virus (WNV) infection. Dr. Billaud also discussed how to interpret the results from a biological perspective.

    In his presentation, Dr. Billaud described the first step in this analytical process as the acquisition of RNA sequence data using next-generation sequencing techniques for the purpose of characterizing and quantifying differential gene expression between an infected and uninfected cell. The CLC Cancer Research Workbench tool is used to process the sequence data, and the results are imported directly into the IPA system.

    Analysis of differential gene expression aims to answer a series of key questions, including the following: What metabolic and/or signaling pathway(s) is activated or inhibited? Is there an overlap of the genes or pathways that are activated or inhibited? What are the potential upstream, downstream, functional, and phenotypic implications of this pathway activation or inhibition?

    Dr. Billaud described other questions researchers might attempt to answer through the use of IPA: What are the identifying the underlying transcriptional programs? Which biological processes are involved and in what way? Are there splice variants of interest? What type of regulation is involved?

    In the WNV case study, IPA predicted activation of the interferon signaling pathway and added statistically and functionally relevant biological processes to the WNV-related biochemical network the system developed. IPA is able to simulate the effects of interferon pathway activation on neighboring molecules and processes, which enables broader modeling of antiviral responses, prediction of the effects on viral replication, and identification of upstream transcriptional regulators of antiviral and related anti-inflammatory processes, for example.

    These data and analytical capabilities may allow researchers to propose new hypotheses that connect molecules in regulatory networks to disease-related pathways in a predictive way, leading to the identification of a “master regulator” that could serve as a disease-specific drug target, according to Dr. Billaud.

    In the WNV example, he described the use of the Molecule Activity Predictor (MAP) function in IPA to test the hypothesis that CLEC7A is a host susceptibility factor required by WNV to stimulate an immune response in the brains of infected patients, contributing to the development of life-threatening encephalitis. The MAP function simulates the inhibition or downregulation of CLEC7A, showing how it would likely reduce the risk of WNV-associated encephalitis. These types of hypotheses would then need to be tested and validated.

    Pathways Driving B-Cell Differentiation

    • Robert C. Rickert, Ph.D., professor and director of the Tumor Microenvironment and Metastasis Program at Sanford-Burnham Medical Research Institute, is using conditional gene targeting to identify the genes and biochemical pathways that play a role at specific stages of B-cell differentiation. With this approach, it is possible to knock out targeted genes in a mouse at different stages of B-cell development, and to do so in an inducible fashion, allowing you “to look at how it affects different signal transduction pathways in a context-specific manner,” says Dr. Rickert.

      When applied to a relevant mouse model of disease—such as a B-cell lymphoma—this inducible genetic system should yield effects similar to those that could be obtained with a drug capable of blocking the activity of the targeted gene product. Dr. Rickert and colleagues are exploring the similarity between the effects achieved with conditional gene targeting and those of recently approved drugs to treat chronic lymphocytic leukemia (CLL) and some forms of lymphoma such as idelalisib and ibrutinib, which are both inhibitors of the B-cell receptor pathway via blocking of PI3K or Bruton’s tyrosine kinase (BTK), respectively.

      Dr. Rickert presented his group’s latest research at a Keystone Symposium Conference, PI 3-Kinase Signaling Pathways in Disease, which took place last January in Vancouver. In his talk, Dr. Rickert emphasized that the phosphatidyl inositol-3 kinase (PI3K) pathway is a major regulator B lymphocyte differentiation and function.

      Dr. Rickert has also applied conditional gene targeting to compare the roles of the NFκB and PI3K pathways in B-cell maturation. He has shown that while both pathways are essential at some stages of B-cell differentiation, only one pathway may be necessary for B-cell maintenance and survival.

      “Ultimately we want to gain more insight at the biochemical level into single cells and the heterogeneity of the cell populations we’re interested in,” says Dr. Rickert. Tumors and cancer cell populations are quite heterogeneic, and better biochemical tools are needed to be able to sort through these populations of cells and “look at some of the more interesting, rogue cells, such as cancer stem cells,” he adds.

    An Evolutionary Approach

    In his laboratory at Hebrew University of Jerusalem, researcher Yuval Tabach, Ph.D., is using computational tools to analyze and compare the genomes and proteins of hundreds of species to identify evolutionary patterns of conservation and loss that point to connections between molecular pathways and disease.

    “The main power of this phylogenetic profiling approach is that if you look at proteins across evolution, some are lost at certain points in certain species,” says Dr. Tabach. For example, proteins involved in the tricarboxylic acid (TCA) cycle have been highly conserved across some species, but have disappeared in others because those species have lost their mitochondria.

    Dr. Tabach and colleagues have shown that sets of genes associated with particular diseases have similar phylogenetic profiles. They are also using this approach to identify genes associated with longevity, cancer resistance, and various extreme environmental conditions.

    Phylogenetic profiling to connect patterns of conservation and loss across millions of years of evolution can be applied to entire proteins, protein domains, and RNA molecules such as microRNAs. The potential applicability of this approach to drug discovery and development is multifaceted.

    For example, given a gene known to be related to a certain disease, the ability to identify other genes with a similar phylogenetic profile might reveal genetic factors that could explain incomplete penetrance or the variability of disease severity in different affected individuals. Alternatively, identification of a candidate gene in one patient could serve as the basis for identifying other key factors in other patients with the same disease using the phylogenetic profile.

    Compared to strategies such as gene expression analysis or protein-protein interaction mapping for identifying disease-related genes, phylogenetic profiling “is much faster” and will become an increasingly powerful tool as the genome sequences of more species become available, explains Dr. Tabach.

    The Israeli start-up company ReThink Pharmaceuticals is using the molecular networks generated through this phylogenetic profiling work for the purpose of drug repositioning. “If you know that a certain drug targets a gene, we can build a network to find other genes/proteins that interact with the drug target,” asserts Dr. Tabach, citing preliminary results that demonstrate the ability to predict additional effects of a drug candidate.

     

 

Measuring siRNA-mediated Knockdown of the IL-8 gene Using the QuantiGene Singleplex Assay

A critical component of RNA interference (RNAi) studies is the validation of gene expression inhibition. RNAi experiments have many sources of variation that make accurate quantitation of target mRNA difficult when qPCR is used. Variation in the potency and stability of short interfering RNA (siRNA), coupled with differences in transfection efficiency and protein turnover, results in varying gene knockdown efficiency.

 

The RNA World Expands

Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.

The RNA World Expands  

Long noncoding RNAs mean more than HOTAIR.

The RNA World Expands

Long noncoding RNA (lncRNAs) can regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels. [© Alila Medicinal Media – Fotolia.com]

  • Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.

    Last year researchers, including Tim Mercer, Ph.D., at the Institute for Molecular Bioscience-University of Queensland, Roche Nimblegen, and John Rinn, Ph.D., and his team in the department of stem cell and regenerative biology at Harvard, reported that “transcriptomic analyses have revealed an ‘unexpected complexity’ to the human transcriptome, the depth and breadth of which exceeds current RNA sequencing capability.”

    These scientists used these techniques to identify and characterize unannotated transcripts whose rare or transient expression is below the detection limits of conventional sequencing approaches. The data also show that intermittent sequenced reads observed in conventional RNA sequencing datasets, previously dismissed as noise, are indicative of unassembled rare transcripts. Collectively, they say these results reveal the range, depth, and complexity of a human transcriptome that is far from fully characterized.

    Noncoding transcripts are RNA molecules that include classical “housekeeping” RNAs such as transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), which are constitutively expressed and play critical roles in protein biosynthesis.

    Among these noncoding RNAs are numerous long noncoding RNAs (lncRNAs), which are defined as endogenous cellular RNAs of more than 200 nucleotides in length that lack an open reading frame of significant length (less than 100 amino acids). The RNA molecules constitute a heterogeneous group, allowing them, scientists point out, to cover a broad spectrum of molecular and cellular functions by implementing different modes of action. lncRNAs are roughly classified based on their position relative to protein-coding genes as intergenic (between genes), intragenic/intronic (within genes), and antisense. Initial efforts to characterize these molecules demonstrated that they function in cis, regulating their immediate genomic neighbors.

    Regulatory Levels

  • lncRNAs can regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels and take part in various physiological and pathological processes, such as cell development, immunity, oncogenesis, clinical disease processes, and more. A classic lncRNA, HOTAIR, was originally identified through work done by Howard Chang, M.D., Ph.D., at Stanford, and Dr. Rinn. Their research eventually led to the discovery of this 2.2 kilobase spliced RNA transcript that interacts with Polycomb group proteins to modify chromatin and repress transcription of the human HOX genes, which regulate development. It remains unclear as to exactly this is accomplished.

    HOTAIR, it was found, originates from the HOXC locus and represses transcription across 40 kb of that locus by altering the chromatin trimethylation state. Hox genes, a highly conserved subgroup of the homeobox superfamily, regulate numerous processes including apoptosis, receptor signaling, differentiation, motility, and angiogenesis. Aberrations in Hox gene expression have been reported in abnormal development and malignancy.

    HOTAIR works to repress Hox gene expression by directing the action of Polycomb chromatin remodeling complexes in trans to govern the cells’ epigenetic state and subsequent gene expression.HOTAIR expression is increased in primary breast tumors and metastases and its expression level in primary tumors can predict eventual metastasis and death. The recent discovery that lncRNA HOTAIRcan link chromatin changes to cancer metastasis furthers the relevance of lncRNAs to human disease.

    Dr. Chang and his colleagues say that the finding that several lncRNAs can control transcriptional alteration implies that the difference in lncRNA profiling between normal and cancer cells is not merely the secondary effect of cancer transformation, and that lncRNAs are strongly associated with cancer progression. The researchers showed that lncRNAs in the HOX loci become systematically dysregulated during breast cancer progression.

    They further demonstrated that enforced expression of HOTAIR in epithelial cancer cells induced genome-wide retargeting of polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2.

    On the other hand they noted loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lncRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy. Thus, the investigators say, differential expression of lncRNAs may be profiled to aid in cancer diagnosis and prognosis and in the selection of potential therapeutics.

    Two years ago the GENCODE consortium, within the framework of the ENCODE project, presented, and analyzed the most complete human lncRNA annotation to date. The data comprise 9,277 manually annotated genes producing 14,880 transcripts. The identification and annotation of this wealth of lncRNAs leaves scientists with a lot of research to do to fully characterize the varied functions of these unusual RNAs. Their identification also challenges technology developers to produce the tools to necessary for these analyses.

     

Transcript Regulation of 18 ADME Genes by Prototypical Inducers in Human Hepatocytes

Drug-drug interactions (DDIs) are of particular concern for regulatory agencies and the pharmaceutical industry for drug safety. Induction of drug metabolizing enzymes by pharmaceuticals, nutraceuticals, and lifestyle influences is one type of DDI in which the influence of a perpetrator molecule increases the enzyme capacity that can metabolize a victim molecule, rendering it ineffective as a therapy. To evaluate this potential, screening assays have been developed, such as the use…

 

Biomarkers Reshape Drug Development

Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk. Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by…

Biomarkers Reshape Drug Development

  • Imanova takes a structured approach to the development of imaging biomarkers, or i-biomarkers.

    Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk.

    Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.
    For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.
    “Imaging biomarkers have been Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

    Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

    For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.

    “Imaging biomarkers have been largely underutilized in drug development,” says Kevin Cox, Ph.D., CEO of London-based Imanova. “But we believe that molecular imaging has the power to assist in successful translation of molecules by reducing the risk of several specific causes of failure in Phase II clinical studies. Imaging biomarkers, or i-biomarkers, are especially valuable in giving confidence of tissue delivery, determination of target engagement, and the evaluation of a drug’s pharmacodynamic effects.”

    While imaging is routinely used in clinical diagnostics for cancer, its acceptance in drug development has been slow. “This is a highly specialized area of knowledge,” Dr. Cox observes. “Designing imaging experiments to answer the right questions is not trivial. Combined with the perceived high costs and dearth of well-equipped facilities, this has slowed down the adoption of imaging as an integral step in drug development.”

    Imanova presents an innovative and highly integrated solution in reducing the barriers for use of molecular imaging. Located in the former GlaxoSmithKline imaging center, Imanova’s staff applies the knowledge needed for translational application of imaging science.

    “Another historical barrier for use of molecular imaging has been the lack of versatile PET tracers for key therapeutic targets,” remarks Dr. Cox. Together with its pharmaceutical clients, Imanova develops proprietary tracers that can answer critical questions about target engagement directly after drug administration. A structured approach for i-biomarker development takes the novel tracer from the candidate pool to clinical validation.

    Uniquely, Imanova utilizes in silico biomathematical modeling to predict a candidate with ideal physicohemical characteristics. “The i-biomarker development pipeline adheres to a strict quality system,” continues Dr. Cox. “We not only provide candidate selection and labeling, but also rigorous preclinical evaluation in several species, combined with blood chemistry or other physiological measurements.”

    The resulting biomarker provides quantitative information to make informed go/no-go decisions. Imanova hopes to develop an open innovation approach to i-biomarker research, and to encourage pharmaceutical companies to collaborate on tracer development.

    “By collaborating in this pre-competitive space, a pharma-academic consortium can de-risk i-biomarker development programs and generate new tools to eliminate costs associated with futile activities downstream,” concludes Dr. Cox. “Most tracers need to be utilized early in the drug development process. Used at the right time, imaging biomarkers are able to inform the design of Phase II studies, including dose ranging and possibly patient selection, saving many months in development and millions of dollars in costs.”

    Answers from Big Data

  • “Clinical bioinformatics is the application of a data-driven, high-tech approach in clinical setting,” says Jerome Wojcik, Ph.D., CEO of Quartz Bio, a clinical bioinformatics service provider located in Plan-Les-Ouates, Switzerland. “We use clinical bioinformatics to adapt treatment to patients, that is, to identify cohorts that respond to the drug in a predictable manner,” says Dr. Wojcik.

    Pharmaceutical partners supply Quartz Bio with data collected in a course of clinical trials. The data (which may include information from protein and RNA expression, genotyping, molecular diagnostics, and flow cytometry studies) often exists in silos within a pharma company. To make sense of the data, Quartz Bio integrates heterogeneously formatted data, analyzes it for consistency, and identifies gaps and outliers.

    Dr. Wojcik’s team dedicates over 40% of the overall analysis time to the biomarker data management. This key step is crucial for the quality of the overall analysis. According to Quartz Bio, all the data-management processes are documented, auditable, and reproducible.

    Once the “Big Data” horde is adequately cleaned up, the team applies adaptive statistical methods to generate multiple hypotheses linking the drug action with subpopulations of patients. “Our challenge is to generate reliable hypotheses on a fairly small statistical patient sample, for example, a thousand patients, but using millions of biomarker datapoints,” continues Dr. Wojcik. “We do not rely on statistics alone. Graphical visualization adapted to the objectives of the study is necessary for interpretation of results.”

    In a recent project, Quartz Bio analyzed multiple oncology biomarkers, such as gene expression, circulating tumor cells, and immunohistochemistry, to identify patient cohorts that would most likely benefit from a novel treatment. Biomarker analysis revealed a subpopulation whose survival rate increased significantly over the population average, bringing a potential application of personalized medicine closer to reality.

     

 

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Insight into lncRNAs

Larry H Bernstein, MD, FCAP, Curator

LPBI

 

Profiling Non-Protein-Coding RNAs

http://www.technologynetworks.com/Genomics/news.aspx?ID=184132

 

Growing insights about a significant, yet poorly understood, part of the genome – the “dark matter of DNA” — have fundamentally changed the way scientists approach the study of diseases.

The human genome contains about 20,000 protein-coding genes – less than 2 percent of the total – but 70 percent of the genome is made into non-coding RNA. Nevertheless, a systematic characterization of these segments, called long non-coding RNAs (lncRNAs), and their alterations in human cancer, is still lacking. Most studies of genomic alterations in cancer have focused on the miniscule portion of the human genome that encodes protein.

An international team, led by researchers at the Perelman School of Medicine at the University of Pennsylvania, has now changed all of that and published their findings this week. A team led by Lin Zhang, MD, the Harry Fields Associate Professor of Obstetrics and Gynecology, and Chi V. Dang, MD, PhD, director of the Abramson Cancer Center, has mined these RNA sequences more fully to identify non-protein-coding segments whose expression is linked to 13 different types of cancer. Zhang first took this approach in 2014 to identify targets for ovarian cancer. Both of these studies are supported by the Basser Center for BRCA at Penn.

“With non-coding RNA sequences constituting almost three quarters of the human genome, there is a great need to characterize genomic, epigenetic, and other alterations of long non-coding segments,” Zhang said. “The present study fills this significant gap in cancer research.”

The team analyzed lncRNAs at transcriptional, genomic, and epigenetic levels in over 5,000 tumor specimens across the different cancer types obtained from The Cancer Genome Atlas (TCGA) and in 935 cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE). They found that lncRNA alterations are highly tumor- and cell line-specific compared to protein-coding genes. In addition, lncRNA alterations are often associated with changes in epigenetic modifiers that act directly on gene expression.

“We believe that the results from this multidimensional analysis provide a rich resource for researchers to investigate the dysregulation of lncRNAs and to identify lncRNAs with diagnostic and therapeutic potential,” Zhang said.

The team also developed two bioinformatics-based platforms to identify cancer-associated lncRNAs and explore their biological functions. One is a searchable database that incorporates clinical information with lncRNA molecular alterations to generate “short lists” of candidate lncRNAs to study. “The molecular profiling data we used for this are linked to clinical and drug response annotations in the TCGA because of its high-quality, multiple-level profiles of human primary tumor specimens and detailed clinical notes for a broad selection of human cancer specimens, along with the CCLE, the best available resource for molecular profiles of cancer cell lines and details about their responses to drugs,” Zhang explained.

The second approach they developed – predicting the biological function of lncRNAs –successfully identified a novel oncogenic lncRNA called BCAL8. They found that BCAL8, when overexpressed, works to promote the cell cycle, which controls cell division. This part of the study provided not only a proof of concept for their lncRNA search strategy, but also a customizable database for other investigators to look for lncRNAs of interest and investigate their function. This database is called the Cancer LncRNome Atlasand is administered by the Abramson Cancer Center at Penn.

 

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lncRNAs in Human Cancers

Curator: Larry H. Bernstein, MD, FCAP

UPDATED 9/26/2021

 

Characterizing lncRNAs in Human Cancers

Changes in long non-coding RNA expression levels are highly indicative of specific cancer types, perhaps even more so than protein-coding genes, according to a new study published today in Cancer Cell.

https://www.genomeweb.com/cancer/penn-scientists-lead-study-characterizing-lncrnas-human-cancers?

Scientists led by first authors Xiaohui Yan, Zhongyi Hu, and Yi Feng and senior authors Chi Dan and Lin Zhang, all from the University of Pennsylvania’s Perelman School of Medicine, characterized alterations in long non-coding RNAs (lncRNAs) in 5,037 tumor specimens comprising 13 cancer types from The Cancer Genome Atlas. They looked at differential changes to lncRNAs at the transcriptional, genomic, and epigenomic level to identify cancer-driving lncRNAs and predict their function.

The study found 2,316 lncRNAs altered in all 13 cancer types. As part of a more detailed analysis, the study looked at disregulation of lncRNA expression in seven cancers. Expression could be both up-regulated and down-regulated compared to normal cells, with, on average, 15 percent being up-regulated and 11 percent being down-regulated in the cancers.

UPDATED 9/26/2021

Here is some recent literature on various lncRNAs that are linked to colorectal cancer growth.

The long noncoding RNA LUCAT1 promotes colorectal cancer cell proliferation by antagonizing Nucleolin to regulate MYC expression.

Wu, R., Li, L., Bai, Y. et al. The long noncoding RNA LUCAT1 promotes colorectal cancer cell proliferation by antagonizing Nucleolin to regulate MYC expression. Cell Death Dis 11, 908 (2020). https://doi.org/10.1038/s41419-020-03095-4

Abstract

The long noncoding RNA (lncRNA) LUCAT1 was recently reported to be upregulated and to play an essential role in multiple cancer types, especially colorectal cancer (CRC), but the molecular mechanisms of LUCAT1 in CRC are mostly unreported. Here, a systematic analysis of LUACT1 expression is performed with data from TCGA database and clinic CRC samples. LUCAT1 is identified as a putative oncogene, which is significantly upregulated in CRC and is associated with poor prognosis. Loss of LUCAT1 restricts CRC proliferative capacities in vitro and in vivo. Mechanically, NCL is identified as the protein binding partner of LUCAT1 by using chromatin isolation by RNA purification coupled with mass spectrometry (ChIRP-MS) and RNA immunoprecipitation assays. We also show that NCL directly binds to LUCAT1 via its putative G-quadruplex-forming regions from nucleotides 717 to 746. The interaction between LUCAT1 and NCL interferes NCL-mediated inhibition of MYC and promote the expression of MYC. Cells lacking LUCAT1 show a decreased MYC expression, and NCL knockdown rescue LUCAT1 depletion-induced inhibition of CRC cell proliferation and MYC expression. Our results suggest that LUCAT1 plays a critical role in CRC cell proliferation by inhibiting the function of NCL via its G-quadruplex structure and may serve as a new prognostic biomarker and effective therapeutic target for CRC.

Long noncoding RNA CMPK2 promotes colorectal cancer progression by activating the FUBP3–c-Myc axis

Gao, Q., Zhou, R., Meng, Y. et al. Long noncoding RNA CMPK2 promotes colorectal cancer progression by activating the FUBP3–c-Myc axis. Oncogene 39, 3926–3938 (2020). https://doi.org/10.1038/s41388-020-1266-8

Abstract

Long noncoding RNAs (lncRNAs) have been shown to play crucial roles in cancer long noncoding RNAs (lncRNAs) have been known to play crucial roles in cancer development and progression by regulating chromatin dynamics and gene expression. However, only a few lncRNAs with annotated functions in the progression of colorectal cancer (CRC) have been identified to date. In the present study, the expression of lncCMPK2 was upregulated in CRC tissues and positively correlated with clinical stages and lymphatic metastasis. The overexpression of lncCMPK2 promoted the proliferation and cell cycle transition of CRC cells. Conversely, the silencing of lncCMPK2 restricted cell proliferation both in vitro and in vivo. lncCMPK2 was localized to the nucleus of CRC cells, bound to far upstream element binding protein 3 (FUBP3), and guided FUBP3 to the far upstream element (FUSE) of the c-Myc gene to activate transcription. lncCMPK2 also stabilized FUBP3. These results provide novel insights into the functional mechanism of lncCMPK2 in CRC progression and highlight its potential as a biomarker of advanced CRC and therapeutic target.

Long Noncoding RNA MIR17HG Promotes Colorectal Cancer Progression via miR-17-5p

Jie Xu, Qingtao Meng, Xiaobo Li, Hongbao Yang, Jin Xu, Na Gao, Hao Sun, Shenshen Wu, Giuseppe 

Familiari, Michela Relucenti, Haitao Zhu, Jiong Wu and Rui ChenDOI: 10.1158/0008-5472.CAN-18-3880 Published October 2019

Abstract

Immune dysregulation plays a vital role in colorectal cancer initiation and progression. Long noncoding RNAs (lncRNA) exhibit multiple functions including regulation of gene expression. Here, we identified an immune-related lncRNA, MIR17HG, whose expression was gradually upregulated in adjacent, adenoma, and colorectal cancer tissue. MIR17HG promoted tumorigenesis and metastasis in colorectal cancer cells both in vitro and in vivo. Mechanistically, MIR17HG increased the expression of NF-κB/RELA by competitively sponging the microRNA miR-375. In addition, RELA transcriptionally activated MIR17HG in a positive feedback loop by directly binding to its promoter region. Moreover, miR-17-5p, one of the transcribed miRNAs from MIR17HG, reduced the expression of the tumor suppressor B-cell linker (BLNK), resulting in increased migration and invasion of colorectal cancer cells. MIR17HG also upregulated PD-L1, indicating its potential role in immunotherapy. Overall, these findings demonstrate that MIR17HG plays an oncogenic role in colorectal cancer and may serve as a promising therapeutic target.

Significance: These findings provide mechanistic insight into the role of the lncRNA MIR17HG and its miRNA members in regulating colorectal cancer carcinogenesis and progression.

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Small but mighty RNAs

Larry H Bernstein, MD, FCAP, Curator

Leaders in Pharmaceutical Intelligence

Series E. 2; 3.5

Revised 9/30/2015

Albert Lasker
Basic Medical Research Award

Victor Ambros, David Baulcombe, and Gary Ruvkun

For discoveries that revealed an unanticipated world of tiny RNAs that regulate gene function in plants and animals

The 2008 Albert Lasker Award for Basic Medical Research honors three scientists who discovered an unanticipated world of tiny RNAs that regulate gene activity in plants and animals. Victor R. Ambros (University of Massachusetts Medical School, Worcester) and Gary B. Ruvkun (Massachusetts General Hospital, Boston, Harvard Medical School) unearthed the first example of this type of molecule in animals and demonstrated how the RNAs turn off genes whose activities are crucial for development. David C. Baulcombe (University of Cambridge) established that small RNAs silence genes in plants as well, thus catalyzing discoveries of many such RNAs in a wide range of living things. His findings led to the identification of the biochemical machinery that unifies numerous processes by which small RNAs govern gene activity.

Ambros, Baulcombe, and Ruvkun did not set out to unveil small regulatory RNAs. Ambros and Ruvkun were studying how the worm Caenorhabditis elegans develops from a newly hatched larva into an adult. Baulcombe, in a seemingly unrelated line of inquiry, was probing how plants defend themselves against viruses. All three investigators possessed the open mindedness, wisdom, and experimental finesse to entertain the possibility—and then verify—that tiny RNAs could perform momentous feats. Their work has led to the realization that these molecules are pivotal regulators of normal physiology as well as disease.

RNA—the little molecule that could
In the early 1980s, Ambros joined the laboratory of Robert Horvitz at the Massachusetts Institute of Technology as a postdoctoral fellow. He wanted to outline the means by which genes choreograph the construction of fully formed adults from single cells. Analyses of flies had revealed that certain genes instruct embryos where to place body parts—for example, wings belong on each side and legs belong on the bottom. But Ambros was intrigued by the notion that other genes might specify the timing—rather than the location—of developmental events; alterations in such genes might cause cells and tissues to adopt fates that are normally associated with earlier or later stages of development.

He directed his attention toward one of the first-known genes of this type, called lin-4, which had been identified earlier in the laboratory of Sydney Brenner (Lasker Special Achievement Award, 2000) and subsequently characterized by Horvitz, Martin Chalfie, and John Sulston. Ambros recognized that, during worms’ trek toward adulthood, those with inactive lin-4 get stuck repeating early larval stages. Consequently, they lack cell types and structures typical of fully formed animals and instead contain extra copies of cells ordinarily produced only at early stages. These observations suggested that normal lin-4 allows immature worms to advance past a particular developmental stage; animals with the defective version cannot overcome that hurdle. Ambros discovered that worms lacking a different gene—lin-14—were the antithesis of those with inactive lin-4. The animals skip early steps in development and prematurely acquire characteristics that normally appear later. These and other results suggested that lin-4 and lin-14 exert opposite effects in worm cells.

To dig further into lin-14‘s function and its possible relationship with lin-4, Ruvkun, who by this time (1982) had joined Horvitz’s laboratory as a postdoctoral fellow, collaborated with Ambros to isolate the lin-14 gene. After the investigators set up independent laboratories in the mid 1980s, Ruvkun, at Massachusetts General Hospital in Boston, established that the protein product of lin-14 is abundant during early larval stages and then its quantities plummet. Under conditions in which it unnaturally remains plentiful, early steps repeat, suggesting that the normal drop in the lin-14 protein allows worms to proceed to later stages. Ambros, at Harvard University, found that lin-4 dampens lin-14 activity and thus a picture emerged about how the genes collaborate. At the appropriate time, lin-4 blocks lin-14 and thus allows worms to continue their developmental trajectory.

Ruvkun sought to identify the portion(s) of lin-14 that lin-4 targets, so he tracked down certain genetic anomalies in lin-14‘s sequence that underlie excess production of the lin-14 protein. He found that these alterations reside in the area of the gene that follows the protein blueprint, a span called the 3′ untranslated region (3′ UTR). The perturbations do not influence amounts of the protein’s messenger RNA (mRNA), the molecule that carries genetic information from DNA to the cell’s protein-making factory, Ruvkun showed. Rather, they alter protein quantities. Therefore, molecules that turn off lin-14 after early stages of development presumably exert their effects through the 3′ UTR region of the lin-14 mRNA and prevent the cell from translating its code into protein.

In the meantime, Ambros’s laboratory was isolating the lin-4 gene, which they assumed encoded a protein; although a few RNAs were known to control gene activity in bacteria, conventional wisdom held that, in animal cells, proteins alone enjoy such powers. The team homed in on smaller and smaller pieces of DNA from normal animals that restore typical developmental behavior to a worm that lacks lin-4. Stretches of DNA that were far shorter than standard genes worked. Eventually, the researchers began considering the possibility that its product was an RNA, but they still assumed that the regulatory molecule they sought would be a respectable size. The smallest RNAs known to do anything important in cells contained about 75 nucleotide (nt) building blocks. Eventually, however, their experiments led them to a tiny RNA, composed of about 22 nucleotides. A larger—61 nt—molecule that contained the smaller RNA appeared as well and Ambros noticed that it could fold into a double-stranded “hairpin”—a structure whose significance would become clear years later.

In an exciting exchange of data, Ambros and Ruvkun realized that the 22-nt lin-4 RNA matched sections within the 3′ UTR of the lin-14 mRNA: These sequences could bind one another by the same base-pairing rules that hold together the Watson and Crick DNA strands. In this view, the tiny lin-4 RNA settles on the target lin-14mRNA—in its 3′ UTR—and the resulting double-stranded structure somehow interferes with translation of the lin-14 mRNA’s genetic information into protein (see illustration).

Image of microRNA
Small but mighty.
This scheme shows how one type of tiny RNA, a microRNA (miRNA), silences genes. It is cut out of a precursor hairpin-shaped pre-miRNA to form a mature miRNA, which binds to the 3′ untranslated region (3′ UTR) of a target gene’s messenger RNA and turns off its activity. [Credit: Carin Cain. Based on an illustration from Victor Ambros]

Despite verification that lin-4 was a tiny RNA with huge regulatory powers, these 1993 findings constituted a mere blip on most biologists’ radar screens: lin-4 resided only in worms, so the phenomenon seemed like an oddity that most organisms did not exploit. Worms were exotic in many ways, experts reasoned, and the observation only fueled that attitude.

Branching out to plants and beyond
Across the Atlantic, David Baulcombe, then of the Sainsbury Laboratory in Norwich, UK, was studying how plants resist viruses. When he and others added to viral-infected plants unusual versions of viral genes, the mRNA copies of the normal genes as well as the newly introduced ones disappeared. Similarly, experimentally added non-viral genes suppressed activity of plant genes that contained similar sequences. Baulcombe proposed that such gene silencing occurs when RNAs embrace target mRNA—through typical Watson-Crick base-pairing—and promote destruction of the mRNA or interfere with its translation into protein. However, no one could find such RNAs.

Baulcombe reasoned that the predicted RNAs might have eluded researchers because the molecules were shorter than anyone imagined and thus, experiments had not been designed to detect them. In 1999, he and a postdoctoral fellow in his laboratory, Andrew Hamilton, devised a hunt specifically for small RNAs. They added test genes to plants and found 25-nt long RNAs that matched; furthermore, these small RNAs appeared only under conditions in which target mRNA activity was shut off. The stunning similarity in size between the plant and worm RNAs suggested that small regulatory RNAs exist in many organisms. Furthermore, it hinted at the presence of cellular machinery that dedicates itself to creating these precisely sized molecules and then uses them to quash gene activity.

In 2000, Ruvkun’s laboratory discovered a second tiny regulatory RNA in worms of exactly the same size as thelin-4 RNA and in the same genetic pathway. Similar to the lin-4 RNA, this let-7 RNA dampens activity of its target gene through its 3′ UTR. Furthermore, its sequence too resides within a larger molecule that folds up on itself to form a double-stranded hairpin structure. Later that year, Ruvkun found that many other creatures, including humans, fruit flies, chickens, frogs, zebrafish, mollusks and sea urchins, carry their own versions of let-7, which could also fold into hairpins. The apparent binding site for let-7 RNA in its target was conserved in some of these organisms as well. Moreover, let-7 RNA appeared and disappeared at similar points during development in many of the animals.

The small RNAs, now called microRNAs (miRNAs), had broken through their designation as “worm curiosities.” Researchers realized that the miRNAs likely execute vital functions during growth and development of other creatures as well. Multiple teams raced to expose regulatory RNAs of approximately 22 nucleotides in length. In 2001, Ambros’s group, now at Dartmouth Medical School, in Hanover, as well as those of David Bartel (Massachusetts Institute of Technology) and Thomas Tuschl (Max Planck Institute for Biophysical Chemistry, G�ttingen) discovered almost 100 of these small regulatory RNAs in flies, humans, and worms.

In addition to revealing that small regulatory RNAs dwell in organisms other than worms, Baulcombe’s finding caught many researchers’ attention because it seemed related to a process called RNA interference (RNAi), which had recently exploded onto the biological scene. In RNAi, long RNAs injected into cells hamper gene activity from similar sequences. No one knew why organisms possessed this ability, but presumably it played some role in normal physiology. In 1998, Andrew Fire (Carnegie Institution of Washington, Baltimore) and Craig Mello (University of Massachusetts Medical School, Worcester), published a watershed paper that defined the fundamental features of RNAi (which garnered them the Nobel Prize in 2006). That work yielded the surprising insight that the process depends on double-stranded RNA. However, the means by which double-stranded RNA triggered silencing remained mysterious.

Experiments from Baulcombe’s laboratory provided the crucial clues. Production of the silencing RNA strand depended on the presence of the other strand, he had noticed. This observation suggested that, at some point during manufacture of the small regulatory RNA, it exists as part of a double-stranded molecule. Suddenly it seemed possible that Baulcombe’s tiny RNAs arose by trimming longer molecules of the type that Fire and Mello had discovered. Furthermore, this notion suggested that the hairpin-like lin-4 and let-7 RNAs similarly gave rise to the mature, 22-nt entities.

Scientists wondered whether the cell deployed the same biochemical machinery to create and use RNA molecules that subdued gene activity in all of these gene-silencing systems. However, the mechanisms of the worm miRNAs seemed to differ from those of the plant molecules as well as RNAi. Unlike the system that Ambros and Ruvkun had been untangling, which allowed mRNA to accumulate but thwarted cells’ abilities to translate the information it contained into protein, the plant system and RNAi destroyed mRNA. For that reason and others, many people doubted that the processes were connected. Still the possibility that they shared a common mechanism and machinery tantalized researchers.

In 2001, the Mello, Ruvkun, and Fire groups collaborated to show that efficient liberation of the lin-4 and let-7RNAs from the hairpin molecules relies on the C. elegans version of Dicer, an enzyme that Gregory Hannon (Cold Spring Harbor Laboratory) discovered and named for its ability to chop dsRNA into uniformly sized, small RNAs that direct mRNA destruction during RNAi. These results and others, including similar ones generated by Philip Zamore (University of Massachusetts Medical School, Worcester), cemented the connection between miRNAs and RNAi, thus providing one biological “reason” for the RNAi machinery. Moreover, they identified the apparatus by which cells generate miRNAs and harness them for key pursuits.

Studies in the past several years have indicated that the human genome contains more than 500 and perhaps as many as 1000 miRNAs that could collectively control a third of all of our protein-producing genes. These regulatory molecules have been implicated in a wide range of normal and pathological activities. They play roles not only in embryonic development, but in blood-cell specialization, cancer, muscle function, heart disease, viral infections, and possibly neurological signaling and stem-cell behavior. Researchers are exploring the possibility of using miRNAs “signatures” for diagnosis and prognosis and are considering manipulating their quantities for therapeutic purposes.

Looking where no one had looked before, Ambros, Baulcombe, and Ruvkun spied an unforeseen universe of potent molecules. Their work has elevated these hitherto unrecognized agents into the spotlight of biology and medicine.

by Evelyn Strauss, Ph.D.

2014 Gruber Genetics Prize
Trio honored for pioneering discoveries of microRNAs
By Jim Fessenden
UMass Medical School Communications

Victor R. Ambros, PhD, professor of molecular medicine, has been awarded the 2014 Gruber Genetics Prize, along with longtime collaborator Gary Ruvkun, PhD, professor of genetics at Massachusetts General Hospital and Harvard Medical School, and David Baulcombe, PhD, professor of botany at the University of Cambridge. They received the prize for their pioneering discoveries of the existence and function of microRNAs and small interfering RNAs, molecules that are now known to play a critical role in gene expression. Dr. Ambros is the Silverman Chair in Natural Sciences and co-director of the RNAi Therapeutics Institute.

Gary Ruvkun, PhD, was awarded the Breakthrough Prize in Life Sciences on November 9, along with Victor Ambros for their work on the discovery of microRNAs and their broad use in biology.

The Breakthrough Prize Foundation announced the recipients of the 2015 Breakthrough Prizes in Fundamental Physics and Life Sciences. These distinguished winners, along with previously announced recipients in the Mathematics category, each receive a $3 million prize.

https://breakthroughprize.org/?controller=Page&action=news&news_id=21

 

Gary Ruvkun, PhD, of the Center for Computational and Integrative Biology and the Department of Molecular Biology, has been awarded the 2014 Gruber Genetics Prize from the Gruber Foundation through Yale University for his work with Victor Ambros, PhD, University of Massachusetts, identifying the existence of microRNAs in animals that control the activity of other genes.

http://gruber.yale.edu/genetics/2014/gary-ruvkun

 

Phillip A. Sharp, PhD
Koch Institute Professor of Integrative Cancer Research

The Sharp Lab focuses on the biology and technology of small RNAs and other types of non-coding RNAs.  RNA interference (RNAi) has dramatically expanded the possibilities for genotype/phenotype analysis in cell biology and for therapeutic intervention.  MicroRNAs (miRNAs) are encoded by endogenous genes and regulate primarily at the stage of translation over half of all genes in mammalian cells.  The Sharp laboratory is working to identify physically the target mRNAs for particular miRNAs.  His laboratory has recently discovered a new class of microRNAs that are produced from sequences adjacent to transcription start sites (TSS-miRNAs).  The functions of the small RNAs are a subject of investigation.  His laboratory is also investigating the relationship between gene regulation by miRNAs and angiogenesis and cellular stress.  Most promoters and enhancers in mammalian cells are transcribed divergently with RNA polymerases initiating in both directions.  Divergent transcription generates thousands of long non-coding RNAs.  The extent of elongation by polymerase in either the sense direction or the antisense direction is controlled by recognition of the nascent RNA by U1 snRNP, a spliceosome component.  The function of the divergent non-coding transcripts is being investigated as well as the relationship of RNA splicing, chromatin modifications and transcription.

 

Noncoding RNAs: A Cache of Cancer Clues?

Rummaging through the Noncoding RNA Attic Has Brought to Light Interesting Baubles—miRNAs and lncRNAs

Kathy Liszewski    GEN Sep  1, 2015 (Vol. 35, No. 15)
http://www.genengnews.com/gen-articles/noncoding-rnas-a-cache-of-cancer-clues/5561/

 

At Weill Cornell Medical College, researchers discovered that estrogen receptors can hijack the androgen-signaling pathway to promote prostate cancer growth. In particular, they found that the estrogen receptor can activate NEAT1, a long noncoding RNA. NEAT1 target genes were determined to be upregulated in several prostate cancer datasets.

http://www.genengnews.com/Media/images/Article/thumb_Cornell_graphics1_Neat1Signature1436235247.jpg

 

At Weill Cornell Medical College, researchers discovered that estrogen receptors can hijack the androgen-signaling pathway to promote prostate cancer growth. In particular, they found that the estrogen receptor can activate NEAT1, a long noncoding RNA. NEAT1 target genes were determined to be upregulated in several prostate cancer datasets.

In the postgenomic era, the numerous and diverse noncoding RNA species once dismissed as “junk RNA” are increasingly seen as treasure. Noncoding RNAs, we now know, have diverse functions in health and disease.

Some in the field believe that we have only started to appreciate the riches of noncoding RNA. The ultimate jewels? They may prove to be previously hidden connections with cancer.

Almost as numerous as newly discovered RNA baubles are the newly organized RNA conferences. One such event, Molecular and Cellular Biology: MicroRNAs and Noncoding RNAs in Cancer, was held June 7–12 in Keystone, CO. This event, a Keystone Symposia conference, focused on the complex universe of RNA biology that is disturbed in cancer.

Providing a perspective on the field was John L. Rinn, Ph.D., an associate professor of stem cell and regenerative biology, Harvard Medical School. He said that if you are not reading a new textbook, your ideas about RNA may be wrong.

“This is a dynamic and fast-moving field,” he insisted. “Recent advances in RNA sequencing technologies have disclosed the existence of thousands of previously unknown noncoding transcripts, including many long noncoding RNAs (lncRNAs) whose functions remain mostly undetermined. However, there are an increasing number of examples that show they are not only key regulators of gene expression, but also direct targets of cancer pathways.”

The laboratory of John L. Rinn, Ph.D., at Harvard Medical School has been studying the role of large intervening ncRNAs (lincRNAs) in establishing the distinct epigenetic states of adult and embryonic cells and their mis-regulation in diseases such as cancer.

http://www.genengnews.com/Media/images/Article/thumb_Harvard_oncolncRNA1512422915.jpg

 

The laboratory of John L. Rinn, Ph.D., at Harvard Medical School has been studying the role of large intervening ncRNAs (lincRNAs) in establishing the distinct epigenetic states of adult and embryonic cells and their mis-regulation in diseases such as cancer.

Noncoding RNAs include the well-known microRNAs (miRNAs) and the lesser-known lncRNAs. Usually defined on the basis of their size, the single-stranded short miRNAs consist of about 22 nucleotides. They regulate gene expression via translation inhibition or degradation of their mRNA targets. Long ncRNAs refer to transcripts that consist of more than 200 nucleotides and lack extended open reading frames. This arbitrary cutoff excludes most known, yet still poorly understood, classes of small RNAs, such as tRNAs and short interfering RNAs.

Recent studies have provided an intriguing hypothesis: Long ncRNAs may be the missing links in cancer. According to Dr. Rinn, “We now know that lncRNAs constitute an important layer of genome regulation over a diverse array of biological processes and diseases, such as cancer.”

Since the ultimate cause of cancer is altered homeostasis of cellular networks and gene expression programs, even the slightest perturbation of these pathways can result in malignant cellular transformation. “These cell circuits are fine-tuned and largely maintained by the coordinated functioning of proteins as well as ncRNAs,” explained Dr. Rinn. “But, beyond the layer of the well-known protein-coding RNAs and miRNAs, lies the realm of lncRNAs that are fast emerging as critical components and regulators of tumor-suppressor and oncogenic pathways.”

Regulator of Metastasis

A precancerous lesion imaged at the University of Minnesota shows abnormal duct morphology and cell proliferation in the mammary gland of a 10-week-old mouse engineered with a single copy number increase of Myc and Pvt1. Gain of Myc alone does not produce such a phenotype.

http://www.genengnews.com/Media/images/Article/thumb_UnivMN_Precancerous1072313061.jpg

A precancerous lesion imaged at the University of Minnesota shows abnormal duct morphology and cell proliferation in the mammary gland of a 10-week-old mouse engineered with a single copy number increase of Myc and Pvt1. Gain of Myc alone does not produce such a phenotype.

The major specific hallmarks of cancer include malignant cell migration, invasion, and metastasis. The latter is the primary cause of cancer recurrence and subsequent death.

“Deregulated lncRNAs may impact a diverse array of human cancers, especially their progression,” said David L. Spector, Ph.D., a professor at the Cold Spring Harbor Laboratory. “One of these lncRNAs is the cancer-associated MALAT1 [metastasis-associated lung adenocarcinoma transcript 1]. It’s not only very abundant in many types of human cells; it is also highly conserved across many mammalian species.”

Dr. Spector’s laboratory identified a novel mechanism for 3′-end processing of this nucleus-restricted lncRNA and is dissecting its mechanism of action: “Since MALAT1 is upregulated in several human cancers, it may play an important role during tumor progression. Because its physiological function at the tissue and organismal levels was unknown, we developed a Malat1 loss-of-function genetic mouse model. Since our in vivo studies demonstrated that Malat1 isn’t essential for mouse development and does not affect global gene expression, we are currently pursuing whether this is due to redundancy or context dependency.”

The team of Sven Diederichs at the German Cancer Research Center DKFZ, in collaboration with the Spector lab, examined the role of MALAT1 by knocking it out in human lung tumor cells. They incorporated an RNA-destabilizing element using zinc finger nucleases. This resulted in a unique loss-of-function model with more than a 1,000-fold silencing. When these cells were utilized in a xenograft mouse model, they found that MALAT1-deficient cells had impaired migration and homing to the lungs. This study supports a role of MALAT1 as a regulator of cell migration that is important in gene expression governing the metastasis of lung cancer cells.

These findings have therapeutic implications, according to Dr. Spector. “MALAT1 could represent a predictive marker of disease and use of antisense oligonucleotides could provide a potential therapeutic strategy,” he concluded.

To extend these studies, Dr. Spector’s group is now examining how altered levels of MALAT1 might impact breast cancer initiation and progression.

Noncoding RNAs: A Cache of Cancer Clues?

Rummaging through the Noncoding RNA Attic Has Brought to Light Interesting Baubles—miRNAs and lncRNAs

http://www.genengnews.com/gen-articles/noncoding-rnas-a-cache-of-cancer-clues/5561/

Cornell_graphics1_Neat1Signature1436235247

http://www.genengnews.com/Media/images/Article/Cornell_graphics1_Neat1Signature1436235247.jpg

At Weill Cornell Medical College, researchers discovered that estrogen receptors can hijack the androgen-signaling pathway to promote prostate cancer growth. In particular, they found that the estrogen receptor can activate NEAT1, a long noncoding RNA. NEAT1 target genes were determined to be upregulated in several prostate cancer datasets.

  • In the postgenomic era, the numerous and diverse noncoding RNA species once dismissed as “junk RNA” are increasingly seen as treasure. Noncoding RNAs, we now know, have diverse functions in health and disease.
  • Some in the field believe that we have only started to appreciate the riches of noncoding RNA. The ultimate jewels? They may prove to be previously hidden connections with cancer.
  • Almost as numerous as newly discovered RNA baubles are the newly organized RNA conferences. One such event, Molecular and Cellular Biology: MicroRNAs and Noncoding RNAs in Cancer, was held June 7–12 in Keystone, CO. This event, a Keystone Symposia conference, focused on the complex universe of RNA biology that is disturbed in cancer.
  • Providing a perspective on the field was John L. Rinn, Ph.D., an associate professor of stem cell and regenerative biology, Harvard Medical School. He said that if you are not reading a new textbook, your ideas about RNA may be wrong.
  • “This is a dynamic and fast-moving field,” he insisted. “Recent advances in RNA sequencing technologies have disclosed the existence of thousands of previously unknown noncoding transcripts, including many long noncoding RNAs (lncRNAs) whose functions remain mostly undetermined. However, there are an increasing number of examples that show they are not only key regulators of gene expression, but also direct targets of cancer pathways.”

The laboratory of John L. Rinn, Ph.D., at Harvard Medical School has been studying the role of large intervening ncRNAs (lincRNAs) in establishing the distinct epigenetic states of adult and embryonic cells and their mis-regulation in diseases such as cancer.

  • Noncoding RNAs include the well-known microRNAs (miRNAs) and the lesser-known lncRNAs. Usually defined on the basis of their size, the single-stranded short miRNAs consist of about 22 nucleotides. They regulate gene expression via translation inhibition or degradation of their mRNA targets. Long ncRNAs refer to transcripts that consist of more than 200 nucleotides and lack extended open reading frames. This arbitrary cutoff excludes most known, yet still poorly understood, classes of small RNAs, such as tRNAs and short interfering RNAs.
  • Recent studies have provided an intriguing hypothesis: Long ncRNAs may be the missing links in cancer. According to Dr. Rinn, “We now know that lncRNAs constitute an important layer of genome regulation over a diverse array of biological processes and diseases, such as cancer.”
  • Since the ultimate cause of cancer is altered homeostasis of cellular networks and gene expression programs, even the slightest perturbation of these pathways can result in malignant cellular transformation. “These cell circuits are fine-tuned and largely maintained by the coordinated functioning of proteins as well as ncRNAs,” explained Dr. Rinn. “But, beyond the layer of the well-known protein-coding RNAs and miRNAs, lies the realm of lncRNAs that are fast emerging as critical components and regulators of tumor-suppressor and oncogenic pathways.”
  • Regulator of Metastasis

A precancerous lesion imaged at the University of Minnesota shows abnormal duct morphology and cell proliferation in the mammary gland of a 10-week-old mouse engineered with a single copy number increase of Myc and Pvt1. Gain of Myc alone does not produce such a phenotype.

A Dangerous PartnershipThe major specific hallmarks of cancer include malignant cell migration, invasion, and metastasis. The latter is the primary cause of cancer recurrence and subsequent death.

  • “Deregulated lncRNAs may impact a diverse array of human cancers, especially their progression,” said David L. Spector, Ph.D., a professor at the Cold Spring Harbor Laboratory. “One of these lncRNAs is the cancer-associated MALAT1 [metastasis-associated lung adenocarcinoma transcript 1]. It’s not only very abundant in many types of human cells; it is also highly conserved across many mammalian species.”
  • Dr. Spector’s laboratory identified a novel mechanism for 3′-end processing of this nucleus-restricted lncRNA and is dissecting its mechanism of action: “Since MALAT1 is upregulated in several human cancers, it may play an important role during tumor progression. Because its physiological function at the tissue and organismal levels was unknown, we developed a Malat1 loss-of-function genetic mouse model. Since our in vivo studies demonstrated that Malat1 isn’t essential for mouse development and does not affect global gene expression, we are currently pursuing whether this is due to redundancy or context dependency.”
  • The team of Sven Diederichs at the German Cancer Research Center DKFZ, in collaboration with the Spector lab, examined the role of MALAT1 by knocking it out in human lung tumor cells. They incorporated an RNA-destabilizing element using zinc finger nucleases. This resulted in a unique loss-of-function model with more than a 1,000-fold silencing. When these cells were utilized in a xenograft mouse model, they found that MALAT1-deficient cells had impaired migration and homing to the lungs. This study supports a role of MALAT1 as a regulator of cell migration that is important in gene expression governing the metastasis of lung cancer cells.
  • These findings have therapeutic implications, according to Dr. Spector. “MALAT1 could represent a predictive marker of disease and use of antisense oligonucleotides could provide a potential therapeutic strategy,” he concluded.
  • To extend these studies, Dr. Spector’s group is now examining how altered levels of MALAT1 might impact breast cancer initiation and progression.

One lncRNA, PVT1, is keeping bad company, at least according to new studies linking it to the key cancer-causing oncogene, MYC. This unexpected partnership has stirred up much interest in the scientific community, especially since MYC is linked to a majority of human cancers.

Anindya Bagchi, Ph.D., an assistant professor of genetics, cell biology and development, University of Minnesota, reported that her group began by looking at structural alterations in cancer genome. “[Of particular interest is the loss or gain of particular segments of the genome that occurs recurrently in cancer,” he notes. “One such region that is of immense interest to us is 8q24, a genomic region often found to be gained in a number of cancers.

“The well-characterized myelocytomatosis (MYC) oncogene resides in the 8q24.21 region. We found that in cancer, MYC is consistently co-gained with an adjacent ‘gene desert’ of about 2 megabases that includes the lncRNA gene PVT1.”

Dr. Bagchi and colleagues utilized chromosomal engineering in mice to construct three iterations to model: MYC only, MYC plus this surrounding area, and the surrounding region alone. “Surprisingly, we found that MYC enhanced tumor growth only when the surrounding region was included,” Dr. Bagchi pointed out. “This verified that MYC is not acting alone.

“We next utilized primary human cancer cell lines and found that PVT1 RNA and MYC protein expression were correlated. Further, we determined that copy number of PVT1 was increased in more than 98% of cancers with MYC gain.”

Finally, Dr. Bagchi’s group definitively fingered PVT1 as the co-conspirator with MYC. The investigators knocked it out of MYC-driven colon cancer cells and found the tumors virtually disappeared. According to Dr. Bagchi, this study complements previous studies and establishes an important finding: Long ncRNA PVT1 interacts with MYC in the nucleus and protects the MYC protein from degradation, probably by reducing phosphorylation of its threonine 58 residue.

“What makes this finding so exciting is that we now may have a much needed tool to target the notoriously elusive MYC protein that has been refractory to small-molecule inhibition,” asserted Dr. Bagchi. “Perhaps by uncoupling this dangerous partnership and targeting PVT1, we could remove the driver that amplifies a major cancer gene.”

  • Prostate Cancer and Noncoding RNA

Given the roles played by ncRNAs in a host of biological processes, it is no surprise that these species also impact prostate cancer progression and therapy resistance. Nonetheless, details of the relationship between ncRNAs and prostate cancer remain to be elucidated, said Dimple Chakravarty, Ph.D., an assistant professor of pathology and laboratory medicine at Weill Cornell Medical College.

“Deregulated or aberrant expression of steroid nuclear receptors are linked with cancer progression and thus are also major targets for therapeutic intervention,” observed Dr. Chakravarty. “But specific therapies are often inadequate.

“For example, the androgen receptor [AR] plays a central role in this malignant progression. Despite the initial effectiveness of therapeutic androgen ablation, resistance inevitably develops to both first generation anti-androgen therapies and to second-generation AR-targeted therapies. The reasons for this are unclear.”

Dr. Chakravarty and colleagues wanted to better understand the role of the estrogen receptor alpha (ERα) that is expressed in prostate cancers. “Our studies identified an ERα-specific noncoding transcriptome signature. This lured us into the noncoding world,” she disclosed.

Dr. Chakravarty and her collaborators, including Mark A Rubin, M.D., a professor of pathology and laboratory medicine at Weill Cornell, scrutinized a combination of chromatin immunoprecipitation (ChIP) and RNA-sequencing data. The investigators found that the most significantly overexpressed and ERα-regulated lncRNA in prostate cancer samples was a transcript called NEAT1, the nuclear enriched abundant transcript 1.

“Our studies utilized a battery of approaches,” detailed Dr. Chakravarty. “We used qRT-PCR and RNA-ISH to examine NEAT1 mRNA levels in prostate cancer tissue and in cell lines, and we analyzed public datasets of normal versus prostate cancer with advanced disease. Epigenetic studies demonstrated that NEAT1 is recruited to the chromatin of prostate cancer genes and contributes to an epigenetic ‘on’ state.”

 

Dr. Chakravarty expressed excitement over these findings: “This study is the first of its kind to demonstrate transcriptional regulation of lncRNAs by an alternative steroid receptor in prostate cancer. We believe NEAT1 could serve as both a prognostic marker for aggressive prostate cancer and also a potential therapeutic target.

 

“Completed and ongoing studies suggest NEAT1 is a good marker for patient risk stratification and a predictor of therapy resistance. We are now exploring the possibility of knocking it out in vivo to see if there is a therapeutic benefit. It could be that targeting NEAT1 and the androgen receptor in combination may provide a unique treatment strategy for a subset of patients who have advanced prostate cancer.”

  • Mouse Models for Noncoding RNA

Genetically engineered mouse models of human cancer have been indispensable in dissecting the molecular mechanisms involved in tumorigenesis. They also provide powerful platforms for preclinically studying drug sensitivity and resistance, said Andrea Ventura, M.D., Ph.D., a cancer biologist at the Memorial Sloan Kettering Cancer Center.

“Mouse models can explore the physiological function of microRNAs such as determining how they affect development and their response to tumor treatments. It is almost impossible to do these studies otherwise,” explained Dr. Ventura. “Another way mouse models are important is for modeling noncoding RNA.”

 

Tools for Studying and Using Small RNAs: From Pathways to Functions to Therapies
This poster provides an overview of the tools that have been developed to understand the functions of small RNAs and, conversely, the use of small RNAs as tools. Tools that are based on small RNAs have been exploited to investigate gene function in cultured cells and in living animals. Small RNA biogenesis, discovery and functional roles are explored in detail.

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Turning genetic information into working proteins

Larry H Bernstein, MD, FCAP, Curator

Leaders in Pharmaceutical Intelligence

Series 2; 3.3

James E. Darnell Jr. (1930— )
Vincent Astor Professor Emeritus
2002 Albert Lasker Award for Special Achievement in Medical Science

Responsible for the various tasks required in turning genetic information into working proteins, ribonucleic acids are one of the most essential players in the life of a cell. First discovered in 1868, RNA today remains the subject of intense scientific scrutiny. Over the course of a career dedicated to understanding the intricate workings of gene transcription, Rockefeller University scientist James E. Darnell Jr. has revealed some of RNA’s most secretive and surprising mechanisms. For his half-century of illuminating research, Dr. Darnell received the 2002 Albert Lasker Award for Special Achievement in Medical Science.

In 1963, Dr. Darnell described a phenomenon he termed “RNA processing,” a step in the process of gene transcription, which had only recently been elucidated in bacterial systems. Working with mammalian cells — which differ from bacterial cells in that they contain a nucleus, where RNA is created — Dr. Darnell observed that very long strings of RNA disappear from the cell nucleus and that subsequently, shorter RNAs resembling the absent longer ones appear in the cytoplasm. Mammalian cells, he concluded, must distill their massive, immature nuclear RNA into shorter, mature forms that are individually coded for specific purposes by specific segments of the genome.

Dr. Darnell carried the principles of his finding — which he made in ribosomal RNA, part of the construction crew that builds cellular proteins — to other long nuclear RNA, including the longest one, which he named heterogeneous nuclear RNA (hnRNA). His hypothesis, that hnRNA is the precursor of the better known messenger RNA — which carries the genetic blueprint for protein building — soon bore fruit when he found a structural correlation between the two. Certain hnRNAs and nearly all messenger RNAs have a “tail” of adenine nucleotides at one end. Dr. Darnell followed this discovery with the observation that when an hnRNA string with an adenine tail disappears from the nucleus, a messenger RNA with the same tail then appears in the cytoplasm, suggesting a causal link between the two. When he found a second similarity — a cap at the end of the string opposite the adenine tail — he faced a conundrum. Scientific dogma had it that the order of nucleotides in any RNA mirrors that of DNA, whether the RNA is modeled from somewhere in the middle of the DNA or from one of the ends. The matching of a nuclear RNA to its cytoplasmic product by two end pieces glued together was surprising, but the concept was soon proven by colleagues at other institutions and called RNA splicing.

After a brief sojourn in Paris to work in François Jacob’s lab, Darnell worked at MIT, the Albert Einstein College of Medicine, and Rockefeller University on the relationship between mRNA and hnRNA. hnRNA was believed to be the precursor to mRNA, and despite making some key discoveries, Darnell admits that he could not free his imagination from the idea of colinearity and envision an hnRNA spliced to produce a smaller mRNA.

At this time, Darnell turned his attention to the question he had pondered since Paris: how were genes regulated in animal cells? This led to the discovery of the STAT and the Jak-STAT pathway of transcription control.

With the knowledge of RNA processing and splicing, Dr. Darnell next examined how cells begin the process of transcription and how they activate particular segments of DNA. Having moved to Rockefeller University in 1974, he found in the early 1980s that cells retain their specificity only in the context of their natural environment. Away from other liver cells, for example, a single liver cell stops producing liver-specific RNA, though it continues to make RNA for more generic cellular tasks. To pinpoint the signals responsible, which he believed must be coming from outside the cell, Dr. Darnell took a closer look at interferons (IFN), proteins that warn a cell when it’s time to raise its genetic defenses against harmful microbes.

Dr. Darnell’s laboratory studies how signals from the cell surface affect transcription of genes in the nucleus. Originally using interferon as a model cytokine, the Darnell group discovered that cell transcription was quickly changed by binding of cytokines to the cell surface. Introducing IFNβ into cell cultures, he watched as a particular type of mRNA accumulated in the cytoplasm, unaccompanied by any new protein synthesis. Analyzing the mRNA led him to the segment of DNA that had been activated, and the lack of new proteins told him that the cell contained its own, usually dormant, IFN-responsive transcription factor. By isolating a particular stretch of DNA from IFN-treated cells, he was able to call out of hiding the proteins that make up that factor, which, partly because they respond to signals very quickly, he called “STATs.” Dr. Darnell then traced the chemical relay that activates the STATs after IFN contact, called the Jak-Stat pathway.

The bound interferon led to the tyrosine phosphorylation of latent cytoplasmic proteins now called STATs (signal transducers and activators of transcription) that dimerize by reciprocal phosphotyrosine-SH2 interchange. They accumulate in the nucleus, bind DNA and drive transcription. This pathway has proved to be of wide importance, with seven STATs now known in mammals that take part in a wide variety of developmental and homeostatic events in all multicellular animals. Crystallographic analysis defined functional domains in the STATs, and current attention is focused on two areas: how the STATs complete their cycle of activation and inactivation, which requires regulated tyrosine dephosphorylation; and how persistent activation of STAT3 that occurs in a high proportion of many human cancers contributes to blocking apoptosis in cancer cells. Current efforts are devoted to inhibiting STAT3 with modified peptides that can enter cells.

 

Dr. Darnell received his M.D. in 1955 from the Washington University School of Medicine. His career has included poliovirus research with Harry Eagle at the National Institute of Allergy and Infectious Diseases, research with François Jacob at the Pasteur Institute in Paris and academic appointments at the Massachusetts Institute of Technology, the Albert Einstein College of Medicine and Columbia University. In 1974 Dr. Darnell joined Rockefeller as Vincent Astor Professor, and from 1990 to 1991 he was vice president for academic affairs.

A member of the National Academy of Sciences since 1973, he has received numerous awards, including the 2012 Albany Medical Center Prize in Medicine and Biomedical Research, the 2003 National Medal of Science, the 2002 Albert Lasker Award for Special Achievement in Medical Science, the 1997 Passano Award, the 1994 Paul Janssen Prize in Advanced Biotechnology and Medicine and the 1986 Gairdner Foundation International Award.

He is the coauthor with S.E. Luria of General Virology and the founding author with Harvey Lodish and David Baltimore of Molecular Cell Biology, now in its seventh edition. His book RNA, Life’s Indispensable Molecule was published in July 2011 by Cold Spring Harbor Laboratory Press. He is a member of the American Academy of Arts and Sciences and a foreign member of The Royal Society and The Royal Swedish Academy of Sciences.

 

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Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Reporter: Stephen S Williams, PhD

Article ID #180: Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle. Published on 8/15/2015

WordCloud Image Produced by Adam Tubman

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series D:

Metabolic Genomics & Pharmaceutics, Vol. I

SACHS FLYER 2014 Metabolomics SeriesDindividualred-page2

which is now available on Amazon Kindle at

http://www.amazon.com/dp/B012BB0ZF0.

This e-Book is a comprehensive review of recent Original Research on  METABOLOMICS and related opportunities for Targeted Therapy written by Experts, Authors, Writers. This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time baccalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon.

All forthcoming BioMed e-Book Titles can be viewed at:

http://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Metabolic Genomics & Pharmaceutics, Vol. I

Chapter 1: Metabolic Pathways

Chapter 2: Lipid Metabolism

Chapter 3: Cell Signaling

Chapter 4: Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8:  Impairments in Pathological States: Endocrine Disorders; Stress

                   Hypermetabolism and Cancer

Chapter 9: Genomic Expression in Health and Disease 

 

Summary 

Epilogue

 

 

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