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Posts Tagged ‘Nucleic acid sequence’


Genomics and epigenetics link to DNA structure

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Sequence and Epigenetic Factors Determine Overall DNA Structure

http://www.genengnews.com/gen-news-highlights/sequence-and-epigenetic-factors-determine-overall-dna-structure/81252592/

http://www.genengnews.com/Media/images/GENHighlight/Atomiclevelsimulationsshowingelectrostaticforcesbetweeneachatom1259202113.jpg

Atomic-level simulations show electrostatic forces between each atom. [Alek Aksimentiev, University of Illinois at Urbana-Champaign]

 

The traditionally held hypothesis about the highly ordered organization of DNA describes the interaction of various proteins with DNA sequences to mediate the dynamic structure of the molecule. However, recent evidence has emerged that stretches of homologous DNA sequences can associate preferentially with one another, even in the absence of proteins.

Researchers at the University of Illinois Center for the Physics of Living Cells, Johns Hopkins University, and Ulsan National Institute of Science and Technology (UNIST) in South Korea found that DNA molecules interact directly with one another in ways that are dependent on the sequence of the DNA and epigenetic factors, such as methylation.

The researchers described evidence they found for sequence-dependent attractive interactions between double-stranded DNA molecules that neither involve intermolecular strand exchange nor are mediated by DNA-binding proteins.

“DNA molecules tend to repel each other in water, but in the presence of special types of cations, they can attract each other just like nuclei pulling each other by sharing electrons in between,” explained lead study author Hajin Kim, Ph.D., assistant professor of biophysics at UNIST. “Our study suggests that the attractive force strongly depends on the nucleic acid sequence and also the epigenetic modifications.”

The investigators used atomic-level supercomputer simulations to measure the forces between a pair of double-stranded DNA helices and proposed that the distribution of methyl groups on the DNA was the key to regulating this sequence-dependent attraction. To verify their findings experimentally, the scientists were able to observe a single pair of DNA molecules within nanoscale bubbles.

“Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation,” the authors wrote. “We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine act as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction.”

The findings from this study were published recently in Nature Communications in an article entitled “Direct Evidence for Sequence-Dependent Attraction Between Double-Stranded DNA Controlled by Methylation.”

After conducting numerous further simulations, the research team concluded that direct DNA–DNA interactions could play a central role in how chromosomes are organized in the cell and which ones are expanded or folded up compactly, ultimately determining functions of different cell types or regulating the cell cycle.

“Biophysics is a fascinating subject that explores the fundamental principles behind a variety of biological processes and life phenomena,” Dr. Kim noted. “Our study requires cross-disciplinary efforts from physicists, biologists, chemists, and engineering scientists and we pursue the diversity of scientific disciplines within the group.”

Dr. Kim concluded by stating that “in our lab, we try to unravel the mysteries within human cells based on the principles of physics and the mechanisms of biology. In the long run, we are seeking for ways to prevent chronic illnesses and diseases associated with aging.”

 

Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

Jejoong Yoo, Hajin Kim, Aleksei Aksimentiev, and Taekjip Ha
Nature Communications 7 11045 (2016)    DOI:10.1038/ncomms11045BibTex

http://bionano.physics.illinois.edu/sites/default/files/styles/large/public/telepathy_figures_0.png?itok=VUJIHX2_

Although proteins mediate highly ordered DNA organization in vivo, theoretical studies suggest that homologous DNA duplexes can preferentially associate with one another even in the absence of proteins. Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation. We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine acts as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction. Indeed, methylation of cytosines makes attraction between GC-rich DNA as strong as that between AT-rich DNA. Recent genome-wide chromosome organization studies showed that remote contact frequencies are higher for AT-rich and methylated DNA, suggesting that direct DNA–DNA interactions that we report here may play a role in the chromosome organization and gene regulation.

Formation of a DNA double helix occurs through Watson–Crick pairing mediated by the complementary hydrogen bond patterns of the two DNA strands and base stacking. Interactions between double-stranded (ds)DNA molecules in typical experimental conditions containing mono- and divalent cations are repulsive1, but can turn attractive in the presence of high-valence cations2. Theoretical studies have identified the ion–ion correlation effect as a possible microscopic mechanism of the DNA condensation phenomena3, 4, 5. Theoretical investigations have also suggested that sequence-specific attractive forces might exist between two homologous fragments of dsDNA6, and this ‘homology recognition’ hypothesis was supported by in vitro atomic force microscopy7 and in vivo point mutation assays8. However, the systems used in these measurements were too complex to rule out other possible causes such as Watson–Crick strand exchange between partially melted DNA or protein-mediated association of DNA.

Here we present direct evidence for sequence-dependent attractive interactions between dsDNA molecules that neither involve intermolecular strand exchange nor are mediated by proteins. Further, we find that the sequence-dependent attraction is controlled not by homology—contradictory to the ‘homology recognition’ hypothesis6—but by a methylation pattern. Unlike the previous in vitro study that used monovalent (Na+) or divalent (Mg2+) cations7, we presumed that for the sequence-dependent attractive interactions to operate polyamines would have to be present. Polyamine is a biological polycation present at a millimolar concentration in most eukaryotic cells and essential for cell growth and proliferation9, 10. Polyamines are also known to condense DNA in a concentration-dependent manner2, 11. In this study, we use spermine4+(Sm4+) that contains four positively charged amine groups per molecule.

Sequence dependence of DNA–DNA forces

To characterize the molecular mechanisms of DNA–DNA attraction mediated by polyamines, we performed molecular dynamics (MD) simulations where two effectively infinite parallel dsDNA molecules, 20 base pairs (bp) each in a periodic unit cell, were restrained to maintain a prescribed inter-DNA distance; the DNA molecules were free to rotate about their axes. The two DNA molecules were submerged in 100mM aqueous solution of NaCl that also contained 20 Sm4+molecules; thus, the total charge of Sm4+, 80 e, was equal in magnitude to the total charge of DNA (2 × 2 × 20 e, two unit charges per base pair; Fig. 1a). Repeating such simulations at various inter-DNA distances and applying weighted histogram analysis12 yielded the change in the interaction free energy (ΔG) as a function of the DNA–DNA distance (Fig. 1b,c). In a broad agreement with previous experimental findings13, ΔG had a minimum, ΔGmin, at the inter-DNA distance of 25−30Å for all sequences examined, indeed showing that two duplex DNA molecules can attract each other. The free energy of inter-duplex attraction was at least an order of magnitude smaller than the Watson–Crick interaction free energy of the same length DNA duplex. A minimum of ΔG was not observed in the absence of polyamines, for example, when divalent or monovalent ions were used instead14, 15.

Figure 1: Polyamine-mediated DNA sequence recognition observed in MD simulations and smFRET experiments.
Polyamine-mediated DNA sequence recognition observed in MD simulations and smFRET experiments.

(a) Set-up of MD simulations. A pair of parallel 20-bp dsDNA duplexes is surrounded by aqueous solution (semi-transparent surface) containing 20 Sm4+ molecules (which compensates exactly the charge of DNA) and 100mM NaCl. Under periodic boundary conditions, the DNA molecules are effectively infinite. A harmonic potential (not shown) is applied to maintain the prescribed distance between the dsDNA molecules. (b,c) Interaction free energy of the two DNA helices as a function of the DNA–DNA distance for repeat-sequence DNA fragments (b) and DNA homopolymers (c). (d) Schematic of experimental design. A pair of 120-bp dsDNA labelled with a Cy3/Cy5 FRET pair was encapsulated in a ~200-nm diameter lipid vesicle; the vesicles were immobilized on a quartz slide through biotin–neutravidin binding. Sm4+ molecules added after immobilization penetrated into the porous vesicles. The fluorescence signals were measured using a total internal reflection microscope. (e) Typical fluorescence signals indicative of DNA–DNA binding. Brief jumps in the FRET signal indicate binding events. (f) The fraction of traces exhibiting binding events at different Sm4+ concentrations for AT-rich, GC-rich, AT nonhomologous and CpG-methylated DNA pairs. The sequence of the CpG-methylated DNA specifies the methylation sites (CG sequence, orange), restriction sites (BstUI, triangle) and primer region (underlined). The degree of attractive interaction for the AT nonhomologous and CpG-methylated DNA pairs was similar to that of the AT-rich pair. All measurements were done at [NaCl]=50mM and T=25°C. (g) Design of the hybrid DNA constructs: 40-bp AT-rich and 40-bp GC-rich regions were flanked by 20-bp common primers. The two labelling configurations permit distinguishing parallel from anti-parallel orientation of the DNA. (h) The fraction of traces exhibiting binding events as a function of NaCl concentration at fixed concentration of Sm4+ (1mM). The fraction is significantly higher for parallel orientation of the DNA fragments.

Unexpectedly, we found that DNA sequence has a profound impact on the strength of attractive interaction. The absolute value of ΔG at minimum relative to the value at maximum separation, |ΔGmin|, showed a clearly rank-ordered dependence on the DNA sequence: |ΔGmin| of (A)20>|ΔGmin| of (AT)10>|ΔGmin| of (GC)10>|ΔGmin| of (G)20. Two trends can be noted. First, AT-rich sequences attract each other more strongly than GC-rich sequences16. For example, |ΔGmin| of (AT)10 (1.5kcalmol−1 per turn) is about twice |ΔGmin| of (GC)10 (0.8kcalmol−1 per turn) (Fig. 1b). Second, duplexes having identical AT content but different partitioning of the nucleotides between the strands (that is, (A)20 versus (AT)10 or (G)20 versus (GC)10) exhibit statistically significant differences (~0.3kcalmol−1 per turn) in the value of |ΔGmin|.

To validate the findings of MD simulations, we performed single-molecule fluorescence resonance energy transfer (smFRET)17 experiments of vesicle-encapsulated DNA molecules. Equimolar mixture of donor- and acceptor-labelled 120-bp dsDNA molecules was encapsulated in sub-micron size, porous lipid vesicles18 so that we could observe and quantitate rare binding events between a pair of dsDNA molecules without triggering large-scale DNA condensation2. Our DNA constructs were long enough to ensure dsDNA–dsDNA binding that is stable on the timescale of an smFRET measurement, but shorter than the DNA’s persistence length (~150bp (ref. 19)) to avoid intramolecular condensation20. The vesicles were immobilized on a polymer-passivated surface, and fluorescence signals from individual vesicles containing one donor and one acceptor were selectively analysed (Fig. 1d). Binding of two dsDNA molecules brings their fluorescent labels in close proximity, increasing the FRET efficiency (Fig. 1e).

FRET signals from individual vesicles were diverse. Sporadic binding events were observed in some vesicles, while others exhibited stable binding; traces indicative of frequent conformational transitions were also observed (Supplementary Fig. 1A). Such diverse behaviours could be expected from non-specific interactions of two large biomolecules having structural degrees of freedom. No binding events were observed in the absence of Sm4+ (Supplementary Fig. 1B) or when no DNA molecules were present. To quantitatively assess the propensity of forming a bound state, we chose to use the fraction of single-molecule traces that showed any binding events within the observation time of 2min (Methods). This binding fraction for the pair of AT-rich dsDNAs (AT1, 100% AT in the middle 80-bp section of the 120-bp construct) reached a maximum at ~2mM Sm4+(Fig. 1f), which is consistent with the results of previous experimental studies2, 3. In accordance with the prediction of our MD simulations, GC-rich dsDNAs (GC1, 75% GC in the middle 80bp) showed much lower binding fraction at all Sm4+ concentrations (Fig. 1b,c). Regardless of the DNA sequence, the binding fraction reduced back to zero at high Sm4+ concentrations, likely due to the resolubilization of now positively charged DNA–Sm4+ complexes2, 3, 13.

Because the donor and acceptor fluorophores were attached to the same sequence of DNA, it remained possible that the sequence homology between the donor-labelled DNA and the acceptor-labelled DNA was necessary for their interaction6. To test this possibility, we designed another AT-rich DNA construct AT2 by scrambling the central 80-bp section of AT1 to remove the sequence homology (Supplementary Table 1). The fraction of binding traces for this nonhomologous pair of donor-labelled AT1 and acceptor-labelled AT2 was comparable to that for the homologous AT-rich pair (donor-labelled AT1 and acceptor-labelled AT1) at all Sm4+ concentrations tested (Fig. 1f). Furthermore, this data set rules out the possibility that the higher binding fraction observed experimentally for the AT-rich constructs was caused by inter-duplex Watson–Crick base pairing of the partially melted constructs.

Next, we designed a DNA construct named ATGC, containing, in its middle section, a 40-bp AT-rich segment followed by a 40-bp GC-rich segment (Fig. 1g). By attaching the acceptor to the end of either the AT-rich or GC-rich segments, we could compare the likelihood of observing the parallel binding mode that brings the two AT-rich segments together and the anti-parallel binding mode. Measurements at 1mM Sm4+ and 25 or 50mM NaCl indicated a preference for the parallel binding mode by ~30% (Fig. 1h). Therefore, AT content can modulate DNA–DNA interactions even in a complex sequence context. Note that increasing the concentration of NaCl while keeping the concentration of Sm4+ constant enhances competition between Na+ and Sm4+ counterions, which reduces the concentration of Sm4+ near DNA and hence the frequency of dsDNA–dsDNA binding events (Supplementary Fig. 2).

Methylation determines the strength of DNA–DNA attraction

Analysis of the MD simulations revealed the molecular mechanism of the polyamine-mediated sequence-dependent attraction (Fig. 2). In the case of the AT-rich fragments, the bulky methyl group of thymine base blocks Sm4+ binding to the N7 nitrogen atom of adenine, which is the cation-binding hotspot21, 22. As a result, Sm4+ is not found in the major grooves of the AT-rich duplexes and resides mostly near the DNA backbone (Fig. 2a,d). Such relocated Sm4+ molecules bridge the two DNA duplexes better, accounting for the stronger attraction16, 23, 24, 25. In contrast, significant amount of Sm4+ is adsorbed to the major groove of the GC-rich helices that lacks cation-blocking methyl group (Fig. 2b,e).

Figure 2: Molecular mechanism of polyamine-mediated DNA sequence recognition.
Molecular mechanism of polyamine-mediated DNA sequence recognition.

(ac) Representative configurations of Sm4+ molecules at the DNA–DNA distance of 28Å for the (AT)10–(AT)10 (a), (GC)10–(GC)10 (b) and (GmC)10–(GmC)10 (c) DNA pairs. The backbone and bases of DNA are shown as ribbon and molecular bond, respectively; Sm4+ molecules are shown as molecular bonds. Spheres indicate the location of the N7 atoms and the methyl groups. (df) The average distributions of cations for the three sequence pairs featured in ac. Top: density of Sm4+ nitrogen atoms (d=28Å) averaged over the corresponding MD trajectory and the z axis. White circles (20Å in diameter) indicate the location of the DNA helices. Bottom: the average density of Sm4+ nitrogen (blue), DNA phosphate (black) and sodium (red) atoms projected onto the DNA–DNA distance axis (x axis). The plot was obtained by averaging the corresponding heat map data over y=[−10, 10] Å. See Supplementary Figs 4 and 5 for the cation distributions at d=30, 32, 34 and 36Å.

If indeed the extra methyl group in thymine, which is not found in cytosine, is responsible for stronger DNA–DNA interactions, we can predict that cytosine methylation, which occurs naturally in many eukaryotic organisms and is an essential epigenetic regulation mechanism26, would also increase the strength of DNA–DNA attraction. MD simulations showed that the GC-rich helices containing methylated cytosines (mC) lose the adsorbed Sm4+ (Fig. 2c,f) and that |ΔGmin| of (GC)10 increases on methylation of cytosines to become similar to |ΔGmin| of (AT)10 (Fig. 1b).

To experimentally assess the effect of cytosine methylation, we designed another GC-rich construct GC2 that had the same GC content as GC1 but a higher density of CpG sites (Supplementary Table 1). The CpG sites were then fully methylated using M. SssI methyltransferase (Supplementary Fig. 3; Methods). As predicted from the MD simulations, methylation of the GC-rich constructs increased the binding fraction to the level of the AT-rich constructs (Fig. 1f).

The sequence dependence of |ΔGmin| and its relation to the Sm4+ adsorption patterns can be rationalized by examining the number of Sm4+ molecules shared by the dsDNA molecules (Fig. 3a). An Sm4+ cation adsorbed to the major groove of one dsDNA is separated from the other dsDNA by at least 10Å, contributing much less to the effective DNA–DNA attractive force than a cation positioned between the helices, that is, the ‘bridging’ Sm4+ (ref. 23). An adsorbed Sm4+ also repels other Sm4+ molecules due to like-charge repulsion, lowering the concentration of bridging Sm4+. To demonstrate that the concentration of bridging Sm4+ controls the strength of DNA–DNA attraction, we computed the number of bridging Sm4+ molecules, Nspm (Fig. 3b). Indeed, the number of bridging Sm4+ molecules ranks in the same order as |ΔGmin|: Nspm of (A)20>Nspm of (AT)10Nspm of (GmC)10>Nspm of (GC)10>Nspm of (G)20. Thus, the number density of nucleotides carrying a methyl group (T and mC) is the primary determinant of the strength of attractive interaction between two dsDNA molecules. At the same time, the spatial arrangement of the methyl group carrying nucleotides can affect the interaction strength as well (Fig. 3c). The number of methyl groups and their distribution in the (AT)10 and (GmC)10 duplex DNA are identical, and so are their interaction free energies, |ΔGmin| of (AT)10Gmin| of (GmC)10. For AT-rich DNA sequences, clustering of the methyl groups repels Sm4+ from the major groove more efficiently than when the same number of methyl groups is distributed along the DNA (Fig. 3b). Hence, |ΔGmin| of (A)20>|ΔGmin| of (AT)10. For GC-rich DNA sequences, clustering of the cation-binding sites (N7 nitrogen) attracts more Sm4+ than when such sites are distributed along the DNA (Fig. 3b), hence |ΔGmin| is larger for (GC)10 than for (G)20.

Figure 3: Methylation modulates the interaction free energy of two dsDNA molecules by altering the number of bridging Sm4+.
Methylation modulates the interaction free energy of two dsDNA molecules by altering the number of bridging Sm4+.

(a) Typical spatial arrangement of Sm4+ molecules around a pair of DNA helices. The phosphates groups of DNA and the amine groups of Sm4+ are shown as red and blue spheres, respectively. ‘Bridging’ Sm4+molecules reside between the DNA helices. Orange rectangles illustrate the volume used for counting the number of bridging Sm4+ molecules. (b) The number of bridging amine groups as a function of the inter-DNA distance. The total number of Sm4+ nitrogen atoms was computed by averaging over the corresponding MD trajectory and the 10Å (x axis) by 20Å (y axis) rectangle prism volume (a) centred between the DNA molecules. (c) Schematic representation of the dependence of the interaction free energy of two DNA molecules on their nucleotide sequence. The number and spatial arrangement of nucleotides carrying a methyl group (T or mC) determine the interaction free energy of two dsDNA molecules.

Genome-wide investigations of chromosome conformations using the Hi–C technique revealed that AT-rich loci form tight clusters in human nucleus27, 28. Gene or chromosome inactivation is often accompanied by increased methylation of DNA29 and compaction of facultative heterochromatin regions30. The consistency between those phenomena and our findings suggest the possibility that the polyamine-mediated sequence-dependent DNA–DNA interaction might play a role in chromosome folding and epigenetic regulation of gene expression.

  1. Rau, D. C., Lee, B. & Parsegian, V. A. Measurement of the repulsive force between polyelectrolyte molecules in ionic solution: hydration forces between parallel DNA double helices. Proc. Natl Acad. Sci. USA 81, 26212625 (1984).
  2. Raspaud, E., Olvera de la Cruz, M., Sikorav, J. L. & Livolant, F. Precipitation of DNA by polyamines: a polyelectrolyte behavior. Biophys. J. 74, 381393 (1998).
  3. Besteman, K., Van Eijk, K. & Lemay, S. G. Charge inversion accompanies DNA condensation by multivalent ions. Nat. Phys. 3, 641644 (2007).
  4. Lipfert, J., Doniach, S., Das, R. & Herschlag, D. Understanding nucleic acid-ion interactions.Annu. Rev. Biochem. 83, 813841 (2014).
  5. Grosberg, A. Y., Nguyen, T. T. & Shklovskii, B. I. The physics of charge inversion in chemical and biological systems. Rev. Mod. Phys. 74, 329345 (2002).
  6. Kornyshev, A. A. & Leikin, S. Sequence recognition in the pairing of DNA duplexes. Phys. Rev. Lett. 86, 36663669 (2001).
  7. Danilowicz, C. et al. Single molecule detection of direct, homologous, DNA/DNA pairing.Proc. Natl Acad. Sci. USA 106, 1982419829 (2009).
  8. Gladyshev, E. & Kleckner, N. Direct recognition of homology between double helices of DNA in Neurospora crassa. Nat. Commun. 5, 3509 (2014).
  9. Tabor, C. W. & Tabor, H. Polyamines. Annu. Rev. Biochem. 53, 749790 (1984).
  10. Thomas, T. & Thomas, T. J. Polyamines in cell growth and cell death: molecular mechanisms and therapeutic applications. Cell. Mol. Life Sci. 58, 244258 (2001).
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Gene Expression: Algorithms for Protein Dynamics

Reporter:  Aviva Lev-Ari, PhD, RN

Stanford-developed algorithm reveals complex protein dynamics behind gene expression

BY KRISTA CONGER

Michael Snyder

In yet another coup for a research concept known as “big data,” researchers at the Stanford University School of Medicine have developed a computerized algorithm to understand the complex and rapid choreography of hundreds of proteins that interact in mindboggling combinations to govern how genes are flipped on and off within a cell.

To do so, they coupled findings from 238 DNA-protein-binding experiments performed by the ENCODE project — a massive, multiyear international effort to identify the functional elements of the human genome — with a laboratory-based technique to identify binding patterns among the proteins themselves.

The analysis is sensitive enough to have identified many previously unsuspected, multipartner trysts. It can also be performed quickly and repeatedly to track how a cell responds to environmental changes or crucial developmental signals.

“At a very basic level, we are learning who likes to work with whom to regulate around 20,000 human genes,” said Michael Snyder, PhD, professor and chair of genetics at Stanford. “If you had to look through all possible interactions pair-wise, it would be ridiculously impossible. Here we can look at thousands of combinations in an unbiased manner and pull out important and powerful information. It gives us an unprecedented level of understanding.”

Snyder is the senior author of a paper describing the research published Oct. 24 in Cell. The lead authors are postdoctoral scholars Dan Xie, PhD, Alan Boyle, PhD, and Linfeng Wu, PhD.

Proteins control gene expression by either binding to specific regions of DNA, or by interacting with other DNA-bound proteins to modulate their function. Previously, researchers could only analyze two to three proteins and DNA sequences at a time, and were unable to see the true complexities of the interactions among proteins and DNA that occur in living cells.

The challenge resembled trying to figure out interactions in a crowded mosh pit by studying a few waltzing couples in an otherwise empty ballroom, and it has severely limited what could be learned about the dynamics of gene expression.

The ENCODE, for the Encyclopedia of DNA Elements, project was a five-year collaboration of more than 440 scientists in 32 labs around the world to reveal the complex interplay among regulatory regions, proteins and RNA molecules that governs when and how genes are expressed. The project has been generating a treasure trove of data for researchers to analyze for the last eight years.

In this study, the researchers combined data from genomics (a field devoted to the study of genes) and proteomics (which focuses on proteins and their interactions). They studied 128 proteins, called trans-acting factors, which are known to regulate gene expression by binding to regulatory regions within the genome. Some of the regions control the expression of nearby genes; others affect the expression of genes great distances away.

The researchers used 238 data sets generated by the ENCODE project to study the specific DNA sequences bound by each of the 128 trans-acting factors. But these factors aren’t monogamous; they bind many different sequences in a variety of protein-DNA combinations. Xie, Boyle and Snyder designed a machine-learning algorithm to analyze all the data and identify which trans-acting factors tend to be seen together and which DNA sequences they prefer.

Wu then performed immunoprecipitation experiments, which use antibodies to identify protein interactions in the cell nucleus. In this way, they were able to tell which proteins interacted directly with one another, and which were seen together because their preferred DNA binding sites were adjoining.

“Before our work, only the combination of two or three regulatory proteins were studied, which oversimplified how gene regulators collaborate to find their targets,” Xie said. “With our method we are able to study the combination of more than 100 regulators and see a much more complex structure of collaboration. For example, it had been believed that a key regulator of cell proliferation called FOS typically only works with JUN protein family members. We show, in addition to JUN, FOS has different partners under different circumstances. In fact, we found almost all the canonical combinations of two or three trans-acting factors have many more partners than we previously thought.”

To broaden their analysis, the researchers included data from other sources that explored protein-binding patterns in five cell types. They found that patterns of co-localization among proteins, in which several proteins are found clustered closely on the DNA to govern gene expression, vary according to cell type and the conditions under which the cells are grown. They also found that many of these clusters can be explained through interactions among proteins, and that not every protein bound to DNA directly.

“We’d like to understand how these interactions work together to make different cell types and how they gain their unique identities in development,” Snyder said. “Furthermore, diseased cells will have a very different type of wiring diagram. We hope to understand how these cells go astray.”

Other Stanford co-authors include life science research assistant Jie Zhai and life science research associate Trupti Kawli, PhD.

The research was supported by the National Human Genome Research Institute (grants U54HG004558 and U54HG006996).

Information about Stanford’s Department of Genetics, which also supported the work, is available at http://genetics.stanford.edu.

PRINT MEDIA CONTACT
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kristac@stanford.edu
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Stanford Medicine integrates research, medical education and patient care at its three institutions – Stanford University School of MedicineStanford Hospital & Clinics and Lucile Packard Children’s Hospital. For more information, please visit the Office of Communication & Public Affairs site at

http://mednews.stanford.edu/.http://med.stanford.edu/ism/2013/october/snyder.html?goback=%2Egde_5180384_member_5799368448383397888#sthash%2EhU03LKIX%2Edpuf

 

Dynamic trans-Acting Factor Colocalization in Human Cells

Cell, Volume 155, Issue 3, 713-724, 24 October 2013
Copyright © 2013 Elsevier Inc. All rights reserved.
10.1016/j.cell.2013.09.043

Authors

    • Highlights
    • Colocalization patterns of 128 TFs in human cells
    • An application of SOMs to study high-dimensional TF colocalization patterns
    • Colocalization patterns are dynamic through stimulation and across cell types
    • Many TF colocalizations can be explained by protein-protein interaction

    Summary

    Different trans-acting factors (TFs) collaborate and act in concert at distinct loci to perform accurate regulation of their target genes. To date, the cobinding of TF pairs has been investigated in a limited context both in terms of the number of factors within a cell type and across cell types and the extent of combinatorial colocalizations. Here, we use an approach to analyze TF colocalization within a cell type and across multiple cell lines at an unprecedented level. We extend this approach with large-scale mass spectrometry analysis of immunoprecipitations of 50 TFs. Our combined approach reveals large numbers of interesting TF-TF associations. We observe extensive change in TF colocalizations both within a cell type exposed to different conditions and across multiple cell types. We show distinct functional annotations and properties of different TF cobinding patterns and provide insights into the complex regulatory landscape of the cell.

    http://www.cell.com/abstract/S0092-8674%2813%2901217-8#!

    Personalized medicine aims to assess medical risks, monitor, diagnose and treat patients according to their specific genetic composition and molecular phenotype. The advent of genome sequencing and the analysis of physiological states has proven to be powerful (Cancer Genome Atlas Research Network, 2011). However, its implementation for the analysis of otherwise healthy individuals for estimation of disease risk and medical interpretation is less clear. Much of the genome is difficult to interpret and many complex diseases, such as diabetes, neurological disorders and cancer, likely involve a large number of different genes and biological pathways (Ashley et al., 2010,Grayson et al., 2011,Li et al., 2011), as well as environmental contributors that can be difficult to assess. As such, the combination of genomic information along with a detailed molecular analysis of samples will be important for predicting, diagnosing and treating diseases as well as for understanding the onset, progression, and prevalence of disease states (Snyder et al., 2009).

    Presently, healthy and diseased states are typically followed using a limited number of assays that analyze a small number of markers of distinct types. With the advancement of many new technologies, it is now possible to analyze upward of 105 molecular constituents. For example, DNA microarrays have allowed the subcategorization of lymphomas and gliomas (Mischel et al., 2003), and RNA sequencing (RNA-Seq) has identified breast cancer transcript isoforms (Li et al., 2011,van der Werf et al., 2007,Wu et al., 2010,Lapuk et al., 2010). Although transcriptome and RNA splicing profiling are powerful and convenient, they provide a partial portrait of an organism’s physiological state. Transcriptomic data, when combined with genomic, proteomic, and metabolomic data are expected to provide a much deeper understanding of normal and diseased states (Snyder et al., 2010). To date, comprehensive integrative omics profiles have been limited and have not been applied to the analysis of generally healthy individuals.

    To obtain a better understanding of: (1) how to generate an integrative personal omics profile (iPOP) and examine as many biological components as possible, (2) how these components change during healthy and diseased states, and (3) how this information can be combined with genomic information to estimate disease risk and gain new insights into diseased states, we performed extensive omics profiling of blood components from a generally healthy individual over a 14 month period (24 months total when including time points with other molecular analyses). We determined the whole-genome sequence (WGS) of the subject, and together with transcriptomic, proteomic, metabolomic, and autoantibody profiles, used this information to generate an iPOP. We analyzed the iPOP of the individual over the course of healthy states and two viral infections (Figure 1A). Our results indicate that disease risk can be estimated by a whole-genome sequence and by regularly monitoring health states with iPOP disease onset may also be observed. The wealth of information provided by detailed longitudinal iPOP revealed unexpected molecular complexity, which exhibited dynamic changes during healthy and diseased states, and provided insight into multiple biological processes. Detailed omics profiling coupled with genome sequencing can provide molecular and physiological information of medical significance. This approach can be generalized for personalized health monitoring and medicine.

     

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    Reporter: Aviva Lev-Ari, PhD, RN

    The 6/13/2013 Supreme Court Decision is covered on this Open Access Online Scientific Journal

    Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

    Geneticist Ricki Lewis, PhD: Genetics Errors in Supreme Court Decision of 6/13/2013

    DNA Science BlogDNA Science Blog

    http://blogs.plos.org/dnascience/2013/06/13/genetics-errors-in-supreme-court-decision/

    Earlier today, my “in” box began to fill with info from everyone I’ve ever met letting me know that the Supreme Court had ruled on the Myriad case about patenting the breast cancer genes BRCA1 and BRCA2. I also received a dozen pitches from PR people offering me all manner of instant interviews with lawyers, doctors, bioethicists, and health care analysts.

    No one offered me an interview with a geneticist – a person who knows something about DNA. So being such a person myself, I decided to take a look at the decision. And I found errors – starting right smack in the opening paragraph.

    “Scientists can extract DNA from cells to isolate specific segments for study. They can also synthetically create exons-only strands of nucleotides known as composite DNA (cDNA). cDNA contains only the exons that occur in DNA, omitting the intervening exons.”

    The definition is correct, the terminology, not. “cDNA” does not stand for “composite DNA.” It stands for “complementary DNA.”

    cDNA came into fashion when I was in grad school, circa 1977. Like many genetics terms, it has a very precise meaning, something I pay attention to because I write human genetics books, including 10 editions of a textbook.

    A cDNA is termed “complementary” because it is complementary in nucleotide base sequence to the messenger RNA (mRNA) that is made from the gene. Enzymes cut from the mRNA the sequences (introns) that do not encode amino acids and retains those (exons) that do encode protein. So a cDNA represents the part of a gene that is actually used to tell the cell to make protein. End of biology lesson.

    A cDNA is created in the laboratory, and it is not a DNA sequence that occurs in nature. Hence, the Supreme Court’s part 2 of the decision, which acknowledges Myriad’s right to use a test based on a complementary, or cDNA.

    I did a google search for “composite DNA” and just found the media parroting of today’s decision, and a few old forensics uses. So a caveat: my conclusion that the term is incorrect and invented is based on negative evidence. If I’m wrong, mea culpa in advance and I will feel like an idiot.

    But cDNA isn’t the only error. I soon found another. On page 16, footnote #8 discusses a pseudogene as resulting from “random incorporation of fragments of cDNA.” That’s not even close to what a pseudogene is.

    A pseudogene results from a DNA replication error that makes an extra copy of a gene. Over time, one copy mutates itself into a form that can’t do its job. The pseudogene remains in the genome like a ghost of a functional gene. The mutations occur at random because the pseudogene, not being used, isn’t subject to natural selection – that’s probably what the Court means by “random.” The globin gene locus on chromosome 11 is chock full of pseudogenes. This is such a classic example of basic genetics that my head is about to explode.

    And how on earth is the Supreme Court’s definition of a pseudogene supposed to happen, in nature or otherwise? A cDNA exists in a lab dish. A gene exists in a cell that is part of an organism. How does the cDNA “randomly incorporate” itself inside the cell? Jump in from the dish? Part of the footnote states, “… given pseudogenes’ apparently random origins … ” Pseudogenes’ origins aren’t random at all. They happen in specific genes that tend to have repeats in the sequence, “confusing” the replication enzymes.

    Today’s decision is undoubtedly a wonderful leap forward for patients, their families, and researchers. And some may think I am nitpicking. But these two errors jumped right out at me — I’d troll for more but I want to post this. What else is wrong? How can we trust the decision if the science is wrong? And what is the background of the people who research the decisions?

    I know nothing about the law, zero, which is why I’m not writing about that. But the science in something as important as a Supreme Court decision should accurately use the language of the field under discussion.

    http://blogs.plos.org/dnascience/2013/06/13/genetics-errors-in-supreme-court-decision/

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    Author: Tilda Barliya PhD

    The field of DNA and RNA nanotechnologies  are considered one of the most dynamic research areas in the field of drug delivery in molecular medicine. Both DNA and RNA have a wide aspect of medical application including: drug deliveries, for genetic immunization, for metabolite and nucleic acid detection, gene regulation, siRNA delivery for cancer treatment (I), and even analytical and therapeutic applications.

    Seeman (6,7) pioneered the concept 30 years ago of using DNA as a material for creating nanostructures; this has led to an explosion of knowledge in the now well-established field of DNA nanotechnology. The unique properties in terms of free energy, folding, noncanonical base-pairing, base-stacking, in vivo transcription and processing that distinguish RNA from DNA provides sufficient rationale to regard RNA nanotechnology as its own technological discipline. Herein, we will discuss the advantages of DNA nanotechnology and it’s use in medicine.

    So What is the rational of using DNA nanotechnology(3)?

    • Genetic studies – its application in various biological fields like biomedicine, cancer research, medical devices  and genetic engineering.
    • Its unique properties of structural stability, programmability of sequences, and predictable self-assembly.
    DNA origami

    Structures made from DNA using the DNA-origami method (Rothemund, 2006)

    Structural DNA nanotechnology rests on three pillars: [1] Hybridization; [2] Stably branched DNA; and [3] Convenient synthesis of designed sequences.

    Hybridization

    Hybridization. The self-association (self=assembly) of complementary nucleic acid molecules or parts of molecules, is implicit in all aspects of structural DNA nanotechnology. Individual motifs are formed by the hybridization of strands designed to produce particular topological species. A key aspect of hybridization is the use of sticky ended cohesion to combine pieces of linear duplex DNA; this has been a fundamental component of genetic engineering for over 35 years (7). Not only is hybridization critical to the formation of structure, but it is deeply involved in almost all the sequence-dependent nanomechanical devices that have been constructed, and it is central to many attempts to build structural motifs in a sequential fashion (7,8 ).

    Stably Branched DNA

    branched DNA molecules are central to DNA nanotechnology. It is the combination of in vitro hybridization and synthetic branched DNA that leads to the ability to use DNA as a construction material. Such branched DNA is thought to be intermediates in genetic recombination (such as Holliday junctions).

    Convenient Synthesis of Designed Sequences

    Biologically derived branched DNA molecules, such as Holliday junctions, are inherently unstable, because they exhibit sequence symmetry; i.e., the four strands actually consist of two pairs of strands with the same sequence. This symmetry enables an isomerization known as branch migration that allows the branch point to relocate.  DNA nanotechnology entailed sequence design that attempted to minimize sequence symmetry in every way possible.

    One of the most remarkable innovations in structural DNA-nanotechnology in recent years is DNA origami, which was invented in 2006 by Paul Rothemund (1) (see Fig above). DNA origami utilizes the genome from a virus together with a large number of shorter DNA strands to enable the creation of numerous DNA-based structures (Figure 1). The shorter DNA strands forces the long viral DNA to fold into a pattern that is defined by the interaction between the long and the short DNA strands (1,2).

    Rothemund believes that an  application of patterned DNA origami would be the creation of a ‘nanobreadboard’, to which diverse components could be added. The attachment of proteins23, for example, might allow novel biological experiments aimed at modelling complex protein assemblies and examining the effects of spatial organization, whereas molecular electronic or plasmonic circuits might be created by attaching nanowires, carbon nanotubes or gold nanoparticles (1).

    DNA nanotechnology and Biological Application

    The physical and chemical properties of nanomaterials such as polymers, semiconductors, and metals present diverse advantages for various in vivo applications (3,9 ). For example:

    • Therapeutics – In cancer for example, nanosystems that are designed from biological materials such as DNA and RNA are ‘programmed’ to be able to evade most, if not all, drug-resistance mechanisms. Based on these properties, most nanosystems are able to deliver high concentrations of drugs to cancer cells while curtailing damage to surrounding healthy cells (2b, 3, 9, 11, 15).
    • Biosensors – capable of picking up very specific biological signals and converting them into electrical outputs that can be analyzed for identification. Biosensors are efficient as they have a high ratio of surface area to volume as well as adjustable electronic, magnetic, optical, and biological properties (3, 12, 13, 14).
    • **Amin and colleagues have developed a biotinylated DNA thin film-coated fiber optic reflectance biosensor for the detection of streptavidin aerosols. DNA thin films were prepared by dropping DNA samples into a polymer optical fiber which responded quickly to the specific biomolecules in the atmosphere. This approach of coating optical fibers with DNA nanostructures could be very useful in the future for detecting atmospheric bio-aerosols with high sensitivity and specificity (3, 14)
    • Computing – Another aspect uses the programmability of DNA to create devices that are capable of computing. Here, the structure of the assembled DNA is not of primary interest. Instead, control of the DNA sequence is used in the creation of computational algorithms, like e.g. artificial neural networks. Qian et al for example, built on the richness of DNA computing and strand displacement circuitry, they showed how molecular systems can exhibit autonomous brain-like behaviours. Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, they systematically transform arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks (3, 10).
    • Additional features: 3rd generation DNA sequencers (II), Biomimetic systems, Energy transfer and photonics etc

    Summary:

    DNA nanotechnology is an evolving field that affects medicine, computation, material sciences, and physics. DNA nanostructures offer unprecedented control over shape, size, mechanical flexibility and anisotropic surface  modification. Clearly, proper control over these aspects can increase  circulation times by orders of magnitude, as can be seen for longcirculating particles such as erythrocytes and various pathogenic particles evolved to overcome this issue.  The use of DNA in DNA/protein-based matrices makes these structures inherently amenable to structural tunability. More research in this direction  will certainly be developed, making DNA a promising biomaterial  in tissue engineering. future development of novel ways in which DNA would be utilized to have a much more comprehensive role in biological computation and data storage is envisaged.

    REFERENCES

    1. Paul W. K. Rothemund. Folding DNA to create nanoscale shapes and patterns. NATURE 2006 (March 16)|Vol 440: 297-302. http://www.nature.com/nature/journal/v440/n7082/full/nature04586.html

    http://www.dna.caltech.edu/Papers/DNAorigami-nature.pdf

    2. Andre V. Pinheiro, Dongran Han, William M. Shih and Hao Yan. Challenges and opportunities for structural DNA nanotechnology. Nature Nanotechnology 2011 Dec | VOL 6: 763-772.  http://www.nature.com/nnano/journal/v6/n12/pdf/nnano.2011.187.pdf

    2b. Thi Huyen La, Thi Thu Thuy Nguyen, Van Phuc Pham, Thi Minh Huyen Nguyen and Quang Huan Le.  Using DNA nanotechnology to produce a drug delivery system. Adv. Nat. Sci.: Nanosci. Nanotechnol. 4 (2013) 015002 (7pp). http://iopscience.iop.org/2043-6262/4/1/015002http://iopscience.iop.org/2043-6262/4/1/015002/pdf/2043-6262_4_1_015002.pdf

    3. Muniza Zahid, Byeonghoon Kim, Rafaqat Hussain, Rashid Amin and Sung H Park. DNA nanotechnology: a future perspective. Nanoscale Research Letters 2013, 8:119. http://www.nanoscalereslett.com/content/8/1/119

    4.By: Cientifica Ltd 2007. The Nanotech Revolution in Drug Delivery.  http://www.cientifica.com/WhitePapers/054_Drug%20Delivery%20White%20Paper.pdf

    5. Gemma Campbell. Nanotechnology and its implications for the health of the E.U citizen: Diagnostics, drug discovery and drug delivery. Institute of Nanotechnology and Nanoforum. http://www.nano.org.uk/nanomednet/images/stories/Reports/diagnostics,%20drug%20discovery%20and%20drug%20delivery.pdf

    6.Peixuan Guo., Haque F., Brent Hallahan, Randall Reif and Hui Li. Uniqueness, Advantages, Challenges, Solutions, and Perspectives in Therapeutics Applying RNA Nanotechnology. Nucleic Acid Ther. 2012 August; 22(4): 226–245. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3426230/

    7. SEEMAN N.C. Nanomaterials based on DNA. Annu. Rev. Biochem. 2010;79:65–87. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3454582/

    8. Yin P, Choi HMT, Calvert CR, Pierce NA. Programming biomolecular self-assembly pathways. Nature.2008;451:318–323.  http://www.ncbi.nlm.nih.gov/pubmed/18202654

    9. Yan Lee P, Wong KY: Nanomedicine: a new frontier in cancer therapeutics. Curr Drug Deliv 2011, 8(3):245-253. OpenURLhttp://www.eurekaselect.com/73728/article

    10. Qian, L.L., Winfree, E., and Bruck, J. Neural Network Computation with DNA Strand Displacement Cascades. Nature 2011 475, 368-372.  http://www.nature.com/nature/journal/v475/n7356/full/nature10262.html

    11. Acharya S, Dilnawaz F, Sahoo SK: Targeted epidermal growth factor receptor nanoparticle bioconjugates for breast cancer therapy. Biomaterials 2009, 30(29):5737-5750. http://www.sciencedirect.com/science/article/pii/S0142961209006929

    12. Bohunicky B, Mousa SA: Biosensors: the new wave in cancer diagnosisNanotechnology, Science and Applications 2011, 4:1-10. http://www.dovepress.com/biosensors-the-new-wave-in-cancer-diagnosis-peer-reviewed-article-NSA-recommendation1

    13. Sanvicens N, Mannelli I, Salvador J, Valera E, Marco M: Biosensors for pharmaceuticals based on novel technologyTrends Anal Chem 2011, 30:541-553. http://www.sciencedirect.com/science/article/pii/S016599361100015X

    14. Amin R, Kulkarni A, Kim T, Park SH: DNA thin film coated optical fiber biosensorCurr Appl Phys 2011, 12(3):841-845. http://www.sciencedirect.com/science/article/pii/S1567173911005888

    15. Choi, Y.; Baker, J. R. Targeting Cancer Cells with DNA Assembled Dendrimers: A Mix and Match Strategy for Cancer. Cell Cycle 2005, 4, 669–671. http://www.ncbi.nlm.nih.gov/pubmed/15846063  http://www.landesbioscience.com/journals/cc/article/1684/

    Other related articles on this Open Access Online Scientific Journal include the following

    I. By: Ziv Raviv PhD. The Development of siRNA-Based Therapies for Cancer. https://pharmaceuticalintelligence.com/2013/05/09/the-development-of-sirna-based-therapies-for-cancer/

    II. By: Tilda Barliya PhD. Nanotechnology, personalized medicine and DNA sequencing. https://pharmaceuticalintelligence.com/2013/01/09/nanotechnology-personalized-medicine-and-dna-sequencing/

    III. By: Larry Bernstein MD FACP. DNA Sequencing Technology. https://pharmaceuticalintelligence.com/2013/03/03/dna-sequencing-technology/

    IV. By: Venkat S Karra PhD. Measuring glucose without needle pricks: nano-sized biosensors made the test easy. https://pharmaceuticalintelligence.com/2012/09/04/measuring-glucose-without-needle-pricks-nano-sized-biosensors-made-the-test-easy/

    Read Full Post »


    Curator: Aviva Lev-Ari, PhD, RN

    In their discussion, the researchers argue that the U.S. Supreme Court now has a chance to shape the balance between the medical good versus inventor protection, adding that, in their opinion, the court should limit the patenting of existing nucleotide sequences, due to their broad scope and non-specificity in the human genome.

    “I am extremely pro-patent, but I simply believe that people should not be able to patent a product of nature,” Dr. Mason says. “Moreover, I believe that individuals have an innate right to their own genome, or to allow their doctor to look at that genome, just like the lungs or kidneys. Failure to resolve these ambiguities perpetuates a direct threat to genomic liberty, or the right to one’s own DNA.”

    http://www.sciencedaily.com/releases/2013/03/130326101614.htm

    Supreme Court May Decide Whether We Own Our Genes

    March 26, 2013
     
    Image Credit: Photos.com

    Brett Smith for redOrbit.com – Your Universe Online

    They may be responsible for everything in your life, from conception to death, they may be inside every living cell in your body – but you do not own your own genes, legally speaking.

    According to a report in Genome Medicine, patents essentially cover the entire human genome, hampering research and raising the question of “genomic liberty.”

    The legal standing of genomic patents could change next month when the Supreme Court reviews patent rights for two key breast and ovarian cancer genes, BRCA1 and BRCA2, which include segments of genetic code as small as 15 nucleotides, known as 15mers.

    “This is, so to speak, patently ridiculous,” said report co-author Dr. Christopher E. Mason of Weill Cornell Medical College. “If patent claims that use these small DNA sequences are upheld, it could potentially create a situation where a piece of every gene in the human genome is patented by a phalanx of competing patents.”

    In their report, Mason and Dr. Jeffrey Rosenfeld, an assistant professor of medicine at the University of Medicine & Dentistry of New Jersey, looked at patents for two different categories of DNA fragments:

    • long and
    • short.

    They revealed 41 percent of the human genome is covered by “long” DNA patents that can include whole genes. Because many genes share similar sequences within their code that are patented, the combination of all these “short” DNA patents covers 100 percent of the genome.

    “This demonstrates that short patent sequences are extremely non-specific and that a 15mer claim from one gene will always cross-match and patent a portion of another gene as well,” Mason said. “This means it is actually impossible to have a 15mer patent for just one gene.”

    To reach their conclusions, the researchers first looked at small sequences within BRCA1 and noticed one of the company’s BRCA1 patents also covered almost 690 other human genes. Some of these genes are unrelated to breast cancer – instead being associated with brain development and heart functioning.

    Next, researchers determined how many known genes are covered by 15mers in current patent claims. They found 58 patents covered at least ten percent of all bases of all human genes. The broadest patent claim matched 91.5 percent of human genes. When the team took patented 15mers and matched them to known genes, they found 100 percent of known genes are patented.

    Finally, the team also looked at “long” DNA sequences from existing gene patents, ranging from a few dozen to thousands of base pairs. They found these long sequences added up to 41 percent of known human genes.

    “There is a real controversy regarding gene ownership due to the overlap of many competing patent claims. It is unclear who really owns the rights to any gene,” Rosenfeld said. “While the Supreme Court is hearing one case concerning just the BRCA1 patent, there are also many other patents whose claims would cover those same genes.

    “Do we need to go through every gene to look at who made the first claim to that gene, even if only one small part? If we resort to this rule, then the first patents to be granted for any DNA will have a vast claim over portions of the human genome,” he added.

    Another legal question surrounds patented DNA sequences that cross species boundaries. The researchers found one company has the rights to 84 percent of all human genes for a patent they received for cow breeding.

    Source: Brett Smith for redOrbit.com – Your Universe Online

    Topics: Health Medical PharmaGeneticsGene patentBiologyGeneLiving modified organismAssociation for Molecular Pathology v. U.S. Patent and Trademark OfficeBRCA1DNASupreme CourtHuman genome

    SOURCE:

    Human Genome: Name Your Price

    Posted March 27, 2013 – 12:51 by a staff writer

    Weill Cornell Medical College researchers have issued a warning that, according to the patent system, the vast majority of humans on the planet don’t ‘own’ their own genes, and in fact their biological make-up is being exploited for profit. Even seemingly innocent research into cow breeding can cover human genetic make-up.

    As spotted by a Slashdot user, two researchers combing through patents on human DNA discovered that over 40,000 patents on DNA molecules have effectively declared the human genome for profit. A report in medical journal Genome Medicine said that humans may be losing their grip on “individual genomic liberty”.

    Looking at two kinds of patented DNA sequences, or long and short fragments, 41 percent of the human genome is covered by DNA patents that can cover entire genes. According to the research, if all of the short sequence patents were allowed in aggregate they could cover 100 percent of the human genome.

    Lead author Dr Christopher E Mason and co-author Dr Jeffrey Rosenfeld warned that short sequences from patents cover “virtually the entire genome, even outside of genes”. A Weill Cornell assistant professor asked: “How is it possible that my doctor cannot look at my DNA without being concerned about patent infringement?”

    There will be a Supreme Court hearing about genomic patent rights next month that will debate the morality of a molecular diagnostic company claiming patents on key cancer genes, as well as on any small sequence of code within the BRCA1 gene. Cornell explained that at present, genes are able to be patented by researchers working in companies and institutions who discover genes that have potentially useful applications, like in testing for cancer risks. Because the patents can be held by companies or organisations, it is possible for the patent owner to charge doctors thousands of dollars for each diagnostic test.

    The authors pointed out that in their studies, while engaged in research, it is common to come across a gene that’s patented “almost every day”. Their paper promises to examine how genes may have been impacted by held patents, and the extent of intellectual property on the genome. Gene patents can also relate between different species – for example, a company may have a patent for breeding cows that also covers a large percentage of human genes. They cited one company that owns 84 percent of all human genes because of a patent for cow breeding.

    “There is a real controversy regarding gene ownership due to the overlap of many competing patent claims. It is unclear who really owns the rights to any gene,” Dr Rosenfeld said. “Do we need to go through every gene to look at who made the first claim to that gene, even if only one small part? If we resort to this rule, then the first patents to be granted for any DNA will have a vast claim over portions of the human genome.”

    Lead author Dr Mason insisted he is pro-patent, but believes people “should not be able to patent a product of nature”.

    “I believe that individals have an innate right to their own genome,” he said.

    http://www.tgdaily.com/hardware-brief/70513-human-genome-name-your-price#BUKfEtjWKb3gq7X3.99 

    Other related articles on Genomics and Ethics on this Open Access Online Scientific Journal include the following:

    Aviva Lev-Ari, PhD, RN

    20.2 Understanding the Role of Personalized Medicine

    Larry H Bernstein, MD, FACP

    20.3 Attitudes of Patients about Personalized Medicine

    Larry H Bernstein, MD, FACP

    20.4  Genome Sequencing of the Healthy

    Larry H. Bernstein, MD, FACP and Aviva Lev-Ari, PhD, RN

    20.5   Genomics in Medicine – Tomorrow’s Promise

    Larry H. Bernstein, MD, FACP

    20.6  The Promise of Personalized Medicine

    Larry H. Bernstein, MD, FACP

    Read Full Post »


    Genomics of Bacterial and Archaeal Viruses

     

    Reporter: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/?p=10187/Genomics of Bacterial and Archaeal Viruses/

    Genomics of Bacterial and Archaeal Viruses: Dynamics within the Prokaryotic Virosphere
    M Krupovic, D Prangishvili, RW Hendrix, and DH Bamford
    Over the past few years, the viruses of prokaryotes have been transformed in the view of microbiologists from simply being convenient experimental model systems into being a major component of the biosphere. They are
    • the global champions of diversity,
    • they constitute a majority of organisms on the planet,
    • they have large roles in the planet’s ecosystems,
    • they exert a significant—some would say dominant—force on
    • the evolution of their bacterial and archaeal hosts, and
    • they have been doing this for billions of years,
    • possibly for as long as there have been cells.
    This transformation in status  or, rather, our expanded appreciation of the importance of these viruses in the biosphere is due to a few significant developments in both understanding and technology.
    (i) It has become clear that the population sizes of these viruses are astoundingly large. This realization grew out of electron microscopic enumerations of tailed phage virions in costal seawater, and numerous measurements in other environments have been made since then. A current estimate based on these measurements is that
    • there are  1031 individual tailed phage virions in the global biosphere—
    • enough to reach for 200 million light years if laid end to end—and measurements of population turnover suggest that
    • it takes roughly 1024 productive infections per second to maintain the global population.
    (ii) Advances in DNA sequencing technology have led to dramatic qualitative improvements in how we understand the
    • genetic structure of viral populations,
    • the mechanisms of viral evolution, and
    • the diversity of viral sequences.
    The majority of newly determined gene and protein sequences of these viruses has no relatives detectable in the public sequence databases, and
    • analysis of metagenomic data provides strong evidence that
    • there is more genetic diversity in the genes of the viruses of prokaryotes
      • than in any other compartment of the biosphere.
    (iii) Facilitated by these conceptual and technical advances, studies of bacterial and archaeal viruses as important components of global biology have flourished. These viruses are revealed as important players in
    • carbon and energy cycling in the oceans and other natural environments and
    • as major agents in the ecology and evolution of their cellular hosts.
    (iv) The isolation and characterization of new viruses have accelerated. This has been especially important for the archaeal viruses, where the discovery of new viruses and of new virus types had lagged behind bacteriophage discovery. For the bacteriophages, the isolation of newly discovered viruses has helped improve the still extremely sparse coverage of sequence diversity and the narrow phylogenetic range of hosts represented by current data.
    (v) High-resolution structures determined
    • for capsid proteins and other virion proteins,
    • together with information about virion assembly mechanisms,
    • have allowed surprising inferences about ancestral connections among genes whose DNA sequences and encoded protein sequences
      • have diverged to the point that they are no longer detectably related.
    English: Schematic diagram of the hexon of a v...

    English: Schematic diagram of the hexon of a virus capsid (Photo credit: Wikipedia)

    English: Adsorption of virions to cells. Portu...

    English: Adsorption of virions to cells. Português do Brasil: Adsorção de vírus a células. (Photo credit: Wikipedia)

    Polio virus (picornavirus)

    Polio virus (picornavirus) (Photo credit: Sanofi Pasteur)

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    Genomics in Medicine – Tomorrow’s Promise

    Reporter: Larry H Bernstein, MD, FCAP

    Genomics in Medicine: Today’s Issues, Tomorrow’s Promise

    KM Beima-Sofie, EH Dorfman, JM Kocarnik, MY Laurino
    Feb 13, 2013 Medscape Genomic Medicine

    What do you think about these issues before reading this piece?

    The Broader Implications of Genetic Sciences
    The 62nd annual meeting of the American Society of Human Genetics (ASHG), which was held in San Francisco, California, in November 2012, featured a diverse array of research in basic, clinical, and population science contributed by human geneticists across the globe.
    Genetic Sequencing Moves Beyond the Laboratory
    Several presentations at the meeting focused on the lessons learned from the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project. The goal of the project was to
    • develop and validate a cost-effective and high-throughput sequencing technology
    • capable of analyzing the DNA sequence in the exome, which
    • consists of all protein-coding regions in the human genome.
    At previous ASHG meetings, presentations and discussions largely focused on
    • the development of sequencing technology and on applications of this technology for research.
    Now that sequencing is an increasing reality, this year’s conference featured presentations on
    • what to do with the resulting information, in both research and clinical settings.
    Issues discussed include the challenges of
    • interpreting sequence data,
    • determining which results should be returned to various parties, and
    • the potential impacts of different testing techniques.
    Results from the NHLBI Exome Sequencing Project and other projects are fueling the discussion on
    legal issues surrounding gene patenting, a hotly debated topic that is currently under consideration by the US Supreme Court. During a plenary session on gene discovery and patent law,
    Of particular focus was the lawsuit brought by the American Civil Liberties Union against Myriad Genetics,
    • contesting the company’s patent of the BRCA1 and BRCA2 genes for hereditary breast and ovarian cancer.
    At present, Myriad has exclusive rights to offer clinical genetic testing for these genes; one of the main arguments of the lawsuit is
    • that gene patents hinder the pursuit of confirmatory tests and limit the testing options available to women.
    DNAPrint Genomics

    DNAPrint Genomics (Photo credit: Wikipedia)

    English: Exome sequencing workflow: Part 2. Ta...

    English: Exome sequencing workflow: Part 2. Target exons are enriched, eluted and then amplified by ligation-mediated PCR. Amplified target DNA is then ready for high-throughput sequencing. (Photo credit: Wikipedia)

    Cost per Megabase of DNA Sequence (Why biologi...

    Cost per Megabase of DNA Sequence (Why biologists panic about compute) (Photo credit: dullhunk)

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