Posts Tagged ‘DNA methylation’

Reporter and Curator: Dr. Sudipta Saha, Ph.D.


The trillions of microbes in the human gut are known to aid the body in synthesizing key vitamins and other nutrients. But this new study suggests that things can sometimes be more adversarial.


Choline is a key nutrient in a range of metabolic processes, as well as the production of cell membranes. Researchers identified a strain of choline-metabolizing E. coli that, when transplanted into the guts of germ-free mice, consumed enough of the nutrient to create a choline deficiency in them, even when the animals consumed a choline-rich diet.


This new study indicate that choline-utilizing bacteria compete with the host for this nutrient, significantly impacting plasma and hepatic levels of methyl-donor metabolites and recapitulating biochemical signatures of choline deficiency. Mice harboring high levels of choline-consuming bacteria showed increased susceptibility to metabolic disease in the context of a high-fat diet.


DNA methylation is essential for normal development and has been linked to everything from aging to carcinogenesis. This study showed changes in DNA methylation across multiple tissues, not just in adult mice with a choline-consuming gut microbiota, but also in the pups of those animals while they developed in utero.


Bacterially induced reduction of methyl-donor availability influenced global DNA methylation patterns in both adult mice and their offspring and engendered behavioral alterations. This study reveal an underappreciated effect of bacterial choline metabolism on host metabolism, epigenetics, and behavior.


The choline-deficient mice with choline-consuming gut microbes also showed much higher rates of infanticide, and exhibited signs of anxiety, with some mice over-grooming themselves and their cage-mates, sometimes to the point of baldness.


Tests have also shown as many as 65 percent of healthy individuals carry genes that encode for the enzyme that metabolizes choline in their gut microbiomes. This work suggests that interpersonal differences in microbial metabolism should be considered when determining optimal nutrient intake requirements.





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Genomics and epigenetics link to DNA structure

Larry H. Bernstein, MD, FCAP, Curator



Sequence and Epigenetic Factors Determine Overall DNA Structure

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

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.

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  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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A Reconstructed View of Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP


There has always been Personalized Medicine if you consider the time a physician spends with a patient, which has dwindled. But the current recognition of personalized medicine refers to breakthrough advances in technological innovation in diagnostics and treatment that differentiates subclasses within diagnoses that are amenable to relapse eluding therapies.  There are just a few highlights to consider:

  1. We live in a world with other living beings that are adapting to a changing environmental stresses.
  2. Nutritional resources that have been available and made plentiful over generations are not abundant in some climates.
  3. Despite the huge impact that genomics has had on biological progress over the last century, there is a huge contribution not to be overlooked in epigenetics, metabolomics, and pathways analysis.

A Reconstructed View of Personalized Medicine

There has been much interest in ‘junk DNA’, non-coding areas of our DNA are far from being without function. DNA has two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), and the pyrimidines (cytosine [C], thymine [T], and  no uracil [U]),  while RNA contains only A, G, C, and U (no T).  The Watson-Crick proposal set the path of molecular biology for decades into the 21st century, culminating in the Human Genome Project.

There is no uncertainty about the importance of “Junk DNA”.  It is both an evolutionary remnant, and it has a role in cell regulation.  Further, the role of histones in their relationship the oligonucleotide sequences is not understood.  We now have a large output of research on noncoding RNA, including siRNA, miRNA, and others with roles other than transcription. This requires major revision of our model of cell regulatory processes.  The classic model is solely transcriptional.

  • DNA-> RNA-> Amino Acid in a protein.

Redrawn we have

  • DNA-> RNA-> DNA and
  • DNA->RNA-> protein-> DNA.

Neverthess, there were unrelated discoveries that took on huge importance.  For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors.  Of huge importance was the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions, which involves the effect of one ligand on the binding or catalysis of another,  demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby the former could inhibit the enzyme without competing with the latter. The current view based on a variety of measurements (e.g., NMR, FRET, and single molecule studies) is a ‘‘dynamic’’ proposal by Cooper and Dryden (1984) that the distribution around the average structure changes in allostery affects the subsequent (binding) affinity at a distant site.

What else do we have to consider?  The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression.  Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.

I have provided mechanisms explanatory for regulation of the cell that go beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, such as, the cell death pathways, expressed in apoptosis and repair.  Nevertheless, there is still a missing part of this discussion that considers the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment.

There is heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics.  There is also heterogeneity in the immediate interstices between cancer cells.  Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. In the case of breast cancer, there is interaction with estrogen , and we refer to an androgen-unresponsive prostate cancer.

Finally,  the interaction between enzyme and substrates may be conditionally unidirectional in defining the activity within the cell.  The activity of the cell is dynamically interacting and at high rates of activity.  In a study of the pyruvate kinase (PK) reaction the catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Experiments found that in there were differences in the active form of pyruvate kinase that were clearly related to the environmental condition of the assay – glycolitic or glyconeogenic. The conformational changes indicated by differential regulatory response were used to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described concluding that induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site. Another example is the inhibition of H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate. An investigation of the inhibition revealed that a covalent bond was formed between the nicotinamide ring of the NAD+ and the enol form of pyruvate.  The isoenzymes of isocitrate dehydrogenase, IDH1 and IDH2 mutations occur in gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. In this case, there is steric hindrance by Asp279 where the isocitrate substrate normally forms hydrogen bonds with Ser94.

Personalized medicine has been largely viewed from a lens of genomics.  But genomics is only the reading frame.  The living activities of cell processes are dynamic and occur at rapid rates.  We have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.


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Writer and curator: Larry H. Bernstein, MD, FCAP and
Curator: Aviva Lev-Ari, PhD, RN

There is an explosion of work-in-progress in applications to regenerative medicine using inducible pluripotent stem cells in both endothelial and cardiomyocyte postischemic repair, and also in post bone marrow radiation restoration, with benefits and hazards.  The following article is quite novel in that it deals with stem cell regulation by DNA methylation.  Therefore, it deals with the essentiality of methylation of DNA in epigenetic regulation.

This is the fourth discussion of a several part series leading from the genome, to protein synthesis (1), posttranslational modification of proteins (2), examples of protein effects on metabolism and signaling pathways (3), and leading to disruption of signaling pathways in disease (4), and effects leading to mutagenesis.

1.  A Primer on DNAand DNA Replication

2.  Overview of translational medicine

3.  Genes, proteomes, and their interaction

4. Regulation of somatic stem cell Function

5.  Proteomics – The Pathway to Understanding and Decision-making in Medicine

6.  Genomics, Proteomics and standards

7.  Long Non-coding RNAs Can Encode Proteins After All

8.  Proteins and cellular adaptation to stress

9.  Loss of normal growth regulation


Posttranslational modification is a step in protein biosynthesis. Proteins are created by ribosomes translating mRNA into polypeptide chains. These polypeptide chains undergo
PTM before becoming the mature protein product.

Regulation of somatic stem cell Function by DNA Methylation and Genomic Imprinting

Mo Li1, Na Young Kim1, Shigeo Masuda1 and Juan Carlos izpisua Belmonte1,2 1Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA. 2Center of Regenerative Medicine in Barcelona, Dr Aiguader, 88, 08003 Barcelona, Spain. Corresponding author email:

Cell & Tissue Transplantation & Therapy 2013:5 19–23
This article is available from


Epigenetic regulation is essential for self-renewal and differentiation of somatic stem cells, including

  • hematopoietic stem cells (HSCs) and
  • neural stem cells (NSCs).

The role of DNA methylation, a key epigenetic pathway,

  • in regulating somatic stem cell function
    • under physiological conditions and during aging

has been intensively investigated.

Accumulating evidence highlights the dynamic nature of

  • the DNAmethylome
    • during lineage commitment of somatic stem cells and
  • the pivotal role of DNAmethyltransferases in
    • stem cell self-renewal and differentiation.

Recent studies on genomic imprinting have shed light on

  • the imprinted gene network (IGN) in somatic stem cells,
  1. where a subset of imprinted genes remain expressed and
  2. are important for maintaining self-renewal of these cells.

Together with emerging technologies, elucidation of the epigenetic mechanisms regulating somatic stem cells with normal or pathological functions may contribute to the development of regenerative medicine.

Keywords: somatic stem cells, epigenetics, DNA methylation, genomic imprinting, hematopoietic stem cells, neural stem cells


In adult animals, somatic stem cells (also known as adult stem cells) are responsible for maintaining tissue homeostasis and participate in tissue regeneration under injury conditions. Self-renewal and differentiation are two important aspects of somatic stem cell function. Epigenetic mechanisms underlying these processes have been intensively investigated. With the increasing ability

  • to identify and manipulate somatic stem cell populations from diverse tissues,
  • it is possible to dissect the epigenetic pathways that are
  1. either unique for a specific tissue or
  2. universally important in regulating stemness and differentiation.

Epigenetic control of somatic stem cell function exists at various levels, including

  • DNA methylation,
  • histone modification, and
  • higher-order chromatin structure dynamics.

Here, we focus on recent progress in our understanding of how

  • DNA methylation regulates somatic stem cell function.

DNA Methylation and stem cell Function

The role of DNA methylation in somatic stem cell compartments has gained increasing attention. Recent  evidence has shown that

  • DNA methylation is dynamically regulated during somatic stem cell differentiation and aging.1

A study of methylomes of human hematopoietic stem cells (HSCs) and two mature hematopoietic lineages,

  • including B cells and neutrophils, showed that
    • hypomethylated regions of lineage-specific genes often become methylated in opposing lineages, and that
    • progenitors display an intermediate methylation pattern

that is poised for lineage-specific resolution.2

Another study compared genome-wide promoter DNA methylation in human cord blood hematopoietic progenitor cells (HPCs) with

  • that in mobilized peripheral blood HPCs from aged individuals.

It was found that aged HPCs lose DNA methylation in a subset of genes that are hypomethylated in differentiated myeloid cells and

  • gain de novo DNA methylation at polycomb repressive complex 2 (PRC2) target sites.3

It was hypothesized that such epigenetic changes contribute to age-related loss of HSC function, such as a bias toward myeloid lineages. Recently, Beerman et al. studied the global DNA methylation landscape of HSCs in the context of

  • age-associated decline of HSC function.4

Over- all, the DNA methylation landscape remains stable during HSC ontogeny. However, HSCs isolated from old mice display higher global DNA methylation. Interestingly, they observed

  • localized DNA methylation changes in genomic regions associated with hematopoietic lineage differentiation.

These methylation changes preferentially map to genes

  • that are expressed in downstream progenitor and effector cells.

For example, genes that are important for the lymphoid and erythroid lineages

  • become methylated in “old” HSCs,

which is consistent with

  • the decline of lymphopoiesis and erythropoiesis during aging.

Additionally, inducing HSC proliferation by 5-fluorouracil treatment or

  • by limiting the number of transplantedHSCs
    • recapitulates the functional decline and DNA methylation changes during physiological aging.

A closer examination of the overlapping genes with significant DNA methylation changes during aging or enforced proliferation showed

  • an enrichment of DNA hypermethylation at PRC2 target loci,

echoing the observation by Bocker et al. in human HSCs.

Interestingly, a recent report showed that epigenetic alterations such as DNA hypermethylation that are accrued during aging,

  • can be fully reset by somatic reprogramming,

raising an interesting possibility that these aging-related epigenetic defects may be reserved by small molecules.5

Methylation of cytosines at CpG dinucleotides is catalyzed by three key enzymes.

DNA (cytosine-5)- methyltransferase 1 (DNMT1) is responsible for maintaining DNA methylation patterns during DNA replication

  • by methylating the newly synthesized hemi-methylated DNA.

The other two DNA methyltransferases, DNMT3a and DNMT3b,

  • are not DNA replication-dependent and can methylate fully unmethylated DNA de novo.

They are responsible for establishing new DNA methylation patterns during development.

DNMT3a, a gene required for neurogenesis,

  • is expressed in postnatal neural stem cells (NSCs).

In NSCs, DNMT3a methylates non-proximal promoter regions, such as gene bodies and intergenic regions. Surprisingly, rather than silencing gene expression,

DNMT3a-mediated DNA methylation in gene bodies antagonizes Polycomb-dependent repression and

  • facilitates the expression of neurogenic genes.6

The role of DNMT3a in HSCs has also been investigated. Both Dnmt3a and Dnmt3b are expressed in HSCs. An earlier study did not identify any defects in HSC function when Dnmt3a or Dnmt3b was removed.  However,

  • HSCs lackingboth of these de novomethyltransferases
    • fail to self-renew, yet retain the capacity to differentiate.7

A more recent study re-examined

  • the consequences of Dnmt3a loss in HSCs and
  • uncovered a progressive defect in differentiation that is only manifested during serial transplantation.8

At the molecular level, while Dnmt3a loss results in the expected hypomethylation at some loci,

  • it counterintuitively causes hypermethylation in even more regions.8

This seemingly paradoxical result echoes the  unconventional role of Dnmt3a in transcriptional  activation in NSCs (as discussed above). Both cases suggest a more complex regulatory function of DNMT3a that is

  • beyond simply methylating DNA.

In contrast, the loss of Dnmt1 produces more dramatic and immediate phenotypes in HSCs, manifested

  • in premature HSC exhaustion and
  • block of lymphoid differentiation,

highlighting the distinct requirements for different DNA methyltransferases in HSCs.9,10

Genomic Imprinting and stemness

DNA methylation also underlies genomic imprinting, which is an

  • evolutionarily conserved epigenetic mechanism of ensuring appropriate gene dosage during development.

One allele of the imprinted genes is

  • epigenetically marked by DNA methylation to be silenced according to the parental origin.

The pattern of imprinting

  • is established in germ cells and maintained in somatic cells.

Imprinted genes are thought to play critical roles in organismal growth and are relatively downregulated after birth.11 Recently, a series of reports demonstrated that

  • a subset of imprinted genes belonging to the purported imprinted gene network (IGN)12
  • remain expressed in somatic stem cells and
  • are important for maintaining self-renewal of these cells.

Through gene expression profiling, one group identified that several members of the IGN are expressed in

  1. murine muscle,
  2. epidermal, and
  3. long-term hematopoietic stem cells
  4. as well as in human epidermal and hematopoietic stem cells.13

In particular, the paternally expressed gene 3 (Peg3) gene was shown by another group

  • to mark cycling and quiescent stem cells in a wide variety of mouse tissues.14

The role of imprinted genes in regulating somatic stem cell function has been examined in two types of tissues.

In bronchioalveolar stem cells (BASCs), a lung epithelial stem cell population,

  • expression of IGN members is required for their self-renewal.

Bmi1, a polycomb repressive  complex 1 (PRC1) subunit,

  • is essential for controlling the expression of imprinted genes in BASCs without affecting their imprinting status.15

In Bmi1 mutant BASCs,  many members of the IGN become derepressed,

  • including p57, H19, Dlk1, Peg3, Ndn, Mest, Gtl2, Grb10, Plagl1, and Igf2.

Knockdown of p57, which is the most differentially expressed imprinted gene between normal and mutant BASCs,

  • partially rescues the self-renewal defect of lung stem cells.

Interestingly, insufficient levels of p57 also inhibit self-renewal of lung stem cells. Because p57 expression

  • remains monoallelic in Bmi1 knockdown cells,
  • Bmi1 is thought to maintain an appropriate level of expression from the expressed allele of p57.15

Another IGN member- delta-like homologue 1 (Dlk1) has been shown to be important for postnatal neurogenesis. Interestingly, in this context,

  • Dlk1 loses its imprinting in postnatal neural stem cells and niche astrocytes.16

These studies suggest that modulating IGN may represent another

  • epigenetic mechanism for balancing self-renewal and differentiation in somatic stem cells.

Thus, somatic stem cells either co-opt or remodel these developmental pathways involving the IGN

  • to fulfill the needs of tissue homeostasis during the adult stage.

In summary, several factors participate in regulating the epigenome of somatic stem cells.

Perturbations in the epigenome of somatic stem cells,

  • either during organismal aging or under pathological conditions,

will tip the balance between self-renewal and differentiation of somatic stem cells (Fig. 1). A detailed understanding of the mechanisms underlying these changes will likely result in novel therapeutic approaches targeting somatic stem cells.

Figure 1. The epigenome of somatic stem cells is regulated by diverse factors.

Future perspectives The epigenetic mechanisms governing self-renewal and differentiation of somatic stem cells are likely to be complex because of the diverse needs of different tissues. It would be interesting to determine whether a common mechanism, such as the IGN, exists across different somatic stem cells. Additionally, study- ing epigenetic pathways that are specific to one type of somatic stem cell requires the isolation of these cells and their differentiated progeny, which is more practical in model organisms than in humans. Along these lines, developing robust in vitro culture methods for human somatic stem cells and protocols for differentiating these cells into specific lineages are critical for uncovering epigenetic pathways that are unique to human somatic stem cells. In recent years, the field has seen a great improvement in methods of directed differentiation of human embryonic stem cells and induced pluripotent stem cells (iPSCs). For example, it is relatively straightforward to produce high-purity cell populations that resemble neural stem cells or mesenchymal stem cells from iPSCs.17

These methodologies not only are useful for studying the normal function of somatic stem cells, but also provide an exciting opportunity for understanding the role of somatic stem cells in disease pathology and a platform to screen for drugs. A recent study under- scored the usefulness of this approach. Liu et al. studied neural stem cells derived from Parkinson’s disease human iPSCs and uncovered previously unknown defects in nuclear morphology and epigenetic regulation in these derived NSCs.18 The cellular defects only menifest in “aged” neural stem cells, which is consistent with the fact that Parkinson’s disease pri- marily manifests in old age. More  importantly, this study identified neural stem cell as a potential target of therapeutic intervention for Parkinson’s disease.

Targeted modification of the human genome is  another technological advancement that is on the horizon to greatly facilitate the dissection of epige- netic pathways in somatic stem cells. Although gene targeting in somatic stem cells has been historically challenging, there have been encouraging successful reports following development of new genome-e diting technologies, such as Helper-dependent adenovi- ral vectors, TALENs, and CAS9/CRISPR. With the development of these new technologies, it seems that the stage has been set for a new wave of discoveries in epigenetic mechanisms of somatic stem cells.


1. Li M, Liu GH, Izpisua Belmonte JC. Navigating the epigenetic landscape of pluripotent stem cells. Nat Rev Mol Cell Biol. 2012;13(8):524–535.

2. Hodges E, Molaro A, Dos Santos CO, et al. Directional DNA methylation changes and complex intermediate states accompany lineage specificity in the adult hematopoietic compartment. Mol Cell. 2011;44(1):17–28.

3. Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F. Genome- wide promoter DNA methylation dynamics of human hematopoietic progen- itor cells during differentiation and aging. Blood. 2011;117(19):e182–e189.

4. Beerman I, Bock C, Garrison BS, et al. Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell. 2013;12(4):413–425.

5. Wahlestedt M, Norddahl GL, Sten G, et al. An epigenetic component of hematopoietic stem cell aging amenable to reprogramming into a young state. Blood. 2013;121(21):4257–4264.

6. Wu H, Coskun V, Tao J, et al. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Science. 2010; 329(5990):444–448.

7. Tadokoro Y, Ema H, Okano M, Li E, Nakauchi H. De novo DNA meth- yltransferase is essential for self-renewal, but not for differentiation, in hematopoietic stem cells. J Exp Med. 2007;204(4):715–722.

8. Challen GA, Sun D, Jeong M, et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat Genet. 2011;44(1):23–31.

9. Broske AM, Vockentanz L, Kharazi S, et al. DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction. Nat Genet. 2009;41(11):1207–1215.

10. Trowbridge JJ, Snow JW, Kim J, Orkin SH. DNA methyltransferase 1 is essential for and uniquely regulates hematopoietic stem and progenitor cells. Cell Stem Cell. 2009;5(4):442–449.

11. Wood AJ, Oakey RJ. Genomic imprinting in mammals: emerging themes and established theories. PLoS Genet. 2006;2(11):e147.

12. Lui JC, Finkielstain GP, Barnes KM, Baron J. An imprinted gene network that controls mammalian somatic growth is down-regulated during postna- tal growth deceleration in multiple organs. Am J Physiol Regul Integr Comp Physiol. 2008;295(1):R189–R196.

13. Berg JS, Lin KK, Sonnet C, et al. Imprinted genes that regulate early mam- malian growth are coexpressed in somatic stem cells. PLoS One. 2011; 6(10):e26410.

14. Besson V, Smeriglio P, Wegener A, et al. PW1 gene/paternally expressed gene 3 (PW1/Peg3) identifies multiple adult stem and progenitor cell popu- lations. Proc Natl Acad Sci U S A. 2011;108(28):11470–11475.

15. Zacharek SJ, Fillmore CM, Lau AN, et al. Lung stem cell self-renewal relies on BMI1-dependent control of expression at imprinted loci. Cell Stem Cell. 2011;9(3):272–281.

16. Ferron SR, Charalambous M, Radford E, et al. Postnatal loss of Dlk1 imprinting in stem cells and niche astrocytes regulates neurogenesis. Nature. 2011;475(7356):381–385.

17. Li W, Sun W, Zhang Y, et al. Rapid induction and long-term self-renewal of primitive neural precursors from human embryonic stem cells by small molecule inhibitors. Proc Natl Acad Sci U S A. 2011;108(20):8299–8304.

18. Liu GH, Qu J, Suzuki K, et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature. 2012;491(7425):603–607.


Additional References in Leaders in Pharmaceutical Intelligence

Proteomics and Biomarker Discovery

Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets

Immune activation, immunity, antibacterial activity

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Research on inflammasomes opens therapeutic ways for treatment of rheumatoid arthritis

Update on mitochondrial function, respiration, and associated disorders

MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

Mitochondrial Damage and Repair under Oxidative Stress

Bzzz! Are fruitflies like us?

Discovery of Imigliptin, a Novel Selective DPP-4 Inhibitor for the Treatment of Type 2 Diabetes

Molecular biology mystery unravelled

Gene Switch Takes Blood Cells to Leukemia and Back Again

Wound-healing role for microRNAs in colon offer new insight to inflammatory bowel diseases

Targeting a key driver of cancer

Tang Prize for 2014: Immunity and Cancer

Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Hemeostasis of Immune Responses for Good and Bad                             Demet Sag, PhD

3:45 – 4:15, 2014, Scott Lowe “Tumor suppressor and tumor maintenance genes”

12:00 – 12:30, 6/13/2014, John Maraganore “Progress in advancement of RNAi therapeutics”

9:30 – 10:00, 6/13/2014, David Bartel “MicroRNAs, poly(A) tails and post-transcriptional gene regulation.”

10:00 – 10:30, 6/13/2014, Joshua Mendell “Novel microRNA functions in mammalian physiology and cancer”

Aviva Lev-Ari, PhD, RN

Targeted genome editing by lentiviral protein transduction of zinc-finger and TAL-effector nucleases          Aviva Lev-Ari, PhD, RN

Illana Gozes discovered Novel Protein Fragments that have proven Protective Properties for Cognitive Functioning

Aviva Lev-Ari, PhD, RN





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Genome Jigsaws

Genome Jigsaws (Photo credit: dullhunk)

Sequencing became the household name.  In 2000s, it was thought to be the key of the Pandora’s box for cure.  Then, after completion of Human Genome Projects showed that there are less number of genes than expected.  This outcome induce to originate yet another set of sequencing programs and collaborations around the world, such as Human Protein Project, Human Microorganisms Projects, ENCODE, Transcriptome Sequencing and Consortiums etc.

It is in humankind to believe in magic and illusion.  The strength of biological diversity and complex mechanism of expression may chalanges the set up of a simple but informative specific essay.  Thus, there is a new developing field to mash rules of biology with mathematical formulas to develop the best bioinformatics or also called computational biology.  Predicting transcription start or termination sites, exon boundaries, possible binding sites of transcription regulators for chromatin modification activities, like histone acetylates and enhancer- and insulator-associated factors based on the human genome sequence.  Deep in mind, this assumption supports that the sequence contains signatures for chromatin modifications essential for gene regulation and development.

There are three primary colors, red, yellow and blue, however, an artist can create many shades. Recently, scientists combining and organizing more data to make sense of our blueprint of life to transfer info generation to generation with the hope to cure diseases of human kind.

Analyzing genome and transcriptome open the door.  These studies suggested that all eukaryotic cells has a rich portfolio of RNAs. Among these long non-coding RNAs has impact on protein coding gene expression, regulating multiple processes even including epigenetic gene expression.

Epigenetics, stemness and non-coding RNAs  play a great role to manipulate and correct the gene expression not only at a proper cell type but also location and time within genome without disturbing the host.

Main concern is differentiation of embryonic stem cells under these epigenetics and influencers.  The best known post-transcriptional modifications, which include methylation, acetylation, ubiquination, and SUMOylation of lysine residues, methylation of arginine residues, and phosphorylation of serines, occur on histone tails. “Epi” means “top” or
“above” so this mechanism give a new direction to the genetic pathways as long as the organism live sometime and may lead into evolutions.  It is critical to show the complexity of
mechanism and relativity of a gene role with a single example for each. 

For example,  DNA methylation occurs mostly on cytosine residues on the CpG islands usually located on promoter regions that are associated with tissue-specific gene expression.  However, there are many other forms of DNA methylations, such as  monoallelic methylation in gene imprinting and inactivation of the X chromosome,  in repetitive elements, like transposons.  There are two main mechanisms but this is not our main topic.  Yet, Myc and hypoxia-inducible factor-1α versus certain methyl-CpG-binding proteins, such as MBD1,MBD2, MBD4, MeCP2, and Kaiso works differently.

Stemness is an important factor for an intervention to correct a pathological condition. In terms of epigenetics, regulation and non-coding RNA Vascular endothelial growth factor A (VEGF-A) is an interesting example for differentiation of endothelial cells and morphogenesis of the vascular system during development with several reasons, epigenetics, gene interactions, time and space.  Everything has to be just right, because neither less nor too much can fulfill the destiny to become a complete adult cell or an organism.   For example, both having only one VEGF-A allele and having two-fold excess of VEGF-A results in death during early embryogenesis, since mice can’t develop proper vascular network.  However, explaining diverse mechanisms and functions of VEGF-A is require more information with specific details.  VEGF-A plays many roles in many pathological cases, such as cancer, inflammation, retinopathies, and arthritis because VEGF-A has also function in epigenetic reprogramming of the promoter regions of Rex1 and Oct4 genes, that are critical for a stem cell. Preferred mechanism is anti-angiogeneic state but tumor cells prefer hypermethylation to induce pro-angiogeneic state, thus VEGF-A stimulates PIGF in tumour cells among many other factors.

Now, let’s turn around to observe development of a cell with Polycomb repressive complexes (PRCs) because they are important chromatin regulators of embryonic stem (ES) cell function.  Originally, RYBP shown to function  as transcriptional repressor in reporter assays from both in tissue culture cells and in fruit fly (Drosophila melanogaster ) and as a direct interactor with Ring1A during embryogenesis through methylation. In addition, RYBP in epigenetic resetting during preimplantation development through repression of germ line genes and PcG targets before formation of pluripotent epiblast cells.  However, I do believe that the most important element is efficient repression of endogenous retroviruses (murine endogenous retrovirus called MuERV class),  preimplantation containing zygotic genome activation stage and germ line specific genes. The selective repressor activity of  RYBP  is in the ES cell state. When RYBP−/− ES cells were analyzed by measuring gene expression during differentiation as embryo bodies formed from mutant and wild-type cells, the result presented that  expression of pluripotency genes Oct4 and Nanog was usually downregulated. However, RYBP is able to bind genomic regions independently of H3K27me3 and there is no relation between altered RYBP binding in Dnmt1-mutant cells to DNA methylation status. In sum, RYBP has a large value in undifferentiated ES cells and may affect or even reset epigenetic landscape during early developmental stages. These are the gaps filled by long non coding RNAs.

We learn more compelling information by comparing and contrasting what is normal and what is abnormal. As a result, pathology is a key learning canvas for basic mechanisms in molecular genetics. Then peppered with functional genomics completes the story for an edible outcome.  We generally refer this as a Translational Research.  For example, recent foundlings suggest that H19 contributes to cancer, including hepatocellular carcinoma (HCC) after reviewing Oncomine resource.  According to these observations, in most HCC cases there is a lower expression of  H19 level is compared to the liver. Thus, in vitro and in vivo studies were undertaken with classical genetic analyzes based on loss- and gain-of-function on H19 to characterize two outcomes depend on H19, that are the effects on gene expression and on HCC metastasis. First, the expression of H19 showed gene expression variation since H19 expression was low in tumor cells than peripheral tumor cells.  Second, the metastasis of cancer based on alteration of miR-200 pathway contributing mesenchymal-to-epithelial transition by H19. Therefore, H19 and miR-200 are targets to be utilized during molecular diagnostics development and establishing targeted therapies in cancer.

Long story short, there is a circle of life where everything is connected even though they look different.  As a result, when we see a sunflower or a baby we remember to smile, because life is still an act to puzzle human.

References and Further Readings:


Non-coding RNAs as regulators of gene expression and epigenetics” Cardiovascular Res 1 June 2011: 430-440.

Epigenetic regulation of key vascular genes and growth factors” Cardiovasc Res 1 June 2011: 441-446.

Epigenetic Regulation by Long Noncoding RNAs” Science 14 December 2012: 1435-1439.

Epigenetic control of embryonic stem cell fate” JEM 25 October 2010: 2287-2295.

Transcribed dark matter: meaning or myth?” Hum Mol Genet 15 October 2010: R162-R168.

Epigenetic activation of the MiR-200 family contributes to H19-mediated metastasis suppression in hepatocellular carcinoma” Carcinogenesis 1 March 2013: 577-586.

Vernalization-Mediated Epigenetic Silencing by a Long Intronic Noncoding RNA” Science 7 January 2011: 76-79.

Predicting the probability of H3K4me3 occupation at a base pair from the genome sequence context” Bioinformatics 1 May 2013: 1199-1205.


Further Readings specific to Embryonic Stem Cell Differentiation and Development :

“BMP Induces Cochlin Expression to Facilitate Self-renewal and Suppress Neural Differentiation of Mouse Embryonic Stem Cells” J. Biol. Chem. 2013 288:8053-8060


“Regulation of DNA Methylation in Rheumatoid Arthritis Synoviocytes”  J. Immunol. 2013 190:1297-1303


“DNA methylome signature in rheumatoid arthritis” Ann Rheum Dis 2013 72:110-117


“The histone demethylase Kdm3a is essential to progression through differentiation” Nucleic Acids Res 2012 40:7219-7232


“Targeted silencing of the oncogenic transcription factor SOX2 in breast cancer” Nucleic Acids Res 2012 40:6725-6740


“Yin Yang 1 extends the Myc-related transcription factors network in embryonic stem cells” Nucleic Acids Res 2012 40:3403-3418


“RYBP Represses Endogenous Retroviruses and Preimplantation- and Germ Line-Specific Genes in Mouse Embryonic Stem Cells” Mol. Cell. Biol. 2012 32:1139-1149


“Polycomb Repressor Complex-2 Is a Novel Target for Mesothelioma Therapy” Clin. Cancer Res. 2012 18:77-90


“OCT4 establishes and maintains nucleosome-depleted regions that provide additional layers of epigenetic regulation of its target genes” Proc. Natl. Acad. Sci. USA 2011 108:14497-14502


“Genome-wide promoter DNA methylation dynamics of human hematopoietic progenitor cells during differentiation and aging” Blood 2011 117:e182-e189


“The CHD3 Chromatin Remodeler PICKLE and Polycomb Group Proteins Antagonistically Regulate Meristem Activity in the Arabidopsis” RootPlant Cell 2011 23:1047-1060


“Chromatin structure of pluripotent stem cells and induced pluripotent stem cells” Briefings in Functional Genomics 2011 10:37-49


Abbreviations used:

DNMT       DNA methyl transferase

ES             embryonic stem

JmjC         Jumonji C

lincRNA     long ncRNA

ncRNA       noncoding RNA

PcG          Polycomb group

PRC          Polycomb repressive complex

PRE          Polycomb repressive element

Previous Posts on Stem Cells:

…  Aviva Lev-Ari, PhD, RN New Life – The Healing Promise of Stem Cells View … p://       Diseases and conditions where stem cell treatment is promising or emerging. Source: Wikipedia Since the …

…  Aviva Lev-Ari, PhD, RN Stem cells create new heart cells in baby mice, but not in adults, study …  picture on the left shows green c-kit+ precursor stem cells within an infarct (lower right) in a

14 January 2013  by Dr. Sudipta Saha on Pharmaceutical Intelligence
…  and Curator: Dr. Sudipta Saha, Ph.D. Germline stem cells that produce oocytes in vitro and fertilization-competent eggs in …  from adult mouse ovaries. A fluorescence-activated cell sorting-based protocol has been standardized that can be used with adult …  compared to the ESC-derived or induced pluripotent stem cell-derived germline cells that are currently used as models for human …

…  PhD, RN The two leading therapy classes are: Cell-based Therapies for angiogenesis and myocardial …  Research Projects Stem Cell biology Embryonic stem cells in cardiovascular repairEarly differentiation of human endothelial …

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…  are not yet known. Some studies suggest a high rate of stem cell activity with differentiation of progenitors to cardiomyocytes. Other …

…  T-cells, said Dr. Margaret Goodell, director of the Stem Cells and Regenerative Medicine Center of Baylor College of Medicine. …  of pediatrics at BCM and a member of the Center for Cell and Gene Therapy at BCM, Texas Children¹s Hospital and The Methodist …  found that mice lacking the gene for this factor had a T-cell deficiency and in particular, too few of these early progenitor …

28 March 2013  by ritusaxena on Pharmaceutical Intelligence
…  and Curator: Ritu Saxena, Ph.D Although cancer stem cells constitute only a small percentage of the tumor burden, their …  after therapeutic target in cancer. The post on cancer stem cells published on the 22nd of March, 2013, describes the identity of CSCs, their functional characteristics, possible cell of origin and biomarkers. This post focuses on the therapeutic potential …

…  programs in the fields of personalized medicine, cell biology, cytogenetics, genotyping, and biobanking drive our …  by playing an important role in induced pluripotent stem (iPS) cell research. Induced pluripotent stem cells are powerful cells which can be made from skin or blood cells, and …

30 November 2012  by sjwilliamspa on Pharmaceutical Intelligence
…  seen in hematologic malignancies such as cutaneous T-cell lymphoma and peripheral T-cell lymphoma and little or no positive outcome …  resistance to chemotherapeutics, and similarity to cancer stem cells(6-10). Figure 1. HDACis led to the induction of EMT phemotype. (A …

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Personalized Medicine and Colon Cancer

Author: Tilda Barliya, PhD

According to Dr. Neil Risch a leading expert in statistical genetics and the director of the UCSF Institute for Human Genetics,  “Personalized medicine, in which a suite of molecules measured in a patient’s lab tests can inform decisions about preventing or treating diseases, is becoming a reality” (7).

Colorectal cancer (CRC) is the third most common cancer and the fourth-leading cause of cancer death worldwide despite advances in screening, diagnosis, and treatment. Staging is the only prognostic classification used in clinical practice to select patients for adjuvant chemotherapy. However, pathological staging fails to predict recurrence accurately in many patients undergoing curative surgery for localized CRC (1,2). Most of the patients who are not eligible for surgery need adjuvant chemotherapy in order to avoid relapse or to increase survival. Unfortunately, only a small portion of them shows an objective response to chemotherapy, becoming problematic to correctly predict patients’ clinical outcome (3).

CRC patients are normally being tested for several known biomarkers which falls into 4 main categories (5):

  1. Chromosomal Instability (CIN)
  2. Microsatellite Instability (MSI)
  3. CpG Island methylator phynotype (CIMP)
  4. Global DNA hypomethylation

In the past few years many studies have exploited microarray technology to investigate gene expression profiles (GEPs) in CRC, but no established signature has been found that is useful for clinical practice, especially for predicting prognosis.  Only a subset of CRC patients with MSI tumors have been shown to have better prognosis and probably respond differently to adjuvant chemotherapy compared to microsatellite stable (MSS) cancer patients (6).

Pritchard & Grady have summarized the selected biomarkers that have been evaluated in colon cancer patients (10).

Table 1

Selected Biomarkers That Have Been Evaluated in Colorectal Cancer

Biomarker Molecular Lesion Frequency
in CRC
Prediction Prognosis Diagnosis
KRAS Codon 12/13 activating
mutations; rarely codon
61, 117,146
40% Yes Possible
BRAF V600E activating
10% Probable Probable Lynch
PIK3CA Helical and kinase
domain mutations
20% Possible Possible
PTEN Loss of protein by IHC 30% Possible
Microsatellite Instability (MSI) Defined as >30%
unstable loci in the NCI
consensus panel or
>40% unstable loci in a
panel of mononucleotide
microsatellite repeats9
15% Probable Yes Lynch
Chromosome Instability (CIN) Aneuploidy 70% Probable Yes
18qLOH Deletion of the long arm
of chromosome 18
50% Probable Probable
CpG Island Methylator
Phenotype (CIMP)
Methylation of at least
three loci from a selected
panel of five markers
15% +/− +/−
Vimentin (VIM) Methylation 75% Early
TGFBR2 Inactivating Mutations 30%
TP53 Mutations Inactivating Mutations 50%
APC Mutations Inactivating Mutations 70% FAP
CTNNB1 (β-Catenin) Activating Mutations 2%
Mismatch Repair Genes Loss of protein by IHC;
methylation; inactivating
1–15% Lynch

CRC- colorectal cancer; IHC- immunohistochemistry; FAP- Familial Adenomatous Polyposis

Examples for the great need of personalized medicine tailored according to the patients’ genetics is clearly seen with two specific drugs for CRC:  Cetuximab and panitumumab are two antibodies that were developed to treat colon cancer. However, at first it seemed as if they were a failure because they did not work in many patients. Then, it was discovered that if a cancer cell has a specific genetic mutation, known as K-ras, these drugs do not work.  This is an excellent example of using individual tumor genetics to predict whether or not treatment will work (8).

According to Marisa L et al, however, the molecular classification of CC currently used, which is based on a few common DNA markers as mentioned above (MSI, CpG island methylator phenotype [CIMP], chromosomal instability [CIN], and BRAF and KRAS mutations), needs to be refined.

Genetic Expression Profiles (GEP)

CRC is composed of distinct molecular entities that may develop through multiple pathways on the basis of different molecular features, as a consequence, there may be several prognostic signatures for CRC, each corresponding to a different entity. GEP studies have recently identified at least three distinct molecular subtypes of CC (4). Dr. Marisa Laetitia and her colleagues from the Boige’s lab however, have conducted a very thorough study and identifies 6 distinct clusters for CC patients. Herein, we’ll describe the majority of this study and their results.

Study  Design:

Marisa L et al (1) performed a consensus unsupervised analysis (using an Affymertix chip) of the GEP on tumor tissue sample from 750 patients with stage I to IV CC. Patients were staged according to the American Joint Committee on Cancer tumor node metastasis (TNM) staging system. Of the 750 tumor samples of the CIT cohort, 566 fulfilled RNA quality requirements for GEP analysis. The 566 samples were split into a discovery set (n = 443) and a validation set (n = 123).

Several known mutations were used as internal controls, including:

  • The seven most frequent mutations in codons 12 and 13 of KRAS .
  • The BRAF c.1799T>A (p.V600E)
  • TP53mutations (exons 4–9)
  • MSI was analyzed using a panel of five different microsatellite loci from the Bethesda reference panel
  • CIMP status was determined using a panel of five markers (CACNA1G, IGF2, NEUROG1, RUNX3, and SOCS1)


The results revealed six clusters of samples based on the most variant probe sets. The consensus matrix showed that C2, C3, C4, and C6 appeared as well-individualized clusters, whereas there was more classification overlap between C1 and C5. In other words:

  • Tumors classified as C1, C5, and C6 were more frequently CIN+, CIMP−TP53 mutant, and distal (p<0.001), without any other molecular or clinicopathological features able to discriminate these three clusters clearly.
  • Tumors classified as C2, C4, and C3 were more frequently CIMP+ (59%, 34%, and 18%, respectively, versus <5% in other clusters) and proximal.
  • C2 was enriched for dMMR (68%) and BRAF- mutant tumors (40%).
  • C3 was enriched for KRAS- mutant tumors (87%).

Note: No association between clusters and TNM stage (histopathology) was found, except enrichment for metastatic (31%) tumors in C4.

Figure: These signaling pathways associated with the molecular subtype (by cluster)

Figure 2 Signaling pathways associated with each molecular subtype.

Marisa L et al. Signaling pathways associated with each molecular subtype

These clusters fall into several signaling pathways:

  • up-regulated immune system and cell growth pathways were found in C2, the subtype enriched for dMMR tumors
  • C4 and C6 both showed down-regulation of cell growth and death pathways and up-regulation of the epithelial–mesenchymal transition/motility pathways. displaying “stem cell phenotype–like” GEPs (91%)
  • Most signaling pathways were down-regulated in C1 and C3.
  • In C1, cell communication and immune pathways were down-regulated.
  • In C5, cell communication, Wnt, and metabolism pathways were up-regulated.

These results are further summarized in table 2:

Figure 3 Summary of the main characteristics of the six subtypes.

Marisa L et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes

The authors have identified six robust molecular subtypes of CC individualized by distinct clinicobiological characteristics (as summarized in table 2).

This classification successfully identified the dMMR tumor subtype, and also individualized five other distinct subtypes among pMMR tumors, including three CIN+ CIMP− subtypes representing slightly more than half of the tumors. As expected, mutation of BRAF was associated with the dMMR subtype, but was also frequent in the C4 CIMP+ poor prognosis subtype. TP53– andKRAS-mutant tumors were found in all the subtypes; nevertheless, the C3 subtype, highly enriched in KRAS-mutant CC, was individualized and validated, suggesting a specific role of this mutation in this particular subgroup of CC.

Current Treatments for colon cancer- Table 3 (11) .

Constant S et al. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies

Exploratory analysis of each subtype GEP with previously published supervised signatures and relevant deregulated signaling pathways improved the biological relevance of the classification.

The biological relevance of our subtypes was highlighted by significant differences in prognosis. In our unsupervised hierarchical clustering, patients whose tumors were classified as C4 or C6 had poorer RFS than the other patients.

Prognostic analyses based solely on common DNA alterations can distinguish between risk groups, but are still inadequate, as most CCs are pMMR CIMP− BRAFwt.

The markers BRAF-mutant, CIMP+, and dMMR may be useful for classifying a small proportion of cases, but are uninformative for a large number of patients.

Unfortunately, 5 of the 9 anti-CRC drugs approved by the FDA today are basic cytotoxic chemotherapeutics that attack cancer cells at a very fundamental level (i.e. the cell division machinery) without specific targets, resulting in poor effectiveness and strong side-effects (Table 3) (11).

An example for side effects induction mechanisms have also been reported in CRC for the BRAF(V600E) inhibitor Vemurafenib that triggers paradoxical EGFR activation (12).


The authors of this study “report a new classification of CC into six robust molecular subtypes that arise through distinct biological pathways and represent novel prognostic subgroups. Our study clearly demonstrates that these gene signatures reflect the molecular heterogeneity of CC. This classification therefore provides a basis for the rational design of robust prognostic signatures for stage II–III CC and for identifying specific, potentially targetable markers for the different subtypes”.

These results further underline the urgent need to expand the standard therapy options by turning to more focused therapeutic strategies: a targeted therapy-for specific subtype profile.. Accordingly, the expansion and the development of new path of therapy, like drugs specifically targeting the self-renewal of intestinal cancer stem cells – a tumor cell population from which CRC is supposed to relapse, remains relevant.

Therefore, the complexity of these results supports the arrival of a personalized medicine, where a careful profiling of tumors will be useful to stratify patient population in order to test drugs sensitivity and combination with the ultimate goal to make treatments safer and more effective.


1. Marisa L,  de Reyniès A, Alex Duval A,  Selves J, Pierre Gaub M, Vescovo L, Etienne-Grimaldi MC, Schiappa R, Guenot D, Ayadi M, Kirzin S, Chazal M, Fléjou JF…Boige V. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Med May 2013 10(5): e1001453. doi:10.1371.

2. Villamil BP, Lopez AR, Prieto SH, Campos GL, Calles A, Lopez- Asenjo JA, Sanz Ortega J, Perez CF, Sastre J, Alfonso R, Caldes T, Sanchez FM and Rubio ED. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC Cancer 2012, 12:260.

3. Diaz-Rubio E, Tabernero J, Gomez-Espana A, Massuti B, Sastre J, Chaves M, Abad A, Carrato A, Queralt B, Reina JJ, et al.: Phase III study of capecitabine plus oxaliplatin compared with continuous-infusion fluorouracil plus oxaliplatin as first-line therapy in metastatic colorectal cancer: final report of the Spanish Cooperative Group for the Treatment of Digestive Tumors Trial. J Clin Oncol 2007, 25(27):4224-4230.

4. Salazar R, Roepman P, Capella G, Moreno V, Simon I, et al. (2011) Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol 29: 17–24.

5.  By: Global Genome Knowledge. Colorectal Cancer- Personalized Medicine, Now a Clinical Reality.

6. Popat S, Hubner R and Houlston RS. Systematic review of microsatellite instability and colorectal cancer prognosis. J Clin Oncol. 2005 Jan 20;23(3):609-618.

7. By: Jeffrey Norris. Value of Genomics and Personalized Medicine Is Wrongly Downplayed.

8. By: James C Salwitz. The Future is now: Personalized Medicine.

9. Jeffrey A. Meyerhardt., and Robert J. Mayer. Systemic Therapy for Colorectal Cancer. N Engl J Med 2005;352:476-487.

10. Pritchard CC and Grady WM. Colorectal Cancer Molecular Biology Moves Into Clinical Practice. Gut. Jan 2011 60(1): 116-129.  Gut. 2011 January; 60(1): 116–129

11. Constant S, Huang S, Wiszniewski L andMas C. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies.  Pharmacology, Toxicology and Pharmaceutical Science » “Drug Discovery”, book edited by Hany A. El-Shemy, ISBN 978-953-51-0906-8.

12. Prahallad, C. Sun, S. Huang, F. Di Nicolantonio, R. Salazar, D. Zecchin, R. L. Beijersbergen, A. Bardelli, R. Bernards, 2012 Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature Jan 2012 483 (7387): 100-103.

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

*. By Tilda Barliya PhD. Colon Cancer.

**. By: Tilda Barliya PhD. CD47: Target Therapy for Cancer.

I. By: Aviva Lev-Ari, PhD, RNCancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed.

II. By: Aviva Lev-Ari, PhD, RN. Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection.

III.  By: Stephen J. Williams, Ph.DIssues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing.

IV. By: Ritu Saxena, Ph.DIn Focus: Targeting of Cancer Stem Cells.

V.  By: Ziv Raviv PhD. Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv.

VI. By: Ritu Saxena, PhD. In Focus: Identity of Cancer Stem Cells.

VII. By: Dror Nir, PhD. State of the art in oncologic imaging of Colorectal cancers.

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Above the Genome – Underlying Disease

June 26-27, 2013 | Hotel Kabuki | San Francisco, CA | Visit website



Michael Snyder


Adventures in Personal Medicine: Integrated Personal Omics Profiling for Following Healthy and Disease States

Michael Snyder, Ph.D., Professor and Chair, Genetics; Director, Stanford Center for Genomics and Personalized Medicine, Stanford University

Joesph Costello


Spontaneous and Therapy Induced Evolution of Tumor Genomes and Epigenomes

Joseph Costello, Ph.D., Professor in Residence, Department of Neurological Surgery; Director, Epigenetics Division, Cell Cycling and Signaling Program, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco


This is just a small sample of presentations that you’ll be able to hear at TCEC: The Clinical Epigenome Conference in San Francisco, CA next month.

Distinguishing between Driver and Passenger Epigenetic Modifications in Cancer

Daniel De Carvalho, Ph.D., Principal Investigator, Ontario Cancer Institute, University Health Network; Assistant Professor, Medical Biophysics, Faculty of Medicine, University of Toronto

Cancer cells typically exhibit aberrant DNA methylation patterns that can drive malignant transformation. Whether cancer cells are dependent on these abnormal epigenetic modifications remains elusive. We used experimental and bioinformatic approaches to unveil genomic regions that require DNA methylation for survival of cancer cells, suggesting these are key epigenetic events associated with tumorigenesis.

Family Proteins and 5-Hydroxymethylcytosine in Stem Cells, Development and Cancer

Yujiang Geno Shi, Ph.D., Associate Biochemist, Division of Endocrinology, Brigham and Women’s Hospital; Assistant Professor, Medicine, Harvard Medical School

Recent studies have shown that ten-eleven translocation (Tet) proteins can catalyze 5mC oxidation and generate 5mC derivatives, including 5-hydroxymethylcytosine (5hmC). Not only are Tet family proteins and 5hmC critical for the identity and normal function of embryonic stem cells and early embryonic process of development, but dysregulation of these newly identified epigenetic factors also plays a major role in cancer development. Here we report an essential role of Tet3 in animal development, and define 5hmC as a potential biomarker for tumor progression. These studies will significantly increase our current understanding of the biological functions of Tet proteins and 5hmC while providing mechanistic insight into the development of epigenetic therapeutics.

Defining the Epigenetic Landscape during Normal and Malignant Hematopoiesis

Lucy A. Godley, M.D., Ph.D., Associate Professor, Department of Medicine, Section of Hematology/Oncology, Cancer Research Center, The University of Chicago

Hematopoietic stem cell commitment and differentiation involves silencing of self-renewal genes and induction of a specific transcriptional program, which is controlled in part through dynamic changes in covalent cytosine modifications. We have studied how the abundance and distribution of these derivatized bases influences hematopoietic stem cell commitment during normal erythropoiesis as well as during leukemia development. The identification of recurrent mutations in several genes important in epigenetic pathways as well as mouse modeling suggest that the balance of covalent cytosine modifications is a key driver of normal blood cell development. I will also discuss how these findings impact our understanding of the activity of the ‘hypomethylating drugs’, now in common use for the treatment of myeloid malignancies.

DNA Methylation Alterations in Lung Adenocarcinoma

Ite A. Laird-Offringa, Ph.D., Associate Professor, Departments of Surgery, Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California

I will discuss our research on integrated genome-scale DNA methylation and mRNA expression data from microdissected lung adenocarcinoma and matched non-tumor lung. We have identified 164 hypermethylated genes showing concurrent downregulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis and detailed examination of individual genes suggests mechanistic contributions of several of these genes to lung adenocarcinoma development and/or progression. I will present information on a number of candidate epigenetic driver genes for lung adenocarcinoma.

Lessons from Surveying the DNA Methylation “Cityscape” of Lethal Metastatic Prostate Cancer

Srinivasan (Vasan) Yegnasubramanian, M.D., Ph.D., Assistant Professor, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine

Alterations in DNA methylation are a hallmark of human cancers, including prostate cancer. We carried out genome-scale analyses of DNA methylation alterations in multiple metastases from each of 13 men that died of metastatic prostate cancer and created DNA methylation cityscapes to visualize these complex data. These analyses revealed that each individual developed a unique DNA methylation signature that was largely maintained across all metastases within that individual. By analyzing their frequency, clonal maintenance, and correlation with expression, we nominated potential “driver” DNA methylation alterations that could be prioritized for development as epigenetic biomarkers and therapeutic targets.

DNA Methylation Detection using Nanopores

George Vasmatzis, Ph.D., Director, Biomarker Discovery Program, Center of Individualized Medicine; Consultant, Department of Molecular Medicine, Mayo Clinic and Foundation

I will discuss some of our latest work around integrating micro-fabrication and solid state technologies with biomarkers. In diagnostics, a biosensor that could look for subtle structural or sequence variations at the single molecule level would be extremely useful . Epigenetic modifications have been linked with cancer, and we have developed nanopore technology to detect the methylation profile of a single molecule. The challenges and the potential of such technology will be discussed.



Genetic & Epigenetic Interplay in Cancer 

Mechanisms in (De)Methylation Underlying 

Development of Disease 

The Predictive Power of Epigenetics: Diagnostic 

& Prognostic Utility 

The Clinical Genome Technology Showcase 

Trends in Analysis & Interpretation 



Wednesday, June 26      View Details     Registration Information

5:30 – 8:30 pm

(SC3) Clinical Combination Economic Conundrums


5:30 – 8:30 pm

(SC4) Advances in Methylation Analysis



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