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Posts Tagged ‘transcription factors’


Gene Editing by creation of a complement without transcription error

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Nanoparticle-Based Artificial Transcription Factor  

NanoScript: A Nanoparticle-Based Artificial Transcription Factor for Effective Gene Regulation

Abstract Image

Transcription factor (TF) proteins are master regulators of transcriptional activity and gene expression. TF-based gene regulation is a promising approach for many biological applications; however, several limitations hinder the full potential of TFs. Herein, we developed an artificial, nanoparticle-based transcription factor, termed NanoScript, which is designed to mimic the structure and function of TFs. NanoScript was constructed by tethering functional peptides and small molecules called synthetic transcription factors, which mimic the individual TF domains, onto gold nanoparticles. We demonstrate that NanoScript localizes within the nucleus and initiates transcription of a reporter plasmid by over 15-fold. Moreover, NanoScript can effectively transcribe targeted genes on endogenous DNA in a nonviral manner. Because NanoScript is a functional replica of TF proteins and a tunable gene-regulating platform, it has great potential for various stem cell applications.

NanoScript_emulates_TF_Structure_and_Function_large.jpg

http://www.energyigert.rutgers.edu/sites/default/files/faculty/kibumlee/NanoScript_emulates_TF_Structure_and_Function_large.jpg

HIGHLIGHTS

  • Transcription Factors (TF) are proteins that regulate transcription and gene expression
  • NanoScript is an versatile, nanoparticle-based platform that mimics TF structure and biological function
  • NanoScript is stable in physiological environments and localizes within the nucleus
  • NanoScript initiates targeted gene expression by over 15-fold to 30 fold, which would be critical for stem cell differentiation and cellular reprogramming
  • NanoScript transcribes endogenous genes on native DNA in a non-viral manner

Transcription factor (TF) proteins are master regulators of transcriptional activity and gene expression. TF-based gene regulation is an essential approach for many biological applications such as stem cell differentiation and cellular programming, however, several limitations hinder the full potential of TFs.

To address this challenge, researchers in Prof. KiBum Lee’s group (Sahishnu Patel and Perry Yin) developed an artificial, nanoparticle-based transcription factor, termed NanoScript, which is designed to mimic the structure and function of TFs. NanoScript was constructed by tethering functional peptides and small molecules called synthetic transcription factors, which mimic the individual TF domains, onto gold nanoparticles. They demonstrated that NanoScript localizes within the nucleus and initiates transcription of a targeted gene with high efficiency. Moreover, NanoScript can effectively transcribe targeted genes on endogenous DNA in a non-viral manner.

NanoScript is a functional replica of TF proteins and a tunable gene-regulating platform. NanoScript has two attractive features that make this the perfect platform for stem cell-based application. First, because gene regulation by NanoScript is non-viral, it serves as an attractive alternative to current differentiation methods that use viral vectors. Second, by simply rearranging the sequence of one molecule on NanoScript, NanoScript can target any differentiation-specific genes and induce differentiation, and thus has excellent prospect for applications in stem cell biology and cellular reprogramming.

Perry To-tien Yin
PhD Candidate, Rutgers University
Prospects for graphene–nanoparticle-based hybrid sensors

PT Yin, TH Kim, JW Choi, KB Lee
Physical Chemistry Chemical Physics 15 (31), 12785-12799
31 2013
Axonal Alignment and Enhanced Neuronal Differentiation of Neural Stem Cells on Graphene‐Nanoparticle Hybrid Structures

A Solanki, STD Chueng, PT Yin, R Kappera, M Chhowalla, KB Lee
Advanced Materials 25 (38), 5477-5482
22 2013
Label‐Free Polypeptide‐Based Enzyme Detection Using a Graphene‐Nanoparticle Hybrid Sensor

S Myung, PT Yin, C Kim, J Park, A Solanki, PI Reyes, Y Lu, KS Kim, …
Advanced Materials 24 (45), 6081-6087
22 2012
Guiding Stem Cell Differentiation into Oligodendrocytes Using Graphene‐Nanofiber Hybrid Scaffolds

S Shah, PT Yin, TM Uehara, STD Chueng, L Yang, KB Lee
Advanced materials 26 (22), 3673-3680
21 2014
Design, Synthesis, and Characterization of Graphene–Nanoparticle Hybrid Materials for Bioapplications

PT Yin, S Shah, M Chhowalla, KB Lee
Chemical reviews 115 (7), 2483-2531
16 2015
Multimodal Magnetic Core–Shell Nanoparticles for Effective Stem‐Cell Differentiation and Imaging

B Shah, PT Yin, S Ghoshal, KB Lee
Angewandte Chemie 125 (24), 6310-6315
16 2013
Nanotopography-mediated reverse uptake for siRNA delivery into neural stem cells to enhance neuronal differentiation

A Solanki, S Shah, PT Yin, KB Lee
Scientific reports 3
14 2013
Combined Magnetic Nanoparticle‐based MicroRNA and Hyperthermia Therapy to Enhance Apoptosis in Brain Cancer Cells

PT Yin, BP Shah, KB Lee
small 10 (20), 4106-4112
11 2014

A highly robust, efficient nanoparticle-based platform to advance stem cell therapeutics

(Nanowerk News) Associate Professor Ki-Bum Lee has developed patent-pending technology that may overcome one of the critical barriers to harnessing the full therapeutic potential of stem cells.
One of the major challenges facing researchers interested in regenerating cells and growing new tissue to treat debilitating injuries and diseases such as Parkinson’s disease, heart disease, and spinal cord trauma, is creating an easy, effective, and non-toxic methodology to control differentiation into specific cell lineages. Lee and colleagues at Rutgers and Kyoto University in Japan have invented a platform they call NanoScript, an important breakthrough for researchers in the area of gene expression. Gene expression is the way information encoded in a gene is used to direct the assembly of a protein molecule, which is integral to the process of tissue development through stem cell therapeutics.
Stem cells hold great promise for a wide range of medical therapeutics as they have the ability to grow tissue throughout the body. In many tissues, stem cells have an almost limitless ability to divide and replenish other cells, serving as an internal repair system.
Nanoscript

Schematic representation of NanoScript’s design and function. (a) By assembling individual STF molecules, including the DBD (DNA-binding domain), AD (activation domain), and NLS (nuclear localization signal), onto a single 10 nm gold nanoparticle, we have developed the NanoScript platform to replicate the structure and function of TFs. This NanoScript penetrates the cell membrane and enters the nucleus through the nuclear receptor with the help of the NLS peptide. Once in the nucleus, NanoScript interacts with DNA to initiate transcriptional activity and induce gene expression. (b) When comparing the structure of NanoScript to representative TF proteins, the three essential domains are effectively replicated. The linker domain (LD) fuses the multidomain protein together and is replicated by the gold nanoparticle (AuNP). (c) The DBD binds to complementary DNA sequences, while the AD recruits transcriptional machinery components such as RNA polymerase II (RNA Pol II), mediator complex, and general transcription factors (GTFs). The synergistic function of the DBD and AD moieties on NanoScript initiates transcriptional activity and expression of targeted genes. (d) The AuNPs are monodisperse and uniform. The NanoScript constructs are shown to effectively localize within the nucleus, which is important because transcriptional activity occurs only in the nucleus. (Reprinted with permission y American Chemical Society) (click on image to enlarge)

Read more: Using nanotechnology to regulate gene expression at the transcriptional level

Transcription factor (TF) proteins are master regulators of gene expression. TF proteins play a pivotal role in regulating stem cell differentiation. Although some have tried to make synthetic molecules that perform the functions of natural transcription factors, NanoScript is the first nanomaterial TF protein that can interact with endogenous DNA.
ACS Nano, a publication of the American Chemical Society (ACS), has published Lee’s research on NanoScript (“NanoScript: A Nanoparticle-Based Artificial Transcription Factor for Effective Gene Regulation”). The research is supported by a grant from the National Institutes of Health (NIH).
“Our motivation was to develop a highly robust, efficient nanoparticle-based platform that can regulate gene expression and eventually stem cell differentiation,” said Lee, who leads a Rutgers research group primarily focused on developing and integrating nanotechnology with chemical biology to modulate signaling pathways in cancer and stem cells. “Because NanoScript is a functional replica of TF proteins and a tunable gene-regulating platform, it has great potential to do exactly that. The field of stem cell biology now has another platform to regulate differentiation while the field of nanotechnology has demonstrated for the first time that we can regulate gene expression at the transcriptional level.”
NanoScript was constructed by tethering functional peptides and small molecules called synthetic transcription factors, which mimic the individual TF domains, onto gold nanoparticles.
“NanoScript localizes within the nucleus and initiates transcription of a reporter plasmid by up to 30-fold,” said Sahishnu Patel, Rutgers Chemistry graduate student and co-author of the ACS Nano publication. “NanoScript can effectively transcribe targeted genes on endogenous DNA in a nonviral manner.”
Lee said the next step for his research is to study what happens to the gold nanoparticles after NanoScript is utilized, to ensure no toxic effects arise, and to ensure the effectiveness of NanoScript over long periods of time.
“Due to the unique tunable properties of NanoScript, we are highly confident this platform not only will serve as a desirable alternative to conventional gene-regulating methods,” Lee said, “but also has direct employment for applications involving gene manipulation such as stem cell differentiation, cancer therapy, and cellular reprogramming. Our research will continue to evaluate the long-term implications for the technology.”
Lee, originally from South Korea, joined the Rutgers faculty in 2008 and has earned many honors including the NIH Director’s New Innovator Award. Lee received his Ph.D. in Chemistry from Northwestern University where he studied with Professor Chad. A. Mirkin, a pioneer in the coupling of nanotechnology and biomolecules. Lee completed his postdoctoral training at The Scripps Research Institute with Professor Peter G. Schultz. Lee has served as a Visiting Scholar at both Princeton University and UCLA Medical School.
The primary interest of Lee’s group is to develop and integrate nanotechnologies and chemical functional genomics to modulate signaling pathways in mammalian cells towards specific cell lineages or behaviors. He has published more than 50 articles and filed for 17 corresponding patents.
Source: Rutgers University

Read more: A highly robust, efficient nanoparticle-based platform to advance stem cell therapeutics

Nanoparticle-based transcription factor mimics

http://nanomedicine.ucsd.edu/blog/article/nanoparticle-based-transcription-factor-mimics

Biologists have been enhancing expression of specific genes with plasmids and viruses for decades, which has been essential to uncovering the function of numerous genes and the relationships among the proteins they encode. However, tools that allow enhancement of expression of endogenous genes at the transcriptional level could be a powerful complement to these strategies. Many chemical biologists have made enormous progress developing molecular tools for this purpose; recent work by a group at Rutgers suggests how nanotechnology might allow application of this strategy in living organisms, and perhaps one day in patients.

In a paper published in ACS Nano, researchers led by KiBum Lee synthesized gold nanoparticles bearing synthetic or shortened versions of the three essential components of transcription factors (TFs), the proteins that “turn on” expression of specific genes in cells. Specifically, polyamides previously designed to bind to a specific promoter sequence, transactivation peptides, and nuclear localization peptides were conjugated to the nanoparticle surface. These nanoparticles enhanced expression of both a reporter plasmid (by ~15-fold) and several endogenous genes (by up to 65%). This enhancement is much greater than that possible using previous constructs lacking nuclear localization sequences; the team incorporated a high proportion of those peptides to ensure efficient delivery to the nucleus.

Nanoscript, a synthetic transciption factor
Diagram of the synthetic TF mimic (termed NanoScript). Decorated particles are ~35 nm in diameter. Letters are amino acid sequences; Py-Im, N-methylpyrrole-N-methylimidazole.

These nanoparticles offer an alternative to delivering protein TFs, which remains extremely challenging despite considerable effort towards the development of delivery systems that transport cargo into cells. Among other barriers to the use of native TFs, incorporating them into polymeric or lipid-based carriers often alters their shape, which would likely reduce their function.

While the group suggests future generations of these nanoparticles might one day be used to treat diseases caused by defects in TF genes, many questions remain. First, the duration of gene expression enhancement is not known; the study only assesses effects at 48 h post-administration. Further, whether gold is the best material for the core remains unclear, as its non-biodegradability means the particles would likely accumulate in the liver over time; synthetic TFs with biodegradable cores might also be considered.

Patel S et al., NanoScript: a nanoparticle-based artificial transcription factor for effective gene regulation,ACS Nano 2014; published online Sep 3.

http://www.wtec.org/bem/docs/BEM-FinalReport-Web.pdf

Biocompatibility and Toxicity of Nanobiomaterials

“Biocompatibility and Toxicity of Nanobiomaterials” is an annual special issue published in “Journal of Nanomaterials.”

http://www.hindawi.com/journals/jnm/toxicity.nanobiomaterials/

Porous Ti6Al4V Scaffold Directly Fabricated by Sintering: Preparation and In Vivo Experiment
Xuesong Zhang, Guoquan Zheng, Jiaqi Wang, Yonggang Zhang, Guoqiang Zhang, Zhongli Li, and Yan Wang
Department of Orthopaedics, Chinese People’s Liberation Army General Hospital, Beijing 100853, China AcademicEditor:XiaomingLi
The interface between the implant and host bone plays a key role in maintaining primary and long-term stability of the implants. Surface modification of implant can enhance bone in growth and increase bone formation to create firm osseo integration between the implant and host bone and reduce the risk of implant losing. This paper mainly focuses on the fabricating of 3-dimensiona interconnected porous titanium by sintering of Ti6Al4V powders, which could be processed to the surface of the implant shaft and was integrated with bone morphogenetic proteins (BMPs). The structure and mechanical property of porous Ti6Al4V was observed and tested. Implant shaft with surface of porous titanium was implanted into the femoral medullary cavity of dog after combining with BMPs. The results showed that the structure and elastic modulus of 3D interconnected porous titanium was similar to cancellous bone; porous titanium combined with BMP was found to have large amount of fibrous tissue with fibroblastic cells; bone formation was significantly greater in 6 weeks postoperatively than in 3 weeks after operation. Porous titanium fabricated by powders sintering and combined with BMPs could induce tissue formation and increase bone formation to create firm osseo integration between the implant and host bone.

Journal of Materials Chemistry B   Issue 39, 2013

Materials for biology and medicine
Synthesis of nanoparticles, their biocompatibility, and toxicity behavior for biomedical applications
J. Mater. Chem. B, 2013,1, 5186-5200    DOI: http://dx.doi.org:/10.1039/C3TB20738B

Nanomaterials research has in part been focused on their use in biomedical applications for more than several decades. However, in recent years this field has been developing to a much more advanced stage by carefully controlling the size, shape, and surface-modification of nanoparticles. This review provides an overview of two classes of nanoparticles, namely iron oxide and NaLnF4, and synthesis methods, characterization techniques, study of biocompatibility, toxicity behavior, and applications of iron oxide nanoparticles and NaLnF4nanoparticles as contrast agents in magnetic resonance imaging. Their optical properties will only briefly be mentioned. Iron oxide nanoparticles show a saturation of magnetization at low field, therefore, the focus will be MLnF4 (Ln = Dy3+, Ho3+, and Gd3+) paramagnetic nanoparticles as alternative contrast agents which can sustain their magnetization at high field. The reason is that more potent contrast agents are needed at magnetic fields higher than 7 T, where most animal MRI is being done these days. Furthermore we observe that the extent of cytotoxicity is not fully understood at present, in part because it is dependent on the size, capping materials, dose of nanoparticles, and surface chemistry, and thus needs optimization of the multidimensional phenomenon. Therefore, it needs further careful investigation before being used in clinical applications.

Graphical abstract: Synthesis of nanoparticles, their biocompatibility, and toxicity behavior for biomedical applications

http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=C3TB20738B

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citations…

HAMLET interacts with lipid membranes and perturbs their structure and integrity

HAMLET (human α-lactalbumin made lethal to tumor cells) is a tumoricidal …. of the alternative complement pathway preserves photoreceptors after retinal injury ….. Life-long in vivo cell-lineage tracing shows that no oogenesis originates from …. ananoparticle-based artificial transcription factor for effective gene regulation …

Authors: Ann-Kristin Mossberg, Maja Puchades, Øyvind Halskau, Anne Baumann, Ingela Lanekoff, Yinxia Chao, Aurora Martinez, Catharina Svanborg, & Roger Karlsson

www.regenerativemedicine.net/NewsletterArchives.asp?qEmpID…

Summary: 

Background – Cell membrane interactions rely on lipid bilayer constituents and molecules inserted within the membrane, including specific receptors. HAMLET (human α-lactalbumin made lethal to tumor cells) is a tumoricidal complex of partially unfolded α-lactalbumin (HLA) and oleic acid that is internalized by tumor cells, suggesting that interactions with the phospholipid bilayer and/or specific receptors may be essential for the tumoricidal effect. This study examined whether HAMLET interacts with artificial membranes and alters membrane structure.

Methodology/Principal Findings – We show by surface plasmon resonance that HAMLET binds with high affinity to surface adherent, unilamellar vesicles of lipids with varying acyl chain composition and net charge. Fluorescence imaging revealed that HAMLET accumulates in membranes of vesicles and perturbs their structure, resulting in increased membrane fluidity. Furthermore, HAMLET disrupted membrane integrity at neutral pH and physiological conditions, as shown by fluorophore leakage experiments. These effects did not occur with either native HLA or a constitutively unfolded Cys-Ala HLA mutant (rHLAall-Ala). HAMLET also bound to plasma membrane vesicles formed from intact tumor cells, with accumulation in certain membrane areas, but the complex was not internalized by these vesicles or by the synthetic membrane vesicles.

Conclusions/Significance – The results illustrate the difference in membrane affinity between the fatty acid bound and fatty acid free forms of partially unfolded HLA and suggest that HAMLET engages membranes by a mechanism requiring both the protein and the fatty acid. Furthermore, HAMLET binding alters the morphology of the membrane and compromises its integrity, suggesting that membrane perturbation could be an initial step in inducing cell death.

Source: Public Library of Science ONE; 5(2) (02/23/10) 

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Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer


Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

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

This summary is the last of a series on the impact of transcriptomics, proteomics, and metabolomics on disease investigation, and the sorting and integration of genomic signatures and metabolic signatures to explain phenotypic relationships in variability and individuality of response to disease expression and how this leads to  pharmaceutical discovery and personalized medicine.  We have unquestionably better tools at our disposal than has ever existed in the history of mankind, and an enormous knowledge-base that has to be accessed.  I shall conclude here these discussions with the powerful contribution to and current knowledge pertaining to biochemistry, metabolism, protein-interactions, signaling, and the application of the -OMICS to diseases and drug discovery at this time.

The Ever-Transcendent Cell

Deriving physiologic first principles By John S. Torday | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41282/title/The-Ever-Transcendent-Cell/

Both the developmental and phylogenetic histories of an organism describe the evolution of physiology—the complex of metabolic pathways that govern the function of an organism as a whole. The necessity of establishing and maintaining homeostatic mechanisms began at the cellular level, with the very first cells, and homeostasis provides the underlying selection pressure fueling evolution.

While the events leading to the formation of the first functioning cell are debatable, a critical one was certainly the formation of simple lipid-enclosed vesicles, which provided a protected space for the evolution of metabolic pathways. Protocells evolved from a common ancestor that experienced environmental stresses early in the history of cellular development, such as acidic ocean conditions and low atmospheric oxygen levels, which shaped the evolution of metabolism.

The reduction of evolution to cell biology may answer the perennially unresolved question of why organisms return to their unicellular origins during the life cycle.

As primitive protocells evolved to form prokaryotes and, much later, eukaryotes, changes to the cell membrane occurred that were critical to the maintenance of chemiosmosis, the generation of bioenergy through the partitioning of ions. The incorporation of cholesterol into the plasma membrane surrounding primitive eukaryotic cells marked the beginning of their differentiation from prokaryotes. Cholesterol imparted more fluidity to eukaryotic cell membranes, enhancing functionality by increasing motility and endocytosis. Membrane deformability also allowed for increased gas exchange.

Acidification of the oceans by atmospheric carbon dioxide generated high intracellular calcium ion concentrations in primitive aquatic eukaryotes, which had to be lowered to prevent toxic effects, namely the aggregation of nucleotides, proteins, and lipids. The early cells achieved this by the evolution of calcium channels composed of cholesterol embedded within the cell’s plasma membrane, and of internal membranes, such as that of the endoplasmic reticulum, peroxisomes, and other cytoplasmic organelles, which hosted intracellular chemiosmosis and helped regulate calcium.

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.  ….

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.

Given that the unicellular toolkit is complete with all the traits necessary for forming multicellular organisms (Science, 301:361-63, 2003), it is distinctly possible that metazoans are merely permutations of the unicellular body plan. That scenario would clarify a lot of puzzling biology: molecular commonalities between the skin, lung, gut, and brain that affect physiology and pathophysiology exist because the cell membranes of unicellular organisms perform the equivalents of these tissue functions, and the existence of pleiotropy—one gene affecting many phenotypes—may be a consequence of the common unicellular source for all complex biologic traits.  …

The cell-molecular homeostatic model for evolution and stability addresses how the external environment generates homeostasis developmentally at the cellular level. It also determines homeostatic set points in adaptation to the environment through specific effectors, such as growth factors and their receptors, second messengers, inflammatory mediators, crossover mutations, and gene duplications. This is a highly mechanistic, heritable, plastic process that lends itself to understanding evolution at the cellular, tissue, organ, system, and population levels, mediated by physiologically linked mechanisms throughout, without having to invoke random, chance mechanisms to bridge different scales of evolutionary change. In other words, it is an integrated mechanism that can often be traced all the way back to its unicellular origins.

The switch from swim bladder to lung as vertebrates moved from water to land is proof of principle that stress-induced evolution in metazoans can be understood from changes at the cellular level.

http://www.the-scientist.com/Nov2014/TE_21.jpg

A MECHANISTIC BASIS FOR LUNG DEVELOPMENT: Stress from periodic atmospheric hypoxia (1) during vertebrate adaptation to land enhances positive selection of the stretch-regulated parathyroid hormone-related protein (PTHrP) in the pituitary and adrenal glands. In the pituitary (2), PTHrP signaling upregulates the release of adrenocorticotropic hormone (ACTH) (3), which stimulates the release of glucocorticoids (GC) by the adrenal gland (4). In the adrenal gland, PTHrP signaling also stimulates glucocorticoid production of adrenaline (5), which in turn affects the secretion of lung surfactant, the distension of alveoli, and the perfusion of alveolar capillaries (6). PTHrP signaling integrates the inflation and deflation of the alveoli with surfactant production and capillary perfusion.  THE SCIENTIST STAFF

From a cell-cell signaling perspective, two critical duplications in genes coding for cell-surface receptors occurred during this period of water-to-land transition—in the stretch-regulated parathyroid hormone-related protein (PTHrP) receptor gene and the β adrenergic (βA) receptor gene. These gene duplications can be disassembled by following their effects on vertebrate physiology backwards over phylogeny. PTHrP signaling is necessary for traits specifically relevant to land adaptation: calcification of bone, skin barrier formation, and the inflation and distention of lung alveoli. Microvascular shear stress in PTHrP-expressing organs such as bone, skin, kidney, and lung would have favored duplication of the PTHrP receptor, since sheer stress generates radical oxygen species (ROS) known to have this effect and PTHrP is a potent vasodilator, acting as an epistatic balancing selection for this constraint.

Positive selection for PTHrP signaling also evolved in the pituitary and adrenal cortex (see figure on this page), stimulating the secretion of ACTH and corticoids, respectively, in response to the stress of land adaptation. This cascade amplified adrenaline production by the adrenal medulla, since corticoids passing through it enzymatically stimulate adrenaline synthesis. Positive selection for this functional trait may have resulted from hypoxic stress that arose during global episodes of atmospheric hypoxia over geologic time. Since hypoxia is the most potent physiologic stressor, such transient oxygen deficiencies would have been acutely alleviated by increasing adrenaline levels, which would have stimulated alveolar surfactant production, increasing gas exchange by facilitating the distension of the alveoli. Over time, increased alveolar distension would have generated more alveoli by stimulating PTHrP secretion, impelling evolution of the alveolar bed of the lung.

This scenario similarly explains βA receptor gene duplication, since increased density of the βA receptor within the alveolar walls was necessary for relieving another constraint during the evolution of the lung in adaptation to land: the bottleneck created by the existence of a common mechanism for blood pressure control in both the lung alveoli and the systemic blood pressure. The pulmonary vasculature was constrained by its ability to withstand the swings in pressure caused by the systemic perfusion necessary to sustain all the other vital organs. PTHrP is a potent vasodilator, subserving the blood pressure constraint, but eventually the βA receptors evolved to coordinate blood pressure in both the lung and the periphery.

Gut Microbiome Heritability

Analyzing data from a large twin study, researchers have homed in on how host genetics can shape the gut microbiome.
By Tracy Vence | The Scientist Nov 6, 2014

Previous research suggested host genetic variation can influence microbial phenotype, but an analysis of data from a large twin study published in Cell today (November 6) solidifies the connection between human genotype and the composition of the gut microbiome. Studying more than 1,000 fecal samples from 416 monozygotic and dizygotic twin pairs, Cornell University’s Ruth Ley and her colleagues have homed in on one bacterial taxon, the family Christensenellaceae, as the most highly heritable group of microbes in the human gut. The researchers also found that Christensenellaceae—which was first described just two years ago—is central to a network of co-occurring heritable microbes that is associated with lean body mass index (BMI).  …

Of particular interest was the family Christensenellaceae, which was the most heritable taxon among those identified in the team’s analysis of fecal samples obtained from the TwinsUK study population.

While microbiologists had previously detected 16S rRNA sequences belonging to Christensenellaceae in the human microbiome, the family wasn’t named until 2012. “People hadn’t looked into it, partly because it didn’t have a name . . . it sort of flew under the radar,” said Ley.

Ley and her colleagues discovered that Christensenellaceae appears to be the hub in a network of co-occurring heritable taxa, which—among TwinsUK participants—was associated with low BMI. The researchers also found that Christensenellaceae had been found at greater abundance in low-BMI twins in older studies.

To interrogate the effects of Christensenellaceae on host metabolic phenotype, the Ley’s team introduced lean and obese human fecal samples into germ-free mice. They found animals that received lean fecal samples containing more Christensenellaceae showed reduced weight gain compared with their counterparts. And treatment of mice that had obesity-associated microbiomes with one member of the Christensenellaceae family, Christensenella minuta, led to reduced weight gain.   …

Ley and her colleagues are now focusing on the host alleles underlying the heritability of the gut microbiome. “We’re running a genome-wide association analysis to try to find genes—particular variants of genes—that might associate with higher levels of these highly heritable microbiota.  . . . Hopefully that will point us to possible reasons they’re heritable,” she said. “The genes will guide us toward understanding how these relationships are maintained between host genotype and microbiome composition.”

J.K. Goodrich et al., “Human genetics shape the gut microbiome,” Cell,  http://dx.doi.org:/10.1016/j.cell.2014.09.053, 2014.

Light-Operated Drugs

Scientists create a photosensitive pharmaceutical to target a glutamate receptor.
By Ruth Williams | The Scentist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41279/title/Light-Operated-Drugs/

light operated drugs MO1

light operated drugs MO1

http://www.the-scientist.com/Nov2014/MO1.jpg

The desire for temporal and spatial control of medications to minimize side effects and maximize benefits has inspired the development of light-controllable drugs, or optopharmacology. Early versions of such drugs have manipulated ion channels or protein-protein interactions, “but never, to my knowledge, G protein–coupled receptors [GPCRs], which are one of the most important pharmacological targets,” says Pau Gorostiza of the Institute for Bioengineering of Catalonia, in Barcelona.

Gorostiza has taken the first step toward filling that gap, creating a photosensitive inhibitor of the metabotropic glutamate 5 (mGlu5) receptor—a GPCR expressed in neurons and implicated in a number of neurological and psychiatric disorders. The new mGlu5 inhibitor—called alloswitch-1—is based on a known mGlu receptor inhibitor, but the simple addition of a light-responsive appendage, as had been done for other photosensitive drugs, wasn’t an option. The binding site on mGlu5 is “extremely tight,” explains Gorostiza, and would not accommodate a differently shaped molecule. Instead, alloswitch-1 has an intrinsic light-responsive element.

In a human cell line, the drug was active under dim light conditions, switched off by exposure to violet light, and switched back on by green light. When Gorostiza’s team administered alloswitch-1 to tadpoles, switching between violet and green light made the animals stop and start swimming, respectively.

The fact that alloswitch-1 is constitutively active and switched off by light is not ideal, says Gorostiza. “If you are thinking of therapy, then in principle you would prefer the opposite,” an “on” switch. Indeed, tweaks are required before alloswitch-1 could be a useful drug or research tool, says Stefan Herlitze, who studies ion channels at Ruhr-Universität Bochum in Germany. But, he adds, “as a proof of principle it is great.” (Nat Chem Biol, http://dx.doi.org:/10.1038/nchembio.1612, 2014)

Enhanced Enhancers

The recent discovery of super-enhancers may offer new drug targets for a range of diseases.
By Eric Olson | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41281/title/Enhanced-Enhancers/

To understand disease processes, scientists often focus on unraveling how gene expression in disease-associated cells is altered. Increases or decreases in transcription—as dictated by a regulatory stretch of DNA called an enhancer, which serves as a binding site for transcription factors and associated proteins—can produce an aberrant composition of proteins, metabolites, and signaling molecules that drives pathologic states. Identifying the root causes of these changes may lead to new therapeutic approaches for many different diseases.

Although few therapies for human diseases aim to alter gene expression, the outstanding examples—including antiestrogens for hormone-positive breast cancer, antiandrogens for prostate cancer, and PPAR-γ agonists for type 2 diabetes—demonstrate the benefits that can be achieved through targeting gene-control mechanisms.  Now, thanks to recent papers from laboratories at MIT, Harvard, and the National Institutes of Health, researchers have a new, much bigger transcriptional target: large DNA regions known as super-enhancers or stretch-enhancers. Already, work on super-enhancers is providing insights into how gene-expression programs are established and maintained, and how they may go awry in disease.  Such research promises to open new avenues for discovering medicines for diseases where novel approaches are sorely needed.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions (Cell, 153:307-19, 2013). They also appear to bind a large percentage of the transcriptional machinery compared to typical enhancers, allowing them to better establish and enforce cell-type specific transcriptional programs (Cell, 153:320-34, 2013).

Super-enhancers are closely associated with genes that dictate cell identity, including those for cell-type–specific master regulatory transcription factors. This observation led to the intriguing hypothesis that cells with a pathologic identity, such as cancer cells, have an altered gene expression program driven by the loss, gain, or altered function of super-enhancers.

Sure enough, by mapping the genome-wide location of super-enhancers in several cancer cell lines and from patients’ tumor cells, we and others have demonstrated that genes located near super-enhancers are involved in processes that underlie tumorigenesis, such as cell proliferation, signaling, and apoptosis.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions.

Genome-wide association studies (GWAS) have found that disease- and trait-associated genetic variants often occur in greater numbers in super-enhancers (compared to typical enhancers) in cell types involved in the disease or trait of interest (Cell, 155:934-47, 2013). For example, an enrichment of fasting glucose–associated single nucleotide polymorphisms (SNPs) was found in the stretch-enhancers of pancreatic islet cells (PNAS, 110:17921-26, 2013). Given that some 90 percent of reported disease-associated SNPs are located in noncoding regions, super-enhancer maps may be extremely valuable in assigning functional significance to GWAS variants and identifying target pathways.

Because only 1 to 2 percent of active genes are physically linked to a super-enhancer, mapping the locations of super-enhancers can be used to pinpoint the small number of genes that may drive the biology of that cell. Differential super-enhancer maps that compare normal cells to diseased cells can be used to unravel the gene-control circuitry and identify new molecular targets, in much the same way that somatic mutations in tumor cells can point to oncogenic drivers in cancer. This approach is especially attractive in diseases for which an incomplete understanding of the pathogenic mechanisms has been a barrier to discovering effective new therapies.

Another therapeutic approach could be to disrupt the formation or function of super-enhancers by interfering with their associated protein components. This strategy could make it possible to downregulate multiple disease-associated genes through a single molecular intervention. A group of Boston-area researchers recently published support for this concept when they described inhibited expression of cancer-specific genes, leading to a decrease in cancer cell growth, by using a small molecule inhibitor to knock down a super-enhancer component called BRD4 (Cancer Cell, 24:777-90, 2013).  More recently, another group showed that expression of the RUNX1 transcription factor, involved in a form of T-cell leukemia, can be diminished by treating cells with an inhibitor of a transcriptional kinase that is present at the RUNX1 super-enhancer (Nature, 511:616-20, 2014).

Fungal effector Ecp6 outcompetes host immune receptor for chitin binding through intrachain LysM dimerization 
Andrea Sánchez-Vallet, et al.   eLife 2013;2:e00790 http://elifesciences.org/content/2/e00790#sthash.LnqVMJ9p.dpuf

LysM effector

LysM effector

http://img.scoop.it/ZniCRKQSvJOG18fHbb4p0Tl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9

While host immune receptors

  • detect pathogen-associated molecular patterns to activate immunity,
  • pathogens attempt to deregulate host immunity through secreted effectors.

Fungi employ LysM effectors to prevent

  • recognition of cell wall-derived chitin by host immune receptors

Structural analysis of the LysM effector Ecp6 of

  • the fungal tomato pathogen Cladosporium fulvum reveals
  • a novel mechanism for chitin binding,
  • mediated by intrachain LysM dimerization,

leading to a chitin-binding groove that is deeply buried in the effector protein.

This composite binding site involves

  • two of the three LysMs of Ecp6 and
  • mediates chitin binding with ultra-high (pM) affinity.

The remaining singular LysM domain of Ecp6 binds chitin with

  • low micromolar affinity but can nevertheless still perturb chitin-triggered immunity.

Conceivably, the perturbation by this LysM domain is not established through chitin sequestration but possibly through interference with the host immune receptor complex.

Mutated Genes in Schizophrenia Map to Brain Networks
From www.nih.gov –  Sep 3, 2013

Previous studies have shown that many people with schizophrenia have de novo, or new, genetic mutations. These misspellings in a gene’s DNA sequence

  • occur spontaneously and so aren’t shared by their close relatives.

Dr. Mary-Claire King of the University of Washington in Seattle and colleagues set out to

  • identify spontaneous genetic mutations in people with schizophrenia and
  • to assess where and when in the brain these misspelled genes are turned on, or expressed.

The study was funded in part by NIH’s National Institute of Mental Health (NIMH). The results were published in the August 1, 2013, issue of Cell.

The researchers sequenced the exomes (protein-coding DNA regions) of 399 people—105 with schizophrenia plus their unaffected parents and siblings. Gene variations
that were found in a person with schizophrenia but not in either parent were considered spontaneous.

The likelihood of having a spontaneous mutation was associated with

  • the age of the father in both affected and unaffected siblings.

Significantly more mutations were found in people

  • whose fathers were 33-45 years at the time of conception compared to 19-28 years.

Among people with schizophrenia, the scientists identified

  • 54 genes with spontaneous mutations
  • predicted to cause damage to the function of the protein they encode.

The researchers used newly available database resources that show

  • where in the brain and when during development genes are expressed.

The genes form an interconnected expression network with many more connections than

  • that of the genes with spontaneous damaging mutations in unaffected siblings.

The spontaneously mutated genes in people with schizophrenia

  • were expressed in the prefrontal cortex, a region in the front of the brain.

The genes are known to be involved in important pathways in brain development. Fifty of these genes were active

  • mainly during the period of fetal development.

“Processes critical for the brain’s development can be revealed by the mutations that disrupt them,” King says. “Mutations can lead to loss of integrity of a whole pathway,
not just of a single gene.”

These findings support the concept that schizophrenia may result, in part, from

  • disruptions in development in the prefrontal cortex during fetal development.

James E. Darnell’s “Reflections”

A brief history of the discovery of RNA and its role in transcription — peppered with career advice
By Joseph P. Tiano

James Darnell begins his Journal of Biological Chemistry “Reflections” article by saying, “graduate students these days

  • have to swim in a sea virtually turgid with the daily avalanche of new information and
  • may be momentarily too overwhelmed to listen to the aging.

I firmly believe how we learned what we know can provide useful guidance for how and what a newcomer will learn.” Considering his remarkable discoveries in

  • RNA processing and eukaryotic transcriptional regulation

spanning 60 years of research, Darnell’s advice should be cherished. In his second year at medical school at Washington University School of Medicine in St. Louis, while
studying streptococcal disease in Robert J. Glaser’s laboratory, Darnell realized he “loved doing the experiments” and had his first “career advancement event.”
He and technician Barbara Pesch discovered that in vivo penicillin treatment killed streptococci only in the exponential growth phase and not in the stationary phase. These
results were published in the Journal of Clinical Investigation and earned Darnell an interview with Harry Eagle at the National Institutes of Health.

Darnell arrived at the NIH in 1956, shortly after Eagle  shifted his research interest to developing his minimal essential cell culture medium, still used. Eagle, then studying cell metabolism, suggested that Darnell take up a side project on poliovirus replication in mammalian cells in collaboration with Robert I. DeMars. DeMars’ Ph.D.
adviser was also James  Watson’s mentor, so Darnell met Watson, who invited him to give a talk at Harvard University, which led to an assistant professor position
at the MIT under Salvador Luria. A take-home message is to embrace side projects, because you never know where they may lead: this project helped to shape
his career.

Darnell arrived in Boston in 1961. Following the discovery of DNA’s structure in 1953, the world of molecular biology was turning to RNA in an effort to understand how
proteins are made. Darnell’s background in virology (it was discovered in 1960 that viruses used RNA to replicate) was ideal for the aim of his first independent lab:
exploring mRNA in animal cells grown in culture. While at MIT, he developed a new technique for purifying RNA along with making other observations

  • suggesting that nonribosomal cytoplasmic RNA may be involved in protein synthesis.

When Darnell moved to Albert Einstein College of Medicine for full professorship in 1964,  it was hypothesized that heterogenous nuclear RNA was a precursor to mRNA.
At Einstein, Darnell discovered RNA processing of pre-tRNAs and demonstrated for the first time

  • that a specific nuclear RNA could represent a possible specific mRNA precursor.

In 1967 Darnell took a position at Columbia University, and it was there that he discovered (simultaneously with two other labs) that

  • mRNA contained a polyadenosine tail.

The three groups all published their results together in the Proceedings of the National Academy of Sciences in 1971. Shortly afterward, Darnell made his final career move
four short miles down the street to Rockefeller University in 1974.

Over the next 35-plus years at Rockefeller, Darnell never strayed from his original research question: How do mammalian cells make and control the making of different
mRNAs? His work was instrumental in the collaborative discovery of

  • splicing in the late 1970s and
  • in identifying and cloning many transcriptional activators.

Perhaps his greatest contribution during this time, with the help of Ernest Knight, was

  • the discovery and cloning of the signal transducers and activators of transcription (STAT) proteins.

And with George Stark, Andy Wilks and John Krowlewski, he described

  • cytokine signaling via the JAK-STAT pathway.

Darnell closes his “Reflections” with perhaps his best advice: Do not get too wrapped up in your own work, because “we are all needed and we are all in this together.”

Darnell Reflections - James_Darnell

Darnell Reflections – James_Darnell

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/8758cb87-84ff-42d6-8aea-96fda4031a1b.jpg

Recent findings on presenilins and signal peptide peptidase

By Dinu-Valantin Bălănescu

γ-secretase and SPP

γ-secretase and SPP

Fig. 1 from the minireview shows a schematic depiction of γ-secretase and SPP

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/c2de032a-daad-41e5-ba19-87a17bd26362.png

GxGD proteases are a family of intramembranous enzymes capable of hydrolyzing

  • the transmembrane domain of some integral membrane proteins.

The GxGD family is one of the three families of

  • intramembrane-cleaving proteases discovered so far (along with the rhomboid and site-2 protease) and
  • includes the γ-secretase and the signal peptide peptidase.

Although only recently discovered, a number of functions in human pathology and in numerous other biological processes

  • have been attributed to γ-secretase and SPP.

Taisuke Tomita and Takeshi Iwatsubo of the University of Tokyo highlighted the latest findings on the structure and function of γ-secretase and SPP
in a recent minireview in The Journal of Biological Chemistry.

  • γ-secretase is involved in cleaving the amyloid-β precursor protein, thus producing amyloid-β peptide,

the main component of senile plaques in Alzheimer’s disease patients’ brains. The complete structure of mammalian γ-secretase is not yet known; however,
Tomita and Iwatsubo note that biochemical analyses have revealed it to be a multisubunit protein complex.

  • Its catalytic subunit is presenilin, an aspartyl protease.

In vitro and in vivo functional and chemical biology analyses have revealed that

  • presenilin is a modulator and mandatory component of the γ-secretase–mediated cleavage of APP.

Genetic studies have identified three other components required for γ-secretase activity:

  1. nicastrin,
  2. anterior pharynx defective 1 and
  3. presenilin enhancer 2.

By coexpression of presenilin with the other three components, the authors managed to

  • reconstitute γ-secretase activity.

Tomita and Iwatsubo determined using the substituted cysteine accessibility method and by topological analyses, that

  • the catalytic aspartates are located at the center of the nine transmembrane domains of presenilin,
  • by revealing the exact location of the enzyme’s catalytic site.

The minireview also describes in detail the formerly enigmatic mechanism of γ-secretase mediated cleavage.

SPP, an enzyme that cleaves remnant signal peptides in the membrane

  • during the biogenesis of membrane proteins and
  • signal peptides from major histocompatibility complex type I,
  • also is involved in the maturation of proteins of the hepatitis C virus and GB virus B.

Bioinformatics methods have revealed in fruit flies and mammals four SPP-like proteins,

  • two of which are involved in immunological processes.

By using γ-secretase inhibitors and modulators, it has been confirmed

  • that SPP shares a similar GxGD active site and proteolytic activity with γ-secretase.

Upon purification of the human SPP protein with the baculovirus/Sf9 cell system,

  • single-particle analysis revealed further structural and functional details.

HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection

From www.pnas.org –  Sep 3, 2013 4:24 PM

Experimental and computational evidence suggests that

  • HLAs preferentially bind conserved regions of viral proteins, a concept we term “targeting efficiency,” and that
  • this preference may provide improved clearance of infection in several viral systems.

To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study
performed during the 2009–2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with

  • IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression).

A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24,

  • were associated with pH1N1 mortality (r = 0.37, P = 0.031) and
  • are common in certain indigenous populations in which increased pH1N1 morbidity has been reported.

HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection.
The computational tools used in this study may be useful predictors of potential morbidity and

  • identify immunologic differences of new variant influenza strains
  • more accurately than evolutionary sequence comparisons.

Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases

  • might advance clinical practices for severe H1N1 infections among genetically susceptible populations.

Metabolomics in drug target discovery

J D Rabinowitz et al.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ.
Cold Spring Harbor Symposia on Quantitative Biology 11/2011; 76:235-46.
http://dx.doi.org:/10.1101/sqb.2011.76.010694 

Most diseases result in metabolic changes. In many cases, these changes play a causative role in disease progression. By identifying pathological metabolic changes,

  • metabolomics can point to potential new sites for therapeutic intervention.

Particularly promising enzymatic targets are those that

  • carry increased flux in the disease state.

Definitive assessment of flux requires the use of isotope tracers. Here we present techniques for

  • finding new drug targets using metabolomics and isotope tracers.

The utility of these methods is exemplified in the study of three different viral pathogens. For influenza A and herpes simplex virus,

  • metabolomic analysis of infected versus mock-infected cells revealed
  • dramatic concentration changes around the current antiviral target enzymes.

Similar analysis of human-cytomegalovirus-infected cells, however, found the greatest changes

  • in a region of metabolism unrelated to the current antiviral target.

Instead, it pointed to the tricarboxylic acid (TCA) cycle and

  • its efflux to feed fatty acid biosynthesis as a potential preferred target.

Isotope tracer studies revealed that cytomegalovirus greatly increases flux through

  • the key fatty acid metabolic enzyme acetyl-coenzyme A carboxylase.
  • Inhibition of this enzyme blocks human cytomegalovirus replication.

Examples where metabolomics has contributed to identification of anticancer drug targets are also discussed. Eventual proof of the value of

  • metabolomics as a drug target discovery strategy will be
  • successful clinical development of therapeutics hitting these new targets.

 Related References

Use of metabolic pathway flux information in targeted cancer drug design. Drug Discovery Today: Therapeutic Strategies 1:435-443, 2004.

Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am J Pharmacogenomics. 2005;5(5):293-302. Review. PMID: 16196499

Medicinal chemistry, metabolic profiling and drug target discovery: a role for metabolic profiling in reverse pharmacology and chemical genetics.
Mini Rev Med Chem.  2005 Jan;5(1):13-20. Review. PMID: 15638788 [PubMed – indexed for MEDLINE] Related citations

Development of Tracer-Based Metabolomics and its Implications for the Pharmaceutical Industry. Int J Pharm Med 2007; 21 (3): 217-224.

Use of metabolic pathway flux information in anticancer drug design. Ernst Schering Found Symp Proc. 2007;(4):189-203. Review. PMID: 18811058

Pharmacological targeting of glucagon and glucagon-like peptide 1 receptors has different effects on energy state and glucose homeostasis in diet-induced obese mice. J Pharmacol Exp Ther. 2011 Jul;338(1):70-81. http://dx.doi.org:/10.1124/jpet.111.179986. PMID: 21471191

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the
[U-C(6)]-d-glucose tracer in mice. Metabolomics. 2009 Sep;5(3):336-345. PMID: 19718458

Metabolic Pathways as Targets for Drug Screening, Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-0046-1, InTech, Available from: http://www.intechopen.com/books/metabolomics/metabolic-pathways-as-targets-for-drug-screening

Iron regulates glucose homeostasis in liver and muscle via AMP-activated protein kinase in mice. FASEB J. 2013 Jul;27(7):2845-54.
http://dx.doi.org:/10.1096/fj.12-216929. PMID: 23515442

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Drug Discov. Today 19 (2014), 171–182     http://dx.doi.org:/10.1016/j.drudis.2013.07.014

Highlights

  • We now have metabolic network models; the metabolome is represented by their nodes.
  • Metabolite levels are sensitive to changes in enzyme activities.
  • Drugs hitchhike on metabolite transporters to get into and out of cells.
  • The consensus network Recon2 represents the present state of the art, and has predictive power.
  • Constraint-based modelling relates network structure to metabolic fluxes.

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are

  • necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs.

To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of

  • the human metabolic network that include the important transporters.

Small molecule ‘drug’ transporters are in fact metabolite transporters, because

  • drugs bear structural similarities to metabolites known from the network reconstructions and
  • from measurements of the metabolome.

Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia:

(i) the effects of inborn errors of metabolism;

(ii) which metabolites are exometabolites, and

(iii) how metabolism varies between tissues and cellular compartments.

However, even these qualitative network models are not yet complete. As our understanding improves

  • so do we recognise more clearly the need for a systems (poly)pharmacology.

Introduction – a systems biology approach to drug discovery

It is clearly not news that the productivity of the pharmaceutical industry has declined significantly during recent years

  • following an ‘inverse Moore’s Law’, Eroom’s Law, or
  • that many commentators, consider that the main cause of this is
  • because of an excessive focus on individual molecular target discovery rather than a more sensible strategy
  • based on a systems-level approach (Fig. 1).
drug discovery science

drug discovery science

Figure 1.

The change in drug discovery strategy from ‘classical’ function-first approaches (in which the assay of drug function was at the tissue or organism level),
with mechanistic studies potentially coming later, to more-recent target-based approaches where initial assays usually involve assessing the interactions
of drugs with specified (and often cloned, recombinant) proteins in vitro. In the latter cases, effects in vivo are assessed later, with concomitantly high levels of attrition.

Arguably the two chief hallmarks of the systems biology approach are:

(i) that we seek to make mathematical models of our systems iteratively or in parallel with well-designed ‘wet’ experiments, and
(ii) that we do not necessarily start with a hypothesis but measure as many things as possible (the ’omes) and

  • let the data tell us the hypothesis that best fits and describes them.

Although metabolism was once seen as something of a Cinderella subject,

  • there are fundamental reasons to do with the organisation of biochemical networks as
  • to why the metabol(om)ic level – now in fact seen as the ‘apogee’ of the ’omics trilogy –
  •  is indeed likely to be far more discriminating than are
  • changes in the transcriptome or proteome.

The next two subsections deal with these points and Fig. 2 summarises the paper in the form of a Mind Map.

metabolomics and systems pharmacology

metabolomics and systems pharmacology

http://ars.els-cdn.com/content/image/1-s2.0-S1359644613002481-gr2.jpg

Metabolic Disease Drug Discovery— “Hitting the Target” Is Easier Said Than Done

David E. Moller, et al.   http://dx.doi.org:/10.1016/j.cmet.2011.10.012

Despite the advent of new drug classes, the global epidemic of cardiometabolic disease has not abated. Continuing

  • unmet medical needs remain a major driver for new research.

Drug discovery approaches in this field have mirrored industry trends, leading to a recent

  • increase in the number of molecules entering development.

However, worrisome trends and newer hurdles are also apparent. The history of two newer drug classes—

  1. glucagon-like peptide-1 receptor agonists and
  2. dipeptidyl peptidase-4 inhibitors—

illustrates both progress and challenges. Future success requires that researchers learn from these experiences and

  • continue to explore and apply new technology platforms and research paradigms.

The global epidemic of obesity and diabetes continues to progress relentlessly. The International Diabetes Federation predicts an even greater diabetes burden (>430 million people afflicted) by 2030, which will disproportionately affect developing nations (International Diabetes Federation, 2011). Yet

  • existing drug classes for diabetes, obesity, and comorbid cardiovascular (CV) conditions have substantial limitations.

Currently available prescription drugs for treatment of hyperglycemia in patients with type 2 diabetes (Table 1) have notable shortcomings. In general,

Therefore, clinicians must often use combination therapy, adding additional agents over time. Ultimately many patients will need to use insulin—a therapeutic class first introduced in 1922. Most existing agents also have

  • issues around safety and tolerability as well as dosing convenience (which can impact patient compliance).

Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics,

  • the quantification and analysis of metabolites produced by the body.

It refers to the direct measurement of metabolites in an individual’s bodily fluids, in order to

  • predict or evaluate the metabolism of pharmaceutical compounds, and
  • to better understand the pharmacokinetic profile of a drug.

Alternatively, pharmacometabolomics can be applied to measure metabolite levels

  • following the administration of a pharmaceutical compound, in order to
  • monitor the effects of the compound on certain metabolic pathways(pharmacodynamics).

This provides detailed mapping of drug effects on metabolism and

  • the pathways that are implicated in mechanism of variation of response to treatment.

In addition, the metabolic profile of an individual at baseline (metabotype) provides information about

  • how individuals respond to treatment and highlights heterogeneity within a disease state.

All three approaches require the quantification of metabolites found

relationship between -OMICS

relationship between -OMICS

http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/OMICS.png/350px-OMICS.png

Pharmacometabolomics is thought to provide information that

Looking at the characteristics of an individual down through these different levels of detail, there is an

  • increasingly more accurate prediction of a person’s ability to respond to a pharmaceutical compound.
  1. the genome, made up of 25 000 genes, can indicate possible errors in drug metabolism;
  2. the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed;
  3. and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions.

Pharmacometabolomics complements the omics with

  • direct measurement of the products of all of these reactions, but with perhaps a relatively
  • smaller number of members: that was initially projected to be approximately 2200 metabolites,

but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is

  • to more closely predict or assess the response of an individual to a pharmaceutical compound,
  • permitting continued treatment with the right drug or dosage
  • depending on the variations in their metabolism and ability to respond to treatment.

Pharmacometabolomic analyses, through the use of a metabolomics approach,

  • can provide a comprehensive and detailed metabolic profile or “metabolic fingerprint” for an individual patient.

Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations,

This approach can then be applied to the prediction of response to a pharmaceutical compound

  • by patients with a particular metabolic profile.

Pharmacometabolomic analyses of drug response are

Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms)

  • within patients that may contribute to altered drug responses and overall outcome of a certain treatment.

The results of pharmacometabolomics analyses can act to “inform” or “direct”

  • pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level.

This concept has been established with two seminal publications from studies of antidepressants serotonin reuptake inhibitors

  • where metabolic signatures were able to define a pathway implicated in response to the antidepressant and
  • that lead to identification of genetic variants within a key gene
  • within the highlighted pathway as being implicated in variation in response.

These genetic variants were not identified through genetic analysis alone and hence

  • illustrated how metabolomics can guide and inform genetic data.

en.wikipedia.org/wiki/Pharmacometabolomics

Benznidazole Biotransformation and Multiple Targets in Trypanosoma cruzi Revealed by Metabolomics

Andrea Trochine, Darren J. Creek, Paula Faral-Tello, Michael P. Barrett, Carlos Robello
Published: May 22, 2014   http://dx.doi.org:/10.1371/journal.pntd.0002844

The first line treatment for Chagas disease, a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi,

  • involves administration of benznidazole (Bzn).

Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active. We used a

  • non-targeted MS-based metabolomics approach to study the metabolic response of T. cruzi to Bzn.

Parasites treated with Bzn were minimally altered compared to untreated trypanosomes, although the redox active thiols

  1. trypanothione,
  2. homotrypanothione and
  3. cysteine

were significantly diminished in abundance post-treatment. In addition, multiple Bzn-derived metabolites were detected after treatment.

These metabolites included reduction products, fragments and covalent adducts of reduced Bzn

  • linked to each of the major low molecular weight thiols:
  1. trypanothione,
  2. glutathione,
  3. g-glutamylcysteine,
  4. glutathionylspermidine,
  5. cysteine and
  6. ovothiol A.

Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase, TcNTRI,

  • were found within the parasites,
  • but low molecular weight adducts of glyoxal, a proposed toxic end-product of NTRI Bzn metabolism, were not detected.

Our data is indicative of a major role of the

  • thiol binding capacity of Bzn reduction products
  • in the mechanism of Bzn toxicity against T. cruzi.

 

 

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Transcription Modulation

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

 

This portion of the transcription series deals with transcription factors and the effects of their binding on metabolism. This also has implications for pharmaceutical target identification.

The Functional Consequences of Variation in Transcription Factor Binding
DA. Cusanovich, B Pavlovic, JK. Pritchard*, Y Gilad*
1 Department of Human Genetics, 2 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA.
PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then

  • examined which genes were impacted by the reduced transcription factor level.

Our results implicate some attributes

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

Many studies have focused on characterizing the genomic locations of TF binding, but

  • it is unclear whether TF binding at any specific locus has
  • functional consequences with respect to gene expression output.

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone. However, the regulatory code of the human genome is much more complicated than

  • the triplet code of protein coding sequences, and is highly context-specific,
  • depending on cell-type and other factors.

Moreover, regulatory regions are not necessarily organized into

  • discrete, easily identifiable regions of the genome and
  • may exert their influence on genes over large genomic distances

Genomic studies addressing questions of the regulatory logic of the human genome have largely taken one of two approaches.

  1. collecting transcription factor binding maps using techniques such as ChIPseq
    and DNase-seq
  2. mapping various quantitative trait loci (QTL), such as gene expression levels
    (eQTLs) [7], DNA methylation (meQTLs) [8] and chromatin accessibility (dsQTLs)

Cumulatively, binding map studies and QTL map studies have

  • led to many insights into the principles and mechanisms of gene regulation.

However, there are questions that neither mapping approach on its own is well equipped to address. One outstanding issue is

  • the fraction of factor binding in the genome that is ‘‘functional’’,
    which we define here to mean that
  • disturbing the protein-DNA interaction leads to a measurable
  • downstream effect on gene regulation.

Transcription factor knockdown could be used to address this problem, whereby

  • the RNA interference pathway is employed to greatly reduce
  • the expression level of a specific target gene by using small interfering RNAs (siRNAs).

The response to the knockdown can then be measured by collecting RNA after the knockdown and

  • measuring global changes in gene expression patterns
  • after specifically attenuating the expression level of a given factor.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

The expression data from all samples were normalized together using

  • quantile  normalization followed by batch correction using the RUV-2 method.

We then performed several quality control analyses to confirm

  1. that the quality of the data was high,
  2. that there were no outlier samples, and
  3. that the normalization methods reduced the influence of confounders

In order to identify genes that were expressed at a significantly different level

  • in the knockdown samples compared to the negative controls,
  • we used likelihood-ratio tests within the framework of a fixed effect linear model.

Following normalization and quality control of the arrays,

  • we identified genes that were differentially expressed between
  • the three knockdown replicates of each factor and the six controls.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

Reassuringly, we did not observe a significant correlation between

  • the knockdown efficiency of a given factor and
  • the number of genes classified as differentially expressed foci.

Because we knocked down 59 different factors in this experiment

  • we were able to assess general patterns associated with the perturbation of transcription factors
  • beyond merely the number of affected target genes.

Globally, despite the range in the number of genes we identified as

  • differentially expressed in each knockdown,
  • the effect sizes of the differences in expression were relatively modest and
  • consistent in magnitude across all knockdowns.

The median effect size following the knockdown experiment for genes classified as

  • differentially expressed at an FDR of 5% in any knockdown was
  • a 9.2% difference in expression level between the controls and the knockdown (Figure 2),
  • while the median effect size for any individual knockdown experiment ranged between 8.1% and 11.0%.
    (this was true whether we estimated the knockdown effect based on qPCR (P = 0.10; Figure 1D) or microarray (P = 0.99; not shown) data.

Nor did we observe a correlation between

  • variance in qPCR-estimated knockdown efficiency (between replicates) and
  • the number of genes differentially expressed (P = 0.94; Figure 1E).

We noticed that the large variation in the number of differentially expressed genes

  • extended even to knockdowns of factors from the same gene family.

Figure 1. Differential expression analysis.
(a) Examples of differential expression analysis results for the genes HCST and IRF4. The top two panels are ‘MA plots’ of the mean Log2(expression level) between the knockdown arrays and the controls for each gene (x-axis) to the Log2(Fold-Change) between the knockdowns and controls (y-axis). Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The gene targeted by the siRNA is highlighted in red. The bottom two panels are ‘volcano plots’ of the Log2(Fold-Change) between the knockdowns and controls (x-axis) to the P-value for differential expression (y-axis). The dashed line marks the 5% FDR threshold. Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The red dot marks the gene targeted by the siRNA.
(b) Barplot of number of differentially expressed genes in each knockdown experiment.
(c) Comparison of the knockdown level measured by qPCR (RNA sample collected 48 hours posttransfection) and the knockdown level measured by microarray.
(d) Comparison of the level of knockdown of the transcription factor at 48 hrs (evaluated by qPCR; x-axis) and the number of genes differentially expressed in the knockdown experiment (y-axis).
(e) Comparison of the variance in knockdown efficiency between replicates for each transcription factor (evaluated by qPCR; x-axis) and the number of differentially expressed genes in the knockdown experiment (y-axis).

Differential expression analysis

Differential expression analysis

http://dx.doi.org:/10.1371/journal.pgen.1004226.g001

Figure 2. Effect sizes for differentially expressed genes.
Boxplots of absolute Log2(fold-change) between knockdown arrays and control arrays for all genes identified as differentially expressed in each experiment. Outliers are not plotted. The gray bar indicates the interquartile range across all genes differentially expressed in all knockdowns. Boxplots are ordered by the number of genes differentially expressed in each experiment. Outliers were not plotted.

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

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

Knocking down SREBF2 (1,286 genes differentially expressed), a key regulator of cholesterol homeostasis,

  • results in changes in the expression of genes that are
  • significantly enriched for cholesterol and sterol biosynthesis annotations.

While not all factors exhibited striking enrichments for relevant functional categories and pathways,

  • the overall picture is that perturbations of many of the factors
  • primarily affected pathways consistent with their known biology.

In order to assess functional TF binding, we next incorporated

  • binding maps together with the knockdown expression data.

We combined binding data based on DNase-seq footprints in 70 HapMap LCLs, reported by Degner et al. (Table S5)

  • and from ChIP-seq experiments in LCL GM12878, published by ENCODE.

We were thus able to obtain genome wide binding maps for a total of 131 factors that were either

  • directly targeted by an siRNA in our experiment (29 factors) or were
  • differentially expressed in one of the knockdown experiments.

We classified a gene as a bound target of a particular factor when

  • binding of that factor was inferred within 10kb of the transcription start site (TSS) of the target gene.

Using this approach, we found that the 131 TFs were bound

  • in proximity to a median of 1,922 genes per factor (range 11 to 7,053 target genes).

We considered binding of a factor to be functional if the target gene

  • was differentially expressed after perturbing the expression level the bound transcription factor.

We then asked about the concordance between

  • the transcription factor binding data and the knockdown expression data.
  •  the extent to which differences in gene expression levels following the knockdowns
  • might be predicted by binding of the transcription factors
  • within the putative regulatory regions of the responsive genes. and also
  • what proportion of putative target (bound) genes of a given TF were
  • differentially expressed following the knockdown of the factor.

Focusing only on the binding sites classified using the DNase-seq data
(which were assigned to a specific instance of the binding motif, unlike the ChIP data),

  • we examined sequence features that might distinguish functional binding.

In particular, whether binding at conserved sites was more likely to be functional  and

  • whether binding sites that better matched the known PWM for the factor were more likely to be functional.

We did not observe a significant shift in the conservation of functional binding sites (Wilcoxon rank sum P = 0.34),

  • but we did observe that binding around differentially expressed genes occurred at sites
  • that were significantly better matches to the canonical binding motif.

Figure 3. Intersecting binding data and expression data for each knockdown.
(a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1).
(b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors.
(c) Boxplot summarizing the distribution of the fraction of bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

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

Considering bound targets determined from either the ChIP-seq or DNase-seq data, we observed that

  • differentially expressed genes were associated with both
  • a higher number of binding events for the relevant factors within 10 kb of the TSS (P,10216; Figure 4A)
  • as well as with a larger number of different binding factors
    (considering the siRNA-targeted factor and any TFs that were DE in the knockdown; P,10216; Figure 4B).

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

Degree of binding correlated with function

Degree of binding correlated with function

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

We examined the distribution of binding about the TSS. Most factor binding was concentrated

  • near the TSS whether or not the genes were classified as differentially expressed (Figure 5A).
  • the distance from the TSS to the binding sites was significantly longer for differentially expressed genes (P,10216; Fig. 5B).

Figure 5. Distribution of functional binding about the TSS.
(a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming the distances and outliers were not plotted.

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

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

We investigated the distribution of factor binding across various chromatin states, as defined by Ernst et al. This dataset lists

  • regions of the genome that have been assigned to different activity states
  • based on ChIP-seq data for various histone modifications and CTCF binding.

For each knockdown, we separated binding events

  • by the genomic state in which they occurred and then
  • tested whether binding in that state was enriched around differentially expressed genes.

After correcting for multiple testing of genes that were differentially expressed.

  • 19 knockdowns showed significant enrichment for binding in ‘‘strong enhancers’’
  • four knockdowns had significant enrichments for ‘‘weak enhancers’’,
  • eight knockdowns showed significant depletion of binding in ‘‘active promoters’’ ,
  • six knockdowns had significant depletions for ‘‘transcription elongation’’,

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

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

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

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

We found no correlation between the number of paralogs and the fraction of bound targets that were differentially expressed. We also did not observe a significant correlation when we considered whether

  • the percent identity of the closest paralog might be predicative of
  • the fraction of bound genes that were differentially expressed following the knockdown (Figure S8).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

In addition to considering

  • the distinguishing characteristics of functional binding, we also examined
  • the direction of effect that perturbing a transcription factor had on the expression level of its direct targets.

We specifically addressed whether

  • knocking down a particular factor tended to drive expression of its putatively direct (namely, bound) targets up or down,
  • which can be used to infer that the factor represses or activates the target, respectively.

Transcription factors have traditionally been thought of primarily as activators, and previous work from our group is consistent with that notion. Surprisingly, the most straightforward inference from the present study is that

  • many of the factors function as repressors at least as often as they function as activators.
  1. EZH2 had a negative regulatory relationship with the largest fraction of direct targets (68.8%),
    consistent with – the known role of EZH2 as the active member of the Polycomb group complex PC2
  2. while JUND seemed to have a positive regulatory relationship with the largest fraction of direct targets (66.7%),
    and with – the biochemical characterization of the AP-1 complex (of which JUND is a component) as a transactivator.

More generally, however, our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

To more explicitly address the effect that our proximity-based definition of target genes might have on our analyses, we reanalyzed

  • the overlap between factor binding and differential expression following the knockdowns
  • using an independent, empirically determined set of target genes.

Thurman et al. used correlations in DNase hypersensitivity between

  • intergenic hypersensitive sites and promoter hypersensitive sites across diverse tissues
  • to assign intergenic regulatory regions to specific genes,
  • independently of proximity to a particular promoter.

We performed this alternative analysis in which we

  • assigned binding events to genes based on the classification of Thurman et al.

We then considered the overlap between binding and differential expression in this new data set. The results were largely

  • consistent with our proximity-based observations.

A median of 9.5% of genes that were bound by a factor were

  • also differentially expressed following the knockdown of that factor
    (compared to 11.1% when the assignment of binding sites to genes is based on proximity).

From the opposite perspective, a median of 28.0% of differentially expressed genes were bound by that factor
(compared to 32.3% for the proximity based definition). The results of this analysis are summarized in Table S7.

Our results should not be considered a comprehensive census of regulatory events in the human genome. Instead, we adopted a gene-centric approach,

  • focusing only on binding events near the genes for which we could measure expression
  • to learn some of the principles of functional transcription factor binding.

In light of our observations a reassessment of our estimates of binding may be warranted. In particular, because functional binding is skewed away from promoters (our system is apparently not well-suited to observe functional promoter binding, perhaps because of protection by large protein complexes),

  • a more conservative estimate of the fraction of binding that is indeed functional would not consider data within the promoter.

Importantly, excluding the putative promoter region from our analysis (i.e. only considering a window .1 kb from the TSS and ,10 kb from the TSS)

  • does not change our conclusions.

Considering this smaller window,

  • a median of 67.0% of expressed genes are still classified as bound by
  1. either the knocked down transcription factor or
  2. a downstream factors that is differentially expressed in each experiment,

yet a median of only 8.1% of the bound genes are

  • also differentially expressed after the knockdowns.

Much of what distinguishes functional binding (as we define it) has yet to be explained. We are unable to explain much of the differential expression observed in our experiments by the presence of least one relevant binding event. This may not be altogether surprising, as

  • we are only considering binding in a limited window around the transcription start site.

To address these issues, more factors should be perturbed to further evaluate the robustness of our results and to add insight. Together, such studies will help us develop a more sophisticated understanding of functional transcription factor binding in particular, the gene regulatory logic more generally.

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

E Shmelkov, Z Tang, I Aifantis, A Statnikov*
Biology Direct 2011; 6(15).  http://www.biology-direct.com/content/6/1/15

Recently the biological pathways have become a common and probably the most popular form of representing biochemical information for hypothesis generation and validation. These maps store wide knowledge of complex molecular interactions and regulations occurring in the living organism in a simple and obvious way, often using intuitive graphical notation. Two major types of biological pathways could be distinguished.

  1. Metabolic pathways incorporate complex networks of protein-based interactions and modifications, while
  2. signal transduction and transcriptional regulatory pathways are usually considered to provide information on mechanisms of transcription

While there are a lot of data collected on human metabolic processes,

  • the content of signal transduction and transcriptional regulatory pathways varies greatly in quality and completeness.

An indicative comparison of MYC transcriptional targets reported in ten different pathway databases reveals that these databases differ greatly from each other (Figure 1). Given that MYC is involved

  • in the transcriptional regulation of approximately 15% of all genes,

one cannot argue that the majority of pathway databases that contain

  • less than thirty putative transcriptional targets of MYC are even close to complete.

More importantly, to date there have been no prior genome-wide evaluation studies (that are based on genome-wide binding and gene expression assays) assessing pathway databases

Background: While pathway databases are becoming increasingly important in most types of biological and translational research, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors:

  1. MYC,
  2. NOTCH1,
  3. BCL6,
  4. TP53,
  5. AR,
  6. STAT1,
  7. RELA.

The employed benchmarking methodology first involves integrating

  • genome-wide binding with functional gene expression data
  • to derive direct targets of transcription factors.

Then the lists of experimentally obtained direct targets

  • are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

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

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

The only exception is MetaCore pathway database which

  • yields statistically significant intersection with experimental results in 84% cases.

The lists of experimentally derived direct targets obtained in this study can be used

  • to reveal new biological insight in transcriptional regulation,  and we
  • suggest novel putative therapeutic targets in cancer.

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

  • solid scientific evidence and rigorous empirical evaluation.

In the current study we perform

(1) an evaluation of ten commonly used pathway databases,

  • assessing the transcriptional regulatory pathways, considered in the current study as
  • the interactions of the type ‘transcription factor-transcriptional targets’.

This involves integration of human genome wide functional microarray or RNA-seq gene expression data with

  • protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms
  • to find direct transcriptional targets of the seven well known transcription factors:
  • MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The choice of transcription factors is based on their important role in oncogenesis and availability of binding and expression data in the public domain.

(2) the lists of experimentally derived direct targets are used to assess the quality and completeness of 84 transcriptional regulatory pathways from four publicly available (BioCarta, KEGG, WikiPathways and Cell Signaling Technology) and six commercial (MetaCore, Ingenuity Pathway Analysis, BKL TRANSPATH, BKL TRANSFAC, Pathway Studio and GeneSpring Pathways) pathway databases.

(3) We measure the overlap between pathways and experimentally obtained target genes and assess statistical significance of this overlap, and we demonstrate that experimentally derived lists of direct transcriptional targets

  • can be used to reveal new biological insight on transcriptional regulation.

We show this by analyzing common direct transcriptional targets of

  • MYC, NOTCH1 and RELA
  • that act in interconnected molecular pathways.

Detection of such genes is important as it could reveal novel targets of cancer therapy.

Figure 1 Number of genes in common between MYC transcriptional targets derived from ten different pathway databases. Cells are colored according to their values from white (low values) to red (high values). (not shown)

statistical methodology for comparison

statistical methodology for comparison

Figure 2 Illustration of statistical methodology for comparison between a gold-standard and a pathway database

Since we are seeking to compare gene sets from different studies/databases, it is essential to transform genes to standard identifiers. That is why we transformed all
gene sets to the HUGO Gene Nomenclature Committee approved gene symbols and names. In order to assess statistical significance of the overlap between the resulting gene sets, we used the hypergeometric test at 5% a-level with false discovery rate correction for multiple comparisons by the method of Benjamini and Yekutieli. The alternative hypothesis of this test is that two sets of genes (set A from pathway
database and set B from experiments) have greater number of genes in common than two randomly selected gene sets with the same number of genes as in sets A and B. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 700 in the experimentally derived list (gold-standard), and their intersection is 16 genes (Figure 2a). If we select on random from a total of
20,000 genes two sets with 300 and 700 genes each, their overlap would be greater or equal to 16 genes in 6.34% times. Thus, this overlap will not be statistically significant at 5% a-level (p = 0.0634). On the other hand, consider that for the pathway database #2, there are 30 direct targets of that transcription factor, and their intersection with the 700-gene gold-standard is only 6 genes. Even though the size of this intersection is rather small, it is unlikely to randomly select 30 genes (out of 20,000) with an overlap greater or equal to 6 genes with a 700-gene gold-standard (p = 0.0005, see Figure 2a). This overlap is statistically significant at 5% a-level.

We also calculate an enrichment fold change ratio (EFC) for every intersection between a gold-standard and a pathway database. For a given pair of a gold-standard and a pathway database, EFC is equal to the observed number of genes in their intersection, divided by the expected size of intersection under the null hypothesis (plus machine epsilon, to avoid division by zero). Notice however that larger values of EFC may correspond to databases that are highly incomplete and contain only a few relations. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 50 in the experimentally derived list (gold-standard), and their intersection is 30 genes (Figure 2b). If we select on random from a total of 20,000 genes two sets with 300 and 50 genes each, their expected overlap under the null hypothesis will be equal to 0.75. Thus, the EFC ratio will be equal to 40 (= 30/0.75). On the other hand, consider that for the pathway database #2, there are 2 direct
targets of that transcription factor, and their intersection with the 50-gene gold-standard is only 1 gene. Even though the expected overlap under the null hypothesis will be equal to 0.005 and EFC equal to 200 (5 times bigger than for the database #1), the size of this intersection with the gold-standard is 30 times less than for database #1 (Figure 2b).

Figure 3 Comparison between different pathway databases and experimentally derived gold-standards for all considered transcription factors. Value in a given cell is a number of overlapping genes between a gold-standard and a pathway-derived gene set. Cells
are colored according to their values from white (low values) to red (high values). Underlined values in red represent statistically significant intersections. (not shown)

Figure 4 Summary of the pathway databases assessment. Green cells represent statistically significant intersections between experimentally derived gold-standards and transcriptional regulatory pathways. White cells denote results that are not statistically significant. Numbers are the enrichment fold change ratios (EFC) calculated for each intersection. (not shown)

At the core of this study was creation of gold-standards of transcriptional regulation in humans that can be compared with target genes reported in transcriptional regulatory pathways. We focused on seven well known transcription factors and obtained gold-standards

  • by integrating genome-wide transcription factor-DNA binding data (from ChIP-chip, ChIP-seq, or ChIP-PET platforms)
  • with functional gene expression microarray and RNA-seq data.

The latter data allows to survey changes in the transcriptomes on a genome-wide scale

  • after the inhibition or over-expression of the transcription factor in question.

However, change in the expression of a particular gene could be caused either by the direct effect of the removal or introduction of a given transcription factor, as well as by an indirect effect, through the change in expression level of some other gene(s). It is essential

  • to integrate data from these two sources to
  • obtain an accurate list of gene targets that are directly regulated by a transcription factor.

It is worth noting that tested pathway databases typically do not give distinction between cell-lines, experimental conditions, and other details relevant to experimental systems in which data were obtained. These databases in a sense propose a ‘universal’ list of transcriptional targets. However, it is known that

  • transcriptional regulation in a cell is dynamic and works differently for different systems and stimuli.

This accentuates the major limitation of pathway databases and emphasizes

  • importance of deriving a specific list of transcriptional targets for the current experimental system.

In this study we followed the latter approach by developing gold-standards for specific cell characterized biological systems and experimental conditions.

The approach used here  for building gold-standards of direct mechanistic knowledge has several limitations. (see article).  Nevertheless, our results suggest that multiple transcription factors can co-operate and control both physiological differentiation and malignant transformation, as demonstrated utilizing combinatorial gene-profiling for

  • NOTCH1, MYC and RELA targets.

These studies might lead us to multi-pathway gene expression “signatures”

  • essential for the prediction of genes that could be targeted in cancer treatments.

In agreement with this hypothesis, several of the genes identified in our analysis have been suggested to be putative therapeutic targets in leukemia, with either preclinical or clinical trials underway (CDK4, CDK6, GSK3b, MYC, LCK, NFkB2, BCL2L1, NOTCH1).

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

I Izeddin†, V Récamier†‡, L Bosanac, II Cissé, L Boudarene, et al.
1Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Inserm, and CNRS UMR; 2Laboratoire Kastler Brossel, CNRS UMR, Departement de Physique et Institut de Biologie
de l’Ecole Normale Supérieure (IBENS), Paris, Fr; 3Transcription Imaging Consortium, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, US; + more.
Biophysics and structural biology | Cell biology eLife 2014;3:e02230. http://dx.doi.org:/10.7554/eLife.02230

Transcription factors are

  • proteins that control the expression of genes in the nucleus, and
  • they do this by binding to other proteins or DNA.

First, however, these regulatory proteins need to overcome the challenge of

  • finding their targets in the nucleus, which is crowded with other proteins and DNA.

Much research to date has focused on measuring how fast proteins can diffuse and spread out throughout the nucleus. However these measurements only make sense if these proteins have access to the same space within the nucleus.

Now, Izeddin, Récamier et al. have developed a new technique to track

  • single protein molecules in the nucleus of mammalian cells.

A transcription factor called c-Myc and another protein called P-TEFb

  • were tracked and while they diffused at similar rates,
  • they ‘explored’ the space inside the nucleus in very different ways.

Izeddin, Récamier et al. found that c-Myc explores the nucleus in a so-called ‘non-compact’ manner: this means that it

  • can move almost everywhere inside the nucleus, and has an equal chance
  • of reaching any target regardless of its position in this space.

P-TEFb, on the other hand, searches

  • the nucleus in a ‘compact’ way.

This means that it is constrained to follow a specific path

  • through the nucleus and is therefore guided to its potential targets.

Izeddin, Récamier et al. explain that

  • the different ‘search strategies’ used by these two proteins
  • influence how long it takes them to find their targets and
  • how far they can travel in a given time.

These findings, together with information about

  • where and when different proteins interact in the nucleus,

will be essential to understand how the organization of the genome within the nucleus

  • can control the expression of genes.

The next challenge will now be to

  • uncover what determines a
  • protein’s search strategy in the nucleus, as well as
  • the potential ways that this strategy might be regulated.

Mueller et al., 2010; Normanno et al., 2012). These transient interactions are essential to ensure a fine regulation of binding site occupancy—by competition or by altering the TF concentration—but must also be persistent enough to enable the assembly of multicomponent complexes (Dundr, 2002; Darzacq and Singer, 2008; Gorski et al., 2008; Cisse et al., 2013).
In parallel to the experimental evidence of the fast diffusive motion of nuclear factors, our understanding of the intranuclear space has evolved from a homogeneous environment to an organelle where spatial arrangement among genes and regulatory sequences play an important role in transcriptional control (Heard and Bickmore, 2007). The nucleus of eukaryotes displays a hierarchy of organized structures (Gibcus and Dekker, 2013) and is often referred to as a
crowded environment.
How crowding influences transport properties of macromolecules and organelles in the cell is a fundamental question in quantitative molecular biology. While a restriction of the available space for diffusion can slow down transport processes, it can also channel molecules towards their targets increasing their chance to meet interacting partners. A widespread observation in quantitative cell biology is that the diffusion of molecules is anomalous, often attributed to crowding in the nucleoplasm, cytoplasm, or in the membranes of the cell (Höfling and Franosch, 2013). An open debate remains on how to determine whether diffusion is anomalous or normal (Malchus and Weiss, 2009; Saxton, 2012), and the mechanisms behind anomalous diffusion (Saxton, 2007). The answer to these questions bears important consequences for the understanding of the biochemical reactions of the cell.
The problem of diffusing molecules in non-homogenous media has been investigated in different fields. Following the seminal work of de Gennes (1982a), (1982b) in polymer physics, the study of diffusivity of particles and their reactivity has been generalized to random or disordered media (Kopelman, 1986; Lindenberg et al., 1991). These works have set a framework to interpret the mobility of macromolecular complexes in the cell, and recently in terms of kinetics of biochemical reactions (Condamin et al., 2007). Experimental evidence has also been found, showing the influence
of the glass-like properties of the bacterial cytoplasm in the molecular dynamics of intracellular processes (Parry et al., 2014). These studies demonstrate that the geometry of the medium in which diffusion takes place has important repercussions for the search kinetics of molecules. The notion of compact and non-compact exploration was introduced by de Gennes (1982a) in the context of dense polymers and describes two fundamental types of diffusive behavior. While a non-compact explorer leaves a significant number of available sites unvisited, a compact explorer performs a redundant
exploration of the space. In chemistry, the influence of compactness is well established to describe dimensional effects on reaction rates (Kopelman, 1986).
In this study, we aim to elucidate the existence of different types of mobility of TFs in the eukaryotic nucleus, as well as the principles governing nuclear exploration of factors relevant to transcriptional control. To this end, we used single-molecule (SM) imaging to address the relationship between the nuclear geometry and the search dynamics of two nuclear factors having distinct functional roles: the proto-oncogene c-Myc and the positive transcription elongation factor (P-TEFb). c-Myc is a basic helix-loop-helix DNA-binding transcription factor that binds to E-Boxes; 18,000 E-boxes are found in the genome, and c-Myc affects the transcription of numerous genes (Gallant and Steiger, 2009).
Recently, c-Myc has been demonstrated to be a general transcriptional activator upregulating transcription of nearly all genes (Lin et al., 2012; Nie et al., 2012). P-TEFb is an essential actor in the transcription regulation driven by RNA Polymerase II. P-TEFb is a cyclin-dependent kinase, comprising a CDK9 and a Cyclin T subunit. It phosphorylates the elongation control factors SPT5 and NELF to allow productive elongation of class II gene transcription (Wada et al., 1998). The carboxy-terminal domain (CTD) of the catalytic subunit RPB1 of polymerase II is also a major target of P-TEFb (Zhou et al., 2012). c-Myc and P-TEFb are therefore two good examples of transcriptional regulators binding to numerous sites in the nucleus; the latter binds to the transcription machinery itself and the former directly to DNA.

Single particle tracking (SPT) constitutes a powerful method to probe the mobility of molecules in living cells (Lord et al., 2010). In the nucleus, SPT has been first employed to investigate the dynamics of mRNAs (Fusco et al., 2003; Shav-Tal et al., 2004) or for rheological measurements of the nucleoplasm using inert probes (Bancaud et al., 2009). Recently, the tracking of single nuclear factors has been facilitated by the advent of efficient in situ tagging methods such as Halo
tags (Mazza et al., 2012). An alternative approach takes advantage of photoconvertible tags (Lippincott-Schwartz and Patterson, 2009) and photoactivated localization microscopy (PALM) (Betzig et al., 2006; Hess et al., 2006). Single particle tracking PALM (sptPALM) was first used to achieve high-density diffusion maps of membrane proteins (Manley et al., 2008). However, spt-PALM experiments have typically been limited to proteins with slow mobility (Manley et al., 2008) or those that undergo restricted motions (Frost et al., 2010; English et al., 2011).

Recently, by inclusion of light-sheet illumination, it has been used to determine the binding characteristics of TFs to DNA (Gebhardt et al., 2013). In this study, we developed a new sptPALM procedure adapted for the recording of individual proteins rapidly diffusing in the nucleus of mammalian cells. We used the photoconvertible fluorophore Dendra2 (Gurskaya et al., 2006) and took advantage of tilted illumination (Tokunaga et al., 2008). A careful control of the photoconversion rate minimized the background signal due to out-of-focus activated molecules, and we could thus follow the motion of individual proteins freely diffusing within the nuclear volume. With this sptPALM technique, we recorded large data sets (on the order of 104 single translocations in a single imaging session), which were essential for a proper statistical analysis of the search dynamics.
We applied our technique to several nuclear proteins and found that diffusing factors do not sense a unique nucleoplasmic architecture: c-Myc and P-TEFb adopt different nuclear space-exploration strategies, which drastically change the way they reach their specific targets. The differences observed between the two factors were not due to their diffusive kinetic parameters but to the geometry of their exploration path. c-Myc and our control protein, ‘free’ Dendra2, showed free diffusion in a three-dimensional nuclear space. In contrast, P-TEFb explored the nuclear volume by sampling a space of reduced dimensionality, displaying characteristics of exploration constrained in fractal structures.
The role of the space-sampling mode in the search strategy has long been discussed from a theoretical point of view (de Gennes, 1982a; Kopelman, 1986; Lindenberg et al., 1991). Our experimental results support the notion that it could indeed be a key parameter for diffusion-limited chemical reactions in the closed environment of the nucleus (Bénichou et al., 2010). We discuss the implications of our observations in terms of gene expression control, and its relation to the spatial organization of genes within the nucleus.

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What is the Future for Genomics in Clinical Medicine?


What is the Future for Genomics in Clinical Medicine?

Author and Curator: Larry H Bernstein, MD, FCAP

 

Introduction

This is the last in a series of articles looking at the past and future of the genome revolution.  It is a revolution indeed that has had a beginning with the first phase discovery leading to the Watson-Crick model, the second phase leading to the completion of the Human Genome Project, a third phase in elaboration of ENCODE.  But we are entering a fourth phase, not so designated, except that it leads to designing a path to the patient clinical experience.
What is most remarkable on this journey, which has little to show in treatment results at this time, is that the boundary between metabolism and genomics is breaking down.  The reality is that we are a magnificent “magical” experience in evolutionary time, functioning in a bioenvironment, put rogether like a truly complex machine, and with interacting parts.  What are those parts – organelles, a genetic message that may be constrained and it may be modified based on chemical structure, feedback, crosstalk, and signaling pathways.  This brings in diet as a source of essential nutrients, exercise as a method for delay of structural loss (not in excess), stress oxidation, repair mechanisms, and an entirely unexpected impact of this knowledge on pharmacotherapy.  I illustrate this with some very new observations.

Gutenberg Redone

The first is a recent talk on how genomic medicine has constructed a novel version of the “printing press”, that led us out of the dark ages.

Topol_splash_image

In our series The Creative Destruction of Medicine, I’m trying to get into critical aspects of how we can Schumpeter or reboot the future of healthcare by leveraging the big innovations that are occurring in the digital world, including digital medicine.

We have this big thing about evidence-based medicine and, of course, the sanctimonious randomized, placebo-controlled clinical trial. Well, that’s great if one can do that, but often we’re talking about needing thousands, if not tens of thousands, of patients for these types of clinical trials. And things are changing so fast with respect to medicine and, for example, genomically guided interventions that it’s going to become increasingly difficult to justify these very large clinical trials.

For example, there was a drug trial for melanoma and the mutation of BRAF, which is the gene that is found in about 60% of people with malignant melanoma. When that trial was done, there was a placebo control, and there was a big ethical charge asking whether it is justifiable to have a body count. This was a matched drug for the biology underpinning metastatic melanoma, which is essentially a fatal condition within 1 year, and researchers were giving some individuals a placebo.

The next observation is a progression of what he have already learned. The genome has a role is cellular regulation that we could not have dreamed of 25 years ago, or less. The role is far more than just the translation of a message from DNA to RNA to construction of proteins, lipoproteins, cellular and organelle structures, and more than a regulation of glycosidic and glycolytic pathways, and under the influence of endocrine and apocrine interactions. Despite what we have learned, the strength of inter-molecular interactions, strong and weak chemical bonds, essential for 3-D folding, we know little about the importance of trace metals that have key roles in catalysis and because of their orbital structures, are essential for organic-inorganic interplay. This will not be coming soon because we know almost nothing about the intracellular, interstitial, and intrvesicular distributions and how they affect the metabolic – truly metabolic events.

I shall however, use some new information that gives real cause for joy.

Reprogramming Alters Cells’ Fate

Kathy Liszewski
Gordon Conference  Report: June 21, 2012;32(11)
New and emerging strategies were showcased at Gordon Conference’s recent “Reprogramming Cell Fate” meeting. For example, cutting-edge studies described how only a handful of key transcription factors were needed to entirely reprogram cells.
M. Azim Surani, Ph.D., Marshall-Walton professor at the Gurdon Institute, University of Cambridge, U.K., is examining cellular reprogramming in a mouse model. Epiblast stem cells are derived from the early-stage embryonic stage after implantation of blastocysts, about six days into development, and retain the potential to undergo reversion to embryonic stem cells (ESCs) or to PGCs.”  They report two critical steps both of which are needed for exploring epigenetic reprogramming.  “Although there are two X chromosomes in females, the inactivation of one is necessary for cell differentiation. Only after epigenetic reprogramming of the X chromosome can pluripotency be acquired. Pluripotent stem cells can generate any fetal or adult cell type but are not capable of developing into a complete organism.”
The second read-out is the activation of Oct4, a key transcription factor involved in ESC development. The expression of Oct4 in epiSCs requires its proximal enhancer.  Dr. Surani said that their cell-based system demonstrates how a systematic analysis can be performed to analyze how other key genes contribute to the many-faceted events involved in reprogramming the germline.
Reprogramming Expressway
A number of other recent studies have shown the importance of Oct4 for self-renewal of undifferentiated ESCs. It is sufficient to induce pluripotency in neural tissues and somatic cells, among others. The expression of Oct4 must be tightly regulated to control cellular differentiation. But, Oct4 is much more than a simple regulator of pluripotency, according to Hans R. Schöler, Ph.D., professor in the department of cell and developmental biology at the Max Planck Institute for Molecular Biomedicine.
Oct4 has a critical role in committing pluripotent cells into the somatic cellular pathway. When embryonic stem cells overexpress Oct4, they undergo rapid differentiation and then lose their ability for pluripotency. Other studies have shown that Oct4 expression in somatic cells reprograms them for transformation into a particular germ cell layer and also gives rise to induced pluripotent stem cells (iPSCs) under specific culture conditions.
Oct4 is the gatekeeper into and out of the reprogramming expressway. By modifying experimental conditions, Oct4 plus additional factors can induce formation of iPSCs, epiblast stem cells, neural cells, or cardiac cells. Dr. Schöler suggests that Oct4 a potentially key factor not only for inducing iPSCs but also for transdifferention.  “Therapeutic applications might eventually focus less on pluripotency and more on multipotency, especially if one can dedifferentiate cells within the same lineage. Although fibroblasts are from a different germ layer, we recently showed that adding a cocktail of transcription factors induces mouse fibroblasts to directly acquire a neural stem cell identity.
Stem cell diagram illustrates a human fetus st...

Stem cell diagram illustrates a human fetus stem cell and possible uses on the circulatory, nervous, and immune systems. (Photo credit: Wikipedia)

English: Embryonic Stem Cells. (A) shows hESCs...

English: Embryonic Stem Cells. (A) shows hESCs. (B) shows neurons derived from hESCs. (Photo credit: Wikipedia)

Transforming growth factor beta (TGF-β) is a s...

Transforming growth factor beta (TGF-β) is a secreted protein that controls proliferation, cellular differentiation, and other functions in most cells. http://en.wikipedia.org/wiki/TGFbeta (Photo credit: Wikipedia)

Pioneer Transcription Factors

Pioneer transcription factors take the lead in facilitating cellular reprogramming and responses to environmental cues. Multicellular organisms consist of functionally distinct cellular types produced by differential activation of gene expression. They seek out and bind specific regulatory sequences in DNA. Even though DNA is coated with and condensed into a thick fiber of chromatin. The pioneer factor, discovered by Prof. KS Zaret at UPenn SOM in 1996, he says, endows the competence for gene activity, being among the first transcription factors to engage and pry open the target sites in chromatin.
FoxA factors, expressed in the foregut endoderm of the mouse,are necessary for induction of the liver program. They found that nearly one-third of the DNA sites bound by FoxA in the adult liver occur near silent genes

A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication

ME Hubbi, K Shitiz, DM Gilkes, S Rey,….GL Semenza. Johns Hopkins University SOM
Sci. Signal 2013; 6(262) 10pgs. [DOI: 10.1126/scisignal.2003417]   http:dx.doi.org/10.1126/scisignal.2003417

http://SciSignal.com/A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication/

Many of the cellular responses to reduced O2 availability are mediated through the transcriptional activity of hypoxia-inducible factor 1 (HIF-1). We report a role for the isolated HIF-1α subunit as an inhibitor of DNA replication, and this role was independent of HIF-1β and transcriptional regulation. In response to hypoxia, HIF-1α bound to Cdc6, a protein that is essential for loading of the mini-chromosome maintenance (MCM) complex (which has DNA helicase activity) onto DNA, and promoted the interaction between Cdc6 and the MCM complex. The binding of HIF-1α to the complex decreased phosphorylation and activation of the MCM complex by the kinase Cdc7. As a result, HIF-1α inhibited firing of replication origins, decreased DNA replication, and induced cell cycle arrest in various cell types. To whom correspondence should be addressed. E-mail: gsemenza@jhmi.edu
Citation: M. E. Hubbi, Kshitiz, D. M. Gilkes, S. Rey, C. C. Wong, W. Luo, D.-H. Kim, C. V. Dang, A. Levchenko, G. L. Semenza, A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication. Sci. Signal. 6, ra10 (2013).

Identification of a Candidate Therapeutic Autophagy-inducing Peptide

Nature 2013;494(7436).    http://nature.com/Identification_of_a_candidate_therapeutic_autophagy-inducing_peptide/   http://www.ncbi.nlm.nih.gov/pubmed/23364696
http://www.readcube.com/articles/10.1038/nature11866

Beth Levine and colleagues have constructed a cell-permeable peptide derived from part of an autophagy protein called beclin 1. This peptide is a potent inducer of autophagy in mammalian cells and in vivo in mice and was effective in the clearance of several viruses including chikungunya virus, West Nile virus and HIV-1.

Could this small autophagy-inducing peptide may be effective in the prevention and treatment of human diseases?

PR-Set7 Is a Nucleosome-Specific Methyltransferase that Modifies Lysine 20 of

Histone H4 and Is Associated with Silent Chromatin

K Nishioka, JC Rice, K Sarma, H Erdjument-Bromage, …, D Reinberg.   Molecular Cell, Vol. 9, 1201–1213, June, 2002, Copyright 2002 by Cell Press   http://www.cell.com/molecular-cell/abstract/S1097-2765(02)00548-8

http://www.sciencedirect.com/science/article/pii/S1097276502005488           http://www.ncbi.nlm.nih.gov/pubmed/12086618
http://www.cienciavida.cl/publications/b46e8d324fa4aefa771c4d6ece4d2e27_PR-Set7_Is_a_Nucleosome-Specific.pdf

We have purified a human histone H4 lysine 20methyl-transferase and cloned the encoding gene, PR/SET07. A mutation in Drosophila pr-set7 is lethal: second in-star larval death coincides with the loss of H4 lysine 20 methylation, indicating a fundamental role for PR-Set7 in development. Transcriptionally competent regions lack H4 lysine 20 methylation, but the modification coincided with condensed chromosomal regions polytene chromosomes, including chromocenter euchromatic arms. The Drosophila male X chromosome, which is hyperacetylated at H4 lysine 16, has significantly decreased levels of lysine 20 methylation compared to that of females. In vitro, methylation of lysine 20 and acetylation of lysine 16 on the H4 tail are competitive. Taken together, these results support the hypothesis that methylation of H4 lysine 20 maintains silent chromatin, in part, by precluding neighboring acetylation on the H4 tail.

Next-Generation Sequencing vs. Microarrays

Shawn C. Baker, Ph.D., CSO of BlueSEQ
GEN Feb 2013
With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications. The immense number of journal articles citing NGS technologies it looks like NGS is no longer just for the early adopters. Once thought of as cost prohibitive and technically out of reach, NGS has become a mainstream option for many laboratories, allowing researchers to generate more complete and scientifically accurate data than previously possible with microarrays.

Gene Expression

Researchers have been eager to use NGS for gene expression experiments for a detailed look at the transcriptome. Arrays suffer from fundamental ‘design bias’ —they only return results from those regions for which probes have been designed. The various RNA-Seq methods cover all aspects of the transcriptome without any a priori knowledge of it, allowing for the analysis of such things as novel transcripts, splice junctions and noncoding RNAs. Despite NGS advancements, expression arrays are still cheaper and easier when processing large numbers of samples (e.g., hundreds to thousands).
Methylation
While NGS unquestionably provides a more complete picture of the methylome, whole genome methods are still quite expensive. To reduce costs and increase throughput, some researchers are using targeted methods, which only look at a portion of the methylome. Because details of exactly how methylation impacts the genome and transcriptome are still being investigated, many researchers find a combination of NGS for discovery and microarrays for rapid profiling.

Diagnostics

They are interested in ease of use, consistent results, and regulatory approval, which microarrays offer. With NGS, there’s always the possibility of revealing something new and unexpected. Clinicians aren’t prepared for the extra information NGS offers. But the power and potential cost savings of NGS-based diagnostics is alluring, leading to their cautious adoption for certain tests such as non-invasive prenatal testing.
Cytogenetics
Perhaps the application that has made the least progress in transitioning to NGS is cytogenetics. Researchers and clinicians, who are used to using older technologies such as karyotyping, are just now starting to embrace microarrays. NGS has the potential to offer even higher resolution and a more comprehensive view of the genome, but it currently comes at a substantially higher price due to the greater sequencing depth. While dropping prices and maturing technology are causing NGS to make headway in becoming the technology of choice for a wide range of applications, the transition away from microarrays is a long and varied one. Different applications have different requirements, so researchers need to carefully weigh their options when making the choice to switch to a new technology or platform. Regardless of which technology they choose, genomic researchers have never had more options.

Sequencing Hones In on Targets

Greg Crowther, Ph.D.

GEN Feb 2013

Cliff Han, PhD, team leader at the Joint Genome Institute in the Los Alamo National Lab, was one of a number of scientists who made presentations regarding target enrichment at the “Sequencing, Finishing, and Analysis in the Future” (SFAF) conference in Santa Fe, which was co-sponsored by the Los Alamos National Laboratory and DOE Joint Genome Institute. One of the main challenges is that of target enrichment: the selective sequencing of genomic or transcriptomic regions. The polymerase chain reaction (PCR) can be considered the original target-enrichment technique and continues to be useful in contexts such as genome finishing. “One target set is the unique gaps—the gaps in the unique sequence regions. Another is to enrich the repetitive sequences…ribosomal RNA regions, which together are about 5 kb or 6 kb.” The unique-sequence gaps targeted for PCR with 40-nucleotide primers complementary to sequences adjacent to the gaps, did not yield the several-hundred-fold enrichment expected based on previously published work. “We got a maximum of 70-fold enrichment and generally in the dozens of fold of enrichment,” noted Dr. Han.

“We enrich the genome, put the enriched fragments onto the Pacific Biosciences sequencer, and sequence the repeats,” continued Dr. Han. “In many parts of the sequence there will be a unique sequence anchored at one or both ends of it, and that will help us to link these scaffolds together.” This work, while promising, will remain unpublished for now, as the Joint Genome Institute has shifted its resources to other projects.
At the SFAF conference Dr. Jones focused on going beyond basic target enrichment and described new tools for more efficient NGS research. “Hybridization methods are flexible and have multiple stop-start sites, and you can capture very large sizes, but they require library prep,” said Jennifer Carter Jones, Ph.D., a genomics field applications scientist at Agilent. “With PCR-based methods, you have to design PCR primers and you’re doing multiplexed PCR, so it’s limited in the size that you can target. But the workflow is quick because there’s no library preparation; you’re just doing PCR.” She discussed Agilent’s recently acquired HaloPlex technology, a hybrid system that includes both a hybridization step and a PCR step. Because no library preparation is required, sequencing results can be obtained in about six hours, making it suitable for clinical uses. However, the hybridization step allows capture of targets of up to 5 megabases—longer than purely PCR-based methods can deliver. The Agilent talk also provided details on the applications of SureSelect, the company’s hybridization technology, to Methyl-Seq and RNA-Seq research. With this technology, 120-mer baits hybridize to targets, then are pulled down with streptavidin-coated magnetic beads.
These are selections from the SFAF conference, which is expected to be a boost to work on the microbiome, and lead to infectious disease therapeutic approaches.

Summary

We have finished a breathtaking ride through the genomic universe in several sessions.  This has been a thorough review of genomic structure and function in cellular regulation.  The items that have been discussed and can be studied in detail include:

  1.  the classical model of the DNA structure
  2. the role of ubiquitinylation in managing cellular function and in autophagy, mitophagy, macrophagy, and protein degradation
  3. the nature of the tight folding of the chromatin in the nucleus
  4. intramolecular bonds and short distance hydrophobic and hydrophilic interactions
  5. trace metals in molecular structure
  6. nuclear to membrane interactions
  7. the importance of the Human Genome Project followed by Encode
  8. the Fractal nature of chromosome structure
  9. the oligomeric formation of short sequences and single nucletide polymorphisms (SNPs)and the potential to identify drug targets
  10. Enzymatic components of gene regulation (ligase, kinases, phosphatases)
  11. Methods of computational analysis in genomics
  12. Methods of sequencing that have become more accurate and are dropping in cost
  13. Chromatin remodeling
  14. Triplex and quadruplex models not possible to construct at the time of Watson-Crick
  15. sequencing errors
  16. propagation of errors
  17. oxidative stress and its expected and unintended effects
  18. origins of cardiovascular disease
  19. starvation and effect on protein loss
  20. ribosomal damage and repair
  21. mitochondrial damage and repair
  22. miscoding and mutational changes
  23. personalized medicine
  24. Genomics to the clinics
  25. Pharmacotherapy horizons
  26. driver mutations
  27. induced pluripotential embryonic stem cell (iPSCs)
  28. The association of key targets with disease
  29. The real possibility of moving genomic information to the bedside
  30. Requirements for the next generation of electronic health record to enable item 29

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

https://pharmaceuticalintelligence.com/2013/01/14/oogonial-stem-cells-purified-a-view-towards-the-future-of-reproductive-biology/   SSaha

https://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/ RSaxena

https://pharmaceuticalintelligence.com/2012/08/22/a-possible-light-by-stem-cell-therapy-in-painful-dark-of-osteoarthritis-kartogenin-a-small-molecule-differentiates-stem-cells-to-chondrocyte-healthy-cartilage-cells/   ASarkar and RSaxena

https://pharmaceuticalintelligence.com/2012/08/07/human-embryonic-pluripotent-stem-cells-and-healing-post-myocardial-infarction/    LHB

https://pharmaceuticalintelligence.com/2013/02/03/genome-wide-detection-of-single-nucleotide-and-copy-number-variation-of-a-single-human-cell/  SJWilliams

https://pharmaceuticalintelligence.com/2013/01/09/gene-therapy-into-healthy-heart-muscle-reprogramming-scar-tissue-in-damaged-hearts/ ALev-Ari

https://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/  SJWilliams

https://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/  TBarliya

Read Full Post »


CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

Curator: Larry H Bernstein, MD, FCAP

Introduction and purpose

This material goes beyond the Initiation Phase of Molecular Biology, Part I.

https://pharmaceuticalintelligence.com/2013/02/08/the-initiation-and-growth-of-molecular-biology-and-genomics/
Part II reviews the Human Genome Project and the decade beyond.

In a three part series:
Part IIA.  CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way
Part IIB.  CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
Part IIC.  CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease

Part III will conclude with Ubiquitin, it’s Role in Signaling and Regulatory Control.
Part I reviewed the huge expansion of the biological research enterprise after the Second World War. It concentrated on the

  • discovery of cellular structures,
  • metabolic function, and
  • creation of a new science of Molecular Biology.

Part II follows the race to delineation of the Human Genome, discovery methods and fundamental genomic patterns that are ancient in both animal and plant speciation. But it explores both the complexity and the systems view of the architecture that underlies and understanding of the genome.

These articles review a web-like connectivity between inter-connected scientific discoveries, as significant findings have led to novel hypotheses and many expectations over the last 75 years. This largely post WWII revolution has driven our understanding of biological and medical processes at an exponential pace owing to successive discoveries of

  • chemical structure,
  • the basic building blocks of DNA  and proteins,
  • nucleotide and protein-protein interactions,
  • protein folding, allostericity,
  • genomic structure,
  • DNA replication,
  • nuclear polyribosome interaction, and
  • metabolic control.

In addition, the emergence of methods for

  • copying,
  • removal,
  • insertion,
  • improvements in structural analysis
  • developments in applied mathematics that have transformed the research framework.

Part IIA:

CRACKING THE CODE OF HUMAN LIFE:

Milestones along the Way

A NOVA interview with Francis Collins (NHGRI) (FC), J. Craig Venter (CELERA)(JCV), and Eric Lander (EL).
RK: For the past ten years, scientists all over the world have been painstakingly trying to read the tiny instructions buried inside our DNA. And now, finally, the “Human Genome” has been decoded.
EL: The genome is a storybook that’s been edited for a couple billion years.
The following will address the odd similarity of genes between man and yeast

EL: In the nucleus of your cell the DNA molecule resides that is about 10 angstroms wide curled up, but the amount of curling is limited by the negative charges that repel one another, but there are folds upon folds. If the DNA is stretched the length of the DNA would be thousands of feet.
EL: We have known for 2000 years that your kids look a lot like you. Well it’s because you must pass them instructions that give them the eyes, the hair color, and the nose shape they have. RK: Cracking the code of those minuscule differences in DNA that influence health and illness is what the Human Genome Project is all about. Since 1990, scientists all over the world have been involved in the effort to read all three billion As, Ts, Gs, and Cs of human DNA.  It took 10 years to find the one genetic mistake that causes cystic fibrosis. Another 10 years to find the gene for Huntington’s disease. Fifteen years to find one of the genes that increase the risk for breast cancer. One letter at a time, painfully slowly…     And then came the revolution. In the last ten years the entire process has been computerized. The computations can do a thousand every second and that has made all the difference. EL: This is basically a parts list with a lot of parts. If you take an airplane, a Boeing 777, I think it has like 100,000 parts. If I gave you a parts list for the Boeing 777 in one sense you’d know 100,000 components, screws and wires and rudders and things like that.  But you wouldn’t know how to put it together, or why it flies. We now have a parts list, and that’s not enough to understand why it flies.

The Human Genome

The Human Genome (Photo credit: dullhunk)

A Quest For Clarity

Tracy Vence is a senior editor of Genome Technology
Tracy Vence @GenomeTechMag
Projects supported by the US National Institutes of Health will have produced 68,000 total human genomes — around 18,000 of those whole human genomes — through the end of this year, National Human Genome Research Institute estimates indicate. And in his book, The Creative Destruction of Medicine, the Scripps Research Institute’s Eric Topol projects that 1 million human genomes will have been sequenced by 2013 and 5 million by 2014.
Daniel MacArthur, a group leader in Massachusetts General Hospital’s Analytic and Translational Genetics Unit estimates that “From a capacity perspective … millions of genomes are not that far off. If you look at the rate that we’re scaling, we can certainly achieve that.”    The prospect of so many genomes has brought clinical interpretation into focus. But there is an important distinction to be made between the interpretation of an apparently healthy person’s genome and that of an individual who is already affected by a disease.
In an April Science Translational Medicine paper, Johns Hopkins University School of Medicine‘s Nicholas Roberts and his colleagues reported that personal genome sequences for healthy monozygotic twin pairs are not predictive of significant risk for 24 different diseases in those individuals. The researchers concluded that whole-genome sequencing was not likely to be clinically useful. Ambiguities have clouded even the most targeted interpretation efforts.

  • Technological challenges,
  • meager sample sizes,
  • a need for increased,
  • fail-safe automation and most important
  • a lack of community-wide standards for the task.

have hampered researchers’ attempts to reliably interpret the clinical significance of genomic variation.

How signals from the cell surface affect transcription of genes in the nucleus.

James Darnell, Jr., MD, Astor Professor, Rockefeller
After graduation from Washington University School of Medicine he worked with Francois Jacob at the Pasteur Institute in Paris and served as Vice President for Academic Affairs at Rockefeller in 1990-91. He is the coauthor with S.E. Luria of General Virology and the founding author with Harvey Lodish and David Baltimore of Molecular Cell Biology, now in its sixth edition. His book RNA, Life’s Indispensable Molecule was published in July 2011 by Cold Spring Harbor Laboratory Press. A member of the National Academy of Sciences since 1973, recipient of  numerous awards, including the 2003 National Medal of Science, the 2002 Albert Lasker Award.
Using interferon as a model cytokine, the Darnell group discovered that cell transcription was quickly changed by binding of cytokines to the cell surface. The bound interferon led to the tyrosine phosphorylation of latent cytoplasmic proteins now called STATs (signal transducers and activators of transcription) that dimerize by

  • reciprocal phosphotyrosine-SH2 interchange.
  • accumulate in the nucleus,
  • bind DNA and drive transcription.

This pathway has proved to be of wide importance with seven STATs now known in mammals that take part in a wide variety of developmental and homeostatic events in all multicellular animals. Crystallographic analysis defined functional domains in the STATs, and current attention is focused on two areas:

  • how the STATs complete their cycle of  activation and inactivation, which requires regulated tyrosine dephosphorylation; and how
  • persistent activation of STAT3 that occurs in a high proportion of many human cancers contributes to blocking apoptosis in cancer cells.

Current efforts are devoted to inhibiting STAT3 with modified peptides that can enter cells.

Cell cycle regulation and the cellular response to genotoxic stress

Stephen J Elledge, PhD, Gregor Mendel Professor of Genetics and Medicine, Investigator, Howard Hughes Medical Institute, Harvard Medical School
As a postdoctoral fellow at Stanford working on eukaryotic homologous recombination, he serendipitously found a family of genes known as ribonucleotide reductases. He subsequently showed that

  • these genes are activated by DNA damage and
  • could serve as tools to help scientists dissect the signaling pathways
  • through which cells sense and respond to DNA damage and replication stress.

At Baylor College of Medicine he made a second major breakthrough with the discovery of the cyclin-dependent kinase 2 gene (Cdk2), which

  • controls the G1-to-S cell cycle transition,
  • an entry checkpoint for the cell proliferation cycle and
  • a critical regulatory step in tumorigenesis.

From there, using a novel “two-hybrid” cloning method he developed, Elledge and Wade Harper, PhD, proceeded to

  • isolate several members of the Cdk2-inhibitory family.

Their discoveries included the p21 and p57 genes, mutations in the latter (responsible for Beckwith-Wiedemann syndrome), characterized by somatic overgrowth and increased cancer risk. Elledge is also recognized for his work in understanding

  • proteome remodeling through ubiquitin-mediated proteolysis.
  • they identified F-box proteins that regulate protein degradation in the cell by
  1. binding to specific target protein sequences and then
  2. marking them with ubiquitin for destruction by the cell’s proteasome machinery.

This breakthrough resulted in

  • the elucidation of the cullin ubiquitin ligase family,
  • which controls regulated protein stability in eukaryotes.

nature10774-f5.2  nature10774-f3.2   ubiquitin structures  Rn1  Rn2

Elledge’s recent research has focused on the cellular mechanisms underlying DNA damage detection and cancer using genetic technologies. In collaboration with Cold Spring Harbor Laboratory researcher Gregory Hannon, PhD, Elledge has generated complete human and mouse short hairpin RNA (shRNA) libraries for genome-wide loss-of-function studies. Their efforts have led to

  • the identification of a number of tumor suppressor proteins
  • genes upon which cancer cells uniquely depend for survival.

This work led to the development of the “non-oncogene addiction” concept. This is noted as follows:

  • proteome remodeling through ubiquitin-mediated proteolysis
  • F-box proteins regulate protein degradation in the cell by binding to specific target protein sequences
  • and then marking them with ubiquitin for destruction by the cell’s proteasome machinery
  • elucidation of the cullin ubiquitin ligase family, which controls regulated protein stability in eukaryotes

Playing the dual roles of inventor and investigator, Elledge developed original techniques to define

  • what drives the cell cycle and
  • how cells respond to DNA damage.

By using these tools, he and his colleagues have identified multiple genes involved in cell-cycle regulation.

Elledge’s work has earned him many awards, including a 2001 Paul Marks Prize for Cancer Research and a 2003 election to the National Academy of Sciences. In his Inaugural Article (1), published in this issue of PNAS, Elledge and his colleagues describe the function of Fbw7, a protein involved in controlling cell proliferation (see below). Elledge studied the error-prone DNA repair mechanism in E-Coli (Escherichia coli) called SOS mutagenesis for his PhD thesis at MIT. His work identified  and described

  • the regulation of a group of enzymes now known as error-prone polymerases,
  • the first members of which were the umuCD genes in E. coli.

It was then that he developed a new cloning tool. Elledge invented a technique that allowed him to approach future cloning problems of this type with great rapidity. With the new technique, “you could make large libraries in lambda that behave like plasmids. We called them `phasmid’ vectors, like plasmid and phage together”. The phasmid cloning method was an early cornerstone for molecular biology research.

Elledge began working on homologous recombination in postdoctoral fellowship at Stanford University, an important niche in the field of eukaryotic genetics. Working with the yeast genome, Elledge searched for rec A, a gene that allows DNA to recombine homologously. Although he never located rec A, he discovered a family of genes known as ribonucleotide reductases (RNRs), which are involved in DNA production. Rec A and RNRs share the same last 4 amino acids, which caused an antibody crossreaction in one of Elledge’s experiments. Initially disappointed with the false positives in his hunt for rec A, Elledge was later delighted with his luck. He found that

  • RNRs are turned  on by DNA damage, and
  • these genes are regulated by the cell cycle.

Prior to leaving Stanford, Elledge attended a talk at the University of California, San Francisco, by Paul Nurse, a leader in cell-cycle research who would later win the 2001 Nobel Prize in medicine. Nurse described his success in isolating the homolog of a key human cell-cycle kinase gene, Cdc2, by using a mutant strain of yeast (8). Although Nurse’s methods were primitive, Elledge was struck by the message he carried: that

  • cell-cycle regulation was functionally conserved, and
  • many human genes could be isolated by looking for complimentary genes in yeast.

Elledge then took advantage of his past successes in building phasmid vectors to build a versatile human cDNA library that could be expressed in yeast. After setting up a laboratory at Baylor, he introduced this library into yeast, screening for complimentary cell-cycle genes.  He quickly identified the same Cdc2 gene isolated by Nurse. However, Elledge also discovered a related gene known as Cdk2. Elledge subsequently found that

  • Cdk2 controlled the G1 to S cell-cycle transition, a step that often goes awry in cancer. These results were published in the EMBO Journal in 1991.

He then continued to use

  • RNRs to perform genetic screens to
  • identify genes involved in sensing and responding to DNA damage.

He subsequently worked out the

  • signal transduction pathways in both yeast and humans that recognize damaged DNA and replication problems.

These “checkpoint” pathways are central to the

  • prevention of genomic instability and a key to understanding tumorigenesis.

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on April 29, 2003.

Defective cardiovascular development and elevated cyclin E and Notch proteins in mice lacking the Fbw7 F-box protein.

Tetzlaff MT, Yu W, Li M, Zhang P, Finegold M, Mahon K , Harper JW, Schwartz RJ, and SJ Elledge. PNAS 2004; 101(10): 3338-3345. cgi doi 10.1073.  pnas.0307875101

The mammalian F-box protein Fbw7 and its Caenorhabditis elegans counterpart Sel-10 have been implicated in

  • the ubiquitin-mediated turnover of cyclin E
  • as well as the Notch Lin-12 family of transcriptional activators. Both unregulated
  1. Notch and cyclin E
  2. promote tumorigenesis, and
  3. inactivate mutations in human

Fbw7 studies suggest that it may be a tumor suppressor. To generate an in vivo system to assess the consequences of such unregulated signaling, we generated mice deficient for Fbw7.  Fbw7-null mice die around 10.5 days post coitus because of a combination of deficiencies in hematopoietic and vascular development and heart chamber mutations. The absence of Fbw7 results in elevated levels of cyclin E, concurrent with inappropriate DNA replication in placental giant trophoblast cells. Moreover, the levels of both Notch 1 and Notch 4 intracellular domains were elevated, leading to stimulation of downstream transcriptional pathways involving Hes1, Herp1, and Herp2. These data suggest essential functions for Fbw7 in controlling cyclin E and Notch signaling pathways in the mouse.

Science as an Adventure

Ubiquitins

Prof. Avram Hershko – Science as an Adventure
Prof. Avram Hershko shared the 2004 Nobel Prize in Chemistry with Aaron Ciechanover and Irwin Rose for “for the discovery of ubiquitin-mediated protein degradation.”

http://www.youtube.com/watch?v=lGJvsmG3mhw&feature=player_detailpage&list=EC8814C902ACB98559

Gene Switches

Nipam Patel is a professor in the Departments of Molecular and Cell Biology and Integrative Biology at UC Berkeley and runs a research laboratory that studies the role, during embryonic development, of homeotic genes (the genetic switches described in this feature). “Ghost in Your Genes” focuses on epigenetic “switches” that turn genes “on” or “off.” But not all switches are epigenetic; some are genetic. That is, other genes within the chromosome turn genes on or off. In an animal’s embryonic stage, these gene switches play a predominant role in laying out the animal’s basic body plan and perform other early functions;

  • the epigenome begins to take over during the later stages of embryogenesis.

Beginning as a fertilized single egg that egg becomes many different kinds of cells.  Altogether, multicellular organisms like humans have thousands of differentiated cells. Each is optimized for use in the brain, the liver, the skin, and so on. Remarkably, the DNA inside all these cells is exactly the same. What makes the cells differ from one another is that different genes in that DNA are either turned on or off in each type of cell.

Take a typical cell, such as a red blood cell. Each gene within that cell has a coding region that encodes the information used to make a particular protein. (Hemoglobin shuttles oxygen to the tissues and carbon dioxide back out to the lungs—or gills, if you’re a fish.) But another region of the gene, called “regulatory DNA,” determines whether and when the gene will be expressed, or turned on, in a particular kind of cell. This precise transcribing of genes is handled by proteins known as transcription factors, which bind to the regulatory DNA, thereby generating instructions for the coding region.

One important class of transcription factors is encoded by the so called homeotic, or Hox, genes. Found in all animals, Hox genes act to “regionalize” the body along the embryo’s anterior-to-posterior (head-to-tail) axis. In a fruit fly, for example, Hox genes lay out the various main body segments—the head, thorax, and abdomen. Amazingly, all animals, from fruit flies to mice to people, rely on the same basic Hox-gene complex. Using different-colored antibody stains, we can see exactly where and to what degree Hox genes are expressed. Each Hox gene is expressed in a specific region along the anterior-to-posterior axis of the embryo.

A fly’s body has three main divisions: head, thorax, and abdomen. We’ll focus on the thorax, which itself has three main segments. In a normal adult fly, the second thoracic segment features a pair of wings, while the third thoracic segment has a pair of small, balloon-shaped structures called halteres. A modified second wing, the haltere serves as a flight stabilizer. In order for the pair of wings and the pair of halteres (as well as all other parts of the fly) to develop properly, the fly’s suite of

  • Hox genes must be expressed in a precise way and at precise times.

During development, the fly’s two wings grow from a structure in the larva known as the wing imaginal disk. (An imago is an insect in its final, adult state.) The haltere grows from the larval haltere imaginal disk. Remember the Ubx Hox gene? Using staining again, we can detect the gene product of Ubx. This reveals that

  • the Ubx gene is naturally “off” in the wing disk—
  • and is “on” in the haltere disk.
  • Now you’ll see what happens when the Ubx gene—just one of a large number of Hox genes—is turned off in the haltere disk. What if a genetic mutation caused the Ubx gene to be turned off, during the larval stage, in the third thoracic segment, the segment that normally produces the haltere? Instead of a pair of halteres, the fly has a second set of wings. With the switch of that single Hox gene, Ubx, from on to off, the third thoracic segment becomes an additional second thoracic segment and the pair of halteres became a second pair of wings. This illustrates the remarkable ability of transcription factors like Ubx to control patterning as well as cell type during development.

ENCODE

A. Data Suggests “Gene” Redefinition

As part of a huge collaborative effort called ENCODE (Encyclopedia of DNA Elements), a research team led by Cold Spring Harbor Laboratory (CSHL) Professor Thomas Gingeras, PhD, publishes a genome-wide analysis of RNA messages, called transcripts, produced within human cells.
Their analysis—one component of a massive release of research results by ENCODE teams from 32 institutes in 5 countries, with 30 papers appearing in 3 different high-level scientific journals—shows that three-quarters of the genome is capable of being transcribed.  This indicates that nearly all of our genome is dynamic and active.  It stands in marked contrast to consensus views prior to ENCODE’s comprehensive research efforts, which suggested that

  • only the small protein-encoding fraction of the genome was transcribed.

The vast amount of data generated with advanced technologies by Gingeras’ group and others in the ENCODE project changes the prevailing understanding of what defines a gene. The current outstanding question concerns

  • the nature and range of those functions.  It is thought that these
  • “non-coding” RNA transcripts act something like components of a giant, complex switchboard, controlling a network of  many events in the cell by
  1. regulating the processes of
  2. replication,
  3. transcription
  4. and translation

– that is, the copying of DNA and the making of proteins is based on information carried by messenger RNAs.  With the understanding that so much of our DNA can be transcribed into RNA comes the realization that there is much less space between what we previously thought of as genes, Gingeras points out.

The full ENCODE Consortium data sets can be freely accessed through

  • the ENCODE project portal as well as at the University of California at Santa Cruz genome browser,
  • the National Center for Biotechnology Information, and
  • the European Bioinformatics Institute.

Topic threads that run through several different papers can be explored via the ENCODE microsite page at http://Nature.com/encode.    Date: September 5, 2012   Source: Cold Spring Harbor Laboratory

1000 Genomes Project Team Reports on Variation Patterns

(from Phase I Data) October 31, 2012 GenomeWeb

In a study appearing online today in Nature, members of the 1000 Genomes Project Consortium presented an integrated haplotype map representing the genomic variation present in more than 1,000 individuals from 14 human populations.  Using data on 1,092 individuals tested by

  • low-coverage whole-genome sequencing,
  • deep exome sequencing, and/or
  • dense genotyping,

the team looked at the nature and extent of the rare and common variation present in the genomes of individuals within these populations. In addition to population-specific differences in common variant profiles, for example, the researchers found distinct rare variant patterns within populations from different parts of the world — information that is expected to be important in interpreting future disease studies. They also encountered a surprising number of the variants that are expected to impact gene function, such as

  • non-synonymous changes,
  • loss-of-function variants, and, in some cases,
  • potentially damaging mutations.

ENCODE was designed to pick up where the Human Genome Project left off.
Although that massive effort revealed the blue­print of human biology, it quickly became clear that the instruction manual for reading the blueprint was sketchy at best. Researchers could identify in its 3 billion letters many of the regions that code for proteins, but they make up little more than 1% of the genome, contained in around 20,000 genes. ENCODE, which started in 2003, is a massive data-collection effort designed to catalogue the

  • ‘functional’ DNA sequences,
  • learn when and in which cells they are active and
  • trace their effects on how the genome is
  1. packaged,
  2. regulated and
  3. read.

After an initial pilot phase, ENCODE scientists started applying their methods to the entire genome in 2007. That phase came to a close with the publication of 30 papers, in Nature, Genome Research and Genome Biology. The consortium has assigned some sort of function to roughly 80% of the genome, including

  • more than 70,000 ‘promoter’ regions — the sites, just upstream of genes, where proteins bind to control gene expression —
  • and nearly 400,000 ‘enhancer’ regions that regulate expression of  distant genes (see page 57)1. But the job is far from done.

Junk DNA? What Junk DNA?

New data reveals that at least 80% of the human genome encodes elements that have some sort of biological function. [© Gernot Krautberger – Fotolia.com] Far from containing vast amounts of junk DNA between its protein-coding genes, at least 80% of the human genome encodes elements that have some sort of biological function, according to newly released data from the Encyclopedia of DNA Elements (Encode) project, a five-year initiative that aims to delineate all functional elements within human DNA. The massive international project, data from which are published in 30 different papers in Nature, Genome Research, Genome Biology, the Journal of Biological Chemistry, Science, and Cell, has identified four million gene switches, effectively

  • regulatory regions in the genome where
  • proteins interact with the DNA to control gene expression.

Overall, the Encode data define regulatory switches that are scattered all over the three billion nucleotides of the genome. In fact, the data suggests,

  • the regions that lie between gene-coding sequences contain a wealth of previously unrecognized functional elements,Including
  • nonprotein-coding RNA transcribed sequences,
  • transcription factor binding sites,
  • chromatin structural elements, and
  • DNA methylation sites.

The combined results suggest that 95% of the genome lies within 8 kb of a DNA-protein interaction, and 99% lies within 1.7 kb of at least one of the biochemical events, the researchers say. Importantly, given the complex three-dimensional nature of DNA, it’s also apparent that

  • a regulatory element for one gene may be located quite some ‘linear’ distance from the gene itself.

“The information processing and the intelligence of the genome reside in the regulatory elements,” explains Jim Kent, director of the University of California, Santa Cruz Genome Browser project and head of the Encode Data Coordination Center. “With this project, we probably went from understanding less than 5% to now around 75% of them.”
The ENCODE results also identified SNPs within regulatory regions that are associated with a range of diseases, providing new insights into the roles that

  • noncoding DNA plays in disease development.

“As much as nine out of 10 times, disease-linked genetic variants are not in protein-coding regions,” comments Mike Pazin, Encode program director at the National Human Genome Research Institute.  “Far from being junk DNA, this regulatory DNA clearly makes important contributions to human disease.”

Other Related Articles on this Open Access Online Scientific Journal, include the following: 

Big Data in Genomic Medicine LHB

https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair S Saha
http://pharmaceuticalintelligence.com/2012/12/04/brca1-a-tumour-suppressor-in-breast-and-ovarian-cancer-functions-in-transcription-ubiquitination-and-dna-repair/

Computational Genomics Center: New Unification of Computational Technologies at Stanford A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/03/computational-genomics-center-new-unification-of-computational-technologies-at-stanford/

Personalized medicine gearing up to tackle cancer ritu saxena
http://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/

Differentiation Therapy – Epigenetics Tackles Solid Tumors sj Williams
http://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/

The Molecular pathology of Breast Cancer Progression tilde barliya`
http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 (pharmaceuticalintelligence.com) A Lev-Ari

http://pharmaceuticalintelligence.com/2013/01/13/paradigm-shift-in-human-genomics-predictive-biomarkers-and-personalized-medicine-part-1/

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/leaders-in-genome-sequencing-of-genetic-mutations-for-therapeutic-drug-selection-in-cancer-personalized-treatment-part-2/

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/personalized-medicine-an-institute-profile-coriell-institute-for-medical-research-part-3/

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com ALA
http://pharmaceuticalintelligence.com/2013/01/13/7000/Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors S Saha
http://pharmaceuticalintelligence.com/2012/11/19/recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes-in-serous-endometrial-tumors/

Personalized medicine-based cure for cancer might not be far away ritu saxena
http://pharmaceuticalintelligence.com/2012/11/20/personalized-medicine-based-cure-for-cancer-might-not-be-far-away/

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/24/human-variome-project-encyclopedic-catalog-of-sequence-variants-indexed-to-the-human-genome-sequence/

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition sjwilliams
http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/10/inspiration-from-dr-maureen-cronins-achievements-in-applying-genomic-sequencing-to-cancer-diagnostics/

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/09/the-cancer-establishments-examined-by-james-watson-co-discover-of-dna-wcrick-41953/

Directions for genomics in personalized medicine lhb
http://pharmaceuticalintelligence.com/2013/01/27/directions-for-genomics-in-personalized-medicine/

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis. SJwilliams
http://pharmaceuticalintelligence.com/2012/10/31/how-mobile-elements-in-junk-dna-prote-cancer-part1-transposon-mediated-tumorigenesis/

Mitochondria: More than just the “powerhouse of the cell” eritu saxena
http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Mitochondrial fission and fusion: potential therapeutic targets? Ritu saxena
http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/

Mitochondrial mutation analysis might be “1-step” away ritu saxena
http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/

mRNA interference with cancer expression lhb
http://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/

Expanding the Genetic Alphabet and linking the genome to the metabolome LHB
http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/

Breast Cancer, drug resistance, and biopharmaceutical targets lhb
http://pharmaceuticalintelligence.com/2012/09/18/breast-cancer-drug-resistance-and-biopharmaceutical-targets/

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/breast-cancer-genomic-profiling-to-predict-survival-combination-of-histopathology-and-gene-expression-analysis/

Gastric Cancer: Whole-genome reconstruction and mutational signatures A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/gastric-cancer-whole-genome-reconstruction-and-mutational-signatures-2/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis lhb
http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology A Lev-Ari
http://pharmaceuticalintelligence.com/2012/08/22/genomic-analysis-fluidigm-technology-in-the-life-science-and-agricultural-biotechnology/

Reveals from ENCODE project will invite high synergistic collaborations to discover specific targets A. Sarkar

https://pharmaceuticalintelligence.com/2012/09/30/reveals-from-encode-project-will-lead-to-confusion-or-specific-target/

ENCODE: the key to unlocking the secrets of complex genetic diseases R. Saxena

https://pharmaceuticalintelligence.com/2012/09/26/encode-the-key-to-unlocking-the-secrets-of-complex-genetic-diseases/

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations s Saha

https://pharmaceuticalintelligence.com/2012/09/20/impact-of-evolutionary-selection-on-functional-regions-the-imprint-of-evolutionary-selection-on-encode-regulatory-elements-is-manifested-between-species-and-within-human-populations/

ENCODE Findings as Consortium A Lev-Ari

https://pharmaceuticalintelligence.com/2012/09/10/encode-findings-as-consortium/

Genomics Orientations for Personalized Medicine SJH, ALA, LHB

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/

2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

https://pharmaceuticalintelligence.com/2013/02/11/2013-genomics-the-era-beyond-the-sequencing-human-genome-francis-collins-craig-venter-eric-lander-et-al/

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Reporter: Sudipta Saha, Ph.D.

 

Beyond characterization of the fundamental anatomy of vascular development, the first investigators in this field participated in one of the classic debates in all of developmental biology: where and when do endothelial cells (and hence blood vessels) arise in the developing embryo? Because blood vessels are first observed in the yolk sac in avian and mammalian embryos. It was initially assumed that all blood vessels arise from extra-embryonic tissues. However, careful histological analysis subsequently indicated that isolated foci of endothelial cells can also be observed in the embryo proper, which suggested that blood vessels arise from an intraembryonic source (specifically, the mesoderm) rather than via colonization. The formation of new blood vessels in the adult organism not only contributed to the progression of diseases such as cancer and diabetic retinopathy but also can be promoted in therapeutic approaches to various ischemic pathologies. Because many of the signals important to blood vessel development during embryogenesis are recapitulated during adult blood vessel formation, much work has been performed to better-understand the molecular control of endothelial differentiation in the developing embryo. Activators and inhibitors of developmental pathways have been tested for their ability to modulate angiogenesis in early phase clinical trials, and in the case of anti-Flk1 antibodies clinical utility has been demonstrated for anti-tumor strategies. Analyses of circulating endothelial progenitor cells, which have angiogenic potential, do indeed suggest that there are similarities in the biology of these cells compared with developmental endothelial precursors. Stem cell therapeutics therefore represents another potential arena for translation of insights from vascular development to clinical practice. Even though our understanding of endothelial development is much richer than it was even a few years ago and despite the potential applications of this knowledge in clinical medicine, there are still a number of key issues on this topic that remain to be resolved. Precisely how early are endothelial precursors specified during development, and what is the nature of this progenitor cell pool? What are the relationships among signaling pathways that specify endothelial fates in a coordinated fashion? Is there a transcriptional hierarchy that regulates vascular development? The answers to these and other questions about endothelial development are likely to be forthcoming in the near future as experimental methods continue to evolve (http://atvb.ahajournals.org/content/25/11/2246.full).

 

The development of the vertebrate heart can be considered an additive process, in which additional layers of complexity have been added throughout the evolution of a simple structure (linear heart tube) in the form of modular elements (atria, ventricles, septa, and valves). Each modular element confers an added capacity to the vertebrate heart and can be identified as individual structures patterned in a precise manner. An understanding of the individual modular steps in cardiac morphogenesis is particularly relevant to congenital heart disease, which usually involves defects in specific structural components of the developing heart. Organ formation requires the precise integration of cell type-specific gene expression and morphological development; both are intertwined in their regulation by transcription factors. Although many transcription factors have been described as regulators of cardiac-specific gene expression, the transcriptional regulation of cardiac morphogenesis is still not well explored. For a transcription factor to be considered directly involved in heart development, it must be expressed in developing heart tissues and exert an influence on processes that impact the morphogenesis of the developing heart. Transcription factors can regulate the expression of other genes in a tissue-specific and quantitative manner and are thus major regulators of embryonic developmental processes. A number of complex transcriptional networks and interactions are involved in the morphogenesis of the developing vertebrate heart. The identities of crucial regulators involved in defined events in cardiogenesis are being uncovered at a rapid rate, but a number of critical questions remain. First and foremost, it is still not known which transcription factors are involved in the earliest differentiation of cardiac cells from the mesoderm. Second, the downstream pathways regulated by transcription factors responsible for key morphogenetic events are still largely unknown. Third, the concept of maintained function or redeployment of functions throughout various stages of development remains to be addressed in detail. The challenge for the future lies in defining pathways downstream from cardiac transcription factors and understanding the intersection of these pathways as the heart develops from a simple patterned structure into a complex multifunctional organ (http://circres.ahajournals.org/content/90/5/509.full).

 

Tissue development and regeneration involve tightly coordinated and integrated processes: selective proliferation of resident stem and precursor cells, differentiation into target somatic cell type, and spatial morphological organization. The role of the mechanical environment in the coordination of these processes is poorly understood. It has been reported that multipotent cells derived from native cardiac tissue continually monitored cell substratum rigidity and showed enhanced proliferation, endothelial differentiation, and morphogenesis when the cell substratum rigidity closely matched that of myocardium. Mechanoregulation of these diverse processes required p190RhoGAP, a guanosine triphosphatase-activating protein for RhoA, acting through RhoA-dependent and -independent mechanisms. Natural or induced decreases in the abundance of p190RhoGAP triggered a series of developmental events by coupling cell-cell and cell-substratum interactions to genetic circuits controlling differentiation (http://www.ncbi.nlm.nih.gov/pubmed/22669846).

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Reporter: Ritu Saxena, Ph.D.

Singapore, May 14, 2012 (ACN Newswire via COMTEX) — Scientists at A*STAR’s Institute of Medical Biology (IMB), in collaboration with doctors and scientists in Jordan, Turkey, Switzerland and USA, have identified the genetic cause of a birth defect known as Hamamy syndrome[1]. Their groundbreaking findings were published on May 13th in the prestigious journal Nature Genetics. The work lends new insights into common ailments such as heart disease, osteoporosis, blood disorders and possibly sterility.

Hamamy syndrome is a rare genetic disorder which is marked by abnormal facial features and defects in the heart, bone, blood and reproductive cells. Its exact cause was unknown until now. The international team, led by scientists at IMB, have pinpointed the genetic mistake to be a mutation in a single gene called IRX5.

This is the first time that a mutation in IRX5 (and the family of IRX genes) has ever been discovered in man. IRX5 is part of a family of transcription factors that is highly conserved in all animals, meaning that this gene is present not only in humans but also in mice, fish, frogs, flies and even worms. Using a frog model, the scientists demonstrated that Irx5 orchestrates cell movements in the developing foetus which underlie head and gonad formation.

Carine Bonnard, a final-year PhD student at IMB and the first author of the paper, said, “Because Hamamy syndrome causes a wide range of symptoms, not just in newborn babies but also in the adult, this implies that IRX5 is critical for development in the womb as well as for the function of many organs in our adult body. For example, patients with this disease cannot evacuate tears from their eyes, and they will also go on to experience repetitive bone fractures (Annex A) or progressive myopia as they age. This discovery of the causative gene is a significant finding that will catalyze research efforts into the role of the Irx gene family and greatly increase our understanding of human health, such as bone homeostasis, or gamete formation for instance.”

“We believe that this discovery could open up new therapeutic solutions to common diseases like osteoporosis, heart disease, anaemia which affect millions of people worldwide,” said Dr Bruno Reversade, Senior Principle Investigator at IMB. “The findings also provide a framework for understanding fascinating evolutionary questions, such as why humans of different ethnicities have distinct facial features and how these are embedded in our genome. IRX genes have been repeatedly co-opted during evolution, and small variation in their activity could underlie fine alterations in the way we look, or perhaps even drastic ones such as the traits seen in an elephant, whale, turtle or frog body pattern.”

Only a handful of people in the world have been identified with Hamamy Syndrome making it a very rare genetic disorder. Rare genetic diseases, usually caused by mutations in a single gene, provide a unique opportunity to better understand more common disease processes. These “natural” experiments are similar to carefully controlled knockout animal experiments in which the function of single genes are analyzed and often give major insights into general health issues.[2]

Prof Birgitte Lane, Executive Director of IMB, said, “Understanding how various pathways in the human body function is the foundation for developing new therapeutic targets. This is an important piece of research that I believe will be of great interest to many scientists and clinicians around the world because of the clinical and genetic insights it brings to a large range of diseases.”

Notes for editor:

The research findings described in this news release can be found on Nature Genetics’s website under the title “Mutations in IRX5 impair craniofacial development and germ cell migration via SDF1” by Carine Bonnard[1], Anna C Strobl[2], Mohammad Shboul1, Hane Lee[3], Barry Merriman[3], Stanley F Nelson[3], Osama H Ababneh[4], Elif Uz[5],[6], Tulay Guran[7], Hulya Kayserili[8], Hanan Hamamy[9],[10] & Bruno Reversade[1],[11].

[1] Institute of Medical Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

[2] Division of Systems Biology, Medical Research Council National Institute for Medical Research, London, UK

[3] Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, USA

[4] Department of Opthalmology, Faculty of Medicine, University of Jordan, Amman, Jordan

[5] Department of Biology, Faculty of Arts and Sciences, Duzce University, Duzce, Turkey

[6] Gene Mapping Laboratory, Department of Medical Genetics, Hacettepe University Medical Faculty, Ankara, Turkey

[7] Pediatric Endocrinology and Diabetes, Marmara University Hospital, Istanbul, Turkey

[8] Medical Genetics Department, Istanbul Medical Faculty, Istanbul University, Istanbul, Turkey

[9] Department of Genetic Medicine and Development, Geneva University Hospital, Geneva, Switzerland

[11] Department of Pediatrics, National University of Singapore, Singapore

Correspondence should be addressed to B.R. (Bruno@reversade.com)

The article can be accessed fromhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2259.html .

About the Institute of Medical Biology (IMB)

IMB is one of the Biomedical Sciences Institutes of the Agency for Science, Technology and Research (A*STAR). It was formed in 2007, the 7th and youngest of the BMRC Research Institutes, with a mission to study mechanisms of human disease in order to discover new and effective therapeutic strategies for improved quality of life. From 2011, IMB also hosts the inter-research institute Skin Biology Cluster platform.

IMB has 20 research teams of international excellence in stem cells, genetic diseases, cancer and skin and epithelial biology, and works closely with clinical collaborators to target the challenging interface between basic science and clinical medicine. Its growing portfolio of strategic research topics is targeted at translational research on the mechanisms of human diseases, with a cell-to-tissue emphasis that can help identify new therapeutic strategies for disease amelioration, cure and eradication. For more information about IMB, please visit www.imb.a-star.edu.sg .

About the Reversade Laboratory

Dr. Reversade, a human geneticist and embryologist holds a Senior Principal Investigator position at IMB and an adjunct faculty position at the Department of Paediatrics in the National University of Singapore. He is a Fellow of the Branco Weiss Foundation based at ETH in Switzerland and also the first recipient of an A*STAR Investigatorship, a programme which provides competitive and prestigious fellowships to support the next generation of international scientific leaders, offering funding and access to state-of-the-art scientific equipment and facilities at A*STAR. For more information about Dr. Reversade’s laboratory, please visit www.reversade.com .

About A*STAR

The Agency for Science, Technology and Research (A*STAR) is the lead agency for fostering world-class scientific research and talent for a vibrant knowledge-based and innovation-driven Singapore. A*STAR oversees 14 biomedical sciences and physical sciences and engineering research institutes, and six consortia & centres, located in Biopolis and Fusionopolis as well as their immediate vicinity. A*STAR supports Singapore’s key economic clusters by providing intellectual, human and industrial capital to its partners in industry. It also supports extramural research in the universities, and with other local and international partners. For more information about A*STAR, please visit www.a-star.edu.sg .

Source: http://www.marketwatch.com/story/scientists-make-groundbreaking-discovery-of-mutation-causing-genetic-disorder-in-humans-2012-05-14?pagenumber=1

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