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Posts Tagged ‘miRNA’


The Role of Exosomes in Metabolic Regulation

Author: Larry H. Bernstein, MD, FCAP

 

On 9/25/2017, Aviva Lev-Ari, PhD, RN commissioned Dr. Larry H. Bernstein to write a short article on the following topic reported on 9/22/2017 in sciencemission.com

 

We are publishing, below the new article created by Larry H. Bernstein, MD, FCAP.

 

Background

During the period between 9/2015  and 6/2017 the Team at Leaders in Pharmaceutical Business Intelligence (LPBI)  has launched an R&D effort lead by Aviva Lev-Ari, PhD, RN in conjunction with SBH Sciences, Inc. headed by Dr. Raphael Nir.

This effort, also known as, “DrugDiscovery @LPBI Group”  has yielded several publications on EXOSOMES on this Open Access Online Scientific Journal. Among them are included the following:

 

QIAGEN – International Leader in NGS and RNA Sequencing, 10/08/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

cell-free DNA (cfDNA) tests could become the ultimate “Molecular Stethoscope” that opens up a whole new way of practicing Medicine, 09/08/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

Detecting Multiple Types of Cancer With a Single Blood Test (Human Exomes Galore), 07/02/2017

Reporter and Curator: Irina Robu, PhD

 

Exosomes: Natural Carriers for siRNA Delivery, 04/24/2017

Reporter: Aviva Lev-Ari, PhD, RN

 

One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI, 01/05/2017

Curator: Marzan Khan, B.Sc

 

SBI’s Exosome Research Technologies, 12/29/2016

Reporter: Aviva Lev-Ari, PhD, RN

 

A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC, 12/28/2016

Curator: Tilda Barliya, PhD

 

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab, 12/28/2016

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

 

Exosomes – History and Promise, 04/28/2016

Reporter: Aviva Lev-Ari, PhD, RN

 

Exosomes, 11/17/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Liquid Biopsy Assay May Predict Drug Resistance, 11/16/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Glypican-1 identifies cancer exosomes, 10/31/2015

Curator: Larry H. Bernstein, MD, FCAP

 

Circulating Biomarkers World Congress, March 23-24, 2015, Boston: Exosomes, Microvesicles, Circulating DNA, Circulating RNA, Circulating Tumor Cells, Sample Preparation, 03/24/2015

Reporter: Aviva Lev-Ari, PhD, RN

 

Cambridge Healthtech Institute’s Second Annual Exosomes and Microvesicles as Biomarkers and Diagnostics Conference, March 16-17, 2015 in Cambridge, MA, 03/17, 2015

Reporter: Aviva Lev-Ari, PhD, RN

 

The newly created think-piece on the relationship between regulatory functions of Exosomes and Metabolic processes is developed conceptually, below.

 

The Role of Exosomes in Metabolic Regulation

Author: Larry H. Bernstein, MD, FCAP

We have had more than a half century of research into the genetic code and transcription leading to abundant work on RNA and proteomics. However, more recent work in the last two decades has identified RNA interference in siRNA. These molecules may be found in the circulation, but it has been a challenge to find their use in therapeutics. Exosomes were first discovered in the 1980s, but only recently there has been a huge amount of research into their origin, structure and function. Exosomes are 30–120 nm endocytic membrane-bound extracellular vesicles (EVs)(1-23) , and more specifically multiple vesicle bodies (MVBs) by a budding process from invagination of the outer cell membrane that carry microRNA (miRNA), and have structures composed of protein and lipids (1,23-27 ). EVs are the membrane vesicles secreted by eukaryotic cells for intracellular communication by transferring the proteins, lipids, and RNA under various physiologic conditions as well as during the disease stage. EVs also act as a signalosomes in many biological processes. Inward budding of the plasma membrane forms small vesicles that fuse. Intraluminal vesicles (ILVs) are formed by invagination of the limiting endosomal membrane during the maturation process of early endosome.

EVs are the MVBs secreted that serve in intracellular communication by transferring a cargo consisting of proteins, lipids, and RNA under various physiologic conditions (4, 23). Exosome-mediated miRNA transfer between cells is considered to be necessary for intercellular signaling and exosome-associated miRNAs in biofluids (23). Exosomes carry various molecular constituents of their cell of origin, including proteins, lipids, mRNAs, and microRNAs (miRNAs) (. They are released from many cell types, such as dendritic cells (DCs), lymphocytes, platelets, mast cells, epithelial cells, endothelial cells, and neurons, and can be found in most bodily fluids including blood, urine, saliva, amniotic fluid, breast milk, hydrothoracic fluid, and ascitic fluid, as well as in culture medium of most cell types.Exosomes have also been shown to be involved in noncoding RNA surveillance machinery in generating antibody diversity (24). There are also a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) that accumulate R-loop structures upon RNA exosome ablation, thereby, resolving deleterious DNA/RNA hybrids arising from active enhancers and distal divergent eRNA-expressing elements (lncRNA-CSR) engaged in long-range DNA interactions (25). RNA exosomes are large multimeric 3′-5′ exo- and endonucleases representing the central RNA 3′-end processing factor and are implicated in processing, quality control, and turnover of both coding and noncoding RNAs. They are large macromolecular cages that channel RNA to the ribonuclease sites (29). A major interest has been developed to characterize of exosomal cargo, which includes numerous non-randomly packed proteins and nucleic acids (1). Moreover, exosomes play an active role in tumorigenesis, metastasis, and response to therapy through the transfer of oncogenes and onco-miRNAs between cancer cells and the tumor stroma. Blood cells and the vascular endothelium is also exosomal shedding, which has significance for cardiovascular,   neurologicological disorders, stroke, and antiphospholipid syndrome (1). Dysregulation of microRNAs and the affected pathways is seen in numerous pathologies their expression can reflect molecular processes of tumor onset and progression qualifying microRNAs as potential diagnostic and prognostic biomarkers (30).

Exosomes are secreted by many cells like B lymphocytes and dendritic cells of hematopoietic and non-hematopoietic origin viz. platelets, Schwann cells, neurons, mast cells, cytotoxic T cells, oligodendrocytes, intestinal epithelial cells were also found to be releasing exosomes (4). They are engaged in complex functions like persuading immune response as the exosomes secreted by antigen presenting cells activate T cells (4). They all have a common set of proteins e.g. Rab family of GTPases, Alix and ESCRT (required for transport) protein and they maintain their cytoskeleton dynamics and participate in membrane fusion. However, they are involved in retrovirus disease pathology as a result of recruitment of the host`s endosomal compartments in order to generate viral vesicles, and they can either spread or limit an infection based on the type of pathogen and its target cells (5).

Upon further consideration, it is understandable how this growing biological work on exosomes has enormous significance for laboratory diagnostics (1, 3, 5, 6, 11, 14, 15, 17-20, 23,30-41) . They are released from many cell types, such as dendritic cells (DCs), lymphocytes, platelets, mast cells, epithelial cells, endothelial cells, and neurons, and can be found in most bodily fluids including blood, urine, saliva, amniotic fluid, breast milk, thoracic and abdominal effusions, and ascitic fluid (1). The involvement of exosomes in disease is broad, and includes: cancer, autoimmune and infectious disease, hematologic disorders, neurodegenerative diseases, and cardiovascular disease. Proteins frequently identified in exosomes include membrane transporters and fusion proteins (e.g., GTPases, annexins, and flotillin), heat shock proteins (e.g., HSC70), tetraspanins (e.g., CD9, CD63, and CD81), MVB biogenesis proteins (e.g., alix and TSG101), and lipid-related proteins and phospholipases. The exosomal lipid composition has been thoroughly analyzed in exosomes secreted from several cell types including DCs and mast cells, reticulocytes, and B-lymphocytes (1). Dysregulation of microRNAs of pathways observed in numerous pathologies (5, 10, 12, 21, 27, 35, 37) including cancers (30), particularly, colon, pancreas, breast, liver, brain, lung (2, 6, 17-20, 30, 33-36, 38, 39). Following these considerations, it is important that we characterize the content of exosomal cargo to gain clues to their biogenesis, targeting, and cellular effects which may lead to identification of biomarkers for disease diagnosis, prognosis and response to treatment (42).

We might continue in pursuit of a particular noteworthy exosome, the NLRP3 inflammasome, which is activated by a variety of external or host-derived stimuli, thereby, initiating an inflammatory response through caspase-1 activation, resulting in inflammatory cytokine IL-1b maturation and secretion (43).
Inflammasomes are multi-protein signaling complexes that activate the inflammatory caspases and the maturation of interleukin-1b. The NLRP3 inflammasome is linked with human autoinflammatory and autoimmune diseases (44). This makes the NLRP3 inflammasome a promising target for anti-inflammatory therapies. The NLRP3 inflammasome is activated in response to a variety of signals that indicate tissue damage, metabolic stress, and infection (45). Upon activation, the NLRP3 inflammasome serves as a platform for activation of the cysteine protease caspase-1, which leads to the processing and secretion of the proinflammatory cytokines interleukin-1β (IL-1β) and IL-18. Heritable and acquired inflammatory diseases are both characterized by dysregulation of NLRP3 inflammasome activation (45).
Receptors of innate immunity recognize conserved moieties associated with either cellular damage [danger-associated molecular patterns (DAMPs)] or invading organisms [pathogen-associated molecular patterns (PAMPs)](45). Either chronic stimulation or overwhelming tissue damage is injurious and responsible for the pathology seen in a number of autoinflammatory and autoimmune disorders, such as arthritis and diabetes. The nucleotide-binding domain leucine-rich repeat (LRR)-containing receptors (NLRs) are PRRs are found intracellularly and they share a unique domain architecture. It consists of a central nucleotide binding and oligomerization domain called the NACHT domain that is located between an N-terminal effector domain and a C-terminal LRR domain (45). The NLR family members NLRP1, NLRP3, and NLRC4 are capable of forming multiprotein complexes called inflammasomes when activated.

The (NLRP3) inflammasome is important in chronic airway diseases such as asthma and chronic obstructive pulmonary disease because the activation results, in pro-IL-1β processing and the secretion of the proinflammatory cytokine IL-1β (46). It has been proposed that Activation of the NLRP3 inflammasome by invading pathogens may prove cell type-specific in exacerbations of airway inflammation in asthma (46). First, NLRP3 interacts with the adaptor protein ASC by sensing microbial pathogens and self-danger signals. Then pro-caspase-1 is recruited and the large protein complex called the NLRP3 inflammasome is formed. This is followed by autocleavage and activation of caspase-1, after which pro-IL-1β and pro-IL-18 are converted into their mature forms. Ion fluxes disrupt membrane integrity, and also mitochondrial damage both play key roles in NLRP3 inflammasome activation (47). Depletion of mitochondria as well as inhibitors that block mitochondrial respiration and ROS production prevented NLRP3 inflammasome activation. Futhermore, genetic ablation of VDAC channels (namely VDAC1 and VDAC3) that are located on the mitochondrial outer membrane and that are responsible for exchanging ions and metabolites with the cytoplasm, leads to diminished mitochondrial (mt) ROS production and inhibition of NLRP3 inflammasome activation (47). Inflammasome activation not only occurs in immune cells, primarily macrophages and dendritic cells, but also in kidney cells, specifically the renal tubular epithelium. The NLRP3 inflammasome is probably involved in the pathogenesis of acute kidney injury, chronic kidney disease, diabetic nephropathy and crystal-related nephropathy (48). The inflammasome also plays a role in autoimmune kidney disease. IL-1 blockade and two recently identified specific NLRP3 inflammasome blockers, MCC950 and β-hydroxybutyrate, may prove to have value in the treatment of inflammasome-mediated conditions.

Autophagosomes derived from tumor cells are referred to as defective ribosomal products in blebs (DRibbles). DRibbles mediate tumor regression by stimulating potent T-cell responses and, thus, have been used as therapeutic cancer vaccines in multiple preclinical cancer models (49). It has been found that DRibbles could induce a rapid differentiation of monocytes and DC precursor (pre-DC) cells into functional APCs (49). Consequently, DRibbles could potentially induce strong innate immune responses via multiple pattern recognition receptors. This explains why DRibbles might be excellent antigen carriers to induce adaptive immune responses to both tumor cells and viruses. This suggests that isolated autophagosomes (DRibbles) from antigen donor cells activate inflammasomes by providing the necessary signals required for IL-1β production.

The Hsp90 system is characterized by a cohort of co-chaperones that bind to Hsp90 and affect its function (50). The co-chaperones enable Hsp90 to chaperone structurally and functionally diverse client proteins. Sahasrabudhe et al. (50) show that the nature of the client protein dictates the contribution of a co-chaperone to its maturation. The study reveals the general importance of the cochaperone Sgt1 (50). In addition to Hsp90, we have to consider Hsp60. Adult cardiac myocytes release heat shock protein (HSP)60 in exosomes. Extracellular HSP60, when not in exosomes, causes cardiac myocyte apoptosis via the activation of Toll-like receptor 4. the protein content of cardiac exosomes differed significantly from other types of exosomes in the literature and contained cytosolic, sarcomeric, and mitochondrial proteins (21).

A new Protein Organic Solvent Precipitation (PROSPR) method efficiently isolates the EV repertoire from human biological samples. Proteomic profiling of PROSPR-enriched CNS EVs indicated that > 75 % of the proteins identified matched previously reported exosomal and microvesicle cargoes. In addition lipidomic characterization of enriched CNS vesicles identified previously reported EV-specific lipid families and novel lipid isoforms not previously detected in human EVs. The characterization of these structures from central nervous system (CNS) tissues is relevant to current neuroscience, especially to advance the understanding of neurodegeneration in amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and Alzheimer’s disease (AD)(15). In addition, study of EVs in brain will enable characterization of the degenerative posttranslational modifications (DPMs) occurring in those proteins.
Neurodegenerative disease is characterized by dysregulation because of NLRP3 inflammasome activation. Alzheimer’s disease (AD) and Parkinson’s disease (PD), both neurodegenerative diseases are associated with the NLRP3 inflammasome. PD is characterized by accumulation of Lewy bodies (LB) formed by a-synuclein (aSyn) aggregation. A recent study revealed that aSyn induces synthesis of pro-IL-1b by an interaction with TLR2 and activates NLRP3 inflammasome resulting in caspase-1 activation and IL-1b maturation in human primary monocytes (43). In addition mitophagy downregulates NLRP3 inflammasome activation by eliminating damaged mitochondria, blocking NLRP3 inflammasome activating signals. It is notable that in this aberrant activation mitophagy downregulates NLRP3 inflammasome activation by eliminating damaged mitochondria, blocking NLRP3 inflammasome activating signals (43).

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

 

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.

 

Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.

 

Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).

 

References:

 

http://link.springer.com/chapter/10.1007%2F978-3-319-31973-5_12

 

http://onlinelibrary.wiley.com/doi/10.1111/andr.12276/abstract;jsessionid=F805A89DCC94BDBD42D6D60C40AD4AB0.f03t03

 

http://www.sciencedirect.com/science/article/pii/S0009279716302241

 

http://link.springer.com/article/10.1007%2Fs10815-016-0657-9

 

http://www.nature.com/articles/srep24976

 

 

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Tumor Shrinking Triple Helices

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Tumor-Shrinking Triple-Helices

A braided structure and some adhesive hydrogel make therapeutic microRNAs both stable and sticky.

By Ruth Williams | April 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/45576/title/Tumor-Shrinking-Triple-Helices

 

MicroRNAs (miRs) are small, noncoding ribonucleic acids that control the translation of target messenger RNAs (mRNAs). Given their roles in development, differentiation, and other cellular processes, misregulation of miRs can contribute to diseases such as cancer. Indeed, “they are recognized as important modulators of cancer progression,” says Natalie Artzi of Harvard Medical School.

In addition to occasionally promoting cancer pathology, miRs also hold the potential to treat it—either by restoring levels of suppressed miRs, or by repressing overactive ones using antisense miRs (antagomiRs). While miRs are promising therapeutic molecules, says Daniel Siegwart of the University of Texas Southwestern Medical Center in Dallas, their use “is currently hindered by at least two issues: nucleic acid instability in vivo, and the development of effective delivery systems to transport miRs into tumor cells.”

Artzi and her team have now addressed both of these issues in one fell swoop. They first assembled two therapeutic miRs—one antagomiR and one that replaced a deficient miR—together with a third miR, a complement of the replacement strand, into triple-helix structures, which increased molecular stability without affecting function. They then complexed these helices with dendrimers—large synthetic branching polymer particles—and mixed these complexes with dextran aldehyde to form an adhesive hydrogel. The gel could then be applied directly to the surface of tumors to deliver the therapeutic miRs into cells with high efficiency.

In mice with induced breast tumors, the triple-helix–hydrogel approach led to dramatic tumor shrinkage and extended life span: the animals survived approximately one month longer than those treated with standard-of-care chemotherapy drugs. Because the RNA-hydrogel mixture must be applied directly to the tumor, the approach will not be suitable for all cancers. But one potential application, says Siegwart, is that “the hydrogel could be applied by a surgeon after performing bulk tumor removal…[and] might kill remaining tumor cells that would otherwise cause tumor recurrence.” (Nature Materials, http://dx.doi.org:/10.1038/NMAT4497, 2015)

STICKING IT TO TUMORS: To deliver therapeutic microRNAs (miRs) to tumors, braids of three microRNAs (miRs)—an antisense strand that blocks a miR overactive in cancer, a strand that replaces a deficient miR, and a stabilizing strand (1)—are added to a dendrimer (2) and mixed with a hydrogel scaffold (3). When researchers introduced the sticky gel onto mouse mammary tumors (4), the malignancies shrank and the animals lived longer (5)© GEORGE RETSECK; J.CONDE ET AL., NATURE MATERIALS

 

miR DELIVERY SYSTEM VEHICLE DOSE TUMOR TARGETING APPLICABLE TUMOR TYPES
Nanoparticles Examples: gold particles, liposomes, peptide nucleic acids, or polymers Usually multiple injections Combining miRs with aptamers or antibodies can guide nanoparticles to target cells, but systemic delivery inevitably leads to some off-target dispersion. Multisite or blood cancers
RNA–triple-helix-hydrogel Dendrimer-dextran hydrogel One Adhesive hydrogel sticks miRs to tumor site with minimal dispersion to other tissues. Solid Tumors

 

Self-assembled RNA-triple-helix hydrogel scaffold for microRNA modulation in the tumour microenvironment

João CondeNuria OlivaMariana AtilanoHyun Seok Song & Natalie Artzi
Nature Materials15,353–363(2016)
                         http://dx.doi.org:/10.1038/nmat4497

The therapeutic potential of miRNA (miR) in cancer is limited by the lack of efficient delivery vehicles. Here, we show that a self-assembled dual-colour RNA-triple-helix structure comprising two miRNAs—a miR mimic (tumour suppressor miRNA) and an antagomiR (oncomiR inhibitor)—provides outstanding capability to synergistically abrogate tumours. Conjugation of RNA triple helices to dendrimers allows the formation of stable triplex nanoparticles, which form an RNA-triple-helix adhesive scaffold upon interaction with dextran aldehyde, the latter able to chemically interact and adhere to natural tissue amines in the tumour. We also show that the self-assembled RNA-triple-helix conjugates remain functional in vitro and in vivo, and that they lead to nearly 90% levels of tumour shrinkage two weeks post-gel implantation in a triple-negative breast cancer mouse model. Our findings suggest that the RNA-triple-helix hydrogels can be used as an efficient anticancer platform to locally modulate the expression of endogenous miRs in cancer.

 

Figure 1: Self-assembled RNA-triple-helix hydrogel nanoconjugates and scaffold for microRNA delivery.

Self-assembled RNA-triple-helix hydrogel nanoconjugates and scaffold for microRNA delivery.

a, Schematic showing the self-assembly process of three RNA strands to form a dual-colour RNA triple helix. The RNA triplex nanoparticles consist of stable two-pair FRET donor/quencher RNA oligonucleotides used for in vivo miRNA inhibit…

 

Figure 4: Proliferation, migration and survival of cancer cells as a function of RNA-triple-helix nanoparticles treatment.close

Proliferation, migration and survival of cancer cells as a function of RNA-triple-helix nanoparticles treatment.

a, miR-205 and miR-221 expression in breast cancer cells at 24, 48 and 72h of incubation (n = 3, statistical analysis performed with a two-tailed Students t-test, , P < 0.01). miRNA levels were normalized to the RNU6B reference gene

 

  1. Kasinski, A. L. & Slack, F. J. MicroRNAs en route to the clinic: Progress in validating and targeting microRNAs for cancer therapy. Nature Rev. Cancer 11, 849864 (2011).
  2. Li, Z. & Rana, T. M. Therapeutic targeting of microRNAs: Current status and future challenges. Nature Rev. Drug Discov. 13, 622638 (2014).
  3. Yin, H. et al. Non-viral vectors for gene-based therapy. Nature Rev. Genet. 15, 541555(2014).
  4. Conde, J., Edelman, E. R. & Artzi, N. Target-responsive DNA/RNA nanomaterials for microRNA sensing and inhibition: The jack-of-all-trades in cancer nanotheranostics? Adv. Drug Deliv. Rev. 81, 169183 (2015).
  5. Chen, Y. C., Gao, D. Y. & Huang, L. In vivo delivery of miRNAs for cancer therapy: Challenges and strategies. Adv. Drug Deliv. Rev. 81, 128141 (2015).
  6. Yin, P. T., Shah, B. P. & Lee, K. B. Combined magnetic nanoparticle-based microRNA and hyperthermia therapy to enhance apoptosis in brain cancer cells. Small 10, 41064112(2014).
  7. Hao, L. L., Patel, P. C., Alhasan, A. H., Giljohann, D. A. & Mirkin, C. A. Nucleic acid–gold nanoparticle conjugates as mimics of microRNA. Small 7, 31583162 (2011).
  8. Endo-Takahashi, Y. et al. Systemic delivery of miR-126 by miRNA-loaded bubble liposomes for the treatment of hindlimb ischemia. Sci. Rep. 4, 3883 (2014).
  9. Chen, Y. C., Zhu, X. D., Zhang, X. J., Liu, B. & Huang, L. Nanoparticles modified with tumor-targeting scFv deliver siRNA and miRNA for cancer therapy. Mol. Ther. 18, 16501656(2010).
  10. Anand, S. et al. MicroRNA-132-mediated loss of p120RasGAP activates the endothelium to facilitate pathological angiogenesis. Nature Med. 16, 909914 (2010).

 

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Pathology Insights

Larry H Bernstein, MD, FCAP, Curator

LPBI

 

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

Ramón Rami-Porta; Hisao Asamura; Peter Goldstraw

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

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

 

 

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

Introduction

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

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

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

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

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

The Future of the TNM Classification

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

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

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

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

MicroRNAs in the Pathobiology of Sarcomas

Anne E Sarver; Subbaya Subramanian

Lab Invest. 2015;95(9):987-984

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

 

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

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

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

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

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

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

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


Figure 1.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Human Papillomavirus Oncogenic mRNA Testing for Cervical Cancer Screening

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

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

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

 

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

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

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

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

Introduction

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

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

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

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

1 of 4

Figure 1.

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

HPV Testing

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

 

HPV and Disease Prevalence

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

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

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

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

AHPV Assay Performance

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

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

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

Figure 2.

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

Figure 3.

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

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

Discussion

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

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

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

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

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RNAi – On Transcription and Metabolic Control

Writer and Curator: Larry H Bernstein, MD, FCAP

 

RNAi

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

 

mRNA, small RNAs, long RNAs, RNAi and DicAR

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

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

 

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

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

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

 

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

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

 

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

 

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

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


Dicers at RISC. The Mechanism of RNAi

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

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

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

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

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

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

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

 

 

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

 

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

 

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

 

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

 

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

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

 

Dicer Promotes Transcription Termination

Dicer Promotes Transcription Termination

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

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

 

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

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

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

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

 

 

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

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

 

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

Cis-acting lncRNAEnhancer-associated lncRNAIntergenic lncRNA

lncRNA

Promoter-associated lncRNA

Proximity transfer

Trans-acting lncRNA

 

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

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

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

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

 

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

 

 

 

 

Table 1 Number of inferred targets for each miRNA tested

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

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

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

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

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

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

hammerhead ribozyme

hammerhead ribozyme

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

 

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

 

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

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

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

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

 

miRNA

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

 

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

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

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

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

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

 

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

 

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

Table 1 – Target mRNAs of IGF2BP3.

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

 

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

Table 1 Companies developing RNAi therapeutics that includes cancer

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

 

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

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

 

miRNAs and cancer targeting

Table 1 of targets

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Table 2. miRNA cancer therapeutics

 

 

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

 

 

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

 

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

 

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

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

 

 

 

 

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RNA and the Transcription the Genetic Code

Curator: Larry H. Bernstein, MD, FCAP

 

 

This portion of the series is a followup on the series on the replication of the genetic code (DNA).  It may be considered alone, or as a tenth article.  Just as DNA has become far more than it was envisioned 60 years ago, the RNA, which was opened to further investigation by Roger Kornberg, Nobel Laureate, and son of the Nobel Laureate, Arthur Kornberg, who studied DNA polymerase, and with his Nobel Associate, attracted the finest minds in biochemistry and built the best academic department of Biochemistry at Stanford University.  RNA is associated with RNA polymerase as DNA is associated with DNA polymerase.  We have already highlighted the several critical reactions involved in the step-by-step replication of DNA.  The classic model has dictated DNA-RNA-protein.  We shall here look at the amazing view that RNA is heterogeneous, and is involved in living processes in health and disease.

 

 

Transcription (Wikipedia)

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA

Both RNA and DNA are nucleic acids, which use base pairs of nucleotides as a complementary language

  • that can be converted back and forth from DNA to RNA by the action of the correct enzymes.

During transcription, a DNA sequence is read by an RNA polymerase,

As opposed to DNA replication, transcription results in

  1. an RNA complement that includes the nucleotide uracil (U) in all instances
  • where thymine (T) would have occurred in a DNA complement.

Also unlike DNA replication where DNA is synthesized, transcription does not involve an RNA primer to initiate RNA synthesis.

Eukaryotic transcription is the elaborate process that eukaryotic cells use to copy genetic information stored in DNA into units of RNA replica. Gene transcription occurs in both eukaryotic and prokaryotic cells.
A eukaryotic cell has a nucleus that separates the processes of transcription and translation. Eukaryotic transcription occurs

The complexity of the eukaryotic genome necessitates a great variety and complexity of gene expression control.

Transcription can be reduced to the following steps, each moving like a wave along the DNA.

  1. One or more sigma factors initiate transcription of a gene by enabling binding of RNA polymerase to promoter DNA.
  2. RNA polymerase moves a transcription bubble, like the slider of a zipper, which splits the double helix DNA molecule into two strands of unpaired DNA nucleotides, by breaking the hydrogen bonds between complementary DNA nucleotides.
  3. RNA polymerase adds matching RNA nucleotides that are paired with complementary DNA nucleotides of one DNA strand.
  4. RNA sugar-phosphate backbone forms with assistance from RNA polymerase to form an RNA strand.
  5. Hydrogen bonds of the untwisted RNA + DNA helix break, freeing the newly synthesized RNA strand.
  6. If the cell has a nucleus, the RNA may be further processed (with the addition of a 3’UTR poly-A tail and a 5’UTR cap) and exits to the cytoplasm through the nuclear pore complex.

The stretch of DNA transcribed into an RNA molecule is called a transcription unit and encodes at least one gene. If the gene transcribed encodes a protein, the result of transcription is messenger RNA (mRNA), which will then be used to create that protein via the process of translation. Alternatively, the transcribed gene may encode for either non-coding RNA genes (such as microRNA, lincRNA, etc.) or ribosomal RNA (rRNA) or transfer RNA (tRNA), other components of the protein-assembly process, or other ribozymes.[1]

Making RNA replication of gene in eukaryotic cells

Transcription is the process of copying genetic information stored in a DNA strand into a transportable complementary strand of RNA.[1] Eukaryotic transcription takes place in the nucleus of the cell and proceeds in three sequential stages: initiation, elongation, and termination.[1] The transcriptional machinery that catalyzes this complex reaction has at its core three multi-subunit RNA polymerases.[1]

Protein coding genes are transcribed into messenger RNAs (mRNAs) that carry the information from DNA to the site of protein synthesis.[1] Although mRNAs possess great diversity, they are not the most abundant RNA species made in the cell. The so-called non-coding RNAs account for the large majority of the transcriptional output of a cell.[2] These non-coding RNAs perform a variety of important cellular functions.[2]

RNA Polymerase

Eukaryotes have three nuclear RNA polymerases, each with distinct roles and properties

Name Location Product
RNA Polymerase I (Pol I, Pol A) nucleolus larger ribosomal RNA (rRNA) (28S, 18S, 5.8S)
RNA Polymerase II (Pol II, Pol B) nucleus messenger RNA (mRNA), most small nuclear RNAs (snRNAs), small interfering RNA (siRNAs) and micro RNA (miRNA).
RNA Polymerase III (Pol III, Pol C) nucleus (and possibly the nucleolus-nucleoplasm interface) transfer RNA (tRNA), other small RNAs (including the small 5S ribosomal RNA (5s rRNA), snRNA U6, signal recognition particle RNA (SRP RNA) and other stable short RNAs

RNA polymerase I (Pol I)

  • catalyzes the transcription of all rRNA genes except 5S.[3][4]

These rRNA genes are organized into a single transcriptional unit and are transcribed into a continuous transcript. This precursor is then processed into

  • three rRNAs: 18S, 5.8S, and 28S.

The transcription of rRNA genes

  1. takes place in a specialized structure of the nucleus called the nucleolus,[5] where
  2. the transcribed rRNAs are combined with proteins to form ribosomes.[6]

RNA polymerase II (Pol II)

  • is responsible for the transcription of all mRNAs, some snRNAs, siRNAs, and all miRNAs.[3][4]

Many Pol II transcripts exist transiently as single strand precursor RNAs (pre-RNAs) that

  • are further processed to generate mature RNAs.[1]
  1.  precursor mRNAs (pre-mRNAs)are extensively processed
  2. before exiting into the cytoplasm through the nuclear pore for protein translation.

RNA polymerase III (Pol III) transcribes small non-coding RNAs, including tRNAs, 5S rRNA, U6 snRNA, SRP RNA, and other stable short RNAs such as ribonuclease P RNA.[7]

Structure of eukaryotic RNA polymerase II (light blue) in complex with α-amanitin (red), a strong poison found in death cap mushrooms that targets this vital enzyme

RNA Polymerases I, II, and III contain 14, 12, and 17 subunits, respectively.[8] All three eukaryotic polymerases have five core subunits that exhibit

  • homology with the β, β’, αI, αII, and ω subunits of E. coli RNA polymerase.

An identical ω-like subunit (RBP6) is used by all three eukaryotic polymerases,

  • while the same α-like subunits are used by Pol I and III.

The three eukaryotic polymerases share four other common subunits among themselves. The remaining subunits are unique to each RNA polymerase.

The additional subunits found in Pol I and Pol III relative to Pol II, are

  • homologous to Pol II transcription factors.[8]

Crystal structures of RNA polymerases I[9] and II [10] provide an opportunity to understand the interactions among the subunits and the molecular mechanism of eukaryotic transcription in atomic detail.

The carboxyl terminal domain (CTD) of RPB1, the largest subunit of RNA polymerase II,

  • plays an important role in bringing together the machinery necessary for the synthesis and processing of Pol II transcripts.[11]

Long and structurally disordered, the CTD

  • contains multiple repeats of heptapeptide sequence YSPTSPS
  1. that are subject to phosphorylation and
  2. other posttranslational modifications during the transcription cycle.

These modifications and their regulation constitute

  • the operational code for the CTD to control
  1. transcription initiation,
  2. elongation and
  3. termination and
  • to couple transcription and RNA processing.[11]

A DNA transcription unit encoding for a protein contains

  • not only the sequence that will eventually be directly translated into the protein (the coding sequence)
  • but also regulatory sequences that direct and regulate the synthesis of that protein.

The regulatory sequence before (i.e., upstream from) the coding sequence is called

the sequence following (downstream from) the coding sequence is called

Initiation

The initiation of gene transcription in eukaryotes occurs in specific steps.[1]

First, an RNA polymerase along with general transcription factors binds to the promoter region of the gene

The subsequent transition of the complex from the closed state to the open state results in

  • the melting or separation of the two DNA strands and
  • the positioning of the template strand to the active site of the RNA polymerase.

Without the need of a primer

  1. RNA polymerase can initiate the synthesis of a new RNA chain using the template DNA strand
  2. to guide ribonucleotide selection and polymerization chemistry.[1]

However, many of the initiated syntheses are aborted

  • before the transcripts reach a significant length (~10 nucleotides).

During these abortive cycles, the polymerase keeps making and releasing short transcripts

  • until it is able to produce a transcript that surpasses ten nucleotides in length.

Once this threshold is attained, RNA polymerase escapes the promoter and

  • transcription proceeds to the elongation phase.[1]

Eukaryotic promoters and general transcription factors

Pol II-transcribed genes contain a region

  • in the immediate vicinity of the transcription start site (TSS) that binds and positions the preinitiation complex.

This region is called the core promoter because of its essential role in transcription initiation.[12][13] Different classes

  • of sequence elements are found in the promoters. For example,
  • the TATA box is the highly conserved DNA recognition sequence for the TATA box binding protein,
  • TBP, whose binding initiates transcription complex assembly at many genes.

Eukaryotic genes

  • contain regulatory sequences beyond the core promoter.

These cis-acting control elements

  • bind transcriptional activators or repressors to increase or decrease transcription from the core promoter.

Well-characterized regulatory elements include

These regulatory sequences

  • can be spread over a large genomic distance, sometimes located
  • hundreds of kilobases from the core promoters.[1]

General transcription factors are

  • a group of proteins involved in transcription initiation and regulation.[1]

These factors typically have DNA-binding domains that bind

  1. specific sequence elements of the core promoter and
  2. help recruit RNA polymerase to the transcriptional start site.

General transcription factors for RNA polymerase II include TFIID, TFIIA, TFIIB, TFIIF, TFIIE, and TFIIH.[1][14][15]

Transcription has some proofreading mechanisms

  • but they are fewer and less effective than the controls for copying DNA; therefore, transcription has a lower copying fidelity than DNA replication.[2]

As in DNA replication, DNA is read from 3′ end → 5′ end during transcription. Meanwhile,

  • the complementary RNA is created from the 5′ end → 3′ end direction.

This means its 5′ end is created first in base pairing. Although DNA is arranged as two antiparallel strands in a double helix, only

one of the two DNA strands, called the template strand, is used for transcription.

This is because RNA is only single-stranded, as opposed to double-stranded DNA. The other DNA strand (the non-template strand) is called the coding strand,

  • because its sequence is the same as the newly created RNA transcript (except for the substitution of uracil for thymine).

The use of only the 3′ end → 5′ end strand eliminates the need for the Okazaki fragments seen in DNA replication.[1]

In virology, the term may also be used when referring to mRNA synthesis from a RNA molecule (i.e. RNA replication). For instance,

  • the genome of an negative-sense single-stranded RNA (ssRNA -) virus
  1. may serve as a template to transcribe a positive-sense single-stranded RNA (ssRNA +) molecule,
  • since the positive-sense strand contains the information needed
  • to translate the viral proteins for viral replication afterwards.

This process is catalysed by a viral RNA replicase.

Transcription is divided into pre-initiation, initiation, promoter clearance, elongation and termination.

Pre-initiation

In eukaryotes, RNA polymerase, and therefore the initiation of transcription, requires

  • the presence of a core promoter sequence in the DNA.

Promoters are regions of DNA that promote transcription and, in eukaryotes, are found at -30, -75, and -90 base pairs

  • upstream from the transcription start site (abbreviated to TSS).

Core promoters are sequences within the promoter that are essential for transcription initiation. RNA polymerase is able to

The most characterized type of core promoter in eukaryotes is

  • a short DNA sequence known as a TATA box, found 25-30 base pairs upstream from the TSS.

The TATA box, as a core promoter, is the binding site for

  1. a transcription factor known as TATA-binding protein (TBP), which
  2. is itself a subunit of another transcription factor, called Transcription Factor II D (TFIID).

After TFIID binds to the TATA box via the TBP,

  • five more transcription factors and RNA polymerase combine around the TATA box
  • in a series of stages to form a preinitiation complex.

One transcription factor, Transcription factor II H, has two components

  • with helicase activity and so
  • is involved in the separating of opposing strands of double-stranded DNA
  • to form the initial transcription bubble.

However, only a low, or basal, rate of transcription is driven by the preinitiation complex alone. Other proteins known as

  1. activators and repressors,
  2. along with any associated coactivators or corepressors,
  3. are responsible for modulating transcription rate.

Thus, preinitiation complex contains:

  1. Core Promoter Sequence
  2. Transcription Factors
  3. RNA Polymerase
  4. Activators and Repressors.

The transcription preinitiation in archaea is, in essence, homologous to that of eukaryotes, but is much less complex.[3]

The archaeal preinitiation complex assembles at a TATA-box binding site; however,

  • in archaea, this complex is composed of only RNA polymerase II, TBP, and TFB (the archaeal homologue of eukaryotic transcription factor II B (TFIIB)).[4][5]

Initiation

Simple diagram of transcription initiation. RNAP = RNA polymerase

In bacteria, transcription begins with the binding of RNA polymerase to the promoter in DNA. RNA polymerase is a core enzyme consisting of five subunits: 2 α subunits, 1 β subunit, 1 β’ subunit, and 1 ω subunit. At the start of initiation,

  • the core enzyme is associated with a sigma factor that
  • aids in finding the appropriate -35 and -10 base pairs downstream of promoter sequences.[6]

When the sigma factor and RNA polymerase combine, they form a holoenzyme.

Transcription initiation is more complex in eukaryotes. Eukaryotic RNA polymerase

  • does not directly recognize the core promoter sequences. Instead,
  • a collection of proteins called transcription factors mediate
  • the binding of RNA polymerase and the initiation of transcription.

Only after certain transcription factors are attached to the promoter does the RNA polymerase bind to it. The completed assembly of

  • transcription factors and RNA polymerase bind to the promoter,
  • forming a transcription initiation complex.

Transcription in the archaea domain is similar to transcription in eukaryotes.[7]

Promoter clearance

After the first bond is synthesized, the RNA polymerase must clear the promoter. During this time

  • there is a tendency to release the RNA transcript and produce truncated transcripts. This is called
  • abortive initiation and is common for both eukaryotes and prokaryotes.[8]

In prokaryotes, abortive initiation continues to occur

  • until an RNA product of a threshold length of approximately 10 nucleotides is synthesized,
  • at which point promoter escape occurs and a transcription elongation complex is formed.

The σ factor is released according to a stochastic model.[9] Mechanistically, promoter escape occurs through a scrunching mechanism, where

  • the energy built up by DNA scrunching provides the energy needed to break interactions between RNA polymerase holoenzyme and the promoter.[10]

In eukaryotes, after several rounds of 10nt abortive initiation,

  • promoter clearance coincides with the TFIIH’s phosphorylation of serine 5 on the carboxy terminal domain of RNAP II,
  • leading to the recruitment of capping enzyme (CE).[11][12]

The exact mechanism of how CE induces promoter clearance in eukaryotes is not yet known.

Elongation

Simple diagram of transcription elongation

One strand of the DNA, the template strand (or noncoding strand), is used as a template for RNA synthesis. As transcription proceeds,

  • RNA polymerase traverses the template strand and uses base pairing complementarity with the DNA template to create an RNA copy.

Although RNA polymerase traverses the template strand from 3′ → 5′, the coding (non-template) strand and newly formed RNA can also be used as reference points,

  • so transcription can be described as occurring 5′ → 3′.

This produces an RNA molecule from 5′ → 3′, an exact copy of the coding strand (except that thymines are replaced with uracils, and the nucleotides are composed of a ribose (5-carbon) sugar where DNA has deoxyribose (one fewer oxygen atom) in its sugar-phosphate backbone).

mRNA transcription can involve multiple RNA polymerases on a single DNA template and multiple rounds of transcription (amplification of particular mRNA),

  • so many mRNA molecules can be rapidly produced from a single copy of a gene.

Elongation also involves a proofreading mechanism

  • that can replace incorrectly incorporated bases.

In eukaryotes,

  • short pauses during transcription allow appropriate RNA editing factors to bind.

These pauses may be intrinsic to the RNA polymerase or due to chromatin structure.

Termination

Main article: Terminator (genetics)

Bacteria use two different strategies for transcription termination –

  1. Rho-independent termination and
  2. Rho-dependent termination.

In Rho-independent transcription termination, also called intrinsic termination,

RNA transcription stops when the newly synthesized RNA molecule forms

  1. a G-C-rich hairpin loop followed by a run of Us. When the hairpin forms,
  2. the mechanical stress breaks the weak rU-dA bonds,
  3. now filling the DNA-RNA hybrid. This pulls the poly-U transcript out of the active site of the RNA polymerase,
  4. in effect, terminating transcription.

In the “Rho-dependent” type of termination, a protein factor called “Rho

  • destabilizes the interaction between the template and the mRNA, thus
  • releasing the newly synthesized mRNA from the elongation complex.[13]

Transcription termination in eukaryotes is less understood but involves cleavage of the new transcript followed by template-independent addition of As at its new 3′ end, in a process called polyadenylation.[14]

Inhibitors

Transcription inhibitors can be used as antibiotics against, for example, pathogenic bacteria (antibacterials) and fungi (antifungals). An example of such an antibacterial is

8-Hydroxyquinoline is an antifungal transcription inhibitor.[15] The effects of histone methylation may also work to inhibit the action of transcription.

Transcription factories

Active transcription units are clustered in the nucleus, in discrete sites called transcription factories or euchromatin. Such sites can be visualized by allowing engaged polymerases to extend their transcripts in tagged precursors (Br-UTP or Br-U) and immuno-labeling the tagged nascent RNA. Transcription factories can also be localized using fluorescence in situ hybridization or marked by antibodies directed against polymerases. There are ~10,000 factories in the nucleoplasm of a HeLa cell, among which are ~8,000 polymerase II factories and ~2,000 polymerase III factories. Each polymerase II factory contains ~8 polymerases. As most active transcription units are associated with only one polymerase, each factory usually contains ~8 different transcription units. These units might be associated through promoters and/or enhancers, with loops forming a ‘cloud’ around the factor.[16]

History

A molecule that allows the genetic material to be realized as a protein was first hypothesized by François Jacob and Jacques Monod. Severo Ochoa won a Nobel Prize in Physiology or Medicine in 1959 for developing a process for synthesizing RNA in vitro with polynucleotide phosphorylase, which was useful for cracking the genetic code. RNA synthesis by RNA polymerase was established in vitro by several laboratories by 1965; however, the RNA synthesized by these enzymes had properties that suggested the existence of an additional factor needed to terminate transcription correctly.

In 1972, Walter Fiers became the first person to actually prove the existence of the terminating enzyme.

Roger D. Kornberg won the 2006 Nobel Prize in Chemistry “for his studies of the molecular basis of eukaryotic transcription”.

Reverse transcription

Some viruses (such as HIV, the cause of AIDS), have the ability to transcribe RNA into DNA. HIV has an RNA genome that is reverse transcribed into DNA. The resulting DNA can be merged with the DNA genome of the host cell. The main enzyme responsible for synthesis of DNA from an RNA template is called reverse transcriptase.

Some eukaryotic cells contain an enzyme with reverse transcription activity called telomerase. Telomerase is a reverse transcriptase that lengthens the ends of linear chromosomes. Telomerase carries an RNA template from which it synthesizes a repeating sequence of DNA, or “junk” DNA. This repeated sequence of DNA is called a telomere and can be thought of as a “cap” for a chromosome. It is important because every time a linear chromosome is duplicated, it is shortened. With this “junk” DNA or “cap” at the ends of chromosomes, the shortening eliminates some of the non-essential, repeated sequence rather than the protein-encoding DNA sequence, that is farther away from the chromosome end.

Telomerase is often activated in cancer cells to enable cancer cells to duplicate their genomes indefinitely without losing important protein-coding DNA sequence. Activation of telomerase could be part of the process that allows cancer cells to become immortal. The immortalizing factor of cancer via telomere lengthening due to telomerase has been proven to occur in 90% of all carcinogenic tumors in vivo with the remaining 10% using an alternative telomere maintenance route called ALT or Alternative Lengthening of Telomeres.[20]

RNA-Seq Dissects the Transcriptome

Transcript Targeting  Kathy Liszewski
GEN    Jul 1, 2014 (Vol. 34, No. 13)

With the rapid rise of next-generation sequencing (NGS), one of its technologies, RNA sequencing (RNA-Seq), has taken center stage for analyzing whole transcriptomes.

Although RNA-Seq is still the new kid on the block,

  • this technology has the potential to revolutionize transcriptomics,
  • revealing the architecture of gene expression in unprecedented detail.

RNA-Seq applications are proliferating and include

  • the elucidation of disease processes,
  • targeted drug development, and
  • personalized medicine.

To orient researchers who are unfamiliar with the differences between  RNA-Seq platforms, Kelli Bramlett, R&D scientist, Life Technologies, poses two key questions:

1. Are you interested in pure discovery, in a nonguided fashion, of every RNA species present in your test samples?

2. Are you mainly focused on measuring expression levels of well-annotated coding RNA transcripts?

You might have a set of genes crucial to

 

  • identifying a disease state, or
  • profiling the stage of a specific type of cancer, or
  • monitoring development in your experimental system,

You then would want to employ a system that

  • “allows you to quickly and efficiently focus on just your genes of interest and screen through many different samples in a short amount of time.”

RNA-Seq allows for true discovery but

  • “requires sequencing depth and
  • requires significant additional time for analysis
  • If a focused panel targeting specific RNAs will better meet your needs, this can be accomplished on systems with
  • much faster turnaround time and less sequencing depth.”( according to Dr. Bramlett)

Enhancing Sensitivity

RNA-Seq has advanced our ability to characterize transcriptomes at high resolution, and the laboratory and data analysis techniques used for this NGS application continue to mature, notes John Tan, Ph.D., senior scientist, Roche NimbleGen. “High sequencing costs combined with the omnipresence of pervasive, abundant transcripts decrease our power to study rare transcripts, decrease throughput, and limit the routine use of this technology.”

For example, notes Dr. Tan, a small number of

  • highly expressed housekeeping genes can be responsible for a large fraction of total sequence reads in an experiment, thus
  • increasing the amount of sequencing required to characterize less abundant transcripts of interest.

To improve the cost-effectiveness, throughput, and sensitivity of RNA-Seq, Dr. Tan and colleagues are developing methods to perform targeted RNA-Seq.
“Targeted enrichment of transcripts of interest

  • circumvents the need to perform separate rRNA depletion or polyA enrichment steps on input RNA,” explains Dr. Tan.

“By targeting their sequencing, researchers can avoid wasting resources on

  • housekeeping transcripts and focus instead on genes or genomic regions of interest.”

Targeted RNA-Seq can allow deeper sequence coverage, increased sensitivity for low-abundance transcripts, less total sequencing per sample, and more samples processed per sequencing instrument run. “Significantly, we observe that the enrichment step also preserves quantitative information very well,” adds Dr. Tan. “These advances will facilitate a more routine use of RNA-Seq technology.”

  • Sample Integrity Issues

“Formalin-fixed, paraffin-embedded (FFPE) patient tissue archives and the clinical data associated with them may provide only limited amounts of sample that may also be degraded,” comments Gary Schroth, Ph.D., distinguished scientist, Illumina. Dr. Schroth says that most labs currently gauge RNA integrity via the RIN (RNA integrity number). but the RIN number from FFPE samples is not a sensitive measure of RNA quality or a good predictor for library preparation. A better predictor is RNA fragment size. We developed the DV200 metric, the percentage of RNA fragments greater than 200 nucleotides, a size needed for accurate construction of libraries.”

Illumina offers its TruSeq® RNA Access Library Preparation Kit especially for FFPE samples. This kit, when used with the DV200 metric, provides cleaner and more accurate library preparation. This new approach allows researchers to start with five-to tenfold less material when making libraries from FFPE samples.

  • Strand Specificity

Most NGS requires initial construction of libraries that may not provide the specificity desired even when prepared from mRNA. “Traditional RNA-Seq library preparation loses the strandedness of transcripts—information that is critical in understanding cellular transcription,” says Jungsoo Park, senior marketing and sales manager, Lexogen.

According to Park, Lexogen tackled this problem

  • by developing a method to generate libraries with greater than 99.9% strand specificity with a simplified process that takes 4.5 hours to complete.

Lexogen’s SENSE mRNA-Seq library kit initially isolates mRNA via

  • the poly A tail and utilizes random hybridization of the transcripts that
  • are bound to the magnetic beads without transcript fragmentation.

“This is a revolutionary method, which keeps high strandedness of the transcripts,” asserts Park.

One of the novel aspects of this approach is the use of starter/stopper heterodimers containing platform-specific linkers that hybridize to the mRNA.
“The starters serve as primers for reverse transcription, which then

  • terminates once the stopper from the next heterodimer is reached,

“At this point, the newly synthesized cDNA and the stopper are ligated while still bound to the RNA template.” According to Park,

  • there is no need for a time-consuming fragmentation step, and library size is determined simply by the protocol itself.

For researchers only intending to see the expression levels, sequencing of the entire mRNA transcript will require subsequent bioinformatics processes such as RPKM, a measure of relative molar RNA concentration.

  RNA-Seq Libraries

NuGEN Technologies offers its Ovation Human Blood RNA-Seq Library System as an end-to-end solution for strand-specific RNA-Seq library construction. NuGEN’s Insert Dependent Adaptor Cleavage (InDA-C) technology can provide targeted depletion of unwanted high-abundance transcripts.
  • Cells possess many thousands of transcripts.
  • uninformative transcript species that can compromise data quality and the cost-effectiveness of sequencing
  • NuGEN Technologies has developed a method for targeted depletion of unwanted transcripts following construction of RNA-Seq libraries. (Insert Dependent Adaptor Cleavage (InDA-C),

employs customized primers that target specific transcripts, such as ribosomal and globin RNAs, to exclude from final RNA-Seq libraries. (hemoglobin RNA derived from blood accounts for at least 60% of transcripts)  “By depleting these two transcript classes, InDA-C quadruples informative reads. and it avoids off-target mRNA cross-hybridization events that can potentially introduce bias. The species and transcript specificity of the workflow relies on the design of InDA-C primers, which can be constructed

  • to target virtually any class of unwanted transcripts for targeted depletion,”  according to Dr. Kain.

NuGEN has developed Single Primer Enrichment Technology, which can be used to prepare targeted NGS libraries from both gDNA or cDNA,

  •  used to identify gene fusion products and alternative splicing patterns from enriched cDNA libraries.

platforms automate the RNA sequencing sample preparation process [Beckman Coulter]

Preparation of libraries for RNA-Seq entails an intensive workflow.  according to Alisa Jackson, senior marketing manager, Genomic Solutions, Beckman Coulter, automation provides four key advantages:

  • Creation of high-quality mRNA libraries. Initial steps in this process include depleting samples of ribosomal RNA. Although it has the greatest abundance, rRNA gives the least amount of information.
  • “We’ve automated this process on our Biomek instruments using popular sample preparation kits from Illumina and New England Biolabs,” notes Jackson. “Accurate pipetting and thorough mixing are critical for this process. The Biomek liquid handler’s 96-channel pipetting head is used in combination with an on-deck orbital shaker to vigorously mix samples. Results show this ‘mix and shake’ approach works well.”
  • Limited exposure to RNAses from human contact. Every scientist’s nemesis when working with RNA is the universal presence of RNA-degrading RNAses. To help overcome this problem, says Jackson, “Biomek consumables such as pipette tips are DNase and RNase-free.”
  • Reduced exposure to toxic chemicals. “An instrument dispenses all reagents involved in the various steps of process.”
  • Enhanced reproducibility. “This is still a very expensive process,” asserts Jackson. “Obtaining accurate results the first time prevents costly repetitions. For this reason, we provide Biomek methods for many NGS library preparation kits. By fully testing these methods with real-life samples, we ensure reliable and repeatable creation of sequence-ready RNA libraries, whether stranded or nonstranded, mRNA or total RNA.”
  • What’s Next?

RNA-seq data analysis

RNA-seq data analysis for target identification. [Boehringer Ingelheim]

  •  “With RNA-Seq, we are closing in on personalized medicine,” suggests Qichao Zhu, Ph.D., principal scientist, Boehringer Ingelheim. “This technology allows more exact identification of patient subgroups. Instead of ‘one drug fits all,’ we can now begin to more appropriately define which drugs will work in which patients. Diseases such as cancer and cystic fibrosis as well as neurodegenerative illnesses have many patient subcategories. Future pharmaceutical drug discovery will be better able to develop targeted therapeutics with the help of RNA-Seq.
  • ”There are still many challenges in the field, however. “A critical aspect is accuracy. Given the large scale set of RNA-Seq, even 99.99% accuracy is not good enough for diagnostics,” insists Dr. Zhu. “Further, as we move forward, we will need to improve many aspects of the technology including
  • disease tissue sample isolation,
  • library construction methodologies, as well as
  • analysis of massive datasets.

“In the future, a patient will go into the doctor’s office and have a whole transcriptome profile test performed.“When PCR technology was discovered, no one knew just how powerful it would become or how many applications it would generate. Now, it is used everywhere. NGS technology and RNA-Seq have a similar potential. ”

 

Gene Paces microRNAs to Set Developmental Rhythms

Kevin Mayer   Jul 18, 2014   GEN News Highlights

http:/www.genengnews.com/gen-news-highlights/gene-paces-micrornas-to-set-developmental-rhythms/81250124/

Using C. elegans as a model researchers identified LIN-42, a gene that is found in animals across the evolutionary tree, as a potent regulator of numerous developmental processes. [C. Hammell, Cold Spring Harbor Laboratory]

  • Although the how of a gene’s function is important, the when, too, is crucial. The ebb and flow ofgene expression can influence a cell’s fate during development, the maturation of entire organisms, and even the evolution of species—helping to explain how species with very similar gene content can differ so dramatically.

Nature’s developmental clockwork

  • depends on the activation or repression of a specific and unique complement of genes. And these genes, in turn,
  • are regulated by microRNA molecules. And, finally,
  • the microRNAs are also subject to regulation.
  •  one must then study the regulators of the regulators of the regulators.

Little is known of the ultimate regulators—the elements that determine the activities of microRNAs. These elements, however, are presumably as subtle as they are powerful—

  1. subtle because microRNAs defined temporal gene expression and cell lineage patterns in a dosage-dependent manner;
  2. powerful because a single microRNA gene can control hundreds of other genes at once.
  3. as always, timing is everything: If a microRNA turns off genes too early or too late, the organism that depends on them will likely suffer severe developmental defects.

To undertake a search for genes that control developmental timing through microRNAs, a team of researchers at Cold Spring Harbor Laboratory relied on a tried-and-true model of animal development, Caenorhabditis elegans. These worms have a fixed number of cells, and each cell division is precisely timed.  “It enables us to understand

  • exactly how a mutation affects development,
  • whether maturation is precocious or delayed,
  • by directly observing defects in the timing of gene expression.” (said team leader Christopher Hammell, Ph.D.)

The researchers described their work in an article entitled, “LIN-42, the Caenorhabditis elegans PERIOD  homolog, Negatively Regulates MicroRNA Transcription,” which appeared July 17 in PLoS Genetics.

the goal to unveil factors that regulate the expression of microRNAs that control developmental timing –

  • they  identified LIN-42, the C. elegans homolog of the human and Drosophila period gene implicated in circadian gene regulation, as a negative regulator of microRNA expression

“By analyzing the transcriptional expression patterns of representative microRNAs, we found that the transcription of many microRNAs is normally highly dynamic and coupled aspects of post-embryonic growth and behavior.”

“LIN-42 shares a significant amount of similarity to the genes that control circadian rhythms in organisms such as mice and humans,” explained Roberto Perales, Ph.D., one of the lead authors of the study. “These are genes that control the timing of cellular processes on a daily basis for you and me. In the worm, these same genes and mechanisms control development, growth, and behavior. This system will provide us with leverage to understand how all of these things are coordinated.”

  1.  LIN-42 controls the repression of numerous genes in addition to microRNAs.
  2.  levels of the protein encoded by LIN-42 tend to
  • oscillate over the course of development and form a part of a developmental clock.

“LIN-42 provides the organism with a kind of cadence or temporal memory, so that

  1. it can remember that it has completed one developmental step before it moves on to the next,” emphasized Dr. Hammell. “This way, LIN-42 coordinates optimal levels of the genes required throughout development.”

 

Intracellular RNA-Seq

This literature review highlights a study led by George Church describing FISSEQ, or fluorescent in situ RNA sequencing.

Anton Simeonov, Ph.D.   Jul 25, 2014

http://www.genengnews.com/insight-and-intelligence/intracellular-rna-seq/77900207/

 

 FISSEQ appears to be sensitive to genes associated with cell type and function, and this in turn could be used for cell typing. [© Alila Medicinal Media – Fotolia.com]

  • Methods such as fluorescence in situ hybridization (FISH) allow gene expression to be observed at the tissue and cellular level; however, only a limited number of genes can be monitored in this manner, making transcriptome-wide studies impractical. George Church’s group* is presenting the further development of their original approach called
  • fluorescent in situ sequencing (FISSEQ) to incorporate a spatially structured sequencing library and an imaging method capable of resolving the amplicons (see Figure 1).

In fixed cells, RNA was reverse transcribed with tagged random hexamers to produce cDNA amplicons.

  1. Aminoallyl deoxyuridine 5-triphosphate (dUTP) was incorporated during reverse transcription and
  2. after the cDNA fragments were circularized before rolling circle amplification (RCA),
  3. an amine-reactive linker was used to cross-link the RCA amplicons containing aminoallyl dUTP.

The team generated RNA sequencing libraries in different cell types, tissue sections, and whole-mount embryos for three-dimensional (3D) visualization that spanned multiple resolution scales (see Figure 1).

Click Image To Enlarge +
Figure 1
  • Figure 1. Construction of 3D RNA-seq libraries in situ. After RT using random hexamers with an adapter sequence in fixed cells, the cDNA is amplified and cross-linked in situ. (A) A fluorescent probe is hybridized to the adapter sequence and imaged by confocal microscopy in human iPS cells (hiPSCs; scale bar: 10 μm) and fibroblasts (scale bar: 25 μm). (B) FISSEQ can localize the total RNA transcriptome in mouse embryo and adult brain sections (scale bar: 1 mm) and whole-mount Drosophila embryos (scale bar: 5 μm), although we have not sequenced these samples. (C) 3D rendering of gene-specific or adapter-specific probes hybridized to cDNA amplicons. 3D, three-dimensional; RT, reverse transcription; FISSEQ, fluorescent in situ sequencing; FISH, fluorescence in situ hybridization.
  • In a proof-of-concept experiment (see Figure 2) the authors sequenced primary fibroblasts in situ after simulating a response to injury, which yielded 156,762 reads, mapped to 8,102 annotated genes. When the 100 highest ranked genes were clustered, cells kept in fetal bovine serum medium were enriched for fibroblast-associated gene hits, while the rapidly dividing cells in epidermal growth factor medium were less fibroblast-like, reaffirming that the FISSEQ platform output reflects the change in transcription status as a function of the cellular environment and stress factors.

 

  • Figure 2. Overcoming resolution limitations and enhancing the signal-to-noise ratio. Ligation of fluorescent oligonucleotides occurs when the sequencing primer ends are perfectly complementary to the template. Extending sequencing primers by one or more bases, one can randomly sample amplicons at 1/4th, 1/16th, and 1/256th of the original density in fibroblasts (scale bar: 5 μm). N, nucleus; C, cytoplasm.
  • The authors further noted that FISSEQ appears to be sensitive to genes associated with cell type and function, and this in turn could be used for cell typing. It was also speculated that FISSEQ might allow for a combined transcriptome profiling and mutation detection in situ.
  • *Abstract from Science 2014, Vol. 343:1360–1363

Understanding the spatial organization of gene expression with single-nucleotide resolution requires

  • localizing the sequences of expressed RNA transcripts within a cell in situ.

Here, we describe fluorescent in situ RNA sequencing (FISSEQ), in which stably cross-linked complementary DNA (cDNA) amplicons are sequenced within a biological sample.

  1. Using 30-base reads from 8102 genes in situ, we examined RNA expression and localization in human primary fibroblasts with a simulated wound-healing assay.
  2. FISSEQ is compatible with tissue sections and whole-mount embryos and
  3. reduces the limitations of optical resolution and noisy signals on single-molecule detection.

Our platform enables massively parallel detection of genetic elements, including

  • gene transcripts and molecular barcodes, and can be used
  • to investigate cellular phenotype, gene regulation, and environment in situ.

Anton Simeonov, Ph.D., works at the NIH.

ASSAY & Drug Development Technologies, is published by Mary Ann Liebert, Inc.
GEN presents here one article that was analyzed in the “Literature Search and Review” column, a paper published in Science titled “Highly multiplexed subcellular RNA sequencing in situ.” Authors of the paper are Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, Terry R, … and Church GM.

 

Completely ablate microRNA genes on the genomic level

  • miR-KOs are transcription activator-like effector (TALE) nucleases that
  • precisely edit specific miRNAs in mammalian cells.
  • SBI designed miR-TALE-nucleases to cleave within the miRNA seed region.

In the absence of HR donor vectors, the cellular machinery repairs such breaks via

  • non-homologous end joining (NHEJ).

This is an error-prone system that typically generates small deletions or insertions (indels) at or near the site of cleavage. Since the seed region (defined as bases 2-8 of the microRNA) directs miRNA binding to its target DNA, indels within the seed region completely abolish miRNA function.

 

Design of miR-KO TALE Nucleases

The miR-KOs are designed to disrupt the miRNA seed region. Pairing miR-KOs with an HR donor

  • replaces the entire miRNA hairpin structure with an insulated selectable marker cassette.

Sample data for miR-KO 21 Knockout

Selection for HR events by puromycin or by FACS-based sorting for RFP can enrich for properly knocked-out alleles. The enriched cell populations are then

  • genotyped to determine whether the knockout is at a single allele or bi-allelic (as in the case of hsa-miR-21).

Genotyping for HR events is performed via junction PCR of genomic DNA-insert junctions at 5′ and/or 3′ ends of an HR site. PCR primer pairs are designed with one of the primer sequences corresponding to the targeted genomic DNA region and the other corresponding to the HR vector.

Primer design strategy for HR-directed genotyping

Genomic DNA PCR was used to to detect HR integration in one or both alleles of hsa-miR-21. Individual cellular clones that display one HR event typically display mutated seed regions in the other allele. miR-KOs, when combined with HR donor vectors have been shown to be highly efficient in generating double miRNA knockouts. For example, a miR-KO strategy against human miR-21 in HEK293T cells resulted in 30 puromycin-resistant lines out of 96 single cell-derived clones. Subsequent PCR-based genotyping of 23 successful PCR amplifications revealed that ~96% (22/23) were mono-allelic (i.e. one allele with HR and other with NHEJ or WT) and ~4% (1/23) were bi-allelic (e.g. both alleles undergone HR) for HR-induced miR-21 deletion. Furthermore, sequencing of PCR products spanning the targeted seed region of miR-21 revealed that 91% (10/11) were NHEJ-modified.

Taken together, these results show a 87% bi-allelic modification rate (20 out of 23 clones)

  • when the miR-KOs are combined with an HR donor vector.

Validation and phenotypic analysis of miR-KO of hsa-miR-21

To confirm complete loss of miRNA-21 expression, we quantified miR-21 expression in three independent miR-21 double knockouts by qPCR.

  1. Clone #1 and #7 carry one deletion of the miR-21 hairpin structure (via HR) and
  2. one indel within the seed region (via NHEJ);
  3. clone #5 carries bi-allelic deletions of the hairpin structure (bi-allelic HR).

We found complete abolishment of miR-21 expression in all three cell lines.

Growth phenotype uncovered in miR-21 KO cell lines

MicroRNA-21 has been characterized as a cell-promoting OncomiR. The abalation of the genomic hsa-miR-21 in human cells resulted in reduced proliferation in all three miR-21 knockout lines tested. Growth curves were plotted for the parental HEK293 cells as well as the three independent knockout lines.

Increase the ease and efficiency of obtaining KOs with matched HR vectors

While the use of miR-KOs alone can successfully abolish miRNA function,

  • screening for bi-allelic indels can be laborious.

Due to the small changes seen with indels, many clonal lines have to be established through limited dilution or single-cell sorting techniques, and

  • subsequently genomic DNA is PCR-amplified,
  • cloned into vectors and
  • subjected to genotyping by Sanger sequencing.

Since many cells will only have either zero or one alleles modified, tremendous work is often required to obtain bi-allelic indels.

To facilitate the screening process,

  • one may combine miRNA-specific TALE-nucleases with HR donor vectors, which enables positive selection and convenient screening of targeted cells.

Because NHEJ occurs more frequently than HR donor integration,

  • the majority of cells that undergo HR integration on one allele carry an indel in the miRNA seed region of the second allele.

This strategy has been shown to be highly efficient in generating bi-allelic miRNA knockouts. A positive selection strategy reveals puromycin-resistant and RFP-positive single-cell derived colonies, majority of which are double knockouts (i.e. HR event on one allele and indel in seed region of second allele).

Shown above is an overview of miR-KO strategies with miR-KOs alone and in combination with an HR donor vector. The HR donor vector enables positive selection, which allows for simple and efficient generation of cells harboring double knockouts.
Gene Described as Critical to Stem Cell Development

GEN News Highlights  Jul 18, 2014
http://www.genengnews.com/gen-news-highlights/gene-described-as-critical-to-stem-cell-development/81250121/

  • Scientists at Michigan State University say they have found that a gene known as ASF1A could be critical to the development of stem cells. ASF1A is at least one of the genes responsible for the mechanism of cellular reprogramming, a phenomenon that can turn one cell type into another, which is key to the making of stem cells, according to the researchers.

In a paper (“Histone chaperone ASF1A is required for maintenance of pluripotency and cellular reprogramming”) published in Science, the MSU team describes

  • how they analyzed more than 5,000 genes from a human oocyte before determining that
  • the ASF1A, along with another gene known as OCT4 and a helper soluble molecule, were the ones responsible for the reprogramming.

In 2006, an MSU team identified the thousands of genes that reside in the oocyte. In 2007, a team of Japanese researchers found that

  • by introducing four other genes into cells, induced pluripotent stem cells (iPSCs) could be created without the use of a human egg.

The researchers say that the genes ASF1A and OCT4 work in tandem with a ligand,

  • a hormone-like substance that also is produced in the oocyte called GDF9, to facilitate the reprogramming process.
  • overexpression of just ASF1A and OCT4 in hADFs exposed to the oocyte-specific paracrine growth factor GDF9 can reprogram hADFs into pluripotent cells

The report underscores the importance of studying the unfertilized MII [metaphase II human] as a means

  • to understand the molecular pathways governing somatic cell reprogramming.

“We believe that ASF1A and GDF9 are two players among many others that remain to be discovered, which are part of the cellular-reprogramming process,” noted Dr. Cibelli. “We hope that in the near future, with what we have learned here, we will be able to test new hypotheses that will reveal more secrets the oocyte is hiding from us. In turn, we will be able to develop new and safer cell therapy strategies.”

  • Although the how of a gene’s function is important, the when, too, is crucial. The ebb and flow of gene expression can influence a cell’s fate during development, the maturation of entire organisms, and even the evolution of species—helping to explain how species with very similar gene content can differ so dramatically.

 

Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers

M Beta, A Venkatesan, M Vasudevan, U Vetrivel, et al. Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers.

Bioinformatics and Biology Insights 2013:7 21–34.   http://dx.doi.org:/10.4137/BBI.S10501

This study was undertaken

  • to identify the differentially expressed miRNAs in the serum of children with RB in comparison with the normal age matched serum,
  • to analyze its concurrence with the existing RB tumor miRNA profile,
  • to identify its novel gene targets specific to RB, and
  • to study the expression of a few of the identified oncogenic miRNAs in the advanced stage primary RB patient’s serum sample.

MiRNA profiling performed on 14 pooled serum from chil­dren with advanced RB and 14 normal age matched serum samples

  • 21 miRNAs found to be upregulated (fold change > 2.0, P < 0.05) and
  • 24 downregulated (fold change > 2.0, P < 0.05).

Intersection of 59 significantly deregulated miRNAs identified from RB tumor profiles with that of miRNAs detected in serum profile revealed that

  • 33 miRNAs had followed a similar deregulation pattern in RB serum.

Later we validated a few of the miRNAs (miRNA 17-92) identified by microarray in the RB patient serum samples (n = 20) by using qRT-PCR.

Expression of the oncogenic miRNAs, miR-17, miR-18a, and miR-20a by qRT-PCR was significant in the serum samples

  • exploring the potential of serum miRNAs identification as noninvasive diagnosis.

Moreover, from miRNA gene target prediction, key regulatory genes of

  • cell proliferation,
  • apoptosis, and
  • positive and negative regulatory networks

involved in RB progression were identified in the gene expression profile of RB tumors.
Therefore, these identified miRNAs and their corresponding target genes could give insights on

  • potential biomarkers and key events involved in the RB pathway.

 

Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes

(M Burton, M Thomassen, Q Tan, and TA Kruse.) Cancer Informatics 2012:11 193–217

http://dx.doi.org:/10.4137/CIN.S10375

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that

  • genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location.

In this study we compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance

  • within the same dataset,
  • feature set validation perfor­mance, and
  • validation performance of entire classifiers in strictly independent datasets

were assessed by

  • 10 times repeated 10-fold cross validation,
  • leave-one-out cross validation, and
  • one-fold validation, respectively.

To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome

  • in strictly independent data sets, both
  • between different and
  • within similar microarray platforms, while
  • classifiers had a poorer performance when validated in strictly independent datasets.

The study showed that MG- and SG-feature sets perform equally well in classifying indepen­dent data. Furthermore, SG-classifiers significantly outperformed MG-classifier

  • when validation is conducted between datasets using similar platforms, while
  • no significant performance difference was found when validation was performed between different platforms.

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

 

Molecular basis of transcription pausing

Jeffrey W. Roberts

Science 13 June 2014;  344(6189), pp. 1226-1227   http://dx.doi.org:/10.1126/science.1255712

+Author Affiliations

  1. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA.
  2. E-mail: jwr7@cornell.edu

During RNA synthesis, RNA polymerase moves erratically along DNA,

  1. frequently resting as it produces an RNA copy of the DNA sequence.

Such pausing helps coordinate the appearance of a transcript with its utilization by cellular processes; to this end,

  • the movement of RNA polymerase is modulated by mechanisms that determine its rate. For example,
  1. pausing is critical to regulatory activities of the enzyme such as the termination of transcription. It is also essential
  2. during early modifications of eukaryotic RNA polymerase II that activate the enzyme for elongation.

Two reports analyzing transcription pausing on a global scale in Escherichia coli, by Larson et al. (1) and by Vvedenskaya et al. (2) on page 1285 of this issue, suggest new functions of pausing and reveal important aspects of its molecular basis.

The studies of Larson et al. and Vvedenskaya et al. follow decades of analysis of bacterial transcription that has illuminated

  • the molecular basis of polymerase pausing events that serve critical regulatory functions.

A transcription pause specified by the DNA sequence

  • synchronizes the translation of RNA into protein with
  • the transcription of leader regions of operons (groups of genes transcribed together) for amino acid biosynthesis;
  • this coordination controls amino acid synthesis in response to amino acid availability (3).

A protein-induced pause occurs when the E. coli initiation factor σ70 restrains RNA polymerase

  • by binding a second occurrence of the “−10” promoter element.

This paused polymerase provides a structure for

  1. engaging a transcription antiterminator (the bacteriophage λ Q protein) (4) that,
  2. inhibits transcription pauses, including those essential for transcription termination.

Knowledge about the interactions between nucleic acids and RNA polymerase that induce pausing

  • comes partly from studies on the E. coli histidine biosynthesis operon.

RNA polymerase pauses at the leader region of this cluster of genes (the “his pause”),

  • allowing an essential RNA hairpin structure to form just upstream of the RNA-DNA hybrid
  • where RNA synthesis is templated in the polymerase’s catalytic cleft.

Importantly, however, other sequence elements are required to induce and stabilize the his pause—particularly

  • the nucleotide at the newly formed, growing end of the RNA (pausing is favored by pyrimidines rather than purines) (5), and
  • at the incoming nucleotide position [pausing is favored particularly by guanine (G)] (6), as well as surrounding elements.

Biochemical and structural analyses have identified an endpoint of the pausing process called the “elemental pause” in which

  • the catalytic structure in the active site is distorted, preventing further nucleotide addition (7).

The elemental paused state also involves distinct conformational changes in the polymerase

  1. that may favor transcription termination and
  2. allow the his and related pauses to be stabilized by RNA hairpins (8).

ILLUSTRATION: V. ALTOUNIAN/SCIENCE

Single-molecule analysis of transcribing RNA polymerase, at nearly single-nucleotide resolution, identified many specific pause sites in the E. coli genome (9). Pausing occurs on essentially any DNA, and very frequently—every 100 nucleotides or so. These “ubiquitous” pauses are only partly efficient (i.e., not always recognized as the enzyme transits), and mostly have not been associated with specific functions. However, their existence is consistent with biochemical experiments showing that the progress of RNA polymerase is generally erratic. A consensus sequence for ubiquitous pauses was identified, with two important elements:

  • a preference for pyrimidine [mostly cytosine (C)] at the newly formed RNA end,
  • followed by G to be incorporated next—just as found for the his pause; and
  • a preference for G at position −10 of the RNA (10 nucleotides before the 3′ end), which is
  • at the upstream boundary of the RNA-DNA templating hybrid.

Remarkably, the tendency of a G in this position to induce pausing was recognized earlier, when DNA could be sequenced only through its transcript (10); it was thought that inhibited unwinding of the RNA-DNA hybrid underlies the pause.

 

Polyymerase, paused.

During transcription, RNA exists in two states as RNA polymerase progresses:

  1. pretranslocated, just after the addition of the last nucleotide [here, cytosine (C)]; and
  2. posttranslocated, after all nucleic acids have shifted in register by one nucleotide relative to the enzyme,
  • exposing the active site for binding of the next substrate molecule [here, guanine (G)].

The pretranslocated state is dominant in the pause. The critical G-C base (RNA-DNA) pair at position −10 in pretranslocated state and

  • the nontemplate DNA strand G bound in the polymerase in the posttranslocated state are marked with an asterisk.

ILLUSTRATION: V. ALTOUNIAN/SCIENCE

This ubiquitous pausing consensus sequence now has been refined and mapped exhaustively in the E. coligenome by Larson et al. and Vvedenskaya et al. (see the figure). In an analysis called native elongating transcript sequencing (NET-Seq) (11), transcripts associated with the whole cellular population of RNA polymerase are isolated from abruptly frozen cells and their growing ends are sequenced, giving a snapshot at nucleotide resolution of global transcription activity; DNA sites that are highly populated by RNA polymerase represent pauses. Larson et al. identified ∼20,000 transcription pause sites in the E. coli genome, including those expected from previous analysis of known sites like the his pause. Their analysis raises interesting questions about the role of such abundant pausing sequences.

Primarily, Larson et al. note that pauses frequently occur

  • exactly at the site of translation initiation, suggesting an important role in gene expression.

This coincidence of events is understandable when you examine the sequences. The consensus sequence in RNA for RNA polymerase pausing is G−10Y−1G+1 [G at position −10 and at the site after the pause; Y denotes either C or uracil (U) at the RNA end] according to Larson et al. and Vvedenskaya et al. The Shine-Dalgarno consensus sequence in RNA that the small-subunit ribosome recognizes is AGGAGG [adenine (A)] providing the G at the −10 position;

  • the downstream initiation codon for RNA translation is AUG, providing (for E. coli) the U at the pause end at position −1, with a following G at position +1.

A slightly modified pausing consensus sequence in the bacterium Bacillus subtilis accommodates the difference in spacing between the Shine-Dalgarno sequence and the initiation codon. What might be the role of a pause exactly at the translation initiation site? Because the ribosome binding site is physically concealed by RNA at the pause,

  • pausing may enable some process that prepares the RNA for translation once RNA polymerase transits the pause site.

Larson et al. suggest that the pause allows upstream RNA secondary structure to resolve in order to present the initiation region properly to the ribosome.

A particularly informative application of NET-Seq that provides new mechanistic information about pausing is based on the discovery of a specific binding site in RNA polymerase [the core recognition element (CRE)] for G in the non-template DNA strand (the strand not transcribed), at position +1 in the “posttranslocated” structure (12).

  • It could be that specific binding of a nucleotide to the enzyme in this position enhances pausing by slowing translocation;

surprisingly, however, Vvedenskaya et al. find the opposite. Cells altered to destroy the G binding site have up to twice as many sites of pausing as in wild-type cells, with

  • a greater preference for G as the incoming nucleotide.

However, this result is understandable in terms of the translocation cycle of RNA polymerase and the ubiquitous pausing sequence that has G at position +1. Binding of G at position +1 to CRE only occurs in the posttranslocated state, which would thus be favored over the pretranslocated state. Hence,

  • if G binding inhibits pausing, then the rate-limiting paused structure must be in the pretranslocated state (a conclusion also made by Larson et al. from biochemical experiments).

This is an important insight into the sequence of protein–nucleic acid interactions that occur in pausing. Vvedenskaya et al. suggest that the actual role of the G binding site is to promote translocation and thus inhibit pausing, to smooth out adventitious pauses in genomic DNA.

The studies by Larson et al. and Vvedenskaya et al. provide a refined and detailed analysis of DNA sequence–induced transcription pausing. As a core process in gene expression, this understanding is relevant not only for the basic biology of transcription, but also has applications in synthetic biology and the design of genetic circuits.

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The editors suggest the following Related Resources on Science sites

In Science Magazine

REPORT Interactions between RNA polymerase and the “core recognition element” counteract pausing

Irina O. Vvedenskaya,  Hanif Vahedian-Movahed, Jeremy G. Bird, Jared G. Knoblauch, Seth R. Goldman,

Yu Zhang, Richard H. Ebright, and Bryce E. Nickels

Science 13 June 2014: 1285-1289.

 

“miR”roring Lupus Control

Angela Colmone

Sci.Signal., 29 July 2014;; 7(336),, p. ec202   http://dx.doi.org:/10.1126/scisignal.2005732

Decreased expression of the B cell signaling inhibitor PTEN may contribute to lupus pathology. Wu et al. found that microRNA (miR)–mediated regulation of PTEN is altered in patients with the autoimmune disease systemic lupus erythematosus (SLE). Patients with SLE have hyperactivated B cells, which results in the production of autoantibodies. The authors found that decreased expression of PTEN in B cells from SLE patients contributes to this B cell hyperactivation. What’s more, they found that PTEN expression in these cells was regulated by miRs and that blocking miR-7 could restore PTEN expression and function to that of healthy controls. These data support exploring miR-7 and PTEN as therapeutic targets for SLE.

X-n. Wu, Y-x. Ye, J-w. Niu, Y. Li, X. Li, X. You, H. Chen, L-d. Zhao, X-f. Zeng, F-c. Zhang, F-l. Tang, W. He, X-t. Cao, X. Zhang, P. E. Lipsky, Defective PTEN regulation contributes to B cell hyperresponsiveness in systemic lupus erythematosus. Sci. Transl. Med. 6, 246ra99 (2014). [Full Text]

Citation:

  1. Colmone, “miR”roring Lupus Control. Sci. Signal.7, ec202 (2014).

 

Long Noncoding RNA Regulating Apoptosis Discovered

Source: © Dmitry Sunagatov – Fotolia.com

  • Scientists from the University of São Paulo (USP) have identified an RNA molecule known as INXS that, although containing no instructions for the production of a protein, modulates the action of an important gene that impactsapoptosis.

According to Sergio Verjovski-Almeida, Ph.D., professor at the USP Chemistry Institute, INXS expression is generally diminished in cancer cells, and methods that are capable of stimulating the production of this noncoding RNA can be used to treat tumors. In experiments on mice, the USP scientists were able to effect a 10-fold reduction in the volume of subcutaneous malignant tumors by administering local injections of a plasmid containing INXS.

The team’s findings (“Long noncoding RNA INXS is a critical mediator of BCL-XS induced apoptosis”) were published in Nucleic Acids Research.

The group headed by Dr. Verjovski-Almeida at USP has been investigating the regulatory role of so-called intronic nonprotein-coding genes—those found in the same region of the genome as a coding gene but on the opposite DNA strand. INXS, for example, is an RNA expressed on the opposite strand of a gene coding for  the BCL-X protein.

“We were studying several protein-coding genes involved in cell death in search of evidence that one of them was regulated by intronic noncoding RNA. That was when we found the gene for BCL-X, which is located on chromosome 20,” he explained.

BCL-X is present in cells in two different forms: one that inhibits apoptosis (BCL-XL) and one that induces the process of cell death (BCL-XS). The two isoforms act on the mitochondria but in opposite ways. The BCL-XS isoform is considered a tumor suppressor because it activates caspases, which are required for the activation of other genes that cause cell death.

“In a healthy cell, there is a balance between the two BCL-X isoforms. Normally, there is already a smaller number of the pro-apoptotic form (BCL-XS). However, in comparing tumor cells to nontumor cells, we observed that tumor cells contain even fewer of the pro-apoptotic form, as well as reduced levels of INXS. We suspect that one thing affects the other,” continued Dr. Verjovski-Almeida.

To confirm the hypothesis, the group silenced INXS expression in a normal cell lineage and the result, as expected, was an increase in the BCL-XL (anti-apoptotic) isoform. “The rate between the two—which was 0.25—decreased to 0.15; in other words, the pro-apoptotic form that previously represented one fourth of the total began to represent only one sixth,” noted Dr. Verjovski-Almeida.

The opposite occurred when the researchers artificially increased the amount of INXS using plasmid expression in a kidney cancer cell line, with the noncoding RNA being reduced. “The pro-apoptotic form increased, and the anti-apoptotic form decreased,” he added.

“In a mouse xenograft model, intra-tumor injections of an INXS-expressing plasmid caused a marked reduction in tumor weight, and an increase in BCL-XS isoform, as determined in the excised tumors,” wrote the investigators. “We revealed an endogenous lncRNA that induces apoptosis, suggesting that INXS is a possible target to be explored in cancer therapies.

 

Scientists map one of the most important proteins in life—and cancer

Mon, 07/21/2014

Scientists have revealed the structure of one of the most important and complicated proteins in cell division—a fundamental process in life and the development of cancer—in research published in Nature.

Images of the gigantic protein in unprecedented detail will transform scientists’ understanding of exactly how cells copy their chromosomes and divide, and could reveal binding sites for future cancer drugs.

A team from The Institute of Cancer Research, London, and the Medical Research Council Laboratory of Molecular Biology in Cambridge produced the first detailed images of the anaphase-promoting complex (APC/C).

The APC/C performs a wide range of vital tasks associated with mitosis,

  1. the process during which a cell copies its chromosomes and
  2. pulls them apart into two separate cells.
  3. Mitosis is used in cell division by all animals and plants.

Discovering its structure could ultimately lead to new treatments for cancer, which

  • hijacks the normal process of cell division to make thousands of copies of harmful cancer cells.

In the study, which was funded by Cancer Research UK,

the researchers reconstituted human APC/C and used a combination of electron microscopy and imaging software to visualize it at a resolution of less than a billionth of a meter.

The resolution was so fine that it allowed the researchers to see the secondary structure—

  • the set of basic building blocks which combine to form every protein.

Alpha-helix rods and folded beta-sheet constructions were clearly visible within the 20 subunits of the APC/C, defining the overall architecture of the complex.

Previous studies led by the same research team had shown

  • a globular structure for APC/C in much lower resolution, but
  • the secondary structure had not previously been mapped.

The new study could identify binding sites for potential cancer drugs.

Each of the APC/C’s subunits bond and mesh with other units at different points in the cell cycle,

  1. allowing it to control a range of mitotic processes including the initiation of DNA replication,
  2. the segregation of chromosomes along protein ‘rails’ called spindles, and
  3. the ultimate splitting of one cell into two, called cytokinesis.

Disrupting each of these processes could

  • selectively kill cancer cells or prevent them from dividing.

Dr David Barford, who led the study as Professor of Molecular Biology at The Institute of Cancer Research, London, before taking up a new position at the Medical Research Council Laboratory of Molecular Biology in Cambridge, said:

“It’s very rewarding to finally tie down the detailed structure of this important protein, which is both

  • one of the most important and most complicated found in all of nature.

We hope our discovery will open up whole new avenues of research that increase our understanding of the process of mitosis, and ultimately lead to the discovery of new cancer drugs.”

Professor Paul Workman, Interim Chief Executive of The Institute of Cancer Research, London, said: “The fantastic insights into molecular structure

  • provided by this study are a vivid illustration of the critical role played by fundamental cell biology in cancer research.

“The new study is a major step forward in our understanding of cell division. When this process goes awry

  • it is a critical difference that separates cancer cells from their healthy counterparts.

Understanding exactly how cancer cells divide inappropriately is crucial to

  • the discovery of innovative cancer treatments to improve outcomes for cancer patients.”

Dr Kat Arney, Science Information Manager at Cancer Research UK, said “Figuring out how the fundamental molecular ‘nuts and bolts’ of cells work is vital

  • if we’re to make progress understanding what goes wrong in cancer cells and how to tackle them more effectively.

Revealing the intricate details of biological shapes is a hugely important step towards identifying targets for future cancer drugs.”

Source: The Institute of Cancer Research, London

 

A cell death avenue evolved from a life-saving path

  1. Harm H. Kampinga

+Author Affiliations

  1. Department of Cell Biology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
  2. E-mail: h.kampinga@umcg.nl

Related Resources

In Science Magazine

Science 20 June 2014: 1389-1392.Published online 22 May 2014

In Science Signaling

Sci. Signal. 24 June 2014: ec175.

Yeast metacaspases are the ancestral enzymes of caspases that execute cellular suicide (“programmed cell death”) in multicellular organisms. Studies on metacaspase 1 (Mca1)

  • have suggested that single-cell eukaryotes can also commit programmed cell death (12). However,

on page 1389 of this issue, Malmgren Hill et al. (3) show that

  • Mca1 has positive rather than negative effects on the life span of the budding yeast Saccharomyces cerevisiae,
  • especially when protein homeostasis is impaired.

Mca1 helps to degrade misfolded proteins that accumulate during aging or that are generated by acute stress, and

  • thereby ensures the continuous and healthy generation of daughter cells
  • that are free of insoluble aggregates that otherwise would limit life span.

View larger version:

 

ILLUSTRATION: V. ALTOUNIAN/SCIENCE

Loss of Mca1 activity has been associated with a reduced appearance of programmed cell death markers (14),

  • implying that its overexpression should decrease the replicative life span of yeast (the number of daughter cells a mother cell can produce throughout its life). Cells lacking Mca1
  • have increased amounts of protein aggregates and oxidized proteins (45).

Malmgren Hill et al. not only show that this is related to decreased survival,

  • but also provide mechanistic insights into the mode of action of Mca1.

Its pro-life action depends on the chaperone heat shock protein 104 (Hsp104), a protein that

  1. can disentangle protein aggregates and
  2. is crucial for the asymmetric segregation of protein aggregates in dividing cells.

Mca1 deficiency does not affect life span of wild-type strains, but

  1. further decreases life span in strains already compromised in protein quality control. In particular,
  2. replicative aging is accelerated in strains lacking the Hsp70 co-chaperone Ydj1.

Mca1 does not improve protein folding but supports

  • degradation of terminally misfolded proteins.

Malmgren Hill et al. show that Mca1 requires proteasomes (protein structures that break down proteins) for all its effects.

The study by Malmgren Hill et al. challenges the idea that

  1. caspases are activated as an altruistic suicide mechanism in single-cell eukaryotes
  2. as a means to provide nutrients for younger and fitter cells in the population (2). Rather,
  3. the data suggest that from an evolutionary perspective, caspase activation is an integrated part of a protective response
  4. to help cells survive toxic stress caused by the accumulation of misfolded proteins.

When, however, activated incorrectly (e.g., in the absence of proteotoxic stress) or too strongly (e.g., in the case of excessive damage to the cell),

  1. the caspase activity may become nonselective and thus
  2. lead to the typical Mca1-dependent hallmarks of programmed cell death (124). Also,
  3. caspase activation in metazoa may function primarily in cell-autonomous protection and cellular remodeling or
  4. pruning. Its role in programmed cell death may also simply reflect overactivation upon severe cellular damage or
  5. hijacking of the caspases in the absence of stress to serve in non–cell-autonomous regulated tissue homeostasis.

View larger version:

Defense against protein damage.

Stress-damaged proteins that form aggregates in cells can be reactivated with the Hsp104-Ssa-Ydj1 chaperone machinery. Mca1 may act

  • in parallel by binding to misfolded proteins during early stages of aggregation for proteasomal degradation (this is independent of Mca1’s enzymatic activity). Alternatively,
  • Mca1 may associate with misfolded proteins formed at late stages of aggregation (together with Hsp104 and Ssa), helping to disentangle
  • the aggregates by its protease cleavage activity before shunting them to the proteasome for degradation.

ILLUSTRATION: V. ALTOUNIAN/SCIENCE

The results of Malmgren Hill et al. also highlight the importance of protein quality control for cellular aging. A collapse of protein homeostasis

  • has been implicated mostly in chronological aging of differentiated cells and, for example,
  • as a cause of neurodegenerative diseases (6).

The authors show that it also plays a prominent role in replicative aging.

  • This supports early findings in yeast (7) and may also be relevant to metazoa,
  • in which stem cells have extremely efficient protein degradation mechanisms (8) and
  • also use asymmetric segregation of protein damage for rejuvenation (9).

The data of Malmgren Hill et al. also suggest the existence of an additional layer of control of protein homeostasis. Beyond the

  • activation and induction of chaperones that assist in protein sorting, refolding, and protein degradation via proteasomes and
  • autophagosomes (membrane structures that deliver proteins to lysosomes for enzymatic destruction) (10),
  • Malmgren Hill et al. show that activation of caspases also belongs to the cell’s repertoire of defense mechanisms against protein damage.
  • Mca1 might act in parallel to the Ssa-Ydj1 machinery. Although
  • Ssa-Ydj1 collaborates with Hsp104 to refold proteins after their aggregation (11),
  • Mca1 primarily supports protein degradation, as its actions require not only Hsp104 but also proteasomal activity (3).

Precisely how Mca1 exerts its effect is yet unclear. It can associate with aggregates independent of other chaperones (35) and

  • independent of its catalytic activity (5), suggesting that
  • it binds directly to misfolded proteins [likely through its amino-terminal “pro-domain”
  • that is rich in glutamine and asparagine repeats].

This interaction may exert chaperone-like activity by keeping unfolded proteins

  • in a proteasome-competent form, which explains why part of Mca1’s protective actions in wild-type strains is independent of its protease activity.

However, the caspase activity of Mca1 is required for protein homeostasis and control of life span in Ydj1-deficient strains. It could be that

  • for more terminally misfolded proteins that accumulate in the absence of Ydj1,
  • protease cleavage may help to dismantle such aggregates in concert with Ssa and Hsp104 (see the figure).

This would also explain why the strongest phenotypes of Mca1 are seen under conditions in which Ydj1 is absent. More biochemical data with purified proteins will be needed to test these ideas.

The study of Malmgren Hill et al. suggests that altruism may not exist among cells. However, life and death seem to be close neighbors, and the things that are life saving may also become lethal. It will therefore be a challenge

  • to make use of these insights into caspase function in order to treat diseases by selectively tipping the balance toward life (e.g., in neurodegenerative diseases) or death (e.g., in cancer).

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 the following Related Report

Life-span extension by a metacaspase in the yeast Saccharomyces cerevisiae

Sandra Malmgren Hill, Xinxin Hao, Beidong Liu, and Thomas Nyström

Science 20 June 2014: 1389-1392.

 

Synthetic biology: the many facets of T7 RNA polymerase

David L Shis, Matthew R Bennett
Molecular Systems Biology(2014)10:745   30.07.2014
http://dx.doi.org:/10.15252/msb.20145492

 

Added 8-2-2014

Split T7 RNA polymerase provides new avenues for creating synthetic gene circuits that are decoupled from host regulatory processes—but how many times can this enzyme be split, yet retain function? New research by Voigt and colleagues (SegallShapiro et al, 2014) indicates that it may be more than you think.

See also: TH Segall‐Shapiro et al (July 2014)

Synthetic gene circuits have become an invaluable tool for studying the design principles of native gene networks and facilitating new biotechnologies (Wayet al2014). Synthetic biologists often strive to build circuits within a framework that enables their consistent and robust operation across a range of hosts and conditions. Currently, however, each circuit must be fastidiously tuned and retuned in order to properly function within a particular host, leading to costly design cycles and esoteric conclusions. As a result, researchers have invested a great deal in developing strategies that

  • decouple synthetic gene circuits from host metabolism and regulation.

In their recent work, Segall‐Shapiro et al (2014) address this problem by

  • expanding the capabilities of orthogonal transcriptional systems in Escherichia coli using fragmented mutants of bacteriophage‐T7 RNA polymerase (T7 RNAP).

T7 RNAP has had a long relationship with biotechnology and

  • is renowned for its compactness and transcriptional activity.

This single subunit polymerase strongly

  • drives transcription from a miniscule 17‐bp promoter
  • that is orthogonally regulated inE. coli.

In this context, orthogonal means that

  • T7 RNAP will not transcribe genes driven by native E. coli promoters, and
  • native polymerases in E. coli will not recognize T7 RNAP’s special promoter—that is
  • the two transcriptional systems leave each other alone.

Interestingly, T7 RNAP drives transcription so strongly that,

  • if left unregulated, it can quickly exhaust cellular resources and lead to cell death.

Because of this, T7 RNAP

  • has been leveraged in many situations calling for protein over‐expression (Studier & Moffatt, 1986).

Additionally, studies examining the binding of T7 RNAP to its promoter have identified

  • a specificity loop within the enzyme that makes direct contact with the promoter
  • between base pairs −11 and −8.

This has led to a number of efforts that have generated T7 RNAP mutants

  • with modified specificities to promoters orthogonal to the original (Chelliserrykattil et al2001).

Given the growing interest in the development of synthetic gene circuits, researchers have taken a renewed interest in T7 RNAP. The orthogonality,

  • transcriptional activity and promoter malleability of T7 RNAP make the enzyme uniquely suited for use in synthetic gene circuits. Importantly,
  • any modifications made to the enzyme increase the possible functionality of circuits. For instance, we recently utilized
  • a split version of T7 RNAP in conjunction with promoter specificity mutants to create a library of transcriptional AND gates (Shis & Bennett, 2013).

The split version of T7 RNAP was originally discovered during purification and shown to be active in vitro (Ikeda & Richardson, 1987). While the catalytic core and DNA‐binding domain

  • are both located on the C‐terminal fragment of split T7 RNAP,
  • the N‐terminal fragment is needed for transcript elongation.

Therefore, if the two halves of split T7 RNAP are placed behind two different inducible promoters,

  1. both inputs must be active in order to form a functional enzyme and
  2. activate a downstream gene.

When the split mutant is combined with promoter specificity mutants,

  • a library of transcriptional AND gates is created.

Segall‐Shapiro et al take the idea of splitting T7 RNAP for novel regulatory architectures one step further. Instead of settling for the one split site already discovered,

  • the authors first streamlined a transposon mutagenesis strategy (Segall‐Shapiro et al2011) to identify four novel cut sites within T7 RNAP.

By expressing T7 RNAP split at two different sites,

  • they create a tripartite T7 RNAP—a polymerase
  • that requires all three subunits for activity.

The authors suggestively designate the fragments of the tripartite enzyme as ‘core’, ‘alpha’, and ‘sigma’ (Fig 1) and they go on to show that

  • tripartite T7 RNAP can not only be used to create 3‐input AND gates, but
  • it also works as a ‘resource allocator’.

In other words, the transcriptional activity of the split polymerase can be regulated

  • by limiting the availability of core and/or alpha fragment, or
  • by expressing additional sigma fragments.

The authors demonstrate strategies to account for common pitfalls in synthetic gene networks

  • such as host toxicity and plasmid copy number variability.

 

Figure 1. Segall‐Shapiro et al extend previous efforts to engineer split T7 RNAP by fragmenting the enzyme at two novel locations to create a tripartite transcription complex.

Co‐expressing different sigma fragments with the alpha and core fragments enables a network of multi‐input transcriptional AND gates.

The tripartite T7 RNAP presented by Segall‐Shapiro et al

  • expands the utility of T7 RNAP in orthogonal gene circuits.

Until now, while T7 RNAP has been attractive for use in synthetic gene circuits,

  • the inability to regulate its activity has often prevented its use.

Splitting the protein into fragments and regulating the transcription complex by fragment availability

  • brings the regulation of T7 RNAP closer to the regulation of multi‐subunit prokaryotic RNA polymerases.

Sigma fragments direct the activity of the transcription complex much like σ‐factors, and the alpha fragment helps activate transcription

  • in the same way as α‐fragments of prokaryotic polymerases.

For additional regulation, the authors note that the tripartite T7 RNAP can be further split at the previously discovered split site to create a four‐fragment enzyme.

More nuanced regulation using split T7 RNAP may be possible

  • with the addition of heterodimerization domains
  • that can drive the specific association of fragments.

This strategy has been successfully applied to engineer specificity and signal diversity

  • in two‐component signaling pathways (Whitaker et al2012).

The activity of T7 RNAP might also be directed to various promoters

  • by using multiple sigma fragments simultaneously,
  • just as σ‐factors do in E. coli.

Finally, synthetic gene circuits driven primarily by T7 RNAP create the possibility of easily transplantable gene circuits. A synthetic gene circuit driven entirely by fragmented T7 RNAP

  • would depend more on fragment availability than unknown interactions with host metabolism.

This would enable rapid prototyping of synthetic gene circuits in laboratory‐friendly strains or cell‐free systems (Shin & Noireaux, 2012) before transplantation into the desired host.

References

  1. Chelliserrykattil J, Cai G, Ellington AD (2001) A combined in vitro/in vivo selection for polymerases with novel promoter specificities. BMC Biotechnol 1: 13

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  1. SegallShapiro TH, Meyer AJ, Ellington AD, Sontag ED, Voigt CA (2014) A “resource allocator” for transcription based on a highly fragmented T7 RNA polymerase.Mol Syst Biol 10: 742

Abstract/FREE Full Text

  1. SegallShapiro TH, Nguyen PQ, Dos Santos ED, Subedi S, Judd J, Suh J, Silberg JJ(2011) Mesophilic and hyperthermophilic adenylate kinases differ in their tolerance to random fragmentation. J Mol Biol 406: 135–148

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  1. Shis DL, Bennett MR (2013) Library of synthetic transcriptional AND gates built with split T7 RNA polymerase mutants. Proc Natl Acad Sci USA 110: 5028–5033

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  1. Studier FW, Moffatt BA (1986) Use of bacteriophageT7 RNApolymerase to direct selective highlevel expression of cloned genes. J Mol Biol 189: 113–130

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  1. Way JC, Collins JJ, Keasling JD, Silver PA (2014) Integrating biological redesign: where synthetic biology came from and where it needs to go. Cell 157: 151–161
  2. Whitaker WR, Davis SA, Arkin AP, Dueber JE (2012) Engineering robust control of twocomponent system phosphotransfer using modular scaffolds. Proc Natl Acad Sci USA 109: 18090–18095

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© 2014 The Authors. Published under the terms of the CC BY 4.0 license

 

 

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Circulating MicroRNAs: Novel Biomarkers and Extracellular Communicators in Cardiovascular Disease?  Esther E. Creemers, Anke J. Tijsen, Yigal M. Pinto.  Circulation Research. 2012; 110: 483-495    http://dx.doi.org:/10.1161/​CIRCRESAHA.111.247452

Novel techniques and targets in cardiovascular microRNA research.  Dangwal S, Bang C, Thum T. Cardiovasc Res. 2012 Mar 15; 93(4):545-54.  http://dx.doi.org:/10.1093/cvr/cvr297

Microparticles: major transport vehicles for distinct microRNAs in circulation. Diehl P, Fricke A, Sander L, Stamm J, Bassler N, Htun N, et al.  Cardiovasc Res. 2012 Mar 15; 93(4):633-44. http://dx.doi.org:/10.1093/cvr/cvs007.

Profiling of circulating microRNAs: from single biomarkers to re-wired networks. A  ZampetakiP Willeit, I Drozdov, S Kiechl and M Mayr. Cardiovasc Res 2012; 93 (4): 555-562.  http://dx.doi.org:/10.1093/cvr/cvr266

Small but smart–microRNAs in the centre of inflammatory processes during cardiovascular diseases, the metabolic syndrome, and ageing. Schroen B, Heymans S.
Cardiovasc Res. 2012; 93(4):605-613http://dx.doi.org:/10.1093/cvr/cvr268

Therapeutic Inhibition of miR-208a Improves Cardiac Function and Survival During Heart Failure. RL Montgomery, TG Hullinger, HM Semus, BA Dickinson, AG Seto, et al.
http://dx.doi.org:/10.1161/​CIRCULATIONAHA.111.030932

Circulating microRNAs to identify human heart failure.  Seto AG, van Rooij E.
Eur J Heart Fail. 2012;14(2):118-119http://dx.doi.org:/10.1093/eurjhf/hfr179.

Use of Circulating MicroRNAs to Diagnose Acute Myocardial Infarction. Y Devaux,
M Vausort, E Goretti, PV Nazarov, F Azuaje. Clin Chem. 2012; 58:559-567. http://dx.doi.org:/10.1373/clinchem.2011.173823

Next Steps in Cardiovascular Disease Genomic Research–Sequencing, Epigenetics, and Transcriptomics  RB Schnabel, A Baccarelli, H Lin, PT Ellinor, and EJ Benjamin.
Clin Chem . 2012 Jan; 58(1): 113–126.  http://dx.doi.org:/10.1373/clinchem.2011.170423

MicroRNA-133 Modulates the {beta}1-Adrenergic Receptor Transduction Cascade.  A Castaldi, T Zaglia, V Di Mauro, P Carullo, G Viggiani, et al.  Circ. Res.. 2014; 115:273-283.
http://dx.doi.org:/10.1161/​CIRCRESAHA.115.303252

Development of microRNA therapeutics is coming of age.  E van Rooij, S Kauppinen.  EMBO Mol Med.. 2014; 6:851-864.  http://dx.doi.org:/10.15252/emmm.201100899

Pitx2-microRNA pathway that delimits sinoatrial node development and inhibits predisposition to atrial fibrillation.   J Wang, Y Bai, N Li, W Ye, M Zhang,et al. PNAS 2014; 111: 9181-9186.

MicroRNA-126 modulates endothelial SDF-1 expression and mobilization of Sca-1+/Lin- progenitor cells in ischaemia  Cardiovasc Res. 2011; 92:449-455,

The use of genomics for treatment is another matter, and has several factors, e.g., age, residual function after AMI, comorbidities

Read Full Post »


Epilogue: Volume 4 – Translational, Post-Translational and Regenerative Medicine in Cardiology

  • Larry H Bernstein, MD, FCAP, Author and Curator, Volume Four, Co-Editor
  • Justin Pearlman, MD, PhD, FACC, Content Consultant for Series A: Cardiovascular Diseases
  • Aviva Lev-Ari, PhD, RN, Co-Editor of Volume Four and Editor-in-Chief, BioMed e-Series

 

This completes Chapter 4 in two parts on the most dynamic developments in the regulatory pathways guiding cardiovascular dynamics and function in health and disease.  I have covered key features of these in two summaries, so I shall try to look further into important expected future directions and their anticipated implications.

1. Mechanisms of Disease

Signal Transduction: Akt Phosphorylates HK-II at Thr-473 and Increases Mitochondrial HK-II Association to Protect Cardiomyocytes

David J. Roberts, Valerie P. Tan-Sah, Jeffery M. Smith and Shigeki Miyamoto
J. Biol. Chem. 2013, 288:23798-23806.  http://dx.doi.org/ 10.1074/jbc.M113.482026

Backgound: Hexokinase II binds to mitochondria and promotes cell survival.
Results: Akt phosphorylates HK-II but not the threonine 473 mutant. The phosphomimetic T473D mutant decreases its dissociation from mitochondria induced by G-6P and increases cell viability against stress.
Conclusion: Akt phosphorylates HK-II at Thr-473, resulting in increased mitochondrial HK-II and cell protection.
Significance: The Akt-HK-II signaling nexus is important in cell survival.

HK-II Phosphorylation

HK-II Phosphorylation

 

 

 

 

 

 

It has been demonstrated that an increased level of HK-II at mitochondria is protective and is increased by protective interventions but decreased under stress.

It   has not  been fully determined   which  molecular  signals  regulate  the    level    of  HK-II at mitochondria.

Thr-473 in HK-II  is phosphorylated by Akt and this phosphorylation  leads to  increases  in  mitochondrial  HK-II binding  through inhibition  of  G-6P-dependent  dissociation, conferring resistance to oxidative stress  (Fig.     7).

Overexpression of  WTHK-II increases mitochondrial HK-II and confers protection against  hydrogen peroxide,  which  is enhanced significantly  in   HK-II   T473D-expressing  cells, whereas  NHK-II, lacking the ability to bind to mitochondria, does not confer protection.   Conversely,  mitochondrial  HK-II from mitochondria (Fig.6, and B) inhibits  the  IGF-1-mediated increase in mitochondrial HK-II and cellular protection.   Similar   dose-dependent  curves were obtained in mitochondrial   HK-II     against stress    (15–25).

Gene Expression and Genetic Variation in Human Atria

Honghuang Lin PhD, Elena V. Dolmatova MD, Michael P. Morley, PhD, Kathryn L. Lunetta PhD, David D. McManus MD, ScM, et al.
Heart Rhythm  2013   http://dx.doi.org/10.1016/j.hrthm.2013.10.051

Background— The human left and right atria have different susceptibilities to develop atrialfibrillation (AF). However, the molecular events related to structural and functional changes that
enhance AF susceptibility are still poorly understood.
Objective— To characterize gene expression and genetic variation in human atria.
Results— We found that 109 genes were differentially expressed between left and right atrial tissues. A total of 187 and 259 significant cis-associations between transcript levels and genetic
variants were identified in left and right atrial tissues, respectively. We also found that a SNP at a known AF locus, rs3740293, was associated with the expression of MYOZ1 in both left and right
atrial tissues.
Conclusion— We found a distinct transcriptional profile between the right and left atrium, and extensive cis-associations between atrial transcripts and common genetic variants. Our results
implicate MYOZ1 as the causative gene at the chromosome 10q22 locus for AF.

Long-Term Caspase Inhibition Ameliorates Apoptosis, Reduces Myocardial Troponin-I Cleavage, Protects Left Ventricular Function, and Attenuates Remodeling in Rats With Myocardial Infarction

Y. Chandrashekhar,  Soma Sen, Ruth Anway,  Allan Shuros,  Inder Anand,

J Am Col  Cardiol  2004; 43(2)   http://dx.doi.org/10.1016/j.jacc.2003.09.026

This study was designed to evaluate whether in vivo caspase inhibition can prevent myocardial contractile protein degradation, improve myocardial function, and attenuate ventricular remodeling.
Apoptosis is thought to play an important role in the development and progression of heart failure (HF) after a myocardial infarction (MI). However, it is not known whether inhibiting apoptosis can attenuate left ventricular (LV) remodeling and minimize systolic dysfunction.

A 28-day infusion of caspase inhibitor was administeredimmediately after an anterior MI. In addition, five sham-operated rats given the caspase inhibitor were compared with 17 untreated sham-operated animals to study effects in non-MI rats. Left ventricular function, remodeling parameters, and hemodynamics were studied four weeks later. Myocardial caspase 3 activation and troponin-I contractile protein cleavage were studied in the non-infarct, remote LV myocardium using Western blots. Apoptosis was assessed using immunohistochemistry for activated caspase-positive cells as well as the TUNEL method. Collagen volume was estimated using morphometry.

Caspase inhibition reduced myocardial caspase 3 activation. This was accompanied by less cleavage of troponin-I, an important component of the cardiac contractile apparatus, and fewer apoptotic cardiomyocytes. Furthermore, caspase inhibition reduced LV-weight-to- body-weight ratio, decreased myocardial interstitial collagen deposition, attenuated LV remodeling, and better preserved LV systolic function after MI.

Caspase inhibition, started soon after MI and continued for four weeks, preserves myocardial contractile proteins, reduces systolic dysfunction, and attenuates ventricular remodeling.

These findings may have important therapeutic implications in post-MI HF. J Am Col Cardiol 2004;43:295–301)

Precardiac deletion of Numb and Numblike reveals renewal of cardiac progenitors

Lincoln T Shenje,  Peter P Rainer , Gun-sik Cho , Dong-ik Lee , Weimin Zhong , Richard P Harvey , David A Kass , Chulan Kwon *,  et al.
eLife 2014.    http://dx.doi.org/10.7554/eLife.02164.001

Cardiac progenitor cells (CPCs) must control their number and fate to sustain the rapid heart growth during development, yet the intrinsic factors and environment governing these processes remain unclear. Here, we show that deletion of the ancient cell-fate regulator Numb (Nb) and its homologue Numblike (Nbl) depletes CPCs in second pharyngeal arches (PA2s) and is associated with an atrophic heart. With histological, fow cytometric and functional analyses, we fnd that CPCs remain undifferentiated and expansive in the PA2, but differentiate into cardiac cells as they exit the arch. Tracing of Nb- and Nbl-defcient CPCs by lineage-specifc mosaicism reveals that the CPCs normally populate in the PA2, but lose their expansion potential in the PA2. These fndings demonstrate that Nb and Nbl are intrinsic factors crucial for the renewal of CPCs in the PA2 and
that the PA2 serves as a microenvironment for their expansion.

2. Diagnostics and Risk Assessment

Classical and Novel Biomarkers for Cardiovascular Risk Prediction in the United States

Aaron R. Folsom
J Epidemiol 2013;23(3):158-162   http://dx.doi.org/10.2188/jea.JE20120157

Cardiovascular risk prediction models based on classical risk factors identified in epidemiologic cohort studies are useful in primary prevention of cardiovascular disease in individuals. This article briefly reviews aspects of
cardiovascular risk prediction in the United States and efforts to evaluate novel risk factors. Even though many novel risk markers have been found to be associated with cardiovascular disease, few appear to improve risk prediction
beyond the powerful, classical risk factors. A recent US consensus panel concluded that clinical measurement of certain novel markers for risk prediction was reasonable, namely,

  1. hemoglobin A1c (in all adults),
  2. microalbuminuria (in patients with hypertension or diabetes), and
  3. C-reactive protein,
  4. lipoprotein-associated phospholipase,
  5. coronary calcium,
  6. carotid intima-media thickness, and
  7. ankle/brachial index (in patients deemed to be at intermediate cardiovascular risk, based on traditional risk factors).

Diagnostic accuracy of NT-proBNP ratio (BNP-R) for early diagnosis of tachycardia-mediated cardiomyopathy: a pilot study

Amir M. Nia, Natig Gassanov, Kristina M. Dahlem, Evren Caglayan, Martin Hellmich, et al.
Clin Res Cardiol (2011) 100:887–896    http://dx.doi.org/10.1007/s00392-011-0319-y

Tachycardia-mediated cardiomyopathy (TMC) occurs as a consequence of prolonged high heart rate due to ventricular and supraventricular tachycardia. In animal models, rapid pacing induces severe biventricular remodeling with dilation and dysfunction [7]. On a cellular basis, cardiomyocytes exert fundamental morphological and functional roles.

When heart failure and tachycardia occur simultaneously, a useful diagnostic tool for early discrimination of patients with benign tachycardia-mediated  cardiomyopathy (TMC) versus major structural heart disease  (MSHD) is not available. Such a tool is required to prevent unnecessary and wearing diagnostics in patients with reversible TMC. Moreover, it could lead to early additional diagnostics and therapeutic approaches in patients with  MSHD.

A total of 387 consecutive patients with supraventricular arrhythmia underwent assessment.  Of these patients, 40 fulfilled the inclusion criteria
with a resting heart rate C100 bpm and an impaired left ventricular ejection fraction \40%. In all patients, successful electrical cardioversion was performed. At baseline, day 1 and weekly for 4 weeks, levels of NT-proBNP and echocardiographic parameters were evaluated.

NT-proBNP ratio (BNP-R) was calculated as a quotient of baseline NT-proBNP/follow-up NT-proBNP. After 4 weeks, cardiac catheterization was performed to identify patients with a final diagnosis of TMC versus MSHD.

Initial NT-proBNP concentrations were elevated and consecutively decreased after cardioversion in all patients studied. The area under the ROC curve for BNP-R to detect TMC was 0.90 (95% CI 0.79–1.00; p \ 0.001) after 1 week  and 0.995 (95% CI 0.99–1.00; p \ 0.0001) after 4 weeks. One week after cardioversion already, a BNP-R cutoff C2.3 was useful for TMC diagnosis indicated by an accuracy of 90%, sensitivity of 84% and specificity of 95%.

BNP-R was found to be highly accurate for the early diagnosis of TMC.

Omega-3 Index and Cardiovascular Health

Clemens von Schacky
Nutrients 2014; 6: 799-814;  http://dx. doi.org/10.3390/nu602099

Fish, marine oils, and their concentrates all serve as sources of the two marine omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), as do some products from algae.
To demonstrate an effect of EPA + DHA on heart health, a number of randomized, controlled intervention studies with clinical endpoints like overall mortality or a combination of adverse cardiac events were conducted in populations with elevated cardiovascular risk. One early intervention study with oily fish, rich in EPA + DHA, and some early studies with fish oil or fish oil concentrate or even purified EPA at doses ranging between 0.9 and 1.8 g/day indeed demonstrated effects in terms of fewer sudden cardiac deaths, fatal or non-fatal myocardial infarctions, or a combination of adverse cardiac events.

Recent meta-analyses found no significant benefits on total mortality, cardiovascular mortality, and other adverse cardiac or cardiovascular events [13–18]. This is in contrast to findings in epidemiologic studies, where intake of EPA + DHA had been found to correlate generally with an up to 50% lower incidence of adverse cardiac events [18,19], and in even sharper contrast to epidemiologic studies based on levels of EPA + DHA, demonstrating e.g., a 10-fold lower incidence of sudden cardiac death associated with high levels of the
fatty acids, as compared to low levels.

This seemingly contradictory evidence has led the American Heart Association to recommend “omega-3 fatty acids from fish or fish oil capsules (1 g/day) for cardiovascular disease risk reduction” for secondary prevention, whereas the European Society for Cardiology recommends “Fish at least twice a week, one of which to be oily fish”, but no supplements for cardiovascular prevention.

A similar picture emerges for atrial fibrillation: In epidemiologic studies, consumption of EPA + DHA or higher levels of EPA + DHA were associated with lower risk for developing atrial fibrillation, while intervention studies found no effect. Pertinent guidelines do not mention EPA + DHA. A similar picture also emerges for severe ventricular rhythm disturbances.

Why is it that trial results are at odds with results from epidemiology? What needs to be done to better translate the epidemiologic findings into trial results? The current review will try to shed some light on this  issue, with a special consideration of the Omega-3 Index.

Recent large trials with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the cardiovascular field did not demonstrate a beneficial effect in terms of reductions of clinical endpoints like

  • total mortality,
  • sudden cardiac arrest or
  • other major adverse cardiac events.

Pertinent guidelines do not uniformly recommend EPA + DHA for cardiac patients. In contrast,

  • in epidemiologic findings, higher blood levels of EPA + DHA were consistently associated with a lower risk for the endpoints mentioned.

The following points argue for the use of erythrocytes: erythrocyte fatty acid
composition has a low biological variability, erythrocyte fat consists almost exclusively of phospholipids, erythrocyte fatty acid composition reflects tissue fatty acid composition, pre-analytical stability, and other points.  In 2004, EPA + DHA in erythrocyte fatty acids were defined as the Omega-3 Index and suggested as a risk factor for sudden cardiac death [39]. Integral to the definition was a specific and standardized analytical procedure, conforming the quality management routinely implemented in the field of clinical chemistry.

The laboratories adhering to the HS-Omega-3 Index methodology perform regular proficiency testing, as mandated in routine Clinical Chemistry labs. So far, the HS-Omega-3 Index is the only analytical procedure used in several laboratories. A standardized analytical procedure is a prerequisite to generate the data base necessary to transport a laboratory parameter from research into clinical routine. Moreover, standardization of the analytical procedure is the first important criterion for establishing a new biomarker for cardiovascular risk set forth by the American Heart Association and the US Preventive Services Task Force.

Because of low biological and analytical variability, a standardized analytical procedure, a large database and for other reasons,

  • blood levels of EPA + DHA are frequently assessed in erythrocytes, using the HS-Omega-3 Index methodology.

Table 1. Mean HS-Omega-3 Index values in various populations, Mean (±standard deviation (SD)). Please note that in every population studied, a lower value was found to be associated with a worse condition than a higher value. References are given, if not, unpublished, n = number of individuals measured.

All levels of fatty acids are determined by the balance of substance entering the body and those leaving the body. Neither a recent meal, even if rich in EPA + DHA, nor severe cardiac events altered the HS-Omega-3 Index. However, while long-term intake of EPA + DHA, e.g., as assessed with food questionnaires, was the main predictor of the HS-Omega-3 Index, long-term intake explained only 12%–25% of its variability. A hereditary component of 24% exists. A number of other factors correlated positively (+) or negatively (−), like age (+), body mass index (−), socioeconomic status (+), smoking (−), but no other conventional cardiac risk factors. More factors determining the level of the HS-Omega-3 Index, especially regarding efflux remain to be  defined. Therefore, it is impossible to predict the HS-Omega-3 Index in an individual, as it is impossible to predict the increase in the HS-Omega-3 Index in an individual in response to a given dose of EPA + DHA. In Table 2, current evidence is presented on the relation of the HS-Omega-3 Index to CV events.

The HS-Omega-3 Index has made it possible to reclassify individuals from intermediate cardiovascular risk into the respective high risk and low risk strata, the third criterion for establishing a new biomarker for CV  risk.

A low Omega-3 Index fulfills the current criteria for a novel cardiovascular risk factor.

Increasing the HS-Omega-3 Index by increased intake of EPA + DHA in randomized controlled trials improved a number of surrogate parameters for cardiovascular risk:

  1. heart rate was reduced,
  2. heart rate variability was increased,
  3. blood pressure was reduced,
  4. platelet reactivity was reduced,
  5. triglycerides were reduced,
  6. large buoyant low-density lipoprotein (LDL)-particles were increased and
  7. small dense LDL-particles were reduced,
  8. large buoyant high-density lipoproteins (HDL)2 were increased,
  9. very low-density lipoprotein (VLDL1) + 2 was reduced,
  10. pro-inflammatory cytokines (e.g., tumor necrosis factor alpha, interleukin-1β, interleukins-6,8,10 and monocyte chemoattractant protein-1) were reduced,
  11. anti-inflammatory oxylipins were increased.

Importantly, in a two-year randomized double-blind angiographic intervention trial, increased erythrocyte EPA + DHA

  • reduced progression and increased regression of coronary lesions, an intermediate parameter.

Taken together, increasing the HS-Omega-3 Index improved surrogate and intermediate parameters for cardiovascular events. A large intervention trial with clinical endpoints based on the HS-Omega-3 Index remains to be conducted. Therefore, the fourth criterion, proof of therapeutic consequence of determining the HS-Omega- Index, is only partially fulfilled.

 

Neutral results of intervention trials can be explained by issues of bioavailability and trial design that surfaced after the trials were initiated.

In the future, incorporating the Omega-3 Index into trial designs by

  1. recruiting participants with a low Omega-3 Index and
  2. treating them within a pre-specified target range (e.g., 8%–11%),
  3. will make more efficient trials possible and
    • provide clearer answers to the questions asked than previously possible.

 

3. Stem Cells and Regenerative Biology

Adult Stem Cells Reverse Muscle Atrophy In Elderly Mice   http://www.science20.com/profile/news_staff

Bioengineers at the University of California, Berkeley in a new study published in Nature say they have identified two key regulatory pathways that control how well adult stem cells repair and replace damaged tissue. They then tweaked how those stem cells reacted to those biochemical signals to revive the ability of muscle tissue in old mice to repair itself nearly as well as the muscle in the mice’s much younger counterparts. Irina Conboy, an assistant professor of bioengineering and an investigator at the Berkeley Stem Cell Center and at the California Institute for Quantitative Biosciences (QB3), led the research team conducting this study. Because the findings relate to adult stem cells that reside in existing tissue, this approach to rejuvenating degenerating muscle eliminates the ethical and medical complications associated with transplanting tissues grown from embryonic stem cells. The researchers focused on

  • the interplay of two competing molecular pathways that control the stem cells,

which sit next to the mature, differentiated cells that make up our working body parts. When the mature cells are damaged or wear out, the stem cells are called into action to begin the process of rebuilding.

old muscle tissue is left with

old muscle tissue is left with

 

 

 

 

 

 

 

 

 

 

 

 

“We don’t realize it, but as we grow our bodies are constantly being remodeled,” said Conboy. “We are constantly falling apart, but we don’t notice it much when we’re young because we’re always being restored. As we age, our stem cells are prevented, through chemical signals, from doing their jobs.” The good news, the researchers said, is that

  • the stem cells in old tissue are still ready and able to perform their regenerative function
  • if they receive the appropriate chemical signals.

Studies have shown that when old tissue is placed in an environment of young blood, the stem cells behave as if they are young again. “Conversely, we have found in a study published last year that even young stem cells rapidly age when placed among blood and tissue from old mice,” said Carlson, who will stay on at UC Berkeley to expand his work on stem cell engineering.

  • Adult stem cells have a receptor called Notch that, when activated,
  • tells them that it is time to grow and divide
  • stem cells also have a receptor for the protein TGF-beta
  • that sets off a chain reaction activatingthemoleculepSmad3 and
    • ultimately producing cyclin-dependent kinase (CDK) inhibitors, which regulate the cell’s ability to divide.
  • activated Notch competeswithactivatedpSmad3 for
    • binding to the regulatory regions of the same CDK inhibitors in the stem cell

“We found that Notch is capable of physically kicking off pSmad3 from the promoters for the CDK inhibitors within the stem cell’s nucleus, which tells us that a precise manipulation of the balance of these pathways would allow the ability to control stem cell responses.” Notch and TGF-beta are well known in molecular biology, but Conboy’s lab is the first to connect them to the process of aging, and the first to show that they act in opposition to each other within the nucleus of the adult stem cell. Aging and the inevitable march towards death are, in part, due to the progressive decline of Notch and the increased levels of TGF-beta , producing a one-two punch to the stem cell’s capacity to effectively rebuild the body, the researchers said.

The researchers disabled the “aging pathway” that tells stem cells to stop dividing by using an established method of RNA interference that reduced levels of pSmad3. The researchers then examined the muscle of the different groups of mice one to five days after injury to compare how well the tissue repaired itself. As expected,

  •  muscle tissue in the young mice easily replaced damaged cells with new, healthy cells. In contrast,
  • the areas of damaged muscle in the control group of old mice were characterized by fibroblasts and scar tissue. However,
  • muscles in the old mice whose stem cell “aging pathway”had been dampened showed levels of cellular regeneration that were
    • comparable to their much younger peers, and that were 3 to 4 times greater than those of the group of “untreated” old mice.

Adult Stem Cells To Repair Damaged Heart Muscle

http://www.science20.com/profile/news_staff

In the first trial of its kind in the world, 60 patients who have recently suffered a major heart attack will be injected with selected stem cells from their own bone marrow during routine coronary bypass surgery. The Bristol trial will test

  • whether the stem cells will repair heart muscle cells damaged by the heart attack,
  • by preventing late scar formation and hence impaired heart contraction.

“ Cardiac stem cell therapy aims to repair the damaged heart as it has the potential to replace the damaged tissue.” We have elected to use a very promising stem cell type selected from the patient’s own bone marrow. This approach ensures no risk of rejection or infection. It also gets around the ethical issues that would result from use of stem cells from embryonic or foetal tissue.

In this trial (known as TransACT), all patients will have bone marrow harvested before their heart operation. Then either stem cells from their own bone marrow or a placebo will be injected into the patients’ damaged hearts during routine coronary bypass surgery. The feasibility and safety of this technique has already been demonstrated. As a result of the chosen double blind placebo-controlled design, neither the patients nor the surgeon knows whether the patient is going to be injected with stem cells or placebo. This ensures that results are not biased in any way, and is the most powerful way to prove whether or not the new treatment is effective.

Research of Stem Cells Repair Damaged Heart

By Kelvinlew Minhan | March 26th 2008

Under highly specific growth conditions in laboratory culture dishes, stem cells

  • can be coaxed into developing as new cardiomyocytes and vascular endothelial cells (Kirschstein and Skirboll, 2001).

Discoveries that have triggered the interest in the application of adult stem cells to heart muscle repair in animal models have been made by researchers in the past few years (Kirschstein and Skirboll, 2001). One  study demonstrated that cardiac tissue can be regenerated in the mouse heart attack model through the introduction of adult stem cells from mouse bone marrow (Kirschstein and Skirboll, 2001). These cells were transplanted into the marrow of irradiated mice approximately 10 weeks before the recipient mice were subjected to heart attack thru tying off different major heart blood vessel, the left anterior descending (LAD) coronary artery. The survival rate was 26 percent at two to four weeks after the induced cardiac injury (Kirschstein and Skirboll, 2001). Another study of the region surrounding the damaged tissue in surviving mice showed the presence of donor-derived cardiomyocytes and endothelial cells (Kirschstein and Skirboll, 2001).

  • the mouse hematopoietic stem cells transplanted into the bone marrow had migrated to the border part of the damaged area, and differentiated into several types of tissue for cardiac repair.

Regenerating heart tissue through stem cell therapy

http://www.mayo.edu/research/discoverys-edge/regenerating-heart-tissue-stem-cell-therapy

Summary

A groundbreaking study on repairing damaged heart tissue through stem cell therapy has given patients hope that they may again live active lives. An international team of Mayo Clinic researchers and collaborators has done it by discovering a way to regenerate heart tissue.

“It’s a paradigm shift,” says Andre Terzic, M.D., Ph.D., director of Mayo Clinic’s Center for Regenerative Medicine and senior investigator of the stem cell trial. “We are moving from traditional medicine, which addresses the symptoms of disease to cure disease.” Treating patients with cardiac disease has typically involved managing heart damage with medication.  In collaboration with European researchers, Mayo Clinic researchers have discovered a novel way to repair a damaged heart. In Mayo Clinic’s breakthrough process,
  • stem cells are harvested from a patient’s bone marrow.
  •  undergo a laboratory treatment that guides them into becoming cardiac cells,
  • which are then injected into the patient’s heart in an effort to grow healthy heart tissue.
The study is the first successful demonstration in people of the feasibility and safety of transforming adult stem cells into cardiac cells. Beyond heart failure, the Mayo Clinic research also is a milestone in the emerging field of regenerative medicine, which seeks to fully heal damaged tissue and organs.

Creating a heart repair kit

Process of converting bone marrow cells to heart cells
This image shows the process used in the clinical trials to repair damaged hearts. Cardioprogenitor cells is another term for cardiopoietic cells, those that were transformed into cardiac cells.
Stem cells transforming to cardiac tissue
Transformation: The cardiopoietic cells on the left react to the cardiac environment, cluster together with like cells and form tissue.
 Mayo Clinic researchers pursued this research, inspired by an intriguing discovery. In the early 2000s, they analyzed stem cells from 11 patients undergoing heart bypass surgery. The stem cells from two of the patients had an unusually high expression of certain transcription factors — the proteins that control the flow of genetic information between cells. Clinically, the two patients appeared no different from the others, yet their stem cells seemed to show unique capacity for heart repair.
That observation drove them to  determine how to convert  nonreparative stem cells to become reparative. Doing so required determining precisely how the human heart naturally develops, at a subcellular level. That painstaking work was led by Atta Behfar, M.D., Ph.D., a cardiovascular researcher at Mayo Clinic in Rochester, Minn. With other members of the Terzic research team, Dr. Behfar identified hundreds of proteins involved in the process of heart development (cardiogenesis). The researchers then set out to identify which of these proteins are essential in driving a stem cell to become a cardiac cell. Using computer models,
  • they simulated the effects of eliminating proteins one by one from the process of heart development.
  • That method yielded about 25 proteins.
    • The team then pared that number down to 8 proteins that their data indicated were essential.
The research team was then able to develop the lab procedure that guides stem cells to become heart cells.
The treated stem cells were dubbed cardiopoietic, or heart creative. A proof of principle study about guided cardiopoiesis, whose results were published in the Journal of the American College of Cardiology in 2010, demonstrated that animal models with heart disease that had been injected with caridiopoietic cells had improved heart function compared with animals injected with untreated stem cells. Hailed as “landmark work,” by the journal’s editorial writer, the study showed it was indeed possible to teach stem cells to become cardiac cells. Stem cells from each patient in the cardiopoiesis group were successfully guided to become cardiac cells. The treated cells were injected into the heart wall of each of those patients without apparent complications.
“Ihis newprocessofcardiopoiesiswas achieved in 100 percent of cases, with a very good safety profile,” Dr.Terzic says. “We are enabling the heart toregainitsinitial structure and function,” Dr.Terzic says, “and we will not stop here.” The clinicaltrialfindingsareexpectedto be published in the Journal of the American College of Cardiology in 2013.  Meanwhile, research to improve the injection process and effectiveness is underway.

Stem Cells from Humans Repair Heart Damage in Monkeys

GEN News Highlights  May1, 2014

GPCR Insights Brighten Drug Discovery Outlook

Ken Doyle, Ph.D.

GEN Apr 15, 2014 (Vol. 34, No. 8)

Recent years have seen major advances in understanding the structure-function relationships of G protein-coupled receptors (GPCRs). This large superfamily of transmembrane receptors comprises over 800 members in humans.

GPCRs regulate a wide variety of physiological processes including

  • sensation (vision, taste, and smell),
  • growth,
  • hormone responses, and
  • regulation of the immune and
  • autonomic nervous systems.

Their involvement in multiple disease pathways makes GPCRs attractive targets for drug discovery efforts.

These multifaceted proteins will be the subject of “GPCR Structure, Function and Drug Discovery,” a Global Technology Community conference scheduled to take place May 22–23 in Boston. The conference is expected to cover a broad range of topics including biased signaling, membrane protein structures, GPCR signaling dynamics, computational approaches to disease.

According to Bryan Roth, M.D., Ph.D., Michael Hooker Distinguished Professor at the University of North Carolina, Chapel Hill,

  • drugs that can selectively target various downstream GPCR pathways hold the most promise.

Dr. Roth’s laboratory studies approximately 360 different GPCRs with therapeutic potential using massively parallel screening methods. His research focuses on “functional selectivity,” which he describes as

  • “the ligand-dependent selectivity for certain signal transduction pathways in one and the same receptor.”

Dr. Roth notes that structural data have demonstrated that GPCRs exist in multiple conformations: “The structures of the 5-hydroxytryptamine 2B receptor and the recent high-resolution delta-opioid receptor structure have provided evidence for conformational rearrangements that contribute to functional selectivity.” Drugs that take advantage of this selectivity by preferentially stabilizing certain conformations may have unique therapeutic utility.

“Generally, we look at G protein versus arrestin-based signaling, although it’s also possible to examine how drugs activate one G protein-mediated signaling pathway versus another.

 

fluorescently tagged Arrestin and GPRC of interest

fluorescently tagged Arrestin and GPRC of interest

 

 

 

 

 

 

 

  • β-Arrestins constitute a major class of intracellular scaffolding proteins that regulate GPCR signaling by preventing or enhancing the binding of GPCRs to intracellular signaling molecules. Laura Bohn, Ph.D., associate professor at Scripps Florida,  studies the roles that β-arrestins play in GPCR-mediated signaling.
  • a particular β-arrestin can play multiple, tissue-specific roles—shutting down the signaling of a receptor in one tissue while activating signaling in another.
  • different ligands can direct GPCR signaling to different effectors, which could result in different physiological effects,” comments Dr. Bohn. “Our challenge is in determining what signaling pathways to harness to promote certain effects, while avoiding others.”
Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

 

 

 

 

 

 

 

 

 

 

 

Using Designer Proteins

The multifunctional signaling abilities of β-arrestins has prompted large-scale study of their properties. Vsevolod Gurevich, Ph.D., professor of pharmacology at Vanderbilt University, studies

  1. the structure,
  2. function, and
  3. biology of arrestin proteins.

β-arrestins have three main functions.

  1. First, they prevent the coupling of GPCRs to G proteins, thereby blocking further G protein-mediated signaling (a process known as desensitization).
  2. Second, the binding of a GCPR releases the β-arrestin’s carboxy-terminal “tail” and promotes internalization of the receptor.
  3. Third, receptor-bound β-arrestins bind other signaling proteins, resulting in a second wave of arrestin-mediated signaling.

Dr. Gurevich’s laboratory studies β-arrestin biology through the use of three types of specially designed mutants—

  1. enhanced phosphorylation-dependent,
  2. receptor-specific, and
  3. signaling-biased mutants.

an enhanced mutant of visual β-arrestin-1 partially compensates for defects of rhodopsin phosphorylation in vivo,

“Several congenital disorders are caused by mutant GPCRs that cannot be normally phosphorylated because they have lost GPCR kinase (GRK) sites. Enhanced super-active arrestins have the potential to compensate for these defects, bringing the signaling closer to normal.”

  • Dr. Gurevich explains the strategy involved in creating designer β-arrestins: “We identify residues critical for individual β-arrestin functions by mutagenesis, using limited structural information as a guide.
  • We also work on getting more structural information. In collaboration with different crystallographers, we solved the crystal structures of all four vertebrate β-arrestin subtypes in the basal state, as well as the structure of the arrestin-1-rhodopsin complex.”
  • Dr. Gurevich believes that designer β-arrestins “are the next step in research and therapy, moving way beyond what small molecules can achieve.
  • The difference in capabilities between redesigned signaling proteins, including β-arrestins, and conventional small molecule drugs is about the same as that between airplanes and horse-driven carriages.”
  • Dr. Gurevich observes that redesigned signaling proteins face considerable obstacles in terms of gene delivery, but that the efforts are worth it. “Using designer signaling proteins, we can tell the cell what to do in a language it cannot disobey,” asserts Dr. Gurevich.

Synthesis and Antihypertensive Screening of Novel Substituted 1,2- Pyrazoline Sulfonamide Derivatives

Avinash M. Bhagwat , Anilchandra R. Bha , Mahesh S. Palled , Anand P. Khadke , Anuradha M. Patil, et al.

Am. J. PharmTech Res. 2014; 4(2).    http://www.ajptr.com/ 

Angiotensin II receptor antagonists, also known as angiotensin receptor blockers , AT1-receptor antagonists or sartans, are a group of pharmaceuticals which modulate the renin-angiotensin-aldosterone system. Their main use is in hypertension, diabetic nephropathy and congestiveheart failure. These substances are AT1-receptor antagonists which

  • block the activationof angiotensin II AT1 receptors.

Blockade of AT1 receptors directly causes

1 vasodilation,

2 reduces secretion of vasopressin,

3 reduces production and secretion of aldosterone, amongst other actions –

4 the combined effect of which is reduction of blood pressure.

Irbesartan is a safe and effectiveangiotensin II receptor antagonist with an affinity for the AT1 receptor that is more than 8,500times greater than its affinity for AT2 receptor. This agent has a higher bioavailability (60-80%) than other drugs in its class . In both Losartan and Irbesartan structures imidazole moiety is being present. A structure analog of losartan and Irbesartan are designed by incorporating the heterocycles like pyrazoline group. We felt it would be interesting to explore the possibilities of 1,2-pyrazoline derivatives for Angiotensin II receptor antagonistic activity.

The Irbesartan structure was a modified Losartan structure, which had all the identity of a Losartan molecule but with groups that would fit the hydrophobic cavity with a tetramethylene group and an alkyl side chain that would fit in the pocket in the AT1 receptor. The hydroxyl methyl group of Losartan being replaced with carbonyl group of Irbesartan. With a view to introduce a hydrogen bonding interaction with AT1 receptor, these structures were further modified with a view of retaining both hydrogen bonding characteristics and as well as lipophilic groups. Losartan and Irbesartan structure contains a diphenyl molecule & imidazole ring.

In Losartan and Irbesartan diphenyl molecule is attached to the nitrogen of the imidazole ring. It is interesting to to see the activity of compounds containing two phenyl rings attached at two different positions namely3,5 position of 1, 2-pyrazoline ring. The sulphonamide derivatives known for its diuretics activity which reduces renal hypertension. We use to synthesize sulphonamide and pyrazoline in one molecule to check its possible Angiotensin II receptor antagonist property. For this reason chalcones were synthesized reacted with hydrazine hydrate to yield the corresponding 1,2-pyrazoline derivatives which further condensed with sulphanilamide and formaldehyde by mannich condensation reaction.

Acute Toxicity Study (LD50)

This study was carried out in order to establish the therapeutic and toxic doses of the newly synthesized 1,2 pyrazoline derivatives. To establish LD50 of these compounds the method described by Miller & Tainter was employed.

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