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Posts Tagged ‘Transcription factor’


Brain Development

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Updated 11/22/2015

Single Gene Found to Play Huge Role in Brain Development

http://www.genengnews.com/gen-news-highlights/single-gene-found-to-play-huge-role-in-brain-development/81251997/

 

Single Gene Found to Play Huge Role in Brain Development

Figure 1: Cells in which NeuroD1 is turned on are reprogrammed to become neurons. Cell nuclei are shown in blue (Höchst stain) and neurons are shown in red (stained with neuronal marker TUJ1). [A. Pataskar,J. Jung, V. Tiwari]

 

Researchers at the Institute of Molecular Biology (IMB) in Mainz, Germany say they have unraveled a complex regulatory mechanism that explains how a single gene can drive the formation of brain cells. Their study (“NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program”), published in The EMBO Journal, is an important step toward a better understanding of how the brain develops. It also harbors potential for regenerative medicine, according to the scientists.

Neurodegenerative disorders, such as Parkinson’s disease, are often characterized by an irreversible loss neurons. Unlike many other cell types in the body, neurons are generally not able to regenerate by themselves, so if the brain is damaged, it stays damaged. One hope of developing treatments for this kind of damage is to understand how the brain develops in the first place, and then try to imitate the process. However, the brain is also one of the most complex organs in the body, and very little is understood about the molecular pathways that guide its development.

 

Figure 2: Diagram showing how NeuroD1 influences the development of neurons. During brain development, expression of NeuroD1 marks the onset of neurogenesis. NeuroD1 accomplishes this via epigenetic reprogramming: neuronal genes are switched on, and the cells develop into neurons. TF: transcription factor; V: ventricle; P: pial surface. [A. Pataskar, J. Jung, V. Tiwari]

 

ijay Tiwari, Ph.D, and his group have been investigating a central gene in brain development, NeuroD1. This gene is expressed in the developing brain and marks the onset of neurogenesis.

In their research article, Dr. Tiwari and his colleagues have shown that during brain development NeuroD1 is not only expressed in brain stem cells but acts as a master regulator of a large number of genes that cause these cells to develop into neurons. They used a combination of neurobiology, epigenetics, and computational biology approaches to show that these genes are normally turned off in development, but NeuroD1 activity changes their epigenetic state in order to turn them on. Strikingly, the researchers show that these genes remain switched on even after NeuroD1 is later switched off. They further show that this is because NeuroD1 activity leaves permanent epigenetic marks on these genes that keep them turned on, in other words it creates an epigenetic memory of neuronal differentiation in the cell.

“Our research has shown how a single factor, NeuroD1, has the capacity to change the epigenetic landscape of the cell, resulting in a gene expression program that directs the generation of neurons,” wrote the screenplay investigators.

“This is a significant step toward understanding the relationship between DNA sequence, epigenetic changes and cell fate. It not only sheds new light on the formation of the brain during embryonic development but also opens up novel avenues for regenerative therapy,” says Dr. Tiwari.

 

NEUROD1 neuronal differentiation 1 [ Homo sapiens (human) ]

Official Symbol NEUROD1 provided by HGNC 

Official Full Name neuronal differentiation 1 provided by HGNC

Primary source HGNC:HGNC:7762 See related Ensembl:ENSG00000162992; HPRD:03428; MIM:601724; Vega:OTTHUMG00000132583

Gene type protein coding

RefSeq status REVIEWED

OrganismHomo sapiens

Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo

Also known asBETA2; BHF-1; MODY6; NEUROD; bHLHa3

Summary This gene encodes a member of the NeuroD family of basic helix-loop-helix (bHLH) transcription factors. The protein forms heterodimers with other bHLH proteins and activates transcription of genes that contain a specific DNA sequence known as the E-box. It regulates expression of the insulin gene, and mutations in this gene result in type II diabetes mellitus. [provided by RefSeq, Jul 2008]

Orthologs mouse all

 

https://en.wikipedia.org/wiki/NEUROD1

Neurogenic differentiation 1 (NeuroD1), also called β2,[1] is a transcription factor of the NeuroD-type. It is encoded by the human gene NEUROD1.

It is a member of the NeuroD family of basic helix-loop-helix (bHLH) transcription factors. The protein forms heterodimers with other bHLH proteins and activates transcription of genes that contain a specific DNA sequence known as the E-box. It regulates expression of the insulin gene, and mutations in this gene result in type II diabetes mellitus.[2]

Contents  [hide

1Interactions

2References

3Further reading

4External links

 

NeuroD1 induces terminal neuronal differentiation in olfactory neurogenesis

Camille BoutinOlaf HardtAntoine de ChevignyNathalie CoréSandra GoebbelsRalph SeidenfadenAndreas Bosio and Harold Cremer

PNAS Jan 19, 2010; 107(3):   1201–1206.   http://dx.doi.org:/10.1073/pnas.0909015107

After their generation and specification in periventricular regions, neuronal precursors maintain an immature and migratory state until their arrival in the respective target structures. Only here are terminal differentiation and synaptic integration induced. Although the molecular control of neuronal specification has started to be elucidated, little is known about the factors that control the latest maturation steps. We aimed at identifying factors that induce terminal differentiation during postnatal and adult neurogenesis, thereby focusing on the generation of periglomerular interneurons in the olfactory bulb. We isolated neuronal precursors and mature neurons from the periglomerular neuron lineage and analyzed their gene expression by microarray. We found that expression of the bHLH transcription factor NeuroD1 strikingly coincides with terminal differentiation. Using brain electroporation, we show that overexpression of NeuroD1 in the periventricular region in vivo leads to the rapid appearance of cells with morphological and molecular characteristics of mature neurons in the subventricular zone and rostral migratory stream. Conversely, shRNA-induced knockdown of NeuroD1 inhibits terminal neuronal differentiation. Thus, expression of a single transcription factor is sufficient to induce neuronal differentiation of neural progenitors in regions that normally do not show addition of new neurons. These results suggest a considerable potential of NeuroD1 for use in cell-therapeutic approaches in the nervous system.

 

Determination of neuronal subtypes is an early event that coincides with cell cycle exit (1, 2). However, after their generation, new neurons have to remain immature for prolonged periods, allowing their migration to final destinations where terminal differentiation occurs (3). Little is known about the factors that maintain the precursor state or induce terminal differentiation.

Olfactory neurogenesis is particularly suited to approach these late steps in neuronal differentiation. Here, stem cell populations first located in the ventricular zone and after the establishment of an ependymal layer positioned in subventricular zone (SVZ) generate migratory neuroblasts throughout life (4). These perform long-distance chain migration via the rostral migratory stream (RMS) into the olfactory bulb (OB), where they migrate into the granule cell layer (GCL) and the glomerular layer (GL) to differentiate into GABA- and dopaminergic neurons (4, 5). Thus, in this system, generation of neurons is permanent and the consecutive steps in the neurogenic sequence are spatially separated.

Determination of newly generated neurons has been studied intensively over the past years. For example, it has been demonstrated that defined areas surrounding the lateral ventricle contain predetermined stem cells that give rise to defined subsets of interneurons (6, 7). Several transcription factors have been implicated in the specification of the different neuronal populations. The zinc finger transcription factor sp8, for instance, appears to be involved in the generation of interneurons expressing calretinin (8), and analysis of Sall3 mutant mice (9) points to a role of this factor in the dopaminergic, tyrosine hydroxylase–positive lineage (10). Furthermore, it appears that interneuron diversity relies on the combinatorial expression of such transcription factors. This is exemplified by Pax6 and Dlx2, which have been shown to interact in the determination of adult generated neuronal precursors toward a dopaminergic fate (9, 11, 12). All of these transcriptional regulators are expressed early during the neurogenic process and remain present until terminal differentiation occurs.

We aimed at the identification of transcription factors that induce terminal differentiation of postnatal generated neurons in the OB. To do so we isolated neuronal precursors and differentiated interneurons from the periglomerular lineage of the OB and compared their gene expression by microarray. We established that the expression of NeuroD1, a bHLH transcription factor that has been implicated in neuronal differentiation in several experimental systems (1317), coincides with the passage from neuronal precursor to mature interneurons. Functionally, we show that premature expression of NeuroD1 in vitro and in vivo induced highly efficiently the differentiation of forebrain progenitors. In vivo, this leads to the transitory appearance of ectopic neurons in the SVZ, RMS and striatum. Conversely, knockdown of NeuroD1 specifically inhibits terminal maturation of periglomerular neurons in the OB. Thus, NeuroD1 is both necessary and sufficient to induce key steps in terminal neuronal differentiation.

 

NeuroD1 Is Specifically Expressed in Mature GL Interneurons.

Subpopulations of neuronal precursors destined for the GCL and GL of the OB are generated by regionally defined stem cell populations in the periventricular region but migrate intermingled in the RMS to the OB. Once there, cells resegregate: granule cell precursors terminate their migration in the GCL, whereas the smaller population of periglomerular neuron precursors traverses this layer and the mitral cell layer (MCL) to invade the peripherally located GL (Fig. 1A). Thus, at a given time point, the GL contains both mature periglomerular neurons and their specific progenitors. Based on this spatial organization we isolated these two populations, concurrently depleting glial cells.

We devised a three-step strategy based on the following: (i) microdissection followed by enzymatic dissociation of the postnatal GL, (ii) depletion of contaminating glial cells by magnetic activated cell sorting (MACS) using an A2B5 specific antibody (18), (iii) separation of PSA-NCAM expressing cells (19) from the remaining fraction containing the mature neurons (Fig. 1B). The same purification strategy was applied to tissue microdissected from the P2 periventricular region (18). Characterization of the different cell population after sorting was performed via immunocytochemistry using the markers used for sorting (A2B5 and PSA-NCAM) as well as the differentiation marker Gad65 (18) (Fig. S1). Thus, as starting material we obtained highly enriched mature OB periglomerular interneurons (PGN), their immature progenitors (PGP), as well as a mixed population of generic progenitors (GP) from the SVZ/RMS.

 

Fig. 1.

Fig. 1.

Expression of NeuroD1 in the olfactory neurogenic system (A) DAPI-stained coronal section through the olfactory bulb of P5. (B) Strategy to isolate neuronal populations at different steps of their maturation. (C) Relative changes in gene expression for selected genes. Expression in GP was considered baseline, and changes are expressed as fold difference. (D–F) NeuroD1 in situ hybridization on sections from P5 mouse brain. No signal was detected along the lateral ventricle or in the RMS (D). In the olfactory bulb, individual NeuroD1+ cells were present in the GCL, whereas the MCL and the GL contained higher amounts (E, high magnification in F). A similar expression pattern was found after β-gal reaction on NeuroD1-lacZ-knockin tissue (G). (Scale bar: 200 μm in A; 100 μm inD and E; 20 μm in F and G).

 

Based on the purified and characterized cell populations, we performed microarray analyses to gain insight into the changes in gene expression during the neurogenic process. Investigation of expression dynamics of genes associated with either the precursor status or neuronal differentiation (Fig. S2 A and B) were used to validate the approach. Furthermore, these data were compared with those from an already available Serial Analysis of Gene Expression (SAGE) study (20).

Serial Analysis of Microarray (SAM) demonstrated the presence of groups of genes with comparable expression patterns (Fig. S2 C–E). Interestingly, only a relatively small fraction of genes were absent in the immature cell populations GP and PGP but highly represented in mature PGN (Fig. S2E). One of the genes showing such a pattern was NeuroD1, which was expressed more than 50-fold higher in PGN than in the immature populations (Fig. 1C). This was in agreement with the above-cited SAGE data, showing that NeuroD1 expression was below the detection level in neuronal precursors of the adult SVZ (20). Thus, expression of NeuroD1 was absent from precursors but coincided with terminal neuronal differentiation.

This late expression of NeuroD1 was in contrast to that of factors that have been functionally implicated in the specification of PGN, including Pax6, Sp8 and Sall3, which were expressed in both the immature populations and in the mature neurons (Fig. 1C; in situ hybridization for Pax6 in Fig. S3). Only Dlx2 showed a moderate increase in the PGN lineage outgoing, however, from an already considerable baseline level in migrating precursors (12) (Fig. 1C).

Next we analyzed the expression of NeuroD1 using in situ hybridization on P5 forebrain sections. Strong expression was found in the GL, whereas weaker expression was observed in the GCL and MCL (Fig. 1 Eand F). The transcript was undetectable in the periventricular region and the RMS (Fig. 1D). This staining was confirmed using NeuroD1-lacZ knockin mice (21) (Fig. 1G). In conclusion, these data demonstrated the absence of NeuroD1 from immature cells of the system and its strong expression in mature PGN. This pattern was coherent with a function in terminal neuronal differentiation.

NeuroD1 Induces Neuronal Differentiation in Vitro.

We studied the neurogenic potential of NeuroD1 in primary cultured neural stem cells using the neurosphere assay. In parallel to NeuroD1, we performed all experiments under the same conditions using the transcription factor Pax6, a well-described neurogenic signal in the system (9, 11, 12), to control for specificity of the observed effects. Neurosphere cells were coelectroporated with NeuroD1 or Pax6 expression vectors and GFP immediately before plating in differentiation conditions. One week after transfection, in the control condition, 14 ± 1% of the GFP-positive cells coexpressed the early neuronal marker Tuj1 (Fig. S4 A and D) whereas NeuroD1 induced Tuj1 expression in virtually all cells (98.0 ± 2%, Fig. S4 B and D). Pax6 gain-of-function led to an intermediate value (60.0 ± 3%, Fig. S4 C and D). NeuN, a later neuronal marker (22), was expressed by 21.1 ± 1% of the Tuj1-positive cells in the control situation (Fig. S4 E and H) but induced by NeuroD1 in almost all cells (93.9 ± 2%; Fig. S4 F and H). Surprisingly, Pax6 expression led to nearly complete disappearance of NeuN (1.7 ± 0.3%; Fig. S4 G and H). We investigated the induction of subtype specific markers by NeuroD1. Whereas tyrosine hydroxylase showed no augmentation, we found a 20% increase in calretinin labeling, in agreement with previous findings (23).

Next we investigated morphological parameters like process length as well as density and length of filopodia. Both NeuroD1 and Pax6 induced a significant, greater than 2-fold increase in process length (Fig. S4 I and L). We analyzed dendritic filopodia, structures that are believed to be precursors of dendritic spines (24). Expression of NeuroD1 induced a doubling in density and length of filopodia (Fig. S4 N, P, and Q). Interestingly, Pax6 reduced filopodia density to a level significantly below that of controls (Fig. S4 O and P), whereas length of the few remaining filopodia was not affected (7.0 ± 0.4 μm; Fig. S4Q).

Thus, the expression of NeuroD1 in neurosphere amplified neural stem cells induced neuronal commitment as well as morphological characteristics of mature neurons. Like NeuroD1, Pax6 favored neuronal commitment but appeared to actively suppress certain characteristics of terminal neuronal differentiation.

NeuroD1 Induces Ectopic Neurons in Vivo.

We asked whether NeuroD1 was also sufficient to induce neuronal differentiation in vivo. We used postnatal forebrain electroporation, an approach that allows efficient genetic manipulation of neural stem cells along the lateral ventricles and, consequently, of all transitory or permanent cell populations that are generated in the olfactory neurogenic process (25). The NeuroD1 expression vector or empty control plasmids were coelectroporated together with a GFP-containing vector that allowed visualization of transfected cells and their progeny at high resolution. Consequences of NeuroD1 gain-of-function were analyzed at 2, 4, 6, 8, and 15 days postelectroporation (dpe). As for the in vitro studies, results were compared with the effects of Pax6 gain-of-function.

At 2 dpe of a control vector into the lateral wall of the forebrain ventricle, 9.8 ± 1.3% (Fig. 2 A and K) of the GFP-expressing cells were localized in the VZ and had the morphology of radial glia (RG) (25). The majority of the GFP + cells, representing mainly neuronal precursors, were localized in the SVZ. Electroporation of a NeuroD1 expression vector induced a loss of GFP-positive RG cells (3.7 ± 0.5%; Fig. 2 B and K). The remaining cells in the VZ showed lower GFP levels than in controls (Fig. 2 A and B asterisks).

Fig. 2.

Fig. 2.

NeuroD1 induces neuronal morphology in vivo. Effect of NeuroD1 gain-of-function at different time points postelectroporation. (A and B) Coronal forebrain sections at the level of the lateral ventricle at 2 dpe. In the control condition, strongly GFP labeled RG are present in the VZ (A, asterisk). Expression of NeuroD1 induced a relative loss of radial glia and fainter GFP label (B, asterisk). (C and D) Coronal sections at the level of the lateral ventricle at 4 dpe. NeuroD1 expression induced an accumulation of transfected cells in the SVZ (D) and the almost total disappearance of radial glia (D). (E–F′) Sagittal sections of the RMS at 4 dpe. In the control situation, cells migrated toward the OB and presented the bipolar morphology specific of migrating precursors (E, E′, arrowheads). NeuroD1 electroporation induced loss of tangential orientation, induction of complex branching (F, F′, arrowhead), and invasion of the surrounding tissues (F, arrowheads). (G and H) Coronal section at the level of the olfactory bulb at 4 dpe. Although the majority of cells have reached the OB in the control situation (G), only a few cells were located in the center of the OB in the presence of NeuroD1 (H). (I and I′) Examples of cells presenting neuronal morphology in the SVZ at 4 dpe. (J) High magnification showing the presence of filopodia covering NeuroD1-expressing cells (arrowheads). (K) Quantification of GFP-positive cells presenting radial glia cell morphology along the lateral ventricle at 2 and 4 dpe. Control: 9.8 ± 1.3% (n= 6) at 2 dpe; 24 ± 11.8% at 4 dpe (n = 3); NeuroD1: 3.7 ± 0.5% at 2 dpe (n = 6); 1.6 ± 0.7% at 4 dpe (n = 3). (l) Distribution of the GFP-positive cells along the rostrocaudal axis. NeuroD1 expressing cells accumulated in proximal parts of the system. (M) Morphological analysis of cells in the SVZ/RMS. Three different classes were defined: (i) bipolar cells presenting tangential orientation, (ii) spherical cells, and (iii) branched cells presenting multiple processes in various directions (compare I). NeuroD1-expressing cells presented a highly branched morphology. Control: bipolar, 80.4%; spherical, 19.5%; branched, 0% (n = 133 cells). NeuroD1: bipolar, 5%; spherical, 16.8%; branched, 78% (n = 119 cells). Statistics: Mann-Whitney test. ns, not significant. **P < 0.01; ***P < 0.005. (Scale bar: 100 μm in E, F, G,and H; 25 μm in A, B, C, D,E, and F’; 10 μm in I; 5 μm in J.)

 

At 4 dpe, in the control situation, considerable amounts of strongly GFP+ RG cells were still present in the VZ (Fig. 2C asterisks), whereas NeuroD1 expression induced an almost complete loss of RG cells (Fig. 2 Dand K). At this time point, control cells were found along the entire SVZ and RMS. They showed generally tangential orientation and the typical morphology of migratory neuronal precursors. Large amounts of such cells were also found in the center of the OB (Fig. 2 G and L). NeuroD1 expression induced an accumulation of GFP-labeled cells in the SVZ (Fig. 2 D and L) at the expense of cells in the RMS (Fig. 2H,quantified in Fig. 2L). The accumulating cells did not have the appearance of migrating precursors but displayed complex multibranched morphologies (Fig. 2 F and F, examples in Fig. 2 I and I, quantified inFig. 2M). All principal processes of these cells were covered with small protrusions resembling filopodia (Fig. 2J). Such morphologically complex cells, strongly resembling neurons, were also predominant in and along proximal parts of the RMS (Fig. 2F). Interestingly, considerable amounts of multibranched cells were found outside of the periventricular region and the RMS, invading neighboring structures such as the striatum (Fig. 2F, arrows). There was a clear correlation between the quantity of transgene expression, as visualized by GFP fluorescence, and the above parameters. Thus, NeuroD1 induced dose-dependently a neuron-like morphology in cells in the SVZ, RMS, and surrounding tissues.

We characterized the NeuroD1 induced neuron-like cell population in the periventricular region using neuronal and glial markers (Fig. 3; examples in Fig. S5). Doublecortin (DCX), a microtubule-associated protein expressed in migratory neuronal precursors (26), was seen in 75.2 ± 4.5% of the cells in the control situation but showed a significant increase after expression of NeuroD1 (91.7 ± 2.2%). NeuN, a marker for most mature neuronal cell types in the brain (22) was low in controls (5.2 ± 1.4%, n = 8) but strongly induced by NeuroD1 (65.9 ± 4.5%, n = 9). Map2, a later generic neuronal marker (27), was also rare in control cells (14.1 ± 1.4%, n = 3) but highly expressed in the NeuroD1 condition (61.9 ± 2.7%, n = 3). GFAP and Olig2 did not show significant alterations due to NeuroD1 expression. Thus, the NeuroD1-induced ectopic cells with neuronal morphology in the SVZ and RMS showed molecular characteristics of neurons.

 

Fig. 3.

Fig. 3.

NeuroD1 induces generic neuronal markers in vivo Molecular phenotype of the cells located in the periventricular region (level 4 in Fig. 2l). Quantification representing the percentage of GFP-positive cells expressing the respective markers. DCX: control, 75.2 ± 4.5%, n = 5; NeuroD1, 91.7 ± 2.2%, n = 5. NeuN: control, 5.2 ± 1.4%, n = 8; NeuroD1, 65.9 ± 4.5%, n = 9. Map2: control, 14.1 ± 1.4%, n = 3; NeuroD1, 61.9 ± 2.7%, n = 3. Olig2: control, 6.8 ± 5%, n = 3; NeuroD1, 2.5 ± 0.5%, n = 3. GFAP: control, 0%, n = 3; NeuroD1, 0%, n = 2. Errors bars indicate SEM. Statistics: DCX and Map2, unpaired ttest; NeuN, Mann-Whitney test. ns, not significant. *P < 0.05; **P < 0.01; ***P < 0.005.

HighWire Press-hosted articles citing this article

  • ……..

    NeuroD1 Is Necessary For OB Interneuron Differentiation in Vivo.

    Next we asked whether NeuroD1 is essential for the generation of PGN. Given that NeuroD1 deficiency in mice is generally associated with perinatal lethality (14, 15, 21), we used a strategy based on RNAi in concert with postnatal in vivo electroporation to knock down NeuroD1 in the olfactory bulb neurogenic system. For validation, three different NeuroD1 specific shRNA vectors were cotransfected with a NeuroD1 expression construct into COS-7 cells. Western blot analysis demonstrated that two of the shRNAs, sh775 and sh776, efficiently inhibited production of the NeuroD1 protein, whereas sh777 induced a less efficient downregulation (sh775, 94.6%; sh776, 96.9%; sh777, 78.4%; corrected for loading against αtubulin; Fig. 4A). All three shRNAs were used for further in vivo studies.

    Fig. 4.

Fig. 4.

In vivo terminal neuronal differentiation of PGC is impaired in absence of NeuroD1. (A) Western blot analysis of protein extracts from cos-7 cells transfected with NeuroD1 or in combination with different NeuroD1 specific shRNAs. sh775 and sh776 strongly repressed NeuroD1 protein expression (94.6% and 96.9%, respectively), whereas sh777 repressed NeuroD1 by 74.8%. (B–H′′) Consequences of loss-of-function of NeuroD1 via in vivo postnatal electroporation at 4 and 15 dpe. (B–E) No differences were observed at the level of the lateral ventricle or in the RMS at 4 dpe. (F) Cell distribution along the rostro-caudal axis was normal (definition of levels in Fig. 2l). (G and H′′) Consequences of NeuroD1 knockdown on PGN morphology at 15 dpe. (G) Whereas shRNAs showing a strong effect on NeuroD1 expression strongly inhibited morphological differentiation, the weakly active shRNA 777 had only a minor effect compared with control. (H) Examples of cells that served for classification of PGN. Class1 cells present primary and secondary branching. Dendritic spines (arrowheads) indicate their synaptic integration in OB circuitry. Class 2 cells present a single primary branch. Class 3 cells present a spherical morphology and no branching. Errors bars indicate SEM. Statistics, unpaired t test. ns, not significant. **P < 0.01; ***P < 0.005. (Scale bar: 100 μm in B–E; 20 μm in H.

………

When the two highly active NeuroD1-specific shRNAs sh775 and sh776 were electroporated, the vast majority of cells in the GL showed simple morphologies with few or no processes (classes 2 and 3), whereas cells with complex neuronal morphologies were sparse (Fig. 4G). When the less-efficient shRNA sh777 was expressed, an intermediate degree of neuronal maturation was observed (Fig. 4G), suggesting a dose-dependent action of NeuroD1 under these conditions. Comparable results were obtained for the GCL. As in the PGL, knockdown of NeuroD1 induced a dose-dependent inhibition of terminal neuronal differentiation (Fig. S9 A and B).

Thus, knockdown of NeuroD1 did not notably interfere with early steps of interneuron generation, but induced a specific defect in the acquisition of the differentiated neuronal phenotype in the OB.

 

Discussion

Although considerable information is available concerning the generation, specification, and migration of neurons, little is known concerning the factors and regulatory cascades that maintain the immature neuronal precursor status or induce the exit from this state and trigger terminal differentiation. Using a systematic approach, we identified NeuroD1 as a candidate for the latter function and validated this role using gain- and loss-of function approaches.

In Xenopus, a late function of NeuroD1 has been suggested based on two lines of evidence (13). First, NeuroD1 is transitorily expressed in territories where neuronal differentiation occurs. Second, misexpression of NeuroD1 causes the premature differentiation of neuronal precursors into neurons. However, the observation that NeuroD1 could also convert presumptive epidermal cells into neurons pointed toward a determination function. Therefore, a doubtless discrimination between a proneural and a terminal differentiation function was not possible.

The above-cited pioneering work in the frog has been extended through the analysis of mice with mutations in the NeuroD1 gene (14, 15, 21). In the hippocampal dentate gyrus of such animals, granule cell precursors are generated correctly in the neuroepithelium and invade the hippocampal anlage. However, in the target structure, precursors show a severe deficit in proliferation, and a defined dentate gryus is not formed (15). In the mutant cerebellum, generation and migration of early precursors appear not to be affected. Nevertheless, once these cells become postmitotic, massive cell death is observed and the cerebellum is severely affected (14). Thus, in these systems a late function of NeuroD1 is already suggested. However, because of the complexity of the models and the relatively low level of resolution, the available information is still fragmentary.

We attempted to clarify the role of NeuroD1 in neuronal differentiation by analyzing its function during olfactory neurogenesis. Using SAGE, microarray, in situ hybridization, and lacZ knockin into the NeuroD1 locus, we have demonstrated that NeuroD1 is expressed in mature neurons of the OB but is absent from immature stages. These findings are in contrast to recent expression data based on a NeuroD1 antibody, suggesting expression of the transcription factor already in the SVZ and RMS (23, 29). However, our loss-of-function approach based on RNAi shows that NeuroD1 is dispensable for generation and migration of precursors but is necessary for their transition into neurons in the target layer. These findings are in agreement with those of a recent study based on conditional NeuroD1 mutants, which showed a comparable defect in the OB (29).

……

This work demonstrates that expression of a single transcription factor can induce massive ectopic neuronal differentiation of neural stem cells in the vertebrate forebrain. The existence of postnatal and adult neurogenesis holds potential for the treatment of neurodegenerative diseases (34). However, in many experimental paradigms, transplanted or recruited cells fail to undergo differentiation into neurons and either transdifferentiate into glia or remain immature precursors (18, 35). It appears conceivable to combine such approaches with the strong neuronal differentiation inducing activity of NeuroD1.

Scientists Unveil Critical Mechanism of Memory Formation

In a new study that could have implications for future drug discovery efforts for a number of neurodegenerative diseases, scientists from the Florida campus of The Scripps Research Institute (TSRI) have found that the interaction between a pair of brain proteins has a substantial and previously unrecognized effect on memory formation.
The study, which was published November 19, 2015 by the journal Cell, focuses on two receptors previously believed to be unrelated—one for the neurotransmitter dopamine, which is involved in learning and memory, reward-motivated behavior, motor control and other functions, and the other for the hormone ghrelin, which is known for regulating appetite as well as the distribution and use of energy.
“Our immediate question was, what is the ghrelin receptor doing in the brain since the natural ligand—ghrelin—for it is missing? What is it’s functional role?” said Roy Smith, chair of TSRI’s Department of Metabolism and Aging. “We found in animal models that when these two receptors interact, the ghrelin receptor changes the structure of the dopamine receptor and alters its signaling pathway.”
“This concept has potentially profound therapeutic implications,” said Andras Kern, the first author of the study and a staff scientist in the Smith lab, “pointing to a possible strategy for selective fine-tuning of dopamine signaling in neurons related to memory. By using small molecules binding to the ghrelin receptor we can enhance or inhibit dopamine signaling.”
Challenging the current theory, which involves canonical dopamine signaling in neurons, the new study shows that the biologically active ghrelin-dopamine receptor complex produces synaptic plasticity, the ability of the brain’s synapses (parts of nerve cells that communicate with other nerve cells) to grow and expand, the biological process underpinning long-term memory formation.
In addition, when the researchers blocked the ghrelin receptor, dopamine-dependent memory formation was inhibited in animal models, demonstrating the mechanism is essential to that process.
Combined with conclusions from earlier studies that showed a significant role for the ghrelin receptor in neurons that regulate food intake, insulin release and immune system deterioration due to aging, the new study further expands the ghrelin receptor’s importance. In animal models, ghrelin inhibits neuronal loss associated with Parkinson’s disease, and stroke, Smith noted, and the new study underlines its possible role in treating memory loss, age related or otherwise.
“All in all, it’s a pretty amazing receptor,” he said.
In addition to Smith and Kern, other authors of the study, “Hippocampal Dopamine/DRD1 Signaling Dependent on the Ghrelin Receptor,” are Maria Mavrikaki, Celine Ullrich, Rosie Albarran-Zeckler and Alicia Faruzzi Brantley of TSRI.
This work was supported by the National Institutes of Health (grant R01AG019230).
Hippocampal Dopamine/DRD1 Signaling Dependent on the Ghrelin Receptor
Andras Kern, Maria Mavrikaki3, Celine Ullrich4, Rosie Albarran-Zeckler, Alicia Faruzzi Brantley, Roy G. Smith
Figure thumbnail fx1
  • In hippocampal neurons GHSR1a and DRD1 forms heteromers in a complex with Gαq
  • DRD1-induced hippocampal synaptic plasticity is dependent on GHSR1a and Gαq
  • DRD1 mediated learning and memory is dependent on Gαq-PLC rather than Gαs signaling
  • DRD1-induced hippocampal memory is regulated by allosteric DRD1:GHSR1a interactions

The ghrelin receptor (GHSR1a) and dopamine receptor-1 (DRD1) are coexpressed in hippocampal neurons, yet ghrelin is undetectable in the hippocampus; therefore, we sought a function for apo-GHSR1a. Real-time single-molecule analysis on hippocampal neurons revealed dimerization between apo-GHSR1a and DRD1 that is enhanced by DRD1 agonism. In addition, proximity measurements support formation of preassembled apo-GHSR1a:DRD1:Gαqheteromeric complexes in hippocampal neurons. Activation by a DRD1 agonist produced non-canonical signal transduction via Gαq-PLC-IP3-Ca2+ at the expense of canonical DRD1 GαscAMP signaling to result in CaMKII activation, glutamate receptor exocytosis, synaptic reorganization, and expression of early markers of hippocampal synaptic plasticity. Remarkably, this pathway is blocked by genetic or pharmacological inactivation of GHSR1a. In mice, GHSR1a inactivation inhibits DRD1-mediated hippocampal behavior and memory. Our findings identify a previously unrecognized mechanism essential for DRD1 initiation of hippocampal synaptic plasticity that is dependent on GHSR1a, and independent of cAMP signaling.

 

 

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RNA polymerase – molecular basis for DNA transcription

Larry H. Bernstein, MD, FCAP, Curator

Leaders in Pharmaceutical Intelligence

Series E: 2; 3.1

Roger Kornberg, MD
Nobel Prize in Chemistry
Stanford University

Son of Arthur Kornberg, who received the Nobel Prize for DNA polymerase, Roger Kornberg spent decades on the problem of transcription of the genetic code in eukaryotic cells. Roger Kornberg made several contributions to the understanding of the transcription model including – recognition of the nucleosomal structure of DNA, characterization of the chromatin modifying factors, and discovering the bridging factor that mediates transcriptional activation (called Mediator). The three types of RNA are termed mRNA, tRNA, and rRNA. Kornberg recognized that chromatin consists of nucleosomes arranged along DNA in the form of beads on a string. He used electron crystallography to determine that lateral diffusion in molecules tethered to the bilayer to pack into two-dimensional crystals suitable for crystallography.   Using yeast, Kornberg identified the role of RNA polymerase II and other proteins in transcribing DNA, and he created three-dimensional images of the protein cluster using X-ray crystallography. Polymerase II is used by all organisms with nuclei, including humans, to transcribe DNA.

While a graduate student working with Harden McConnell at Stanford in the late 1960s, he discovered the “flip-flop” and lateral diffusion of phospholipids in bilayer membranes. While a postdoctoral fellow working with Aaron Klug and Francis Crick at the MRC in the 1970s, Kornberg discovered the nucleosome as the basic protein complex packaging chromosomal DNA in the nucleus of eukaryotic cells (chromosomal DNA is often termed “Chromatin” when it is bound to proteins in this manner, reflecting Walther Flemming‘s discovery that certain structures within the cell nucleus would absorb dyes and become visible under a microscope).[10] Within the nucleosome, Kornberg found that roughly 200 bp of DNA are wrapped around an octamer of histone proteins.

Kornberg’s research group at Stanford later succeeded in the development of a faithful transcription system from baker’s yeast, a simple unicellular eukaryote, which they then used to isolate in a purified form all of the several dozen proteins required for the transcription process. Through the work of Kornberg and others, it has become clear that these protein components are remarkably conserved across the full spectrum of eukaryotes, from yeast to human cells.

Using this system, Kornberg made the major discovery that transmission of gene regulatory signals to the RNA polymerase machinery is accomplished by an additional protein complex that they dubbed Mediator.[11] As noted by the Nobel Prize committee, “the great complexity of eukaryotic organisms is actually enabled by the fine interplay between tissue-specific substances, enhancers in the DNA and Mediator. The discovery of Mediator is therefore a true milestone in the understanding of the transcription process.”[12]

Kornberg took advantage of expertise with lipid membranes gained from his graduate studies to devise a technique for the formation of two-dimensional protein crystals on lipid bilayers. These 2D crystals could then be analyzed using electron microscopy to derive low-resolution images of the protein’s structure. Eventually, Kornberg was able to use X-ray crystallography to solve the 3-dimensional structure of RNA polymerase at atomic resolution.[13][14] He extended these studies to obtain structural images of RNA polymerase associated with accessory proteins.[15] Through these studies, Kornberg created an actual picture of how transcription works at a molecular level.

“I measured the molecular weight of the purified H3/H4 preparation by equilibrium ultracentrifugation, while Jean Thomas offered to analyze the material by chemical cross-linking. Both methods showed unequivocally that H3 and H4 were in the form of a double dimer, an (H3)2(H4)2 tetramer (Kornberg and Thomas, 1974). I pondered this result for days, and came to the following conclusions (Kornberg, 1974). First, the exact equivalence of H3 and H4 in the tetramer implied that the differences in relative amounts of the histones from various sources measured in the past must be due to experimental error. This and the stoichiometry of the tetramer implied a unit of structure in chromatin based on two each of the four histones, or an (H2A)2(H2B)2(H3)2(H4)2 octamer. Second, since chromatin from all sources contains roughly one of each histone for every 100 bp of DNA, a histone octamer would be associated with 200 bp of DNA. Finally, the (H3)2(H4)2 tetramer was reminiscent of hemoglobin, an a2b2 tetramer. The X-ray structures of hemoglobin and other oligomeric proteins available at the time were compact, with no holes through which a molecule the size of DNA might pass. Rather, the DNA in chromatin must be wrapped on the outside of the histone octamer.

As I turned these ideas over in mind, it struck me how I might explain the results of Hewish and Burgoyne. What if their sedimentation coefficient of unit length DNA fragments was measured under neutral rather than alkaline conditions? Then the DNA would have been double stranded and about 250 bp in length. Allowing for the approximate nature of the result, the correspondence with my prediction of 200 bp was electrifying. Then I recalled a reference near the end of the Hewish and Burgoyne paper to a report of a similar pattern of DNA fragments by Williamson. I rushed to the library and found that Williamson had obtained a ladder of DNA fragments from the cytoplasm of necrotic cells and measured the unit size by sedimentation under neutral conditions: the result was 205 bp! … with colleagues in Cambridge, I proved the existence of the histone octamer and the equivalence of the 200 bp unit with the particle seen in the electron microscope (Kornberg, 1977). This chapter of the chromatin story concluded with the X-ray crystal structure determination of the particle, now known as the nucleosome, showing a histone octamer surrounded by DNA, in near atomic detail (Luger et al., 1997).

I had decided to pursue the function rather than the structure of the nucleosome, and was joined in this by Yahli Lorch, who became my lifelong partner in chromatin research, and also my partner in life. We investigated the consequences of the nucleosome for transcription. It was believed that histones are generally inhibitory to transcription. We found, to the contrary, that RNA polymerases are capable of reading right through a nucleosome. Coiling of promoter DNA in a nucleosome, however, abolished initiation by RNA polymerase II (pol II) (Lorch et al., 1987). This finding, together with genetic studies of Michael Grunstein and colleagues, identified a regulatory role of the nucleosome in transcription. It has since emerged that nucleosomes play regulatory roles in a wide range of chromosomal transactions. A whole new field has emerged, one of the most active in bioscience today. It involves a bewildering variety of posttranslational modifications of the histones, and a protein machinery of great complexity for applying, recognizing, and removing these modifications.”

 

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The role and importance of transcription factors

Larry H. Bernstein, MD, FCAP, Writer and Curator

https://pharmaceuticalintelligence.com/2014/8/05/The-role-and-importance-of-transcripton-factors

The following is a second in the 2nd series that is focused on the topic of the impact of genomics and transcriptomics in the evolution of 21st century of medicine, which shall have to be more efficient and more effective by the end of this decade, if the prediction for the funding of Medicare is expected to run out. Even so, Social Security was devised by none other than the Otto von Bismarck, who unified Germany, and United Kingdom has had a charity hospital care system begun to protect the widows of the ravages of war, and nursing was developed by Florence Nightengale as a result of the experience of war. It can only be concluded that the care for the elderly, the infirm, and those who have little resources to live on has a long history in western civilization, and it will not cease to exist as a public social obligation anytime soon. The 20th century saw an explosive development of physics; organic, inorganic, biochemistry, and medicinal chemistry, and the elucidation of the genetic code and its mechanism of translation in plants, microorganisms, and eukaryotes.  All of which occurred irrespective of the most horrendous wars that have reshaped the world map.

The following are the second portions of a puzzle in construction that is intended to move into deeper complexities introduced by proteomics, cell metabolism, metabolomics, and signaling.  This is the only manner by which I can begin to appreciate what a wonder it is to view and live in this world with all its imperfections.

We have already visited the transcription process, by which an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. We shall examine this further.

  1. RNA and the transcription the genetic code

Larry H. Bernstein, MD, FCAP, Writer and Curator
https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/

  1. The role and importance of transcription factors?
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    https://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs
  2. What is the meaning of so many RNAs?

Larry H. Bernstein, MD, FCAP, Writer and Curator
https://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs

  1. Pathology Emergence in the 21st Century
    Larry Bernstein, MD, FCAP, Author and Curator
    https://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  2. The Arnold Relman Challenge: US HealthCare Costs vs US HealthCare Outcomes

Larry H. Bernstein, MD, FCAP, Reviewer and Curator; and
Aviva Lev-Ari, PhD, RN, Curator
https://pharmaceuticalintelligence.com/2014/08/05/the-relman-challenge/

 

 

 

Quantifying transcription factor kinetics: At work or at play?

Posted online on September 11, 2013. (doi:10.3109/10409238.2013.833891)

Florian Mueller1,2, Timothy J. Stasevich3, Davide Mazza4, and James G. McNally5
1Institut Pasteur, Computational Imaging and Modeling Unit, CNRS, Paris, Fr
2Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure, Paris, Fr
3Graduate School of Frontier Biosciences, Osaka University, Osaka, Jp
4Istituto Scientifico Ospedale San Raffaele, Centro di Imaging Sperimentale e Università Vita-Salute
San Raffaele, Milano, It, and
5Fluorescence Imaging Group, National Cancer Institute, NIH, Bethesda, MD, USA

Read More: http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

Abstract

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Here we summarize and compare the four different techniques that are currently used to measure these kinetics in live cells, namely fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single molecule tracking (SMT) and competition ChIP (CC). We highlight the principles underlying each of these approaches as well as their advantages and disadvantages. A comparison of data from each of these techniques raises an important question: do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or do they reflect non-specific interactions (i.e. playing TFs)? To help resolve this dilemma we discuss five key unresolved biological questions related to the functionality of transient and prolonged binding events at both specific promoter response elements as well as non-specific sites. In support of functionality, we review data suggesting that TF residence times are tightly regulated, and that this regulation modulates transcriptional output at single genes. We argue that in addition to this site-specific regulatory role, TF residence times also determine the fraction of promoter targets occupied within a cell thereby impacting the functional status of cellular gene networks. Thus, TF residence times are key parameters that could influence transcription in multiple ways.

Keywords: Competition-ChIP, kinetic modeling, live-cell imaging, non-specific binding, specific binding, transcription, transcription factor dynamics http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

The Transcription Factor Titration Effect Dictates Level of Gene ExpressionCalifornia Institute of Technology

Robert C. Brewster, Franz M. Weinert, Hernan G. Garcia, Dan Song, Mattias Rydenfelt, and Rob Phillips  CalTech
 Cell Mar 13, 2014; 156:1312–1323,.

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number—in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle.

 

 

Optimal reference genes for normalization of qRT-PCR data from archival formalin-fixed, paraffin-embedded breast tumors controlling for tumor cell content and decay of mRNA.

Tramm TSørensen BSOvergaard JAlsner J.

Diagn Mol Pathol. 2013 Sep;22(3):181-7. http://dx.doi.org:/10.1097/PDM.0b013e318285651e

Gene-expression analysis is increasingly performed on degraded mRNA from formalin-fixed, paraffin-embedded tissue (FFPE), giving the option of examining retrospective cohorts. The aim of this study was to select robust reference genes showing stable expression over time in FFPE, controlling for various content of tumor tissue and decay of mRNA because of variable length of storage of the tissue.

Sixteen reference genes were quantified by qRT-PCR in 40 FFPE breast tumor samples, stored for 1 to 29 years. Samples included 2 benign lesions and 38 carcinomas with varying tumor content. Stability of the reference genes were determined by the geNorm algorithm. mRNA was successfully extracted from all samples, and the 16 genes quantified in the majority of samples.

Results showed 14% loss of amplifiable mRNA per year, corresponding to a half-life of 4.6 years. The 4 most stable expressed genes were CALM2, RPL37A, ACTB, and RPLP0. Several of the other examined genes showed considerably instability over time (GAPDH, PSMC4, OAZ1, IPO8).

In conclusion, we identified 4 genes robustly expressed over time and independent of neoplastic tissue content in the FFPE block.   PMID:23846446

 

Structures of Cas9 Endonucleases Reveal RNA-Mediated Conformational Activation

Martin Jinek1,*,Fuguo Jiang2,*David W. Taylor3,4,*Samuel H. Sternberg5,*Emine Kaya2, et al.

 

1Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland. 2Department of Molecular and Cell Biology,3Howard Hughes Medical Institute, 4California Institute for Quantitative Biosciences, 5Department of Chemistry, 6Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,. 7The Laboratory for Molecular Infection Medicine Sweden, Umeå University, Umeå S-90187, Sweden. 8Helmholtz Centre for Infection Research, Department of Regulation in Infection Biology, D-38124 Braunschweig, Germany. 9Hannover Medical School, D-30625 Hannover, Germany. 10Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720.

‡ Present address: Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66 CH-4058 Basel, Switzerland.

§ Present address: Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA.

 

Science  http://dx.doi.org:/10.1126/science.1247997

 

Type II CRISPR-Cas systems use an RNA-guided DNA endonuclease, Cas9,

  • to generate double-strand breaks in invasive DNA during an adaptive bacterial immune response.

Cas9 has been harnessed as a powerful tool for genome editing and gene regulation in many eukaryotic organisms.

Here, we report 2.6 and 2.2 Å resolution crystal structures of two major Cas9 enzymes subtypes,

  • revealing the structural core shared by all Cas9 family members.

The architectures of Cas9 enzymes define nucleic acid binding clefts, and

single-particle electron microscopy reconstructions show that the two structural lobes harboring these clefts undergo guide

  • RNA-induced reorientation to form a central channel where DNA substrates are bound.

The observation that extensive structural rearrangements occur before target DNA duplex binding

  • implicates guide RNA loading as a key step in Cas9 activation.

MicroRNA function in endothelial cells
Dr. Virginie Mattot
Angiogenesis, endothelium activation
Solving the mystery of an unknown target gene using microRNA Target Site Blockers

Dr. Virgine Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets which functions are yet unknown.

What is the main focus of the research conducted in your lab?

We are studying endothelial cell functions with a particular interest in angiogenesis and endothelium activation during physiological and tumoral vascular development.

How did your research lead to the study of microRNAs?

A few years ago, we identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

We have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

What is the aim of your current project?

While we were searching for the functions of the intronic microRNA,

  • we identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and if regulation of this gene by the microRNA was involved in endothelial cell function. We had already characterized the endothelial cell phenotype associated with the inhibition of our intronic microRNA. We then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

How did you perform the experiments and analyze the results?

LNA™ enhanced target site blockers (TSB) for our microRNA were designed by Exiqon. We

  • transfected the TSBs into endothelial cells using our standard procedure and
  • analysed the induced phenotype.

As a control for these experiments, a mutated version of the TSB was designed by Exiqon and transfected into endothelial cells. We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

What do you find to be the main benefits/advantage of the LNA™ microRNA target site blockers from Exiqon?

Target Site Blockers are efficient tools to demonstrate the specific involvement of

  • putative microRNA targets in the function played by this microRNA.

What would be your advice to colleagues about getting started with microRNA functional analysis?

  • it is essential to perform both gain and loss of functions experiments.

 Changing the core of transcription

Different members of the TAF family of proteins work in differentiated cells, such as motor neurons or brown fat cells, to control the expression of genes that are specific to each cell type.

Katherine A Jones
Jones. eLife 2014;3:e03575. http://dx.doi.org:/10.7554/eLife.03575

 

Related research articles: Herrera FJ, Yamaguchi T, Roelink H, Tjian R. 2014. Core promoter factor TAF9B regulates neuronal gene expression. eLife 3:e02559. http://dx.doi.org:/10.7554eLife.02559

Zhou H, Wan B, Grubisic I, Kaplan T, Tjian R. 2014. TAF7L modulates brown adipose tissue formation. eLife 3:e02811. Http://dx.doi.org:/10.7554/eLife.02811

 

Motor neurons (green) being grown in vitro

Motor neurons (green) being grown in vitro

Image Motor neurons (green) being grown in vitro

 

In a developing organism, different genes are expressed at different times

 

  • the pattern of gene expression can often change abruptly.

 

Expressing a gene involves multiple steps:

 

  • the DNA must be transcribed into a molecule of messenger RNA,
  • which is then trans­lated into a protein.

 

The mechanisms that start the transcription of protein-coding genes in rap­idly growing cells are reasonably well understood: two types of proteins—

 

  • DNA-binding activators and general transcription factors—

 

cooperate to recruit an enzyme called RNA polymerase, which then transcribes the gene (Kadonaga, 2012).

 

These proteins bind to a region of the gene called the promoter, which is

 

  • upstream from the protein-coding region of the gene.

 

TATA-binding protein is a general transcrip­tion factor that

  • binds to certain sequences of DNA bases found within promoters

14 TATA-binding protein associated factors (TAFs) are included into two different protein complexes called TFIID and SAGA (Müller et al., 2010). which, in budding yeast, can recruit TATA-binding protein to gene promoters (Basehoar et al., 2004), but not all genes require all of the general transcription factors, and some genes require both TFIID and SAGA complexes.

Although the steps that are required to switch on genes when cells are rapidly dividing are fairly well known,

  • the same is not true for cells that are differentiating into specialised cell types.

In these cells, many transcription factors are downregulated and

  • the entire pattern of gene expression changes dramatically.

Moreover, certain TAFs are strongly up-regulated during differentiation. The core transcriptional machinery is essentially rebuilt at the genes that are expressed in differentiated cells.

Over the years Robert Tjian of the University of California Berkeley and co-workers have illu­minated how individual TAFs can affect how a cell differentiates in different contexts (Figure 1). Now, in eLife, Francisco Herrera of UC Berkeley and co-workers—including Teppei Yamaguchi, Henk Roelink and Tjian—have identified a critical role for a TAF called TAF9B in the expression of genes in motor neurons (Herrera et al., 2014).

Herrera et al. found that TAF9B predominantly associates with the SAGA complex, rather than the TFIID complex, in the motor neuron cells. Mice in which the gene for TAF9B had been deleted had less neuronal tissue in the developing spinal cord. Moreover, the genes that are involved in forming the branches of neurons were not properly regu¬lated in these mice.

Recently, in another eLife paper, Tjian and co-workers at Berkeley, Fudan University and the Hebrew University of Jerusalem—including Haiying Zhou as first author, Bo Wan, Ivan Grubisic and Tommy Kaplan—reported that another TAF protein, called TAF7L, works as part of the TFIID complex to up-regulate genes that direct cells to become brown adipose tissue (Zhou et al., 2014).

 

TATA-binding protein associated factors

TATA-binding protein associated factors

Figure 1. TATA-binding protein associated factors (TAFs) regulate transcription in specific cell types. TAF3, for example, works with another transcription factor to regulate the expression of genes that are critical for the differentiation of the endoderm in the early embryo (Liu et al., 2011). TAF3 also forms a complex with the TATA-related factor, TRF3, to regulate Myogenin and other muscle-specific genes to form myotubes (Deato et al., 2008). TAF7L interacts with another transcription factor to activate genes involved in the formation of adipocytes (‘fat cells’) and adipose tissue (Zhou et al., 2013; Zhou et al., 2014). Finally, TAF9B is a key regulator of transcription in motor neurons (Herrera et al., 2014). The names of some of the genes regulated by the TAFs are shown in brackets.

TAF9B

Deleting the gene for TAF9B in mouse embryonic stem cells revealed that this TAF

  • is not needed for the growth of stem cells, or
  • required for the expression of genes that prevent differentiation:

both of these processes are known to be highly-dependent upon the TFIID complex
(Pijnappel et al., 2013). However,

  • genes that would normally be expressed specifically in neurons were not
  • up-regulated when cells without the TAF9B gene started to specialise.

Herrera et al. identified numerous genes that can only be switched on when the TAF9B protein is present, which means that it joins a growing list of TAF proteins that are dedicated to controllingthe expression of genes in specialised cell types.

TAF9B activates neuron-specific genes by binding to sites that

  • reside outside of these genes’ core promoters.

Further, many of these sites were also bound by a master regulator of motor neuron-specific genes.

TAF7L

 

Whilst most of the fat tissue in humans is white adipose tissue, which contains cells that store fatty molecules, some is brown adipose tissue, or ‘brown fat’, that instead generates heat. When TAF7L promotes the differentiation of brown fat, it up-regulates genes that are targeted by a tran­scription

factor called PPAR-γ; last year it was shown that this transcription factor also promotes the differentiation of white adipose tissue (Zhou et al., 2013).
Mice without the TAF7L gene had 40% less brown fat than wild-type mice, and also grew too much skeletal muscle tissue. TAF7L was specifi­cally required to activate genes that control how brown fat develops and functions. Thus TAF7L expression appears to shift the fate of a stem cell towards brown adipose tissue, potentially at the expense of skeletal muscle, as both cell types develop from the same group of stem cells.

When stem cells with less TAF7L than normal are differentiated in vitro, they yield more muscle than fat cells. Conversely, cells with an excess of TAF7L express brown fat-specific genes and switch off muscle-specific genes.

The work of Herrera et al. and Zhou et al. reinforces the idea that different TAFs

  • provide the flexibility needed to control gene expression in a tissue-specific manner, and
  • enable differenti­ating cells to change which genes they express rapidly.

However many interesting questions remain:

Which signals lead to the destruction of core transcription factors?
Are core promoter ele­ments at tissue-specific genes designed to rec­ognise variant TAFs?
What determines whether variant TAFs are incorporated within TFIID, SAGA, or other complexes?

Shortly after RNA polymerase II starts to tran­scribe a gene, it briefly pauses. Interestingly, a DNA sequence associated with this pausing, called the pause button, closely matches the sequences that bind to two subunits of TFIID (TAF6 and TAF9; Kadonaga, 2012). Consequently, TAF6 and TAF9 might be involved in pausing transcription, and if so, the variant TAF9B could play a similar role at motor neuron genes.

Molecular basis of transcription pausing

Jeffrey W. Roberts
Science 344, 1226 (2014);  http://dx.doi.org:/10.1126/science.1255712
http://www.sciencemag.org/content/344/6189/1226.full.html

During RNA synthesis, RNA polymerase moves erratically along DNA, 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,
  • pausing is critical to regulatory activities of the enzyme such as the termination of transcription. It is also
  • essential 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 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 engaging a transcription antiterminator (the bacteriophage λ Q protein) ( 4) that, in turn, inhibits transcription

pauses, including those essential for transcription termination.

 

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

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)

 

 

Polymerase, paused

Polymerase, paused

Polymerase, paused. During transcription, RNA exists in two states as RNA polymerase progresses: pretranslocated, just after the addition of the last nucleotide [here, cytosine (C)];

and 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 the pretranslocated state and the nontemplate DNA strand G bound in the

polymerase in the posttranslocated state are marked with an asterisk.
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.
Processive Antitermination

Robert A. Weisberg1* and Max E. Gottesman2

Section on Microbial Genetics, Laboratory of Molecular Genetics, National Institute of Child Health and

Human Development, National Institutes of Health, Bethesda, Maryland 20892-2785,1 and

Institute of Cancer Research, Columbia University, New York, New York 100322

Journal Of Bacteriology, Jan. 1999; 181(2): 359–367.
After initiating synthesis of RNA at a promoter, RNA polymerase (RNAP) normally continues to elongate the transcript until it reaches a termination site. Important elements of termination sites are transcribed before polymerase translocation stops, and the resulting RNA is an active element of the termination pathway. Nascent transcripts of intrinsic sites can halt transcription without the assistance of additional factors, and

those of Rho-dependent sites recruit the Rho termination protein to the elongation complex. In both cases, RNAP, the transcript, and the template dissociate (reviewed in references 76 and 80).

 

Termination is rarely, if ever, completely efficient, and the expression of downstream genes can be controlled by altering the efficiency of terminator readthrough. Two distinct mechanisms of elongation control have been reported for bacterial RNA polymerases. In one, exemplified by attenuation of the his and trp operons of Salmonella typhimurium and Escherichia coli, respectively,

  • a single terminator is inactivated by interaction with an upstream sequence in the transcript, with a terminator-specific protein, or with a translating ribosome that follows closely behind RNAP (reviewed in references 35 and 104).

In a second, whose prototype is antitermination of phage l early transcription,

  • polymerase is stably modified to a terminator-resistant form after it leaves the promoter.

In this case, the modified enzyme not only transcribes through sequential downstream terminators,

  • but also it is less sensitive to the pause sites that normally delay transcript elongation.

Both pathways are widespread in nature, but in this minireview we consider only the second,

  • known as processive antitermination
    (for previous reviews, see references 22, 23, 27, and 32).

The recent explosive growth in our understanding of transcription elongation (reviewed in references 57, 96, and 99) make this an especially appropriate time to survey regulatory elements that target the transcription elongation complex.

Antitermination in l is induced by two quite distinct mechanisms.

  • the result of interaction between l N protein and its targets in the early phage transcripts,
  • an interaction between the l Q protein and its target in the late phage promoter.

We describe the N mechanism first. Lambda N, a small basic protein of the arginine- rich motif (ARM) (Fig. 1) family of RNA binding proteins, binds to a 15-nucleotide (nt) stem-loop called BOXB (17) (Fig. 2).

 

FIG. 1. [not shown] (A) Alignment of phage N proteins and the HK022 Nun protein. The color groupings reflect the frequency of amino acid substitutions in evolutionarily related protein domains: an amino acid is more likely to be replaced by one in the same color group than by one in a different color group in related proteins (34).

The amino-proximal ARM regions were aligned by eye and according to the structures of the P22 and l ARMs complexed to their cognate nut sites (see text and Fig. 2), and the remainder of the proteins was aligned by ClustalW (38). The dots indicate gaps introduced to improve the alignment. Aside from the ARM regions, the

proteins fall into three very distantly related (or unrelated) families: (i) l and phage 21; (ii) P22, phage L, and HK97; and (iii) HK022 Nun.

 

FIG. 2. [not shown] BOXA and BOXB RNAs and their interaction with the ARM of their cognate N proteins. The amino acid-nucleotide interactions are shown to the left except for BOXB of phage 21, for which the structure of the complex is unknown. The sequences of BOXA and BOXA-BOXB spacer are shown to the right. The dots

to the left and right of the spacer sequences are for alignment. (A) l N-ARM-BOXB complex (adapted from reference 48 with permission of the publisher). Open circles, pentagons, and rectangles represent phosphates, riboses, and bases, respectively. Watson-Crick base pairs (????) are indicated. The zigzag line denotes a sheared

G z A base pair. Open circles, open rectangles, and arrowheads depict ionic, hydrophobic, and hydrogen-bonding interactions, respectively. Guanine-11, indicated by a bold rectangle, is extruded from the BOXB loop (see text). (B) P22 N-ARM-BOXB complex (adapted from reference 15 with permission of the publisher). Open

circles, pentagons, rectangles, and ovals represent phosphates, riboses, bases, and amino acids, respectively. The solid pentagons indicate riboses with a C29-endo pucker.

Base stacking ( ), intermolecular hydrogen bonding or electrostatic interactions (,—–), intermolecular hydrophobic or van der Waals interactions (4), intramolecular hydrogen bonds (– – – –) and Watson-Crick base pairs (?????) are indicated. Cytosine-11 is extruded from the loop (see text). Note that the amino-terminal amino acid

residue in the complex corresponds to Asn-14 in the complete protein (Fig. 1), and the displayed amino acids are numbered accordingly. (C) NUTL site of phage 21. The arrows indicate the inverted sequence repeats of BOXB.

 

FIG. 3. [not skown] HK022 put sites and folded PUT RNAs. (A) Alignment of putL and putR (43). The numbers give distances from the start sites of the PL and PR promoters, respectively, and the pairs of arrows indicate inverted sequence repeats. (B) Folded PUTL and PUTR RNAs. The structures, which were generated by energy

minimization as described (43), have been partially confirmed by genetic and biochemical studies (7, 43).
The active bacterial elongation complex consists of

  • core RNAP,
  • template, and
  • RNA product.

The 39 end of the RNA

  • is engaged in the active site of the enzyme,
  • The following ;8 nt are hybridized to the template strand of the DNA, and
  • the next ;9 nt remain closely associated with RNAP (64).
  • About 17 nt of the nontemplate DNA strand are separated from the template strand in the transcription bubble.

Elongation complexes can also contain NusA and/or NusG. These proteins, which

  • increase the stability of the N-mediated antitermination complex (see above),
  • have different effects on elongation.
  • NusA decreases and NusG increases the elongation rate, and
  • both proteins alter termination efficiency in a terminator-specific manner (13, 14, 86; see reference 76).

An elongation complex, unless located at a terminator, is extraordinarily stable,

  • even when translocation is prevented by removal of substrates.

Recent observations suggest that this stability depends mainly on

  • interactions between RNAP and the RNA-DNA hybrid as well as
  • between polymerase and the downstream duplex DNA template (63, 87).

Nascent RNA emerging from the hybrid region and upstream duplex DNA

  • do not appear to be required.

The strength of the RNA-DNA hybrid is believed to

  • assure the lateral stability of the complex.

 

Reducing the strength of the RNA-DNA bonds, for example

  • by incorporation of nucleotide analogs,
  • favors backsliding of RNAP on the template, with consequent
  • disengagement of the 39 RNA end from the active site, and
  • concerted retreat of the RNA-DNA hybrid region from the 39 end (65).

Such a disengaged complex retains its resistance to dissociation and

  • is capable of resuming elongation if the original or a newly created 39 end reengages with the active site (10, 44, 45, 65, 71, 95).

Intrinsic terminators consist of a guanine- and cytosine-rich RNA hairpin stem

  • immediately followed by a short uracil-rich segment
  • within which termination can occur.

 

If termination does not occur at this point,

  • polymerase continues to elongate the transcript with normal processivity
  • until it reaches the next terminator.

Neither the stem nor the uracil-rich segment

  • is sufficient for termination, although
  • either can transiently slow elongation.

The weakness of base pairing between rU and dA

  • destabilizes the RNA-DNA hybrid in the uracil-rich segment, and
  • this probably contributes to termination.

Formation of the hairpin stem as nascent terminator RNA emerges from polymerase

  • destabilizes the RNA-DNA hybrid and
  • interrupts contacts between the emerging nascent RNA and RNAP (62a).

It might also interfere with the stabilizing interactions between

  • RNAP and the hybrid or those between RNAP and
  • the downstream region of the template.

Cross-linking of nucleic acid to RNAP suggests that

  • both the downstream DNA and the nascent RNA
  • that emerges from the hybrid region, and
  • within which the terminator hairpin might form,
  • are located close to the same regions of the enzyme (64).

Conversely, modifications that render RNAP termination resistant

  • could prevent the terminator stem from destabilizing one or more of these targets,
  • at least while the 39 end of the RNA is within the uracil rich segment of the terminator.

The l N and Q proteins and HK022 PUT RNA

  • also suppress Rho-dependent terminators (43a, 79, 103) which,
  • in contrast to intrinsic terminators, lack a precisely determined termination point.

Rho is an RNA-dependent ATPase that binds to cytosine-rich, unstructured regions in nascent RNA and acts preferentially

  • to terminate elongation complexes that are paused at nearby downstream sites
    (19, 29, 46, 47, 59, 60).

Rho possesses RNA-DNA helicase activity, and this activity is directional,

  • unwinding DNA paired to the 39 end of the RNA molecule (11, 90).
  • This corresponds to the location of the hybrid and of RNAP
    in an active ternary elongation complex.

The ability of antiterminators to suppress Rho-dependent and -independent terminators

  • suggests that they prevent a step that is common to both classes.

Given the helicase activity of Rho, a likely candidate for this step is disruption of the RNA-DNA

hybrid. However, other candidates, such as destabilization of RNAP-template or RNAP-hybrid interactions, are also plausible.

Alternatively, the ability of N, Q, and PUT to suppress RNAP pausing (31, 43, 54, 74)

  • suggests that they prevent Rho-dependent termination
  • by accelerating polymerase away from Rho bound at upstream RNA sites.

This explanation raises the problem of why NusG,

  • which also accelerates polymerase,
  • enhances rather than suppresses Rho-dependent termination (see above).

Clearly, the molecular details of processive antitermination remain poorly understood despite the 30 years that have elapsed since its discovery.

 

 

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

OPEN ACCESS

Jingyi Jessica Li1, 2, Peter J Bickel1 and Mark D Biggin3

1Department of Statistics, University of California, Berkeley, CA, USA

2Departments of Statistics and Human Genetics, University of California, Los Angeles, CA, USA

3Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Academic editor – Barbara Engelhardt   http://dx.doi.org:/10.7717/peerj.270

Distributed under Creative-Commons CC-0

ABSTRACT

Large scale surveys in mammalian tissue culture cells suggest that the protein ex-

pressed at the median abundance is present at 8,000_16,000 molecules per cell and

that differences in mRNA expression between genes explain only 10_40% of the dif-

ferences in protein levels. We find, however, that these surveys have significantly un-

derestimated protein abundances and the relative importance of transcription.

Using individual measurements for 61 housekeeping proteins to rescale whole proteome

data from Schwanhausser et al. (2011), we find that the median protein detected is

expressed at 170,000 molecules per cell and that our corrected protein abundance

estimates show a higher correlation with mRNA abundances than do the uncorrected

protein data. In addition, we estimated the impact of further errors in mRNA and

protein abundances using direct experimental measurements of these errors.

The resulting analysis suggests that mRNA levels explain at least

  • 56% of the differences in protein abundance for the 4,212 genes

detected by Schwanhausser et al. (2011), though because one major source of error

could not be estimated the true percent contribution should be higher.
We also employed a second, independent strategy to

  • determine the contribution of mRNA levels to protein expression.

The variance in translation rates directly measured by ribosome profiling is only 12%

of that inferred by Schwanhausser et al. (2011), and

  • the measured and inferred translation rates correlate poorly (R2 D 13).

Based on this, our second strategy suggests that

  • mRNA levels explain _81% of the variance in protein levels.

We also determined the percent contributions of

  • transcription,
  • RNA degradation,
  • translation
  • and protein degradation

to the variance in protein abundances using both of our strategies.

While the magnitudes of the two estimates vary, they both suggest that

  • transcription plays a more important role than the earlier studies implied and
  • translation a much smaller role.

Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately

  • 40% of genes in a given cell within a population express no mRNA.

Since there can be no translation in the absence of mRNA, we argue that

  • differences in translation rates can play no role in determining the expression levels for the _40% of genes that are non-expressed.

Subjects Bioinformatics, Computational Biology

Keywords Transcription, Translation, Mass spectrometry, Gene expression, Protein abundance

How to cite this article Li et al. (2014), System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2:e270; 

http://dx.doi.org:/10.7717/peerj.270

 

 

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

Evgeny Shmelkov1,2, Zuojian Tang2, Iannis Aifantis3, Alexander Statnikov2,4

Shmelkov et al. Biology Direct 2011, 6:15  http://www.biology-direct.com/content/6/1/15

 

Background: Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The employed benchmarking methodology first

  • involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors.
  • Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

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

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

The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that

  • the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and
  • suggest novel putative therapeutic targets in cancer.

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

 

Illustration of statistical methodology

Illustration of statistical methodology

 

Figure 2 Illustration of statistical methodology for comparison

between a gold-standard and a pathway database

 

Additional material

Additional file 1: Supplementary Information. Table S1: Functional gene expression data. Table 2: Transcription factor-DNA binding data. Table S3: Most confident direct transcriptional targets of each of the four transcription factors. These targets were obtained by overlapping several gold-standards obtained with different datasets for the same transcription factor. Table S4: Genes directly regulated by two or more of the three transcription factors: MYC, NOTCH1, and RELA. Figure S1: Comparison of gene sets of transcriptional targets derived from ten different pathway databases by Jaccard index. In case, where Jaccard index of an overlap could not be determined due to comparison of two empty gene lists, we assigned value 0. Cells are colored according to the Jaccard index, from white (Jaccard index equal to 0) to dark-orange (Jaccard index equal to 1). Each sub-figure gives results for a different transcription factor: (a) AR, (b) BCL6, (c) MYC, (d) NOTCH1, (e) RELA, (f) STAT1, (g) TP53

 

http://dx.doi.org:/10.1186/1745-6150-6-15

 

Cite this article as: Shmelkov et al.: Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale. Biology Direct 2011 6:15

 

 

The Functional Consequences of Variation in Transcription Factor Binding
Darren A. Cusanovich1, Bryan Pavlovic1,2, Jonathan K. Pritchard1,2,3*, Yoav Gilad1*

1 Department of Human Genetics, University of Chicago, 2 Howard Hughes Medical Institute, University of Chicago, Chicago,

Illinois, 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, California,

 

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output.

To evaluate the context of functional TF binding we knocked down

  • 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line.
  • We identified genes whose expression was affected by the knockdowns.
  • We intersected the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites

This combination of data allowed us to infer functional TF binding.

  • we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that
  • most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes.
  • functional TF binding is enriched in regulatory elements that harbor
    • a large number of TF binding sites,
    • at sites with predicted higher binding affinity, and
    • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

Author Summary

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to

study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might

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

Citation: Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y (2014) The Functional Consequences of Variation in Transcription Factor Binding. PLoS Genet 10(3):e1004226. http://dx.doi.org:/10.1371/journal.pgen.1004226

Editor: Yitzhak Pilpel, Weizmann Institute of Science, Israel

 

 

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

Figure 2. Effect sizes for differentially expressed genes.

Boxplots of absolute Log2(fold-change) between knockdown arrays

and control arrays for all genes identified as differentially expressed in

each experiment. Outliers are not plotted. The gray bar indicates the

interquartile range across all genes differentially expressed in all

knockdowns. Boxplots are ordered by the number of genes differentially

expressed in each experiment. Outliers were not plotted.

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

 

 

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

 

 

 

 

 

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

bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

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

 

Degree of binding correlated with function

Degree of binding correlated with function

 

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

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

 

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

 

Figure 5. Distribution of functional binding about the TSS. (a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming

the distances and outliers were not plotted.

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

 

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

 

 

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

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

 

To test whether the number of paralogs or the degree of similarity with the closest paralog for each transcription factor knocked down might influence the number of genes differentially expressed in our experiments, we obtained definitions of paralogy and the calculations of percent identity for 29 different factors from Ensembl’s BioMart (http://useast.ensembl.org/biomart/martview/) [31]. We used genome build GRCh37.p13.

For each gene, we counted the number of paralogs classified as a ‘‘within_species_paralog’’. After selecting only genes considered a ‘‘within_species_paralog’’, we also assigned the maximum percent identity as the closest paralog.

To evaluate the effect that an independent assignment of target genes to regulatory regions might have on our analyses, we used the definition of target genes defined by Thurman et al. (ftp://ftp.ebi.ac.uk/pub/databases/…)

which use correlations in DNase hypersensitivity between distal and proximal regulatory regions across different cell types to link distal elements to putative target genes [38].

We intersected the midpoints of our called binding events (defined above) with these regulatory elements in order to assign our binding events to specific target genes and then re-analyzed the overlap between

binding and differential expression in our experiments.

PLOS Genetics 6 Mar 2014; 10 (3), e1004226

 

 

 

The essential biology of the endoplasmic reticulum stress response

for structural and computational biologists

Sadao Wakabayashia, Hiderou Yoshidaa,*

aDepartment of Molecular Biochemistry, Graduate School of Life Science,

University of Hyogo, Hyogo 678-1297, Japan

CSBJ Mar 2013; 6(7), e201303010, http://dx.doi.org/10.5936/csbj.201303010

 

Abstract: The endoplasmic reticulum (ER) stress response is a cytoprotective mechanism that maintains homeostasis of the ER by

  • upregulating the capacity of the ER in accordance with cellular demands.

If the ER stress response cannot function correctly, because of reasons such as aging, genetic mutation or environmental stress,

  • unfolded proteins accumulate in the ER and cause ER stress-induced apoptosis,
  • resulting in the onset of folding diseases,
    • including Alzheimer’s disease and diabetes mellitus.

Although the mechanism of the ER stress response has been analyzed extensively by biochemists, cell biologists and molecular biologists, many aspects remain to be elucidated. For example,

  • it is unclear how sensor molecules detect ER stress, or
  • how cells choose the two opposite cell fates
    (survival or apoptosis) during the ER stress response.

To resolve these critical issues, structural and computational approaches will be indispensable, although the mechanism of the ER stress response is complicated and difficult to understand holistically at a glance. Here, we provide a concise introduction to the mammalian ER stress response for structural and computational biologists.

The basic mechanism of the mammalian ER stress response

The mammalian ER stress response consists of three pathways: the ATF6, IRE1 and PERK pathways, of which the main functions are

  • augmentation of folding and ERAD capacity, and
  • translational attenuation, respectively.

Although these response pathways cross-talk with each other and have several branched subpathways, we focus on the main pathways in this section.

  • The ATF6 pathway regulates the transcriptional induction of ER chaperone genes
  • pATF6(P) is a sensor molecule comprising a type II transmembrane protein residing on the ER membrane (Figure 2).

When pATF6(P) detects ER stress,

  • the protein is transported to the Golgi apparatus through vesicular transport in a COP-II vesicle
  • and is sequentially cleaved by two proteases residing in the Golgi,
    • namely site 1 protease (S1P) and site 2 protease (S2P)

The cytoplasmic portion of pATF6(P) (pATF6(N)) is

  1. released from the Golgi membrane,
  2. translocates into the nucleus,
  3. binds to an enhancer element called the ER stress response element (ERSE),
  4. and activates the transcription of ER chaperone genes,
  • including BiP, GRP94, calreticulin and protein disulfide isomerase (PDI)

The consensus nucleotide sequence of ERSE is CCAAT(N9)CCACG, and pATF6(N) recognizes both the CCACG portion and another transcription factor NF-Y,

  • which binds to the CCAAT portion

NF-Y is a general transcription factor required for

  • the transcription of various human genes

 

Figure 2. The ATF6 pathway. The sensor molecule pATF6(P) located on the ER membrane is transported to the Golgi apparatus by transport vesicles in response to ER stress. In the Golgi apparatus, pATF6(P) is sequentially cleaved by two proteases, S1P and S2P, resulting in release of the cytoplasmic portion pATF6(N) from the ER membrane. pATF6(N) translocates into the nucleus and activates transcription of ER chaperone genes through binding to the cis-acting enhancer ERSE.

 

Figure 3. The IRE1 pathway. In normal growth conditions, the sensor molecule IRE1 is an inactive monomer, whereas IRE1 forms an active oligomer in response to ER stress. Activated IRE1 converts unspliced XBP1 mRNA to mature mRNA by the cytoplasmic mRNA splicing. From mature XBP1 mRNA, an active transcription factor pXBP1(S) is translated and activates the transcription of ERAD genes through binding to the enhancer UPRE.

 

Figure 4. The PERK pathway. When PERK detects unfolded proteins in the ER, PERK phosphorylates eIF2α, resulting in translational attenuation and translational induction of ATF4. ATF4 activates the transcription of target genes encoding translation factors, anti-oxidation factors and a transcription factor CHOP. Other kinases such as PKR, GCN2 and HRI also phosphorylate eIF2α, and phosphorylated eIF2α is dephosphorylated by CReP, PP1C-GADD34 and p58IPK

 

Figure 7. Three functions of pXBP1(U). pXBP1(U) translated from XBP1(U) mRNA binds to pXBP1(S) and enhances its degradation. The CTR region of pXBP1(U) interacts with the ribosome tunnel and slows translation, while the HR2 region anchors XBP1(U) mRNA to the ER membrane, in order to enhance splicing of XBP1(U) mRNA by IRE1.

 

Figure 8. Major pathways of ER stress-induced apoptosis. ER stress induces apoptosis through various pathways, including transcriptional induction of CHOP by the PERK and ATF6 pathways, the IRE1-TRAF2 pathway and the caspase-12 pathway.

If cells are damaged by strong and sustained ER stress that they cannot deal with and ER stress still persists and hampers the survival of the organism, the ER stress response activates the apoptotic pathways and disposes of damaged cells from the body.

Computational simulation of response pathways to analyze the decision mechanism that determines cell fate (survival or apoptosis) provides a valuable analysis tool, although there have been few such studies to date.

Read Full Post »


Liver Endoplasmic Reticulum Stress and Hepatosteatosis

Larry H Bernstein, MD, FCAP

 

1. Absence of adipose triglyceride lipase protects from hepatic endoplasmic reticulum stress in mice.

Fuchs CD, Claudel T, Kumari P, Haemmerle G, et al.
LabExpMol Hepatology, Medical Univ of Graz, Austria.
Hepatology. 2012 Jul;56(1):270-80.   http://dx.doi.org/10.1002/hep.25601. Epub 2012 May 29.

Nonalcoholic fatty liver disease (NAFLD) is characterized by

  • triglyceride (TG) accumulation and
  • endoplasmic reticulum (ER) stress.

Fatty acids (FAs) may trigger ER stress, therefore,

  •  the absence of adipose triglyceride lipase (ATGL/PNPLA2)-
    • the main enzyme for intracellular lipolysis,
  • releasing FAs, and
  • closest homolog to adiponutrin (PNPLA3)

recently implicated in the pathogenesis of NAFLD-

  • could protect against hepatic ER stress.

Wild-type (WT) and ATGL knockout (KO) mice

  •  were challenged with tunicamycin (TM) to induce ER stress.

Markers of hepatic

  •  lipid metabolism,
  • ER stress, and
  • inflammation were explored
    • for gene expression by
    •  serum biochemistry,
    • hepatic TG and FA profiles,
    • liver histology,
    • cell-culture experiments were performed in Hepa1.6 cells
  • after the knockdown of ATGL before FA and TM treatment.

TM increased hepatic TG accumulation in ATGL KO, but not in WT mice. Lipogenesis and β-oxidation
were repressed at the gene-expression level
(sterol regulatory element-binding transcription factor 1c,
fatty acid synthase, acetyl coenzyme A carboxylase 2, and carnitine palmitoyltransferase 1 alpha) in
both WT and ATGL KO mice. Genes for very-low-density lipoprotein (VLDL) synthesis (microsomal
triglyceride transfer protein and apolipoprotein B)

  •  were down-regulated by TM in WT
  • and even more in ATGL KO mice,
  • which displayed strongly reduced serum VLDL cholesterol levels.

ER stress markers were induced exclusively in TM-treated WT, but not ATGL KO, mice:

  •  glucose-regulated protein,
  • C/EBP homolog protein,
  • spliced X-box-binding protein,
  • endoplasmic-reticulum-localized DnaJ homolog 4, and
  • inflammatory markers Tnfα and iNos.

Total hepatic FA profiling revealed a higher palmitic acid/oleic acid (PA/OA) ratio in WT mice.
Phosphoinositide-3-kinase inhibitor-

  • known to be involved in FA-derived ER stress and
  • blocked by OA-
  • was increased in TM-treated WT mice only.

In line with this, in vitro OA protected hepatocytes from TM-induced ER stress. Lack of ATGL may protect from
hepatic ER stress through alterations in FA composition. ATGL could constitute a new therapeutic strategy
to target ER stress in NAFLD.
PMID: 22271167 Diabetes Obes Metab. 2010 Oct;12 Suppl 2:83-92.
http://dx.doi.org/10.1111/j.1463-1326.2010.01275.x.

2. Hepatic steatosis: a role for de novo lipogenesis and the transcription factor SREBP-1c.
Ferré P, Foufelle F. INSERM, and Université Pierre et Marie Curie-Paris, Paris, France.    PMID: 21029304

Excessive availability of plasma fatty acids and lipid synthesis from glucose (lipogenesis) are important determinants of steatosis.
Lipogenesis is an insulin- and glucose-dependent process that is under the control of specific transcription factors,

Insulin induces the maturation of SREBP-1c in the endoplasmic reticulum (ER).

  • SREBP-1c in turn activates glycolytic gene expression,
    • allowing glucose metabolism, and
    • lipogenic genes in conjunction with ChREBP.

Lipogenesis activation in the liver of obese markedly insulin-resistant steatotic rodents is then paradoxical.
It appears the activation of SREBP-1c and thus of lipogenesis is

  •  secondary in the steatotic liver to an ER stress.

The ER stress activates the

  •  cleavage of SREBP-1c independent of insulin,
  • explaining the paradoxical stimulation of lipogenesis
  • in an insulin-resistant liver.

Inhibition of the ER stress in obese rodents

  •  decreases SREBP-1c activation and lipogenesis and
  • improves markedly hepatic steatosis and insulin sensitivity.
  • ER is thus worth considering as a potential therapeutic target for steatosis and metabolic syndrome.

3. SREBP-1c transcription factor and lipid homeostasis: clinical perspective
Ferré P, Foufelle F
Inserm, Centre de Recherches Biomédicales des Cordeliers, Paris, France.
Horm Res. 2007;68(2):72-82. Epub 2007 Mar 5. PMID:17344645

Insulin has long-term effects on glucose and lipid metabolism through its control on the expression of specific genes.
In insulin sensitive tissues and particularly in the liver,

  •  the transcription factor sterol regulatory element binding protein-1c (SREBP-1c) transduces the insulin signal, which is
  • synthetized as a precursor in the membranes of the endoplasmic reticulum
  • which requires post-translational modification to yield its transcriptionally active nuclear form.

Insulin activates the transcription and the proteolytic maturation of SREBP-1c, which induces the

  •  expression of a family of genes
  • involved in glucose utilization and fatty acid synthesis and
  • can be considered as a thrifty gene.

Since a high lipid availability is

  •  deleterious for insulin sensitivity and secretion,
  • a role for SREBP-1c in dyslipidaemia and type 2 diabetes
  • has been considered in genetic studies.

SREBP-1c could also participate in

  •  hepatic steatosis observed in humans
  • related to alcohol consumption and
  • hyperhomocysteinemia
  • concomitant with a ER-stress and
  • insulin-independent SREBP-1c activation.

4. Hepatic steatosis: a role for de novo lipogenesis and the transcription factor SREBP-1c
Ferré P, Foufelle F
INSERM, Centre de Recherches des Cordeliers and Université Pierre et Marie Curie-Paris, Paris, France.
Diabetes Obes Metab. 2010 Oct;12 Suppl 2:83-92. PMID: 21029304
http://dx.doiorg/10.1111/j.1463-1326.2010.01275.x.

Lipogenesis in liver steatosis is

  •  an insulin- and glucose-dependent process
  • under the control of specific transcription factors,
  • sterol regulatory element binding protein 1c (SREBP-1c),
  • activated by insulin and carbohydrate response element binding protein (ChREBP)

Insulin induces the maturation of SREBP-1c in the endoplasmic reticulum (ER).
SREBP-1c in turn activates glycolytic gene expression, allowing –

  •  glucose metabolism in conjunction with ChREBP.

activation of SREBP-1c and lipogenesis is secondary in the steatotic liver to ER stress, which

  •  activates the cleavage of SREBP-1c independent of insulin,
  • explaining the stimulation of lipogenesis in an insulin-resistant liver.
  • Inhibition of the ER stress in obese rodents decreases SREBP-1c activation and improves
  • hepatic steatosis and insulin sensitivity.

ER is thus a new partner in steatosis and metabolic syndrome

5. Pharmacologic ER stress induces non-alcoholic steatohepatitis in an animal model
Jin-Sook Leea, Ze Zhenga, R Mendeza, Seung-Wook Hac, et al.
Wayne State University SOM, Detroit, MI
Toxicology Letters 20 May 2012; 211(1):29–38      http://dx.doi.org/10.1016/j.toxlet.2012.02.017

Endoplasmic reticulum (ER) stress refers to a condition of

  •  accumulation of unfolded or misfolded proteins in the ER lumen, which is known to
  • activate an intracellular stress signaling termed
  • Unfolded Protein Response (UPR).

A number of pharmacologic reagents or pathophysiologic stimuli

  •  can induce ER stress and activation of the UPR signaling,
  • leading to alteration of cell physiology that is
  • associated with the initiation and progression of a variety of diseases.

Non-alcoholic steatohepatitis (NASH), characterized by hepatic steatosis and inflammation, has been considered the
precursor or the hepatic manifestation of metabolic disease. In this study, we delineated the

  • toxic effect and molecular basis
  • by which pharmacologic ER stress,
  • induced by a bacterial nucleoside antibiotic tunicamycin (TM),
  • promotes NASH in an animal model.

Mice of C57BL/6J strain background were challenged with pharmacologic ER stress by intraperitoneal injection of TM. Upon TM injection,

  •  mice exhibited a quick NASH state characterized by
  • hepatic steatosis and inflammation.

TM-treated mice exhibited an increase in –

  •  hepatic triglycerides (TG) and a –
  • decrease in plasma lipids, including
  • plasma TG,
  • plasma cholesterol,
  • high-density lipoprotein (HDL), and
  • low-density lipoprotein (LDL),

In response to TM challenge,

  •  cleavage of sterol responsive binding protein (SREBP)-1a and SREBP-1c,
  •  the key trans-activators for lipid and sterol biosynthesis,
  • was dramatically increased in the liver.

Consistent with the hepatic steatosis phenotype, expression of

  •  some key regulators and enzymes in de novo lipogenesis and lipid droplet formation was up-regulated,
  • while expression of those involved in lipolysis and fatty acid oxidation was down-regulated
  • in the liver of mice challenged with TM.

TM treatment also increased phosphorylation of NF-κB inhibitors (IκB),

  •  leading to the activation of NF-κB-mediated inflammatory pathway in the liver.

Our study not only confirmed that pharmacologic ER stress is a strong “hit” that triggers NASH, but also demonstrated

  •  crucial molecular links between ER stress,
  • lipid metabolism, and
  • inflammation in the liver in vivo.

Highlights
► Pharmacologic ER stress induced by tunicamycin (TM) induces a quick NASH state in vivo.
► TM leads to dramatic increase in cleavage of sterol regulatory element-binding protein in the liver.
► TM up-regulates lipogenic genes, but down-regulates the genes in lipolysis and FA oxidation.
► TM activates NF-κB and expression of genes encoding pro-inflammatory cytokines in the liver.
Abbreviations
ER, endoplasmic reticulum; TM, tunicamycin; NASH, non-alcoholic steatohepatitis; NAFLD,
non-alcoholic fatty liver disease; TG, triglycerides; SREBP, sterol responsive binding protein;
NF-κB, activation of nuclear factor-kappa B; IκB, NF-κB inhibitor
Keywords: ER stress; Non-alcoholic steatohepatitis; Tunicamycin; Lipid metabolism; Hepatic inflammation
Figures and tables from this article:

Fig. 1. TM challenge alters lipid profiles and causes hepatic steatosis in mice. (A) Quantitative real-time RT-PCR analysis of liver mRNA isolated from mice challenged with TM or vehicle control. Total liver mRNA was isolated at 8 h or 30 h after injection with vehicle or TM (2 μg/g body weight) for real-time RT-PCR analysis. Expression values were normalized to β-actin mRNA levels. Fold changes of mRNA are shown by comparing to one of the control mice. Each bar denotes the mean ± SEM (n = 4 mice per group); **P < 0.01. Edem1, ER degradation enhancing, mannosidase alpha-like 1. (B) Oil-red O staining of lipid droplets in the livers of the mice challenged with TM or vehicle control (magnification: 200×). (C) Levels of TG in the liver tissues of the mice challenged with TM or vehicle control. (D) Levels of plasma lipids in the mice challenged with TM or vehicle control. TG, triglycerides; TC, total plasma cholesterol; HDL, high-density lipoproteins; VLDL/LDL, very low and low density lipoproteins. For C and D, each bar denotes mean ± SEM (n = 4 mice per group); *P < 0.05; **P < 0.01.

 Fhttp://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr1.jpgigure options

Fig. 2. TM challenge leads to a quick NASH state in mice. (A) Histological examination of liver tissue sections of the mice challenged with TM (2 μg/g body weight) or vehicle control. Upper panel, hematoxylin–eosin (H&E) staining of liver tissue sections; the lower panel, Sirius staining of collagen deposition of liver tissue sections (magnification: 200×). (B) Histological scoring for NASH activities in the livers of the mice treated with TM or vehicle control. The grade scores were calculated based on the scores of steatosis, hepatocyte ballooning, lobular and portal inflammation, and Mallory bodies. The stage scores were based on the liver fibrosis. Number of mice examined is given in parentheses. Mean ± SEM values are shown. P-values were calculated by Mann–Whitney U-test.

 http://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr2.jpg

Fig. 3. TM challenge significantly increases levels of cleaved/activated forms of SREBP1a and SREBP1c in the liver. Western blot analysis of protein levels of SREBP1a (A) and SREBP1c (B) in the liver tissues from the mice challenged with TM (2 μg/g body weight) or vehicle control. Levels of GAPDH were included as internal controls. For A and B, the values below the gels represent the ratios of mature/cleaved SREBP signal intensities to that of SREBP precursors. The graph beside the images showed the ratios of mature/cleaved SREBP to precursor SREBP in the liver of mice challenged with TM or vehicle. The protein signal intensities shown by Western blot analysis were quantified by NIH imageJ software. Each bar represents the mean ± SEM (n = 3 mice per group); **P < 0.01. SREBP-p, SREBP precursor; SREBP-m, mature/cleaved SREBP.

 http://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr3.jpg

Fig. 4. TM challenge up-regulates expression of genes involved in lipogenesis but down-regulates expression of genes involved in lipolysis and FA oxidation. Quantitative real-time RT-PCR analysis of liver mRNAs isolated from the mice challenged with TM (2 μg/g body weight) or vehicle control, which encode regulators or enzymes in: (A) de novo lipogenesis: PGC1α, PGC1β, DGAT1 and DGAT2; (B) lipid droplet production: ADRP, FIT2, and FSP27; (C) lipolysis: ApoC2, Acox1, and LSR; and (D) FA oxidation: PPARα. Expression values were normalized to β-actin mRNA levels. Fold changes of mRNA are shown by comparing to one of the control mice. Each bar denotes the mean ± SEM (n = 4 mice per group); **P < 0.01. (E and F) Isotope tracing analysis of hepatic de novo lipogenesis. Huh7 cells were incubated with [1-14C] acetic acid for 6 h (E) or 12 h (F) in the presence or absence of TM (20 μg/ml). The rates of de novo lipogenesis were quantified by determining the amounts of [1-14C]-labeled acetic acid incorporated into total cellular lipids after normalization to cell numbers.

 http://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr4.jpg

Fig. 5. TM activates the inflammatory pathway through NF-κB, but not JNK, in the liver. Western blot analysis of phosphorylated Iκ-B, total Iκ-B, phosphorylated JNK, and total JNK in the liver tissues from the mice challenged with TM (2 μg/g body weight) or vehicle control. Levels of GAPDH were included as internal controls. The values below the gels represent the ratios of phosphorylated protein signal intensities to that of total proteins.

 http://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr5.jpg

Fig. 6. TM induces expression of pro-inflammatory cytokines and acute-phase responsive proteins in the liver. Quantitative real-time RT-PCR analyses of liver mRNAs isolated from the mice challenged with TM (2 μg/g body weight) or vehicle control, which encode: (A) pro-inflammatory cytokine TNFα and IL6; and (B) acute-phase protein SAP and SAA3. Expression values were normalized to β-actin mRNA levels. Fold changes of mRNA are shown by comparing to one of the control mice. (C–E) ELISA analyses of serum levels of TNFα, IL6, and SAP in the mice challenged with TM or vehicle control for 8 h ELISA. Each bar denotes the mean ± SEM (n = 4 mice per group); *P < 0.05, **P < 0.01.

http://ars.els-cdn.com/content/image/1-s2.0-S0378427412000732-gr6.jpg

Corresponding author at: Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, 540 E. Canfield Avenue, Detroit, MI 48201, USA. Tel.: +1 313 577 2669; fax: +1 313 577 5218.

The SREBP regulatory pathway. Brown MS, Goldst...

The SREBP regulatory pathway. Brown MS, Goldstein JL (1997). “The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor”. Cell 89 (3) : 331–340. doi:10.1016/S0092-8674(00)80213-5. PMID 9150132. (Photo credit: Wikipedia)

English: Structure of the SREBF1 protein. Base...

English: Structure of the SREBF1 protein. Based on PyMOL rendering of PDB 1am9. (Photo credit: Wikipedia)

The SREBP regulatory pathway

The SREBP regulatory pathway (Photo credit: Wikipedia)

English: Diagram of rough endoplasmic reticulu...

English: Diagram of rough endoplasmic reticulum by Ruth Lawson, Otago Polytechnic. (Photo credit: Wikipedia)

Micrograph demonstrating marked (macrovesicula...

Micrograph demonstrating marked (macrovesicular) steatosis in non-alcoholic fatty liver disease. Masson’s trichrome stain. (Photo credit: Wikipedia)

 

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Reprogramming Cell Fate

 

Reporter: Larry H.Bernstein, MD, FCAP

Kathy Liszewski: reporting Gordon Conference “Reprogramming Cell Fate” meeting
M. Azim Surani, Ph.D., Univ Cambridge
Source unknown: June 21, 2012;32(11)
They report two critical steps both of which are needed for exploring epigenetic reprogramming.  While females have two X chromosomes ,
  • the inactivation of one is necessary for cell differentiation.
  • Only after epigenetic reprogramming of the X chromosome can pluripotency be acquired.

Pluripotent stem cells can generate – any fetal or adult cell type but

    • don’t develop into a complete organism.
Pioneer transcription factors take the lead in – facilitating cellular reprogramming – and responses to environmental cues.
Multicellular organisms consist of
  • functionally distinct cellular types
  • produced by differential activation of gene expression.
They seek out and bind specific regulatory sequences in DNA, even though DNA is coated with and condensed into a thick fiber of chromatin.
The pioneer factor, discovered by Prof. KS Zaret at UPenn SOM in 1996, endows the competence for gene activity,
  • being among the first transcription factors to
  • engage and pry open the target sites in chromatin.
FoxA factors, expressed in the foregut endoderm of the mouse,are necessary for
  • induction of the liver program.
    •  nearly one-third of the DNA sites bound by FoxA in the adult liver occur near silent genes.
organ regeneration example from induced plurip...

organ regeneration example from induced pluripotent stem cells(iPS cell) (Photo credit: Wikipedia)

English: Pathway of stem cell differentiation

English: Pathway of stem cell differentiation (Photo credit: Wikipedia)

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ENCODE data reveals important information from Genome Wide Association Studies relevant to understanding complex genetic diseases

Author: Ritu Saxena, Ph.D.

Introduction

“The depth, quality, and diversity of the ENCODE data are unprecedented” is what was stated by John Stamatoyannopoulos, professor of genomic sciences at the University of Washington and one of the many principle investigators of ENCODE project. ENCODE (Encyclopedia of DNA elements), indeed, was an ambitious project launched as a pilot in 2003 and then expanded in 2007 for the whole genome analysis and identification of all the functional elements of the human genome. The findings were striking as they challenged the definition of “gene” and ‘the central dogma of genetics (Gene-mRNA-protein). Infact, the non-coding part that constitutes about 80% of the genome or the so-called “junk DNA” was found to contain elements crucial for gene regulation. The elements, in large part, include RNA transcripts that are not transcribed into proteins but might have a regulatory role. For detailed reading, refer to the findings published in the issue of Nature, The ENCODE Project Consortium Nature 489, 57–74 (2012) An integrated encyclopedia of DNA elements in the human genome

Key features of the data, as explained in the National Human Genome Research Institute website (National Human Genome Research Institute News feature), include comprehensive mapping of:

  • Protein-coding genes — Proteins are molecules made of amino acids linked together in a specific sequence; the amino acid sequence is encoded by the sequence of DNA subunits called nucleotides that make up genes.
  • Non-coding genes — Stretches of DNA that are read by the cell as if they were genes but do not encode proteins. These appear to help regulate the activity of the genome.
  • Chromatin structure features — Complex physical structures made from a combination of DNA and binding proteins that make up the contents of the nucleus and affects genome function.
  • Histone modifications — Histones are the proteins that make up the chromatin structures that help shape and control the genome. In addition, histone proteins can be physically modified by adding chemical groups, such as a methyl molecule, that further regulates genomic activity.
  • DNA methylation — Just like histones, methyl groups can be added to DNA itself in a process called DNA methylation. Chemically attaching methyl groups to DNA physically changes the ability of enzymes to reach the DNA and thus alters the gene expression pattern in cells. Methylation helps cells “remember what they are doing” or alter levels of gene expression, and it is a crucial part of normal development and cellular differentiation in higher organisms.
  • Transcription factor binding sites — Transcription factors are proteins that bind to specific DNA sequences, controlling the flow (or transcription) of genetic information from DNA to mRNA. Mapping the binding sites can help researchers understand how genomic activity is controlled.

How could ENCODE be helpful in the study of complex human diseases?

Complex diseases and Genome wide association studies (GWAS)

Coronary artery disease, type 2 diabetes and many forms of cancer are complex human diseases that have a significant genetic component. Unlike mendelian disorders that have defined loci, the genetic component of complex disorders lies in the form of genetic variations in the genome making an individual susceptible to these complex diseases.

Researchers have performed Genome-wide association studies (GWAS) of the human genome, leading to the identification of thousands of DNA variants that could be linked with complex traits and diseases. However, identifying the variants, referred to as SNPs (Single Nucleotide Polymorphisms), that actually contribute to the disease, and understanding how they exert influence on a disease has been more of a mystery.

How would ENCODE solve the puzzle?

The puzzle lies in interpreting how the SNPs found in the genome affect a person’s susceptibility to a particular trait or disease and what is the mechanism behind it. As identified in the GWAS, most variants that are associated with the phenotype of the trait or disease lie in the non-coding region of the genome. Infact, in more than 400 studies compiled in the GWAS catalog only a small minority of the trait/disease-associated SNPs occur in protein-coding regions; the large majority (89%) are in noncoding regions. These variants fall in the gene deserts that lie far from protein-coding region, similar to those where cis-regulatory modules (CRMs) are found. CRMs such as promoters and enhancers are a group of binding sites for transcription factors, and the presence of transcription factors bound to these sites is a good indicator of the potential regulatory regions.

The integrative analysis of ENCODE data has give important insights to the results of GWAS studies. Investigators have employed ENCODE data as an initial guide to discover regulatory regions in which genetic variation is affecting a complex trait. Additionally, ENCODE study when examined the SNPs from GWAS that were associated with the phenotype of the trait, found that these regions are enriched in DNase-sensitive regions i.e, lie in the function-associated DNA region of the genome as it could be bound by transcription factors affecting the regulation of gene expression. Thus, the project demonstrates that non-coding regions must be considered when interpreting GWAS results, and it provides a strong motivation for reinterpreting previous GWAS findings.

Using ENCODE Data to Interpret GWAS Results

ENCODE and predisposition to CANCER:

C-Myc, a proto-oncogene, codes for a transcripton factor, when expressed constitutively leads to uninhibited cell proliferation resulting in cancer. It has been observed that common variants within a ~1 Mb region upstream of c-Myc gene have been associated with cancers of the colon, prostate, and breast. Several SNPs have been reported in this region, that although affect the phenotype, lie in the distal cis-region of the MYC gene. Alignment of the ENCODE data in this region with the significant variants from the GWAS also reveals that key variants are found in the transcription factor occupied DNA segments mapped by this consortium. One variant rs698327, lies within a DNase hypersensitive site that is bound by several transcription factors, enhancer-associated protein p300, and contains histone modifications relative to enhancers (high H3K4me1, low H3K4me3). ENCODE data indicates that non-coding regions in the human chromosome 8q24 loci are associated with cancer and as observed in the case of c-myc gene, similar studies on cancer-related genes could help explain predisposition to cancer.

ENCODE and fetal hemoglobin expression:

Another example of the use of ENCODE data is that of gene regulation of fetal hemoglobin. Several regions were predicted via ENCODE that were involved in the regulation of fetal hemoglobin. It was found that these predicted regions are close to the SNPs in the BLC11A gene that is associated with persistent expression of fetal hemoglobin.

Future perspective

As evident from the above examples, the ENCODE data shows that genetic variants do affect regulated expression of a target gene. Recently, several research groups in the UK performed a large-scale GWAS study to determine the genetic predisposition to fracture risk. The collaborative effort, published in a recent issue of the PLoS journal, was made to identify genetic variants associated with cortical bone thickness (CBT) and bone mineral density (BMD) with data from more than 10,000 subjects. http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002745 The study generated a wealth of data including the result – identification of SNPs in the WNT16 and its adjacent gene, FAM3C were found to be relevant to CBT and BMD. ENCODE data, in this case, could be helpful in interpreting more detailed information including determining additional SNPs, the regulatory information of the genes involved and much more. Thus, it could be concluded that ENCODE data could be immensely useful in interpreting associations between disease and DNA sequences that can vary from person to person.

Sources:

Research articles

An integrated encyclopedia of DNA elements in the human genome

A User’s Guide to the Encyclopedia of DNA Elements (ENCODE)

What does our genome encode?

Genome-wide Epigenetic Data Facilitate Understanding of Disease Susceptibility Association Studies

Genomics: ENCODE explained

ENCODE Project Writes Eulogy For Junk DNA

WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk

 News articles

ENCODE project: In massive genome analysis new data suggests ‘gene’ redefinition

National Human Genome Research Institute News feature

Related posts

Expanding the Genetic Alphabet and linking the genome to the metabolome

Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes

ENCODE Findings as Consortium

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Reported by: Dr. Venkat S. Karra, Ph.D.

 

Brain structures involved in dealing with fear...

 

Major depression or chronic stress can cause the loss of brain volume, a condition that contributes to both emotional and cognitive impairment. Now a team of researchers led by Yale University scientists has discovered one reason why this occurs—a single genetic switch that triggers loss of brain connections in humans and depression in animal models.

 

The findings, reported in Nature Medicine, show that the genetic switch known as a transcription factor represses the expression of several genes that are necessary for the formation of synaptic connections between brain cells, which in turn could contribute to loss of brain mass in the prefrontal cortex.

 

“We wanted to test the idea that stress causes a loss of brain synapses in humans,” said senior author Ronald Duman, the Elizabeth Mears and House Jameson Professor of Psychiatry and professor of neurobiology and of pharmacology. “We show that circuits normally involved in emotion, as well as cognition, are disrupted when this single transcription factor is activated.”

 

The research team analyzed tissue of depressed and non-depressed patients donated from a brain bank and looked for different patterns of gene activation. The brains of patients who had been depressed exhibited lower levels of expression in genes that are required for the function and structure of brain synapses. Lead author and postdoctoral researcher H.J. Kang discovered that at least five of these genes could be regulated by a single transcription factor called GATA1. When the transcription factor was activated, rodents exhibited depressive-like symptoms, suggesting GATA1 plays a role not only in the loss of connections between neurons but also in symptoms of depression.

 

Duman theorizes that genetic variations in GATA1 may one day help identify people at high risk for major depression or sensitivity to stress.

 

“We hope that by enhancing synaptic connections, either with novel medications or behavioral therapy, we can develop more effective antidepressant therapies,” Duman said.

 

source:

 

http://www.rdmag.com/News/2012/08/Life-Sciences-Team-Discovers-How-Stress-Depression-Can-Shrink-The-Brain/

 

 

 

 

 

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