This project received funding from the I-CORE Program of the Planning and Budgeting Committee and the Israel Science Foundation (Grant No. 152/11), Marie Curie Reintegration Grant No. PCIG11-GA- 2012-321675, and from the European Union&#39;s Horizon 2020 Research and Innovation Program under grant agreement no. 664918 - MRG-Grammar.

1. System Preparation
<ol>
	<li>Design of binding-site plasmids 
	<ol>
		<li>Design the binding site cassette as depicted in Figure 1. Each minigene contains the following parts (5&#39; to 3&#39;): Eagl restriction site, &#8764;40 bases of the 5&#39; end of the kanamycin (Kan) resistance gene, pLac-Ara promoter, ribosome binding site (RBS), AUG of the mCherry gene, a spacer (&#948;), an RBP binding site, 80 bases of the 5&#39; end of the mCherry gene, and an ApaLI restriction site. 
		NOTE: To increase the success rate of the assay, design three binding-site cassettes for each binding site, with spacers consisting of at least one, two, and three bases. See Representative Results section for further guidelines.</li>
	</ol>
	</li>
	<li>Cloning of binding site plasmids
	<ol>
		<li>Order the binding-site cassettes as double-stranded DNA (dsDNA) minigenes. Each minigene is &#8764;500&#8201;bp long and contains an Eagl restriction site and an ApaLI restriction site at the 5&#39; and 3&#39; ends, respectively (see step 1.1.1). 
		NOTE: In this experiment, mini-genes with half of the kanamycin gene were ordered to facilitate screening for positive colonies. However, Gibson assembly17 is also suitable here, in which case the binding site can be ordered as two shorter complementary single-stranded DNA oligos.</li>
		<li>Double-digest both the mini-genes and the target vector with Eagl-HF and ApaLI by the restriction protocol18, and column purify19.</li>
		<li>Ligate the digested minigenes to the binding-site backbone containing the rest of the mCherry reporter gene, terminator, and a kanamycin resistance gene20.</li>
		<li>Transform the ligation solution into Escherichia coli TOP10 cells21.</li>
		<li>Identify positive transformants via Sanger sequencing.
		<ol>
			<li>Design a primer 100 bases upstream to the region of interest (see Table 1 for primer sequences).</li>
			<li>Miniprep a few bacterial colonies22.</li>
			<li>Prepare 5 &#181;L of a 5 mM solution of the primer and 10 &#181;L of the DNA at 80 ng/&#181;L concentration.</li>
			<li>Send the two solution to a convenient facility for Sanger sequencing23.</li>
		</ol>
		</li>
		<li>Store purified plasmids at -20 &#176;C, and bacterial strains as glycerol stocks24, both in the 96-well format. DNA will then be used for transformation into E. coli TOP10 cells containing one of four fusion-RBP plasmids (see step 1.3.5).</li>
	</ol>
	</li>
	<li>Design and construction of the RBP plasmid 
	NOTE: Amino acid and nucleotide sequences of the coat proteins used in this study are listed in Table 2.
	<ol>
		<li>Order the required RBP sequence lacking a stop codon as a custom-ordered dsDNA minigene lacking a stop codon with restriction sites at the ends (Figure 1).</li>
		<li>Clone the tested RBP lacking a stop codon immediately downstream of an inducible promoter and upstream of a fluorescent protein lacking a start codon (Figure 1), similar to steps 1.2.2-1.2.4. Make sure that the RBP plasmid contains a different antibiotic resistance gene than the binding-site plasmid.</li>
		<li>Identify positive transformants via Sanger sequencing, similar to step 1.2.5 (see Table 1 for primer sequences).</li>
		<li>Choose one positive transformant and make it chemically-competent25. Store as glycerol purified plasmids at -20 &#176;C and glycerol stocks of bacterial strains24 at -80 &#176;C in 96-well plates.</li>
		<li>Transform the binding-site plasmids (from step 1.2.6) stored in 96-well plates into chemically-competent bacterial cells already containing an RBP-mCerulean plasmid21. To save time, instead of plating the cells on Petri dishes, plate them using an 8-channel pipettor on 8-lane plates containing Luria-Bertani (LB)26 agar with relevant antibiotics (Kan and Amp). Colonies should appear in 16 h.</li>
		<li>Select a single colony for each double transformant and grow overnight in LB medium with the relevant antibiotics (Kan and Amp) and store as glycerol stocks24 at -80 &#176;C in 96-well plates.</li>
	</ol>
	</li>
</ol>
2. Experiment Setup
NOTE: The protocol presented here was performed using a liquid-handling robotic system in combination with an incubator and a plate reader. Each measurement was carried out for 24 inducer concentrations, with two duplicates for each strain + inducer combination. Using this robotic system, data for 16 strains per day with 24 inducer concentrations was collected. However, if such a device is unavailable, or if fewer experiments are necessary, these can easily be done by hand using an 8-channel multi-pipette and adapting the protocol accordingly. For example, preliminary results for four strains per day with 12 inducer concentrations and four time-points were acquired in this manner.
<ol>
	<li>Prepare, in advance, 1 L of bioassay buffer (BA) by mixing 0.5 g of tryptone, 0.3 mL of glycerol, 5.8 g of NaCl, 50 mL of 1 M MgSO4, 1 mL of 10x phosphate-buffered saline (PBS) buffer pH 7.4, and 950 mL of double distilled water (DDW). Autoclave or sterile filter the BA buffer.</li>
	<li>Grow the double-transformant strains at 37 &#176;C and 250 rpm shaking in 1.5 mL LB with appropriate antibiotics (kanamycin at a final concentration of 25 &#956;g/mL and ampicillin at a final concentration of 100 &#956;g/mL), in 48-well plates, over a period of 18 h (overnight).</li>
	<li>In the morning, make the following preparations.
	<ol>
		<li>Inducer plate. In a clean 96-well plate, prepare wells with semi-poor medium (SPM) consisting of 95% BA and 5% LB26 in the incubator at 37 &#176;C. The number of wells corresponds to the desired number of inducer concentrations. Add C4-HSL to the wells in the inducer plate that will contain the highest inducer concentration (218 nM).</li>
		<li>Program the robot to serially dilute medium from each of the highest-concentration wells into 23 lower concentrations ranging from 0 to 218 nM. The volume of each inducer dilution should be sufficient for all strains (including duplicates).</li>
		<li>While the inducer dilutions are being prepared, warm 180 &#956;L of SPM in the incubator at 37 &#176;C, in 96-well plates.</li>
		<li>Dilute the overnight strains from step 2.2 by a factor of 100 by serial dilutions: first dilute by a factor of 10 by mixing 100 &#956;L of bacteria with 900 &#956;L of SPM in 48-well plates, and then dilute again by a factor of 10 by taking 20 &#956;L from the diluted solution into 180 &#956;L of pre-warmed SPM, in 96-well plates suitable for fluorescent measurements.</li>
		<li>Add the diluted inducer from the inducer plate to the 96-well plates with the diluted strains according to the final concentrations.</li>
	</ol>
	</li>
	<li>Shake the 96-well plates at 37 &#176;C for 6 h, while taking measurements of optical density at 595 nm (OD595), mCherry (560 nm/612 nm) and mCerulean (460 nm/510 nm) fluorescence via a plate reader every 30 min. For normalization purposes, measure growth of SMP with no cells added.</li>
</ol>
3. Preliminary Results Analysis
<ol>
	<li>For each day of experiment, choose a time interval of logarithmic growth according to the measured growth curves, between the linear growth phase and the stationary (T&#173;0, Tfinal). Take approximately 6&#8722;8 time points, while discarding the first and last measurements to avoid error derived from inaccuracy of exponential growth detection (see Figure 2A, top panel). 
	NOTE: Discard strains that show abnormal growth curves or strains where logarithmic growth phase could not be detected and repeat the experiment.</li>
	<li>Calculate the average normalized fluorescence of mCerulean and rate of production of mCherry, from the raw data of both mCerulean and mCherry fluorescence for each inducer concentration (Figure 2A).
	<ol>
		<li>Calculate normalized mCerulean as follows: 
		<img alt="Equation 1" src="/files/ftp_upload/59611/59611eq1.jpg" /> 
		&#8203;where blank(mCerulean) is the mCerulean level [a.u.] for medium only, blank(OD) is the optical density for medium only, and mCerulean and OD are the mCerulean fluorescence and optical density values, respectively.</li>
		<li>Average mCerulean over the different time points (Figure 2B, top two panels) as follows: 
		<img alt="Equation 2" src="/files/ftp_upload/59611/59611eq2.jpg" /> 
		&#8203;where #Time points is the number of data timepoints taken into account, T0 is the time at which the exponential growth phase begins, and Tfinal is the time at which the exponential growth phase ends.</li>
		<li>Calculate mCherry rate of production (Figure 2B, bottom two panels) as follows: 
		<img alt="Equation 3" src="/files/ftp_upload/59611/59611eq3.jpg" /> 
		where mCherry(t) is the mCherry level [a.u.] at time t, OD is the optical density value, T0 is the time at which the exponential growth phase begins, and Tfinal is the time at which the exponential growth phase ends.</li>
	</ol>
	</li>
	<li>Finally, plot the mCherry rate of production as a function of mCerulean, creating dose response curves as a function of RBP-mCerulean fusion fluorescence (Figure 2C). Such plots represent production of the reporter gene as a function of RBP presence in the cell.</li>
</ol>
4. Dose Response Function Fitting Routine and KRBP Extraction
<ol>
	<li>Under the assumption that the ribosome rate of translation with the RBP bound is constant, model the mCherry production rate as follows (see Figure 2D, green line): 
	<img alt="Equation 4" src="/files/ftp_upload/59611/59611eq4.jpg" /> 
	where [x] is the normalized average mCerulean fluorescence calculated according to Eq. 2, mCherry production rate is the value calculated according to Eq. 3, KRBP is the relative binding affinity [a.u.], Kunbound is the ribosome rate of translation with the RBP unbound, n is the cooperativity factor, and C is the base fluorescence [a.u.]. C, n, Kunbound, and KRBP are found by fitting the mCherry production rate data to the model (Eq. 4).</li>
	<li>Using data analysis software, conduct a fitting procedure on plots depicting mCherry production rate as a function of averaged mCerulean (step 3.3), and extract the fit parameters according to the formula in Eq. 4. 
	NOTE: Only fitting results with R2 &#62; 0.6 are taken into account. For those fits, KRBP error is mostly in the range of 0.5% to 20% of KRBP values, for a 0.67 confidence interval, while those with higher KRBP error can be also verified by eye.</li>
	<li>Normalize KRBP values by the respective maximal value of averaged mCerulean for each dose-response function. 
	<img alt="Equation 5" src="/files/ftp_upload/59611/59611eq5.jpg" /> 
	where KRBP in [a.u.] is the value extracted from the fitting procedure in Eq. 4, and max (averaged mCerulean) is the maximal averaged mCerulean signal [a.u] observed for the current strain. 
	NOTE: The normalization facilitates correct comparison of the regulatory effect across strains by eliminating the dependence on the particular maximal RBP expression levels.</li>
</ol>

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The authors have nothing to disclose.

The method described in this article facilitates quantitative in vivo measurement of RBP-RNA binding affinity in E. coli cells. The protocol is relatively easy and can be conducted without the use of sophisticated machinery, and data analysis is straightforward. Moreover, the results are produced immediately, without the relatively long wait-time associated with next generation sequencing (NGS) results.
One limitation to this method is that it works only in bacterial cells. However, a...

RNA binding protein (RBP)-based post-transcriptional regulation, specifically characterization of the interaction between RBPs and RNA, has been studied extensively in recent decades. There are multiple examples of translational down-regulation in bacteria originating from RBPs inhibiting, or directly competing with, ribosome binding1,2,3. In the field of synthetic biology, RBP-RNA interactions are emerging as a significant tool for the design of transcription-based genetic circuits4,5. Therefore, there is an increase in demand for characterization of such RBP-RNA interactions in a cellular context.
The most common methods for studying protein-RNA interactions are the electrophoretic mobility shift assay (EMSA)6, which is limited to in vitro settings, and various pull-down assays7, including the CLIP method8,9. While such methods enable the discovery of de novo RNA binding sites, they suffer from drawbacks such as labor-intensive protocols and expensive deep sequencing reactions and may require a specific antibody for RBP pull-down. Due to the susceptible nature of RNA to its environment, many factors can affect RBP-RNA interactions, emphasizing the importance of interrogating RBP-RNA binding in the cellular context. For example, we and others have demonstrated significant differences between RNA structures in vivo and in vitro10,11.
Based on the approach of a previous study12, we recently demonstrated10 that when placing pre-designed binding sites for the capsid RBPs from the bacteriophages GA13, MS214, PP715, and Q&#946;16 in the translation initiation region of a reporter mRNA, reporter expression is strongly repressed. We present a relatively simple and quantitative method, based on this repression phenomenon, to measure the affinity between RBPs and their corresponding RNA binding sites in vivo.

<table>
	<tbody>
		<tr>
			<td>Ampicillin sodium salt</td>
			<td>SIGMA</td>
			<td>A9518</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnesium sulfate (MgSO4)</td>
			<td>ALFA AESAR</td>
			<td>33337</td>
			<td></td>
		</tr>
		<tr>
			<td>48 plates</td>
			<td>Axygen</td>
			<td>P-5ML-48-C-S</td>
			<td></td>
		</tr>
		<tr>
			<td>8- lane plates</td>
			<td>Axygen</td>
			<td>RESMW8I</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well plates</td>
			<td>Axygen</td>
			<td>P-DW-20-C</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well plates for plate reader</td>
			<td>Perkin Elmer</td>
			<td>6005029</td>
			<td></td>
		</tr>
		<tr>
			<td>ApaLI</td>
			<td>NEB</td>
			<td>R0507</td>
			<td></td>
		</tr>
		<tr>
			<td>Binding site sequences</td>
			<td>Gen9 Inc. and Twist Bioscience</td>
			<td></td>
			<td>see Table 1</td>
		</tr>
		<tr>
			<td>E. coli TOP10 cells</td>
			<td>Invitrogen</td>
			<td>C404006</td>
			<td></td>
		</tr>
		<tr>
			<td>Eagl-HF</td>
			<td>NEB</td>
			<td>R3505</td>
			<td></td>
		</tr>
		<tr>
			<td>glycerol</td>
			<td>BIO LAB</td>
			<td>071205</td>
			<td></td>
		</tr>
		<tr>
			<td>incubator</td>
			<td>TECAN</td>
			<td>liconic incubator</td>
			<td></td>
		</tr>
		<tr>
			<td>Kanamycin solfate</td>
			<td>SIGMA</td>
			<td>K4000</td>
			<td></td>
		</tr>
		<tr>
			<td>KpnI- HF</td>
			<td>NEB</td>
			<td>R0142</td>
			<td></td>
		</tr>
		<tr>
			<td>ligase</td>
			<td>NEB</td>
			<td>B0202S</td>
			<td></td>
		</tr>
		<tr>
			<td>liquid-handling robotic system</td>
			<td>TECAN</td>
			<td>EVO 100, MCA 96-channel</td>
			<td></td>
		</tr>
		<tr>
			<td>Matlab analysis software</td>
			<td>Mathworks</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>multi- pipette 8 lanes</td>
			<td>Axygen</td>
			<td>BR703710</td>
			<td></td>
		</tr>
		<tr>
			<td>N-butanoyl-L-homoserine lactone (C4-HSL)</td>
			<td>cayman</td>
			<td>K40982552 019</td>
			<td></td>
		</tr>
		<tr>
			<td>PBS buffer</td>
			<td>Biological Industries</td>
			<td>020235A</td>
			<td></td>
		</tr>
		<tr>
			<td>platereader</td>
			<td>TECAN</td>
			<td>Infinite F200 PRO</td>
			<td></td>
		</tr>
		<tr>
			<td>Q5 HotStart Polymerase</td>
			<td>NEB</td>
			<td>M0493</td>
			<td></td>
		</tr>
		<tr>
			<td>RBP seqeunces</td>
			<td>Addgene</td>
			<td>27121 &#38; 40650</td>
			<td>see Table 2</td>
		</tr>
		<tr>
			<td>SODIUM CHLORIDE (NaCL)</td>
			<td>BIO LAB</td>
			<td>190305</td>
			<td></td>
		</tr>
		<tr>
			<td>SV Gel and PCR Clean-Up System</td>
			<td>Promega</td>
			<td>A9281</td>
			<td></td>
		</tr>
		<tr>
			<td>Tryptone</td>
			<td>BD</td>
			<td>211705</td>
			<td></td>
		</tr>
	</tbody>
</table>

an assay for quantifying protein-rna binding in bacteria

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding of an RNA binding protein (RBP) to the initiation region of the mRNA, which interferes with ribosome binding. In the presented method, we utilize this blocking phenomenon to quantify the binding affinity of RBPs to their cognate and non-cognate binding sites. To do this, we insert a test binding site in the initiation region of a reporter mRNA and induce the expression of the test RBP. In the case of RBP-RNA binding, we observed a sigmoidal repression of the reporter expression as a function of RBP concentration. In the case of no-affinity or very low affinity between binding site and RBP, no significant repression was observed. The method is carried out in live bacterial cells, and does not require expensive or sophisticated machinery. It is useful for quantifying and comparing between the binding affinities of different RBPs that are functional in bacteria to a set of designed binding sites. This method may be inappropriate for binding sites with high structural complexity. This is due to the possibility of repression of ribosomal initiation by complex mRNA structure in the absence of RBP, which would result in lower basal reporter gene expression, and thus less-observable reporter repression upon RBP binding.

The presented method utilizes the competition between an RBP and the ribosome for binding to the mRNA molecule (Figure 1). This competition is reflected by decreasing mCherry levels as a function of increased production of RBP-mCerulean, due to increasing concentrations of inducer. In the case of increasing mCerulean fluorescence, with no significant changes in mCherry, a lack of RBP binding is deduced. Representative results for both a positive and a negativ...

Watch this Scientific Journal Video about An Assay for Quantifying Protein-RNA Binding in Bacteria at JoVE.com

An Assay for Quantifying Protein-RNA Binding in Bacteria

Bakterilerde protein RNA bağlayıcı ölçümü için bir tahlil

In this method, we quantify the binding affinity of RNA binding proteins (RBPs) to cognate and non-cognate binding sites using a simple, live, reporter assay in bacterial cells. The assay is based on repression of a reporter gene.

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding ...

an-assay-for-quantifying-protein-rna-binding-in-bacteria

Research

JoVE Journal

Genetics

6.5K Görüntüleme.  Technion-Israel Institute of Technology. Protein in vivo'ya bağlanmanın, RNA'nın karmaşık doğası ve proteinlerle etkileşimini etkileyen birçok faktör nedeniyle önemli bir sorun olmaya devam etmektedir. RNA bağlayıcı protein veya RBP-RNA afinite canlı bakteri hücrelerinin içinde ölçülür. Hücresel ortam hem RNA yapısını hem de ortaya çıkan RBP-RNA bağlanmasını etkilediği için, vivo'daki yakınlığın ölçülmesi doğal sistemlerdeki bu etkileşimleri anlamak için çok önemlidir.Başarının s...

Video: An Assay for Quantifying Protein-RNA Binding in Bacteria

Methyl-binding DNA capture Sequencing for Patient Tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Hasta Dokularda metil-bağlayıcı DNA yakalama Dizi

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

Genetik

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) refers to the experimental situation in which evolution is observed using living organisms under controlled conditions and stressors; organisms are thereby artificially forced to make evolutionary changes. Microorganisms are subject to a variety of stressors in the environment and are capable of regulating certain stress-inducible proteins to increase their chances of survival. Naturally occurring spontaneous mutations bring about changes in a microorganism's genome that affect its chances of survival. Long-term exposure to chemostat culture provokes an accumulation of spontaneous mutations and renders the most adaptable strain dominant. Compared to the colony transfer and serial transfer methods, chemostat culture entails the highest number of cell divisions and, therefore, the highest number of diverse populations. Although chemostat culture for ALE requires more complicated culture devices, it is less labor intensive once the operation begins. Comparative genomic and transcriptome analyses of the adapted strain provide evolutionary clues as to how the stressors contribute to mutations that overcome the stress. The goal of the current paper is to bring about accelerated evolution of microorganisms under controlled laboratory conditions.

Here, we present a protocol to obtain adaptive laboratory evolution of microorganisms under conditions using chemostat culture. Also, genomic analysis of the evolved strain is discussed.

Bir Kemostat kullanarak Mikroorganizmaların Adaptif Laboratuvarı Evolution Prosedürü

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) ...

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

RNA-binding proteins (RBPs) play important roles in every aspect of RNA metabolism and regulation. Their identification is a major challenge in modern biology. Only a few in vitro and in vivo methods enable the identification of RBPs associated with a particular target mRNA. However, their main limitations are the identification of RBPs in a non-cellular environment (in vitro) or the low efficiency isolation of RNA of interest (in vivo). An RNA-binding protein purification and identification (RaPID) methodology was designed to overcome these limitations in yeast and enable efficient isolation of proteins that are associated in vivo. To achieve this, the RNA of interest is tagged with MS2 loops, and co-expressed with a fusion protein of an MS2-binding protein and a streptavidin-binding protein (SBP). Cells are then subjected to crosslinking and lysed, and complexes are isolated through streptavidin beads. The proteins that co-purify with the tagged RNA can then be determined by mass spectrometry. We recently used this protocol to identify novel proteins associated with the ER-associated PMP1 mRNA. Here, we provide a detailed protocol of RaPID, and discuss some of its limitations and advantages.

RNA-protein interactions lie at the heart of many cellular processes. Here, we describe an in vivo method to isolate specific RNA and identify novel proteins that are associated with it. This could shed new light on how RNAs are regulated in the cell.

Rapid Metodoloji tarafından Roman RNA Bağlayıcı Proteinler İzolasyon

RNA-binding proteins (RBPs) play important roles in every aspect of RNA metabolism and regulation. Their identification is a major challenge in modern ...

Analysis of Cap-binding Proteins in Human Cells Exposed to Physiological Oxygen Conditions

Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex is by far the most common pathway to initiate protein synthesis in eukaryotic cells, but stress-specific variations of this complex are now emerging. Purifying cap-binding proteins with an affinity resin composed of Agarose-linked m7GTP (a 5' mRNA cap analog) is a useful tool to identify factors involved in the regulation of translation initiation. Hypoxia (low oxygen) is a cellular stress encountered during fetal development and tumor progression, and is highly dependent on translation regulation. Furthermore, it was recently reported that human adult organs have a lower oxygen content (physioxia 1-9% oxygen) that is closer to hypoxia than the ambient air where cells are routinely cultured. With the ongoing characterization of a hypoxic eIF4F complex (eIF4FH), there is increasing interest in understanding oxygen-dependent translation initiation through the 5' mRNA cap. We have recently developed a human cell culture method to analyze cap-binding proteins that are regulated by oxygen availability. This protocol emphasizes that cell culture and lysis be performed in a hypoxia workstation to eliminate exposure to oxygen. Cells must be incubated for at least 24 hr for the liquid media to equilibrate with the atmosphere within the workstation. To avoid this limitation, pre-conditioned media (de-oxygenated) can be added to cells if shorter time points are required. Certain cap-binding proteins require interactions with a second base or can hydrolyze the m7GTP, therefore some cap interactors may be missed in the purification process. Agarose-linked to enzymatically resistant cap analogs may be substituted in this protocol. This method allows the user to identify novel oxygen-regulated translation factors involved in cap-dependent translation.

Here, we present human cell culture protocols to analyze translation initiation factors that bind the 5' cap of mRNA during physiological oxygen conditions. This method utilizes an Agarose-linked m7GTP cap analog and is suitable to investigate cap-binding factors and their interacting partners.

Fizyolojik Oksijen Koşullarına Maruz İnsan hücrelerinde Cap-bağlayıcı proteinlerin analizi

Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex ...

Sequential Salt Extractions for the Analysis of Bulk Chromatin Binding Properties of Chromatin Modifying Complexes

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous nature of most mammalian chromatin-modifying complexes. In order to overcome these hurdles, we have adapted a sequential salt extraction (SSE) assay for evaluating the relative binding affinities of chromatin-bound complexes. This easy and straightforward assay can be used by non-experts to evaluate the relative difference in binding affinity of two related complexes, the changes in affinity of a complex when a subunit is lost or an individual domain is inactivated, and the change in binding affinity after alterations to the chromatin landscape. By sequentially re-suspending bulk chromatin in increasing amounts of salt, we are able to profile the elution of a particular protein from chromatin. Using these profiles, we are able to determine how alterations in a chromatin-modifying complex or alterations to the chromatin environment affect binding interactions. Coupling SSE with other in vitro and in vivo assays, we can determine the roles of individual domains and proteins on the functionality of a complex in a variety of chromatin environments.

Sequential salt extraction of chromatin bound proteins is a useful tool for determining the binding properties of large protein complexes. This method can be employed to evaluate the role of individual subunits or domains in the overall affinity of a protein complex to bulk chromatin.

Kromatin kompleksleri değiştirme özelliklerini bağlama toplu Kromatin analizi için sıralı tuz çekimi

Elucidation of the binding properties of chromatin-targeting proteins can be very challenging due to the complex nature of chromatin and the heterogeneous ...

Quantifying Abdominal Pigmentation in Drosophila melanogaster

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for understanding the development and evolution of morphological phenotypes. Abdominal pigmentation in Drosophila melanogaster has been particularly useful, allowing researchers to identify the loci that underlie inter- and intraspecific variations in morphology. Hitherto, however, D. melanogaster abdominal pigmentation has been largely assayed qualitatively, through scoring, rather than quantitatively, which limits the forms of statistical analysis that can be applied to pigmentation data. This work describes a new methodology that allows for the quantification of various aspects of the abdominal pigmentation pattern of adult D. melanogaster. The protocol includes specimen mounting, image capture, data extraction, and analysis. All the software used for image capture and analysis feature macros written for open-source image analysis. The advantage of this approach is the ability to precisely measure pigmentation traits using a methodology that is highly reproducible across different imaging systems. While the technique has been used to measure variation in the tergal pigmentation patterns of adult D. melanogaster, the methodology is flexible and broadly applicable to pigmentation patterns in myriad different organisms.

Quantifying Abdominal Pigmentation in Drosophila melanogaster

This work presents a method to quickly and precisely quantify the abdominal pigmentation of Drosophila melanogaster using digital image analysis. This method streamlines the procedures between phenotype acquisition and data analysis and includes specimen mounting, image acquisition, pixel value extraction, and trait measurement.

Karın Pigmentasyonunun Karaciğer Miktarında Nicelleştirilmesi<em&gt; Drosophila melanogaster</em

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for ...

In Vitro Bioluminescence Assay to Characterize Circadian Rhythm in Mammary Epithelial Cells

The circadian rhythm is a fundamental physiological process present in all organisms that regulates biological processes ranging from gene expression to sleep behavior. In vertebrates, circadian rhythm is controlled by a molecular oscillator that functions in both the suprachiasmatic nucleus (SCN; central pacemaker) and individual cells comprising most peripheral tissues. More importantly, disruption of circadian rhythm by exposure to light-at-night, environmental stressors and/or toxicants is associated with increased risk of chronic diseases and aging. The ability to identify agents that can disrupt central and/or peripheral biological clocks, and agents that can prevent or mitigate the effects of circadian disruption, has significant implications for prevention of chronic diseases. Although rodent models can be used to identify exposures and agents that induce or prevent/mitigate circadian disruption, these experiments require large numbers of animals. In vivo studies also require significant resources and infrastructure, and require researchers to work all night. Thus, there is an urgent need for a cell-type appropriate in vitro system to screen for environmental circadian disruptors and enhancers in cell types from different organs and disease states. We constructed a vector that drives transcription of the destabilized luciferase in eukaryotic cells under the control of the human PERIOD 2 gene promoter. This circadian reporter construct was stably transfected into human mammary epithelial cells, and circadian responsive reporter cells were selected to develop the in vitro bioluminescence assay. Here, we present a detailed protocol to establish and validate the assay. We further provide details for proof of concept experiments demonstrating the ability of our in vitro assay to recapitulate the in vivo effects of various chemicals on the cellular biological clock. The results indicate that the assay can be adapted to a variety of cell types to screen for both environmental disruptors and chemopreventive enhancers of circadian clocks.

In Vitro Bioluminescence Assay to Characterize Circadian Rhythm in Mammary Epithelial Cells

An in vitro bioluminescence assay to determine cellular circadian rhythm in mammary epithelial cells is presented. This method utilizes mammalian cell reporter plasmids expressing destabilized luciferase under the control of the PERIOD 2 gene promoter. It can be adapted to other cell types to evaluate organ-specific effects on circadian rhythm.

Vitro Sirkadiyen ritim meme epitel hücreleri ayırdetmek için bioluminescence tahlil

The circadian rhythm is a fundamental physiological process present in all organisms that regulates biological processes ranging from gene expression to ...

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Gerçek zamanlı analiz bağlama, transkripsiyon, çeviri ve ciro hücresel etkinleştirme sırasında küresel olayları görüntülemek üzere transkripsiyon faktörü

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Dictyostelium discoideum is an intriguing model organism for the study of cell differentiation processes during development, cell signaling, and other important cellular biology questions. The technologies available to genetically manipulate Dictyostelium cells are well-developed. Transfections can be performed using different selectable markers and marker re-cycling, including homologous recombination and insertional mutagenesis. This is supported by a well-annotated genome. However, these approaches are optimized for axenic cell lines growing in liquid cultures and are difficult to apply to non-axenic wild-type cells, which feed only on bacteria. The mutations that are present in axenic strains disturb Ras signaling, causing excessive macropinocytosis required for feeding, and impair cell migration, which confounds the interpretation of signal transduction and chemotaxis experiments in those strains. Earlier attempts to genetically manipulate non-axenic cells have lacked efficiency and required complex experimental procedures. We have developed a simple transfection protocol that, for the first time, overcomes these limitations. Those series of large improvements to Dictyostelium molecular genetics allow wild-type cells to be manipulated as easily as standard laboratory strains. In addition to the advantages for studying uncorrupted signaling and motility processes, mutants that disrupt macropinocytosis-based growth can now be readily isolated. Furthermore, the entire transfection workflow is greatly accelerated, with recombinant cells that can be generated in days rather than weeks. Another advantage is that molecular genetics can further be performed with freshly isolated wild-type Dictyostelium samples from the environment. This can help to extend the scope of approaches used in these research areas.

Genetic Engineering of Dictyostelium discoideum Cells Based on Selection and Growth on Bacteria

Dictyostelium discoideum is a popular model organism to study complex cellular processes such as cell migration, endocytosis, and development. The utility of the organism is dependent on the feasibility of genetic manipulation. Here, we present methods to transfect Dictyostelium discoideum cells that overcome existing limitations of culturing cells in liquid media.

Genetik Mühendisliği hücreleri Dictyostelium discoideum alan seçimi ve bakteri büyüme

Dictyostelium discoideum is an intriguing model organism for the study of cell differentiation processes during development, cell signaling, and other ...

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Gene regulation plays an important role in all cells. Transcriptional, post-transcriptional (or RNA processing), translational, and post-translational steps are used to regulate specific genes. Sequence-specific nucleic acid-binding proteins target specific sequences to control spatial or temporal gene expression. The binding sites in nucleic acids are typically characterized by mutational analysis. However, numerous proteins of interest have no known binding site for such characterization. Here we describe an approach to identify previously unknown binding sites for RNA-binding proteins. It involves iterative selection and amplification of sequences starting with a randomized sequence pool. Following several rounds of these steps-transcription, binding, and amplification-the enriched sequences are sequenced to identify a preferred binding site(s). Success of this approach is monitored using in vitro binding assays. Subsequently, in vitro and in vivo functional assays can be used to assess the biological relevance of the selected sequences. This approach allows identification and characterization of a previously unknown binding site(s) for any RNA-binding protein for which an assay to separate protein-bound and unbound RNAs exists.

Sequence specificity is critical for gene regulation. Regulatory proteins that recognize specific sequences are important for gene regulation. Defining functional binding sites for such proteins is a challenging biological problem. An iterative approach for identification of a binding site for an RNA-binding protein is described here and is applicable to all RNA-binding proteins.

Düzenleyici RNA Bağlayıcı Proteinler için Bağlayıcı Siteleri Belirlemek için Sıra Alanını Keşfetmek

Gene regulation plays an important role in all cells. Transcriptional, post-transcriptional (or RNA processing), translational, and post-translational ...