Gene Regulation - Science topic
Regulation of gene expression (or gene regulation) includes the processes that cells and viruses use to regulate the way that the information in genes is turned into gene products. Although a functional gene product can be an RNA, the majority of known mechanisms regulate protein coding genes. Any step of the gene's expression may be modulated, from DNA-RNA transcription to the post-translational modification of a protein.
Questions related to Gene Regulation
We want to use some natural oily material as the nutrient for HeLa cells. According to the Insoluble material in medium, if we want to treat HeLa cells with oily material in order to study the effects of the oil in the growth and division also in the transcription of treated HeLa cells, what is the best method and has somebody done projects like this?
I am designing a plasmid with an SV40 promoter-driven antibiotic resistance. Does expression from an SV40 promoter require a TATA box upstream of the transcription start site? The original vector had a TATA box at -30, however this is lost in my cloning strategy. With my current plan, the transcription start site is just 8bp from the end of the SV40 promoter. Will this allow for expression, or is a TATA box needed?
I'd like to know that what are the different ways to know/identify whether a particular Gene is expressed or not ?
Few points from my side are :
1) identifying it's corresponding m-RNA transcripts level.
2) identifying the protein that was produced by the expression of that particular Gene.
Any other points ?
When staining with hematoxylin and eosin of a muscle biopsy from a patient with T341P desminopathy, we observe accumulations of inclusions similar to nuclei (arrows in figures 1 and 2, x280). And outside of these accumulations - adipose tissue, which used to be muscle tissue. There are no such massive accumulations of inclusions in adjacent muscle fibers. We assume that clusters of inclusions are not nuclei? Figure 2 is the inverted figure 1.
I am looking to model how expression of a target gene from a state of dysfunction (i.e., knockout; CRISPR-Cas9 induced) to overexpression may influence aspects of neuronal functioning/morphology using patient-derived hiPSCs. The gene itself is associated with several neurodevelopmental phenotypes, so I would like to measure the effects from the iPS cell state -> NPCs -> neuronal state to try and capture whether the degree of expression influences the ability of the cells to mature into neurons.
The target gene itself is quite large (>195kb), so it was suggested to me to apply CRISPRa to achieve overexpression as this makes use of endogenous transcriptional machinery to upregulate the target. However, I have not come across any published articles where (1) gene upregulation is initiated prior to and sustained throughout the differentiation process (i.e., the CRISPRa system is introduced once the cells are at the desired cellular phenotype), or (2) upregulation is maintained over an extended time course. The latter may be necessary to allow me to measure functional outputs of interest. I am thinking an inducible system approach would be useful here, but am open to suggestions!
I am very green in this area of research, and CRISPRa has not been previously attempted in my lab, so would immensely appreciate any advice/recommendations on how I might approach this!
A patient with desminopathy survived Covid-19 six months ago without pneumonia, but with a temporary loss of smell and taste. After Covid-19, we note an accelerated progression of desminopathy, penetration accelerates, new muscles are quickly involved in the pathological process, muscle mass decreases, and heart function worsens. Perhaps the infection or its consequences are somehow connected with the mechanism of progression of desminopathy?
To save life in desminopathy, can the body purposefully reduce muscle mass, for example, due to decreased heart function or for another reason?
It is known that when hypothermia, the body sacrifices limbs for survival. Is it possible with desminopathy a similar phenomenon?
Hi, Iinserted gene of Reverse transcriptase into pPLc245 plazmid. If I'm right, this plazmid doesn't code cI857 represor. For expression od reverse transcriptase from this plasmid I used E. coli DH10B. RT was expressed after increase of cultivation temperature to 37 °C. Before this thermo induction I cultivated E. coli DH10B with pPLc245 at 28 °C. But at this temperature RT was not expressed. Why is this possible? Is E. coli DH10B coding cI represor or pPLc245 could contain gene for this represor?
Thank you for all responses.
I want to know the kinetics of a transcriptomic response to infection. I am interested in the earliest time points, how minutes does it take a cell to activate a gene upon infection?
Thank you for your insights
I am looking for a "not too old" review or paper on the regulation of NR or in general denitrification in bacteria. In particular I like to find an answer on how nitrite accumulation could inhibit nitrate turn over.
I need to identify and compare TFBSs in a number of sequences. I've used Transfac with the Match program (http://gene-regulation.com/) before but it seems that the website has been having issues lately. Are there other programs that can accomplish similar tasks? Ideally I'm looking for something browser based, but I can work with command line if that's the next best thing.
I am using DAVID (https://david.ncifcrf.gov/home.jsp) to cluster some genes I found upregulated in my RNAseq data. I am just using the official gene symbol without any quantitative data. However, the KEGG pathway results are giving me p-values which are extremely high. It does not make any sense to me. How the p-value can be calculated without any number? Can the p-value be significant?
I'm trying to purify a natively expressed protein from a large operon, native expression levels are very low, does anyone have any experience with changing the innate promoter with let's say an inducible one? Is it possible? I'm trying to find any literature on that, but don't see much.
I'd appreciate any help :)
(heterologous expression from a vector is not an option)
All the best!
I have analysed some bioinformatically important information relevant to gene regulation. I want to publish it.
I would like to create a biosensor in which gene1 produces the cellular receptors for a ligand. Upon the binding of this ligand I would like the transcription of gene2 to be activated. This can be done with a histidine kinase receptor.
Mostly, I want to know if this can be done with 1 vector and not 2. If yes, then how can I promote the expression of gene1 under all conditions and not the gene2.
I am working on molecular validation of co-expressed genes in Rice under moisture stress.
I have a list of genes that are co-expressed in three rice varieties, under a particular set of physiological conditions. How do I plot/draw a gene regulation network ?
What else data do I need to do the same? Is there a Bioinformatics tool?
We recently identified a novel transcriptional isoform of a gene in brain. It's endogenous expression is very low compared to the annotated one. Exon 1 of the gene is missing, and a portion of a long terminal repeat (33bp) spliced into Exon 2. Thus, the first ATG for this new isoform is found in Exon 6 due to the loss of Exon 1. my question is: 1. is the new isoform translated into protein? 2. if not, how can we test it is a non-coding RNA? 3. If it is translated, how can we test the protein it makes.
Thank you very much!
With the weird social isolation issues we are all facing i am hoping to find a few people to start some discussions.
If we can't attend conferences, then why not try some other means of trading some knowledge to help get our experiments moving again when we are back in the lab (if you find yourself in isolation that is).
I have a project at the moment looking at epithelial mesenchymal transition in CF airways. I am a little bit stuck at the moment after developing a lineage tracing vector, which possibly stems from not enough fore thought regarding E and N-Cadherin gene regulation.
If anyone has any experience with EMT, E or N-Cadherin gene regulation, or lineage tracing vectors/methods, it would be great to chat to you.
Happy to discuss via email and or zoom
I am looking for putative transcription factors binding sites (which I know the sequences of) on some promoters (which I also know the sequences). What I want is a software where I can upload my promoter, the consensus sequence of one TF, and it tells me how many of putative binding sites (with a certain degree of liberty) can be found on my promoter sequence.
(FYI, I work with budding yeast)
Thanks for you help
I am sure if the binding site of a microRNA can be implicated on a gene or mRNA there one can conclude about the mechanism of action and in a cancer case, its parthenogenesis.
Is the binding on both 3' and 5' end of mRNA, just 3' UTR or other coding region?
Recall also that they have multiple targets, if this microRNA binds to more than one sites, can can one implicate the specific one responsible for regulation?
Suppose i have a DNA sequence and i want to find transcription strat site, CDS, poly A signal etc., which software will be useful to find this out?
The attached image shows chromatograms from the same FAME sample run three times on our GC-MS. As seen, additional peaks show up if running the sample several times. From the MS spectra the additional peaks seem to be plasticizers. If I heat the column to max for 30 min and then rerun the sample the peaks are gone. The additional peaks elute at approximately the same in all samples. I have tried changing septum, liner, injector needle, needle wash whiteout any improvements. Any suggestion how to lose these peaks? Thanks in advance.
I am working on the interaction between C. elegans and the nematode-trapping fungus. Now I found a gene in C .elegans called epc-1 which will have the phenotype I want when it was deleted. It is to contribute to histone acetyltransferase activity. Now I am wondering how to find the genes regulated by it.
Could anyone give me some suggestions? I will very much appreciate it.
I have got a promoter region which is size about 1000bp from human genome. I'm trying to find cis acting elements corresponds for gene regulation in multiple myeloma. What are the freely available methods for this?
In a patient with hereditary desminopathy (Thr341Pro DES mutation in the heterozygous state), a significant loss of muscle mass is observed after a night's sleep, with its replacement by adipose tissue. How to reduce muscle loss during sleep?
i am currently looking for an experiment alternative to southern blot. I heard that pcr based assays give results quickly ( let's say within few hours). I want to perform sybr green relied pcr to detect copy number of my interested gene in the human genome like in southern blot. however, I couldn't find a proper protocol. If you address a protocol or share your experience, I will appreciate.
I want to check whether any response element is present for a particular set of transcription factors on my gene of interest. I have already found EPD (http://epd.epfl.ch/) for promoter sequence and motif search and JASPAR (http://jaspar.genereg.net/) for response sequences. But the problem is, I am not able to merge those information.
The problem with EPD is, many gene promoters are not available. Even if they are available and I find transcription factor motifs on my gene of interest, there is no way to see them as sequence and annotate it.
So, if someone knows any software/ database/ technique for this purpose, it will be immensely helpful for me.
Predicted G2-M phase cell cycle defect mutant change ploidy distribution , but most of the cell cycle progression gene up-regulate in mutant background.
There are specific gene for specific expression. But if any one gene regulated ( up or down ) then other gene will also effected for their regulation or not?Though they may or may not be interlinked.
I have very limited resources in my lab and very narrow budget. I used to use the red blood lysis solution, but I'm now trying to find a new protocol. Can anyone help?
When we have a positive regulation the control is tight, so we have low background expression under non-induced conditions.
Negative regulation can not be fully controlled, but as far as I understand it is more popular than positive regulation.
Are there any other important features of these two types of regulations?
Thank you in advance!
To assess whether expression of the gene of our interest is associated with mutant status of p53 we have treated FaDu cells( mut-p53) with CP-31398, a Known p53 stabilizer, that restore a wild-type DNA-binding conformation of mutant p53. we check the expression by TaqMan based q-PCR, and got expected result, but when we treated the Fadu cells with Pifithrin-α( an inhibitor of p53 ) we again got the same trend( in both cases RQ is 0.2 & 0.4 respectively), while we are expecting No change or Induction. According to the publication: Cancer Biol Ther. 2002 Jan-Feb;1(1):47-55, FaDu expressed only 50% of the normal level of p53 mRNA, either because only one allele was present (A431), or because only one of the two alleles was transcribed. What could be the possible reason of same trend of gene expression after the exposure of CP-31398 & Pifithrin-α in Fadu Cells?
I am eager to know your opinions about site-specific expression of secondary metabolite genes in plants. Which one is more involved: epigenetic regulation, transcription factor-mediated gene regulation or post-transcriptional gene regulations?
P.S: It is clear that all mentioned items (and other mechanisms) are involved, but my question is which one is more engaged, specifically for secondary metabolite differential biosynthesis?
Many thanks for all your consultations,
I'm trying to look at the induction of candidate genes involved in plant defence when I block the signalling pathways related to JAs and ethylene and apply different stresses. For ethylene I can use the AVG, an inhibitor of ethylene biosynthesis, but as far as I know, there are no inhibitors of the jasmonate signaling pathway. Do you have any info about it?
Recently I am performing RT-PCRs and Western blots of a mammalian cell line in which I overexpressed two vectors (wild-type and mutated) of equal promoter and sequence length. However, in both RT-PCR and Western blot I observed very different expression levels between both vectors, despite always transfecting the cells with the same amount of DNA. Could these results be acceptable for publication? Could I assume that the mutation is affecting the gene regulation of its vector in a different way? Or should I normalize this effect in some way in order to explore changes caused in other genes/proteins?
Dr. Magnus Nordborg recently pointed out in a presentation that is no evidence that methylation is involved in either development or adaptation and there is still little evidence of the involvement with gene regulation.
Also, the paper "Epigenomic Diversity in a Global Collection of Arabidopsis thaliana Accessions" (Kawakatsu et al. 2016) showed methylation in Arabidopsis strongly correlated with geography and climate of origin.
It seems that environment controls both methylation and gene expression but there is not necessarily a cause-effect relationship between these two.
Gene regulation and environment are quite related with each other thats why the circadian rhythm is maintained but what are the exact factor for that specific gene expression and the molecular mechanism behind detecting the environmental changes by the human body?
We are using lipofectamine for transfecting synthetic miRNA mimics for the miRNA functional analyses and miRNA target site validation experiments that we are developing with human cancer cell lines. However I would like to know if in your experience those experiments work as well with jet-PEY or similar reagents, which seems to be more cost effective. Thank you, Inma
We will performed ChIp and reporter gene assays to functional validate rSNPs identified within breast cancer-relevant regulatory regions. Which breast cancer line is most suitable for this study? or alternatively, how to select it among the available breast cancer lines such as MCF10 MCF7 HCC1954.
Regulation of gene expression operates at different levels. If you are working on laboratory animals which have a problem with accumulation of a certain protein earlier than the stage when the organisms need it, how would you apply the concept of gene regulation to them? Assume that you have maximal power, capability and all the equipment needed.
Many articles have used cloning method for understanding gene regulation. what other methods can be used to know the expression of common promotor in different gene regulation.
I would like to prepare in silico analysis of role chosen by me transcriptor factor(TF) in the regulation few human gene expression.
Earlier I used the TRED: a Transcriptional Regulatory Element Database to the prediction present of transcriptional regulatory elements (TRE) specific for analyzed transcriptor factor(TF) in the promoters of analyzed genes.
TRED: a Transcriptional Regulatory Element Database (http://rulai.cshl.edu/TRED) was designed as a resource for functional studies and gene regulation studies.
Can you suggest me the free simple online tools to predict the presence of a transcriptional regulatory element (TRE) in the promotor of interested genes?
Gene regulation network of rice with respect to soil salinity and drought are used by researchers to study the genes and their interplay as these stress conditions lead to reduced production of crop produce.
Enhancers and target promoters are often found "in contact". Some 'in contact' enhancer-promoter pairs stay dormant, i.e., there is little detectable promoter-driven transcription. But other enhancer-promoter pairs 'in contact' are 'active'. That is, both the enhancer and promoter 'in contact' are actively transcribing, generating eRNAs at the enhancers and mRNAs from the promoter. So, if we look at the 'in contact' enhancer-promoter pairs, they can either both be transcribing, or both not transcribing. My question is, is there any enhancer 'in contact' with a transcribing promoter but is itself not transcribing? That is, only the promoter is active, but the enhancer is not, despite both being in contact with each other? Shall appreciate any insightful answer that provides evidence, e.g., comparative 3C/HiC-RNA-seq data from same cell populations.
I have a bunch of differentially expressed genes and a long intergenic non-coding RNA, what can do (bioinformatically first) to check the hypothesis that some of these genes may be directly regulated by this linc-RNA?
Does anyone know which gene regulate the MyD88 gene expression in the intestinal epithelial cells ? or whether PI3K/AKT regulate the MyD88 expression or not? if MyD88 downregulated in your model, how will you check the signal pathway? Thanks!
For studying the role of these receptors in my gene of interest regulation in liver I need to have these cell lines
We have a nuclear/cytoplasmic fractionation protocol that works well, but the problem is that the isolated nuclei are EXTREMELY sticky and very difficult to work with.
We want to fix these cells, stain for cell-type specific intracellular markers, and FACS sort for RNA-seq. We can do this routinely with whole cells, but isolated nuclei are totally unmanageable.
All buffers are made with Ca/Mg-free PBS, and supplementation of 5mM EDTA does not help. I have also tried treatment with 50U/ml DNase + 5mM MgCl2 to no avail.
Straining cells through a 70um nylon mesh is off the cards, as the nuclei gloops get stuck on the mesh.
Does anyone have ideas/tips and tricks to deal with this problem?
It has been a long time that I think about one of the important aspects of miRNA research: how many genes mediate miRNA effects? According to the literature, most if not all of miRNAs exert their effects through modulating tens (or hundreds) of mRNA targets (although in different contexts and cell types). For some miRNAs, a small number of key mRNA targets mediate the effects elicited by the miRNA, but for others, a large(r) set of mRNAs are fine-tuned by the miRNA and it is the collective effects of these minor downregulations which lead to a cellular behavior promoted by the miRNA. However, what I see is that almost all of the journals insist on finding a single target for the miRNA of interest! The situation gets more complicated to me when the miRNA influences several behaviors of a cell at the same time (e.g. survival, differentiation, colony formation, migration), yet the reviewer expects authors to find a 'single' target which probably mediates all these diverse effects. Actually, this rationale does not make sense to me, since I think there should be more than one miRNA targets that are mediating the miRNA effects in different cellular contexts.
I would highly appreciate if you comment on my question and let me know what you think in this regard as well as how we should manage a situation where the reviewer requests you to find a single target but you think (or expect) that there should be several miRNA targets, since I think insisting on finding a single target may result in bias (which is not good science).
I observed this in one of my microarray experiments in which the first two gene got upregulated and last gene was downregulated. these three genes belonged to the same operon. Kindly suggest
I have 8 different starch samples from stressed wheat (drought and heat). I want to plan an assay for define differences of amounts for amylose and amylopectin. I have found one assay kit for it but cannot afford the money (kit is for 100 tests and more than 550€ in Turkey). I was looking for some protocols online for wet lab and found 1 about it “iodine complex”. But couldn’t understand the quantification methods. Do you have any suggestions for it?
I want to knock down one protein by 50% in HCC cell lines. I want to know whether it is possible and how to achieve it?
Thanks sincerely for your answer.
Would like to check in vivo if a gene were interested in is indeed flow-regulated. Collaborators will be co-author on the final publication. Your help is highly appreciated
I am planning to perform a viability drug screen with Selleckchem bioactive compounds library (~2000 drugs).
My goal is to find drugs that differentially kill cells with wt or knockout of my gene of interest. Since the potency of drugs varies widely (nM to >50 uM range of IC50) I wonder what is the best design strategy. I saw that people just use 1 uM and/or 10 uM in primary screens but this would miss the relevant range of many drugs, where I have a chance to see the effect of my gene.
Obviously, doing the screen with more concentrations will decrease the replicates for each (I am thinking to use duplicates since CellTiter-GLo gives very tight replicates).
I would appreciate any input regarding the screen design.
I'm looking for mRNA markers for general IFN response. Also, can the type I and II interferon responses be identified using mRNA expression of defined marker gene sets by qPCR?
I'm purifying a recombinant protein. It's secreted protein. After chromatography procedure, I have some samples of protein:
- Fermentation sample (applied sample): the supernatant from BSM medium (pH 7.1 - 7.3, conductivity: 35 mS/cm, approximately)
- Column flow sample: pH 4.0
- Eluted sample: pH 7.0, NaCl 0.9 M, Tris-HCl 20mM,
I see these three protein samples have three different buffer. I want to measure the amount of protein in these samples by Bradford assay method, but I don't know which buffer should I use to balance. I'm using mQ for this step.
Please give me advice.
I used genomatyx to analyze transcription factors in promoters sequences, but now I have many problems to login, so does anyone knows another facility to do this?
It is known that around 1000 TFs are discovered in human cells (bionumbers website). But the statistics about TF-gene interactions are not clarified enough. The answer to this question is crucial in my simulation project. Anybody who could reply to my question based on scientific references would be appreciated as co-author in the final reports.
I am trying to knockdown basal level expression of my target microRNA by transfected Anti-Mir,miRNA Inhibitor, but I didn’t get significant level of knockdown result by real time PCR.
Have any one any idea please help me?
· Cell line-MCF12A (The MCF-12A cell line is a non-tumorigenic epithelial cell line). For cell culture I am using MEGM, Kit Catalog No. CC-3150 by Lonza/Clonetics with 100 ng/ml cholera toxin.
· Name of the transfection reagent- lifofectamin 2000(forward transfection), RNAiMAX(reverse transfection). Transfection down by according to the thermos fisher scientific. I did also forward and reverse but results is same after 24h and also 48h of incubation.
· For the RNA isolation, used miRCURY RNA isolation kit(EXICON). For cDNA making, used miRCURY LNA universal RT microRNA PCR/Universal cDNA Synthesis kit(exiqon), for real Time PCR, used miRCURY LNA universal RT microRNA PCR/ExiLENT SYBER Green(exiqon). For endogenous control I used U6. Everything I did according to company protocol.
Locus specific hydroxymethylation quantification using NEB EpiMark Analysis Kit. Does anyone has a template for data analysis using this kit? I want to use raw ct values for my hydroxymethylation analysis and tried to do qpcr but for some samples I don't see any ct values? I am not sure if my conversion protocol failed or my qPCR is not working well. Any suggestions?
I am going to produce zebrafish knock-in by CRISPR/Cas method. So I want to know proper concentration of CRISPR/Cas components for knock-in (sgRNA, Donor plasmid, Cas9 mRNA). Does anyone have experience with this?
We have used 16 S rRNA seq to look at differences in gut microbial composition between patients and controls. Now we want to use qPCR to verify the findings, however, I have only used qPCR before for gene expression, where you use a reference gene to normalise to in order to do relative quant. What do you use when you want to quantify microbial levels?
I want to compare two set of data containing 8 observations and even though they follow a normal distribution, I am not sure it's correct to perform a student-t test on it because the set of data is very small. Or is it better to use a non parametric test?
In search of articles, I found that neutrophil migration could be blocked by several chemicals. However, the specificity and time and space control is a big issue. Is there any way to specifically block the neutrophil migration?