Mutation - Science topic

Q5 is a proof reading polymerase so will try to correct your mutations.

Dr Loubna Youssar thank you for your contribution to the discussion

The significance of gene mutation burden in endometrial cancer data collated from different studies can be assessed using statistical methods such as Fisher’s exact test and logistic regression.

The viral genome is very large and consists of two main parts: the non-synthetic protein region, which includes genes responsible for DNA replication and frontal expression, and these regions are somewhat coherent, so they do not suffer from rapid and frequent genetic mutations. After a large number of years, as was the case in Covid 2019, as for the other region, it is the synthetic protein region, and it includes genetic genes responsible for the structural parts of the virus, such as the envelope, capsid, and spines, and it is fragile, so it suffers from continuous genetic mutations, and for this reason we find the emergence of the so-called modified corona or mutated virus, As these mutations are at the level of the structural sequence of the glycoproteins of the viral spines, which play a major role as specialized sites for binding to cellular receptors. This type of simple mutation is not dangerous, as the body can overcome it through the immunity it has gained through previous infections and vaccination doses, but it may be a danger to certain pathological traits, but healthy, vaccinated people and those who have acquired immunity from previous infections are safe from it.

Thank you, Victor Omoboyede

In the user's guide, there is no method discussion. If you share some documentation, it would be a great help.

The DE-MEDT algorithm, or Differential Evolution combined with Modified Extremal Directed Tabu search, is a potent hybrid optimization algorithm aimed at solving both discrete and continuous variables in structural optimization problems.

In the context of truss structure optimization, the algorithm attempts to find an optimal solution considering both the size (usually a continuous variable) and the topology (usually a discrete variable) of the structure. This problem is challenging due to the mixed nature of the variables and the non-linear constraints often associated with engineering problems.

Here's how the DE-MEDT algorithm manages the trade-off between these variables:

Differential Evolution (DE): DE is a population-based optimization algorithm that deals with continuous variables. It uses a simple and powerful mutation strategy to generate new solutions. In truss structure optimization, DE can be used to optimize the size of the truss elements. The size variables are continuous and can be any real number within the specified range. DE is good at exploring the search space and escaping from local optima.

Modified Extremal Directed Tabu (MEDT) search: MEDT is used for dealing with discrete variables. The Tabu search is a meta-heuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. In the context of truss structure optimization, MEDT could be used to optimize the topology of the truss, i.e., deciding where to place the truss elements. The topology variables are discrete and can only be 0 (no bar) or 1 (bar exists). MEDT is capable of executing global exploration and local exploitation simultaneously.

The DE-MEDT algorithm manages the trade-off by incorporating DE and MEDT into a single framework, allowing them to work cooperatively to find the global optimum. The algorithm first uses DE to perturb the continuous variables (sizes) and then applies MEDT to adjust the discrete variables (topology). This process continues iteratively until a termination condition is met.

The success of DE-MEDT in managing the trade-off between discrete and continuous variables depends on how well it balances exploration (searching new areas of the solution space) and exploitation (refining solutions in a currently promising area). The hybrid nature of DE-MEDT, with DE providing powerful exploration and MEDT providing effective exploitation, makes it a capable tool for managing this trade-off in truss structure optimization.

With the progression of the detected case of desminopathy, the appearance of viruses and gram-negative rods was established, there was an excessive bacterial growth of the fecal microbiota with a pronounced increase in transient microorganisms, an increase in endotoxin. The results are presented in the article: https://www.researchgate.net/publication/372952519_CHANGE_CHARACTERISTICS_IN_SALIVA_AND_FECES_MICROBIOTA_OF_A_DESMINOPATHY_T341P_PATIENT

Although the number of individuals is small to conduct a statistical test and draw strong conclusions, you can still use the Chi-square test with null and alternative hypotheses. For more details, you may search any statistics homepage and apply the test in R or Python.

Hope it helps.

Best

J. Ramirez-Franco. Thank you so much for your fast replies. My images are already at 8-bit. They are coloured. GFP channel. They are quite dark when I open them on image J- this is because of the laser intensity on Zeiss that I wanted to moderate and I had to adjust brightness and contrast to decrease saturation of pixels.

I am attaching an example, but I had to increase the brightness & adjust the contrast so that u can see.

When I quantify them I normally increase the brightness/contrast without pressing 'apply' this allows me to identify where the ROI are.

thank you so much for the valuable suggestions

@tomas Hlsuka

You can look up the improtant oncogenic mutations (including p53) of that cell line in its Cellosaurus entry:

In it you will see that the p53 mutation for that cell line has been reported in five different papers.

Best

Amos

Mishal Shaheen the primers you are using are the entire sequence, right, 28 bp roughly that would make sense for the Tm you write. I don't think the temperatures are so different, i can't see that as a reason. The outer primers match 100% but the inner ones have 1 mismatch, so the temperature for the initial annealing steps will also go down a bit and be similar to the outer ones. And IF and IR are also similar, and one of them works. I am a bit skeptic about touchdown, because you get a pretty specific fragment already, and actually you can use a higher annealing T later (because after a few initial rounds of amplification there will be more template that already has the altered primer incorporated, so you could go up a couple of degrees.) What temperature do you use? 65, 67? I think oligo concentration is a better shot for optimization. If you want to try touchdown, here is a reference for how to set it up:

As mentioned I would see what temperature works well for the short fragment alone only using outer and inner primer for that fragment (but check the annotation, i think the actual 175 bp fragment is with IF and OF not as mentioned in the table. If i m not wrong the 175bp mutation corresponds to the reference human genome (i don't know anything about humans but maybe with that in mind you can get a control from some colleages somewhere, a clone or so?). If this strategy doesn't work, i think your polymorphism is also inside an ApaI site (GGGCCC), if this is true, you can use the outer primer fragment and digest with ApaI to see if you have the polymorphism or not.

With a purified, intact protein of uniform size, the resolution of basic MALDI-TOF should be sufficient to tell whether the molecular weight of the protein has changed by the expected amount, give or take a few daltons, as long as the protein is not too large.

Performing docking analysis for a protein-ligand complex is not exactly going to give comprehensive explanation on an activity potential. Like the routine goes, you identify the activation site then build a pocket around it to predict a poses (Rigid or flexible) that the ligand can form to sit well in the site, then score them based on RMSD for affinity strength. If you notice this condition, only the activation site is essential for this two function (pose forming and scoring). So if your protein structure is to have a mutant amino acid residue outside this box it has no influence in the docking profiling. Nonetheless, docking is not exactly a functional evaluation because the several other biochemical and chemical conditions that is needed is not present, the algorithm only mimic for structural effect.

If you need to do more on mutant functional effect on disease condition then that's a different analysis to pick on.

Hi Jude

in diploid organisms, running PCR for an heterozygous target just means that you have amplification of part of both chromosomes, not due to the double helix state of the genomic template.

fred

The phrase is referring to SNPs at the following genomic positions.

Hello Meilani Salsabila Daulay

The information is available on google. Nevertheless, I came across these links below which may be helpful.

Best.

That's a good approach Susanta Roy I would add that once you are working with your multiple sequence alignment (MSA) in Jalview (https://www.jalview.org), you load an experimental 3D protein structure, or an AlphaFold model (all possible from Jalview, just right-click on a sequence label), and visualise the mutations and conservation scores on the structure too. Jalview makes this easy by colouring the structure by the sequence, so you can choose to colour by conservation and add features to represent your mutations and they will instantly be viewable on the structure.

The other thing I would add is that in addition to BLASTing the full-length protein, have a look at it on InterPro and see what domains it has. Then you can work with curated MSAs from the individual domains too.

Great question Muhammad Abrar Yousaf !

What is your exact question? The first reply is a nice, but vague summary written by a chatbot. Any of those related to your research project?

Tomáš Hluska Thanks for your answer! My colleagues and I finally found out that there is another non-specific binding site for our primer within the target gene, and it is a problem without a solution, so we decide to use another method. Just cut off the tiny fragment of DNA containing the mutation site but not the non-specific binding site within the target gene and reinsert them into the target gene after mutation. Wish that would work. Otherwise, we have to let the company synthesise the target gene by its sequence.

If a mutation changes the recognition sequence for a restriction enzyme, the enzyme will not cut at that position, changing the band pattern of the digest. If the mutation is an insertion or deletion, the change in the size of the restriction fragment may be apparent in the digest by a change in the mobility of one of the bands.

When I try to use the gamultiobj, I set the lower and upper bound for the variables but it does not use them. The variables exceed them.

This is the code:

fit_funct=@obj_functs_taller

num_vab=2

A=[];b=[];

Aeq=[];beq=[];

lb=-1.5;

ub=1.5;

options = optimoptions(@gamultiobj,'PlotFcn',{@gaplotpareto,@gaplotscorediversity});

[xr,fvalr]=gamultiobj(fit_funct,num_vab,A,b,Aeq,beq,lb,ub,options);

And the function used:

function y=obj_functs_taller(x)

y(1)=exp(-prod(x-sqrt(1/3)));

y(2)=exp(-prod(x+sqrt(1/3)));

end

Ma'Mon Abu Hammad I really appreciate the information and tips we received. Thanks

Do you have a specific question?

This is an intriguing question. First, defining the protein's binding pocket by setting the grid box does not necessarily stop the ligand from interacting with other residues outside the grid box. Hence, mutating the residue in silico could have a potential impact on the docking scores which would be a major criterion in screening out the small molecules.

Also, taking into consideration the charge of the wildtype and mutant proteins, the modification of the protein would perturb the charge of the protein and could potentially have an impact on the interaction profiles of the protein with the compounds.

It will be good to perform both pre- and post-modification docking on the protein to take into account the potential impact of the residue change. Furthermore, I would advise you to get the sequence of the protein and conduct homology modelling using the crystal structure you currently have as the template as opposed to conducting modification using software like Chimera and PyMol.

The results of the transcriptome analysis of F1 hybrids suggest that the relationship between expression levels and allelic levels can be complex. However, the researchers were able to identify some of the factors that contribute to this complexity. This information could be used to develop new methods for predicting the expression of genes in F1 hybrids.

There are a number of factors that can contribute to the complexity of the relationship between expression levels and allelic levels. One factor is that the expression of a gene can be affected by a number of different factors, including the environment, the developmental stage of the organism, and the presence of other genes. Another factor is that the expression of a gene can be influenced by the interaction of different alleles. One allele may be dominant over another, meaning that it is expressed more strongly.

There are two concepts related to optimization.

1) Exploration

2) Exploitation

Through crossover you are exploiting (mostly) or searching solution within the found solutions i.e. new chromosomes are formed using same values (genes) of the chromosomes. Whereas mutation can help to bring some more diversity in the population and can mutate a gene to any random point. Hence, mutation can introduce more diversity through exploration of other solutions rather than existing solutions.

Mohammad Reza Moravej

Hello Rohit Rajan

Children are born with mutation in the Rb gene that they inherit from one of their parents or this gene change may happen during the very early stages of their development in the womb. Each cell normally has two Rb genes. As long as a retinal cell has at least one Rb gene that works as it should, it will not form a retinoblastoma. But when both of the Rb genes are mutated or missing, a cell can grow unchecked. This can lead to further gene changes, which in turn may cause cells to become cancerous.

On the other hand, p53 gene follows a different pattern in carcinogenesis. It may be mutated during life time when the body is exposed to cancer-causing substances in the environment (carcinogens) such as tobacco smoke, other toxic chemicals, ultraviolet light, radiation, etc. Somatic mutations in p53 gene are one of the most frequent alterations in human cancers, and germline mutations are the underlying cause of Li-Fraumeni syndrome, which predisposes to a wide spectrum of early-onset cancers.

A number of environmental mutagens have been identified that react with a specific set of DNA sequences which are found in the TP53 gene. As such they could contribute to an enhanced frequency of specific p53 mutant alleles. It is common that these mutagens further show tissue specificity for the cancers they promote and that enhances the evidence for a role by these mutagens. Changes in mutant p53 proteins, produces loss of function (DNA binding and transcription) or some mutant proteins have an allele-specific gain of function that promotes cancer.

Best.

If it is an X linked disorder and the child is a male then it seems quite normal. Alternatively if the biological father has a deletion of that locus then the child will be affected and hemizygous which looks homozygous on sequencing . Non paternity must always be considered in genetic disorders.I am assuming that this is not a mitochondrial disorder.

I'm not seeing anything that looks like a plasmid. Did you try including a sample of just the pet28a as a reference?

In your case, since both I-Tasser and Robetta predict that the mutation is located in a loop region of the protein, it's possible that the loop region is structurally flexible and can adopt different conformations. Additionally, it's possible that the loop region is not well conserved among homologous proteins, which can make it difficult to identify suitable templates for homology modeling. To choose the most reliable model, you can consider several factors, such as the quality of the model as assessed by various metrics (e.g., MolProbity, QMEAN), the completeness of the model (e.g., missing residues or side chains), the accuracy of the active site and other important regions, and the degree of similarity to available experimental structures (if any). It may also be helpful to perform molecular dynamics simulations or other validation methods to assess the stability and flexibility of the models and to further refine the models if necessary. Finally, it's important to keep in mind that homology modeling is a computational prediction method and the resulting models should be considered as hypotheses that require experimental validation.

Just to add a bit more complexity, unless you have very strong molecular evidence to back up the gene model, any PREDICTED introns/exons/promotors/etc are just predictions. Bioinformatics tools use "generally true, most of the time" rules. But if you care about a particular gene, you'll have to spend time either in the literature or at the bench to see the evidence that supports/refutes that model for that specific gene.

The "intron" might not even actually be an intron.

What is the fundamental understanding of the bystander effect in the development of drug resistance in cells?

The bystander effect refers to the phenomenon where cells that are not directly exposed to a drug can still develop resistance to it. This effect can occur through several mechanisms, including:

1. Secretion of signaling molecules: Cells that are directly exposed to a drug can secrete signaling molecules that can affect neighboring cells. These signaling molecules can induce changes in gene expression and cellular pathways that result in drug resistance.

2. Transfer of genetic material: Cells that are directly exposed to a drug can release small vesicles, such as exosomes or microvesicles, that contain genetic material such as RNA or DNA. These vesicles can be taken up by neighboring cells, resulting in the transfer of genetic material and the development of drug resistance.

3. Altered microenvironment: Cells that are directly exposed to a drug can change the microenvironment around them, leading to the development of drug resistance in neighboring cells. For example, the secretion of extracellular matrix proteins or cytokines can create a protective environment that shields cells from the effects of drugs.

The bystander effect can contribute to the development of drug resistance in cancer cells, where a small population of drug-resistant cells can emerge and propagate in the tumor microenvironment. This phenomenon can lead to the failure of chemotherapy and the recurrence of cancer.

Understanding the mechanisms underlying the bystander effect can help in the development of new therapeutic strategies that can overcome drug resistance. For example, targeting the signaling pathways or genetic material transfer mechanisms that mediate the bystander effect can prevent the development of drug resistance and improve the efficacy of chemotherapy.

I sincerely appreciate your help.

500ng of DNA template sounds like a lot to me. The quantity of DNA template will be a large proportion of pcr product. I know you are using the Dpn1 but thats not always perfect

One more point, is that gDNA is the only one that truly identifies a mutation. You can have an error that arose during transcription or cDNA synthesis that exists in the cDNA but since it is not hereditable, by definition it is not a mutation.

Hi...

It is possible that the CRISPR-Cas9 system was able to edit the DNA at the target site, even though the guide RNA sequence itself did not show any mutations. CRISPR-Cas9 works by using a guide RNA molecule to direct the Cas9 enzyme to a specific location in the genome. Once the enzyme is at the target site, it can make cuts in the DNA, resulting in deletions, insertions, or substitutions.

If the sequences flanking the guide RNA and PAM sequence show deletions, insertions, and substitutions, it is likely that the CRISPR-Cas9 system was able to make edits at the target site. However, it is important to carefully analyze the sequence data to confirm the presence and nature of the edits.

In some cases, the CRISPR-Cas9 system may not make any edits at the target site, even though the guide RNA is correctly targeted. This can occur if the DNA at the target site is resistant to cutting or if the repair mechanisms of the cell are able to accurately repair the cuts made by the enzyme.

I hope this information is helpful.

I am genuinely thankful for your assistance.

Zexiong Xie

pls try this and let me know

NSDUH_adults <- NSDUH_adults %>%

mutate(k6scale = cut(K6SCMON, breaks=c(-1,4,12,25), labels=c("Low k6", "Moderate k6", "High k6")))

What tissue type was used to determine the 50% VAF? Let's say it was cheek cells. Classically that person would be called "heterozygous" for that particular variant). Then you would expect all cells in their body to start out as heterozygous.

Somatic mutations are very common in cancers.

Run a full set of PCR controls AND include a sample from another tissue type if possible (e.g. cheek swab).

If you have the mutation then a strong band is expected. Ideally you should have a dna control sample which does have the mutation as a control sample in your pcr assay. This would tell you that the mutation is amplifying so that you can have more confidence that any small amplified band is the right thing and also that no band is a real result rather than a failed pcr

I agree with Katie A S Burnette that mutations are normally pretty rare. However if the protein being expressed is somewhat toxic to the cell then you may have a fairly strong selection for mutants that fail to express. This might include a variety of deletions in the plasmid or other rearrangements in addition to point mutations. It really would depend upon the product.

I appreciate the information and advice you have shared.

No, I want to amplify only the region containing these mutations is the target region for amplification.

UNIPROT

thank you all for your answers, would you please give more details, I am new to this area

With high fidelity Taq polymerase or DNA polymerase I, this will be no issue

Hello Amr; The only familiar place that I encounter the "waiting time problem" is in the creationist literature. In papers published by anthropologists and evolutionary biologists I don't think that you will encounter the term. Please correct me if I'm wrong. best regards, Jim Des Lauriers

Thank you Hanna,

This is a great example. I wasn't aware of it.

Daniel

Hi Lilian,

qPCR is used for measuring quantity of your target (ie, number copies of genes or precise sequences for qPCR on DNA, and expression of a gene for RT-qPCR on RNA or cDNA), whereas the castPCR will only used to genotype a variant, one mutation.

castPCR means "Competitive allele-specific TaqMan PCR", one evolution for the old but reliable ARMS-PCR.

see this paper for more explanations:

all the best

fred

You can install Bioedit software to visualize the sequence and also you can check the nucleotide alterations in comparison to reference genome.

Yes, as Sajed Sarabandi wrote, you can use any structure viewer to generate a static 2D image of your protein structure - however, in any single such an image, It would be near impossible to see und distinguish ~50 different mutation. In contrast, using an embedded 3D image, the user can rotate the protein to optimally see the residue of interest. In JSmol, you could attach a Javascript command to each residue in the sequence to temporally highlight that specific residue in the 3D image.

What are you using to plot with? You need to plot your tree as a cladogram rather than a phylogram. Both Dendroscope and Figtree have options for this (either explicitly setting plot type as cladogram, or something like "use equal branch lengths"), or if plotting in R using ape:::plot.phylo you can set use_edge_length=FALSE

Francisco J. Enguita

You can change the trajectory files to AMBER format using VMD.

In Hb T Cam

bodia, both the mutations (of Hb E and Hb D) are on same beta gene, but it will not give two bands of Hb E and Hb D; instead there will be a single variant band cathodal to Hb A2.

Thanks to all for taking the trouble to answer.

Best regards

Can I ask, what benefit do you see in using the R program for designing the minigene? Doing a from-start design, or starting on the primary suggestion, you may just place a start codon wherever it's useful.

Hi Zexu.

I think you can use this tool to pronounce any English word. You also have the option to choose between British English and American English.

Bye.

Yiheng Yang Hi

Hope you doing well

As per your queries, You can perform the MD of protein that is uploaded on SWISSMODEL or AlphaFold but one condition you should have accurate data from the servers.

my priority for choosing qPCR instead of Fish relates to the fact that qPCR is cost-effective and more easily paid for by patients. By the way, your answer helped me. thank you for your time.

It could be a trick question. How do we define "wild type" in a population with some diversity? If you have one or a few differences between two sequences, which is "normal" and which is "mutant". But maybe you should be viewing the sequences as amino acid (control-t to translate in BioEdit; but has to be in the right frame) and see if there is a mutation that introduces a stop codon.

Thank you, Abdul Basit.

Dear Akshay Shirsath

it depends if there is a PDB structure avaialble you can use softwares that calculate the energy changes induced by aminoacid replacment. I the past i used the Popmusic program (https://soft.dezyme.com/) and it works quite well.

If there is not structure avaialble, the prediction is much more difficult.

°One think that you can try is check how the mutation can change the prediction of the secondary structure of the protein, because a mutation that is predicted to destabilize a stable secondary strcture region as alpha-elix or Beta-shhet can have certanilly more important effect than a mutation that seems to do not alter it.

to calculate 2ndari structures i'm generally using the

which allow to use in parallel different algoritms.

good luck

Manuele

You have multiple bands on your gel, including the second PCR sample (from either the left or the right). I assume that means you did a gel excision and clean up before sequencing? Otherwise you will have overlapping sequence from two different PCR products.

Strange results. Could you show some 2D plots? At first glance, it looks like the Mu allele is only bound by the Mu probe (fig 1), but the WT allele can be bound by both the WT and Mu alleles (fig 2). If this is the case, the first thing I would try is raising the annealing/extension temp.

You should be looking to produce a VCF (variant call format) file for your samples. This requires all samples to be sequenced, then the reads mapped to a reference genome (same across all samples), then variants called.

Once you have done this, you can get the information you need about each variant position in the genome across all samples from this file.

df[df == 0] <- NA

df==0 when you have missing values as 0 if not just write code as df[df == ]

Hello Bishoy Abdel Malek

ClinVar is restricted to those variants that have been interpreted for clinical or functional significance. Each submission to ClinVar has five major categories of data: submitter, variation, condition, interpretation, and evidence. The interpretation of the variant is the focus of the ClinVar database and therefore it is a required field.

Please refer to the references attached below for more information. They will be helpful.

Best.

No answers in 2 years? For shame. I suggest MOE for mutating and simulating the effects. MOE makes it easy. You can mutate and energy minimize, or you can go all the way and run molecular dynamics with explicit solvent. MD can be done with atoms fixed or turned off to speed the calcualtions. MOE costs money but is worth it.

Sure, Mam, I will try and check for multiple clones. Thank you very much for your time and suggestions.

Your best option would be to align the sequences of all your alleles to look for conserved regions/domains and then design your gRNAs for those. For multiple sequence alignment I like to use MUSCLE but that is just my preference.

Hello Boudjema; Your last reply to Mr. Dsugutov is perfect! The Grants' research is the clearest demonstration of the phenomenon available to a nonspecialist audience. Well done! Best regards, Jim Des Lauriers