Gene Regulation - Science topic

Dissolve the oil using dmso and test the efficacy, a dmso alone control can be maintained to ensure the undesirable effects of dmso. Usually less than 0.05% dmso is effective. Best wishes.

The SV40 (Simian virus 40) promoter is a strong viral promoter commonly used for driving gene expression in various experimental systems. While the presence of a TATA box upstream of the transcription start site is a common feature in many promoters, the SV40 promoter is unique in that it lacks a canonical TATA box.

The SV40 promoter utilizes an alternative mechanism for transcription initiation called the "TATA-less" promoter. Instead of relying on a TATA box, it utilizes other elements and transcription factors to initiate transcription. The absence of a TATA box in the SV40 promoter does not necessarily impair its ability to drive gene expression.

Therefore, in your current cloning strategy where the transcription start site is located just 8bp from the end of the SV40 promoter, it is likely that the expression can still occur without the presence of a TATA box. The SV40 promoter contains other regulatory elements and transcription factor binding sites that can facilitate transcription initiation.

However, it's worth noting that the exact transcriptional activity may depend on the specific context and the downstream sequence elements present in your plasmid. Experimental verification, such as measuring the expression levels of your gene of interest, can help confirm the functionality of the modified SV40 promoter in your specific system.

Hi,

You can do qPCR to check the expression of the target genes.

Dear Geir Bjorklund, Duc M. Hoang, John Hildyard, thank you very much for your answers and recommendations!

Thank you for your suggestion Shambhabi Chatterjee!

It may be the case that I cam unable to effectively upregulate the target gene expression during the differentiation process due to to a reduction doxycycline response.

If your plasmid really is pPLc245 (and not some derivative of it) then it should not carry the lambda repressor gene and you should get constitutive expression at any temperature. So I can't really explain your result unless RT does not fold properly to have activity at RT or 28.

I'm actually a bit surprised this worked at all for you though, generally plasmids with superstrong promoters like lambda pL are not very stable under conditions where there is no repressor.

The cascade leading to the transcriptional factor activation is short, in the case of the canonical ones (NFkB, IMD). The accumulation of the transcripts is another subject matter. The structure of the promoters of AMPs suggest non immune related transcription factors are likely to bind and interfere with the specific transcription. HIF binding site, for instance, is frequently found nearby these genes, suggesting a potential hindrance upon redox stress.

Frontiers | Transcriptional and Post-transcriptional Control of the Nitrate Respiration in Bacteria | Molecular Biosciences (frontiersin.org)

Dear Jacob

Kindly read following review article in which they mentioned few online tools other than TRANSFAC

Best regards

DAVID adopts Fisher's Exact test to measure the gene-enrichment in annotation terms. It is just a matter of making a 2x2 contingency table, as in the example here: https://david.ncifcrf.gov/content.jsp?file=functional_annotation.html (section 2.2).

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A Hypothetical Example In the human genome background (30,000 genes total; Population Total (PT)), 40 genes are involved in the p53 signaling pathway (Population Hits (PH)). A given gene list has found that three genes (List Hits (LH)) out of 300 total genes in the list (List Total (LT)) belong to the p53 signaling pathway. Then we ask if 3/300 is more than a random chance compared to the human background of 40/30000. A 2 x 2 contingency table is built based on the above numbers: List Hits (LH) = 3 List Total (LT) = 300 Population Hits (PH) = 40 Population Total (PT) = 30,000

Exact p-value = 0.007. Since p-value < 0.05, this user's gene list is specifically associated (enriched) in the p53 signaling pathway by more than random chance.

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Hence, quantitative data are not considered in such enrichment analyses unless you don't want to calculate an additional activation/inhibition score, as computed by Ingenuity Pathway Analysis, for example.

alright then you can use lamda red to take the regulatory region of a plasmid for induction and recombine it instead of the native promoter. You can for example use pQE-5 as template (https://www.snapgene.com/resources/plasmid-files/?set=qiagen_vectors&plasmid=pQE-5) you add homology arms that are 50bp start from the atg codon from your target locus and extend 50bp and do the same with the other homology arm (I cannot advice where to stop since I do not know the locus you are targeting therefore you will have to use your own judgement for that). The pQE-5 plasmid has an IPTG regulated t5 promoter hence no need for a t7 strain and it also has an amp maker close to the promoter add your homology to this fragment allows to easily identify the recombinants that took up the promoter + amp (hence the wt promoter has been switched). Of course you can use whatever plasmid you want as template depending on the system you want to incorporate just make sure you gel purify your fragment before electroporation otherwise you will end you with the plasmid itself rather than the promoter swapped region.

awo! great, thanks

Hi,

Pharmacognosy Magazine, Bioinformation, Advances in Bioinformatics, Current Drug Discovery Technologies, Chemistry Research Journal, In silico Pharmacology, Medical Hypotheses, Journal of Biomolecular Structure and Dynamics.

Great! Hope you figured out how to get one induce the other. Good luck.

Is the recipe by Mark working? Please share the experience. Note section is little confusing. Can you please elaborate it @Mark

Dear Jeeja Jaya Krishnan ,

Does this tool answer your question?

DiVENN

northern blot analysis

Legitemedly, sure it would not be expressed, because as you knock-out the first exon probably by frameshift, all you get is residual of proteins which are not functional and decays swift. Sometimes, however, there are secondary starting codons in different part of exons which can produce sort of pseudogenes but rearly functional.

Nice Topic , Following

Good Answer Satyajeet Salunkhe

#GlimmerHMM is a new gene finder based on a Generalized Hidden Markov Model (GHMM). It actually helps you to annotate the draft genome by running a few simple commands.

https://twitter.com/H3nRasouli/status/1255052886985396224

What is m/z for plasticizers peaks?

Hi Hanwen,

There are a number of websites that compile gene information, but the biggest determinant for you in choosing a website is probably the one with the most relevant and detailed information for your organism. Some general websites include Uniprot and NCBI.

If you have a set of genes that you want to identify pathways, GO-term searches can be helpful and can be acquired from websites like http://amigo.geneontology.org/amigo.

In the end, I think the best source of information would be searching the primary literature or reviews that cover your gene of interest. You can find literature in Web of Science, Pubmed, or even Google Scholar.

If your gene has never been studied before, there still are ways to get some hypotheses on relevant pathways, namely to search for sequence homologues of your gene in other organisms where there may be more information (websites such as BLAST or https://www.flyrnai.org/cgi-bin/DRSC_orthologs.pl).

Thanks,

Barry

In brief,

1. Use the genomic sequence to find out potential transcription factor binding site(s) using the TRANSFAC program (or any other free program);

2. Generate a reporter construct with the promoter sequence to be tested;

3. Use potential sequence(s) for a candidate TF to perform binding assays using the labelled cis-sequence;

4. Mutate the potential TF binding site(s) in the reporter to demonstrate the specificity of binding;

5. Perform ChIP assays to demonstrate binding of a TF to cis-sequence in vivo (in cells).

You can visit this website http://www.sciomics.de/services/microarray-printing. Hope this helps

Regards

Dear Ali Javadmanesh, Adrian Fierl, Abdulnabi Abdullamer Matruod, thank you very much for your answers!

Agree with the author above. If there are multiple copies of GOI scattered around genome, Southern blot may not resolve all of them, and qPCR may also be inconclusive. You may need to do NGS using long read platform to convulsively determine the copy number. qPCR is handy for an estimate.

Hi Andrea,

RNA found in both plasma and serum is normally fragmented RNA or small RNA , mainly miRNA, and is either bound to proteins or contained inside extracellular vesicles. If your target is to purify mRNA from plasma/serum then you have to be aware that this mRNA may not be the full length, original RNA, that you may be getting when isolating RNA from whole blood.

I personally recommend for you using one of Norgen's Plasma/Serum RNA Purification kits ( https://norgenbiotek.com/sites/default/files/resources/Plasma-Serum-RNA-Purification-Kit-PI55000-5.pdf). These kits covers a sample volume range from 50ul up to 5mL without using any phenol or carrier RNA. Unlike other purification kits, Norgen's kits uses Silicon carbide as its separating matrix instead of silica. Some literature has mentioned that silica-based technology with/without phenol has a bias towards binding large RNA sizes as well as a bias towards binding RNA with sequences containing high GC contents whereas Norgen's Silicon carbide doesn't have such a bias.

For the isolation of RNA from blood, this should be a straight forward isolation but you have to use a different isolation method for this. I also recommend using Norgen's Total RNA Purificiation kit (Cat. 17200) for this.

When isolating RNA from plasma/serum you should be expecting low RNA amounts (normally in the picogram range) therefore you can't quantify you plasma/serum RNA using conventional methods such as regular spectrometer or Nanodrop since they are not sensitive enough to quantify such low RNA amounts. I recommend using Agilent Bioanalyzer RNA Pico Chip for quantifying plasma/serum RNA. Also don't relay on the RIN values from the bioanalyzer chips cause it will be low and won;t reflect the quality of the purified RNA. RIN values are calculated based on the ratio of the 28S rRNA and the 18S rRNA and since plasma/serum doesn't have cells therefore the purified RNA won't have these two bands which will lead to a very low RIN value. The best way to evaluate the quality of the purified RNA is to amplify a highly abundant small RNA target such as the 5s rRNA or to amplify a highly abundant miRNA such as miR-21. I believe that what Norgen does when performing small RNA sequencing services. https://norgenbiotek.com/services/small-rna-and-microrna-next-gen-sequencing

Sorry for the long message but I just want to give you some detailed information so you don't face any issues through out you project.

I hope this answered all of your concerns.

Best Regards and Good luck.

Biswajit Biswas I'd also recommend looking at the various ENCODE tracks. For example: https://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=725021383_RGLKNnOIuYWhC8gnLaWXEWCGidL3&c=chr21&g=wgEncodeTfBindingSuper gives you actual TFBS data from experiments. You can click on the names of any track to see detailed information. Keep clicking through the links on the subsequent pages to turn on/off specific TFs and cell lines.

For mouse, there are several experimental TFBS tracks in the mm9 assembly under "Expression and Regulation".

As Frederic Lepretre pointed out, the table browser option will allow you to pull the associated information for your genes/regions of interest. I'd also be happy to help further by private message, if you need it.

Good luck,

Ryan

Thank you, Mr. Nashaat Ghalib, for your interest. I found the opposite expression pattern of some important downstream gene.

Thank you for your suggestion.

How I can isolate WBC from whole blood ?

I think many of the respondents are mixing up the difference between positive and negative regulation, which is defined as the behavior of the regulatory protein, which is not the same as a inducible or co-repressible system. For example in E. coli both the arabinose operon and the lactose operon are inducible by the sugar, but lac operon is primarily controlled by a repressor and hence negative regulation whereas ara operon is positively regulated by a transcriptional activator (I realize that is an oversimplification for both but primarily correct).

For controlling gene expression and inducible system is normally easier to regulate because you just need to add the small molecule inducer to turn on gene expression, whereas in a co-repressible system you would need to remove the co-repressor (frequently an amino acid or something similar). So from that context inducible is much easier.

Whereas the difference between a negatively regulated vs a positively regulated system is about the behavior of the regulatory protein. The problem with a negatively regulated system is that for controlling a multi copy plasmid you need to have excess repressor present, and usually a single copy of the repressor gene on the chromosome does not suffice. So often negatively regulated promoters are not as tightly regulated and may have substantial basal expression. Whereas for positive regulation you don't have the same difficulty and the basal uninduced expression levels are usually lower (more tightly regulated).

As to why negative regulation is more common in plasmid systems, I think it primarily is historical. The lac operon was extremely well studied and understood in E. coli at the time many cloning and expression plasmids were being developed and it has just stayed with us.

May the the molecule of your interest which you are working is simply not involved in cascade. The result can be interpreted as it has no role in particular pathway or may be any other signaling deivn the proces. Thanks

Hello, look for your topic at science direct link below.

Neomycin is effective against jasmonates

good question

Hi there,

First of all you have to repeat the experiment in order to be able to validate the hypothesis that mutation has a significant effect at RNA/protein levels. If you want to normalize the data you definitely need an internal control (from a housekeeping gene both for RNA and protein levels).

If I may complete the previous answers: GSEA calculate enrichment of a set of genes over the entire genome. If you get hits in C3 TFT, it means that your set of genes are probable targets of the transcription factors that are indicated by GSEA. Therefore if what you call "hits" are the C3 TFT results, it's understandable that they are not (all) in your gene subset. However, if you look at the genes which make the C3 TFT result significant, they should all be a subset of your gene list. Now, if your set of genes is regulated by a protein of interest and you get indication of putative regulation by some TF, it might be that your protein of interest is another TF working in dimer or a transcriptional cofactor and it may be worth investigating.

I support the comments of MR Schaefer and I can contribute that epigenetic changes also interfere in the behavior of transgenic plants in field conditions.

Ok sir.

It all depends on the cell line, and the transfection reagent for it as well. Given the similarities between miRNA and siRNA, if you find reagents that work well for siRNA delivery it's likely they'll work for miRNA delivery as well. See this catalog (https://altogen.com/products-index/) and you'll see that different cell lines have different efficiencies, even for optimized reagents. Generics will work, but they won't be stellar. Look through previous studies to see what reagents they used for given cell lines, and you should be set for getting the empirical values for efficiency as well.

Dear Laura,

You don't give details about which association with breast cancer you are talking about but I assume it is association with risk (not outcomes) of breast cancer.

In this case (examining risk), the assumption is that this candidate SNPs would have its effects driving risk in a "normal cell" (the cell of origin of the tumor being studied) and therefore your best bet is to use a immortalized 'normal' MCF10A.

Sometimes 'normal' cells are not available or suffer from technical limitations (low transfection efficiency, or clumping during FACS analysis, for example). In this case, you could resort to cancer cell lines - understanding this is the 'next best model'.

If you decide to use cancer cells make sure you try to use a line that is representative of the tumor subtype associated with the SNP. Sometimes, a SNP is associated with risk of ER-negative breast cancer only and it would not be wise to use MCF7 or other ER-positive cell lines. You should be able to justify your choice of cell lines in light of the question you are asking.

Good luck,

alvaro

This is a very imprecise question. Is this specific for a strain of animals? Is it genetic and heritable? Does it cause a phenotype that is deterimental to fitness? Lot of genes are expresed in cells that may be superficial or unnecessary, but if they cause no harm then the cell has no reason to suppress expression. When you say earlier, do you mean in embryonic development? If so then the cells are likley to see a signal prematurely. They may have increased sensitivity to a peptide or morphogen, perhaps due to a variant in the receptor. Or, they may be missing a repressor that acts to inhibit expression early but is then switched off later. There are many models for how this could happen.

Methylation is a very location-specific mechanism for gene repression, such that hyper-methylation of the gene promoter is primarily linked to reduced transcriptional activity.

In the context of adolescent diseases, imprinting disorders such as Angelman and Prader-Willi syndromes are classical examples of abberent imprinting disorders:

If interested in somatic diseases associated with DNA hypermethylation, the above mention of cancer is a great example. Specifically for adolescents, osteosarcoma and various leukemias (e.g. Acute Myelogenous Leukemia, AML) are malignancies affective younger adults:

Hello guys, '

Is it a must to add PMSF ? I only have protease/phosphates inhibitor cocktail ?

You can insert a fragment of your promoter of interest in some plasmid, (for example, pGL3 Basic Vector) conjugated to a luciferase gene (reporter gene). Then you could create mutations in that recombinant plasmid to evaluate how the function of the promoter is affected. I did a similar approach using pGL3 Basic Vector from promega and I created different mutations using Q5® Site-Directed Mutagenesis Kit from NEB. At the end, you can evaluate the response of luciferase gene using the appropriate equipment. However, you need to consider the type of cells that you will use, the reporter plasmid and the length of the promoter that you want to evaluate.

I hope this helps.

Frances M

Thank you all for your answer!

Has it really been 5 years since I posted this question? How time flies! I no longer work in this field but thank you anyway.

PFA

The attached content shall be useful to you.

Hi Anil,

Indeed, there may be eRNAs at the enhancer of FT that we overlooked so I cannot guarantee their absence. Several non-coding RNAs, smRNAs, and mRNA datasets did not reveal them though. About the contact of the enhancer with the promoter at the FT locus, there are two papers published (including one from my previous group): Cao et al., 2014 (doi/10.1105/tpc.113.120352) and Liu et al., 2014 (DOI: 10.1038/ncomms5558). In these papers, interactions within the FT locus were investigated using 3C (chromosome conformation capture). Unfortunately, the interactions between the promoter of FT and its upstream region are discordant between the 2 studies. Several factors can explain this discrepancy, including the fact that relatively short distances were probed (less than 5 kb). At the end, I would be really carefull when interpreting 3C data since this method is really tricky (see Jamge et al., 2017, doi.org/10.1186/s13007-017-0251-x).

About other enhancers at FT locus, yes, there are others, but you will have to wait a bit more for our next paper to get out ;)

What do you mean by mutualism exactly?

To get more information about enhancers in plants, I refer you to our review (Weber et al., 2016, doi: 10.1016/j.tplants.2016.07.013).

Best regards,

Johan

Try to see the correlation between the gene expression and the lnc RNA expressions, as a preliminary analysis by writing a simple R script. You could choose the targets for the correlation analysis using online tools suggested by the answer above. There might be good tools which could do all this and much more bioinformatics analysis.

Hi Xin, You might want to take a look on this webpage:http://www.genecards.org/cgi-bin/carddisp.pl?gene=MYD88 It has info on regulatory elements from different sources:"...This subsection describes genomic regulatory elements related to the gene from GeneHancer. GeneHancer is a database of genome-wide enhancer-to-gene associations, embedded in GeneCards. Regulatory elements were mined from the following sources:

It also includes Transcription Factor Binding Sites within enhancers and Gene Targets for Enhancer. I hope is useful, irepan

Thanks a lot

Hi there. I am working on unfixed nuclei separation from tissue for chromatin studies. What I found was that sucrose in the buffer is essential for the integarity of nuclei, for it helps to maintain the osmatic pressure. 5%-15% sucrose all helps. Mg++ might be also important for the integarity of nuclei. When I simply use PBS, the nuclei don't look good. The DNA was leaking and thus clump them together.

Hi Sharif

Excellent question and very interesting reflection.

I suffered the same critics from reviewers. Thinking in a very simplistic way, complex eukaryotic organisms are very redundant. So, they have many ways to do the same things. In consequence it should be very difficult to find a "master" miRNA which will regulate a very important gene or pathway. On the other hand, a multiple and complex effect is expected, involving many miRNAs that are working together. Of course that you can find "master" miRNAs in simple organisms, but in humans this is very difficult.

If you want to find interesting miRNAs in a particular process, I would suggest you to use a complex approach, combining information about target recognition and also about pathways. You can distinguish different categories of targeted mRNAs if you pinpoint them into biochemical pathways. More relevant miRNAs will target "receptors" instead of "effectors".

Of course this is not an easy task.. Analysis of many variables is needed. More relevant miRNAs will be the ones that regulate more targets. On the other hand, the most relevant targets will be those regulated with more miRNAs at the same time.

Take a look at this paper where we did this kind of approach.

Best of luck. Waiting for your feedback.

Paco

Hello Gaurav, DO you know about this 

Hope that will help you. You can use transcription units data. 

Hi

there are lot of tools, but what you can do is a look on biogps (http://biogps.org/#goto=welcome) , the reactome (http://www.reactome.org) or DAVID (https://david.ncifcrf.gov) to analyze your lists of genes, just to begin. otherwise, my comments match Heena's ones, you should provide more details to get better answers.

fred

If you are sure that these genes are in the operon and can confirm these results using qRT-PCR, then the difference can be due transcriptional attenuation, e. g.  premature termination of transcription. Transcriptional termination is one of the mechanisms to regulate gene expression. 

The reaction is pretty straight forward. A potassium iodide solution is used as iodine itself is not very soluble and in aqueous solution s the triiodide ion is formed which binds with the spiral structure of the starch molecule and turn to purple-blue color from brown. This blue-black color can easily be measured using a spectrophotometer usually at 600nm. You can easily construct a standard curve using pure starch samples.

As far as a distinction between amylose and amylopectin is concerned, there are some fairly old but tried and tested protocols available that can be used to separate amylose and amylopectin, or if you know the ratio of the two in wheat samples you can get away with measuring just amylose. Also you may need to do a multi-wavelength study to get more accurate results. It can be a bit time consuming but not at all difficult. And yes your concerns about the kits are not unfounded, their prices can be exorbitant but mostly because they are enzyme based.

Double nicking in a 22-bp region can generate, in some cases, a "double stranded break" with free DNA-ends like a true (wild type) Cas9-mediated DSB. These ends are targets for exonucleases removing a lot of nucleotides ending up with a 100-bp deletion. 

Getting these free-ended DNA fragments can be generated due to replication or transcription, both of which are in most cases not easily controlled. 

I agree with Joseph Papamatheakis. If you need to maintain this for any length of time, you would have to make a heterozygous null using genome edits tools such as CRISPR/Cas9.

no,. it is about Tie2

We do automated high throughput screening in our facility for Chemical Genomics.  For an initial screen, all compounds are used at 10 uM in DMSO.  If you identify a hit, we then do a dose response curve over a broader range from 1 nM to 100 uM. 

Dear Kristiina,

mRNA markers for  IFN responses can be very cell/tissue specific.

But you can consider the following "general" interferons:

Interferon-α&β Receptor Ligands: IFNA1, IFNA4, IFNB1.

Cytokine Receptor Ligands: IFNA14, IFNA16, IFNA2, IFNA21, IFNA5, IFNA6, IFNA7, IFNA8, IFNE, IL15.

Interferon Related Genes: IFRD1, IFRD2, IFNL2, IFNL1, IL6.

You can also monitor the general Interferon Receptors:

Interferon-α&β Receptors: IFNAR1, IFNAR2.

Hematopoietin & Interferon-Class Receptors: IL10RB, IL11RA, IL12B, IL13RA1, IL20RA, IL21R, IL22RA2, IFNLR1, IL2RB...(there are few more)

Monitoring the "common" Interferon Regulatory Factors may also be a good idea:

IRF1, IRF2, IRF3, IRF4, IRF5, IRF6...

Usually we  initially test a broad range of mRNA markers to ID those best expressed in the specific cell line/tissue.

Good luck!

Hi, in the Bradford assay you have to prepare a standard curve, in particular you have a linear range of the assay for your standard protein, for example BSA, between 1 to 20ug or 20-140 ug. In the standard curve you have to add the ul of lysis buffer in which you resuspended your protein of interest. The same ul of buffer the you add to your blank sample. In your situation, if I understand correctly, the buffers are different (prepared with completely different reagents), so you have to prepare a blank and curve samples for each buffers, in this case 3 curves and 3 blanks (all in duplicates at least). Another way, not properly correct, it would be to prepare only a standard curve, without adding any buffers, only water, and prepare at least 3 different blanks.

For mouse gene ontology analysis, DAVID tool can be used.

Check this review for bioinformatics tools for lncRNA research

Use JASPAR

I read somwhere (I have to find this reference) that a mammalian cell has 5000-6000 expressed genes.

If the human genome has let's say 30000 genes, then around 1/5th of the genes are active in each cell type. And  around 4/5th of the genes then are repressed. Maybe this gives a starting point for some guesstimation work?

Maybe this researchgate discussion and the article attached can help you to resolve your issue

5hmC levels could be very low and not even detectable with qPCR. Did you use the control DNA (5mC, 5hmC and unmodified)? If those gave the expected results, then did you run a qPCR of your fragment of interest without any restriction enzyme? Was the result positive (do you have enough DNA to start with)? NEB Epimark is based on the same technology / concept (beta-glycosylaytion of 5hmC) than Active Motif Specific Chemical Labelling (SCL) precipitation of 5hmC that can also be used for detection at a single locus, except that NEB doesn't use precipitation but 5hmC sensitive restriction enzymes. As a SCL user, I have also been confronted to very high Ct values (> 38) at some loci which are very poorly enriched in 5hmC. How many cycles of qPCR are you doing? I usually run at least 45 cycles. Good luck !

I can recommend the CRISPR/Cas google group, things like this are discussed there in detail: https://groups.google.com/forum/#!forum/crispr

Stefanie,

Presumably, you have 16S  primers "specific" for different groups like Bacteroidetes, Firmicutes, Actinobacteria etc. and you are interested in verifying differences in their levels between different samples.  One approach is to use a "universal" 16S primer pair to generate a reference.  This should give you a measure of the "total" 16S DNA in each sample (which may or may  not be closely correlated with the weight of the original stool sample--you could look at that).  Then you are essentially comparing the proportion of each specific group within the total of all 16S sequences in that sample.  This approach is similar to the sequencing approach, which also measures the proportion of each group in the total sample without giving any information on the absolute number of each type.  A general problem with this approach is the specificity of the primers. Ideally, you would include controls for every type of bacteria that you are measuring and assess the degree to which your primers are specific for that type.  This may be a particular concern if you are trying to measure a type that has a low relative content in the total mix and the primers you are using will also weakly amplify a type of bacteria that is common in the total mix. You will not be able to detect a specific signal.

The question is: do you expect your data to follow a normal distribution? Do you know from earlier studies or logical reasoning that this kind of data is usually normally distributed?

Normality is an assumption for a t-test, which is no trivial thing. You explicitly "tell" your test that for each group, the further you move away from the mean in either direction, the less likely the observation. If you have good reason to assume that your data is indeed normal, and according to whichever test for normality (they all have their issues, especially with small sample size), your data indeed seems to be normal (or at least kind of symmetrically distributed around the mean), then the t-test is absolutely the way forward. In this way, you will have a lot more power to draw conclusions. 

I argue that for simple questions (I'm not talking about super high dimensional data, or machine learning), parametric testing is to be preferred if you have somewhat of an idea about where your data came from and what kind of distributions you expect.

Many people apply some kind of ad hoc method, where each set of observations is first tested for normality and depending on the outcome, a parametric or non-parametric test is chosen. There are several issues with this method. First, it leads to intellectual laziness by taking away the need for a scientist to wonder what kind of process generated his/her data. Next, it leads to a situation where outliers or deviations from expected distributions are overlooked ("hidden" behind a non-parametric test) and finally, just by the definition of many of those normality-tests, it will lead to the wrong choice of a test in about 5% of cases :)

Dear Lucia,

The following publications demonstrate specific blocking of the neutrophil migration:

Blood. 2012 Aug 16; 120(7): 1489–1498.

Prepublished online 2012 Jun 1. doi:  10.1182/blood-2012-01-404046

PMCID: PMC3423786

Jun-Xia Wang,1 Angela M. Bair,2 Sandra L. King,3 Ruslan Shnayder,1 Ya-Fang Huang,4 Chi-Chang Shieh,4 Roy J. Soberman,2 Robert C. Fuhlbrigge,3,5 and Peter A. Nigrovic 1,5

Author information ► Article notes ► Copyright and License information ►

This article has been cited by other articles in PMC.

Ly6G is a glycosylphosphatidylinositol (GPI)–anchored protein of unknown function that is commonly targeted to induce experimental neutrophil depletion in mice. In the present study, we found that doses of anti-Ly6G Abs too low to produce sustained neutropenia remained capable of inhibiting experimental arthritis, leaving joint tissues free of infiltrating neutrophils. Thioglycollate-stimulated peritonitis was also attenuated. No alteration in neutrophil apoptosis was observed, implicating impaired recruitment. Indeed, Ly6G ligation abrogated neutrophil migration toward LTB4 and other chemoattractants in a transwell system. Exploring the basis for this blockade, we identified colocalization of Ly6G and β2-integrins by confocal microscopy and confirmed close association by both coimmunoprecipitation and fluorescence lifetime imaging microscopy. Anti-Ly6G Ab impaired surface expression of β2-integrins in LTB4-stimulated neutrophils and mimicked CD11a blockade in inhibiting both ICAM-1 binding and firm adhesion to activated endothelium under flow conditions. Correspondingly, migration of β2-integrin–deficient neutrophils was no longer inhibited by anti-Ly6G. These results demonstrate that experimental targeting of Ly6G has functional effects on the neutrophil population and identify a previously unappreciated role for Ly6G as a modulator of neutrophil migration to sites of inflammation via a β2-integrin–dependent mechanism.

NATURE REVIEWS IMMUNOLOGY | REVIEW

Sofia de Oliveira,

Emily E. Rosowski & Anna Huttenlocher

Nature Reviews Immunology 16, 378–391 (2016) doi:10.1038/nri.2016.49

Yuan Liu,2 * Miriam B. O’Connor,2 * Kenneth J. Mandell,* Ke Zen,* Axel Ullrich,‡

Hans-Jo¨rg Bu¨hring,† and Charles A. Parkos3

CD47, a cell surface transmembrane Ig superfamily member, is an extracellular ligand for signal regulatory protein (SIRP). Interactions between CD47 and SIRP regulate many important immune cell functions including neutrophil (PMN) transmigration. Here we report identification of a novel function-blocking peptide, CERVIGTGWVRC, that structurally mimics an epitope on CD47 and binds to SIRP. The CERVIGTGWVRC sequence was identified by panning phage display libraries on the inhibitory CD47 mAb, C5D5. In vitro PMN migration assays demonstrated that peptide CERVIGTGWVRC specifically inhibited PMN migration across intestinal epithelial monolayers and matrix in a dose-dependent fashion. Further studies using recombinant proteins indicated that the peptide specifically blocks CD47 and SIRP binding in a dose-dependent fashion. Protein binding assays using SIRP domain-specific recombinant proteins demonstrated that this peptide directly bound to the distal-most Ig loop of SIRP, the same loop where CD47 binds. In summary, these findings support the relevance of CD47-SIRP interactions in

regulation of PMN transmigration and provide structural data predicting the key residues involved on the surface of CD47. Such peptide reagents may be useful for studies on experimental models of inflammation and provide a template for the design of anti-inflammatory agents. The Journal of Immunology, 2004, 172: 2578–2585.

Thomas S. Liang, Ji-Liang Gao, Omid Fatemi, Mark Lavigne, Thomas L. Leto and

Philip M. Murphy1

Laboratory of Host Defenses, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892

Spinorphin is an endogenous heptapeptide (leucylvalylvalyltyrosylprolyltryptophylthreonine), first isolated from bovine spinal cord, whose sequence matches a conserved region of β-hemoglobin. Also referred to as LVV-hemorphin-4 and a member of the nonclassical opioid hemorphin family, spinorphin inhibits enkephalin-degrading enzymes and is analgesic. Recently, spinorphin was reported to block neutrophil activation induced by the chemotacticN-formylpeptide N-formylmethionylleucylphenylalanine (fMLF), suggesting a potential role as an endogenous negative regulator of inflammation. Here we use both gain- and loss-of-function genetic tests to identify the specific mechanism of spinorphin action on neutrophils. Spinorphin induced calcium flux in normal mouse neutrophils, but was inactive in neutrophils from mice genetically deficient in the fMLF receptor subtype FPR (N-formylpeptide receptor). Consistent with this, spinorphin induced calcium flux in human embryonic kidney 293 cells transfected with mouse FPR, but had no effect on cells expressing the closely related fMLF receptor subtype FPR2. Despite acting as a calcium-mobilizing agonist at FPR, spinorphin was a weak chemotactic agonist and effectively blocked neutrophil chemotaxis induced by fMLF at concentrations selective for FPR. Spinorphin did not affect mouse neutrophil chemotaxis induced by concentrations of fMLF that selectively activate FPR2. Thus, spinorphi