It's about transcriptomics. mid-log phase bacteria divided into 2 conditions, with and without antibiotic but I wanna know the incubation time before RNA extraction
some paper said 30min, some about 15min
could I try qPCR at time points to check one of known gene that upregulated when exposed to the antibiotics
or are there any easier methods or research papers mention about incubation time after exposed to the drug before perform RNA extraction
thank you in advance
It very much depends what you are measuring and how. To look at only de-novo transcription in that window, you need a specialised method such as incorporation of modified NTP, or ChIP of nascent RNA chains, or run-off transcription. You don't mention anything like that, so I assume you are measuring transcript levels using a generic method, such as a microarray or RNA-seq. In that case, with a short time course, you will only detect UPREGULATED transcripts, because the transcripts of genes whose transcription slows down (or even stops completely) have a certain half-life and may linger for a long time before you can observe a drop in their levels. In fast cycling cells, such as bacteria, a lot of the effect is down to the dilution after cell division. If your antibiotic affects that, the maths is off anyway. It will vary between transcripts, so global assumptions needed for microarray or RNA-seq analysis need to be carefully applied.
There are many other factors that affect the choice of time point: the strength of the promoter before and after drug application (inactive promoter which is switched ON is easy to detect quickly. An active promoter that is enhanced is much harder and will take longer to be reliably measurable), the length of the transcription unit and the position of your real-time PCR primers within it (close to promoter or many kb away from it on a polycistronic transcript?).
If you have a candidate gene, definitely do a detailed time-course real-time PCR evaluation as a first step. Then you can take 2-3 key time points for genome-wide measurements.
Good luck!
It very much depends what you are measuring and how. To look at only de-novo transcription in that window, you need a specialised method such as incorporation of modified NTP, or ChIP of nascent RNA chains, or run-off transcription. You don't mention anything like that, so I assume you are measuring transcript levels using a generic method, such as a microarray or RNA-seq. In that case, with a short time course, you will only detect UPREGULATED transcripts, because the transcripts of genes whose transcription slows down (or even stops completely) have a certain half-life and may linger for a long time before you can observe a drop in their levels. In fast cycling cells, such as bacteria, a lot of the effect is down to the dilution after cell division. If your antibiotic affects that, the maths is off anyway. It will vary between transcripts, so global assumptions needed for microarray or RNA-seq analysis need to be carefully applied.
There are many other factors that affect the choice of time point: the strength of the promoter before and after drug application (inactive promoter which is switched ON is easy to detect quickly. An active promoter that is enhanced is much harder and will take longer to be reliably measurable), the length of the transcription unit and the position of your real-time PCR primers within it (close to promoter or many kb away from it on a polycistronic transcript?).
If you have a candidate gene, definitely do a detailed time-course real-time PCR evaluation as a first step. Then you can take 2-3 key time points for genome-wide measurements.
Good luck!
Yes ...You can check the regualtion of gene expression at different intervals using Real time PCR or quantitate RNA expression by next gen sequencing.
all the best
I would like looking at the OD value of the bacterial culture under different antibiotics and trying to establish a growth curve based on which you can find the time points at equal OD value ( as growth matters on gene transcript levels ), then you follow gene expression analysis in a time -course manner. Good Luck
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