<p>I am planning some experiments to investigate how mono cultures of microbes respond to different nutrient concentrations. I want to use an -omics based approach to understand how, on a molecular level, the microbes utilise these nutrients by looking for up regulation of associated genes. With the idea looking out for upregulation in mind, I was going to go for transcriptomics. But now I am beginning to realise that regulatory changes in genes does not necessarily translate to the molecular utilisation of a nutrient by a cell. To that end, perhaps I would be better using quantitative proteomics to capture changes in the concentration of functional proteins which are actually interacting with the nutrients. However, I worry that by relying on proteomics I might miss important regulatory changes for microbe species where the proteome is less well defined.</p>
<p>I should say that it's an either/or situation, there is only budget for one.
I'd appreciate any thoughts on the best approach.
Thanks.</p>

CC BY-SA 4.0
2021-08-17T15:18:07.493Z
My gut feeling is that you will be figuring out a lot more causality using the transcriptomic approach, but that might be just because I am a lot more familiar with that type of data. This is a really open-ended question I think, a lot more suited to discussion forums than Q&A sites.
Kamil S Jaron
CC BY-SA 4.0
2021-08-17T15:25:11.540Z
I agree that this is a discussion question. Proteomics is less available but also less sensitive, albeit requiring less corrections analysis-wise. For transcription factors and other low copy number protein transcriptomics is the best option. Bacteria do not go around sequestering mRNA as much as Eukaryotes, which is nice, but some phyla, produce compounds that make them problematic for RNA work (e.g. the osmolyte in Firmicutes spores (something-picolinate)), so proteomics > transcriptomics for Geobacillus etc. Also... there is metabolomics if enzymes are involved (but way less sensitive).
Matteo Ferla
CC BY-SA 4.0
2021-08-17T15:33:48.360Z
Also, before starting any -omics, check the quality of your reference genome if it is a non-standard species. This sounds unimportant, but if your have an auto-annotated reference genome, you will spend more time fixing it than anything else —e.g. pathway X is missing enzyme Y, but in the operon with WYZ there is a mystery enzyme, which guilty by association would suggest is a candidate, but you have to get more evidence.
Matteo Ferla
CC BY-SA 4.0
2021-08-18T13:30:37.200Z
@MatteoFerla Thanks for this. When you say quality, are you referring to coverage? The species I'm interested in has a reference genome with 39% coverage, is that likely to be sufficient?
phytofan
CC BY-SA 4.0
2021-08-18T14:50:39.790Z
No, I meant the quality of the annotations: the data is meaningless if too many of the genes are genes of unknown function or worse not what the automatic annotation claimed. I am assuming you have full coverage. `reference genome with 39% coverage` is unusable.
Matteo Ferla
<transcriptome><proteomics><microbiology>
Transcriptomics vs Proteomics for understanding molecular responses
# Transcriptomics vs Proteomics for understanding molecular responses

(Asked by: username_0 on 2021-08-16)

I am planning some experiments to investigate how mono cultures of microbes respond to different nutrient concentrations. I want to use an -omics based approach to understand how, on a molecular level, the microbes utilise these nutrients by looking for up regulation of associated genes. With the idea looking out for upregulation in mind, I was going to go for transcriptomics. But now I am beginning to realise that regulatory changes in genes does not necessarily translate to the molecular utilisation of a nutrient by a cell. To that end, perhaps I would be better using quantitative proteomics to capture changes in the concentration of functional proteins which are actually interacting with the nutrients. However, I worry that by relying on proteomics I might miss important regulatory changes for microbe species where the proteome is less well defined.

I should say that it's an either/or situation, there is only budget for one.
I'd appreciate any thoughts on the best approach.
Thanks.

**Tags:** transcriptome, proteomics, microbiology
> **Comments:**
> **username_1**: My gut feeling is that you will be figuring out a lot more causality using the transcriptomic approach, but that might be just because I am a lot more familiar with that type of data. This is a really open-ended question I think, a lot more suited to discussion forums than Q&A sites.  
> **username_2**: I agree that this is a discussion question. Proteomics is less available but also less sensitive, albeit requiring less corrections analysis-wise. For transcription factors and other low copy number protein transcriptomics is the best option. Bacteria do not go around sequestering mRNA as much as Eukaryotes, which is nice, but some phyla, produce compounds that make them problematic for RNA work (e.g. the osmolyte in Firmicutes spores (something-picolinate)), so proteomics > transcriptomics for Geobacillus etc. Also... there is metabolomics if enzymes are involved (but way less sensitive).  
> **username_2**: Also, before starting any -omics, check the quality of your reference genome if it is a non-standard species. This sounds unimportant, but if your have an auto-annotated reference genome, you will spend more time fixing it than anything else —e.g. pathway X is missing enzyme Y, but in the operon with WYZ there is a mystery enzyme, which guilty by association would suggest is a candidate, but you have to get more evidence.  
> **username_0**: @MatteoFerla Thanks for this. When you say quality, are you referring to coverage? The species I'm interested in has a reference genome with 39% coverage, is that likely to be sufficient?  
> **username_2**: No, I meant the quality of the annotations: the data is meaningless if too many of the genes are genes of unknown function or worse not what the automatic annotation claimed. I am assuming you have full coverage. `reference genome with 39% coverage` is unusable.  

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## 0 answers

