Biología holística de microorganismos

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.title[
# Biología holística de microorganismos
]
.subtitle[
## Genómica, Transcriptómica y proteómica
]
.author[
### Sergio Paolo Barahona Padilla
]
.institute[
### DBT-735 Principios de Bioinformática
]
.date[
### 2024-05-22
]

---

# Book: Microbial Proteomics

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## here() starts at G:/Mi unidad/RProjects/xaringan
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<img src="omics0.png" width="900" />
Webpage: [Wiley Online Library](https://onlinelibrary.wiley.com/doi/10.1002/0471973165.ch1)
---
# Content
1. Introduction
2. Proteome in Perspective
3. Amalgamation of Tools for a Proteomic “Tool Box”
4. Relationship between Genome, Transcriptome, and Proteome
5. What have Model Organisms Contributed to our Understanding of Biological Systems?
6. Conclusion
---
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# 1. Introducction
---
# 'Omes'

<img src="omics1.png" width="900" />
Reference: Virág et al. (2020) doi: [10.1007/s10337-019-03796-9](https://doi.org/10.1007/s10337-019-03796-9)
---
# Classic relationships between omes
<img src="omics26.png" width="700" />

Reference: Wasinger & Corthals (2002) doi: [10.1016/s1570-0232(02)00125-3](https://www.sciencedirect.com/science/article/pii/S1570023202001253)
---
# Actual relationships between 'omes'

<img src="omics2.png" width="900" />
Reference: Dunn et al. (2011) doi: [10.1039/B906712B](https://doi.org/10.1039/B906712B)
---
# Network Across Multiple Omic Layers

<img src="omics3.png" width="900" />
Reference: Yugi et al. (2016) doi: [10.1016/j.tibtech.2015.12.013](http://dx.doi.org/10.1016/j.tibtech.2015.12.013)
---
# Trans-omic analysis in *E. coli*

<img src="omics4.png" width="900" />
Image's reference: Yugi et al. (2016) doi: [10.1016/j.tibtech.2015.12.013](http://dx.doi.org/10.1016/j.tibtech.2015.12.013)

Original paper: Ishii et al. (2007) doi: [10.1126/science.1132067](http://dx.doi.org/10.1126/science.1132067)
---
# Trans-omic analysis in *B. subtilis*

<img src="omics5.png" width="900" />
Image's reference: Yugi et al. (2016) doi: [10.1016/j.tibtech.2015.12.013](http://dx.doi.org/10.1016/j.tibtech.2015.12.013)

Original paper: Buescher et al. (2012) doi: [10.1126/science.1206871](http://dx.doi.org/10.1126/science.1206871)
---
# 'Omes' + environment = phenotype

<img src="omics6.png" width="500" />

Reference: Yugi et al. (2016) doi: [10.1016/j.tibtech.2015.12.013](http://dx.doi.org/10.1016/j.tibtech.2015.12.013)
---
# Genomics alone is not enough

<img src="omics7.png" width="900" />
---
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# 2. Proteome in Perspective
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# Proteome 

Set of all proteins expressed in a given condition and given location. 
<img src="omics8.png" width="900" />
Image credit: [PastelBio](https://x.com/PastelBio/status/720536450803097600/photo/3) on X 
---
# Proteome 

*“The sum of proteoforms expressed in a selected type of biological material at a certain time”.* (Ponomarenko et al., 2016).

Reference: Carbonara et al. (2021) doi: [10.3390/proteomes9030038](https://doi.org/10.3390/proteomes9030038)
---
# Isoformes vs Proteoformes
<img src="omics22.png" width="900" />

Reference: Hollas et al. (2022) doi: [10.1093/nar/gkab1086](https://doi.org/10.1093/nar/gkab1086)
---
# Proteome complexity: reasons?
Proteome complexity can be explained from three diverse perspectives:
1. **Evolutionary complexity**
2. Internal complexity
3. Sample complexity
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# 2.1 Evolutionary complexity 
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# Evolutionary complexity 
Evolutionary complexity does not depend on **genome size** (c-value paradox)
<img src="omics11.png" width="550" />

Image credit: [Animal Genome Size Database](http://www.genomesize.com/statistics.php) 
---
# Evolutionary complexity 
Evolutionary complexity does not depend on **genome size** (c-value paradox)
<img src="omics12.png" width="700" />

Reference: Lackotia (2023) doi: [10.1016/j.bbadva.2023.100107](http://dx.doi.org/10.1016/j.bbadva.2023.100107)
---
# Evolutionary complexity 
Evolutionary complexity also does not depend on the **number of genes**.
<img src="omics13.png" width="700" />

Reference: Wasinger (2005) doi: [10.1002/0471973165.ch1](https://onlinelibrary.wiley.com/doi/10.1002/0471973165.ch1)
---
# Evolutionary complexity 
Evolutionary complexity depends on **protein regulation**:
- Protein abundance
- Proteome complexity
---
# Protein abundance: highly regulated
- Protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. These rates are controlled by diverse **regulators**.
- Apparently, there are infinite translation/transcription ratios ("Crick Space")

Reference: Hausser et al. (2019) doi: [10.1038/s41467-018-07391-8](http://dx.doi.org/10.1038/s41467-018-07391-8)
---
# Protein abundance: highly regulated

- Genes prefer combining high transcription rates with low translation rates
- Depleted space of Crick space: almost no gene
<img src="omics16.png" width="500" />

Reference: Hausser et al. (2019) doi: [10.1038/s41467-018-07391-8](http://dx.doi.org/10.1038/s41467-018-07391-8)
---
# Protein abundance: highly regulated
<img src="omics15.png" width="900" />
Reference: Harper & Bennett (2016) doi: [10.1038/nature19947](http://dx.doi.org/10.1038/nature19947)
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# Proteome complexity
*Homo sapiens*: 40 proteins/gen, *E. coli*: 1 proteins/gen, *S. cerevisiae*: 3 proteins/gen

Reference: Harper & Bennett (2016) doi: [10.1038/nature19947](http://dx.doi.org/10.1038/nature19947)
---
# Proteome complexity: reasons?
Proteome complexity can be explained from three diverse perspectives: 
1. Evolutionary complexity
2. **Internal complexity**
3. Sample complexity
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# 2.2 Internal complexity 
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# Protein interactome networks
<img src="omics17.png" width="550" />

Reference: Carrera et al. (2017) doi: [10.3389/fmicb.2017.02458](https://doi.org/10.3389/fmicb.2017.02458)
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# Protein interactome networks
<img src="omics18.png" width="500" />

Reference: Elhabashy et al. (2022) doi: [10.1016/j.str.2022.02.004](https://doi.org/10.1016/j.str.2022.02.004)
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# Protein-Protein interations (PPI)
Supramolecular complexes formed through protein-protein interactions.
<img src="omics19.png" width="900" />

Reference: Elhabashy et al. (2022) doi: [10.1016/j.str.2022.02.004](https://doi.org/10.1016/j.str.2022.02.004)
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# 3. Amalgamation of Tools for a Proteomic “Tool Box”
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# Technologies for proteomics
<img src="omics27.png" width="450" />

Reference: Wasinger & Corthals (2002) doi: [10.1016/s1570-0232(02)00125-3](https://www.sciencedirect.com/science/article/pii/S1570023202001253)
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# Types of proteomics workflow
<img src="omics20.png" width="650" />

Image credit: [Sanju Tamang](https://microbenotes.com/proteomics/) on Microbe Notes 
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# Top-down proteomics in bacteria
<img src="omics21.png" width="900" />
Image credit: [Sanju Tamang](https://microbenotes.com/proteomics/) on Microbe Notes 
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# Challenges of proteomics
- Proteins with low abundance, i.e. insufficient concentration for biochemical analyses, e.g. 2D-PAGE (*dark proteome*): **dynamic range of protein expression**
- High abundance of chemically similar proteins due to PTMs (proteoformes): **complex samples**
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# Benefits of proteomics
## Discovery and development of vaccines
- Antigen discovery (immunogenic proteins as potential vaccine targets)
- Vaccine development
- Vaccine characterization
---
# Benefits of proteomics
## Discovery and development of vaccines
<img src="omics24.png" width="600" />

Adamczyk-Poplawska et al. (2011) doi: [/10.1016/j.jprot.2011.01.019
](http://dx.doi.org/10.1016/j.jprot.2011.01.019) 
---
# Benefits of proteomics
## Discovery and development of vaccines
<img src="omics23.png" width="550" />

Reference: Galassie & Link (2015) doi: [10.1002/prca.201500054](https://doi.org/10.1002/prca.201500054)
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# Benefits of proteomics
## Discovery and development of vaccines in *H. pylori*
<img src="omics25.png" width="900" />
Reference: Hornburg et al. (2019) doi: [10.1038/s41598-019-53493-8](http://dx.doi.org/10.1038/s41598-019-53493-8)
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# 4. Relationship between Genome, Transcriptome, and Proteome
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# Transcriptomics and proteomics

## Correlation between mRNA and protein
- mRNA level (transcriptomics) **is not** a good predictor of protein abundance (proteomics).
<img src="omics28.png" width="900" />
Reference: Kumar et al. (2016) doi: [10.1002/pmic.201600140](http://dx.doi.org/10.1002/pmic.201600140) 
---
# Transcriptomics and proteomics
## Correlation between mRNA and protein
In yeast, abundant (high copy number) proteins are well correlated with mRNA abundance
<img src="omics31.png" width="800" />

Reference: Futcher et al. (1999) doi: [10.1128/MCB.19.11.7357](http://dx.doi.org/10.1128/MCB.19.11.7357) 
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# Transcriptomics and proteomics
## Correlation between mRNA and protein
In yeast, low copy number proteins are poorly correlated with mRNA abundance
 
<img src="omics29.png" width="600" />

Reference: Gygi et al. (1999) doi: [10.1128/MCB.19.3.1720](http://dx.doi.org/10.1128/MCB.19.3.1720)
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# Transcriptomics and proteomics
## Correlation between mRNA and protein

 
<img src="omics30.png" width="600" />

Reference: Gygi et al. (1999) doi: [10.1128/MCB.19.3.1720](http://dx.doi.org/10.1128/MCB.19.3.1720)
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# Transcriptomics and proteomics
## Correlation between mRNA and protein
2D electrophoresis and LC/MS methods are highly biased towards the detection of highly abundant proteins. Low-abundance proteins are usually not detected.
<img src="omics32.png" width="600" />

Reference: Ghaemmaghami et al. (2003) doi: [10.1038/nature02046](http://dx.doi.org/10.1038/nature02046)
---
# Transcriptomics and proteomics
## Correlation between mRNA and protein

<img src="omics33.png" width="600" />

Reference: Ghaemmaghami et al. (2003) doi: [10.1038/nature02046](http://dx.doi.org/10.1038/nature02046)
---
# Transcriptomics and proteomics
## Correlation between mRNA and protein

<img src="omics34.png" width="600" />

Reference: Ghaemmaghami et al. (2003) doi: [10.1038/nature02046](http://dx.doi.org/10.1038/nature02046)
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# 5. What have Model Organisms Contributed to our Understanding of Biological Systems?
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# Model organisms
<img src="omics36.png" width="900" />
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# Thank you