I am a third year student of genetics in Peru 🇵🇪 I’m interested in understanding biological phenomena from different approaches and multiple scales with the aim of developing comprehensive and effective treatments and technologies (mainly in chronic diseases) I enjoy doing a variety of things; lately I’ve been exploring mathematical modeling, music, and quantum physics. I love playing volleyball, cycling, and open water swimming.
Week 1 HW: Principles and Practices
Week 2 HW: Read, write and edit DNA
Includes question solving as preparation
Using Python to run Opentrons liquid handling robot
Weeek 4 HW: Protein design - Part 1
Exploring the world of AI
It’s achieved by inducing the expression of factors called “Yamanaka factors”, which enable a cell to regain a pluripotent state in which DNA methylation patterns and chromatin architecture are reset to a younger state in a “rejuvenation” process that enhances health and lifespan for individuals. (1, 2) Recently, a treatment intended to treat optic neuropathies based on this mechanism was approved by the FDA for its first clinical trials; this treatment consists of an AAV2 vector that carries the Oct4, Sox2, and Klf4 factors. Systemic doxycycline administration is needed for almost two months to activate OSK expression. (3) What if we could engineer a self-regulated genetic circuit that, by utilizing biosensors, detects aging or disease markers like transcription factors that enhance senescence-associated secretory phenotypes (SASPs), and thus activates itself and begins rewiring? To ensure non-malfeasance, the system incorporates a failsafe kill switch as a safety module that induces apoptosis if the cell loses its differentiated identity or if markers of full pluripotency, such as Nanog, are detected, to reduce the risk of cancer. By the time a “safe-by-design” technology like this becomes approved and available to the general public, there must be some important regulations upheld.
Actors: Researchers, Medics, Industry, Government and Patients
Main cast: Researchers, Industry
Main cast: Government, Medics, Patients
Main cast: Medics, Researchers
| Does the option: | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Enhance Biosecurity | |||
| • By preventing incidents | 1 | - | - |
| • By helping respond | - | - | 1 |
| Foster Lab Safety | |||
| • By preventing incident | - | - | - |
| • By helping respond | - | - | - |
| Protect the environment | |||
| • By preventing incidents | - | - | - |
| • By helping respond | - | - | - |
| Other considerations | |||
| • Minimizing costs and burdens to stakeholders | 3 | 1 | 3 |
| • Feasibility? | 1 | 3 | 2 |
| • Not impede research | 1 | 2 | 1 |
| • Promote constructive applications | 3 | 2 | 3 |
I believe that options 1 and 3 are the most important in this case, so I would recommend developing a standard circuit design that is self-regulating and has an external regulation mechanism in case, in a physician’s judgment, it is considered necessary to stop the process in any patient. I would suggest this kind of approach as a prerequisite for any autonomous reprogramming system intended for human use in the FDA.
What is the error rate of polymerase? 1:10^6
How does this compare to the length of the human genome? Human genome consist on 3.2 Giga base pairs, so at least more than 3 000 errors could be made in the synthesis of an entire human genome by one polymerase. But, since human methabolic pathways are at most 10kbp long, its negligible in the majority of context.
How does biology deal with that discrepancy? There are a lot of polymerase and DNA genome is separated in chromosomes, so chances of committing errors in replication are low, also thera are DNA mismatch reparation systems (MutS Repair System).
How many different ways are there to code for an average human protein? Considering that the average human protein has 1036 bp, and the genetic code is degenerate, you can make the same protein with different codons that in the end encode the same aminoacid, so there should be a lot of different ways.
In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest? This is primarily because, when using different base conjugations, some bases will be compatible with others, and in the case of biological synthesis, we could risk the formation of hairpin-like structures between them (as in the case of RNA proteins), which would force the termination of the translation when we want to obtain the proteins. There is also the error rate.
What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
Google Search: “Lysine Contigency”
(Part 2 consisted of “Gel Art - Restriction of Digests and Gel Electrophoresis”, intended to be carried out in the laboratory, since I do not have access to one, I did not complete it)
Part 1: Benchling & In silico gel art
Using a ladder Life 1 kb Plus and different restriction enzymes (EcoRI, XhoI, SalI, EcoRV, NdeI, BamHI, HindIII and KpnI) on the Lambda fage genome, I tried to draw a Teletubbie.
All started choosing my favourite protein…
Part 4: DNA Synthesis order, building my first plasmid
Stitching together the components to form a plasmid
Why and how
I went to UniProt page and retrieved Homo sapien’s PO5F1 sequence
https://www.uniprot.org/uniprotkb/Q01860/entry#sequences
Q01860-1: This isoform has been chosen as the canonical sequence
sp|Q01860|PO5F1_HUMAN POU domain, class 5, transcription factor 1 OS=Homo sapiens OX=9606 GN=POU5F1 PE=1 SV=1 MAGHLASDFAFSPPPGGGGDGPGGPEPGWVDPRTWLSFQGPPGGPGIGPGVGPGSEVWGI PPCPPPYEFCGGMAYCGPQVGVGLVPQGGLETSQPEGEAGVGVESNSDGASPEPCTVTPG AVKLEKEKLEQNPEESQDIKALQKELEQFAKLLKQKRITLGYTQADVGLTLGVLFGKVFS QTTICRFEALQLSFKNMCKLRPLLQKWVEEADNNENLQEICKAETLVQARKRKRTSIENR VRGNLENLFLQCPKPTLQQISHIAQQLGLEKDVVRVWFCNRRQKGKRSSSDYAQREDFEA AGSPFSGGPVSFPLAPGPHFGTPGYGSPHFTALYSSVPFPEGEAFPPVSVTTLGSPMHSN
OCT4-SOX2-bound nucleosome - SHL-6
From: Science 368 1460-1465 (2020) PMID 32327602
DOI 10.1126/science.abb0074
PO5F1 protein DNA sequence ATGGCGGGACACCTGGCTTCGGATTTCGCCTTCTCGCCCCCTCCAGGTGGTGGAGGTGATGGGCCAGGGGGGCCGGAGCCGGGCTGGGTTGATCCTCGGACCTGGCTAAGCTTCCAAGGCCCTCCTGGAGGGCCAGGAATCGGGCCGGGGGTTGGGCCAGGCTCTGAGGTGTGGGGGATTCCCCCATGCCCCCCGCCGTATGAGTTCTGTGGGGGGATGGCGTACTGTGGGCCCCAGGTTGGAGTGGGGCTAGTGCCCCAAGGCGGCTTGGAGACCTCTCAGCCTGAGGGCGAAGCAGGAGTCGGGGTGGAGAGCAACTCCGATGGGGCCTCCCCGGAGCCCTGCACCGTCACCCCTGGTGCCGTGAAGCTGGAGAAGGAGAAGCTGGAGCAAAACCCGGAGGAGTCCCAGGACATCAAAGCTCTGCAGAAAGAACTCGAGCAATTTGCCAAGCTCCTGAAGCAGAAGAGGATCACCCTGGGATATACACAGGCCGATGTGGGGCTCACCCTGGGGGTTCTATTTGGGAAGGTATTCAGCCAAACGACCATCTGCCGCTTTGAGGCTCTGCAGCTTAGCTTCAAGAACATGTGTAAGCTGCGGCCCTTGCTGCAGAAGTGGGTGGAGGAAGCTGACAACAATGAAAATCTTCAGGAGATATGCAAAGCAGAAACCCTCGTGCAGGCCCGAAAGAGAAAGCGAACCAGTATCGAGAACCGAGTGAGAGGCAACCTGGAGAATTTGTTCCTGCAGTGCCCGAAACCCACACTGCAGCAGATCAGCCACATCGCCCAGCAGCTTGGGCTCGAGAAGGATGTGGTCCGAGTGTGGTTCTGTAACCGGCGCCAGAAGGGCAAGCGATCAAGCAGCGACTATGCACAACGAGAGGATTTTGAGGCTGCTGGGTCTCCTTTCTCAGGGGGACCAGTGTCCTTTCCTCTGGCCCCAGGGCCCCATTTTGGTACCCCAGGCTATGGGAGCCCTCACTTCACTGCACTGTACTCCTCGGTCCCTTTCCCTGAGGGGGAAGCCTTTCCCCCTGTCTCCGTCACCACTCTGGGCTCTCCCATGCATTCAAAC
https://www.ncbi.nlm.nih.gov/nuccore/NM_002701.6?report=genbank
I went to NCBI genbank and retrieved POU5F1 mARN transcript variant, 1409bp long
I learned that there are spliced variants of OCT4 gene, that includes some introns and can have a more pivotal role in the induction of stemness properties (Yazd et al., 2011)
Reading the Yang et al. (2023) article, I saw in their “Materials and Methods” section, that they transfected pLVX-EF1alpha 2xGFP:NES-IRES-2xRFP:NLS to generate NCC-stable cells, so I thought it would be a good backbone for the assembly. This “shuttle vector” was originally described by Mertens et al. (2015) (AddGene ID: 71396) Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Mertens J, Paquola AC, Ku M, Hatch E, Bohnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy JR, Goncalves JT, Toda T, Kim Y, Winkler J, Yao J, Hetzer MW, Gage FH. Cell Stem Cell. 2015 Oct 6. pii: S1934-5909(15)00408-7. doi: 10.1016/j.stem.2015.09.001. 10.1016/j.stem.2015.09.001 PubMed 26456686
I selected restriction enzymes BamHI and EcoRI to cut the GFP reporter out, and then I built my DNA insert sequence (POU5F1 gene for Oct4) with sticky ends ideal to connect with the backbone.
I I incorporated a Kozak consensus sequence (GCCACC) upstream of the ATG instead of an RBS
And to add the 7xHis at the C-terminus of the protein, before the stop codon
The design leverages the backbone’s endogenous promoter and polyadenylation signal, so I didn’t incorporate those to the insert
https://benchling.com/s/seq-dcl08hA5mckl7k6SyMfX?m=slm-6JoFb5a3gBdbPekfEuDq
I tried ordering, but couldn’t so I made another insert, with a Trp-less GFP like in recitation, and got my Hight Copy cassette from Twist: https://benchling.com/s/seq-tiZifTIvfRfmji3ky9IP?m=slm-Xdd23sZE45BAObyU1yP8
I would like to sequence the plasmid I modified (pLVX-EF1alpha-POU5F1-7xHis-IRES-mCherry) as a way of experimentally verifying that there were no mutations and that the junctions integrated properly.
I would use the “Oxford Nanopore” sequencing method because it would allow me to find accidental recombinations and deletions by being able to sequence the entire plasmid.
I would like to synthesize de novo the entire modified POU5F1 (Oct4) cassett, because that way I can perform codon optimization and add the necessary elements (histidine tag and Kozak sequence) in the most efficient way.
I would use silicon-based DNA synthesis on microchips. Given that the Oct4 cDNA is approximately 1 kb, I really appreciate the ability to synthesize thousands of oligonucleotides in parallel with extremely high accuracy and at a much lower cost than traditional column synthesis.
I would like to edit the genome of the plasmid recipient cells so that they possess a specific locus where the POU5F1 cassette can be inserted without activating oncogenes or disrupting vital genes.
I would use Prime Editing because it would allow me to edit a small portion of the genome with considerable precision and turn it into a specific recognition site. Since it uses a Reverse Transcriptase (fused to Cas9 nickase) to write the new sequence directly into the DNA, it works perfectly in neurons or resting fibroblasts.
I wanted to recreate a solar disc
Part 2: Use of automation tools
References and what I intend to do
Well, of the three projects I’ve proposed, I’ll start with the second one, “Nostoc’s smart gummies”.
The objective here is to Characterize the sensitivity of heavy metal biosensors in Nostoc colonies and standardize the globular shape for the production of “gummies”
Echo: Precise gradient transfer of heavy metal solutions (lead and arsenic) at parts-per-billion (ppb) concentrations to Nostoc culture plates.
Multiflo: Dispensing of culture media optimized for cyanobacteria growth (BG-11).
PHERAstar: Measurement of the color intensity (absorbance) of reporter chromoproteins to establish the biosensor calibration curve.
Evolutionary Scale Model (ESM): A model that generates likelihood values for the existence of an amino acid at a specific position within a protein sequence. It is essentially a Masked Language Modeling (MLM) architecture.
Deep Mutational Scanning (DMS): A 2D map representing the probability values generated by the ESM for every possible mutation in a sequence.
Latent Space: Upon inputting a FASTA sequence, the language model adds start and end tokens, generating a high-dimensional vector (320 dimensions in the case of the recitation model). This vector classifies the protein; when projected into a 3D space, it creates a map that clusters the protein alongside others from a provided database based on functional or family similarities.
Multi-Layer Perceptron (MLP): Consist on two linear transformation with an non-linear activation (https://www.ultralytics.com/glossary/gelu-gaussian-error-linear-unit) in between.
Likelihood Generation: We utilize the fundamental property of the ESM (Masked Language Modeling) to calculate amino acid probabilities based on the sequence context.
Attention Mechanism: These data points are processed as Queries (Q), Keys (K), and Values (V). This generates an Attention Matrix, where vectors for each amino acid indicate the relevance and position of other residues in the chain.
Structural Extraction (Simple ESM): The raw attention matrix is symmetrized to ensure physically plausible distances and refined using Average Product Correction (APC) to eliminate correlation noise. Through regression, a 2D contact map is obtained.
Model Optimization: This map is compared against structural databases (like the PDB) to adjust the model. This feedback loop updates the model’s weights, maximizing the log-likelihood of real-world protein structures found in nature.
Enrichment via MLP: In parallel, the correlation vectors from the attention matrix are processed by a Multi-Layer Perceptron (MLP). This generates enriched vectors that describe the physicochemical implications of the attention data.
3D Prediction (ESM-2/ESMFold): In the ESM-2 architecture, these MLP-enriched vectors are fed into a “Folding Trunk” module. This module applies learned rules to predict the 3D structure, including bond angles and atomic coordinates.
