![sfGFP amino acids](<./sgGFP Aas.jpg>)
MSKGEELFTGVVPILVELDGDVNGHKFSVRGEGEGDATNGKLTLKFICTTGKLPVPWPTL
VTTLTYGVQCFSRYPDHMKRHDFFKSAMPEGYVQERTISFKDDGTYKTRAEVKFEGDTLV
NRIELKGIDFKEDGNILGHKLEYNFNSHNVYITADKQKNGIKANFKIRHNVEDGSVQLAD
HYQQNTPIGDGPVLLPDNHYLSTQSVLSKDPNEKRDHMVLLEFVTAAGITHGMDELYKGS
HHHHHH
![sgGFP Aas](/attachments/d12c476e-50db-469a-82d0-41c5496d9b00)
![sfGFP protein PDB](/attachments/8e400e42-cded-484d-97f0-8e28a82b2fdf)

Back-translated / codon-usage–matched CDS (low GC target):
ATGTCAAAAGGTGAGGAATTATTTACCGGAGTAGTACCAATACTGGTAGAATTAGATGGCG
ATGTTAATGGGCATAAGTTTTCAGTGCGTGGAGAAGGAGAAGGCGATGCTACAAATGGAAA
ATTAACGTTAAAATTTATTTGTACTACTGGGAAACTACCTGTACCTTGGCCAACTTTAGTT
ACAACCTTAACATATGGTGTACAATGTTTTTCTCGTTATCCAGATCATATGAAACGTCATG
ATTTTTTTAAAAGTGCGATGCCTGAAGGTTACGTTCAAGAAAGAACTATATCTTTTAAAGAT
GATGGTACATATAAAACACGAGCTGAAGTAAAATTTGAAGGTGATACTTTGGTTAATAGAAT
TGAACTTAAAGGGATTGATTTTAAGGAAGATGGAAATATTCTCGGACACAAATTAGAATACA
ATTTTAATTCACATAATGTTTACATAACAGCTGATAAACAAAAAAATGGCATAAAAGCAAAT
TTTAAAATAAGACATAATGTAGAAGATGGAAGTGTCCAATTAGCAGATCATTATCAGCAAAA
CACACCAATTGGTGATGGTCCTGTCCTTTTACCAGATAATCATTATTTATCAACCCAATCTG
TTTTGTCAAAAGATCCGAATGAAAAAAGAGATCATATGGTTTTATTGGAATTTGTAACAGCA
GCAGGTATTACTCATGGCATGGATGAATTATATAAAGGCTCTCATCATCATCATCATCAT


Codon optimization for E. coli

I then codon-optimized the CDS for Escherichia coli using a “use best codon” strategy. As expected, the amino-acid sequence is unchanged, but the nucleotide sequence changes due to synonymous codon choices that better match E. coli translation preferences.

Nucleotide identity (baseline vs optimized): 76.96%

GC content (baseline, codon-usage–matched): 33.0%

GC content (optimized, best-codon): 50.0%

Rare codons: 11 (baseline) vs 0 (optimized)

Hairpins (reported by the tool): 0 in both

Thymine fraction (reported by the tool): 0.30 (baseline) vs 0.21 (optimized)
ATGAGCAAAGGCGAAGAACTGTTTACCGGCGTGGTGCCGATTCTGGTGGAACTGGATGGCGAT
GTGAACGGCCATAAATTTAGCGTGCGCGGCGAAGGCGAAGGCGATGCGACCAACGGCAAACT
GACCCTGAAATTTATTTGCACCACCGGCAAACTGCCGGTGCCGTGGCCGACCCTGGTGACCA
CCCTGACCTATGGCGTGCAGTGCTTTAGCCGCTATCCGGATCATATGAAACGCCATGATTTT
TTTAAAAGCGCGATGCCGGAAGGCTATGTGCAGGAACGCACCATTAGCTTTAAAGATGATGG
CACCTATAAAACCCGCGCGGAAGTGAAATTTGAAGGCGATACCCTGGTGAACCGCATTGAAC
TGAAAGGCATTGATTTTAAAGAAGATGGCAACATTCTGGGCCATAAACTGGAATATAACTTT
AACAGCCATAACGTGTATATTACCGCGGATAAACAGAAAAACGGCATTAAAGCGAACTTTAA
AATTCGCCATAACGTGGAAGATGGCAGCGTGCAGCTGGCGGATCATTATCAGCAGAACACCC
CGATTGGCGATGGCCCGGTGCTGCTGCCGGATAACCATTATCTGAGCACCCAGAGCGTGCTG
AGCAAAGATCCGAACGAAAAACGCGATCATATGGTGCTGCTGGAATTTGTGACCGCGGCGGGC
ATTACCCATGGCATGGATGAACTGTATAAAGGCAGCCATCATCATCATCATCATCAT

Best way to obtain the DNA

For a ~0.74 kb CDS like sfGFP, the most straightforward approach is gene synthesis (ordering a dsDNA fragment). It is fast, accurate, and does not require an existing template. If a plasmid template is already available, an alternative is PCR amplification + cloning (e.g., restriction cloning or Gibson), but synthesis avoids PCR-introduced mutations and simplifies the workflow.
Codon-optimized CDS (best codons, medium GC target)

## Part 4 — DNA Write (Ordering + Construct Design)

### 4.1 Expression cassette design (what I would build)
To express **sfGFP in *E. coli***, I would build a standard bacterial expression cassette:

- **Promoter:** T7 promoter (for high expression in BL21(DE3)-like strains) or a strong constitutive promoter if T7 is not desired  
- **RBS:** strong bacterial RBS (e.g., a consensus Shine–Dalgarno / gene10-like RBS)  
- **CDS:** sfGFP coding sequence, codon-optimized for *E. coli* (AA sequence unchanged)  
- **Tag / stop:** optional **C-terminal 6xHis** tag for purification + **stop codon**  
- **Terminator:** strong transcription terminator (e.g., T7 terminator / bacterial terminator)

This design is simple, robust, and makes fluorescence an immediate readout for “does expression work?”.

### 4.2 What I would order (DNA “write” step)
Because the sfGFP CDS is short (~0.7–0.8 kb), the most straightforward approach is **DNA synthesis** (a dsDNA fragment or a cloned gene). Concretely, I would order one of these:

**Option A — Gene fragment (fast + flexible)**
- Order the **sfGFP insert as dsDNA** with flanking overlaps for Gibson/HiFi assembly (or with restriction sites).
- Then clone into an expression plasmid in the lab.

**Option B — Cloned gene in a plasmid (one-step ready)**
- Order **sfGFP already cloned** into a high-copy plasmid backbone.

### 4.3 Twist Bioscience access limitation (Argentina) + workaround plan
From my location (Argentina), the Twist ordering portal is not accessible and prompts me to contact a local operator. In a real order scenario, I would do one of the following:

![Twist screenshot](<./twist.jpg>)

1) **Contact Twist local sales/support** (as requested) and place the order via email (sequence + vector + cloning format).  
2) Use an **alternative synthesis provider** that ships to my region (e.g., ordering a dsDNA fragment from another vendor) and then perform the same assembly into an equivalent plasmid backbone.

For the purposes of this homework, I describe the intended order and construct as if placing a standard synthesis + cloning order.

### 4.4 Vector choice and final construct
If using Twist’s catalog, I would choose a standard **high-copy AmpR plasmid backbone** (e.g., a pTwist Amp high-copy–type vector), and insert the sfGFP expression cassette into it.

Final construct conceptually looks like:

**[T7 promoter] – [RBS] – [sfGFP CDS (E. coli optimized)] – [6xHis] – [STOP] – [Terminator]**

### 4.5 How I would obtain protein from this DNA (high-level workflow)
1) **Assemble** the insert into the plasmid (Gibson/HiFi or restriction cloning).  
2) **Transform** into *E. coli* (expression strain if using T7).  
3) **Verify** by sequencing (to confirm sfGFP is correct and in-frame).  
4) **Express** and measure fluorescence as a fast functional readout.  
5) (Optional) **Purify** via His-tag if purification is required.

This approach separates “DNA write” (ordering/synthesis) from “DNA read” (sequencing verification) and “DNA function” (fluorescence output).

## Part 5 — DNA Read / Write / Edit (Dengue focus: Argentina)

### 5.1 DNA Read

**(i) What DNA/RNA would I want to sequence and why?**  
I would focus on **genomic surveillance of Dengue virus (DENV) in Argentina**, integrating **clinical** and **environmental** sequencing to support public health decisions in real time.

Concretely, I would sequence:

1) **Clinical DENV genomes (RNA → cDNA)** from a **representative subset** of confirmed cases:
- **Across regions** (e.g., AMBA vs. northern provinces where dengue burden can be higher).
- **Across time** (weekly/biweekly sampling during season peaks).
- **Across epidemiological contexts** (outbreak clusters, travel-associated cases, and sporadic detections).

**Why:**  
- To track **serotype dynamics** (DENV-1/2/3/4) and detect shifts that may correlate with outbreak intensity.  
- To monitor **lineage introductions** (new clades entering a province) and infer **transmission connectivity** between regions.  
- To support **molecular epidemiology**: identify clusters, potential superspreading contexts, and genomic signatures associated with rapid spread (without overclaiming causality).  
- To generate local datasets that strengthen **regional capacity** and reduce dependence on external sequencing pipelines.

2) **Environmental DENV surveillance in Aedes aegypti pools** (and optionally wastewater as exploratory):
- **Mosquito pools** (RT-PCR confirmed) from vector surveillance programs: this can provide early hints of circulating serotypes/lineages even before clinical case counts surge.
- **Wastewater** is less standard for DENV than for enteric viruses, but could be explored as a research add-on; vector-based sampling is usually more direct for arboviruses.

**Why:**  
- To get **earlier warning signals** and a broader picture of circulation beyond who shows up at clinics.
- To link **vector circulation** with **human cases**, improving outbreak models.

---

**(ii) What sequencing technology would I use and why?**  
I would use a **two-tier strategy**:

- **Illumina short-read sequencing (2nd generation)** for routine surveillance:
  - High per-base accuracy, scalable multiplexing, strong variant calling.
  - Great for producing reliable consensus genomes and phylogenies.

- **Oxford Nanopore sequencing (3rd generation)** for rapid, field-forward situations:
  - Faster turnaround when you need same-week answers (e.g., suspected new introduction or unusual outbreak).
  - Useful for decentralized labs or mobile workflows, at the cost of higher raw read error (mitigated by coverage + consensus polishing).

This hybrid approach fits a realistic public health workflow: Illumina as the “gold standard backbone”, Nanopore as the “rapid response tool”.

---

**1) Is it first-, second-, or third-generation? How so?**  
- **Illumina = second-generation**: massively parallel short reads (sequencing-by-synthesis).  
- **Nanopore = third-generation**: single-molecule sequencing, long reads, electrical signal through nanopores.

---

**2) What is the input? How do you prepare your input? Essential steps.**  
**Input:** Dengue is an **RNA virus**, so the primary input is **viral RNA** extracted from samples, then converted to **cDNA**.

A practical pipeline:

**Clinical samples (serum/plasma/whole blood, depending on stage):**
1. **Sample + metadata collection** (date, location, Ct value, suspected serotype if known, etc.).  
2. **RNA extraction**.  
3. **RT step → cDNA**.  
4. **Target enrichment strategy** (choose one):
   - **Amplicon tiling PCR** (common for viral genomes; efficient and cheap).  
   - OR **capture-based enrichment** (more flexible but more expensive).  
5. **Library preparation**:
   - Illumina: adapter ligation + indexes (multiplexing), optional PCR.  
   - Nanopore: end-repair + adapter ligation, optional barcoding.  
6. **Sequencing run**.  
7. **Bioinformatics**: QC → mapping → consensus → variants → phylogeny.

**Mosquito pool samples:**
1. **Pool preparation** (Aedes aegypti pools, ideally with RT-qPCR confirmation).  
2. **RNA extraction** (often with inhibitors → extra QC).  
3. RT → cDNA, then same as above.

**Key practical note:** For DENV, sampling time matters: early infection tends to have higher viremia (better genome recovery). Also, using Ct thresholds to select samples improves success rate.

---

**3) How does it decode the bases (base calling)?**  
- **Illumina**: fluorescent signals from nucleotide incorporation per cycle → base calls + quality scores.  
- **Nanopore**: ionic current shifts as molecules pass through the pore → signal-to-sequence base calling (model-based), then consensus polishing.

---

**4) What is the output?**  
- **FASTQ** reads (with quality scores).  
- **BAM/CRAM** alignments to a reference genome.  
- **Consensus genome FASTA** per sample.  
- **Variant calls (VCF)** (when appropriate).  
- **QC reports** (coverage depth, % genome recovered, contamination checks).  
- Downstream: **phylogenetic trees** and **lineage/cluster summaries** for epidemiological interpretation.

---

### 5.2 DNA Write

**(i) What DNA would I want to synthesize and why? (Dengue-focused)**  
I would “write” DNA that enables **faster and more deployable dengue diagnostics** and/or supports local R&D.

Three concrete synthesis targets:

1) **DENV diagnostic standards and controls** (safe, non-infectious):
- Synthetic **gene fragments** (e.g., conserved regions of DENV genome used in RT-qPCR/CRISPR assays).
- **Positive control templates** for assay development and QA/QC.
**Why:** robust controls are crucial for reliable diagnostics, especially across multiple labs and seasons.

2) **CRISPR-based dengue detection components** (research prototype):
- Synthetic DNA templates to generate **RNA targets** (IVT) or **reporter constructs** for assay benchmarking.
- If building cell-free or isothermal detection workflows, you can synthesize the necessary templates without needing infectious material.
**Why:** safer, faster iteration.

3) **Aedes-related biosensor modules** (optional):
- DNA parts for sensor chassis optimization (e.g., expression cassettes for reporters in E. coli cell-free systems).
**Why:** create modular “plug-and-play” parts to accelerate prototyping.

---

**(ii) What technology would I use for DNA synthesis and why?**  
- For ~0.3–3 kb fragments: **commercial gene synthesis** (dsDNA fragments or cloned gene in a plasmid).
- For many variants: **oligo pools** (array-based synthesis) + assembly.

**Why:** speed + reliability, avoids PCR errors, and supports rapid iteration (especially when you want multiple versions: different primers, target regions, or assay designs).

---

**1) Essential steps (high-level)**  
- Design sequence (include constraints: avoid repeats/extreme GC, include needed cloning sites/overlaps).  
- Order as dsDNA fragment (or oligos + assembly).  
- If needed: clone into plasmid backbone (Gibson/HiFi or restriction cloning).  
- Verify by sequencing (at least Sanger for inserts, or NGS for pools).  
- Use as template/control in downstream assays.

---

**2) Limitations (speed, accuracy, scalability)**  
- **Length & complexity**: longer sequences or high repeat content may fail or take longer.  
- **Error rate**: increases with length; sometimes error correction or clone screening is needed.  
- **Sequence constraints**: extreme GC, hairpins, homopolymers can reduce success.  
- **Regulatory/shipping**: international access can be limited; some vendors require regional sales contact.  
- **Cost**: scales with length and number of variants.

---

### 5.3 DNA Edit

**(i) What DNA would I want to edit and why? (Dengue context)**  
I would focus on edits that are **ethically appropriate, feasible, and beneficial**, avoiding speculative or high-risk human germline scenarios.

Two realistic editing directions:

1) **Editing lab strains (E. coli or cell-free chassis) to improve dengue diagnostic prototyping**  
Examples (conceptual):
- Reduce background nuclease activity that can degrade reporters.  
- Improve expression stability of reporter proteins or enzymes used in readouts.  
**Why:** more robust, reproducible diagnostics and faster prototyping cycles.

2) **Vector biology research (Aedes aegypti) — in controlled research settings**  
Examples (high-level):
- Knock-in/knock-out genes to study **vector competence** or immune pathways relevant to arbovirus replication.  
**Why:** better understanding of transmission biology can support long-term control strategies (with strong oversight and biosafety/ethics review).

---

**(ii) What technology would I use and why?**  
- **CRISPR-Cas9** for knock-outs and knock-ins in model systems.  
- **Base editing** for precise point mutations (when you want to avoid double-strand breaks).  
- **Prime editing** for flexible small edits (insertions/deletions/substitutions) with less HDR dependence.

Choice depends on the edit:
- Big insertions → Cas9 + HDR (or targeted integration strategies).  
- Single base changes → base editor.  
- Small flexible edits → prime editor.

---

**1) How does it edit DNA? (conceptual steps)**  
- Guide RNA targets a specific locus.  
- Editor performs cut or base conversion.  
- Cellular repair/processing results in the desired change.  
- Screen and validate clones/lines.

---

**2) What preparation is needed and what is the input?**  
- Target selection + guide design + off-target risk assessment.  
- Editor delivery strategy (plasmid, mRNA, RNP).  
- Optional donor template for HDR edits.  
- Validation plan:
  - PCR across the locus, Sanger/NGS confirmation,
  - phenotype/functional assay relevant to the edit,
  - off-target screening where appropriate.

---

**3) Limitations (efficiency/precision)**  
- **Delivery** limitations (some cell types/organisms are difficult).  
- **Off-targets** and unintended edits (varies with editor/guide).  
- **HDR efficiency** can be low; requires careful design and screening.  
- Need for **strong controls**, replication, and transparent reporting.