Week 1 HW: Principles and Practices

Class Assignment

1. Describe a biological engineering application or tool you want to develop and why.

The project aims to develop a tool to promote Parkinson’s disease phenotype manifestation in human brain organoids by controllable induction of alpha-synuclein protein expression in dopaminergic neurons. The tool is a genetic construct containing switches and regulators to produce alpha-synuclein beyond normal levels in a subpopulation of cells in patient-derived brain organoids for the investigation of patient-specific pathogenic mechanisms, pathways, and phenotypes.

Patient-derived brain organoids naturally recapitulate some neurodegenerative and neurodevelopmental disease features and are used as models to study human-specific pathology and test potential therapeutics. One of the significant and costly problems of these models is the time needed (months) for organoid maturation and manifestation of pathological phenotypes, involving protein accumulation, mitochondrial dysfunction, and neuronal death. Therefore, approaches to speed up growth, maturation, and phenotype development are currently needed and being devised. In Parkinson’s disease in particular, death of dopaminergic neurons causing movement deficiencies is caused by alpha-synuclein protein misfolding and accumulation, triggered by failures in different interconnected processes (mitochondrial dysfunction, dopamine metabolism, inflammation, autophagy dysfunction) and various mutations (SNCA, LRRK2, PINK1, Parkin, DJ-1, GBA). The tool for premature controllable production of alpha-synuclein will allow standardized promotion of Parkinson’s disease phenotype to be used in studies of individual and patient-specific factors leading to the disease (specific dysfunctions leading to inefficient protein degradation and alpha-synuclein protein accumulation) and devise personalized treatment strategies within platforms working with patient-derived brain organoids/assembloids.

2. Describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals.

One goal can be related to environmental and resource considerations. The tool may substantially reduce the time and resources for Parkinson’s research, so these benefits need to be documented and promoted. Therefore, systematic quantification, validation, and dissemination of resource efficiency of this method for accelerated Parkinson’s phenotype modeling is needed to promote adoption of sustainable research practices and resource allocation decisions in neurodegenerative research and drug discovery.

3. Describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g., 3D printing, drones, financial systems, etc.).

Action 1: Conduct and share the analysis of the process

Purpose: measure costs and environmental footprint of the tool versus old approaches; the results need to be published. 

Design: data needs to be collected by a lab; environmental specialists, including institutional ones, need to be involved to share their expertise on the methodology and the analysis, as well as consider both relative and absolute environmental impact; additional funding can be requested from the Parkinson’s Foundation.

What could be wrong, incorrect assumptions: The environmental and resource benefits may not be sufficient or comparable to old protocols like fibril seeding; there could be hidden costs for monitoring or equipment that cancel out the saved time; when these organoids are produced at scale, the dynamics and the resources needed  may be different from those for lab scales.

Risks: initial monitoring, as well as monitoring in the labs that acquire this method, may require considerable investments that can be hard to acquire, and therefore, this tool may lack monitoring, be less preferred; the results of monitoring may show that the method is not saving time or resources but rather is more resource-intensive

Action 2: Develop and promote a framework for resource efficiency reporting

Purpose: create a standardized method for reporting the use of resources that can become a standard for the field and include environmental measures into research workflow. This could be a template or checklist to increase transparency and improve reproducibility across studies and labs.

Design: tool developers need to agree on metrics, sharing the standard with agencies, societies, and databases that could adopt and promote it (SFN, CAN, Stem Cell Research foundations, etc.); software developers can create tools for efficient and easy reporting of data across the tool users

What could be wrong, incorrect assumptions: the reporting burden might be too large for researchers to comply with; standardization may not be possible across different research environments; the chosen metrics fail in reporting real efficiency gains 

Risks: users may refuse to adopt or ignore the standard because it’s too complex, expensive, or not enforced

Action 3: Develop an open-access resource optimization database and tools

Purpose: create a community-maintained platform where researchers working with organoids and the developed tool share their protocols, techniques that save resources, troubleshooting, quality control, and cost-benefit analysis.

Design: this database can be part of an already existing open-access platform for organoid research, such as the one maintained by the Early Drug Discovery Unit at McGill University. Tool developers need to develop the initial database and report their content there. Contributing researchers need to volunteer time to share their data and protocols; the university needs to approve sharing potentially patentable information; moderators are required to guide and encourage participation. 

What could be wrong, incorrect assumptions: publications can be sufficient to report protocols and sustainability gains, and no new resource is needed; efficiency improvements in different contexts may not translate to universal strategies.

Risks: the resource can become outdated and not used; companies might take advantage to promote their reagents; funding may not be sufficient; researchers may choose not to share the most valuable information; bad protocols might be propagated. 

4. Score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

5. Drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties.

Among the proposed 3, Action 1, i.e., the monitoring, needs to be prioritized because it will generate and assess evidence for the tool and for whether the other two actions are needed. The data obtained through Action 1 will provide the base for protocol optimization. Action 1 also mainly depends on the tool developers rather than on other contributors, and so it’s easier to lead. The main risk of this action is choosing the wrong metrics. Also, the efficiency of the tool in terms of the environment may not be accepted as a priority by the community of researchers in the field (Parkinson’s or neurodegenerative disorders). Also, if in fact, there is no environmental benefit of the tool, some lessons on the disease-accelerating approach, metrics approach, and costs, need to nevertheless be learned and published. An uncertainty is what agencies would be willing to fund this.

6. Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues. This should be included on your class page for this week.

I’ve learned about governance for synthetic biology projects in general and about the boundary between technical practice in science and governance. Regarding the latter, the design of controls is part of scientific methodology. But control design can actually become governance if new and specific standards for controls are disseminated, when control requirements are institutionalized, standardized control frameworks are proposed or built onto a tool, minimal control standards are created, or when expectations about reporting controls are established to foster transparency. This is applicable to my project on accelerated disease phenotype manifestation as well, and some related governance actions can be developed on how to ensure proper controls are used. These actions can include 1) establishing minimum control standards both for within-organoid controls (the organoids are intended to be chimeras) and parallel controls with natural manifestation (normally aging organoids); 2) creating a standardized control protocol repository; 3) providing training on the protocols and certifications; 4) other (to be added).

Assignment (Week 2 Lecture Prep)

Homework Questions from Professor Jacobson:

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

The error rate of DNA polymerase depends on the organism and on the type of polymerase. The error rate of replicative human polymerases δ and ε before proofreading with their endonuclease activity is estimated as about one wrong nucleotide in every hundred thousand to million nucleotides added. Compared to diploid human genome 6 billion base pairs long, this error rate means that replication mediated by polymerase alone will produce about 610-910-5 = ~60,000 errors per genome with 10-5 rate, which is why proofreading is essential. To deal with that discrepancy, errors are corrected through immediate proofreading, reducing mistakes to about 1-2 orders and through mismatch repair reducing mistakes to about another 1-2 orders, eventually reducing the mistakes to ~10⁻9–10⁻10 rate. To immediately correct the mistakes of Pol ε, Pols δ and ε conduct proofreading themselves by shifting the DNA strand to the exonuclease site. Right after replication, post-replication errors are corrected in the process called mismatch repair correcting patches of mismatching pairs and involving proteins recognising a mistake on a new strand, endonucleases and free exonucleases to remove the mismatch, the polymerase delta to fill the gap with the correct sequence, and ligase to seal the strand.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

The number of ways to code an average human protein is equal to the average number of codons per amino acid (~3) to the power of the average protein size (~400 amino acids) = 3400. Some of the reasons all these different codes don’t work and codons are not equally likely include: sequence proofreading correcting mistakes in codons; evolution leading to codon selection removing sequences that caused problems in mRNA folding, splicing, were slow in translation and affect protein folding making the protein non-functional; the polymerase being prone to some errors but not others; certain mutations accruing non-randomly, with pyrimidine-purine less likely than purine-purine or pyrimidine- pyrimidine.

Homework Questions from Professor Proust:

1. What’s the most commonly used method for oligo synthesis currently?

Phosphoramidite synthesis is the most common.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

Its difficult to synthesise oligos longer than 200 nucleotides because phosphoramidite synthesis efficiency decreases with length due to errors accumulation (as more synthesis steps are needed and more opportunities for mistakes arise, nucleotides may fail to attach, chemical errors upon nucleotide attachment may occur, and inefficient capping may allow for next synthesis cycles while truncated sequences are produced that compromise purification), enzymatic synthesis capable of making longer oligos expensive, less standardized, and still under development, and microarray-based synthesis produces shorter sequences.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?

Such a long synthesis will mainly produce incorrect sequences and a tiny fraction of correct ones due to the accumulation of errors, and extracting the correct ones will be expensive, time-consuming, impossible, and overall impractical.

Homework Questions from Professor Church:

1. [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The 10 essential amino acids in all animals are: Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine, and Arginine. ‘Lysine Contingency’ was intended as a method to make dinosaurs unable to synthesize Lysine, which makes no sense, as lysine is not produced by animals, regardless of the species they used for cloning.