Reading a biology lab protocol effectively ~ 2

Example: Guiding student through genomic DNA extraction from Arabidopsis leaves

Genomic DNA extraction from Arabidopsis leaves

1. Understand the goal and the challenges.

Goal: to isolate DNA from plant cells.

– What is there in plant cells?

Cell contains DNA, RNA, proteins, lipid, metabolites, polysaccharides, etc.

Challenges: to remove unwanted materials – RNA, proteins, etc.

2. Breakdown into parts

– Cell wall breakdown – mechanical disruption by bead-beating or grinding with a mortar and pestle.

– Cell membrane breakdown – SDS aid in lysing cell.

– DNA protection – EDTA chelates divalent cations, such as Mg2+ and Ca2+, which are cofactors of DNases, and make DNases non-functional.

– RNA removal – treat with Ribonuclease A (RNase A).

– proteins removal – SDS is a detergent that forms complexes with proteins, thus removing protein contaminants.

– NaCl (Salt) is used to remove proteins that are bound to DNA. It also helps keep the proteins dissolved in the aqueous layer, avoiding DNA co-precipitate with proteins.

– DNA precipitation – precipitated with isopropanol.

3. Understand the principles of procedures

– For example: Use Ethanol or Isopropanol?

Use Ethanol If:

You have the space to fit two volumes of ethanol to sample in your tube.

The sample needs to be stored for a long period of time and will be chilled.

You need to precipitate very small DNA fragments.

Use Isopropanol If:

Your sample volume is large and you can only fit 1 volume of solvent into your tube.

You need large molecular weight species.

The DNA concentration in your sample is low.

You are in a hurry and want to accelerate the precipitation of nucleic acids at room temperature.

Reference: https://bitesizebio.com/2839/dna-precipitation-ethanol-vs-isopropanol/

4. Understand the function of chemicals used

For example: Why liquid nitrogen is used to grind plant leaves?

– Liquid nitrogen was used to break the cell wall and disrupt the cell membrane (Clark 1997) while keeping cellular enzymes and other undesired chemicals deactivated, thus reducing shearing and damaging of the DNA. 

5. Understand the advantages and disadvantages

– SDS-based DNA extraction method does not use organic compounds that may affect human health.

– CTAB-based DNA extraction method is advisable for plants containing high polysaccharides and polyphenol content. CTAB is an amine-based cationic quaternary surfactant. It interacts with anionic polysaccharide (ionic interaction) and then decreases their solubility.

6. Ask questions throughout the reading process

– Why fresh and young leaves is preferred for DNA extraction?

– Can we extract DNA from dried leaves?

– Can we extract DNA from old leaves?

Reading a biology lab protocol effectively ~ 1

Steps for Reading a Protocol Efficiently

1. Understand the goal and the challenges.

Ask ‘How to achieve this goal?’

2. Breakdown into parts

Ask ‘What are the fundamental steps?’

3. Understand the principles of procedures

Ask ‘What is the purpose of this step?’

4. Understand the function of chemicals used

Ask ‘What is the function of this chemical in this step?’

5. Look for the critical steps in this experiment

Ask ‘What to avoid?’

6. Look for the appropriate step to stop

Stop for overnight, or stop for break.

7. Calculate the approximate time required for each section

Split into days.

Split into time period, morning and afternoon.

Find break time

8. Understand the alternative approaches

Ask ‘What steps can be change?’

Ask ‘What parts can be switch?’

9. Understand the advantages and disadvantages

Ask ‘Is there any chemicals that are toxic?’

10. Draw a flowchart for lab protocol

Drawing flow chart can improve understanding

11. Ask questions throughout the reading process

Asking questions is a reading comprehension strategy.

12. Learn from experts – read protocol from reliable sites

– Nature Protocols – Nature Protocols is a collection of vigorously peer-reviewed protocols that have been approved for publishing.

– Journal of Visualized Experiments – JoVE is a peer-reviewed journal that publishes visual experiments such as videos or images.

– Bio-Protocol – Bio-protocol is a site created by a group of biologists from Stanford.

– Supplier Protocols

Scientific terminology: Ghost

Ghost: to describe remnants of organelle, membrane, cells based on their appearance under the microscope.

Example:

Peroxisomal ghosts: peroxisomal membrane remnants with nearly empty matrix.

Plant protoplast ghosts: aggregations of microtubules in the absence of a plasma membrane.

Bacterial protoplast ghosts: empty membranes formed after osmotic lysis of protoplasts.

Red blood cell ghosts: cell devoid of their cytosolic content with the retainment of their membrane and cytoskeletal structures.

Ghost cell: A dead cell in which the outline remains visible, but whose nucleus and cytoplasmic structures are not stainable.

Modulators of plant hormone transport

1. N-1-naphthylphthalamic acid (NPA) – a key inhibitor of directional (polar) transport of the hormone auxin in plants.
2. naphthalene-2-acetic acid (2-NAA) – inhibit auxin influx carriers.
3. 2,3,5-triiodobenzoic acid (TIBA) – an auxin transport inhibitor and is used to study the role of auxin flow during plant growth and development.
4. Brefeldin A (BFA) – an inhibitor of vesicle trafficking
5. 2-[4-(diethylamino)-2-hydroxybenzoyl] benzoic acid (BUM) – an ABCB-specific auxin efflux inhibitor.

Modulators of plant hormone

1. cyclic olefin 2,5-norbornadiene (NBD) – an inhibitor of ethylene.
2. Uniconazole – a triazole growth retardant inhibiting GA biosynthesis.
3. Abamine – ABA biosynthesis inhibitor to target NCED

Modulators of ubiquitination and sumoylation

1. MG132 – widely used to block the proteolytic activity of the 26S proteasome complex.
2. MG115 – a potent, reversible peptide aldehyde inhibitor of proteasome chymotrypsin-like and caspase-like activities
3. N-ethylmaleimide (NEM) – a potent SUMO protease inhibitor

Modulators of Oxidative Stress

1. Hydrogen peroxide (H2O2) – induced reactive oxygen species (ROS) formation.
2. Diphenyleneiodonium chloride (DPI) – an inhibitor of NADPH oxidase and also a potent, irreversible, and time-, temperature-dependent iNOS/eNOS inhibitor. DPI also functions as a TRPA1 activator and selectively inhibits intracellular reactive oxygen species (ROS).
3. Mn-TMPP [Manganese (III) 5,10,15,20-tetra (4-pyridyl)-21H,23H-porphine] – a ROS scavenger that acts as a SOD and catalase mimic.
4. Dimethylthiourea (DMTU) – a small, permeable, and relatively nontoxic scavenger of hydrogen peroxide (H2O2).
5. Methyl viologen (MV) – induces excessive ROS generation in chloroplasts.
6. Menadione (MN) – induce mitochondrial ROS
7. nitric oxide (NO) – acts in a concentration and redox-dependent manner to counteract oxidative stress either by directly acting as an antioxidant through scavenging reactive oxygen species (ROS), such as superoxide anions (O(2)(-)*), to form peroxynitrite (ONOO(-)) or by acting as a signaling molecule, thereby altering gene expression

Modulators of Transcription and Translation

1. actinomycin D – a transcription inhibitor which intercalates into DNA. Actinomycin D forms a very stable complex with DNA, preventing the unwinding of the DNA double-helix, thus inhibiting the DNA-dependent RNA polymerase activity.
2. Cycloheximide (CHX) – a protein synthesis inhibitor in eukaryotes. It is a small molecule that inhibits translation elongation through binding to the E-site of the 60S ribosomal unit and interfering with deacetylated tRNA
3. Lactimidomycin – a glutarimide antibiotic and a translation inhibitor drug. Although cycloheximide (CHX) freezes all translating ribosomes, the translation inhibitor lactimidomycin (LTM) acts preferentially on the initiating ribosome but not on the elongating ribosome.
4. Puromycin – a Tyr-tRNA mimetic that enters the ribosome A site and terminates translation by ribosome-catalyzed covalent incorporation into the nascent chain C terminus.

COVID-19 Vaccine papers

Current Status of COVID-19 Vaccine Development: Focusing on Antigen Design and Clinical Trials on Later Stages

https://immunenetwork.org/DOIx.php?id=10.4110/in.2021.21.e4

Moderna

https://www.nature.com/articles/s41586-020-2622-0

Pfizer/BioNTech

https://www.nature.com/articles/s41541-021-00311-w

AstraZeneca/Oxford University

https://www.nature.com/articles/s41586-020-2608-y

Novavax

https://www.nature.com/articles/s41467-020-20653-8

Janssen / Johnson & johnson

https://www.nature.com/articles/s41586-020-2607-z

Gamaleya research institute /Sputnik V

https://www.sciencedirect.com/science/article/pii/S0140673620318663?via%3Dihub

Curevac

https://www.nature.com/articles/s41541-021-00311-w

Inovio

https://www.nature.com/articles/s41467-020-16505-0

Medigen

https://www.nature.com/articles/s41598-020-77077-z

Context‐specific marker‐assisted selection

Notes

Reference: Sebastian, S. A., et al. “Context‐specific marker‐assisted selection for improved grain yield in elite soybean populations.” Crop science 50.4 (2010): 1196-1206.

• Yield quantitative trait loci (QTL) are often detected within the context of specific soybean breeding populations and specific environments (Guzman et al., 2007; Orf et al., 1999; Reyna and Sneller, 2001).
• In a thorough review of molecular markers and selection for complex traits, Bernardo (2008) summarizes the variables that can affect QTL detection and confirmation and concedes that “because estimated QTL effects for traits such as grain yield or plant height have limited transferability across populations, QTL mapping for such traits will likely have to be repeated for each breeding population.”
• The current study investigates the possibility of a context- specific MAS (CSM) approach for improving grain yield. The term “context-specific” is used herein to distinguish it from “population-specific” and to acknowledge that yield QTL are a function of both population-specific (the genetic context) and environmental-specific (the environmental context) factors.
• CSM within commercially elite cultivars has several logistical and commercially appealing advantages: First it permits detection and MAS of multiple yield QTL within the context of a population that has typically been fixed for yield confounding traits such as relative maturity, plant height, and disease resistance. Second, the base population would have already been characterized and deemed commercially suitable for a given TPE. Third, if a higher-yielding haplotype is selected from the base population, it can be released immediately as an improved version of the original cultivar.
• The bulking method therefore minimized the field resources needed in the multi-environment confirmation phase by concentrating testing resources on the comparison of most interest to the study: the CSM haplotype versus the unselected mother line bulk. The trade-off of this design was that the experiment could not simultaneously prove that CSM-selected sublines performed better than phenotypically selected sublines. But this was not the goal of the current study since we were already very aware from decades of experience that selections based on individual progeny row yield phenotypes had very low repeatability (low heritability) in subsequent trials.
• The actual field environments chosen for confirmation of genetic gain were considered to be representative of the TPE for which the mother line was specifically adapted for commercial production.
• The unique aspect of CSM is that it focuses the power of genetic markers to construct a target genotype customized for a specific population and TPE. This eliminates the requirement to validate QTL across other populations and other environments that lie outside of the TPE. The only validation required for CSM is the confirmation of significant genetic gain of the selected haplotype within the TPE.
• It is noteworthy that the current studies required minimal field and marker resources to demonstrate CSM and to release significantly improved commercial cultivars. For example, during the QTL detection phase, a small sample of one to three distinct environments sampled from the larger reference TPE within 1 yr (2005) were needed to identify potentially useful yield QTL within any given genetic context. Although one to three environments in 1 yr might appear to be poor sampling of the TPE, this is actually representative of the way commercial soybean breeding programs conduct early-generation yield testing: inbred lines derived from a given population are typically tested in small plots at a single environment that hopefully will be representative of the TPE. But, if the early-generation test environment is not representative of the TPE, this might not be predictive of genotypes that are favorable across the broader sample of TPE environments encountered in subsequent replicated trials (Bernardo, 2008).
• Breeders prefer testing environments that permit expression of high yield potential yet have low spatial variation in soil type, soil depth, slope, and drainage properties. Such environments are more likely to expose differences in genetic potential and minimize differences due to nongenetic factors. It seems logical that these environments also should be favored for effective CSM.
• Other studies (in progress) are being conducted to quantify the relative effi ciency of CSM versus phenotypic selection for yield and to determine the feasibility of CSM within populations of broader genetic diversity such as biparental and backcross populations. However, based on the examples shown here and progress in ongoing experiments, CSM has already been adopted as a major component of MAS strategies known commercially as Accelerated Yield Technology (AYT) at Pioneer Hi-Bred International.

Reference: Smallwood, Christopher J., et al. “Context‐Specific Genomic Selection Strategies Outperform Phenotypic Selection for Soybean Quantitative Traits in the Progeny Row Stage.” Crop Science 59.1 (2019): 54-67.

• Historically, oil and protein in soybean seed are negatively correlated (Yaklich et al., 2002). Oil and yield share a positive relationship, and protein and yield have a negative relationship (Morrison et al., 2008). Because of this, increases in soybean oil and yield must be sought after while simultaneously seeking to maintain adequate protein levels (Cober et al., 2009).
• In soybean cultivar development, after crossing segregating parents and developing inbred populations through naturally occurring self-pollination, it is common to evaluate progeny rows derived from inbred single plants based on appearance or phenotypic score for advancement into replicated testing and eventual cultivar release (Fehr, 1987).
• Quantitative trait loci (QTL)-based selection strategies are inherently biased, as they only account for a limited amount of genetic information. A more robust method such as genomic selection (GS), which accounts for the entire genome (Nakaya and Isobe, 2012), would be worth investigating.
• However, for complex traits with low heritability, GS may be prone to limited success (Nakaya and Isobe, 2012). For marker-assisted selection (MAS) in complex traits, a context specific MAS (CSM) approach can be beneficial for increasing the selection efficiency within target environments by reducing the potential for genotype ´ environment interaction (Sebastian et al., 2012). In a CSM breeding approach, biparental populations are ideal for training predictions due to the reduced genetic complexity and larger recombination blocks (Sebastian et al., 2012). The greater control exhibited with CSM using a biparental population grown in a limited number of environments can benefit the selection potential for complex traits that are otherwise difficult to improve (Sebastian et al., 2012).