Noga Mano | Research Methods B | June 2026

Introduction:

The Necessity of Post-qPCR Data Analysis:

Following a qPCR run analyzing gene reactions for stressors or inhibitors, we obtain outputs raw Cycle Threshold (C_t) values. These values reflect initial cDNA transcript abundance, but they cannot be directly compared or interpreted biologically, due to inherent experimental variations.

To extract meaningful scientific insights, processing raw data through the calculation method is essential for understanding the gene reaction under stress conditions, compare to normal conditions.

Post-qPCR mathematical processing is required to eliminate technical-background “noises”, establish a relative baseline and translate exponential data into true biological trends.

Preliminary Step: Experimental Setup & Gene Selection

Targeted Genes Defining:

Define your target gene(s) of interest and a stable internal reference (housekeeping) gene:

:memo: Reference (housekeeping) gene: a stable gene which the known stressor/factor we chose to check will not effect it.

:memo: Target gene: gene or genes that we expect to react upon known stressor/factor.

Experimental Design:

Design and conduct a controlled experiment consisting of:

:memo: Experimental/Treatment Group: Exposed to the specific stressor/inhibitor.

:memo: Control Group: Keep under unstressed conditions.

Quantification:

Perform quantitative PCR (qPCR) to obtain raw cycle threshold (C_t) values for all samples.

Arrange the result of the qPCR in table:

Table 1: Cycle Threshold (C_t) - an example

¹ Reference gene

Step 1: Internal Data Normalization (ΔC_t)

Normalize the target gene expression against the internal reference gene to account for variations in initial cDNA template concentration.

Minor variations in RNA quality, reverse transcription efficiency and/or pipetting are inevitable. Subtracting the Ct values of a stable reference gene (e.g., Tubulin) from the target gene controls for these confounding factors, ensuring that observed differences reflect true biological responses rather than technical artifacts.

Calculated Parameter: ΔC_t

Formula:

Example:

Tubulin DMSO Control = 23.30

ascs DMSO Control = 29.09

DMSO Control (Δ𝐶𝑡) = 29.09 - 23.30 = 5.798

Tubulin Inhibtior treatment = 23.30 (the right reference gene - no change under stress treatment)

ascs Inhibtior treatment = 28.51 (a clear difference between control and stress treatment)

Inhibtior treatment (Δ𝐶𝑡) = 28.51 - 23.30 = 5.213

As shown in the table 2:

Table 2: ΔCt calculation

Step 2: Normalization to the Control Group (ΔΔCt)

Calculate the relative change of the target gene within the stress treatment group compared to its baseline expression in the control group.

Baseline Comparison (ΔΔCt): Gene expression changes are relative. Subtracting the baseline expression of the control group (e.g., DMSO control) from the experimental group, isolates the net effect of the treatment.

Calculated Parameter: ΔΔCt

Formula:

Note: Typically, the average ΔCt of the control group is subtracted from each individual sample’s ΔCt.

Example

ascs gene:

DMSO Control (Δ𝐶𝑡) = 5.798

Inhibtior treartment (Δ𝐶𝑡) = 5.213

ΔΔ𝐶𝑡 = 5.213 - 5.798 = -0.585

As shown in table 3:

Table 3: ΔΔCt calculation

Step 3: Calculation of Relative Expression = Fold Change

Convert the logarithmic ΔΔCt value into a linear value representing the exponential amplification of the PCR process.

Because PCR amplification is exponential, C_t values exist on a logarithmic scale (where a difference of 1 cycle represents a 2-fold change). Utilizing the $2^{-ΔΔCt}$ equation transforms these values into a linear Fold Change scale, providing an intuitive, publishable measure of gene upregulation or downregulation.

Calculated Parameter: Fold Change (Relative mRNA Expression)

Formula:

Example

ascs gene fold change:

ΔΔCt = -0.585

Fold change = 2^(-0.585) = 1.50

As shown in table 4:

Table 3: Fold change calculation

Step 4: Data Visualization and Biological Interpretation

Generate a graphical representation (e.g., a bar chart) displaying gene expression levels under the tested stress condition. (X-axis: Experimental Groups [targeted genes]; Y-axis: Fold Change).

Graph 1: Fold change results

Looking at the bar chart, you can see which genes were upregulated / downregulated:

Fold change = 1 : inhibitor / stress have no effect on gene expression. Reference gene’s fold change should be = 1.

Fold change > 1 : gene expression increased (upregulated) under stress compared to the control.

Fold change < 1 : gene expression decreased (downregulated) under stress compared to the control.

Introduction

Over the last several decades, rising global sea surface temperatures (SST) have driven a severe deterioration of marine ecosystems worldwide by inducing the breakdown of the coral-algal symbiosis under heat stress, leading to widespread coral bleaching and accelerated mortality (Swinhoe et al., 2026). These escalating thermal anomalies critically disrupt the early life-history stages of reef-building species, compromising larval and juvenile survival and directly threatening long-term coral recruitment and reef persistence (Drury et al., 2022). Consequently, major research efforts have focused on unraveling the underlying molecular, genetic, and physiological mechanisms that govern either damage susceptibility or adaptive thermal tolerance pathways within scleractinian holobionts.

The majority of reef-building corals maintain a mutualistic symbiosis with symbiotic dinoflagellates, a foundational relationship upon which their survival and capacity to withstand environmental perturbations heavily depend. Because holobiont performance and bleaching susceptibility are directly dictated by the complex interactions between these compartments, corals must be holistically evaluated as an integrated holobiont rather than isolated entities. Consequently, any comprehensive assessment of thermal tolerance must account for the distinct physiological strategies, evolutionary pressures, and mechanisms of mutual support shared between the coral hosts and their endosymbionts.

Reference / Housekeeping Genes

In their study, Colín-García et al. (2025) examined gene expression dynamics under elevated temperatures, utilized two specialized housekeeping genes to accurately normalize molecular stress responses via quantitative PCR (qPCR) (Colín-García et al., 2025):

• Name: Elongation Factor 1-alpha (EF1α):

  Known biological activity: A core housekeeping protein, essential for cellular translation during the process of protein synthesis, facilitating translational elongation (Sasikumar et al., 2012). EF1α is a common housekeeping protein across genera.

• Name: Cyclophilin (CYC):

  Known biological activity: A molecular chaperone that catalyzes cellular protein folding and maintains structural stability under stress conditions. CYC is universal, evulotionary highly conserved protein, and found to be stable in photosynthetic organisms (Rosic et al., 2011).

Ultimately, the reference gene EF1α was established as the internal reference control for the coral host to provide a stable baseline for cellular normalization, while CYC was validated as the internal reference control for the algal symbiont to ensure robust transcriptional evaluation under thermal stress conditions (Colín-García et al., 2025).

Target Genes

Pelagic larval stages are considered the dominant dispersal mechanism in corals, meaning that thermal tolerance and responsiveness to environmental cues play a key role in the preservation of coral reefs in an era of climate change. In a comprehensive RNA-Seq analysis, Meyer et al. (2011) demonstrated that hundreds of genes exhibit significant differential expression during these early stages, highlighting the complex molecular shifts triggered by environmental stress. Among these critical pathways, coral larvae under heat stress, triggers cell apoptosis.

• Name: tnfsf10 gene:

  Known biological activity: Initiating the extrinsic programmed cell death (apoptosis) pathway. Upon ligand binding to specific death receptors within the Tumor Necrosis Factor Receptor Superfamily (TNFRSF), it triggers a downstream intracellular caspase cascade, leading to regulated cellular degradation and mortality in response to irreversible environmental stress (Quistad et al., 2014). Apotosis prevents secondary damage (e.g from ROS) and conserves energy waste on unviable cells. Although corals possess nature’s most diverse TNFRSF repertoire, this extrinsic pathway remains largely uninvestigated, providing a strong scientific justification for analyzing the target gene tnfsf10 via qPCR (Quistad et al., 2014).

• Name: Heat Shock Protein (HSPs):

  Known biological activity: The group of key target genes comprises the heat shock protein (HSP) family (such as Hsp70 and Hsp90), encode molecular chaperones (Swinhoe et al., 2026). The chaperones are fundamentally involved in maintaining regular cellular functions, playing a crucial role in preventing protein denaturation under thermal stress. In addition, these highly conserved proteins participate in cell differentiation, morphogenesis, cell signaling, and the overall protection of cells against severe stress and apoptosis (Rosic et al., 2011).

Experimental Framework and Hypotheses of Gene Expression Under Thermal Stress

Thermal stress acts as a major environmental perturbation that disrupts cell homeostasis, destabilizes protein conformations, and induces the breakdown of the coral-algal symbiosis. To unravel the underlying molecular mechanisms governing damage susceptibility or adaptive resilience in scleractinian corals, I chose to evaluate the transcriptional responses of pelagic larval stages subjected to an acute thermal stress manipulation compared to an ambient control group (normal SST temperature conditions).

Organism:

General Hermatypic Coral

Experiment (schematic design):

Thresholds of newly-released (brooding species) planula-larvae settlement and transcriptomic response under theraml stress treatments:

25°C (Ambient Control)

28°C (Moderate Stress)

31°C (Acute Stress)

Replicates (n) = 4

Planulae per Replicate = 25

Daily observation: survival and settlement rates.

Analysis of Transcriptomic Response: A group of planulae (n=4) will be removed daily from each replicate. Under RNA-safe fixation process, will be frozen for molecular analysis.

1. Internal Reference Genes (Expected Stability)

Elongation Factor 1-alpha (EF1α)

• Expected Expression: Highly stable, constant baseline expression levels.
• Rationale: Given its continuous and fundamental role in translational elongation (Sasikumar et al., 2012), its transcription will remain unaffected by environmental shifts, making it an ideal baseline for coral host normalization under thermal stress.

Cyclophilin (CYC)

• Expected Expression: Constant, unaltered transcription levels across all groups.
• Rationale: As a highly conserved chaperone required for baseline protein folding, its transcriptional resilience under severe thermal and light stress makes it a validated reference control for the Symbiodiniaceae compartment (Rosic et al., 2011).

2. Experimental Target Genes (Expected Dynamics)

Heat Shock Proteins (HSP, e.g., Hsp70 and Hsp90)

• Expected Expression: Sharp, significant upregulation under elevated temperatures.

• Rationale: Because heat stress directly denatures cellular proteins, the dynamic overexpression of these molecular chaperones is critically required to mitigate oxidative damage, refold damaged proteins, and prevent thermal bleaching (Rosic et al., 2011; Swinhoe et al., 2026). Furthermore, an early, synchronized upregulation of hsp70 across both symbiont and host compartments represents a critical molecular strategy that minimizes bleaching susceptibility.

• Tumor Necrosis Factor Receptor-Associated Ligand 10 (tnfsf10)
  • Expected Expression: Pronounced upregulation corresponding to stress intensity.
  • Rationale: When thermal stress surpasses cellular protective thresholds, the initiation of programmed cell death triggers the extrinsic apoptotic pathway. Tracking tnfsf10 transcript dynamics serves as a highly sensitive biomarker for quantifying stress-induced cellular mortality (Quistad et al., 2014).

References:

Colín Garcia, N. A., Ocaña-Mendoza, C., Carrara, X. C., Rioja-Nieto, R., Calle-Triviño, J., & Pérez-Ángel, D. A. (2025). Characterization and Expression of Hsp70 Gene in Corals: A Comparative Responses of Coral Hosts and Symbiodinium to Thermal Stress in Three Coral Species. Available at SSRN 5119646.‏

Drury, C., Bean, N. K., Harris, C. I., Hancock, J. R., Huckeba, J., Roach, T. N., … & Gates, R. D. (2022). Intrapopulation adaptive variance supports thermal tolerance in a reef-building coral. Communications biology, 5(1), 486.‏

Quistad, S. D., Stotland, A., Barott, K. L., Smurthwaite, C. A., Hilton, B. J., Grasis, J. A., … & Rohwer, F. L. (2014). Evolution of TNF-induced apoptosis reveals 550 My of functional conservation. Proceedings of the National Academy of Sciences, 111(26), 9567-9572.

Rosic, N. N., Pernice, M., Dove, S., Dunn, S., & Hoegh-Guldberg, O. (2011). Gene expression profiles of cytosolic heat shock proteins Hsp70 and Hsp90 from symbiotic dinoflagellates in response to thermal stress: possible implications for coral bleaching. Cell Stress and Chaperones, 16(1), 69-80.

Sasikumar, A. N., Perez, W. B., & Kinzy, T. G. (2012). The many roles of the eukaryotic elongation factor 1 complex. Wiley Interdisciplinary Reviews: RNA, 3(4), 543-555.‏

Swinhoe, N., Tinoco, A. I., Sarfati, D. N., Henderson, C. F., Kowalewski, G. P., Meier, E. K., … & Cleves, P. A. (2026). CRISPR/Cas9-mutagenesis reveals that varying dependence on HSF1 is associated with differences in coral heat tolerance. bioRxiv, 2026-04.

Noga Mano, 029354719

03/06/2026

Designing primers for algal species identification

Introduction:

Primer design is widely used for the genetic identification of organisms of scientific and/or economic importance (e.g., in agriculture and biotechnology). In a survey conducted, a red alga was identified as belonging to the genus Gracilaria, and the aim is to determine whether it represents a local species or a potentially invasive one.

For species identification, primers were designed for the 18S rRNA gene. This gene is a conserved nuclear marker widely used for molecular identification due to its combination of highly conserved regions, which enable reliable primer design, and variable regions, which allow taxonomic discrimination. However, its relatively low evolutionary rate limits its resolution at the species level, particularly among closely related taxa, and it often requires the use of additional, more variable markers. The designed primers are intended to amplify the target region for sequencing and subsequent phylogenetic analysis to determine the species identity and origin.

Primer designing workflow:

1. Collecting 18S rRNA sequences from NCBI

• Enter NCBI: https://www.ncbi.nlm.nih.gov/nuccore
• In the search line choose: ‘Nucleotide’
• Type the name of the target organism and/or the gene (e.g. Gracilaria 18S rRNA)
• Choose DNA
• In the results:

o Make sure you choose Gracilaria

o Avoid “partial sequence”

o Look for: “complete sequence”

• Open a WORD document.
• Choose 6-8 results.

• Enter each result and press FASTA gracilaria_seq.md

• Select sequences containing minimal ambiguous bases (Ns) within the target gene region.

Figure 1: FASTA sequence containing ambiguous bases (multi Ns)

Figure 2: FASTA sequence sequence without ambiguous bases

• Copy and paste the sequence, including its header, to the document.
• Edit the sequenced species names according to your WORD doc. (or go back to NCBI search): Mark the sequences number and ‘edit sequence name’.
• Save the document.

2. Performing multiple sequence alignment (ClustalW in MEGA)

• Open MEGA software
• Choose: ‘Align’ –> Edit/Build Alignment –> Create a New Alignment
• Copy each sequence, including headers from the Word document
• Choose: Alignment –> Align by ClustalW
• Save as .MAS file

Figure 3: Alignment of selected Gracilaria sp. 18S rRNA gene sequences

3. Identifying conserved regions across species

• Look at the uniformity and differences between the sequences: choose a representative complete sequence, with a conserved region without gaps, yet variable to allow discrimination between species.

Figure 4: Identifying conserved areas and variances after sequence alignment

4. Sequence extract for primer design

• Open Primer3web - https://primer3.ut.ee/
• From the word document, copy a sequence segment of ~1000 bases from the result that you chose, and paste it into the window.

NOTE: Primer3 should receive a single clean DNA sequence. Make sure you are not pasting a ClustalW alignment with gaps, stars, spaces, or multiple species

• Press “Pick Primers” - you get the output of the primer you designed.

Figure 5: Pasting the sequense into the central window

• Look at the first table of the output, and identify:

o Forward primer (LEFT = »»») sequence & position

o Reverse primer (RIGHT = «««) sequence & position

o Primer length

o Tm - the difference should not be >3

o %GC - 40%-60%

o Hairpins - low <2

o Expected amplicon size

Figure 6: The output table of the primer designed for Gracilaria

Primer3 Output link: https://primer3.ut.ee/cgi-bin/primer3/primer3web_results.cgi

Primer designed data for Gracilaria

o Forward primer sequence: TGAGAGACGGCTACCACATC. Position - starts at 354

o Reverse primer sequence: TCTGCTGGCTCCTCGATAAG. Position - starts at 507

o Primer length: 20 | 20

o Tm = 58.90 | 58.96 –> ✔

o %GC = 55% | 55% –> ✔

o Hairpins = 0.00 –> ✔

o Expected amplicon size: 1050

5. Verification of Primer Specificity

• Open NCBI Primer BLAST - https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?GROUP_TARGET=on
• In the line “Forward primer” - copy the sequence of LEFT PRIMER from the output table of the primer designed
• In the line “Reverse primer” - copy the sequence of RIGHT PRIMER from the output table of the primer designed
• Change “Max” to 250 (fig. 7)

Figure 7: Primer-Blast primer input

• In the section of “Primer Pair Specifity Checking Parameters”: o Change ‘Database’ to “nt” o Change ‘Organism’ to the species you designed you primer o Change ‘Max target amplicon size’ to 4000
• Press Get Primers

Figure 8: Primer-Blast for Gracilaria sp.

6. Recieving specific primers to PCR template

• Get your report at the next stage of the Primer-BLAST process and save it.
• To check the specificity of the primer you have designed, repeat the process with wider options: go back one step and change ‘Organism’ twice (or more) to: (1) group name (e.g red algae, Rhodophyta, macroalgae) or, (2) totaly different organism (e.g fish, invertebrate).

See Primer-BLAST results:

1. Gracilaria 18S rRNA results/primerblast_gracilaria_18S_rRNA.txt
2. Red algae - Rhodophyta results/primerblast_rhodophyta.txt
3. Animal - wild sheep results/primerblast_wild-sheep.txt
4. General - “Organism” results/primerblast_organism.txt

Construct a phylogenetic tree

A phylogenetic tree is constructed to infer evolutionary relationships among organisms and to accurately identify species by comparing genetic sequences and determining their similarity to known taxa.

1. Based on the protocol above, go back to MEGA software
2. Open your .MAS file
3. Press ‘PHYLOGENY’ icon
4. Choose ‘Construct/Test Neighbour-Joining Tree’ –> OK
5. In the openning table, change:
   • ‘Test of phylogeny’ to ‘Boostrap method’
   • ‘Substitution’ to ‘Nucleotide’
   • ‘Model/Method’ to ‘Kimura 2-parameter model’
6. Get the output and save.

Figure 9: Gracilaria Phylogeny Tree output

Gracilaria Phylogeny Tree output file: results/Gracilaria18SrRNA_02062026.2.mtsx

Summary

The Gracilaria phylogenetic tree (fig. 9) was constructed using the Neighbor‑Joining method with the Kimura 2-parameter model and 100 bootstrap replicates. The phylogenetic tree shows the relationships among several Gracilaria species based on 18S rRNA sequences. All included species (Gracilaria textorii, G. debilis, G. salicornia, G. foliifera, and G. fergusonii) cluster within the same general group, indicating their genetic similarity at the genus level. The analyzed sequence groups within this Gracilaria cluster, supporting its classification within the genus. However, the relatively low bootstrap values (43 and 64), suggest weak to moderate support for the branching pattern, indicating limited resolution. This result is consistent with the conserved nature of the 18S rRNA gene, which may not provide sufficient variability for reliable species-level discrimination.

GITHUB POST

Noga Mano, 029354719

13/05/2026

Experiment Protocol

Experiment title: The effect of bacteria on planulae settlement

Introduction

Coral reefs are essential for marine ecosystems and human livelihoods, yet they have experienced significant, global, rapid decline due to natural and human-induced stressors. A promising way to recover coral reefs is to enhance larval recruitment. Indeed, many present scientists are focusing on finding the key factors for successful larval settlement – physically, chemically and biologically.

Research Question:

How do different bacterial species affect larval recruitment, settlement and survival of primary polyps of the coral Stylophora pistillata?

Research hypothesis (for every bacteria species):

• H0 – The bacterial strain has no effect on larval recruitment of the coral Stylophora pistillata
• H1 - The bacterial strain affects larval recruitment of the coral Stylophora pistillata

Methods

Variables:

· Independent Variable: Bacteria strain · Dependent Variable: Settlement rate, survival

List of materials and equipment:

• 360 fresh planulae of the coral Stylophora pistillata
• 5 Bacterial strains
• 12 Sterile six-wells plates, inoculated with a single-species bacterial biofilm
• MB medium
• 20µ Filtered artificial seawater 40ppt
• Sterile pipettes
• Incubator
• Binocular

Experiment Design:

Two sets of six wells plates inoculated with bacteria:

• Treatment 1: Bacteria strain #1
• Treatment 2: Bacteria strain #2
• Treatment 3: Bacteria strain #3
• Treatment 4: Bacteria strain #4
• Treatment 5: Bacteria strain #5
• Treatment 6: Control (MB medium) Planulae will be divided into groups (n=10).

Each group will be located in a well X six replicates. Larvae were randomly distributed among treatments to avoid bias.

Figure 1: An entirely well photo

Table 1: Survival rate bacteria strain #3

Scientific Refernce

Strain-specific surface polysaccharides mediate bacterial induction of metamorphosis in the coral Pocillopora acuta

Device Protocol

LCEN-404 Clinical Centrifuge Manual