|Disclaimer: This is not a peer-reviewed article, but an example of a white paper based on an undergraduate biotechnology course at California State University-San Marcos.|
Author: Benison P. Zerrudo
Microalgae have been an important organism in a wide range of industries. They are used in the production of biofuels, used in the food industry, and applied in the cosmetic world. Chemical-regulated systems that control gene expression have numerous potential applications in biotechnology however, these systems are usually invasive and toxic to some organisms. Advanced technology allows the idea of developing transgene expression system which can be controlled by humans in a non-invasive technique. Utilization of light-inducible promoters has subdued numerous problems of conventional chemically regulated system. We exploit genetic components of Isochrysis galbana exposed to light with different wavelengths and aim to determine differential gene expression and identify upstream sequences that are potential light-sensitive promoter elements. RNAs were extracted from the microalgae, purified cDNA libraries were produced, cDNA library amplifications were performed using quantitative polymerase chain reaction (qPCR), and bioinformatics tools were utilized to analyze potential promoter sequence. Our results reveal 5 upstream conserved sequences of candidate loci that were significantly expressing under blue light. The 5 potential promoter elements were found to be at 153-160 bp, 190-198 bp, 285-290 bp, 293-301 bp, and 408-413 bp upstream of the candidate loci.
Microscopic algae have established an importance in the biotechnology industry which includes biofuels, food supplements, and aesthetic effects (Bancroft 2016). Growing algae can contribute to solutions for the depleting fossil fuels because they have the ability to collect light energy and capture carbon dioxide (Razeghifard, 2013). Microalgae are capable of growing rapidly under different conditions including open ponds and photobioreactors. Biomass compounds from microalgae can be used as a source for mass producing different types of biofuels. Some microalgae have biological properties which possess importance to human health. Microalgae has been used in the food industry for many years and some species contain an array of functional nutrients which includes proteins, omega-3 polyunsaturated fatty acids, polysaccharides, vitamins, and minerals (Panahi et. Al., 2016). One study revealed that microalgal supplementation using certain species can treat hyperlipidemia and hyperglycemia, and they can also serve as a protection against oxidative stress, pulmonary disease, and cancer (Panahi et. Al., 2016). Microalgae have also been applied in the cosmetic industry. Its cosmetic applications recently gained more attention in treating skin problems including aging, tanning, and pigment disorders. Genetic engineering of microalgae involves transformation and selection methods which are the key steps in gene modification (Ng et. Al., 2017). To develop a metabolically targeted microalgal engineering system, data were produced which encompasses expressed sequence tag, metabolic pathways, and the whole-genome sequence of microalgae (Baek et. Al., 2016). Novel genome editing tools such as RNAi, ZNFs, and CRISPR/Cas9 have been utilized in microalgae research (Fayyaz et. Al., 2020). As a result of advanced technology and rising information about microalgae, proposal to develop a transgene expression system which can be controlled by humans is on the rise.
Promoters are sequences of DNA that turn gene expression on or off. The transcription process begins at the promoter regions usually found upstream of the gene coding region. RNA polymerase and transcription factors bind to the promoter regions to produce the messenger RNA molecule. The core promoter region is 30 to 100 nucleotides in length and located about 40 bp upstream of the transcription start site. A number of sequence motifs can be found within the core promoter including the TATA box, Inr initiator, TFBII recognition element, and downstream promoter element. Further upstream of the transcription start site are the regulatory-containing proximal and distal promoter regions where enhancers, silencers, and insulators can bind to. Sequence-specific proteins bind to promoter and distal regions which leads to precise regulation of gene expression. Proximal promoter regions are located within the 500 bp relative to the transcription start site and contain the GC box, CAAT box, and cis-regulatory modules. Distal promoter region has varying length and can extend up to 10 kb from the transcription start site in either direction. Transcription activators interact with distal promoters to increase the rate of transcription. The complex interaction between DNA and proteins at these regions can activate, enhance, or inhibit gene expression. The type, amount, and combination of regulatory motifs present in the promoter region as well as the activity of transcription factors can contribute to the regulation of transcription.
In biotechnology industry, promoters are classified as constitutive, spatiotemporal, inducible, or synthetic. Constitutive promoters are unregulated promoter segments of DNA that allow continuous transcription of its associated gene (Hernandez-Garcia and Finer, 2014). They are important in metabolic engineering and synthetic biology. With differing strengths, constitutive promoters are useful for fine-tuning gene expression which can improve the pathway of the desired chemicals for an increased production. Strong constitutive promoters are also associated with the expression of some cryptic clusters which can lead to the discovery of new natural products (Li et. Al., 2015). Spatiotemporal promoters activate genes within specific tissue of an organism at specific times during growth and development (Hernandez-Garcia and Finer, 2014). It has a wide range of applications which includes tissue-specific targeting of pharmaceutical compounds and development of nutritionally and/or functionally improved transgenic seeds. Spatiotemporal promoters are also used to generate fruits with improved market quality and enhanced nutritional value through genetic engineering. Inducible promoters regulate gene expression in response to environmental stimuli such as nutrients or light (Hernandez-Garcia and Finer, 2014). The application of light-inducible promoters has overcome many problems of conventional chemical-regulated systems. Inducible promoters are also useful for the regulation of lethal or stress-related genes that respond to biotic or abiotic stresses. Many promoters are responsive to hormones from insects, mammals, and plants. For example, the insect ecdysone receptor ligand-binding domain are inducible in plants using either tebufenozide or methoxyfenozide, a component of insecticides. Synthetic promoters are different from native promoters because they can provide gene expression profiles that do not exist naturally (Hernandez-Garcia and Finer, 2014). These promoters are constructed by engineering of cis elements including activators, repressors, and enhancers. One highly active synthetic promoter is called DR5 auxin promoter. DR5 auxin promoter contains tandem repeats of the auxin responsive TGTCTC element. It has been used to study auxin response mechanisms in plants. Synthetic promoters can be designed to provide huge advantages over natural promoters in terms of specificity and strength.
Isochrysis galbana is a marine microalgal species of Haptophyta. It is an excellent food for a number of bivalve larvae, and it is now used in bivalve aquaculture industry. I. galbana contains high amount of fibers and it is also a promising prebiotic (Herrmann et. Al., 2010). One study revealed that I. galbana treatment increased the number of lactic-acid bacteria in rat feces (ScienceDirect). Extract from I. galbana is said to have some sort of cosmetic and hair-growth properties when using isopropanol, methanol, ethanol, ethyl acetate, or hexane as extractants. More importantly, I. galbana is rich in docosahexaenoic acid and alkenones, and they can be cultured and scaled easily. Docosahexaenoic acid is a polyunsaturated fatty acid with significant health and nutritional value. However, mass cultivation of I. galbana for docosahexaenoic acid production has been disappointing due to its low productivity (Lui et. Al., 2013). As the importance of I. galbana expands, interest in using as a microbial cell factory has also been increasing. CRISPR is an efficient and versatile tool to fine tune the biosynthetic pathways, nevertheless determining and characterizing promoter sequences (strong, weak, inducible) is important in controlling gene expression. Further techniques which include targeting the Cas9 to a specific studied promoter can we then realize a favorable gene expression by activating, repressing, or silencing genes to enhance metabolites or to increase docosahexaenoic acid production.
We aim to determine the effects of lights with different wavelengths on I. galbana gene expression and identify promoter elements as constitutive, inducible, strong, or weak. Cultures of I. galbana will be grown in filtered oceanwater media illuminated constantly with red, yellow, violet, orange, blue, or white light. High quality RNA will be extracted from the microalgal sample and gel electrophoresis will be performed to determine the integrity of total RNA. Messenger RNA will be isolated, fragmented, and added with primers. First and second strand complementary DNA will be synthesized using the messenger RNA. Double-stranded complementary DNA will be purified, and adaptors will be ligated. Adaptor ligated DNA will be enriched using polymerase chain reaction. DNA library purification and quality check will be performed. Data will be analyzed to quantitate expression under different lighting environments and sequences will be map back to a reference genome to extract matching promoter sequences. Sequence comparison will be performed, and conserved structural elements will be systematically listed. The results of the experiment should give an idea on the sequences that will be affected by different wavelengths of light and to whether a gene expression is associated with light responsive, constitutive, strong, or weak promoter elements. Knowing the type of promoter element, for example light-inducible promoters, should succor future research to develop a system such as controlling the gene expression by turning the light on or off, or by fine-tuning the gene expression by adjusting the light intensity or wavelength (Baek et. Al., 2016).
MATERIALS and METHODS
Pre-RNA Extraction Preparation
Isochrysis galbana was cultured under different wavelengths of light (red, orange, yellow, blue, violet, green, or white). The cells were harvested by centrifugation and washed by 100% ethanol. The cells were stored at -80°C prior to RNA extraction.
RNA Extraction and Quality Analysis
I. galbana cells were ground in liquid nitrogen. We added 10 ml RNA extraction buffer to the ground cells. One ml of 2M sodium acetate (pH 4.0), 10 ml water-saturated phenol (pH 4.3), and 2 ml chloroform:isoamyl alcohol (24:1) were added to the sample. Sample was centrifuged for 10 minutes at room temperature with the setting 5000xg. Equal volume of isopropanol was added and incubated at -20°C for at least 45 minutes. The sample was centrifuged for 10 minutes at 4°C with setting at 10,000xg. The pellet was resuspended using 500 μL sterile water. Equal volume of 4M lithium chloride was added. The pellet was washed in cold 70% ethanol and resuspended in 50 μL of water. The concentration of RNA was determined from its absorbance at 260 nm. At A260, 1 absorbance equates to 40 ug/ml of RNA.
We diluted 5 μL of total RNA with 15 μL water. The diluted sample was heated for 3 minutes at 65°C and then cooled on ice for 1 minute. The sample was loaded onto Invitrogen™ E-Gel™ EX Agarose 1% Gel. The marker used was RiboRuler™ High Range RNA Ladder. The gel containing the RNA sample was ran in E-Gel™ Power Snap Electrophoresis System using E-Gel™ EX 1-2% program (program 7) for 10 minutes. The size of the ribosomal band was determined, and the quality of the RNA was assessed.
NEBNext Oligo d(T)25 Beads Preparation
We aliquoted 20 μL of NEBNext Oligo d(T)25 beads into a sterile 0.2 ml PCR tube. The beads were washed with 100 μL RNA binding buffer (2X) and the supernatant was removed. The beads were resuspended in 50 μL RNA binding buffer (2X).
MRNA Isolation, Fragmentation, and Priming
Total RNA sample was diluted with nuclease-free water to a final volume of 50 μL. We added the 50 μL total RNA sample to the NEBNext Oligo d(T)25 that was prepared earlier. The sample was heated at 65°C for 5 minutes and held at 4°C. The separated beads were washed with 200 μL of wash buffer. The sample was added with 50 μL of Tris Buffer. The sample was then heated at 80°C for 2 minutes and held at 25°C. We added adding 50 μL of RNA binding buffer (2X). The beads were washed by adding 200 μL of wash buffer. We added 11.5 μL of the First Strand Synthesis Reaction Buffer and Primer mix (2X). The sample was incubated in thermal cycler with settings 94°C for 15 minutes, heated lid at 105°C, and hold at 94°C. We collected 10 μL of the supernatant containing the purified mRNA.
First and Second Strand cDNA Synthesis
The first strand cDNA synthesis reaction was assembled by combining 8 μL NEBNext Strand Specificity Reagent, 2 μL NEBNext First Strand Synthesis Enzyme Mix, and the purified mRNA. The sample was incubated in thermal cycler with settings heated lid at 105°C, 10 minutes at 25°C, 15 minutes at 42°C, 15 minutes at 70°C, and hold at 4°C. The second strand cDNA synthesis reaction was assembled by combining 8 μL NEBNext Second Strand Synthesis Reaction Buffer with dUTP Mix (10X), 4 μL NEBNext Second Strand Synthesis Enzyme Mix, 48 μL nuclease-free water, and the First Strand Synthesis reaction. The sample was incubated in a thermal cycler for 1 hour at 16°C with heated lid at 40°C.
Purification of Double-Stranded cDNA
We added 144 μL (1.8X) SPRIselect beads to the second strand synthesis reaction. We added 200 μL 80% ethanol to the separated beads. Target DNA was separated from the beads by adding 53 μL 0.1 TE Buffer. We collected 50 μL supernatant containing the purified cDNA and stored at -20°C.
End Prep of cDNA Library
End prep reaction was assembled by adding 7 μL NEBNext Ultra II End Prep Reaction Buffer and 3 μL NEBNext Ultra II End Prep Enzyme Mix to the 50 μL purified cDNA. The sample was incubated in a thermocycler with settings heated lid at ≥75°C, 30 minutes at 20°C, 30 minutes at 65°C, and hold at 4°C.
NEBNext Adaptor was diluted in ice-cold Adaptor Dilution Buffer. Dilution was performed according to table 1 below:
|Total RNA Input||Dilution Required|
|1000-250 ng||5-fold dilution|
|249-100 ng||25-fold dilution|
|99-10 ng||100-fold dilution|
Ligation reaction was assembled by adding 2.5 μL of NEBNext Diluted Adaptor, 1 μL NEBNext Ligation Enhancer, and 20 μL NEBNext Ultra II Ligation Master Mix into the End Prepped DNA. Each component of the ligation reaction was added according to the order mentioned. The sample was incubated in a thermocycler with setting 15 minutes at 20°C. After incubation, 3 μL USER Enzymes was added to the ligation reaction. The sample was incubated at 37°C for 15 minutes with heated lid set to ≥45°C.
Purification of Ligation Reaction
After adaptor ligation, the sample was added with 87 μL (0.9X) resuspended SPRIselect Beads We added 200 μL 80% ethanol to the separated beads. Target DNAs were separated by adding 17 μL 0.1X TE Buffer. We collected 15 μL of the supernatant.
PCR Enrichment of Adaptor Ligated DNA
PCR enrichment reaction was assembled by adding 25 μL NEBNext Ultra II Q5 Master Mix, 5 μL Index #8 Primer/i7 Primer and 5 μL Universal PCR Primer/i5 Primer into the Adaptor Ligated DNA. The sample was PCR-amplified on thermocycler with settings heated lid at 105°C, initial denaturation (98°C, 30 sec, 1 cycle), denaturation (98°C, 10 sec, 8-16 cycles), annealing/extension (65°C, 75 sec, 8-16 cycles), final extension (65°C, 5 mins, 1 cycle), hold (4°C). The cycles were adjusted according to the total RNA input: 1000ng (8-9 cycles), 100ng (12-13 cycles), 10ng (15-16 cycles).
Purification of the PCR Reaction
We added 45 μL (0.9X) SPRIselect beads into the PCR reaction sample. We added 200 μL 80% ethanol to the separated beads. Target DNA was separated by adding 23 μL 0.1X TE Buffer. We collected 20 μL of the supernatant and stored at -20°C.
Preparing Master Mix and Primer Mix for Determining Library Concentration
The library concentration was determined using the NEBNext Library Quant Kit Quick Protocol (E7630). We obtained 5 ng to 1 μg of fragmented DNA sample. Master Mix and Primer Mix were prepared by adding 100 μL NEBNext Library Quant Primer Mix to NEBNext Library Quant Master Mix (1.5 mL).
Preparing Library Dilutions
NEBNext Library Quant Dilution Buffer (1X) was prepared by making 1:10 dilution of the 10X buffer in nuclease-free water. An initial 1:1000 dilution of each library sample in NEBNext Library Quant Dilution (1x) was prepared. To create 1:1000 dilution, 1 μL library sample was added to 999 μL NEBNext Library Quant Dilution Buffer (1X). To create 1:10000 dilution, 10 μL of the 1:1000 dilution was added to 90 μL NEBNext Library Quant Dilution Buffer (1X). To create 1:100000 dilution, 10 μL of the 1:10000 dilution was added to 90 μL NEBNext Library Quant Dilution Buffer (1X).
Preparing qPCR Assays
DNA standards and diluted library samples were prepared by combining 16 μL NEBNext Library Quant Master Mix (with Primers) and 4 μL of DNA standards. The no-template control was prepared by combining 16 μL NEBNext Library Quant Master Mix (with Primers) and 4 μL Library Dilution Buffer (1X).
Running qPCR Assay in a Real-Time Thermal Cycler Using FAM/SYBR Setting
QPCR cycling conditions were set to: Initial Denaturation (95°C, 1 min, 1 cycle), Denaturation (95°C, 15 sec, 35 cycles), Extension (63°C, 45 sec, 0 cycle). The standard concentrations were annotated using real-time instrument as follow: DNA Standard 1 (10 pM), DNA Standard 2 (1 pM), DNA Standard 3 (0.1 pM), DNA Standard 4 (0.01 pM). Concentrations were calculated using standard curve generated with qPCR instrument software. Standard curve and library concentrations were calculated using NEB qPCR webtool.
Sizing Library Fragments Using E-Gel
We combined 100 ng of the library sample with sterile water to bring the final volume to 20 μL. The entire volume of the diluted library sample was loaded into a well of the E-Gel™ EX. Diluted 20 μL of DNA ladder was also loaded into the E-Gel™ EX. The E-Gel® iBase™ was programmed according to table 2 below:
|Gel Type||Program||Default Run Time||Maximal Run Time|
|E-Gel® (0.8%, 1.2%, 2%)||RUN E-Gel 0.8-2.0%||26 mins||40 mins|
|E-Gel® 4%||RUN E-Gel 4%||30 mins||40 mins|
|E-Gel® double comb (0.8%, 2%)||RUN E-Gel DC||13 mins||20 mins|
FASTA/FASTQ files were produced from the Illumina NGS Sequencing. Read quality was analyzed using FASTQC (Galaxy Version 0.72+galaxy1) tool in Galaxy 21.01.rc1 (usegalaxy.org) using default settings. Reads were aligned to the Isochrysis genome using HISAT2 (Galaxy Version 2.1.0+galaxy7) with the following settings: reference genome set to Isochrysis_galbana.faa.txt, paired-end, strand information set to reverse, print summary alignment to file (Yes). Aligned reads were counted using HTSEQ-Count (Galaxy Version 0.9.1) with settings GFF file (iso_final_annot.gff3) and stranded (Yes). Differential gene expressions were identified using DESEQ2 (Galaxy Version 126.96.36.199+galaxy1) with the following settings: visualizing the analysis (Yes), normalized count tables (Yes). Fifteen of the most differentially expressed genes were identified using the statistics table produced by DESEQ2 following the sort criteria: log2 fold change ≥2 and ≤-2 then p-value < 0.05. Using JGI Phychocosm, 1000 base-pairs upstream of the most differentially expressed genes were identified. Multiple sequence alignment was performed using ClustalW (Galaxy Version 2.1) with default settings and FASTA output. Sequence logos (Galaxy Version 3.5.0) were generated using default settings.
Library Quality Control
After the libraries were quantified using qPCR, cDNA library samples ran through gel electrophoresis to determine purified cDNA library sizes. 6 out of 8 samples display broad bands of cDNA library at around 400 bp (Figure 3). QPCR amplification plot was generated to compare the threshold cycles between two different diluted cDNA libraries. As expected, the 1:1000 diluted cDNA libraries passed the threshold at fewer cycles (15.6, 15.7, 13.9 cycles) compared with the 1:10000 dilution (19.1, 18.7, 17.5 cycles). The no-template control libraries passed threshold at a significantly higher cycles compared with the cDNA libraries (Figure 4). Melt curve analysis was performed to determine the dissociation temperatures of the cDNA libraries. Both dissociation temperature of the 1:1000 and 1:10000 cDNA libraries are 78°C (Figure 5). Standard curve plot was generated to determine the concentration cDNA. Using the equation of the line (y = -3.685x + 12.67) and the threshold cycles for each diluted cDNA library, the undiluted concentrations were calculated to be 225.2 pM and 270.9 pM for 1:1000 and 1:10000 dilution, respectively (Figure 6). The initial concentrations of the two library samples are not significantly different. The quality control results suggest that the purification processes and pipetting techniques were performed accurately.
HISAT2 and HTSEQ-Count Analysis
FASTA files were aligned with the Isochrysis genome to create the BAM files. Aligned reads were counted using HTSEQ-Count in Galaxy. The numbers of reads that aligned with the Isochrysis genome are 1692220 and 13013342 for blue 1 and blue 2 libraries, respectively, while white 1 and white 2 libraries have aligned read numbers of 11886112 and 9464630, respectively. The expression ranges for blue 1 and blue 3 libraries are 1520 and 5392, respectively. Since white light is composed of broad electromagnetic spectrum, white 1 and white 2 libraries have higher expression ranges of 183640 and 49873, respectively. The average gene expressions for blue 1, blue 3, white 1, and white 2 libraries are 28, 23, 716, and 542, respectively. Median gene expressions and the number of expressed genes also appear to be lower in blue libraries than in white libraries. The average proportions of genes expressed are 27.5% and 88% for blue and white libraries, respectively (Table 3). The results in this analysis revealed that 450-495 nanometers wavelength of the electromagnetic spectrum can interact and induce expressions in certain genes. Summary of this analysis is shown in Table 1.
Differential gene expressions were identified using DESEQ2 and heat map graph (Figure 7) was generated to reveal relationship distances between the libraries. Green 1 library is most closely related to yellow 1, yellow 2, orange 1, orange 2, violet 1, violet 2, green 2, and blue 2. Green 1 library is next closely related to red 2 and is least closely related to blue 1 and red 1. In contrast, all white libraries do not show significant relationship with the other non-white color libraries. DESEQ2 summary table comparing blue and white libraries was generated. The range of log2 fold change is -14.24 to 9.48 while the range of fold change is 0.00005 to 714. The number of up-regulated genes under blue light was determined using the following criteria: log2<-4, p-value<0.05. The number of down-regulated genes under blue light was determined using the following criteria: log2>4, p-value<0.05. The number of up-regulated genes and down-regulated genes are 292 and 3964, respectively (Table 4). This data show that a specific wavelength of light can induce different gene expressions compared with white light that has all the magnetic spectrum.
Candidate loci were identified using the statistics table from DESEQ2 analysis. For up-regulated loci, candidates were determined by sorting the table according to log2 fold change from lowest to highest and the top 10 loci were selected. For down-regulated loci, candidates were determined by sorting the table according to log2 fold change from highest to lowest and the top 10 loci were selected. Table 2 shows the top 10 candidate loci that were up-regulated and down-regulated under blue light.
Using JGI Phychocosm, 500 base-pairs upstream of the candidate loci were identified and multiple sequence alignment of the upstream base-pairs was performed using Rgenetics Sequence Logo program. Overrepresented sequences were identified by comparing the logos of up-regulated genes and down-regulated genes. Comparison analysis revealed 153-160 bp, 190-198 bp, 285-290 bp, 293-301 bp, and 408-413 bp upstream of the candidate loci are conserved sequences in the up-regulated genes but lacking in the down-regulated genes. The data provides information about conserved upstream sequences of the up-regulated genes that are potentially light-sensitive promoters under blue-colored light. Figure 2 shows the comparison summary of conserved upstream sequences between up-regulated genes and down-regulated genes.
We utilized RNASEQ and determined upstream sequences that are potential light-sensitive promoters of the candidate loci significantly up-regulated under different colors of light. Although only the blue light libraries were used for the logo analysis, other colored light libraries show distinct analytical results compared with the white light libraries. The library-to-library heat map revealed that none of the colored light libraries is closely related with the white light libraries suggesting that the colored light induces different amount of gene expression compared with white light. Our data also reveal the amount of gene expression and the number of expressed genes when comparing blue and white light libraries. Five consensus sequences were identified as overrepresented in the logo analysis and were considered as potential light-sensitive promoters.
Differential gene expressions were revealed however, verification with the qPCR is needed. Further research should include identifying specific genes that are correlated with different wavelengths of light. The activity of the potential promoter elements should be tested in synthetic reporter construct. For example, it would be beneficial if blue light can promote the expression of green fluorescent protein construct with blue light-specific motif because the translated protein is recognized by gene therapy researchers and was used in various therapeutic proteins (Wahlfors, et. Al). Investigating the genes identified in this research may illuminate the mechanism of light-induced growth and metabolism. Ultimately, a successful light-sensitive promoter system should provide quick, non-invasive, switchable control of the gene expression in any suitable cell by simply exposing to a source of light.
Baek, K., Lee, Y., Nam, O., Park, S., Sim, S.J. and Jin, E. (2016), Introducing Dunaliella LIP promoter containing light‐inducible motifs improves transgenic expression in Chlamydomonas reinhardtii. Biotechnology Journal, 11: 384-392.
Bancroft D. (2016). The Unlimited Biotech Potential of Algae. Labiotech. Accessed March 5, 2021.
Fayyaz, M., Chew, K. W., Show, P. L., Ling, T. C., Ng, I. S., & Chang, J. S. (2020). Genetic engineering of microalgae for enhanced biorefinery capabilities. Biotechnology advances, 43, 107554.
Hernandez-Garcia C., Finer J. (2014). Identification and validation of promoters and cis-acting regulatory elements. Plant Science. Volumes 217–218, March 2014, Pages 109-119.
Herrmann, M., Joppe, H., Pertile, P. (2010). Extracts of isochrysis sp. Accessed 2017-04-17
Li, S., Wang, J., Li, X. et al. (2015). Genome-wide identification and evaluation of constitutive promoters in streptomycetes. Microb Cell Fact 14, 172.
Liu, J., Sommerfeld, M., & Hu, Q. (2013). Screening and characterization of Isochrysis strains and optimization of culture conditions for docosahexaenoic acid production. Applied microbiology and biotechnology, 97(11), 4785–4798.
Ng, I. S., Tan, S. I., Kao, P. H., Chang, Y. K., & Chang, J. S. (2017). Recent Developments on Genetic Engineering of Microalgae for Biofuels and Bio-Based Chemicals. Biotechnology journal, 12(10), 10.1002/biot.201600644.
Panahi, Y., Darvishi, B., Jowzi, N., Beiraghdar, F., & Sahebkar, A. (2016). Chlorella vulgaris: A Multifunctional Dietary Supplement with Diverse Medicinal Properties. Current pharmaceutical design, 22(2), 164–173.
Razeghifard R. (2013). Algal biofuels. Photosynth Research. 117(1-3):207-19. doi: 10.1007/s11120-013-9828-z. Epub 2013 Apr 21. PMID: 23605290.
Read, B. RNASeq Aimed at Identifying Light Responsive Promoter Elements. California State University San Marcos – Biological Science Department.
Shimizu-Sato, A., Huq, E. (2002). A Light-Switchable Gene Promoter System. Nature Biotechnology. 20(10):1041-4 DOI:10.1038/nbt734
ScienceDirect. Isochrysis galbana. Accessed March 5, 2021. https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/isochrysis-galbana
Wahlfors J, Loimas S, Pasanen T, Hakkarainen T. (2001). Green fluorescent protein (GFP) fusion constructs in gene therapy research. Histochem Cell Biol. 115(1):59-65. doi: 10.1007/s004180000219. PMID: 11219609.
Yella, Venkata Rajesh & Bansal, Manju. (2014). In silico Identification of Eukaryotic Promoters. 10.1007/978-94-017-9514-2_4.