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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261680
72381
10.1038/s41598-024-72381-4
Article
Analysis of oil accumulation mechanisms in plasma induced mutant Scenedesmus strains compared to original Scenedesmus strains
Wei Wenqian weiweiwenqian@126.com

1
Huang Feng 2
1 Shaanxi Institute of Fashion Engineering, Xianyang, 712046 Shaanxi China
2 Xi’an Energy Conservation and Green Development Research Institute Co., Ltd., Xi’an, 710016 Shaanxi China
11 9 2024
11 9 2024
2024
14 2125011 5 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Scenedesmus sp. is a species of the Scenedesmus genus within the phylum Chlorophyta, commonly found as a planktonic algal species in freshwater and known for its rapid growth rate. This study employs room-temperature, atmospheric-pressure plasma mutagenesis for the breeding of Scenedesmus sp., utilizing transcriptomic analysis to investigate the biosynthesis mechanism of triglycerides. Further analysis of differentially expressed genes in transcriptome by measuring the macroscopic biological indicators of mutant and original algal strains. The findings of the study suggest that the mutant strain's photosynthesis has been enhanced, leading to improved light energy utilization and CO2 fixation, thereby providing more carbon storage and energy for biomass and lipid production. The intensification of glycolysis and the TCA (tricarboxylic acid) cycle results in a greater shift in carbon flux towards lipid accumulation. An elevated expression level of related enzymes in starch and protein degradation pathways may enhance acetyl CoA accumulation, facilitating a larger substrate supply for fatty acid production and thereby increasing lipid yield.

Keywords

Transcriptome
Plasma mutagenesis
Lipid accumulation
Scenedesmus sp.
Subject terms

Cell biology
Plant sciences
Key R&D Program Project in Xianyang CityL2023-ZDYF-SF-029 2023 Campus level Scientific Research Project of Shaanxi Institute of Fashion Engineering2023XKZ58 2024 Campus level Industry University Research Project24JX24 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Research has indicated that high oil production are primarily found in the phyla Chlorophyta, Diatom, and Chlorella1. Scenedesmus sp., a species of planktonic algae commonly found in freshwater, belongs to the family Scenedesmus of the phylum Chlorophyta and exhibits a rapid growth rate. ARTP (Atmospheric Room Temperature Plasma) is a radio frequency discharge technology that uses gas discharge to generate a large amount of high-energy active particles under atmospheric and room temperature conditions; Research has shown that active particles can effectively be used for genetic material in cells and cause DNA structural damage, thereby utilizing the high fault tolerance repair mechanism of cells to generate a large number of mutation sites and ultimately obtain a large capacity gene mutation library2,3. Numerous studies have demonstrated that ARTP operation is both feasible and safe, enabling rapid and diverse mutations in microbial genomes, leading to alterations in microbial gene sequences4,5. Transcriptome sequencing is a technique that reveals the total RNA content within a specific cell and can be utilized to study gene transcription and metabolic regulation6. Through omics research, we can explore the differential expression of genes in mutant algal strains, as well as the enzymes that play a crucial role in oil production7. This study explores the differential gene expression of mutant algal strains and the enzymes that play a key role in oil production. The results indicate that an increase in the expression levels of related enzymes in the degradation pathways of starch and protein may promote the accumulation of acetyl CoA, providing more substrates for fatty acid production and increasing lipid production, providing theoretical guidance for plasma induced improvement of lipid yield in Scenedesmus.

Materials and methods

Selection of algal species

The algal species used in this experiment are Scenedesmus, which were purchased from the freshwater algae seed bank of Institute of Hydrobiology, Chinese Academyof Sciences. Wash with distilled water to eliminate the influence of the medium, and further separate and purify on BG11 medium containing agar powder. Which is cultured in a 250 mL sterilized conical flask containing 120 mL of BG11 medium. Typically, three replicates are established during the cultivation process. The conical flask is sealed with sterile film and placed in a constant temperature and light incubator. The light intensity is maintained at 80 μmol photons m−2 s−1, with a light cycle of L:D (12 h:12 h), and the cultivation temperature is set at 25 °C.

Plasma mutation conditions

Before the mutagenesis experiment, a mixture of glycerol and culture medium was prepared. The mutant protection solution and nutrient solution were obtained after mutagenesis. 190 mL of sterilized BG-11 and 5 mL of glycerol were mixed well, and 10 mL of each mixture was added to a 50 mL sterile centrifuge tube, Cultivate the algal solution to the logarithmic phase, with a cell concentration of about 106 algal cells per milliliter. At this time, the cell viability is strong and more suitable for mutagenesis. Take 0.1 mL of algal solution for mutagenesis, maintain a distance of 2 mm between the transmitter and the sample, air gas flow rate of 5 L/min, and a mutagenic time of 60 s. By changing different mutagenic voltage and current conditions, mutate the diatom and set a control group.

Protein determination

The Coomassie Brilliant Blue method8 was used for determination, with bovine serum protein as the standard solution. First, 5 mL of algal liquid was centrifuged at 8000 rpm for 10 min, and the supernatant was discarded. Then, 5 mL of phosphate buffer salt solution (PBS) was added for ultrasonic crushing and then placed in a refrigerator at 4 °C for 12 h. The solution was then centrifuged at 8000 rpm for 10 min, 1 mL of the supernatant was removed, and 5 mL of Coomassie brilliant blue solution was added. After standing at room temperature for 10 min, a spectrophotometer was used to start the determination at a wavelength of 595 nm, and determination was completed within 20 min. In the process of measuring protein, the influence of protein concentration on growth was considered. We have continuously monitored the protein concentration changes of the original algal strain and the mutant algal strain six times within a 36 day culture cycle, and compared the protein concentration during transcriptome sequencing, i.e. the end stage of growth.

Determination of triglycerides

During the cultivation process, regularly take 3 mL of algal solution, add 1 mL of 25% DMSO, place it in an ultrasound instrument and sonicate at 500W power for 10 min to enhance the permeability of algal cells, then add 0.05 mL of 100 μg/mL Nile Red staining solution to stain the algal cells., shake the mixture with a fixed mixer for 20 s to ensure even mixing, then place it in a black box for 15 min to react. Finally, measure the value of OD570 A1, and set a blank control group without Nile Red dye solution. Measure the background fluorescence value A2, and calculate the concentration of triglycerides by substituting the difference between the two (A1–A2) into the standard curve.

Determination of carbohydrates

Using the phenol sulfuric acid method9, a standard curve was established between glucose and absorbance OD490 using glucose as the standard solution. Take 1 mL of extraction solution, add 1 mL of newly prepared 5% phenol solution and 5 mL of concentrated sulfuric acid, shake to mix evenly, let it stand for 20 min until the color is completed and cooled to room temperature, measure the value of OD680, and then substitute it into the formula to calculate the carbohydrate concentration of algal cells.

RNA extraction method

Using a gun head without RNase to absorb logarithmic phase (OD680 = 0.5–0.8) The microalgae sample was placed in a 10 mL enzyme free tube. Centrifuge at 8000 r min−1 and 4 °C for 5 min to fully remove the supernatant, and repeat the operation to take approximately 0.3–0.6 g. Quickly freeze in liquid nitrogen for 15 min and store in a − 80 °C refrigerator. Extract RNA using the RNArep Pure Plant kit for polysaccharide and polyphenol plant assay (DP441). After RNA extraction, use RNA specific agarose gel electrophoresis to detect the concentration and purity. Purify the mRNA from the total RNA using the unique poly A structure of mRNA, and break the mRNA into 200–300 bp fragments through ion disruption.

Screening methods for dominant algal strains

In the screening of dominant algal strains, the combination of specific growth rate and relative fluorescence value, as well as the product of specific growth rate and relative fluorescence value, is used as the screening principle. The specific screening principle is as follows: the first screening principle is to have a growth rate greater than 10% of the original plant and a relative fluorescence value greater than 20% of the original plant; The principle of fill in screening is that the product of specific growth rate and relative fluorescence value is greater than 35% of the original strain. This condition mainly involves filling in high specific growth rate mutants or high relative fluorescence value mutants. Algae strains that meet the above two principles are considered to have positive mutations, while the rest are considered to have negative mutations. The selected positive mutant strains are inoculated into a six well plate and passaged under the same conditions to verify their genetic stability.

Through mutagenesis of microalgae with different emission power at a certain mutagenic time. To illustrate the effect of ARTP on the microalgae, chlorophyll fluorescence was measured immediately after irradiation to reflect the growth condition of the mutant algae after the radiation. Chlorophyll fluorescence was considered as one of the important parameters for the activity of photosystem II. The mutant and control groups were cultivated on agar plates with a diameter of 90 mm. After 10 days, the colonies were clearly visible in the solid medium. According to modern breeding theory, high mortality rate is usually accompanied by high mutation rate. When the mortality rate exceeds 90%, the positive mutation rate is higher. Single and larger colonies were selected from the plates with mutation mortality greater than 90% for subsequent experiments. The selected colonies and the control group were cultured in 6-well plates. The optical density was measured on the 3rd and 8th day, and the fluorescence intensity was measured on the 8th day. To determine the relationship between mutation and mortality, the definitions for both positive and negative mutations were established. Positive mutation is defined by a specific growth rate that was > 10% higher than the control group and relative fluorescence that was > 20% higher than the control group, or the product of the specific growth rate value and relative fluorescence value was > 35% higher than the control group. Negative mutation is defined by the observation that any of the indexes could not achieve the specified value. This combination was suggested as a screening method that ensures that highly oleaginous algal strains would be screened instead of relying on a single indicator such as specific growth rate or relative fluorescence. The dominant strains in this manuscript were finally screened according to the above screening methods.

Statistical analysis

The experimental results were shown as the means of three repeated experiments. The error bars indicated the standard deviation and the statistical significance of the results was evaluated using one-way ANOVA. The software of SPSS Statistics 22 was used for data analysis, where a p-value of < 0.05 was considered statistically significant.

Results and discussion

Figure 1 shows the changes in Fv/Fm and triglyceride concentrations with culture time, while Fig. 2 shows the changes in carbohydrate and protein concentrations with culture time between mutant and original strains By statistically analyzing the transcriptome expression of high oil yield mutant and original strains during the rapid oil accumulation period in the later growth stage, differentially expressed genes and enzymes related to the oil accumulation pathway were screened, and the gene expression and metabolic regulation of oil accumulation in Scenedesmus were analyzed to reveal the underlying reasons for macroscopic index changes, providing a theoretical basis for further targeted gene modification of oil producing algae strains.Fig. 1 Fv/Fm and triglyceride concentrations of the original and mutant strains.

Fig. 2 Carbohydrate and protein concentrations of the original and mutant strains.

Sequencing results and quality evaluation

Around the 20th day, the original and mutant strains were sequenced, and the raw data of each sample were statistically analyzed as shown in Table 1. Y represents the original strain and T represents the mutant strain. Two replicates were taken for each sample, and the total readings for Y1, Y2, T1, and T2 were 48.08 million, 47.93 million, 49.04 million, and 52.09 million, respectively. According to the preliminary evaluation parameters Q30 (%), they are 95.6%, 95.66%, 95.68%, and 95.69%, all reaching above 95%. The sequencing data has high quality, is very reliable, and can meet the corresponding requirements.Table 1 Raw data statistics.

Sample	Total reads	Total number of bases (bp)	Q30 (bp)	N (%)	Q20 (%)	Q30 (%)	
Y1	48,089,994	7,213,499,100	6,896,222,838	0.000681	98.38	95.6	
Y2	47,925,582	7,188,837,300	6,877,217,622	0.000697	98.41	95.66	
T1	49,038,874	6,305,831,100	6,033,483,384	0.000686	98.41	95.68	
T2	52,090,052	7,813,507,800	7,476,928,082	0.000702	98.41	95.69	
Q30 (bp): The total number of bases with a base recognition accuracy of 99.9% or higher; N (%): The percentage of fuzzy bases; Q20 (%): The percentage of bases with a base recognition accuracy of over 99%; Q30 (%): The percentage of bases with a base recognition accuracy of 99.9% or higher.

Filter the raw data of the offline machine to remove low-quality reads with connectors that interfere greatly with subsequent analysis. The high-quality sequences (Clean Reads) obtained after filtering are concatenated and assembled from scratch. Using Trinity software 2.5.1 for De Novo assembly and splicing of transcriptome, obtain new transcripts. The new data is statistically analyzed to select the representative sequence Unigene under each gene, and then annotated and analyzed in depth. The statistical analysis results of the overall sequence are shown in Table 2. As shown in the table, a total of 26,749 Unigenes were obtained, with total sequence length, total number of sequences, N50, and GC proportion of 35,507,238 bp, 26,749, 2070 bp, and 61.05%, respectively.Table 2 Sequence overall statistics table.

	Total length of sequence (bp)	Total number of sequences	Maximum sequence length (bp)	Average sequence length (bp)	N50 (bp)	N50 seqence No	N90 (bp)	N90 seqence No	GC%	
Transcript	77,919,983	50,161	19,648	1553.40	2313	10,032	697	33,193	61.00	
Unigene	35,507,238	26,749	19,648	1327.42	2070	5086	541	17,756	61.05	
N50 and N90 represent when a certain sequence is added to all previous sequences, the length at this time just exceeds 50% or 90% of the total length; GC (%): The GC content of a sequence.

Functional annotation of Scenedesmus Unigene

Summary of annotation results

After performing non parametric concatenation on Clean Reads, Unigene was compared and annotated with databases such as NR, GO, KEGG10, Pfam, Swissprot, eggNOG, etc. The results of annotation in each database are shown in Table 3. The most annotated is NR, with a maximum of 11,588 comments, accounting for 43.32% of the total. Next is the eggNOG library, which annotates 9546 unigenes, accounting for 35.69% of the total. The Swissprot, Pfam, KEGG, and GO libraries annotate 30.51%, 28.36%, 23.66%, and 19.08% of the total genes, respectively. Among them, 10.78% of unigenes are annotated in each library.Table 3 Summary of annotation results.

Database name	Unigene quantity	Proportion (%)	
NR	11,588	43.32	
GO	5104	19.08	
KEGG	6328	23.66	
Pfam	7586	28.36	
eggNOG	9546	35.69	
Swissprot	8160	30.51	
In all database	2884	10.78	

NR annotation statistics

By comparing and annotating with the NR database, it is possible to clearly understand which species has significant similarities in gene sequences with the algae species studied in this experiment, as well as to understand some gene functional information of this algae species through the proportion of each part. The comparison results are shown in Fig. 3. Figure 3 lists the top 7 species with the highest predicted similarity to the studied species, and the results show significant differences in their proportions among different species. The species with the highest proportion are Monoraphidium neglectum (38.57%), followed by Gonium pectorate (11.5%), Volvox carteri f. nagariensis (11.09%), Chlamydomonas reinhardtii (9.7%), Coccomyxa subellipsoidea C-169 (5.67%), Chlorella variabilis (4.56%), and Zea mays (2.89%). The six species with the highest proportion belong to algae, and the largest proportion is Monoraphidium neglectum, which was identified by Bogen as an oil containing green algae in previous studies11.Fig. 3 Distribution of annotated species in NR library.

GO annotation statistics

The GO annotation results are divided into three categories: biological process, cellular component, and molecular function12. GO annotation can provide a preliminary understanding of the functions and effects of different genes on organisms, and can provide a basis for subsequent GO enrichment analysis of differentially expressed genes. The annotation result is shown in Fig. 4. From the figure, it can be seen that in the Biological Process category, the categories with more annotated genes are cellular processes (2583) and metabolic processes (2530), followed by single organism processes (1613). In the Cellular Component category, the maximum number of genes annotated in the cellular part is 2197, followed by organelles (1592), cell membrane (1172), organelle composition (852), and macromolecular complexes (833). In Molecular Function, genes with binding activity (2168) and catalytic function (2832) account for a large proportion of the total annotated genes, and these genes may play important roles, far exceeding the functions of structural molecular activity (318), transport activity (43), and molecular function regulators (34) in this category.Fig. 4 GO database annotation statistics chart.

KEGG annotation statistics

Using the KOBAS annotation system, a similar species set was selected and automatically annotated. The statistical results are shown in Fig. 5. It can be seen that the total number of annotated unigenes is 6610, and KEGG is divided into 5 major categories at the first level. Among them, 2582 unigenes are classified into the metabolic part, accounting for 43.15% of the total, followed by genetic information processing (23.66%), biological systems (15.36%), cellular processes (9.79%), and environmental information processing (8.04%).Fig. 5 KEGG database annotation statistics chart.

EggNOG annotation statistics

EggNOG refers to the clustering of homologous proteins in eukaryotic organisms, where genes are annotated through eggNOG alignment, and the best result of the alignment is assigned an eggNOG number to the corresponding gene. Further utilizing the correspondence between the eggNOG number and the eggNOG classification directory, classify each gene into the eggNOG classification directory, and macroscopically understand the processes in which the genes of Scenedesmus participate, their roles, and the differences in each classification. The statistical results are shown in Table 4.Table 4 eggNOG classification notes statistical table.

Abbreviation	Annotation	Unigene number (individual)	Proportion	
A	RNA processing and modification	474	4.16%	
B	The structure and movement of chromatin	158	1.39%	
C	Energy production and conversion	473	4.15%	
D	Cell cycle control and mitosis	207	1.81%	
E	Amino acid transport and metabolism	401	3.52%	
F	Nucleotide transport and metabolism	128	1.12%	
G	Carbohydrate transport and metabolism	532	4.66%	
H	Coenzyme transport and metabolism	224	1.96%	
I	Lipid transport and metabolism	368	3.23%	
J	Translation, ribosome structure and biosynthesis	739	6.48%	
K	Transcription	507	4.44%	
L	Copy, compile, and repair	480	4.21%	
M	Biosynthesis of cell walls/membranes	104	0.91%	
N	Cellular movement	5	0.04%	
O	Posttranslational modifications, protein folding, and molecular chaperones	1083	9.49%	
P	Transport and metabolism of inorganic ions	291	2.55%	
Q	Biosynthesis, Transportation, and Catabolism of secondary metabolites	281	2.46%	
R	General functional prediction	1597	14.00%	
S	Function unknown	1843	16.16%	
T	Signal transduction mechanism	697	6.11%	
U	Intracellular transport, secretion, and vesicular transport	446	3.91%	
V	Defense mechanism	90	0.79%	
W	Extracellular structure	14	0.12%	
Y	Cell nucleus structures	31	0.27%	
Z	Cytoskeleton	234	2.05%	

From Table 4, it can be seen that a total of 11,407 unigenes were annotated by eggNOG, divided into 25 categories. 1597 unigenes were classified as general functional prediction (R category), accounting for 14.00%; The proportion of post-translational modifications (O-type) is also relatively high, at 9.49%, and their role can help protein synthesis and maintain a relatively stable spatial structure; Class J accounts for 6.48%; Signal transduction mechanisms (Class T) account for 6.11%; The transportation and metabolism of carbohydrates (G class) account for 4.66%; Lipid transport and metabolism (Class I) account for 3.23%. From the above results, it can be seen that mutant algae strains respond to external environmental signals through their own reaction mechanisms and signal transduction under the action of plasma, activating the transcription, replication, compilation, and repair processes of related genes, thereby affecting cell growth and death, and regulating energy metabolism such as amino acids, carbohydrates, and lipids in the cell body.

Analysis of differentially expressed genes

Based on the gene expression between the original group and the mutant group, differentially expressed genes (DEGs) between the mutant group and the original group were detected using DESeq. If the screening condition is P-value < 0.05 and | log2 Fold Change |> 1, the gene is considered DEG. For the detected differentially expressed genes, if their log2 Fold Change > 0, it means that the gene expression in the mutant group is upregulated compared to the original group; If log2 Fold Change < 0, it is considered that the gene expression in the mutant group is downregulated compared to the original strain group. From Fig. 6, it can be seen that compared with the original group, the mutant group had a total of 527 genes upregulated and 139 genes downregulated. The volcano plot was drawn using the R language13 ggplots2 software package. According to the volcano plot principle, the further the value of log2 FC is from the far point, the larger the p-value value value, indicating that the expression difference of this gene in the mutant group compared to the original group is greater and more significant. Therefore, it can be seen that most of the upregulated genes in the mutant group have higher expression multiples compared to the downregulated genes. The differential expression of these genes may result in significant differences in the metabolic processes within algal cells.Fig. 6 Volcano map of differential expression gene between original and mutant groups.

Differential gene enrichment analysis

GO enrichment analysis of differentially expressed genes

By analyzing the genes annotated in the GO database using the top-GO software GO enrichment analysis, the GO enrichment results are usually classified according to three parts: molecular function MF, biological process BP, and cellular component CC. The top 10 GO term entries with the smallest p-value value or the most significant enrichment in each GO classification are selected and summarized, as shown in Fig. 7. In order to further identify the most significantly enriched GO terms, based on the GO enrichment results, the total number of genes enriched on a certain GO term is counted as an indicator to evaluate the degree of enrichment. Screen the top 20 GO Term entries with the lowest FDR value and draw a bubble chart as shown in Fig. 8. Among them, there are significant differences in membrane composition, calcium ion transmembrane transport, and phosphate ester hydrolase activity. These differences may enhance the permeability of algal cell membranes, affect the utilization of various elements in the culture medium and the activity of related enzymes during growth, leading to differences in algal cell growth and metabolic processes.Fig. 7 Histogram of GO enrichment analysis. The vertical axis represents the terms of Go level 2, and the horizontal axis represents the − log10 (p-value) enriched by each term.

Fig. 8 Bubble plot of GO enrichment analysis.

Differential gene KEEG enrichment analysis

In the physiological and biochemical activities of algal cells, each activity requires the participation of many genes, which regulate each other and play important roles. KEEG analysis can provide a more comprehensive understanding of the differential expression of related genes in lipid synthesis, protein synthesis, and other processes between mutant and original strains, and thus affect their metabolism14. According to the KEGG enrichment analysis results, the top 20 pathways with the lowest p-value were selected, which represent the most significant functional enrichment15. As shown in Fig. 9, it can be seen that the MAPK signaling pathway, protein processing in the endoplasmic reticulum, photosynthesis, glutathione metabolism, carbon sequestration by photosynthetic organisms, and glycerophospholipid metabolism are significantly enriched. In metabolic pathways, the focus is on identifying upregulated differentially expressed genes related to fatty acid synthesis, which can provide a foundation for later molecular breeding and targeted modification.Fig. 9 Histogram of KEGG enrichment analysis.

Analysis of metabolic pathways related to oil synthesis in mutant and original strains

Pathways of photosynthesis

Algae cells require light during their growth process. By capturing light energy, they fix carbon dioxide in the air, maintain their own growth, and produce sufficient carbon sources for the synthesis of biochemical components such as lipids, carbohydrates, and proteins. In the mutant strain, the gene encoding photosystem II oxygen evolution enhancing protein (psbO) was significantly upregulated (log2FC = 3.94). psbO is an important enzyme in photosystem II, and its increased expression promotes water decomposition, increases oxygen release rate, synthesizes NADPH and ATP, and provides energy and raw materials for intracellular carbon fixation16. The transcriptional expression of phosphoenolpyruvate carboxyl kinase (log2FC = 1.05) and 3-phosphate glyceraldehyde dehydrogenase (log2FC = 1.84) genes involved in the process of photosynthetic carbon fixation were significantly upregulated. These two genes play an important role in the carbon fixation process. Upregulation of these genes can enable mutant strains to have higher photosynthetic efficiency and carbon dioxide fixation efficiency17, ensuring the growth of algal cells and good photosynthesis. This also confirms the reason why the mutant strain we studied earlier has a higher maximum quantum yield Fv/Fm during the growth process.

Protein metabolic pathways

According to protein analysis, the protein synthesis rate of the mutant strain is slower than that of the original strain, and there will be a decrease in protein content in the later stages of cultivation. Therefore, it is speculated that plasma mutagenesis has a significant impact on enzymes or genes related to protein generation and degradation in algal cells. The transcriptome results showed that in the arginine and proline metabolic pathways, the expression of the enzyme SMOX gene associated with it was downregulated in the mutant strain (Table 5), which was not conducive to amino acid synthesis. Tyrosine and phenylalanine are precursor substances for algae cells to respond to environmental stress during growth. The upregulation of genes encoding these two enzymes in mutant strains helps them resist damage caused by plasma and is a self-protection measure18. In this study, it was also found that the transcription level of nitrate reductase (NR) gene in the mutant strain was significantly downregulated compared to the original strain (log2FC = − 1.38). This enzyme controls the first step of nitrate assimilation in algal cells, and protein synthesis cannot be separated from nitrogen absorption. The synthesis of proteins is inseparable from the absorption of nitrogen, and mutant strains have a decrease in nitrogen utilization efficiency. At the same time, nitrogen limitation is also a factor affecting lipid accumulation in algal cells19. To cope with the slowdown of nitrogen absorption, mutant strains require nitrogen from the breakdown metabolism of proteins or other non essential amino acids, and the resulting nitrogen is transferred to essential metabolic pathways such as the TCA cycle, which is important for lipid biosynthesis20. This is consistent with our study that the protein accumulation rate of mutant strains is slow and may decrease in the later stages of growth. Multiple genes in the protein hydrolysis pathway have significantly upregulated transcriptional levels (Table 5), which is the main reason for the decrease in protein content of mutant strains. Algae cells also require amino acids for their own growth or other metabolism, which further promotes protein hydrolysis.Table 5 Differentially expressed genes between mutant and original strains.

Pathway	Gene id	Name	log2 FC	p value	
Photosynthesis	TRINITY_DN20622_c0_g1	psbO	3.94	1.96041E−07	
TRINITY_DN3406_c0_g2	pckA	1.05	7.17912E−05	
TRINITY_DN3567_c0_g2	pckA	2.375	0.00022	
TRINITY_DN4852_c0_g1	gapA	1.84	0.00954	
Protein degradation	TRINITY_DN4105_c0_g1	NR	− 1.38	0.00013	
TRINITY_DN4105_c0_g1	SMOX	− 1.28	0.00034	
TRINITY_DN104_c6_g2	BiP	1.35	4.9728E−08	
TRINITY_DN847_c0_g2	GRP94	1.51	3.81512E−15	
TRINITY_DN1280_c2_g2	PDIA1	2.96	3.68236E−06	
TRINITY_DN65_c5_g1	PDIA4	2.51	1.16851E−18	
TRINITY_DN2792_c0_g1	EPS1	1.52	5.42127E−11	
TRINITY_DN1280_c2_g1	CRYAA	2.40	6.19692E−22	
TRINITY_DN529_c0_g1	CRYAB	1.35	2.37078E−14	
TRINITY_DN122_c2_g2	NEF	1.40	6.02185E−08	
Starch and sucrose metabolism	TRINITY_DN10_c13_g1	endoglucanase	1.93	3.87696E−27	
TRINITY_DN2876_c0_g1	AMY	1.21	0.00020018	
TRINITY_DN3102_c2_g1	malS	1.29	8.96699E−06	
TRINITY_DN4940_c0_g2	amyA	2.00	2.09986E−09	
Fatty acid biosynthesis	TRINITY_DN1561_c0_g1	pflD	1.32	5.61056E−06	
TRINITY_DN1290_c1_g1	KCS	1.03	0.000067538	
TRINITY_DN2144_c0_g2	FASN	1.09	8.53086E−10	
TRINITY_DN25397_c0_g1	FABG	1.14	0.000540	
TRINITY_DN25643_c0_g1	FABL	1.68	0.000046	
Kennedy pathway	TRINITY_DN26688_c0_g1	DGAT	1.46	0.001516	

Carbohydrate metabolism pathways

In the analysis of starch and sucrose metabolism pathways, it was found that genes involved in various enzymes involved in starch hydrolysis, such as AMY (log2FC = 1.21), malS (log2FC = 1.29), amyA (log2FC = 2.00), and endolucanase (log2FC = 1.93), were upregulated. This indicates that the enhanced starch hydrolysis metabolism in the mutant strain promotes the conversion of starch to glucose, upregulates the expression of enzymes involved in starch hydrolysis, and leads to the accumulation and transformation of carbon towards lipids21. The mutant strain is involved in the glycolytic pathway, we found that the expression of pckA in the glycolytic pathway of the mutant strain was upregulated by more than twice, which promoted the conversion of oxaloacetic acid (OAA), an intermediate product in the tricarboxylic acid cycle, to Phosphoenol acetate. Finally, under the catalytic action of pyruvate kinase, pyruvate was generated. Due to the enhancement of OAA bypass conversion, the amount of OAA involved in the TCA cycle decreased compared to the original strain, indicating that the TCA cycle was weakened, and the inflow of acetylCoA into the TCA cycle was reduced, thereby increasing the accumulation of acetylCoA. At the same time, the expression of gapA was upregulated by more than twice, which also accelerated the production of pyruvate, which can be further converted to acetyl CoA. The expression level of formate C-acetyltransferase involved in the conversion of pyruvate to acetyl CoA in the mutant strain was upregulated by more than twice (log2FC = 1.32), which increased the production of acetyl CoA and provided abundant raw materials for lipid synthesis. The combined effect of these genes is to increase the content of acetyl CoA, which is a precursor for de novo synthesis of fatty acids and plays a decisive role in lipid metabolism. The enhancement of glycolysis has been repeatedly verified to promote the production of acetyl CoA and lipid accumulation22,23.

Synthesis pathways of fatty acids

The precursor substance for the biosynthesis of fatty acids is acetyl CoA, which can be obtained from different substances in organisms. The first pathway is through the catalysis of ACS enzyme, which consumes ATP to produce acetic acid; the second pathway is obtained through the decomposition of pyruvate; the third pathway is through the TCA cycle, which can decompose citric acid into acetyl CoA and oxaloacetic acid; The fourth pathway is the breakdown of lipids, amino acids, and other substances. According to the transcriptome results, differential expression maps of genes related to fatty acid and TAG synthesis pathways were drawn (Fig. 10). No high expression levels were found for the genes encoding acetyl CoA synthase, but a large amount of pyruvate and acetyl CoA were produced during the carbon fixation pathway of photosynthesis, protein and amino acid hydrolysis, and glycolysis processes. The upregulation of gapA and pckA increases the conversion of pyruvate to acetyl CoA and reduces the flow of acetyl CoA to the TCA cycle, which leads to a significant increase in the residual amount of acetyl CoA and directly promotes subsequent lipid generation. Then, under the action of Accase, it is converted into malonyl CoA, which is crucial for the entire lipid metabolism because Accase is a key enzyme that controls the synthesis rate24. The expression level of the enzyme KCS encoding malonyl CoA conversion was more than twice that of the original strain, which also accelerated the accumulation of malonyl-ACP. Under the catalysis of fatty acid synthase (FASN), the carbon chain was continuously extended to synthesize long-chain fatty acids. The expression of 3-oxoacyl-ACP reductase (FABG) and enoylated ACP reductase (FABL) in the fatty acid biosynthesis pathway is upregulated. Free fatty acids are actively transported to the endoplasmic reticulum and bind to glycerol 3-phosphate (G3P), completing the synthesis of diacylglycerol (DAG). Finally, under the catalytic action of diacylglycerol acyltransferase (DGAT), DAG is converted to triacylglycerol (TAG), and the upregulation of DGAT gene expression promotes this process25.Fig. 10 Differential expression of genes related to fatty acid and triglyceride synthesis pathway between mutant and original strains. Upregulated genes in the figure are shown in red, while downregulated genes are shown in green. Boxes represent intermediate products.

Conclusion

The sequencing results showed that the Q30 values of samples Y1, Y2, T1, and T2 were 95.6%, 95.66%, 95.68%, and 95.69%, respectively, all reaching over 95%. The reliability of the results is relatively high and meets the requirements of subsequent analysis. According to the sequencing results, a total of 527 genes were upregulated and 139 genes were downregulated in the mutant group, and some genes showed significant differences. GO enrichment showed that there were significant differences between the mutant strain and the original strain in membrane composition, calcium ion transmembrane transport, and phosphate ester hydrolase activity. These differences may cause changes in the growth of the mutant strain and the permeability of the cell membrane, thereby affecting the enzyme activity of the algal strain and causing differences in its metabolic process. KEGG mainly exhibits significant differences in pathways such as MAPK signaling pathway, protein processing in endoplasmic reticulum, photosynthesis, carbon sequestration in photosynthetic organisms, and glycerophospholipid metabolism. The photosynthesis of the mutant strain has been enhanced, resulting in higher light energy utilization efficiency and CO2 fixation efficiency, providing more carbon storage and energy for biomass and lipid production. The enhancement of glycolysis and the TCA cycle transfer more carbon flow to the accumulation of lipids. The increased expression levels of related enzymes in the degradation pathways of starch and proteins may promote the accumulation of acetyl CoA, providing more substrates for fatty acid production and increasing lipid production. Provide theoretical guidance for improving the oil yield of Scenedesmus by plasma mutagenesis.

Acknowledgements

Thank you to the Xianyang Science and Technology Bureau for providing funding for key research and development projects, and the Shaanxi Institute of Fashion Engineering for providing funding for scientific research projects.

Author contributions

W.W. is responsible for the main paper writing, while H.F. provides experimental support.

Funding

This study was funded by Key R&D Program Project in Xianyang City (Grant no. L2023-ZDYF-SF-029), 2023 Campus level Scientific Research Project of Shaanxi Institute of Fashion Engineering (2023XKZ58), 2024 Campus level Industry University Research Project (24JX24).

Data availability

Plasma Induced Mutant Algae Strains and Purified Algae Strains (Original data) (NCBI): www.ncbi.nlm.nih.gov/bioproject/PRJNA1111801. Sequence data that support the findings of this study has been deposited in the NCBI database, with the primary accession code PRJNA1111801.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Qiuling L Min L Zhihong Y Screening of oleaginous microalgae and assessment of its oil producing capability Acta Microbiol. Sin. 2020 60 08 1648 1660
Qiuling, L. et al. Screening of oleaginous microalgae and assessment of its oil producing capability. Acta Microbiol. Sin. 60(08), 1648–1660 (2020).
2. Huan L Ling S Xiaodong S Application of atmospheric and room temperature plasma mutagenesis in microbial and edible fungi mutation breeding J. Biol. 2023 40 04 92 97
Huan, L. et al. Application of atmospheric and room temperature plasma mutagenesis in microbial and edible fungi mutation breeding. J. Biol. 40(04), 92–97 (2023).
3. Zhang X Zhang C Zhou QQ Quantitative evaluation of DNA damage and mutation rate by atmospheric and room temperature plasma (ARTP) and conventional mutagenesis Appl. Microbiol. Biotechnol. 2015 99 13 5639 5646 10.1007/s00253-015-6678-y 26025015
Zhang, X. et al. Quantitative evaluation of DNA damage and mutation rate by atmospheric and room temperature plasma (ARTP) and conventional mutagenesis. Appl. Microbiol. Biotechnol. 99(13), 5639–5646 (2015).26025015 10.1007/s00253-015-6678-y
4. Liu K Fang H Cui F ARTP mutation and adaptive laboratory evolution improve probiotic performance of Bacillus coagulans Appl. Microbiol. Biotechnol. 2020 104 14 6363 6373 10.1007/s00253-020-10703-y 32474797
Liu, K. et al. ARTP mutation and adaptive laboratory evolution improve probiotic performance of Bacillus coagulans. Appl. Microbiol. Biotechnol. 104(14), 6363–6373 (2020).32474797 10.1007/s00253-020-10703-y
5. Zhang X Zhang XF Li HP Atmospheric and room temperature plasma (ARTP) as a new powerful mutagenesis tool Appl. Microbiol. Biotechnol. 2014 98 12 5387 5396 10.1007/s00253-014-5755-y 24769904
Zhang, X. et al. Atmospheric and room temperature plasma (ARTP) as a new powerful mutagenesis tool. Appl. Microbiol. Biotechnol. 98(12), 5387–5396 (2014).24769904 10.1007/s00253-014-5755-y
6. Lin L Qinhong W Hailin Y De novotranscriptomic analysis of Chlorella sorokiniana: Pathway description and gene discovery for lipid production Acta Microbiol. Sin. 2014 54 9 1010 1021
Lin, L. et al. De novotranscriptomic analysis of Chlorella sorokiniana: Pathway description and gene discovery for lipid production. Acta Microbiol. Sin. 54(9), 1010–1021 (2014).
7. Manfred GG Full-length transcriptome assembly from RNA-seq data without a reference genome Nat. Biotechnol. 2011 29 7 644 10.1038/nbt.1883 21572440
Manfred, G. G. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29(7), 644 (2011).21572440 10.1038/nbt.1883
8. Bradford MM A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding Anal. Biochem. 1976 72 1 248 10.1016/0003-2697(76)90527-3 942051
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72(1), 248 (1976).942051 10.1016/0003-2697(76)90527-3
9. Nielsen, S. S. Phenol-sulfuric Acid Method for Total Carbohydrates 47–53 (Springer US, 2010).
10. Kanehisa M Goto S KEGG: Kyoto encyclopedia of genes and genomes Nucleic Acids Res. 2000 28 1 27 30 10.1093/nar/28.1.27 10592173
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000).10592173 10.1093/nar/28.1.27
11. Bogen C Klassenet V Wichmann J Identification of Monoraphidium contortum as a promising species for liquid biofuel production Bioresour. Technol. 2013 133 622 626 10.1016/j.biortech.2013.01.164 23453981
Bogen, C. et al. Identification of Monoraphidium contortum as a promising species for liquid biofuel production. Bioresour. Technol. 133, 622–626 (2013).23453981 10.1016/j.biortech.2013.01.164
12. Aiyun H Dingfang G Sha L Transcriptome analysis for the mechanism of high-yield of DHA by mutagenic Schizochytrium China Oils Fats 2019 44 12 120 126
Aiyun, H., Dingfang, G. & Sha, L. Transcriptome analysis for the mechanism of high-yield of DHA by mutagenic Schizochytrium. China Oils Fats 44(12), 120–126 (2019).
13. Love MI Huber W Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol. 2014 15 550 10.1186/s13059-014-0550-8 25516281
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).25516281 10.1186/s13059-014-0550-8
14. Kanehisa M Toward understanding the origin and evolution of cellular organisms Protein Sci. 2019 28 11 1947 1951 10.1002/pro.3715 31441146
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28(11), 1947–1951 (2019).31441146 10.1002/pro.3715
15. Kanehisa M Furumichi M Sato Y KEGG for taxonomy-based analysis of pathways and genomes Nucleic Acids Res. 2023 51 D1 D587 D592 10.1093/nar/gkac963 36300620
Kanehisa, M. et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51(D1), D587–D592 (2023).36300620 10.1093/nar/gkac963
16. Lu H Cheng J Wang Z Enhancing photosynthetic characterization and biomass productivity of nannochloropsis oceanica by nuclear radiation Front. Energy Res. 2020 8 143 10.3389/fenrg.2020.00143
Lu, H. et al. Enhancing photosynthetic characterization and biomass productivity of nannochloropsis oceanica by nuclear radiation. Front. Energy Res. 8, 143 (2020).10.3389/fenrg.2020.00143
17. Guo J Bai Y Chen Z Transcriptomic analysis suggests the inhibition of DNA damage repair in green alga raphidocelis subcapitata exposed to roxithromycin Ecotoxicol. Environ. Saf. 2020 201 110737 10.1016/j.ecoenv.2020.110737 32505758
Guo, J. et al. Transcriptomic analysis suggests the inhibition of DNA damage repair in green alga raphidocelis subcapitata exposed to roxithromycin. Ecotoxicol. Environ. Saf. 201, 110737 (2020).32505758 10.1016/j.ecoenv.2020.110737
18. Fu S Xue S Chen J Effects of different short-term UV-B radiation intensities on metabolic characteristics of porphyra haitanensis Int. J. Mol. Sci. 2021 22 4 2180 10.3390/ijms22042180 33671697
Fu, S. et al. Effects of different short-term UV-B radiation intensities on metabolic characteristics of porphyra haitanensis. Int. J. Mol. Sci. 22(4), 2180 (2021).33671697 10.3390/ijms22042180
19. Benhima R Arroussi HE Kadmiri IM Nitrate reductase inhibition induces lipid enhancement of dunaliella tertiolecta for biodiesel production Sci. World J. 2018 1 1 8 10.1155/2018/6834725
Benhima, R. et al. Nitrate reductase inhibition induces lipid enhancement of dunaliella tertiolecta for biodiesel production. Sci. World J. 1, 1–8 (2018).10.1155/2018/6834725
20. Hui C Zheng Y Jiao Z Comparative metabolic profiling of the lipid-producing green microalga Chlorella reveals that nitrogen and carbon metabolic pathways contribute to lipid metabolism Biotechnol. Biofuels 2017 10 1 1 20 28053662
Hui, C. et al. Comparative metabolic profiling of the lipid-producing green microalga Chlorella reveals that nitrogen and carbon metabolic pathways contribute to lipid metabolism. Biotechnol. Biofuels 10(1), 1–20 (2017).28053662
21. Nzayisenga JC Farge X Groll SL Effects of light intensity on growth and lipid production in microalgae grown in wastewater Biotechnol. Biofuels 2020 13 1 1 8 10.1186/s13068-019-1646-x 31911817
Nzayisenga, J. C. et al. Effects of light intensity on growth and lipid production in microalgae grown in wastewater. Biotechnol. Biofuels 13(1), 1–8 (2020).31911817 10.1186/s13068-019-1646-x
22. Liu T Li Y Liu F The enhanced lipid accumulation in oleaginous microalga by the potential continuous nitrogen-limitation (CNL) strategy Bioresour. Technol. 2016 203 150 159 10.1016/j.biortech.2015.12.021 26724547
Liu, T. et al. The enhanced lipid accumulation in oleaginous microalga by the potential continuous nitrogen-limitation (CNL) strategy. Bioresour. Technol. 203, 150–159 (2016).26724547 10.1016/j.biortech.2015.12.021
23. Li J Niu X Pei G Identification and metabolomic analysis of chemical modulators for lipid accumulation in crypthecodinium cohnii Bioresour. Technol. 2015 191 362 368 10.1016/j.biortech.2015.03.068 25818259
Li, J. et al. Identification and metabolomic analysis of chemical modulators for lipid accumulation in crypthecodinium cohnii. Bioresour. Technol. 191, 362–368 (2015).25818259 10.1016/j.biortech.2015.03.068
24. Muthuraj M Selvaraj B Palabhanvi B Enhanced lipid content in Chlorella Sp. FC2 IITG via high energy irradiation mutagenesis Korean J. Chem. Eng. 2018 36 1 63 70 10.1007/s11814-018-0180-z
Muthuraj, M. et al. Enhanced lipid content in Chlorella Sp. FC2 IITG via high energy irradiation mutagenesis. Korean J. Chem. Eng. 36(1), 63–70 (2018).10.1007/s11814-018-0180-z
25. Huerlimann, R. S. Microalgal Lipid Biosynthesis: Phylogeny of Acetyl-coa Carboxylase and Gene Expression Patterns of Key Enzymes (James Cook University, 2014).
