전체메뉴
검색
Article Search

JMB Journal of Microbiolog and Biotechnology

QR Code QR Code

Research article


References

  1. Piovan A, Filippini R, Corbioli G, Costa VD, Giunco EMV, Burbello G, et al. 2021. Carotenoid extract derived from Euglena gracilis overcomes lipopolysaccharide-induced neuroinflammation in microglia: role of NF-κB and Nrf2 signaling pathways. Mol. Neurobiol. 58: 3515-3528.
    Pubmed PMC CrossRef
  2. Nakashima A, Horio Y, Suzuki K, Isegawa Y. 2021. Antiviral activity and underlying action mechanism of Euglena extract against influenza virus. Nutrients 13: 3911.
    Pubmed PMC CrossRef
  3. Kondo Y, Kato A, Hojo H, Nozoe S, Takeuchi M, Ochi K. 1992. Cytokine-related immunopotentiating activities of paramylon, a β-(1→3)-D-glucan from Euglena gracilis. J. Pharmacobiodyn. 15: 617-621.
    Pubmed CrossRef
  4. Barsanti L, Vismara R, Passarelli V, Gualtieri P. 2001. Paramylon (β-1,3-glucan) content in wild type and WZSL mutant of Euglena gracilis. Effects of growth conditions. J. Appl. Phycol. 13: 59-65.
  5. Gissibl A, Sun A, Care A, Nevalainen H, Sunna A. 2019. Bioproducts from Euglena gracilis: synthesis and applications. Front. Bioeng. Biotechnol. 7: 108.
    Pubmed PMC CrossRef
  6. Sugiyama A, Hata S, Suzuki K, Yoshida E, Nakano R, Mitra S, et al. 2010. Oral administration of paramylon, a beta-1,3-D-glucan isolated from Euglena gracilis Z inhibits development of atopic dermatitis-like skin lesions in NC/Nga mice. J. Vet. Med. Sci. 72: 755-763.
    Pubmed CrossRef
  7. Okouchi R, E S, Yamamoto K, Ota T, Seki K, Imai M, et al. 2019. Simultaneous intake of Euglena gracilis and vegetables exerts synergistic anti-obesity and anti-inflammatory effects by modulating the gut microbiota in diet-induced obese mice. Nutrients 11: 204.
    Pubmed PMC CrossRef
  8. Yang H, Choi K, Kim KJ, Park SY, Jeon JY, Kim BG, et al. 2022. Immunoenhancing effects of Euglena gracilis on a cyclophosphamideinduced immunosuppressive mouse model. J. Microbiol. Biotechnol. 32: 228-237.
    Pubmed PMC CrossRef
  9. Park S, Kim KJ, Jo SM, Jeon JY, Kim BR, Hwang JE, et al. 2023. Euglena gracilis (Euglena) powder supplementation enhanced immune function through natural killer cell activity in apparently healthy participants: a randomized, double-blind, placebo-controlled trial. Nutr. Res. 119: 90-97.
    Pubmed CrossRef
  10. Jo KA, Kim KJ, Park S, Jeon JY, Hwang JE, Kim JY. 2023. Evaluation of the effects of Euglena gracilis on enhancing immune responses in RAW264.7 cells and a cyclophosphamide-induced mouse model. J. Microbiol. Biotechnol. 33: 493-499.
    Pubmed PMC CrossRef
  11. Maggini S, Beveridge S, Sorbara P, Senatore G. 2008. Feeding the immune system: the role of micronutrients in restoring resistance to infections. CABI Reviews. https://doi.org/10.1079/PAVSNNR20083098.
    CrossRef
  12. Germic N, Frangez Z, Yousefi S, Simon HU. 2019. Regulation of the innate immune system by autophagy: monocytes, macrophages, dendritic cells and antigen presentation. Cell Death Differ. 26: 715-727.
    Pubmed PMC CrossRef
  13. Gottschalk RA, Martins AJ, Angermann BR, Dutta B, Ng CE, Uderhardt S, et al. 2016. Distinct NF-κB and MAPK activation thresholds uncouple steady-state microbe sensing from anti-pathogen inflammatory responses. Cell Syst. 2: 378-390.
    Pubmed PMC CrossRef
  14. Fink LN, Frøkiær H. 2008. Dendritic cells from Peyer's patches and mesenteric lymph nodes differ from spleen dendritic cells in their response to commensal gut bacteria. Scand. J. Immunol. 68: 270-279.
    Pubmed CrossRef
  15. Keely S, Walker MM, Marks E, Talley NJ. 2015. Immune dysregulation in the functional gastrointestinal disorders. Eur. J. Clin. Investig. 45: 1350-1359.
    Pubmed CrossRef
  16. Rerknimitr P, Otsuka A, Nakashima C, Kabashima K. 2017. The etiopathogenesis of atopic dermatitis: barrier disruption, immunological derangement, and pruritus. Inflamm. Regen. 37: 14.
    Pubmed PMC CrossRef
  17. Luebke RW, Parks C, Luster MI. 2004. Suppression of immune function and susceptibility to infections in humans: association of immune function with clinical disease. J. Immunotoxicol. 1: 15-24.
    Pubmed CrossRef
  18. Shirani K, Hassani FV, Razavi-Azarkhiavi K, Heidari S, Zanjani BR, Karimi G. 2015. Phytotrapy of cyclophosphamide-induced immunosuppression. Environ. Toxicol. Pharmacol. 39: 1262-1275.
    Pubmed CrossRef
  19. Meng Y, Li B, Jin D, Zhan M, Lu J, Huo G. 2018. Immunomodulatory activity of Lactobacillus plantarum KLDS1.0318 in cyclophosphamide-treated mice. Food Nutr. Res. 21: 62.
    Pubmed PMC CrossRef
  20. Jimenez-Valera M, Moreno E, Amat MA, Ruiz-Bravo A. 2003. Modification of mitogen-driven lymphoproliferation by ceftriaxone in normal and immunocompromised mice. Int. J. Antimicrob. Agents 22: 607-612.
    Pubmed CrossRef
  21. Bendich A, Shapiro SS. 1986. Effect of beta-carotene and canthaxanthin on the immune responses of the rat. J. Nutr. 116: 2254-2262.
    Pubmed CrossRef
  22. Sudhagar A, Kumar G, El-Matbouli M. 2018. Transcriptome analysis based on RNA-Seq in understanding pathogenic mechanisms of diseases and the immune system of fish: a comprehensive review. Int. J. Mol. Sci. 19: 245.
    Pubmed PMC CrossRef
  23. Harding JJ, Nandakumar S, Armenia J, Khalil DN, Albano M, Ly M, et al. 2019. Prospective genotyping of Hepatocellular Carcinoma: clinical implications of next-generation sequencing for matching patients to targeted and immune therapies. Clin. Cancer Res. 25: 2116-2126.
    Pubmed PMC CrossRef
  24. Dheilly NM, Adema C, Raftos DA, Gourbal B, Grunau C, Du Pasquier L. 2014. No more non-model species: the promise of next generation sequencing for comparative immunology. Dev. Compar. Immunol. 45: 56-66.
    Pubmed PMC CrossRef
  25. Bray NL, Pimentel H, Melsted P, Pachter L. 2016. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34: 525-527.
    Pubmed CrossRef
  26. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. 2022. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50: W216-w221.
    Pubmed PMC CrossRef
  27. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. 2019. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47: D607-d613.
    Pubmed PMC CrossRef
  28. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13: 2498-2504.
    Pubmed PMC CrossRef
  29. Binns D, Dimmer E, Huntley R, Barrell D, O'Donovan C, Apweiler R. 2009. QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25: 3045-3046.
    Pubmed PMC CrossRef
  30. Chen J, Zhang XD, Jiang Z. 2013. The application of fungal β-glucans for the treatment of colon cancer. Anticancer Agents Med. Chem. 13: 725-730.
    Pubmed CrossRef
  31. Wang J, Tong X, Li P, Cao H, Su W. 2012. Immuno-enhancement effects of Shenqi Fuzheng injection on cyclophosphamide-induced immunosuppression in Balb/c mice. J. Ethnopharmacol. 139: 788-795.
    Pubmed CrossRef
  32. Williams MB, Butcher EC. 1997. Homing of naive and memory T lymphocyte subsets to Peyer's patches, lymph nodes, and spleen. J. Immunol. 159: 1746-1752.
    Pubmed CrossRef
  33. Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. 2017. Transcriptome profiling in human diseases: new advances and perspectives. Int. J. Mol. Sci. 18: 1652.
    Pubmed PMC CrossRef
  34. Suzuki K, Nakashima A, Igarashi M, Saito K, Konno M, Yamazaki N, et al. 2018. Euglena gracilis Z and its carbohydrate storage substance relieve arthritis symptoms by modulating Th17 immunity. PLoS One 13: e0191462.
    Pubmed PMC CrossRef
  35. Pandya AD, Al-Jaderi Z, Høglund RA, Holmøy T, Harbo HF, Norgauer J, et al. 2011. Identification of human NK17/NK1 cells. PLoS One 6: e26780.
    Pubmed PMC CrossRef
  36. Huang L, Wang M, Yan Y, Gu W, Zhang X, Tan J, et al. 2018. OX40L induces helper T cell differentiation during cell immunity of asthma through PI3K/AKT and P38 MAPK signaling pathway. J. Transl. Med. 16: 74.
    Pubmed PMC CrossRef
  37. Wolken J, Mellon A. 1956. The relationship between chlorophyll and the carotenoids in the algal flagellate, Euglena. J. Gen. Physiol. 39: 675.
    Pubmed PMC CrossRef
  38. Foletta VC, Segal DH, Cohen DR. 1998. Transcriptional regulation in the immune system: all roads lead to AP-1. J. Leukoc. Biol. 63: 139-152.
    Pubmed CrossRef
  39. Wang X, Sun L, He N, An Z, Yu R, Li C, et al. 2021. Increased expression of CXCL2 in ACPA-positive rheumatoid arthritis and its role in osteoclastogenesis. Clin. Exp. Immunol. 203: 194-208.
    Pubmed PMC CrossRef
  40. Kelley JM, Hughes LB, Bridges SL Jr. 2008. Does gamma-aminobutyric acid (GABA) influence the development of chronic inflammation in rheumatoid arthritis? J. Neuroinflammation 5: 1.
    Pubmed PMC CrossRef
  41. Bhandage AK, Barragan A. 2021. GABAergic signaling by cells of the immune system: more the rule than the exception. Cell. Mol. Life Sci. 78: 5667-5679.
    Pubmed PMC CrossRef
  42. Cai W, Li H, Zhang Y, Han G. 2020. Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. PeerJ. 8: e8390.
    Pubmed PMC CrossRef
  43. Monmai C, Park SH, You S, Park WJ. 2018. Immuno-enhancement effect of polysaccharide extracted from Stichopus japonicus on cyclophosphamide-induced immunosuppression mice. Food Sci. Biotechnol. 27: 565-573.
    Pubmed PMC CrossRef
  44. Lin X, Sun Q, Zhou L, He M, Dong X, Lai M, et al. 2018. Colonic epithelial mTORC1 promotes ulcerative colitis through COX-2-mediated Th17 responses. Mucosal Immunol. 11: 1663-1673.
    Pubmed CrossRef
  45. Calvayrac R, Laval-Martin D, Briand J, Farineau J. 1981. Paramylon synthesis by Euglena gracilis photoheterotrophically grown under low O2 pressure. Planta 153: 6-13.
    Pubmed CrossRef
  46. Bendich A. 1989. Carotenoids and the immune response. J. Nutr. 119: 112-115.
    Pubmed CrossRef
  47. Yao R, Fu W, Du M, Chen ZX, Lei AP, Wang JX. 2022. Carotenoids biosynthesis, accumulation, and applications of a model microalga Euglenagracilis. Mar. Drugs 20: 496.
    Pubmed PMC CrossRef
  48. Pyo MY, Park B, Choi JJ, Yang M, Yang HO, Cha JW, et al. 2013. Pheophytin a and chlorophyll a identified from environmentally friendly cultivation of green pepper enhance interleukin-2 and interferon-γ in Peyer's patches ex vivo Biol. Pharm. Bull. 36: 1747-1753.
    Pubmed CrossRef
  49. Piovan A, Filippini R, Corbioli G, Costa VD, Giunco EMV, Burbello G, et al. 2021. Carotenoid extract derived from Euglena gracilis overcomes lipopolysaccharide-induced neuroinflammation in microglia: role of NF-κB and Nrf2 signaling pathways. Mol. Neurobiol. 58: 3515-3528.
    Pubmed PMC CrossRef

Related articles in JMB

More Related Articles

Article

Research article

J. Microbiol. Biotechnol. 2024; 34(4): 880-890

Published online April 28, 2024 https://doi.org/10.4014/jmb.2401.01006

Copyright © The Korean Society for Microbiology and Biotechnology.

Unveiling Immunomodulatory Effects of Euglena gracilis in Immunosuppressed Mice: Transcriptome and Pathway Analysis

Seon Ha Jo1†, Kyeong Ah Jo1†, Soo-yeon Park1, and Ji Yeon Kim1,2*

1Department of Food Science and Biotechnology, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
2Department of Nano Bio Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Correspondence to:Ji Yeon Kim,       jiyeonk@seoultech.ac.kr

Seon Ha Jo and Kyeong Ah Jo contributed equally to this work.

Received: January 8, 2024; Revised: January 31, 2024; Accepted: February 3, 2024

Abstract

The immunomodulatory effects of Euglena gracilis (Euglena) and its bioactive component, β-1,3-glucan (paramylon), have been clarified through various studies. However, the detailed mechanisms of the immune regulation remain to be elucidated. This study was designed not only to investigate the immunomodulatory effects but also to determine the genetic mechanisms of Euglena and β-glucan in cyclophosphamide (CCP)-induced immunosuppressed mice. The animals were orally administered saline, Euglena (800 mg/kg B.W.) or β-glucan (400 mg/kg B.W.) for 19 days, and CCP (80 mg/kg B.W.) was subsequently administered to induce immunosuppression in the mice. The mice exhibited significant decreases in body weight, organ weight, and the spleen index. However, there were significant improvements in the spleen weight and the spleen index in CCP-induced mice after the oral administration of Euglena and β-glucan. Transcriptome analysis of the splenocytes revealed immune-related differentially expressed genes (DEGs) regulated in the Euglena- and β-glucantreated groups. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that pathways related with interleukin (IL)-17 and cAMP play significant roles in regulating T cells, B cells, and inflammatory cytokines. Additionally, Ptgs2, a major inflammatory factor, was exclusively expressed in the Euglena-treated group, suggesting that Euglena’s beneficial components, such as carotenoids, could regulate these genes by influencing immune lymphocytes and inflammatory cytokines in CCP-induced mice. This study validated the immunomodulatory effects of Euglena and highlighted its underlying mechanisms, suggesting a positive contribution to the determination of phenotypes associated with immune-related diseases and the research and development of immunotherapies.

Keywords: Cyclophosphamide, Euglena, beta-glucan, immunomodulation, transcriptome

Introduction

Euglena gracilis, commonly referred to as Euglena, stands out as a microalgae that is rich in a variety of beneficial nutrients, such as vitamins, minerals, carotenoids, and chlorophyll [1]. Possessing attributes of both a plant and an animal, Euglena is a promising biological resource frequently employed as a food or health supplement [2]. Additionally, Euglena species accumulate a storage polysaccharide in their body characterized by linear β-1,3-glucan chains [3, 4]. In contrast to the β-glucans in the cell walls of plants and bacteria, the β-glucan in Euglena is stored in granules called paramylon in the cytoplasm, constituting up to 90% of Euglena [5]. Euglena and β-glucan have been shown to improve health by enhancing immunity, inhibiting atopic dermatitis, and modulating gut microbial composition [6, 7]. In vitro, in vivo, and clinical trial studies have demonstrated that Euglena and β-glucan activate the immune system by releasing cytokines and inducing the immune cells such as natural killer cells or T lymphocytes [8-10]. They also activate the production of TNF-alpha in macrophages and modulate the expression of various cytokines [10]. Although they are known for their antitumor, anti-inflammatory, and antiallergic effects, the detailed genetic immune mechanisms involved remain to be elucidated. The human immune system protects the body from pathogens and cancer cells, providing defense through immune cells and antibodies against infections and diseases [11]. Serving as the primary barrier, the innate immune system initiates immune responses through collaborative interactions among macrophages, lymphocytes, and their products, such as cytokines [12]. Immune cells form a complex network to respond to pathogens, eliminate abnormal cells, and ultimately strengthen immune defenses across the nuclear factor (NF)-kB and mitogen-activated protein kinase (MAPK) signaling pathways, primarily in the spleen and lymph nodes [13, 14]. Disruptions in immune balance and immune deficiency may lead to conditions such as atopic disease and intestinal disorders [15, 16]. Research indicates that many diseases and complications are epidemiologically associated with immune suppression [17]. Cyclophosphamide (CCP) is a cytotoxic alkylating agent, that is widely used as an anticancer agent for a variety of diseases [18]. CCP induces cytotoxicity in proliferating lymphocytes, demonstrating immunosuppressive effects [19]. Thus, it was used to establish an experimental model applicable to the evaluation of immunomodulation by antibiotics in a previous study [20]. Advancements in DNA sequencing technologies, particularly next-generation sequencing (NGS), offer powerful tools for analyzing diverse gene expression patterns and identifying differentially expressed genes (DEGs) [21]. NGS has enabled scientific achievements and biological applications that were previously inconceivable. Recent research utilizing NGS for comprehensive transcriptome analyses of diverse organisms has demonstrated promising prospects for immunotherapy for immune-related disorders [22, 23]. Moreover, NGS can provide significant insights into the immunological characteristics of organisms by examining the impact of transcriptional profiles on the immune response [24]. Advanced RNA sequencing (RNA-seq) technology, an NGS method, can be used to interpret the genetic aspects of immune regulation and identify previously unknown immunological points. Despite previous studies confirming the immunomodulatory effects of Euglena, the underlying cellular and genetic mechanisms have not been elucidated. Therefore, the primary objective of this study was to validate the underlying immunomodulatory mechanisms through comprehensive genetic analysis, identifying specific genes and pathways selectively expressed in response to Euglena and β-glucan. In the experimental section, Euglena and β-glucan were administered to CCP-induced immunosuppressed murine models to explore their immune-enhancing effects. After the identification of the DEGs in the mouse model through RNA sequencing, the network between pathways and genes was visualized in various formats, providing a systematic overview. This procedure was subsequently validated to confirm significantly enhanced pathways and genes.

Materials and Methods

Materials

High-glucose Dulbecco’s modified Eagle’s medium (DMEM), Dulbecco’s phosphate-buffered saline (DPBS), fetal bovine serum (FBS), 1 M hydroxyethyl piperazine ethane sulfonic acid (HEPES) buffer, and penicillin–streptomycin mixtures were obtained from Biowest (France). Roswell Park Memorial Institute (RPMI)-1640 medium was obtained from Welegene (Korea). CCP, red blood cell (RBC) lysis buffer, amphotericin B, and trypan blue were obtained from Sigma–Aldrich (USA). The YAC-1 cell line, utilized for measuring NK cell activity, was obtained from the Korean Cell Line Bank (Republic of Korea).

Sample Preparation: Euglena and β-glucan Powder

Euglena and β-glucan powder were obtained from Daesang Corp. R&D Center (Republic of Korea). Euglena gracilis DSW1 cells (KCTC 13930BP) were cultured for 6 days in medium supplemented with L-glutamic acid, DL-malic acid, glucose, dibasic potassium phosphate, and cyanocobalamin. Following the 6-day incubation period in a jar fermenter, the Euglena cells were subjected to centrifugation at 4,000 ×g for 10 min and then washed with distilled water. The collected cells were sterilized and dried. β-glucan powder was prepared by centrifuging Euglena cells at 4,000 ×g for 10 min. After the pH was adjusted to 12.5 with NaOH, the cells were extracted at 60°C for 1 h. Subsequent centrifugation under the same conditions was performed, followed by sterilization and drying. The resulting paramylon exhibited a β-glucan content of 97%. Analysis of the β-glucans was conducted using 1H NMR spectroscopy at the Korea Basic Science Institute (Republic of Korea), confirming their identity as β-1,3-glucans. The prepared Euglena powder contained 665.61 mg/g of β-glucans. Based on this, the concentration of the β-glucan sample administered to the animals was set at half the concentration of the Euglena sample. Thus, the concentrations of the Euglena and β-glucan samples were set at 800 mg/kg B.W. and 400 mg/kg B.W., respectively.

Animal Study Design

Five-week-old male ICR mice were procured from Hana Biotech (Republic of Korea) for this study. The mice were housed under a 12 h light/dark cycle with unrestricted access to food and water. Following a one-week acclimation period at the Dongnam Chemical Research Institutés animal facility (Animal Facility Registration No. 412, Republic of Korea), the mice were grouped as follows (13 mice per group): normal control (CON), 80 mg/kg body weight (B.W.) cyclophosphamide-only (CCP), CCP + 800 mg/kg B.W. Euglena (E800), and CCP + 400 mg/kg B.W. β-glucan (B400). The CON and CCP groups received oral saline, while the E800 and B400 groups (sample groups) were orally administered Euglena and β-glucan, respectively, for 19 days. The E800 and B400 groups also received daily intraperitoneal injections of CCP diluted in saline for 3 days to induce immunosuppression. This immunosuppressive treatment started on the 17th day of test substance administration. On the 19th day, the experimental animals were euthanized with CO2, followed by laparotomy. Blood samples were collected from the abdominal aorta using a 1 ml syringe. The spleen, Peyer's patches, and mesenteric lymph nodes were harvested and weighed. Additional specimens were preserved in RPMI 1640 medium for further analysis. Changes in body weight were calculated using the following formula: change in body weight (BW) (%) = BW (g) on Day 19/BW (g) on Day 17 × 100. This research was conducted in accordance with the policies and regulations of the Institutional Animal Care and Use Committee (IACUC) of Dongnam Chemical Research Institute (Approval No. SEMI-23-007).

Sample Preparation for RNA Sequencing Analysis

The purity of the RNA (n = 5 per group) was assessed by analyzing 1 μl of total RNA extracted on a NanoDrop8000 spectrophotometer. The integrity of the total RNA was verified using an Agilent Technologies 2100 Bioanalyzer (Agilent, USA), which provided an RNA integrity number (RIN) value. mRNA sequencing libraries were generated following the manufacturer's instructions for the TruSeq stranded mRNA library prep kit (Illumina, USA). To isolate and fragment mRNA from total RNA, poly-T-oligo-attached magnetic beads were used for two rounds of purification. Cleaved RNA fragments primed with random hexamers were reverse transcribed into first-strand cDNA using reverse transcriptase, random primers, and dUTP in place of dTTP. The resulting products were purified and enriched through PCR to create the final strand-specific cDNA library. The quality of the amplified libraries was confirmed through automated electrophoresis (Agilent). Following quantitative polymerase chain reaction (qPCR) using KAPA SYBR FAST qPCR Master Mix (Kapa Biosystems, USA), index-tagged libraries were combined in equimolar amounts for pooling. RNA sequencing was conducted on a NovaSeq 6000 system (Illumina) in accordance with the manufacturer’s protocols.

Identification of DEGs

Reads for each sample were mapped to a reference (Mus musculus GRCm39) by Kallisto v0.46.1 (USA) [25]. The alignment results were subsequently added to the edgeR package to determine the DEGs. The DEG analysis was conducted with two different comparison designs: between the CON and the E800 groups (CON vs. E800) and between the CON and the B400 groups (CON vs. B400). Significant DEGs were selected based on the criteria of a false discovery rate (FDR) < 0.05 and |log2 Fold Change| ≥ 1.

Pathway Enrichment Analysis

For a deeper understanding of the biological processes involved and the gene pathways enriched, functional enrichment analysis of DEGs was conducted utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/) [26]. Gene Ontology (GO) enrichment terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment data were considered significant at thresholds of p < 0.05. Additionally, a protein‒protein interaction (PPI) network was established using the online Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db.org/), aiming to identify possible relationships [27]. In the constructed network, nodes and connecting edges symbolize biological molecules and their relationships, respectively. The DEGs and pathways were visually represented using Cytoscape (https://cytoscape.org/) for network visualization [28].

Quantitative Reverse Transcription Polymerase Chain Reaction (RT–qPCR)

Total RNA was isolated from the splenocytes using TRIzol reagent (Life Technologies, USA). Subsequently, cDNA was synthesized with a cDNA reverse transcription kit (Roche, Switzerland). RT‒qPCR was carried out with specific primers for interferon-γ (IFN-γ), interleukin (IL)-17, Ptgs2, IL-2, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as shown in Table 1. The gene expression levels of IFN-γ, IL-17, Ptgs2, and IL-2 were quantified using a Light Cycler 96 system (Hoffmann La Roche) based on the Universal Probe Library (UPL) method. The relative mRNA expression, normalized to that of GAPDH, was calculated using the comparative CT method.

Table 1 . Primers used for RT‒qPCR analysis..

GeneForward (5'-3')Reverse (3'-5')
IFN-γatctggaggaactggcaaaattcaagacttcaaagagtctgagg
IL-17gctgacccctaagaaaccccgaagcagtttgggacccctt
Ptgs2gcccattgaacctggactgacccaatcagcgtttctcgt
IL-2gctgttgatggacctacaggattcaattctgtggcctgctt
GAPDHaagagggatgctgcccttacccattttgtctacgggacga

TNF-α, tumor necrosis factor-alpha; IFN-γ, interferon-γ; IL-17, interleukin-17; Ptgs2, Cyclooxygenase-1; IL-2, interleukin-2; GAPDH, glyceraldehyde 3-phosphate dehydrogenase.



Statistical Analysis

All the results are expressed as the mean ± standard error of the mean (SEM). Statistical significance was assessed using one-way ANOVA followed by Duncan’s multiple range test (SAS 9.4, SAS Institute, USA). Different letters indicate significant differences between the groups (p < 0.05).

Results

Effects of Euglena on Body Weight in a CCP-Induced Mouse Model

To investigate the immunosuppressive effects of CCP on a mouse model, changes in B.W. were measured on Day 19 compared to Day 17, the first day of CCP administration. As shown in Fig. 1, the percentage of B.W. change (%) was significantly lower in the CCP-induced groups (CCP, E800, and B400) than in the CON group (p < 0.0001).

Figure 1. Rate of body weight change after treatment with cyclophosphamide. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). Rate of body weight change after CCP injection. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.

Effects of Euglena on Organ Weight in a CCP-Induced Mouse Model

Critical immune-related organs, such as the spleen, Peyer’s patches, and mesenteric lymph nodes (MLNs), were measured to explore the immune-enhancing effects of Euglena and β-glucan on immunosuppressed mice (Table 2). The weights of the spleen, Peyer’s patches, and MLN were significantly lower in the CCP-induced groups than in the CON groups (p < 0.0001, consistent across all three analyzed organs). Among these groups, the spleen weights of the E800 and B400 groups were significantly greater than that of the CCP group. However, there were no significant differences in Peyer’s patch or MLN weights between the CCP, E800, and B400 groups.

Table 2 . Effects of Euglena and β-glucan on the immune-related organ weight..

GroupOrgan weight (g)
SpleenPeyer’s patchMesenteric Lymph Node
CON0.133 ± 0.004a0.053 ± 0.003a0.079 ± 0.001a
CCP0.053 ± 0.003d0.026 ± 0.002b0.027 ± 0.002b
E8000.080 ± 0.004b0.032 ± 0.003b0.027 ± 0.002b
B4000.069 ± 0.002c0.030 ± 0.002b0.023 ± 0.001b

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test..



Effects of Euglena on the Spleen Index of a CCP-Induced Mouse Model

As illustrated in Fig. 2, the spleen indices were significantly lower in the CCP group than in the CON group (p < 0.0001). In the CCP-induced groups, the spleen indices were significantly greater than those in the CCP group, with approximately 1.56-fold and 1.43-fold increases in the E800 and B400 groups, respectively.

Figure 2. Effects of Euglena and β-glucan on the spleen index. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). The spleens of all the mice were weighed, and the spleen indices were calculated. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.

Identification of DEGs

Based on the FDR < 0.05 and |log2 Fold Change|≥ 1 criteria, a total of 2312, 167, and 32 DEGs were identified in the three DEG sets of immunosuppressed mice treated with Euglena and β-glucan: CON vs. CCP, CCP vs. E800 and CCP vs. B400, respectively (Table 3). Within the CCP vs. E800 comparison, 167 DEGs were observed, comprising 161 upregulated genes and 6 downregulated genes. In the CCP vs. B400 comparison, 32 DEGs were detected, including 30 upregulated genes and 2 downregulated genes. GO analysis revealed 1922, 30, and 15 DEGs in the CON vs. CCP, CCP vs. E800, and CCP vs. B400 comparisons, respectively. Moreover, KEGG analysis revealed 905, 12, and 10 DEGs in the CON vs. CCP, CCP vs. E800, and CCP vs. B400 comparisons, respectively. The DEGs associated with all the upregulated and downregulated genes were visualized with different plots, such as MA plots and volcano plots (Figs. 3 and 4), which clearly illustrated the distribution of genes whose expression significantly changed. Notably, the CON vs. CCP comparison exhibited the highest number of DEGs, with the CCP vs. E800 comparison showing more DEGs than the CCP vs. B400 comparison.

Table 3 . Numbers of DEGs annotated to GO and KEGG..

DEG setFiltered IDUp-regulated geneDown-regulated geneTotal gene (DEGs)Total gene (GO)Total gene (KEGG)
CON vs. CCP16,3151,3839292,3121,922905
CCP vs. E80017,30016161673012
CCP vs. B40016,998302321510

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Filtered ID, the number of samples with a read count > 2; Upregulated gene, the number of genes with a differential expression at FDR < 0.05 & log2 Fold Change ≥ 1; Downregulated gene, the number of genes with a differential expression at FDR < 0.05 & log2 Fold Change ≤ -1..



Figure 3. MA and volcano plot of DEGs after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Differentially expressed genes with an FDR < 0.05 are marked in red.

Figure 4. Visualization of immune-related pathways and genes in CCP-induced immunosuppressed mice after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). (A) Network visualization of immune-related pathways and genes in the CCP vs. E800 comparison. (B) Network visualization of immune-related pathways and genes in the CCP vs. B400 comparison. The colors of the shapes (red for GO terms, blue for KEGG pathways, and green for genes) reflect the corresponding entities. The brightness of a node in the network reflects the p value, and the size of the node indicates the log (fold change) value of gene expression. (C) Comparison of immune-related pathways and genes between the CCP and E800 cohorts using a bubble chart visualization. (D) Comparison of immune-related pathways and genes between CCP and B400 using a bubble chart visualization. The color of the bubble and enrichment score denote the –log (p value), and the size of the bubble denotes the gene count.

Immune-Related GO and KEGG Analyses of DEGs

To clarify the mechanism of the immune response to Euglena and β-glucan, immune-related pathways were selected from the GO and KEGG analyses based on the QuickGO database about immune responses [29]. The outcomes of the analysis of DEGs are summarized in Table 4, which shows the numbers of immune-related pathways and genes. According to the KEGG analysis, five pathways were identified in the CCP vs. E800 comparison, and three were identified in the CCP vs. B400 comparison. The sets of DEGs associated with Euglena and β-glucan treatment were analyzed, and 7 and 5 genes, respectively, were identified. GO_BP analysis revealed 11 immune-related biological process terms in the CCP vs. E800 comparison and 10 out of 18 in the CCP vs. B400 comparison. Additionally, 5 and 3 immune-related genes were identified in the Euglena- and β-glucan-treated DEG sets, respectively.

Table 4 . Numbers of immune-related KEGG pathways and GO Biological Process (GO_BP) terms..

MethodDEG setPathwayGene
TotalImmune-relatedTotalImmune-related
KEGGCCP vs. E800165107
CCP vs. B40011365
GO_BPCCP vs. E8002511144
CCP vs. B400181063

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). The ‘Gene’ category indicates the total number of genes annotated to the pathways and GO_BP..



As indicated in Table 5, according to KEGG analysis, the IL-17 signaling pathway exhibited the most significant enrichment (p = 3.32E-06) and the highest number of annotated DEGs in the CCP vs. E800 comparison. Other notable immune-related pathways included the tumor necrosis factor (TNF), estrogen, cAMP, and MAPK signaling pathways. All the KEGG results for the CCP vs. E800 comparison, except for the estrogen-related pathway, were consistent with those for the CCP vs. B400 comparison (Table 6).

Table 5 . Immune-related KEGG pathways and GO Biological Process (GO_BP) terms after treatment with Euglena..

TypeCodeTermP-valueCountGene
KEGGmmu04657IL-17 signaling pathway3.32E-065Fosb, Fos, Ptgs2, Jun, Cxcl2
KEGGmmu04668TNF signaling pathway2.94E-044Fos, Ptgs2, Jun, Cxcl2
KEGGmmu04915Estrogen signaling pathway1.11E-023Fos, Gabbr1, Jun
KEGGmmu04024cAMP signaling pathway2.82E-023Fos, Gabbr1, Jun
KEGGmmu04010MAPK signaling pathway4.80E-023Fos, Dusp1, Jun
GO_BPGO:0051591Response to cAMP1.03E-033Jun, Fos, Fosb
GO_BPGO:0034097Response to cytokine2.90E-033Jun, Fos, Ptgs2
GO_BPGO:0009410Response to xenobiotic stimulus3.49E-034Jun, Fos, Ptgs2, Fosb
GO_BPGO:0071277Cellular response to calcium ion3.72E-033Jun, Fos, Fosb
GO_BPGO:0032496Response to lipopolysaccharide1.42E-023Jun, Fos, Ptgs2
GO_BPGO:0035994Response to muscle stretch1.87E-022Jun, Fos
GO_BPGO:0051412Response to corticosterone2.55E-022Fos, Fosb
GO_BPGO:0071276Cellular response to cadmium ion3.04E-022Jun, Fos
GO_BPGO:0032570Response to progesterone3.52E-022Fos, Fosb
GO_BPGO:0032870Cellular response to hormone stimulus4.00E-022Fos, Fosb
GO_BPGO:0034614Cellular response to reactive oxygen species4.19E-022Jun, Fos


Table 6 . Immune-related KEGG pathways and GO Biological Process (GO_BP) terms after treatment with β-glucan..

TypeCodeTermP-valueCountGene
KEGGmmu04657IL-17 signaling pathway8.55E-054Fosb, Fos, Jun, Cxcl2
KEGGmmu04668TNF signaling pathway5.30E-033Fos, Jun, Cxcl2
KEGGmmu04010MAPK signaling pathway3.28E-023Fos, Dusp1, Jun
GO_BPGO:0051591Response to cAMP4.98E-043Fosb, Fos, Jun
GO_BPGO:0071277Cellular response to calcium ion1.82E-033Fosb, Fos, Jun
GO_BPGO:0035994Response to muscle stretch1.31E-022Fos, Jun
GO_BPGO:0051412Response to corticosterone1.79E-022Fosb, Fos
GO_BPGO:0009410Response to xenobiotic stimulus1.94E-023Fosb, Fos, Jun
GO_BPGO:0071276Cellular response to cadmium ion2.14E-022Fos, Jun
GO_BPGO:0032570Response to progesterone2.48E-022Fosb, Fos
GO_BPGO:0032870Cellular response to hormone stimulus2.82E-022Fosb, Fos
GO_BPGO:0034614Cellular response to reactive oxygen species2.95E-022Fos, Jun
GO_BPGO:0009612Response to mechanical stimulus4.03E-022Fosb, Jun


The GO term “response to cAMP” showed the most significant enrichment in both the CCP vs. E800 and CCP vs. B400 comparisons (p = 1.03E-03, 4.98E-04), with annotated genes such as Jun, Fos, and Fosb. The GO terms included “response to cytokine” and “response to lipopolysaccharide” only for the CCP vs. E400 comparison, and “cellular response to reactive oxygen species” was included for both the CCP vs. E800 and CCP vs. B400 comparisons.

The DEGs identified from the KEGG analysis of the CCP vs. E800 comparison revealed a total of seven upregulated genes, namely, Fosb, Fos, Ptgs2, Jun, Cxcl2, Gabbr1, and Dusp1. The results from the GO_BP analysis demonstrated enrichment of DEGs such as Fosb, Fos, Ptgs2, and Jun. In the CCP vs. B400 comparison, 5 upregulated genes (Fosb, Fos, Jun, Cxcl2, and Dusp1) were identified via KEGG analysis, and 3 upregulated genes (Fosb, Fos, and Jun) were identified via GO_BP related to the immune system. The analyzed immune-related DEGs were consistent between the CCP vs. E800 and CCP vs. B400 comparisons, except for Ptgs2 and Gabbr1.

Visualization of the Immune-Related Network between Pathways and Genes

Fig. 4A and 4B illustrate immune-related DEGs following the ingestion of Euglena and β-glucan, along with the analysis of KEGG pathways and GO_BP terms. Notably, Fosb, Ptgs2, and Cxcl2 exhibited relatively high fold changes in expression in the CCP vs. E800 comparison. Gabbr1 and Fosb exhibited significantly pronounced differences in expression compared to the other genes with high p values in the same DEG set. Additionally, compared with those of the other genes, the enrichment of Fos, Fosb, Jun, and Ptgs2 in the CCP vs. E800 comparison showed intricate associations with various immune-related pathways and terms. Both Fosb and Cxcl2 exhibited significant changes in gene expression in response to β-glucan. Cxcl2 and Fosb exhibited significantly higher p-values in the CCP vs. B400 comparison. Additionally, the pathway with the highest gene count and p value in both the CCP vs. E800 and CCP vs. B400 comparisons was the IL-17 signaling pathway. Moreover, the GO_BP terms with the highest gene count and p value were associated with the cAMP signaling pathway (Fig. 4C and 4D).

Biological Interpretation via PPI Network Analysis

To unravel the interactions among DEGs, a PPI network was constructed for the seven selected immune-related DEGs (Fosb, Fos, Jun, Cxcl2, Dusp1, Ptgs2, and Gabbr1). All the analyzed immune-related DEGs were matched with proteins in the STRING database (species: Mus musculus). The constructed PPI network for the CCP vs. B400 comparison contained 5 nodes and 7 edges, while that for the CCP vs. E800 comparison contained 7 nodes and 12 edges (Fig. 5). Fosb, Fos, and Jun exhibited the most intimate relationships in both the CCP vs. E800 and CCP vs. B400 comparisons. Fosb, Fos, Jun, Cxcl2, and Dusp1 were annotated in both DEG sets, while Ptgs2 and Gabbr1 were enriched only in the CCP vs. E800 comparison. Ptgs2 was more closely related to other genes than was Gabbr1.

Figure 5. Protein‒protein interaction (PPI) network for immune-related genes in CCP-induced immunosuppressed mice after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800mg/kg B.W.) and cyclophosphamide (80mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). (A) Gene network of a total of 7 immune-related genes in the CCP vs. E800 comparison. (B) Gene network of a total of 5 immune-related genes in the CCP vs. B400 comparison. The network nodes represent the genes, and the edges represent gene–gene associations.

Validation of Gene Expression

The most significantly regulated immune pathway in both the CCP vs. E800 and CCP vs. B400 comparisons was the IL-17 signaling pathway, and the most significantly modified DEG regulated by Euglena was Ptgs2. The expression levels of IL-17 pathway-related genes (IFN-γ, IL-17, and IL-2) and Ptgs2 were validated using RT‒qPCR (Fig. 6). The results revealed that, compared with those in the CON group, the expression levels of IFN-γ, IL-17, Ptgs2, and IL-2 in the CCP group decreased by 41.5%, 21.4%, 33.3%, and 14%, respectively. Notably, Ptgs2 gene expression in the E800 group was significantly greater than that in the CCP group (p = 0.006). The E800 group also exhibited approximately 77.9%, 46.9%, and 7.2% increases in the relative mRNA expression levels of IFN-γ, IL-17, and IL-2, respectively, compared to those in the CCP group, although these differences were not statistically significant. The expression levels of these genes tended to increase by 13.1%, 12.8%, and 19.8% in the B400 group compared to the CCP group.

Figure 6. Effects of Euglena and β-glucan on the relative gene expression levels of cytokines and enzymes related to immunomodulation in CCP-induced immunosuppressed mice. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Total RNA was extracted from the splenocytes of the model mice. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.

Discussion

Euglena gracilis contains a carbohydrate storage substance, β-glucan, which is a naturally occurring polysaccharide in the biological realm and exhibits various activities within the human body, including immune-enhancing effects [30]. Despite their recognized positive impact on the immune system, there remains a gap in direct research elucidating the genetic mechanisms involved in immune-regulatory effects. Therefore, in this study, we aimed to unravel these mechanisms through transcriptome analysis. However, it is difficult to confirm the immunomodulatory effect of a sample in healthy people or animals. The immunosuppressive function of CCP provides valuable insights into the challenging assessment of immune-related effects. Notably, research has highlighted enhancements in T and B lymphocyte proliferation and an increased count of immune cells following sample treatment in comparison to those in the CCP-induced mouse model [31]. In this study, significant decreases in the B.W. and weight of immune organs, including the spleen, Peyer's patches, and mesenteric lymph nodes (MLNs), after CCP treatment suggested an immunosuppressive effect of CCP. Immune activity occurs predominantly at key sites, such as the spleen, where immune functions are affected and potential damage occurs [32]. Specifically, the spleen, which includes numerous immune cells, is recognized for its role in diverse cytokine secretion pathways and the regulation of immune responses. Following the reduction in the spleen index induced by CCP, subsequent increases in the spleen index were observed in the E800 and B400 groups, signifying the immune-enhancing effects of Euglena and β-glucan. Additionally, splenocytes were extracted from the spleen and used for analyzing the transcriptome in the present study to confirm the importance of the modified immune system.

Transcriptome analysis is primarily utilized to determine the overall patterns of gene expression in cells or tissues, providing insights into biological processes and diseases [33]. The validated RNA sequencing results indicated that most of the immune-related pathways exhibited similar trends in the CON vs. E800 and CCP vs. B400 sets. However, compared with those in the β-glucan set, the DEG set in the Euglena cohort showed a more diverse and intricate array of DEGs and associated mechanisms.

The IL-17 signaling pathway was the most significantly modulated pathway in both the Euglena- and β-glucan-treated groups, as indicated by the KEGG database analysis. CD4+ T cells differentiate into various phenotypes, including Th1, Th2, and Th17 phenotypes [34]. IFN-γ produced by NK cells usually activates IL-17 receptors, contributing to the modulation of autoimmune diseases such as arthritis by inhibiting the differentiation of Th17 cells [34, 35]. Additionally, pathways associated with TNF and MAPK were significantly impacted by Euglena and β-glucan. Various immune receptors act through the MAPK signaling pathway to regulate the differentiation of cytokines and T cells [36]. These findings suggest that oral consumption of Euglena and β-glucan primarily influences T-cell differentiation and activation through the MAPK signaling pathway and concurrently impacts immune regulation through the secretion of cytokines such as IL-17 and TNF-α. GO analysis revealed that the genes related to the response to cytokines and lipopolysaccharides were specifically modulated by Euglena, which was not evident in the β-glucan DEG set. The diverse immune modulation effects of Euglena, surpassing those of β-glucan, may signify the immunomodulatory abilities of other constituents within Euglena, such as chlorophylls and carotenoids [37].

The functional connections among the 7 immune-related genes (Fosb, Fos, Jun, Cxcl2, Dusp1, Ptgs2, and Gabbr1) were confirmed by the PPI network and showed trends similar to but different from those of the Euglena and β-glucan DEG sets. Notably, Fosb, Fos, and Jun, which are closely related, encode the transcription factor activator protein-1 (AP-1), which plays crucial roles in activating T and B cells, differentiating T helper cells, and modulating cytokines through the MAPK signaling pathway [38]. Cxcl2, the gene most significantly regulated by β-glucan, is a chemokine produced by binding to G protein-coupled receptors found in immune cells [39]. This gene holds promise for offering valuable insights into the treatment of inflammatory and immune-related diseases, including rheumatoid arthritis [39].

Gabbr1 and Ptgs2 were exclusively identified in the Euglena-treated group but not in the β-glucan group. Gabbr1, which encodes the GABA receptor, regulates MAPK activity, influencing cytokine production and contributing to the reduction of inflammation [40]. GABA, which is secreted by NK cells, plays multifaceted roles in T-cell regulation, cytokine secretion, proliferation, and cytotoxicity [41]. Ptgs2, the key enzyme in prostaglandin biosynthesis, regulates inflammation and immune responses through Toll-like receptors and is induced by transcription factors such as NF-kB, MAPK, and JNK [42]. Ptgs2, also known as COX-2, primarily generates PGE2, promoting the differentiation of Th17 cells and exacerbating inflammation [43]. In one study, COX-2 was shown to interact more closely with other immune-related genes than Gabbr, and controlling Ptgs2 regulated by Euglena could lead to a potent anti-inflammatory effect [44]. Euglena synthesizes carbohydrate reserves utilizing the energy obtained through photosynthesis in its chloroplasts [45]. Carotenoids, which are synthesized in the chloroplasts and chromoplasts of microalgae and plants, are well known for their antioxidant effects and anti-inflammatory properties, as are chlorophyll. Carotenoids in Euglena promote T and B lymphocyte responses, enhance cytotoxic T cells and NK cell activity, and regulate COX-2 [21, 46, 47]. Additionally, chlorophyll components impact immunity, as indicated by increased IL-2 and IFN-γ levels within Peyer's patches and an increase in CD4+ T cells [48]. Previous research has shown that carotenoids extracted from Euglena exhibit anti-inflammatory effects by modulating the NF-kB signaling pathway [49]. It is reasonable to hypothesize that Gabbr1 and Ptgs2 are ultimately regulated by these pigment components in Euglena, extending beyond the influence of β-glucan. Further study of the beneficial components of Euglena could significantly contribute to elucidating the immune modulation mechanisms of Euglena.

In this study, we identified immune pathway and gene modifications in CCP-induced mice treated with Euglena and β-glucan, successfully constructing a network. Transcriptome profiling revealed increases in the expression levels of immune and inflammatory genes within the IL-17 and cAMP signaling pathways after treatment with Euglena. Euglena exhibited more diverse and abundant immune mechanisms than β-glucan, specifically involving two genes, Ptgs2 and Gabbr1, whose expression was suggested to be regulated by beneficial components such as carotenoids, which are known for enhancing the immune system. The transcriptome database can contribute to identifying key mechanisms influenced by immune modulation in Euglena. Further research into the positive effects of Euglena's metabolically active compounds on immune responses is imperative to explore the benefits of these compounds as functional materials. Phenotypes of targeted immune diseases, such as rheumatoid arthritis, can be selected to contribute positively to therapeutic research. For accurate construction of the immune network and obtaining results, integrated data will be needed, utilizing not only RNA expression but also a broad range of biotechnological methods.

Supplemental Materials

Acknowledgments

This research was part of a project titled 'Development of functional food material derived from marine resources, microalgae Euglena gracilis', which was funded by the Ministry of Oceans and Fisheries, Korea.

Conflict of Interest

The authors have no financial conflicts of interest to declare.

Fig 1.

Figure 1.Rate of body weight change after treatment with cyclophosphamide. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). Rate of body weight change after CCP injection. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Fig 2.

Figure 2.Effects of Euglena and β-glucan on the spleen index. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). The spleens of all the mice were weighed, and the spleen indices were calculated. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Fig 3.

Figure 3.MA and volcano plot of DEGs after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Differentially expressed genes with an FDR < 0.05 are marked in red.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Fig 4.

Figure 4.Visualization of immune-related pathways and genes in CCP-induced immunosuppressed mice after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). (A) Network visualization of immune-related pathways and genes in the CCP vs. E800 comparison. (B) Network visualization of immune-related pathways and genes in the CCP vs. B400 comparison. The colors of the shapes (red for GO terms, blue for KEGG pathways, and green for genes) reflect the corresponding entities. The brightness of a node in the network reflects the p value, and the size of the node indicates the log (fold change) value of gene expression. (C) Comparison of immune-related pathways and genes between the CCP and E800 cohorts using a bubble chart visualization. (D) Comparison of immune-related pathways and genes between CCP and B400 using a bubble chart visualization. The color of the bubble and enrichment score denote the –log (p value), and the size of the bubble denotes the gene count.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Fig 5.

Figure 5.Protein‒protein interaction (PPI) network for immune-related genes in CCP-induced immunosuppressed mice after treatment with Euglena and β-glucan. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800mg/kg B.W.) and cyclophosphamide (80mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). (A) Gene network of a total of 7 immune-related genes in the CCP vs. E800 comparison. (B) Gene network of a total of 5 immune-related genes in the CCP vs. B400 comparison. The network nodes represent the genes, and the edges represent gene–gene associations.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Fig 6.

Figure 6.Effects of Euglena and β-glucan on the relative gene expression levels of cytokines and enzymes related to immunomodulation in CCP-induced immunosuppressed mice. CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Total RNA was extracted from the splenocytes of the model mice. The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test.
Journal of Microbiology and Biotechnology 2024; 34: 880-890https://doi.org/10.4014/jmb.2401.01006

Table 1 . Primers used for RT‒qPCR analysis..

GeneForward (5'-3')Reverse (3'-5')
IFN-γatctggaggaactggcaaaattcaagacttcaaagagtctgagg
IL-17gctgacccctaagaaaccccgaagcagtttgggacccctt
Ptgs2gcccattgaacctggactgacccaatcagcgtttctcgt
IL-2gctgttgatggacctacaggattcaattctgtggcctgctt
GAPDHaagagggatgctgcccttacccattttgtctacgggacga

TNF-α, tumor necrosis factor-alpha; IFN-γ, interferon-γ; IL-17, interleukin-17; Ptgs2, Cyclooxygenase-1; IL-2, interleukin-2; GAPDH, glyceraldehyde 3-phosphate dehydrogenase.


Table 2 . Effects of Euglena and β-glucan on the immune-related organ weight..

GroupOrgan weight (g)
SpleenPeyer’s patchMesenteric Lymph Node
CON0.133 ± 0.004a0.053 ± 0.003a0.079 ± 0.001a
CCP0.053 ± 0.003d0.026 ± 0.002b0.027 ± 0.002b
E8000.080 ± 0.004b0.032 ± 0.003b0.027 ± 0.002b
B4000.069 ± 0.002c0.030 ± 0.002b0.023 ± 0.001b

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 13 for each group). The data represent the mean ± SEM. Different letters indicate significant differences (p < 0.05) according to Duncan’s multiple range test..


Table 3 . Numbers of DEGs annotated to GO and KEGG..

DEG setFiltered IDUp-regulated geneDown-regulated geneTotal gene (DEGs)Total gene (GO)Total gene (KEGG)
CON vs. CCP16,3151,3839292,3121,922905
CCP vs. E80017,30016161673012
CCP vs. B40016,998302321510

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.); B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). Filtered ID, the number of samples with a read count > 2; Upregulated gene, the number of genes with a differential expression at FDR < 0.05 & log2 Fold Change ≥ 1; Downregulated gene, the number of genes with a differential expression at FDR < 0.05 & log2 Fold Change ≤ -1..


Table 4 . Numbers of immune-related KEGG pathways and GO Biological Process (GO_BP) terms..

MethodDEG setPathwayGene
TotalImmune-relatedTotalImmune-related
KEGGCCP vs. E800165107
CCP vs. B40011365
GO_BPCCP vs. E8002511144
CCP vs. B400181063

CON, normal control; CCP, cyclophosphamide (80 mg/kg B.W.); E800, Euglena (800 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.). B400, β-glucan (400 mg/kg B.W.) and cyclophosphamide (80 mg/kg B.W.) (n = 5, randomly selected mice for each group). The ‘Gene’ category indicates the total number of genes annotated to the pathways and GO_BP..


Table 5 . Immune-related KEGG pathways and GO Biological Process (GO_BP) terms after treatment with Euglena..

TypeCodeTermP-valueCountGene
KEGGmmu04657IL-17 signaling pathway3.32E-065Fosb, Fos, Ptgs2, Jun, Cxcl2
KEGGmmu04668TNF signaling pathway2.94E-044Fos, Ptgs2, Jun, Cxcl2
KEGGmmu04915Estrogen signaling pathway1.11E-023Fos, Gabbr1, Jun
KEGGmmu04024cAMP signaling pathway2.82E-023Fos, Gabbr1, Jun
KEGGmmu04010MAPK signaling pathway4.80E-023Fos, Dusp1, Jun
GO_BPGO:0051591Response to cAMP1.03E-033Jun, Fos, Fosb
GO_BPGO:0034097Response to cytokine2.90E-033Jun, Fos, Ptgs2
GO_BPGO:0009410Response to xenobiotic stimulus3.49E-034Jun, Fos, Ptgs2, Fosb
GO_BPGO:0071277Cellular response to calcium ion3.72E-033Jun, Fos, Fosb
GO_BPGO:0032496Response to lipopolysaccharide1.42E-023Jun, Fos, Ptgs2
GO_BPGO:0035994Response to muscle stretch1.87E-022Jun, Fos
GO_BPGO:0051412Response to corticosterone2.55E-022Fos, Fosb
GO_BPGO:0071276Cellular response to cadmium ion3.04E-022Jun, Fos
GO_BPGO:0032570Response to progesterone3.52E-022Fos, Fosb
GO_BPGO:0032870Cellular response to hormone stimulus4.00E-022Fos, Fosb
GO_BPGO:0034614Cellular response to reactive oxygen species4.19E-022Jun, Fos

Table 6 . Immune-related KEGG pathways and GO Biological Process (GO_BP) terms after treatment with β-glucan..

TypeCodeTermP-valueCountGene
KEGGmmu04657IL-17 signaling pathway8.55E-054Fosb, Fos, Jun, Cxcl2
KEGGmmu04668TNF signaling pathway5.30E-033Fos, Jun, Cxcl2
KEGGmmu04010MAPK signaling pathway3.28E-023Fos, Dusp1, Jun
GO_BPGO:0051591Response to cAMP4.98E-043Fosb, Fos, Jun
GO_BPGO:0071277Cellular response to calcium ion1.82E-033Fosb, Fos, Jun
GO_BPGO:0035994Response to muscle stretch1.31E-022Fos, Jun
GO_BPGO:0051412Response to corticosterone1.79E-022Fosb, Fos
GO_BPGO:0009410Response to xenobiotic stimulus1.94E-023Fosb, Fos, Jun
GO_BPGO:0071276Cellular response to cadmium ion2.14E-022Fos, Jun
GO_BPGO:0032570Response to progesterone2.48E-022Fosb, Fos
GO_BPGO:0032870Cellular response to hormone stimulus2.82E-022Fosb, Fos
GO_BPGO:0034614Cellular response to reactive oxygen species2.95E-022Fos, Jun
GO_BPGO:0009612Response to mechanical stimulus4.03E-022Fosb, Jun

References

  1. Piovan A, Filippini R, Corbioli G, Costa VD, Giunco EMV, Burbello G, et al. 2021. Carotenoid extract derived from Euglena gracilis overcomes lipopolysaccharide-induced neuroinflammation in microglia: role of NF-κB and Nrf2 signaling pathways. Mol. Neurobiol. 58: 3515-3528.
    Pubmed KoreaMed CrossRef
  2. Nakashima A, Horio Y, Suzuki K, Isegawa Y. 2021. Antiviral activity and underlying action mechanism of Euglena extract against influenza virus. Nutrients 13: 3911.
    Pubmed KoreaMed CrossRef
  3. Kondo Y, Kato A, Hojo H, Nozoe S, Takeuchi M, Ochi K. 1992. Cytokine-related immunopotentiating activities of paramylon, a β-(1→3)-D-glucan from Euglena gracilis. J. Pharmacobiodyn. 15: 617-621.
    Pubmed CrossRef
  4. Barsanti L, Vismara R, Passarelli V, Gualtieri P. 2001. Paramylon (β-1,3-glucan) content in wild type and WZSL mutant of Euglena gracilis. Effects of growth conditions. J. Appl. Phycol. 13: 59-65.
  5. Gissibl A, Sun A, Care A, Nevalainen H, Sunna A. 2019. Bioproducts from Euglena gracilis: synthesis and applications. Front. Bioeng. Biotechnol. 7: 108.
    Pubmed KoreaMed CrossRef
  6. Sugiyama A, Hata S, Suzuki K, Yoshida E, Nakano R, Mitra S, et al. 2010. Oral administration of paramylon, a beta-1,3-D-glucan isolated from Euglena gracilis Z inhibits development of atopic dermatitis-like skin lesions in NC/Nga mice. J. Vet. Med. Sci. 72: 755-763.
    Pubmed CrossRef
  7. Okouchi R, E S, Yamamoto K, Ota T, Seki K, Imai M, et al. 2019. Simultaneous intake of Euglena gracilis and vegetables exerts synergistic anti-obesity and anti-inflammatory effects by modulating the gut microbiota in diet-induced obese mice. Nutrients 11: 204.
    Pubmed KoreaMed CrossRef
  8. Yang H, Choi K, Kim KJ, Park SY, Jeon JY, Kim BG, et al. 2022. Immunoenhancing effects of Euglena gracilis on a cyclophosphamideinduced immunosuppressive mouse model. J. Microbiol. Biotechnol. 32: 228-237.
    Pubmed KoreaMed CrossRef
  9. Park S, Kim KJ, Jo SM, Jeon JY, Kim BR, Hwang JE, et al. 2023. Euglena gracilis (Euglena) powder supplementation enhanced immune function through natural killer cell activity in apparently healthy participants: a randomized, double-blind, placebo-controlled trial. Nutr. Res. 119: 90-97.
    Pubmed CrossRef
  10. Jo KA, Kim KJ, Park S, Jeon JY, Hwang JE, Kim JY. 2023. Evaluation of the effects of Euglena gracilis on enhancing immune responses in RAW264.7 cells and a cyclophosphamide-induced mouse model. J. Microbiol. Biotechnol. 33: 493-499.
    Pubmed KoreaMed CrossRef
  11. Maggini S, Beveridge S, Sorbara P, Senatore G. 2008. Feeding the immune system: the role of micronutrients in restoring resistance to infections. CABI Reviews. https://doi.org/10.1079/PAVSNNR20083098.
    CrossRef
  12. Germic N, Frangez Z, Yousefi S, Simon HU. 2019. Regulation of the innate immune system by autophagy: monocytes, macrophages, dendritic cells and antigen presentation. Cell Death Differ. 26: 715-727.
    Pubmed KoreaMed CrossRef
  13. Gottschalk RA, Martins AJ, Angermann BR, Dutta B, Ng CE, Uderhardt S, et al. 2016. Distinct NF-κB and MAPK activation thresholds uncouple steady-state microbe sensing from anti-pathogen inflammatory responses. Cell Syst. 2: 378-390.
    Pubmed KoreaMed CrossRef
  14. Fink LN, Frøkiær H. 2008. Dendritic cells from Peyer's patches and mesenteric lymph nodes differ from spleen dendritic cells in their response to commensal gut bacteria. Scand. J. Immunol. 68: 270-279.
    Pubmed CrossRef
  15. Keely S, Walker MM, Marks E, Talley NJ. 2015. Immune dysregulation in the functional gastrointestinal disorders. Eur. J. Clin. Investig. 45: 1350-1359.
    Pubmed CrossRef
  16. Rerknimitr P, Otsuka A, Nakashima C, Kabashima K. 2017. The etiopathogenesis of atopic dermatitis: barrier disruption, immunological derangement, and pruritus. Inflamm. Regen. 37: 14.
    Pubmed KoreaMed CrossRef
  17. Luebke RW, Parks C, Luster MI. 2004. Suppression of immune function and susceptibility to infections in humans: association of immune function with clinical disease. J. Immunotoxicol. 1: 15-24.
    Pubmed CrossRef
  18. Shirani K, Hassani FV, Razavi-Azarkhiavi K, Heidari S, Zanjani BR, Karimi G. 2015. Phytotrapy of cyclophosphamide-induced immunosuppression. Environ. Toxicol. Pharmacol. 39: 1262-1275.
    Pubmed CrossRef
  19. Meng Y, Li B, Jin D, Zhan M, Lu J, Huo G. 2018. Immunomodulatory activity of Lactobacillus plantarum KLDS1.0318 in cyclophosphamide-treated mice. Food Nutr. Res. 21: 62.
    Pubmed KoreaMed CrossRef
  20. Jimenez-Valera M, Moreno E, Amat MA, Ruiz-Bravo A. 2003. Modification of mitogen-driven lymphoproliferation by ceftriaxone in normal and immunocompromised mice. Int. J. Antimicrob. Agents 22: 607-612.
    Pubmed CrossRef
  21. Bendich A, Shapiro SS. 1986. Effect of beta-carotene and canthaxanthin on the immune responses of the rat. J. Nutr. 116: 2254-2262.
    Pubmed CrossRef
  22. Sudhagar A, Kumar G, El-Matbouli M. 2018. Transcriptome analysis based on RNA-Seq in understanding pathogenic mechanisms of diseases and the immune system of fish: a comprehensive review. Int. J. Mol. Sci. 19: 245.
    Pubmed KoreaMed CrossRef
  23. Harding JJ, Nandakumar S, Armenia J, Khalil DN, Albano M, Ly M, et al. 2019. Prospective genotyping of Hepatocellular Carcinoma: clinical implications of next-generation sequencing for matching patients to targeted and immune therapies. Clin. Cancer Res. 25: 2116-2126.
    Pubmed KoreaMed CrossRef
  24. Dheilly NM, Adema C, Raftos DA, Gourbal B, Grunau C, Du Pasquier L. 2014. No more non-model species: the promise of next generation sequencing for comparative immunology. Dev. Compar. Immunol. 45: 56-66.
    Pubmed KoreaMed CrossRef
  25. Bray NL, Pimentel H, Melsted P, Pachter L. 2016. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34: 525-527.
    Pubmed CrossRef
  26. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. 2022. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50: W216-w221.
    Pubmed KoreaMed CrossRef
  27. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. 2019. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47: D607-d613.
    Pubmed KoreaMed CrossRef
  28. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13: 2498-2504.
    Pubmed KoreaMed CrossRef
  29. Binns D, Dimmer E, Huntley R, Barrell D, O'Donovan C, Apweiler R. 2009. QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25: 3045-3046.
    Pubmed KoreaMed CrossRef
  30. Chen J, Zhang XD, Jiang Z. 2013. The application of fungal β-glucans for the treatment of colon cancer. Anticancer Agents Med. Chem. 13: 725-730.
    Pubmed CrossRef
  31. Wang J, Tong X, Li P, Cao H, Su W. 2012. Immuno-enhancement effects of Shenqi Fuzheng injection on cyclophosphamide-induced immunosuppression in Balb/c mice. J. Ethnopharmacol. 139: 788-795.
    Pubmed CrossRef
  32. Williams MB, Butcher EC. 1997. Homing of naive and memory T lymphocyte subsets to Peyer's patches, lymph nodes, and spleen. J. Immunol. 159: 1746-1752.
    Pubmed CrossRef
  33. Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. 2017. Transcriptome profiling in human diseases: new advances and perspectives. Int. J. Mol. Sci. 18: 1652.
    Pubmed KoreaMed CrossRef
  34. Suzuki K, Nakashima A, Igarashi M, Saito K, Konno M, Yamazaki N, et al. 2018. Euglena gracilis Z and its carbohydrate storage substance relieve arthritis symptoms by modulating Th17 immunity. PLoS One 13: e0191462.
    Pubmed KoreaMed CrossRef
  35. Pandya AD, Al-Jaderi Z, Høglund RA, Holmøy T, Harbo HF, Norgauer J, et al. 2011. Identification of human NK17/NK1 cells. PLoS One 6: e26780.
    Pubmed KoreaMed CrossRef
  36. Huang L, Wang M, Yan Y, Gu W, Zhang X, Tan J, et al. 2018. OX40L induces helper T cell differentiation during cell immunity of asthma through PI3K/AKT and P38 MAPK signaling pathway. J. Transl. Med. 16: 74.
    Pubmed KoreaMed CrossRef
  37. Wolken J, Mellon A. 1956. The relationship between chlorophyll and the carotenoids in the algal flagellate, Euglena. J. Gen. Physiol. 39: 675.
    Pubmed KoreaMed CrossRef
  38. Foletta VC, Segal DH, Cohen DR. 1998. Transcriptional regulation in the immune system: all roads lead to AP-1. J. Leukoc. Biol. 63: 139-152.
    Pubmed CrossRef
  39. Wang X, Sun L, He N, An Z, Yu R, Li C, et al. 2021. Increased expression of CXCL2 in ACPA-positive rheumatoid arthritis and its role in osteoclastogenesis. Clin. Exp. Immunol. 203: 194-208.
    Pubmed KoreaMed CrossRef
  40. Kelley JM, Hughes LB, Bridges SL Jr. 2008. Does gamma-aminobutyric acid (GABA) influence the development of chronic inflammation in rheumatoid arthritis? J. Neuroinflammation 5: 1.
    Pubmed KoreaMed CrossRef
  41. Bhandage AK, Barragan A. 2021. GABAergic signaling by cells of the immune system: more the rule than the exception. Cell. Mol. Life Sci. 78: 5667-5679.
    Pubmed KoreaMed CrossRef
  42. Cai W, Li H, Zhang Y, Han G. 2020. Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. PeerJ. 8: e8390.
    Pubmed KoreaMed CrossRef
  43. Monmai C, Park SH, You S, Park WJ. 2018. Immuno-enhancement effect of polysaccharide extracted from Stichopus japonicus on cyclophosphamide-induced immunosuppression mice. Food Sci. Biotechnol. 27: 565-573.
    Pubmed KoreaMed CrossRef
  44. Lin X, Sun Q, Zhou L, He M, Dong X, Lai M, et al. 2018. Colonic epithelial mTORC1 promotes ulcerative colitis through COX-2-mediated Th17 responses. Mucosal Immunol. 11: 1663-1673.
    Pubmed CrossRef
  45. Calvayrac R, Laval-Martin D, Briand J, Farineau J. 1981. Paramylon synthesis by Euglena gracilis photoheterotrophically grown under low O2 pressure. Planta 153: 6-13.
    Pubmed CrossRef
  46. Bendich A. 1989. Carotenoids and the immune response. J. Nutr. 119: 112-115.
    Pubmed CrossRef
  47. Yao R, Fu W, Du M, Chen ZX, Lei AP, Wang JX. 2022. Carotenoids biosynthesis, accumulation, and applications of a model microalga Euglenagracilis. Mar. Drugs 20: 496.
    Pubmed KoreaMed CrossRef
  48. Pyo MY, Park B, Choi JJ, Yang M, Yang HO, Cha JW, et al. 2013. Pheophytin a and chlorophyll a identified from environmentally friendly cultivation of green pepper enhance interleukin-2 and interferon-γ in Peyer's patches ex vivo Biol. Pharm. Bull. 36: 1747-1753.
    Pubmed CrossRef
  49. Piovan A, Filippini R, Corbioli G, Costa VD, Giunco EMV, Burbello G, et al. 2021. Carotenoid extract derived from Euglena gracilis overcomes lipopolysaccharide-induced neuroinflammation in microglia: role of NF-κB and Nrf2 signaling pathways. Mol. Neurobiol. 58: 3515-3528.
    Pubmed KoreaMed CrossRef