Bioinformatic Analysis Identifies Biomarkers and Treatment Targets in Primary Sjögren’s Syndrome Patients with Fatigue

Chen, Guangshu; Che, Li; Cai, Xingdong; Zhu, Ping; Ran, Jianmin

doi:https://doi.org/10.1155/2022/7697558

BioMed Research International

On this page

Abstract Introduction Materials and Methods Results Discussion Data Availability Ethical Approval Consent Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 7697558 | https://doi.org/10.1155/2022/7697558

Bioinformatic Analysis Identifies Biomarkers and Treatment Targets in Primary Sjögren’s Syndrome Patients with Fatigue

Guangshu Chen,¹Li Che,²Xingdong Cai,²Ping Zhu,¹and Jianmin Ran¹

Academic Editor: Paul Harrison

Received03 Nov 2021

Accepted23 Dec 2021

Published15 Jan 2022

Abstract

We aim to identify the common genes, biological pathways, and treatment targets for primary Sjögren’s syndrome patients with varying degrees of fatigue features. We select datasets about transcriptomic analyses of primary Sjögren’s syndrome (pSS) patients with different degrees of fatigue features and normal controls in peripheral blood. We identify common differentially expressed genes (DEGs) to find shared pathways and treatment targets for pSS patients with fatigue and design a protein-protein interaction (PPI) network by some practical bioinformatic tools. And hub genes are detected based on the PPI network. We perform biological pathway analysis of common genes by Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Lastly, potential treatment targets for pSS patients with fatigue are found by the Enrichr platform. We discovered that 27 DEGs are identified in pSS patients with fatigue features and the severe fatigued pSS-specific gene is RTP4. DEGs are mainly localized in the mitochondria, endosomes, endoplasmic reticulum, and cytoplasm and are involved in the biological process by which interferon acts on cells and cells defend themselves against viruses. Molecular functions mainly involve the process of RNA synthesis. The DEGs of pSS are involved in the signaling pathways of viruses such as hepatitis C, influenza A, measles, and EBV. Acetohexamide PC3 UP, suloctidil HL60 UP, prenylamine HL60 UP, and chlorophyllin CTD 00000324 are the four most polygenic drug molecules. PSS patients with fatigue features have specific gene regulation, and chlorophyllin may alleviate fatigue symptoms in pSS patients.

1. Introduction

Primary Sjögren’s syndrome (pSS) is an all-body autoimmune disease that mainly affects middle-aged women [1]. The main clinical feature of the disease is dryness of the mouth and eyes, and the pathophysiology is characterized by focal lymphocyte infiltration in exocrine glands [2, 3]. Fatigue is commonly seen in pSS patients as an extraglandular manifestation and closely links with poor life quality [4–6]. Fatigue affects approximately 70% of pSS patients [7, 8]. Normally, fatigue and depression are considered manifestations of psychological disorders and interact with physical pain and discomfort, which creates a vicious cycle. Fatigue in pSS is induced and regulated by genetic and molecular mechanisms, with the innate immune system playing an important role in the production of fatigue [9–11]. Although pSS always comes with fatigue, not all patients exhibit fatigue, which provides a good model for exploring the underlying biological mechanisms.

High-throughput methods play an increasingly essential role in biology spheres, and microarray data analysis highlights its advantage in large-scale analysis of gene expression among high-throughput applications [12, 13]. Former studies [14, 15] have shown the high-throughput sequencing analysis result for pSS patients with fatigue features but do not offer further analysis based on varying degrees of fatigue. This study tries to present characteristic genes and biological pathways in pSS patients with manifestations of fatigue, as well as drugs of potential benefit.

The GSE66795 dataset from the GPL10558 platform on the GEO database is selected for gene expression of pSS with fatigue. The GSE66795 dataset was first identified for differentially expressed genes (DEGs) in pSS patients with different levels of fatigue, and based on the coexpressed genes, further analyses including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway are performed to understand the biological process. The top ten target genes from the protein-protein interaction (PPI) network will be obtained to identify potential drugs that may alleviate fatigue in pSS patients.

2. Materials and Methods

2.1. Dataset Collection

We search “Primary Sjögren’s syndrome” and “fatigue” in the GEO database [16] and select the dataset (GSE66795) demonstrating gene expression in pSS patients with varying degrees of fatigue characteristics and normal controls. The GSE66795 dataset is extracted from the GPL10558 platform (Illumina HumanHT-12 V4.0 expression microbead chip) for RNA sequence analysis. The data of GSE66795 is obtained from the UK registry of primary Sjögren’s syndrome. It includes whole genome microarray profiles of pSS patients with varying degrees of fatigue characteristics and normal controls in peripheral blood. One hundred and thirty-one patients with pSS are involved, including 21 patients with mild fatigue, 74 patients with moderate fatigue, 36 patients with severe fatigue, and 29 normal controls.

2.2. Differential Expression Analysis

Differential expression analysis is performed using the online analysis tool GEO2R; gene expression profiles of pSS patients with mild, moderate, and severe fatigue were compared with normal controls separately to identify DEGs. values and adjusted values are calculated using -tests. Genes with the following criteria were retained for each sample: (1) log2-fold change (log2FC) absolute value greater than 1 and (2) adjusted value less than 0.05. After identifying DEGs in pSS patients with varying degrees of fatigue, the online website (https://www.xiantao.love/gds) is used to plot a Venn diagram.

2.3. Gene Ontology and Pathway Discovery in Gene Set Enrichment Analysis

Gene set enrichment analysis is used to understand the general biological function and the chromosomal location of a gene [17]. For gene product annotation, the terms of Gene Ontology (GO) are used, including biological process (BP), molecular function (MF), and cellular component (CC) [18]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are commonly used to describe metabolic pathways [19]. GO terms and KEGG pathways were gotten through the platform Enrichr (https://amp.pharm.mssm.edu/Enrichr/) based on the DEGs [20].

2.4. Protein-Protein Interaction (PPI) Network

The information generated from the PPI network improves the understanding of protein function [21]. PPI networks are made by STRING (https://string-db.org/) after inputting the common DEGs. We analyze PPIs through Cytoscape (https://cytoscape.org/) to further present the network and identify target genes.

2.5. Transcription Factor- (TF-) Gene Interactions

We use NetworkAnalyst (https://www.networkanalyst.ca/) to identify interactions of TF-genes with DEGs [22]. NetworkAnalyst plays a comprehensive network platform for gene expression across a wide range of species and enables them to be subjected to a meta-analysis [23].

2.6. Identification of Potential Treatment Targets

Identification of drug molecules is a vital component of genomics research. We input the DEGs in the Drug Signature Database (DSigDB). Then, we get the designed drug molecules, which may have promising clinical application. DSigDB is obtained through the Enrichr (https://amp.pharm.mssm.edu/Enrichr/) platform. Enrichr is primarily used as an enrichment analysis platform, providing extensive visual details of the common functions of inputted genes [24].

3. Results

3.1. DEG Identifications

We use the GSE66795 dataset to identify the DEGs of pSS with fatigue. 37, 29, and 33 DEGs are obtained for pSS with mild, moderate, and severe fatigue, respectively. The collected DEGs are further compared by using the online website (https://www.xiantao.love/gds) for gathering common genes in pSS with varying degrees of fatigue. And 27 (OAS1, OAS2, GBP1, IRF7, EIF2AK2, IFIT2, USP18, SAMD9L, HES4, IFI44L, SERPING1, IFIT3, IFITM3, IFI6, XAF1, MX1, OASL, OTOF, HERC5, LY6E, EPSTI1, OAS3, ISG15, IFIT1, RSAD2, IFI44, and IFI27) common DEGs are identified. The specific genes to pSS with mild fatigue are DDX60, IFIH1, GBP5, LAP3, and TIMM10. The specific genes to pSS with moderate fatigue are HLA-DRB4 and HLA-DRB6, and that to pSS with severe fatigue is RTP4. The Venn diagram (Figure 1) shows that common DEGs accounted for 67.5% out of a total of 40 DEGs.

3.2. GO Terms and KEGG Pathways

We analyzed 27 common DEGs for both GO and KEGG pathways. Both of the results are taken from the top 10 GO entries. GO terms in Table 1 suggest that DEGs are mainly localized in the mitochondria, endosomes, endoplasmic reticulum, and cytoplasm. They are involved in the biological processes of interferon action on cells and cellular defense against viruses. And the molecular functions are mainly engaged in the process of RNA synthesis. KEGG pathways in Table 2 suggest that the DEGs of pSS with fatigue are involved in the signaling pathways of viruses such as hepatitis C, influenza A, measles, and EBV. Both are seen in Figures 2(a) and 2(b).

(a)

(b)

3.3. Identification of Hub Genes by PPI Networks

We put common DEGs into the STRING website, and the files generated after analysis are further entered into Cytoscape software for visual analysis. PPI networks are designed to detect hub genes for identifying drug molecules for pSS with fatigue. PPI networks involve 24 nodes and 552 edges, which are shown in Figure 3(a). We present the top 20 genes in Figure 3(b) and Table 3.

(a)

(b)

Figure 3

(a) Protein-protein interaction (PPI) network for DEGs of patients with varying degrees of fatigue. Nodes in blue color indicate common DEGs with the highest connection degree value, and edges specify the interconnection in the middle of two genes. The network involves 24 nodes and 552 edges. (b) The top 20 genes are detected from the PPI network of common DEGs. The nodes in red color represent 14 genes with the highest degree value which are MX1, IFIT1, ISG15, RSAD2, IFI44L, IFI44, IFIT3, OAS2, OAS1, IFI6, XAF1, IFIT2, GBP1, and OAS3. Based on the topological analysis, the degree value of these 14 genes is both 23.

3.4. TF-Gene Interactions

The interactions of TF and genes are shown in Figure 4. The network has 60 nodes and 108 edges. Sixteen TF-genes regulate IFIT1, and IFIT3 is handled by 14 TF-genes. The network involves 60 TF-genes. Figure 4 shows the network of TF-gene interactions.

3.5. Identification of Drug Candidates

We identify drug molecules for the top 10 hub genes on the Enrichr platform. We collect drug candidates judged on adjusted values. The analysis reveals that acetohexamide PC3 UP, suloctidil HL60 UP, prenylamine HL 60 UP, and chlorophyllin CTD 00000324 are the four most polygenic drug molecules that interact with genes. Figure 5 and Table 4 present the drug candidates in DSigDB.

4. Discussion

Fatigue is an annoying experience that means physical and mental tiredness [25]. Mengshoel et al. [26] reveal that most pSS patients literally suffer from fluctuating fatigue out of control regardless of their health condition. Fatigue has a significant influence on patients’ daily life, and patients must adapt to their behavior and lives. Although the underlying mechanisms are still unclear, former studies take depression and pain as the prominent factors associated with fatigue [5, 27]. Currently, growing evidence suggests that fatigue has a molecular and genetic basis on its production and regulation. Therefore, most scholars view fatigue as a biological and brain phenomenon [9–11].

IL-1β tends to increase rapidly secreted from macrophages to activate the immune system when meeting tissue injury or infection. IL-1β plays its role by binding with the IL-1 receptor coming with the downstream of IL-1 response [28]. Then, immune and inflammation systems are activated, which induce the behavior of disease, with fatigue being involved as an important component [29]. All these inflammatory signaling pathways go on working and turn fatigue into a chronic state. In the brain, IL-1 β signaling pathways may explain the ultimate pathway of fatigue [30, 31], and IL-1 blocker treatment may effectively release fatigue [32, 33]. Thus, fatigue and other unpleasant mood in those patients with autoimmune disease not only should be understood by the unfortunate development of chronic illness but also may be related to some signaling pathways and activation of genes that regulate the mood in the cerebral system.

Genome-wide association analysis of pSS patients has been conducted, and a gene (RTP4) is identified as highly relevant. Similarly, we confirm that RTP4 is highly expressed in pSS patients with severe fatigue through bioinformatic analysis, suggesting that this gene is critical in the mechanisms of fatigue. RTP4 encodes a protein associated with the expression of opioid receptors on the cell surface. These receptors are also expressed in the lymph system and pain-regulated pathways in the brain [34]. However, the former study did not stratify pSS based on the degree of fatigue, and it is unclear which degree of fatigue expresses the RTP4 gene. Our study finds that pSS patients with severe fatigue specifically express the RTP4 gene, providing clues for further studies on the genomics of fatigue features in pSS patients.

OAS1, a coexpressed gene for pSS in our study, has been established in previous studies as a risk locus of pSS and impacts the flaw of virus clearance because of the altering response of IFN [35]. Our gene pathway analysis points out that DEGs for pSS with fatigue are mainly localized intracellularly and involved in signaling pathways of common viruses in the respiratory and digestive tracts, suggesting that pSS is a systemic disease with an uncertain etiology and that viral infection may be a predisposing factor.

Fatigue always accompanies pSS patients, but it is hard work to manage these bad feelings [36]. The clinical practice guidelines (CPG) committee emphasizes the many causes of fatigue in pSS; therefore, the comprehensive evaluation for diagnosis is essential. So far, the treatment for fatigue in pSS with solid recommendation is mere taking exercise, which is also practical in other autoimmune diseases [37]. In America, hydroxychloroquine (HCQ) is the most widely used drug therapy for pSS with fatigue, but the recommendation strength is not strong enough [34]. It is not recommended to release fatigue in pSS using dehydroepiandrosterone (DHEA) [34]. Both the tumor necrosis factor inhibitor is discouraged for the treatment of fatigue in pSS [38, 39]. Our bioinformatic study reveals that besides chloroquine and testosterone drugs that help improve fatigue, chlorophyllin, the sulphonylurea hypoglycaemic drug acetylhexane, and the antiallergic drug terfenadine may have improved fatigue in pSS. However, chloroquine and testosterone are not strongly recommended as we mentioned before. Acetohexamide has been discontinued in the American market due to its significant hypoglycaemic risk. Terfenadine is not suitable for long-term use since its central depression as an antiallergy drug. And chlorophyllin appears to hold some promise for reducing fatigue in pSS.

Chlorophyll is an ingredient of the derifil drug which is available as an over-the-counter medicine [40]. And chlorophyllin, obtained by hydrolyzing chlorophyll to remove phytyl alcohol, is a water-soluble derivative. Chlorophyll has been shown to exert its anticancer properties by playing a role as an antioxidant [41], a CYP inhibitor [42], an apoptosis inducer [43], a phase II enzyme stimulator [44], and a carcinogen transport modulator [45]. Currently, COVID-19 has swept the world and may last for a long time because of its rapid mutation. Almost 5,000,000 people have died in this epidemic [46], and the reduction of lymphocytes in COVID-19 patients is considered an important risk factor for poor prognosis [47–49]. Recent studies suggest that the chlorophyll derivative sodium copper chlorophyllin (SCC) may improve survival in critically ill COVID-19 patients by increasing the total number of lymphocytes [50]. Increasing consumers choose dietary chlorophyll which is derived from SCC for diet supplements for the sake of keeping healthy [51, 52]. Dietary chlorophyll is safe and has been shown to have a higher absorption rate in the human body, which may trigger ionic compound chelation [53, 54]. Zeng et al. [55] cognize one functional food called barley grass powder which is rich in chlorophyll, and other nutrients can effectively alleviate fatigue in chronic patients. The mechanism of chlorophyll’s role in relieving fatigue in pSS patients is unclear. It may be related to the nature of the hepatic enzyme inhibitors that increase the concentrations of immunosuppressant like hydroxychloroquine, which has better control of fatigue. And the capacity of scavenging the oxygen radical as an antioxidant may somewhat improve the fatigue of body.

We have identified gene expression profiles in peripheral blood specific to pSS with fatigue characteristics. The analysis of identified DEGs and pathways in this study will deepen our understanding of the essence of fatigue in pSS. The discovery that chlorophyllin may improve fatigue symptoms provides a theoretical basis for better improving the quality of life in pSS patients. And a preprint has previously been published [56].

Data Availability

The dataset supporting the conclusions of this article is available in the UK registry of primary Sjögren’s syndrome repository and in the hyperlink (https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE66795).

Ethical Approval

GEO belongs to public databases. The patients we choose involved in the database have obtained ethical approval. It is available for all users to download relevant data for free. Our study is based on open-source data, so there is no need to offer ethics approval.

There is no need for consent to participate.

Conflicts of Interest

The authors declare that they have no competing interests.

Authors’ Contributions

Guangshu Chen and Li Che contributed equally to this work.

Acknowledgments

We sincerely acknowledge the GEO database for providing their platforms and contributors for uploading their meaningful datasets. This study was financially supported by the Guangdong Science and Technology Project Fund for Key Scientific Research Base under Grant no. 2019B020230001.

References

R. I. Fox, F. V. Howell, R. C. Bone, and P. E. Michelson, “Primary Sjogren syndrome: clinical and immunopathologic features,” Seminars in Arthritis and Rheumatism, vol. 14, no. 2, pp. 77–105, 1984.
View at: Publisher Site | Google Scholar
S. E. Gabriel and K. Michaud, “Epidemiological studies in incidence, prevalence, mortality, and comorbidity of the rheumatic diseases,” Arthritis Research & Therapy, vol. 11, no. 3, p. 229, 2009.
View at: Publisher Site | Google Scholar
K. Asmussen, V. Andersen, G. Bendixen, M. Schiodt, and P. Oxholm, “A new model for classification of disease manifestations in primary Sjögren's syndrome: evaluation in a retrospective long-term study,” Journal of Internal Medicine, vol. 239, no. 6, pp. 475–482, 1996.
View at: Publisher Site | Google Scholar
K. Haldorsen, I. Bjelland, A. I. Bolstad, R. Jonsson, and J. Brun, “A five-year prospective study of fatigue in primary Sjögren's syndrome,” Arthritis Research & Therapy, vol. 13, no. 5, p. R167, 2011.
View at: Publisher Site | Google Scholar
T. Karageorgas, S. Fragioudaki, A. Nezos, D. Karaiskos, H. M. Moutsopoulos, and C. P. Mavragani, “Fatigue in primary Sjögren's syndrome: clinical, laboratory, psychometric, and biologic associations,” Arthritis Care & Research, vol. 68, no. 1, pp. 123–131, 2016.
View at: Publisher Site | Google Scholar
C. L. Overman, M. B. Kool, J. A. P. da Silva, and R. Geenen, “The prevalence of severe fatigue in rheumatic diseases: an international study,” Clinical Rheumatology, vol. 35, no. 2, pp. 409–415, 2016.
View at: Publisher Site | Google Scholar
A. Lerdal, A. Wahl, T. Rustoen, B. R. Hanestad, and T. Moum, “Fatigue in the general population: a translation and test of the psychometric properties of the Norwegian version of the fatigue severity scale,” Scandinavian Journal of Public Health, vol. 33, no. 2, pp. 123–130, 2005.
View at: Publisher Site | Google Scholar
N. Howard Tripp, J. Tarn, A. Natasari et al., “Fatigue in primary Sjögren's syndrome is associated with lower levels of proinflammatory cytokines,” RMD Open, vol. 2, no. 2, article e000282, 2016.
View at: Publisher Site | Google Scholar
K. Brække Norheim, J. Imgenberg-Kreuz, K. Jonsdottir et al., “Epigenome-wide DNA methylation patterns associated with fatigue in primary Sjögren's syndrome,” Rheumatology, vol. 55, no. 6, pp. 1074–1082, 2016.
View at: Publisher Site | Google Scholar
K. Bårdsen, M. M. Nilsen, J. T. Kvaløy, K. B. Norheim, G. Jonsson, and R. Omdal, “Heat shock proteins and chronic fatigue in primary Sjögren's syndrome,” Innate Immunity, vol. 22, no. 3, pp. 162–167, 2016.
View at: Publisher Site | Google Scholar
R. Dantzer, C. J. Heijnen, A. Kavelaars, S. Laye, and L. Capuron, “The neuroimmune basis of fatigue,” Trends in Neurosciences, vol. 37, no. 1, pp. 39–46, 2014.
View at: Publisher Site | Google Scholar
A. Sturn, J. Quackenbush, and Z. Trajanoski, “Genesis: cluster analysis of microarray data,” Bioinformatics, vol. 18, no. 1, pp. 207-208, 2002.
View at: Publisher Site | Google Scholar
M. L. Lee, F. C. Kuo, G. A. Whitmore, and J. Sklar, “Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 18, pp. 9834–9839, 2000.
View at: Publisher Site | Google Scholar
L. Zhang, P. Xu, X. Wang et al., “Identification of differentially expressed genes in primary Sjögren's syndrome,” Journal of Cellular Biochemistry, vol. 120, no. 10, pp. 17368–17377, 2019.
View at: Publisher Site | Google Scholar
G. G. Song, J. H. Kim, Y. H. Seo, S. J. Choi, J. D. Ji, and Y. H. Lee, “Meta-analysis of differentially expressed genes in primary Sjogren's syndrome by using microarray,” Human Immunology, vol. 75, no. 1, pp. 98–104, 2014.
View at: Publisher Site | Google Scholar
E. Clough and T. Barrett, “The Gene Expression Omnibus database,” Methods in Molecular Biology, vol. 1418, pp. 93–110, 2016.
View at: Publisher Site | Google Scholar
A. Subramanian, P. Tamayo, V. K. Mootha et al., “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 43, pp. 15545–15550, 2005.
View at: Publisher Site | Google Scholar
A. Doms and M. Schroeder, “GoPubMed: exploring PubMed with the Gene Ontology,” Nucleic Acids Research, vol. 33, no. Web Server, pp. W783–W786, 2005.
View at: Publisher Site | Google Scholar
M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000.
View at: Publisher Site | Google Scholar
M. V. Kuleshov, M. R. Jones, A. D. Rouillard et al., “Enrichr: a comprehensive gene set enrichment analysis web server 2016 update,” Nucleic Acids Research, vol. 44, no. W1, pp. W90–W97, 2016.
View at: Publisher Site | Google Scholar
A. Ben-Hur and W. S. Noble, “Kernel methods for predicting protein-protein interactions,” Bioinformatics, vol. 21, Suppl 1, pp. i38–i46, 2005.
View at: Publisher Site | Google Scholar
Z. Ye, F. Wang, F. Yan et al., “Bioinformatic identification of candidate biomarkers and related transcription factors in nasopharyngeal carcinoma,” World Journal of Surgical Oncology, vol. 17, no. 1, p. 60, 2019.
View at: Publisher Site | Google Scholar
G. Zhou, O. Soufan, J. Ewald, R. E. W. Hancock, N. Basu, and J. Xia, “NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis,” Nucleic Acids Research, vol. 47, no. W1, pp. W234–W241, 2019.
View at: Publisher Site | Google Scholar
E. Y. Chen, C. M. Tan, Y. Kou et al., “Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool,” BMC Bioinformatics, vol. 14, no. 1, p. 128, 2013.
View at: Publisher Site | Google Scholar
L. B. Krupp and D. A. Pollina, “Mechanisms and management of fatigue in progressive neurological disorders,” Current Opinion in Neurology, vol. 9, no. 6, pp. 456–460, 1996.
View at: Publisher Site | Google Scholar
A. M. Mengshoel, K. B. Norheim, and R. Omdal, “Primary Sjögren's syndrome: fatigue is an ever-present, fluctuating, and uncontrollable lack of energy,” Arthritis Care & Research, vol. 66, no. 8, pp. 1227–1232, 2014.
View at: Publisher Site | Google Scholar
L. C. Pollard, E. H. Choy, J. Gonzalez, B. Khoshaba, and D. L. Scott, “Fatigue in rheumatoid arthritis reflects pain, not disease activity,” Rheumatology, vol. 45, no. 7, pp. 885–889, 2006.
View at: Publisher Site | Google Scholar
C. A. Dinarello, A. Simon, and J. W. van der Meer, “Treating inflammation by blocking interleukin-1 in a broad spectrum of diseases,” Nature Reviews. Drug Discovery, vol. 11, no. 8, pp. 633–652, 2012.
View at: Publisher Site | Google Scholar
B. L. Hart, “Biological basis of the behavior of sick animals,” Neuroscience and Biobehavioral Reviews, vol. 12, no. 2, pp. 123–137, 1988.
View at: Publisher Site | Google Scholar
S. Rossi, V. Studer, C. Motta et al., “Neuroinflammation drives anxiety and depression in relapsing-remitting multiple sclerosis,” Neurology, vol. 89, no. 13, pp. 1338–1347, 2017.
View at: Publisher Site | Google Scholar
J. M. Kim, H. J. Kang, J. W. Kim et al., “Associations of tumor necrosis factor-α and interleukin-1β levels and polymorphisms with post-stroke depression,” The American Journal of Geriatric Psychiatry, vol. 25, no. 12, pp. 1300–1308, 2017.
View at: Publisher Site | Google Scholar
R. Omdal and R. Gunnarsson, “The effect of interleukin-1 blockade on fatigue in rheumatoid arthritis--a pilot study,” Rheumatology International, vol. 25, no. 6, pp. 481–484, 2005.
View at: Publisher Site | Google Scholar
C. Cavelti-Weder, R. Furrer, C. Keller et al., “Inhibition of IL-1beta improves fatigue in type 2 diabetes,” Diabetes Care, vol. 34, no. 10, article e158, 2011.
View at: Publisher Site | Google Scholar
S. E. Carsons, F. B. Vivino, A. Parke et al., “Treatment guidelines for rheumatologic manifestations of Sjögren's syndrome: use of biologic agents, management of fatigue, and inflammatory musculoskeletal pain,” Arthritis Care & Research, vol. 69, no. 4, pp. 517–527, 2017.
View at: Publisher Site | Google Scholar
H. Li, T. R. Reksten, J. A. Ice et al., “Identification of a Sjögren's syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons,” PLoS Genetics, vol. 13, no. 6, article e1006820, 2017.
View at: Publisher Site | Google Scholar
B. Segal, “Fatigue in primary Sjogren's syndrome,” in Sjogren's Syndrome: Diagnosis and Therapeutics, M. Ramos-Casals, Ed., pp. 129–143, Springer Verlag, London, 2012.
View at: Google Scholar
B. E. Strombeck, E. Theander, and L. T. Jacobsson, “Effects of exercise on aerobic capacity and fatigue in women with primary Sjogren's syndrome,” Rheumatology, vol. 46, no. 5, pp. 868–871, 2007.
View at: Publisher Site | Google Scholar
V. Sankar, M. T. Brennan, M. R. Kok et al., “Etanercept in Sjögren's syndrome: a twelve-week randomized, double-blind, placebo-controlled pilot clinical trial,” Arthritis and Rheumatism, vol. 50, no. 7, pp. 2240–2245, 2004.
View at: Publisher Site | Google Scholar
X. Mariette, P. Ravaud, S. Steinfeld et al., “Inefficacy of infliximab in primary Sjögren's syndrome: results of the randomized, controlled Trial of Remicade in Primary Sjögren's Syndrome (TRIPSS),” Arthritis and Rheumatism, vol. 50, no. 4, pp. 1270–1276, 2004.
View at: Publisher Site | Google Scholar
S. Suryavanshi, D. Sharma, R. Checker et al., “Amelioration of radiation-induced hematopoietic syndrome by an antioxidant chlorophyllin through increased stem cell activity and modulation of hematopoiesis,” Free Radical Biology & Medicine, vol. 85, pp. 56–70, 2015.
View at: Publisher Site | Google Scholar
K. K. Boloor, J. P. Kamat, and T. P. Devasagayam, “Chlorophyllin as a protector of mitochondrial membranes against γ-radiation and photosensitization,” Toxicology, vol. 155, no. 1-3, pp. 63–71, 2000.
View at: Publisher Site | Google Scholar
C. H. Yun, H. G. Jeong, J. W. Jhoun, and F. P. Guengerich, “Non-specific inhibition of cytochrome P450 activities by chlorophyllin in human and rat liver microsomes,” Carcinogenesis, vol. 16, no. 6, pp. 1437–1440, 1995.
View at: Publisher Site | Google Scholar
L. C. Chiu, C. K. Kong, and V. E. Ooi, “The chlorophyllin-induced cell cycle arrest and apoptosis in human breast cancer MCF-7 cells is associated with ERK deactivation and cyclin D1 depletion,” International Journal of Molecular Medicine, vol. 16, no. 4, pp. 735–740, 2005.
View at: Google Scholar
J. W. Fahey, K. K. Stephenson, A. T. Dinkova-Kostova, P. A. Egner, T. W. Kensler, and P. Talalay, “Chlorophyll, chlorophyllin and related tetrapyrroles are significant inducers of mammalian phase 2 cytoprotective genes,” Carcinogenesis, vol. 26, no. 7, pp. 1247–1255, 2005.
View at: Publisher Site | Google Scholar
J. E. Mata, Z. Yu, J. E. Gray, D. E. Williams, and R. Rodriguez-Proteau, “Effects of chlorophyllin on transport of dibenzo(a, l)pyrene, 2-amino-1-methyl-6-phenylimidazo-[4,5- b ]pyridine, and aflatoxin B₁ across Caco-2 cell monolayers,” Toxicology, vol. 196, no. 1-2, pp. 117–125, 2004.
View at: Publisher Site | Google Scholar
Coronavirus Resource Center, Johns Hopkins University, “COVID-19 Dashboard,” November 2021, https://coronavirus.jhu.edu/map.html.
View at: Google Scholar
E. Terpos, I. Ntanasis-Stathopoulos, I. Elalamy et al., “Hematological findings and complications of COVID-19,” American Journal of Hematology, vol. 95, no. 7, pp. 834–847, 2020.
View at: Publisher Site | Google Scholar
Q. Zhao, M. Meng, R. Kumar et al., “Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a systemic review and meta-analysis,” International Journal of Infectious Diseases, vol. 96, pp. 131–135, 2020.
View at: Publisher Site | Google Scholar
M. Zheng, Y. Gao, G. Wang et al., “Functional exhaustion of antiviral lymphocytes in COVID-19 patients,” Cellular & Molecular Immunology, vol. 17, no. 5, pp. 533–535, 2020.
View at: Publisher Site | Google Scholar
N. F. Clark and A. W. Taylor-Robinson, “COVID-19 therapy: could a copper derivative of chlorophyll a be used to treat lymphopenia associated with severe symptoms of SARS-CoV-2 infection?” Frontiers in Medicine, vol. 8, article 620175, 2021.
View at: Publisher Site | Google Scholar
C. Ulbricht, R. Bramwell, M. Catapang et al., “An evidence-based systematic review of chlorophyll by the Natural Standard Research Collaboration,” Journal of Dietary Supplements, vol. 11, no. 2, pp. 198–239, 2014.
View at: Publisher Site | Google Scholar
B. Mysliwa-Kurdziel and K. Solymosi, “Phycobilins and phycobiliproteins used in food industry and medicine,” Mini Reviews in Medicinal Chemistry, vol. 17, no. 13, pp. 1173–1193, 2017.
View at: Publisher Site | Google Scholar
M. G. Ferruzzi, M. L. Failla, and S. J. Schwartz, “Sodium copper chlorophyllin: in vitro digestive stability and accumulation by Caco-2 human intestinal cells,” Journal of Agricultural and Food Chemistry, vol. 50, no. 7, pp. 2173–2179, 2002.
View at: Publisher Site | Google Scholar
S. Bhatia, P. N. Prabhu, A. C. Benefiel et al., “Galacto-oligosaccharides may directly enhance intestinal barrier function through the modulation of goblet cells,” Molecular Nutrition & Food Research, vol. 59, no. 3, pp. 566–573, 2015.
View at: Publisher Site | Google Scholar
Y. Zeng, X. Pu, J. Yang et al., “Preventive and therapeutic role of functional ingredients of barley grass for chronic diseases in human beings,” Oxidative Medicine and Cellular Longevity, vol. 2018, Article ID 3232080, 15 pages, 2018.
View at: Publisher Site | Google Scholar
G. Chen, L. Che, X. Cai, P. Zhu, J. Ran, and S. Liu, Bioinformatic analysis identifies biomarkers and treatment targets in primary Sjögren's syndrome patients with fatigue, Research Square, 2021.
View at: Publisher Site

Copyright

Copyright © 2022 Guangshu Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

486

Downloads

907

Citations