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. 2021 Oct 9;12:e59. doi: 10.34172/jlms.2021.59

Low-Level Laser Therapy Effects on Rat Blood Hemostasis Via Significant Alteration in Fibrinogen and Plasminogen Expression Level

Babak Arjmand 1, Mahmood Khodadoost 2, Somayeh Jahani Sherafat 3, Mostafa Rezaei Tavirani 4,*, Nayebali Ahmadi 4, Farshad Okhovatian 5, Majid Rezaei Tavirani 6
PMCID: PMC8837859  PMID: 35155144

Abstract

Introduction: There are many documents about the significant role of low-level laser therapy (LLLT) in different processes such as regenerator medicine and bone formation. The aim of this study is to assess the role of LLLT in blood hemostasis in rats via bioinformatic investigation.

Methods: The differentially expressed plasma proteins of treated rats via LLLT from the literature and the added 50 first neighbors were investigated via network analysis to find the critical dysregulated proteins and biological processes by using Cytoscape software, the STRING database, and ClueGO.

Results: A scale-free network including 55 nodes was constructed from queried and added first neighbor proteins. Fibrinogen gamma, fibrinogen alpha, and plasminogen were highlighted as the central genes of the analyzed network. Fibrinolysis was determined as the main group of biological processes that were affected by LLLT.

Conclusion: Findings indicate that LLLT affects blood hemostasis which is an important point in approving the therapeutic application of LLLT and also in preventing its possible complication.

Keywords: Laser therapy, Differentially expressed proteins, Bioinformatics, Rat, Blood hemostasis

Introduction

The application of a laser in different fields of medicine implies more investigation to discover its molecular mechanism and possible complication. 1 Hemostasis as a process that is involved in the cessation of bleeding from a blood vessel is studied in the presence of many stress conditions. 2-4 There are many documents about the molecular aspect of low-level laser therapy (LLLT), which are concerned with proteomics, genomics, and bioinformatics. 5-7 Since the high throughput methods provide large numbers of data, bioinformatics as a useful tool is applied to organize and interpret the findings to present a new concept. 8

Network analysis as one of the bioinformatics fields is an approach that studies the relationship between the assessed data. In this mode of analysis, the large numbers of proteins can include in a unique interacted unit to form a scale-free network. In the scale-free networks, there are few nodes (proteins in this study) that play crucial roles in network construction. 9 Centrality parameters of the studied nodes are important characters of the nodes of the network that rank the node as critical and usual nodes. Two significant central parameters are degree and betweenness centrality. The nodes that are characterized with high values of degree and betweenness centrality are known as hub and bottleneck nodes respectively. The common hubs and bottlenecks are hub-bottlenecks which are potent central elements of the analyzed network. 10-12 The central nodes of a network determine the possible function of the network. 13,14

Gene ontology is a method that determines related biological processes, molecular functions, biochemical pathways, and cellular components for the studied genes. 15,16 Results of gene ontology are used to assess the molecular mechanism of many diseases. 17,18 In many cases, network analysis and gene ontology are tied together to solve the complexity of the studied systems. 19-21 In the present study, the limited numbers of dysregulated plasma proteins of rats that are treated via LLLT in interaction with the main numbers of the first neighbors are selected to be analyzed via network analysis. The central proteins are investigated via gene ontology to find the critical modulated biological processes after LLLT.

Methods

Six dysregulated rat plasma proteins after LLLT were extracted from the published data by Kilik et al. 22 Based on the methods of this article, 8 male Wistar rats in two groups, control group (C) and irradiated group (I), were investigated to find the effect of LLLT. Radiation is administrated by the gallium–aluminum–arsenide (GaAlAs) diode laser (Maestro/CCM, Medicom Prague, Czech Republic, λ = 830 nm, oval shape of beam-spot size * 1 cm2, power density 450 mW/cm2, total daily dose 60,3 J/cm2, irradiation time 134 seconds) via transcutaneous irradiation for 9 days. Plasma proteins were analyzed via two-dimensional gel electrophoresis and the digested spots were identified by MALDI-TOF mass spectrometry. Details of the procedure are described in the published article. 22 The queried differentially expressed proteins (DEPs) plus 50 first neighbors from the STRING database were analyzed by using Cytoscape software v3.7.2. The central nodes were assessed via gene ontology by ClueGO plugin of Cytoscape. The related significant biological processes were selected from “GO, Biological Process, EBI, UniProt-GOA-ACAP-ARAP, 08.05.2020”.

Results

Six identified proteins including haptoglobin (HP), hemopexin (HPX), fibrinogen gamma (FGG), fetuin-A (FETUA), Fetuin-B (FETUB), and alpha-1-antitrypsin (A1AT) were extracted from the published data by Kilik et al. 22 The first three proteins (HP, HPX, and FGG) are the up-regulated proteins while the other ones are de-regulated individuals. Among the 6 queried differentially expressed genes (DEGs), A1AT was not recognized by the STRING database. The network including 55 nodes (five queried DEPs and 50 added first neighbors) and 1034 edges was created (Figure 1). The nodes of the network and the centrality parameters of the nodes are presented in Table 1. The last queried DEP is located in the row of 23 in Table 1 and its degree is 40. The degree of 40 was selected as cutoff and the 24 nodes that were characterized by degree value above 40 were considered for more analysis. The results of gene ontology of the selected 24 proteins are presented in Table 2. 56 terms clustered in four classes of biological processes are presented in Table 2. Each one of the two first groups includes one term while the third group is characterized by 18 terms. The larger group contains 36 biological processes.

Figure 1.

Figure 1

Network Including 5 Recognized Queried DEPs and 50 Added First Neighbors. The nodes are layout based on degree value. Red to blue color and increasing size of nodes refer to higher values of degree.

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Table 1. The 55 nodes of the analyzed network and the related centrality parameters are presented.

R Display Name Query Term Degree BC CC Stress
1 Ahsg FETUA 54 0.017 1.000 902
2 Fgg FGG 54 0.017 1.000 902
3 Plg 54 0.017 1.000 902
4 Fga 53 0.015 0.982 828
5 Serpinc1 50 0.013 0.931 694
6 Fgb 49 0.012 0.915 666
7 Hrg 49 0.013 0.915 692
8 F2 48 0.010 0.900 592
9 Kng1 48 0.011 0.900 602
10 Apoa1 47 0.009 0.885 540
11 Hp HP 47 0.011 0.885 584
12 Alb 46 0.009 0.871 510
13 Apoa2 46 0.008 0.871 498
14 Gc 45 0.008 0.857 460
15 Apob 44 0.008 0.844 458
16 Serpind1 44 0.008 0.844 476
17 Hpx HPX 43 0.007 0.831 434
18 Itih2 43 0.008 0.831 452
19 Ttr 43 0.006 0.831 396
20 Ambp 42 0.006 0.818 348
21 Serpina1 42 0.005 0.818 334
22 Apoh 41 0.005 0.806 314
23 Fetub FETUB 40 0.006 0.794 316
24 Orm1 40 0.006 0.794 346
25 C3 39 0.005 0.783 314
26 A2m 38 0.005 0.771 292
27 Apoa4 37 0.005 0.761 272
28 Cp 37 0.004 0.761 258
29 Itih4 37 0.003 0.761 202
30 Afp 35 0.003 0.740 180
31 Apoe 34 0.003 0.730 180
32 App 34 0.003 0.730 198
33 Cpb2 34 0.003 0.730 180
34 Itih3 34 0.003 0.730 198
35 Serpinf2 34 0.005 0.730 254
36 Zpi 34 0.004 0.730 224
37 Fn1 33 0.003 0.720 188
38 Il6 33 0.003 0.720 170
39 Pzp 33 0.002 0.720 128
40 Apoc3 32 0.001 0.711 104
41 Clu 32 0.004 0.711 226
42 Serpina3c 32 0.002 0.711 106
43 Kng2 31 0.002 0.701 104
44 Timp1 31 0.003 0.701 164
45 A1bg 30 0.004 0.692 204
46 Fabp1 29 0.001 0.684 90
47 Serpina10 28 0.001 0.675 78
48 C4a 26 0.001 0.659 76
49 Serpina3m 25 0.001 0.651 96
50 Serpine1 25 0.002 0.651 100
51 C9 24 0.001 0.643 76
52 F13b 23 0.001 0.635 54
53 Spp2 23 0.000 0.635 36
54 Pros1 22 0.001 0.628 58
55 RGD1310507 17 0.000 0.593 8
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Note: Three queried genes appear in the third column.

Table 2. Biological processes related to the 24 top nodes of the analyzed network.

GO Term % AP Associated protein found
Cysteine-type endopeptidase inhibitor activity 5.33 [Ahsg, Fetub, Hrg, Kng1]
Acute-phase response 7.55 [Ahsg, F2, Hp, Orm1]
Regulation of plasma lipoprotein particle levels 4.69 [Apoa1, Apoa2, Apob]
Plasma lipoprotein particle organization 8.57 [Apoa1, Apoa2, Apob]
Plasma lipoprotein particle remodeling 14.29 [Apoa1, Apoa2, Apob]
Protein-containing complex remodeling 13.64 [Apoa1, Apoa2, Apob]
Regulation of lipid catabolic process 4.62 [Apoa1, Apoa2, Apoh]
Protein-lipid complex subunit organization 7.69 [Apoa1, Apoa2, Apob]
Neutral lipid catabolic process 10.81 [Apoa1, Apoa2, Apob, Apoh]
Glycerolipid catabolic process 6.35 [Apoa1, Apoa2, Apob, Apoh]
Positive regulation of lipid catabolic process 10.00 [Apoa1, Apoa2, Apoh]
Intermembrane lipid transfer 6.82 [Apoa1, Apoa2, Apob]
Protein-lipid complex remodeling 14.29 [Apoa1, Apoa2, Apob]
Sterol transporter activity 9.38 [Apoa1, Apoa2, Apob]
Acylglycerol catabolic process 10.81 [Apoa1, Apoa2, Apob, Apoh]
Lipid transfer activity 6.82 [Apoa1, Apoa2, Apob]
Triglyceride catabolic process 11.54 [Apoa1, Apob, Apoh]
Cholesterol efflux 6.25 [Apoa1, Apoa2, Apob]
Sterol transfer activity 13.64 [Apoa1, Apoa2, Apob]
Cholesterol transfer activity 14.29 [Apoa1, Apoa2, Apob]
Regulation of coagulation 10.34 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1]
Regulation of hemostasis 10.71 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1]
Protein activation cascade 41.67 [Apoh, Fga, Fgb, Fgg, Serpinc1]
Blood coagulation 5.78 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1, Serpind1]
Hemostasis 5.71 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1, Serpind1]
Heterotypic cell-cell adhesion 6.78 [Apoa1, Fga, Fgb, Fgg]
Negative regulation of coagulation 15.09 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg]
Positive regulation of coagulation 12.90 [Apoh, F2, Hrg, Plg]
Negative regulation of hemostasis 15.38 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg]
Positive regulation of hemostasis 14.29 [Apoh, F2, Hrg, Plg]
Platelet activation 5.88 [F2, Fga, Fgb, Fgg, Hrg]
Blood coagulation, fibrin clot formation 62.50 [Apoh, Fga, Fgb, Fgg, Serpinc1]
Regulation of response to wounding 4.46 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1]
Negative regulation of response to wounding 8.33 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg]
Positive regulation of response to wounding 4.60 [Apoh, F2, Hrg, Plg]
Regulation of blood coagulation 10.84 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1]
Platelet aggregation 6.12 [Fga, Fgb, Fgg]
Positive regulation of blood coagulation 14.29 [Apoh, F2, Hrg, Plg]
Negative regulation of blood coagulation 15.69 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg]
Regulation of heterotypic cell-cell adhesion 16.00 [Apoa1, Fga, Fgb, Fgg]
Regulation of wound healing 5.66 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg, Serpinc1]
Negative regulation of wound healing 10.26 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Kng1, Plg]
Positive regulation of wound healing 5.97 [Apoh, F2, Hrg, Plg]
Positive regulation of heterotypic cell-cell adhesion 21.43 [Fga, Fgb, Fgg]
Endothelial cell apoptotic process 5.08 [Fga, Fgb, Fgg]
Zymogen activation 5.97 [Apoh, Fga, Fgb, Fgg]
Fibrinolysis 31.82 [Apoh, F2, Fga, Fgb, Fgg, Hrg, Plg]
Negative regulation of fibrinolysis 42.86 [Apoh, Hrg, Plg]
Positive regulation of vasoconstriction 6.38 [Fga, Fgb, Fgg]
Regulation of fibrinolysis 27.27 [Apoh, Hrg, Plg]
Negative regulation of epithelial cell apoptotic process 5.56 [Fga, Fgb, Fgg]
Plasminogen activation 16.00 [Apoh, Fga, Fgb, Fgg]
Regulation of extrinsic apoptotic signaling pathway via death domain receptors 5.56 [Fga, Fgb, Fgg]
Regulation of endothelial cell apoptotic process 5.45 [Fga, Fgb, Fgg]
Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors 10.00 [Fga, Fgb, Fgg]
Negative regulation of endothelial cell apoptotic process 9.38 [Fga, Fgb, Fgg]
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Note: Term P value, term P value corrected with Bonferroni step down, group P value, and group P value corrected with Bonferroni step down were less than 0.001. The four groups are shown in different colors and the name of the groups is bolded. %AP refers to the percentage of associated proteins.

Discussion

As it is shown in Figure 1, the collection of queried DEPs and the added first neighbor proteins formed a scale-free network. In the many published documents, the importance of the scale-free network in the interpretation of the molecular mechanism is emphasized. Centrality analysis revealed that there are numbers of critical nodes that can play a role as central nodes. As it is represented in Table 1, FETUA and FGG are the top nodes based on degree value and also betweenness centrality; therefore, these two queried DEPs can be considered as hub-bottlenecks. Like FETUA and FGG, PLG and FGA appear as hub-bottleneck nodes (see Figure 1 and Table 1). Based on these results, it is possible that the two queried DEPs (FETUA and FGG) and also the two added first neighbors (PLG and FGA) be considered as the critical proteins which are dysregulated by laser irradiation.

As it is depicted in Table 2, the group of terms, which is categorized as “neutral lipid catabolic process” is not related to the central proteins. Then this group of terms was ignored from more analysis. FETUA (AHSG) is involved in the two terms of the top two groups of the term including “cysteine-type endopeptidase inhibitor activity” and “acute-phase response”. FGG and PLG are involved in the 29 and 20 terms of the last group of terms respectively. FGA, the last central protein, is related to the 29 terms of the last group of terms.

It can be concluded that the critical group of terms is the group which is classed as “fibrinolysis” and the crucial proteins are FGG, FGA, and PLG. FGG is a queried up-regulated DEP and FGA and PLG are two added first neighbors. Fibrinolysis is a significant term which effects on hemostatic balance. 23 Fibrinolysis and platelet function are tied together to control blood coagulation. 24 As it can be seen in Table 2, most terms in the “fibrinolysis” group are characterized by platelet, coagulation, hemostasis, and fibrinolysis. The important role of fibrinogen in blood hemostasis and the pathological condition due to dysregulation of fibrinogen expression is described in many documents. Dysregulation of fibrinogen expression is highlighted in COVID-19 infection disease. 25 Plasminogen is an inactive form of plasmin, the enzyme that degrades fibrin and activates matrix metalloproteinases, which leads to extracellular matrix degradation. 26 The closed relationship between plasminogen and fibrinogen in body hemostasis was studied about 50 years ago and is a well-known association. 27

Many effects such as wound healing, improving muscular function, effect on bacterial growth, effect on oral mucositis in cancer patients, bone formation, dentistry, and many other subjects are attributed to LLLT. 28-31 In the present study, a significant effect of LLLT on blood hemostasis is highlighted. This finding may be a crucial point in the application of LLLT as a therapeutic tool or in preventing the disadvantage of using LLLT.

Conclusion

It can be concluded that LLLT results in the modulation of body blood hemostasis. Fibrinogen and plasminogen were highlighted as the two important elements in this process.

Ethical Considerations

This project was approved by the ethical committee of Shahid Beheshti University of Medical Sciences. (code: IR.SBMU.RETECH.REC.1400.006)

Conflict of Interests

There is no conflict of interest.

Acknowledgment

This project is supported by Shahid Beheshti University of Medical Sciences.

Please cite this article as follows: Arjmand B, Khodadoost M, Jahani Sherafat S, Rezaei Tavirani M, Ahmadi N, Okhovatian F, Rezaei Tavirani M. Low-level laser therapy effects on rat blood hemostasis via significant alteration in fibrinogen and plasminogen expression level. J Lasers Med Sci. 2021;12:e59. doi:10.34172/jlms.2021.59.

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