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There Are No or a Little Increase, Then Decrease of Changed Values of Specific Cytokines In or Markers on Helper or iNK T Cells Dependently on Their Initial Values Not to Be Detected by Conventional Analyses

Submitted:

01 December 2024

Posted:

02 December 2024

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Abstract
Background: It is our purpose to identify changes in expression of cytokines in or markers on helper T cells by treatments not to be identified by conventional statistical analysis. Methods: The studies showing cytokines or markers’ values in individual subjects before and after treatments were selected from the PubMed Central® databases with the keywords of “CD4 cells”. The significant differences from control values but no changes between before and after treatments with conven-tional statistical analyses were selected from the studies to be examined with Ishida’s t-test1. Results: Only Ishida’s t-test1 suggested that Sema4A-PlexinD1 axis with anti-plexinD1 antibody, Cat-PAD, and Intravenous immunoglobulin increased (when initial values X < X-intercept values γ) and de-creased (when X > γ) the changed value of expression of GATA3 mRNA in Th2 cells, interleukin-22 and PlexinB1 mRNA in Th17 cells of healthy subjects, CRTh2 on Th2 cells of cat allergen patients, and Cytotoxic T lymphocyte antigen 4 on helper T cells and Programmed death 1 on invariant natural killer T cells of common variable immunodeficiency patients, respectively. Conclusion: Ishida’s t-test1 also should be used for analysis of the change of expression of specific cytokines in or markers on helper T cells with conventional statistical analysis.
Keywords: 
α-plexinD1
;  
cat-PAD
;  
IVIg
;  
PlexinD1
;  
PlexinB1
;  
CRTh2
;  
CTLA4
;  
PD-1
;  
CD4 cells
;  
Th1
;  
Th2
;  
Th17
;  
helper T cells
;  
cat allergen
;  
CVID
;  
regression lines between X and C
;  
Ishida’s t-test1
Subject: 
Biology and Life Sciences  -   Immunology and Microbiology

1. Introduction

According to Sun, et al., T cells are crucial for immune functions to maintain health and prevent disease. T cell development occurs in a stepwise process in the thymus and mainly generates CD4+ and CD8+ T cell subsets. Upon antigen stimulation, naïve T cells differentiate into CD4+ helper and CD8+ cytotoxic effector and memory cells, mediating direct killing, diverse immune regulatory function, and long-term protection. In response to acute and chronic infections and tumors, T cells adopt distinct differentiation trajectories and develop into a range of heterogeneous populations with various phenotype, differentiation potential, and functionality under precise and elaborate regulations of transcriptional and epigenetic programs. [1]
According to Luo, et al., immune response in the asthmatic respiratory tract is mainly driven by CD4+ T helper (Th) cells, represented by Th1, Th2, and Th17 cells. Asthma is a heterogeneous and progressive disease, reflected by distinct phenotypes orchestrated by Th2 or non-Th2 (Th1 and Th17) immune responses at different stages of the disease course. Heterogeneous cytokine expression within the same Th effector state in response to changing conditions in vivo and interlineage relationship among CD4+ T cells shape the complex immune networks of the inflammatory airway, making it difficult to find one panacea for all asthmatics [2].
Thus, it is important to know exactly the difference in changes in cytokines in or markers on helper T cells with treatments. However, as already we mentioned in the previous paper [3], Paris et al. [4] reported that combination antiretroviral therapy (cART) did not change the percent of PD-1highCTLA-4lowCD127high early/intermediated CD4+ T cells of human immunodeficiency virus (HIV) infected patients (n=14, p=0.194 with Wilcoxon signed-rank test [5]) but increased the percent of the marker limited to initial CD4 counts less than 200 (n=9, p=0.0273 with Wilcoxon signed-rank test [5]). Thus, we hypothesized that there were two kinds of patients, that one was the patients whose initial percent of the marker was less than a particular value and the other was the patients whose initial percent of the marker was greater than the particular value, that the treatment increased percent of the marker of the former and decreased percent of the marker of the latter and that by treating the increase and decrease as the same way as variation, Wilcoxon signed-rank test [5] misled Paris et al. that cART did not increase percent of the marker with signed-rank without separating its increase reaction and decrease reaction, and that such a reaction was not detected with the regular regression line between the initial marker values and the marker values after a treatment [6].
Thus, we searched figures with keyword of “CD4 cells” in all fields of PMC database and selected literatures showing cytokines or markers’ values in individual subjects before and after treatments and extracted literatures having the significant differences from control values of such as medium or placebo values but no changes between before and after treatments with conventional statistical analyses from the studies and examined p-values for the changes dependent on initial values with Ishida’s t-test1 [3]. In this paper, we will report the results.

2. Methods

2.1. Terminology

Data Sources, Regression line between initial values (X) and changed values (C), C axis-intercept (α), slope of the regression line (β), X axis-intercept (γ), p value for β, Ishida’s t-test1, Mean of positive expected value of C (Mp), Mean of negative expected value of C (Mn), Mean of expected value of C (Me), t-value for Me, p value for Me, Validation of Ishida’s t-test1, RTM (the measurement errors due to double measurement), Conventional statistical analysis were described in the previous paper [3].

2.2. Calclation

The α, β and p value for β were obtained by inputting the values before and after treatments into SAS JMP 10 (Corporate Headquarters 100 SAS Campus Drive Cary, NC 27513, USA), Mp, Mn, Me, t-value and p-value for Me were obtained by inputting the values before and after treatments, the number of degrees of freedom and t-value for Me into Excel 2019 T.DIST.2T function, where the formulae for Ishida’s t-test1 described in the previous paper [3] were incorporated. A p-value for β of <0.05 and p-value for Me of <0.05 were considered statistically significant.

2.3. Selection of Literatures and Extraction of the Objective Dynamics

The studies showing cytokines or markers’ values in individual subjects before and after treatments were selected from the PubMed Central® databases with the keywords of “CD4 cells”. Then the significant differences from control values such as medium or placebo values to excluded RTM [7,8] but no changes between before and after treatments with conventional statistical analyses were selected from the studies. Then the no changes were examined with the regression line whether the values of changes were dependent on initial values. Lastly the no changes dependent on initial values were examined with Ishida’s t-test1 [3].

2.4. Risk of Bias in Methods

There was risk of bias due to limitation of databases, limitation of numbers of subjects whose data were able to be estimated from the spots or lines drawn in figures and its estimation errors and limitation of literatures showing RTM [7,8].

3. Results

3.1. Selection of Literatures and Extraction and Examination of Dynamics

According to the procedure described in Methods and Figure 1, we searched figures with keyword of “CD4 cells” to obtain 172,714 figures concerning CD4 cells and its related cells from PMC databases, selected literatures showing cytokines or markers values in individual subjects before and after treatments from the figures to obtain 140 literatures, extracted literatures having the significant differences from control values such as media or placebo values from the literatures to obtain 3 literatures, extracted dynamics having no changes between before and after treatments described in the literatures to obtain 25 dynamics (See Table 1).
We examined p-values for slope (β) of the regression lines between initial values (X) and changed values (C) of the 25 dynamics with regression line analysis (See Figure 2 and the left column in Table 2) and then, we calculated p-values for the C having the p-values of <0.05 for β with Ishida’s t-test1 to obtain 6 objective dynamics (001i, 0.01F, 001M, 003J, 016e, and 016k) from the 25 dynamics (See the right column in Table 2).
According to regression line analysis and Ishida’s t-test1 analysis, in 001F dynamics, Sema4A-PlexinD1 axis decreased changed values of 2-ΔCt of intracellular GATA3 in Th2 cells from healthy naïve subjects with anti plexinD1 antibody dependently on their initial values and in the rest 5 (001i, 001M, 003J, 016e and 016k) dynamics, Sema4A-PlexinD1 axis, Cat-PAD, and IVIg increased changed value of 2-ΔCt of IL22 mRNA and PLEXIND1 mRNA in Th17 cells from healthy naive subjects with anti plexinD1 antibody, MFI of CRTh2 on Th2 cells from cat allergen patients, % of CTLA4 on CD4 cells and PD-1 on iNKT cells from CVID patients when their initial values were less than γ, and then decreased their changed values when their initial values were bigger than γ, dependently on their initial values (See the Figure 2 and the left column in Table 2).
There was risk of bias in these results due to limitation of databases, limitation of numbers of subjects whose data were able to be estimated from the spots or lines drawn in figures and its estimation errors, and limitation of validity of Ishida’s t-test1.

4. Discussions

Immune responses vary from person to person. Thus, it is necessary to measure the immune response before and after treatment in each individual.
The measurement of the immune response before and after treatment in each individual includes RTM error [7, 8] based on double measurements. However, there was no literature in 140 literatures having control which measured the values twice before treatments to know the size of RTM error [7, 8]. There were only 3 literatures [9, 11, 24] having control which measured the values of media or placebo which excluded the changes of experimental group less than those of media or placebo with RTM error [7, 8]. There were many literatures in 140 literatures having comparison of changes between experimental groups, which also solved RTM error [7, 8] but no significant difference between two groups does not mean no change of immune responses in the group between before and after treatments.
There is another error based on conventional statistics misinterpreting the changes depend on the initial value as random changes described in our previous paper [3]. The 3 literatures have doubt within each two judgment or between two judgments on observation as follows.
Concerning 001i dynamics, there was doubt between judgment 1 of “the Sema4A-PlexinD1 axis did not increase 2-ΔCt of IL22 mRNA in Th1 cells and Th2 cells but increased that in Th17 cells from healthy naive subjects at presence of IgG for 7 days after treatment significantly more than that in media and also did not increase that in the Th1 cells, Th2 cells, and Th17 cells from the subjects at presence of anti-PLEXIND1 antibody for 7 days after treatment significantly more than that in media” and judgment 2 of “there was no difference between the 2-ΔCt in Th17 cells at presence of IgG and at presence of anti-PLEXIND1 antibody” according to a Parametric ANOVA test [151] analysis shown in Figure 2 described in Carvalheiro et al. literature [9]. However, judgment 3 of “the 2-ΔCt in Th17 cells with the axis at presence of anti-PLEXIND1 antibody was significantly lower than that at presence of IgG” according to Ishida’s t-test1 [3] analysis instead of the judgment 2 resolved the above doubt.
Concerning 001F dynamics, there was doubt within each judgment 1 of “the Sema4A-PlexinD1 axis did not increase 2-ΔCt of GATA3 mRNA in Th1 cells and Th17 cells but increased that in Th2 cells from healthy naive subjects at presence of IgG for 7 days after treatment significantly more than that in media and also did not increase that in the Th1 cells and Th17 cells but increased that in Th2 cells from the subjects at presence of anti-PLEXIND1 antibody for 7 days after treatment significantly more than that in media” and judgment 2 of “there was no difference between the 2-ΔCt in Th2 cells at presence of IgG and at presence of anti-PLEXIND1 antibody” according to a Parametric ANOVA test [151] analysis shown in Figure 4B described in Carvalheiro et al. literature [9]. The data resolving the doubt of the judgment 1 was not shown in the Figure but judgment 3 of “the 2-ΔCt in Th2 cells with the axis at presence of anti-PLEXIND1 antibody was significantly lower than that at presence of IgG” according to Ishida’s t-test1 [3] analysis instead of the judgment 2 resolved the doubt of the judgment 2.
Concerning 001M dynamics, there was doubt between judgment 1 of “the Sema4A-PlexinD1 axis did not increase 2-ΔCt of PLEXINB1 mRNA in Th1 cells and Th2 cells but increased that in Th17 cells from healthy naive subjects at presence of IgG for 7 days after treatment significantly more than that in media and also did not increase that in the Th1 cells, Th2 cells, and Th17 cells from the subjects at presence of anti-PLEXIND1 antibody for 7 days after treatment significantly more than that in media” and judgment 2 of “there was no difference between the 2-ΔCt in Th17 cells at presence of IgG and at presence of anti-PLEXIND1 antibody” according to a Parametric ANOVA test [151] analysis shown in Figure 2 described in Carvalheiro et al. literature [9]. However, judgment 3 of “the 2-ΔCt in Th17 cells with the axis at presence of anti-PLEXIND1 antibody was significantly lower than that at presence of IgG” according to Ishida’s t-test1 [3] analysis instead of the judgment 2 resolved the doubt.
Concerning 003J dynamics, there was doubt between judgment 1 of “there was no difference between MFI of CRTh2+ in Th2 cells from cat allergen patients before and after vaccination with the placebo vaccine and there was also no difference between the MFI before and after vaccination with Cat-PAD (a Fel d1 synthetic peptide vaccine for cat allergy)” and judgment 2 of “there was significant difference between the MFI after vaccination with the placebo vaccine and Cat-PAD” according to a Parametric ANOVA test [151] analysis shown in Figure 5 described in Rudulier et al. literature [11]. However, judgment 3 of “there was no difference between MFI of CRTh2+ in Th2 cells from cat allergen patients before and after vaccination with the placebo vaccine but there was difference between the MFI before and after vaccination with Cat-PAD” according to Ishida’s t-test1 [3] analysis instead of the judgment 1 resolved the doubt.
Concerning 0016e dynamics, there was doubt between judgment 1 of “the values of % of CTLA-4 on CD4 T cells from CVID patients were significantly bigger than that from healthy subjects according to Mann Whitney test [152] analysis” and judgment 2 of “IVIg did not decrease significantly the % from CVID patients according to Wilcoxon marched-pairs signed rank test [5] analysis shown in Figure 3B described in Paquin-Prouclx et al. literature [24], although IVIg is used to treat various autoimmune diseases [150 Velikova et al.]. However, judgment 3 of “there was no difference between MFI of CRTh2+ in Th2 cells from cat allergen patients before and after vaccination with the placebo vaccine but there was difference between the MFI before and after vaccination with Cat-PAD” according to Ishida’s t-test1 [3] analysis instead of the judgment 1 resolved the doubt.
Concerning 0016k dynamics, there was doubt between judgment 1 of “the values of % of PD-1 on iNKT cells from CVID patients were significantly bigger than that from healthy subjects according to Mann Whitney test [152] analysis” and judgment 2 of “IVIg did not decrease significantly the % from CVID patients according to Wilcoxon marched-pairs signed rank test [5] analysis shown in Figure 3B described in Paquin-Prouclx et al. literature [24], although IVIg is used to treat various autoimmune diseases [150 Velikova et al.]. However, judgment 3 of “there was no difference between MFI of CRTh2+ in Th2 cells from cat allergen patients before and after vaccination with the placebo vaccine but there was difference between the MFI before and after vaccination with Cat-PAD” according to Ishida’s t-test1 [3] analysis instead of the judgment 1 resolved the doubt.
Thus, it is important to apply Ishida’s t-test1 for analysis of dynamics having the significant differences from control values and having no changes between before and after treatments with conventional statistical analysis.
Variance of 001M dynamics in parametric ANOVA test was equal to (Σ(Ci-M)2)/5. Against this, variance in Ishida’s t-test1 was equal to (Σ(Ci-Ei)2)/5. The latter was much smaller than the former for p-value to be less than 0.05 (See Figure 3).
That is, the 3 literatures [9,11,24] with Ishida’s t-test [3] analysis showed that T helper cells from healthy naive subjects were differentiated to Th1 cells, Th2 cells and Th17 cells with the Sema4A-PlexinD1axis not to increase 2-ΔCt of IL22 mRNA and 2-ΔCt of PLEXINB1 mRNA in a part of T helper cells (defined as Th1 cells) and another part of T helper cells (defined as Th2 cells) but to increase those in another part of T helper cells (defined as Th17 cells) and not to increase 2-ΔCt of GATA3 mRNA in the cells defined as Th1 cells and Th17 cells but to increase that in the cells defined as Th2 cells and a Fel d1 synthetic peptide vaccine for cat allergy decreased the increase of MFI of CRTh2+ in Th2 cells from cat allergen patients and IVIg decreased the increase of % of CTLA-4 on CD4 T cells and PD-1 on iNKT cells from CVID patients.

4. Conclusions

Thus, it is important to apply also Ishida’s t-test1 for analysis of dynamics having the significant differences from control values such as media or placebo values and having no changes between before and after treatments with conventional analysis.

Funding

Research funding was not provided by any organization.

Author Contributions statement: T.I. designed and run the study and wrote the manuscript. K.T., G.W., N.T., J.K., H.T., Y.G., N.M. contributed to discussions for the study. All authors reviewed the manuscript.

Acknowledgments

T.I. thanks Mr. Jun-ichi Takagi, Chairman of SUMS for his financial support.
Competing interests: The authors declare no competing interests associated with this manuscript.

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Figure 1. Procedures for selection, extraction and examination of the objective dynamics in subjects.
Figure 1. Procedures for selection, extraction and examination of the objective dynamics in subjects.
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Figure 2. Regression line between initial values and changed values of the 25 dynamics described in Table 1.
Figure 2. Regression line between initial values and changed values of the 25 dynamics described in Table 1.
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Figure 3. The regression line between the initial value and the changed value of 2-ΔCt of IL22 mRNA in Th17 cells from healthy naive subjects with Sema4A-PlexinD1 axis treatment. The close circles, open circles, and cross indicated each measurement value (C1~C6), each expected value on the regression line in Ishida’s t-test1(E1~E6), the mean (M) of each measurement value of 2-ΔCt of IL22 mRNA at those each initial value (X1~X6), respectively.
Figure 3. The regression line between the initial value and the changed value of 2-ΔCt of IL22 mRNA in Th17 cells from healthy naive subjects with Sema4A-PlexinD1 axis treatment. The close circles, open circles, and cross indicated each measurement value (C1~C6), each expected value on the regression line in Ishida’s t-test1(E1~E6), the mean (M) of each measurement value of 2-ΔCt of IL22 mRNA at those each initial value (X1~X6), respectively.
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Table 1. Contents of the 25 dynamics with their source.
Table 1. Contents of the 25 dynamics with their source.
Name of dynamics　 Contents and source of dynamics
001a, b, c, d, f, i, u, v, x, B, F, M, X, Y The significant differences from control (medium) values but no changes of 2−ΔCt of IFNγ mRNA in Th1 cells (a), TNF mRNA in Th1 cells (b), Th2 cells (c), Th17 cells (d), IL4 mRNA in Th2 cells (f), IL22 mRNA in Th17 (i), TBX21 mRNA in Th2 cells (u), Th17 cells (v), GATA3 mRNA in Th2 cells (x), TBX21 mRNA in Th1 (B) from healthy naïve subjects for 7days, GATA3 mRNA in Th2 cells from healthy naïve subjects for 3 days (F), plexinD1 mRNA in Th17 cells (M), plexinB2 mRNA in Th1 cells (X), plexinB2 mRNA in Th2 cells (Y) from healthy naïve subjects for 7days between presence of IgG and anti plexinD1 antibody with Sema4A-PlexinD1 axis by a parametric ANOVA analysis shown in Figure 2 (a,b,c,d,f,i), 4A (u,v,x), 4B (B,F), 5A (M), 5B (X,Y) described by Carvalheiro et al., respectively [9].
003J The significant differences from control (vs Cat-PAD) values but no changes of MFI of CRTh2+ in Th2 cells from cat allergen patients between before and after Cat-PAD (a Fel d 1 synthetic peptide vaccine for cat allergy) with a paired t-test analyses shown in Figure 5 by Rudulier et al. [11].
016b, c, d, e, g, h, i, j, k, l The significant differences from control (healthy subjects) values but no changes of % of Ki67+ (b), CD38+HLA-DR+(c), PD-1+ (d), CTLA4+ (e), CD83+ (g), MFI of CD86+ (h) in CD4 cells, % of iNKT+ in T cells (i), HLA-DR+ (j) in CD4 cells, PD-1+ (k) in iNKT cells, Treg cells in T cells (l) from CVID patients between before and after IVIg treatments with conventional analyses shown in Figure 2B (b), 2C (c), 3A (d), 3B (e), 4E (g), 4F (h), 6B (i), 6C (J), 6E (k), 7B (l) described by Paquin-Proulx et al., respectively [24].
Remarks. 2ΔCt: Unite for comparative quantitative method, IL22: Interleukin 22, Th17 cell: T helper 17 cell, plexinD1: Cell surface receptor for SEMA4A, MFI: Mean fluorescence intensity, CRTh2: One of the seven transmembrane G protein-coupled receptors (GPCRs) and a chemoattractant receptor expressed in Th2 cells, Th2 cells: T helper 2 cell, Ki67: Indicator showing the proliferative power (proliferation speed) of cancer cells, CD83: Dendritic cell maturation markers, CTLA4: Cytotoxic T-lymphocyte antigen-4 and a protein that suppresses the activation of T cells that attack cancer cells, PD-1: Programmed cell death-1 and a transmembrane protein that suppresses immune responses expressed on the surface of immune cells such as T cells and pro-B cells, and plays a role in suppressing lymphocyte activation signals. CD4: A 55kDa transmembrane glycoprotein expressed on helper T cells, CVID: Common variable immunodeficiency, IVIg: Intravenous immunoglobulin.
Table 2. Analysis of the 25 dynamics and oo3I dynamics with regression line and Ishida’s t-test1 analysis.
Table 2. Analysis of the 25 dynamics and oo3I dynamics with regression line and Ishida’s t-test1 analysis.
Grp Regression line analysis Ishida’s t-test1 analysis
β α γ SDβ pβ Mp Mn Me SDMe pMe
001a 0.583639 -0.965513 1.65429 0.275019 0.1011 － － － － －
001b -0.342565 0.2412552 0.70426 0.413352 0.4538 － － － － －
001c -0.435417 0.0547073 0.12564 0.292764 0.2112 － － － － －
001d -0.529443 0.42177 0.79662 0.286389 0.1382 － － － － －
001f 0.1322071 0.0149402 -0.11300 0.235374 0.6043 － － － － －
001i -0.759324 0.0235881 0.03106 0.087184 0.0010 0.002519 -0.05979 -0.057276 0.018451 0.000625
001u -0.29946 0.0853122 0.28488 0.458729 0.5495 － － － － －
001v -0.130475 0.1034944 0.79321 0.311543 0.6969 － － － － －
001x -0.166337 0.9113585 5.47898 0.323471 0.6342 － － － － －
001B 0.141773 1.4551323 -10.2638 0.822327 0.8790 － － － － －
001F -0.94878 63.240418 66.6544 0.1289 0.0180 0 -20.625 -20.625 3.610198 0.001438
001M -0.625954 0.0107729 0.01721 0.135225 0.0098 0.000491 -0.018408 -0.017916 0.008157 0.002990
001X -0.341355 1.2472585 3.65384 0.282244 0.3501 － － － － －
001Y 0.7407407 -1.665185 2.24799 0.436955 0.2321 － － － － －
003I -0.003816 -2.374166 -622.160 0.144456 0.9794 － － － － －
003J -0.763118 1250.5817 1638.77 0.114928 <0.0001 13.03715 -346.3704 -333.3333 347.2614 0.006769
016b -0.664866 2.256452 3.39384 0.410998 0.1498 － － － － －
016c -0.395623 .4300786 1.08709 0.577484 0.5153 － － － － －
016d 0.3148861 -10.08834 32.0380 0.42777 0.4856 － － － － －
016e 0.749586 .3172256 -0.42320 0.181662 0.0044 0.181703 -1.888369 -1.706666 2.165108 0.045616
016g -1.663256 919.90458 553.074 0.755043 0.0635 － － － － －
016h -1.021933 150.67662 147.442 0.406617 0.0402 36.86415 -47.14193 -10.27777 117.5184 0.799665
016i -0.290638 0.0111808 0.03846 0.259921 0.3004 － － － － －
016j -0.3528 24.963342 70.7577 0.334907 0.3271 － － － － －
016k -1.041275 4.4103124 4.23549 0.232528 0.0029 1.354406 -13.83588 -12.48148 10.52224 0.007415
016l -0.511875 0.6682842 1.30556 0.355122 0.1927 － － － － －
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