Skip to content

Advertisement

  • Research
  • Open Access

δ-Tocotrienol feeding modulates gene expression of EIF2, mTOR, protein ubiquitination through multiple-signaling pathways in chronic hepatitis C patients

  • 1Email author,
  • 2,
  • 2,
  • 1, 3, 4,
  • 1, 3 and
  • 1, 5
Lipids in Health and Disease201817:167

https://doi.org/10.1186/s12944-018-0804-7

  • Received: 17 April 2018
  • Accepted: 26 June 2018
  • Published:

Abstract

Background

δ-Tocotrienol is a naturally occurring proteasome inhibitor, which has the capacity to inhibit proliferation and induce apoptosis in several cancer cells obtained from several organs of humans, and other cancer cell lines. Moreover, results of plasma total mRNAs after δ-tocotrienol feeding to hepatitis C patients revealed significant inhibition in the expression of pro-inflammatory cytokines (TNF-α, VCAM1, proteasome subunits) and induction in the expression of ICAM1 and IFN-γ after post-treatment. This down-regulation of proteasome subunits leads to autophagy, apoptosis of immune cells and several genes. The present study describes RNA-sequence analysis of plasma total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients on gene expression regulated by proteasome.

Methods

Pooled specimens of plasma total mRNAs of pre-dose versus post-dose of δ-tocotrienol treatment of hepatitis C patients were submitted to RNA-sequence analyses. The data based on > 1 and 8-fold expression changes of 2136 genes were uploaded into “Ingenuity Pathway Analyses (IPA)” for core analysis, which describes possible canonical pathways, upstream regulators, diseases and functional metabolic networks.

Results

The IPA of “molecules” indicated fold change in gene expression of 953 molecules, which covered several categories of biological biomarkers. Out of these, gene expression of 220 related to present study, 12 were up-regulated, and 208 down-regulated after δ-tocotrienol treatment. The gene expression of transcription regulators (ceramide synthase 3 and Mohawk homeobox) were up-regulated, and gene expression of 208 molecules were down-regulated, involved in several biological functions (HSP90AB1, PSMC3, CYB5R4, NDUFB1, CYP2R1, TNFRF1B, VEGFA, GPR65, PIAS1, SFPQ, GPS2, EIF3F, GTPBP8, EIF4A1, HSPA14, TLR8, TUSSC2). IPA of “causal network” indicated gene regulators (676), in which 76 down-regulated (26 s proteasomes, interleukin cytokines, and PPAR-ligand-PPA-Retinoic acid-RXRα, PPARγ-ligand-PPARγ-Retinoic acid-RARα, IL-21, IL-23) with significant P-values. The IPA of “diseases and functions” regulators (85) were involved with cAMP, STAT2, 26S proteasome, CSF1, IFNγ, LDL, TGFA, and microRNA-155-5p, miR-223, miR-21-5p. The IPA of “upstream analysis” (934) showed 57 up-regulated (mainly 38 microRNAs) and 64 gene regulators were down-regulated (IL-2, IL-5, IL-6, IL-12, IL-13, IL-15, IL-17, IL-18, IL-21, IL-24, IL-27, IL-32), interferon β-1a, interferon γ, TNF-α, STAT2, NOX1, prostaglandin J2, NF-κB, 1κB, TCF3, and also miRNA-15, miRNA-124, miRNA-218-5P with significant activation of Z-Score (P < 0.05).

Conclusions

This is first report describing RNA-sequence analysis of δ-tocotrienol treated plasma total mRNAs obtained from chronic hepatitis C patients, that acts via multiple-signaling pathways without any side-effects. These studies may lead to development of novel classes of drugs for treatment of chronic hepatitis C patients.

Keywords

  • δ-Tocotrienol
  • Chronic hepatitis C
  • RNA-sequence
  • Gene expression of biomarkers
  • Causal network
  • Diseases and functions
  • Up-stream regulators
  • Canonical pathways

Background

We have recently reported that δ-tocotrienol is a potent anti-cancer agent (liver, pancreas, prostrate, breast cancer cell lines, Hela, melanoma, B lymphocytes and T-cells), and also a modulator of proteasome function, as compared to other outstanding proteasome inhibitors (thiostrepton, 2-methoxyestradiol, and quercetin) [1]. Moreover, plasma total mRNAs obtained from δ-tocotrienol treated hepatitis C patients showed significant inhibition in the expression of pro-inflammatory cytokines (TNF-α and VCAM-1), and induction in expression of ICAM-1, IFN-γ, whereas proteasome subunits X, Y, Z, LMP7, LMP2, LMP10 (22–44%) were significantly inhibited compared to pre-dose values, and this down-regulation of proteasome subunits leads to autophagy and apoptosis of cells [1]. The present study is an extension of these findings to study the effect of δ-tocotrienol (Fig. 1) treatment of chronic hepatitis C patients in their plasma mRNAs using RNA-Sequencing by Ingenuity Pathway Analysis (IPA). The viral infection with hepatitis C is responsible for a vast majority of chronic hepatitis cases over 180 million people worldwide, which is further supported by epidemiological and clinical studies have also demonstrated a causative role of viral infection of hepatitis C in the development of hepatocellular carcinoma [2]. These figures are alarming, as patients currently asymptomatic with relatively mild disease may eventually progress to complications of chronic liver diseases, like cirrhosis, and hepatocellular carcinoma [3]. The mechanisms of liver disease are not fully understood.
Fig. 1
Fig. 1

Chemical structure of δ-tocotrienol (similar figure was published in our publication-54. Qureshi et al., Journal of Clinical & Experimental Cardiology. 2015;6:4. https://doi.org/10.4172/2155-9880.1000367 [54]

The mechanisms that contribute to the pathogenesis of hepatitis virus-related liver infections are diverse and very complex. Investigation of altered cellular mechanisms through gene profiling techniques has improved the clear understanding of various disease processes and development of novel therapeutic targets [4]. Earlier, techniques applied for studying gene expression profiling included microarrays, which analyzes quantitative expression of thousands of genes, and time consuming real-time PCR assays that gives only small number of expression of genes. These tools have been used previously for identification of differentially expressed genes in hepatitis C virus associated cirrhosis and carcinoma [5]. In summary, these changes in gene expression were associated with immune response, fibrosis, cellular growth, proliferation, and apoptosis [57]. Nowadays, similar estimation carried out by RNA-sequence procedure, which will provide very accurate gene expression of several virus important biological functions and biomarkers.

The genotype hepatitis C is an important determinant of the response to treatment, and differences found in clinical outcomes of the disease with respect to infection of various genotypes [68]. The genotype 3 is the most prevalent genotype around the world compared to other genotype infection [8]. In the present study we will identify altered cellular processes in chronic hepatitis C patients after treatment with δ-tocotrienols. The main purpose of this preliminary study was to isolate plasma total mRNAs from a few participants after δ-tocotrienol treatment of chronic hepatitis C patients, and to carry out RNA-sequence analysis, which quantified mRNA expression of a large number of genes in pooled specimens of pre-dose versus post-dose of δ-tocotrienol treatment of chronic hepatitis C patients. The gene expression data was analyzed by “Ingenuity Pathway Analysis”, which would reveal the cellular and biological mechanisms at the molecular level in plasma total mRNAs obtained from chronic hepatitis C patients.

Methods

Materials

DeltaGold 125 mg softgels from annatto seeds (typical composition 90% δ-tocotrienol and 10% γ-tocotrienol) were supplied by American River Nutrition, Inc. (Hadley, MA, USA). RNeasy mini kit was obtained from QIAGEN Sciences (Germantown, MD, USA).

Impact of δ-tocotrienol in chronic hepatitis C patients

The study was carried out in Pakistan Ordinance Factory (POF) Hospital, Wah Cantonment, Rawalpindi, Pakistan; in collaboration with department of biomedical Sciences, University of Missouri-Kansas City, MO, USA. The study protocol was registered (IRB # 129–2015) was approved by Institutional Review Board of POF, Rawalpindi, Pakistan. The study was carried out under a FDA approved IND number 36906. The hepatitis C antibody test was purchased from Sigma Chemical Co., St. Louis, USA. The second diagnosing hepatitis C test is RNA PCR test was obtained from the EDTA treated fresh whole blood by using total RNA purification kit # 17200 (NORGEN Bioteck Corporation, Thorold, ON, Canada).

RNA-Sequence Analyses of plasma total RNAs obtained from EDTA treated whole blood after feeding δ-tocotrienol for 6-weeks to hepatitis C patients

The details of study design, inclusion/exclusion criteria, experimental design, and physical characteristics of hepatitis C patients were same as reported [1]. In short, the total mRNA was extracted from plasma of EDTA treated fresh whole blood of each hepatitis C patients (n = 14) fed δ-tocotrienol (500 mg/d) for 6 weeks by total RNA purification kit (NORGEN Bioteck Corporation, Thorold, ON, Canada). The purity of total RNAs (stored − 80 °C) was estimated by the ratios of 260/280 (2.02–2.08) of all samples, which was determined using Thermo Scientific NanoDrop 1000 Spectrophotometer. The mRNAs samples from Pakistan were brought in person (by Dr. Dilshad A. Khan in dry ice to avoid any degradation of RNAs) to UMKC, Medical School after approval by (Compliance officer Mr. Christopher Winders, and Chemical/Biological Safety officer Mr. Mike Philips) members of University of Missouri Kansas City institutional review board.

The results of most important cytokines and other biomarkers associated with the present investigation were estimated by real-time RT-PCR by using plasma total RNAs purified from pre-dose versus post-dose samples after feeding δ-tocotrienol for 6-weeks to chronic hepatitis C patients has been published recently [1], therefore present manuscript lacks in vitro estimations of RT-PCR data. The same plasma total RNAs were used in the present study.

The RNA-Sequence analyses were carried out at Division of Experimental and Translational Genetics, Children’s Mercy Hospital, Kansas City, MO. Five randomized samples selected of total RNAs of hepatitis C patients, and combined. Total mRNAs of combined samples were purified by Biostic Blood Total RNA Isolation Kit (MOBIO Laboratories, Inc). The purified total mRNAs were further purified and concentrated to 10.0 μl by using by Gene Jet RNA Clean up and Concentration Micro Kit (Thermo Scientific, EU, Lithuania). The purity of these RNAs was further determined in the Division of Experimental and Translational Genetic & Core of Omic Research (The Children Mercy Hospital, Kansas City, MO) by their own instruments for quality control and quantity of each sample to make sure that each sample is up to standard before putting into a NGS run. The concentrated total mRNAs of each set was converted to cDNA, and total RNA-Seq carried out. Gene expression level and fold change (post vs pre-dose) of FPKM were calculated at > 1, > 2, or > 5 levels at 2-fold, 4-fold, and 8-fold after filtering several million fold up-regulated and down-regulated genes (Table 1).
Table 1

Estimation of basic RNA-sequence expresion unit (FPKM) of δ-tocotrienol treated hepatitis C patients1

#

RNA-Seq expression unit

Number of genes

Genes based on 2-fold

Genes based on 4-fold

Genes based on 8-fold

1

FPKM > 1

12614

9480

5369

2136

2

FPKM > 2

7426

1366

696

527

3

FPKM > 5

3323

379

285

268

1The gene expression level and fold change (post-dose vs pre-dose) of FPKM were calculated at more than 1, 2, or 5 at 2-fold, 4-fold, and 8-fold after filtering million-fold up-regulation and down-regulation. The RNA-seq analyses data based on FPKM >1 and 8-fold change of 2136 genes (0 values were replaced with 0.001) of ratios of post-dose over pre-dose treatment of δ-tocotrienol to hepatitis C patients was submitted into “Ingenuity Pathway Analyses (IPA)” for core analysis (Ingenuity Systems, Redwood City, CA)

Statistical analyses

These data were analyzed by IPA program of treatment-mediated effects as post-dose versus pre-dose. The statistical significance level was set at 5% (P < 0.05).

Results

Genome-wide profiling experiment of plasma mRNAs obtained from pre-dose and post-dose δ-tocotrienol treatment of hepatitis C patients

The RNA-Sequence analysis was based on FPKM > 1 and 8-fold change of 2136 genes (0 values replaced with 0.001; Table 1) ratios of post-dose over pre-dose treatment of δ-tocotrienol to hepatitis C patients were uploaded into “Ingenuity Pathway Analyses (IPA)” for core analysis (Ingenuity Systems, Redwood City, CΑ). The various genes associated with different biological functions and biomarkers are from “Ingenuity Knowledge Base” generated molecular networks, according to biological as well as molecular functions. These include canonical pathways, upstream regulatory analysis, and disease-based functional network, which helped discovering the list of several biomarkers. The core analysis was carried out with the settings of indirect and direct relationship between focused molecules based on experimentally observed data and human databases in the “Ingenuity Knowledge Base” were considered as the data sources in these analyses and pathways.

“Molecules” affected by δ-tocotrienol feeding to hepatitis C patients

The IPA of “molecules section” indicates fold changes in gene expression of 953 genes, which covered several categories of biological biomarkers, which are presented in the heat-map of this section (Fig. 2). Out of these, expression of 220 genes were related to present study, and only 12 genes were up-regulated (Table 2), and remaining 208 genes of various biomarkers were down-regulated after δ-tocotrienol treatment (Table 3). The ceramide synthase 3 and Mohawk homeobox were only two up-regulated genes involved as transcription regulators. The down-regulated gene expression of 208 molecules are involved in several biological functions (Additional file 1: Table S1, Additional file 2: Table S2 and Additional file 3: Table S3). The functions of these regulators are ATPase NA+/K+ transporting subunit α1, apolipoprotein B, proteasome 26S subunits, NADH ubiquinone oxidoreductase subunits B1, B9, cytochrome b5 reductase 4, autophagy related 4 ~ 5, cytochrome P450 family, TNF receptor superfamily 1B, RAS P21 protein activator 2, ubiquitin conjugating enzyme B2 J1, several other types of ubiquitin proteasome subunits, and protein inhibitor of activated STAT1 (Table 3). Similarly, gene regulator of G-protein signaling 2, nuclear factor of activated T-cells 2 interacting protein, TNF-α induced protein 8, C-X-C motif chemokines ligand 1, RNA polymerase II subunit H, tumor suppressor candidate 2, splicing factor 3b subunit 5, and several miRNAs (877, 1250,140), RNAs, tRNAs are reported in Table 3. The summary of most important down-regulated biomarkers are HSP90AB1, IL-16, autophagy, TNFSF1B, VEGFA, NFIL3, UBP1, USP25, RASA3, USP15, UBE4A, USP19, PSMG3, IL-27RA, SCP2, IFNGR1, ID2, TUSC2, IL-1R2, IL18RP, IRF2, PCNA1250,77,40 and several tRNAs (Table 3).
Fig. 2
Fig. 2

Effect of several biological biomarkers in “diseases and functions” of heat map in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The fold change expression of several biological functions (hematological system, function development, cell death, survival, inflammatory response, cell to cell signaling, cancer, organism injury, organism abnormalities, cellular development and immunological diseases) are illustrated in heat map

Table 2

Effect of δ-tocotrienol on up-regulation of fold change gene expression of “Molecules” section (12) of IPA analysis in hepatitis C patients

Up-regulation

#

Symbol

Entrez Gene Name

Expr Fold Change

Type(s)

1

HIST1H2AD

histone cluster 1 H2A family member d

1804955.068

other

2

HHIPL2

HHIP like 2

28.710

other

3

RPP38

ribonuclease P/MRP subunit p38

24.946

enzyme

4

CERS3

ceramide synthase 3

19.082

transcription regulator

5

HBG1

hemoglobin subunit gamma 1

17.945

other

6

MT-TQ

tRNA

14.252

other

7

AKR1D1

aldo-keto reductase family 1 member D1

14.056

enzyme

8

TSPAN15

tetraspanin 15

11.523

other

9

HBG2

hemoglobin subunit gamma 2

11.413

other

10

MKX

mohawk homeobox

9.573

transcription regulator

12

P4HA3

prolyl 4-hydroxylase subunit alpha 3

8.686

enzyme

Table 3

Effect of δ-tocotrienol on down-regulation of fold change gene expression of “Molecules” section (64) of IPA analysis in hepatitis C patients

Down-regulation

#

Symbol

Entrez Gene Name

Expr Fold Change

Type(s)

1

ATP1A1

ATPase Na+/K+ transporting subunit alpha 1

-8.014

transporter

2

HSP90AB1

heat shock protein 90 alpha family class B member 1

-8.049

enzyme

3

APOBEC3A

apolipoprotein B mRNA editing enzyme catalytic subunit 3A

-8.163

enzyme

4

CXCR2

C-X-C motif chemokine receptor 2

-8.208

G-protein coupled receptor

5

IL16

interleukin 16

-8.239

cytokine

6

PSMC3

proteasome 26S subunit, ATPase 3

-8.346

transcription regulator

7

NDUFB9

NADH:ubiquinone oxidoreductase subunit B9

-8.354

enzyme

8

CYB5R4

cytochrome b5 reductase 4

-8.367

enzyme

9

ATG3

autophagy related 3

-8.376

enzyme

10

CREB1

cAMP responsive element binding protein 1

-8.452

transcription regulator

12

NDUFB1

NADH:ubiquinone oxidoreductase subunit B1

-8.566

enzyme

13

PDE3B

phosphodiesterase 3B

-8.568

enzyme

14

IGF2R

insulin like growth factor 2 receptor

-8.68

transmembrane receptor

15

CYP2R1

cytochrome P450 family 2 subfamily R member 1

-8.682

enzyme

16

NDUFA11

NADH:ubiquinone oxidoreductase subunit A11

-8.686

enzyme

17

IGSF6

immunoglobulin superfamily member 6

-8.712

transmembrane receptor

18

TNFRSF1B

TNF receptor superfamily member 1B

-8.746

transmembrane receptor

19

PRPF18

pre-mRNA processing factor 18

-8.777

transporter

20

SERP1

stress associated endoplasmic reticulum protein 1

-8.872

other

21

UBE2J1

ubiquitin conjugating enzyme E2 J1

-8.874

enzyme

22

VEGFA

vascular endothelial growth factor A

-8.933

growth factor

23

GYS1

glycogen synthase 1

-9.027

enzyme

24

GPR65

G protein-coupled receptor 65

-9.054

G-protein coupled receptor

25

ILF2

interleukin enhancer binding factor 2

-9.105

transcription regulator

26

OSBPL11

oxysterol binding protein like 11

-9.201

other

27

PSMA5

proteasome subunit alpha 5

-9.31

peptidase

28

PIAS1

protein inhibitor of activated STAT 1

-9.326

transcription regulator

29

TRAF7

TNF receptor associated factor 7

-9.341

enzyme

30

COX14

COX14, cytochrome c oxidase assembly factor

-9.447

other

31

RPS26

ribosomal protein S26

-9.456

other

32

SFPQ

splicing factor proline and glutamine rich

-9.469

other

33

ATF4

activating transcription factor 4

-9.515

transcription regulator

34

PECAM1

platelet and endothelial cell adhesion molecule 1

-9.552

other

35

GPS2

G protein pathway suppressor 2

-9.56

transcription regulator

36

NFIL3

nuclear factor, interleukin 3 regulated

-9.568

transcription regulator

37

PSMB8

proteasome subunit beta 8

-9.709

peptidase

38

UBP1

upstream binding protein 1 (LBP-1a)

-9.718

transcription regulator

39

RAP2C

RAP2C, member of RAS oncogene family

-9.792

enzyme

40

PIBF1

progesterone immunomodulatory binding factor 1

-9.876

other

41

USP25

ubiquitin specific peptidase 25

-9.911

peptidase

42

FRS2

fibroblast growth factor receptor substrate 2

-9.962

kinase

43

PSMB4

proteasome subunit beta 4

-10.119

peptidase

44

USP15

ubiquitin specific peptidase 15

-10.16

peptidase

45

UBA52

ubiquitin A-52 residue ribosomal protein fusion product 1

-10.176

enzyme

46

UBE4A

ubiquitination factor E4A

-10.189

enzyme

47

GTPBP8

GTP binding protein 8 (putative)

-10.19

other

48

USP19

ubiquitin specific peptidase 19

-10.713

peptidase

49

TNFAIP8

TNF alpha induced protein 8

-10.974

other

50

HSPA14

heat shock protein family A (Hsp70) member 14

-10.978

peptidase

51

TLR8

toll like receptor 8

-11.975

transmembrane receptor

52

IL27RA

interleukin 27 receptor subunit alpha

-12.004

transmembrane receptor

53

SCP2

sterol carrier protein 2

-13.672

transporter

54

IFNGR2

interferon gamma receptor 2

-13.844

transmembrane receptor

55

ID2

inhibitor of DNA binding 2, HLH protein

-14.133

transcription regulator

56

TUSC2

tumor suppressor candidate 2

-15.922

other

57

IL2RG

interleukin 2 receptor subunit gamma

-16.787

transmembrane receptor

58

IL1R2

interleukin 1 receptor type 2

-19.547

transmembrane receptor

59

IRF2

interferon regulatory factor 2

-22.655

transcription regulator

60

PTGS2

prostaglandin-endoperoxide synthase 2

-25.841

enzyme

61

mir-877

microRNA 877

-4497.07

microRNA

62

mir-1250

microRNA 1250

-4755.79

microRNA

63

mir-140

microRNA 140

-5668.259

microRNA

64

KLRC4-KLRK1/KLRK1

killer cell lectin like receptor K1

-1565687.642

transmembrane receptor

“Causal Networks” affected by δ-tocotrienol feeding to hepatitis C patients

The down-regulation of several biomarkers of “causal network” of IPA of RNA samples obtained after treatment with δ-tocotrienol of chronic hepatitis C patients is described in Tables 4 and 5.
Table 4

Effect of δ-tocotrienol on up-regulation (24) of fold change gene expression in "causal netwworks" section of IPA analysis in hepatitis C patients

#

Master Regulator

Molecule Type

Part. regulators1

Depth

Pred Acti State2

Act. Z-Score3

P-Value Over4

Network Bi-Corr5

Causal Net6

Target-Con-Re7

A

Up-regulation

         

1

leuprolide

biologic drug

26s Proteasome,AKT1

3

Activated

2.104

8.5E-10

0.0032

217 (71)

69

2

HLA-DR

complex

26s Proteasome,AR,ATR

3

Activated

5.458

3.44E-09

0.0145

260 (87)

86

3

PRDX1

enzyme

26s Proteasome,ABL1

3

Activated

7.084

1.73E-08

0.0427

250 (76)

75

4

alefacept

bilologic drug

alefacept, AP1,CD2

3

Activated

2.278

2.50E-07

0.0222

85 (20)

20

5

juglone

chemical toxicant

CASP3,FOS,juglone,JUN

2

Activated

2.449

0.00000682

0.0272

54 (9)

9

6

mir-148

microRNA

mir-148

1

Activated

2.000

0.00103

0.0055

4 (1)

1

7

26s Proteasome

complex

26s Proteasome

1

Activated

2.840

0.00167

0.0476

15 (1)

1

8

mir-122

microRNA

mir-122

1

Activated

3.317

0.00189

0.022

11 (1)

1

9

mir-19

microRNA

mir-19

1

Activated

2.236

0.002

0.0185

5 (1)

1

10

mir-9

microRNA

mir-9

1

Activated

2.000

0.00473

0.0203

4 (1)

1

11

IL2RG

transmembrane

IL2RG

1

Activated

0.000

0.00181

0.0188

8 (1)

1

12

miR-2682-5p (other miRNAs w/seed AGGC)

mature microRNA

miR-2682-5p (miRNAs)

1

Activated

1.414

0.00584

0.0073

2 (1)

1

13

alpha-tocopherol succinate

chemical drug

alpha-tocopherol succinate

1

Activated

0.000

0.00597

0.0316

4 (1)

1

14

mir-199

microRNA

mir-199

1

Activated

1.732

0.00849

0.0258

3 (1)

1

15

mir-138

microRNA

mir-138

1

Activated

1.414

0.0113

0.0239

2 (1)

1

16

miR-330-5p (other miRNAs w/seed CUCU)

mature microRNA

miR-330-5p (and other

1

Activated

1.414

0.0113

0.0209

2 (1)

1

17

mir-326

microRNA

mir-326

1

Activated

1.414

0.0113

0.0191

2 (1)

1

18

mir-32

microRNA

mir-32

1

Activated

1.414

0.0113

0.0304

2 (1)

1

19

LAMP2

enzyme

LAMP2

1

Activated

0.000

0.0113

0.0251

2 (1)

1

20

mir-218

microRNA

mir-218

1

Activated

1.732

0.0183

0.0398

3 (1)

1

21

UBA7

enzyme

UBA7

1

Activated

1.414

0.0183

0.0416

2 (1)

1

22

miR-147a (miRNAs w/seed UGUGUGG)

mature microRNA

miR-147a (other miRNAs)

1

Activated

1.000

0.0448

0.0417

1 (1)

1

23

miR-504-5p (other miRNAs w/seed GACC)

mature microRNA

miR-504-5p (miRNAs)

1

Activated

1.000

0.0448

0.0417

1 (1)

1

24

BI 2536

chemical drug

26s Proteasome,ABL1

3

Activated

1.331

2.06E-12

0.0034

249 (50)

49

1Part. Regulators = Paticipating Regulators; 2Pred Acti state = Predicted Acitivation State; 3Act. Z-Score = Activation Z-Score; 4P-Value Over. = P-Value Overlap; 5Network Bi-Corr = Network Bias-Corrected P-Values; 7Target-Con-Re. = Target Connected regulators

Table 5

Effect of δ-tocotrienol on down-regulation (74) of fold change gene expression in "causal netwworks" section of IPA analysis in hepatitis C patients

#

Master Regulator

Molecule Type

Part. regulators1

Depth

Pred Acti State2

Act. Z-Score3

P-Value Over4

Network Bi-Corr5

Causal Net6

Target-Con-Re7

B

Down-regulation

         

25

JAK1/2

group

26s Proteasome,Akt,AKT1

3

Inhibited

-7.511

2.54E-14

0.0008

295 (81)

80

26

PPAR ligand-PPAR-Retinoic acid-RXRα

complex

26s Proteasome,Akt,AKT1

3

Inhibited

-4.459

3.31E-13

0.0131

306 (61)

60

27

LXR ligand-LXR-Retinoic acid-RXRα

complex

26s Proteasome,Akt,AR

3

Inhibited

-4.815

4.17E-13

0.0085

290 (58)

57

28

PPARγ ligand-PPARγ-Retinoic acid-RARα

complex

26s Proteasome,Akt,AKT1

3

Inhibited

-4.230

4.23E-13

0.0121

306 (66)

65

29

PXR ligand-PXR-Retinoic acid-RXRα

complex

26s Proteasome,AKT1

3

Inhibited

-4.432

3.33E-12

0.0221

294 (58)

58

30

RAR ligand-RARα-Retinoic acid-RXRα

complex

26s Proteasome,Akt,AKT1

3

Inhibited

-5.396

3.52E-12

0.039

297 (57)

56

31

Vegf Receptor

group

26s Proteasome,ABL1,Akt

3

Inhibited

-5.056

1.56E-11

0.0052

276 (93)

90

32

FXR ligand-FXR-Retinoic acid-RXRα

complex

26s Proteasome,Akt,AKT1

3

Inhibited

-5.100

1.96E-11

0.0484

291 (56)

55

33

hydrogen sulfide

chemical - endogenous mammalian

26s Proteasome,Akt,AKT1

3

Inhibited

-4.222

2.15E-11

0.0013

237 (92)

89

34

NLK

kinase

26s Proteasome,AKT1,Alp

3

Inhibited

-3.429

8.72E-11

0.0375

248 (50)

45

35

CD80

transmembrane receptor

CD28,CD80,IFNG,IL4

2

Inhibited

-6.267

1.32E-10

0.003

132 (8)

8

36

Pdgfra-Pdgfrb

complex

26s Proteasome,AKT1,AR

3

Inhibited

-7.878

1.37E-10

0.0184

285 (93)

89

37

Klra7 (includes others)

transmembrane receptor

26s Proteasome,Akt,AR

3

Inhibited

-7.445

1.44E-10

0.0324

291 (93)

93

38

FLT4

transmembrane receptor

26s Proteasome,Akt,AR

3

Inhibited

-5.020

1.46E-10

0.0177

280 (80)

78

39

Vegfr dimer

complex

26s Proteasome,AKT1,AR

3

Inhibited

-7.071

1.59E-10

0.0178

242 (61)

58

40

lipopolysaccharide

chemical drug

lipopolysaccharide

1

Inhibited

-7.668

2.75E-10

0.0045

120 (1)

1

41

TEK

kinase

26s Proteasome,ADRB2

3

Inhibited

-4.954

3E-10

0.0124

274 (93)

93

42

LATS1

kinase

26s Proteasome,ARID4A

3

Activated

4.680

3.43E-10

0.0322

250 (56)

54

43

NYAP1

other

26s Proteasome,Akt,AKT1

3

Inhibited

-6.264

3.54E-10

0.0304

281 (86)

85

44

MYO16

other

26s Proteasome,Akt,AKT1

3

Inhibited

-6.264

3.54E-10

0.0304

281 (86)

85

45

NYAP2

other

26s Proteasome,Akt,AKT1

3

Inhibited

-6.264

3.54E-10

0.0304

281 (86)

85

46

IRS

group

26s Proteasome,ADRB2

3

Inhibited

-5.548

1.63E-09

0.0456

269 (77)

74

47

FAK-Src

complex

26s Proteasome,ABL1,Akt

3

Inhibited

-6.839

2.41E-09

0.043

273 (90)

86

48

Plk

group

26s Proteasome,Akt,AKT1

3

Inhibited

-2.500

2.77E-09

0.0425

219 (55)

50

49

G-protein beta

group

26s Proteasome,ADORA2A

3

Inhibited

-5.647

3.22E-09

0.0309

283 (103)

99

50

ADRA1B

G-protein coupled receptor

26s Proteasome,ADRA1B

3

Inhibited

-6.238

4.49E-09

0.0406

278 (86)

85

51

IL2

cytokine

IL2

1

Inhibited

-4.619

8.23E-09

0.0004

48 (1)

1

52

propolis

biologic drug

26s Proteasome,AKT1

3

Inhibited

-2.829

1.78E-08

0.0482

231 (76)

73

53

exenatide

biologic drug

26s Proteasome,Akt,AMPK

3

 

-1.432

2.36E-08

0.0088

236 (88)

88

54

imidazole

chemical - endogenous mammalian

26s Proteasome,ADORA2A

3

 

1.091

2.79E-08

0.05

243 (75)

70

55

LETM1

other

Akt,AMPK,APP,AR

3

 

-1.023

0.000000069

0.036

215 (64)

63

56

IL-2R

complex

IL-2R,IL2RA,IL2RG,JAK1

2

Inhibited

-3.491

0.00000012

0.0103

84 (14)

13

57

IL23

complex

IL12B,IL23,JAK2,MTOR

2

Inhibited

-7.155

0.000000165

0.0112

80 (9)

9

58

IL15

cytokine

IL15

1

Inhibited

-2.121

0.000000551

0.0009

32 (1)

1

59

TH17 Cytokine

group

IL17A,IL21,IL22,TH17

2

Inhibited

-4.323

0.000000813

0.0037

39 (4)

4

60

IL4R

transmembrane receptor

IL4,IL4R,IRS1,IRS2,JAK

2

Inhibited

-4.503

0.00000102

0.0252

75 (13)

12

61

IL21

cytokine

IL21

1

Inhibited

-2.985

0.00000527

0.0028

22 (1)

1

62

SATB1

transcription regulator

SATB1

1

 

1.528

0.00000669

0.0011

21 (1)

1

63

cyclosporin A

biologic drug

cyclosporin A

1

 

1.441

0.0000108

0.0163

39 (1)

1

64

IL12RB2

transmembrane receptor

IL12 (family),IL12RB2

2

Inhibited

-4.116

0.0000233

0.0103

34 (4)

3

65

mir-26

microRNA

Akt,mir-26

2

 

0.192

0.0000247

0.0126

27 (2)

2

66

mir-221

microRNA

Akt,mir-221

2

 

-0.192

0.0000247

0.0129

27 (2)

2

67

IL5

cytokine

IL5

1

Inhibited

-4.914

0.0000541

0.0136

28 (1)

1

68

ropivacaine

chemical drug

Akt,NOS3,Pkc(s)

2

 

-1.029

0.0000544

0.0289

34 (5)

4

69

UCP3

transporter

IRS1,IRS2,PI3K

2

 

-1.961

0.0000657

0.0231

26 (4)

3

70

AIF1

other

AIF1,Akt,BAD

2

 

-1.177

0.0000657

0.0211

26 (3)

3

71

IFN Beta

group

IFN Beta

1

Inhibited

-2.138

0.00082

0.043

14 (1)

1

72

PDGFD

growth factor

PDGFD

1

 

-0.577

0.000838

0.0044

3 (1)

1

73

PARP9

enzyme

PARP9

1

Inhibited

-2.236

0.00123

0.0073

5 (1)

1

74

PPP1R14B

phosphatase

PPP1R14B

1

 

-1.732

0.00162

0.005

3 (1)

1

1Part. Regulators = Paticipating Regulators; 2Pred Acti state = Predicted Acitivation State; 3Act. Z-Score = Activation Z-Score; 4P-Value Over. = P-Value Overlap; 5Network Bi-Corr = Network Bias-Corrected P-Values; 7Target-Con-Re. = Target Connected regulators

There were 676 gene regulators identified in this section, and only 98 regulators were associated with present study, indicating significant P-values for all regulators (Tables 4 and 5). The fold change gene expression of 24 was up-regulated (Table 4) and 74 down-regulated (Table 5). This section includes down-regulated gene expression of 26S proteasomes, interleukin cytokines, and PPAR-ligand-PPA-Retinoic acid-RXTα, PPARγ-ligand-PPARγ-Retinoic acid-RARα, IL-7R, CD80, IRS, IL-2, IL-2RG, IL-5, IL-15, IL-21, IL-23 and several types of microRNAs (miRNAs) as shown in Table 5. The activation Z-Score, P-values, network bias-corrected and causal network values were in descending order of all these gene biomarkers (Tables 4 and 5).

“Diseases and functions” affected by δ-tocotrienol feeding to hepatitis C patients

The IPA of RNAs obtained from effect of δ-tocotrienol treatment of chronic hepatitis C patients on relative percentage relationship of gene regulators (70) of “diseases and functions” reported in Table 6. In this section, percentage relationships of main regulators were AP1, cAMP, EIF2AK2 2RL1, IL-17A, IL-1RN, KITLG, miRNA-155-5p, STAT2 (48%; 43/90), 26S proteasome, CSF1, IFNG, IL-17A, IRF4, LDL, RELA, TGFA (43%; 17/40); mir-223 (0%; 0/2), IL-15 (100%; 1/1), IL-1Β (0%; 0/1), and miR-21-5p (100%; 1/1) (Table 6). The consistency score of these regulators varied from 1.73 ~ 36.34, total regulars (1–9), total node (5–57), diseases and functions total varied 1–10 as shown in Table 6.
Table 6

Effects of δ-tocotrienol treatment on "Regulator Effects" section (70) of IPA analysis of "Diseases and Functions" in hepatitis C patients

ID

Consistency

Node

Regulator

Regulators

Target

Disease &

Diseases & Functions

Known Regulator-Disease/

 

Score

Total

Total

Total

Fuunctions Totals

Function Relationship

1

36.338

57

9

Ap1,CAMP,EIF2AK2,IL17A,IL1R,miR-155-5,STAT2

38

10

activation of phagocytes

48% (43/90)

2

32.199

69

13

26s Proteasome,ANGPT2,Ap1,BCL2,CAMP,CEBPA,TGFA

45

11

activation of antigen presenting cells

40% (57/143)

3

30.414

57

12

26s Proteasome,CAMP,CSF1,F2RL1,IL17A,miR-21-5p,TGFA

37

8

activation of myeloid cells

32% (31/96)

4

30.375

97

13

Ap1,CAMP,CCL5,EIF2AK2,F2RL1,FGF10,IL17A,

64

20

accumulation of l cells,leukopoiesis

38% (99/260)

5

28.605

56

10

26s Proteasome,BCL2,CAMP,STAT3,TGFA,TGM2

37

9

adhesion of blood cells

36% (32/90)

6

25.456

49

8

26s Proteasome,F2RL1,IL1RN,IRF4,KLF3,STAT3,TGFA,

32

9

adhesion of immune cells

26% (19/72)

7

25.126

127

20

ANGPT2,Ap1,CAMP,CST5,ETS1,F2RL1,IFNL1,IGF1,IL17A,

92

15

cell movement of granulocytes

40% (121/300)

8

24.82

53

8

26s Proteasome,BCL2,CSF1,F2RL1,IL1RN,STAT3,TGFA,

38

7

adhesion of blood cells

41% (23/56)

9

23.333

50

7

CAMP,F2RL1,IL17A,mir-10,NRG1,TGFA,Tlr

36

7

cell viability of tumor cell lines

63% (31/49)

10

23.026

36

7

26s Proteasome,BCL2,CREB1,F2RL1,IFNA2,IL1RN,TGFA

22

7

binding of leukocytes

24% (12/49)

11

22.687

55

11

26s Proteasome,Calcineurin protein(s),CD38,EIF4E,F2RL1,

37

7

migration of macrophages

23% (18/77)

12

21.651

23

5

CIITA,EBI3,IL27,PARP9,PDCD1

12

6

activation of lymphatic system cells

53% (16/30)

13

21.355

41

6

F2RL1,IL1RN,miR-155-5p (miRNAs w/seed UAAUGCU),

28

7

cell viability of mononuclear leukocytes

36% (15/42)

14

20.788

42

5

F2RL1,IL1RN,Pkc(s),TNFSF11,VEGFA

28

9

adhesion of immune cells

47% (21/45)

15

20.715

50

7

BTNL2,CIITA,Ifn,Ifnar,IL27,SYVN1,TGM2

33

10

activation of leukocytes

20% (14/70)

16

19.856

54

8

Ap1,CAMP,CSF2,EIF2AK2,F2RL1,IL1RN,miR-155-5p

39

7

chemotaxis of granulocytes

38% (21/56)

17

19.73

30

3

CAMP,miR-155-5p (miRNAs w/seed UAAUGCU),PSMD10

19

8

cell death of connective tissue cells

33% (8/24)

18

19.1

50

8

F2,F2RL1,IL17A,MIF,mir-1,PPRC1,REL,TGFA

35

7

cell viability of lymphatic system cells

46% (26/56)

19

18.764

67

13

Ap1,BCR (complex),CAMP,CSF2,IL12 (complex),IL21,STAT1,

48

6

synthesis of reactive oxygen species

41% (32/78)

20

18.475

41

7

F2RL1,IL17A,LDL,mir-1,PPRC1,REL,RELA

27

7

cell viability of mononuclear leukocytes

39% (19/49)

21

18.429

75

8

CCL5,F2RL1,IL1RN,miR-155-5pPSMD10,STAT4,TGFA

49

18

apoptosis of fibroblast cell lines

31% (45/144)

22

17.098

34

6

F2RL1,Igm,IL1RN,IL6,STAT3,VEGFA

23

5

binding of myeloid cells

37% (11/30)

23

16.585

33

7

CEBPA,EGF,FLT3LG,IL17A,MIF,mir-1,REL

21

5

NK cell proliferation

37% (13/35)

24

16.44

50

7

CAMP,F2RL1,IL17A,JUN,LDL,NRG1,TGFA

37

6

activation of antigen presenting cells,

50% (21/42)

25

15.167

50

7

CAMP,ETS1,F2,F2RL1,IL17A,MIF,TGFA

36

7

accumulation of cells

55% (27/49)

26

14.732

52

8

26s Proteasome,CSF1,IFNG,IL17A,IRF4,LDL,RELA,TGFA

39

5

chemotaxis of kidney cell lines

43% (17/40)

27

14.467

47

5

26s Proteasome,AKT1,LDL,TGFA,TGM2

37

5

cellular homeostasis

48% (12/25)

28

12.928

70

11

26s Proteasome,APP,CREB1,CSF1,IFNA2,IFNG,IL17A,TGFA

54

5

translation of mRNA

44% (24/55)

29

12.667

50

5

CEBPA,F2RL1,IL1RN,TNFSF11,VEGFA

36

9

quantity of IgG,recruitment of cells

31% (14/45)

30

12.33

50

7

CAMP,EIF2AK2,F2RL1,HRAS,IL17A,IL1RN,STAT2

37

6

homing of neutrophils,recruitment of cells

40% (17/42)

31

12.221

76

6

CD40LG,GAST,miR-155-5p,TNFSF11

63

7

production of reactive oxygen species

45% (19/42)

32

11.939

32

6

CAMP,ETS1,IL17A,KITLG,miR-155-5,miR-21-5p

22

4

infiltration by myeloid cells

38% (9/24)

33

11.839

34

4

BTNL2,Hbb-b2,Ifnar,TRIM24

24

6

diabetes mellitus,hypersensitive reaction

8% (2/24)

34

10.818

46

5

CEBPA,EGF,FLT3LG,IL17A,MIF

35

6

cell viability of tumor cell lines

43% (13/30)

35

9.707

21

5

F2,F2RL1,IL1RN,IL6,VEGFA

13

3

migration of antigen presenting cells

60% (9/15)

36

8.693

13

4

CD3,F2RL1,IL1RN,VEGFA

7

2

binding of myeloid cells

25% (2/8)

37

8.521

22

5

26s Proteasome,FOXO3,IL18,Pkc(s),TNFSF11

15

2

response of lymphatic system cells

60% (6/10)

38

8.01

74

8

A2M,CD40LG,GAST,mir-17,miR-17-5p,other miRNAs

58

8

anemia,binding of tumor cell lines

28% (18/64)

39

7.649

36

5

GAST,PARP9,PIK3R1,SOX4,TGFA

26

5

anemia,autophagy,organismal death

16% (4/25)

40

7.464

87

13

CD40LG,EP300,ERG,Igm,IL7,miR-19b-3p,miR-291a-3

69

5

cell death of fibroblast cell lines

28% (18/65)

41

7.181

14

6

CSF2,EDN1,F2,IL1B,KITLG,SPI1

7

1

migration of granulocytes

33% (2/6)

42

6.791

26

5

EDN1,F2,PRKCA,TNFSF11,VEGFA

17

4

Nephritis,synthesis of eicosanoid

40% (8/20)

43

6.633

17

3

IRF5,miR-155-5p (miRNAs w/seed UAAUGCU),PSMD10

11

3

apoptosis of connective tissue cells

0% (0/9)

44

6.379

18

3

ETS1,GFI1,PRL

13

2

quantity of hematopoietic progenitor cells

100% (6/6)

45

6.306

22

3

miR-155-5p (miRNAs w/seed UAAUGCU),miR-21-5p

17

2

cell death of connective tissue cells

17% (1/6)

46

6.183

27

3

CREB1,IFNA2,PDCD1

22

2

activation of leukocytes

67% (4/6)

47

5.667

14

1

GFI1

9

4

HIV infection,proliferation of blood cells

75% (3/4)

48

5.345

19

1

IL5

14

4

inflammation of body cavity

50% (2/4)

49

5.292

34

4

CAMP,CSF2,IFNG,IL12 (complex)

28

2

synthesis of leukotriene

75% (6/8)

50

4.907

17

3

EGF,PRDM1,SMARCA4

12

2

endocytosis,phagocytosis of cells

17% (1/6)

51

4.276

18

2

GFI1,Pkc(s)

14

2

differentiation of mononuclear leukocytes

50% (2/4)

52

4.199

37

3

IL2,IL21,IL4

30

4

apoptosis of connective tissue cells

42% (5/12)

53

4.16

17

3

CAMP,CSF1,Immunoglobulin

13

1

mobilization of Ca2+

67% (2/3)

54

3.889

12

2

mir-8,miR-92a-3p (and other miRNAs w/seed AUUGCAC)

8

2

cell cycle progression

0% (0/4)

55

3.13

8

1

FOXO1

5

2

hyperplasia of lymphoid organ,

0% (0/2)

56

3.024

11

3

Igm,Interferon alpha,STAT1

7

1

apoptosis of kidney cell lines

0% (0/3)

57

3

13

3

CEBPA,IFN Beta,mir-223

9

1

production of protein

33% (1/3)

58

2.236

8

1

mir-223

5

2

Bacterial Infections,production of protein

0% (0/2)

59

1.789

7

1

E2F1

5

1

cell death of fibroblasts

100% (1/1)

60

1.789

7

1

IL15

5

1

cytotoxicity of natural killer cells

100% (1/1)

61

1.789

7

1

IL1B

5

1

binding of lymphatic system cells

100% (1/1)

62

1.732

5

1

CD28

3

1

hyperplasia of lymphoid organ

0% (0/1)

63

1.508

13

1

TP53

11

1

catabolism of protein

100% (1/1)

64

0.802

17

2

HRAS,TCR

14

1

expression of mRNA

0% (0/2)

65

0.577

32

4

IFNA2,IRF7,TGFB1,TNF

27

1

systemic lupus erythematosus

25% (1/4)

66

-2.714

13

1

IL4

11

1

infection of cells

100% (1/1)

67

-4.082

8

1

miR-21-5p (and other miRNAs w/seed AGCUUAU)

6

1

cell death

100% (1/1)

68

-6.5

6

1

TCF7L2

4

1

apoptosis of fibroblast cell lines

0% (0/1)

69

-16.743

5

1

TRAP1

3

1

synthesis of reactive oxygen species

100% (1/1)

70

-23.519

58

1

APP

56

1

cancer

100% (1/1)

“Upstream analysis” affected by δ-tocotrienol feeding to hepatitis C patients

The most interesting results of present IPA was “upstream analysis” of δ-tocotrienol treated hepatitis C patients. There were 934 gene regulators identified in this section. The 57 genes regulator correspond to present study were up-regulated (Table 7), and 64 gene regulators down-regulated (Table 8). There were several miRNAs (38), which were up-regulated and remaining other important biomarkers gene were down-regulated (Table 8). The activation Z-Scores (3.79–1.26) and P-values (5.39E-8 – 1.26) were significant from each biomarkers. The down-regulated biomarkers included several cytokines (IL-2, Il-5, IL-6, IL-7, IL-12, IL-13, IL-15, IL-17, IL-17A, IL-18, IL-21, IL-24, IL-27, IL-32), as well as miRNA-15, miRNA-124, miRNA-218-5P, interferon β-1a, interferon γ, TNF-α, STAT2, NOX1, prostaglandin J2, NF-κB, IκB, and TCF3 (transcription regulator), with significant activation Z-Score (− 4.56–2.531), and P-values were 9.17–14.00; P < 0.05, respectively (Table 8).
Table 7

Effect of δ-tocotrienol on up-regulation of fold change expression in “upstream regulator” section (57) of IPA analysis in hepatitis C patients

Upstream Regulator

Molecule Type

Predicted Activation State

Activation Z-Score

P-value of overlap

Mechanistic Network

#

Up-regulated

1

miR-17-5p (and other miRNAs w/seed AAAGUGC)

mature microrna

Activated

3.798

5.39E-08

127 (7)

2

miR-155-5p (miRNAs w/seed UAAUGCU)

mature microrna

Activated

4.518

9.04E-06

137 (7)

3

miR-19b-3p (and other miRNAs w/seed GUGCAAA)

mature microrna

Activated

2.198

0.00017

 

4

miR-92a-3p (and other miRNAs w/seed AUUGCAC)

mature microrna

Activated

2.187

0.00744

 

5

miR-214-3p (and other miRNAs w/seed CAGCAGG)

mature microrna

  

0.0113

 

6

miR-291a-3p (and other miRNAs w/seed AAGUGCU)

mature microrna

Activated

2.994

0.017

 

7

miR-21-5p (and other miRNAs w/seed AGCUUAU)

mature microrna

Activated

2.595

0.0159

 

8

miR-330-5p (and other miRNAs w/seed CUCUGGG)

mature microrna

  

0.0113

 

9

miR-122-5p (miRNAs w/seed GGAGUGU)

mature microrna

Activated

2.586

0.0279

 

10

miR-2682-5p (and other miRNAs w/seed AGGCAGU)

mature microrna

  

0.00584

 

11

miR-205-5p (and other miRNAs w/seed CCUUCAU)

mature microrna

  

0.0325

 

12

miR-200b-3p (and other miRNAs w/seed AAUACUG)

mature microrna

 

1.960

0.0273

 

13

miR-542-3p (miRNAs w/seed GUGACAG)

mature microrna

  

0.0363

 

14

miR-221-3p (and other miRNAs w/seed GCUACAU)

mature microrna

 

1.957

0.0349

 

15

miR-147a (miRNAs w/seed UGUGUGG)

mature microrna

  

0.0448

 

16

miR-450a-5p (and other miRNAs w/seed UUUGCGA)

mature microrna

  

0.0448

 

17

miR-216a-5p (miRNAs w/seed AAUCUCA)

mature microrna

  

0.0448

 

18

miR-504-5p (and other miRNAs w/seed GACCCUG)

mature microrna

  

0.0448

 

19

miR-657 (miRNAs w/seed GCAGGUU)

mature microrna

  

0.0448

 

20

mir-17

microrna

Activated

2.581

0.00091

 

21

mir-122

microrna

Activated

3.300

0.00189

 

22

mir-19

microrna

Activated

2.204

0.002

 

23

mir-1

microrna

Activated

2.72

0.00354

128 (6)

24

mir-214

microrna

  

0.00906

 

25

mir-326

microrna

  

0.0113

 

26

mir-138

microrna

  

0.0113

 

27

mir-32

microrna

  

0.0113

 

28

mir-155

microrna

 

1.965

0.00691

173 (8)

29

mir-148

microrna

 

1.997

0.00103

 

30

mir-199

microrna

  

0.0028

164 (7)

31

mir-218

microrna

  

0.0183

 

32

mir-515

microrna

  

0.0225

 

33

mir-132

microrna

  

0.0349

 

34

mir-10

microrna

Activated

2.786

0.0366

 

35

mir-8

microrna

Activated

2.128

0.0344

 

36

mir-25

microrna

 

1.972

0.0349

 

37

mir-622

microrna

  

0.0448

 

38

mir-181

microrna

 

0.988

0.0498

 

39

Immunoglobulin

complex

Activated

2.345

0.00024

283 (16)

40

prednisolone

chemical drug

 

1.763

0.00025

235 (13)

41

26s Proteasome

complex

Activated

2.921

0.000933

326 (16)

42

IgG

complex

 

1.003

0.00824

295 (16)

43

TRAP1

enzyme

Activated

2.236

0.0169

 

44

IL1RN

cytokine

Activated

3.235

0.0275

 

45

prostaglandin A1

chemical - endogenous non-mammalian

 

0.686

0.00249

159 (8)

46

AGTR1

g-protein coupled receptor

 

1.067

0.0291

 

47

MAPK1

kinase

 

1.017

0.0361

 

48

Ubiquitin

group

  

0.039

 

49

IL18RAP

transmembrane receptor

  

0.0363

 

50

TAB1

enzyme

 

1.258

0.0349

 

51

eIF2B

complex

  

0.0448

 

52

SNRPN

other

  

0.0448

 

53

SNORD21

other

  

0.0448

 

54

SOS2

other

  

0.0448

 

55

IL1RL2

transmembrane receptor

  

0.0469

 

56

IL18BP

other

  

0.0469

 

57

IL10RA

transmembrane receptor

Activated

2.688

0.229

 
Table 8

Effect of δ-tocotrienol on down-regulation of fold change expression in “upstream regulators” section (64) of IPA analysis in hepatitis C patients

#

Upstream Regulator

Molecule Type

Predicted Activation State

Activation z-score

p-value of overlap

Mechanistic Network

Down-regulated

1

interferon beta-1a

biologic drug

  

9.17E-14

 

2

IL2

cytokine

Inhibited

-4.562

2.23E-09

297 (17)

3

IL15

cytokine

Inhibited

-2.247

1.37E-08

299 (19)

4

FAS

transmembrane receptor

 

-1.461

3.94E-08

263 (17)

5

TNF

cytokine

Inhibited

-5.914

0.000000294

378 (19)

6

IL21

cytokine

Inhibited

-2.747

0.00000339

264 (15)

7

GATA1

transcription regulator

 

-0.822

0.00000497

243 (11)

8

IRF1

transcription regulator

Inhibited

-3.223

0.000011

245 (13)

9

EGF

growth factor

Inhibited

-5.15

0.0000204

303 (15)

10

TGFB1

growth factor

Inhibited

-3.491

0.00004

350 (17)

11

IL6

cytokine

Inhibited

-3.043

0.0000566

284 (15)

12

IL5

cytokine

Inhibited

-4.866

0.0000654

243 (13)

13

Interferon alpha

group

Inhibited

-4.069

0.000154

150 (9)

14

STAT4

transcription regulator

Inhibited

-4.536

0.000489

111 (6)

15

IL7

cytokine

Inhibited

-2.665

0.00064

243 (18)

16

IL13

cytokine

 

-1.516

0.000806

295 (16)

17

STAT1

transcription regulator

Inhibited

-4.582

0.000877

241 (14)

18

IL1B

cytokine

Inhibited

-4.367

0.000982

330 (17)

19

STAT2

transcription regulator

Inhibited

-2.219

0.00105

173 (9)

20

PARP9

enzyme

Inhibited

-2.200

0.00123

142 (6)

21

FOXC1

transcription regulator

 

-1.961

0.002

 

22

IL2RG

transmembrane receptor

 

-0.113

0.00233

 

23

IL12 (complex)

complex

Inhibited

-2.378

0.00251

246 (17)

24

TGFA

growth factor

Inhibited

-2.888

0.00327

283 (17)

25

CD14

transmembrane receptor

 

-1.768

0.00332

298 (16)

26

TNFSF10

cytokine

 

-1.376

0.00477

297 (17)

27

mir-223

microrna

Inhibited

-2.060

0.00527

167 (7)

28

IL27

cytokine

Inhibited

-2.937

0.00527

317 (16)

29

beta-estradiol

chemical - endogenous mammalian

Inhibited

-4.574

0.00546

358 (17)

30

IL10

cytokine

 

-0.803

0.00582

247 (17)

31

ADORA2A

g-protein coupled receptor

Inhibited

-2.365

0.00599

175 (9)

32

IFNL1

cytokine

Inhibited

-2.925

0.00622

224 (11)

33

IL18

cytokine

Inhibited

-2.26

0.00701

326 (19)

34

NOX1

ion channel

 

-1.951

0.00741

263 (14)

35

SOX4

transcription regulator

Inhibited

-3.033

0.00834

 

36

prostaglandin J2

chemical - endogenous non-mammalian

 

-1.432

0.0115

 

37

E2F1

transcription regulator

Inhibited

-2.081

0.0142

 

38

CREB1

transcription regulator

Inhibited

-3.766

0.0143

 

39

IGF1

growth factor

Inhibited

-2.385

0.0158

 

40

IL12 (family)

group

 

-0.500

0.016

 

41

IRF5

transcription regulator

Inhibited

-2.155

0.0162

 

42

FOXO4

transcription regulator

 

-1.98

0.0179

 

43

PGF

growth factor

 

-1.959

0.0237

 

44

BTG2

transcription regulator

 

1.165

0.0239

 

45

mir-15

microrna

 

-0.927

0.0279

 

46

STAT5A

transcription regulator

 

-0.896

0.0294

 

47

NFE2L2

transcription regulator

Inhibited

-3.644

0.0295

 

48

MIF

cytokine

Inhibited

-2.642

0.0304

 

49

FGF10

growth factor

Inhibited

-2.200

0.0305

 

50

miR-26a-5p (and other miRNAs w/seed UCAAGUA)

mature microrna

 

1.916

0.0309

 

51

NOX4

enzyme

 

-1.941

0.0309

 

52

NFKBIB

transcription regulator

 

-1.400

0.0331

 

53

IFNA1/IFNA13

cytokine

 

-1.77

0.0331

 

54

FLT3LG

cytokine

Inhibited

-2.411

0.0331

 

55

IL17F

cytokine

 

-1.917

0.0349

 

56

IL32

cytokine

 

-1.15

0.0416

 

57

CCL5

cytokine

Inhibited

-2.621

0.042

 

58

IL17A

cytokine

Inhibited

-3.075

0.0422

 

59

MIR124

group

 

1.941

0.0435

 

60

miR-218-5p (and other miRNAs w/seed UGUGCUU)

mature microrna

  

0.0443

 

61

CXCR4

g-protein coupled receptor

 

-0.842

0.0447

 

62

CD38

enzyme

Inhibited

-3.429

0.0482

 

63

IL24

cytokine

 

-0.277

0.0498

 

64

TCF3

transcription regulator

Inhibited

-2.530

0.231

 

“Diseases or functions annotation” affected by δ-tocotrienol feeding in hepatitis C patients

The effect of δ-tocotrienol on gene expression in “diseases or functions annotation” of IPA of mRNAs sample of chronic hepatitis C patients resulted in determining 500 types of diseases and functions. Out of these 11 type genes of diseases and functions were up-regulated, while 49 were down regulated (Table 9A and B). The up-regulated genes (11) of functions include cell death/survival cell death, organismal injury and abnormalities, cellular function and maintenance, gene expression, protein synthesis, metabolic disease, and neurological diseases as shown in Table 9A. Their p-values and activation Z-Scores varied from 3.94E21–8.54E6 2.64–0.71 (P < 0.01), respectively (Table 9A). The gene expression of 49 were down-regulated after δ-tocotrienol treatment of chronic hepatitis C patients. These genes are involved in cellular development, cellular growth, proliferation hematology, infectious diseases, cell-to-cell signaling/interaction, cardiovascular disease, antimicrobial response, cell morphology, inflammatory response, neurological disease, humoral immune response, free radical scavenging, immunological diseases, lipid metabolism, gene expression, cancer, RNA post-transcriptional modification and many other diseases as outlined in Table 9B.
Tables 9

Effect of δ-tocotrienol on "diseases or functions annotation" section of IPA analysis of total mRNAs of hepatitis C patients

#

Categories

Diseases or Functions Annotation

P-Value

Predicted Activation

Act Z-Score

Molecules

# Molecules

A

Up-regulated (11)

      

1

Cell Death and Survival

cell death

3.94E-21

Increased

2.645

ABCD1,ABL1,ACO2

349

2

Cancer, Cell Death and Survival

necrosis of malignant tumor

4.75E-21

Increased

3.412

ABL1,B2M,BCL2L11

76

3

Cellular Function and Maintenance

function of lymphatic system cells

2.1E-16

 

0.273

ABL1,ARHGEF,

60

4

Cellular Function and Maintenance

function of leukocytes

1.25E-15

 

0.051

ARHGEF6,ARRB2,B2M

77

5

Gene Expression, Protein Synthesis

translation of mRNA

1.6E-12

Increased

2.941

BTG2,DNAJC1,EIF2S3

36

6

Gene Expression

expression of mRNA

3.44E-12

Increased

2.115

BTG2,CD47,DNAJC1

43

7

Metabolic Disease

glucose metabolism disorder

2.76E-08

 

1.558

ABHD16A,ALOX5AP,ANAPC13

136

8

Organismal Survival

organismal death

0.00000495

Increased

11.544

ABL1,ADORA2A,APRT

210

9

Cancer, Hematological Disease

lymphoproliferative malignancy

0.00000592

 

1.725

ABL1,ADORA2A,AIMP1

203

10

Neurological Disease, Organismal

disorder of basal ganglia

0.0000781

 

1.538

ABCD1,ABL1,ADORA2A

76

11

Cancer, Organismal Injury

carcinoma

0.0000854

 

0.711

ABCD1,ABHD16A,ABL1

749

B

Down-regulated (49)

     

12

Cellular Development, Cellular

proliferation of immune cells

1.29E-24

Decreased

-2.128

ABL1,ADORA2A,ARHGEF6

128

13

Cellular Development, Cellular

proliferation of mononuclear leukocytes

6.29E-24

Decreased

-2.073

ABL1,ADORA2A,ARHGEF6

123

14

Infectious Diseases

Viral Infection

6.4E-24

Decreased

-5.928

ABL1,ADORA2A,AGO4

207

15

Cellular Growth and Proliferation

proliferation of lymphatic system cells

8.63E-24

Decreased

-2.019

ABL1,ADORA2A,ARHGEF6

129

16

Immunological Disease

systemic autoimmune syndrome

2.37E-23

 

-0.774

ABHD16A,ADORA2A,AKR1D1

163

17

Hematological System Development

quantity of mononuclear leukocytes

6.64E-19

Decreased

-4.691

ABL1,ADORA2A,ARHGEF6

113

18

Lymphoid Tissue Structure

quantity of lymphatic system cells

1.46E-18

Decreased

-4.679

ABL1,ADORA2A,ARHGEF6

115

19

Hematological System Development

quantity of blood cells

6.22E-16

Decreased

-4.724

ABL1,ADD3,ADORA2A

134

20

Cell-To-Cell Signaling and Interaction

activation of cells

2E-15

Decreased

-5.698

ADORA2A,AFP,ARRB2

127

21

Connective Tissue Disorders

inflammation of joint

2.16E-13

 

-1.573

ABL1,ADORA2A,AKR1D1

128

22

Cardiovascular Disease, Developmental

Diamond-Blackfan anemia

4.55E-11

  

CD52,FLVCR1,RPL11

13

23

Antimicrobial Response, Inflammatory

antimicrobial response

8.55E-09

 

-1.395

APOBEC3A,ATG5,BCL10

44

24

Embryonic Development, Hematological

formation of lymphoid tissue

1.45E-08

Decreased

-2.618

ABL1,B2M,BCL2L11

48

25

Free Radical Scavenging

metabolism of reactive oxygen species

1.56E-08

Decreased

-2.89

ABL1,ATG5,ATP7A

63

26

Neurological Disease, Skeletal

neuromuscular disease

5.12E-07

 

-0.200

ABL1,ADORA2A,ALAS1

95

27

Cell Morphology

morphology of blood cells

7.37E-07

  

ABCD1,ABL1,ADD3

52

28

Inflammatory Response, Neurological

inflammation of central nervous system

0.00000109

 

-1.099

ADORA2A,B2M,C3AR1

48

29

Humoral Immune Response, Protein

production of antibody

0.00000114

 

-1.497

B2M,BCL10,BCL2L11

40

30

Endocrine System Disorders

diabetes mellitus

0.00000166

Decreased

-2.058

ABHD16A,ALOX5AP,ANAPC13

110

31

Digestive System Development

morphology of Peyer's patches

0.00000208

  

DDX58,ID2,IGKC

12

32

Cellular Compromise, Inflammatory

degranulation of cells

0.0000021

Decreased

-3.08

C3AR1,C5AR1,CAMP

31

33

Cell Signaling, Molecular Transport

mobilization of Ca2+

0.00000212

Decreased

-2.95

ADORA2A,ARRB2,B2M

42

34

Cell-To-Cell Signaling and Interaction

binding of leukocytes

0.00000273

Decreased

-4.799

ABL1,ADORA2A,ARRB2

46

35

Immunological Disease

allergy

0.00000286

 

-1.655

ABL1,ACO2,ADORA2A

49

36

Humoral Immune Response, Protein

quantity of immunoglobulin

0.00000494

 

-1.731

B2M,BCL10,BCL2L11

37

37

RNA Post-Transcriptional Modification

processing of RNA

0.0000059

 

-0.670

ADAT1,AFF2,CELF1

36

38

Hematological System Development

quantity of thymocytes

0.00000592

Decreased

-3.599

ABL1,B2M,BCL10

30

39

Immunological Disease

abnormal morphology of immune

0.00000593

  

ABCD1,ABL1,B2M

37

40

Cancer, Hematological Disease

mature B-cell lymphoma

0.00000888

  

ABL1,B2M,BCL10

38

41

Digestive System Development

abnormal morphology of Peyer's

0.00000906

  

DDX58,ID2,IGKC

11

42

Lipid Metabolism, Small Molecule

synthesis of eicosanoid

0.00000989

Decreased

-3.209

ALOX5AP,ATP5J,C5AR1

29

43

Cellular Growth and Proliferation

expansion of cells

0.0000113

 

-0.717

ADORA2A,B2M,BMI1

37

44

Lipid Metabolism, Small Molecule

synthesis of leukotriene C4

0.0000148

Decreased

-2.753

ALOX5AP,C5AR1,COTL1

8

45

Gene Expression

activation of DNA endogenous

0.000016

Decreased

-3.846

ARRB2,ATF4,BMI1

111

46

Antigen Presentation, Inflammatory

antigen presentation

0.0000715

 

-1.556

ARL8B,CD74,CST3

14

47

Cell Death and Survival, Organismal

cell death of kidney cells

0.0000715

 

-1.863

ATG5,ATP1A1,BCL10

39

48

Cellular Movement, Hematological

chemotaxis of granulocytes

0.0000723

Decreased

-2.235

ADORA2A,BST1,C3AR1

24

49

Cancer, Hematological Disease

large-cell lymphoma

0.0000741

  

B2M,BCL2L11,CAMLG

34

50

Cell-To-Cell Signaling and Interaction

binding of mononuclear leukocytes

0.0000753

Decreased

-3.212

CD47,CD48,CD58

25

51

Cellular Movement, Embryonic

chemotaxis of embryonic cell lines

0.0000767

Decreased

-2.587

ARRB2,CAMP,CXCL1

7

52

Cellular Movement, Hair and Skin

chemotaxis of epithelial cell lines

0.0000767

Decreased

-2.587

ARRB2,CAMP,CXCL1

7

53

Cell Death and Survival, Skeletal

cell death of smooth muscle cells

0.0000775

 

-0.332

ARRB2,CAMP,CASP3

16

54

Cell Death and Survival

cell viability of phagocytes

0.0000775

Decreased

-2.939

BCL2A1,CD48,CEBPB

16

55

Cell Death and Survival

killing of lymphatic system cells

0.0000789

Decreased

-2.016

BCL2L11,CD47,CD48

10

56

Cell Death and Survival

cell viability of mononuclear leukocytes

0.0000805

Decreased

-3.491

ATG3,BCL10,BCL2L11

25

57

Cellular Development, Cellular Growth

differentiation of myeloid leukocytes

0.0000809

 

-1.081

ABL1,CAMP,CD47

31

58

Cell-To-Cell Signaling and Interaction

binding of lymphatic system cells

0.0000847

Decreased

-3.360

CD47,CD48,CD58

23

59

RNA Post-Transcriptional Modification

unwinding of mRNA

0.000086

  

EIF4A1,EIF4A2,EIF4B

3

60

Cell Death and Survival, Organismal

cell death of epithelial cells

0.000136

 

-1.105

ARRB2,ATG5,BCL10

51

The results described so far are summarized in Table 10. The data were divided into 12 categories, each category has 5 topics (total 60), and out of these 60 topics, only 13 topics were further investigated in detail for their functions related to present studies. For example, the “diseases and disorder” category (III) includes infectious diseases, immunological diseases, cancer, and organismal injury/abnormalities and tumor morphology (Table 10). The “molecular and cellular functions” category (IV) includes cellular development, cellular growth and proliferation, death/survival, cell-to-cell signal ligand interaction and cellular function and maintenance. Table 10 also includes a list of expression log ratio of 10 up-regulated genes (SNORD15A, SNORA32, SNORA56, SNORA9, SNORA3B, SNORA3A, HIST1H2AD, LINC00305, HHIPL2), and 10 down-regulated genes (HMGN1P3, SNHG25, SNORA67, RPL17-C18orf32, ISY1-RAB43, ARHGEF18, KLRC4-KLRK1/KLRK1, HIST1H3J, MTHFS, SNORA16A) were related to present investigation. At the end, out of 360 “canonical pathways” of IPA of total mRNAs samples of effects of δ-tocotrienol treatment to hepatitis C patients, 33 pathways are selected, which are associated with various signaling and biomarkers relative to present results (Table 11). The heat map (Fig. 2) also depicts same diseases and functions as outlined in Tables 9A, B and 10.
Table 10

Summary of IPA analyses of RNAs obtained from δ-tocotrienol treatment of hepatitis C patients

#

Subjects

P-Value ovrlap

Overlap

#

Subjects

P-Value ovrlap

# Molecules

I

Top Canonical Pathways

  

VII

Cardiotoxicity

  

1

EIF2 Signaling

1.28E-37

30.3 % 67/221

31

Cardiac Infarction

3.62E-01 - 5.40E-04

23

2

Regulation of eIF4 and p70S6K Signaling

5.38 E-140

21.0 % 33/157

32

Caediac Necrosis/Cell Death

1.65E-01 - 2.56E-03

23

3

mTOR Signaling

1.28 E-13

18.4 % 37/102

33

Cardiac Dycfunction

4.31E-01 - 2.63E-03

11

4

B Cell Receptor Signaling

8.35 E-08

14.2 % 27/190

34

Cardiac Fibrosis

1.77E-01 - 5.68E-03

14

5

Signaling

1.72E-06

16.2 % 18/111

35

Cardiac Transformation

1.10E-02 - 1.10E-02

2

II

Top Upstream Regulators

 

Predicted Activation

VIII

Hepatotoxicity

  

6

ST 1926

5.62E-20

Activated

36

Liver Proliferation

2.15E-01 - 5.85E-05

26

7

Sirolimus

2.32E-18

Activated

37

Liver Necrosis/Cell Death

6.13E-01 - 6.59E-05

29

8

CD 437

1.45E-17

Activated

38

Liver Damage

4.69E-01 - 1.81E-04

35

9

RICTOR

1.64E-17

Activated

39

Liver Inflamma/Hepatitistion

4.52E-01 - 5.02E-04

36

10

MYCN

3.22E-15

Inhibited

40

Liver Cirrhosis

4.19E-02 - 1.65E-03

21

III

Diseases and Disorder

 

# Molecules

IX

Nephrotoxicity

  

11

Infectious Diseases

1.14E-04 - 1.29E-24

244

41

Renal Necrosis/Cell Death

3.32E-01 - 7.15E-05

46

12

Immunological Disease

7.41E-05 - 2.37E-23

372

42

Renal Inflammation

3.74E-01 - 1.69E-03

33

13

Cancer

1.25E-04 - 4.75E-22

839

43

Renal Nephritis

3.70E-01 - 1.69E-03

33

14

Organismal Injury and Abnormalities

1.36E-04 - 4.75E-21

865

44

Renal Damage

5.15E01 - 3.12E-03

21

15

Tumor Morphology

1.19E-04 - 4.75E-21

82

45

Glomerular Injury

1.00E-00 - 1.47E-02

22

IV

Molecular and Cellular Functions

 

# Molecules

X

Top Regulator Effect Networks

Disease & Functions

Consistency Score

16

Cellular Development

1.24E-04 - 1.29E-24

222

46

Ap1,CAMP,F2RL1,IL17A,IL1RN,KITLG,mir10,NRG1,SELP (+2 >)

Activationof antigen presenting cells (+11 >)

40.848

17

Cellular Growth and Proliferation

1.24E-04 - 1.29E-24

206

47

AP1,CAMP,EIF2AK2,F2RL1,IL17A,IL1RN, KITLG (+2 >)

Activationof phagocytes (+9 >)

36.338

18

Cell Death and Survival

1.36E -04 - 3.94E-21

371

48

26s Proteasome,ANGPT2,AP1,BCL2,CAMP,CEBPA,F2RL (+6 >)

Activationof antigen presenting cells (+10 >)

32.199

19

Cell-To-Cell Signalingand Interaction

1.34EE-18-04 - 7.04

183

49

26s Proteasome,CAMP,CSF1,IL17A,JUN,LDL (+5 >)F2RL (+6 >)

Activationof antigen presenting cells (+7 >)

30.414

20

Cellular Function and Maintenance

1.02E-04 - 2.10E-16

232

50

AP1,CAMP,CCL5,EIF2AK2,F2RL1,FGF10,IL17A,IL1RN (+5 >)

Accumulation of leukocytes (+19 >)

30.375

V

Physiological System Development and Function

 

# Molecules

XI

Top Networks (Associated Network Functions)

 

Score

21

Hematological System Development and Function

1.34E-04 -1.29E-24

255

51

Developmentall Disorder, Hereditary Disorder, Metabolic Diseases

46

22

Lymphoid Tissue Structure and Development

1.33E-04 -1.29E-24

194

52

Cancer, Cell Death and Survival, Organismal Injury and Abnormalities

44

23

Tissue Morphology

1.19E-04 - 2.45E-19

184

53

Post-Translational Midification, Cell Cycle, Cellular Development

44

24

Immune Cell Trafficking

1.34E-04 - 7.04E-18

160

54

Cancer, Hematological Disease, Immunological Disease

 

41

25

Hematopoiesis

1.02E004 - 6.87E-14

130

55

Protein Synthesis, RNA Post-Transcriptional Modification, Gene Expression

39

VI

Top Tox Functions (Clinical Chemistry and Hematology)

 

# Molecules

XII

Top Toxicology Lists

p-value

Overlap

26

Increased Levels of Albumin

2.38E-01 - 1.24E-02

4

56

Renal Necrosis/Cell Death

1.58E-05

8.60 % 46/538

27

Increased Levels of Alkaline Phosphatase

2.12E-01 - 4,42E-02

6

57

Liver Prolification

1.80E-05

11.0 % 26/236

28

Decreased Levels of Hematocrit

5.71E-02 - 5.71E-02

2

58

Liver Necrosis/ Cell Death

8.35E-05

9.6 % 29/303

29

Increased Levels of Hematocrit

6.20E-02 - 6,20E-02

8

59

Mechanism of Gene regulation by Peroxisome

2.74E-04

13.7 % 13/95

30

Increased Levels of Potassium

5.36E-01 - 8.64E-02

2

60

Increases Liver Damage

7.40E-04

11.4 % 15/132

A

Gene Expression Fold Change (Up-regulated)

Expression Value

 

B

Gene Expression Fold Change (Down-regulated)

Expression Value

 

1

SNORD15A

581.151

 

1

HMGN1P3

-381.06

 

2

SNORA32

390.353

 

2

SNHG25

-350.0555

 

3

SNORA56

185.194

 

3

SNORA67

-148.69

 

4

SNORA9

124.698

 

4

RPL17-C18orf32

-67.253

 

5

SNORS3B

102.91

 

5

ISY1-RAB43

-51.147

 

6

SNORA3A

93.09

 

6

ARHGEF18

-41.381

 

7

HIST1H2AD

20.784

 

7

KLRC4-KLRK1/KLK1

-20.578

 

8

SNORD3D

17.157

 

8

HIST1H3J

-19.795

 

9

LINC00305

4.853

 

9

MTHFS

-18.71

 

10

HHIPL2

4.844

 

10

SNORA16A

-18.285

 
Table 11

Effect of δ-tocotrienol on canonical pathways (33) of IPA ingenuity canonical pathways analysis (360) in hepatitis C patients

#

Ingenuity Canonical Pathways (Fold Change Expression)

-log (p-value)

Ratio

Z-Score

Molecules

1

EIF2 Signaling; Eukaryotic translation initiation factors (221)

36.900

0.303

-5.692

RPL7A,EIF3G,RPL13A,RPL32,RPS24,RPL37A,RPL23,RPL26,RPS13

2

Regulation of eIF4 and p70S6K signaling (157)

13.300

0.210

0.000

PPP2R5E, EIF3G, RPS26

3

Protein ubiquitination pathway (265)

3.130

0.091

#NUM!

UBE2J1, USP19, UBA52

4

mTOR signaling; Mammalian target of rapamycin (201)

12.900

0.184

-2.138

PPP2R5E, EIF3G, RPS26

5

Type I Diabetes Mellitus Signaling (111)

5.760

0.162

-2.496

NFKB1,MAP3K5,JAK2,HLA-DQB1,IFNGR2,TNFRSF1B,PIAS1,TRADD

6

Th1 and Th2 Activation Pathway (185)

5.640

0.130

#NUM!

NFKB1,JAK2,NOTCH1,HLA-DQB1,IFNGR2,PIK3R1,HLA-DRA

7

Interferon Signaling (36)

4.700

0.250

-2.333

IFNGR1,OAS1,IFIT1,JAK2,IFITM1,IFNGR2,IFITM2,PIAS1,PSMB8

8

Role of IL-17F (44)

3.960

0.205

-3.000

NFKB1,ATF4,CREB1,RPS6KA3,CXCL1,MAPK1,CXCL8,RPS6KA4

9

IL-8 Signaling (197)

3.320

0.102

-4.123

NFKB1,GNA13,GNB4,RACK1,VEGFA,MYL12B,PIK3R1,ARRB2,NCF2

10

NF-κB Signaling (181)

2.940

0.099

-4.243

GSK3B,SIGIRR,NFKB1,CSNK2B,TNFRSF1B,IL1R2,PIK3R1,TRADD

11

IL-17A Signaling in Fibroblasts (35)

2.400

0.171

#NUM!

GSK3B,NFKB1,CEBPD,CEBPB,MAPK1,TRAF6

12

IL-6 Signaling (128)

2.360

0.102

-3.051

NFKB1,JAK2,CSNK2B,TNFRSF1B,VEGFA,IL1R2,PIK3R1,CXCL8,FRS2

13

Induction of Apoptosis by HIV1 (61)

2.280

0.131

-2.828

CXCR4,NFKB1,MAP3K5,TNFRSF1B,CASP3,TRADD,RIPK1,SLC25A13

14

HMGB1 Signaling (133)

2.220

0.098

-3.606

OSM,NFKB1,IFNGR2,TNFRSF1B,PIK3R1,SP1,CXCL8,IFNGR1,HMGB1

15

PPAR Signaling (95)

2.040

0.105

1.897

NFKB1,TNFRSF1B,PTGS2,IL18RAP,MAPK1,IL1R2,HSP90AB1,SCAND1

16

IL-10 Signaling (69)

1.960

0.116

#NUM!

NFKB1,IL18RAP,MAPK1,IL1R2,SP1,FCGR2A,TRAF6,IL10RA

17

iNOS Signaling (45)

1.860

0.133

-2.449

IFNGR1,NFKB1,JAK2,IFNGR2,MAPK1,TRAF6

18

Insulin Receptor Signaling (141)

1.650

0.085

-1.508

GSK3B,PPP1CC,PTEN,JAK2,GYS1,PDE3B,FRS2,MAPK1,GSK3A

19

p53 Signaling (111)

1.600

0.090

0.000

GSK3B,DRAM1,PTEN,HIF1A,FRS2,ATR,ST13,PIK3R1,PIAS1,PCNA

20

Role of IL-17A in Arthritis (69)

1.490

0.101

#NUM!

NFKB1,FRS2,PTGS2,CXCL1,MAPK1,PIK3R1,CXCL8

21

Toll-like Receptor Signaling (76)

1.300

0.092

-1.000

SIGIRR,TLR8,UBA52,NFKB1,MAP3K1,MAPK1,TRAF6

22

IL-1 Signaling (92)

1.300

0.087

-2.449

GNAQ,NFKB1,GNA13,GNB4,RACK1,MAP3K1,MAPK1,TRAF6

23

Apoptosis Signaling (90)

0.987

0.078

-0.378

NFKB1,MAP3K5,BCL2L11,BCL2A1,TNFRSF1B,MAPK1,CASP3

24

PDGF Signaling (90)

0.987

0.078

-2.646

ABL1,JAK2,CSNK2B,MAP3K1,FRS2,MAPK1,PIK3R1

25

Type II Diabetes Mellitus Signaling (128)

0.944

0.070

-2.333

NFKB1,MAP3K5,TNFRSF1B,MAP3K1,FRS2,CEBPB,MAPK1,PIK3R1

26

IL-15 Signaling (76)

0.904

0.107

#NUM!

NFKB1,JAK2,TXK

27

autophagy (62)

0.859

0.081

#NUM!

CTSW,ATG3,ATG5,CTSC,LAMP2

28

IL-2 Signaling (64)

0.818

0.078

-2.000

CSNK2B,FRS2,MAPK1,PIK3R1,IL2RG

29

PPARα/RXRα Activation (180)

0.759

0.061

3.000

TGS1,GNAQ,TGFBR2,NFKB1,JAK2,IL18RAP,MAPK1,MED12,IL1R2

30

TNFR1 (32)

2.210

0.140

-2.646

NFKB1,MAP4K2,MAP3K1,PAK1,CASP3,TRADD,RIPK1

31

STAT3 Pathway (74)

0.641

0.068

-1.342

TGFBR2,JAK2,MAPK1,PTPN6,IGF2R

32

Nitric Oxide Signaling in the Cardiovascular System (113)

0.633

0.062

-2.646

ITPR2,VEGFA,PDE3B,FRS2,MAPK1,PIK3R1,HSP90AB1

33

Osteoarthritis Pathway (210)

3.370

0.100

-2.524

NFKB1,CREB1,NOTCH1,TNFRSF1B,VEGFA,KEF1,IL-1R2,mir-140

Discussion

The fold-change gene expression data analyzed by Ingenuity Pathway Analysis describes cellular and biological mechanisms at the molecular level on the effect of δ-tocotrienol in chronic hepatitis C patients. It involves metabolic and cellular processes, mainly associated with catalytic activity of structural molecules. It also reveals an insight of correlation of signaling pathways and transcriptional factors, and subsequently describes inhibition or activation of anti- and pro-inflammatory genes. The results of these functional genomics produced a huge amount of data analyzed by biological networks using differentially gene expression after treatment with δ-tocotrienol to chronic hepatitis C patients. It predicts possible canonical pathways, upstream regulators, diseases and functional metabolic networks. The differential gene expressions of several biological functions illustrated in the heat map is shown in Fig. 2.

The present data revealed that genes responsible for replication of virus, infection by RNA viruses, infection of tumor cell lines, HIV infection and replication of influenza virus were all down-regulated, while cell death processes were all up-regulated. Moreover, as mentioned earlier, that Table 10 includes a list of expression log ratio of 10 up-regulated and 10 down-regulated genes. The forgoing information is mainly from “Ingenuity Knowledge Base” including as the information source for these facts and pathways.

The first up-regulated gene, SNORD15 is a non-coding RNA (ncRNA) gene which involves in the modification of other small nuclear RNAs (snRNAs), located in the nucleolus of the eukaryotic cell, which is a major site of snRNA biogenesis, and known as small nuclear RNA (snoRNA) [9]. It belongs to C/D box class of snoRNA, which function in directing site-specific 2-O-methylation of substrate RNAs [9]. In humans, there are two closely related copies of the U15 snoRNA (called SNORD15A and SNORD15B) [10]. Histone H2A type 1-D encoded by HIST1H2AD gene in humans. Histones are basic nuclear proteins that are responsible for the nucleosome structure of chromosomal fiber in eukaryotes. LINC00305 is associated with atherosclerotic plagues and monocytes [11]. Overexpression of LINC00305 promoted the expression of inflammation-associated genes in THP-1cells and reduced the expression of contractile markers in co-cultured human aortic smooth muscle cells. LINC00305 overexpression activated NF-κB and inhibition of NF-κB abolished LINC00305-mediated activation of cytokine expression [12]. HHIPL-2 identified as a candidate gene involved in iron-related modulation of osteoblast markers. The excess of iron limits HHIP-2 gene expression and decreases osteoblastic activity in human MG-63 cell [13].

Whereas, the “High Mobility group Nucleosome Domain 1 Pseudogene 3” (HMGN1P3) is a down-regulated pseudogene 3, and belongs to NURSA nuclear receptor signaling pathways expression of HMGN1P3 gene, and involves in all type of cancers (from breast, prostate, pancreas, colon kidney, lung, ovary, uterus) [14, 15]. The small nuclear RNA (SNORA67) is also a down-regulated non-coding RNA molecule that belongs to the H/ACA class of snoRNA, which guide the sites of modification of uridines and pseudouridines [16]. The ISY1-RAB43 is the naturally occurring read-through transcription gene, which act between the neighboring ISY1 (splicing factor homolog) and RAB43 (member RAS oncogene family) gene on chromosome 3. The read-through transcript encodes is a protein that shares sequence identity with the upstream gene product, but its C-terminus is distinct due to a frameshift relative to the downstream gene [17]. The Rho/Rac guanine nucleotide exchange factor 18 (ARHGEF18) is GTP binding proteins that regulate a number of cellular functions such as, cytoskeletal rearrangements, gene transcription, cell growth and motility [18].

The KLRC4-KLRK1 gene represents also naturally occurring down-regulated read-through transcription gene, which acts between the neighboring KLRK4 (killer cell lectin-like receptor subfamily C, member 4) family. This protein and its ligands are therapeutic targets for the treatment of immune diseases and cancers [19]. Histone H3.1 is a protein that in human encoded by the HIST1H3J gene [20, 21]. Histones are basic nuclear proteins that are responsible for the nucleosomes fiber in eukaryotes. The methenyltetrahydrofolate synthetase (MTHFS) is down- regulated encoded an enzyme that catalyzes the conversion of 5-formyltetrahydrofolate to 5, 10-methenyltetrahydrofolate, and helps regulate carbon flow through the folate-dependent one-carbon metabolic network [22, 23]. The small nucleolar RNA, H/ACA box 16A (SNORA16A) gene provides a unified query environment for genes defined by sequence [24].

The study also provides an insight of correlation of signaling pathways and transcriptional factors and subsequently describes the modulation of anti- as well as pro-inflammatory genes. It described the effects δ-tocotrienol in chronic hepatitis C patients on gene expression of liver cancer, liver hyperplasia, cell proliferation, cell growth, cell death/survival, infections, inflammatory diseases, and apoptosis. Collectively, the effects of δ-tocotrienol on “canonical pathways” observed in IPA of total mRNA sample of hepatitis C patients resulted in modulation of over 360 pathways, which are associated with multiple signaling pathways. It is conceivable that some or most of these pathways may be controlled by the proteasome, since the protein ubiquitination pathway was down-regulated by δ-tocotrienol treatment as described previously [1].

The important signaling pathways modulated by tocotrienols are as follows: at the top of the list is “eukaryotic translation initiation factors” (EIF2) signaling pathway (Fig. 3). This is involved in protein synthesis, and requires a large number of polypeptides. EIF2 is a GTP-binding protein, which initiates specific forms of met-tRNA onto the ribosome. Its important function is to deliver charged initiator met-tRNA to the ribosome, it also identifies the translational starting site [9]. This is followed by protein ubiquitination pathway, which plays a major role in the degradation of short-lived or regulatory proteins. It plays a role in a variety of cellular processes, such as cell cycle, cell proliferation, apoptosis, DNA repair, transcriptional regulation, cell surface receptors, ion channels regulation and antigen presentation, as outlined in Fig. 4 [10]. We have discussed the importance of ubiquitination in our several earlier publications [1115].
Fig. 3
Fig. 3

Effect on eukaryotic translation initiation factors (EIF2) signaling pathway in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. EIF2 was down-regulated by δ-tocotrienol treatment, which is involved in protein synthesis, requires a large number of polypeptides. EIF2 is a GTP-binding protein, which initiates specific form of met-tRNA onto the ribosome

Fig. 4
Fig. 4

Effect on protein ubiquitination signaling pathway in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The protein ubiquitination pathway was down-regulated by δ-tocotrienol treatment. It plays a major role in the degradation of regulatory proteins, including a variety of cellular processes, such as cell cycle, cell proliferation, DNA repair, apoptosis, transcription regulation, cell surface receptors, ion channel regulation and antigen presentation

δ-Tocotrienol treatment of chronic hepatitis C patients also affects several other regulators in canonical pathways, we will limit our discussion to only important signaling and biomarkers associated with present investigation. The toll-like receptor signaling (TLRs) belongs to the family of pathogen-associated pattern recognition receptors, and bind to specific molecular patterns in bacteria and viruses. The pathogen-associated ligands include bacterial flagellin, viral DNA, lipopolysaccharide (LPS) and CpG DNA motifs. TLRs form a complex with different combinations of adopter molecules like MYD88, TRAF6 and TIRAP to initiate signal transduction upon ligand binding. This binding triggers a cascade of signaling events via the TLR-adapter complex, and downstream sigling molecules like p38MAPK. JNK. NF-κB activated and translocated into the nucleus, where they activate transcription regulators like c-Fos and c-Jun, leading to the induction of several pro-inflammatory cytokines, eventually leading to antibacterial and antiviral responses [25, 26]. Tocotrienol treatment causes a downregulation of the TLR pathways in hepatitis C patients. The toll-like receptor signaling pathways outlined in Fig. 5.
Fig. 5
Fig. 5

Effect on toll-like receptor (TLRs) signaling pathways in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The TLRs were down-regulated by δ-tocotrienol treatment, these belong to the family of pathogen-associated receptors, and bind to a number of bacteria and viruses, such as viral DNA, lipopolysaccharide, and CpG DNA motifs. TLRs form a complex with different combinations of adapter molecules like MYD88, TRAF6 and TIRAP to initiate signal transduction upon ligand binding

The signal transducers and activators of transcription (STATs) are a family of cytoplasmic proteins with Src homology-2 (SH2) domains. STATs acts as a signal messenger and transcription factors. It participates in normal cellular responses to cytokines and growth factors. STATs pathways activated via tyrosine phosphorylation cascade after ligand binding by stimulation of the cytokine receptor-kinase complex and growth factor-receptor complex. The IL-6 cytokine activates STAT3 and STAT1. STAT3 encoded in human gene. The STAT3 signaling pathway (Fig. 6) plays an important role in normal development, particularly hematopoiesis, and regulates cancer metastasis by regulating the expression of genes that are critical to cell survival, cell proliferation, invasion, angiogenesis, and tumor immune evasion [2729].
Fig. 6
Fig. 6

Effect on signal transducer and activators of transcription (STATs) signaling pathways in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The STATs were down-regulated by δ-tocotrienol treatment, and belong to a family of cytoplasmic proteins with Src homology-2 (SH2) domains that acts as signal messenger and transcriptional factors and responses to cytokines and growth factors. The STAT pathways are activated via tyrosine phosphorylation cascade and play an important role in normal development of hematopoiesis, and regulates cancer metastasis by regulating the expression of genes that are critical to cell survival, cell proliferation, invasion, angiogenesis, and tumor immune evasion

The nuclear factor kappa B (NF-κB) transcription factors are key regulators of gene expression and acts in response to stress and the development of innate and acquired immunity [30]. A multitude of extracellular stimuli (such as cytokines, infections, oxidative, DNA-damaging agents, UV light, osmotic shock) can lead to NF-κB activation. NF-κB activators mediate the site-specific phosphorylation of serine on IκB (inhibitor of NF-κB), resulting in IκB ubiquitination and subsequent proteasomal destruction [31]. The pathway highlights the important components of the NF-κB signaling pathway outlined in (Fig. 7). Inhibiting this pathway by proteasome inhibitors would possibly expected to cause cell death of infected hepatic cells.
Fig. 7
Fig. 7

Effect on nuclear factor kappaB (NF-κB) in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. δ-Tocotrienol modulates NF-κB transcription factors, which are key regulators of gene expression and act in response to stress and the development of innate and acquired immunity. A number of NF-κB activators mediate the site-specific phosphorylation of serine on IκB (inhibitor of NF-κB), there by marking IκB for ubiquitination and subsequent proteasomal destruction

The catalytic activity of iNOS is to kill or inhibit the growth of invading viruses and microorganisms. It produces nitric oxide from L-arginine [32, 33]. Nitric oxide is a free radical effector of the innate immune system that can directly inhibit pathogen replication. A variety of extracellular stimuli can activate signaling pathways that converge to initiate expression of iNOS. Moreover, components of cell wall of bacteria (lipopolysaccharide; LPS) or fungi trigger the innate immune signaling cascade leading to expression of iNOS [3436]. This leads to activation of NF-κB and p38 MAPK signaling pathways [37]. NF-κB in the nucleus binds to NF-κB elements in the iNOS 5′ flanking region, triggering iNOS transcription. Cytokines released from the infected host cell also activate nitric oxide production. IFNγ activates JAK family kinases to trigger JAK/STAT signaling, leading to synthesis of the transcription factor IRF1 and stimulation of a large number of iNOS mRNA transcription [38]. The iNOS signaling pathways (Fig. 8) shows all possible regulators of production of nitric oxide, and highlights the important molecular events leads to production in macrophages. Collectively, IFN-γ induced by δ-tocotrienols would be expected to modulate the JAK/STAT pathway and NO production.
Fig. 8
Fig. 8

Effect on nitric oxide synthase (iNOS) in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The iNOS was down-regulate by δ-tocotrienol treatment. It produces nitric oxide from L-arginine, a cytotoxic weapon generated by macrophages. The catalytic activity of iNOS is to kill or inhibit the growth of invading microorganisms. Nitric oxide is a free radical effector of the innate immune system that inhibits pathogen replication. A variety of extracellular stimuli (components of bacteria and fungi) can activate signaling pathways that help to initiate expression of iNOS

Interleukin-6 (IL-6) is a regulator of acute phase responses and a lymphocyte stimulatory factor. The central role of IL-6 is for the management of infectious and inflammatory diseases [39]. IL-6 responses transmitted through glycoprotein 130 (GP130), which serves as the universal signal-transducing receptor subunit for all IL-6 related cytokines. Moreover, IL-6-type cytokines utilize tyrosine kinases of the Janus kinase (JAK) family and signal transducer/activators of STAT transcription family as major mediators of signal transduction [40]. In addition to the JAK/STAT pathway of signal transduction, IL-6 also activates the extracellular signal-regulated kinases (ERK1/2) of the mitogen activated protein kinase (MAPK) pathway (Fig. 9). The upstream regulators of ERK1/2 include RAS and the src homology-2 containing proteins GRB2 and SHC. The SCH protein activate by JAK2 and thus serves as a link between the IL-6 activated JAK/STAT and RAS-MAPK pathways shown in IL-6 signaling pathway Fig. 9 [41]. Furthermore, phosphorylation of MAPKs in response to IL-6 activated RAS results in the activation of nuclear factor IL-6 (NF-IL-6), which in turn stimulates the transcription of the IL-6 gene. IL-6 gene transcription is also stimulated by TNF-α and IL-1 via activation of NF-κB [4143]. The tumor necrosis factor receptor (TNFR1) belongs to a family of 20 in mammalian cells.
Fig. 9
Fig. 9

Effect on interleukin-6 (IL-6) regulator of gene expression in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The IL-6 was down-regulated by δ-tocotrienol treatment, and is considered a regulator of acute phase responses and a lymphocyte stimulatory factor. The most important role of IL-6 is for the management of infection and inflammatory diseases. The transcription of IL-6 gene is stimulated by TNF-α and IL-1 via activation of NF-κB

TNF-α, an important cytokine involves in cell proliferation, differentiation, and apoptosis modulate immune responses and induction of inflammation [44]. TNF-α functions through two receptors, TNFR1 TNFR2. TNFR1 is expressed in human tissue and TNFR2 expressed in immune cells (Fig. 10) [44, 45]. δ-Tocotrienol also inhibits expression of IL-6 and TNFR induction in chronic hepatitis C patients.
Fig. 10
Fig. 10

Effect on tumor necrosis factor receptor1 (TNFR1) regulator of gene expression in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The TNFR1 was down-regulated by δ-tocotrienol treatment, and belongs to a family of 20 in mammalian cells. TNF-α is an important cytokine involved in cell proliferation, differentiation, apoptosis, modulates immune responses and induction of inflammation. TNF-α functions through two receptors, TNFR1 and TNFR2. TNFR1 is expressed in human tissue, and TNFR2 is expressed in immune cells

Autophagy is a basic catabolic mechanism that involves cellular degradation of unnecessary or dysfunctional cellular components through the actions of liposome [46, 47]. Autophagy is generally activate by condition of nutrient deprivation but has also been associated with physiological as well as pathological processes such as development, differentiation, neurodegenerative diseases, stress, infection, and cancer [4749]. The mammalian target of rapamycin (mTOR) kinase is a critical regulator of autophagy induction, with activated mTOR (AKT and MAPK signaling) suppressing autophagy, and negative regulation of mTOR (AMPK and p53 signaling) promoting it [48]. The autophagy pathway (Fig. 11) highlights the key molecular events involved in triggering autophagy. Inhibiting the proteasome activity also causes the onset of autophagy, as observed with δ-tocotrienol treatment.
Fig. 11
Fig. 11

Effect on autophagy in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. The autophagy modulated by δ-tocotrienol treatment of hepatitis C patients:. Autophagy is a general term for the basic catabolic mechanism that involves cellular degradation of unnecessary or dysfunctional cellular components through the actions of lysosome. Autophagy is generally activated by conditions of nutrient deprivation but it has also been associated with physiological as well as pathological processes such as development, differentiation, neurodegenerative diseases, stress, infection, and cancer. The mammalian target of rapamycin (mTOR) kinase is a critical regulator of autophagy induction

Whereas, apoptosis is a coordinated energy-dependent process that involves the activation of a group of cysteine proteases called caspases and a cascade of events that link the initiating stimuli to programmed cell death [50]. The two main pathways of apoptosis are the intrinsic and extrinsic pathways. Each pathway requires specific triggers to initiate a cascade of molecular events that converge at the stage of caspase-3 activation [50]. The activation of caspase-3 in turn triggers an execution pathway resulting in characteristic cytomorphological features including cell shrinkage, membrane blabbing, chromatin condensation and DNA fragmentation [51]. Further details of intrinsic and extrinsic pathways were found in the attached Ingenuity Apoptosis Signaling Pathway (Fig. 12), which highlights the key molecular events involved in trigging apoptosis.
Fig. 12
Fig. 12

Effect on apoptosis in plasma of total mRNAs obtained from δ-tocotrienol treatment of hepatitis C patients. Apoptosis modulated by δ-tocotrienol treatment of hepatitis C patients. Apoptosis is a coordinated energy-dependent process that involves the activation of a group of cysteine proteases called caspases and a cascade of events that link the initiating stimuli to programmed cell death. There are two main pathways of apoptosis, the intrinsic and extrinsic as shown here

Beside these, other regulators were also affected by δ-tocotrienol treatment of hepatitis C patients, and they are interferon signaling, IL-2 signaling, and HMGB1 signaling, Cardiac hypertrophy signaling, Th1 and Th2 activation pathway, production of nitric oxide and reactive oxygen species in macrophages, Osteoarthritis pathway, PPAR signaling, type,I diabetes mellitus signaling, Type II diabetes mellitus, and insulin receptor signaling. In summary, EIF2 signaling regulator is at the top of the canonical pathway list but its fold change expression value is 221 as compared to protein ubiquitination pathway is 265 fold. On the other hand, osteoarthritis (210 fold), mammalian target of rapamycin (mTOR-201 fold), IL-8 (197 fold), Th1-Th2 (185 fold), PPARα/RXRα activation (180 fold), NF-κB (181 fold), IL-6 (128 fold), Type II diabetes mellitus signaling (128 fold), and nitric oxide signaling in cardiovascular system (113 fold), all have lower fold change expression compared to EIF2. This indicates the importance of δ-tocotrienol on so many biological activities and signaling pathways (Table 11). The importance of most of these regulators was discussed in our several publications during course of the last decade [1, 1115].

Conclusions

Present results of fold-change expression data analyzed by “Ingenuity Pathway Analysis” describe the effect of δ-tocotrienol in chronic hepatitis C patients on biological mechanisms at molecular level. It also revealed an insight of correlation of signaling pathways and transcriptional factors. Recently, two comprehensive reviews on the several biological activities of tocotrienols as hypocholesterolemic, anti-inflammatory, anticancer, antioxidant, neuroprotective, skin protection benefits, bone health and longevity have been published [52, 53]. These articles also cover the beneficial properties of different isomers of tocotrienols treatment along with possible mechanisms, signaling pathways in breast, prostate, pancreas, rectal cancers in cell lines and humans [52, 53]. Major signaling pathways that were affected by δ-tocotrienol treatment in chronic hepatitis C subjects are summarized in the Table 12. The collective results indicate that tocotrienols inhibit cancer cell proliferation, promotes cell cycle arrest, decreases angiogenesis and acts via multiple signaling pathways [1]. Our present results are consistent with these conclusions and δ-tocotrienol treatment of hepatitis C patients, acts by increasing cell death, and necrosis of malignant tumors, and by decreasing viral infection, cellular growth and proliferation, decreasing endocrine system disorders such as diabetes mellitus, and mobilization of calcium. Therefore, tocotrienols can safely be used for hepatitis C patients, without any side effects.
Table 12

Major signaling pathways affected by δ-tocotrienol treatment in chronic hepatitis C subjects

Down-regulated by δ -tocotrienol treatment

Up-regulated by δ-tocotrienol treatment

Proliferation of immune cells

Cell death and survival

Proliferation of mononuclear leukocytes

Necrosis of malignant tumor

Viral infection

Gene expression

Free radical scavenging

Organismal Death

Endocrine system disorder, Diabetes mellitus

Cell death of cancer cells

Mobilization of Ca2+

Cell death of tumors

Replication of virus

 

HIV infection, replication of Influenza virus

 

Abbreviations

EIF2: 

Eukaryotic translation initiation factors

ICAM1: 

Intercellular adhesion molecule1

IL-6: 

Interleukin-6

IPA: 

Ingenuity Pathway Analysis

mTOR: 

Mammalian target of rapamycin

NF-κB: 

Nuclear factor kappaB

TNF-α: 

Τumor necrosis factor-α

VCAM1: 

Vascular cell adhesion molecule1

Declarations

Acknowledgements

We thank Ms. Suman Chaudhary as coordinator of collecting total mRNAs samples and estimation of quality control of total mRNAs for RNA-sequence analyses of various samples. The study was carried out under a FDA approved IND number 36906.

Funding

The study supported in part by Advanced Medical Research, Madison, Wisconsin and NIH funds RO1 GM50870, 3RO1 GM631S1, and 5RO1 GM10263.

Availability of data and materials

All data generated or analyzed during this study are included in this article.

Author’s contributions

AAQ and DAK conceived and planned the study to carry out RNA-sequence analysis after feeding δ-tocotrienol to chronic hepatitis C patients; AAQ wrote the manuscript. DAK and SM carried out human study and prepared total mRNAs after feeding δ-tocotrienol to chronic hepatitis C patients. SQY and MX have carried out RNA-sequence analyses, including data analyses. NQ has edited the manuscript and also involves in data analyses of RNA-sequence. NQ, and DAK were also involved in proof reading of this manuscript. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

The study carried out at the Pakistan Ordinance Factory (POF) Hospital, Wah Cantonment, Rawalpindi, 64,000, Pakistan, in collaboration with the Department of Basic Medical Sciences, University of Missouri-Kansas City, MO, USA. The study protocol registered (IRB # 129–2015) and approved by Institutional Review Board of POF Hospital, Rawalpindi, 64,000, Pakistan.

All subjects signed an informed-consent form, which approved by Institutional Board of POF Hospital, Rawalpindi, 64,000, Pakistan. The purified total RNA samples delivered at UMKC, School of Medicine after getting approval by the members “Compliance Officer (Christopher Winders)” and “Chemical Biological Safety Officer (Timothy Sturgis, RBP)” of Institution Board of UMKC School of Medicine, Kansas City, MO. USA.

Consent for publication

All contributing authors agree to the publication of this article.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biomedical Science, School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA
(2)
Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology (AFIP), National University of Medical Sciences, Rawalpindi, 64000, Pakistan
(3)
Division of Experimental and Translational Genetics, Department of Pediatrics, Childern’s Mercy Hospital, 2401 Gillham Road, Kansas City, MO 64108, USA
(4)
Department of Biomedical and Health Informatics, School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA
(5)
Pharmacology/Toxicology, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA

References

  1. Qureshi AA, Eleanor Z, khan DA, Shahida M, Silswal N, Qureshi N. Proteasomes inhibitors modulate anticancer and anti-proliferative properties via NF-κB signaling, and ubiquitin-proteasome pathways in cancer cell lines of different organs. Lipids Health Dis. 2018;17:62. https://doi.org/10.1186/s12944-018-0697-5.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Shepard CW, Finelli L, Alter MJ. Global epidemiology of hepatitis C virus infection. Lancet Infect Dis. 2005;5(9):558–67.View ArticlePubMedGoogle Scholar
  3. Hamid S, Umar M, alam A, Siddiqui A, Qureshi H, Butt J. PSG consensus statement on management of hepatitis C virus infection-2003. J Pak Med Assoc. 2004;54(3):146–50.PubMedGoogle Scholar
  4. DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM. Use of a cDNA microarray to analyse patients in human cancer. Nat Genet. 1996;14(4):457–60.View ArticlePubMedGoogle Scholar
  5. Patil MA, Chua MS, Pan KH, Lin R, Leh, Cheung ST, Ho C, Li R, Fan ST, Cohen SN, Chen X, So S. An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma. Oncogene. 2005;24(23):3737–47.View ArticlePubMedGoogle Scholar
  6. Shackel NA, McGuinness PH, Abbott CA, Correll MD, McCaughan GW. Insight into the pathobiology of hepatitis C virus associated cirrhosis: analysis of intrahepatic differential gene expression. Am J Pathol. 2002;160(2):641–54.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Smith MW, Yue ZN, Korth MJ, Do HJ, Boix L, Fausto N, Bruix J, Carithers RL Jr, Katze MG. Hepatitis C virus and liver disease: global transcriptional profiling and identification of potential markers. Hepatology (Baltomore, MD). 2003;38(6):1458–67.View ArticleGoogle Scholar
  8. Zein NN. Clinical significance of hepatitis C virus genotypes. Clin Microbiol Rev. 2000;13(2):223–35.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Galardi S, Fatica A, Bachi A, Scaloni A, Presutti C, Bozzoni I. Purified box C/D snoRNAs are able to reproduce site-specific 2;-O-methylation of target RNA in vitro. Mol Cell Biol. 2002;22(19):6663–8.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Tycowski KT, Shu MD, Steitz JA. A small molecular RNA is processed from an intron of the human gene encoding ribosomal protein S3. Genes Dev. 1993;7(7A):1176–90.View ArticlePubMedGoogle Scholar
  11. Albig W, Kioschis P, Poustka A, Meergans K, Doeneck D. Human histone gene organization: nonregular arrangement within a large cluster. Genomics. 1997;40(2):314–22.View ArticlePubMedGoogle Scholar
  12. Zhang DD, Wang WT, Xiong J, Xie XM, Cui SS, Zhao ZG, Li MJ, Zhang ZQ, Hao DL, Zhao X, Li J, Wang J, Chen HZ, Lv X, Liu DP. Long noncoding RNA LINC00305 promotes inflammation by activating the AHRR-NF-κB pathway in human monocytes. Sci Rep. 2017;10(7):46204. https://doi.org/10.1038/srep46204.View ArticleGoogle Scholar
  13. Doyard M, Fatih N, Monnier A, Island ML, Aubry M, Leroyen P, Bouvet R, Charles G, Loreal O, Guggenbuhl P. Iron excess limits HHIPL-2 gene expression and decreases osteoblastic activity in human MG-63 cells. Osteoporos Int. 2012;10:2435–45. https://doi.org/10.1007/s00198-011-1871-z. PMID 22237814View ArticleGoogle Scholar
  14. Strichman-Almashanu L, Bustin M, Landsman D. Retroposed copies of the HMG genes: a window to genome dynamics. Genome Res. 2003;13:800–12.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Rogalla P, Botda Z, Meyer-Bolte K, Tran KH, Hauke S, Nimzyk R, Bullerdiek J. Mapping and molecular characterization of five HMG1-related DNA sequences. Cytogen Cell Genet. 1998;83:124–9.View ArticleGoogle Scholar
  16. Kiss T. Small nuclear RNAs: an abundant group of noncoding RNAs with diverse cellular functions. Cell. 2002;109(2):145–8.View ArticlePubMedGoogle Scholar
  17. Stove EH, Konstantinopoulos PA, Matulonis UA, Swisher EM. Biomarkers of response and resistance to DNA repair targeted therapies. Clin Cancer Res. 2016;22(23):5651–60.View ArticleGoogle Scholar
  18. Arno G, Carss KJ, Hull S, Zihni C, Robson AG, Fiorentino A, UK Inherited Retinal Disease Consortium, Hardcastle AJ, Holder GE, Cheetham ME, Plagnol V, NIHR Bioresource-Rare Disease Consortium, Moore AT, Raymond FL, Matter K, Balda MS, Webster AR. Biallelic mutation of ARHGEF18, involved in the determination of epithelial apicobasal polarity, causes adult-onset retinal degeration. Am J Hum Genet. 2017;100(2):334–42.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Houchins JP, Yabe T, McSherry C, Bach FH. DNA sequence analyses of NKG2, a family of related cDNA clones encoding type II integral membrane proteins on human natural killer cells. J Exp Med. 1991;173:1017–20.View ArticlePubMedGoogle Scholar
  20. Albig W, Doenecke D. The human histone gene cluster at the D6S105 locus. Human Genet. 1997;101(3):284–94.View ArticleGoogle Scholar
  21. Marzluff WF, Gongidi P, Woods KR, Jin J, Maltais LJ. The human and mouse replication-dependent histone genes. Genomics. 2002;80(5):487–98.View ArticlePubMedGoogle Scholar
  22. Dayan A, Bertrand R, Beachemin M, Chahla D, Mamo A, Filion M, Skup D, Massie B, Jolivet J. Cloning and characterization of the human 5,10-methenyltetrahdrofolate synthase-encoding cDNA. J Gene. 1995;165(2):307–11.View ArticleGoogle Scholar
  23. Bertrand R, Beauchemin M, Dayan A, Quimet M, Jolivet J. Identification and characterization of human mitochondrial methenyltetrahdrofolate synthetase activity. Biochem Biophys Acta. 1995;1266:245–9.View ArticlePubMedGoogle Scholar
  24. Murry JL, Sheng J, Rubin DH. A role for H/ACA and C/D small nucleolar RNAs in viral replication. Mol Biotechnol. 2014;56:429–37.View ArticleGoogle Scholar
  25. Zhang D, Zhang G, Hayden MS, Greenbaltt MB, Bussey C, Flavell RA, Ghosh S. A toll-like receptor that prevents infection by urophathogenic bacteria. Science. 2004;303:1522–6.View ArticlePubMedGoogle Scholar
  26. Kien E, Means TK, Heine H, et al. Toll-like receptor 4 imparts lagand-specific recognition of bacterial lipopolysaccharide. J Clin Invest. 2000;105:497–504.View ArticleGoogle Scholar
  27. Silva CM. Role of STATs as downstream signal transducers in Src family kinase-mediated tumorigenesis. Oncogene. 2004;23(48):8017–23.View ArticlePubMedGoogle Scholar
  28. Lin CP, Cao X. Structure, function, and regulation of STAT protein. Mol BioSyst. 2006;2(11):536–50.View ArticleGoogle Scholar
  29. Yuan ZL, Guan YJ, Wei W, Wang L, Kane AB, Chin YE. Central role of the threonine residue within the p+1 loop of trceptor tyrosine kinase in STAT3 constitutive phosphorylation in metastatic cancer cells. Mol Cell Biol. 2004;24(21):9390–400.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Karin M. The beginning of the end: IκB kinase (IKK) and NF-κB activation. J Bol Chem. 1999;274:27339–42.View ArticleGoogle Scholar
  31. Palombella VJ, Rando OJ, Goldberg AL, Maniatis T. The ubiquitin-proteasome pathway is required for processing the NF-κB precursor protein and activation of NF-κB. Cell. 1994;78:773–85.View ArticlePubMedGoogle Scholar
  32. Moncada S, Higgs A. The L-arginine-nitric oxide pathway. N Engl J Med. 1993;329:2002–12.View ArticlePubMedGoogle Scholar
  33. Moncada S, Palmer RM, Higgs EA. Nitric oxide: physiology, pathophysiology and pharmacology. Pharmacol Rev. 1991;43:109–42.PubMedGoogle Scholar
  34. Forstemann U, Closs EI, Pollock JS, Nakane M, Schwarz P, Gath I, Kleinert H. Nitric oxide synthase isozyme: characterization, purification, molecular cloning and functions. Hypertension. 1994;23(pt 2):1121–31.View ArticleGoogle Scholar
  35. Nadaud S, Sobrier F. Molecular biology and molecular genetics of nitric oxide synthase genes. Clin Exp Hypertens. 1996;18:113–43.View ArticlePubMedGoogle Scholar
  36. Nathan C, Xie O. Nitric oxide synthase: roles, tolls and controls. Cell. 1994;78:915–8.View ArticlePubMedGoogle Scholar
  37. Qureshi N, Vogel SN, Van Way C III, Papasian CJ, Qureshi AA, Morrison DC. The proteasome. A central regulator of inflammation and macrophage function. Immunol Res. 2005;31(3):243–60.View ArticlePubMedGoogle Scholar
  38. Ma C, Wang DL, Li M, Cai W. Anti-inflammatory effect of resveratrol through the suppression of NF-kB and JAK/STAT signaling pathway. Acta Biochim Biophys Sin. 2015;17(3):207–13.View ArticleGoogle Scholar
  39. Kallen KJ, zum Buschenfelde KH, Rose-John S. The therapeutic potential of interleukin-6 hyperagonists and antagonists. Expert Opin Investig Drugs. 1997;6(3):237–66.View ArticlePubMedGoogle Scholar
  40. Heinrich PC, Behrmann I, Muller-newen G, Schaper F, Graeve F. Interleukin-6-type cytokine signaling through the gp 130/Jak/STAT pathway. Biochem J. 1998;334(pt 2):297–314.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Brandt C, Pedersen BK. The role of exercise-induced myokines in muscle homeostasis and the defense against chronic diseases. J Biomed Biotechnol 2010; Article ID 520258, 6 pages. Doi:https://doi.org/10.1155/2010/520258.
  42. Munoz-Canoves P, Scheele C, Pedersen BK, Serrano AL. Interkin-6 myokine signaling in skeletal muscle: a double-edged sword? FEBS J. 2013;280(17):4131–48.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Meador BM, Krzyszton CP, Johnson RW, Huey KA. Effects of IL-10, and age on IL-6, IL-1b, and TNF-α responses in mouse skeletal and cardiac muscle to an acute inflammatory insult. J Appl Physiol. 2008;104:991–7.View ArticlePubMedGoogle Scholar
  44. Beutler B, Greenwald D, Hulmes JD, Chan M, Pan YC, Matuison J, Ulevith R, Cerami A. Identity of tumor necrosis factor and macrophage-secreted factor cachectin. Nature. 1985;316(6028):552–4.View ArticlePubMedGoogle Scholar
  45. Soranzo C, Perego P, Zunino F. Effect of tumor necrosis factor on human tumor cell lines sensitive and resistant to cytotoxic drugs, and its interaction with chemotherapeutic agents. Anti-Cancer Drugs. 1990;1(2):157–63.View ArticlePubMedGoogle Scholar
  46. Ziparo E, Petrungaro S, Marini ES, Starace D, Conti S, Facchiano A, Filippini A, Giampietri C. Autophagy in prostate cancer and androgen suppressioin therapy. Int J Mol Sci. 2013;12:12090–106. https://doi.org/10.3390/ijms140612090. (ISSN 1422-0067)View ArticleGoogle Scholar
  47. Rubinsztein DC, Bento CF, Deretic V. Therapeutic targeting of autophagy in neurodegenerative and infectious diseases. J Exp Med. 2015;212(7):979–90.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Nedelsky NB, Todd PK, Taylor JP. Autophagy and ubiquitin-proteasome system: collaborators in neuroprotection. Biochim Biophys Acta. 2008;1782:691–9.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Zhu K, Dunner K Jr, McConkey DJ. Proteasome inhibitors activate autophagy as a cytoprotective response in human prostate cancer cells. Oncogene. 2010;29:451–62.View ArticlePubMedGoogle Scholar
  50. King LB, Ashwell JD. Thymocyte and T cell apoptosis: is all death created equal? Thymus. 1994–1995;23(3–4):209–30.PubMedGoogle Scholar
  51. Zhang N, Hartig H, Dzhagalov I, Draper D, He YW. The role of apoptosis in the development and function of T lymphocytes. Cell Res. 2005;15(10):749–69.View ArticlePubMedGoogle Scholar
  52. Kanchi MM, Shanmugan MK, Rane G, Sethi G, Kumar AP. Tocotrienols: the unsaturated sidekick shifting new paradigms in vitamin E therapeutics. Drug Discov Today. 2017;22(12):1765–81.View ArticlePubMedGoogle Scholar
  53. Sailo BL, Banik K, Padmavathi G, Javadi M, Bordoloi D, Kunnumakkara AB. Tocotrienols: the promising analogue of vitamin E for cancer therapeutics. Pharmacol Res. 2018;130:259–72.View ArticlePubMedGoogle Scholar
  54. Qureshi AA, Khan DA, Mahjabeen W, Trias AM, Silswal N, Qureshi N. Impact of δ-tocotrienol on inflammatory biomarkers and oxidative stress in hypercholesterolemic subjects. J Clin Exp Cardiolog. 2015;6:4. https://doi.org/10.4172/2155-9880.1000367.View ArticleGoogle Scholar

Copyright

Advertisement