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Article

Transcriptional Modulation of the Host Immunity Mediated by Cytokines and Transcriptional Factors in Plasmodium falciparum-Infected Patients of North-East India

by
Md Zohaib Ahmed
1,2,
Nitin Bhardwaj
1,2,
Supriya Sharma
1,
Veena Pande
2 and
Anupkumar R Anvikar
1,*
1
Epidemiology and Clinical Research Division, ICMR-National Institute of Malaria Research, New Delhi 110077, India
2
Department of Biotechnology, Kumaun University, Bhimtal, Uttarakhand, 263136, India
*
Author to whom correspondence should be addressed.
Biomolecules 2019, 9(10), 600; https://doi.org/10.3390/biom9100600
Submission received: 18 July 2019 / Revised: 20 September 2019 / Accepted: 21 September 2019 / Published: 11 October 2019
(This article belongs to the Section Cellular Biochemistry)
Browse Figures
Review Reports Versions Notes

Abstract

:
Complications due to malaria are caused mostly by host immunological responses. Plasmodium falciparum subverts host immunity by various strategies, including modulation in the host immune responses by regulating cytokines. The transcriptional alterations of major cytokines and immunoregulators were analyzed in this study through gene expression profiling in clinically defined subgroups of P. falciparum patients. Malaria patients were included from Dhalai district hospital of Tripura with uncomplicated malaria (UC) and severe malaria (SM) and healthy controls from endemic and non-endemic areas of India. qPCR gene expression analysis was performed for all factors and they were grouped into three clusters based on their altered expressions. The first cluster was downregulated with an increased parasitic burden which included T-BET, GATA3, EOMES, TGF-β, STAT4, STAT6 and cytokines IFN-γ, IL-12, IL-4, IL-5, and IL-13. RANTES, IL-8, CCR8, and CXCR3 were decreased in the SM group. The second cluster was upregulated with severity and included TNF-α, IL-10, IL-1β and IL-7. PD-1 and BCL6 were increased in the SM group. The third cluster comprised of NF-κB and was not altered. The level of perforin was suppressed while GrB expression was elevated in SM. P. falciparum malaria burden is characterized by the modulation of host immunity via compromization of T cell-mediated responses and suppression of innate immune-regulators.

1. Introduction

Malaria is a multifactorial, life-threatening disease [1] and continues to be a public health problem worldwide with approximately 219 million infected cases worldwide in 2017 by the World Health Organization [2]. P. falciparum, which is known to cause severe malaria, is responsible for about half the malaria burden in India [3]. P. falciparum malaria is widespread in rural areas of the country. However, endemicity is stratified differently for different parts of the country. North-eastern states (Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura) of the country account for only 4% of the country’s population, yet contribute to 10% P. falciparum cases [4]. Tripura, the study state, has reported 12,390 malaria cases in 2018, most of them being P. falciparum [3]. These north-eastern states are prone sites as the first case of chloroquine drug resistance P. falciparum malaria in India was too documented in Assam in the year 1973 [5]. These areas continue to be at risk of drug resistance, with reduced efficacy of artesunate + sulpha-pyrimethamine in P. falciparum [6]. This resistance pressure is thought to infiltrate through the international borders along these states [4,7] The immunological responses of the host in the area may be altered due to this drug resistance pressure.
P. falciparum malaria is prone to become severe due to various factors like PfEMP1, cytoadherence or sequestration, rosetting, etc. [8]. Apart from the parasitic factors, host immunity plays a role in deciding the disease outcome. Coordination of pro- and anti-inflammatory cytokines network is well known to contribute to disease progression and have diverse functionality which could be accountable for either causing malaria severity or disease resolution. They differentiate between the diverse forms of clinical malaria through differential regulation of TH1 and TH2 paradigm [8,9,10,11]. Although, previous studies have been reported to comprehend the regulations of cytokines and associated molecules in malaria [8,11,12], a detailed study on transcriptional profiling of host immune responses by cytokines and other associated signaling molecules in malaria is much needed. Their precise involvement in terms of different clusters and groups should be described in detail to conclude how the parasitic factors are regulating host’s molecular signaling during malaria progression. For an improved illustration of the role displayed by CD4 + TH1 and TH2 together with factors of innate immunoregulators like CD8+Tc cells, macrophage, and natural killer [NK] cells in malaria pathogenesis, transcriptional profiling of associated chief cytokines and other regulatory molecules were analyzed in the present study in diverse clinical conditions. The signaling proteins selected in the panel consisted of TH1 specific T-BET (TBX21), STAT1 (signal transducer and activator of transcription), STAT4, lymphotoxin alpha (LT-α), TNF-α, IFN-γ and IL-12Rβ2. TH2 specific GATA3, STAT6, IL-4, IL-5, IL-13, and IL-10. Some general transcription factors which are critical in immunological signaling included were NF-κB, cMAF, AP-1(cJUN), NFAT1, BATF, RUNX1, and RUNX3. Signaling proteins for innate immune responses selected were CSF1 (macrophage), IL-7, IRF1, ETS1, PERFORIN (PERF), GRANZYME-B (GrB) and EOMES. Chemokine associated molecules like CCR8, CXCR3, RANTES (CCL5), and CXCL8 (IL-8) along with significant factors like TGF-β, IL-1β, IL-12 and p38MAPK were also included. The expression of negative regulators of transcription like SOCS1 (suppressor of cytokine signaling), SOCS3, BCL6 and PD1 were measured.
All the transcription factors, cytokines, and chemokines which were included in the study, suggested a compromised TH1 and TH2 status and distressed innate immunoregulation. Chemokines and its receptors along with the other major transcription factors were found to be downregulated during the malaria progression. Though, some of the critical immunoregulators were found to be elevated which may endow with some protection against P. falciparum infection significantly in severe malaria.

2. Materials and Methods

2.1. Study Site and Population

The study was carried out at Dhalai district hospital, Dhalai, Tripura, India. Tripura shares its major parts with the international border of Bangladesh. Dhalai is the largest district among the total eight districts of Tripura and is an endemic site for P. falciparum malaria in north-east India. The district’s 70% area comprises dense forests and hills with an annual rainfall of about ~2–2.5 m [4]. The area has a hot and humid climate with an average temperature range from 17 to 36 °C and relative humidity of about 70–80%, thus making it highly suitable for malaria transmission. Ethnic tribes staying in remote hilly areas predominate the district. The district is categorized as the most socio-economically backward with poor healthcare services and minimal disease and prevention awareness, making it difficult for malaria control program operations [13].

2.2. Study Design

A total of twenty-two P. falciparum malaria-infected and nine healthy control subjects were enrolled for the study from September 2016 to October 2017. Malaria was diagnosed using microscopy and confirmed malaria patients were treated according to the national drug policy [14]. Parasite density was estimated by Giemsa-stained thick and thin blood smear examination in a conventional light microscope. Patients were categorized into uncomplicated and severe malaria groups on the basis of severity criteria classified according to the World Health Organization [15]. Other than severe malaria (SM; n = 05), uncomplicated cases were stratified into uncomplicated malaria1 (UC1; n = 05) and uncomplicated malaria2 (UC2; n = 12) based on parasite density where UC1 < 25,000 parasite/μL and UC2 > 25,000 parasite/μL, respectively. Healthy endemic control samples (EC; n = 06) were collected from patient’s relatives with no malaria infection. Samples of healthy non-endemic controls (NEC; n = 03) with no malaria history were also collected from other non-endemic areas (Bihar state) for comparison and analysis purposes.

2.3. Sample Collection

Peripheral venous blood samples (1 mL) were withdrawn into Tempus blood RNA tube (Applied Biosystems, Foster City, CA, USA) from the enrolled P. falciparum malaria patients and healthy controls. It was mixed several times gently and stored at −80 °C until use. Other clinical parameters were obtained and mentioned in demographic details (Table 1).

2.4. Sample Preparation and cDNA Synthesis

Before processing, samples were kept at room temperature for a minimum of two hours. Total RNA was extracted using the Tempus RNA isolation kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer’s instructions. The isolated RNA was quantified using NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA, USA). cDNA synthesis was carried out with 500 ng RNA as starting material by high capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA, USA) using random hexamer primer.

2.5. Primer Designing and Gene Expression Analysis

RT-qPCR primers were designed using Primer3 software as per method defined elsewhere [16]. Desalted primers were synthesized by GCC Biotech (India) Pvt. Ltd. (Kirtankhola, West Bengal, India). Accuracy and specificity of primers were checked by Blast algorithm and their products were electrophoretically checked on 1.5% agarose gel for amplification accuracy. Accession numbers were obtained from the DNA Data Bank of Japan (DDBJ) for all primers (Table 2). cDNA was diluted in a ratio of 1:5 by adding molecular grade water. Real-time PCR was performed on LightCycler 480, Roche, in 20 μL reaction volumes in 96-well plate (Bio-rad, Hercules, CA, USA) containing 2 X SYBR green PCR master mix (KAPA Biosystems, Wilmington, MA, USA), 0.25μM of each primer, cDNA and water to adjust the volume using standard conditions. All experiments were performed in replicates. Normalization of gene expression levels was performed using human β-actin gene as an endogenous control while healthy controls were used as calibrators. The relative fold change was calculated using 2−∆∆CT method while melting curve analysis was done to assure the presence of specific amplification of the products [17].

2.6. Statistical Analyses

Mean, standard deviation and standard error bar were calculated for all regulatory factors in each study subgroup, i.e., endemic control (EC) and non-endemic control (NEC), uncomplicated malaria1 (UC1), uncomplicated malaria 2 (UC2) and severe malaria (SM). Analysis of variance-ANOVA post-hoc (LSD) analysis was performed for regulatory factors/cytokines comparison between all study subgroups. Canonical discriminant function analysis was performed to discriminate study subgroups from each other for all 39 regulatory factors and cytokines. Pearson correlation test was used to assess the correlation between regulatory factors/cytokines and parasitemia versus regulatory factors. Graphical representations for Pearson correlation test and standard error bar graphs at 95% confidence interval (C.I) were executed. Heat-map was generated using ClustVis tool [18]. Statistical Package for Social Sciences version 17 software (SPSS Inc. in Chicago, IL, USA) was used for all statistical analyses and p-value < 0.05 was considered statistically significant.

3. Results

3.1. Clinical and Demographic Characteristics

Demographic and clinical features among different malaria subgroups were compared, as depicted in Table 1. Gender distribution between the groups was markedly discriminated with a higher percentage of males affected by severe malaria. Positive correlation of parasitemia with respiratory distress (p < 0.0001) was observed. Age was correlated with BMI (p = 0.001) but independent of parasitemia. Elevated pulse rate and respiratory rate (RR) were observed among malaria cases compared to healthy controls. Lower levels of hemoglobin were recorded among severe malaria patients (SM) along with one anemia case (Hb = 6.80 g/dL). Overall, severe malaria (SM) patients were differentiated from the other groups by higher parasitemia levels and respiratory distress.

3.2. Altered Expression Levels of Cytokines, Transcription Factors, and Other Signaling Molecules Among Different Malaria Sub-Groups

Cytokines and TF’s level profiling were performed on patient’s blood RNA samples having differential disease burden and were grouped accordingly. A significantly augmented level of IL-1β, IL-10, and TNF-α was found in the SM group compared to the EC and NEC control groups. Interestingly, UC2 groups of these factors showed a marginal decreased level compared to UC1. T helper cell1 (TH1) specific master transcription factor T-BET and T helper cell2 (TH2) key transcription factor GATA3 levels were found inversely proportional to disease severity among UC1, UC2, and SM malaria sub-groups and were considerably downregulated compared to both of the healthy controls. The consistent decrease in levels was more intense among severe cases than uncomplicated groups with a fold change reduction of 4.74 and 2.01 for T-BET and GATA3, respectively, compared to endemic control. IL-12, as well as receptor IL-12R levels, were also found decreased in all malaria groups compared to healthy subjects. Downregulation of IL-12, coupled with the upregulation of IL-10 with parasitic burden was observed which depicts the host protective mechanism in response to parasitemia load. Evidently, considering the differential expression pattern of the cytokines and other regulatory factors according to disease severity, they were grouped into three major clusters (Table 3).
Cluster 1 factors showed a marked decrease in the expression level with malaria burden. This comprises a total of 23 factors out of 39 cytokines and regulatory factors taken into consideration (Figure 1). On contrary, levels of interferon-gamma (IFN-γ) and Runt-related transcription factor 1 (RUNX1) were found increased during early infection (UC1). However, with malaria progression, they decreased with an intense decrease in SM group. IL-8 and IL-12R, although they showed decreased levels in comparison with the NEC control, they showed their upregulated levels in SM and UC1 groups, respectively, implicating the significance of the endemic over non-endemic healthy controls. Transcription factor STAT4 (signal transducer and activator of transcription 4) was found intensely depressed, similar to STAT6 in the severe group, with a fold change reductions of 2.86 and 1.43, respectively, compared to endemic control.
The second cluster consisted of 15 factors, expression levels of which were found significantly increased with the malaria severity (Figure 2). Not all regulatory factors and cytokine behavior were found idealistic in this cluster as well. For example, the expression level of STAT1 had an upregulated status among all the malaria groups. It was mostly expressed during mild infections (UC1 and UC2 groups) compared to severe cases. Similarly, programmed death-1 (PD-1) which accomplish the immunoregulation during infection along with the GrB, showed elevated expressions during early infections among the UC1 group. However, the levels of GrB together with cMAF and cJUN were found depressed in the UC2 group compared to both healthy control groups. On the other hand, IL-7 was depressed during early infection in both UC1 and UC2 groups. However, its expression was found increased with the malaria severity by a fold change of 1.32 compared to endemic control. It could be explained by upregulation of CD127 receptor following lower expression of T-BET [19] and hence, emerged as a notable factor in host protective mechanism against malaria burden. IL-1β expression was unchanged throughout uncomplicated malaria condition but got upregulated drastically among severe cases by 1.19-fold compared to EC control. Correspondingly, interferon regulatory factor 1 (IRF1) which is an essential regulator of immune responses and inflammation against infection, was found increased but with a constant expression level from the beginning of the infection till it reaches severity. BCL6 (B cell CLL/Lymphoma 6) expression level was also found elevated in the SM group which suggest suppression of macrophage cells proliferation and inhibition of Th1-Th2 cells differentiation in response to malaria severity [20,21]. Surprisingly, the third cluster comprised of single factor NF-κB and was observed discriminated from the other cytokines and regulatory factors by depicting an unchanged or moderately affected expression pattern in all the clinical forms of malaria (Figure 3).

3.3. Differential Categorization of Cytokines and Other Regulatory Factors in Malaria Sub-Groups

Canonical discriminant function (CDF) analysis was performed, it separated the different malaria sub-groups notably distinguished from each other. Healthy control groups were separated from the malaria subgroups with an observable distance of endemic control from the non-endemic subjects (Figure 4). A marked distance was more noticeable between SM and EC groups than UC2 and EC groups, which showed the significant parasitic burden on the SM group. Heat-map was generated using log2 fold values for all the cytokines and regulatory factors in all malaria sub-groups using healthy groups as calibrators. Two major gene clusters were observed among cytokines and regulatory factors and grouped into upregulated and downregulated factors (Figure 5). Lower expressions of transcription factor EOMES and PERF by a fold change reduction of 3.90 and 2.20 in SM compared to endemic control were observed. The distressed expression of EOMES may ease natural killer cells, NKT cells, and CD8+ T cells lower proliferation and differentiation which is in accord with malaria burden progression [19]. Similarly, macrophage colony-stimulating factor 1 (CSF1) displayed a significant downregulated level during early infection stage (UC1) and it may facilitate compromised macrophage proliferation and autophagy functionality [22].
Transforming growth factor β (TGF-β) and chemokines like CXCL8 (IL-8), CXCR3, Rantes, and CCR8 were also found downregulated in SM cases by the fold change reduction values of 1.53, 1.25, 1.98, 3.08, and 5.69, respectively, compared to endemic control. Remarkably, immunoregulatory molecules like SOCS1 and SOCS3 were found altered in each malaria sub-groups by the fold change value of 4.04 and 1.88 in UC1 while 4.55 and 3.12 in severe case group, respectively, compared to endemic control. The parasitic burden may craft cytokines and other transcriptional factors differential expression and could be noted for disease manifestation. Fold change analyses for all the factors compared to endemic and non-endemic healthy controls are mentioned in Supplementary Table S1.

3.4. Correlation Analyses of Cytokines, Transcription Factors and Other Signaling Molecules in Diverse Malaria Subgroups

The correlation analyses of cytokines, TFs, and other parameters were performed to assess the expression consequences of the diverse factors in all malaria groups. IFN-γ was found positively associated with the transcription factors GATA3, EOMES, T-BET, STAT4 and cMAF (Figure 6A). Correlation analysis revealed a positive association of IL-8, IL-10, and cJUN with TNF-α expression, while IL-12, RUNX1, and STAT1 were negatively associated with TNF-α (Figure 6B). Hemoglobin correlation with cytokines and regulatory factors were performed which interestingly, unveiled positive associated with relative mRNA expressions (Ct values) of IL-8, IL-10, GrB, and RUNX3 and an inverse association with STAT1 transcription factor (Figure 6C). Parasitemia was positively correlated with IFN-γ, T-BET, RUNX1, and STAT1 and negatively correlated with NFκB factor (Figure 6D). IL-5, GATA3, RUNX3, and RANTES were inversely associated with IL-1β expression, while NFκB and SOCS3 were positively correlated to IL-1β production (Figure 6E). An interesting and associated regulation of IL-12R was observed with amplified IL-10 production. In addition, overproduction of IL-10 was positively associated with IL-8, GrB, cMAF, and BATF but negatively correlated with STAT1 transcription factor (Figure 6F). IL-10, IL-12R positive correlation was observed with T-BET, GATA3, PERF, RUNX3, STAT4 and cJUN transcription factors (Figure 6G). TH2 key transcription factor GATA3 had a positive correlation with T-BET, EOMES, RUNX3, STAT4 and STAT6 factors, while it was found negatively associated with SOCS3 (Figure 6H). PD-1 receptor, which is expressed by several immunoregulatory cells including CD4+ and CD8+ T cells, revealed an increased expression (see Figure 2B) along with decreased T-BET, GATA3 and EOMES expression post-malaria infection (see Figure 1A) which support an inverse association of T-BET with PD-1 [23]. Contrary to this, a positive correlation of PD-1 to T-BET, GATA, and PERFORIN was observed while an inverse correlation was seen with IL-1β, SOCS3 and BCL6 (Figure 6I). It shows more population of PD1+CTLA4+CD4+ T cells over CD4+ T cells during acute malaria [24]. Suppressor of cytokine signaling, SOCS3 was found in a negative association with T-BET and RUNX3 like regulatory factors, while it positively correlated with NFκB, SOCS1 and BCL6 (Figure 6J).

3.5. Role of Innate Immunity Factors in Regulation of TH1 and TH2

A linear regression model was used to conclude the dependencies, as well as the strength of individual singling molecules, among factors of immunoregulatory molecules. Standardized coefficient (Beta) was used to predict the TH1 specific cell-mediated immunity factor T-BET and TH2 key factor GATA3 as dependent variable and innate immunoregulatory molecules as independent variable. The panel consisted of IL-1β, EOMES, PERFORIN, GRANZY-B, LT alpha, CXCR3, CCR8, ETS1, IRF1, RANTES, BCL6, PD-1 for T-BET and IL-1β, PERFORIN, GRANZY-B, LT alpha, CXCR3, CCR8, ETS1, IRF1, RANTES, BCL6 and PD-1 for GATA3 prediction. Each innate factor was found significantly predicting and hence regulating the cytokines expression and Th1 and Th2 proliferation by influencing master transcription factors T-BET and GATA3 (Table 4).

4. Discussion

Immunoregulatory responses and regulations of P. falciparum malaria pathogenesis are maintained by the complex coordination of various signaling molecules along with interplay between pro-inflammatory and anti-inflammatory cytokines and other associated molecules (Supplementary Figure S1). In the present study, the relationship between clinical malaria severity, intricate cytokines, and other signaling molecules multifarious coordination network in subjects from Dhalai district of north-east India was observed. The balance between pro- and anti-inflammatory responses in malaria progression could be best characterized by the expression levels of IL-12 and IL-10 in malaria clinical forms and are equivocally debated too. In contrast to previous reports of higher levels of IL-12 [25,26], IFN-γ [25,27] and lower IL-10 [25,28] expressions in uncomplicated and SM, a lower level of IL-12 in uncomplicated malaria was observed which got further depressed in SM. Further, elevated levels of IL-10 in uncomplicated malaria with a high peak during SM were seen. This data are consistent with the previous findings of low IL-12 [29,30,31] and high IL-10 [27,29,30] expression pattern, depending on malaria severity.
Few reports have also observed a higher IL-12 as well as IL-10 levels following malaria parasitic load [12,26]. Initially, IFN-γ level was found increased and influencing the STAT1-dependent signaling in this study. However, with the parasite burden, both of them got depressed. On contrary to reports of higher circulatory concentrations of TGF-β during malaria infection [12,25], it was found depressed in the present study which is in parallel to previous reports [29,31]. It is crucial for providing protection against malaria severity, significantly in lower concentrations [32,33] given the fact that it was found inversely correlated with parasitemia [34]. Besides, TNF-α and IL-1β levels were also found elevated during SM, similar to previous findings [12,26,35], which reflect the role of malaria pigment or hemozoin in the disease outcome [36]. Monocyte/macrophage arbitrated hemozoin uptake may result in the induction of TNF-α and IL-1β levels [37]. However, TNF-α upregulation was also reported in CD16 dendritic cells along with the induced IL-10 production in response to P. falciparum burden [38]. Upregulation of TNF-α could result in augmented expression of Fc receptor on monocytes or modulation of Fc receptor-based signaling pathway mechanism, thus, an increased phagocytic activity against P. falciparum. In addition, an augmented TNF-α expression in response to malaria infection could be helpful in parasite clearance and resolution of fever [36]. Hence, following P. falciparum infection, the study results obtained insights into a compromised CD4+ TH1 activity by downregulation of transcription factor T-BET, master regulator STAT4, LT-α, IFN-γ and receptor IL-12Rβ2. Similarly, a skewed CD4+ TH2 condition was observed due to the suppressed transcription factor GATA3, master regulator STAT6, IL-4, IL-5, and IL-13 gene expressions [28].
The reduced level of transcription factor EOMES with lower T-BET expression suggest a compromised innate immunity (lower CD8+Tc, natural killer (NK) cell, and natural killer T (NKT) populations [39]). However, initial augmentation in IFN-γ and STAT1 expression levels in this study depicted their effector functionality during early parasitic infection. Lower levels of major transcription factors like STAT4, ETS1, PERF, and CSF1 (M-CSF) provide validation of compromised innate immunity. Moreover, elevated expression of basic leucine zipper transcription factor ATF like (BATF) factor under the influence of upregulated PD-1, thought to contribute to the suppression of the effector activity of exhausted CD8+T cells while negatively regulating the expression of PERF protein [40,41]. Notably, overexpression of PD-1, observed in this study, authenticates T-cell exhaustion, specifically CD8+T-cell and T-cell dysfunction which is produced in a reduced cytotoxicity and effector functionality of associated cells. Therefore, PD-1 depletion could promote accelerated parasite clearance and T-cell functionality [42,43]. Transcriptional repressor BCL6 promotes the expression of PD-1 and upregulation in both BCL6 and its target PD-1 as observed in the present study and it could facilitate the upregulation of T-follicular helper cell (TFH) differentiation [21,44]. A study in Malian children has observed the frequency of circulating PD1+CXCR3−CXCR5+CD4+ TFH cells post-malaria infection, which could support B-cells functionality [45]. In favor of this, a lower expression of CXCR3 along with an augmented BCL6 and PD-1 expression in the present study, possibly suggest the increased population of TFH cell following malaria pathology. Majority of the innate immunity regulators like CTLs and NK cells requires the synergy of both PERF and GrB for the caspase-independent killing of an intracellular pathogen [46]. Interestingly, we have witnessed a suppressed PERF expression while GrB mRNA expression was found significantly upregulated in SM. Although PERF is crucial for GrB-mediated initiation of apoptosis, it could not refuse the possibility of augmented GrB uptake by the infected cells via receptor-mediated endocytotic pathway [46,47].
RUNX1 and RUNX3 are well known to play a critical role in T-cell immunity. RUNX1, recognized as a significant hematopoietic regulator in mammals and RUNX3 which acts as an essential neurogenesis controller were also measured through mRNA expression levels [48,49]. RUNX3 is chiefly expressed by TH1 committed cells, while TH2 requires the synergy of both the RUNX1 and RUNX3 [50]. RUNX3, in association with master transcription regulator TBET enhances TH1 differentiation by silencing the expression of IL-4 and promoting IFN-gamma production, this effect could be opposed by GATA3 making a complex with it [51]. In the present study, the downregulated expression pattern of RUNX1 and RUNX3 factors were observed. This reduction was found intensely associated with RUNX1 in the SM group by the fold change reduction of 1.6 and 2.3, respectively, compared to endemic and non-endemic controls. During the early infection (UC1), expression of RUNX1 was found elevated by the fold change value of 1.15 compared to endemic control. Level of RUNX3 was also found decreased in all the malaria sub-groups with a significantly decreased status among UC2 groups compared to both the control subjects.
The present study shows the subordinate expression pattern of chemokine receptors CXCR3 and CCR8 in response to increased malaria burden, it exemplifies the reduced trafficking of TH1, CD8+T cells, NK cells, monocytes and TH2 cells within lymphoid organ or in the peripheral tissues [52,53]. However, a study in the murine model revealed an upregulated CXCR3 expression in NK cell and T-cell and its association with lymphocyte trafficking during cerebral malaria [54] and mice deficient in CXCR3 was found efficiently protected from cerebral malaria [55]. The reduced level of RANTES (CCL5) was also observed which seem to be associated with increased disease severity and was similar to the previous finding [56]. Additionally, reduced circulatory level of RANTES was found correlated with severe malarial anemia in children and associated with suppression of erythropoiesis and parasite-induced thrombocytopenia [56,57]. On the other hand, contrary to the reports of the higher circulating level of IL-8 (CXCL8) in response to P. falciparum malaria [58,59], depressed expression of IL-8 was observed frequently during early infection. Notably, the expression of IL-8 was found increased in correlation with the parasite densities during severe condition compared to uncomplicated cases.
The study illustrates the differential transcriptional regulations and suppression of innate as well as cell-mediated immunity in the diverse clinical subgroups of P. falciparum malaria infection. Various signaling molecules collectively produce complex signaling cascade which proclaims an active role in the malaria progression with their differential expression levels in accord with disease burden. These could be helpful as indicative biomarkers of malaria as well as for monitoring the disease severity. Enhanced TNF-α, IL-1β, IL-10, and IL-7 production and lower TGF-β concentrations, however, may together provide some protection against the parasitic deleterious effects. It was a model study with a modest sample size aimed to identify the biomarkers responsible for the progression from uncomplicated to severe malaria. A study in a large sample size could be more beneficial in predicting the malaria diagnosis using measurement of the panel’s one or more cytokines, transcription factors, or other signaling molecules.

Supplementary Materials

The following are available online at https://www.mdpi.com/2218-273X/9/10/600/s1, Supplementary Table S1: Fold change analysis of cytokines and signaling molecules compared to both the Endemic and Non-Endemic healthy controls. Supplementary Figure S1: Protein-protein interaction network.

Author Contributions

A.R.A. and M.Z.A. conceptualized and designed the study. A.R.A. and N.B. were involved in patient recruitment. M.Z.A. performed the experiments. S.S. helped in writing, review and editing. N.B. and M.Z.A. analyzed and interpreted the data. M.Z.A. and A.R.A. wrote the manuscript. V.P. provided intellectual inputs. All authors reviewed the manuscript.

Funding

This work was supported by the intramural funding of ICMR-National Institute of Malaria Research.

Acknowledgments

The author MZA is thankful to the Department of Biotechnology (DBT) and BCIL, India for providing fellowship and also grateful to Kumaun University (Uttarakhand) for his registration and overall support. Authors would like to thank all staffs of ICMR-NIMR and Kumaun University for their support.

Conflicts of Interest

Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Gazzinelli, R.T.; Kalantari, P.; Fitzgerald, K.A.; Golenbock, D.T. Innate sensing of malaria parasites. Nat. Rev. Immunol. 2014, 14, 744–757. [Google Scholar] [CrossRef]
  2. WHO. World Malaria Report 2018. WHO. Available online: http://www.who.int/malaria/publications/world-malaria-report-2018/en/ (accessed on 15 February 2019).
  3. Malaria Situation in India 2018. National Vector Borne Disease Control Programme. Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India. Available online: http://www.nvbdcp.gov.in (accessed on 22 January 2019).
  4. Dev, V.; Adak, T.; Singh, O.; Nanda, N.; Baidya, B. Malaria transmission in Tripura: Disease distribution & determinants. Indian J. Med. Res. 2015, 142, 12. [Google Scholar] [CrossRef]
  5. Sharma, J. Antimalarial drug resistance malaria parasite: A review in NE states of India. Adv. Appl. Sci. Res. 2015, 6, 254–257. [Google Scholar]
  6. Shah, N.K.; Dhillon, G.P.; Dash, A.P.; Arora, U.; Meshnick, S.R.; Valecha, N. Antimalarial drug resistance of Plasmodium falciparum in India: Changes over time and space. Lancet Infect. Dis. 2011, 11, 57–64. [Google Scholar] [CrossRef]
  7. Dhiman, S.; Goswami, D.; Rabha, B.; Gopalakrishnan, R.; Baruah, I.; Singh, L. Malaria epidemiology along Indo-Bangladesh border in Tripura state, India. Southeast Asian J. Trop. Med. Public Health 2010, 41, 1279. [Google Scholar]
  8. Day, N.P.J.; Hien, T.T.; Schollaardt, T.; Loc, P.P.; Chuong, L.V.; Chau, T.T.H.; Mai, N.T.H.; Phu, N.H.; Sinh, D.X.; White, N.J.; et al. The Prognostic and Pathophysiologic Role of Pro- and Anti-inflammatory Cytokines in Severe Malaria. J. Infect. Dis. 1999, 180, 1288–1297. [Google Scholar] [CrossRef]
  9. Miller, L.H.; Baruch, D.I.; Marsh, K.; Doumbo, O.K. The pathogenic basis of malaria. Nature 2002, 415, 673–679. [Google Scholar] [CrossRef]
  10. Spellberg, B.; Edwards, J.E. Type 1/Type 2 Immunity in Infectious Diseases. Clin. Infect. Dis. 2001, 32, 76–102. [Google Scholar] [CrossRef]
  11. Liberman, A.C.; Refojo, D.; Arzt, E. Cytokine Signaling/Transcription Factor Cross–Talk in T Cell Activation and Th1-Th2 Differentiation. Arch. Immunol. Ther. Exp. 2003, 51, 351–366. [Google Scholar]
  12. Prakash, D.; Fesel, C.; Jain, R.; Cazenave, P.; Mishra, G.C.; Pied, S. Clusters of Cytokines Determine Malaria Severity in Plasmodium falciparum—Infected Patients from Endemic Areas of Central India. J. Infect. Dis. 2006, 194, 198–207. [Google Scholar] [CrossRef]
  13. District Profile of Dhalai District. Office of the District Magistrate & Collector. Government of Tripura. Available online: https://dhalai.nic.in/document-category/district-profile/ (accessed on 10 October 2018).
  14. Guidelines for Diagnosis and Treatment of Malaria in India. National Institute of Malaria Research and National Vector Borne Disease Control Programme. 2014. Available online: http://nimr.org.in/guidelines_books/ (accessed on 23 October 2018).
  15. Severe Malaria. Trop. Med. Int. Health 2014, 19, 7–131. [CrossRef]
  16. Thornton, B.; Basu, C. Real-Time PCR [qPCR] primer design using free online software. Biochem. Mol. Biol. Educ. 2011, 39, 145–154. [Google Scholar] [CrossRef]
  17. Schmittgen, T.D.; Livak, K.J. Analyzing real-Time PCR data by the comparative CT method. Nat. Protoc. 2008, 3, 1101–1108. [Google Scholar] [CrossRef]
  18. Metsalu, T.; Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015, 43, W566–W570. [Google Scholar] [CrossRef]
  19. Knox, J.J.; Cosma, G.L.; Betts, M.R.; McLane, L.M. Characterization of T-Bet and Eomes in Peripheral Human Immune Cells. Front. Immunol. 2014, 5, 217. [Google Scholar] [CrossRef]
  20. Yu, R.Y.L. BCL-6 negatively regulates macrophage proliferation by suppressing autocrine IL-6 production. Blood 2005, 105, 1777–1784. [Google Scholar] [CrossRef] [Green Version]
  21. Xie, M.M.; Koh, B.H.; Hollister, K.; Wu, H.; Sun, J.; Kaplan, M.H.; Dent, A.L. Bcl6 promotes follicular helper T-Cell differentiation and PD-1 expression in a Blimp1-independent manner in mice. Eur. J. Immunol. 2017, 47, 1136–1141. [Google Scholar] [CrossRef]
  22. Boulakirba, S.; Pfeifer, A.; Mhaidly, R.; Obba, S.; Goulard, M.; Schmitt, T.; Chaintreuil, P.; Calleja, A.; Furstoss, N.; Orange, F.; et al. IL-34 and CSF-1 display an equivalent macrophage differentiation ability but a different polarization potential. Sci. Rep. 2018, 8. [Google Scholar] [CrossRef]
  23. Oestreich, K.J.; Weinmann, A.S. T-Bet employs diverse regulatory mechanisms to repress transcription. Trends Immunol. 2012, 33, 78–83. [Google Scholar] [CrossRef]
  24. Mackroth, M.S.; Abel, A.; Steeg, C.; zur Wiesch, J.S.; Jacobs, T. Acute Malaria Induces PD1 + CTLA4 + Effector T Cells with Cell-Extrinsic Suppressor Function. PLoS Pathog. 2016, 12, e1005909. [Google Scholar] [CrossRef]
  25. Mahanta, A.; Kar, S.K.; Kakati, S.; Baruah, S. Heightened inflammation in severe malaria is associated with decreased IL-10 expression levels and neutrophils. Innate Immun. 2015, 21, 546–552. [Google Scholar] [CrossRef] [PubMed]
  26. Lyke, K.E.; Burges, R.; Cissoko, Y.; Sangare, L.; Dao, M.; Diarra, I.; Kone, A.; Harley, R.; Plowe, C.V.; Doumbo, O.K.; et al. Serum Levels of the Proinflammatory Cytokines Interleukin-1 Beta [IL-1], IL-6, IL-8, IL-10, Tumor Necrosis Factor Alpha, and IL-12[p70] in Malian Children with Severe Plasmodium falciparum Malaria and Matched Uncomplicated Malaria or Healthy Controls. Infect. Immun. 2004, 72, 5630–5637. [Google Scholar] [CrossRef] [PubMed]
  27. Abel, A.; Steeg, C.; Aminkiah, F.; Addai-Mensah, O.; Addo, M.; Gagliani, N.; Casar, C.; Yar, D.D.; Owusu-Dabo, E.; Jacobs, T.; et al. Differential expression pattern of co-Inhibitory molecules on CD4 + T cells in uncomplicated versus complicated malaria. Sci. Rep. 2018, 8. [Google Scholar] [CrossRef]
  28. Mahanta, A.; Baruah, S. Lower expression of GATA3 and T-bet correlates with downregulated IL-10 in severe falciparum malaria. Clin. Transl. Immunol. 2015, 4, e49. [Google Scholar] [CrossRef] [PubMed]
  29. Perkins, D.J.; Weinberg, J.B.; Kremsner, P.G. Reduced interleukin-12 and transforming growth factor—B 1 in severe childhood malaria: Relationship of cytokine balance with disease severity. J. Infect. Dis. 2000, 182, 988–992. [Google Scholar] [CrossRef] [PubMed]
  30. Luty, A.J.F.; Perkins, D.J.; Lell, B.; Schmidt-Ott, R.; Lehman, L.G.; Luckner, D.; Greve, B.; Matousek, P.; Herbich, K.; Schmid, D.; et al. Low Interleukin-12 Activity in Severe Plasmodium falciparum Malaria. Infect. Immun. 2000, 68, 3909–3915. [Google Scholar] [CrossRef] [PubMed]
  31. Chaiyaroj, S.C.; Rutta, A.S.M.; Muenthaisong, K.; Watkins, P.; Na Ubol, M.; Looareesuwan, S. Reduced levels of transforming growth factor-β1, interleukin-12 and increased migration inhibitory factor are associated with severe malaria. Acta Trop. 2004, 89, 319–327. [Google Scholar] [CrossRef]
  32. Luckhart, S.; Lieber, M.J.; Singh, N.; Zamora, R.; Vodovotz, Y. Low levels of mammalian TGF-β1 are protective against malaria parasite infection, a paradox clarified in the mosquito host. Exp. Parasitol. 2008, 118, 290–296. [Google Scholar] [CrossRef]
  33. Wenisch, C.; Parschalk, B.; Burgmann, H.; Looareesuwan, S.; Graninger, W. Decreased serum levels of TGF-β in patients with acute Plasmodium falciparum malaria. J. Clin. Immunol. 1995, 15, 69–73. [Google Scholar] [CrossRef]
  34. Omer, F.M.; Kurtzhals, J.A.L.; Riley, E.M. Maintaining the immunological balance in parasitic infections: A role for TGF-β? Parasitol. Today 2000, 16, 18–23. [Google Scholar] [CrossRef]
  35. Dodoo, D.; Omer, F.M.; Todd, J.; Akanmori, B.D.; Koram, K.A.; Riley, E.M. Absolute Levels and Ratios of Proinflammatory and Anti-inflammatory Cytokine Production In Vitro Predict Clinical Immunity to Plasmodium falciparum Malaria. J. Infect. Dis. 2002, 185, 971–979. [Google Scholar] [CrossRef] [PubMed]
  36. Malaguarnera, L.; Musumeci, S. The immune response to Plasmodium falciparum malaria. Lancet Infect. Dis. 2002, 2, 472–478. [Google Scholar] [CrossRef]
  37. Chua, C.L.L.; Brown, G.; Hamilton, J.A.; Rogerson, S.; Boeuf, P. Monocytes and macrophages in malaria: Protection or pathology? Trends Parasitol. 2013, 29, 26–34. [Google Scholar] [CrossRef] [PubMed]
  38. Loughland, J.R.; Woodberry, T.; Boyle, M.J.; Tipping, P.E.; Piera, K.A.; Amante, F.H.; Kenangalem, E.; Price, R.N.; Engwerda, C.R.; Anstey, N.M. Plasmodium falciparum Activates CD16+ Dendritic Cells to Produce Tumor Necrosis Factor and Interleukin-10 in Subpatent Malaria. J. Infect. Dis. 2018, 219, 660–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Glimcher, L.H.; Townsend, M.J.; Sullivan, B.M.; Lord, G.M. Recent developments in the transcriptional regulation of cytolytic effector cells. Nat. Rev. Immunol. 2004, 4, 900–911. [Google Scholar] [CrossRef]
  40. Kurachi, M.; Barnitz, R.A.; Yosef, N.; Odorizzi, P.M.; DiIorio, M.A.; Lemieux, M.E.; Yates, K.; Godec, J.; Klatt, M.G.; Regev, A.; et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 2014, 15, 373–383. [Google Scholar] [CrossRef] [PubMed]
  41. Quigley, M.; Pereyra, F.; Nilsson., B.; Porichis, F.; Fonseca, C.; Eichbaum, Q.; Julg, B.; Jesneck, J.L.; Brosnahan, K.; Imam, S.; et al. Transcriptional analysis of HIV-Specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 2010, 16, 1147–1151. [Google Scholar] [CrossRef]
  42. Wei, F.; Zhong, S.; Ma, Z.; Kong, H.; Medvec, A.; Ahmed, R.; Freeman, G.J.; Krogsgaard, M.; Riley, J.L. Strength of PD-1 signaling differentially affects T-cell effector functions. Proc. Natl. Acad. Sci. USA 2013, 110, E2480–E2489. [Google Scholar] [CrossRef] [Green Version]
  43. Horne-Debets, J.M.; Faleiro, R.; Karunarathne, D.S.; Liu, X.Q.; Lineburg, K.E.; Poh, C.M.; Grotenbreg, G.M.; Hill, G.R.; MacDonald, K.P.A.; Good, M.F.; et al. PD-1 Dependent Exhaustion of CD8+ T Cells Drives Chronic Malaria. Cell Rep. 2013, 5, 1204–1213. [Google Scholar] [CrossRef] [Green Version]
  44. Poholek, A.C.; Hansen, K.; Hernandez, S.G.; Eto, D.; Chandele, A.; Weinstein, J.S.; Dong, X.; Odegard, J.M.; Kaech, S.M.; Dent, A.L. In vivo regulation of Bcl6 and T follicular helper cell development. J. Immunol. 2010, 185, 313–326. [Google Scholar] [CrossRef]
  45. Obeng-Adjei, N.; Portugal, S.; Tran, T.M.; Yazew, T.B.; Skinner, J.; Li, S.; Jain, A.; Felgner, P.L.; Doumbo, O.K.; Kayentao, K.; et al. Circulating Th1-Cell-type Tfh Cells that Exhibit Impaired B Cell Help Are Preferentially Activated during Acute Malaria in Children. Cell Rep. 2015, 13, 425–439. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Trapani, J.A.; Smyth, M.J. Functional significance of the perforin/granzyme cell death pathway. Nat. Rev. Immunol. 2002, 2, 735–747. [Google Scholar] [CrossRef] [PubMed]
  47. Froelich, C.J.; Orth, K.; Turbov, J.; Seth, P.; Gottlieb, R.; Babior, B.; Shah, G.M.; Bleackley, R.C.; Dixit, V.M.; Hanna, W. New Paradigm for Lymphocyte Granule-Mediated Cytotoxicity: Target cells bind and internalize granzyme b, but an endosomolytic agent is necessary for cytosolic delivery and subsequent apoptosis. J. Biol. Chem. 1996, 271, 29073–29079. [Google Scholar] [CrossRef] [PubMed]
  48. De Bruijn, M.; Dzierzak, E. Runx transcription factors in the development and function of the definitive hematopoietic system. Blood 2017, 129, 2061–2069. [Google Scholar] [CrossRef] [PubMed]
  49. Wong, W.F.; Kohu, K.; Chiba, T.; Sato, T.; Satake, M. Interplay of transcription factors in T-Cell differentiation and function: The role of Runx: Runx transcription factor and T lymphocytes. Immunology 2011, 132, 157–164. [Google Scholar] [CrossRef] [PubMed]
  50. Djuretic, I.M.; Levanon, D.; Negreanu, V.; Groner, Y.; Rao, A.; Ansel, K.M. Transcription factors T-Bet and Runx3 cooperate to activate Ifng and silence Il4 in T helper type 1 cells. Nat. Immunol. 2007, 8, 145. [Google Scholar] [CrossRef] [PubMed]
  51. Yagi, R.; Junttila, I.S.; Wei, G.; Urban, J.F., Jr.; Zhao, K.; Paul, W.E.; Zhu, J. The transcription factor GATA3 actively represses RUNX3 protein-Regulated production of interferon-γ. Immunity 2010, 32, 507–517. [Google Scholar] [CrossRef] [PubMed]
  52. Griffith, J.W.; Sokol, C.L.; Luster, A.D. Chemokines and Chemokine Receptors: Positioning Cells for Host Defense and Immunity. Annu. Rev. Immunol. 2014, 32, 659–702. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Ioannidis, L.J.; Nie, C.Q.; Hansen, D.S. The role of chemokines in severe malaria: More than meets the eye. Parasitology 2014, 141, 602–613. [Google Scholar] [CrossRef] [PubMed]
  54. Hansen, D.S.; Bernard, N.J.; Nie, C.Q.; Schofield, L. NK Cells Stimulate Recruitment of CXCR3+ T Cells to the Brain during Plasmodium berghei-Mediated Cerebral Malaria. J. Immunol. 2007, 178, 5779–5788. [Google Scholar] [CrossRef] [PubMed]
  55. Campanella, G.S.V.; Tager, A.M.; El Khoury, J.K.; Thomas, S.Y.; Abrazinski, T.A.; Manice, L.A.; Colvin, R.A.; Luster, A.D. Chemokine receptor CXCR3 and its ligands CXCL9 and CXCL10 are required for the development of murine cerebral malaria. Proc. Natl. Acad. Sci. USA 2008, 105, 4814–4819. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Ochiel, D.O.; Awandare, G.A.; Keller, C.C.; Hittner, J.B.; Kremsner, P.G.; Weinberg, J.B.; Perkins, D.J. Differential Regulation of —Chemokines in Children with Plasmodium falciparum Malaria. Infect. Immun. 2005, 73, 4190–4197. [Google Scholar] [CrossRef] [PubMed]
  57. Were, T.; Hittner, J.B.; Ouma, C.; Otieno, R.O.; Orago, A.S.; Ong’echa, J.M.; Vulule, J.M.; Keller, C.C.; Perkins, D.J. Suppression of RANTES in children with Plasmodium falciparum malaria. Haematologica 2006, 91, 1396–1399. [Google Scholar] [PubMed]
  58. Burgmann, H.; Hollenstein, U.; Wenisch, C.; Thalhammer, F.; Looareesuwan, S.; Graninger, W. Serum concentrations of MIP-1 α and interleukin-8 in patients suffering from acute Plasmodium falciparum malaria. Clin. Immunol. Immunopathol. 1995, 76, 32–36. [Google Scholar] [CrossRef] [PubMed]
  59. Lopera-Mesa, T.M.; Mita-Mendoza, N.K.; van de Hoef, D.L.; Doumbia, S.; Konaté, D.; Doumbouya, M.; Gu, W.; Traoré, K.; Diakité, S.A.S.; Remaley, A.T.; et al. Plasma Uric Acid Levels Correlate with Inflammation and Disease Severity in Malian Children with Plasmodium falciparum Malaria. PLoS ONE 2012, 7, e46424. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Relative mRNA expression profiling of cytokines and other regulatory factors showing differential disease burden among malaria subgroups are presented as Ct values. Expressions of these signaling molecules were found decreased with the P. falciparum malaria burden and were grouped as cluster 1. Major TH1, TH2 factors, and players of innate immunity were found depressed (A) IFNG displayed gene upregulation during early malaria progressions. Master transcription factor of TH1 (T-BET), TH2 (GATA3) as well as CD8+ Tc (EOMES) cells got depressed as malaria progress. Expressions of PERFORIN and macrophage cells specific transcription factor CSF1 were found downregulated. (B) Grouped as innate immunity regulatory molecules, RUNX1 and RUNX3 were found depressed. However, RUNX1 displayed gene upregulation during early malaria progressions compared to healthy endemic control subjects. Chemokines and chemokines receptors, including STAT4, STAT6, and ETS1 levels were found altered. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at a 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
Figure 1. Relative mRNA expression profiling of cytokines and other regulatory factors showing differential disease burden among malaria subgroups are presented as Ct values. Expressions of these signaling molecules were found decreased with the P. falciparum malaria burden and were grouped as cluster 1. Major TH1, TH2 factors, and players of innate immunity were found depressed (A) IFNG displayed gene upregulation during early malaria progressions. Master transcription factor of TH1 (T-BET), TH2 (GATA3) as well as CD8+ Tc (EOMES) cells got depressed as malaria progress. Expressions of PERFORIN and macrophage cells specific transcription factor CSF1 were found downregulated. (B) Grouped as innate immunity regulatory molecules, RUNX1 and RUNX3 were found depressed. However, RUNX1 displayed gene upregulation during early malaria progressions compared to healthy endemic control subjects. Chemokines and chemokines receptors, including STAT4, STAT6, and ETS1 levels were found altered. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at a 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
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Figure 2. Expressions of these signaling molecules got upregulated following malaria progression and were grouped as Cluster 2. (A) Cytokines like TNF-α, IL10, and IL1β levels were upregulated with an increase in malaria burden. STAT1 expression was moderately upregulated during early infections. Despite downregulated perforin expression, GrB mRNA expression was elevated in severe malaria. Expression levels of IL7 and cJUN were low during the early stages (UC1 and UC2) but were considerably upregulated in severe malaria (SM). (B) p38MAPK mRNA expressions were upregulated. Negative regulators of cytokine signaling like SOCS1 and SOCS3 found elevated following malaria burden. T cell exhaustion indicator, PD1 was also got upregulated following malaria burden. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at a 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
Figure 2. Expressions of these signaling molecules got upregulated following malaria progression and were grouped as Cluster 2. (A) Cytokines like TNF-α, IL10, and IL1β levels were upregulated with an increase in malaria burden. STAT1 expression was moderately upregulated during early infections. Despite downregulated perforin expression, GrB mRNA expression was elevated in severe malaria. Expression levels of IL7 and cJUN were low during the early stages (UC1 and UC2) but were considerably upregulated in severe malaria (SM). (B) p38MAPK mRNA expressions were upregulated. Negative regulators of cytokine signaling like SOCS1 and SOCS3 found elevated following malaria burden. T cell exhaustion indicator, PD1 was also got upregulated following malaria burden. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at a 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
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Figure 3. NF-κB expression was not much influenced following malaria burden in any of the clinical subgroups and was grouped in Cluster 3 separately. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
Figure 3. NF-κB expression was not much influenced following malaria burden in any of the clinical subgroups and was grouped in Cluster 3 separately. Uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL), severe malaria (SM), endemic control (EC), non-endemic control (NEC). β-actin was used as an endogenous control. Mean and standard error bars were calculated at 95% confidence interval (CI). Gene expression decreases as Ct value increases and vice versa.
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Figure 4. Canonical discriminant function (CDF) illustrates the characteristics of the different clinical subgroups considering regulatory factors/cytokine expression profiles in Plasmodium falciparum-infected patients and healthy control subjects. Function 1 and 2 resulting from CDF, discriminating the EC and NEC from uncomplicated malaria1 (UC1), uncomplicated malaria2 (UC2) and severe malaria (SM) groups according to their relative mRNA expressions (Ct values). Annotation indicates the factors (cytokines and TF’s) that turned out to be relevant for each type of discrimination. CDF discrimination of the clinical subgroups was analyzed using all 39 factors considered for the study. 1 = UC1 (parasitemia < 25,000/μL), 2 = UC2 (parasitemia > 25,000/μL), 3 = SM, 4 = EC and 5 = NEC.
Figure 4. Canonical discriminant function (CDF) illustrates the characteristics of the different clinical subgroups considering regulatory factors/cytokine expression profiles in Plasmodium falciparum-infected patients and healthy control subjects. Function 1 and 2 resulting from CDF, discriminating the EC and NEC from uncomplicated malaria1 (UC1), uncomplicated malaria2 (UC2) and severe malaria (SM) groups according to their relative mRNA expressions (Ct values). Annotation indicates the factors (cytokines and TF’s) that turned out to be relevant for each type of discrimination. CDF discrimination of the clinical subgroups was analyzed using all 39 factors considered for the study. 1 = UC1 (parasitemia < 25,000/μL), 2 = UC2 (parasitemia > 25,000/μL), 3 = SM, 4 = EC and 5 = NEC.
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Figure 5. Heat-map illustrates comparative fold change of each group per gene expression profile. Each cell of the 2D plot denotes log2 fold value of a single factor in one group with standardized levels represented by color as per color scale on the right. Fold change was calculated by 2−ΔΔCt method considering β-actin as an endogenous control. Top horizontal and left vertical dendrogram indicates average Euclidean clustering of genes taken into panel and malaria clinical subgroups, respectively. UC1 was marked and sub-grouped from the UC2 and SM group. The figure shows a consequence of higher parasitic burden displayed by the latter two groups despite other reasonable associated causes. UC1-EC, uncomplicated malaria group (parasitemia < 25,000/μL) fold change compared to endemic control; UC1-NEC, uncomplicated malaria group (parasitemia < 25,000/μL) fold change compared to non-endemic control; UC2-EC, uncomplicated malaria group (parasitemia > 25,000/μL) fold change compared to endemic control; UC2-NEC, uncomplicated malaria group (parasitemia > 25,000/μL) fold change compared to non-endemic control; SM-EC, severe malaria group fold change compared to endemic control; SM-NEC, severe malaria group fold change compared to non-endemic control.
Figure 5. Heat-map illustrates comparative fold change of each group per gene expression profile. Each cell of the 2D plot denotes log2 fold value of a single factor in one group with standardized levels represented by color as per color scale on the right. Fold change was calculated by 2−ΔΔCt method considering β-actin as an endogenous control. Top horizontal and left vertical dendrogram indicates average Euclidean clustering of genes taken into panel and malaria clinical subgroups, respectively. UC1 was marked and sub-grouped from the UC2 and SM group. The figure shows a consequence of higher parasitic burden displayed by the latter two groups despite other reasonable associated causes. UC1-EC, uncomplicated malaria group (parasitemia < 25,000/μL) fold change compared to endemic control; UC1-NEC, uncomplicated malaria group (parasitemia < 25,000/μL) fold change compared to non-endemic control; UC2-EC, uncomplicated malaria group (parasitemia > 25,000/μL) fold change compared to endemic control; UC2-NEC, uncomplicated malaria group (parasitemia > 25,000/μL) fold change compared to non-endemic control; SM-EC, severe malaria group fold change compared to endemic control; SM-NEC, severe malaria group fold change compared to non-endemic control.
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Figure 6. Pearson correlation statistical tests were performed to assess the correlation between cytokines, transcription factors, parasitemia and hemoglobin in all malaria sub-groups (UC1+ UC2+ SM). (A) r = 0.512, 0.656, 0.831, 0.658, 0.561, p = 0.015, 0.001, 0.000, 0.001, 0.007 (B) r = 0.519, −0.509, −0.454, −0.551, 0.571, 0.542, p = 0.013, 0.016, 0.034, 0.008, 0.005, 0.009 (C) r =0.669, 0.504, 0.442, −0.455, 0.617, p = 0.001, 0.017, 0.039, 0.033, 0.002 (D) r = 0.442, 0.438, −0.443, 0.471, 0.539, p = 0.039, 0.041, 0.039, 0.027, 0.010 (E) r = −0.561, −0.619, 0.651, −0.571, −0.521, 0.905, p = 0.007, 0.002, 0.001, 0.006, 0.013, 0.000 (F) r =0.454, 0.588, −0.619, 0.632, 0.482, 0.581, p = 0.034, 0.004, 0.002, 0.002, 0.023, 0.005 (G) r = 0.804, 0.651, 0.856, 0.843, 0.853, 0.559, p = 0.000, 0.001, 0.000, 0.000, 0.000, 0.007 (H) r = 0.768, 0.840, 0.804, 0.896, 0.676, −0.622, p = 0.000, 0.000, 0.000, 0.000, 0.001, 0.002 (I) r = −0.487, 0.869, 0.696, 0.850, −0.484, −0.756, p = 0.022, 0.000, 0.000, 0.000, 0.023, 0.000 (J) r = −0.662, 0.646, −0.555, 0.777, 0.699, 0.694, p = 0.001, 0.001, 0.007, 0.000, 0.000, 0.000.
Figure 6. Pearson correlation statistical tests were performed to assess the correlation between cytokines, transcription factors, parasitemia and hemoglobin in all malaria sub-groups (UC1+ UC2+ SM). (A) r = 0.512, 0.656, 0.831, 0.658, 0.561, p = 0.015, 0.001, 0.000, 0.001, 0.007 (B) r = 0.519, −0.509, −0.454, −0.551, 0.571, 0.542, p = 0.013, 0.016, 0.034, 0.008, 0.005, 0.009 (C) r =0.669, 0.504, 0.442, −0.455, 0.617, p = 0.001, 0.017, 0.039, 0.033, 0.002 (D) r = 0.442, 0.438, −0.443, 0.471, 0.539, p = 0.039, 0.041, 0.039, 0.027, 0.010 (E) r = −0.561, −0.619, 0.651, −0.571, −0.521, 0.905, p = 0.007, 0.002, 0.001, 0.006, 0.013, 0.000 (F) r =0.454, 0.588, −0.619, 0.632, 0.482, 0.581, p = 0.034, 0.004, 0.002, 0.002, 0.023, 0.005 (G) r = 0.804, 0.651, 0.856, 0.843, 0.853, 0.559, p = 0.000, 0.001, 0.000, 0.000, 0.000, 0.007 (H) r = 0.768, 0.840, 0.804, 0.896, 0.676, −0.622, p = 0.000, 0.000, 0.000, 0.000, 0.001, 0.002 (I) r = −0.487, 0.869, 0.696, 0.850, −0.484, −0.756, p = 0.022, 0.000, 0.000, 0.000, 0.023, 0.000 (J) r = −0.662, 0.646, −0.555, 0.777, 0.699, 0.694, p = 0.001, 0.001, 0.007, 0.000, 0.000, 0.000.
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Table
Table 1. Age, gender distribution, parasitemia level and other demographic details among different malaria clinical groups.
Table 1. Age, gender distribution, parasitemia level and other demographic details among different malaria clinical groups.
UC1 (n = 05)UC2 (n = 12)SM (n = 05)EC (n = 06)NEC (n = 03)
Age16.8 ± 4.96 *26.58 ± 10.7022.8 ± 11.7830 ± 8.32 *34 ± 5.29
Gender F [%]4 (80)2 (16.7)1(20)3 (50)1(33.3)
BMI17.92 ± 1.44 *18.54 ± 2.53 **19.18 ± 2.24 &22.80 ± 3.60 *, **, &24.96 ± 2.38
Parasite/μL9104 ± 3242.4290184.75 ± 28871.36289859.6 ± 98092.5NANA
Systolic B.P116.2 ± 10.92108.16 ± 18.38 **100.6 ± 14.10 &133.33 ± 8.62 **, &116 ± 8.89
Diastolic B.P79.4 ± 9.42 #70.33 ± 9.54 **68.2 ± 4.87 #, &86.66 ± 4.13 **, &76.33 ± 5.69
Pulse110 ± 16.19 *92.41 ± 18.4897 ± 18.1782.5 ± 11.15 *84 ± 2.0
RR22.6 ± 1.52 #, $25.83 ± 2.48 **, ##, $36.4 ± 1.14 #, ##, &20.83 ± 1.83 **, &19 ± 1.0
Hb [g/dL]12.34 ± 1.0513.09 ± 0.86 ##11.2 ± 3.17 ##12.60 ± 1.6914.53 ± 0.32
HCT [%]37.6 ± 3.2139.08 ± 3.4534.8 ± 10.9939.83 ± 5.3443.33 ± 1.53
p-values were calculated using ANOVA post-hoc (LSD) analysis. Parameters were represented as mean and standard deviation (±). Note: Endemic control (EC), non-endemic control (NEC), uncomplicated malaria1 (UC1 = parasitemia < 25,000/μL), uncomplicated malaria2 (UC2 = parasitemia > 25,000/μL) and severe malaria (SM).* = UC1 vs. EC, p-value < 0.05; **= UC2 vs. EC, p-value < 0.05; # = UC1 vs. SM, p-value < 0.05; ## = UC2 vs. SM, p-value < 0.05; $ = UC1 vs. UC2, p-value < 0.05; & = SM vs. EC, p-value < 0.05.
Table
Table 2. List of primer sequences designed for SYBR green-based quantitative real-time polymerase chain reaction.
Table 2. List of primer sequences designed for SYBR green-based quantitative real-time polymerase chain reaction.
S.NGenes (Homo Sapiens)HGNC Gene IDForward Primer Reverse Primer Product Size (bp)TmDDBJ Accession NumbersNCBI Chromosome Location
1IFN-γ (IFNG)HGNC:5438GCAGCCAACCTAAGCAAGATCAAACCGGCAGTAACTGGAT10360LC461674 12q15
2TNF-α (TNF-alpha)HGNC:11892GCCCGACTATCTCGACTTTGGGTTGAGGGTGTCTGAAGGA14160LC461675 6p21.33
3TGF-β1 (TGFB1)HGNC:11766CCCTGGACACCAACTATTGCCAGAAGTTGGCATGGTAGCC13060LC461676 19q13.2
4β-ACTIN (ACTB)HGNC:132TCGTGCGTGACATTAAGGAGGTCAGGCAGCTCGTAGCTCT11060LC4616777p22.1
5IL-1β (IL1B)HGNC:5992GGCGGCCAGGATATAACTCCCTAGGGATTGAGTCCACA10060LC461678 2q14.1
6IL-4HGNC:6014GGCTTGAATTCCTGTCCTGTATGATCGTCTTTAGCCTTTC7760LC461679 5q31.1
7IL-5HGNC:6016AGGGCCAAGAAAGAGTCAGGTGCCTGGAGGAAAATACTTC15360LC461680 5q31.1
8IL-7HGNC:6023TTCCTCTGGTCCTCATCCAG ATCCGCCAGCAGTGTACTTT 14060LC461681 8q21.13
9IL-8 (CXCL8)HGNC:6025CTAGGACAAGAGCCAGGAAGAA AACTGCACCTTCACACAGAGC12860LC461682 4q13.3
10IL-10HGNC:5962TTGGGGCTTCCTAACTGCTACAGTGGTTGGGGAATGAGGTTAG11862LC461683 1q32.1
11IL-12BHGNC:5970ATTGTGCCACTGCATACCAGAGGACTGCCATGGAAGCTAA10162LC461684 5q33.3
12IL-12Rβ2HGNC:5972ACTGGAGCCTCAGCACATCTAGCCTCACCACTCAGAGCAT13860LC461685 1p31.3
13IL-13HGNC:5973GCCAAGGGTTCAGAGACTCAGACCCCAGTGAGGTAGCAGA10260LC461686 5q31.1
14RUNX1HGNC:10471GGGAACTGTCAAGCTGGTGTCTGTGTACCGTGGACTGTGGA12658LC461687 21q22.12
15RUNX3HGNC:10473TGAGAGGTGGGGAGTACTGGGGCAAGACTTCACCTCGGAA10260LC461688 1p36.11
16IRF1HGNC:6116GAAGAACATGGATGCCACCTTCTCTGCACCATATCCACCA15660LC461689 5q31.1
17T-BET (TBX21)HGNC:11599GGAAACGGATGAAGGACTGAATCCTTCTTGAGCCCCACTT8958LC461690 17q21.32
18GATA3HGNC:4172GAGGGTAGCAGTGTATGAGCTCACTAACACAGAACACGACAGG11258LC46169110p14
19STAT1HGNC:11362ACAAAGTCATGGCTGCTGAGAAGTTCCATTGGCTCTGGTG12860LC461692 2q32.2
20STAT4HGNC:11365CAACCAACGATTCCCAGAACTCTGCCAGCATATGGAGTTG14258LC461693 2q32.2-q32.3
21STAT6HGNC:11368AACATCCAGCCATTCTCTGCTTGGGCTTCTTGGGATAGAG10158LC461694 12q13.3
22NF-KB1 (NFKB1)HGNC:7794CTGGAAGCACGAATGACAGATGAGGTCCATCTCCTTGGTC17260LC461695 4q24
23EOMESHGNC:3372CCACTGCCCACTACAATGTGCTCATCCAGTGGGAACCAGT16660LC461696 3p24.1
24GrB (GZMB)HGNC:4709CCAGGGCATTGTCTCCTATGATTACAGCGGGGGCTTAGTT13860LC461697 14q12
25PERFORIN (PRF1)HGNC:9360CATGTAACCAGGGCCAAAGTGGCTTAGGAGTCACGTCCAG10460LC461698 10q22.1
26CSF1HGNC:2432TAAGAGACCCTGCCCTACCTGCAAGTTCACTGCCCTTCCCTA12758LC461699 1p13.3
27LT-ALPHA (LTA)HGNC:6709CCTGATGTCTGTCTGGCTGATGCTCTTCCTCTGTGTGTGG11360LC461700 6p21.33
28CXCR3HGNC:4540AGCTTTGACCGCTACCTGAAGCCGACAGGAAGATGAAGTC14060LC461701 Xq13.1
29CCR8HGNC:1609CCCTGTGATGCGGAACTTATCAGACCACAAGGACCAGGAT11960LC4617023p22.1
30cMAFHGNC:6776CTTTGCTCTCTGCCTCGTCT CGCTCTCTACCTCTGTGCAA 14160LC461703 16q23.2
31NFAT1 (NFATC2)HGNC:7776CTGGAGGTGGGTTTCTACCAAGGGGCAGAAGGGATCTTTA13460LC461704 20q13.2
32cJUN(AP1)HGNC:6204CACGTGAAGTGACGGACTGTCAGGGTCATGCTCTGTTTCA14360LC461705 1p32.1
33p38 MAPK (MAPK14)HGNC:6876TGCACATGCCTACTTTGCTC AGGTCAGGCTTTTCCACTCA11660LC4617066p21.31
34SOCS1HGNC:19383AGACCCCTTCTCACCTCTTGATAGGAGGTGCGAGTTCAGGT11760LC461707 16p13.13
35SOCS3HGNC:19391GAGACGGGACATCTTTCACCT CAGGCTGAGTATGTGGCTTTC 15260LC461708 17q25.3
36BATFHGNC:958GGAGTGAACACGGGAACTGT CCATGGGACTTGAGCATCTT 14860LC461709 14q24.3
37BCL6HGNC:1001CAGCCACAAGACCGTCCATAC CGAGTGTGGGTTTTCAGGTTG 9660LC461710 3q27.3
38ETS1HGNC:3488TGGTCTAGCTGGGTGAAACC CCAGAATGGAGAAGGGAACA 10260LC461711 11q24.3
39PD-1 (PDCD1)HGNC:8760CCTGCAGGCCTAGAGAAGTTT GGGCATGTGTAAAGGTGGAG 9160LC461712 2q37.3
40RANTES (CCL5)HGNC:10632TCTGTGACCAGGAAGGAAGTCGTTTGCCAGTAAGCTCCTGTG10860LC461713 17q12
Table
Table 3. Differential clustering of TF’s and cytokines based on their similar expression order.
Table 3. Differential clustering of TF’s and cytokines based on their similar expression order.
Cluster 1Cluster 2Cluster 3
IFNγRunx3TNFαp38NFκB
TGF-βStat4IL10BATF
IL12Stat6IL1βBCL6
GATA3IL12Rβ2Granzyme BIRF1
TbetCXCR3Stat1PD-1
EomesCCR8IL7
IL4LTαcMAF
IL5NFATcJUN
IL13ETS1Socs1
CSF1IL8Socs3
PerforinRantes
Runx1
Note. Cluster 1= Factors with decreased expression following the disease severity, Cluster 2 = Factors with increased expression following the disease severity, Cluster 3 = Factors with moderate or unchanged expression following the disease severity.
Table
Table 4. Linear regression predictive analysis of immunoregulatory factors.
Table 4. Linear regression predictive analysis of immunoregulatory factors.
Factors (All Malaria Cases)TBET **Factors (All Malaria Cases)GATA3 ***
Standardized Coefficient (Beta)p-Value *Standardized Coefficient (Beta)p-Value *
IL-1β−0.6450.001IL-1β−0.6190.002
EOMES0.9090.000PERFORIN0.6390.001
PERFORIN0.8820.000GRANZY-B0.4180.053
GRANZ-B0.6390.001LT alpha0.7400.000
LT alpha0.6100.003CXCR30.7690.000
CXCR30.8640.000CCR80.4740.026
CCR80.4350.043ETS10.8590.000
ETS10.8100.000IRF1−0.5030.017
IRF1−0.5550.007RANTES0.7470.000
RANTES0.8680.000BCL6−0.5490.008
BCL6−0.7380.000PD-10.6960.000
PD-10.8690.000
* p-values were calculated using a linear regression model analysis. ** Dependent variable: T-BET, *** dependent variable: GATA3.

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Ahmed, M.Z.; Bhardwaj, N.; Sharma, S.; Pande, V.; Anvikar, A.R. Transcriptional Modulation of the Host Immunity Mediated by Cytokines and Transcriptional Factors in Plasmodium falciparum-Infected Patients of North-East India. Biomolecules 2019, 9, 600. https://doi.org/10.3390/biom9100600

AMA Style

Ahmed MZ, Bhardwaj N, Sharma S, Pande V, Anvikar AR. Transcriptional Modulation of the Host Immunity Mediated by Cytokines and Transcriptional Factors in Plasmodium falciparum-Infected Patients of North-East India. Biomolecules. 2019; 9(10):600. https://doi.org/10.3390/biom9100600

Chicago/Turabian Style

Ahmed, Md Zohaib, Nitin Bhardwaj, Supriya Sharma, Veena Pande, and Anupkumar R Anvikar. 2019. "Transcriptional Modulation of the Host Immunity Mediated by Cytokines and Transcriptional Factors in Plasmodium falciparum-Infected Patients of North-East India" Biomolecules 9, no. 10: 600. https://doi.org/10.3390/biom9100600

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