Abstract

   Infections of humans with the protozoan parasite Toxoplasma gondii
   (T. gondii) can lead to the disease's development, even in an
   asymptomatic status. However, the mechanisms that result in these
   clinical outcomes after infection are poorly understood. This study
   aimed to explore the molecular pathogenesis of toxoplasmosis-related
   inflammation through next-generation sequencing, to assess RNA
   expression profiles in peripheral blood from 5 female patients with
   chronic toxoplasmosis and 5 healthy female controls. All plasma samples
   were analyzed for anti-Toxoplasma IgG and IgM antibody titers by using
   electrochemiluminescence. Detection of acute and chronic toxoplasmosis
   was carried out using the ELISA IgG avidity. We evaluated the levels of
   INF-γ, IL-2, IL-12, TNF-α, IL-10, and IL-1β in culture supernatants of
   Peripheral Blood Mononuclear Cells infected with Toxoplasma lysate
   antigen (TLA) prepared with tachyzoites of strain T. gondii RH.
   Differential expression analysis was performed using DESeq2, pathway
   and enrichment analysis of DEGs was done on WEB-based Gene SeT AnaLysis
   Toolkit (WebGestalt) and Protein-protein interaction was carried out
   using NetworkAnalyst with STRING. In older people with chronic
   asymptomatic infection, a significant difference in the levels of
   inflammatory cytokines INF-γ and IL-2 was observed compared to
   seronegative individuals. Our results revealed differences in the
   regulation of critical biological processes involved in host responses
   to chronic T. gondii infection. Gene ontology analysis revealed several
   biologically relevant inflammatory and immune-related pathways.

   Keywords: Toxoplasma gondii, Host-pathogen interaction, Older people,
   Inflammation, Cytokines, RNA Ion AmpliSeq, Hub genes
     __________________________________________________________________

   Toxoplasma gondii; Host-pathogen interaction; Older people;
   Inflammation; Cytokines; RNA Ion AmpliSeq; Hub genes.

1. Introduction

   Aging is generally a complex process characterized by a gradual
   deterioration of biological processes, affecting the immune system and
   central nervous system [[29]1]. Although aging is a potent risk factor
   for many diseases, are mostly unknown molecular mechanisms that lead to
   susceptibility [[30]2]. Aging has proven challenging to dissect in part
   due to its interactions with environmental influences, other genetic
   factors, and a large number of age-related diseases [[31]3]. This
   immune aging, important in current clinical practice, is known as
   immunosenescence. Understanding these subtle biological changes provide
   us with tools to carry out suitable immunotherapy in the clinical field
   [[32]4].

   Among the main causes of morbidity and mortality in the elderly
   population are infectious diseases, and precisely a key microorganism
   contributing to a chronic state of host immune activity (inflamm-aging)
   and neuroinflammation in humans is T. gondii, with the ability to alter
   behavior and personality among other. [[33]5]. Latent toxoplasmosis is
   a prevalent chronic parasitic infection, with the ability to alter the
   host's behavior, due to the parasite's ability to induce neurochemical
   changes, cytokines release such as tumor necrosis factor-α (TNF-α),
   interleukin-12 (IL-12), interferon-gamma (IFN-γ) and IL-1 β or neuronal
   cell death [[34]6].

   The effect of parasitism on host behavior has been demonstrated between
   healthy immunocompetent populations, and T. gondii seropositivity
   including personality changes, suicide, schizophrenia, depression, and
   in very preliminary investigations, Alzheimer's disease (AD). One
   hypothesis suggests that parasite evolution is a sophisticated host
   manipulation product rather than the parasite's physiological
   activities. However, there is no characterization of the effects of
   aging on the seropositive older population, especially the age-related
   changes of the immune system. The current study aimed to determine
   which genes and pathways show differential expression with age in older
   population blood, which could provide the basis for understanding the
   complex heterogeneity in toxoplasmosis in older people. We used Ion
   AmpliSeq Transcriptome technology and performed functional enrichment
   analysis for these DEGs and evaluated INF-γ, IL-2, IL-12, TNF- α,
   IL-10, and IL-1β levels in supernatants from Peripheral Blood
   Mononuclear Cells cultures infected with Toxoplasma lysate antigen
   (TLA) prepared with tachyzoites of the T. gondii RH. This study's
   collective data may represent a valuable resource for further advances
   in immunological research in the older population. Moreover,
   understanding the older population's immune system's principles is
   vital for developing vaccines that can elicit protective immunity
   [[35]7].

2. Materials and methods

2.1. Samples

   The Universidad Autónoma de Manizales's ethics committee approved the
   study protocol. The study was conducted on 10 subjects (females) over
   70 years old: 5 patients with chronic toxoplasmosis and 5 healthy
   controls. Subjects were recruited through an open call; the exclusion
   criteria were: history of psychiatric or neurological disorders,
   allergic disease, systemic inflammatory disease or unstable medical
   conditions, or those who consumed systemic corticosteroids at least one
   month before the test. After obtaining written informed consent, we
   collected blood samples from all the participants according to the
   Manizales city Ministry of Health's biosafety regulations and
   guidelines. Blood samples were collected in predetermined daytime hours
   between 8:00 a.m. and 9:00 a.m. to avoid the effect of diurnal
   variation.

2.2. Biological samples and diagnostic assays for toxoplasmosis

   The participants were classified as either Toxoplasma-positive or
   –negative based on the concentration of specific IgM and IgG
   anti-Toxoplasma gondii antibodies in their plasma. All plasma samples
   were analyzed using Electro-chemiluminescence immunoassay (ECLIA) by
   Cobas modular platform (Cobas e 411 analyzer). Both tests (IgG and IgM)
   were performed by the device automatically according to the
   manufacturer's setting. Samples with IgM Ab titer <0.8 IU/mL were
   regarded as negative, 0.81–1.0 IU/mL as borderline, and >1.0 IU/mL as
   positive results. Samples with IgG Ab titer <0.9 IU/mL were regarded as
   negative, 1.0–3.0 IU/mL as borderline, and >3.0 IU/mL as positive
   results. Detection of acute and chronic toxoplasmosis was carried out
   using the ELISA IgG avidity. Using the ELISA IgG avidity method, it was
   observed that all IgG positive cases had chronic toxoplasmosis. A low
   T. gondii IgG avidity index shows a probable recent infection (less
   than 4 months) whereas a high avidity for this parasite rules out a
   possibility for recent infection.

2.3. Preparation of Toxoplasma gondii lysate antigen (TLA)

   Toxoplasma lysate antigen (TLA) was prepared with tachyzoites of the
   described T. gondii RH strain, after making minor changes [[36]8]. The
   tachyzoites were repeatedly frozen and thawed to lyse the parasite
   cells in liquid nitrogen for four times and sonication (8 cycles at 20
   W for 20 s). The resulting protein extract was centrifuged for 10 min
   at 3000 g and 4 °C to remove the cell debris, the supernatant
   containing native T. gondii antigens (TLA) was stored at -80 °C and
   used as antigen in the in vitro assays. Before use, they were
   reconstituted to 1 mg/ml in PBS.

2.4. In vitro lymphocyte proliferation assays

   The PBMCs isolated from Toxoplasma-seronegative and
   Toxoplasma-seropositive donors were counted, and cell viability was
   determined by trypan blue. PBMCs were isolated from heparin-treated
   peripheral blood samples by Fycoll gradient. To determine the optimal
   concentration, they were cultivated for three days at 37 °C with 5%
   CO2. They were then cultured in 96-well culture plates at a density of
   1 × 105 cells per well in 0.2 ml of RPMI-1640 supplemented with
   antibiotics and 10% human AB serum in the presence or absence of 10
   μg/ml of concanavalin and 10 μg/ml of TLA. 5 wells per sample were used
   in duplicate, 1 well as control (-).

2.5. Cytokine measurement

   The production of INF-γ, IL-2, IL-12, TNF- α, IL-10, and IL-1β was
   quantified in supernatants from PBMC previously stimulated for 72 h
   with TLA or medium alone. Cytokine production was measured in duplicate
   using the Human Cytokine Magnetic Bead Panel (MILLIPLEX MAP) according
   to the manufacturer's protocol (EMD Millipore, MA, USA). Before
   analysis, samples were centrifuged at 3000 rpm for 15 min at 4C.
   Detection was then performed using the multiplex technology of Luminex
   (MAGPIX).

2.6. RNA isolation

   Total RNA was isolated from white blood cells from cases and controls
   (N = 10) at Genetracer Biotech Lab using the Tempus ™ Spin RNA
   Isolation Kit (ThermoFisher Scientific, USA) following manufacturer's
   instruction. RNA quality was measured using Agilent Bioanalyzer 2100
   (Agilent Technologies, Palo Alto, Calif.).

2.7. Library preparation and amplicon sequencing

   Libraries were prepared from 40 ng of total RNA processed using the
   AmpliSeq Transcriptome Kit v2 (ThermoFisher Scientific, USA) combined
   with barcodes Ion Xpress™ 01–16 Kit following the manufacturer's
   protocol. RNA sequencing libraries were validated using the Ion Library
   TaqMan™ Quantification Kit (ThermoFisher Scientific, USA) in a final
   volume of 10ul. A reaction mixture was prepared with library
   concentration of each sample, mixed with the Ion Spheres Particles
   (ISPs) and the rest of the reagents required for the emulsion PCR.
   Next, the chip was loaded with the ISPs in the automated Ion Chef
   system (Thermo Fisher Scientific, USA) using the Ion PI™ Hi-Q™ Chef Kit
   and the Ion PI™ chips v3 (ThermoFisher Scientific, USA). Then,
   sequencing was performed on the Ion Proton sequencer (Thermo Fisher
   Scientific, USA), which generated more than 80 million 150bp single end
   reads. Finally, Raw data were analyzed with the Torrent Suite 5.0.2
   (Thermo Fisher Scientific, USA) to generate the sequence of reads,
   eliminate bar codes, and low-quality reads. FASTQ files were generated
   using the File Exporter 4.6.0.0 plugin (Thermo Fisher Scientific, USA).

2.8. Gene expression analysis

   AmpliSeq data were analyzed and adjusted by quality using the tool
   SAMStat v 1.5 [[37]9]. Later, cleaned reads were aligned onto the human
   reference genome UCSC Hg19 (TDB, 2014) using the Ion Torrent Mapping
   Alignment Program (TMAP). Follow by estimation of abundances with BED
   Tools v 2.26 [[38]10]. The DeSeq2 v.1.12.4 R package [[39]11] was used
   to perform differential expression analysis of sequenced data. First,
   genes were filtered based on reading coverage, and only genes that had
   average coverage of at least 50 reads across all samples were retained.
   P-values were corrected for significance and the Benjamini-Hochberg
   correction was applied to estimate the false discovery rate (FDR). The
   value of FDR ≤0.1, |log2FC| > 0.5 and corrected p-value ≤0.05 were used
   as the cutoff point for statistical significance.

2.9. Gene ontology (GO) enrichment analysis of differentially expressed genes

   The detected DEGs were subjected to functional enrichment analyses
   using WEB-based Gene SeT AnaLysis Toolkit (WebGestalt,
   [40]http://www.webgestalt.org/), an online software toolkit comprising
   information from various public resources for biological analysis
   [[41]12]. The enrichment analysis such pathways, diseases, and drugs
   were carried out with top 10 results as significant using a
   hypergeometric test and Benjamini & Hochberg method.

2.10. Protein-protein interaction (PPI) network of the (DEGs)

   The protein-protein interaction (PPI) network of the (DEGs) encoding
   proteins was analyzed, and the first-order network was performed using
   Network Analyst with STRING interactome database [[42]13] at the
   confidence score cutoff 900.

2.11. In silico analysis

   To understand the potential association between miRNAs and
   differentially expressed mRNAs obtained in the study, the
   differentially expressed mRNAs were imported into the miRWalk algorithm
   (version 3.0, [43]http://mirwalk.umm.uni-heidelberg.de/) [[44]14] based
   on six bioinformatics algorithms (miRanda, DIANAmT, PicTar, miRDB,
   TargetScan, and miRWalk) to predict their target miRNAs. The
   co-predicted target miRNAs by three or more mentioned software were
   considered putative targets for subsequent analysis. For designing the
   Gene-miRNA Interaction network, the web-based application
   NetworkAnalyst was used.

   The miRmap software [[45]15] addresses the challenges in
   posttranscriptional repression of miRNAs in the human genome by
   probabilistic thermodynamic, evolutionary, and sequence-based features.
   In miRmap, the seed pairing of miRNA with the mRNA of the corresponding
   gene was analyzed.

   Gene regulatory events play a crucial role in several developmental and
   physiological processes in a cell. Macromolecules such as proteins,
   RNA, and genes work in coordination to generate operative responses
   under different conditions [[46]16]. The RegNetworks [[47]17] is a
   database that contains types (Transcription Factor-Gene, Transcription
   Factor-Transcription factor, microRNA-Transcription Factor) of
   transcriptional and posttranscriptional regulatory relationships for
   mouse and human. The conservation of knowledge about the binding site
   of transcription factor (TFBS) can also be implemented to couple the
   potential regulation between targets and their regulators. For
   designing the TF-Gene Interaction network, the web-based application
   NetworkAnalyst was used.

2.12. Statistical analysis

   Statistical analysis was performed using GraphPad Prism 8.0 software.
   The Kolmogorov-Smirnov test was applied for assessing the normality of
   the data distribution. The concentration of each cytokine was expressed
   in pg/mL. Cytokines levels among the two groups were compared using
   t-test and Mann-Whitney U test. Differences with a two-tailed value of
   p < 0.05 were considered as statistically significant.

3. Results

   The Ethics Committee of the Universidad Autónoma de Manizales approved
   this study's protocol [project 515–075]. A total of 10 blood donors
   participated in this study. RNA was isolated from whole blood from 5
   patients with T. gondii seropositive (mean age ±standard deviation
   [SD], 75.37 ± 17.27 years) and 5 healthy controls (mean age ±standard
   deviation [SD], 74.29 ± 13.81 years). All participants were females.

3.1. Cytokines production by PBMCs infected with TLA of T. gondii

   We first investigated the cytokines secreted by PBMCs obtained from
   T. gondii-seropositive elderly people and 5 healthy controls, after
   stimulation with Toxoplasma lysate antigen (TLA) prepared with
   tachyzoites of the RH strain of T. gondii. In response to the infection
   with T. gondii, the host sets up an immune reaction, mainly of cellular
   type, via T lymphocytes—essentially helper T lymphocytes (Th1),
   characterized by pro-inflammatory cytokine production such as
   interferon-gamma (IFN-γ), interleukins 2 and 12 (IL-2 and IL-12) and
   tumor necrosis factor (TNF-α) [[48]18]. We first investigated the
   cytokines secreted by the PBMCs obtained from older people T. gondii
   seropositive and 5 healthy controls, upon stimulation with Toxoplasma
   lysate antigen (TLA) prepared with tachyzoites of the T. gondii RH
   strain. INF-γ production and IL-2 after TLA infection was higher in
   PBMCs from the seropositive individual's group than in the seronegative
   individual's group (P = 0.038 and P = 0.0079, respectively), indicating
   that these cytokines tended to be higher in seropositive older people.
   It is well established that IFN-γ is important to promote Th1-directed
   adaptive immune responses and to control parasite replication during
   chronic infection via IFN-γ mediated NO synthesis in different host
   cells such as macrophages. Although the principal cytokine controlling
   this infection is IFN-γ, other cytokines have been implicated [[49]19].
   IL-2 is an important growth factor involved in the activation and
   expansion of T-cell responses during an immune response. Since
   production of IFN-γ by T cells is the major mediator of resistance to
   T. gondii, IL-2 have been demonstrated to be involved in the optimal
   production of IFN-γ by T cells [[50]20]. Contrarily, IL-10 inhibits the
   killing of T. gondii by human macrophages and leads to suppression of
   cell-mediated immunity in response to this parasite and decreases the
   pathology linked to its infection [[51]21] ([52]Figure 1).

Figure 1.

   [53]Figure 1
   [54]Open in a new tab

   Comparison of cytokine production by PBMCs infected with TLA of
   T. gondii in culture supernatants at 24 h ∗p < 0.05, ∗∗p < 0.01;
   Mann-Whitney U test. IgG (+) T. gondii seropositive individuals; IgG
   (-) T. gondii seronegative individuals.

   In contrast, the results indicate that, under experimental conditions
   used, no significant differences were observed for IL-12, TNF-α, IL-10,
   and IL-1β secretion among groups. The monitoring of the immune response
   can provide valuable information that can help in understanding the
   mechanisms of immune system control over parasites ([55]Table 1).

Table 1.

   Cytokine secretion by PBMCs infected with the TLA of T. gondii in
   patients IgG (+) and IgG (-).
   Cytokine[56]^a Patients IgG (+)[57]^b Medium Controls IgG (-)[58]^c
   Medium P-value[59]^d Patients IgG (+)[60]^bTLA Controls IgG
   (-)[61]^cTLA P-value[62]^d
   INF-γ 33.31 ± 7.51 27.65 ± 8.86 0.640 97.99 ± 25.64 23.75 ± 15.20 0.038
   IL-2 14.37 ± 1.61 9.00 ± 1.71 0.06 116.37 ± 23.15 50.07 ± 6.04 0.0079
   IL-12 7.06 ± 1.39 4.58 ± 2.07 0.352 32.02 ± 22.47 4.53 ± 2.34 0.259
   TNF-α 20.81 ± 4.03 41.24 ± 16.65 0.267 305.44 ± 88.86 394.04 ± 11.88
   0.567
   IL-10 3.90 ± 0.97 2.55 ± 1.05 0.376 254.02 ± 67.39 416.17 ± 16.54 0.392
   IL-1β 27.98 ± 3.26 24.23 ± 3.31 0.444 335.44 ± 58.36 376.71 ± 65.84
   0.652
   [63]Open in a new tab
   ^a

   Levels (pg/mL).
   ^b

   n = 5.
   ^c

   n = 5.
   ^d

   Results are represented as Mean ± SD; p < 0.05, Mann-Whitney U test.

3.2. Differential expression analysis and gene ontology enrichment

   Amplicon RNA sequencing from peripheral blood of subjects generated an
   average of ∼12 million reads per sample, of which more than 94% aligned
   to the reference genome, data analysis identified 13227 annotated
   amplicons genes as expressed in the sequenced samples. DESeq2
   identified a total of 75 genes as differentially expressed (p-value ≤
   0.05 after FDR correction) between cases and controls, of which 46 were
   up-regulated, and 29 were down-regulated (Table S1). GO enrichment
   analysis was performed to obtain insights into the biological roles of
   DEGs. Only the annotated DEGs were selected and tested against the
   background set of all genes with GO annotation. GO categories were
   examined separately.

   We found that for up-regulated genes, almost all of the significantly
   enriched GO terms in the biological process category were associated
   with the immune system, such as immune response (GO:0006955), immune
   effector process (GO: 0002252), defense response (GO: 0006952), Innate
   immune response (GO:0045087) and Cytokine production (GO:0001816). The
   overview of GO analysis results for the DEG with adjusted p-value with
   the number of genes involved is summarized in ([64]Table 2).

Table 2.

   Gene ontology enrichment analysis of up-regulated differentially
   expressed genes associated with chronic toxoplasmosis in older people.
   Category Term Description Count Genes P-Value
   GO Biological Processes GO:0006955 Immune response 18 IRAK3, IRF1,
   MCEMP1, PARP9, PPP1R14B, PRKCD, SLC11A1, TRIM25, BCL6, C3AR1, CD68,
   CEBPB, CLEC4D, CYBB, FCGR1A, FLOT2, GCA, IL1R2 1.3707 e-8
   GO Biological Processes GO:0002252 Immune effector process 14 BCL6,
   C3AR1, CD68, CLEC4D, CYBB, FCGR1A, GCA, IRAK3, IRF1, MCEMP1, PARP9,
   PRKCD, SLC11A1, TRIM25 4.1845e-8
   GO Biological Processes GO:0006952 Defense response 15 PARP9, PPP1R14B,
   PRKCD, SLC11A1, TRIM25, BCL6, C3AR1, CEBPB, CLEC4D, CYBB, FCGR1A,
   FLOT2, IL1R2, IRAK3, IRF1 2.0243e-7
   GO Biological Processes GO:0045087 Innate immune response 11 TRIM25,
   CLEC4D, CYBB, FCGR1A, FLOT2, IRAK3, IRF1, PARP9, PPP1R14B, PRKCD,
   SLC11A1 8.0621e-7
   GO Biological Processes GO:0001816 Cytokine production 10 BCL6, C3AR1,
   CEBPB, CYBB, IL1R2, IRAK3, IRF1, PRKCD, SLC11A1, TRIM25 1.7609e-6
   GO Biological Processes GO:0060333 Interferon-gamma-mediated signaling
   pathway 5 FCGR1A, IRF1, PARP9, PRKCD, TRIM25 1.0812e-6
   GO Biological Processes GO:0031347 Regulation of defense response 10
   BCL6, C3AR1, CEBPB, CLEC4D, FLOT2, IL1R2, IRAK3, IRF1, PARP9, PRKCD
   1.5299e-6
   GO Biological Processes GO:0034341 Response to interferon-gamma 6
   FCGR1A, IRF1, PARP9, PRKCD, SLC11A1, TRIM25 3.1586e-6
   GO Biological Processes GO:0045321 Leukocyte activation 12 PRKCD,
   SLC11A1, BCL6, C3AR1, CD68, CEBPB, CLEC4D, CYBB, FLOT2, GCA, IRF1,
   MCEMP1 3.9474e-6
   GO Biological Processes GO:0001817 Regulation of cytokine production 9
   BCL6, C3AR1, CEBPB, CYBB, IL1R2, IRAK3, IRF1, SLC11A1, TRIM25 6.5352e-6
   [65]Open in a new tab

   Emerging transcriptomic approaches can help decipher how aging
   interventions prioritize which transcripts to regulate and, in general,
   how the aging brain maintains this decreased translation rate without
   compromising cellular health and integrity. Here, we focus specifically
   on how the parasite alters the immune response, as this is crucial to
   our understanding of how infection is controlled and since the host's
   immune response is a primary target of T. gondii virulence factors.
   Unbiased analysis of gene expression has identified new host genes
   associated with T. gondii infection, which can be validated in
   subsequent functional studies. We show that some parts of the host
   response can be used as potential biomarkers of disease, representing
   viable targets for drug therapy. Finally, this study is a necessary
   preliminary analysis to reveal the starting points of this pathological
   cascade in older adults infected with T. gondii. By applying the wide
   range of recently developed tools, it is hoped that we can identify
   critical elements that can be addressed therapeutically, linking
   telltale nodes of aging with T. gondii infection. Results revealed that
   the main diseases associated with hub genes were infection,
   inflammation, Spondylarthritis, immune system disease, antiinflammatory
   agents, antiinfectives, glucocorticoids, antivirals, and interferons
   are the top 5 drugs found in drug enrichment analysis ([66]Table 3).

Table 3.

   Enrichment analysis result of diseases and drugs for the identified
   up-regulated differentially expressed genes.
   Disease Enrichment description P value Drug Enrichment Description P
   value
   [67]PA444614 Infection 1.2534e-8 PA164712458 Antiinflammatory agents
   4.6583e-6
   [68]PA444620 Inflammation 6.0664e-7 PA164712447 Antiinfectives
   9.7563e-6
   [69]PA447162 Spondylarthritis 2.4415e-6 [70]PA452347 Glucocorticoids
   1.5139e-5
   [71]PA444602 Immune system diseases 1.1127e-5 [72]PA452174 Antivirals
   2.0080e-5
   [73]PA445723 Spondylitis 1.6932e-5 [74]PA451999 Interferons 6.6049e-5
   PA166048719 Gram-positive bacterial infections 1.8858e-5 PA164712462
   Antiinflammatory agents, non-steroids 1.3995e-4
   [75]PA447163 Spondylarthropathies 5.6891e-5 PA164712839 Interleukins
   7.4562e-4
   [76]PA446038 Virus diseases 7.5408e-5 PA10832 Corticosteroids 8,2240e-4
   PA166048822 Actinomycetales infections 1.3979e-4 PA164713378 Vaccines
   8.5288e-4
   [77]PA445526 Respiratory tract infections 1.5201e-4 PA164713047 Other
   antivirals 9.9999e-4
   [78]Open in a new tab

   For down-regulated genes, the most significantly enriched biological
   process GO terms were lymphocyte differentiation (GO: 0030098),
   lymphocyte activation (GO: 0046649), and leukocyte differentiation (GO:
   0002521) ([79]Table 4).

Table 4.

   Gene ontology enrichment analysis of down-regulated differentially
   expressed genes associated with chronic toxoplasmosis in older people.
   Category Term Description Count Genes P-Value
   GO Biological Processes GO:0030098 Lymphocyte differentiation 6 CAMK4,
   CCR7, FCRL3, ITM2A, LEF1, RHOH 6.6925e-6
   GO Biological Processes GO:0046649 Lymphocyte activation 7 CAMK4, CCR7,
   FCRL3, ITM2A, LEF1, RHOH, SIRPG 2,4740 e-5
   GO Biological Processes GO:0002521 Leukocyte differentiation 6 CAMK4,
   CCR7, FCRL3, ITM2A, LEF1, RHOH 5.7465e-5
   GO Biological Processes GO:0009125 Nucleoside monophosphate catabolic
   process 2 DNPH1, NT5E 1.3002e-4
   GO Biological Processes GO:0002577 Regulation of antigen processing and
   presentation 2 CCR7, HLA-DOB 2.3558e-4
   GO Biological Processes GO:0030217 T cell differentiation 4 CAMK4,
   CCR7, LEF1, RHOH 3.0223e-4
   GO Biological Processes GO:0042110 T cell activation 5 CAMK4, CCR7,
   LEF1, RHOH, SIRPG 3.9816e-4
   GO Biological Processes GO:0030097 Hemopoiesis 6 CAMK4, CCR7, FCRL3,
   ITM2A, LEF1, RHOH 7.2340e-4
   GO Biological Processes GO:0048534 Hematopoietic or lymphoid organ
   development 6 CAMK4, CCR7, FCRL3, ITM2A, LEF1, RHOH 9.4902e-4
   GO Biological Processes GO:0045321 Leukocyte activation 7 CAMK4, CCR7,
   FCRL3, ITM2A, LEF1, RHOH, SIRPG 6.5352e-6
   [80]Open in a new tab

   The results provide an important collection of differential gene
   expression data in chronic versus healthy donors. To verify the utility
   of our processed gene signature, WebGestalt database was utilized for
   finding suitable drug molecules that may be used for treatment of
   chronic toxoplasmosis caused by hub genes expressed differentially. The
   enriched disease terms for the DEG were leukemia, B-Cell, Leukemia,
   Lymphocytic, Chronic, B-Cell, Leukemia, Lymphoid and Lymphoid leukemia
   NOS. Drug association analysis was performed for all hub gene
   candidates by using WebGestalt database and results obtained were
   depicted in [81]Table 5. Vanillin, Angiotensin II antagonists and
   diuretics, bisoprolol, gliclazide and immunoglobulins are identified
   best drug candidates.

Table 5.

   Enrichment analysis result of diseases an drugs for the identified
   differentially expressed genes.
   Disease Enrichment Description P-Value Drug Enrichment Description
   P-Value
   [82]PA446156 Leukemia, B-Cell 9.5390e-8 [83]PA451852 vanillin 0.010408
   [84]PA446169 Leukemia, Lymphocytic, Chronic, B-Cell 1.1057e-7
   PA164712369 Angiotensin II antagonists and diuretics 0.011702
   [85]PA444756 Leukemia, Lymphoid 0.0000025048 [86]PA448641 bisoprolol
   0.011702
   PA165108377 Lymphoid leukemia NOS 0.0000025048 PA10892 gliclazide
   0.011702
   [87]PA444602 Immune System Diseases 0.000029889 PA164712817
   Immunoglobulins 0.011931
   [88]PA446727 Lymphoma, B-Cell, Marginal Zone 0.000050652 PA164754884
   immune globulin 0.011931
   [89]PA444833 Lymphatic Diseases 0.00012698 PA164713118 Other
   potassium-sparing agents 0.012994
   [90]PA444849 Lymphoproliferative Disorders 0.00014607 PA164749409
   drospirenone 0.012994
   PA153627493 Autoimmune Thyroid Disease 0.00030827 PA164743961
   fludrocortisone 0.012994
   [91]PA444755 Leukemia, Hairy Cell 0.00074118 [92]PA450061 iohexol
   0.012994
   [93]Open in a new tab

3.3. Pathway enrichment analysis of differentially expressed genes

   To further evaluate the biological significance of the DEGs, we also
   performed Reactome pathway enrichment analysis. Hypergeometric tests
   with a cut-off value of p < 0.05 were used as the criterion for pathway
   detection. We found that up-regulated genes were significantly enriched
   in 10 pathways, among which five pathways were immune-related
   ([94]Table 6).

Table 6.

   Reactome pathway analysis of up-regulated differentially expressed
   genes associated with chronic toxoplasmosis in older people.
   Gene Set Description P-Value FDR
   R-HSA-168256 Immune System 2.7866e-6 0.0036058
   R-HSA-168249 Innate Immune System 5.1225e-6 0.0036058
   R-HSA-877300 Interferon-gamma signaling 7.5429e-6 0.0036058
   R-HSA-6798695 Neutrophil degranulation 8.3468e-6 0.0036058
   R-HSA-913531 Interferon Signaling 2.8781e-4 0.099468
   R-HSA-1280215 Cytokine Signaling in the Immune system 8.1074e-4 0.23349
   R-HSA-2029485 Role of phospholipids in phagocytosis 2.5581e-3 0.63150
   R-HSA-1236975 Antigen processing-Cross presentation 3.2600e-3 0.70416
   R-HSA-1222556 ROS, RNS production in phagocytes 4.1739e-3 0.80139
   R-HSA-3371497 HSP90 chaperone cycle for steroid hormone receptors (SHR)
   1.1146e-2 1
   [95]Open in a new tab

   [96]Table 7 shows down-regulated genes were significantly enriched in
   10 pathways, among which three pathways were metabolism-related.

Table 7.

   Reactome pathway analysis of Down-regulated differentially expressed
   genes associated with chronic toxoplasmosis in older people.
   Gene Set Description P-Value FDR
   R-HSA-74259 Purine catabolism 3.6298e-4 0.37597
   R-HSA-15869 Metabolism of nucleotides 4.3515e-4 0.37597
   R-HSA-8956319 Nucleobase catabolism 1.3914e-3 0.80144
   R-HSA-111932 CaMK IV-mediated phosphorylation of CREB 7.5585e-3 1
   R-HSA-9022535 Loss of phosphorylation of MECP2 at T308 7.5585e-3 1
   R-HSA-442717 CREB phosphorylation through the activation of CaMKK
   9.0638e-3 1
   R-HSA-5358493 Synthesis of diphthamide-EEF2 1.2068e-2 1
   R-HSA-4411364 Binding of TCF/LEF: CTNNB1 to target gene promoters
   1.2068e-2 1
   R-HSA-8951430 RUNX3 regulates WNT signaling 1.2068e-2 1
   R-HSA-4641265 Repression of WNT target genes 1.8051e-2 1
   [97]Open in a new tab

3.4. Protein-protein interaction network of differentially expressed genes

   Protein-protein interaction analysis was performed on all DEGs. Since
   the biological processes occur in a network biology context, to provide
   functional insights into the identified DEGs roles, we have
   reconstructed the PPI network around the proteins encoded by the DEGs
   using the publicly available PPI database STRING via NetworkAnalyst
   tool. Through a topological measure degree, we have detected the
   presence of eight hub proteins in DEGs up-regulated: YWHAB, PRKCD,
   CEBPB, BCL6, IRF1, TRIM25, UBC, and HLA-C ([98]Figure 2).

Figure 3.

   [99]Figure 3
   [100]Open in a new tab

   PPI network of hub genes (down-regulated) of older people with chronic
   toxoplasmosis. The nodes represent proteins, and edges represent
   connections/interactions. The gradual color represents the degree.

Figure 2.

   [101]Figure 2
   [102]Open in a new tab

   PPI network of hub genes (up-regulated) of older people with chronic
   toxoplasmosis. The nodes represent proteins, and edges represent
   connections/interactions. The gradual color represents the degree.
   LEF1, TRAF4, CAMK4, CREBBP, and BIRC3 in DEGs down-regulated
   ([103]Figure 3).

3.5. The regulatory network of differentially expressed mRNAs and miRNAs
associated with chronic toxoplasmosis in older people

   In miRWalk, 16 miRNAs (hsa-miR-181b-5p, hsa-miR-485–5p,
   hsa-miR-216b-5p, hsa-miR-504–3p, hsa-miR-4753–3p, hsa-miR-6715b-5p,
   hsa-miR-5011–5p, hsa-miR-203a-5p, hsa-miR-423–5p, hsa-miR-346,
   hsa-miR-93–5p, hsa-miR-3666, hsa-miR-378a-5p, hsa-miR-1182,
   hsa-miR-651–3p, hsa-miR-4660) were associated with 8 genes up-regulated
   ([104]Figure 4).

Figure 4.

   [105]Figure 4
   [106]Open in a new tab

   A PPI network for eight common responsible genes. The network consists
   of 295 nodes and 317 edges, and eight seed nodes. Nodes are proteins,
   and the edges establish a relationship between proteins.

   15 miRNAs (hsa-miR-649, hsa-miR-571, hsa-miR-449a, hsa-miR-300,
   hsa-miR-381–3p, hsa-miR-449b-5p, hsa-miR-129-1-3p, hsa-miR-17–5p,
   hsa-miR-29a-3p, hsa-miR-33a-5p, hsa-miR-29b-3p, hsa-miR-34a-5p,
   hsa-miR-4319, hsa-miR-153–3p, hsa-miR-6729–3p) were associated with 5
   genes down-regulated ([107]Figure 5).

Figure 5.

   [108]Figure 5
   [109]Open in a new tab

   A PPI network for five common responsible genes. The network consists
   of 57 nodes and 56 edges, and three seed nodes. Nodes are proteins, and
   the edges establish a relationship between proteins.

3.6. Identification of transcription factors from RegNetworks

   We identified TFs that may regulate the expression of DEGs at
   transcriptional levels. In RegNetworks, 80 transcription factors (ABL1,
   GABPA, HDAC5, CREBBP, DAB2, EEF1A1, EGR2, EGR3, EP300, ESRRA, FOXA1,
   HABP4, HNF4A, HNRNPK, ATF2, ATF4, CEBPA, BACH1, ATF2, AHR, ARNT, CREB1,
   CTCF, EIF2AK2, ELK1, EP300, HMGA1, JUN, MAX, MYC, PPARG, TFAP2A,
   TFAP2C, USF1, XBP1, SPI1, SP1, SFPQ, RXRG, RXRB, RXRA, BACH, CREB1,
   DAZAP2, E2F1, E2F2, E2F3, E2F4, E2F5, E2F6, E2F7, EGR1, FOS, FOSB,
   FOSL1, HDAC5, NFKB1, NFKB2, NFYAABL1, GABPA, HDAC5, DAB2, EGR2, EP300,
   FOXA1, CREB1, EGR1, STAT3, MYB, STAT1, STAT5B, NFKB1, EIF2AK2, STAT4,
   MYC, USF1, PPARD, RXRA, RXRB, RXRG) were associated with the 8 genes
   up-regulated ([110]Figure 6).

Figure 6.

   [111]Figure 6
   [112]Open in a new tab

   Analysis of Transcription Factors interaction networks based on
   differentially expressed genes (up-regulated) identified in chronic
   toxoplasmosis in the older population. The network consists of 351
   nodes and 2842 edges, and 60 seed nodes.

   18 transcription factors (ELK1, NFKB1, NFKB2, RELA, TP53, CTBP1,
   CTNNB1, CREBBP, EP300, RELA, ATF4, ATF4, CREB1, CREB3, CREB3L1,
   CREB3L3, CREB3L4, CREB5) were associated with the five genes
   down-regulated ([113]Figure 7).

Figure 7.

   [114]Figure 7
   [115]Open in a new tab

   Analysis of Transcription Factors interaction networks based on
   differentially expressed genes (down-regulated) identified in chronic
   toxoplasmosis in the older population. The network consists of 269
   nodes and 732 edges, and 20 seed nodes.

   Up-regulated and down-regulated gene search in chronic toxoplasmosis
   results in 13 genes with a validated efficacy in clinical research. The
   miRNA search for these 13 genes in miRWalk resulted in identifying 31
   miRNAs and 98 transcription factors with a maximum probability of
   becoming biomarkers for chronic toxoplasmosis based on an association
   of genes and transcription factors ([116]Table 8).

Table 8.

   Genes, miRNAs & Transcription Factors associated with regulation of
   chronic toxoplasmosis in the older population.
   Genes miRNAs (miRWalk) Gene-miRNA score (miRmap) Transcription factors
   (RegNetworks)
   YWHAB hsa-miR-181b-5p 3.42% ABL1, GABPA, HDAC5
   hsa-miR-485–5p 90.86%
   hsa-miR-216b-5p 80.13%
   hsa-miR-504–3p Nil
   hsa-miR-4753–3p 58.95%
   hsa-miR-6715b-5p 90.78%
   PRKCD hsa-miR-5011–5p 99.85% CREBBP, DAB2, EEF1A1, EGR2, EGR3, EP300,
   ESRRA, FOXA1, HABP4, HNF4A, HNRNPK
   hsa-miR-203a-5p Nil
   CEBPB hsa-miR-423–5p Nil ATF2, ATF4, CEBPA
   BCL6 hsa-miR-346 97.99% BACH1, ATF2
   IRF1 hsa-miR-93–5p 74.01% AHR, ARNT, CREB1, CTCF, EIF2AK2, ELK1, EP300,
   HMGA1
   hsa-miR-3666 96.59%
   hsa-miR-378a-5p 94.76%
   TRIM25 hsa-miR-1182 99.70% JUN, MAX, MYC, PPARG, TFAP2A, TFAP2C, USF1,
   XBP1
   UBC hsa-miR-651–3p Nil SPI1, SP1, SFPQ, RXRG, RXRB, RXRA, BACH, CREB1,
   DAZAP2, E2F1, E2F2, E2F3, E2F4, E2F5, E2F6, E2F7, EGR1, FOS, FOSB,
   FOSL1, HDAC5
   HLA-C hsa-miR-4660 92.19% NFKB1, NFKB2, NFYA
   BIRC3 hsa-miR-649 Nil ELK1, NFKB1, NFKB2, RELA, TP53
   hsa-miR-571 81.55%
   LEF1 hsa-miR-449a 92.82% CTBP1, CTNNB1, CREBBP, EP300
   hsa-miR-300 88.35%
   hsa-miR-381–3p 85.79%
   hsa-miR-449b-5p 92.98%
   TRAF4 hsa-miR-129-1-3p 73.02% NFKB1, NFKB2, RELA
   hsa-miR-17–5p 78.07%
   CAMK4 hsa-miR-29a-3p Nil ATF4, CREBBP, EP300
   hsa-miR-33a-5p Nil
   hsa-miR-29b-3p Nil
   hsa-miR-34a-5p Nil
   hsa-miR-4319 Nil
   CREBBP hsa-miR-153–3p 84.06% ATF4, CREB1, CREB3, CREB3L1, CREB3L3,
   CREB3L4, CREB5, CTNNB1
   hsa-miR-6729–3p Nil
   [117]Open in a new tab

   [118]Table 9 summarizes the hub proteins identified from the
   protein-protein interactions encoded by the up-differentially expressed
   and down-differentially genes in older people with chronic
   toxoplasmosis.

Table 9.

   The summary of the hub proteins identified.
   Gene Symbol Full Name Feature
   YWHAB Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation
   protein beta Role in linking mitogenic signaling and the cell cycle
   machinery
   PRKCD Protein kinase C delta Role in the regulation of various cellular
   processes, including cell proliferation, apoptosis, and differentiation
   CEBPB CCAAT enhancer-binding protein beta Transcription factor
   regulating the expression of genes involved in immune and inflammatory
   responses
   BCL6 BCL6 transcription repressor Potential transcriptional regulatory
   activity
   IRF1 Interferon regulatory factor 1 Transcriptional regulator which
   displays a remarkable functional diversity in the regulation of
   cellular responses
   TRIM25 Tripartite motif-containing 25 Involved in innate immune defense
   UBC Ubiquitin C Plays a crucial role in maintaining cellular ubiquitin
   levels under stressful conditions
   HLA-C Major histocompatibility complex, class I, C Play a central role
   in the immune system by presenting peptides derived from endoplasmic
   reticulum lumen
   BIRC3 baculoviral IAP repeat-containing 3 Modulates inflammatory
   signaling and immunity
   LEF1 lymphoid enhancer-binding factor 1 Regulates T-cell receptor alpha
   enhancer function
   TRAF4 TNF receptor-associated factor 4 Plays a role in the activation
   of JNK and NF-kappa-B and the regulation of apoptosis and cell survival
   CAMK4 calcium/calmodulin-dependent protein kinase IV It is involved in
   synaptic plasticity, neuronal transmission, and neuronal gene
   expression required for neuronal homeostasis and brain development.
   CREBBP CREB binding protein Plays a critical role in the control of
   cell proliferation, homeostasis, and embryonic development
   [119]Open in a new tab

4. Discussion

   The aging process is characterized by a continuous remodeling. The main
   actors are apoptosis, oxidative stress, DNA repair, immune response and
   inflammation. One of the most recent theories on aging focuses on the
   chronic low-grade inflammation named ‘‘inflammaging’’ [[120]22].

   A major contributor of inflammaging is the antigenic load of persistent
   infection, It is also clear that chronic T. gondii infection alters
   human monocytic cell phenotypes and functions across lifetimes
   [[121]23], and is not immunologically silent as large quantities of pro
   inflammatory cytokines are produced/released and are the expression of
   a system that involves environment, genes and polymorphisms. Some
   studies have found alterations in individuals҆ behavior associated with
   T. gondii seropositivity. T. gondii causes latent chronic infections of
   worldwide concern. Its subclinical influence on behavior and cognition
   is not well understood [[122]24].

   The long-term host effects caused by the parasite T. gondii are poorly
   understood. Mice with chronic infection have alterations in
   connectivity [[123]25], neurotransmitters [[124]26], seizures, memory,
   neurobehaviors [[125]27] and form long-lasting chronic infections in
   the central nervous system (CNS) of humans [[126]28]. Some
   epidemiologic serologic studies show associations between
   seropositivity for T. gondii and some human neurologic diseases,
   including memory loss [[127]29].

   In the present study, we focused on the effects of chronic, latent
   Toxoplasma infection in the PBMCs from older people to produce
   cytokines, when cultured in the presence of Toxoplasma lysate antigen
   (TLA) prepared with tachyzoites of the T. gondii RH strain. The
   significant production of IL-2 and INF-γ highlights the importance of
   these two cytokines in the immunopathology associated with
   toxoplasmosis. Interestingly, neither IL-12, TNF- α, IL-10, and IL-1β
   concentrations showed any alterations in supernatants from Peripheral
   Blood Mononuclear Cells cultures infected with Toxoplasma lysate
   antigen (TLA).

   IL-2 is considered important in mediating resistance against a variety
   of different intracellular parasites [[128]30]. Kelly et al. described
   the IL-2 and INF-γ production from peripheral blood mononuclear cells
   stimulated in vitro with Toxoplasma antigen from a seropositive donor
   [[129]31]. To our knowledge, there are no other studies evaluating IL-2
   and IFN-γ production from PBMCs stimulated with Toxoplasma lysate
   antigen (TLA) in older people. IL-2 is produced by T-cells and controls
   the expression of the transcription factors, effector cytokines and
   chromatin regulators. Activates NK-cells, stimulates B-cells and is
   necessary for the proliferation of T-lymphocytes [[130]32].

   During chronic toxoplasmosis, the production of IFN-γ has been shown to
   play an important role in the switch to bradyzoite production, as is
   the activation of CD8+ cytotoxic T cells and the induction of
   immunity-related GTPases (IRG) and is indispensable for the survival of
   the host [[131]33].

   Transcriptomics great promise for personalized and precision medicine
   and can provide a comprehensive understanding of biological processes
   [[132]34]. Differential gene expression analysis is used to identify
   key genes that undergo changes in expression relative to healthy
   individuals, as well as patients with other diseases. These key genes
   can act as diagnostic, prognostic and predictive markers for disease.
   Gene expression “signatures” in blood have the potential to be used for
   diagnosis of infectious diseases, where current diagnostics are
   unreliable, ineffective, or of limited potential. With methods more
   sophisticated blood transcriptome profiling may become suitable for use
   in clinical practice for diagnosing, predicting, and personalizing
   treatment for chronic toxoplasmosis.

   Our study identified specific blood gene expression patterns associated
   with chronic toxoplasmosis when compared to healthy individuals. In our
   study, gene expression profiling analysis revealed the core genes and
   pathways associated with chronic toxoplasmosis and allowed the
   identification of targets for therapeutic strategy. Bioinformatics
   methods were applied to analyze the raw data and we identified 75 DEGs,
   including 46 up-regulated and 29 down-regulated DEGs. Various
   bioinformatics analyses such as GO, pathways, disease, drugs, and
   networks were utilized for the integrated approach. The enrichment
   analysis of these genes gave an overview of possible associated
   diseases and dysregulated pathways. The PPI network of DEG was
   constructed using the STRING database and the main genes YWHAB, PRKCD,
   CEBPB, BCL6, IRF1, TRIM25, UBC and HLA-C were obtained. Druggable genes
   (DG) are vital and can act as a therapeutic target for disease.

   The CEBPB (Enhancer Binding Protein β-CCAAT) transcription factor
   regulates numerous genes involved in essential functions such as
   hematopoiesis, cell cycle, inflammation, and host immune responses
   [[133]35], is activated by multiple inflammatory stimuli, including LPS
   and IL-17 [[134]36]. Furthermore, IL-17 activates the CCAAT/Enhancer
   Binding Protein family of transcription factors, particularly CEBPB
   [[135]37], but the biological role of this pathway in chronic
   toxoplasmosis is not well defined. Cebpb−/− mice are highly susceptible
   to systemic Listeria monocytogenes [[136]38]. In contrast, Cebpb−/−
   mice were resistant to oral candidiasis [[137]39].

   The transcriptional repressor B-cell Lymphoma 6 (BCL6), as a
   transcriptional repressor, mediates the innate immune response's
   counter-regulation and represses NF-κB activity [[138]40]. Also, BCL6
   can directly repress its target proinflammatory cytokines and
   chemokines by specifically binding to the target gene promoters in
   macrophages [[139]41]. We also found that PRKCD and IRF1 were
   significantly overexpressed in older people with chronic toxoplasmosis.
   PRKCD is a serine-threonine kinase that encodes protein kinase C-δ and
   phosphorylates myristoylated alanine-rich C kinase substrate regulating
   differentiation apoptosis and cell cycle in several cellular models.
   The phosphorylation is known to induce neurite degeneration via
   instability of the actin network in mouse and humans brains [[140]42].
   We identified IRF1 (interferon regulatory factor 1) itself as a
   transcription factor, regulated by STAT1, the absence of which
   increases susceptibility to Toxoplasma infection [[141]43].

   Formin-like protein 1 (FMNL1) is expressed in various tissues, such as
   the spleen, peripheral blood leukocytes and thymus [[142]44]. FMNL1
   expression is correlated with cell adhesion, phagocytosis, survival,
   and cell migration in macrophages [[143]45]. On the other hand, TRIM25
   is an E3 ubiquitin ligase enzyme crucial for antiviral immune response
   associated with IFN signaling involved in various cellular processes.
   TRIM25 [[144]46] has recently been identified as an RNA-binding
   protein, important for its role in regulating intracellular signaling,
   innate immunity, cell development, and participating NF-κB signaling
   pathway implicating regulatory functions in inflammation and immunity
   [[145]47].

   GO analysis revealed genes involved in lymphocyte differentiation (GO:
   0030098), lymphocyte activation (GO: 0046649), and leukocyte
   differentiation (GO: 0002521). The top hub genes were obtained: BIRC3,
   LEF1, TRAF4, CAMK4, and CREBBP.

   Recently, studies have demonstrated a pivotal role for (miRNAs) to
   infection by apicomplexan parasites as well as functionality of host
   mature various immune cells [[146]19] and can directly affect the
   expression of thousands of genes [[147]48]. For example, miR-125b
   appear to strongly impact on proinflammatory cytokine production, such
   as IFN-γ and IL-2 [[148]49]. Additionally, there have been fewer
   reports the role of each mRNA/miRNA target in specific pathways during
   infection T. gondii. In this context, an increased understanding of the
   interactions among altered behavior, neuroinflammation, and infection
   may lead to new approaches for the treatment and prevention of these
   disorders and insight into the interaction between host and parasite.

   These new data and analysis will provide insights extending beyond
   infection, to common pathways of neurodegeneration and shared upstream
   regulators parasites perturb [[149]50]. Data also are accumulating that
   a variety of infections cause cognitive impairment. Thus, a new
   paradigm to integrate knowledge of pathogenesis, prevention, and
   treatment is needed.

   Analysis of differentially expressed genes have allowed us to identify
   core genes and relevant immune pathways that are activated in
   asymptomatic older patients with chronic toxoplasmosis that could
   represent potential biomarkers for early diagnosis and treatment.

   The study is an initial investigation with a small sample size.
   However, future expression studies with blood will require larger
   sample sizes including older men to establish the robustness of the
   findings.

5. Conclusions

   Our results revealed that cytokine signaling responses as an
   inflammatory hallmark in Toxoplasma gondii-infected older people, may
   be associated with modulation of immune responses of Th1 CD4+ T cells.
   The results indicate that T. gondii infection of IgG positive
   individuals induces changes in gene expression profile in relevant
   immunological pathways that could predict disease trajectories.

Declarations

Author contribution statement

   Carlos Andrés Naranjo-Galvis: conceived and designed the experiments;
   performed the experiments; analyzed and interpreted the data;
   contributed reagents, materials, analysis tools or data; wrote the
   paper.

   Kelly Y Cardona-Londoño: performed the experiments; analyzed and
   interpreted the data; contributed reagents, materials, analysis tools
   or data.

   Mary Orrego-Cardozo: performed the experiments; contributed reagents,
   materials, analysis tools or data.

   Xabier Elcoroaristizabal-Martín: performed the experiments; contributed
   reagents, materials, analysis tools or data.

Funding statement

   This work was supported by the Universidad Autónoma de Manizales
   (project number 515–075).

Data availability statement

   Data included in article/supplementary material/referenced in article.

Declaration of interests statement

   The authors declare no conflict of interest. Xabier Elcoroaristizabal
   Martín is employed by Genetracer Biotech and collaborates with
   Universidad Autónoma de Manizales. The funder had no role in the design
   of the study; in the collection, analyses, or interpretation of data;
   in the writing of the manuscript, or in the decision to publish the
   results.

Additional information

   No additional information is available for this paper.

Acknowledgements