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Published in final edited form as: J Neurosci Methods. 2020 Jan 22;334:108601. doi: 10.1016/j.jneumeth.2020.108601

Transcardial Perfusion is not Required to Accurately Measure Cytokines within the Brain

William H Walker II 1,*, Jacob R Bumgarner 1, Randy J Nelson 1, A Courtney DeVries 1,2
PMCID: PMC7374017  NIHMSID: NIHMS1553926  PMID: 31981570

Abstract

Background

Cytokines are key signaling molecules within the immune system that regulate a host’s response to pathogens and neuronal damage. Aberrant cytokine signaling has been implicated in many neurological diseases. Therefore, accurately measuring cytokine concentrations within the brain is crucial.

New Method

This study demonstrates that removing blood within brain vasculature via saline perfusion does not alter brain parenchymal cytokine protein concentrations or mRNA expression.

Results

Hippocampal protein and mRNA data demonstrate that brain parenchymal cytokine concentrations do not significantly differ based on the method of euthanasia (i.e., perfusion or no perfusion). These results are consistent within naive and immune challenged mice.

Comparison with existing method

Due to the potential of cytokine contamination from circulating blood, it is believed that transcardial perfusion is required for accurate measurement of cytokine concentrations and gene expression within the brain. However, our data indicate that cytokine concentrations are unaffected by not perfusing mice with saline prior to tissue collection.

Conclusions

Brain cytokine concentrations are unaffected by perfusing with saline prior to tissue collection; this holds true regardless of immune status (homeostatic or immune challenged), suggesting that this time-consuming step may be unnecessary.

Keywords: cytokines, neuroinflammation, perfusion

1. Introduction

Cytokines are a diverse group of signaling proteins secreted by immune cells in response to antigens or other stimuli. In 1957, the first cytokine, interferon (later interferon-α), was identified; the name reflected its ability to interfere with viral replication in chick chorio-allantoic membranes (Isaacs and Lindenmann, 1987). Today the number of identified cytokines has increased extensively; currently it is estimated that over 100 genes code for cytokine-like activities (Dinarello, 2007). Cytokines are broadly classified as pro-inflammatory or anti-inflammatory, and were originally loosely categorized into six groups including lymphokines, monokines, interleukins, interferons, colony stimulating factors, and chemokines based on their production/release and function. However, given the redundancy and pleiotropic effects of many cytokines, this classifying system has become increasingly obsolete. Once released, these signaling proteins can act in an autocrine, paracrine, or endocrine manner to affect numerous aspects of cellular function including transcription, cell growth, differentiation, and survival (Hirano et al., 2000; Schluns and Lefrancois, 2003; De Simone et al., 2015; Mackay et al., 2015).

Cytokines perform critical roles in various developmental processes, including embryogenesis and hematopoiesis (Guzeloglu-Kayisli et al., 2009; Asada et al., 2017; Robertson et al., 2017), but they have been most extensively studied for governing immune responses to infection or trauma (Luster, 1998; Dinarello, 2000). These signaling proteins are critical for host defense against pathogens, as animals lacking cytokines or cytokine signaling components quickly succumb to bacterial, viral, and parasite infections (Gazinelli et al., 1996; Takeuchi et al., 2000; Drennan et al., 2004; Tabeta et al. 2004). However, regulation of cytokine signaling and immune activation plays an equally critical role, as over-activation of the immune response and chronic inflammation are thought to underlie many diseases such as cancer, arthritis, diabetes mellitus, Crohn’s disease, heart disease, and many others (Danesh et al., 2000; Wong et al., 2006; Doneath et al., 2009; Grivennikov et al., 2010; Strober et al., 2010).

Notably, cytokines are not restricted to the periphery. These signaling proteins perform similar important developmental and homeostatic functions within the brain parenchyma, including roles in regulating neurogenesis, gliogenesis, neurotransmission, and blood brain barrier permeability (Mossner et al., 1998; Abbott, 2000; Abbott et al., 2006; Muller et al., 2006; Deverman and Patterson, 2009). Further, chronic inflammation (i.e., over active cytokine signaling) has been implicated several neurological diseases, including multiple sclerosis, stroke, depression, Alzheimer’s disease, and schizophrenia (Raison et al., 2006; Frischer et al., 2009; Monji et al., 2009; Farooq et al., 2017; Piirainen et al., 2017). Due to the important roles of central (i.e., brain) cytokine signaling in homeostatic and disease processes, it is crucial that these signaling molecules are accurately measured within the brain parenchyma. Due to the high vascularity of the brain, it is believed that transcardial perfusion is required to accurately measure cytokine concentrations within the brain (Amsen et al., 2009; Kvichansky et al., 2019). However, to date few experiments have explicitly tested this question (Nguyen et al., 1998; Kvichansky et al., 2019) and no experiment has extensively examined this question in mice. We hypothesized that transcardial perfusion is not required to accurately measure cytokine concentrations within the brain parenchyma, and we predicted that residual blood contamination would have no appreciable effect on central cytokine concentrations. To test this hypothesis, we measured central cytokine concentrations at the protein and mRNA level in both homeostatic and immune challenged mice and demonstrated that transcardial perfusion does not significantly alter brain measures of a panel of cytokines.

2. Methods

Adult (7–8 weeks) male and female Balb/C mice were purchased from Charles Rivers Laboratories and allowed one week to acclimate in our laboratory prior to any experimental manipulation. During this time mice were allowed ad libitum access to food (Harlan Teklad #7912) and reverse osmosis purified water. Following one week of acclimation in the vivarium, male and female mice were randomly assigned treatment groups, and injected at the start of the inactive phase (ZT0) with either 0.33 mg/kg lipopolysaccharide (LPS), a component of Gram-negative bacteria, (Sigma Aldrich, St. Louis, MO) or equal volume of saline (Baxter, Deerfield, IL). Four hours after the injection (ZT4) of LPS or saline, a blood sample was obtained from the submandibular vein and treatment groups (LPS or saline) were randomly further subdivided to determine the method of euthanasia. Mice that underwent perfusion (LPS+perfusion and saline+perfusion) first received an intraperitoneal injection of Euthasol (Virbac, Fort Worth, TX) at a dose of 270 mg/kg to allow for sedation. Euthasol contains two active ingredients: sodium pentobarbital, which causes cerebral death and respiratory arrest, and sodium phenytoin, which induces cardiac arrest. After verifying sedation via a toe pinch, mice were transcardially perfused via perfusion pump with ice cold 1X phosphate-buffered saline for 5 min (~20 mL/min) and decapitated. Adequate perfusion was confirmed based on clearance of blood from the liver. Mice that were not perfused (LPS+no perfusion and saline+no perfusion) underwent cervical dislocation and decapitation. Note, perfused mice were not cervically dislocated after completion of the perfusion as this does not normally occur (i.e., perfusion is followed by decapitation). Brains were extracted and the hemispheres separated at the midline. Both hemispheres of the brain were placed in a tube containing RNAlater solution (Invitrogen, Waltham, MA), and stored at −80°C overnight. Following overnight storage, the hippocampus was extracted for subsequent protein quantification and qRT-PCR. All experiments were approved by West Virginia University Institutional Animal Care and Use Committee.

2.1. Protein Extraction and Multiplex

Protein Extraction and Multiplex followed a protocol as previously described (Walker II et al, 2017). Hippocampi were placed in a 1.5ml tube containing a solution of RIPA buffer (Thermo Fisher Scientific, Waltham, MA) and Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific, Waltham, MA) at a concentration of 1ml/100mg of tissue and homogenized via sonication. After sonication samples were allowed to incubate on ice for 30 min. Next, samples were centrifuged at 13,300 rpm for 15 min at 4°C. The supernatant was removed and placed in a new 1.5 ml tube for subsequent BCA protein assay and Meso Scale Discover V-Plex Pro-inflammatory Mouse Panel. To insure for equal amount of protein load in each well during protein multiplexing a Pierce BCA Protein Assay (Thermo Fisher Scientific, Waltham, MA) was run according to manufacturer’s instructions. To determine cytokine protein concentrations in the serum and hippocampus, samples were analyzed using Meso Scale Discover V-Plex Pro-inflammatory Mouse Panel according to manufacturer’s instructions. This kit measures protein concentrations of the following cytokines: IFNγ, IL-10, IL-12p70, IL-1β, IL-2, IL-4, IL-5, IL-6, KC/Gro (CXCL1), and TNFα. The plates were read using a Meso Quickplex machine and the data analyzed via MSD Discovery Workbench software v4.0.

2.2. RNA extraction, cDNA synthesis, and qRT-PCR

RNA extraction, cDNA, and qRT-PCR followed a protocol previously described (Walker II et al., 2017). RNA was extracted using Trizol Reagent (Ambion, Waltham, MA) according to manufacturer’s instructions. The quantity and quality of the RNA was measured using a Nanodrop One (Wilmington, DE) spectrophotometer. cDNA was synthesized using M-MLV reverse transcriptase and diluted 1:10. For qRT-PCR 4μl (40ng) of diluted cDNA was combined with 16μl of master mix solution containing: Taqman Fast Advanced Master Mix (Life Technologies, Carlsbad, CA), an inventoried probe from Applied Biosystems (Life Technologies, Carlsbad, CA) (Table 1), a primer-limited probe for the endogenous eukaryotic control 18s rRNA, and water. Each sample was run in duplicate. The qRT-PCR cycling conditions used were 95° C for 20 s, 40 cycles of 95° C for 3 s, and then 60° C for 30 s. Gene expression was quantified using the Pfaffl Method.

Table 1:

Taqman inventoried primer-probes used for qRT-PCR

Gene Name Assay ID
IL-1β Mm00434228_m1
IL-6 Mm00446190_m 1
TNFα Mm00443258_m1
18s Hs99999901_s1
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2.3. Statistical Analysis

Outliers were detected and removed prior to analysis using the Grubb’s Test. An a priori decision was made to compare only within saline or LPS groups as comparisons between saline and LPS groups does not address the hypothesis being tested. All data were analyzed using an unpaired two-tailed t-test. However, if variances between groups significantly differed, then data were analyzed using an unpaired two-tailed t-test with Welch’s correction. Because the protein multiplex measures 10 cytokines simultaneously in the same sample, a correction was made to reduce the likelihood of type 1 error; therefore, a Bonferroni correction was applied and a group difference of p<0.005 (0.05/10=0.005) was considered statistically significant for data acquired using the Meso Scale Discover V-Plex Pro-inflammatory Mouse Panel. Note, this is consistent with our previously published data using the Meso Scale Discover V-Plex Pro-inflammatory Mouse Panel (Walker et al., 2017; Borniger et al., 2018). For qRT-PCR data p<0.05 was considered significant. All statistical analyses were performed using GraphPad Prism 8.0 software.

3. Results

As a control to confirm equal serum cytokine concentrations, a submandibular blood sample was obtained prior to euthanasia in the perfusion and non-perfusion groups. As expected, serum cytokine concentrations did not significantly differ between perfusion and no perfusion groups within saline or LPS treatment for IL-1β, IL-6, TNFα, IL-2, IFN-γ, CXCL1, IL-10, IL-4, IL-5, and IL-12p70 (Fig. 1, IL-12p70 data not shown). Further, hippocampal protein cytokine concentrations were measured and demonstrate no significant differences between perfusion and no perfusion groups within saline or LPS treatment for any cytokine measured (Fig. 2). One cytokine (IL-10) failed to reach detectable range in the hippocampus. Additionally, these protein data were verified via qRT-PCR for il-1β, il-6, and tnfα, further demonstrating no group differences (Fig. 3).

Figure 1:

Figure 1:

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Serum cytokine concentrations do not significantly differ within saline or LPS treatment groups. A. Cytokine concentrations for IL-1β (saline t(29)=2.128, p>0.005; LPS t(29)=0.8277, p>0.005) IL-6 (saline t(30)=0.0323, p>0.005; LPS t(30)=0.1329, p>0.005), TNFα (saline t(29)=1.377, p>0.005; LPS t(30)=0.1801, p>0.005), IL-2 (saline t(28)=2.363, p>0.005; LPS t(24)=0.1217, p>0.005), IFN-γ (saline t(18.75)=0.9847, p>0.005, Welch’s correction; LPS t(29)=0.8015, p>0.005), CXCL1 (saline t(28)=0.8107, p>0.005; LPS t(30)=0.1044, p>0.005), IL-10 (saline t(23.57)=2.037, p>0.005, Welch’s correction; LPS t(30)=0.1475, p>0.005), IL-4 (saline t(24)=0.0255, p>0.005; LPS t(23)=0.4656, p>0.005), and IL-5 (saline t(21.78)=1.502, p>0.005, Welch’s correction; LPS t(30)=1.428, p>0.005). Data are presented as mean±SEM; n =12–16 animals per group. Left y-axis represents saline treatment and the right y-axis represents LPS treatment.

Figure 2:

Figure 2:

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Hippocampal cytokine concentrations do not significantly differ within saline or LPS treatment groups. A. Cytokine concentrations for IL-1β (saline t(21.95)=0.352, p>0.005, Welch’s correction; LPS t(30)=0.2727, p>0.005), IL-6 (saline t(29)=0.1053, p>0.005; LPS t(30)=0.4749, p>0.005), TNFα (saline t(29)=0.7387, p>0.005; LPS t(23.56)=0.0027, p>0.005, Welch’s correction), IL-2 (saline t(29)=1.207, p>0.005; LPS t(30)=0.9208, p>0.005), IFN-γ (saline t(30)=0.2054, p>0.005; LPS t(30)=0.7036, p>0.005), CXCL1(saline t(29)=0.1419, p>0.005; LPS t(30)=2.612, p>0.005), IL-12p70 (saline t(29)=2.123, p>0.005; LPS t(24.29)=2.973, p>0.005, Welch’s correction), IL-4 (saline t(29)=0.1072, p>0.005; LPS t(30)=0.6571, p>0.005), and IL-5 (saline t(30)=0.0545, p>0.005; LPS t(30)=2.510, p>0.005). Data are presented as mean±SEM; n=15–16 animals per group. Left y-axis represents saline treatment and the right y-axis represents LPS treatment.

Figure 3:

Figure 3:

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Hippocampal mRNA cytokine levels do not significantly differ within saline or LPS treatment groups. A. Cytokine levels for il-1β (saline t(17.10)=1.913, p>0.005, Welch’s correction; LPS t(29)=0.7499, p>0.005) il-6 (saline t(28)=1.377, p>0.05; LPS t(28)=0.6346, p>0.05), and tnfα (saline t(27)=1.507, p>0.05; LPS t(29)=0.9035, p>0.05). Data are presented as mean fold change±SEM; n =14–16 animals per group. Left y-axis represents saline treatment and the right y-axis represents LPS treatment.

4. Discussion

Cytokines are key signaling proteins within the immune system and are known to regulate important developmental and homeostatic functions both peripherally and centrally (Abbott et al., 2006; Muller et al., 2006; Deverman and Patterson, 2009; Guzeloglu-Kayisli et al., 2009; Asada et al., 2017; Robertson et al., 2017). These proteins are most extensively studied in the context of a host’s immune response to pathogens or evolving neurological damage. Aberrant cytokine signaling has been implicated in numerous peripheral and central diseases (Abbott et al., 2006; Muller et al., 2006; Deverman and Patterson, 2009, Doneath et al., 2009; Grivennikov et al., 2010; Strober et al., 2010). Therefore, accurately measuring cytokine concentrations within the brain parenchyma is critically important for experimental rigor and replicability. This study demonstrates that contrary to previous recommendations (Nguyen et al., 1998; Kvichansky et al., 2019), cytokine concentrations within the brain parenchyma are unaffected by whether or not the brain tissue is or is is not perfused with isotonic saline prior to tissue collection. Additionally, by challenging mice with LPS and collecting tissue during the peak of the neuroinflammatory response (4 h; Dantzer et al., 2008), we demonstrate that this effect holds true during an immune challenge. Notably, hippocampal cytokines IL-1β, IL-6 and TNFα were measured via protein ELISA (Fig.2) and verified unchanged via qRT-PCR (Fig.3), demonstrating consistency between mRNA and protein results. These results are consistent with previous studies which demonstrated no changes in IL-1β or corticosterone within the brain parenchyma following perfusion (Nguyen et al., 1998; Little et al., 2008).

Not doing perfusion during tissue collection simplifies IACUC protocols by removing a non-survival surgery, reduces cost without confounding measures of multiple commonly analyzed cytokines, including IL-1β, IL-6, and TNF-α, and reduces the time of tissue collection by approximately three to five minutes per mouse (Boiven et al., 2017). By reducing the amount of time needed during tissue collection, data are more consistent in studies with large sample sizes by allowing collection of all samples within a smaller temporal window, which is necessary due to the circadian regulation of cytokine expression (Keller et al., 2009; Fonken et al., 2015) Further, delays in storage of samples by as little as one hour results in significant changes in cytokine mRNA levels (Duvigneau et al., 2003). Therefore, depending on the cytokine being measured, studies implementing transcardial perfusions and involving large cohorts of animals have the potential for discrepancies between measured and true physiological cytokine mRNA expression. Further, not perfusing mice removes the requirement for using opiate based sedative drugs (i.e., pentobarbital), isoflurane, or carbon dioxide prior to tissue collection. Notably, exposure to isoflurane or carbon dioxide prior to tissue collection significantly alters cytokine mRNA and protein expression (Kotani et al., 1999; Abolhassani et al., 2009). Additionally, pentobarbital-phenytoin, the active ingredient in Euthasol takes approximately three minutes on average after injection to result in the death of the mouse (Boiven et al., 2017). Three minutes is a sufficient amount of time for a mouse to activate the hypothalamic-pituitary-adrenal axis and release corticosterone, which is a well-known modulator of the immune response, potentially further altering measured cytokines expression from true physiological levels (Boiven et al., 2017; Padgett and Glaser, 2003).

There are limitations to this study that should be discussed. First, the hippocampus is the only region of the brain examined in this study. The hippocampus was chosen due to the dense vascularization within the region (Quintana et al., 2019. Future studies should examine other regions of the brain to determine if alterations in cytokines are present in regions outside of the hippocampus. Indeed, a recent study in rats demonstrated alterations in TNF in the cortex following perfusion (Kvichansky et al., 2019). Additionally, this study demonstrated that not perfusing mice prior to tissue collection had no effect on the concentration of nine cytokines within the brain (Fig. 2). However, the generalizability of these results to other proteins not measured within this study remains to be determined.

In sum, we demonstrate in male and female mice that brain parenchymal cytokine concentrations are unaffected by not perfusing mice with saline prior to tissue collection; this holds true regardless of immune status (homeostatic or immune challenged). Removing perfusion from experimental methods will reduce the amount of time for tissue collection and remove the requirement for using opiate based sedative drugs (i.e., pentobarbital), isoflurane, or carbon dioxide, thus, leading to a more rapid collection and presumably a more accurate and physiologically relevant measure of cytokine concentrations.

Highlights.

  • Brain parenchymal cytokine concentrations are unaffected in absence of perfusion

  • The previous holds true regardless of immune status (homeostatic/immune challenged)

  • Not doing perfusion allows for more efficient tissue collection

Acknowledgements

We thank the West Virginia University Laboratory Animal Resources for their exceptional care of the mice used in this study. We further acknowledge Cornelius Braam and Connor Jacob for their assistance. The authors were supported by grants from NCI (R01CA194924 ACD) and NIGMS under award number 5U54GM104942-03. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

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Declaration of Interests

The authors declare no conflict of interest associated with the studies presented in this manuscript.

References

  1. Abbott NJ, (2000). Inflammatory mediators and modulation of blood–brain barrier permeability. Cellular and Molecular Neurobiology, 20(2), pp.131–147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abbott NJ, Rönnbäck L and Hansson E, (2006). Astrocyte–endothelial interactions at the blood–brain barrier. Nature Reviews Neuroscience, 7(1), p.41. [DOI] [PubMed] [Google Scholar]
  3. Abolhassani M, Guais A, Chaumet-Riffaud P, Sasco AJ, & Schwartz L (2009). Carbon dioxide inhalation causes pulmonary inflammation. American Journal of Physiology-Lung Cellular and Molecular Physiology, 296(4), L657–L665. [DOI] [PubMed] [Google Scholar]
  4. Amsen D, de Visser KE, & Town T (2009). Approaches to determine expression of inflammatory cytokines. In Inflammation and Cancer (pp. 107–142). Humana Press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Asada N, Kunisaki Y, Pierce H, Wang Z, Fernandez NF, Birbrair A, Ma’ayan A and Frenette PS, (2017). Differential cytokine contributions of perivascular haematopoietic stem cell niches. Nature Cell Biology, 19(3), p.214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boivin GP, Bottomley MA, Schiml PA, Goss L and Grobe N, (2017). Physiologic, behavioral, and histologic responses to various euthanasia methods in C57BL/6NTac male mice. Journal of the American Association for Laboratory Animal Science, 56(1), pp.69–78. [PMC free article] [PubMed] [Google Scholar]
  7. Borniger JC, Walker II WH, Emmer KM, Zhang N, Zalenski AA, Muscarella SL, ... & DeVries AC (2018). A role for hypocretin/orexin in metabolic and sleep abnormalities in a mouse model of non-metastatic breast cancer. Cell Metabolism, 28(1), 118–129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Danesh J, Whincup P, Walker M, Lennon L, Thomson A, Appleby P, Gallimore JR and Pepys MB, 2000. Low grade inflammation and coronary heart disease: prospective study and updated meta-analyses. Bmj, 321(7255), pp.199–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dantzer R, O’Connor JC, Freund GG, Johnson RW, & Kelley KW (2008). From inflammation to sickness and depression: when the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Deverman BE and Patterson PH, (2009). Cytokines and CNS development. Neuron, 64(1), pp.61–78. [DOI] [PubMed] [Google Scholar]
  11. De Simone V, Franze E, Ronchetti G, Colantoni A, Fantini MC, Di Fusco D, Sica GS, Sileri P, MacDonald TT, Pallone F and Monteleone G, (2015). Th17-type cytokines, IL-6 and TNF-α synergistically activate STAT3 and NF-kB to promote colorectal cancer cell growth. Oncogene, 34(27), p.3493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dinarello CA (2000). Proinflammatory cytokines. Chest, 118(2), 503–508. [DOI] [PubMed] [Google Scholar]
  13. Dinarello CA (2007). Historical insights into cytokines. European Journal of Immunology, 37(S1), S34–S45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Donath MY, Böni-Schnetzler M, Ellingsgaard H and Ehses JA, (2009). Islet inflammation impairs the pancreatic β-cell in type 2 diabetes. Physiology, 24(6), pp.325–331. [DOI] [PubMed] [Google Scholar]
  15. Drennan MB, Nicolle D, Quesniaux VJ, Jacobs M, Allie N, Mpagi J, Frémond C, Wagner H, Kirschning C and Ryffel B, (2004). Toll-like receptor 2-deficient mice succumb to Mycobacterium tuberculosis infection. The American Journal of Pathology, 164(1), pp.49–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Duvigneau JC, Hartl RT, Teinfalt M and Gemeiner M, (2003). Delay in processing porcine whole blood affects cytokine expression. Journal of Immunological Methods, 272(1–2), pp.11–21. [DOI] [PubMed] [Google Scholar]
  17. Farooq RK, Asghar K, Kanwal S and Zulqernain A, 2017. Role of inflammatory cytokines in depression: Focus on interleukin-1β. Biomedical Reports, 6(1), pp.15–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fonken LK, Frank MG, Kitt MM, Barrientos RM, Watkins LR, & Maier SF (2015). Microglia inflammatory responses are controlled by an intrinsic circadian clock. Brain, Behavior, and Immunity, 45, 171–179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Frischer JM, Bramow S, Dal-Bianco A, Lucchinetti CF, Rauschka H, Schmidbauer M, Laursen H, Sorensen PS and Lassmann H, 2009. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain, 132(5), pp. 1175–1189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gazzinelli RT, Wysocka M, Hieny S, Scharton-Kersten T, Cheever A, Kühn R, Müller W, Trinchieri G and Sher A, (1996). In the absence of endogenous IL-10, mice acutely infected with Toxoplasma gondii succumb to a lethal immune response dependent on CD4+ T cells and accompanied by overproduction of IL-12, IFN-gamma and TNF-alpha. The Journal of Immunology, 157(2), pp.798–805. [PubMed] [Google Scholar]
  21. Grivennikov SI, Greten FR and Karin M, (2010). Immunity, inflammation, and cancer. Cell, 140(6), pp.883–899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Guzeloglu-Kayisli O, Kayisli UA and Taylor HS, (2009), January. The role of growth factors and cytokines during implantation: endocrine and paracrine interactions. In Seminars in reproductive medicine (Vol. 27, No. 1, p. 62). NIH Public Access. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hirano T, Ishihara K and Hibi M, (2000). Roles of STAT3 in mediating the cell growth, differentiation and survival signals relayed through the IL-6 family of cytokine receptors. Oncogene, 19(21), p.2548. [DOI] [PubMed] [Google Scholar]
  24. Isaacs A and Lindenmann J, (1987). Virus interference. I. The interferon. Journal of Interferon Research, 7(5), pp.429–438. [DOI] [PubMed] [Google Scholar]
  25. Little HJ, Croft AP, O’callaghan MJ, Brooks SP, Wang G, & Shaw SG (2008). Selective increases in regional brain glucocorticoid: a novel effect of chronic alcohol. Neuroscience, 156(4), 1017–1027. [DOI] [PubMed] [Google Scholar]
  26. Luster AD (1998). Chemokines—chemotactic cytokines that mediate inflammation. New England Journal of Medicine, 338(7), 436–445. [DOI] [PubMed] [Google Scholar]
  27. Keller M, Mazuch J, Abraham U, Eom GD, Herzog ED, Volk HD, ... & Maier B (2009). A circadian clock in macrophages controls inflammatory immune responses. Proceedings of the National Academy of Sciences, 106(50), 21407–21412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kotani N, Hashimoto H, Sessler DI, Yasuda T, Ebina T, Muraoka M, & Matsuki A (1999). Expression of genes for proinflammatory cytokines in alveolar macrophages during propofol and isoflurane anesthesia. Anesthesia & Analgesia, 89(5), 1250–1256. [PubMed] [Google Scholar]
  29. Kvichansky AA, Volobueva MN, Spivak YS, Tret’yakova LV, Gulyaeva NV, & Bolshakov AP (2019). Expression of mRNAs for IL-1β, IL-6, IL-10, TNFα, CX3CL1, and TGFβ1 Cytokines in the Brain Tissues: Assessment of Contribution of Blood Cells with and without Perfusion. Biochemistry (Moscow), 84(8), 905–910. [DOI] [PubMed] [Google Scholar]
  30. Mackay LK, Wynne-Jones E, Freestone D, Pellicci DG, Mielke LA, Newman DM, Braun A, Masson F, Kallies A, Belz GT and Carbone FR, (2015). T-box transcription factors combine with the cytokines TGF-β and IL-15 to control tissue-resident memory T cell fate. Immunity, 43(6), pp.1101–1111. [DOI] [PubMed] [Google Scholar]
  31. Mössner R, Heils A, Stöber G, Okladnova O, Daniel S and Lesch KP, (1998). Enhancement of serotonin transporter function by tumor necrosis factor alpha but not by interleukin-6. Neurochemistry International, 33(3), pp.251–254. [DOI] [PubMed] [Google Scholar]
  32. Monji A, Kato T and Kanba S, (2009). Cytokines and schizophrenia: Microglia hypothesis of schizophrenia. Psychiatry and Clinical Neurosciences, 63(3), pp.257–26 [DOI] [PubMed] [Google Scholar]
  33. MÜller N and Schwarz M, (2006). Schizophrenia as an inflammation-mediated dysbalance of glutamatergic neurotransmission. Neurotoxicity Research, 10(2), pp.131–148. [DOI] [PubMed] [Google Scholar]
  34. Nguyen KT, Deak T, Owens SM, Kohno T, Fleshner M, Watkins LR, & Maier SF (1998). Exposure to acute stress induces brain interleukin-1β protein in the rat. Journal of Neuroscience, 18(6), 2239–2246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Padgett DA and Glaser R, 2003. How stress influences the immune response. Trends in Immunology, 24(8), pp.444–448. [DOI] [PubMed] [Google Scholar]
  36. Piirainen S, Youssef A, Song C, Kalueff AV, Landreth GE, Malm T and Tian L, 2017. Psychosocial stress on neuroinflammation and cognitive dysfunctions in Alzheimer’s disease: the emerging role for microglia?. Neuroscience & Biobehavioral Reviews, 77, pp.148–164 [DOI] [PubMed] [Google Scholar]
  37. Quintana DD, Lewis SE, Anantula Y, Garcia JA, Sarkar SN, Cavendish JZ, … & Simpkins JW (2019). The cerebral angiome: High resolution MicroCT imaging of the whole brain cerebrovasculature in female and male mice. NeuroImage, 202, 116109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Raison CL, Capuron L and Miller AH, 2006. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends in Immunology, 27(1), pp.24–31 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Robertson SA, Chin PY, Femia JG and Brown HM, (2017). Embryotoxic cytokines—Potential roles in embryo loss and fetal programming. Journal of Reproductive Immunology. [DOI] [PubMed] [Google Scholar]
  40. Schluns KS and Lefrançois L, (2003). Cytokine control of memory T-cell development and survival. Nature Reviews Immunology, 3(4), p.269. [DOI] [PubMed] [Google Scholar]
  41. Strober W, Zhang F, Kitani A, Fuss I and Fichtner-Feigl S, (2010). Pro-inflammatory cytokines underlying the inflammation of Crohn’s disease. Current Opinion in Gastroenterology, 26(4), p.310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Tabeta K, Georgel P, Janssen E, Du X, Hoebe K, Crozat K, Mudd S, Shamel L, Sovath S, Goode J and Alexopoulou L, (2004). Toll-like receptors 9 and 3 as essential components of innate immune defense against mouse cytomegalovirus infection. Proceedings of the National Academy of Sciences of the United States of America, 101(10), pp.3516–3521 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Takeuchi O, Hoshino K and Akira S, (2000). Cutting edge: TLR2-deficient and MyD88-deficient mice are highly susceptible to Staphylococcus aureus infection. The Journal of Immunology, 165(10), pp.5392–5396. [DOI] [PubMed] [Google Scholar]
  44. Walker II WH, Borniger JC, Zalenski AA, Muscarella SL, Fitzgerald JA, Zhang N, Gaudier-Diaz MM and DeVries AC, (2017). Mammary tumors induce central pro-inflammatory cytokine expression, but not behavioral deficits in balb/c mice. Scientific Reports, 7(1), p.8152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wong PK, Quinn JM, Sims NA, van Nieuwenhuijze A, Campbell IK and Wicks IP, 2006. Interleukin-6 modulates production of T lymphocyte–derived cytokines in antigen-induced arthritis and drives inflammation-induced osteoclastogenesis. Arthritis & Rheumatology, 54(1), pp.158–168. [DOI] [PubMed] [Google Scholar]

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