Role of inflammatory signaling pathways involving the CD40–CD40L–TRAF cascade in diabetes and hypertension—insights from animal and human studies

CD40L–CD40–TRAF signaling plays a role in atherosclerosis progression and affects the pathogenesis of coronary heart disease (CHD). We tested the hypothesis that CD40L–CD40–TRAF signaling is a potential therapeutic target in hyperlipidemia, diabetes, and hypertension. In mouse models of hyperlipidemia plus diabetes (db/db mice) or hypertension (1 mg/kg/d angiotensin-II for 7 days), TRAF6 inhibitor treatment (2.5 mg/kg/d for 7 or 14 days) normalized markers of oxidative stress and inflammation. As diabetes and hypertension are important comorbidities aggravating CHD, we explored whether the CD40L–CD40–TRAF signaling cascade and their associated inflammatory pathways are expressed in CHD patients suffering from comorbidities. Therefore, we analyzed vascular bypass material (aorta or internal mammary artery) and plasma from patients with CHD with diabetes and/or hypertension. Our Olink targeted plasma proteomic analysis using the IMMUNO-ONCOLOGY panel revealed a pattern of step-wise increase for 13/92 markers of low-grade inflammation with significant changes. CD40L or CD40 significantly correlated with 38 or 56 other inflammatory targets. In addition, specific gene clusters that correlate with the comorbidities were identified in isolated aortic mRNA of CHD patients through RNA-sequencing. These signaling clusters comprised CD40L–CD40–TRAF, immune system, hemostasis, muscle contraction, metabolism of lipids, developmental biology, and apoptosis. Finally, immunological analysis revealed key markers correlated with comorbidities in CHD patients, such as CD40L, NOX2, CD68, and 3-nitrotyrosine. These data indicate that comorbidities increase inflammatory pathways in CHD, and targeting these pathways will be beneficial in reducing cardiovascular events in CHD patients with comorbidities. Supplementary Information The online version contains supplementary material available at 10.1007/s00395-024-01045-1.

Supplemental Tables Table S1.Significant correlations between CD40L and other targets of inflammation (based on the olink plasma proteome data).Linear regression graphs are shown for selected targets in suppl.Figure S3.Table S2.Significant correlations between CD40 and other targets of inflammation (based on the olink plasma proteome data).Linear regression graphs are shown for selected targets in suppl.Figure S4.

Supplemental figures
Supplemental Figure S1: CD40L-CD40-TRAF cascade markers and selected markers of inflammation are increased (two by minor trend) in sera from CHD patients with hypertension plus diabetes as co-morbidity.Olink analysis in sera from CHD patients with either hypertension or hypertension and diabetes was performed.The following six increased targets in CHD patients with hypertension and diabetes were identified: CD70 (A), LAG3 (B), Gal9 (C), IL8 (D), MMP7 (E), and CAIX (F) Supplemental Figure S3: Correlations of CD40L levels with other selected markers of inflammation.Linear regression analysis revealed significant correlation of CD40L with other markers of inflammation in sera from CHD patients with either hypertension or hypertension and diabetes.Graphical presentation of correlations were selected from suppl.Table S1 for specific targets that are central to TNF signaling as shown by cluster analysis in Figure 5 of the main manuscript.

Measurement of glucose levels in whole blood
The blood glucose was measured in the whole blood of non-fasting animals using the ACCU-CHEK sensor system from Roche Diagnostics.

Non-invasive blood pressure recordings
Before the final non-invasive blood pressure measurement, the mice were trained at least three times to minimize stress reactions and to adapt the mice to the process.
Blood pressure was recorded on day 6 after the beginning of the treatment.The final data points reflect a mean value of 5 independent measurements.The accuracy of the tail-cuff method was already demonstrated compared to the radio telemetry measurement [13].

Vascular isometric tension studies
Isometric tension recordings were performed as previously described in reference [46,51].Endothelia intact and fat-free isolated aortic rings (thoracic aorta, 3 mm in length) were mounted in organ chambers, and vasodilator responses to acetylcholine (ACh) were assessed.Maximal relaxation was determined after stimulation with 3.3 µM ACh.

Superoxide anion detection by HPLC
Heart tissue was incubated in 50 µM DHE for 30 min at 37°C.After weight determination, the heart pieces were homogenized in 300 µl 50 % acetonitrile/50 % PBS, centrifuged at 20000 g for 20 min, and 50 µl of the supernatant was used for HPLC analysis.The HPLC analysis was performed as described in reference [15].2hydroxyethidium (superoxide-specific product) and ethidium (unspecific ROSdependent product) concentrations were determined by external and internal standards.

Western Blot Analysis of Protein Expression
Western blot analysis was performed as described previously [11,14,47].The protein was isolated from frozen tissue through homogenization with liquid nitrogen, followed by 60 min incubation in a homogenization buffer containing protease and phosphatase inhibitors on ice with intermittent vortexing.After vortexing and centrifugation (10 min, 10000 g, 4 °C), the protein-rich supernatant was transferred to fresh tubes, and the concentration was measured by Bradford assay.The samples were prepared and diluted to 5 µg/µl in Lämmli buffer.Gel electrophoresis was performed with 30 µg protein on 10% SDS pages and transferred onto nitrocellulose imager from Intas Science imaging.Densitometric quantification was performed using software (Version 3.0.00.00).

Dot Blot Analysis of Protein Modification and Expression
The renal protein isolation was performed as described before in the Western Blot analysis part, whereas a Bradford assay was directly done from blood plasma or serum.Dot blot analysis was performed according to our previous reports [11,27,38].

Targeted proteomics
To address the role of co-morbidities arterial hypertension and diabetes on the severity of coronary artery disease and thromboinflammation, the 92 CVD-related human protein biomarkers of the Olink T96 IMMUNO-ONCOLOGY panel were measured using the Proximity Extension Assay (PEA) technology (Olink Biosciences, Uppsala, Sweden), as described elsewhere [3,19,33].In brief, once-thawed ethylenediaminetetraacetic acid (EDTA)-blood plasma was used for analysis.For each target antigen, the affinity-based PEA technique uses a pair of antibodies linked to unique, partially complementary single-stranded DNA oligonucleotides.After the simultaneous binding of both antibodies to an antigen molecule, close proximity allows for the formation of a PCR target sequence by hybridization.After unspecific preamplification, amplicons were quantified by qPCR using protein-specific primer pairs.The resulting C t -value of each protein (Fluidigm Real-Time PCR Analysis Software, Version 4.3.1,San Francisco, USA) was transformed to normalized protein expression (NPX) units using software from the manufacturer (Olink® NPX Manager, Version 1.1.4.0,Uppsala, Sweden).NPX units represent relative quantifications of protein concentrations on a log2-scale (i.e., an increase by 1 NPX represents a duplication of protein concentration).

Bioinformatical analysis of proteomic data
Samples were grouped, and differential expression analysis was performed using the Perseus software suite.Briefly, differential expression was determined by applying the Student's t-test using a cut-off of p<0.05.Proteins that were differentially expressed in at least one group were retained.To visualize expression changes across the groups, we determined the mean value, Z-normalised, and proteins were clustered using Euclidean distance.Network analysis and data representation were performed using Cytoscape.Differentially expressed proteins were mapped onto a network generated by String.Edges represent interactions based on genetic, experimental, biochemical, and text-mining data using a threshold of >0.4.

RNA-sequencing
RNA sequencing was carried out at Novogene Bioinformatics Technology Co., Ltd., in Cambridge (UK), and in analogy to our previous studies [27,38], except that we used the human library for the present analysis.According to the manufacturer's protocol, aortic mRNA was isolated using the RNeasy Mini kit (Qiagen, Hilden,  S8).

Extended Discussion of Human Protein Data
In our proteomic analysis of CHD patients' plasma, we identified some step-wise increased targets in association with the number of their comorbidities (CHD<CHD+HT<CHD+HT+T2DM).Cluster analysis identified TNFα as a central player.TNFRSF9 (CD137 / 4-1BB) and TNFRSF4 (CD134 / ox40) belong to the TNF receptor superfamily, like CD40 (TNFRSF5).Both proteins are present on the surface of activated T cells.Like CD40, TNFRSF9 and TNFRSF4 can directly interact with TRAF2 and promote NF-κB activation [21,24,34].Further, TNFRSF9 expression is enhanced by pro-inflammatory stimuli via CD40 or IL12 and functions as a T cell costimulator [16].CD27 belongs to the TRAF interacting receptors and mediates T-T and T-B cell interactions.Like other co-stimulatory receptors, CD27 functions similarly and is involved in NF-κB activation via TRAF2/5 signaling [1].Inflammatory cytokines like TNFα also belong to the group of TRAF interacting proteins, and we observed a significantly increased protein expression in our CHD patients.Besides TRAF2-mediated signaling, TNFα can induce apoptosis via caspase8/caspase3 signaling [6].In addition, TNFRSF21 (or DR6), significantly increased in the patient's plasma, contains a death domain and induces apoptosis via NF-κB signaling.Since TNFRSF21 also belongs to the family of TNF receptors superfamily, it may also play a role in inflammation and regulation of the immune system [41].
Gal1 is another significantly upregulated protein that can induce apoptosis, especially in T cells.Among others, the upregulation of Gal1 expression and secretion is upregulated in activated B-cells via CD40L (on T-cells) and CD40 (on Bcells) interaction, which could lead to apoptosis in T-cells and increased cytokine production in B-cells.Gal1 and, therefore, indirect CD40L-CD40 mediated signaling seems to be an important modulator for B-cell-regulated T-cell function and survival [54].ICOSLG, which is also step-wise increased in our proteomic studies, is expressed on germinal center B-cells and interacts with its receptor ICOS (on T follicular helper cells), which mediates calcium flux.The altered calcium flux in T follicular helper cells leads to CD40L externalization, which triggers CD40 on germinal center B cells and leads to ICOSLG expression.The positive feedback mechanism of upregulated ICOSLG by CD40 signaling is important in bone marrow plasma cell development and T follicular helper cell-controlled, long-lived humoral immunity [32].Recently, through RNA-seq analysis in human tonsils, a new population of CCL4/CCL3 chemokine-expressing B cells was described.This cell population's activation and chemokine expression are accompanied by CD40 costimulation [12], which indicates the significant increase in CVD patients' plasma of CCL4 and CCL3 is probably mediated by CD40L-CD40 signaling.For example, CD40 signaling in macrophages is activated after interaction with T cell-expressed CD40L, leading to p38 MAPK activation that induces the pro-inflammatory cytokine IL12 expression [36].CD5 is upregulated after strong T and B cell activation.On CD5+ lymphocytes, the IL10 secretion is mediated through CD40 signaling [52].
CD244 and KLRD1 are involved in NK (natural killer) cell function and NK cellmediated immunity [2].It is not clear to what extent CD40L-CD40 signaling is involved in their signaling.Also, the significantly increased CD68 expression in our human plasma is linked to the CD40L-CD40 signaling pathway.Indeed, enhanced CD40 expression co-stimulates CD68 expression, indicating an involvement of monocytes and macrophages in inflammation [10].
Extended Discussion of Human RNA-seq Data The summary of IPA analysis of the comparison of CHD+HT vs. CHD showed the upregulation of pro-inflammatory and pro-oxidative gene expression (see suppl.Fig. S7A).Also, the top 5 regulated canonical pathways (see suppl.for the observed gene expression changes.Inhibition of the Yes-TEAD interaction markedly inhibited the Angiotensin-II-mediated vascular remodeling in a rat hypertension model [31].The peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A) has been linked to energy metabolism, which may affect heart function.PPARGC1A is dysregulated in heart failure [40,45] and plays a role in endothelial regulation [9] and atherosclerotic lesions generation [22].The IPAnoted inclusion of CD40 in the top 5 upstream regulators of the expression changes seen in the CHD HT vs. the CHD group matched perfectly with our animal data of this study and already published data [46].Also, the presence of the insulin receptor INSR in the 5 top upstream regulators is in good accordance with the published literature [37].Only the presence of the Hbb-b1 gene (human HBB) in the top 5 upstream regulators does not fit well in the literature.However, several data suggest sickle cell disease (SCD; mutation in the β-globin gene HBB) can be viewed as panvasculopathy associated with multiple mechanisms [50].
Interestingly, suppl.Fig. S8A showed that the effects of all these upstream regulators are markedly reduced in comparing CHD+HT+T2DM vs. CHD+HT.This unexpected result likely correlates to the drugs used in the CHD+HT+T2DM group.
All patients in this group received glucose-lowering drugs (insulin, metformin).High glucose is well noticed to be a pro-inflammatory and pro-oxidative factor [8].
Therefore, it is reasonable that drug-mediated blood-glucose-lowering resulted in the reversion of glucose-induced gene expression regulation.In addition, there also antioxidative effects described for metformin (via AMPK activation and inhibition of mTOR pathways [4,17]) and insulin (via NO [48]) that may explain the reversion of gene expression in CHD HT+T2DM vs. CHD+HT patients.
The summaries of the IPA analyses of the comparisons CHD+HT+T2DM vs. CHD and CHD+HT+T2DM vs. CHD+HT (suppl.Fig. S7B and C) both showed mainly inhibitory effects of the inclusion of T2DM in the disease complex on gene expressional regulation.This is also primarily true for the top 5 canonical pathways identified by IPA for these comparisons (suppl.Fig. S8B and C).Also, most of the  In both comparisons, the IPA program identified T2DM-induced activation of the pathway involving the transcription factor Sine Oculis Homeobox Homolog 1 (SIX1).
SIX1 is a homeobox transcription factor important for development and differentiation.SIX1 regulates the expression of different proteins involved in organ development and cancer [43].SIX1 is upregulated in a murine asthma model, and silencing of SIX1 resulted in reduced NF-κB signaling as SIX1 has been identified as an integral component of the non-canonical NF-κB signaling cascade [53].No data about the specific involvement of SIX1 in CHD, hypertension, or T2DM have been published.Also, in the comparisons of CHD+HT+T2DM vs. CHD and CHD+HT+T2DM vs. CHD+HT, the IPA-analysis showed reduced effects of the cytoskeletal/membrane protein dystrophin (DMD).It is well known that mutations in dystrophin can result in familial forms of dilated cardiomyopathy [7].In viral myocarditis, the transcription factor STAT3 blocks the dilated cardiac phenotype by maintaining membrane integrity resulting from STAT3-regulated dystrophin expression [28].
In comparing CHD+HT+T2DM vs. CHD (suppl.Fig. S9B), IPA indicated reduced effects of both transcription factors MYOD1 and MEF2C.In hypertensive rats, the cardiac remodeling induced by the lncRNA MALAT1 was related to the inhibition of MyoD expression [30].The transcription factor MEF2C is mainly involved in cell proliferation, migration, and cellular homeostasis in cardiovascular systems, which play important roles in modulating cell phenotype [42].MEF2C has also been described as an important regulator of smooth muscle differentiation, and dysregulation of MEF2C has been linked to hypertension-related vascular changes [44].The IPA analysis also indicated reduced effects of beta-estradiol in this group.
Estrogen treatment is commonly believed to reduce/block the development of atherosclerosis.Estrogen has been shown to enhance endothelial NOS expression and thereby enhance bioactive NO [26], cause vasodilation, suppress arterial collagen secretion and extracellular matrix deposition, act as calcium antagonist properties, diminish calcification, and decrease the secretion of inflammatory cytokines (reviewed in [20]).However, studies with estrogens in human cardiovascular disease have resulted in conflicting results [5,35].
In the comparison of CHD+HT+T2DM vs. CHD+HT (suppl.Fig. S9C), IPA indicated enhanced effects of the serine protease caseinolytic protease proteolytic subunit (CLPP) and the fatty acid oxidation enzyme carnitine palmitoyl transferase 1B (CPT1B).In rats treated with a high-fat diet for 18 weeks, systemic insulin resistance was induced, and the CLPP expression in slow-twitch muscle was enhanced compared to the control animals [29].As stated by the authors of this study, insulin sensitivity in skeletal muscle is related to mitochondrial homeostasis.As CLPP is a marker protein for the mitochondrial unfolded protein response (UPRmt), the enhancement of CLPP may be a mechanism to counteract mitochondrial dysfunction.
In a cardiac hypertrophy mouse model, overexpression of CPT1B could reverse cardiomyocyte hypertrophy induced by the transcribed ultra-conserved region us.323[49].Gastric bypass surgery of T2DM patients improved whole-body insulin sensitivity and enhanced expression of fatty acid oxidation enzymes like CPT1B [23].
Therefore, CPT1B seems to be involved in T2DM pathogenesis.
: Correlations of CD40 levels with other selected markers of inflammation.Linear regression analysis revealed significant correlation of CD40 with other markers of inflammation in sera from CHD patients with either hypertension or hypertension and diabetes.Graphical presentation of correlations were selected from suppl.TableS2for specific targets that are central to TNF signaling as shown by cluster analysis in Figure5of the main manuscript.SupplementalFigure S5: In plasma from CHD patients with either hypertension or hypertension and diabetes, protein expression levels of selected inflammatory markers, are stepwise increased with each additional comorbidity.Protein expression analysis of NOX2 (A), CD40L (B), CD68 (C), and 3NT (D) was determined by dot blot analysis in the plasma of CHD patients with either hypertension or diabetes or hypertension and diabetes.Original representative blots are shown below.Data are mean±SD CHD n=8-15, CHD+HT n=9-15, and CHD+HT+T2DM n=11-14 patients per group.* p≤0.05, ** p≤0.01 and *** p≤0.001.CHD=coronary heart disease, HT=hypertension, T2DM=type 2 diabetes mellitus, NOX2=NADPH oxidase 2, 3NT=3-nitrotyrosine.A B CHD + HT vs CHD CHD + HT + T2DM vs CHD C Supplemental Figure S6: Comparison of the gene expression in the aortic samples from (A) CHD+HT versus CHD patients, (B) CHD+HT+T2DM versus CHD patients, and (C) CHD+HT+T2DM versus CHD+HT patients.Aortic tissue samples from CHD, CHD+HT, or CHD+HT+T2DM patients were analyzed for total mRNA expression using the RNA-Seq method and analyzed with CLC genomics workbench.Shown are the Volcano plots (provided by the CLC genomic workbench) of the comparison (A) CHD+HT versus CHD, (B) CHD+HT+T2DM versus CHD, and (C) CHD+HT+T2DM versus CHD+HT.CHD=coronary heart disease, HT=hypertension, T2DM= type 2 diabetes mellitus.CHD + HT + T2DM vs CHD + HT A B CHD + HT vs CHD CHD + HT + T2DM vs CHD C Supplemental Figure S7: Graphical summary of the pathways identified by the ingenuity software involved in the regulation of gene expression comparing the aortic samples from (A) CHD+HT versus CHD patients, (B) CHD+HT+T2DM versus CHD patients and (C) CHD+HT+T2DM versus CHD+HT patients.Aortic tissue samples from CHD, CHD+HT, or CHD+HT+T2DM patients were analyzed for total mRNA expression using the RNA-Seq method and analyzed with CLC genomics workbench.The DEG (p < 0.05) lists were imported into the ingenuity software and analyzed.Shown are the summaries of the comparison (A) CHD+HT versus CHD, (B) CHD+HT+T2DM versus CHD, and (C) CHD+HT+T2DM versus CHD+HT.Activated pathways are indicated in red; inhibited pathways are shown in blue.CHD=coronary heart disease, HT=hypertension, T2DM= type 2 diabetes mellitus.CHD + HT + T2DM vs CHD + HT A B M i t o c h o n d r i a l D y s f u n c t i o n O x i d a t i v e P h o s p h o r y l a t i o n S i r t u i n N e u t r o .E x t r a c e l l u l a r T r + T2DM vs CHD CHD + HT vs CHD CHD + HT + T2DM vs CHD + HT CHD + HT + T2DM vs CHD C Supplemental Figure S8: Top 5 regulated signal pathways identified by the ingenuity software comparing the gene expression in the aortic samples from (A) CHD+HT versus CHD patients, (B) CHD+HT+T2DM versus CHD patients, and (C) CHD+HT+T2DM versus CHD+HT patients.Aortic tissue samples from CHD, CHD+HT, or CHD+HT+T2DM patients were analyzed for total mRNA expression using the RNA-Seq method and analyzed with CLC genomics workbench.The DEG lists were imported into the ingenuity software and analyzed for the top 5 regulated canonical signal pathways.To normalize the negative log 2, p-values were multiplicated with the log 2 expression values.Shown are the top 5 pathways for the comparison (A) CHD+HT versus CHD, (B) CHD+HT+T2DM versus CHD, and (C) CHD+HT+T2DM versus CHD+HT.In each case, the values of the other comparisons were included as well.CHD=coronary heart disease, HT=hypertension, T2DM= type 2 diabetes mellitus.
Germany).RNA sample quality control, mRNA library preparation (poly A enrichment), NovaSeq PE150 sequencing (9 G raw data per sample), and data quality control were performed by Novogene.Data analysis was performed using CLC genomic workbench (22.0.2) and IPA (both from Qiagen, Hilden, Germany).The data (fastq.gz)were imported in the CLC data format (.clc).from the Novogen sequencing results report (RAW data).Then the reads were trimmed using the parameter provided by the manufacturer (quality score: 0.05; the maximum number of ambiguities: 2).These trimmed RNA-Seq data were mapped to the human genome (Homo_sapiens_hg38-2022-11-25-22-33, ENSEMBL) using the following parameters (Mismatch cost: 3; Insertion cost: 3; Deletion cost: 3; Length fraction: 0.9; Similarity fraction: 0.9; Maximum number of hits for a read: 10).The rpkm data were used for calculation of the fold enhancement of the mRNA expression.The statistical comparisons provided by CLC genomics workbench were uploaded to the IPA server to analyze relevant changes in signal pathways (upload p < 0,05; analysis p < 0,05).Volcano plots to envisage the number of changed genes by the three different therapeutic interventions created by the CLC genomic workbench.Canonical pathways analyses to envisage the biochemical processes that are changed by the two co-morbidities arterial hypertension and diabetes in CHD patients were performed with IPA (suppl.Figures S7 and Fig. S8A) showed activation of mitochondrial dysfunction, oxidative phosphorylation, enhanced neutrophil extracellular trap generation, and enhanced granzyme A activation.These pathways are important for pro-inflammatory and pro-oxidative mechanisms leading to cardiovascular diseases.The IPA-analysis of the top 5 upstream regulators (suppl.Fig S9A) revealed the importance of the transcription factor TEAD1, the transcriptional coactivator PPARGC1A, CD40 (receptor of CD40L), the insulin receptor INSR, and the beta adult major chain of hemoglobin Hbb-b1 (human HBB)