Alterations of soluble TWEAK and CD163 concentrations in patients with chronic heart failure

Clinical utility of circulating TWEAK and CD163 as biomarkers of iron-induced cardiac decompensation in transfusion dependent thalassemia major

BACKGROUND AND AIM

Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) affects most of the cells involved in cardiac fibrosis like inflammatory cells, cardiomyocytes and fibroblasts. CD163, the receptor of TWEAK on the surface of type 2 macrophages, is shed into plasma upon macrophages activation. This work aimed to evaluate serum TWEAK and its decoy receptor CD163 as probable biomarkers to monitor myocardial iron overload (MIO) in transfusion dependent thalassemia major (TDTM) patients and to predict iron-induced cardiac decompensation (IICD).

METHODS

A total of 140 TDTM patients were enrolled. Patients were categorized into two groups; group I (n = 70) diagnosed with IICD while group II (n = 70) had no evidence of IICD. sTWEAK and sCD163 were quantitated utilizing Enzyme-linked-immunosorbent- assay.

RESULTS

sTWEAK was evidently lower in group I than group II (medians, 412 and 1052 pg/mL respectively). sCD163 was higher in group I than group II (medians, 615.5 and 323.5 ng/mL respectively). sTWEAK positively correlated with cardiac MRI-T2 mapping and ventricular ejection fractions and negatively correlated with B-Natriuretic peptide and cardiac troponin. An inverse relationship between TWEAK and CD163 was documented throughout the study. sTWEAK, sCD163 and TWEAK/CD163 ratio proved to be significant predictors of IICD in TDTM patients. TWEAK/CD163 ratio < 1.04 discriminated IICD in TDTM patients with 100 % clinical sensitivity and specificity.

CONCLUSION

Circulating TWEAK and CD163 appears to be promising biomarkers for monitoring MIO and predicting IICD in TDTM patients.

Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure

OBJECTIVE

Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF.

METHOD

The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers.

RESULTS

The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified.

CONCLUSION

The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Regulation of CD163 Receptor in Patients with Abdominal Aortic Aneurysm and Associations with Antioxidant Enzymes HO-1 and NQO1

Red blood cells are found within the abdominal aortic aneurysm (AAA), in the intraluminal thrombus (ILT), and in neovessels. Hemolysis promotes aortic degeneration, e.g., by heme-induced reactive oxygen species formation. To reduce its toxicity, hemoglobin is endocytosed by the CD163 receptor and heme is degraded by heme oxygenase-1 (HO-1). A soluble form (sCD163) is discussed as an inflammatory biomarker representing the activation of monocytes and macrophages. HO-1 and NAD(P)H quinone dehydrogenase 1 (NQO1) are antioxidant genes that are induced by the Nrf2 transcription factor, but their regulation in AAA is only poorly understood. The aim of the present study was to analyze linkages between CD163, Nrf2, HO-1, and NQO1 and to clarify if plasma sCD163 has diagnostic and risk stratification potential. Soluble CD163 was 1.3-fold (p = 0.015) higher in AAA compared to patients without arterial disease. The difference remained significant after adjusting for age and sex. sCD163 correlated with the thickness of the ILT (r_s = 0.26; p = 0.02) but not with the AAA diameter or volume. A high aneurysmal CD163 mRNA was connected to increases in NQO1, HMOX1, and Nrf2 mRNA. Further studies are needed to analyze the modulation of the CD163/HO-1/NQO1 pathway with the overall goal of minimizing the detrimental effects of hemolysis.

Baseline interleukin-10, CD163, and tumor necrosis factor-like weak inducer of apoptosis (TWEAK) gingival tissue levels in relation to clinical periodontal treatment outcomes: A 12-week follow-up study

BACKGROUND

The aim of this study was to examine the relationship between healing response after non-surgical periodontal treatment and baseline gingival tissue levels of M2 macrophage activation-related proteins CD163, interleukin (IL)-10, interferon (IFN)-γ, and tumor necrosis factor-like weak inducer of apoptosis (TWEAK), and the CD163/TWEAK ratio.

METHODS

Eighty-eight gingival tissue samples from 44 Stage III/IV, Grade C periodontitis patients (18 smokers) and 41 tissue samples from 41 periodontally healthy participants (18 smokers) were evaluated. Clinical parameters were recorded in periodontally healthy individuals at baseline and in periodontitis patients at pre-treatment and 2, 6, and 12 weeks following therapy. IL-10, IFN-γ, CD163, and TWEAK levels were analyzed with Luminex technique.

RESULTS

Tissue levels (median, 1^st -3^rd quartile) of IL-10 (pg/ng protein), CD163 (pg/μg protein) and TWEAK (pg/μg protein) were as follows: IL-10 periodontitis: 2.08, 0.86-5.32 and periodontally healthy: 5.22, 3.20-10.25; CD163 periodontitis: 8.85, 4.92-14.06 and periodontally healthy: 18.36, 12.51-34.02; TWEAK periodontitis: 0.08, 0.05-0.11 and periodontally healthy: 0.16, 0.12-0.21. IL-10, CD163, and TWEAK levels were higher (P < 0.001) in periodontally healthy tissues than in periodontitis tissues. Pocket closure at 12 weeks was associated with elevated baseline gingival CD163 levels (P = 0.047) and CD163/TWEAK ratio (P = 0.001). Elevated baseline gingival CD163/TWEAK ratio was associated with pocket reduction at 6 (P = 0.022) and 12 weeks (P = 0.002).

CONCLUSION

Associations of pocket closure with pre-treatment gingival tissue CD163 levels and CD163/TWEAK ratio indicate that baseline M2 macrophage activation profile may play a role in periodontal wound healing.

Low sCD163/TWEAK Ratio at First Day After Acute Myocardial Infarction Associated with Adverse Cardiac Remodeling in Non-Elderly Patients

Aim    In this study, we aimed to investigate the role of sCD163 / tumor necrosis factor-like weak apoptosis-inducing (TWEAK) ratio in cardiac remodeling in non-elderly patients diagnosed with first acute myocardial infarction (MI).Material and Methods    Forty-four patients (age ranges: 40–64 years) diagnosed with first-time acute ST-elevation MI in the emergency department were evaluated with cardiac magnetic resonance (CMR) imaging. Adverse remodeling (AR) was defined the increases of left ventricular end-diastolic volume by ≥12 % by CMR at 6‑month post-MI TWEAK and sCD163 were measured at the first day (baseline), 2 weeks and 6 weeks post-MI.Results    The average age of patients included in the study was 53.6±5.1 years. AR was detected in 18 patients at the 6 months post-MI. At the first day post-MI, median sCD163 concentration (116 069 vs 86 394 pg / mL, p=0.040) and median TWEAK concentration (759.4 vs 220.1 pg / mL, p<0.001) were higher in AR group compared to group without AR (the non-AR group), median sCD163 / TWEAK ratio (101.4 vs. 406.8; p<0.001) was lower. At the first day post-MI, concentrations of TWEAK and sCD163 showed a positive correlation in AR group and group without AR s. At 2 weeks post-MI, positive correlation continued in the non-AR group, but no significant correlation was found in the AR group. At the first day post-MI, sCD163 / TWEAK ratio was higher diagnostic performance compared to TWEAK and sCD163.Conclusion    In the early phase post-MI, the relationship between sCD163 – TWEAK may have an important role in AR pathogenesis. A lower sCD163 / TWEAK ratio on the first day after MI was associated with an increase in left ventricular end-diastolic volume after 6 months of follow-up.

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.

The impact of probiotic yogurt versus ordinary yogurt on serum sTWEAK, sCD163, ADMA, LCAT and BUN in patients with chronic heart failure: a randomized, triple-blind, controlled trial

BACKGROUND

To date, no study has investigated the effects of probiotic yogurt as a functional food in patients with chronic heart failure (CHF). Therefore, the aim of this study was to compare the impact of probiotic yogurt versus ordinary yogurt on inflammatory, endothelial, lipid and renal indices in CHF patients. In this randomized, triple-blind clinical trial, 90 patients with CHF were randomly allocated into two groups to take either probiotic or ordinary yogurt for 10 weeks. Serum levels of soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK), soluble cluster of differentiation 163 (sCD163), asymmetric dimethylarginine (ADMA), and lecithin cholesterol acyltransferase (LCAT) were measured by using ELISA kits, and blood urea nitrogen (BUN) was measured by calorimetry method at baseline and at the end of trial. The P-value <0.05 was defined as statistically significant.

RESULTS

Seventy-eight patients completed the study. At the end of the intervention, the levels of sTWEAK in both groups increased significantly, and this increase was greater in the probiotic yogurt group [691.84 (335.60, 866.95)] compared to control group [581.96 (444.99, 929.40)], and the difference between the groups was statistically significant after adjusting for confounders (P-value: 0.257, adjusted P-value: 0.038). However, no significant differences were found between the groups in the cases of other study indices.

CONCLUSION

Probiotic yogurt may be useful for improving the inflammatory status in patients with CHF through increasing sTWEAK levels, however, further studies are needed in this area. © 2022 Society of Chemical Industry.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Targeting of CD163+ Macrophages in Inflammatory and Malignant Diseases

The macrophage is a key cell in the pro- and anti-inflammatory response including that of the inflammatory microenvironment of malignant tumors. Much current drug development in chronic inflammatory diseases and cancer therefore focuses on the macrophage as a target for immunotherapy. However, this strategy is complicated by the pleiotropic phenotype of the macrophage that is highly responsive to its microenvironment. The plasticity leads to numerous types of macrophages with rather different and, to some extent, opposing functionalities, as evident by the existence of macrophages with either stimulating or down-regulating effect on inflammation and tumor growth. The phenotypes are characterized by different surface markers and the present review describes recent progress in drug-targeting of the surface marker CD163 expressed in a subpopulation of macrophages. CD163 is an abundant endocytic receptor for multiple ligands, quantitatively important being the haptoglobin-hemoglobin complex. The microenvironment of inflammation and tumorigenesis is particular rich in CD163+ macrophages. The use of antibodies for directing anti-inflammatory (e.g., glucocorticoids) or tumoricidal (e.g., doxorubicin) drugs to CD163+ macrophages in animal models of inflammation and cancer has demonstrated a high efficacy of the conjugate drugs. This macrophage-targeting approach has a low toxicity profile that may highly improve the therapeutic window of many current drugs and drug candidates.

Regular Training Increases sTWEAK and Its Decoy Receptor sCD163-Does Training Trigger the sTWEAK/sCD163-Axis to Induce an Anti-Inflammatory Effect?

Background: Low levels of soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK) were reported in patients with coronary artery disease, heart failure, chronic kidney disease and diabetes mellitus. Soluble cluster differentiation 163 (sCD163) serum levels are related to M2 macrophages, having anti-inflammatory attributes. As sport is well-known for its anti-inflammatory and cardioprotective effects we aimed to investigate the influence of eight months of physical activity on serum sCD163 and sTWEAK levels. Methods: In total, 109 subjects with at least one cardiovascular risk factor were asked to perform endurance training within the calculated training pulse for eight months. Overall, 98 finished the study. The performance gain was measured/quantified by bicycle stress tests at the beginning and end of the observation period. The cohort was divided into four groups, dependent on their baseline performance and performance gain. sCD163 and sTWEAK were measured at baseline and after two, six and eight months by ELISA. Results: Those participants who had a performance gain of ≤2.9% (mean gain 12%) within eight months showed a significant increase in sTWEAK (group 2: from 133 to 200 pg/mL, p = 0.002 and group 4: from 166 to 212 pg/mL, p = 0.031) and sCD163 levels (group 2: from 255 to 348 ng/mL, p = 0.035 and group 4: from 247 to 288 ng/mL, p = 0.025) in contrast to subjects without performance gain (sTWEAK: group 1: from 161 to 177 pg/mL, p = 0.953 and group 3: from 153 to 176 pg/mL, p = 0.744; sCD163: group 1: from 289 to 256 ng/mL, p = 0.374 and group 4: from 291 to 271 ng/mL, p = 0.913). Baseline sCD163 correlated with erythrocyte count, hematocrit, ASAT and lipoprotein a, the presence of hypertension and a BMI > 30 kg/m². Conclusion: Regular physical activity leads to a significant increase in sCD163 and sTWEAK levels of up to 37% and 50%, respectively. It is well-known that physical activity prevents or retards the onset and genesis of chronic inflammatory disease. One possible way of how training evolves its beneficial effect might be by modifying the inflammation status using the sTWEAK–sCD163 axis. Brief Summary: Regular physical activity leads to a significant increase in sTWEAK and sCD163 levels. Both factors are diminished in patients with chronic (inflammation-based) diseases, such as coronary artery disease, heart failure, pulmonary artery hypertension, chronic kidney disease and diabetes mellitus. It seems that the amounts of soluble TWEAK and CD163 are essential for a healthy balance and modulation between pro- and anti-inflammatory processes, and regular physical training could use the sCD163–sTWEAK axis to unfold its beneficial effect.

Temporal patterns of macrophage- and neutrophil-related markers are associated with clinical outcome in heart failure patients

AIMS

Evidence on the association of macrophage- and neutrophil-related blood biomarkers with clinical outcome in heart failure patients is limited, and, with the exception of C-reactive protein, no data exist on their temporal evolution. We aimed to investigate whether temporal patterns of these biomarkers are related to clinical outcome in patients with stable chronic heart failure (CHF).

METHODS AND RESULTS

In 263 patients with CHF, we performed serial plasma measurements of scavenger receptor cysteine-rich type 1 protein M130 (CD163), tartrate-resistant acid phosphatase type 5 (TRAP), granulins (GRN), spondin-1 (SPON1), peptidoglycan recognition protein 1 (PGLYRP1), and tissue factor pathway inhibitor (TFPI). The Cardiovascular Panel III (Olink Proteomics AB, Uppsala, Sweden) was used. During 2.2 years of follow-up, we collected 1984 samples before the occurrence of the composite primary endpoint (PE) or censoring. For efficiency, we selected 567 samples for the measurements (all baseline samples, the last two samples preceding the PE, and the last sample before censoring in event-free patients). The relationship between repeatedly measured biomarker levels and the PE was evaluated by joint models. Mean (±standard deviation) age was 67 ± 13 years; 189 (72%) were men; left ventricular ejection fraction (%) was 32 ± 11. During follow-up, 70 (27%) patients experienced the PE. Serially measured biomarkers predicted the PE in a multivariable model adjusted for baseline clinical characteristics [hazard ratio (95% confidence interval) per 1-standard deviation change in biomarker]: CD163 [2.07(1.47-2.98), P < 0.001], TRAP [0.62 (0.43-0.90), P = 0.009], GRN [2.46 (1.64-3.84), P < 0.001], SPON1 [3.94 (2.50-6.50), P < 0.001], and PGLYRP1 [1.62 (1.14-2.31), P = 0.006].

CONCLUSIONS

Changes in plasma levels of CD163, TRAP, GRN, SPON1, and PGLYRP1 precede adverse cardiovascular events in patients with CHF.

Proteomic Bioprofiles and Mechanistic Pathways of Progression to Heart Failure

Background Identifying the mechanistic pathways potentially associated with incident heart failure (HF) may provide a basis for novel preventive strategies. Methods and Results To identify proteomic biomarkers and the potential underlying mechanistic pathways that may be associated with incident HF defined as the first hospitalization for HF, a nested-matched case-control design was used with cases (incident HF) and controls (without HF) selected from 3 cohorts (>20 000 individuals). Controls were matched on cohort, follow-up time, age, and sex. Two independent sample sets (a discovery set with 286 cases and 591 controls and a replication set with 276 cases and 280 controls) were used to discover and replicate the findings. Two hundred fifty-two circulating proteins in the plasma were studied. Adjusting for the matching variables age, sex, and follow-up time (and correcting for multiplicity of tests), 89 proteins were found to be associated with incident HF in the discovery phase, of which 38 were also associated with incident HF in the replication phase. These 38 proteins pointed to 4 main network clusters underlying incident HF: (1) inflammation and apoptosis, indicated by the expression of the TNF (tumor necrosis factor)-family members; (2) extracellular matrix remodeling, angiogenesis and growth, indicated by the expression of proteins associated with collagen metabolism, endothelial function, and vascular homeostasis; (3) blood pressure regulation, indicated by the expression of natriuretic peptides and proteins related to the renin-angiotensin-aldosterone system; and (4) metabolism, associated with cholesterol and atherosclerosis. Conclusions Clusters of biomarkers associated with mechanistic pathways leading to HF were identified linking inflammation, apoptosis, vascular function, matrix remodeling, blood pressure control, and metabolism. These findings provide important insight on the pathophysiological mechanisms leading to HF. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02556450.

Chemokines profile in patients with chronic heart failure treated with cardiac resynchronization therapy

PURPOSE

Inflammatory mechanisms have been suggested to play a role in the heart failure with reduced ejection fraction (HF-REF) development, but the role of chemokines is largely unknown. Cardiac resynchronization therapy (CRT) may reverse the HF-REF course. We aimed to evaluate selected chemokines concentrations in HF-REF patients and their relationship with disease severity and clinical response to CRT.

MATERIALS AND METHODS

The study included 37 patients (64.1 ± 11.04 years, 6 females) with HF-REF subjected to CRT, controlled prior to implantation and after 6 months. The control population included 26 healthy volunteers (63.9 ± 8.1 years, 8 females). Serum chemokines concentrations were determined using multiplex method.

RESULTS

HF-REF patients were characterized by the higher baseline MIF, NAP-2 and PF4 concentrations and lower Axl, BTC, IL-9, and IL-18 BPa concentrations comparing to controls. After 6 months of CRT only NAP-2 concentration decreased significantly in comparison to the baseline values.

CONCLUSIONS

HF-REF patients present altered chemokines profile compared to the control group. The CRT-related alleviation of HF-REF causes only slight changes in the chemokines concentrations especially in the platelet-associated ones. The precise chemokines role in the HF-REF pathogenesis and their prognostic value remains to be established.

Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

The C-terminal region of tumor necrosis factor like weak inducer of apoptosis is required for interaction with advanced glycation end products

Previously, we found that endogenously produced pro-inflammatory molecules, advanced glycation end products (AGEs), interact with tumor necrosis factor-like weak inducer of apoptosis (TWEAK), and attenuate its immunomodulatory function. In the present study, to elucidate the mechanism by which AGEs attenuate TWEAK function, we searched for regions responsible for TWEAK-AGE interaction using TWEAK deletion mutants. Pull-down assays with the TWEAK mutants and AGEs revealed that the C-terminal half of TWEAK, which is the region essential for receptor stimulation, was required for this interaction. On the other hand, the N-terminal deletion mutants did not exhibit a significant decrease in AGE binding. Moreover, a moderate decrease in the AGE binding by double-deletion in quartered C-terminal half regions and a substantial decrease by triple-deletion in this region were observed. In addition, full-length TWEAK stimulated IL-8 gene expression in endothelial EA.hy.926 cells, whereas the triple-deletion mutant lost much of this activity, suggesting that the TWEAK-AGE interaction sites overlap with the region needed to exert normal function of TWEAK. Our present findings may help to elucidate the pathophysiological roles of the TWEAK-AGE interaction for prevention and treatment of AGE-related inflammatory diseases.

Potential biomarkers for heart failure

In this study, we identified candidate biomarkers for heart failure (HF). The gene expression profile GSE57338, containing 117 ischemic cardiomyopathic HF and 136 control samples, was downloaded and analyzed using various bioinformatics approaches. In total, 376 differentially expressed genes (DEGs) were identified, and four modules were explored in protein-protein interaction networks. DEGs (including ankyrin repeat and SOCS box-containing 14 [ASB14]) in the modules were mainly categorized by the function. Several relationships including interferon regulatory factor 1 (IRF1)-C-C motif chemokine ligand 5 (CCL5) were revealed in the transcription factor microRNA target gene regulatory network. Gene-drug analysis revealed 11 DEGs (such as the cluster of differentiation 163 [CD163]) for the target drugs. Data verification analysis identified 118 overlapping DEGs including ASB14, CD163, and CCL5. ASB14 may be involved in HF progression via protein ubiquitination and CCL5 may be involved in HF via the IRF1-CCL5 interaction. Genes including CD163 are potential biomarkers for HF.

Bioinformatics identification of potential candidate blood indicators for doxorubicin-induced heart failure

The care of individual patients requiring anthracyclines remains challenging as uncertainty persists on predictors of cardiotoxicity. The aim of the present study was to identify potential candidate blood indicators of doxorubicin-induced heart failure. The gene expression profiles of GSE40447 and GSE9128 microarray data were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) using the R/Limma package or GEO2R. Functional and pathway enrichment analysis on DEGs were performed using DAVID database. The cardiovascular disease (CVD)-related DEGs were screen out based on the CardioGenBase database. The protein-protein interaction (PPI) network was constructed with STRING database and visualized by using Cytoscape. Then, the CVD-related DEGs were validated by intersection analysis with DEGs in GSE9128. The overlapping DEGs with a consistent expression pattern in GSE40447 and GSE9128 were identified as candidate indicators for doxorubicin-induced heart failure. A total of 516 DEGs potentially associated with doxorubicin-induced heart failure in GSE40447 were identified, which were mainly enriched in the gene ontology terms related to B cells, leukocytes, lymphocyte activation and B cell receptor signaling pathway. Of the DEGs, 42 were screened out as CVD-related DEGs by using CardioGenBase. Seven genes with high connectivity degree were presented in the PPI network. Finally, 5/6 CVD-related DEGs revealed by the intersection analysis were validated by GSE9128 and highlighted as candidate indicators of doxorubicin-induced heart failure: CD163, CD28, SLC25A20, ANPEP and TLR5. Several genes, including the 5 previously mentioned, were proposed as potential candidate blood indicators for doxorubicin-induced heart failure. Further experimental validations are greatly warranted for future clinical application.

T cell and monocyte/macrophage activation markers associate with adverse outcome, but give limited prognostic value in anemic patients with heart failure: results from RED-HF

BACKGROUND

Activated leukocytes may contribute to the development and progression of heart failure (HF). We investigated the predictive value of circulating levels of stable and readily detectable markers reflecting both monocyte/macrophage and T-cell activity, on clinical outcomes in HF patients with reduced ejection fraction (HFrEF).

METHODS

The association between baseline plasma levels of soluble CD163 (sCD163), macrophage migration inhibitory factor (MIF), granulysin, soluble interleukin-2 receptor (sIL-2R), and activated leukocyte cell adhesion molecule (ALCAM) and the primary endpoint of death from any cause or first hospitalization for worsening of HF was evaluated using multivariable Cox proportional hazard models in 1541 patients with systolic HF and mild to moderate anemia, enrolled in the Reduction of Events by darbepoetin alfa in Heart Failure (RED-HF) trial. Modifying effects and interaction with darbepoetin alfa treatment were also assessed.

RESULTS

All leukocyte markers, except granulysin, were associated with the primary outcome and all-cause death in univariate analysis (all p < 0.01) and remained significantly associated in multivariable analysis adjusting for conventional clinical variables (e.g. age, gender, BMI, NYHA class, creatinine, LVEF, etiology) and CRP. However, after final adjustment for TnT and NT-proBNP no associations were found with outcomes. No interaction with darbepoetin alpha treatment was observed for any marker.

CONCLUSIONS

Leukocyte activation markers sCD163, MIF, sIL-2R, and ALCAM were associated with adverse outcome in patients with HFrEF, but add little as prognostic markers on top of established biochemical risk markers.

CLINICAL TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT00358215 .

Repair Injured Heart by Regulating Cardiac Regenerative Signals

Cardiac regeneration is a homeostatic cardiogenic process by which the sections of malfunctioning adult cardiovascular tissues are repaired and renewed employing a combination of both cardiomyogenesis and angiogenesis. Unfortunately, while high-quality regeneration can be performed in amphibians and zebrafish hearts, mammalian hearts do not respond in kind. Indeed, a long-term loss of proliferative capacity in mammalian adult cardiomyocytes in combination with dysregulated induction of tissue fibrosis impairs mammalian endogenous heart regenerative capacity, leading to deleterious cardiac remodeling at the end stage of heart failure. Interestingly, several studies have demonstrated that cardiomyocyte proliferation capacity is retained in mammals very soon after birth, and cardiac regeneration potential is correspondingly preserved in some preadolescent vertebrates after myocardial infarction. There is therefore great interest in uncovering the molecular mechanisms that may allow heart regeneration during adult stages. This review will summarize recent findings on cardiac regenerative regulatory mechanisms, especially with respect to extracellular signals and intracellular pathways that may provide novel therapeutics for heart diseases. Particularly, both in vitro and in vivo experimental evidences will be presented to highlight the functional role of these signaling cascades in regulating cardiomyocyte proliferation, cardiomyocyte growth, and maturation, with special emphasis on their responses to heart tissue injury.