Chemokine receptor 4 expression on blood T lymphocytes predicts severity of major depressive disorder

Freff, Jana; Beins, Eva C.; Bröker, Lisa; Schwarte, Kathrin; Dantas, Rafael Leite; Maj, Carlo; Arolt, Volker; Dannlowski, Udo; Nöthen, Markus M.; Baune, Bernhard T.; Forstner, Andreas J.; Alferink, Judith

doi:10.1016/j.jad.2022.05.003

Cited by 6 publications

(8 citation statements)

References 82 publications

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“…CFTR (CF transmembrane conductance regulator) [380], ABCA2 [381], HK2 [382], NGFR (nerve growth factor receptor) [383], TF (transferrin) [384], GLUL (glutamate-ammonia ligase) [324], ADAMTS4 [385], BIN1 [386], HSPA2 [387], LMNA (lamin A/C) [388], HSD11B1 [389], HTRA1 [390], APLP1 [391], MT3 [392], ADORA1 [393], DYSF (dysferlin) [394], SLC24A4 [395], RHOB (ras homolog family member B) [396], NINJ2 [397], LRP2 [398], LIPC (lipase C, hepatic type) [399], DHCR7 [400], SCD (stearoyl-CoA desaturase) [401], F11 [402], PFKL (phosphofructokinase, liver type) [403], ALDH1A1 [404], SPON1 [405] and CTNNA3 [406] are significantly associated with the Alzheimer’s Disease. CCR4 [407], CCR2 [408], CD48 [409], CD28 [410], CCL3 [411], CTLA4 [412], C6 [413], MICB (MHC class I polypeptide-related sequence B) [414], DRD3 [415], HGF (hepatocyte growth factor) [416], DIO3 [417], TTR (transthyretin) [418], GRHL3 [419], VEGFA (vascular endothelial growth factor A) [420], ADM (adrenomedullin) [421], CHI3L1 [422], SOX3 [423], MAG (myelin associated glycoprotein) [424], CNP (2’,3’-cyclic nucleotide 3’ phosphodiesterase) [425], SREBF1 [426], OLIG2 [427], ATF3 [428], NGFR (nerve growth factor receptor) [429], TF (transferrin) [430], GLUL (glutamate-ammonia ligase) [324]. MOG (myelin oligodendrocyte glycoprotein) [431], ERBB3 [432], NHLH2 [433], GADD45B [434], PADI2 [435], ADORA1 [436], NINJ2 [437], WNT7A [438], DAO (D-amino acid oxidase) [439], PRODH (proline dehydrogenase 1) [440] and HCRTR1 [441] could regulate the development of psychiatric disorders.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have shown that TAB2 [131], LTF (lactotransferrin) [487], BRCA1 [621], NFKB1 [622], TEAD1 [623], FOXO3 [624] and NRF1 [625] can be involved in the disease progression of obesity ., Altered expression of CCR4 [280], LTF (lactotransferrin) [295], CDH1 [313], CFTR (CF transmembrane conductance regulator) [317], hsa-mir-146a-5p [626], hsa-mir-195-5p [627], hsa-mir-4739 [628], HNF4A [629], NFKB1 [630], NR2F1 [631], FOXO3 [632] and NRF1 [633] are observed in diabetes mellitus. CCR4 [407], hsa-mir-146a-5p [634] and NFKB1 [635] were associated with psychiatric disorders. LTF (lactotransferrin) [362], RHOB (ras homolog family member B) [396], CDH1 [377], CFTR (CF transmembrane conductance regulator) [380], hsa-mir-146a-5p [636], hsa-mir-195-5p [637], BRCA1 [638], ELK4 [639] and FOXO3 [640] might offer useful information for treating Alzheimer’s Disease.…”

Section: Discussionmentioning

confidence: 99%

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Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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“…Previous studies had shown that pathways include neutrophil degranulation [74], immune system [75], extracellular matrix organization [76], toll-like receptor cascades [77], cytokine signaling in immune system [78], metabolism of RNA [79] and gene expression [80] were involved in the progression of CF. NTRK3 [81], MMP9 [82], IGF2 [83], SOCS3 [84], IGFBP2 [85], SLC1A3 [86], NOS1AP [87], HP (haptoglobin) [88], TLR5 [89], ARG1 [90], NRG1 [91], RGS5 [92], LCN2 [93], TSPO (translocator protein) [94], PLAU (plasminogen activator, urokinase) [95], MME (membrane metalloendopeptidase) [96], BST1 [97], NR6A1 [98], TLR4 [99], NRN1 [100], ABCA13 [101], CTSD (cathepsin D) [102], NCAM1 [103], SIGMAR1 [104], CNR2 [105], CCR4 [106], ABCB1 [107], TIMELESS (timeless circadian regulator) [108], IRF8 [109], TXNIP (thioredoxin interacting protein) [110], SUV39H1 [111], CRY1 [112], CRY2 [113], PDCD4 [114] and MGAT5 [115] played an important role in depression and anxiety. Accumulating evidence has demonstrated that MMP9 [116], S100A12 [117], HP (haptoglobin) [118], TLR5 [119], NRG1 [120], PLAU (plasminogen activator, urokinase) [121], SLC11A1 [122], AQP9 [123], CHIT1 [124], TLR4 [125], SLC26A8 [126], CTSD (cathepsin D) [127], SERPINB1 [128], FASLG (Fas ligand) [129], SLC4A4 [130], AQP3 [131] and IRF8 [132] appears to be constitutively associated with CF .…”

Section: Discussionmentioning

confidence: 99%

“…In this present investigation, we downloaded the GSE136371 NGS dataset from GEO, and a total of 917 DEGs (479 up regulated genes and 438 down regulated genes) between CF and normal control samples were identified by using bioinformatics tools. CHGA (chromogranin A) [48] and IL23R [49] [99], NRN1 [100], ABCA13 [101], CTSD (cathepsin D) [102], NCAM1 [103], SIGMAR1 [104], CNR2 [105], CCR4 [106], ABCB1 [107], TIMELESS (timeless circadian regulator) [108], IRF8 [109], TXNIP (thioredoxin interacting protein) [110], SUV39H1 [111], CRY1 [112], CRY2 [113], PDCD4 [114] and MGAT5 [115] played an important role in depression and anxiety. Accumulating evidence has demonstrated that MMP9 [116], S100A12 [117], HP (haptoglobin) [118], TLR5 [119], NRG1 [120], PLAU (plasminogen activator, urokinase) [121], SLC11A1 [122], AQP9 [123], CHIT1 [124], TLR4 [125], SLC26A8 [126], CTSD (cathepsin D) [127], SERPINB1 [128], FASLG (Fas ligand) [129], SLC4A4 [130], AQP3 [131] and IRF8 [132] appears to be constitutively associated with CF.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Cystic fibrosis (CF) is one of the inherited autosomal recessive disorders with very complicated pathogenesis. Identifying the molecular signatures and specific biomarkers of CF might provide novel clues for CF and and CF associated complications prognosis and targeted therapy. Based on the nexgeneration sequencing (NGS) dataset GSE136371 downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between CF samples and normal controls were identified by using DESeq2 bioconductor package of R software. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Then protein-protein interaction (PPI) network of these DEGs was visualized by Cytoscape with IMEx interactome database. The most significant module from the PPI network was selected for GO and pathway enrichment analysis. Subsequently, a miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Finally, receiver operating characteristic curve (ROC) analysis was established to validate these hub genes. Total of 917 DEGs were identified between CF and normal control samples in GSE136371 dataset, including 479 up regulated and 438 down regulated genes. The most enriched DEGs in GO and pathway enrichment analysis were mainly associated with response to stimulus, regulation of cellular process, Neutrophil degranulation and rRNA processing. PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network predicted ten hub genes (FN1, UBE2D1, SRPK1, MAPK14, CEBPB, HSP90AB1, HSPA8, XRCC6, NCL and PARP1). In conclusion, the DEGs, relative pathways and hub genes identified in the present study might aid in understanding of the molecular mechanisms underlying CF and CF associated complications progression and provide potential molecular targets and biomarkers for CF and CF associated complications.

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“…The whole blood staining procedure for surface marker expression was performed similar as described before ( 77 ). In brief, for identification of T cells and their subsets, monoclonal antibodies anti-human CD3-PerCP, anti-human CD4-APC-Cy7, and anti-human CD8-BV510 were used.…”

Section: Methodsmentioning

confidence: 99%

Expression of CXCR4 on CD4+ T cells predicts body composition parameters in female adolescents with anorexia nervosa

et al. 2022

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Anorexia nervosa (AN) is a severe eating disorder characterized by excessive weight loss and lack of recognition of the seriousness of the current low body weight. Individuals with AN frequently exhibit an enhanced inflammatory state and altered blood levels of cytokines and chemokines. However, the expression of chemokine receptors in AN and the association with body composition parameters and treatment effects are still unknown. In this study, we examined the expression of CCR4, CCR6, CXCR3, and CXCR4 on peripheral blood T cells in female adolescents with AN before (T0, n = 24) and after 6 weeks of multimodal therapy (T1, n = 20). We also investigated their value to predict body mass index (BMI) and fat mass index (FMI) at baseline. Using multi-parameter flow cytometry, we found increased expression of CCR4, CXCR3, and CXCR4, but not CCR6, on CD4+ T cells in AN at T0 when compared to healthy controls (HC, n = 20). At T1, CXCR3 and CXCR4 expression decreased in AN. We found a close link between CCR4, CCR6 and CXCR4 expression and the adolescent mental health status in the study cohort as determined by the Strengths and Difficulties Questionnaire (SDQ). Specifically, CXCR4 expression correlated positively with emotional symptoms and peer relationship problems, as well as with the total sum score of the SDQ. In addition, CXCR4 expression on CD4+ T cells was a significant predictor of BMI and FMI in female adolescents. Our findings that CXCR4 expression on T cells is altered in adolescents with AN and predicts body composition parameters in adolescents suggest an impact of this chemokine receptor in the pathogenesis of AN.

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Chemokine receptor 4 expression on blood T lymphocytes predicts severity of major depressive disorder

Cited by 6 publications

References 82 publications

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Identification of Key Genes and Underlying Mechanisms in Cystic Fibrosis and its Associated Complications Based on Bioinformatics Analysis of Next Generation Sequencing Data Analysis

Expression of CXCR4 on CD4+ T cells predicts body composition parameters in female adolescents with anorexia nervosa

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