XCL1, a serum biomarker in neurological diseases; HTLV-1-associated myelopathy and multiple sclerosis

Saeidi, Morteza; Vahidi, Zohreh; Nahayati, Mohammad Ali; Rezaiyan, Majid Khadem; Zemorshidi, Fariba; Mahdifar, Maryam; Hafezi, Fatemeh; Moghadam, Saeedeh Mehraban; Saghi, Effat; Akbarpour, Ensieh; Boostani, Reza; Rafatpanah, Houshang

doi:10.1016/j.micpath.2022.105962

Cited by 6 publications

(5 citation statements)

References 61 publications

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“…The Markov Chain Monte Carlo (MCMC) method was used to assess the sensitivity indices between inflammaging and MS. As a result, the 35 sensitive triples (by calculating the absolute difference frequency) were shown in Table 3 (Sarasin-Filipowicz et al, 2009 ; Chen and D'Mello, 2010 ; Bergbold and Lemberg, 2013 ; Liu et al, 2013 ; Malhotra et al, 2013 ; Wan, 2014 ; Charbit et al, 2015 ; Fusco et al, 2015 ; An et al, 2017 ; Arentsen et al, 2017 ; Maridas et al, 2017 ; Mathur et al, 2017 ; Xiao et al, 2019 ; Immler et al, 2020 ; Sato et al, 2020 ; Tong et al, 2020 ; Buhelt et al, 2021 ; Correale, 2021 ; Fadul et al, 2021 ; Ma et al, 2021 ; Peng et al, 2021 ; Bogacka et al, 2022 ; Franceschi et al, 2022 ; Hjæresen et al, 2022 ; Khurana and Goswami, 2022 ; Liu S. et al, 2022 ; Schebb et al, 2022 ; Watanabe et al, 2022 ; Saeidi et al, 2023 ). For example, the sensitive triple with maximum difference was “TMPRSS13-USP18-DCHS2” (the absolute difference value was 0.270469).…”

Section: Resultsmentioning

confidence: 99%

“…The disease predictor 0.6853 0.7233 19 Chen and D'Mello, 2010;Malhotra et al, 2013;Charbit et al, 2015;An et al, 2017;Arentsen et al, 2017;Lee et al, 2018;Xiao et al, 2019;Sato et al, 2020;Peng et al, 2021;Franceschi et al, 2022;Saeidi et al, 2023). For example, the top aging and inflammatory markers were also TMPRSS13 and USP18, respectively.…”

Section: Markers Used For Classificationmentioning

confidence: 99%

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Identification of crucial inflammaging related risk factors in multiple sclerosis

Xu,

Wang,

Ren

et al. 2024

Front. Mol. Neurosci.

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BackgroundMultiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.MethodsFirst, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential “aging-inflammation-disease” triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.ResultsA series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.ConclusionIn conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Markers Used For Classificationmentioning

confidence: 99%

Identification of crucial inflammaging related risk factors in multiple sclerosis

Xu,

Wang,

Ren

et al. 2024

Front. Mol. Neurosci.

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“…Also, XCL1 serum levels are significantly higher in MS patients compared to healthy control. XCL1 is a ligand for ITGA9, and antibodies neutralizing XCL1 result in abolished disease progression in EAE mice 66 . There are also reports about interleukin 10 (IL10) involvement with MS.…”

Section: Discussionmentioning

confidence: 99%

Unbiased multiplex antigen screening of Cerebrospinal Fluid detects microbial and autoantigenic epitopes associated with Multiple Sclerosis

Barton,

Tran,

Olson

et al. 2024

Preprint

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To comprehensively investigate the intrathecal antibody profile of multiple sclerosis (MS), we examined the cerebrospinal fluid of 195 patients (92 MS and 103 non-MS) for antibodies using a multiplex unbiased bacteria peptide library. We first tested against Epstein-Barr nuclear antigen 1 (EBNA1) for epitope sites enriched in MS and found a significant enrichment at position 407-419. We then used the data to perform a high-throughput screen against a library of 129 viruses known to infect humans. We discovered several additional epitopes from viruses such as Hantaan virus, Human Herpesvirus 6A and Human respiratory syncytial virus B associated with MS. Besides viral epitopes, we also screened for potential autoantigens of the central nervous system (CNS). We discovered several autoantigenic epitopes in proteins such as ADRB3, HTR3A and MPO that were significantly enriched for MS. Because of previous associations of Toxoplasma gondii infection with MS, we also performed a Toxoplasma gondii specific analysis and discovered additional epitopes enriched for MS. We further assessed epitope-epitope correlations within the patient samples and found distinct patterns of association between these microbial and autoantigenic epitopes. Finally, we performed machine-learning to determine if these epitopes are predictive for MS and found that the model incorporating all the epitopes could most effectively discriminate between MS and non-MS (ROC-AUC score = 0.91). Our results demonstrate the effectiveness of multiplex unbiased screens to detect the identity of potentially cross-reactive antibodies targeting MS CNS epitopes and they can also be used as effective biomarkers for MS.One Sentence SummaryWe performed an unbiased multiplex bacteria peptide antibody library screen on cerebrospinal fluid samples of patients with multiple sclerosis (MS) as well as non-MS controls and detected multiple viral and autoantigenic epitopes that are significantly enriched in MS patient samples.

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“…RYR1 [169], IRGM (immunity related GTPase M) [170], TRPC3 [171], PDE2A [172], SCML4 [173], SEMA3F [155], CUX2 [174], ROBO4 [175], DRD2 [176], GP6 [177], TRPM5 [178], ABI3BP [179], ACAN (aggrecan) [180] and NPC1L1 [181] plays a key role in cardiovascular diseases. Previous studies have reported that the XCL1 [182], HLA-DMB [183], CD40 [184], HLA-DRA [185], RUNX1 [186], IL18R1 [187], NINJ2 [188], ACE (angiotensin I converting enzyme) [189], CD44 [190], IL4R [191], MYD88 [192], WNT9B [193], CXCL16 [194], CXCL13 [195], RORB (RAR related orphan receptor B) [196], GDF15 [197], THEMIS (thymocyte selection associated) [198], KCNH7 [199], BTK (Bruton tyrosine kinase) [200] and MOBP (myelin associated oligodendrocyte basic protein) [201] are a key regulators of multiple sclerosis. Recently, increasing evidence demonstrated that HLA-DMB [202], VIP (vasoactive intestinal peptide) [203], GATA6 [204], CD40 [205], TFAP2B [206], HFE (homeostatic iron regulator) [207], IGFBP7 [208], NPY2R [209], CCL2 [210], AQP5 [211], HLA-DMA [212], RUNX1 [81], PPY (pancreatic polypeptide) [213], ASPA (aspartoacylase) [214], NOS1 [215], ADAM12 [216], NPPC (natriuretic peptide C) [217], COL1A1 [218], IL1R1 [219], ABCG2 [220], ACE (angiotensin I converting enzyme) [221], CD34 [222], HLA-DPA1 [223], A2M [224], MEOX2 [225], CDKN2A [226], SERPINE1 [227], CD44 [228], FABP4 [108], ITGB3 [229], ALOX5AP [230], SFRP4 [231], ISM1 [232], IL4R [233], RUNX2 [234], CASP1 […”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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XCL1, a serum biomarker in neurological diseases; HTLV-1-associated myelopathy and multiple sclerosis

Cited by 6 publications

References 61 publications

Identification of crucial inflammaging related risk factors in multiple sclerosis

Identification of crucial inflammaging related risk factors in multiple sclerosis

Unbiased multiplex antigen screening of Cerebrospinal Fluid detects microbial and autoantigenic epitopes associated with Multiple Sclerosis

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

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