Neurodegeneration and Astrogliosis in the Human CA1 Hippocampal Subfield Are Related to hsp90ab1 and bag3 in Alzheimer’s Disease

González-Rodríguez, Melania; Villar‐Conde, Sandra; Astillero‐Lopez, Veronica; Villanueva-Anguita, Patricia; Úbeda-Bañón, Isabel; Flores‐Cuadrado, Alicia; Martı́nez-Marcos, Alino; Saiz-Sánchez, Daniel

doi:10.3390/ijms23010165

Cited by 32 publications

(19 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bioinformatics analysis indicated that the downregulation of PPP3R1 can serve as novel biomarkers for patients with AD and the potential mechanisms of low PPP3R1 involved in AD pathogenesis mainly involve in axon guidance, glutamatergic-mediated synapses transport, LTP, and MAPK signaling pathway ( 62 ). HSP90AB1, which is a chaperone of Hsp90 family that is closely linked to astrocytes, was reported to be a neuroprotective factor in AD patients via reducing the accumulation of cerebral Aβ and tau ( 63 ). As a vital chemokine, a higher concentration of CXCR10 was observed in the brain tissues of AD animal models, indicating its pathogenic role in contributing to AD progression ( 64 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Lai

Lin²,

Lin³

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Lai

Lin²,

Lin³

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…LMNA (lamin A/C) [187], PFKFB3 [188], TRAF2 [189], ADORA2B [190], ACADS (acyl-CoA dehydrogenase short chain) [191], IRF7 [192], ASL (argininosuccinatelyase) [193], IL15 [194], PIK3R1 [195], OSM (oncostatin M) [196], SOCS3 [197], CHD9 [198], IFI44L [199], GNLY (granulysin) [200], FLNB (filamin B) [201], IL1R1 [202], SETD3 [203], TP53 [204], BRD4 [205], COX2 [206], HSP90AB1 [207], SLC7A1 [208], ND2 [209] and COX1 [210] have been reported to be associated with hypertension. LMNA (lamin A/C) [211], INPP5K [212], SCYL1 [213], AKR7A2 [214], TRAF2 [215], SLC11A2 [216], NEU1 [217], SNAP29 [218], DUSP22 [219], P2RX4 [220], ADORA2B [221], NAXD (NAD(P)HX dehydratase) [222], FEZ1 [223], TBK1 [224], GRN (granulin precursor) [225], ATG4D [226], CTSD (cathepsin D) [227], PPP2R5D [228], IRF7 [229], ACAA1 [230], S1PR1 [231], PGRMC1 [169], MAPK14 [232], IL15 [233], OSM (oncostatin M) [234], GBA (glucosylceramidase beta) [235], SOCS3 [236], GLUL (glutamate-ammonia ligase) [175], MON1A [237], LMAN2L [238], RHOC (ras homolog family member C) [239], TUBB2A [240], CHP1 [241], TP53 [242], ATXN1 [243], JARID2 [244], IFNG (interferon gamma) [245], NUCKS1 [246], CTBP1 [247], DNAJC3 [180], ATP6 [248], BRD4 [249], EIF4B [250], COX2 [251], SFPQ (splicing factor proline and glutamine rich) [252], PCBP1 [253], RPL5 [254], HSP90AB1 [255], TRA2A […”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

View full text Add to dashboard Cite

Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

show abstract

“…Prior to morphometric analyses, we delimited the hippocampal subfields of the dentate gyrus, CA 3/2, and CA 1. These regions were delimited following the anatomical criteria described in previously published papers [ 6 , 45 , 46 ]. To ensure the accuracy of the region delimitation, we overlaid the ISH images with the corresponding Nissl sections using the software FIJI ImageJ version 1.52p (National Institutes of Health, Bethesda, MD, USA).…”

Section: Methodsmentioning

confidence: 99%

Age, Education Years, and Biochemical Factors Are Associated with Selective Neuronal Changes in the Elderly Hippocampus

Castro

Silva

Rabelo

et al. 2022

Cells

View full text Add to dashboard Cite

Brain aging involves regional alterations of specific cellular subpopulations in the human hippocampus: a network hub for memory consolidation. The present study investigates whether age, sex, education years, and the concentration of neuropathological and inflammatory proteins influence neuronal-type marker expression in the elderly hippocampus. We analyzed the digital images (1 µm/pixel) of postmortem hippocampal sections from 19 non-demented individuals (from 78 to 99 years). This material was obtained from the “Aging Dementia and TBI Study” open database. Brain samples were processed through in situ hybridization (ISH) for the immunodetection of VGLUT1 (glutamatergic transporter) and GAT1 (GABAergic transporter) and mRNAs and Luminex protein quantifications. After image acquisition, we delineated the dentate gyrus, CA 3/2, and CA1 hippocampal subdivisions. Then, we estimated the area fraction in which the ISH markers were expressed. Increased VGLUT1 was observed in multiple hippocampal subfields at late ages. This glutamatergic marker is positively correlated with beta-amyloid and tau proteins and negatively correlated with interleukin-7 levels. Additionally, education years are positively correlated with GAT1 in the hippocampus of elderly women. This GABAergic marker expression is associated with interferon-gamma and brain-derived neurotrophic factor levels. These associations can help to explain how hippocampal sub-regions and neurotransmitter systems undergo distinct physiological changes during normal aging.

show abstract

Neurodegeneration and Astrogliosis in the Human CA1 Hippocampal Subfield Are Related to hsp90ab1 and bag3 in Alzheimer’s Disease

Cited by 32 publications

References 72 publications

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Age, Education Years, and Biochemical Factors Are Associated with Selective Neuronal Changes in the Elderly Hippocampus

Contact Info

Product

Resources

About