Brain CHID1 Expression Correlates with NRGN and CALB1 in Healthy Subjects and AD Patients

Abstract

Alzheimer’s disease is a progressive, devastating, and irreversible brain disorder that, day by day, destroys memory skills and social behavior. Despite this, the number of known genes suitable for discriminating between AD patients is insufficient. Among the genes potentially involved in the development of AD, there are the chitinase-like proteins (CLPs) CHI3L1, CHI3L2, and CHID1. The genes of the first two have been extensively investigated while, on the contrary, little information is available on CHID1. In this manuscript, we conducted transcriptome meta-analysis on an extensive sample of brains of healthy control subjects (n = 1849) (NDHC) and brains of AD patients (n = 1170) in order to demonstrate CHID1 involvement. Our analysis revealed an inverse correlation between the brain CHID1 expression levels and the age of NDHC subjects. Significant differences were highlighted comparing CHID1 expression of NDHC subjects and AD patients. Exclusive in AD patients, the CHID1 expression levels were correlated positively to calcium-binding adapter molecule 1 (IBA1) levels. Furthermore, both in NDHC and in AD patient’s brains, the CHID1 expression levels were directly correlated with calbindin 1 (CALB1) and neurogranin (NRGN). According to brain regions, correlation differences were shown between the expression levels of CHID1 in prefrontal, frontal, occipital, cerebellum, temporal, and limbic system. Sex-related differences were only highlighted in NDHC. CHID1 represents a new chitinase potentially involved in the principal processes underlying Alzheimer’s disease.

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

“…Husemoen et al 51 , Zhang et al 52 , Hartz et al 53 , Słomiński et al 54 , Johansson et al 55 , Pan et al 56 , Lopez-Sanz et al 57 , Grant 58 , Słomiński et al 59 , Galán et al 60 , Jordan et al 61 , Winkler et al 62 , Yip et al 63 , Crookshank et al 64 , Lempainen et al 65 , Qu and Polychronakos 66 , Morrison et al 67 , Zhang et al 68 , Gerlinger-Romero et al 69 , Belanger et al 70 , Dieter et al 71 , Wanic et al 72 , Ushijima Wanic et al 73 , Guo et al 74 , Davis et al 75 , Elbarbary et al 76 , Villasenor et al 77 , Zhang et al 78 , Lee et al 79 , Zhi et al 80 , Li Calzi et al 81 , Sebastiani et al 82 , Cherney et al 83 , Doggrell 84 and Yanagihara et al 85 studied the clinical and prognostic values of FLG (filaggrin), FGF21, PEMT (phosphatidylethanolamine N -methyltransferase) KL (klotho), CEL (carboxyl ester lipase), FOSL2, STAT1, TCF7L2, TP53, EGFR (epidermal growth factor receptor), ETS1, KCNJ8, DEAF1, GCG (glucagon), IKZF4, OAS1, IRS1, ABCG2, FBXO32, PTBP1, BACH2, CNDP2, KLF11, MT1E, DPP4, SLC29A3, RGS16, MAS1, GCGR (glucagon receptor), HLA-C, VASP (vasodilator stimulated phosphoprotein), CCR2, PTGS2, GLP1R and JMJD6 in patients with T1DM. Vassilev et al 86 , Qin et al 87 , Ma et al 88 , West et al 89 , Hoffmann et al 90 , Deary et al 91 , Belangero et al 92 , Jung et al 93 , Tang et al 94 , Goodier et al 95 , Petyuk et al 96 , Roux et al 97 , Castrogiovanni et al 98 , Suleiman et al 99 , Haack et al ...…”

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

“…Furthermore, we investigated the biological functions of these DEGs by using online website, and GO and pathway enrichment analysis. Husemoen et al [ [94], Tang et al [95], Goodier et al [96], Petyuk et al [97], Roux et al [98], Castrogiovanni et al [99], Suleiman et al [100] [111], Ma et al [112], Chabbert et al [113], Abramsson et al [114], Aeby et al [115] and Roll et al [116] found that the expression of DCC (DCC netrin 1 receptor), PLP1, SNX19, SH3RF1, TNFRSF1A, NCSTN (nicastrin), DGCR2, NPAS2, CDNF (cerebral dopamine neurotrophic factor), SMCR8, HSPA2, STUB1, CHID1, ATP13A2, SQSTM1, LIG3, SP4, ACSL6, ERN1, ATF6B, LRFN2, NRG3, LRRTM3, GABRA2, ADAM30, GABRR2, TSHZ3, LOXL1, SCN1B and SRPX2 are associated with the prognosis of patients with cognitive impairment, but these genes might be novel target for T1DM. Recent studies found that KCP (kielin cysteine rich BMP regulator) [117], NOG (noggin) [118], COL6A3 [119], BTG2 [120], RPS6 [121], KLF15 [122], KLF3 [123], ZFP36 [124], ETV5 [125], TLE3 [126], NNMT (nicotinamide Nmethyltransferase) [127], WDTC1 [128], ZFHX3 [129], SIAH2 [130], MBOAT7 [131], RUNX1T1 [132], MAPK4 [133], KLF9 [134], SELENBP1 [135], HELZ2 [136], ELK1 [137], SERTAD2 [138], CRTC3 [139], ABCB11 [140], TACR1 [141], SLC22A11 [142], PER3…”

“…Husemoen et al [52], Zhang et al [53], Hartz et al [54], Słomiń ki et al [55], Johansson et al [56], sPan et al [57], Lopez-Sanz et al [58], Grant, [59], Słomiń i et al [60], Galán et al sk [61], Jordan et al [62], Winkler et al [63], Yip et al [64], Crookshank et al [65], Lempainen et al [66], Qu and Polychronakos, [67], Morrison et al [68], Zhang et al [69], Gerlinger-Romero et al [70], Belanger et al [71], Dieter et al [72], Wanic et al [73], Ushijima Wanic et al [74], Guo et al [75], Davis et al [76], Elbarbary et al [77], Villasenor et al [78], Zhang et al [79], Lee et al [80], Zhi et al [81], Li Calzi et al [82], Sebastiani et al [83], Cherney et al [84], Doggrell, [85] and Yanagihara et al [86] found that FLG (filaggrin), FGF21, PEMT (phosphatidylethanolamine N-methyltransferase) KL (klotho), CEL (carboxyl ester lipase), FOSL2, STAT1, TCF7L2, TP53, EGFR (epidermal growth factor receptor), ETS1, KCNJ8, DEAF1, GCG (glucagon), IKZF4, OAS1, IRS1, ABCG2, FBXO32, PTBP1, BACH2, CNDP2, KLF11, MT1E, DPP4, SLC29A3, RGS16, MAS1, GCGR (glucagon receptor), HLA-C, VASP (vasodilator stimulated phosphoprotein), CCR2, PTGS2, GLP1R and JMJD6 are involved in the progression of T1DM. Vassilev et al [87], Qin et al [88], Ma et al [89], West et al [90], Hoffmann et al [91], Deary et al [92], Belangero et al [93], Jung et al [94], Tang et al [95], Goodier et al [96], Petyuk et al [97], Roux et al [98], Castrogiovanni et al [99], Suleiman et al [100], Haack et al [101], Kwiatkowski et al [102], Pinacho et al [103], Luo et al [104], He et al [105], Moudi et al [106], Thevenon et al [107], Li et al [108], Reitz et al [109], Jenkins and Escayg [110], Letronne et al [111], Ma et al [112], Chabbert et al [113], Abramsson et al [114], Aeby...…”

Lactate Metabolism Regulates Tumour Growth and Progression in Glioblastoma

“…The e ciency of each biomarker across the different tumor grade was assessed by the receiver operating characteristic (ROC) curve analyses [38][39][40]. The ROC curves analyzed brain biopsies of healthy subjects (NT) vs glioblastoma patients, astrocytoma vs glioblastoma, and oligodendroglioma vs glioblastoma.…”