The pathophysiology of many neuropsychiatric disorders is still poorly understood. Identification of biomarkers for these diseases could benefit patients due to better classification and stratification. Exosomes excreted into the circulatory system can cross the blood-brain barrier and carry a cell type-specific set of molecules. Thus, exosomes are a source of potential biomarkers for many diseases, including neuropsychiatric disorders. Here, we investigated exosomal proteins produced from human-induced pluripotent stem cells (iPSCs) and iPSC-derived neural stem cells, neural progenitors, neurons, astrocytes, microglia-like cells, and brain capillary endothelial cells. Of the 31 exosome surface markers analyzed, a subset of biomarkers were significantly enriched in astrocytes (CD29, CD44, and CD49e), microglia-like cells (CD44), and neural stem cells (SSEA4). To identify molecular fingerprints associated with disease, circulating exosomes derived from healthy control (HC) individuals were compared against schizophrenia (SCZ) patients and late-onset Alzheimer\'s disease (LOAD) patients. A significant epitope pattern was identified for LOAD (CD1c and CD2) but not for SCZ compared to HC. Thus, analysis of cell type- and disease-specific exosome signatures of iPSC-derived cell cultures may provide a valuable model system to explore proteomic biomarkers for the identification of novel disease profiles.

BACKGROUND: The involvement of the peripheral immune system in the etiology of neurodegenerative diseases has recently been emphasized. Genome-wide association studies (GWAS) have recently identified several candidate immune genes linked to development of both Alzheimer\'s disease (AD) and depression. TBX21 (T-bet) which drives the Th1 immune response, is linked to the major depressive disorder (MDD) phenotype. This study investigated the association between the TBX21 immune gene and the possibility of late-onset Alzheimer\'s disease (LOAD) incidence in 194 LOAD and 200 control subjects using the real-time qPCR and the Tetra-ARMS-PCR methods. We also used an in silico approach to analyze the potential effects imparted by TBX21 rs17244587 and rs41515744 polymorphisms in LOAD pathogenesis.
RESULTS: We found that the TBX21 \"immune gene\" had significantly elevated mRNA expression levels in the leukocytes of peripheral blood in patients with LOAD (P < 0.0001). We also found an upward trend in TBX21 expression with increasing age in LOAD patients compared to the control group (P < 0.05; CI = 95%). We noticed that the TT genotype of rs41515744 plays a protective role in LOAD incidence, as it attenuates the expression of TBX21 in the control group. We observed that the dominant model of rs41515744 represented a substantial association with LOAD (P = 0.019).
CONCLUSIONS: Our results show for the first time the likely impact of the TBX21 (T-bet) immune gene in LOAD development and that the elevated TBX21 mRNAs in the WBCs of LOAD patients may represent a new easy diagnostic test for Alzheimer\'s disease.

Apolipoprotein E4 (ApoE4) is the most recognized genetic risk factor for late-onset Alzheimer\'s disease (LOAD), whereas ApoE2 reduces the risk for LOAD. The underlying mechanisms are unclear but may include effects on brain energy metabolism. Here, we used neuro-2a (N2a) cells that stably express human ApoE isoforms (N2a-hApoE), differentiated N2a-hApoE neuronal cells, and humanized ApoE knock-in mouse models to investigate relationships among ApoE isoforms, glycolytic metabolism, and neuronal health and aging. ApoE2-expressing cells retained robust hexokinase (HK) expression and glycolytic activity, whereas these endpoints progressively declined with aging in ApoE4-expressing cells. These divergent ApoE2 and ApoE4 effects on glycolysis directly correlated with markers of cellular wellness. Moreover, ApoE4-expressing cells upregulated phosphofructokinase and pyruvate kinase with the apparent intent of compensating for the HK-dependent glycolysis reduction. The introduction of ApoE2 increased HK levels and glycolysis flux in ApoE4 cells. PI3K/Akt signaling was distinctively regulated by ApoE isoforms but was only partially responsible for the ApoE-mediated effects on HK. Collectively, our findings indicate that human ApoE isoforms differentially modulate neuronal glycolysis through HK regulation, with ApoE2 upregulating and ApoE4 downregulating, which markedly impacts neuronal health during aging. These findings lend compelling support to the emerging inverse-Warburg theory of AD and highlight a therapeutic opportunity for bolstering brain glycolytic resilience to prevent and treat AD.

Aging is the greatest risk factor for late-onset Alzheimer\'s disease (LOAD), which accounts for >95% of Alzheimer\'s disease (AD) cases. The mechanism underlying the aging-related susceptibility to LOAD is unknown. Cellular senescence, a state of permanent cell growth arrest, is believed to contribute importantly to aging and aging-related diseases, including AD. Senescent astrocytes, microglia, endothelial cells, and neurons have been detected in the brain of AD patients and AD animal models. Removing senescent cells genetically or pharmacologically ameliorates β-amyloid (Aβ) peptide and tau-protein-induced neuropathologies, and improves memory in AD model mice, suggesting a pivotal role of cellular senescence in AD pathophysiology. Nonetheless, although accumulated evidence supports the role of cellular senescence in aging and AD, the mechanisms that promote cell senescence and how senescent cells contribute to AD neuropathophysiology remain largely unknown. This review summarizes recent advances in this field. We believe that the removal of senescent cells represents a promising approach toward the effective treatment of aging-related diseases, such as AD.

Young-onset and late-onset Alzheimer\'s disease has different clinical presentations with late-onset presenting most often with memory deficits while young-onset often presents with a non-amnestic syndrome. However, it is unknown whether there are differences in presentation and progression of neuropsychiatric symptoms in young- versus late-onset Alzheimer\'s disease. We aimed to investigate differences in the prevalence and severity of neuropsychiatric symptoms in patients with young- and late-onset Alzheimer\'s disease longitudinally with and without accounting for the effect of medication usage. Sex differences were also considered in these patient groups. We included 126 young-onset and 505 late-onset Alzheimer\'s disease patients from National Alzheimer\'s Coordinating Center-Uniform Data Set (NACC-UDS) and Alzheimer\'s Disease Neuroimaging Initiative (ADNI). We investigated the prevalence and severity of neuropsychiatric symptoms using the Neuropsychiatric Inventory-Questionnaire over 4 visits with 1-year intervals, using a linear mixed-effects model. The prevalence of depression was significantly higher in young-onset than late-onset Alzheimer\'s disease over a 4-year interval when antidepressant usage was included in our analyses. Our findings suggest that neuropsychiatric symptom profiles of young- and late-onset Alzheimer\'s disease differ cross-sectionally but also display significant differences in progression.

BACKGROUND: Developing biomarker tools for identification of individuals at high-risk for late-onset Alzheimer\'s disease (LOAD) is important for prognosis and early treatment. This review focuses on genetic factors and their potential role for precision medicine in LOAD.
UNASSIGNED: APOEe4 is the strongest genetic risk factor for non-Mendelian LOAD, and the APOE-linkage disequilibrium (LD) region has produced the most significant association signal in multi-center genome-wide-association-studies (GWAS). Consideration of extended haplotypes in the APOE-LD region and specifically, non-coding variants in putative enhancer elements, such as the TOMM40-polyT, in-addition to the coding variants that comprise the APOE-genotypes, may be useful for predicting subjects at high-risk of developing LOAD and estimating age-of-onset of early disease-stage symptoms. A genetic-biomarker based on APOE-TOMM40-polyT haplotypes, and age is currently applied in a clinical trial for prevention/delay of LOAD onset. Additionally, we discuss LOAD-GWAS discoveries and the development of new genetic risk scores based on LOAD-GWAS findings other than the APOE-LD region.
UNASSIGNED: Deciphering the precise causal genetic-variants within LOAD-GWAS regions will advance the development of genetic-biomarkers to complement and refine the APOE-LD region based prediction model. Collectively, the genetic-biomarkers will be translational for early diagnosis and enrichment of clinical trials with subjects at high-risk.

Late-onset Alzheimer\'s disease (LOAD) is a complex and multifactorial disease. So far ten loci have been identified for LOAD, including APOE, PICALM, CLU, BIN1, CD2AP, CR1, CD33, EPHA1, ABCA7, and MS4A4A/MS4A6E, but they explain about 50% of the genetic risk and thus additional risk genes need to be identified. Amyloid beta (Aβ) plaques develop in the brains of LOAD patients and are considered to be a pathological hallmark of this disease. Recently 12 new Aβ toxicity modifier genes (ADSSL1, PICALM, SH3KBP1, XRN1, SNX8, PPP2R5C, FBXL2, MAP2K4, SYNJ1, RABGEF1, POMT2, and XPO1) have been identified that potentially play a role in LOAD risk. In this study, we have examined the association of 222 SNPs in these 12 candidate genes with LOAD risk in 1291 LOAD cases and 958 cognitively normal controls. Single site and haplotype analyses were performed using PLINK. Following adjustment for APOE genotype, age, sex, and principal components, we found single nucleotide polymorphisms (SNPs) in PPP2R5C, PICALM, SH3KBP1, XRN1, and SNX8 that showed significant association with risk of LOAD. The top SNP was located in intron 3 of PPP2R5C (P=0.009017), followed by an intron 19 SNP in PICALM (P=0.0102). Haplotype analysis revealed significant associations in ADSSL1, PICALM, PPP2R5C, SNX8, and SH3KBP1 genes. Our data indicate that genetic variation in these new candidate genes affects the risk of LOAD. Further investigation of these genes, including additional replication in other case-control samples and functional studies to elucidate the pathways by which they affect Aβ, are necessary to determine the degree of involvement these genes have for LOAD risk.