Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis

Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer's disease

Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. Finally, we assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.

Exploring the feasibility of using mice as a substitute model for investigating microglia in aging and Alzheimer's disease though single cell analysis

OBJECTIVE

To guide animal experiments, we investigated the similarities and differences between humans and mice in aging and Alzheimer's disease (AD) at the single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing (scRNA-seq) level.

METHODS

Microglia cells were extracted from dataset GSE198323 of human post-mortem hippocampus. The distributions and proportions of microglia subpopulation cell numbers related to AD or age were compared. This comparison was done between GSE198323 for humans and GSE127892 for mice, respectively. The Seurat R package and harmony R package were used for data analysis and batch effect correction. Differentially expressed genes (DEGs) were identified by FindMarkers function with MAST test. Comparative analyses were conducted on shared genes in DEGs associated with age and AD. The analyses were done between human and mouse using various bioinformatics techniques. The analysis of genes in DEGs related to age was conducted. Similarly, the analysis of genes in DEGs related to AD was performed. Cross-species analyses were conducted using orthologous genes. Comparative analyses of pseudotime between humans and mice were performed using Monocle2.

RESULTS

(1) Similarities: The proportion of microglial subpopulation Cell_APOE/Apoe shows consistent trends, whether in AD or normal control (NC) groups in both humans and mice. The proportion of Cell_CX3CR1/Cx3cr1, representing homeostatic microglia, remains stable with age in NC groups across species. Tuberculosis and Fc gamma R-mediated phagocytosis pathways are shared in microglia responses to age and AD across species, respectively. (2) Differences: IL1RAPL1 and SPP1 as marker genes are more identifiable in human microglia compared to their mouse counterparts. Most genes of DEGs associated with age or AD exhibit different trends between humans and mice. Pseudotime analyses demonstrate varying cell density trends in microglial subpopulations, depending on age or AD across species.

CONCLUSIONS

Mouse Apoe and Cell_Apoe maybe serve as proxies for studying human AD, while Cx3cr1 and Cell_Cx3cr1 are suitable for human aging studies. However, AD mouse models (App_NL_G_F) have limitations in studying human genes like IL1RAPL1 and SPP1 related to AD. Thus, mouse models cannot fully replace human samples for AD and aging research.

Brain-Region-Specific Differences in Protein Citrullination/Deimination in a Pre-Motor Parkinson's Disease Rat Model

The detection of early molecular mechanisms and potential biomarkers in Parkinson's disease (PD) remains a challenge. Recent research has pointed to novel roles for post-translational citrullination/deimination caused by peptidylarginine deiminases (PADs), a family of calcium-activated enzymes, in the early stages of the disease. The current study assessed brain-region-specific citrullinated protein targets and their associated protein-protein interaction networks alongside PAD isozymes in the 6-hydroxydopamine (6-OHDA) induced rat model of pre-motor PD. Six brain regions (cortex, hippocampus, striatum, midbrain, cerebellum and olfactory bulb) were compared between controls/shams and the pre-motor PD model. For all brain regions, there was a significant difference in citrullinated protein IDs between the PD model and the controls. Citrullinated protein hits were most abundant in cortex and hippocampus, followed by cerebellum, midbrain, olfactory bulb and striatum. Citrullinome-associated pathway enrichment analysis showed correspondingly considerable differences between the six brain regions; some were overlapping for controls and PD, some were identified for the PD model only, and some were identified in control brains only. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways identified in PD brains only were associated with neurological, metabolic, immune and hormonal functions and included the following: "Axon guidance"; "Spinocerebellar ataxia"; "Hippo signalling pathway"; "NOD-like receptor signalling pathway"; "Phosphatidylinositol signalling system"; "Rap1 signalling pathway"; "Platelet activation"; "Yersinia infection"; "Fc gamma R-mediated phagocytosis"; "Human cytomegalovirus infection"; "Inositol phosphate metabolism"; "Thyroid hormone signalling pathway"; "Progesterone-mediated oocyte maturation"; "Oocyte meiosis"; and "Choline metabolism in cancer". Some brain-region-specific differences were furthermore observed for the five PAD isozymes (PADs 1, 2, 3, 4 and 6), with most changes in PAD 2, 3 and 4 when comparing control and PD brain regions. Our findings indicate that PAD-mediated protein citrullination plays roles in metabolic, immune, cell signalling and neurodegenerative disease-related pathways across brain regions in early pre-motor stages of PD, highlighting PADs as targets for future therapeutic avenues.

Exploring the hub Genes and Potential Mechanisms of Complement system-related Genes in Parkinson Disease: Based on Transcriptome Sequencing and Mendelian Randomization

An accurate diagnosis of Parkinson's disease (PD) remains challenging and the exact cause of the disease is unclean. The aims are to identify hub genes associated with the complement system in PD and to explore their underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) and key module genes related to PD were mined through differential expression analysis and WGCNA. Then, differentially expressed CSRGs (DE-CSRGs) were obtained by intersecting the DEGs, key module genes and CSRGs. Subsequently, MR analysis was executed to identify genes causally associated with PD. Based on genes with significant MR results, the expression level and diagnostic performance verification were achieved to yield hub genes. Functional enrichment and immune infiltration analyses were accomplished to insight into the pathogenesis of PD. qRT-PCR was employed to evaluate the expression levels of hub genes. After MR analysis and related verification, CD93, CTSS, PRKCD and TLR2 were finally identified as hub genes. Enrichment analysis indicated that the main enriched pathways for hub genes. Immune infiltration analysis found that the hub genes showed significant correlation with a variety of immune cells (such as myeloid-derived suppressor cell and macrophage). In the qRT-PCR results, the expression levels of CTSS, PRKCD and TLR2 were consistent with those we obtained from public databases. Hence, we mined four hub genes associated with complement system in PD which provided novel perspectives for the diagnosis and treatment of PD.

Spatially resolved transcriptome of the aging mouse brain

Brain aging is associated with cognitive decline, memory loss and many neurodegenerative disorders. The mammalian brain has distinct structural regions that perform specific functions. However, our understanding in gene expression and cell types within the context of the spatial organization of the mammalian aging brain is limited. Here we generated spatial transcriptomic maps of young and old mouse brains. We identified 27 distinguished brain spatial domains, including layer-specific subregions that are difficult to dissect individually. We comprehensively characterized spatial-specific changes in gene expression in the aging brain, particularly for isocortex, the hippocampal formation, brainstem and fiber tracts, and validated some gene expression differences by qPCR and immunohistochemistry. We identified aging-related genes and pathways that vary in a coordinated manner across spatial regions and parsed the spatial features of aging-related signals, providing important clues to understand genes with specific functions in different brain regions during aging. Combined with single-cell transcriptomics data, we characterized the spatial distribution of brain cell types. The proportion of immature neurons decreased in the DG region with aging, indicating that the formation of new neurons is blocked. Finally, we detected changes in information interactions between regions and found specific pathways were deregulated with aging, including classic signaling WNT and layer-specific signaling COLLAGEN. In summary, we established a spatial molecular atlas of the aging mouse brain (http://sysbio.gzzoc.com/Mouse-Brain-Aging/), which provides important resources and novel insights into the molecular mechanism of brain aging.

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease

There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.

Passive Immunotherapies Targeting Amyloid-β in Alzheimer's Disease: A Quantitative Systems Pharmacology Perspective

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid-β (Aβ) protein accumulation in the brain. Passive immunotherapies using monoclonal antibodies for targeting Aβ have shown promise for AD treatment. Indeed, recent US Food and Drug Administration approval of aducanumab and lecanemab, alongside positive donanemab Phase III results demonstrated clinical efficacy after decades of failed clinical trials for AD. However, the pharmacological basis distinguishing clinically effective from ineffective therapies remains unclear, impeding development of potent therapeutics. This study aimed to provide a quantitative perspective for effectively targeting Aβ with antibodies. We first reviewed the contradicting results associated with the amyloid hypothesis and the pharmacological basis of Aβ immunotherapy. Subsequently, we developed a quantitative systems pharmacology (QSP) model that describes the non-linear progression of Aβ pathology and the pharmacologic actions of the Aβ-targeting antibodies. Using the QSP model, we analyzed various scenarios for effective passive immunotherapy for AD. The model revealed that binding exclusively to the Aβ monomer has minimal effect on Aβ aggregation and plaque reduction, making the antibody affinity toward Aβ monomer unwanted, as it could become a distractive mechanism for plaque reduction. Neither early intervention, high brain penetration, nor increased dose could yield significant improvement of clinical efficacy for antibodies targeting solely monomers. Antibodies that bind all Aβ species but lack effector function exhibited moderate effects in plaque reduction. Our model highlights the importance of binding aggregate Aβ species and incorporating effector functions for efficient and early plaque reduction, guiding the development of more effective therapies for this devastating disease. SIGNIFICANCE STATEMENT: Despite previous unsuccessful attempts spanning several decades, passive immunotherapies utilizing monoclonal antibodies for targeting amyloid-beta (Aβ) have demonstrated promise with two recent FDA approvals. However, the pharmacological basis that differentiates clinically effective therapies from ineffective ones remains elusive. Our study offers a quantitative systems pharmacology perspective, emphasizing the significance of selectively targeting specific Aβ species and importance of antibody effector functions. This perspective sheds light on the development of more effective therapies for this devastating disease.

Spatiotemporal characterization of glial cell activation in an Alzheimer's disease model by spatially resolved transcriptomics

The molecular changes that occur with the progression of Alzheimer's disease (AD) are well known, but an understanding of the spatiotemporal heterogeneity of changes in the brain is lacking. Here, we investigated the spatially resolved transcriptome in a 5XFAD AD model at different ages to understand regional changes at the molecular level. Spatially resolved transcriptomic data were obtained from 5XFAD AD models and age-matched control mice. Differentially expressed genes were identified using spots clustered by anatomical structures. Gene signatures of activation of microglia and astrocytes were calculated and mapped on the spatially resolved transcriptomic data. We identified early alterations in the white matter (WM) of the AD model before the definite accumulation of amyloid plaques in the gray matter (GM). Changes in the early stage of the disease involved primarily glial cell activation in the WM, whereas the changes in the later stage of pathology were prominent in the GM. We confirmed that disease-associated microglia (DAM) and astrocyte (DAA) signatures also showed initial changes in WM and that activation spreads to GM. Trajectory inference using microglial gene sets revealed the subdivision of DAMs with different spatial patterns. Taken together, these results help to understand the spatiotemporal changes associated with reactive glial cells as a major pathophysiological characteristic of AD. The heterogeneous spatial molecular changes apply to identifying diagnostic and therapeutic targets caused by amyloid accumulation in AD.

DNA methylation at birth and fine motor ability in childhood: an epigenome-wide association study with replication

Lower fine motor performance in childhood has been associated with poorer cognitive development and neurodevelopmental conditions such as autism spectrum disorder, yet, biological underpinnings remain unclear. DNA methylation (DNAm), an essential process for healthy neurodevelopment, is a key molecular system of interest. In this study, we conducted the first epigenome-wide association study of neonatal DNAm with childhood fine motor ability and further examined the replicability of epigenetic markers in an independent cohort. The discovery study was embedded in Generation R, a large population-based prospective cohort, including a subsample of 924 ~ 1026 European-ancestry singletons with available data on DNAm in cord blood and fine motor ability at a mean (SD) age of 9.8 (0.4) years. Fine motor ability was measured using a finger-tapping test (3 subtests including left-, right-hand and bimanual), one of the most frequently used neuropsychological instruments of fine motor function. The replication study comprised 326 children with a mean (SD) age of 6.8 (0.4) years from an independent cohort, the INfancia Medio Ambiente (INMA) study. Four CpG sites at birth were prospectively associated with childhood fine motor ability after genome-wide correction. Of these, one CpG (cg07783800 in GNG4) was replicated in INMA, showing that lower levels of methylation at this site were associated with lower fine motor performance in both cohorts. GNG4 is highly expressed in the brain and has been implicated in cognitive decline. Our findings support a prospective, reproducible association between DNAm at birth and fine motor ability in childhood, pointing to GNG4 methylation at birth as a potential biomarker of fine motor ability.

Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions

The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.

Age-Dependent Sex Differences in Perineuronal Nets in an APP Mouse Model of Alzheimer's Disease Are Brain Region-Specific

Alzheimer's disease (AD) is the most common form of dementia, which disproportionately affects women. AD symptoms include progressive memory loss associated with amyloid-β (Aβ) plaques and dismantled synaptic mechanisms. Perineuronal nets (PNNs) are important components of the extracellular matrix with a critical role in synaptic stabilisation and have been shown to be influenced by microglia, which enter an activated state during AD. This study aimed to investigate whether sex differences affected the density of PNNs alongside the labelling of microglia and Aβ plaques density.We performed neurochemistry experiments using acute brain slices from both sexes of the APPNL-F/NL-F mouse model of AD, aged-matched (2-5 and 12-16 months) to wild-type mice, combined with a weighted gene co-expression network analysis (WGCNA). The lateral entorhinal cortex (LEC) and hippocampal CA1, which are vulnerable during early AD pathology, were investigated and compared to the presubiculum (PRS), a region unscathed by AD pathology. The highest density of PNNs was found in the LEC and PRS regions of aged APPNL-F/NL-F mice with a region-specific sex differences. Analysis of the CA1 region using multiplex-fluorescent images from aged APPNL-F/NL-F mice showed regions of dense Aβ plaques near clusters of CD68, indicative of activated microglia and PNNs. This was consistent with the results of WGCNA performed on normalised data on microglial cells isolated from age-matched, late-stage male and female wild-type and APP knock-in mice, which revealed one microglial module that showed differential expression associated with tissue, age, genotype, and sex, which showed enrichment for fc-receptor-mediated phagocytosis. Our data are consistent with the hypothesis that sex-related differences contribute to a disrupted interaction between PNNs and microglia in specific brain regions associated with AD pathogenesis.

Transcriptomics Meta-Analysis Reveals Phagosome and Innate Immune System Dysfunction as Potential Mechanisms in the Cortex of Alzheimer's Disease Mouse Strains

Immune-related pathways can affect the immune system directly, such as the chemokine signaling pathway, or indirectly, such as the phagosome pathway. Alzheimer's disease (AD) is reportedly associated with several immune-related pathways. However, exploring its underlying mechanism is challenging in animal studies because AD mouse strains differentially express immune-related pathway characteristics. To overcome this problem, we performed a meta-analysis to identify significant and consistent immune-related AD pathways that are expressed in different AD mouse strains. Next-generation RNA sequencing (RNA-seq) and microarray datasets for the cortex of AD mice from different strains such as APP/PSEN1, APP/PS2, 3xTg, TREM, and 5xFAD were collected from the NCBI GEO database. Each dataset's quality control and normalization were already processed from each original study source using various methods depending on the high-throughput analysis platform (FastQC, median of ratios, RMA, between array normalization). Datasets were analyzed using DESeq2 for RNA-seq and GEO2R for microarray to identify differentially expressed (DE) genes. Significantly DE genes were meta-analyzed using Stouffer's method, with significant genes further analyzed for functional enrichment. Ten datasets representing 20 conditions were obtained from the NCBI GEO database, comprising 116 control and 120 AD samples. The DE analysis identified 284 significant DE genes. The meta-analysis identified three significantly enriched immune-related AD pathways: phagosome, the complement and coagulation cascade, and chemokine signaling. Phagosomes-related genes correlated with complement and immune system. Meanwhile, phagosomes and chemokine signaling genes overlapped with B cells receptors pathway genes indicating potential correlation between phagosome, chemokines, and adaptive immune system as well. The transcriptomic meta-analysis showed that AD is associated with immune-related pathways in the brain's cortex through the phagosome, complement and coagulation cascade, and chemokine signaling pathways. Interestingly, phagosome and chemokine signaling pathways had potential correlation with B cells receptors pathway.

Anxiety adds the risk of cognitive progression and is associated with axon/synapse degeneration among cognitively unimpaired older adults

BACKGROUND

Mental symptoms have been shown to be associated with dementia. As the most common neuropsychiatric disorder, it is unclear whether and why anxiety increases the risk of cognitive progression in elderly.

METHODS

The aim of this study was to investigate the longitudinal effects of anxiety on cognitive impairment in non-dementia elderly and to explore the underlying biological processes using multi-omics including microarray-based transcriptomics, mass spectrometry-based proteomics and metabolomics, cerebrospinal fluid (CSF) biochemical markers, and brain diffusion tensor imaging (DTI). The Alzheimer's Disease Neuroimaging Initiative (ADNI), Chinese Longitudinal Healthy Longevity Survey (CLHLS) and Shanghai Mental Health Centre (SMHC) cohorts were included.

FINDINGS

Anxiety was found to increase the risk of subsequent cognitive progression in the ADNI, and a similar result was observed in the CLHLS cohort. Enrichment analysis indicated activated axon/synapse pathways and suppressed mitochondrial pathways in anxiety, the former confirmed by deviations in frontolimbic tract morphology and altered levels of axon/synapse markers, and the latter supported by decreased levels of carnitine metabolites. Mediation analysis revealed that anxiety's effect on the longitudinal cognition was mediated by brain tau burden. Correlations of mitochondria-related expressed genes with axon/synapse proteins, carnitine metabolites, and cognitive changes were found.

INTERPRETATION

This study provides cross-validated epidemiological and biological evidence that anxiety is a risk factor for cognitive progression in non-dementia elderly, and that axon/synapse damage in the context of energy metabolism imbalance may contribute to this phenomenon.

FUNDING

The National Natural Science Foundation of China (82271607, 81971682, and 81830059) for data analysis and data collection.

Identification and validation of novel signature associated with hepatocellular carcinoma prognosis using Single-cell and WGCNA analysis

Background: Hepatocellular carcinoma is a rapidly advancing malignancy with a poor prognosis. Therefore, further research is needed on its potential pathogenesis and therapeutic targets. Methods: In this study, the relevant datasets were downloaded from the TCGA database and the key modules were identified using WGCNA in the necroptosis-related gene set, while single-cell datasets were scored using the necroptosis gene set. Differential genes in the high- and low-expression groups were determined using the WGCNA module genes as intersection sets to identify key genes involved in necroptosis in liver cancer. Then, prognostic models were constructed using LASSO COX regression followed by multi-faceted validation. Finally, model genes were found to be correlated with key proteins of the necroptosis pathway and used to identify the most relevant genes, followed by their experimental validation. Subsequently, on the basis of the analysis results, the most relevant SFPQ was selected for cell-level verification. Results: We constructed a prognosis model that included five necroptosis-related genes (EHD1, RAC1, SFPQ, DAB2 and PABPC4) to predict the prognosis and survival of HCC patients. The results showed that the prognosis was more unfavorable in the high-risk group compared to the low-risk group, which was corroborated using ROC curves and risk factor plots. In addition, we further checked the differential genes using GO and KEGG analyses and found that they were predominantly enriched in the neuroactive ligand-receptor interaction pathway. The results of the GSVA analysis demonstrated that the high-risk group was mainly enriched in DNA replication, regulation of the mitotic cycle, and regulation of various cancer pathways, while the low-risk group was predominantly enriched in the metabolism of drugs and xenobiotics using cytochrome P450. SFPQ was found to be the main gene that affects the prognosis and SFPQ expression was positively correlated with the expression of RIPK1, RIPK3 and MLKL. Furthermore, the suppression of SFPQ could inhibit hyper-malignant phenotype HCC cells, while the WB results showed that inhibition of SFPQ expression also resulted in lower expression of necroptosis proteins, compared to the sh-NC group. Conclusions: Our prognostic model could accurately predict the prognosis of patients with HCC to further identify novel molecular candidates and interventions that can be used as alternative methods of treatment for HCC.

Accelerated aging-related transcriptome alterations in neurovascular unit cells in the brain of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia with no effective therapies. Aging is a dominant risk factor for AD. The neurovascular unit (NVU) plays an important role in maintaining homeostasis of the brain microenvironment. The accelerated aging of NVU cells may directly impair NVU function and contribute to AD pathogenesis. However, the expression patterns of aging-related genes (AGs) in NVU cells of AD remain unclear. In this study, we performed single-nucleus transcriptome analysis of 61,768 nuclei from prefrontal cortical samples of patients with AD and normal control (NC) subjects. Eight main cell types were identified, including astrocytes, microglia, excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, pericytes, and endothelial cells. Transcriptomic analysis identified the expression patterns of AGs in NVU cells of AD. Gene set enrichment analysis confirmed the key aging-associated cellular pathways enriched in microglia and oligodendrocytes. These aging-related transcriptomic changes in NVU were cross-validated using bulk transcriptome data. The least absolute shrinkage and selection operator regression method was used to select the crucial AGs most associated with AD: IGF1R, MXI1, RB1, PPARA, NFE2L2, STAT5B, FOS, PRKCD, YWHAZ, HTT, MAPK9, HSPA9, SDHC, PRKDC, and PDPK1. This 15-gene model performed well in discriminating AD from NC samples. Among them, IGF1R, MXI1, PPARA, YWHAZ, and MAPK9 strongly correlated with pathologic progression in AD, were identified as critical regulators of AD. Although most AGs showed similar trends of expression changes in different types of NVU cells in AD, certain AGs were expressed in a cell-specific manner. Our comprehensive analysis of brain NVU from patients with AD reveals previously unknown molecular changes associated with aging that may underlie the functional dysregulation of NVU, providing important insights for exploring potential cell-specific therapeutic targets to restore brain homeostasis in AD.

Intestinal Flora Balance Therapy Based on Probiotic Support Improves Cognitive Function and Symptoms in Patients with Alzheimer's Disease: A Systematic Review and Meta-analysis

Objective

The clinical value of intestinal flora balance therapy based on probiotic support in improving cognitive function and symptoms of patients with Alzheimer's disease was to systematically evaluate, so as to provide evidence-based medicine basis for the promotion and use of this therapy.

Methods

The randomized controlled trials (RCTs) were searched for the improvement of cognitive function and symptoms of patients with Alzheimer's disease by intestinal flora balance therapy supported mainly by probiotics in PubMed, EMBASE, ScienceDirect, Cochrane Library, China Knowledge Network Database (CNKI), China VIP database, Wanfang database, and China Biomedical Literature Database (CBM) online database (RCT). Data were extracted independently by two researchers, and the literature was assessed for risk of bias according to the Cochrane Handbook 5.1.0 criteria. The data were meta-analyzed using RevMan 5.4 statistical software.

Results

Finally, 5 randomized controlled trials were included, with a total sample size of 386 cases. The results of meta-analysis showed that Chi2 = 13.14, df = 2, P = 0.001, and I 2 = 85% showed significant heterogeneity in the inclusion of the study data. Probiotic-supported intestinal microflora balance therapy improves cognitive function in patients with Alzheimer's disease. Through meta-analysis of transient memory scores, it is concluded that intestinal flora balance therapy based on probiotic support can improve transient memory in patients with Alzheimer's disease. Meta-analysis of ADAS-COG score showed that intestinal flora balance therapy supported by probiotics could improve the cognitive function of patients with Alzheimer's disease. The ADL score was analyzed by meta, and the heterogeneity test result was Chi2 = 0.79, df = 1, P = 0.37 > 0.05, and I 2 = 0%, indicating that the intestinal flora balance therapy supported by probiotics can improve the ability of daily living of patients with Alzheimer's disease.

Conclusion

Intestinal flora balance therapy based on probiotic support can effectively improve cognitive function, instantaneous memory, and ability of daily life in patients with Alzheimer's disease. However, more studies and long-term follow-up studies with higher methodological quality are needed to further verify.

Network Biology Approaches to Uncover Therapeutic Targets Associated with Molecular Signaling Pathways from circRNA in Postoperative Cognitive Dysfunction Pathogenesis

Postoperative cognitive dysfunction (POCD) is a cognitive deterioration and dementia that arise after a surgical procedure, affecting up to 40% of surgery patients over the age of 60. The precise etiology and molecular mechanisms underlying POCD remain uncovered. These reasons led us to employ integrative bioinformatics and machine learning methodologies to identify several biological signaling pathways involved and molecular signatures to better understand the pathophysiology of POCD. A total of 223 differentially expressed genes (DEGs) comprising 156 upregulated and 67 downregulated genes were identified from the circRNA microarray dataset by comparing POCD and non-POCD samples. Gene ontology (GO) analyses of DEGs were significantly involved in neurogenesis, autophagy regulation, translation in the postsynapse, modulating synaptic transmission, regulation of the cellular catabolic process, macromolecule modification, and chromatin remodeling. Pathway enrichment analysis indicated some key molecular pathways, including mTOR signaling pathway, AKT phosphorylation of cytosolic targets, MAPK and NF-κB signaling pathway, PI3K/AKT signaling pathway, nitric oxide signaling pathway, chaperones that modulate interferon signaling pathway, apoptosis signaling pathway, VEGF signaling pathway, cellular senescence, RANKL/RARK signaling pathway, and AGE/RAGE pathway. Furthermore, seven hub genes were identified from the PPI network and also determined transcription factors and protein kinases. Finally, we identified a new predictive drug for the treatment of SCZ using the LINCS L1000, GCP, and P100 databases. Together, our results bring a new era of the pathogenesis of a deeper understanding of POCD, identified novel therapeutic targets, and predicted drug inhibitors in POCD.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

Screening of Crucial Differentially-Methylated/Expressed Genes for Alzheimer's Disease

Background: We aimed to make an integrated analysis of published transcriptome and DNA methylation dataset to ascertain the key differentially methylated and differentially expressed genes for Alzherimer's disease (AD). Methods: Two gene expression microarrays and 1 gene methylation microarray were downloaded for identification of differentially expressed genes and differentially methylated genes. Then, we used various biological information databases to annotate the functions of the differentially-methylated/expressed genes, and screen out key genes and important signaling pathways. Finally, we validate the differentially-methylated/expressed genes in the additional online datasets and in blood from AD patients.Results: A total of 8 hub hypomethylated-high expression genes were obtained, including Rac family small GTPase 2, FGR proto-oncogene, Src family tyrosine kinase, LYN proto-oncogene, Src family tyrosine kinase, protein kinase C delta, myosin IF, integrin subunit alpha 5, semaphorin 4D, and growth arrest specific protein 7. Some enriched signaling pathways of hypomethylated-high expression genes were identified, including regulation of actin cytoskeleton, chemokine signaling pathway, Fc gamma R-mediated phagocytosis, and axon guidance. Conclusion: Differentially-methylated/expressed genes are likely to be associated with AD.

MicroRNA-Target Interaction Regulatory Network in Alzheimer's Disease

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer's disease

BACKGROUND

The interaction between the brain and periphery might play a crucial role in the development of Alzheimer's disease (AD).

METHODS

Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers.

RESULTS

A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-β deposition.

CONCLUSION

An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD.

BMI1 is associated with CS8F amyloid-β and rates of cognitive decline in Alzheimer's disease

Background

Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer’s disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-β.

Methods

We used genetic, longitudinal cognition, and cerebrospinal fluid (CSF) biomarker data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). First, we performed a gene-based association analysis of common single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) > 5%) located within ± 20 kb of the gene boundary of BMI1, an optimal width for including potential regulatory SNPs in the 5′ and 3′ untranslated regions (UTR) of BMI1, with CSF Aβ_1-42 levels. Second, we performed cross-sectional and longitudinal association analyses of SNPs in BMI1 with cognitive performance using linear and mixed-effects models. We replicated association of SNPs in BMI1 with cognitive performance in an independent cohort (N=1084), Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP).

Results

Gene-based genetic association analysis showed that BMI1 was significantly associated with CSF Aβ_1-42 levels after adjusting for multiple testing using permutation (permutation-corrected p value=0.005). rs17415557 in BMI1 showed the most significant association with CSF Aβ_1-42 levels. Participants with minor alleles of rs17415557 have increased CSF Aβ_1-42 levels compared to those with no minor alleles. Further analysis identified and replicated the minor allele of rs17415557 as being significantly associated with slower cognitive decline rates in AD.

Conclusions

Our findings provide fundamental evidence that BMI1 rs17415557 may serve as a protective mechanism related to AD pathogenesis, which supports the results of previous studies linking BMI1 to protection against AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00906-4.

The plasma peptides of Alzheimer's disease

Background

A practical strategy to discover proteins specific to Alzheimer’s dementia (AD) may be to compare the plasma peptides and proteins from patients with dementia to normal controls and patients with neurological conditions like multiple sclerosis or other diseases. The aim was a proof of principle for a method to discover proteins and/or peptides of plasma that show greater observation frequency and/or precursor intensity in AD. The endogenous tryptic peptides of Alzheimer’s were compared to normals, multiple sclerosis, ovarian cancer, breast cancer, female normal, sepsis, ICU Control, heart attack, along with their institution-matched controls, and normal samples collected directly onto ice.

Methods

Endogenous tryptic peptides were extracted from blinded, individual AD and control EDTA plasma samples in a step gradient of acetonitrile for random and independent sampling by LC–ESI–MS/MS with a set of robust and sensitive linear quadrupole ion traps. The MS/MS spectra were fit to fully tryptic peptides within proteins identified using the X!TANDEM algorithm. Observation frequency of the identified proteins was counted using SEQUEST algorithm. The proteins with apparently increased observation frequency in AD versus AD Control were revealed graphically and subsequently tested by Chi Square analysis. The proteins specific to AD plasma by Chi Square with FDR correction were analyzed by the STRING algorithm. The average protein or peptide log₁₀ precursor intensity was compared across disease and control treatments by ANOVA in the R statistical system.

Results

Peptides and/or phosphopeptides of common plasma proteins such as complement C2, C7, and C1QBP among others showed increased observation frequency by Chi Square and/or precursor intensity in AD. Cellular gene symbols with large Chi Square values (χ2 ≥ 25, p ≤ 0.001) from tryptic peptides included KIF12, DISC1, OR8B12, ZC3H12A, TNF, TBC1D8B, GALNT3, EME2, CD1B, BAG1, CPSF2, MMP15, DNAJC2, PHACTR4, OR8B3, GCK, EXOSC7, HMGA1 and NT5C3A among others. Similarly, increased frequency of tryptic phosphopeptides were observed from MOK, SMIM19, NXNL1, SLC24A2, Nbla10317, AHRR, C10orf90, MAEA, SRSF8, TBATA, TNIK, UBE2G1, PDE4C, PCGF2, KIR3DP1, TJP2, CPNE8, and NGF amongst others. STRING analysis showed an increase in cytoplasmic proteins and proteins associated with alternate splicing, exocytosis of luminal proteins, and proteins involved in the regulation of the cell cycle, mitochondrial functions or metabolism and apoptosis. Increases in mean precursor intensity of peptides from common plasma proteins such as DISC1, EXOSC5, UBE2G1, SMIM19, NXNL1, PANO, EIF4G1, KIR3DP1, MED25, MGRN1, OR8B3, MGC24039, POLR1A, SYTL4, RNF111, IREB2, ANKMY2, SGKL, SLC25A5, CHMP3 among others were associated with AD. Tryptic peptides from the highly conserved C-terminus of DISC1 within the sequence MPGGGPQGAPAAAGGGGVSHRAGSRDCLPPAACFR and ARQCGLDSR showed a higher frequency and highest intensity in AD compared to all other disease and controls.

Conclusion

Proteins apparently expressed in the brain that were directly related to Alzheimer’s including Nerve Growth Factor (NFG), Sphingomyelin Phosphodiesterase, Disrupted in Schizophrenia 1 (DISC1), the cell death regulator retinitis pigmentosa (NXNl1) that governs the loss of nerve cells in the retina and the cell death regulator ZC3H12A showed much higher observation frequency in AD plasma vs the matched control. There was a striking agreement between the proteins known to be mutated or dis-regulated in the brains of AD patients with the proteins observed in the plasma of AD patients from endogenous peptides including NBN, BAG1, NOX1, PDCD5, SGK3, UBE2G1, SMPD3 neuronal proteins associated with synapse function such as KSYTL4, VTI1B and brain specific proteins such as TBATA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09320-2.

Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays

Aim: In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patients. Materials & methods: Differentially expressed genes associated with PD were screened from the GSE99039 dataset using weighted gene co-expression network analysis, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, gene-gene interaction network analysis and receiver operator characteristics analysis. Results: We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, CS, PRKCD, RHOG and VAMP2 directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. Conclusion: A DFG-analysis identified a four-gene panel (CS, PRKCD, RHOG, VAMP2) as a potential diagnostic predictor to diagnose PD.

Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease

OBJECTIVE

To determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD).

METHODS

Blood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis.

RESULTS

The TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, CD33 and PILRA were significantly upregulated, and TRAPPC6A was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed.

CONCLUSIONS

The blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, CD33 and PILRA were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD.

CLASSIFICATION OF EVIDENCE

The study is rated Class III because of the case control design and the risk of spectrum bias.