Genome-wide haplotype association study identify TNFRSF1A, CASP7, LRP1B, CDH1 and TG genes associated with Alzheimer's disease in Caribbean Hispanic individuals

Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies.

Integrated transcriptomics reveals the brain and blood biomarkers in Alzheimer's disease

BACKGROUND

The systematic molecular associations between the peripheral blood cells and brain in Alzheimer's disease (AD) remains unclear, which hinders our understanding of AD pathological mechanisms and the exploration of new diagnostic biomarkers.

METHODS

Here, we performed an integrated analysis of the brain and peripheral blood cells transcriptomics to establish peripheral biomarkers of AD. By employing multiple statistical analyses plus machine learning, we identified and validated multiple regulated central and peripheral network in patients with AD.

RESULTS

By bioinformatics analysis, a total of 243 genes were differentially expressed in the central and peripheral systems, mainly enriched in three modules: immune response, glucose metabolism and lysosome. In addition, lysosome related gene ATP6V1E1 and immune response related genes (IL2RG, OSM, EVI2B TNFRSF1A, CXCR4, STAT5A) were significantly correlated with Aβ or Tau pathology. Finally, receiver operating characteristic (ROC) analysis revealed that ATP6V1E1 showed high-diagnostic potential for AD.

CONCLUSION

Taken together, our data identified the main pathological pathways in AD progression, particularly the systemic dysregulation of the immune response, and provided peripheral biomarkers for AD diagnosis.

Topical Application of Baicalin Combined with Echinacoside Ameliorates Psoriatic Skin Lesions by Suppressing the Inflammation-Related TNF Signaling Pathway and the Angiogenesis-Related VEGF Signaling Pathway

Baicalin (BAI), the main active component of Scutellaria baicalensis, has significant anti-inflammatory and antibacterial effects. Echinacoside (ECH), an active component from Echinacea purpurea, has significant antiangiogenesis and antioxidant effects. In previous studies, BAI or ECH has been used for some skin inflammation problems by topical treatment. Psoriasis (PSO) is a common inflammatory skin disease with typical features such as excessive inflammatory response and vascular proliferation in skin lesions. Because of the anti-inflammatory effect of BAI and the antiangiogenic activity of ECH, it is proposed that the combination of BAI and ECH can ameliorate psoriatic skin lesions better than a single component. This study aims to explore the effects and potential mechanisms of BAI combined with ECH on imiquimod (IMQ)-induced psoriatic skin lesions by topical treatment. Transcriptome analysis first showed that the TNF signaling pathway and the VEGF signaling pathway were significantly enriched in IMQ-induced psoriatic skin lesions. Topical application of BAI combined with ECH could ameliorate IMQ-induced skin lesions in mice, especially the better effects of B2-E1 (BAI/ECH = 2:1). Network pharmacology analysis and molecular docking indicated that BAI-treated PSO on the skin by regulating the TNF signaling pathway, and ECH treated PSO on the skin by regulating the VEGF signaling pathway. Meanwhile, the ELISA test and the qPCR assay showed that BAI combined with ECH could inhibit the expression of key cytokines and genes related to the TNF signaling pathway and the VEGF signaling pathway. Zebrafish experiments demonstrated the anti-inflammatory and antiangiogenic effects of BAI combined with ECH and revealed the potential mechanisms associated with regulating the inflammation-related TNF signaling pathway and the angiogenesis-related VEGF signaling pathway. This suggested that BAI combined with ECH may be a promising topical agent to ameliorate psoriatic skin lesions in the future.

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease

Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.

Bayesian model and selection signature analyses reveal risk factors for canine atopic dermatitis

Canine atopic dermatitis is an inflammatory skin disease with clinical similarities to human atopic dermatitis. Several dog breeds are at increased risk for developing this disease but previous genetic associations are poorly defined. To identify additional genetic risk factors for canine atopic dermatitis, we here apply a Bayesian mixture model adapted for mapping complex traits and a cross-population extended haplotype test to search for disease-associated loci and selective sweeps in four dog breeds at risk for atopic dermatitis. We define 15 associated loci and eight candidate regions under selection by comparing cases with controls. One associated locus is syntenic to the major genetic risk locus (Filaggrin locus) in human atopic dermatitis. One selection signal in common type Labrador retriever cases positions across the TBC1D1 gene (body weight) and one signal of selection in working type German shepherd controls overlaps the LRP1B gene (brain), near the KYNU gene (psoriasis). In conclusion, we identify candidate genes, including genes belonging to the same biological pathways across multiple loci, with potential relevance to the pathogenesis of canine atopic dermatitis. The results show genetic similarities between dog and human atopic dermatitis, and future across-species genetic comparisons are hereby further motivated.

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10⁻⁶) and cell adhesion molecules (corrected p-value = 5.44 × 10⁻⁴). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Genetic associations with healthy ageing among Chinese adults

The genetic basis of overall healthy ageing, especially among the East-Asian population is understudied. We conducted a genome-wide association study among 1618 Singapore Chinese elderly participants (65 years or older) ascertained to have aged healthily and compared their genome-wide genotypes to 6221 participants who did not age healthily, after a 20-year follow-up. Two genetic variants were identified (PMeta < 2.59 × 10-8) to be associated with healthy aging, including the LRP1B locus previously associated in long-lived individuals without cognitive decline. Our study sheds additional insights on the genetic basis of healthy ageing.

Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer's disease

The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.

Proteins in the pathway from high red blood cell width distribution to all-cause mortality

Background

The pathophysiological mechanisms underlying the association between red blood cell distribution width (RDW) and all-cause mortality are unknown. We conducted a data-driven discovery investigation to identify plasma proteins that mediate the association between RDW and time to death in community-dwelling adults.

Methods

At baseline, 962 adults (women, 54·4%; age range, 21–98 years) participated in the InCHIANTI, “Aging in the Chianti Area” study, and proteomics data were generated from their plasma specimens. Of these, 623 participants had proteomics data available at the 9-year follow-up. For each visit, a total of 1301 plasma proteins were measured using SOMAscan technology. Complete data on vital status were available up to the 15-year follow-up period. Protein-specific exponential distribution accelerated failure time, and linear regression analyses adjusted for possible covariates were used for mortality and mediation analyses, respectively (survival data analysis).

Findings

Baseline values of EGFR, GHR, NTRK3, SOD2, KLRF1, THBS2, TIMP1, IGFBP2, C9, APOB, and LRP1B mediated the association between baseline RDW and all-cause mortality. Changes in IGFBP2 and C7 over 9 years mediated the association between changes in RDW and 6-year all-cause mortality.

Interpretation

Cellular senescence may contribute to the association between RDW and mortality.

Funding

This study was funded by grants from the National Institutes of Health (NIH) and the National Institute on Aging (NIA) contract and was supported by the Intramural Research Program of the NIA, NIH. The InCHIANTI study was supported as a ‘targeted project’ by the Italian Ministry of Health and in part by the U.S. NIA.

Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease

Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.

Admixture mapping reveals the association between Native American ancestry at 3q13.11 and reduced risk of Alzheimer's disease in Caribbean Hispanics

BACKGROUND

Genetic studies have primarily been conducted in European ancestry populations, identifying dozens of loci associated with late-onset Alzheimer's disease (AD). However, much of AD's heritability remains unexplained; as the prevalence of AD varies across populations, the genetic architecture of the disease may also vary by population with the presence of novel variants or loci.

METHODS

We conducted genome-wide analyses of AD in a sample of 2565 Caribbean Hispanics to better understand the genetic contribution to AD in this population. Statistical analysis included both admixture mapping and association testing. Evidence for differential gene expression within regions of interest was collected from independent transcriptomic studies comparing AD cases and controls in samples with primarily European ancestry.

RESULTS

Our genome-wide association study of AD identified no loci reaching genome-wide significance. However, a genome-wide admixture mapping analysis that tests for association between a haplotype's ancestral origin and AD status detected a genome-wide significant association with chromosome 3q13.11 (103.7-107.7Mb, P = 8.76E-07), driven by a protective effect conferred by the Native American ancestry (OR = 0.58, 95%CI = 0.47-0.73). Within this region, two variants were significantly associated with AD after accounting for the number of independent tests (rs12494162, P = 2.33E-06; rs1731642, P = 6.36E-05). The significant admixture mapping signal is composed of 15 haplotype blocks spanning 5 protein-coding genes (ALCAM, BBX, CBLB, CCDC54, CD47) and four brain-derived topologically associated domains, and includes markers significantly associated with the expression of ALCAM, BBX, CBLB, and CD47 in the brain. ALCAM and BBX were also significantly differentially expressed in the brain between AD cases and controls with European ancestry.

CONCLUSION

These results provide multiethnic evidence for a relationship between AD and multiple genes at 3q13.11 and illustrate the utility of leveraging genetic ancestry diversity via admixture mapping for new insights into AD.

How understudied populations have contributed to our understanding of Alzheimer's disease genetics

The majority of genome-wide association studies have been conducted using samples with a broadly European genetic background. As a field, we acknowledge this limitation and the need to increase the diversity of populations studied. A major challenge when designing and conducting such studies is to assimilate large samples sizes so that we attain enough statistical power to detect variants associated with disease, particularly when trying to identify variants with low and rare minor allele frequencies. In this review, we aimed to illustrate the benefits to genetic characterization of Alzheimer's disease, in researching currently understudied populations. This is important for both fair representation of world populations and the translatability of findings. To that end, we conducted a literature search to understand the contributions of studies, on different populations, to Alzheimer's disease genetics. Using both PubMed and Alzforum Mutation Database, we systematically quantified the number of studies reporting variants in known disease-causing genes, in a worldwide manner, and discuss the contributions of research in understudied populations to the identification of novel genetic factors in this disease. Additionally, we compared the effects of genome-wide significant single nucleotide polymorphisms across populations by focusing on loci that show different association profiles between populations (a key example being APOE). Reports of variants in APP, PSEN1 and PSEN2 can initially determine whether patients from a country have been studied for Alzheimer's disease genetics. Most genome-wide significant associations in non-Hispanic white genome-wide association studies do not reach genome-wide significance in such studies of other populations, with some suggesting an opposite effect direction; this is likely due to much smaller sample sizes attained. There are, however, genome-wide significant associations first identified in understudied populations which have yet to be replicated. Familial studies in understudied populations have identified rare, high effect variants, which have been replicated in other populations. This work functions to both highlight how understudied populations have furthered our understanding of Alzheimer's disease genetics, and to help us gauge our progress in understanding the genetic architecture of this disease in all populations.

Identification of dysregulated genes and pathways of different brain regions in Alzheimer's disease

Background: Alzheimer's disease (AD) is a degenerative neurologic disease. The study aimed to identify the key differentially expressed genes (DEGs) and pathways in AD pathogenesis and obtain potential biomarkers in AD diagnosis.Methods: An integrated analysis of publicly available Gene Expression Omnibus datasets of AD was performed. DEGs in hippocampus tissue (HIP), temporal gyrus tissue (TG), frontal gyrus tissue (FG) and whole blood (WB) were identified. Bioinformatics analyses were used to insight into the functions of DEGs. The expression levels of candidate DEGs were preliminarily validated in GSE1297. The discriminatory ability of candidate DEGs in WB samples of AD patients and healthy individuals was evaluated in GSE63060 and GSE63061 datasets through receiver operating characteristic (ROC) analysis.Results: The DEGs in HIP, TG and FG tissues of AD were identified. Functions involved in regulation of apoptotic process, apoptotic process and cell death were significantly enriched from DEGs in AD. MAPK signaling pathway and Wnt signaling pathway were significantly enriched. YAP1, MAPK9 and GJA1 were the hub proteins in protein-protein interaction network in HIP, TG and FG. The expression levels of 14 DEGs in GSE1297 dataset were consistent with our integrated analysis. Moreover, 7 out of 14 DEGs had the diagnostic value in distinguishing AD patients from healthy controls in both GSE630060 and GSE630061 datasets.Conclusion: The DEGs including YAP1, MAPK1, GJA1 and pathways including MAPK signaling pathway and Wnt signaling pathway may be related to AD progression. RAD51C, SAFB2, SSH3 and TXNDC9 might be potential biomarkers in AD diagnosis.

Exploring effective approaches for haplotype block phasing

BACKGROUND

Knowledge of phase, the specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations. One approach for detecting such mutations is through phased haplotype association analysis. While the accuracy of methods for phasing genotype data has been widely explored, there has been little attention given to phasing accuracy at haplotype block scale. Understanding the combined impact of the accuracy of phasing tool and the method used to determine haplotype blocks on the error rate within the determined blocks is essential to conduct accurate haplotype analyses.

RESULTS

We present a systematic study exploring the relationship between seven widely used phasing methods and two common methods for determining haplotype blocks. The evaluation focuses on the number of haplotype blocks that are incorrectly phased. Insights from these results are used to develop a haplotype estimator based on a consensus of three tools. The consensus estimator achieved the most accurate phasing in all applied tests. Individually, EAGLE2, BEAGLE and SHAPEIT2 alternate in being the best performing tool in different scenarios. Determining haplotype blocks based on linkage disequilibrium leads to more correctly phased blocks compared to a sliding window approach. We find that there is little difference between phasing sections of a genome (e.g. a gene) compared to phasing entire chromosomes. Finally, we show that the location of phasing error varies when the tools are applied to the same data several times, a finding that could be important for downstream analyses.

CONCLUSIONS

The choice of phasing and block determination algorithms and their interaction impacts the accuracy of phased haplotype blocks. This work provides guidance and evidence for the different design choices needed for analyses using haplotype blocks. The study highlights a number of issues that may have limited the replicability of previous haplotype analysis.

Genome-wide selection signatures analysis of litter size in Dazu black goats using single-nucleotide polymorphism

Litter size is considered to be the most important index for estimating domestic animal productivity. The number of indigenous goats in China with higher litter sizes than those of commercial breeds in other countries may be helpful for accelerating genetic improvements in goat breeding. We performed a genome-wide selective sweep analysis of 31 Dazu black goats with extreme standard deviation in litter size within the third fetus to identify significant genomic regions and candidate genes through different analyses. The analysis identified a total of 33,917,703 variants, including 32,262,179 SNPs and 1,655,524 indels. In addition, two novel candidate genes (LRP1B and GLRB), which are related to litter size, were obtained with π, Tajima's D, πA/πB, and F _ST at the individual level with a 95% threshold for each parameter. These two genes were annotated in five GO terms (localization, binding, macromolecular complex, membrane part, and membrane) and two pathways (long-term depression and neuroactive ligand-receptor interaction pathway). Regarding the result of linkage disequilibrium (LD) analysis, in LRP1B and GRID2, the high-yield Dazu black goats exhibit significantly different LD patterns from low-yield goats. Litter size variability has low heritability and is related to multiple complex factors found in domestic animals. Obtaining a clear explanation and significant signal by genome-wide selective sweep analysis with a small sample size is difficult. However, we investigated some candidate genes, particularly LRP1B and GLRB, which may provide useful information for further research.

LRP1B Polymorphisms Are Associated with Multiple Myeloma Risk in a Chinese Han Population

Multiple myeloma (MM) is an extremely complex plasma cell malignancy that is genetically heterogeneous. A recent Genome-wide association study (GWAS) indicated that variation at 2q22 (rs61070260) influences MM risk. This association has not been validated to date in a Chinese Han population. In this study, we evaluated the association between rs61070260 in LRP1B and MM risk in a Chinese Han population involving 739 MM patients and 592 healthy controls. Our results indicated that rs61070260 in LRP1B was significantly associated with MM susceptibility (P=3.937×10-37). Furthermore, the linkage disequilibrium (LD) analysis of rs61070260 revealed an LD block encompassing exons 26, 27 and 28 of the LRP1B gene, and a subsequent sequencing analysis identified three SNPs (rs762074421, rs756168629, rs113600691) in exons 26 and 28 of LRP1B. For the SNP rs756168629 in exon 26, a missense mutation which results in a transition from arginine to histidine at position 1661 of the LRP1B protein, has not been found in Chinese populations according to the Chinese Millionome Database and Genome Aggregation Database (EAS), and this mutation was predicted to be deleterious or damaging by SIFT and PolyPhen. These findings firmly establish the role of LRP1B in contributing to MM susceptibility. In addition, the identification of a rare coding mutation (p.R1661H) in LRP1B detected in MM individuals was suggested to be harmful to the encoded protein, which was characterized as a candidate tumour suppressor; thus, LRP1B is likely to be a disease-associated gene that is implicated in the development and progression of MM.

Early Life Stress and Epigenetics in Late-onset Alzheimer's Dementia: A Systematic Review

Involvement of life stress in Late-Onset Alzheimer's Disease (LOAD) has been evinced in longitudinal cohort epidemiological studies, and endocrinologic evidence suggests involvements of catecholamine and corticosteroid systems in LOAD. Early Life Stress (ELS) rodent models have successfully demonstrated sequelae of maternal separation resulting in LOAD-analogous pathology, thereby supporting a role of insulin receptor signalling pertaining to GSK-3beta facilitated tau hyper-phosphorylation and amyloidogenic processing. Discussed are relevant ELS studies, and findings from three mitogen-activated protein kinase pathways (JNK/SAPK pathway, ERK pathway, p38/MAPK pathway) relevant for mediating environmental stresses. Further considered were the roles of autophagy impairment, neuroinflammation, and brain insulin resistance. For the meta-analytic evaluation, 224 candidate gene loci were extracted from reviews of animal studies of LOAD pathophysiological mechanisms, of which 60 had no positive results in human LOAD association studies. These loci were combined with 89 gene loci confirmed as LOAD risk genes in previous GWAS and WES. Of the 313 risk gene loci evaluated, there were 35 human reports on epigenomic modifications in terms of methylation or histone acetylation. 64 microRNA gene regulation mechanisms were published for the compiled loci. Genomic association studies support close relations of both noradrenergic and glucocorticoid systems with LOAD. For HPA involvement, a CRHR1 haplotype with MAPT was described, but further association of only HSD11B1 with LOAD found; however, association of FKBP1 and NC3R1 polymorphisms was documented in support of stress influence to LOAD. In the brain insulin system, IGF2R, INSR, INSRR, and plasticity regulator ARC, were associated with LOAD. Pertaining to compromised myelin stability in LOAD, relevant associations were found for BIN1, RELN, SORL1, SORCS1, CNP, MAG, and MOG. Regarding epigenetic modifications, both methylation variability and de-acetylation were reported for LOAD. The majority of up-to-date epigenomic findings include reported modifications in the well-known LOAD core pathology loci MAPT, BACE1, APP (with FOS, EGR1), PSEN1, PSEN2, and highlight a central role of BDNF. Pertaining to ELS, relevant loci are FKBP5, EGR1, GSK3B; critical roles of inflammation are indicated by CRP, TNFA, NFKB1 modifications; for cholesterol biosynthesis, DHCR24; for myelin stability BIN1, SORL1, CNP; pertaining to (epi)genetic mechanisms, hTERT, MBD2, DNMT1, MTHFR2. Findings on gene regulation were accumulated for BACE1, MAPK signalling, TLR4, BDNF, insulin signalling, with most reports for miR-132 and miR-27. Unclear in epigenomic studies remains the role of noradrenergic signalling, previously demonstrated by neuropathological findings of childhood nucleus caeruleus degeneration for LOAD tauopathy.

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning

Background

Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.

Methods

We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome.

Results

We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale.

Conclusions

In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Electronic supplementary material

The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.

Functional Roles of the Interaction of APP and Lipoprotein Receptors

The biological fates of the key initiator of Alzheimer’s disease (AD), the amyloid precursor protein (APP), and a family of lipoprotein receptors, the low-density lipoprotein (LDL) receptor-related proteins (LRPs) and their molecular roles in the neurodegenerative disease process are inseparably interwoven. Not only does APP bind tightly to the extracellular domains (ECDs) of several members of the LRP group, their intracellular portions are also connected through scaffolds like the one established by FE65 proteins and through interactions with adaptor proteins such as X11/Mint and Dab1. Moreover, the ECDs of APP and LRPs share common ligands, most notably Reelin, a regulator of neuronal migration during embryonic development and modulator of synaptic transmission in the adult brain, and Agrin, another signaling protein which is essential for the formation and maintenance of the neuromuscular junction (NMJ) and which likely also has critical, though at this time less well defined, roles for the regulation of central synapses. Furthermore, the major independent risk factors for AD, Apolipoprotein (Apo) E and ApoJ/Clusterin, are lipoprotein ligands for LRPs. Receptors and ligands mutually influence their intracellular trafficking and thereby the functions and abilities of neurons and the blood-brain-barrier to turn over and remove the pathological product of APP, the amyloid-β peptide. This article will review and summarize the molecular mechanisms that are shared by APP and LRPs and discuss their relative contributions to AD.

Fragility Extraordinaire: Unsolved Mysteries of Chromosome Fragile Sites

Chromosome fragile sites are a fascinating cytogenetic phenomenon now widely implicated in a slew of human diseases ranging from neurological disorders to cancer. Yet, the paths leading to these revelations were far from direct, and the number of fragile sites that have been molecularly cloned with known disease-associated genes remains modest. Moreover, as more fragile sites were being discovered, research interests in some of the earliest discovered fragile sites ebbed away, leaving a number of unsolved mysteries in chromosome biology. In this review we attempt to recount some of the early discoveries of fragile sites and highlight those phenomena that have eluded intense scrutiny but remain extremely relevant in our understanding of the mechanisms of chromosome fragility. We then survey the literature for disease association for a comprehensive list of fragile sites. We also review recent studies addressing the underlying cause of chromosome fragility while highlighting some ongoing debates. We report an observed enrichment for R-loop forming sequences in fragile site-associated genes than genomic average. Finally, we will leave the reader with some lingering questions to provoke discussion and inspire further scientific inquiries.

A loss of function variant in CASP7 protects against Alzheimer's disease in homozygous APOE ε4 allele carriers

Background

Alzheimer’s disease (AD) represents the most common form of dementia in elder populations with approximately 30 million cases worldwide. Genome wide genotyping and sequencing studies have identified many genetic variants associated with late-onset Alzheimer’s disease (LOAD). While most of these variants are associated with increased risk of developing LOAD, only limited number of reports focused on variants that are protective against the disease.

Methods

Here we applied a novel approach to uncover protective alleles against AD by analyzing genetic and phenotypic data in Mount Sinai Biobank and Electronic Medical Record (EMR) databases.

Results

We discovered a likely loss-of-function small deletion variant in the caspase 7 (CASP7) gene associated with significantly reduced incidence of LOAD in carriers of the high-risk APOE ε4 allele. Further investigation of four independent cohorts of European ancestry revealed the protective effect of the CASP7 variant against AD is most significant in homozygous APOE ε4 allele carriers. Meta analysis of multiple datasets shows overall odds ratio = 0.45 (p = 0.004). Analysis of RNA sequencing derived gene expression data indicated the variant correlates with reduced caspase 7 expression in multiple brain tissues we examined.

Conclusions

Taken together, these results are consistent with the notion that caspase 7 plays a key role in microglial activation driving neuro-degeneration during AD pathogenesis, and may explain the underlying genetic mechanisms that anti-inflammatory interventions in AD show greater benefit in APOE ε4 carriers than non-carriers. Our findings inform potential novel therapeutic opportunities for AD and warrant further investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2725-z) contains supplementary material, which is available to authorized users.