Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer's disease

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

New Insights into the Genetics and Epigenetics of Aging Plasticity

Biological aging is characterized by irreversible cell cycle blockade, a decreased capacity for tissue regeneration, and an increased risk of age-related diseases and mortality. A variety of genetic and epigenetic factors regulate aging, including the abnormal expression of aging-related genes, increased DNA methylation levels, altered histone modifications, and unbalanced protein translation homeostasis. The epitranscriptome is also closely associated with aging. Aging is regulated by both genetic and epigenetic factors, with significant variability, heterogeneity, and plasticity. Understanding the complex genetic and epigenetic mechanisms of aging will aid the identification of aging-related markers, which may in turn aid the development of effective interventions against this process. This review summarizes the latest research in the field of aging from a genetic and epigenetic perspective. We analyze the relationships between aging-related genes, examine the possibility of reversing the aging process by altering epigenetic age.

A Structured Brain-wide and Genome-wide Association Study Using ADNI PET Images

A multi-stage variable selection method is introduced for detecting association signals in structured brain-wide and genome-wide association studies (brain-GWAS). Compared to conventional single-voxel-to-single-SNP approaches, our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain and the genome, respectively. It avoids large number of multiple comparisons while effectively controls the false discoveries. Validity of the proposed approach is demonstrated by both theoretical investigation and numerical simulations. We apply the proposed method to a brain-GWAS using ADNI PET imaging and genomic data. We confirm previously reported association signals and also find several novel SNPs and genes that either are associated with brain glucose metabolism or have their association significantly modified by Alzheimer's disease status.

DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex

Background

Epigenome-wide association studies (EWAS) based on human brain samples allow a deep and direct understanding of epigenetic dysregulation in Alzheimer’s disease (AD). However, strong variation of cell-type proportions across brain tissue samples represents a significant source of data noise. Here, we report the first EWAS based on sorted neuronal and non-neuronal (mostly glia) nuclei from postmortem human brain tissues.

Results

We show that cell sorting strongly enhances the robust detection of disease-related DNA methylation changes even in a relatively small cohort. We identify numerous genes with cell-type-specific methylation signatures and document differential methylation dynamics associated with aging specifically in neurons such as CLU, SYNJ2 and NCOR2 or in glia RAI1,CXXC5 and INPP5A. Further, we found neuron or glia-specific associations with AD Braak stage progression at genes such as MCF2L, ANK1, MAP2, LRRC8B, STK32C and S100B. A comparison of our study with previous tissue-based EWAS validates multiple AD-associated DNA methylation signals and additionally specifies their origin to neuron, e.g., HOXA3 or glia (ANK1). In a meta-analysis, we reveal two novel previously unrecognized methylation changes at the key AD risk genes APP and ADAM17.

Conclusions

Our data highlight the complex interplay between disease, age and cell-type-specific methylation changes in AD risk genes thus offering new perspectives for the validation and interpretation of large EWAS results.

Electronic supplementary material

The online version of this article (10.1186/s13072-018-0211-3) contains supplementary material, which is available to authorized users.

Age-dependent alterations in expression and co-localization of Pax6 and Ras-GAP in brain of aging mice

As the brain ages, the survival and plasticity of neurons and glia are compromised. The data-mining and in silico studies suggest interactions of Pax6 with Ras and binding sites in Ras-GAP promoter. The Pax6 also shows age-dependent alterations. Therefore, it is presumed that Pax6 may be associated with the Ras-GAP, a synaptic protein, either directly or indirectly in brain. The expression, co-localization and interaction of Pax6 and Ras-GAP in different regions of brain of mice during aging were investigated through immunofluorescence assay, co-immunoprecipitation and western blotting, respectively. The co-localization of Pax6 and Ras-GAP were observed in dentate gyrus (DG) and sub-granular zone (SGZ) of hippocampus, in glomerular (GlLa) and mitral cells (MiCe) of olfactory lobe, granular cells (GrCe), Purkinje cell (PuCe) and molecular cell layer (MoLa) of cerebellum, internal plexiform layer (InPl), molecular layer (MoLa) of cerebral cortex and in intercalated cells of amygdala (ITC), caudate nucleus regions in brain of aging mice. The expression of Pax6 and Ras-GAP was altered in hippocampus, amygdala, caudate nucleus, olfactory lobe, cerebral cortex and cerebellum from young to old mice. The Pax6 interacts with Ras-GAP in brain of mice. Results indicate impact of Pax6 on Ras-GAP-mediated activities of synapses, learning and memory, emotions and fear as well as motor functions. Alterations in expression and co-localization of Pax6 and Ras-GAP during aging may be responsible for age-associated compromised survival and plasticity of neurons and glia.

YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA

N6-methyladenosine (m6A) is the most abundant internal modification in eukaryotic messenger RNAs (mRNAs), and plays important roles in cell differentiation and tissue development. It regulates multiple steps throughout the RNA life cycle including RNA processing, translation, and decay, via the recognition by selective binding proteins. In the cytoplasm, m6A binding protein YTHDF1 facilitates translation of m6A-modified mRNAs, and YTHDF2 accelerates the decay of m6A-modified transcripts. The biological function of YTHDF3, another cytoplasmic m6A binder of the YTH (YT521-B homology) domain family, remains unknown. Here, we report that YTHDF3 promotes protein synthesis in synergy with YTHDF1, and affects methylated mRNA decay mediated through YTHDF2. Cells deficient in all three YTHDF proteins experience the most dramatic accumulation of m6A-modified transcripts. These results indicate that together with YTHDF1 and YTHDF2, YTHDF3 plays critical roles to accelerate metabolism of m6A-modified mRNAs in the cytoplasm. All three YTHDF proteins may act in an integrated and cooperative manner to impact fundamental biological processes related to m6A RNA methylation.

Understanding cellular function and disease with comparative pathway analysis

Pathway analysis is important in interpreting the functional implications of high-throughput experimental results, but robust comparison across platforms and species is problematic. A new approach, Pathprinting, provides a cross-platform, cross-species comparative analysis of pathway expression signatures. This method calculates pathway-level statistics from gene expression across nearly 180,000 microarrays in the Gene Expression Omnibus. Pathprinting can accurately retrieve phenotypically similar samples and identify sets of human and mouse genes that are prognostic in cancer.

See related Research paper, http://genomemedicine.com/content/5/7/68

The transcription factor ultraspiracle influences honey bee social behavior and behavior-related gene expression

Behavior is among the most dynamic animal phenotypes, modulated by a variety of internal and external stimuli. Behavioral differences are associated with large-scale changes in gene expression, but little is known about how these changes are regulated. Here we show how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive (primarily “nursing” brood) to foraging outside. We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone. These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites, as revealed by ChIP–chip. Instead, behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites. Many modules of JH– and maturation-related genes were co-regulated in both the fat body and brain, predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues. Our findings demonstrate how “single gene effects” on behavioral plasticity can involve complex transcriptional networks, in both brain and peripheral tissues.

Animals use behavior as one of the principal means of meeting their basic needs and responding flexibly to changes in their environment. An emerging insight is that changes in behavior are associated with massive changes in gene expression in the brain, but we know relatively little about how these changes are regulated. One important class of gene regulators are transcription factors (TF), proteins that orchestrate the expression of tens to thousands of genes. We discovered that ultraspiracle (USP), a TF previously known primarily for its role in development, regulates behavioral change in the honey bee; and we show that USP causes behaviorally related changes in gene expression by mediating responses to an endocrine regulator, juvenile hormone. We present evidence that these effects on gene expression occur through combinatorial interactions between USP and other TFs, and that these hormonally related transcriptional networks are preserved between two tissues with causal roles in behavioral plasticity: the brain and the fat body, a peripheral nutrient-sensing organ. These results suggest that behavior is subserved by complex interactions between genes and gene networks, occurring both in the brain and in peripheral tissues. More generally our results suggest that molecular systems biology is a promising paradigm by which to understand the mechanistic basis for behavior.

Caffeinated coffee, decaffeinated coffee, and the phenolic phytochemical chlorogenic acid up-regulate NQO1 expression and prevent H₂O₂-induced apoptosis in primary cortical neurons

Neurodegenerative disorders are strongly associated with oxidative stress, which is induced by reactive oxygen species including hydrogen peroxide (H₂O₂). Epidemiological studies have suggested that coffee may be neuroprotective, but the molecular mechanisms underlying this effect have not been clarified. In this study, we investigated the protective effects of caffeinated coffee, decaffeinated coffee, and the phenolic phytochemical chlorogenic acid (5-O-caffeoylquinic acid), which is present in both caffeinated and decaffeinated coffee, against oxidative neuronal death. H₂O₂-induced apoptotic nuclear condensation in neuronal cells was strongly inhibited by pretreatment with caffeinated coffee, decaffeinated coffee, or chlorogenic acid. Pretreatment with caffeinated coffee, decaffeinated coffee, or chlorogenic acid inhibited the H₂O₂-induced down-regulation of anti-apoptotic proteins Bcl-2 and Bcl-X(L) while blocking H₂O₂-induced pro-apoptotic cleavage of caspase-3 and pro-poly(ADP-ribose) polymerase. We also found that caffeinated coffee, decaffeinated coffee, and chlorogenic acid induced the expression of NADPH:quinine oxidoreductase 1 (NQO1) in neuronal cells, suggesting that these substances protect neurons from H₂O₂-induced apoptosis by up-regulation of this antioxidant enzyme. The neuroprotective efficacy of caffeinated coffee was similar to that of decaffeinated coffee, indicating that active compounds present in both caffeinated and decaffeinated coffee, such as chlorogenic acid, may drive the effects.

Clusters of temporal discordances reveal distinct embryonic patterning mechanisms in Drosophila and anopheles

Evolutionary innovations can be driven by spatial and temporal changes in gene expression. Several such differences have been documented in the embryos of lower and higher Diptera. One example is the reduction of the ancient extraembryonic envelope composed of amnion and serosa as seen in mosquitoes to the single amnioserosa of fruit flies. We used transcriptional datasets collected during the embryonic development of the fruit fly, Drosophila melanogaster, and the malaria mosquito, Anopheles gambiae, to search for whole-genome changes in gene expression underlying differences in their respective embryonic morphologies. We found that many orthologous gene pairs could be clustered based on the presence of coincident discordances in their temporal expression profiles. One such cluster contained genes expressed specifically in the mosquito serosa. As shown previously, this cluster is re-deployed later in development at the time of cuticle synthesis. In addition, there is a striking difference in the temporal expression of a subset of maternal genes. Specifically, maternal transcripts that exhibit a sharp reduction at the time of the maternal-zygotic transition in Drosophila display sustained expression in the Anopheles embryo. We propose that gene clustering by local temporal discordance can be used for the de novo identification of the gene batteries underlying morphological diversity.

COMODO: an adaptive coclustering strategy to identify conserved coexpression modules between organisms

Increasingly large-scale expression compendia for different species are becoming available. By exploiting the modularity of the coexpression network, these compendia can be used to identify biological processes for which the expression behavior is conserved over different species. However, comparing module networks across species is not trivial. The definition of a biologically meaningful module is not a fixed one and changing the distance threshold that defines the degree of coexpression gives rise to different modules. As a result when comparing modules across species, many different partially overlapping conserved module pairs across species exist and deciding which pair is most relevant is hard. Therefore, we developed a method referred to as conserved modules across organisms (COMODO) that uses an objective selection criterium to identify conserved expression modules between two species. The method uses as input microarray data and a gene homology map and provides as output pairs of conserved modules and searches for the pair of modules for which the number of sharing homologs is statistically most significant relative to the size of the linked modules. To demonstrate its principle, we applied COMODO to study coexpression conservation between the two well-studied bacteria Escherichia coli and Bacillus subtilis. COMODO is available at: http://homes.esat.kuleuven.be/∼kmarchal/Supplementary_Information_Zarrineh_2010/comodo/index.html.

Cross-species queries of large gene expression databases

MOTIVATION

Expression databases, including the Gene Expression Omnibus and ArrayExpress, have experienced significant growth over the past decade and now hold hundreds of thousands of arrays from multiple species. Since most drugs are initially tested on model organisms, the ability to compare expression experiments across species may help identify pathways that are activated in a similar way in humans and other organisms. However, while several methods exist for finding co-expressed genes in the same species as a query gene, looking at co-expression of homologs or arbitrary genes in other species is challenging. Unlike sequence, which is static, expression is dynamic and changes between tissues, conditions and time. Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species.

RESULTS

To facilitate queries in large databases, we developed a new method for comparing expression experiments from different species. We define a distance metric between the ranking of orthologous genes in the two species. We show how to solve an optimization problem for learning the parameters of this function using a training dataset of known similar expression experiments pairs. The function we learn outperforms previous methods and simpler rank comparison methods that have been used in the past for single species analysis. We used our method to compare millions of array pairs from mouse and human expression experiments. The resulting matches can be used to find functionally related genes, to hypothesize about biological response mechanisms and to highlight conditions and diseases that are activating similar pathways in both species.

AVAILABILITY

Supporting methods, results and a Matlab implementation are available from http://sb.cs.cmu.edu/ExpQ/.

Uncovering molecular biomarkers that correlate cognitive decline with the changes of hippocampus' gene expression profiles in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is characterized by a neurodegenerative progression that alters cognition. On a phenotypical level, cognition is evaluated by means of the MiniMental State Examination (MMSE) and the post-mortem examination of Neurofibrillary Tangle count (NFT) helps to confirm an AD diagnostic. The MMSE evaluates different aspects of cognition including orientation, short-term memory (retention and recall), attention and language. As there is a normal cognitive decline with aging, and death is the final state on which NFT can be counted, the identification of brain gene expression biomarkers from these phenotypical measures has been elusive.

METHODOLOGY/PRINCIPAL FINDINGS

We have reanalysed a microarray dataset contributed in 2004 by Blalock et al. of 31 samples corresponding to hippocampus gene expression from 22 AD subjects of varying degree of severity and 9 controls. Instead of only relying on correlations of gene expression with the associated MMSE and NFT measures, and by using modern bioinformatics methods based on information theory and combinatorial optimization, we uncovered a 1,372-probe gene expression signature that presents a high-consensus with established markers of progression in AD. The signature reveals alterations in calcium, insulin, phosphatidylinositol and wnt-signalling. Among the most correlated gene probes with AD severity we found those linked to synaptic function, neurofilament bundle assembly and neuronal plasticity.

CONCLUSIONS/SIGNIFICANCE

A transcription factors analysis of 1,372-probe signature reveals significant associations with the EGR/KROX family of proteins, MAZ, and E2F1. The gene homologous of EGR1, zif268, Egr-1 or Zenk, together with other members of the EGR family, are consolidating a key role in the neuronal plasticity in the brain. These results indicate a degree of commonality between putative genes involved in AD and prion-induced neurodegenerative processes that warrants further investigation.

Modeling co-expression across species for complex traits: insights to the difference of human and mouse embryonic stem cells

Complex interactions between genes or proteins contribute substantially to phenotypic evolution. We present a probabilistic model and a maximum likelihood approach for cross-species clustering analysis and for identification of conserved as well as species-specific co-expression modules. This model enables a “soft” cross-species clustering (SCSC) approach by encouraging but not enforcing orthologous genes to be grouped into the same cluster. SCSC is therefore robust to obscure orthologous relationships and can reflect different functional roles of orthologous genes in different species. We generated a time-course gene expression dataset for differentiating mouse embryonic stem (ES) cells, and compiled a dataset of published gene expression data on differentiating human ES cells. Applying SCSC to analyze these datasets, we identified conserved and species-specific gene regulatory modules. Together with protein-DNA binding data, an SCSC cluster specifically induced in murine ES cells indicated that the KLF2/4/5 transcription factors, although critical to maintaining the pluripotent phenotype in mouse ES cells, were decoupled from the OCT4/SOX2/NANOG regulatory module in human ES cells. Two of the target genes of murine KLF2/4/5, LIN28 and NODAL, were rewired to be targets of OCT4/SOX2/NANOG in human ES cells. Moreover, by mapping SCSC clusters onto KEGG signaling pathways, we identified the signal transduction components that were induced in pluripotent ES cells in either a conserved or a species-specific manner. These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network.

A major goal in biology is to understand the evolution of complex traits, such as the development of multicellular body plans. To a certain extent, complex traits are governed by regulated gene expression. The comparison expression data between species requires extra considerations than sequence comparison, because gene expression is not static and the level of expression is influenced by external conditions. Considering that co-expression patterns are often comparable across species, we developed a statistical model for cross-species clustering analysis. The model allows each species to create its own clusters of the genes but also encourages the species to borrow strength from each others' clusters of orthologous genes. The result is a pairing of clusters, one from each species, where the paired clusters share many but not necessarily all orthologous genes. The model-based approach not only reduces subjective influence but also enables effective use of evolutionary dependence. Applying this model to analyze human and mouse embryonic stem (ES) cell data, we identified the transcription factors and the signaling proteins that are specifically expressed in either human or mouse ES cells. These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network.

Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer's disease cortex reveals altered miRNA regulation

Although microRNAs are being extensively studied for their involvement in cancer and development, little is known about their roles in Alzheimer's disease (AD). In this study, we used microarrays for the first joint profiling and analysis of miRNAs and mRNAs expression in brain cortex from AD and age-matched control subjects. These data provided the unique opportunity to study the relationship between miRNA and mRNA expression in normal and AD brains. Using a non-parametric analysis, we showed that the levels of many miRNAs can be either positively or negatively correlated with those of their target mRNAs. Comparative analysis with independent cancer datasets showed that such miRNA-mRNA expression correlations are not static, but rather context-dependent. Subsequently, we identified a large set of miRNA-mRNA associations that are changed in AD versus control, highlighting AD-specific changes in the miRNA regulatory system. Our results demonstrate a robust relationship between the levels of miRNAs and those of their targets in the brain. This has implications in the study of the molecular pathology of AD, as well as miRNA biology in general.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background

Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.

Results

We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.

Conclusion

ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Cross species analysis of microarray expression data

MOTIVATION

Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied.

RESULTS

In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them.

AMIC@: All MIcroarray Clusterings @ once

The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algorithms and inspection of results. Among the salient features AMIC@ offers, there are: (i) automatic file format detection, (ii) suggestions on the number of clusters using a variant of the stability-based method of Tibshirani et al. (iii) intuitive visual inspection of the data via heatmaps and (iv) measurements of the clustering quality using cluster homogeneity. Large data sets can be processed efficiently by selecting algorithms (such as FPF-SB and k-Boost), specifically designed for this purpose. In case of very large data sets, the user can opt for a batch-mode use of the system by means of the Clustering wizard that runs all algorithms at once and delivers the results via email. AMIC@ is freely available and open to all users with no login requirement at the following URL http://bioalgo.iit.cnr.it/amica.