Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease

miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach

Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD-related miRNAs in cerebrospinal fluid (CSF) found in the GeoDataSets, evaluated by machine learning, with miRNAs listed in a systematic review, and a pathway analysis. Using machine learning approaches, we identified most differentially expressed miRNAs in Gene Expression Omnibus (GEO), which were validated by the systematic review, using the acronym PECO-Population (P): Patients with AD, Exposure (E): expression of miRNAs, Comparison (C): Healthy individuals, and Objective (O): miRNAs differentially expressed in CSF. Additionally, pathway enrichment analysis was performed to identify the main pathways involving at least four miRNAs selected. Four miRNAs were identified for differentiating between patients with and without AD in machine learning combined to systematic review, and followed the pathways analysis: miRNA-30a-3p, miRNA-193a-5p, miRNA-143-3p, miRNA-145-5p. The pathways epidermal growth factor, MAPK, TGF-beta and ATM-dependent DNA damage response, were regulated by these miRNAs, but only the MAPK pathway presented higher relevance after a randomic pathway analysis. These findings have the potential to assist in the development of diagnostic tests for AD using miRNAs as biomarkers, as well as provide understanding of the relationship between different pathophysiological mechanisms of AD.

Construction of a circRNA- lincRNA-lncRNA-miRNA-mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility

Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq. Methods: Thirty samples from Ji'ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs). Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤-5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine-cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks. We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes’ sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles.

lncRNA-miRNA-mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach

Understanding the molecular pattern of fertility is considered as an important step in breeding of different species, and despite the high importance of the fertility, little success has been achieved in dissecting the interactome basis of sheep fertility. However, the complex mechanisms associated with prolificacy in sheep have not been fully understood. Therefore, this study aimed to use competitive endogenous RNA (ceRNA) networks to evaluate this trait to better understand the molecular mechanisms responsible for fertility. A competitive endogenous RNA (ceRNA) network of the corpus luteum was constructed between Romanov and Baluchi sheep breeds with either good or poor genetic merit for prolificacy using whole-transcriptome analysis. First, the main list of lncRNAs, miRNAs, and mRNA related to the corpus luteum that alter with the breed were extracted, then miRNA−mRNA and lncRNA−mRNA interactions were predicted, and the ceRNA network was constructed by integrating these interactions with the other gene regulatory networks and the protein−protein interaction (PPI). A total of 264 mRNAs, 14 lncRNAs, and 34 miRNAs were identified by combining the GO and KEGG enrichment analyses. In total, 44, 7, 7, and 6 mRNAs, lncRNAs, miRNAs, and crucial modules, respectively, were disclosed through clustering for the corpus luteum ceRNA network. All these RNAs involved in biological processes, namely proteolysis, actin cytoskeleton organization, immune system process, cell adhesion, cell differentiation, and lipid metabolic process, have an overexpression pattern (Padj < 0.01). This study increases our understanding of the contribution of different breed transcriptomes to phenotypic fertility differences and constructed a ceRNA network in sheep (Ovis aries) to provide insights into further research on the molecular mechanism and identify new biomarkers for genetic improvement.

Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration

Aging is a prominent risk factor for neurodegenerative disorders (NDDs); however, the molecular mechanisms rendering the aged brain particularly susceptible to neurodegeneration remain unclear. Here, we aim to determine the link between physiological aging and NDDs by exploring protein turnover using metabolic labeling and quantitative pulse-SILAC proteomics. By comparing protein lifetimes between physiologically aged and young adult mice, we found that in aged brains protein lifetimes are increased by ~20% and that aging affects distinct pathways linked to NDDs. Specifically, a set of neuroprotective proteins are longer-lived in aged brains, while some mitochondrial proteins linked to neurodegeneration are shorter-lived. Strikingly, we observed a previously unknown alteration in proteostasis that correlates to parsimonious turnover of proteins with high biosynthetic costs, revealing an overall metabolic adaptation that preludes neurodegeneration. Our findings suggest that future therapeutic paradigms, aimed at addressing these metabolic adaptations, might be able to delay NDD onset.

Integrated Bioinformatics Analysis to Identify Alternative Therapeutic Targets for Alzheimer's Disease: Insights from a Synaptic Machinery Perspective

Alzheimer's disease (AD), the most common type of dementia, is a serious neurodegenerative disease that has no cure yet, but whose symptoms can be alleviated with available medications. Therefore, early and accurate diagnosis of the disease and elucidation of the molecular mechanisms involved in the progression of pathogenesis are critically important. This study aimed to identify dysregulated miRNAs and their target mRNAs through the integrated analysis of miRNA and mRNA expression profiling in AD patients versus unaffected controls. Expression profiles in postmortem brain samples from AD patients and healthy individuals were extracted from the Gene Expression Omnibus database and were analyzed using bioinformatics approaches to identify gene ontologies, pathways, and networks. Finally, the module analysis of the PPI network and hub gene selection was carried out. A total of five differentially expressed miRNAs were extracted from the miRNA dataset, and 4312 differentially expressed mRNAs were obtained from the mRNA dataset. By comparing the DEGs and the putative targets of the altered miRNAs, 116 (3 upregulated and 113 downregulated) coordinated genes were determined. Also, six hub genes (SNAP25, GRIN2A, GRIN2B, DLG2, ATP2B2, and SCN2A) were identified by constructing a PPI network. The results of the present study provide insight into mechanisms such as synaptic machinery and neuronal communication underlying AD pathogenesis, specifically concerning miRNAs.

A Study to Decipher the Potential Effects of Butylphthalide against Central Nervous System Diseases Based on Network Pharmacology and Molecular Docking Integration Strategy

Background

Butylphthalide (NBP), approved by the China National Medical Products Administration (NMPA) for the treatment of ischemic stroke (IS), showed pleiotropic potentials against central nervous system (CNS) diseases, including neuroprotection and cognitive deficits improvement. However, the effects and corresponding modes of action were not fully explored. This study was designed to investigate the potential of NBP against IS-associated CNS diseases based on network pharmacology (NP) and molecular docking (MD).

Methods

IS was inputted as the index disease to retrieve the “associated diseases” in DisGeNET. Three-database-based IS genes were obtained and integrated (DisGeNET, Malacards, and OMIM). Then, IS-associated genes were identified by combining these genes. Meanwhile, PubMed references and online databases were applied to identify NBP target genes. The IS-related disease-disease association (DDA) network and NBP-disease regulation network were constructed and analyzed in Cytoscape. In silico MD and references were used to validate the binding affinity of NBP with critical targets and the potential of NBP against certain IS-related CNS disease regulation.

Results

175 NBP target genes were obtained, while 312 IS-related disease genes were identified. 36 NBP target genes were predicted to be associated with IS-related CNS diseases, including Alzheimer's disease (AD), epilepsy, major depressive disorder (MDD), amyotrophic lateral sclerosis (ALS), and dementia. Six target genes (i.e., GRIN1, PTGIS, PTGES, ADRA1A, CDK5, and SULT1E1) indicating disease specificity index (DSI) >0.5 showed certain to good degree binding affinity with NBP, ranging from −9.2 to −6.7 kcal/mol. And the binding modes may be mainly related to hydrogen bonds and hydrophobic “bonds.” Further literature validations inferred that these critical NBP targets had a tight association with AD, epilepsy, ALS, and depression.

Conclusions

Our study proposed a drug-target-disease integrated method to predict the drug repurposing potentials to associated diseases by application of NP and MD, which could be an attractive alternative to facilitate the development of CNS disease therapies. NBP may be promising and showed potentials to be repurposed for treatments for AD, epilepsy, ALS, and depression, and further investigations are warranted to be carefully designed and conducted.

Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue

Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of "proxy" genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.

Identification of the ZEB2 gene as a potential target for epilepsy therapy and the association between rs10496964 and ZEB2 expression

Objective

An association between the rs10496964 polymorphism and the ZEB2 gene has not yet been reported, and the role of ZEB2 in epilepsy therapy is also unclear. The aims of this research were to evaluate the role of ZEB2 in the therapy of epilepsy and to explore the association between rs10496964 and ZEB2 expression.

Methods

We used the expression quantitative trait loci (eQTL) dataset resource from the Brain eQTL Almanac to evaluate the association between rs10496964 and ZEB2 expression in human brain tissue. Pathway and process enrichment analysis, protein–protein interaction analysis, and PhosphoSitePlus® analysis were then performed to further evaluate the role of ZEB2 in the therapy of epilepsy.

Results

The rs10496964 polymorphism was found to regulate the expression of ZEB2 in human brain tissue. The ZEB2 protein interacts with the targets of approved antiepileptic drugs, and a post-translational acetylation modification of ZEB2 was associated with an epilepsy drug therapy.

Conclusion

Our findings suggest that ZEB2 may be involved in the therapy of epilepsy, and rs10496964 regulates ZEB2 expression in human brain tissue.

Interdisciplinary Research in Alzheimer's Disease and the Roles International Societies Can Play

An ever-increasing ageing population has elevated Alzheimer's disease to be one of the biggest challenges in modern medicine. Alzheimer's disease is highly complex, and we are still no closer to understanding the causes, let alone an effective treatment. The lack of good experimental models and lack of critical understanding has led to high failure rates of clinical trials with high associated costs, as well as difficulties in implementing treatments. The multifaceted nature of this disease highlights the need for an interdisciplinary approach to address these concerns. In this essay, we suggest how collaborative work can be useful in addressing some of the above issues. We then propose that international organisations and publishers need to support interdisciplinary research by creating platforms, lobbying funders, and pushing for interdisciplinary publications. We further highlight some of the issues involved in implementing these suggestions and argue that willpower of the research community, together with a re-evaluation of evaluation metrics and incentive systems, are needed in order to foster interdisciplinary research. Overall, we emphasise the need for interdisciplinary research in Alzheimer's disease and suggest that international societies should play a huge role in this endeavour.

Integrated Genomic Analysis Revealed Associated Genes for Alzheimer's Disease in APOE4 Non-Carriers

BACKGROUND

APOE4 is the strongest genetic risk factor for late-onset Alzheimer's disease (LOAD). LOAD patients carrying or not carrying APOE4 manifest distinct clinico-pathological characteristics. APOE4 has been shown to play a critical role in the pathogenesis of AD by affecting various aspects of pathological processes. However, the pathogenesis involved in LOAD not-carrying APOE4 remains elusive.

OBJECTIVE

We aimed to identify the associated genes involved in LOAD not-carrying APOE4.

METHODS

An integrated genomic analysis of datasets of genome-wide association study, genome-wide expression profiling and genome-wide linkage scan and protein-protein interaction network construction were applied to identify associated gene clusters in APOE4 non-carriers. The role of one of hub gene of an APOE4 non-carrier-associated gene cluster in tau phosphorylation was studied by knockdown and western blot.

RESULTS

We identified 12 gene clusters associated with AD APOE4 non-carriers. The hub genes associated with AD in these clusters were MAPK8, POU2F1, XRCC1, PRKCG, EXOC6, VAMP4, SIRT1, MME, NOS1, ABCA1 and LDLR. The associated genes for APOE4 non-carriers were enriched in hereditary disorder, neurological disease and psychological disorders. Moreover, knockdown of PRKCG to reduce the expression of protein kinase Cγ isoform enhanced tau phosphorylation at Thr181 and Thr231 and the expression of glycogen synthase kinase 3β and cyclin-dependent kinase 5 in the presence of APOE3 but not APOE4.

CONCLUSION

The study provides new insight into the mechanism of distinct pathogenesis of LOAD not carrying APOE4 and prompts the functional exploration of identified genes based on APOE genotypes.

Meta-Analysis of Gene Expression Changes in the Blood of Patients with Mild Cognitive Impairment and Alzheimer's Disease Dementia

Background: Dementia is a major public health concern affecting approximately 47 million people worldwide. Mild cognitive impairment (MCI) is one form of dementia that affects an individual’s memory with or without affecting their daily life. Alzheimer’s disease dementia (ADD) is a more severe form of dementia that usually affects elderly individuals. It remains unclear whether MCI is a distinct disorder from or an early stage of ADD. Methods: Gene expression data from blood were analyzed to identify potential biomarkers that may be useful for distinguishing between these two forms of dementia. Results: A meta-analysis revealed 91 genes dysregulated in individuals with MCI and 387 genes dysregulated in ADD. Pathway analysis identified seven pathways shared between MCI and ADD and nine ADD-specific pathways. Fifteen transcription factors were associated with MCI and ADD, whereas seven transcription factors were specific for ADD. Mir-335-5p was specific for ADD, suggesting that it may be useful as a biomarker. Diseases that are associated with MCI and ADD included developmental delays, cognition impairment, and movement disorders. Conclusion: These results provide a better molecular understanding of peripheral changes that occur in MCI and ADD patients and may be useful in the identification of diagnostic and prognostic biomarkers.

Elucidating Critical Proteinopathic Mechanisms and Potential Drug Targets in Neurodegeneration

Neurodegeneration entails progressive loss of neuronal structure as well as function leading to cognitive failure, apathy, anxiety, irregular body movements, mood swing and ageing. Proteomic dysregulation is considered the key factor for neurodegeneration. Mechanisms involving deregulated processing of proteins such as amyloid beta (Aβ) oligomerization; tau hyperphosphorylation, prion misfolding; α-synuclein accumulation/lewy body formation, chaperone deregulation, acetylcholine depletion, adenosine 2A (A2A) receptor hyperactivation, secretase deregulation, leucine-rich repeat kinase 2 (LRRK2) mutation and mitochondrial proteinopathies have deeper implications in neurodegenerative disorders. Better understanding of such pathological mechanisms is pivotal for exploring crucial drug targets. Herein, we provide a comprehensive outlook about the diverse proteomic irregularities in Alzheimer's, Parkinson's and Creutzfeldt Jakob disease (CJD). We explicate the role of key neuroproteomic drug targets notably Aβ, tau, alpha synuclein, prions, secretases, acetylcholinesterase (AchE), LRRK2, molecular chaperones, A2A receptors, muscarinic acetylcholine receptors (mAchR), N-methyl-D-aspartate receptor (NMDAR), glial cell line-derived neurotrophic factor (GDNF) family ligands (GFLs) and mitochondrial/oxidative stress-related proteins for combating neurodegeneration and associated cognitive and motor impairment. Cross talk between amyloidopathy, synucleinopathy, tauopathy and several other proteinopathies pinpoints the need to develop safe therapeutics with ability to strike multiple targets in the aetiology of the neurodegenerative disorders. Therapeutics like microtubule stabilisers, chaperones, kinase inhibitors, anti-aggregation agents and antibodies could serve promising regimens for treating neurodegeneration. However, drugs should be target specific, safe and able to penetrate blood-brain barrier.

Biological Hallmarks of Cancer in Alzheimer's Disease

Although Alzheimer's disease (AD) is an international health research priority for our aging population, little therapeutic progress has been made. This lack of progress may be partially attributable to disease heterogeneity. Previous studies have identified an inverse association of cancer and AD, suggesting that cancer history may be one source of AD heterogeneity. These findings are particularly interesting in light of the number of common risk factors and two-hit models hypothesized to commonly drive both diseases. We reviewed the ten hallmark biological alterations of cancer cells to investigate overlap with the AD literature and identified overlap of all ten hallmarks in AD, including (1) potentially common underlying risk factors, such as increased inflammation, deregulated cellular energetics, and genome instability; (2) inversely regulated mechanisms, including cell death and evading growth suppressors; and (3) functions with more complex, pleiotropic mechanisms, some of which may be stage-dependent in AD, such as cell adhesion/contact inhibition and angiogenesis. Additionally, we discuss the recent observation of a biological link between cancer and AD neuropathology. Finally, we address the therapeutic implications of this topic. The significant overlap of functional pathways and molecules between these diseases, some similarly and some oppositely regulated or functioning in each disease, supports the need for more research to elucidate cancer-related AD genetic and functional heterogeneity, with the aims of better understanding AD risk mediators, as well as further exploring the potential for some types of drug repurposing towards AD therapeutic development.

Overactive BRCA1 Affects Presenilin 1 in Induced Pluripotent Stem Cell-Derived Neurons in Alzheimer's Disease

The BRCA1 protein, one of the major players responsible for DNA damage response has recently been linked to Alzheimer's disease (AD). Using primary fibroblasts and neurons reprogrammed from induced pluripotent stem cells (iPSC) derived from familial AD (FAD) patients, we studied the role of the BRCA1 protein underlying molecular neurodegeneration. By whole-transcriptome approach, we have found wide range of disturbances in cell cycle and DNA damage response in FAD fibroblasts. This was manifested by significantly increased content of BRCA1 phosphorylated on Ser1524 and abnormal ubiquitination and subcellular distribution of presenilin 1 (PS1). Accordingly, the iPSC-derived FAD neurons showed increased content of BRCA1(Ser1524) colocalized with degraded PS1, accompanied by an enhanced immunostaining pattern of amyloid-β. Finally, overactivation of BRCA1 was followed by an increased content of Cdc25C phosphorylated on Ser216, likely triggering cell cycle re-entry in FAD neurons. This study suggests that overactivated BRCA1 could both influence PS1 turnover leading to amyloid-β pathology and promote cell cycle re-entry-driven cell death of postmitotic neurons in AD.

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

MicroRNA-16 targets mRNA involved in neurite extension and branching in hippocampal neurons during presymptomatic prion disease

The mechanisms that lead to neuronal death in neurodegenerative diseases are poorly understood. Prion diseases, like many more common disorders such as Alzheimer's and Parkinson's diseases, are characterized by the progressive accumulation of misfolded disease-specific proteins. The earliest changes observed in brain tissue include a reduction in synaptic number and retraction of dendritic spines, followed by reduced length and branching of neurites. These pathologies are observable during presymptomatic stages of disease and are accompanied by altered expression of transcripts that include miRNAs. Here we report that miR-16 localized within hippocampal CA1 neurons is increased during early prion disease. Modulating miR-16 expression in mature murine hippocampal neurons by expression from a lentivirus, thus mimicking the modest increase seen in vivo, was found to induce neurodegeneration. This was characterized by retraction of neurites and reduced branching. We performed immunoprecipitation of the miR-16 enriched RISC complex, and identified associated transcripts from the co-immunoprecipitated RNA (Ago2 RIP-Chip). These transcripts were enriched with predicted binding sites for miR-16, including the validated miR-16 targets APP and BCL2, as well as numerous novel targets. In particular, genes within the neurotrophin receptor mediated MAPK/ERK pathway were potentially regulated by miR-16; including TrkB (NTRK2), MEK1 (MAP2K1) and c-Raf (RAF). Increased miR-16 expression in neurons during presymptomatic prion disease and reduction in proteins involved in MAPK/ERK signaling represents a possible mechanism by which neurite length and branching are decreased during early stages of disease.

A novel network analysis approach reveals DNA damage, oxidative stress and calcium/cAMP homeostasis-associated biomarkers in frontotemporal dementia

Frontotemporal Dementia (FTD) is the form of neurodegenerative dementia with the highest prevalence after Alzheimer’s disease, equally distributed in men and women. It includes several variants, generally characterized by behavioural instability and language impairments. Although few mendelian genes (MAPT, GRN, and C9orf72) have been associated to the FTD phenotype, in most cases there is only evidence of multiple risk loci with relatively small effect size. To date, there are no comprehensive studies describing FTD at molecular level, highlighting possible genetic interactions and signalling pathways at the origin FTD-associated neurodegeneration. In this study, we designed a broad FTD genetic interaction map of the Italian population, through a novel network-based approach modelled on the concepts of disease-relevance and interaction perturbation, combining Steiner tree search and Structural Equation Model (SEM) analysis. Our results show a strong connection between Calcium/cAMP metabolism, oxidative stress-induced Serine/Threonine kinases activation, and postsynaptic membrane potentiation, suggesting a possible combination of neuronal damage and loss of neuroprotection, leading to cell death. In our model, Calcium/cAMP homeostasis and energetic metabolism impairments are primary causes of loss of neuroprotection and neural cell damage, respectively. Secondly, the altered postsynaptic membrane potentiation, due to the activation of stress-induced Serine/Threonine kinases, leads to neurodegeneration. Our study investigates the molecular underpinnings of these processes, evidencing key genes and gene interactions that may account for a significant fraction of unexplained FTD aetiology. We emphasized the key molecular actors in these processes, proposing them as novel FTD biomarkers that could be crucial for further epidemiological and molecular studies.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.

Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology

Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD), Parkinson’s (PD) and Huntington’s diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.

Copy Number Variations in Amyotrophic Lateral Sclerosis: Piecing the Mosaic Tiles Together through a Systems Biology Approach

Amyotrophic lateral sclerosis (ALS) is a devastating and still untreatable motor neuron disease. Despite the molecular mechanisms underlying ALS pathogenesis that are still far from being understood, several studies have suggested the importance of a genetic contribution in both familial and sporadic forms of the disease. In addition to single-nucleotide polymorphisms (SNPs), which account for only a limited number of ALS cases, a consistent number of common and rare copy number variations (CNVs) have been associated to ALS. Most of the CNV-based association studies use a traditional candidate-gene approach that is inadequate for uncovering the genetic architectures of complex traits like ALS. The emergent paradigm of “systems biology” may offer a new perspective to better interpret the wide spectrum of CNVs in ALS, enabling the characterization of the complex network of gene products underlying ALS pathogenesis. In this review, we will explore the landscape of CNVs in ALS, putting specific emphasis on the functional impact of common CNV regions and genes consistently associated with increased risk of developing disease. In addition, we will discuss the potential contribution of multiple rare CNVs in ALS pathogenesis, focusing our attention on the complex mechanisms by which these proteins might impact, individually or in combination, the genetic susceptibility of ALS. The comprehensive detection and functional characterization of common and rare candidate risk CNVs in ALS susceptibility may bring new pieces into the intricate mosaic of ALS pathogenesis, providing interesting and important implications for a more precise molecular biomarker-assisted diagnosis and more effective and personalized treatments.

Differential DNA Methylation of MicroRNA Genes in Temporal Cortex from Alzheimer's Disease Individuals

This study investigated for the first time the genomewide DNA methylation changes of noncoding RNA genes in the temporal cortex samples from individuals with Alzheimer's disease (AD). The methylome of 10 AD individuals and 10 age-matched controls were obtained using Illumina 450 K methylation array. A total of 2,095 among the 15,258 interrogated noncoding RNA CpG sites presented differential methylation, 161 of which were associated with miRNA genes. In particular, 10 miRNA CpG sites that were found to be hypermethylated in AD compared to control brains represent transcripts that have been previously associated with the disease. This miRNA set is predicted to target 33 coding genes from the neuregulin receptor complex (ErbB) signaling pathway, which is required for the neurons myelination process. For 6 of these miRNA genes (MIR9-1, MIR9-3, MIR181C, MIR124-1, MIR146B, and MIR451), the hypermethylation pattern is in agreement with previous results from literature that shows downregulation of miR-9, miR-181c, miR-124, miR-146b, and miR-451 in the AD brain. Our data implicate dysregulation of miRNA methylation as contributor to the pathogenesis of AD.

Application of Systems Theory in Longitudinal Studies on the Origin and Progression of Alzheimer's Disease

This chapter questions the prevailing "implicit" assumption that molecular mechanisms and the biological phenotype of dominantly inherited early-onset alzheimer's disease (EOAD) could serve as a linear model to study the pathogenesis of sporadic late-onset alzheimer's disease (LOAD). Now there is growing evidence to suggest that such reductionism may not be warranted; these suppositions are not adequate to explain the molecular complexities of LOAD. For example, the failure of some recent amyloid-centric clinical trials, which were largely based on the extrapolations from EOAD biological phenotypes to the molecular mechanisms in the pathogenesis of LOAD, might be due to such false assumptions. The distinct difference in the biology of LOAD and EOAD is underscored by the presence of EOAD cases without evidence of familial clustering or Mendelian transmission and, conversely, the discovery and frequent reports of such clustering and transmission patterns in LOAD cases. The primary thesis of this chapter is that a radically different way of thinking is required for comprehensive explanations regarding the distinct complexities in the molecular pathogenesis of inherited and sporadic forms of Alzheimer's disease (AD). We propose using longitudinal analytical methods and the paradigm of systems biology (using transcriptomics, proteomics, metabolomics, and lipidomics) to provide us a more comprehensive insight into the lifelong origin and progression of different molecular mechanisms and neurodegeneration. Such studies should aim to clarify the role of specific pathophysiological and signaling pathways such as neuroinflammation, altered lipid metabolism, apoptosis, oxidative stress, tau hyperphosphorylation, protein misfolding, tangle formation, and amyloidogenic cascade leading to overproduction and reduced clearance of aggregating amyloid-beta (Aβ) species. A more complete understanding of the distinct difference in molecular mechanisms, signaling pathways, as well as comparability of the various forms of AD is of paramount importance. The development of knowledge and technologies for early detection and characterization of the disease across all stages will improve the predictions regarding the course of the disease, prognosis, and response to treatment. No doubt such advances will have a significant impact on the clinical management of both EOAD and LOAD patients. The approach propped here, combining longitudinal studies with the systems biology paradigm, will create a more effective and comprehensive framework for development of prevention therapies in AD.

Dissecting Complex and Multifactorial Nature of Alzheimer's Disease Pathogenesis: a Clinical, Genomic, and Systems Biology Perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by loss of memory and other cognitive functions. AD can be classified into familial AD (FAD) and sporadic AD (SAD) based on heritability and into early onset AD (EOAD) and late onset AD (LOAD) based on age of onset. LOAD cases are more prevalent with genetically complex architecture. In spite of significant research focused on understanding the etiological mechanisms, search for diagnostic biomarker(s) and disease-modifying therapy is still on. In this article, we aim to comprehensively review AD literature on established etiological mechanisms including role of beta-amyloid and apolipoprotein E (APOE) along with promising newer etiological factors such as epigenetic modifications that have been associated with AD suggesting its multifactorial nature. As genomic studies have recently played a significant role in elucidating AD pathophysiology, a systematic review of findings from genome-wide linkage (GWL), genome-wide association (GWA), genome-wide expression (GWE), and epigenome-wide association studies (EWAS) was conducted. The availability of multi-dimensional genomic data has further coincided with the advent of computational and network biology approaches in recent years. Our review highlights the importance of integrative approaches involving genomics and systems biology perspective in elucidating AD pathophysiology. The promising newer approaches may provide reliable means of early and more specific diagnosis and help identify therapeutic interventions for LOAD.

Impaired mitochondrial energy metabolism in Alzheimer's disease: Impact on pathogenesis via disturbed epigenetic regulation of chromatin landscape

The amyloid cascade hypothesis for the pathogenesis of Alzheimer's disease (AD) was proposed over twenty years ago. However, the mechanisms of neurodegeneration and synaptic loss have remained elusive delaying the effective drug discovery. Recent studies have revealed that amyloid-β peptides as well as phosphorylated and fragmented tau proteins accumulate within mitochondria. This process triggers mitochondrial fission (fragmentation) and disturbs Krebs cycle function e.g. by inhibiting the activity of 2-oxoglutarate dehydrogenase. Oxidative stress, hypoxia and calcium imbalance also disrupt the function of Krebs cycle in AD brains. Recent studies on epigenetic regulation have revealed that Krebs cycle intermediates control DNA and histone methylation as well as histone acetylation and thus they have fundamental roles in gene expression. DNA demethylases (TET1-3) and histone lysine demethylases (KDM2-7) are included in the family of 2-oxoglutarate-dependent oxygenases (2-OGDO). Interestingly, 2-oxoglutarate is the obligatory substrate of 2-OGDO enzymes, whereas succinate and fumarate are the inhibitors of these enzymes. Moreover, citrate can stimulate histone acetylation via acetyl-CoA production. Epigenetic studies have revealed that AD is associated with changes in DNA methylation and histone acetylation patterns. However, the epigenetic results of different studies are inconsistent but one possibility is that they represent both coordinated adaptive responses and uncontrolled stochastic changes, which provoke pathogenesis in affected neurons. Here, we will review the changes observed in mitochondrial dynamics and Krebs cycle function associated with AD, and then clarify the mechanisms through which mitochondrial metabolites can control the epigenetic landscape of chromatin and induce pathological changes in AD.

Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network

Background

Most cases of idiopathic autism spectrum disorder (ASD) likely result from unknown environmental triggers in genetically susceptible individuals. These triggers may include maternal exposure of a fetus to minute concentrations of pharmaceuticals, such as carbamazepine (CBZ), venlafaxine (VNX) and fluoxetine (FLX). Unmetabolized pharmaceuticals reach drinking water through a variety of routes, including ineffectively treated sewage. Previous studies in our laboratory examined the extent to which gene sets were enriched in minnow brains treated with pharmaceuticals. Here, we tested the hypothesis that genes in fish brains and human cell cultures, significantly enriched by pharmaceuticals, would have distinct characteristics in an ASD-associated protein interaction network. We accomplished this by comparing these groups using 10 network indices.

Results

A network of 7212 proteins and 33,461 interactions was generated. We found that network characteristics for enriched gene sets for particular pharmaceuticals were distinct from each other, and were different from non-enriched ASD gene sets. In particular, genes in fish brains, enriched by CBZ and VNX 1) had higher network importance than that in the overall network, and those enriched by FLX, and 2) were distinct from FLX and non-enriched ASD genes in multivariate network space. Similarly, genes in human cell cultures enriched by pharmaceutical mixtures (at environmental concentrations) and valproate (at clinical dosages) had similar network signatures, and had greater network importance than genes in the overall ASD network.

Conclusions

The results indicate that important gene sets in the ASD network are particularly susceptible to perturbation by pharmaceuticals at environmental concentrations.

Integrating -Omics: Systems Biology as Explored Through C. elegans Research

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

Pathway and network approaches for identification of cancer signature markers from omics data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

Genomics in neurological disorders

Neurological disorders comprise a variety of complex diseases in the central nervous system, which can be roughly classified as neurodegenerative diseases and psychiatric disorders. The basic and translational research of neurological disorders has been hindered by the difficulty in accessing the pathological center (i.e., the brain) in live patients. The rapid advancement of sequencing and array technologies has made it possible to investigate the disease mechanism and biomarkers from a systems perspective. In this review, recent progresses in the discovery of novel risk genes, treatment targets and peripheral biomarkers employing genomic technologies will be discussed. Our major focus will be on two of the most heavily investigated neurological disorders, namely Alzheimer’s disease and autism spectrum disorder.

Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach

Airway remodeling is a pathophysiologic process at the clinical, cellular, and molecular level relating to chronic obstructive airway diseases such as chronic obstructive pulmonary disease (COPD), asthma and mustard lung. These diseases are associated with the dysregulation of multiple molecular pathways in the airway cells. Little progress has so far been made in discovering the molecular causes of complex disease in a holistic systems manner. Therefore, pathway and network reconstruction is an essential part of a systems biology approach to solve this challenging problem. In this paper, multiple data sources were used to construct the molecular process of airway remodeling pathway in mustard lung as a model of airway disease. We first compiled a master list of genes that change with airway remodeling in the mustard lung disease and then reconstructed the pathway by generating and merging the protein-protein interaction and the gene regulatory networks. Experimental observations and literature mining were used to identify and validate the master list. The outcome of this paper can provide valuable information about closely related chronic obstructive airway diseases which are of great importance for biologists and their future research. Reconstructing the airway remodeling interactome provides a starting point and reference for the future experimental study of mustard lung, and further analysis and development of these maps will be critical to understanding airway diseases in patients.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: a long-range point of view beyond 2020

Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD.

A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Association of the dopamine β-hydroxylase 19 bp insertion/deletion polymorphism with positive symptoms but not tardive dyskinesia in schizophrenia

OBJECTIVE

Overactivity of dopaminergic neurotransmission is a putative mechanism of tardive dyskinesia (TD). Dopamine beta-hydroxylase (DBH) is a key enzyme in the conversion of dopamine to norepinephrine, and plasma DBH activity is altered in TD patients. This study examined whether the functional DBH 5'-Ins/Del polymorphism was associated with TD severity in Chinese patients with schizophrenia.

METHODS

We compared the rate of this polymorphism in patients with (n = 312) and without TD (n = 435), and healthy controls (n = 625). The severity of TD was assessed using the Abnormal Involuntary Movement Scale (AIMS) and psychopathology using the Positive and Negative Syndrome Scale (PANSS).

RESULTS

There were no significant differences in the distribution of the allele and genotype frequencies between the patients and controls, or between the patients with and without TD. Also, there was no significant difference in the AIMS total score between the three genotype groups. However, the PANSS positive symptom subscore was significantly higher in patients with Del/Del genotype (13.2 ± 5.2) than those with Ins/Del (11.2 ± 4.9) and Ins/Ins (11.1 ± 3.1) genotypes (both p < 0.05).

CONCLUSION

These results suggest that although the DBH 5'-Ins/Del polymorphism was not associated with susceptibility to TD in patients with schizophrenia, it might be related to positive symptoms of schizophrenia.

Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.

The ways in which cells respond to changes in their environment are controlled by networks of physical links among the proteins and genes. The initial signal of a change in conditions rapidly passes through these networks from the cytoplasm to the nucleus, where it can lead to long-term alterations in cellular behavior by controlling the expression of genes. These cascades of signaling events underlie many normal biological processes. As a result, being able to map out how these networks change in disease can provide critical insights for new approaches to treatment. We present a computational method for reconstructing these networks by finding links between the rapid short-term changes in proteins and the longer-term changes in gene regulation. This method brings together systematic measurements of protein signaling, genome organization and transcription in the context of protein-protein and protein-DNA interactions. When used to analyze datasets from an oncogene expressing cell line model of human glioblastoma, our approach identifies key nodes that affect cell survival and functional transcriptional regulators.

Chapter 4: Protein interactions and disease

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

Intraneuronal β-amyloid and its interactions with proteins and subcellular organelles

Amyloidogenic aggregation and misfolding of proteins are linked to neurodegeneration. The mechanism of neurodegeneration in Alzheimer's disease, which gives rise to severe neuronal death and memory loss, is not yet fully understood. The amyloid hypothesis remains the most accepted theory for the pathomechanism of the disease. It was suggested that β-amyloid accumulation may play a key role in initiating the neurodegenerative processes. The recent intracellular β-amyloid (iAβ) hypothesis emphasizes the primary role of iAβ to initiate the disease by interaction with cytoplasmic proteins and cell organelles, thereby triggering apoptosis. Sophisticated methods (proteomics, protein microarray, and super resolution microscopy) have been used for studying iAβ interactions with proteins and membraneous structures. The present review summarizes the studies on the origin of iAβ and the base of its neurotoxicity: interactions with cytosolic proteins and several cell organelles such as endoplasmic reticulum, endosomes, lysosomes, ribosomes, mitochondria, and the microtubular system.

Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets

BACKGROUND

Alzheimer's disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.

RESULTS

Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA/SVM. GA/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods.

CONCLUSION

Compared to the other methods, GA/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. The biological significance of the genes and gene pairs is discussed.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Consolidation and translation regulation

mRNA translation, or protein synthesis, is a major component of the transformation of the genetic code into any cellular activity. This complicated, multistep process is divided into three phases: initiation, elongation, and termination. Initiation is the step at which the ribosome is recruited to the mRNA, and is regarded as the major rate-limiting step in translation, while elongation consists of the elongation of the polypeptide chain; both steps are frequent targets for regulation, which is defined as a change in the rate of translation of an mRNA per unit time. In the normal brain, control of translation is a key mechanism for regulation of memory and synaptic plasticity consolidation, i.e., the off-line processing of acquired information. These regulation processes may differ between different brain structures or neuronal populations. Moreover, dysregulation of translation leads to pathological brain function such as memory impairment. Both normal and abnormal function of the translation machinery is believed to lead to translational up-regulation or down-regulation of a subset of mRNAs. However, the identification of these newly synthesized proteins and determination of the rates of protein synthesis or degradation taking place in different neuronal types and compartments at different time points in the brain demand new proteomic methods and system biology approaches. Here, we discuss in detail the relationship between translation regulation and memory or synaptic plasticity consolidation while focusing on a model of cortical-dependent taste learning task and hippocampal-dependent plasticity. In addition, we describe a novel systems biology perspective to better describe consolidation.