Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

Transcriptional Dysregulation Study Reveals a Core Network Involving the Progression of Alzheimer's Disease.

Background: The pathogenesis of Alzheimer's disease is associated with dysregulation at different levels from transcriptome to cellular functioning. Such complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes. Methods: Applying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states. Results: Ninety- seven genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimer's disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles. Conclusion: Overall, our study reveals a core network of transcriptional dysregulation associated with the progression of Alzheimer's disease by affecting the aging and synaptic functions related genes; the gene regulation is not conserved in the human and mouse brains.

Systemic evaluation of cellular reprogramming processes exploiting a novel R-tool: eegc.

MOTIVATION: Cells derived by cellular engineering, i.e. differentiation of induced pluripotent stem cells and direct lineage reprogramming, carry a tremendous potential for medical applications and in particular for regenerative therapies. These approaches consist in the definition of lineage-specific experimental protocols that, by manipulation of a limited number of biological cues-niche mimicking factors, (in)activation of transcription factors, to name a few-enforce the final expression of cell-specific (marker) molecules. To date, given the intricate complexity of biological pathways, these approaches still present imperfect reprogramming fidelity, with uncertain consequences on the functional properties of the resulting cells. RESULTS: We propose a novel tool eegc to evaluate cellular engineering processes, in a systemic rather than marker-based fashion, by integrating transcriptome profiling and functional analysis. Our method clusters genes into categories representing different states of (trans)differentiation and further performs functional and gene regulatory network analyses for each of the categories of the engineered cells, thus offering practical indications on the potential lack of the reprogramming protocol. AVAILABILITY AND IMPLEMENTATION: eegc R package is released under the GNU General Public License within the Bioconductor project, freely available at https://bioconductor.org/packages/eegc/. CONTACT: christine.nardini.rsrc@gmail.com or hongkang.k.mei@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BET bromodomain inhibition promotes neurogenesis while inhibiting gliogenesis in neural progenitor cells.

Neural stem cells and progenitor cells (NPCs) are increasingly appreciated to hold great promise for regenerative medicine to treat CNS injuries and neurodegenerative diseases. However, evidence for effective stimulation of neuronal production from endogenous or transplanted NPCs for neuron replacement with small molecules remains limited. To identify novel chemical entities/targets for neurogenesis, we had established a NPC phenotypic screen assay and validated it using known small-molecule neurogenesis inducers. Through screening small molecule libraries with annotated targets, we identified BET bromodomain inhibition as a novel mechanism for enhancing neurogenesis. BET bromodomain proteins, Brd2, Brd3, and Brd4 were found to be downregulated in NPCs upon differentiation, while their levels remain unaltered in proliferating NPCs. Consistent with the pharmacological study using bromodomain selective inhibitor (+)-JQ-1, knockdown of each BET protein resulted in an increase in the number of neurons with simultaneous reduction in both astrocytes and oligodendrocytes. Gene expression profiling analysis demonstrated that BET bromodomain inhibition induced a broad but specific transcription program enhancing directed differentiation of NPCs into neurons while suppressing cell cycle progression and gliogenesis. Together, these results highlight a crucial role of BET proteins as epigenetic regulators in NPC development and suggest a therapeutic potential of BET inhibitors in treating brain injuries and neurodegenerative diseases.

A Systematic Investigation into Aging Related Genes in Brain and Their Relationship with Alzheimer's Disease.

Aging, as a complex biological process, is accompanied by the accumulation of functional loses at different levels, which makes age to be the biggest risk factor to many neurological diseases. Even following decades of investigation, the process of aging is still far from being fully understood, especially at a systematic level. In this study, we identified aging related genes in brain by collecting the ones with sustained and consistent gene expression or DNA methylation changes in the aging process. Functional analysis with Gene Ontology to these genes suggested transcriptional regulators to be the most affected genes in the aging process. Transcription regulation analysis found some transcription factors, especially Specificity Protein 1 (SP1), to play important roles in regulating aging related gene expression. Module-based functional analysis indicated these genes to be associated with many well-known aging related pathways, supporting the validity of our approach to select aging related genes. Finally, we investigated the roles of aging related genes on Alzheimer's Disease (AD). We found that aging and AD related genes both involved some common pathways, which provided a possible explanation why aging made the brain more vulnerable to Alzheimer's Disease.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology.

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network.

Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric "S-score" that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by the case studies.

Drug targets in the cytokine universe for autoimmune disease.

In autoimmune disease, a network of diverse cytokines is produced in association with disease susceptibility to constitute the 'cytokine milieu' that drives chronic inflammation. It remains elusive how cytokines interact in such a complex network to sustain inflammation in autoimmune disease. This has presented huge challenges for successful drug discovery because it has been difficult to predict how individual cytokine-targeted therapy would work. Here, we combine the principles of Chinese Taoism philosophy and modern bioinformatics tools to dissect multiple layers of arbitrary cytokine interactions into discernible interfaces and connectivity maps to predict movements in the cytokine network. The key principles presented here have important implications in our understanding of cytokine interactions and development of effective cytokine-targeted therapies for autoimmune disorders.

Opportunities in systems biology to discover mechanisms and repurpose drugs for CNS diseases.

Therapies for central nervous system (CNS) diseases remain an unmet medical need. This is largely due to multiple unknown disease-modifying genes and pathways. Systems biology through network modeling has shown promise in discovering novel therapeutic targets, deciphering disease mechanisms, and suggesting drug repurposing opportunities. In this article we cover current progress in systems biology and its role, applications, and challenges in the pharmaceutical industry. We also outline a practical strategy to infer drug repositioning candidates for rare CNS diseases by describing Multiple Level Network Modeling (MLNM) analysis.

Role of the low-affinity binding site in electron transfer from cytochrome C to cytochrome C peroxidase.

The interaction of yeast iso-1-cytochrome c (yCc) with the high- and low-affinity binding sites on cytochrome c peroxidase compound I (CMPI) was studied by stopped-flow spectroscopy. When 3 microM reduced yCc(II) was mixed with 0.5 microM CMPI at 10 mM ionic strength, the Trp-191 radical cation was reduced from the high-affinity site with an apparent rate constant >3000 s(-1), followed by slow reduction of the oxyferryl heme with a rate constant of only 10 s(-1). In contrast, mixing 3 microM reduced yCc(II) with 0.5 microM preformed CMPI *yCc(III) complex led to reduction of the radical cation with a rate constant of 10 s(-1), followed by reduction of the oxyferryl heme in compound II with the same rate constant. The rate constants for reduction of the radical cation and the oxyferryl heme both increased with increasing concentrations of yCc(II) and remained equal to each other. These results are consistent with a mechanism in which both the Trp-191 radical cation and the oxyferryl heme are reduced by yCc(II) in the high-affinity binding site, and the reaction is rate-limited by product dissociation of yCc(III) from the high-affinity site with apparent rate constant k(d). Binding yCc(II) to the low-affinity site is proposed to increase the rate constant for dissociation of yCc(III) from the high-affinity site in a substrate-assisted product dissociation mechanism. The value of k(d) is <5 s(-1) for the 1:1 complex and >2000 s(-1) for the 2:1 complex at 10 mM ionic strength. The reaction of horse Cc(II) with CMPI was greatly inhibited by binding 1 equiv of yCc(III) to the high-affinity site, providing evidence that reduction of the oxyferryl heme involves electron transfer from the high-affinity binding site rather than the low-affinity site. The effects of CcP surface mutations on the dissociation rate constant indicate that the high-affinity binding site used for the reaction in solution is the same as the one identified in the yCc*CcP crystal structure.