Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer's disease

Potential for New Therapeutic Approaches by Targeting Lactate and pH Mediated Epigenetic Dysregulation in Major Mental Diseases

Multiple lines of evidence have shown that lactate-mediated pH alterations in the brains of patients with neuropsychiatric diseases such as schizophrenia (SCZ), Alzheimer's disease (AD) and autism may be attributed to mitochondrial dysfunction and changes in energy metabolism. While neuronal activity is associated with reduction in brain pH, astrocytes are responsible for rebalancing the pH to maintain the equilibrium. As lactate level is the main determinant of brain pH, neuronal activities are impacted by pH changes due to the binding of protons (H+) to various types of proteins, altering their structure and function in the neuronal and non-neuronal cells of the brain. Lactate and pH could affect diverse types of epigenetic modifications, including histone lactylation, which is linked to histone acetylation and DNA methylation. In this review, we discuss the importance of pH homeostasis in normal brain function, the role of lactate as an essential epigenetic regulatory molecule and its contributions to brain pH abnormalities in neuropsychiatric diseases, and shed light on lactate-based and pH-modulating therapies in neuropsychiatric diseases by targeting epigenetic modifications. In conclusion, we attempt to highlight the potentials and challenges of translating lactate-pH-modulating therapies to clinics for the treatment of neuropsychiatric diseases.

Endosomal Acid-Base Homeostasis in Neurodegenerative Diseases

Neurodegenerative disorders are debilitating and largely untreatable conditions that pose a significant burden to affected individuals and caregivers. Overwhelming evidence supports a crucial preclinical role for endosomal dysfunction as an upstream pathogenic hub and driver in Alzheimer's disease (AD) and related neurodegenerative disorders. We present recent advances on the role of endosomal acid-base homeostasis in neurodegeneration and discuss evidence for converging mechanisms. The strongest genetic risk factor in sporadic AD is the ε4 allele of Apolipoprotein E (ApoE4), which potentiates pre-symptomatic endosomal dysfunction and prominent amyloid beta (Aβ) pathology, although how these pathways are linked mechanistically has remained unclear. There is emerging evidence that the Christianson syndrome protein NHE6 is a prominent ApoE4 effector linking endosomal function to Aβ pathologies. By functioning as a dominant leak pathway for protons, the Na⁺/H⁺ exchanger activity of NHE6 limits endosomal acidification and regulates β-secretase (BACE)-mediated Aβ production and LRP1 receptor-mediated Aβ clearance. Pathological endosomal acidification may impact both Aβ generation and clearance mechanisms and emerges as a promising therapeutic target in AD. We also offer our perspective on the complex role of endosomal acid-base homeostasis in the pathogenesis of neurodegeneration and its therapeutic implications for neuronal rescue and repair strategies.

Amyloid clearance defect in ApoE4 astrocytes is reversed by epigenetic correction of endosomal pH

Endosomes have emerged as a central hub and pathogenic driver of Alzheimer's disease (AD). The earliest brain cytopathology in neurodegeneration, occurring decades before amyloid plaques and cognitive decline, is an expansion in the size and number of endosomal compartments. The strongest genetic risk factor for sporadic AD is the ε4 allele of Apolipoprotein E (ApoE4). Previous studies have shown that ApoE4 potentiates presymptomatic endosomal dysfunction and defective endocytic clearance of amyloid beta (Aβ), although how these two pathways are linked at a cellular and mechanistic level has been unclear. Here, we show that aberrant endosomal acidification in ApoE4 astrocytes traps the low-density lipoprotein receptor-related protein (LRP1) within intracellular compartments, leading to loss of surface expression and Aβ clearance. Pathological endosome acidification is caused by ε4 risk allele-selective down-regulation of the Na⁺/H⁺ exchanger isoform NHE6, which functions as a critical leak pathway for endosomal protons. In vivo, the NHE6 knockout (NHE6^KO) mouse model showed elevated Aβ in the brain, consistent with a causal effect. Increased nuclear translocation of histone deacetylase 4 (HDAC4) in ApoE4 astrocytes, compared with the nonpathogenic ApoE3 allele, suggested a mechanistic basis for transcriptional down-regulation of NHE6. HDAC inhibitors that restored NHE6 expression normalized ApoE4-specific defects in endosomal pH, LRP1 trafficking, and amyloid clearance. Thus, NHE6 is a downstream effector of ApoE4 and emerges as a promising therapeutic target in AD. These observations have prognostic implications for patients who have Christianson syndrome with loss of function mutations in NHE6 and exhibit prominent glial pathology and progressive hallmarks of neurodegeneration.

Neurological Diseases from a Systems Medicine Point of View

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article's supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer's disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html.

KPP: KEGG Pathway Painter

Background

High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns. Functional analysis of GE patterns is a daunting task as it requires often recourse to the public repositories of biological knowledge. On the other hand, in many cases researcher's inquiry can be served by a comprehensive glimpse. The KEGG PATHWAY database is a compilation of manually verified maps of biological interactions represented by the complete set of pathways related to signal transduction and other cellular processes. Rapid mapping of the differentially expressed genes to the KEGG pathways may provide an idea about the functional relevance of the gene lists corresponding to the high-throughput expression data.

Results

Here we present a web based graphic tool KEGG Pathway Painter (KPP). KPP paints pathways from the KEGG database using large sets of the candidate genes accompanied by "overexpressed" or "underexpressed" marks, for example, those generated by microarrays or miRNA profilings.

Conclusion

KPP provides fast and comprehensive visualization of the global GE changes by consolidating a list of the color-coded candidate genes into the KEGG pathways. KPP is freely available and can be accessed at http://web.cos.gmu.edu/~gmanyam/kegg/

Emerging bioinformatics approaches for analysis of NGS-derived coding and non-coding RNAs in neurodegenerative diseases

Neurodegenerative diseases in general and specifically late-onset Alzheimer’s disease (LOAD) involve a genetically complex and largely obscure ensemble of causative and risk factors accompanied by complex feedback responses. The advent of “high-throughput” transcriptome investigation technologies such as microarray and deep sequencing is increasingly being combined with sophisticated statistical and bioinformatics analysis methods complemented by knowledge-based approaches such as Bayesian Networks or network and graph analyses. Together, such “integrative” studies are beginning to identify co-regulated gene networks linked with biological pathways and potentially modulating disease predisposition, outcome, and progression. Specifically, bioinformatics analyses of integrated microarray and genotyping data in cases and controls reveal changes in gene expression of both protein-coding and small and long regulatory RNAs; highlight relevant quantitative transcriptional differences between LOAD and non-demented control brains and demonstrate reconfiguration of functionally meaningful molecular interaction structures in LOAD. These may be measured as changes in connectivity in “hub nodes” of relevant gene networks (Zhang etal., 2013). We illustrate here the open analytical questions in the transcriptome investigation of neurodegenerative disease studies, proposing “ad hoc” strategies for the evaluation of differential gene expression and hints for a simple analysis of the non-coding RNA (ncRNA) part of such datasets. We then survey the emerging role of long ncRNAs (lncRNAs) in the healthy and diseased brain transcriptome and describe the main current methods for computational modeling of gene networks. We propose accessible modular and pathway-oriented methods and guidelines for bioinformatics investigations of whole transcriptome next generation sequencing datasets. We finally present methods and databases for functional interpretations of lncRNAs and propose a simple heuristic approach to visualize and represent physical and functional interactions of the coding and non-coding components of the transcriptome. Integrating in a functional and integrated vision coding and ncRNA analyses is of utmost importance for current and future analyses of neurodegenerative transcriptomes.