Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease

A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network

The inference of large-scale gene regulatory networks is essential for understanding comprehensive interactions among genes. Most existing methods are limited to reconstructing networks with a few hundred nodes. Therefore, parallel computing paradigms must be leveraged to construct large networks. We propose a generic parallel framework that enables any existing method, without re-engineering, to infer large networks in parallel, guaranteeing quality output. The framework is tested on 15 inference methods (not limited to) employing in silico benchmarks and real-world large expression matrices, followed by qualitative and speedup assessment. The framework does not compromise the quality of the base serial inference method. We rank the candidate methods and use the top-performing method to infer an Alzheimer's Disease (AD) affected network from large expression profiles of a triple transgenic mouse model consisting of 45,101 genes. The resultant network is further explored to obtain hub genes that emerge functionally related to the disease. We partition the network into 41 modules and conduct pathway enrichment analysis, revealing that a good number of participating genes are collectively responsible for several brain disorders, including AD. Finally, we extract the interactions of a few known AD genes and observe that they are periphery genes connected to the network's hub genes. Availability: The R implementation of the framework is downloadable from https://github.com/Netralab/GenericParallelFramework.

Respirasome Proteins Are Regulated by Sex-Hormone Interactions in the Brain

The existence of sex differences in disease incidence is attributed, in part, to sex differences in metabolism. Uncovering the precise mechanism driving these differences is an extraordinarily complex process influenced by genetics, endogenous hormones, sex-specific lifetime events, individual differences and external environmental/social factors. In fact, such differences may be subtle, but across a life span, increase susceptibility to a pathology. Whilst research persists in the hope of discovering an elegant biological mechanism to underpin sex differences in disease, here, we show, for the first time, that such a mechanism may be subtle in nature but influenced by multiple sex-specific factors. A proteomic dataset was generated from a gonadectomized mouse model treated with Tibolone, a menopausal hormone therapy. Following functional enrichment analysis, we identified that Alzheimer's disease and the electron transport chain-associated pathways were regulated by sex-hormone interactions. Specifically, we identified that the expression of three respirasome proteins, NDUFA2, NDUFA7 and UQCR10, is significantly altered by compounding factors that contribute to sex differences. These proteins function in bioenergetics and produce reactive oxygen species, which are each dysregulated in many diseases with sex differences in incidence. We show sex-specific reprogrammed responses to Tibolone following gonadectomy, which primarily influence the expression of proteins contributing to metabolic pathways. This further infers that metabolic differences may underpin the observed sex differences in disease, but also that hormone therapy research now has potential in exploring sex-specific interventions to produce an effective method of prevention or treatment.

Single-Cell RNA-Seq of Cisplatin-Treated Adult Stria Vascularis Identifies Cell Type-Specific Regulatory Networks and Novel Therapeutic Gene Targets

The endocochlear potential (EP) generated by the stria vascularis (SV) is necessary for hair cell mechanotransduction in the mammalian cochlea. We sought to create a model of EP dysfunction for the purposes of transcriptional analysis and treatment testing. By administering a single dose of cisplatin, a commonly prescribed cancer treatment drug with ototoxic side effects, to the adult mouse, we acutely disrupt EP generation. By combining these data with single cell RNA-sequencing findings, we identify transcriptional changes induced by cisplatin exposure, and by extension transcriptional changes accompanying EP reduction, in the major cell types of the SV. We use these data to identify gene regulatory networks unique to cisplatin treated SV, as well as the differentially expressed and druggable gene targets within those networks. Our results reconstruct transcriptional responses that occur in gene expression on the cellular level while identifying possible targets for interventions not only in cisplatin ototoxicity but also in EP dysfunction.

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression.

METHODS

We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD.

RESULTS

We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions.

DISCUSSION

Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD.

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.