Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Leveraging DNA methylation to predict treatment response in major depressive disorder: A critical review.

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

FKBP52 overexpression accelerates hippocampal-dependent memory impairments in a tau transgenic mouse model.

Abnormal accumulation of hyperphosphorylated tau induces pathogenesis in neurodegenerative diseases, like Alzheimer's disease. Molecular chaperones with peptidyl-prolyl cis/trans isomerase (PPIase) activity are known to regulate these processes. Previously, in vitro studies have shown that the 52 kDa FK506-binding protein (FKBP52) interacts with tau inducing its oligomerization and fibril formation to promote toxicity. Thus, we hypothesized that increased expression of FKBP52 in the brains of tau transgenic mice would alter tau phosphorylation and neurofibrillary tangle formation ultimately leading to memory impairments. To test this, tau transgenic (rTg4510) and wild-type mice received bilateral hippocampal injections of virus overexpressing FKBP52 or GFP control. We examined hippocampal-dependent memory, synaptic plasticity, tau phosphorylation status, and neuronal health. This work revealed that rTg4510 mice overexpressing FKBP52 had impaired spatial learning, accompanied by long-term potentiation deficits and hippocampal neuronal loss, which was associated with a modest increase in total caspase 12. Together with previous studies, our findings suggest that FKBP52 may sensitize neurons to tau-mediated dysfunction via activation of a caspase-dependent pathway, contributing to memory and learning impairments.

Small heat shock protein 22 kDa can modulate the aggregation and liquid-liquid phase separation behavior of tau.

Alzheimer's disease is a progressive fatal neurodegenerative disease with no cure or effective treatments. The hallmarks of disease include extracellular plaques and intracellular tangles of aggregated protein. The intracellular tangles consist of the microtubule associated protein tau. Preventing the pathological aggregation of tau may be an important therapeutic approach to treat disease. In this study we show that small heat shock protein 22 kDa (Hsp22) can prevent the aggregation of tau in vitro. Additionally, tau can undergo liquid-liquid phase separation (LLPS) in the presence of crowding reagents which causes it to have an increased aggregation rate. We show that Hsp22 can modulate both the aggregation and LLPS behavior of tau in vitro.

RANDOMIZE: A Web Server for Data Randomization.

The microarray-based Illumina Infinium MethylationEpic BeadChip (Epic 850k) has become a useful and standard tool for epigenome wide deoxyribonucleic acid (DNA) methylation profiling. Data from this technology may suffer from batch effects due to improper handling of the samples during the plating process. Batch effects are a significant issue and can give rise to spurious and inaccurate results and reduction in power to detect real biological differences. Careful study design, such as randomizing the samples to uniformly distribute the samples across the factors responsible for batch effects, is crucial to address batch effects and other technical artifacts. Randomization helps to reduce the likelihood of bias and impact of difference among groups. This process of randomizing the samples can be a tedious, error-prone, and time-consuming task without a user-friendly and efficient tool. We present RANDOMIZE, a web-based application designed to perform randomization of relevant metadata to evenly distribute samples across the factors typically responsible for batch effects in DNA methylation microarrays, such as rows, chips and plates. We demonstrate that the tool is efficient, fast and easy to use. The tool is freely available online at https://coph-usf.shinyapps.io/RANDOMIZE/ and can be accessed using any web browser. Sample data and tutorial is also available with the tool.