Ellis Patrick

MicroRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis.

The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh‐frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome and mutation. Associations with prognosis were replicated for miR‐150‐5p, miR‐142‐3p and miR‐142‐5p. Co‐analysis of miRNA and mRNA uncovered a network associated with poor prognosis (PP) that paradoxically favoured expression of miRNAs opposing tumorigenesis. These miRNAs are likely part of an autoregulatory response to oncogenic drivers, rather than drivers themselves. Robust association of miR‐150‐5p and the miR‐142 duplex with good prognosis and earlier stage metastatic melanoma supports their potential as biomarkers. miRNAs overexpressed in association with PP in an autoregulatory fashion will not be suitable therapeutic targets.

An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome

We report a multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation and histone acetylation data from the dorsolateral prefrontal cortex of 411 older adults who have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also reanalyze published genome wide association studies using an xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based expression eQTL resource.

A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease

There is a need for new therapeutic targets with which to prevent Alzheimer’s disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.The authors constructed and validated a molecular network of the aging human cortex from RNA sequencing data from 478 individuals and identified genes that affect cognitive decline or neuropathology in Alzheimer’s disease.

Hepatic metallothionein expression in chronic hepatitis C virus infection is IFNL3 genotype-dependent.

The IFNL3 genotype predicts the clearance of hepatitis C virus (HCV), spontaneously and with interferon (IFN)-based therapy. The responder genotype is associated with lower expression of interferon stimulated genes (ISGs) in liver biopsies from chronic hepatitis C patients. However, ISGs represent many interacting molecular pathways, and we hypothesised that the IFNL3 genotype may produce a characteristic pattern of ISG expression explaining the effect of genotype on viral clearance. For the first time, we identified an association between a cluster of ISGs, the metallothioneins (MTs) and IFNL3 genotype. Importantly, MTs were significantly upregulated (in contrast to most other ISGs) in HCV-infected liver biopsies of rs8099917 responders. An association between lower fibrosis scores and higher MT levels was demonstrated underlying clinical relevance of this association. As expected, overall ISGs were significantly downregulated in biopsies from subjects with the IFNL3 rs8099917 responder genotype (P=2.38 × 10−7). Peripheral blood analysis revealed paradoxical and not previously described findings with upregulation of ISGs seen in the responder genotype (P=1.00 × 10−4). The higher MT expression in responders may contribute to their improved viral clearance and MT-inducing agents may be useful adjuncts to therapy for HCV. Upregulation of immune cell ISGs in responders may also contribute to the IFNL3 genotype effect.

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information

BackgroundThe cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation.ResultsWe have proposed a novel approach called Tshrink+ for inferring differential gene expression through improved modelling of the gene-wise variances. Existing methods share information between genes of similar average expression by shrinking, or moderating, the gene-wise variances to a fitted common variance. We have been able to achieve improved estimation of the common variance by using gene-wise sample variances from external experiments, as well as gene length.ConclusionsUsing biological data we show that utilising additional external information can improve the modelling of the common variance and hence the calling of differentially expressed genes. These sources of additional information include gene length and gene-wise sample variances from other RNA-Seq and microarray datasets, of both related and seemingly unrelated tissue types. The results of this are promising, with our differential expression test, Tshrink+, performing favourably when compared to existing methods such as DESeq and edgeR when considering both gene ranking and sensitivity. These improved variance models could easily be implemented in both DESeq and edgeR and highlight the need for a database that offers a profile of gene variances over a range of tissue types and organisms.

KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis

Mass spectrometry (MS)‐based quantitative phosphoproteomics has become a key approach for proteome‐wide profiling of phosphorylation in tissues and cells. Traditional experimental design often compares a single treatment with a control, whereas increasingly more experiments are designed to compare multiple treatments with respect to a control. To this end, the development of bioinformatic tools that can integrate multiple treatments and visualise kinases and substrates under combinatorial perturbations is vital for dissecting concordant and/or independent effects of each treatment. Here, we propose a hypothesis driven kinase perturbation analysis (KinasePA) to annotate and visualise kinases and their substrates that are perturbed by various combinatorial effects of treatments in phosphoproteomics experiments. We demonstrate the utility of KinasePA through its application to two large‐scale phosphoproteomics datasets and show its effectiveness in dissecting kinases and substrates within signalling pathways driven by unique combinations of cellular stimuli and inhibitors. We implemented and incorporated KinasePA as part of the “directPA” R package available from the comprehensive R archive network (CRAN). Furthermore, KinasePA also has an interactive web interface that can be readily applied to annotate user provided phosphoproteomics data (http://kinasepa.pengyiyang.org).

Estimation of data-specific constitutive exons with RNA-Seq data

BackgroundRNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons— exons which are consistently conserved after splicing— offers an unbiased estimation of the overall transcription of a gene.ResultsWe propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are “constitutive” in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq.ConclusionWhen used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.

A cortical immune network map identifies a subset of human microglia involved in Tau pathology

Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimers disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five gene modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 541 subjects from two cohort studies of brain aging. Two of these transcriptional programs – modules 113 and 114 – relate to the accumulation of β-amyloid, while module 5 relates to tau pathology. These modules are also detectable in the human brains epigenome, where we replicate these associations. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that VASP, a representative module 5 gene, encodes a protein that is upregulated in activated microglia in AD.

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.

MOTIVATION In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. RESULTS We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. AVAILABILITY AND IMPLEMENTATION This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages. CONTACT jean.yang@sydney.edu.au SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A molecular network of the aging brain implicates INPPL1 and PLXNB1 in Alzheimer's disease

The fact that only symptomatic therapies of small effect are available for Alzheimer’s disease (AD) today highlights the need for new therapeutic targets with which to prevent a major contributor to aging-related cognitive decline. Here, we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first identify the role of modules of coexpressed genes, and then confirm them in independent AD datasets. Then, we prioritize influential genes in AD-related modules and test our predictions in human model systems. We functionally validate two putative regulator genes in human astrocytes: INPPL1 and PLXNB1, whose activity in AD may be related to semaphorin signalling and type II diabetes, which have both been implicated in AD. This arc of network identification followed by statistical and experimental validation provides specific new targets for therapeutic development and illustrates a network approach to a complex disease. One sentence summary Molecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimer’s and cognitive decline genes.