Mark F. Rogers

An integrative approach to predicting the functional effects of non-coding and coding sequence variation.

Motivation: Technological advances have enabled the identification of an increasingly large spectrum of single nucleotide variants within the human genome, many of which may be associated with monogenic disease or complex traits. Here, we propose an integrative approach, named FATHMM-MKL, to predict the functional consequences of both coding and non-coding sequence variants. Our method utilizes various genomic annotations, which have recently become available, and learns to weight the significance of each component annotation source. Results: We show that our method outperforms current state-of-the-art algorithms, CADD and GWAVA, when predicting the functional consequences of non-coding variants. In addition, FATHMM-MKL is comparable to the best of these algorithms when predicting the impact of coding variants. The method includes a confidence measure to rank order predictions. Availability and implementation: The FATHMM-MKL webserver is available at: http://fathmm.biocompute.org.uk Contact: H.Shihab@bristol.ac.uk or Mark.Rogers@bristol.ac.uk or C.Campbell@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Treating the placenta to prevent adverse effects of gestational hypoxia on fetal brain development.

Some neuropsychiatric disease, including schizophrenia, may originate during prenatal development, following periods of gestational hypoxia and placental oxidative stress. Here we investigated if gestational hypoxia promotes damaging secretions from the placenta that affect fetal development and whether a mitochondria-targeted antioxidant MitoQ might prevent this. Gestational hypoxia caused low birth-weight and changes in young adult offspring brain, mimicking those in human neuropsychiatric disease. Exposure of cultured neurons to fetal plasma or to secretions from the placenta or from model trophoblast barriers that had been exposed to altered oxygenation caused similar morphological changes. The secretions and plasma contained altered microRNAs whose targets were linked with changes in gene expression in the fetal brain and with human schizophrenia loci. Molecular and morphological changes in vivo and in vitro were prevented by a single dose of MitoQ bound to nanoparticles, which were shown to localise and prevent oxidative stress in the placenta but not in the fetus. We suggest the possibility of developing preventative treatments that target the placenta and not the fetus to reduce risk of psychiatric disease in later life.

FATHMM-XF: accurate prediction of pathogenic point mutations via extended features

Summary We present FATHMM‐XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM‐XF outperforms competitors on benchmark tests, particularly in non‐coding regions where the majority of pathogenic mutations are likely to be found. Availability and implementation The FATHMM‐XF web server is available at http://fathmm.biocompute.org.uk/fathmm‐xf/, and as tracks on the Genome Tolerance Browser: http://gtb.biocompute.org.uk. Predictions are provided for human genome version GRCh37/hg19. The data used for this project can be downloaded from: http://fathmm.biocompute.org.uk/fathmm‐xf/

RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or ‘podocytes’, the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes.

Sex‐specific differences in cardiovascular and metabolic hormones with integrated signalling in the paraventricular nucleus of the hypothalamus

What is the topic of this review? We describe roles of crucial signalling molecules in the paraventricular nucleus of the hypothalamus and highlight recent data suggesting sex‐specific changes in the expression of crucial signalling molecules and their receptors, which may underlie sex differences in both cardiovascular and metabolic function. What advances does it highlight? This review highlights the integrative capacity of the paraventricular nucleus in mediating cardiovascular and metabolic effects by integrating information from multiple signalling molecules. It also proposes that these signalling molecules have sex‐specific differential gene expression, indicating the importance of considering these differences in our ongoing search to understand the female–male differences in the regulation of crucial autonomic systems.

iCLIP identifies novel roles for SAFB1 in regulating RNA processing and neuronal function.

BackgroundSAFB1 is a RNA binding protein implicated in the regulation of multiple cellular processes such as the regulation of transcription, stress response, DNA repair and RNA processing. To gain further insight into SAFB1 function we used iCLIP and mapped its interaction with RNA on a genome wide level.ResultsiCLIP analysis found SAFB1 binding was enriched, specifically in exons, ncRNAs, 3’ and 5’ untranslated regions. SAFB1 was found to recognise a purine-rich GAAGA motif with the highest frequency and it is therefore likely to bind core AGA, GAA, or AAG motifs. Confirmatory RT-PCR experiments showed that the expression of coding and non-coding genes with SAFB1 cross-link sites was altered by SAFB1 knockdown. For example, we found that the isoform-specific expression of neural cell adhesion molecule (NCAM1) and ASTN2 was influenced by SAFB1 and that the processing of miR-19a from the miR-17-92 cluster was regulated by SAFB1. These data suggest SAFB1 may influence alternative splicing and, using an NCAM1 minigene, we showed that SAFB1 knockdown altered the expression of two of the three NCAM1 alternative spliced isoforms. However, when the AGA, GAA, and AAG motifs were mutated, SAFB1 knockdown no longer mediated a decrease in the NCAM1 9–10 alternative spliced form. To further investigate the association of SAFB1 with splicing we used exon array analysis and found SAFB1 knockdown mediated the statistically significant up- and downregulation of alternative exons. Further analysis using RNAmotifs to investigate the frequency of association between the motif pairs (AGA followed by AGA, GAA or AAG) and alternative spliced exons found there was a highly significant correlation with downregulated exons. Together, our data suggest SAFB1 will play an important physiological role in the central nervous system regulating synaptic function. We found that SAFB1 regulates dendritic spine density in hippocampal neurons and hence provide empirical evidence supporting this conclusion.ConclusionsiCLIP showed that SAFB1 has previously uncharacterised specific RNA binding properties that help coordinate the isoform-specific expression of coding and non-coding genes. These genes regulate splicing, axonal and synaptic function, and are associated with neuropsychiatric disease, suggesting that SAFB1 is an important regulator of key neuronal processes.

HIPred: an integrative approach to predicting haploinsufficient genes

Motivation: A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein‐protein interaction networks that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well‐studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1000 genomes project, NHLBI Exome Sequencing Project and the Exome Aggregation Consortium creates an urgent need for unbiased haploinsufficiency prediction methods. Results: Here, we describe a machine learning approach, called HIPred, that integrates genomic and evolutionary information from ENSEMBL, with functional annotations from the Encyclopaedia of DNA Elements consortium and the NIH Roadmap Epigenomics Project to predict haploinsufficiency, without the study bias described earlier. We benchmark HIPred using several datasets and show that our unbiased method performs as well as, and in most cases, outperforms existing biased algorithms. Availability and Implementation: HIPred scores for all gene identifiers are available at: https://github.com/HAShihab/HIPred. Contact: h.shihab@bristol.ac.uk or tom.gaunt@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

CScape: a tool for predicting oncogenic single-point mutations in the cancer genome

For somatic point mutations in coding and non-coding regions of the genome, we propose CScape, an integrative classifier for predicting the likelihood that mutations are cancer drivers. Tested on somatic mutations, CScape tends to outperform alternative methods, reaching 91% balanced accuracy in coding regions and 70% in non-coding regions, while even higher accuracy may be achieved using thresholds to isolate high-confidence predictions. Positive predictions tend to cluster in genomic regions, so we apply a statistical approach to isolate coding and non-coding regions of the cancer genome that appear enriched for high-confidence predicted disease-drivers. Predictions and software are available at http://CScape.biocompute.org.uk/.

GTB - an online genome tolerance browser

BackgroundAccurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods.ResultsWe present the Genome Tolerance Browser (GTB, http://gtb.biocompute.org.uk): an online genome browser for visualizing the predicted tolerance of the genome to mutation. The server summarizes several in silico prediction algorithms and conservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithms.ConclusionThe GTB enables users to visualize the similarities and differences between several prediction algorithms and to upload their own data as additional tracks; thereby facilitating the rapid identification of potential regions of interest.

An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.

BackgroundSmall insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome.ResultsWe present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk.ConclusionsFATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.