Corticosterone pattern-dependent glucocorticoid receptor binding and transcriptional regulation within the liver.

Ultradian glucocorticoid rhythms are highly conserved across mammalian species, however, their functional significance is not yet fully understood. Here we demonstrate that pulsatile corticosterone replacement in adrenalectomised rats induces a dynamic pattern of glucocorticoid receptor (GR) binding at ~3,000 genomic sites in liver at the pulse peak, subsequently not found during the pulse nadir. In contrast, constant corticosterone replacement induced prolonged binding at the majority of these sites. Additionally, each pattern further induced markedly different transcriptional responses. During pulsatile treatment, intragenic occupancy by active RNA polymerase II exhibited pulsatile dynamics with transient changes in enrichment, either decreased or increased depending on the gene, which mostly returned to baseline during the inter-pulse interval. In contrast, constant corticosterone exposure induced prolonged effects on RNA polymerase II occupancy at the majority of gene targets, thus acting as a sustained regulatory signal for both transactivation and repression of glucocorticoid target genes. The nett effect of these differences were consequently seen in the liver transcriptome as RNA-seq analysis indicated that despite the same overall amount of corticosterone infused, twice the number of transcripts were regulated by constant corticosterone infusion, when compared to pulsatile. Target genes that were found to be differentially regulated in a pattern-dependent manner were enriched in functional pathways including carbohydrate, cholesterol, glucose and fat metabolism as well as inflammation, suggesting a functional role for dysregulated glucocorticoid rhythms in the development of metabolic dysfunction.

Prediction of driver variants in the cancer genome via machine learning methodologies.

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.

Nucleotide diversity of functionally different groups of immune response genes in Old World camels based on newly annotated and reference-guided assemblies.

BACKGROUND: Immune-response (IR) genes have an important role in the defense against highly variable pathogens, and therefore, diversity in these genomic regions is essential for species' survival and adaptation. Although current genome assemblies from Old World camelids are very useful for investigating genome-wide diversity, demography and population structure, they have inconsistencies and gaps that limit analyses at local genomic scales. Improved and more accurate genome assemblies and annotations are needed to study complex genomic regions like adaptive and innate IR genes. RESULTS: In this work, we improved the genome assemblies of the three Old World camel species - domestic dromedary and Bactrian camel, and the two-humped wild camel - via different computational methods. The newly annotated dromedary genome assembly CamDro3 served as reference to scaffold the NCBI RefSeq genomes of domestic Bactrian and wild camels. These upgraded assemblies were then used to assess nucleotide diversity of IR genes within and between species, and to compare the diversity found in immune genes and the rest of the genes in the genome. We detected differences in the nucleotide diversity among the three Old World camelid species and between IR gene groups, i.e., innate versus adaptive. Among the three species, domestic Bactrian camels showed the highest mean nucleotide diversity. Among the functionally different IR gene groups, the highest mean nucleotide diversity was observed in the major histocompatibility complex. CONCLUSIONS: The new camel genome assemblies were greatly improved in terms of contiguity and increased size with fewer scaffolds, which is of general value for the scientific community. This allowed us to perform in-depth studies on genetic diversity in immunity-related regions of the genome. Our results suggest that differences of diversity across classes of genes appear compatible with a combined role of population history and differential exposures to pathogens, and consequent different selective pressures.

Rapid, robust plasmid verification by de novo assembly of short sequencing reads.

Plasmids are a foundational tool for basic and applied research across all subfields of biology. Increasingly, researchers in synthetic biology are relying on and developing massive libraries of plasmids as vectors for directed evolution, combinatorial gene circuit tests, and for CRISPR multiplexing. Verification of plasmid sequences following synthesis is a crucial quality control step that creates a bottleneck in plasmid fabrication workflows. Crucially, researchers often elect to forego the cumbersome verification step, potentially leading to reproducibility and-depending on the application-security issues. In order to facilitate plasmid verification to improve the quality and reproducibility of life science research, we developed a fast, simple, and open source pipeline for assembly and verification of plasmid sequences from Illumina reads. We demonstrate that our pipeline, which relies on de novo assembly, can also be used to detect contaminating sequences in plasmid samples. In addition to presenting our pipeline, we discuss the role for verification and quality control in the increasingly complex life science workflows ushered in by synthetic biology.

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome.

MOTIVATION: Next-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations. RESULTS: For somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions. AVAILABILITY AND IMPLEMENTATION: Predictions and software are available at http://CScape-somatic.biocompute.org.uk/. CONTACT: mark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours.

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.

Improving Illumina assemblies with Hi-C and long reads: An example with the North African dromedary.

Researchers have assembled thousands of eukaryotic genomes using Illumina reads, but traditional mate-pair libraries cannot span all repetitive elements, resulting in highly fragmented assemblies. However, both chromosome conformation capture techniques, such as Hi-C and Dovetail Genomics Chicago libraries and long-read sequencing, such as Pacific Biosciences and Oxford Nanopore, help span and resolve repetitive regions and therefore improve genome assemblies. One important livestock species of arid regions that does not have a high-quality contiguous reference genome is the dromedary (Camelus dromedarius). Draft genomes exist but are highly fragmented, and a high-quality reference genome is needed to understand adaptation to desert environments and artificial selection during domestication. Dromedaries are among the last livestock species to have been domesticated, and together with wild and domestic Bactrian camels, they are the only representatives of the Camelini tribe, which highlights their evolutionary significance. Here we describe our efforts to improve the North African dromedary genome. We used Chicago and Hi-C sequencing libraries from Dovetail Genomics to resolve the order of previously assembled contigs, producing almost chromosome-level scaffolds. Remaining gaps were filled with Pacific Biosciences long reads, and then scaffolds were comparatively mapped to chromosomes. Long reads added 99.32 Mbp to the total length of the new assembly. Dovetail Chicago and Hi-C libraries increased the longest scaffold over 12-fold, from 9.71 Mbp to 124.99 Mbp and the scaffold N50 over 50-fold, from 1.48 Mbp to 75.02 Mbp. We demonstrate that Illumina de novo assemblies can be substantially upgraded by combining chromosome conformation capture and long-read sequencing.

Glucocorticoid Receptor-Tethered Mineralocorticoid Receptors Increase Glucocorticoid-Induced Transcriptional Responses.

Mineralocorticoid and glucocorticoid receptors (MRs and GRs) constitute a functionally important dual receptor system detecting and transmitting circulating corticosteroid signals. High expression of MRs and GRs occurs in the same cells in the limbic system, the primary site of glucocorticoid action on cognition, behavior, and mood; however, modes of interaction between the receptors are poorly characterized. We used chromatin immunoprecipitation with nucleotide resolution using exonuclease digestion, unique barcode, and single ligation (ChIP-nexus) for high-resolution genome-wide characterization of MR and GR DNA binding profiles in neuroblastoma cells and demonstrate recruitment to highly similar DNA binding sites. Expressed MR or GR showed differential regulation of endogenous gene targets, including Syt2 and Ddc, whereas coexpression produced augmented transcriptional responses even when MRs were unable to bind DNA (MR-XDBD). ChIP confirmed that MR-XDBD could be tethered to chromatin by GR. Our data demonstrate that MR can interact at individual genomic DNA sites in multiple modes and suggest a role for MR in increasing the transcriptional response to glucocorticoids.

Differential aging-related changes in neurophysiology and gene expression in IB4-positive and IB4-negative nociceptive neurons.

Despite pain prevalence altering with age, the effects of aging on the properties of nociceptors are not well understood. Nociceptors, whose somas are located in dorsal root ganglia, are frequently divided into two groups based on their ability to bind isolectin B4 (IB4). Here, using cultured neurons from 1-, 3-, 5-, 8-, 12-, and 18-month-old mice, we investigate age-dependent changes in IB4-positive and IB4-negative neurons. Current-clamp experiments at physiological temperature revealed nonlinear changes in firing frequency of IB4-positive, but not IB4-negative neurons, with a peak at 8 months. This was likely due to the presence of proexcitatory conductances activated at depolarized membrane potentials and significantly higher input resistances found in IB4-positive neurons from 8-month-old mice. Repetitive firing in nociceptors is driven primarily by the TTX-resistant sodium current, and indeed, IB4-positive neurons from 8-month-old mice were found to receive larger contributions from the TTX-resistant window current around the resting membrane potential. To further address the mechanisms behind these differences, we performed RNA-seq experiments on IB4-positive and IB4-negative neurons from 1-, 8-, and 18-month-old mice. We found a larger number of genes significantly affected by age within the IB4-positive than IB4-negative neurons from 8-month-old mice, including known determinants of nociceptor excitability. The above pronounced age-dependent changes at the cellular and molecular levels in IB4-positive neurons point to potential mechanisms behind the reported increase in pain sensitivity in middle-aged rodents and humans, and highlight the possibility of targeting a particular group of neurons in the development of age-tailored pain treatments.

Preeclamptic placentae release factors that damage neurons: implications for foetal programming of disease.

Prenatal development is a critical period for programming of neurological disease. Preeclampsia, a pregnancy complication involving oxidative stress in the placenta, has been associated with long-term health implications for the child, including an increased risk of developing schizophrenia and autism spectrum disorders in later life. To investigate if molecules released by the placenta may be important mediators in foetal programming of the brain, we analysed if placental tissue delivered from patients with preeclampsia secreted molecules that could affect cortical cells in culture. Application of culture medium conditioned by preeclamptic placentae to mixed cortical cultures caused changes in neurons and astrocytes that were related to key changes observed in brains of patients with schizophrenia and autism, including effects on dendrite lengths, astrocyte number as well as on levels of glutamate and γ-aminobutyric acid receptors. Treatment of the placental explants with an antioxidant prevented neuronal abnormalities. Furthermore, we identified that bidirectional communication between neurons and astrocytes, potentially via glutamate, is required to produce the effects of preeclamptic placenta medium on cortical cells. Analysis of possible signalling molecules in the placenta-conditioned medium showed that the secretion profile of extracellular microRNAs, small post-transcriptional regulators, was altered in preeclampsia and partially rescued by antioxidant treatment of the placental explants. Predicted targets of these differentially abundant microRNAs were linked to neurodevelopment and the placenta. The present study provides further evidence that the diseased placenta may release factors that damage cortical cells and suggests the possibility of targeted antioxidant treatment of the placenta to prevent neurodevelopmental disorders.

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/. Contact: mark.rogers@bristol.ac.uk or c.campbell@bristol.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

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

BACKGROUND: Small 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. RESULTS: We 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. CONCLUSIONS: FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.

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/ .

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

NEW FINDINGS: 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. Many traditional cardiovascular hormones have been implicated in metabolic function. Conversely, many hormones traditionally involved in metabolic regulation have an effect on cardiovascular function. Many of these signalling molecules exert such effects through specific actions in the paraventricular nucleus, an integrative autonomic control centre located in the hypothalamus. Here, we focus on four cardiovascular/metabolic peptide hormones that signal within the paraventricular nucleus, namely angiotensin II, orexin, adiponectin and nesfatin-1. Each of these hormones has specific electrophysiological effects on paraventricular nucleus neurons that can be related to its physiological actions. In addition, we introduce preliminary transcriptomic data indicating that the genes for some of these hormones and their receptors have sex-specific differential expression.

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.

Recognition memory-induced gene expression in the perirhinal cortex: A transcriptomic analysis.

We have used transcriptome analysis to identify genes and pathways that are activated during recognition memory formation in the perirhinal cortex. Rats were exposed to objects either repeatedly, so that the objects become familiar, or to novel objects in a bow-tie maze over six consecutive days. On the final day, one hour after the last exposure to the series of objects, RNA from the perirhinal cortex was sequenced to compare the transcriptome of naïve control rats and rats exposed to either novel or familiar stimuli. Differentially expressed genes were identified between group Novel and group Familiar rats. These included genes coding for transcription factors, GDNF receptors and extracellular matrix-related proteins. Moreover, differences in alternative splicing were also detected between the two groups, which suggests that this post-transcriptional mechanism may play a role in the consolidation of object recognition memory. To conclude, this study shows that RNA sequencing can be used as a tool to identify differences in gene expression in behaving animals undergoing the same task but encountering different exposures.

Predicting the Pathogenic Impact of Sequence Variation in the Human Genome.

Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.

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.