Genetic linkage analysis of a large family identifies FIGN as a candidate modulator of reduced penetrance in heritable pulmonary arterial hypertension.

BACKGROUND: Mapping the genetic component of molecular mechanisms responsible for the reduced penetrance (RP) of rare disorders constitutes one of the most challenging problems in human genetics. Heritable pulmonary arterial hypertension (PAH) is one such disorder characterised by rare mutations mostly occurring in the bone morphogenetic protein receptor type 2 (BMPR2) gene and a wide heterogeneity of penetrance modifier mechanisms. Here, we analyse 32 genotyped individuals from a large Iberian family of 65 members, including 22 carriers of the pathogenic BMPR2 mutation c.1472G>A (p.Arg491Gln), 8 of them diagnosed with PAH by right-heart catheterisation, leading to an RP rate of 36.4%. METHODS: We performed a linkage analysis on the genotyping data to search for genetic modifiers of penetrance. Using functional genomics data, we characterised the candidate region identified by linkage analysis. We also predicted the haplotype segregation within the family. RESULTS: We identified a candidate chromosome region in 2q24.3, 38 Mb upstream from BMPR2, with significant linkage (LOD=4.09) under a PAH susceptibility model. This region contains common variants associated with vascular aetiology and shows functional evidence that the putative genetic modifier is located in the upstream distal promoter of the fidgetin (FIGN) gene. CONCLUSION: Our results suggest that the genetic modifier acts through FIGN transcriptional regulation, whose expression variability would contribute to modulating heritable PAH. This finding may help to advance our understanding of RP in PAH across families sharing the p.Arg491Gln pathogenic mutation in BMPR2.

GenomicScores: seamless access to genomewide position-specific scores from R and Bioconductor.

Summary: Genomewide position-specific scores, such as those estimating conservation, constraint, fitness or mutation tolerance, are ubiquitous in current genome analyses. The diversity of sources and formats of these scores, as well as their size, increase the burden to use them. We present GenomicScores, a Bioconductor package that provides efficient storage and seamless access of genomewide position-specific scores from R, facilitating their use in genome analysis workflows. Availability and implementation: GenomicScores is implemented in R and available at https://bioconductor.org/packages/GenomicScores under the open source 'Artistic-2.0' license. Supplementary information: Supplementary data are available at Bioinformatics online.

Public data and open source tools for multi-assay genomic investigation of disease.

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Umbilical cord gene expression reveals the molecular architecture of the fetal inflammatory response in extremely preterm newborns.

BACKGROUND: The fetal inflammatory response (FIR) in placental membranes to an intrauterine infection often precedes premature birth raising neonatal mortality and morbidity. However, the precise molecular events behind FIR still remain largely unknown, and little has been investigated at gene expression level. METHODS: We collected publicly available microarray expression data profiling umbilical cord (UC) tissue derived from the cohort of extremely low gestational age newborns (ELGANs) and interrogate them for differentially expressed (DE) genes between FIR and non-FIR-affected ELGANs. RESULTS: We found a broad and complex FIR UC gene expression signature, changing up to 19% (3,896/20,155) of all human genes at 1% false discovery rate. Significant changes of a minimum 50% magnitude (1,097/3,896) affect the upregulation of many inflammatory pathways and molecules, such as cytokines, toll-like receptors, and calgranulins. Remarkably, they also include the downregulation of neurodevelopmental pathways and genes, such as Fragile-X mental retardation 1 (FMR1), contactin 1 (CNTN1), and adenomatous polyposis coli (APC). CONCLUSION: The FIR expression signature in UC tissue contains molecular clues about signaling pathways that trigger FIR, and it is consistent with an acute inflammatory response by fetal innate and adaptive immune systems, which participate in the pathogenesis of neonatal brain damage.

Microproteins encoded by noncanonical ORFs are a major source of tumor-specific antigens in a liver cancer patient meta-cohort.

The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Here, we investigate if microproteins encoded by noncanonical open reading frames (ncORFs) are a relevant source of tumor-specific antigens. We analyze RNA sequencing data from 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue together with ribosome profiling and immunopeptidomics data. Combining human leukocyte antigen-epitope binding predictions and experimental validation experiments, we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs, including two peptides that can trigger an immune response in humanized mice. We identify a subset of 33 tumor-specific long noncoding RNAs expressing novel cancer antigens shared by more than 10% of the HCC samples analyzed, which, when combined, cover a large proportion of the patients. The results of the study open avenues for extending the range of anticancer vaccines.

GSVA: gene set variation analysis for microarray and RNA-seq data.

BACKGROUND: Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. RESULTS: To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. CONCLUSIONS: GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.

BACKGROUND: High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user. RESULTS: Here we show that expression profiles produced by extensively-replicated RNA-seq experiments lead to a rich diversity of count data distributions beyond the Poisson and the negative binomial, such as Poisson-Inverse Gaussian or Pólya-Aeppli, which can be captured by a more general family of count data distributions called the Poisson-Tweedie. The flexibility of the Poisson-Tweedie family enables a direct fitting of emerging features of large expression profiles, such as heavy-tails or zero-inflation, without the need to alter a single configuration parameter. We provide a software package for R called tweeDEseq implementing a new test for differential expression based on the Poisson-Tweedie family. Using simulations on synthetic and real RNA-seq data we show that tweeDEseq yields P-values that are equally or more accurate than competing methods under different configuration parameters. By surveying the tiny fraction of sex-specific gene expression changes in human lymphoblastoid cell lines, we also show that tweeDEseq accurately detects differentially expressed genes in a real large RNA-seq data set with improved performance and reproducibility over the previously compared methodologies. Finally, we compared the results with those obtained from microarrays in order to check for reproducibility. CONCLUSIONS: RNA-seq data with many replicates leads to a handful of count data distributions which can be accurately estimated with the statistical model illustrated in this paper. This method provides a better fit to the underlying biological variability; this may be critical when comparing groups of RNA-seq samples with markedly different count data distributions. The tweeDEseq package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org.

Distinct regulatory programs establish widespread sex-specific alternative splicing in Drosophila melanogaster.

In Drosophila melanogaster, female-specific expression of Sex-lethal (SXL) and Transformer (TRA) proteins controls sex-specific alternative splicing and/or translation of a handful of regulatory genes responsible for sexual differentiation and behavior. Recent findings in 2009 by Telonis-Scott et al. document widespread sex-biased alternative splicing in fruitflies, including instances of tissue-restricted sex-specific splicing. Here we report results arguing that some of these novel sex-specific splicing events are regulated by mechanisms distinct from those established by female-specific expression of SXL and TRA. Bioinformatic analysis of SXL/TRA binding sites, experimental analysis of sex-specific splicing in S2 and Kc cells lines and of the effects of SXL knockdown in Kc cells indicate that SXL-dependent and SXL-independent regulatory mechanisms coexist within the same cell. Additional determinants of sex-specific splicing can be provided by sex-specific differences in the expression of RNA binding proteins, including Hrp40/Squid. We report that sex-specific alternative splicing of the gene hrp40/squid leads to sex-specific differences in the levels of this hnRNP protein. The significant overlap between sex-regulated alternative splicing changes and those induced by knockdown of hrp40/squid and the presence of related sequence motifs enriched near subsets of Hrp40/Squid-regulated and sex-regulated splice sites indicate that this protein contributes to sex-specific splicing regulation. A significant fraction of sex-specific splicing differences are absent in germline-less tudor mutant flies. Intriguingly, these include alternative splicing events that are differentially spliced in tissues distant from the germline. Collectively, our results reveal that distinct genetic programs control widespread sex-specific splicing in Drosophila melanogaster.

Genome-wide postnatal changes in immunity following fetal inflammatory response.

The fetal inflammatory response (FIR) increases the risk of perinatal brain injury, particularly in extremely low gestational age newborns (ELGANs, < 28 weeks of gestation). One of the mechanisms contributing to such a risk is a postnatal intermittent or sustained systemic inflammation (ISSI) following FIR. The link between prenatal and postnatal systemic inflammation is supported by the presence of well-established inflammatory biomarkers in the umbilical cord and peripheral blood. However, the extent of molecular changes contributing to this association is unknown. Using RNA sequencing and mass spectrometry proteomics, we profiled the transcriptome and proteome of archived neonatal dried blood spot (DBS) specimens from 21 ELGANs. Comparing FIR-affected and unaffected ELGANs, we identified 782 gene and 27 protein expression changes of 50% magnitude or more, and an experiment-wide significance level below 5% false discovery rate. These expression changes confirm the robust postnatal activation of the innate immune system in FIR-affected ELGANs and reveal for the first time an impairment of their adaptive immunity. In turn, the altered pathways provide clues about the molecular mechanisms triggering ISSI after FIR, and the onset of perinatal brain injury. DATABASES: EGAS00001003635 (EGA); PXD011626 (PRIDE).

Origin of primate orphan genes: a comparative genomics approach.

Genomes contain a large number of genes that do not have recognizable homologues in other species and that are likely to be involved in important species-specific adaptive processes. The origin of many such "orphan" genes remains unknown. Here we present the first systematic study of the characteristics and mechanisms of formation of primate-specific orphan genes. We determine that codon usage values for most orphan genes fall within the bulk of the codon usage distribution of bona fide human proteins, supporting their current protein-coding annotation. We also show that primate orphan genes display distinctive features in relation to genes of wider phylogenetic distribution: higher tissue specificity, more rapid evolution, and shorter peptide size. We estimate that around 24% are highly divergent members of mammalian protein families. Interestingly, around 53% of the orphan genes contain sequences derived from transposable elements (TEs) and are mostly located in primate-specific genomic regions. This indicates frequent recruitment of TEs as part of novel genes. Finally, we also obtain evidence that a small fraction of primate orphan genes, around 5.5%, might have originated de novo from mammalian noncoding genomic regions.

Evolution of primate orphan proteins.

Genomes contain a large number of genes that do not have recognizable homologues in other species. These genes, found in only one or a few closely related species, are known as orphan genes. Their limited distribution implies that many of them are probably involved in lineage-specific adaptive processes. One important question that has remained elusive to date is how orphan genes originate. It has been proposed that they might have arisen by gene duplication followed by a period of very rapid sequence divergence, which would have erased any traces of similarity to other evolutionarily related genes. However, this explanation does not seem plausible for genes lacking homologues in very closely related species. In the present article, we review recent efforts to identify the mechanisms of formation of primate orphan genes. These studies reveal an unexpected important role of transposable elements in the formation of novel protein-coding genes in the genomes of primates.

The BIOMEPOC Project: Personalized Biomarkers and Clinical Profiles in Chronic Obstructive Pulmonary Disease. / Proyecto de biomarcadores y perfiles clínicos personalizados en la enfermedad pulmonar obstructiva crónica (proyecto BIOMEPOC).

Chronic obstructive pulmonary disease (COPD) is an entity with a heterogeneous presentation. For this reason, attempts have been made to characterize different phenotypes and endotypes to enable a more individualized approach. The aim of the Biomarkers in COPD (BIOMEPOC) project is to identify useful biomarkers in blood to improve the characterization of patients. Clinical data and blood samples from a group of patients and healthy controls will be analyzed. The project will consist of an exploration phase and a validation phase. Analytical parameters in blood will be determined using standard techniques and certain 'omics' (transcriptomics, proteomics, and metabolomics). The former will be hypothesis-driven, whereas the latter will be exploratory. Finally, a multilevel analysis will be conducted. Currently, 269 patients and 83 controls have been recruited, and sample processing is beginning. Our hope is to use the results to identify new biomarkers that, alone or combined, will allow a better characterization of patients.

Comparative gene finding in chicken indicates that we are closing in on the set of multi-exonic widely expressed human genes.

The recent availability of the chicken genome sequence poses the question of whether there are human protein-coding genes conserved in chicken that are currently not included in the human gene catalog. Here, we show, using comparative gene finding followed by experimental verification of exon pairs by RT-PCR, that the addition to the multi-exonic subset of this catalog could be as little as 0.2%, suggesting that we may be closing in on the human gene set. Our protocol, however, has two shortcomings: (i) the bioinformatic screening of the predicted genes, applied to filter out false positives, cannot handle intronless genes; and (ii) the experimental verification could fail to identify expression at a specific developmental time. This highlights the importance of developing methods that could provide a reliable estimate of the number of these two types of genes.

Gene finding in the chicken genome.

BACKGROUND: Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method. RESULTS: We performed experimental evaluation by RT-PCR of three different computational gene finders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram was computed and each component of it was evaluated. The results showed that de novo comparative methods can identify up to about 700 chicken genes with no previous evidence of expression, and can correctly extend about 40% of homology-based predictions at the 5' end. CONCLUSIONS: De novo comparative gene prediction followed by experimental verification is effective at enhancing the annotation of the newly sequenced genomes provided by standard homology-based methods.

Splice site identification by idlBNs.

MOTIVATION: Computational identification of functional sites in nucleotide sequences is at the core of many algorithms for the analysis of genomic data. This identification is based on the statistical parameters estimated from a training set. Often, because of the huge number of parameters, it is difficult to obtain consistent estimators. To simplify the estimation problem, one imposes independent assumptions between the nucleotides along the site. However, this can potentially limit the minimum value of the estimation error. RESULTS: In this paper, we introduce a novel method in the context of identifying functional sites, that finds a reasonable set of independence assumptions supported by the data, among the nucleotides, and uses it to perform the identification of the sites by their likelihood ratio. More importantly, in many practical situations it is capable of improving its performance as the training sample size increases. We apply the method to the identification of splice sites, and further evaluate its effect within the context of exon and gene prediction.

The propagation of perturbations in rewired bacterial gene networks.

What happens to gene expression when you add new links to a gene regulatory network? To answer this question, we profile 85 network rewirings in E. coli. Here we report that concerted patterns of differential expression propagate from reconnected hub genes. The rewirings link promoter regions to different transcription factor and σ-factor genes, resulting in perturbations that span four orders of magnitude, changing up to ∼ 70% of the transcriptome. Importantly, factor connectivity and promoter activity both associate with perturbation size. Perturbations from related rewirings have more similar transcription profiles and a statistical analysis reveals ∼ 20 underlying states of the system, associating particular gene groups with rewiring constructs. We examine two large clusters (ribosomal and flagellar genes) in detail. These represent alternative global outcomes from different rewirings because of antagonism between these major cell states. This data set of systematically related perturbations enables reverse engineering and discovery of underlying network interactions.

Thrombin stimulates insulin secretion via protease-activated receptor-3.

The disease mechanisms underlying type 2 diabetes (T2D) remain poorly defined. Here we aimed to explore the pathophysiology of T2D by analyzing gene co-expression networks in human islets. Using partial correlation networks we identified a group of co-expressed genes ('module') including F2RL2 that was associated with glycated hemoglobin. F2Rl2 is a G-protein-coupled receptor (GPCR) that encodes protease-activated receptor-3 (PAR3). PAR3 is cleaved by thrombin, which exposes a 6-amino acid sequence that acts as a 'tethered ligand' to regulate cellular signaling. We have characterized the effect of PAR3 activation on insulin secretion by static insulin secretion measurements, capacitance measurements, studies of diabetic animal models and patient samples. We demonstrate that thrombin stimulates insulin secretion, an effect that was prevented by an antibody that blocks the thrombin cleavage site of PAR3. Treatment with a peptide corresponding to the PAR3 tethered ligand stimulated islet insulin secretion and single ß-cell exocytosis by a mechanism that involves activation of phospholipase C and Ca(2+) release from intracellular stores. Moreover, we observed that the expression of tissue factor, which regulates thrombin generation, was increased in human islets from T2D donors and associated with enhanced ß-cell exocytosis. Finally, we demonstrate that thrombin generation potential in patients with T2D was associated with increased fasting insulin and insulinogenic index. The findings provide a previously unrecognized link between hypercoagulability and hyperinsulinemia and suggest that reducing thrombin activity or blocking PAR3 cleavage could potentially counteract the exaggerated insulin secretion that drives insulin resistance and ß-cell exhaustion in T2D.

Mapping eQTL networks with mixed graphical Markov models.

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene-gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.

Inference of regulatory networks from microarray data with R and the bioconductor package qpgraph.

Regulatory networks inferred from microarray data sets provide an estimated blueprint of the functional interactions taking place under the assayed experimental conditions. In each of these experiments, the gene expression pathway exerts a finely tuned control simultaneously over all genes relevant to the cellular state. This renders most pairs of those genes significantly correlated, and therefore, the challenge faced by every method that aims at inferring a molecular regulatory network from microarray data, lies in distinguishing direct from indirect interactions. A straightforward solution to this problem would be to move directly from bivariate to multivariate statistical approaches. However, the daunting dimension of typical microarray data sets, with a number of genes p several orders of magnitude larger than the number of samples n, precludes the application of standard multivariate techniques and confronts the biologist with sophisticated procedures that address this situation. We have introduced a new way to approach this problem in an intuitive manner, based on limited-order partial correlations, and in this chapter we illustrate this method through the R package qpgraph, which forms part of the Bioconductor project and is available at its Web site.

Reverse engineering molecular regulatory networks from microarray data with qp-graphs.

Reverse engineering bioinformatic procedures applied to high-throughput experimental data have become instrumental in generating new hypotheses about molecular regulatory mechanisms. This has been particularly the case for gene expression microarray data, where a large number of statistical and computational methodologies have been developed in order to assist in building network models of transcriptional regulation. A major challenge faced by every different procedure is that the number of available samples n for estimating the network model is much smaller than the number of genes p forming the system under study. This compromises many of the assumptions on which the statistics of the methods rely, often leading to unstable performance figures. In this work, we apply a recently developed novel methodology based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data with p >> n. Using experimental and functional annotation data from Escherichia coli, here we show how qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, we also show that the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions. An R package, called qpgraph implementing this method is part of the Bioconductor project and can be downloaded from (www.bioconductor.org). A parallel standalone version for the most computationally expensive calculations is available from (http://functionalgenomics.upf.xsedu/qpgraph).