Effect of Wave Dark Matter on Equal Mass Black Hole Mergers.

For dark matter to be detectable with gravitational waves from binary black holes, it must reach higher than average densities in their vicinity. In the case of light (wavelike) dark matter, the density of dark matter between the binary can be significantly enhanced by accretion from the surrounding environment. Here we show that the resulting dephasing effect on the last ten orbits of an equal mass binary is maximized when the Compton wavelength of the scalar particle is comparable to the orbital separation, 2π/µâˆ¼d. The phenomenology of the effect is different from the channels that are usually discussed, where dynamical friction (along the orbital path) and radiation of energy and angular momentum drive the dephasing, and is rather dominated by the radial force (the spacetime curvature in the radial direction) towards the overdensity between the black holes. While our numerical studies limit us to scales of the same order, this effect may persist at larger separations and/or particle masses, playing a significant role in the merger history of binaries.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Deep learning for drug response prediction in cancer.

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:  mrocha@di.uminho.pt.

Scalable transcriptomics analysis with Dask: applications in data science and machine learning.

BACKGROUND: Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary. METHODS: In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics. RESULTS: This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask . CONCLUSION: By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.

Time-dependent genetic effects on gene expression implicate aging processes.

Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature.

Putative cis-regulatory drivers in colorectal cancer.

The cis-regulatory effects responsible for cancer development have not been as extensively studied as the perturbations of the protein coding genome in tumorigenesis. To better characterize colorectal cancer (CRC) development we conducted an RNA-sequencing experiment of 103 matched tumour and normal colon mucosa samples from Danish CRC patients, 90 of which were germline-genotyped. By investigating allele-specific expression (ASE) we show that the germline genotypes remain important determinants of allelic gene expression in tumours. Using the changes in ASE in matched pairs of samples we discover 71 genes with excess of somatic cis-regulatory effects in CRC, suggesting a cancer driver role. We correlate genotypes and gene expression to identify expression quantitative trait loci (eQTLs) and find 1,693 and 948 eQTLs in normal samples and tumours, respectively. We estimate that 36% of the tumour eQTLs are exclusive to CRC and show that this specificity is partially driven by increased expression of specific transcription factors and changes in methylation patterns. We show that tumour-specific eQTLs are more enriched for low CRC genome-wide association study (GWAS) P values than shared eQTLs, which suggests that some of the GWAS variants are tumour specific regulatory variants. Importantly, tumour-specific eQTL genes also accumulate more somatic mutations when compared to the shared eQTL genes, raising the possibility that they constitute germline-derived cancer regulatory drivers. Collectively the integration of genome and the transcriptome reveals a substantial number of putative somatic and germline cis-regulatory cancer changes that may have a role in tumorigenesis.

CPEB1 coordinates alternative 3'-UTR formation with translational regulation.

More than half of mammalian genes generate multiple messenger RNA isoforms that differ in their 3' untranslated regions (3' UTRs) and therefore in regulatory sequences, often associated with cell proliferation and cancer; however, the mechanisms coordinating alternative 3'-UTR processing for specific mRNA populations remain poorly defined. Here we report that the cytoplasmic polyadenylation element binding protein 1 (CPEB1), an RNA-binding protein that regulates mRNA translation, also controls alternative 3'-UTR processing. CPEB1 shuttles to the nucleus, where it co-localizes with splicing factors and mediates shortening of hundreds of mRNA 3' UTRs, thereby modulating their translation efficiency in the cytoplasm. CPEB1-mediated 3'-UTR shortening correlates with cell proliferation and tumorigenesis. CPEB1 binding to pre-mRNAs not only directs the use of alternative polyadenylation sites, but also changes alternative splicing by preventing U2AF65 recruitment. Our results reveal a novel function of CPEB1 in mediating alternative 3'-UTR processing, which is coordinated with regulation of mRNA translation, through its dual nuclear and cytoplasmic functions.

Transcriptome and genome sequencing uncovers functional variation in humans.

Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.

Biased allelic expression in human primary fibroblast single cells.

The study of gene expression in mammalian single cells via genomic technologies now provides the possibility to investigate the patterns of allelic gene expression. We used single-cell RNA sequencing to detect the allele-specific mRNA level in 203 single human primary fibroblasts over 133,633 unique heterozygous single-nucleotide variants (hetSNVs). We observed that at the snapshot of analyses, each cell contained mostly transcripts from one allele from the majority of genes; indeed, 76.4% of the hetSNVs displayed stochastic monoallelic expression in single cells. Remarkably, adjacent hetSNVs exhibited a haplotype-consistent allelic ratio; in contrast, distant sites located in two different genes were independent of the haplotype structure. Moreover, the allele-specific expression in single cells correlated with the abundance of the cellular transcript. We observed that genes expressing both alleles in the majority of the single cells at a given time point were rare and enriched with highly expressed genes. The relative abundance of each allele in a cell was controlled by some regulatory mechanisms given that we observed related single-cell allelic profiles according to genes. Overall, these results have direct implications in cellular phenotypic variability.

Molecular signatures of plastic phenotypes in two eusocial insect species with simple societies.

Phenotypic plasticity is important in adaptation and shapes the evolution of organisms. However, we understand little about what aspects of the genome are important in facilitating plasticity. Eusocial insect societies produce plastic phenotypes from the same genome, as reproductives (queens) and nonreproductives (workers). The greatest plasticity is found in the simple eusocial insect societies in which individuals retain the ability to switch between reproductive and nonreproductive phenotypes as adults. We lack comprehensive data on the molecular basis of plastic phenotypes. Here, we sequenced genomes, microRNAs (miRNAs), and multiple transcriptomes and methylomes from individual brains in a wasp (Polistes canadensis) and an ant (Dinoponera quadriceps) that live in simple eusocial societies. In both species, we found few differences between phenotypes at the transcriptional level, with little functional specialization, and no evidence that phenotype-specific gene expression is driven by DNA methylation or miRNAs. Instead, phenotypic differentiation was defined more subtly by nonrandom transcriptional network organization, with roles in these networks for both conserved and taxon-restricted genes. The general lack of highly methylated regions or methylome patterning in both species may be an important mechanism for achieving plasticity among phenotypes during adulthood. These findings define previously unidentified hypotheses on the genomic processes that facilitate plasticity and suggest that the molecular hallmarks of social behavior are likely to differ with the level of social complexity.

Cosmology and fundamental physics with the Euclid satellite.

Euclid is a European Space Agency medium-class mission selected for launch in 2020 within the cosmic vision 2015-2025 program. The main goal of Euclid is to understand the origin of the accelerated expansion of the universe. Euclid will explore the expansion history of the universe and the evolution of cosmic structures by measuring shapes and red-shifts of galaxies as well as the distribution of clusters of galaxies over a large fraction of the sky. Although the main driver for Euclid is the nature of dark energy, Euclid science covers a vast range of topics, from cosmology to galaxy evolution to planetary research. In this review we focus on cosmology and fundamental physics, with a strong emphasis on science beyond the current standard models. We discuss five broad topics: dark energy and modified gravity, dark matter, initial conditions, basic assumptions and questions of methodology in the data analysis. This review has been planned and carried out within Euclid's Theory Working Group and is meant to provide a guide to the scientific themes that will underlie the activity of the group during the preparation of the Euclid mission.

Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes-most of which are not differentially expressed-exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable (IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.

Short term exposure of beta cells to low concentrations of interleukin-1ß improves insulin secretion through focal adhesion and actin remodeling and regulation of gene expression.

Type 2 diabetes involves defective insulin secretion with islet inflammation governed in part by IL-1ß. Prolonged exposure of islets to high concentrations of IL-1ß (>24 h, 20 ng/ml) impairs beta cell function and survival. Conversely, exposure to lower concentrations of IL-1ß for >24 h improves these same parameters. The impact on insulin secretion of shorter exposure times to IL-1ß and the underlying molecular mechanisms are poorly understood and were the focus of this study. Treatment of rat primary beta cells, as well as rat or human whole islets, with 0.1 ng/ml IL-1ß for 2 h increased glucose-stimulated (but not basal) insulin secretion, whereas 20 ng/ml was without effect. Similar differential effects of IL-1ß depending on concentration were observed after 15 min of KCl stimulation but were prevented by diazoxide. Studies on sorted rat beta cells indicated that the enhancement of stimulated secretion by 0.1 ng/ml IL-1ß was mediated by the NF-κB pathway and c-JUN/JNK pathway acting in parallel to elicit focal adhesion remodeling and the phosphorylation of paxillin independently of upstream regulation by focal adhesion kinase. Because the beneficial effect of IL-1ß was dependent in part upon transcription, gene expression was analyzed by RNAseq. There were 18 genes regulated uniquely by 0.1 but not 20 ng/ml IL-1ß, which are mostly involved in transcription and apoptosis. These results indicate that 2 h of exposure of beta cells to a low but not a high concentration of IL-1ß enhances glucose-stimulated insulin secretion through focal adhesion and actin remodeling, as well as modulation of gene expression.

Tensor interpretation of BICEP2 results severely constrains axion dark matter.

The recent detection of B modes by the BICEP2 experiment has nontrivial implications for axion dark matter implied by combining the tensor interpretation with isocurvature constraints from Planck observations. In this Letter the measurement is taken as fact, and its implications considered, though further experimental verification is required. In the simplest inflation models, r=0.2 implies HI=1.1×10(14) GeV. If the axion decay constant fa1 accounts for theoretical uncertainty). If fa>HI/2π then vacuum fluctuations of the axion field place conflicting demands on axion DM: isocurvature constraints require a DM abundance which is too small to be reached when the backreaction of fluctuations is included. High-fa QCD axions are thus ruled out. Constraints on axionlike particles, as a function of their mass and DM fraction, are also considered. For heavy axions with ma≳10(-22) eV we find Ωa/Ωd≲10(-3), with stronger constraints on heavier axions. Lighter axions, however, are allowed and (inflationary) model-independent constraints from the CMB temperature power spectrum and large scale structure are stronger than those implied by tensor modes.

Cosmology on ultralarge scales with intensity mapping of the neutral hydrogen 21 cm emission: limits on primordial non-Gaussianity.

The large-scale structure of the Universe supplies crucial information about the physical processes at play at early times. Unresolved maps of the intensity of 21 cm emission from neutral hydrogen HI at redshifts z=/~1-5 are the best hope of accessing the ultralarge-scale information, directly related to the early Universe. A purpose-built HI intensity experiment may be used to detect the large scale effects of primordial non-Gaussianity, placing stringent bounds on different models of inflation. We argue that it may be possible to place tight constraints on the non-Gaussianity parameter f(NL), with an error close to σ(f(NL))~1.

Cosmology and Fundamental Physics with the Euclid Satellite.

Euclid is a European Space Agency medium-class mission selected for launch in 2019 within the Cosmic Vision 2015-2025 program. The main goal of Euclid is to understand the origin of the accelerated expansion of the universe. Euclid will explore the expansion history of the universe and the evolution of cosmic structures by measuring shapes and red-shifts of galaxies as well as the distribution of clusters of galaxies over a large fraction of the sky. Although the main driver for Euclid is the nature of dark energy, Euclid science covers a vast range of topics, from cosmology to galaxy evolution to planetary research. In this review we focus on cosmology and fundamental physics, with a strong emphasis on science beyond the current standard models. We discuss five broad topics: dark energy and modified gravity, dark matter, initial conditions, basic assumptions and questions of methodology in the data analysis. This review has been planned and carried out within Euclid's Theory Working Group and is meant to provide a guide to the scientific themes that will underlie the activity of the group during the preparation of the Euclid mission.

Lifestyle integrated functional exercise for people with interstitial lung disease (iLiFE): A mixed-methods feasibility study.

BACKGROUND: People with interstitial lung disease (ILD) present low levels of physical activity (PA) and spend most of their time at home, especially in advanced stages of the disease. The Lifestyle Integrated Functional Exercise for people with ILD (iLiFE) embedding PA in patients' daily routines was developed and implemented. OBJECTIVES: This study aimed to explore the feasibility of iLiFE. METHODS: A pre/post mixed-methods feasibility study was conducted. Feasibility of iLiFE was determined by participant recruitment/retention, adherence, feasibility of outcome measures and adverse events. Measures of PA, sedentary behaviour, balance, muscle strength, functional performance/capacity, exercise capacity, impact of the disease, symptoms (i.e., dyspnoea, anxiety, depression, fatigue and cough) and health-related quality of life were collected at baseline and post-intervention (12-weeks). Semi-structured interviews with participants were conducted in-person immediately after iLiFE. Interviews were audio-recorded, transcribed and analysed by deductive thematic analysis. RESULTS: Ten participants (5♀, 77±3y; FVCpp 77.1 ± 4.4, DLCOpp 42.4 ± 6.6) were included, but only nine completed the study. Recruitment was challenging (30%) and retention high (90%). iLiFE was feasible, with excellent adherence (84.4%) and no adverse events. Missing data were associated with one dropout and non-compliance with the accelerometer (n = 1). Participants reported that iLiFE contributed to (re)gain control in their daily life, namely through improving their well-being, functional status and motivation. Weather, symptoms, physical impairments and lack of motivation were identified as threats to keep an active lifestyle. CONCLUSIONS: iLiFE seems to be feasible, safe and meaningful for people with ILD. A randomised controlled trial is needed to strengthen these promising findings.

The landscape of expression and alternative splicing variation across human traits.

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.

Evolutionary patterns at the RNase based gametophytic self - incompatibility system in two divergent Rosaceae groups (Maloideae and Prunus).

BACKGROUND: Within Rosaceae, the RNase based gametophytic self-incompatibility (GSI) system has been studied at the molecular level in Maloideae and Prunus species that have been diverging for, at least, 32 million years. In order to understand RNase based GSI evolution within this family, comparative studies must be performed, using similar methodologies. RESULT: It is here shown that many features are shared between the two species groups such as levels of recombination at the S-RNase (the S-pistil component) gene, and the rate at which new specificities arise. Nevertheless, important differences are found regarding the number of ancestral lineages and the degree of specificity sharing between closely related species. In Maloideae, about 17% of the amino acid positions at the S-RNase protein are found to be positively selected, and they occupy about 30% of the exposed protein surface. Positively selected amino acid sites are shown to be located on either side of the active site cleft, an observation that is compatible with current models of specificity determination. At positively selected amino acid sites, non-conservative changes are almost as frequent as conservative changes. There is no evidence that at these sites the most drastic amino acid changes may be more strongly selected. CONCLUSIONS: Many similarities are found between the GSI system of Prunus and Maloideae that are compatible with the single origin hypothesis for RNase based GSI. The presence of common features such as the location of positively selected amino acid sites and lysine residues that may be important for ubiquitylation, raise a number of issues that, in principle, can be experimentally addressed in Maloideae. Nevertheless, there are also many important differences between the two Rosaceae GSI systems. How such features changed during evolution remains a puzzling issue.