Starburst Galactic Nuclei as Light Dark Matter Laboratories.

Starburst galaxies are well-motivated astrophysical emitters of high-energy gamma rays. They are well-known cosmic-ray "reservoirs," thanks to their expected large magnetic field turbulence which confine high-energy protons for ∼10^{5} years. Over such long times, cosmic-ray transport can be significantly affected by scatterings with sub-GeV dark matter. Here we point out that this scattering distorts the cosmic-ray spectrum, and the distortion can be indirectly observed by measuring the gamma rays produced by cosmic rays via hadronic collisions. Present gamma-ray data show no sign of such a distortion, leading to stringent bounds on the cross section between protons and dark matter. These are highly complementary with current bounds and have large room for improvement with the future gamma-ray measurements in the 0.1-10 TeV range from the Cherenkov Telescope Array, which can strengthen the limits by as much as 2 orders of magnitude.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

Modeling DNA Methylation Profiles through a Dynamic Equilibrium between Methylation and Demethylation.

DNA methylation is a heritable epigenetic mark that plays a key role in regulating gene expression. Mathematical modeling has been extensively applied to unravel the regulatory mechanisms of this process. In this study, we aimed to investigate DNA methylation by performing a high-depth analysis of particular loci, and by subsequent modeling of the experimental results. In particular, we performed an in-deep DNA methylation profiling of two genomic loci surrounding the transcription start site of the D-Aspartate Oxidase and the D-Serine Oxidase genes in different samples (n = 51). We found evidence of cell-to-cell differences in DNA methylation status. However, these cell differences were maintained between different individuals, which indeed showed very similar DNA methylation profiles. Therefore, we hypothesized that the observed pattern of DNA methylation was the result of a dynamic balance between DNA methylation and demethylation, and that this balance was identical between individuals. We hence developed a simple mathematical model to test this hypothesis. Our model reliably captured the characteristics of the experimental data, suggesting that DNA methylation and demethylation work together in determining the methylation state of a locus. Furthermore, our model suggested that the methylation status of neighboring cytosines plays an important role in this balance.

Nucleotide distance influences co-methylation between nearby CpG sites.

The tendency of individual CpG sites to be methylated is distinctive, non-random and well-regulated throughout the genome. We investigated the structural and spatial factors influencing CpGs methylation by performing an ultra-deep targeted methylation analysis on human, mouse and zebrafish genes. We found that methylation is not a random process and that closer neighboring CpG sites are more likely to share the same methylation status. Moreover, if the distance between CpGs increases, the degree of co-methylation decreases. We set up a simulation model to analyze the contribution of both the intrinsic susceptibility and the distance effect on the probability of a CpG to be methylated. Our finding suggests that the establishment of a specific methylation pattern follows a universal rule that must take into account of the synergistic and dynamic interplay of these two main factors: the intrinsic methylation susceptibility of specific CpG and the nucleotide distance between two CpG sites.

Tracking the evolution of epialleles during neural differentiation and brain development: D-Aspartate oxidase as a model gene.

We performed ultra-deep methylation analysis at single molecule level of the promoter region of developmentally regulated D-Aspartate oxidase (Ddo), as a model gene, during brain development and embryonic stem cell neural differentiation. Single molecule methylation analysis enabled us to establish the effective epiallele composition within mixed or pure brain cell populations. In this framework, an epiallele is defined as a specific combination of methylated CpG within Ddo locus and can represent the epigenetic haplotype revealing a cell-to-cell methylation heterogeneity. Using this approach, we found a high degree of polymorphism of methylated alleles (epipolymorphism) evolving in a remarkably conserved fashion during brain development. The different sets of epialleles mark stage, brain areas, and cell type and unravel the possible role of specific CpGs in favoring or inhibiting local methylation. Undifferentiated embryonic stem cells showed non-organized distribution of epialleles that apparently originated by stochastic methylation events on individual CpGs. Upon neural differentiation, despite detecting no changes in average methylation, we observed that the epiallele distribution was profoundly different, gradually shifting toward organized patterns specific to the glial or neuronal cell types. Our findings provide a deep view of gene methylation heterogeneity in brain cell populations promising to furnish innovative ways to unravel mechanisms underlying methylation patterns generation and alteration in brain diseases.

ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons.

BACKGROUND: CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation. RESULTS: Here we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions. CONCLUSIONS: ampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net .

Modeling DNA methylation by analyzing the individual configurations of single molecules.

DNA methylation is often analyzed by reporting the average methylation degree of each cytosine. In this study, we used a single molecule methylation analysis in order to look at the methylation conformation of individual molecules. Using D-aspartate oxidase as a model gene, we performed an in-depth methylation analysis through the developmental stages of 3 different mouse tissues (brain, lung, and gut), where this gene undergoes opposite methylation destiny. This approach allowed us to track both methylation and demethylation processes at high resolution. The complexity of these dynamics was markedly simplified by introducing the concept of methylation classes (MCs), defined as the number of methylated cytosines per molecule, irrespective of their position. The MC concept smooths the stochasticity of the system, allowing a more deterministic description. In this framework, we also propose a mathematical model based on the Markov chain. This model aims to identify the transition probability of a molecule from one MC to another during methylation and demethylation processes. The results of our model suggest that: 1) both processes are ruled by a dominant class of phenomena, namely, the gain or loss of one methyl group at a time; and 2) the probability of a single CpG site becoming methylated or demethylated depends on the methylation status of the whole molecule at that time.

Evidence for evolutionary and nonevolutionary forces shaping the distribution of human genetic variants near transcription start sites.

The regions surrounding transcription start sites (TSSs) of genes play a critical role in the regulation of gene expression. At the same time, current evidence indicates that these regions are particularly stressed by transcription-related mutagenic phenomena. In this work we performed a genome-wide analysis of the distribution of single nucleotide polymorphisms (SNPs) inside the 10 kb region flanking human TSSs by dividing SNPs into four classes according to their frequency (rare, two intermediate classes, and common). We found that, in this 10 kb region, the distribution of variants depends on their frequency and on their localization relative to the TSS. We found that the distribution of variants is generally different for TSSs located inside or outside of CpG islands. We found a significant relationship between the distribution of rare variants and nucleosome occupancy scores. Furthermore, our analysis suggests that evolutionary (purifying selection) and nonevolutionary (biased gene conversion) forces both play a role in determining the relative SNP frequency around TSSs. Finally, we analyzed the potential pathogenicity of each class of variant using the Combined Annotation Dependent Depletion score. In conclusion, this study provides a novel and detailed view of the distribution of genomic variants around TSSs, providing insight into the forces that instigate and maintain variability in such critical regions.

A distinct group of CpG islands shows differential DNA methylation between replicas of the same cell line in vitro.

BACKGROUND: CpG dinucleotide-rich genomic DNA regions, known as CpG islands (CGIs), can be methylated at their cytosine residues as an epigenetic mark that is stably inherited during cell mitosis. Differentially methylated regions (DMRs) are genomic regions showing different degrees of DNA methylation in multiple samples. In this study, we focused our attention on CGIs showing different DNA methylation between two culture replicas of the same cell line. RESULTS: We used methylation data of 35 cell lines from the Encyclopedia of DNA Elements (ENCODE) consortium to identify CpG islands that were differentially methylated between replicas of the same cell line and denoted them Inter Replicas Differentially Methylated CpG islands (IRDM-CGIs). We identified a group of IRDM-CGIs that was consistently shared by different cell lines, and denoted it common IRDM-CGIs. X chromosome CGIs were overrepresented among common IRDM-CGIs. Autosomal IRDM-CGIs were preferentially located in gene bodies and intergenic regions had a lower G + C content, a smaller mean length, and a reduced CpG percentage. Functional analysis of the genes associated with autosomal IRDM-CGIs showed that many of them are involved in DNA binding and development. CONCLUSIONS: Our results show that several specific functional and structural features characterize common IRDM-CGIs. They may represent a specific subset of CGIs that are more prone to being differentially methylated for their intrinsic characteristics.

CpG islands under selective pressure are enriched with H3K4me3, H3K27ac and H3K36me3 histone modifications.

BACKGROUND: Histone modification is an epigenetic mechanism that influences gene regulation in eukaryotes. In particular, histone modifications in CpG islands (CGIs) are associated with different chromatin states and with transcription activity. Changes in gene expression play a crucial role in adaptation and evolution. RESULTS: In this paper, we have studied, using a computational biology approach, the relationship between histone modifications in CGIs and selective pressure in Homo sapiens. We considered three histone modifications: histone H3 lysine 4 trimethylation (H3K4me3), histone H3 lysine 27 acetylation (H3K27ac) and histone H3 lysine 36 trimethylation (H3K36me3), and we used the publicly available genomic-scale histone modification data of thirteen human cell lines. To define regions under selective pressure, we used three distinct signatures that mark selective events from different evolutionary periods. We found that CGIs under selective pressure showed significant enrichments for histone modifications. CONCLUSION: Our result suggests that, CGIs that have undergone selective events are characterized by epigenetic signatures, in particular, histone modifications that are distinct from CGIs with no evidence of selection.

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2.

BACKGROUND: The analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods. RESULTS: The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive, the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Python language and takes advantage of operators and modules provided by the simuPOP simulation environment. It can be used through a graphical or a command-line interface and is freely available from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0. CONCLUSIONS: Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.

Signs of selective pressure on genetic variants affecting human height.

Many decades of scientific investigation have proved the role of selective pressure in Homo Sapiens at least at the level of individual genes or loci. Nevertheless, there are examples of polygenic traits that are bound to be under selection, but studies devoted to apply population genetics methods to unveil such occurrence are still lacking. Stature provides a relevant example of well-studied polygenic trait for which is now available a genome-wide association study which has identified the genes involved in this trait, and which is known to be under selection. We studied the behavior of F(ST) in a simulated toy model to detect population differentiation on a generic polygenic phenotype under selection. The simulations showed that the set of alleles involved in the trait has a higher mean F(ST) value than those undergoing genetic drift only. In view of this we looked for an increase in the mean F(ST) value of the 180 variants associated to human height. For this set of alleles we found F(ST) to be significantly higher than the genomic background (p = 0.0356). On the basis of a statistical analysis we excluded that the increase was just due to the presence of outliers. We also proved as marginal the role played by local adaptation phenomena, even on different phenotypes in linkage disequilibrium with genetic variants involved in height. The increase of F(ST) for the set of alleles involved in a polygenic trait seems to provide an example of symmetry breaking phenomenon concerning the population differentiation. The splitting in the allele frequencies would be driven by the initial conditions in the population dynamics which are stochastically modified by events like drift, bottlenecks, etc, and other stochastic events like the born of new mutations.

CpG islands undermethylation in human genomic regions under selective pressure.

DNA methylation at CpG islands (CGIs) is one of the most intensively studied epigenetic mechanisms. It is fundamental for cellular differentiation and control of transcriptional potential. DNA methylation is involved also in several processes that are central to evolutionary biology, including phenotypic plasticity and evolvability. In this study, we explored the relationship between CpG islands methylation and signatures of selective pressure in Homo Sapiens, using a computational biology approach. By analyzing methylation data of 25 cell lines from the Encyclopedia of DNA Elements (ENCODE) Consortium, we compared the DNA methylation of CpG islands in genomic regions under selective pressure with the methylation of CpG islands in the remaining part of the genome. To define genomic regions under selective pressure, we used three different methods, each oriented to provide distinct information about selective events. Independently of the method and of the cell type used, we found evidences of undermethylation of CGIs in human genomic regions under selective pressure. Additionally, by analyzing SNP frequency in CpG islands, we demonstrated that CpG islands in regions under selective pressure show lower genetic variation. Our findings suggest that the CpG islands in regions under selective pressure seem to be somehow more "protected" from methylation when compared with other regions of the genome.

Schizophrenia and vitamin D related genes could have been subject to latitude-driven adaptation.

BACKGROUND: Many natural phenomena are directly or indirectly related to latitude. Living at different latitudes, indeed, has its consequences with being exposed to different climates, diets, light/dark cycles, etc. In humans, one of the best known examples of genetic traits following a latitudinal gradient is skin pigmentation. Nevertheless, also several diseases show latitudinal clinals such as hypertension, cancer, dismetabolic conditions, schizophrenia, Parkinson's disease and many more. RESULTS: We investigated, for the first time on a wide genomic scale, the latitude-driven adaptation phenomena. In particular, we selected a set of genes showing signs of latitude-dependent population differentiation. The biological characterization of these genes showed enrichment for neural-related processes. In light of this, we investigated whether genes associated to neuropsychiatric diseases were enriched by Latitude-Related Genes (LRGs). We found a strong enrichment of LRGs in the set of genes associated to schizophrenia. In an attempt to try to explain this possible link between latitude and schizophrenia, we investigated their associations with vitamin D. We found in a set of vitamin D related genes a significant enrichment of both LRGs and of genes involved in schizophrenia. CONCLUSIONS: Our results suggest a latitude-driven adaptation for both schizophrenia and vitamin D related genes. In addition we confirm, at a molecular level, the link between schizophrenia and vitamin D. Finally, we discuss a model in which schizophrenia is, at least partly, a maladaptive by-product of latitude dependent adaptive changes in vitamin D metabolism.

A novel approach to simulate gene-environment interactions in complex diseases.

BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. RESULTS: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. CONCLUSIONS: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

Genome-wide scan for signatures of human population differentiation and their relationship with natural selection, functional pathways and diseases.

Genetic differences both between individuals and populations are studied for their evolutionary relevance and for their potential medical applications. Most of the genetic differentiation among populations are caused by random drift that should affect all loci across the genome in a similar manner. When a locus shows extraordinary high or low levels of population differentiation, this may be interpreted as evidence for natural selection. The most used measure of population differentiation was devised by Wright and is known as fixation index, or F(ST). We performed a genome-wide estimation of F(ST) on about 4 millions of SNPs from HapMap project data. We demonstrated a heterogeneous distribution of F(ST) values between autosomes and heterochromosomes. When we compared the F(ST) values obtained in this study with another evolutionary measure obtained by comparative interspecific approach, we found that genes under positive selection appeared to show low levels of population differentiation. We applied a gene set approach, widely used for microarray data analysis, to detect functional pathways under selection. We found that one pathway related to antigen processing and presentation showed low levels of F(ST), while several pathways related to cell signalling, growth and morphogenesis showed high F(ST) values. Finally, we detected a signature of selection within genes associated with human complex diseases. These results can help to identify which process occurred during human evolution and adaptation to different environments. They also support the hypothesis that common diseases could have a genetic background shaped by human evolution.

Transitions at CpG dinucleotides, geographic clustering of TP53 mutations and food availability patterns in colorectal cancer.

BACKGROUND: Colorectal cancer is mainly attributed to diet, but the role exerted by foods remains unclear because involved factors are extremely complex. Geography substantially impacts on foods. Correlations between international variation in colorectal cancer-associated mutation patterns and food availabilities could highlight the influence of foods on colorectal mutagenesis. METHODOLOGY: To test such hypothesis, we applied techniques based on hierarchical clustering, feature extraction and selection, and statistical pattern recognition to the analysis of 2,572 colorectal cancer-associated TP53 mutations from 12 countries/geographic areas. For food availabilities, we relied on data extracted from the Food Balance Sheets of the Food and Agriculture Organization of the United Nations. Dendrograms for mutation sites, mutation types and food patterns were constructed through Ward's hierarchical clustering algorithm and their stability was assessed evaluating silhouette values. Feature selection used entropy-based measures for similarity between clusterings, combined with principal component analysis by exhaustive and heuristic approaches. CONCLUSION/SIGNIFICANCE: Mutations clustered in two major geographic groups, one including only Western countries, the other Asia and parts of Europe. This was determined by variation in the frequency of transitions at CpGs, the most common mutation type. Higher frequencies of transitions at CpGs in the cluster that included only Western countries mainly reflected higher frequencies of mutations at CpG codons 175, 248 and 273, the three major TP53 hotspots. Pearson's correlation scores, computed between the principal components of the datamatrices for mutation types, food availability and mutation sites, demonstrated statistically significant correlations between transitions at CpGs and both mutation sites and availabilities of meat, milk, sweeteners and animal fats, the energy-dense foods at the basis of "Western" diets. This is best explainable by differential exposure to nitrosative DNA damage due to foods that promote metabolic stress and chronic inflammation.