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Gradual divergence and diversification of mammalian duplicate gene functions

10.1101/004267

Raquel Assis;Doris Bachtrog;

Gene duplication provides raw material for the evolution of functional innovation. We recently developed a phylogenetic method to classify the evolutionary processes underlying the retention and functional evolution of duplicate genes by quantifying divergence of their gene expression profiles. Here, we apply our method to pairs of duplicate genes in eight mammalian genomes, using data from 11 distinct tissues to construct spatial gene expression profiles. We find that young mammalian duplicates are often functionally conserved, and that functional divergence gradually increases with evolutionary distance between species. Examination of expression patterns in genes with conserved and new functions supports the \"out-of-testes\" hypothesis, in which new genes arise with testis-specific functions and acquire functions in other tissues over time. While new functions tend to be tissue-specific, there is no bias toward expression in any particular tissue. Thus, duplicate genes acquire a diversity of functions outside of the testes, possibly contributing to the origin of a multitude of complex phenotypes during mammalian evolution.

2014-04-17

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

10.1101/004283

Matthew J. Peloqiun;Dave Bridges;

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

2014-04-17

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

10.1101/004283

Matthew J. Peloqiun;Dave Bridges;

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

2014-04-23

Weight Loss in Response to Food Deprivation Predicts The Extent of Diet Induced Obesity in C57BL/6J Mice

10.1101/004283

Matthew J. Peloqiun;Dave Bridges;

Inbred C57BL/6J mice have been used to study diet-induced obesity and the detrimental physiological effects associated with it. Little is understood about predictive factors that predispose an animal to weight gain. To address this, mice were fed a high fat diet, control diet or normal chow diet. Several measurements including pre-diet serum hormone levels and pre-diet body weight were analyzed, but these had limited predictive value regarding weight gain. However, baseline measurements of weight loss in response to food deprivation showed a strong negative correlation with high fat diet-induced weight gain. These data suggest that fasting-induced weight loss in adolescent mice is a useful predictor of diet-induced weight gain.

2014-06-30

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

10.1101/004291

Luwen Ning;Guan Wang;Zhoufang Li;Wen Hu;Qingming Hou;Yin Tong;Meng Zhang;Li Qin;Xiaoping Chen;Hengye Man;Pinghua Liu;Jiankui He;

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

2014-04-18

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

10.1101/004317

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

2014-04-18

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

10.1101/004317

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

2014-04-21

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

10.1101/004317

Alexandre Melnikov;Peter Rogov;Li Wang;Andreas Gnirke;Tarjei S Mikkelsen;

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

2014-06-10

Exact Reconstruction of Gene Regulatory Networks using Compressive Sensing

10.1101/004242

Young Hwan Chang;Joe W. Gray;Claire J. Tomlin;

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the networks sparseness.\n\nResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. For each problem, a set of numerical examples is presented.\n\nConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

2014-04-20

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

10.1101/004325

Thomas Turner;Christopher C Giauque;Daniel R Schrider;Andrew D Kern;

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

2014-04-21

Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large- effect at the foraging gene in a population from North Carolina, USA

10.1101/004325

Thomas Turner;Christopher C Giauque;Daniel R Schrider;Andrew D Kern;

Thirty four years ago, it was postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed \"rover\" and \"sitter\", and that this variation is caused mainly by large-effect alleles at a single locus. Since that time, considerable data has been amassed that compares the behavior and physiology of these morphs. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 natural strains, performed a genome-wide association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of long term balancing selection We propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature. Genome-wide association does support a role for natural variation at several other loci, including the sulfateless gene, though these associations should be considered preliminary until validated with a larger sample size.

2015-01-16

Phylogenetic ANOVA: The Expression Variance and Evolution (EVE) model for quantitative trait evolution

10.1101/004374

Rori Rohlfs;Rasmus Nielsen;

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within and between-species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous HKA test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between-species as candidates for expression level adaptation, and genes with high expression diversity within-species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within-species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

2014-04-21

Phylogenetic ANOVA: The Expression Variance and Evolution (EVE) model for quantitative trait evolution

10.1101/004374

Rori Rohlfs;Rasmus Nielsen;

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within and between-species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous HKA test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between-species as candidates for expression level adaptation, and genes with high expression diversity within-species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within-species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

2015-06-02

Regulatory variants explain much more heritability than coding variants across 11 common diseases

10.1101/004309

Alexander Gusev;S Hong Lee;Benjamin M Neale;Gosia Trynka;Bjarni J Vilhjalmsson;Hilary Finucane;Han Xu;Chongzhi Zang;Stephan Ripke;Eli Stahl;n/a Schizophrenia Working Group of the PGC;n/a SWE-SCZ Consortium;Anna K Kahler;Christina M Hultman;Shaun M Purcell;Steven A McCarroll;Mark Daly;Bogdan Pasaniuc;Patrick F Sullivan;Naomi R Wray;Soumya Raychaudhuri;Alkes L Price;

Common variants implicated by genome-wide association studies (GWAS) of complex diseases are known to be enriched for coding and regulatory variants. We applied methods to partition the heritability explained by genotyped SNPs [Formula] across functional categories (while accounting for shared variance due to linkage disequilibrium) to genotype and imputed data for 11 common diseases. DNaseI Hypersensitivity Sites (DHS) from 218 cell-types, spanning 16% of the genome, explained an average of 79% of [Formula] (5.1x enrichment; P < 10-20); further enrichment was observed at enhancer and cell-type specific DHS elements. The enrichments were much smaller in analyses that did not use imputed data or were restricted to GWAS-associated SNPs. In contrast, coding variants, spanning 1% of the genome, explained only 8% of [Formula] enrichment; P = 5 x 10-4). We replicated these findings but found no significant contribution from rare coding variants in an independent schizophrenia cohort genotyped on GWAS and exome chips.

2014-04-21

Quantifying selection in immune receptor repertoires

10.1101/004341

Yuval Elhanati;Anand Murugan;Curtis G Callan Jr.;Thierry Mora;Aleksandra Walczak;

The efficient recognition of pathogens by the adaptive immune system relies on the diversity of receptors displayed at the surface of immune cells. T-cell receptor diversity results from an initial random DNA editing process, called VDJ recombination, followed by functional selection of cells according to the interaction of their surface receptors with self and foreign antigenic peptides. To quantify the effect of selection on the highly variable elements of the receptor, we apply a probabilistic maximum likelihood approach to the analysis of high-throughput sequence data from the {beta}-chain of human T-cell receptors. We quantify selection factors for V and J gene choice, and for the length and amino-acid composition of the variable region. Our approach is necessary to disentangle the effects of selection from biases inherent in the recombination process. Inferred selection factors differ little between donors, or between naive and memory repertoires. The number of sequences shared between donors is well-predicted by the model, indicating a purely stochastic origin of such \"public\" sequences. We find a significant correlation between biases induced by VDJ recombination and our inferred selection factors, together with a reduction of diversity during selection. Both effects suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution.

2014-04-21

Quantitative comparison of single-cell sequencing methods using hippocampal neurons

10.1101/004366

Luwen Ning;Guan Wang;Zhoufang Li;Wen Hu;Qingming Hou;Yin Tong;Meng Zhang;Li Qin;Xiaoping Chen;Heng-Ye Man;Pinghua Liu;Jiankui He;

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

2014-04-21

Notorious Novel Avian Influenza Viruses H10N8 and H7N9 in China in 2013 Co-originated from H9N2

10.1101/004390

Liang Chen;Feng Zhu;Chenglong Xiong;Zhijie Zhang;Lufang Jiang;Rui Li;Genming Zhao;Yue Chen;Qingwu Jiang;

In 2013, two new avian influenza viruses (AIVs) H7N9 and H10N8 emerged in China caused worldwide concerns. Previous studies have studied their originations independently; this study is the first time to investigate their co-originating characteristics. Gene segments of assorted subtype influenza A viruses, as well as H10N8 and H7N9, were collected from public database. 26 With the help of series software, small and large-scale phylogenetic trees, mean evolutionary rates, and divergence years were obtained successionally. The results demonstrated the two AIVs co-originated from H9N2, and shared a spectrum of mutations in common on many key sites related to pathogenic, tropism and epidemiological characteristics. For a long time, H9N2 viruses had been circulated in eastern and southern China; poultry was the stable and lasting maintenance reservoir. High carrying rate of AIVs H9N2 in poultry had an extremely high risk of co-infections with other influenza viruses, which increased the risk of virus reassortment. It implied that novel AIVs reassortants based on H9N2 might appear and prevail at any time in China; therefore, surveillance of H9N2 AIVs should be given a high priority.

2014-04-21

Cell size regulation in bacteria

10.1101/004457

Ariel Amir;

Various bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive Bacillus subtilis divide symmetrically after they approximately double their volume. Their size at division is not constant, but is typically distributed over a narrow range. Here, we propose an analytically tractable model for cell size control, and calculate the cell size and inter-division time distributions, and the correlations between these variables. We suggest ways of extracting the model parameters from experimental data, and show that existing data for E. coli supports partial size control, and a particular explanation: a cell attempts to add a constant volume from the time of initiation of DNA replication to the next initiation event. This hypothesis accounts for the experimentally observed correlations between mother and daughter cells as well as the exponential dependence of size on growth rate.\n\nPACS numbers: 87.17.Ee, 87.17.Aa, 87.10.Mn, 87.81Tt

2014-04-23

Cell size regulation in bacteria

10.1101/004457

Ariel Amir;

Various bacteria such as the canonical gram negative Escherichia coli or the well-studied gram positive Bacillus subtilis divide symmetrically after they approximately double their volume. Their size at division is not constant, but is typically distributed over a narrow range. Here, we propose an analytically tractable model for cell size control, and calculate the cell size and inter-division time distributions, and the correlations between these variables. We suggest ways of extracting the model parameters from experimental data, and show that existing data for E. coli supports partial size control, and a particular explanation: a cell attempts to add a constant volume from the time of initiation of DNA replication to the next initiation event. This hypothesis accounts for the experimentally observed correlations between mother and daughter cells as well as the exponential dependence of size on growth rate.\n\nPACS numbers: 87.17.Ee, 87.17.Aa, 87.10.Mn, 87.81Tt

2014-04-24

Soft selective sweeps in complex demographic scenarios

10.1101/004424

Benjamin A Wilson;Dmitri Petrov;Philipp W Messer;

Recent studies have shown that adaptation from de novo mutation often produces so-called soft selective sweeps, where adaptive mutations of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that soft sweeps should be likely if the product of the population size and the mutation rate towards the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such 'hardening' of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.

2014-04-23

The evolution of genetic diversity in changing environments

10.1101/004440

Oana Carja;Uri Liberman;Marcus W. Feldman;

The production and maintenance of genetic and phenotypic diversity under temporally fluctuating selection and the signatures of environmental and selective volatility in the patterns of genetic and phenotypic variation have been important areas of focus in population genetics. On one hand, stretches of constant selection pull the genetic makeup of populations towards local fitness optima. On the other, in order to cope with changes in the selection regime, populations may evolve mechanisms that create a diversity of genotypes. By tuning the rates at which variability is produced, such as the rates of recombination, mutation or migration, populations may increase their long-term adaptability. Here we use theoretical models to gain insight into how the rates of these three evolutionary forces are shaped by fluctuating selection. We compare and contrast the evolution of recombination, mutation and migration under similar patterns of environmental change and show that these three sources of phenotypic variation are surprisingly similar in their response to changing selection. We show that knowing the shape, size, variance and asymmetry of environmental runs is essential for accurate prediction of genetic evolutionary dynamics.

2014-04-23

A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

10.1101/004465

Josef C Uyeda;Luke J Harmon;

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

2014-04-23

An integrated RNA and CRISPR/Cas toolkit for multiplexed synthetic circuits and endogenous gene regulation in human cells

10.1101/004432

Lior Nissim;Samuel D Perli;Alexandra Fridkin;Pablo Perez-Pinera;Timothy Lu;

RNA-based regulation, such as RNA interference, and CRISPR/Cas transcription factors (CRISPR-TFs), can enable scalable synthetic gene circuits and the modulation of endogenous networks but have yet to be integrated together. Here, we combined multiple mammalian RNA regulatory strategies, including RNA triple helix structures, introns, microRNAs, and ribozymes, with Cas9-based CRISPR-TFs and Cas6/Csy4-based RNA processing in human cells. We describe three complementary strategies for expressing functional gRNAs from transcripts generated by RNA polymerase II (RNAP II) promoters while allowing the harboring gene to be translated. These architectures enable the multiplexed expression of proteins and multiple gRNAs from a single compact transcript for efficient modulation of synthetic constructs and endogenous human promoters. We used these regulatory tools to implement tunable synthetic gene circuits, including multi-stage transcriptional cascades. Finally, we show that Csy4 can rewire regulatory connections in RNA-dependent gene circuits with multiple outputs and feedback loops to achieve complex functional behaviors. This multiplexable toolkit will be valuable for the construction of scalable gene circuits and the perturbation of natural regulatory networks in human cells for basic biology, therapeutic, and synthetic-biology applications.

2014-04-23

Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

10.1101/004259

Young Hwan Chang;Jim Korkola;Dhara N. Amin;Mark M. Moasser;Jose M. Carmena;Joe W. Gray;Claire J. Tomlin;

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.\n\nAuthor SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.

2014-04-23

Disentangling Multidimensional Spatio-Temporal Data into their Common and Aberrant Responses

10.1101/004259

Young Hwan Chang;Jim Korkola;Dhara N. Amin;Mark M. Moasser;Jose M. Carmena;Joe W. Gray;Claire J. Tomlin;

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data.\n\nAuthor SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.

2014-04-28

Crowdsourced analysis of ash and ash dieback through the Open Ash Dieback project: A year 1 report on datasets and analyses contributed by a self-organising community.

10.1101/004564

Diane Saunders;Kentaro Yoshida;Christine Sambles;Rachel Glover;Bernardo Clavijo;Manuel Corpas;Daniel Bunting;Suomeng Dong;Ghanasyam Rallapalli;Matthew Clark;David Swarbreck;Sarah Ayling;Matthew Bashton;Steve Collin;Tsuyoshi Hosoya;Anne Edwards;Lisa Crossman;Graham Etherington;Joe Win;Liliana Cano;David Studholme;J Allan Downie;Mario Caccamo;Sophien Kamoun;Dan MacLean;

Ash dieback is a fungal disease of ash trees caused by Hymenoscyphus pseudoalbidus that has swept across Europe in the last two decades and is a significant threat to the ash population. This emergent pathogen has been relatively poorly studied and little is known about its genetic make-up. In response to the arrival of this dangerous pathogen in the UK we took the unusual step of providing an open access database and initial sequence datasets to the scientific community for analysis prior to performing an analysis of our own. Our goal was to crowdsource genomic and other analyses and create a community analysing this pathogen. In this report on the evolution of the community and data and analysis obtained in the first year of this activity, we describe the nature and the volume of the contributions and reveal some preliminary insights into the genome and biology of H. pseudoalbidus that emerged. In particular our nascent community generated a first-pass genome assembly containing abundant collapsed AT-rich repeats indicating a typically complex genome structure. Our open science and crowdsourcing effort has brought a wealth of new knowledge about this emergent pathogen within a short time-frame. Our community endeavour highlights the positive impact that open, collaborative approaches can have on fast, responsive modern science.

2014-04-25

Crowdsourced analysis of ash and ash dieback through the Open Ash Dieback project: A year 1 report on datasets and analyses contributed by a self-organising community.

10.1101/004564

Diane Saunders;Kentaro Yoshida;Christine Sambles;Rachel Glover;Bernardo Clavijo;Manuel Corpas;Daniel Bunting;Suomeng Dong;Ghanasyam Rallapalli;Matthew Clark;David Swarbreck;Sarah Ayling;Matthew Bashton;Steve Collin;Tsuyoshi Hosoya;Anne Edwards;Lisa Crossman;Graham Etherington;Joe Win;Liliana Cano;David Studholme;J Allan Downie;Mario Caccamo;Sophien Kamoun;Dan MacLean;

Ash dieback is a fungal disease of ash trees caused by Hymenoscyphus pseudoalbidus that has swept across Europe in the last two decades and is a significant threat to the ash population. This emergent pathogen has been relatively poorly studied and little is known about its genetic make-up. In response to the arrival of this dangerous pathogen in the UK we took the unusual step of providing an open access database and initial sequence datasets to the scientific community for analysis prior to performing an analysis of our own. Our goal was to crowdsource genomic and other analyses and create a community analysing this pathogen. In this report on the evolution of the community and data and analysis obtained in the first year of this activity, we describe the nature and the volume of the contributions and reveal some preliminary insights into the genome and biology of H. pseudoalbidus that emerged. In particular our nascent community generated a first-pass genome assembly containing abundant collapsed AT-rich repeats indicating a typically complex genome structure. Our open science and crowdsourcing effort has brought a wealth of new knowledge about this emergent pathogen within a short time-frame. Our community endeavour highlights the positive impact that open, collaborative approaches can have on fast, responsive modern science.

2014-08-04

MOHCA-seq: RNA 3D models from single multiplexed proximity-mapping experiments

10.1101/004556

Clarence Cheng;Fang-Chieh Chou;Wipapat Kladwang;Siqi Tian;Pablo Cordero;Rhiju Das;

Large RNAs control myriad biological processes but challenge tertiary structure determination. We report that integrating multiplexed *OH cleavage analysis with tabletop deep sequencing (MOHCA-seq) gives nucleotide-resolution proximity maps of RNA structure from single straightforward experiments. After achieving 1-nm resolution models for RNAs of known structure, MOHCA-seq reveals previously unattainable 3D information for ligand-induced conformational changes in a double glycine riboswitch and the sixth community-wide RNA puzzle, an adenosylcobalamin riboswitch.

2014-04-25

Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions

10.1101/004572

Sacha Laurent;Marc Robinson-Rechavi;Nicolas Salamin;

AO_SCPCAPBSTRACTC_SCPCAPRecent methodological advances are enabling better examination of speciation and extinction processes and patterns. A major open question is the origin of large discrepancies in species number between groups of the same age. Existing frameworks to model this diversity either focus on changes between lineages, neglecting global effects such as mass extinctions, or focus on changes over time which would affect all lineages. Yet it seems probable that both lineages differences and mass extinctions affect the same groups. Here we used simulations to test the performance of two widely used methods, under complex scenarios. We report good performances, although with a tendency to over-predict events when increasing the complexity of the scenario. Overall, we find that lineage shifts are better detected than mass extinctions. This work has significance for assessing the methods currently used for estimating changes in diversification using phylogenies and developing new tests.

2014-04-25

Detecting patterns of species diversification in the presence of both rate shifts and mass extinctions

10.1101/004572

Sacha Laurent;Marc Robinson-Rechavi;Nicolas Salamin;

AO_SCPCAPBSTRACTC_SCPCAPRecent methodological advances are enabling better examination of speciation and extinction processes and patterns. A major open question is the origin of large discrepancies in species number between groups of the same age. Existing frameworks to model this diversity either focus on changes between lineages, neglecting global effects such as mass extinctions, or focus on changes over time which would affect all lineages. Yet it seems probable that both lineages differences and mass extinctions affect the same groups. Here we used simulations to test the performance of two widely used methods, under complex scenarios. We report good performances, although with a tendency to over-predict events when increasing the complexity of the scenario. Overall, we find that lineage shifts are better detected than mass extinctions. This work has significance for assessing the methods currently used for estimating changes in diversification using phylogenies and developing new tests.

2014-11-24

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

10.1101/004481

Amir Shahmoradi;Dariya K. Sydykova;Stephanie J. Spielman;Eleisha L. Jackson;Eric T. Dawson;Austin G. Meyer;Claus O. Wilke;

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

2014-04-24

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

10.1101/004481

Amir Shahmoradi;Dariya K. Sydykova;Stephanie J. Spielman;Eleisha L. Jackson;Eric T. Dawson;Austin G. Meyer;Claus O. Wilke;

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

2014-07-21

The Evolution of Power and the Divergence of Cooperative Norms

10.1101/004416

Michael D Makowsky;Paul E Smaldino;

We consider a model of multilevel selection and the evolution of institutions that distribute power in the form of influence in a groups collective interactions with other groups. In the absence of direct group-level interactions, groups with the most cooperative members will outcompete less cooperative groups, while within any group the least cooperative members will be the most successful. Introducing group-level interactions, however, such as raiding or warfare, changes the selective landscape for groups. Our model suggests that as the global population becomes more integrated and the rate of intergroup conflict increases, selection increasingly favors unequally distributed power structures, where individual influence is weighted by acquired resources. The advantage to less democratic groups rests in their ability to facilitate selection for cooperative strategies - involving cooperation both among themselves and with outsiders - in order to produce the resources necessary to fuel their success in inter-group conflicts, while simultaneously selecting for leaders (and corresponding collective behavior) who are unburdened with those same prosocial norms. The coevolution of cooperative social norms and institutions of power facilitates the emergence of a leadership class of the selfish and has implications for theories of inequality, structures of governance, non-cooperative personality traits, and hierarchy. Our findings suggest an amendment to the well-known doctrine of multilevel selection that \"Selfishness beats altruism within groups. Altruistic groups beat selfish groups.\" In an interconnected world, altruistic groups led by selfish individuals can beat them both.

2014-04-24

The Evolution of Power and the Divergence of Cooperative Norms

10.1101/004416

Michael D Makowsky;Paul E Smaldino;

We consider a model of multilevel selection and the evolution of institutions that distribute power in the form of influence in a groups collective interactions with other groups. In the absence of direct group-level interactions, groups with the most cooperative members will outcompete less cooperative groups, while within any group the least cooperative members will be the most successful. Introducing group-level interactions, however, such as raiding or warfare, changes the selective landscape for groups. Our model suggests that as the global population becomes more integrated and the rate of intergroup conflict increases, selection increasingly favors unequally distributed power structures, where individual influence is weighted by acquired resources. The advantage to less democratic groups rests in their ability to facilitate selection for cooperative strategies - involving cooperation both among themselves and with outsiders - in order to produce the resources necessary to fuel their success in inter-group conflicts, while simultaneously selecting for leaders (and corresponding collective behavior) who are unburdened with those same prosocial norms. The coevolution of cooperative social norms and institutions of power facilitates the emergence of a leadership class of the selfish and has implications for theories of inequality, structures of governance, non-cooperative personality traits, and hierarchy. Our findings suggest an amendment to the well-known doctrine of multilevel selection that \"Selfishness beats altruism within groups. Altruistic groups beat selfish groups.\" In an interconnected world, altruistic groups led by selfish individuals can beat them both.

2015-01-29

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

10.1101/004507

Reda Younsi;Dan MacLean;

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

2014-04-24

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

10.1101/004507

Reda Younsi;Dan MacLean;

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

2014-10-27

Lack of association between Toll Like Receptor-2 &amp;amp; Toll Like Receptor-4 Gene Polymorphisms and Iranian Asthmatics risk or features

10.1101/004382

hamid bahrami;saeed daneshmandi;Ali Akbar Pourfathollah;hasan haidarnazhad;

BackgroundAsthma as chronic inflammatory airway disease is considered to be the most common chronic disease that is involving genetic and environmental factors. Toll like receptors (TLRs) and other inflammatory mediators are important in modulation of inflammation. In this study we evaluated the role of TLR2 Arg753Gln and TLR4 Asp299Gly polymorphisms in the asthma susceptibility, progress, control levels and lung functions in Iranian subjects.\n\nMethodsOn 99 asthmatic patients and 120 normal subjects, TLR2 Arg753Gln and TLR4 Asp299Gly polymorphism were evaluated by PCR-RFLP method recruiting Msp1 and Nco1 restriction enzymes, respectively. IgE serum levels by ELISA technique were determined and asthma diagnosis, treatment and control levels were considered using standard schemes and criteria.\n\nResultsOur results indicated that the genotype and allele frequencies of the TLR2 Arg753Gln and TLR4 Asp299Gly polymorphisms were not significantly different between control subjects and asthmatics (p > 0.05) or even in asthma features such as IgE levels, asthma history and pulmonary factors (p > 0.05).\n\nConclusionsMeanwhile some previous studies indicated TLRs and their polymorphisms role in asthma incidence and features, our data demonstrated that TLR2 Arg753Gln and TLR4 Asp299Gly gene variants were not risk factor of asthma or its features in Iranian patients. Genetic complexity, ethnicity, influence of other genes or polymorphisms may overcome these polymorphisms in our asthmatics.

2014-04-28

The invariance hypothesis implies domain-specific regions in visual cortex

10.1101/004473

Joel Z. Leibo;Qianli Liao;Fabio Anselmi;Tomaso Poggio;

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition systems optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

2014-04-24

The invariance hypothesis implies domain-specific regions in visual cortex

10.1101/004473

Joel Z. Leibo;Qianli Liao;Fabio Anselmi;Tomaso Poggio;

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition systems optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions.

2015-04-26

Regulatory RNA design through evolutionary computation and strand displacement

10.1101/004580

William Rostain;Thomas E Landrain;Guillermo Rodrigo;Alfonso Jaramillo;

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

2014-04-28

In vivo generation of DNA sequence diversity for cellular barcoding

10.1101/004499

Ian Peikon;Diana Gizatullina;Anthony Zador;

Heterogeneity is a ubiquitous feature of biological systems. A complete understanding of such systems requires a method for uniquely identifying and tracking individual components and their interactions with each other. We have developed a novel method of uniquely tagging individual cells in vivo with a genetic \"barcode\" that can be recovered by DNA sequencing. We demonstrate the feasibility of this technique in bacterial cells. This method should prove useful in tracking interactions of cells within a network, and/or heterogeneity within complex biological samples.

2014-04-24

Graph-based data integration predicts long-range regulatory interactions across the human genome

10.1101/004622

Sofie Demeyer;Tom Michoel;

Transcriptional regulation of gene expression is one of the main processes that affect cell diversification from a single set of genes. Regulatory proteins often interact with DNA regions located distally from the transcription start sites (TSS) of the genes. We developed a computational method that combines open chromatin and gene expression information for a large number of cell types to identify these distal regulatory elements. Our method builds correlation graphs for publicly available DNase-seq and exon array datasets with matching samples and uses graph-based methods to filter findings supported by multiple datasets and remove indirect interactions. The resulting set of interactions was validated with both anecdotal information of known long-range interactions and unbiased experimental data deduced from Hi-C and CAGE experiments. Our results provide a novel set of high-confidence candidate open chromatin regions involved in gene regulation, often located several Mb away from the TSS of their target gene.

2014-04-29

Characterizing a collective and dynamic component of chromatin immunoprecipitation enrichment profiles in yeast

10.1101/004614

Lucas D. Ward;Junbai Wang;Harmen J. Bussemaker;

Recent chromatin immunoprecipitation (ChIP) experiments in fly, mouse, and human have revealed the existence of high-occupancy target (HOT) regions or hotspots that show enrichment across many assayed DNA-binding proteins. Similar co-enrichment observed in yeast so far has been treated as artifactual, and has not been fully characterized. Here we reanalyze ChIP data from both array-based and sequencing-based experiments to show that in the yeast S. cerevisiae, the collective enrichment phenomenon is strongly associated with proximity to noncoding RNA genes and with nucleosome depletion. DNA sequence motifs that confer binding affinity for the proteins are largely absent from these hotspots, suggesting that protein-protein interactions play a prominent role. The hotspots are condition-specific, suggesting that they reflect a chromatin state or protein state, and are not a static feature of underlying sequence. Additionally, only a subset of all assayed factors is associated with these loci, suggesting that the co-enrichment cannot be simply explained by a chromatin state that is universally more prone to immunoprecipitation. Together our results suggest that the co-enrichment patterns observed in yeast represent transcription factor co-occupancy. More generally, they make clear that great caution must be used when interpreting ChIP enrichment profiles for individual factors in isolation, as they will include factor-specific as well as collective contributions.

2014-04-29

Ontogenic, phenotypic, and functional characterization of XCR1+ dendritic cells leads to a consistent classification of intestinal dendritic cells based on the expression of XCR1 and SIRPα

10.1101/004648

Martina Becker;Steffen Güttler;Annabell Bachem;Evelyn Hartung;Ahmed Mora;Anika Jäkel;Andreas Hutloff;Volker Henn;Hans Werner Mages;Stephanie Gurka;Richard A. Kroczek;

In the past, lack of lineage markers confounded the classification of dendritic cells (DC) in the intestine and impeded a full understanding of their location and function. We have recently shown that the chemokine receptor XCR1 is a lineage marker for cross-presenting DC in the spleen. Now we provide evidence that intestinal XCR1+ DC largely, but not fully, overlap with CD103+ CD11b- DC, the hypothesized correlate of \"cross-presenting DC\" in the intestine, and are selectively dependent in their development on the transcription factor Batf3. XCR1+ DC are located in the villi and epithelial crypts of the lamina propria of the small intestine, the T cell zones of Peyers Patches, and in the T cell zones and sinuses of the draining mesenteric lymph node. Functionally, we could demonstrate for the first time that XCR1+ / CD103+ CD11b- DC excel in the cross-presentation of orally applied antigen. Together, our data show that XCR1 is a lineage marker for cross-presenting DC also in the intestinal immune system. Further, extensive phenotypic analyses reveal that expression of the integrin SIRP consistently demarcates the XCR1- DC population. We propose a simplified and consistent classification system for intestinal DC based on the expression of XCR1 and SIRP.

2014-04-30

Functional knockout of forebrain protein 14-3-3 disrupts conditioned taste aversion learning

10.1101/004663

Adam Kimbrough;Yuying Wu;Yi Zhou;Thomas A. Houpt;

Protein 14-3-3 isoforms are key to many cellular processes and are ubiquitous throughout the brain. 14-3-3 is a regulator of ser/thr phospho-signaling by binding and sequestering phosphorylated substrates including kinases, histone deactylases, and transcription factors. The role of protein 14-3-3 in conditioned taste aversion learning (CTA) has not previously been examined. We parameterized CTA learning in difopein- YFP transgenic mice, which have widespread by expression of the artificial peptide difopein in the forebrain, including the basolateral amygdala and insular cortex, resulting in functional knock-out (FKO) of all 14-3-3 isoforms . We found that a single pairing of saccharin or NaCl (CS) and LiCl injection (US) was not sufficient to induce CTA in FKO mice. Multiples pairings of CS and US did lead to CTA acquisition in the FKO mice; however, the CTA rapidly extinguished within 30 minutes to 24 hours after acquisition. Additionally, we found that 14-3-3 FKO mice have an attenuated visceral neuraxis response to LiCl as measured by c-Fos induction. The deficit in FKO was not due to an inability to discriminate or avoid tastants, because they showed normal unconditioned taste preferences for both palatable (saccharin, maltodextrin , low concentration NaCl) and unpalatable tastants (quinine, HCl, and high concentration NaCl) and they were able to reduce intake of a maltodextrin solution adulterated with quinine. The FKO did not have a global deficit in ingestive learning, because they were able to form a conditioned flavor-nutrient preference. Thus, FKO of forebrain 14-3-3 appears to disrupt CTA learning leading to forgetting, rapid extinction, or failure to reconsolidate. This further implicates ser/thr phospho-signaling pathways in the regulation of long-term CTA learning.

2014-04-30

Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

10.1101/004655

Christopher Yau;

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy number alterations. This paper describes how uncertainty in classification and differential detection rates can introduce biases in measures of clonal diversity. A simulation strategy is introduced that allows differential detection rates to be adjusted for and this process is shown to minimise bias.

2014-04-30

The Landscape of Human STR Variation

10.1101/004671

Thomas F. Willems;Melissa Gymrek;Gareth Highnam;- The 1000 Genomes Project;David Mittelman;Yaniv Erlich;

Short Tandem Repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across over 1,000 individuals in phase 1 of the 1000 Genomes Project. This process nearly saturated common STR variations. After employing a series of quality controls, we utilize this call set to analyze determinants of STR variation, assess the human reference genomes representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations. The resource is publicly available at http://strcat.teamerlich.org/ both in raw format and via a graphical interface.

2014-05-01

The Landscape of Human STR Variation

10.1101/004671

Thomas F. Willems;Melissa Gymrek;Gareth Highnam;- The 1000 Genomes Project;David Mittelman;Yaniv Erlich;

Short Tandem Repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across over 1,000 individuals in phase 1 of the 1000 Genomes Project. This process nearly saturated common STR variations. After employing a series of quality controls, we utilize this call set to analyze determinants of STR variation, assess the human reference genomes representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations. The resource is publicly available at http://strcat.teamerlich.org/ both in raw format and via a graphical interface.

2014-07-10

Detection and Polarization of Introgression in a Five-taxon Phylogeny

10.1101/004689

James B Pease;Matthew W. Hahn;

In clades of closely related taxa, discordant genealogies due to incomplete lineage sorting (ILS) can complicate the detection of introgression. The D-statistic (a.k.a. the ABBA/BABA test) was proposed to infer introgression in the presence of ILS for a four-taxon clade. However, the original D-statistic cannot be directly applied to a symmetric five-taxon phylogeny, and the direction of introgression cannot be inferred for any tree topology. Here we explore the issues associated with previous methods for adapting the D-statistic to a larger tree topology, and propose new \"DFOIL\" tests to infer both the taxa involved in and the direction of introgressions for a symmetric five-taxon phylogeny. Using theory and simulations, we find that previous modifications of the D-statistic to five-taxon phylogenies incorrectly identify both the pairs of taxa exchanging migrants as well as the direction of introgression. The DFOIL statistics are shown to overcome this deficiency and to correctly determine the direction of introgressions. The DFOIL tests are relatively simple and computationally inexpensive to calculate, and can be easily applied to various phylogenomic datasets. In addition, our general approach to the problem of introgression detection could be adapted to larger tree topologies and other models of sequence evolution.

2014-05-01

Spatial localization of recent ancestors for admixed individuals

10.1101/004713

Wen-Yun Yang;Alexander Platt;Charleston Wen-Kai Chiang;Eleazar Eskin;John Novembre;Bogdan Pasaniuc;

Ancestry analysis from genetic data plays a critical role in studies of human disease and evolution. Recent work has introduced explicit models for the geographic distribution of genetic variation and has shown that such explicit models yield superior accuracy in ancestry inference over non-model-based methods. Here we extend such work to introduce a method that models admixture between ancestors from multiple sources across a geographic continuum. We devise efficient algorithms based on hidden Markov models to localize on a map the recent ancestors (e.g. grandparents) of admixed individuals, joint with assigning ancestry at each locus in the genome. We validate our methods using empirical data from individuals with mixed European ancestry from the POPRES study and show that our approach is able to localize their recent ancestors within an average of 470Km of the reported locations of their grandparents. Furthermore, simulations from real POPRES genotype data show that our method attains high accuracy in localizing recent ancestors of admixed individuals in Europe (an average of 550Km from their true location for localization of 2 ancestries in Europe, 4 generations ago). We explore the limits of ancestry localization under our approach and find that performance decreases as the number of distinct ancestries and generations since admixture increases. Finally, we build a map of expected localization accuracy across admixed individuals according to the location of origin within Europe of their ancestors.\n\nAuthor SummaryInferring ancestry from genetic data forms a fundamental problem with applications ranging from localizing disease genes to inference of human history. Recent approaches have introduced models of genetic variation as a function of geography and have shown that such models yield high accuracies in ancestry inference from genetic data. In this work we propose methods for modeling the mixing of genetic data from different sources (i.e. admixture process) in a genetic-geographic continuum and show that using these methods we can accurately infer the ancestry of the recent ancestors (e.g. grandparents) from genetic data.

2014-05-04

Comparison of Y-chromosomal lineage dating using either evolutionary or genealogical Y-STR mutation rates

10.1101/004705

Chuan-Chao Wang;Li Hui;

We have compared the Y chromosomal lineage dating between sequence data and commonly used Y-SNP plus Y-STR data. The coalescent times estimated using evolutionary Y-STR mutation rates correspond best with sequence-based dating when the lineages include the most ancient haplogroup A individuals. However, the times using slow mutated STR markers with genealogical rates fit well with sequence-based estimates in main lineages, such as haplogroup CT, DE, K, NO, IJ, P, E, C, I, J, N, O, and R. In addition, genealogical rates lead to more plausible time estimates for Neolithic coalescent sublineages compared with sequence-based dating.

2014-05-03

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

10.1101/004739

Arnold Wiliem;Conrad Sanderson;Yongkang Wong;Peter Hobson;Rodney Minchin;Brian Lovell;

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

2014-05-05

Pre-trial exogenous visual flicker does not affect behavioral or EEG signatures of conflict processing

10.1101/004747

Michael X Cohen;Fraser William Steel;

Activity in the theta frequency band (4-8 Hz) over medial prefrontal regions has been consistently implicated in top-down cognitive control processes, including recognizing and resolving response conflict. It remains an unanswered question whether these theta-band dynamics are a neural mechanism of cognitive control, or instead are epiphenomenal to the neural computational machinery but are useful indices of brain function. Here we addressed this question by attempting to boost conflict processing (or its EEG theta-band signatures) via pre-trial exogenous theta-band visual flicker. Although the flicker successfully entrained posterior brain networks, there were no effects of flicker on behavior or on EEG signatures of conflict processing. In this paper, we detail our attempts and discuss possible future directions for using exogenous flicker in the study of the role of endogenous brain oscillations in conflict processing.

2014-05-05

Sequence co-evolution gives 3D contacts and structures of protein complexes

10.1101/004762

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

2014-05-06

Sequence co-evolution gives 3D contacts and structures of protein complexes

10.1101/004762

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

2014-05-23

Sequence co-evolution gives 3D contacts and structures of protein complexes

10.1101/004762

Thomas A. Hopf;Charlotta P.I. Schärfe;João P.G.L.M. Rodrigues;Anna G. Green;Chris Sander;Alexandre M.J.J. Bonvin;Debora S. Marks;

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

2014-09-15

N-BLR, a primate-specific non-coding transcript, modulates the epithelial-to-mesenchymal transition and leads to colorectal cancer invasion and migration

10.1101/004796

Isidore Rigoutsos;Sang Kil Lee;Su Youn Nam;Tina Catela Ivkovic;Martin Pichler;Simona Rossi;Peter Clark;Jing Yi;Hui Ling;Masayoshi Shimizu;Roxana Simona Redis;Maitri Yogen Shah;Xinna Zhang;Eun Jung Jung;Aristotelis Tsirigos;Li Huang;Jana Ferdin;Roberta Gafa;Riccardo Spizzo;Milena Nicoloso;Maryam Shariati;Aida Tiron;Jen Jen Yeh;Raul Teruel;Sonia Melo;Lianchun Xiao;Elsa Renee Flores;Massimo Negrini;Menashe Bar Eli;Sendurai Mani;Chang-Gong Liu;Ioana Berindan-Neagoe;Manel Esteller;Michael Keating;Giovanni Lanza;George Calin;

Non-coding RNAs have been commanding increasingly greater attention in recent years as the few that have been functionalized to date play important roles in key cellular processes. Here we show that N-BLR, a ~900 bp non-coding RNA, modulates the epithelial-to-mesenchymal transition, increases colorectal cancer invasion, and functions as a migration enabler by affecting the expression of ZEB1 and E-cadherin. In patients with colorectal cancer, N-BLR expression associates with tumor stage and invasion potential. As N-BLR contains several instances of a category of DNA motifs known as pyknons, we also designed a custom-made array to investigate the possibility that other pyknon loci may be transcribed. For several of the loci probed by the array we found that the corresponding pyknons are differentially expressed between cancer and normal tissue samples. Taken together the data suggest that a systematic study of other pyknon-containing non-coding RNAs like N-BLR may be warranted in the context of colorectal cancer.

2014-05-06

Cooperation between Noncanonical Ras Network Mutations in Cancer

10.1101/004846

Edward C Stites;Paul C Trampont;Lisa B Haney;Scott F Walk;Kodi S Ravichandran;

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell, and whether there is selection for certain combinations of mutations, are not well understood. Using a Ras signaling network mathematical model we tested potential synergistic combinations within the Ras network. Intriguingly, our modeling, including a \"computational random mutagenesis\" approach, and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of mutations in multiple other components of the Ras pathway, including weakly activating, noncanonical, Ras mutants. Since conventional wisdom holds that mutations within the same pathway do not co-occur, it was surprising that modeling and experiments both suggested a functional benefit for co-occurring Ras pathway mutations. Furthermore, we analyzed >3900 sequenced cancer specimens from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) and we uncovered an increased rate of co-occurrence between mutations the model predicted could display synergy. Overall, these data suggest that selective combinations of Ras pathway mutations could serve the role of cancer \"driver\". More generally, this work presents a mechanism by which the context created by one mutation influences the evolutionary trajectories of cancer development, and this work suggests that mutations that result in \"network instability\" may promote cancer in a manner analogous to genomic instability.

2014-05-06

Some plants don&amp;#146;t play games: An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game.

10.1101/004820

Gordon McNickle;Joel S Brown;

This is the pre-peer-reviewed version of the following article: G.G. McNickle and J.S. Brown. (2014) An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game. Journal of Ecology. DOI: 10.1111/1365-2745.12259, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12259/abstract\n\nO_LIGame theoretic models that seek to predict the most competitive strategy plants use for competition in soil are clear; they generally predict that over-proliferation of roots is the only evolutionarily stable strategy. However, empirical studies are equally clear that not all plants employ this strategy of over-proliferation of roots. Here, our goal was to develop and test an alternative non-game theoretic model that can be used to develop alternative hypotheses for plants that do not appear to play games.\nC_LIO_LIThe model is similar to previous models, but does not use a game theoretic optimization criterion. Instead, plants use only nutrient availability to select a root allocation strategy, ignoring neighbours. To test the model we compare root allocation and seed yield of plants grown either alone or with neighbours.\nC_LIO_LIThe model predicted plants that do not sense neighbours (or ignore neighbours) should allocate roots relative to resource availability following an ideal free distribution. This means that if a soil volume of quality R contains x roots, then a soil volume of quality R/n will contain x/n roots. The experimental data were consistent with this prediction. That is, plants grown with 1.2g of slow release fertilizer resources produced 0.043 g of roots, while plants grown with neighbours, or plants grown with half as much fertilizer produced half as much root mass (0.026g, and 0.24g respectively). Seed yield followed a similar pattern.\nC_LIO_LIThis model presents an alternative predictive framework for those plant species that do not seem to play a tragedy of the commons game for belowground competition.\nC_LIO_LISynthesis: It remains unclear why some plants do not engage in belowground games for competition. Models suggest over-proliferation is an unbeatable evolutionary stable strategy, yet plants that do not play the game apparently coexist with plants that do. We suggest that a greater understanding of trade-offs among traits that are important for other biotic interactions (above-ground competition, enemy defence, mutualisms) will lead to a greater understanding of why some species over-proliferate roots when in competition but other species do not.\nC_LI

2014-05-06

Some plants don&amp;#146;t play games: An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game.

10.1101/004820

Gordon McNickle;Joel S Brown;

This is the pre-peer-reviewed version of the following article: G.G. McNickle and J.S. Brown. (2014) An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game. Journal of Ecology. DOI: 10.1111/1365-2745.12259, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12259/abstract\n\nO_LIGame theoretic models that seek to predict the most competitive strategy plants use for competition in soil are clear; they generally predict that over-proliferation of roots is the only evolutionarily stable strategy. However, empirical studies are equally clear that not all plants employ this strategy of over-proliferation of roots. Here, our goal was to develop and test an alternative non-game theoretic model that can be used to develop alternative hypotheses for plants that do not appear to play games.\nC_LIO_LIThe model is similar to previous models, but does not use a game theoretic optimization criterion. Instead, plants use only nutrient availability to select a root allocation strategy, ignoring neighbours. To test the model we compare root allocation and seed yield of plants grown either alone or with neighbours.\nC_LIO_LIThe model predicted plants that do not sense neighbours (or ignore neighbours) should allocate roots relative to resource availability following an ideal free distribution. This means that if a soil volume of quality R contains x roots, then a soil volume of quality R/n will contain x/n roots. The experimental data were consistent with this prediction. That is, plants grown with 1.2g of slow release fertilizer resources produced 0.043 g of roots, while plants grown with neighbours, or plants grown with half as much fertilizer produced half as much root mass (0.026g, and 0.24g respectively). Seed yield followed a similar pattern.\nC_LIO_LIThis model presents an alternative predictive framework for those plant species that do not seem to play a tragedy of the commons game for belowground competition.\nC_LIO_LISynthesis: It remains unclear why some plants do not engage in belowground games for competition. Models suggest over-proliferation is an unbeatable evolutionary stable strategy, yet plants that do not play the game apparently coexist with plants that do. We suggest that a greater understanding of trade-offs among traits that are important for other biotic interactions (above-ground competition, enemy defence, mutualisms) will lead to a greater understanding of why some species over-proliferate roots when in competition but other species do not.\nC_LI

2014-05-07

MOSAIC EPIGENETIC DYSREGULATION OF ECTODERMAL CELLS IN AUTISM SPECTRUM DISORDER

10.1101/004853

Esther R. Berko;Masako Suzuki;Faygel Beren;Christophe Lemetre;Christine M. Alaimo;R. Brent Calder;Karen Ballaban-Gil;Batya Gounder;Kaylee Kampf;Jill Kirschen;Shahina B. Maqbool;Zeineen Momin;David M. Reynolds;Natalie Russo;Lisa Shulman;Edyta Stasiek;Jessica Tozour;Maria Valicenti-McDermott;Shenglong Wang;Brett S. Abrahams;Joseph Hargitai;Dov Inbar;Zhengdong Zhang;Joseph D. Buxbaum;Sophie Molholm;John J. Foxe;Robert W. Marion;Adam Auton;John Greally;

DNA mutational events are increasingly being identified in autism spectrum disorder (ASD), but the potential additional role of dysregulation of the epigenome in the pathogenesis of the condition remains unclear. The epigenome is of interest as a possible mediator of environmental effects during development, encoding a cellular memory reflected by altered function of progeny cells. Advanced maternal age (AMA) is associated with an increased risk of having a child with ASD for reasons that are not understood. To explore whether AMA involves covert aneuploidy or epigenetic dysregulation leading to ASD in the offspring, we tested an homogeneous ectodermal cell type from 47 individuals with ASD compared with 48 typically developing (TD) controls born to mothers of [&ge;]35 years, using a quantitative genome-wide DNA methylation assay. We show that DNA methylation patterns are dysregulated in ectodermal cells in these individuals, having accounted for confounding effects due to subject age, sex and ancestral haplotype. We did not find mosaic aneuploidy or copy number variability to occur at differentially-methylated regions in these subjects. Of note, the loci with distinctive DNA methylation were found at genes expressed in the brain and encoding protein products significantly enriched for interactions with those produced by known ASD-causing genes, representing a perturbation by epigenomic dysregulation of the same networks compromised by DNA mutational mechanisms. The results indicate the presence of a mosaic subpopulation of epigenetically-dysregulated, ectodermally-derived cells in subjects with ASD. The epigenetic dysregulation observed in these ASD subjects born to older mothers may be associated with aging parental gametes, environmental influences during embryogenesis or could be the consequence of mutations of the chromatin regulatory genes increasingly implicated in ASD. The results indicate that epigenetic dysregulatory mechanisms may complement and interact with DNA mutations in the pathogenesis of the disorder.\n\nAUTHOR SUMMARYOlder mothers have a higher than expected risk of having a child with an autism spectrum disorder (ASD). The reason for this increased risk is unknown. The eggs of older mothers are more prone to abnormalities of chromosome numbers, suggesting this as one possible mechanism of the increased ASD risk. Age is also associated with a loss of control of epigenetic regulatory patterns that govern gene expression, indicating a second potential mechanism. To test both possibilities, we sampled cells from the same developmental origin as the brain, and performed genome-wide tests looking for unusual chromosome numbers and DNA methylation patterns. The studies were performed on individuals with ASD and typically developing controls, all born to mothers at least 35 years of age at the time of birth. We found the cells from individuals with ASD to have changes in DNA methylation at a number of loci, especially near genes encoding proteins known to interact with those already implicated in ASD. We conclude that epigenetic dysregulation occurring in gametes or early embryonic life may be one of the contributors to the development of ASD.

2014-05-06

Missing kinaesthesia challenges precise naturalistic cortical prosthetic control

10.1101/004861

Ferran Galán;Mark R Baker;Kai Alter;Stuart N Baker;

A major assumption of brain-machine interface (BMI) research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography (EEG) in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.

2014-05-06

Visual areas exert feedforward and feedback influences through distinct frequency channels

10.1101/004804

Andre M Bastos;Julien Vezoli;Conrado A Bosman;Jan-Mathijs Schoffelen;Robert Oostenveld;Jarrod R Dowdall;Peter De Weerd;Henry Kennedy;Pascal Fries;

Visual cortical areas are thought to form a hierarchy and to subserve cognitive functions by interacting in both feedforward and feedback directions. While feedforward influences convey sensory signals, feedback influences modulate brain responses to a given sensory stimulus according to the current behavioural context. Many studies have demonstrated effects of feedback influences on feedforward driven responses and on behaviour. Also, anatomical projections in both directions have been identified. However, although these studies have revealed the anatomical paths and the neurophysiological consequences of influences in both directions, the neurophysiological mechanisms through which these influences are exerted remain largely elusive. Here we show that in the primate visual system, feedforward influences are carried by theta-band (~4 Hz) and gamma-band (~60-80 Hz) synchronization, and feedback influences by beta-band (~14-18 Hz) synchronization. These frequency-specific asymmetries in directed influences were revealed by simultaneous local field potential recordings from eight visual areas and an analysis of Granger-causal influences across all 28 pairs of areas. The asymmetries in directed influences correlated directly with asymmetries in anatomy and enabled us to build a visual cortical hierarchy from the influence asymmetries alone. Across different task periods, most areas stayed at their hierarchical position, whereas particularly frontal areas moved dynamically. Our results demonstrate that feedforward and feedback signalling use different frequency channels, which might subserve their differential communication requirements and lead to differential local consequences. The possibility to infer hierarchical relationships through functional data alone might make it possible to derive a cortical hierarchy in the living human brain.

2014-05-06

A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate

10.1101/004788

Max V Staller;Charless C Fowlkes;Meghan D.J. Bragdon;Zeba B. Wunderlich;Angela DePace;

In developing embryos, gene regulatory networks canalize cells towards discrete terminal fates. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos depleted of a key maternal input, bicoid (bcd), by building a cellular- resolution gene expression atlas containing measurements of 12 core patterning genes over 6 time points in early development. With this atlas, we determine the precise perturbation each cell experiences, relative to wild type, and observe how these cells assume cell fates in the perturbed embryo. The first zygotic layer of the network, consisting of the gap and terminal genes, is highly robust to perturbation: all combinations of transcription factor expression found in bcd depleted embryos were also found in wild type embryos, suggesting that no new cell fates were created even at this very early stage. All of the gap gene expression patterns in the trunk expand by different amounts, a feature that we were unable to explain using two simple models of the effect of bcd depletion. In the second layer of the network, depletion of bcd led to an excess of cells expressing both even skipped and fushi tarazu early in the blastoderm stage, but by gastrulation this overlap resolved into mutually exclusive stripes. Thus, following depletion of bcd, individual cells rapidly canalize towards normal cell fates in both layers of this gene regulatory network. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further modeling of canalization in this transcriptional network.

2014-05-06

Pushed beyond the brink: Allee effects, environmental stochasticity, and extinction

10.1101/004887

Gregory Roth;Sebastian Schreiber;

To understand the interplay between environmental stochasticity and Allee effects, we analyze persistence, asymptotic extinction, and conditional persistence for stochastic difference equations. Our analysis reveals that persistence requires that the geometric mean of fitness at low densities is greater than one. When this geometric mean is less than one, asymptotic extinction occurs with high probability for low initial population densities. Additionally, if the population only experiences positive density-dependent feedbacks, conditional persistence occurs provided the geometric mean of fitness at high population densities is greater than one. However, if the population experiences both positive and negative density-dependent feedbacks, conditional persistence only occurs if environmental fluctuations are sufficiently small. We illustrate counter-intuitively that environmental fluctuations can increase the probability of persistence when populations are initially at low densities, and can cause asymptotic extinction of populations experiencing intermediate predation rates despite conditional persistence occurring at higher predation rates.

2014-05-07

Pushed beyond the brink: Allee effects, environmental stochasticity, and extinction

10.1101/004887

Gregory Roth;Sebastian Schreiber;

To understand the interplay between environmental stochasticity and Allee effects, we analyze persistence, asymptotic extinction, and conditional persistence for stochastic difference equations. Our analysis reveals that persistence requires that the geometric mean of fitness at low densities is greater than one. When this geometric mean is less than one, asymptotic extinction occurs with high probability for low initial population densities. Additionally, if the population only experiences positive density-dependent feedbacks, conditional persistence occurs provided the geometric mean of fitness at high population densities is greater than one. However, if the population experiences both positive and negative density-dependent feedbacks, conditional persistence only occurs if environmental fluctuations are sufficiently small. We illustrate counter-intuitively that environmental fluctuations can increase the probability of persistence when populations are initially at low densities, and can cause asymptotic extinction of populations experiencing intermediate predation rates despite conditional persistence occurring at higher predation rates.

2014-09-02

Genetic dissection of MAPK-mediated complex traits across S. cerevisiae

10.1101/004895

Sebastian Treusch;Frank W Albert;Joshua S Bloom;Iulia E Kotenko;Leonid Kruglyak;

Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we determined the genetic architectures of phenotypes critically dependent on MAPK signaling cascades. Genetic variants identified fell within MAPK signaling networks themselves as well as other interconnected signaling pathways, illustrating how genetic variation can shape the phenotypic output of highly conserved signaling cascades.

2014-05-07

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

10.1101/004911

Mario Fruzangohar;Esmaeil Ebrahimie;David L Adelson;

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

2014-05-08

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

10.1101/004911

Mario Fruzangohar;Esmaeil Ebrahimie;David L Adelson;

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

2014-05-14

Differential relationships between habitat fragmentation and within-population genetic diversity of three forest-dwelling birds

10.1101/004903

Benjamin Zuckerberg;Matt Carling;Roi Dor;Elise Ferree;Garth Spellman;Andrea Townsend;

Habitat fragmentation is a major driver of environmental change affecting wildlife populations across multiple levels of biological diversity. Much of the recent research in landscape genetics has focused on quantifying the influence of fragmentation on genetic variation among populations, but questions remain as to how habitat loss and configuration influences within-population genetic diversity. Habitat loss and fragmentation might lead to decreases in genetic diversity within populations, which might have implications for population persistence over multiple generations. We used genetic data collected from populations of three species occupying forested landscapes across a broad geographic region: Mountain Chickadee (Poecile gambeli; 22 populations), White-breasted Nuthatch (Sitta carolinensis; 13 populations) and Pygmy Nuthatch (Sitta pygmaea; 19 populations) to quantify patterns of haplotype and nucleotide diversity across a range of forest fragmentation. We predicted that fragmentation effects on genetic diversity would vary depending on dispersal capabilities and habitat specificity of the species. Forest aggregation and the variability in forest patch area were the two strongest landscape predictors of genetic diversity. We found higher haplotype diversity in populations of P. gambeli and S. carolinensis inhabiting landscapes characterized by lower levels of forest fragmentation. Conversely, S. pygmaea demonstrated the opposite pattern of higher genetic diversity in fragmented landscapes. For two of the three species, we found support for the prediction that highly fragmented landscapes sustain genetically less diverse populations. We suggest, however, that future studies should focus on species of varying life-history traits inhabiting independent landscapes to better understand how habitat fragmentation influences within-population genetic diversity.

2014-05-08

Spatial variation in water loss predicts terrestrial salamander distribution and population dynamics

10.1101/004986

William Peterman;Raymond D Semlitsch;

Many patterns observed in ecology, such as species richness, life history variation, habitat use, and distribution have physiological underpinnings. For many ectothermic organisms temperature relations shape these patterns, but for terrestrial amphibians, water balance may supersede temperature as the most critical physiologically-limiting factor. Many amphibian species have little resistance to water loss, which restricts them to moist microhabitats and may significantly affect foraging, dispersal, and courtship. Using plaster models as surrogates for terrestrial plethodontid salamanders, we measured water loss under ecologically-relevant field conditions to estimate the duration of surface activity time across the landscape. Surface activity time was significantly affected by topography, solar exposure, canopy cover, maximum air temperature, and time since rain. Spatially, surface activity times were highest in ravine habitats and lowest on ridges. Surface activity time was a significant predictor of salamander abundance, as well as a predictor of successful recruitment; the probability of a juvenile salamander occupying an area with high surface activity time was two times greater than an area with limited predicted surface activity. Our results suggest that survival, recruitment, or both are demographic processes that are affected by water loss and the ability of salamanders to be surface active. Results from our study extend our understanding of plethodontid salamander ecology, emphasize the limitations imposed by their unique physiology, and highlight the importance of water loss to spatial population dynamics. These findings are timely to understanding the effects that fluctuating temperature and moisture conditions predicted for future climates will have on plethodontid salamanders.

2014-05-09

Data-intensive modeling of forest dynamics

10.1101/005009

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

2014-05-10

Data-intensive modeling of forest dynamics

10.1101/005009

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

2014-08-06

Data-intensive modeling of forest dynamics

10.1101/005009

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

2014-12-08

Data-intensive modeling of forest dynamics

10.1101/005009

Jean F Lienard;Dominique Gravel;Nikolay Strigul;

Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

2015-05-02

An appraisal of the classic forest succession paradigm with the shade tolerance index

10.1101/004994

Jean F Lienard;Ionut Florescu;Nikolay Strigul;

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the \"shade tolerance index\" and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic -variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

2014-05-10

An appraisal of the classic forest succession paradigm with the shade tolerance index

10.1101/004994

Jean F Lienard;Ionut Florescu;Nikolay Strigul;

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the \"shade tolerance index\" and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic -variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

2014-06-03

Background selection as baseline for nucleotide variation across the Drosophila genome

10.1101/005017

Josep M Comeron;

The constant removal of deleterious mutations by natural selection causes a reduction in neutral diversity and efficacy of selection at genetically linked sites (a process called Background Selection, BGS). Population genetic studies, however, often ignore BGS effects when investigating demographic events or the presence of other types of selection. To obtain a more realistic evolutionary expectation that incorporates the unavoidable consequences of deleterious mutations, we generated high-resolution landscapes of variation across the Drosophila melanogaster genome under a BGS scenario independent of polymorphism data. We find that BGS plays a significant role in shaping levels of variation across the entire genome, including long introns and intergenic regions distant from annotated genes. We also find that a very large percentage of the observed variation in diversity across autosomes can be explained by BGS alone, up to 70% across individual chromosome arms, thus indicating that BGS predictions can be used as baseline to infer additional types of selection and demographic events. This approach allows detecting several outlier regions with signal of recent adaptive events and selective sweeps. The use of a BGS baseline, however, is particularly appropriate to investigate the presence of balancing selection and our study exposes numerous genomic regions with the predicted signature of higher polymorphism than expected when a BGS context is taken into account. Importantly, we show that these conclusions are robust to the mutation and selection parameters of the BGS model. Finally, analyses of protein evolution together with previous comparisons of genetic maps between Drosophila species, suggest temporally variable recombination landscapes and thus, local BGS effects that may differ between extant and past phases. Because genome-wide BGS and temporal changes in linkage effects can skew approaches to estimate demographic and selective events, future analyses should incorporate BGS predictions and capture local recombination variation across genomes and along lineages.

2014-05-09

Module organizational principles and dynamics in biological networks

10.1101/005025

Chun-Yu Lin;Tsai-ling Lee;Yi-Wei Lin;Yu-Shu Lo;Chih-Ta Lin;Jinn-Moon Yang;

A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying organizational principles of a module remain unclear. In this study, we collected CORUM module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores (PPIES) and interface evolution scores (IES) to infer module elements, including core and ring components. Functions of core components were highly correlated (Pearsons r = 0.98) with those of 11,384 essential genes. In comparison with ring components, core proteins and PPIs were conserved in multiple species. Subsequently, protein dynamics and module dynamics of biological networks and functional diversities confirmed that core components form dynamic biological network hubs and play key roles in various biological functions. PPIES and IES can reflect module organization principles and protein/module dynamics in biological networks. On the basis of the analyses of gene essentiality, module dynamics, network topology, and gene co-expression, the module organizational principles can be described as follows: 1) a module consists of core and ring components; 2) the core components play major roles in biological functions and collaborate with ring components to perform certain functions in some cases; 3) the core components are conserved and essential in module dynamics in time and space.

2014-05-11

Mycoplasma stress response: adaptive regulation or broken brakes?

10.1101/004960

Pavel V Mazin;Gleb Y Fisunov;Alexey Y Gorbachev;Ilya A Altukhov;Tatiana A Semashko;Dmitry G Alexeev;Vadim M Govorun;

The avian bacterial pathogen Mycoplasma gallisepticum is a good model for transcriptional regulation studies due to its small genome and relative simplicity. In this study, we used RNA-Seq experiments combined with MS-based proteomics to accurately map coding sequences (CDSs), transcription start sites (TSSs) and transcription terminators (TTs) and to decipher their roles in stress-induced transcriptional responses. We identified 1061 TSSs at an FDR (false discovery rate) of 10% and showed that almost all transcription in M. gallisepticum is initiated from classic TATAAT promoters, which are surrounded by A/T-rich sequences and rarely accompanied by a -35 element. Our analysis revealed the pronounced complexity of the operon structure: on average, each coding operon has one internal TSS and TT in addition to the primary ones. Our new transcriptomic approach based on the intervals between the two closest transcription initiators and/or terminators allowed us to identify two classes of TTs: strong, unregulated and hairpin-containing TTs and weak, heat shock-regulated and hairpinless TTs. Comparing the gene expression levels under different conditions (such as heat, osmotic and peroxide stresses) revealed widespread and divergent transcription regulation in M. gallisepticum. Modeling suggested that the structure of the core promoter plays a major role in gene expression regulation. We have shown that the heat stress activation of cryptic promoters combined with the suppression of hairpinless TTs leads to widespread, seemingly non-functional transcription.

2014-05-09

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

10.1101/005082

Paula Tataru;Jasmine A. Nirody;Yun S. Song;

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: [email protected]

2014-05-12

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

10.1101/005082

Paula Tataru;Jasmine A. Nirody;Yun S. Song;

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: [email protected]

2014-05-13

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

10.1101/005082

Paula Tataru;Jasmine A. Nirody;Yun S. Song;

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: [email protected]

2014-09-03

bíogo: a simple high-performance bioinformatics toolkit for the Go language

10.1101/005033

R Daniel Kortschak;David L Adelson;

biogo is a framework designed to ease development and maintenance of computationally intensive bioinformatics applications. The library is written in the Go programming language, a garbage-collected, strictly typed compiled language with built in support for concurrent processing, and performance comparable to C and Java. It provides a variety of data types and utility functions to facilitate manipulation and analysis of large scale genomic and other biological data. biogo uses a concise and expressive syntax, lowering the barriers to entry for researchers needing to process large data sets with custom analyses while retaining computational safety and ease of code review. We believe biogo provides an excellent environment for training and research in computational biology because of its combination of strict typing, simple and expressive syntax, and high performance.

2014-05-12

bíogo: a simple high-performance bioinformatics toolkit for the Go language

10.1101/005033

R Daniel Kortschak;David L Adelson;

biogo is a framework designed to ease development and maintenance of computationally intensive bioinformatics applications. The library is written in the Go programming language, a garbage-collected, strictly typed compiled language with built in support for concurrent processing, and performance comparable to C and Java. It provides a variety of data types and utility functions to facilitate manipulation and analysis of large scale genomic and other biological data. biogo uses a concise and expressive syntax, lowering the barriers to entry for researchers needing to process large data sets with custom analyses while retaining computational safety and ease of code review. We believe biogo provides an excellent environment for training and research in computational biology because of its combination of strict typing, simple and expressive syntax, and high performance.

2015-03-27

CasFinder: Flexible algorithm for identifying specific Cas9 targets in genomes

10.1101/005074

John Aach;Prashant Mali;George M Church;

CRISPR/Cas9 systems enable many molecular activities to be efficiently directed in vivo to user-specifiable DNA sequences of interest, including generation of dsDNA cuts and nicks, transcriptional activation and repression, and fluorescence. CRISPR targeting relies on base pairing of short RNA transcripts with their target DNA sequences that must also be adjacent to fixed DNA motifs. However, rules for Cas9 targeting specificity are incompletely known. With increasing numbers of Cas9 systems being developed and deployed in more and more organisms, there is now strong need for a flexible and rational method for finding Cas9 sites with low off-targeting potential. We address this through the CasFinder system, which we demonstrate by generating human and mouse exome-wide catalogs of specific sites for three varieties of Cas9 - S. pyogenes, S. thermophilus (ST1), and N. meningitidis - that each target 56-74% of all exons. We also generate reduced sets of up to 3 targets per gene for use in high-throughput Cas9-based gene knockout screens that target 75-80% of all genes.

2014-05-12

Diversity and evolution of centromere repeats in the maize genome

10.1101/005058

Paul Bilinski;Kevin Distor;Jose Gutierrez-Lopez;Gabriela Mendoza Mendoza;Jinghua Shi;R. Kelly Dawe;Jeffrey Ross-Ibarra;

Centromere repeats are found in most eukaryotes and play a critical role in kinetochore formation. Though CentC repeats exhibit considerable diversity both within and among species, little is understood about the mechanisms that drive cen- tromere repeat evolution. Here, we use maize as a model to investigate how a complex history involving polyploidy, fractionation, and recent domestication has impacted the diversity of the maize CentC repeat. We first validate the existence of long tan- dem arrays of repeats in maize and other taxa in the genus Zea. Although we find considerable sequence diversity among CentC copies genome-wide, genetic similar- ity among repeats is highest within these arrays, suggesting that tandem duplica- tions are the primary mechanism for the generation of new copies. Genetic clustering analyses identify similar sequences among distant repeats, and simulations suggest that this pattern may be due to homoplasious mutation. Although the two ancestral subgenomes of maize have contributed nearly equal numbers of centromeres, our analysis shows that the vast majority of all CentC repeats derive from one of the parental genomes. Finally, by comparing maize with its wild progenitor teosinte, we find that the abundance of CentC has decreased through domestication while the peri- centromeric repeat Cent4 has drastically increased.

2014-05-12

The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

10.1101/005041

Bargavi Thyagarajan;Jesse D Bloom;

Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the {approx} 104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the proteins structure and function, and can be used to create a model that describes hemagglutinins evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenzas antigenic evolution.

2014-05-12

The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

10.1101/005041

Bargavi Thyagarajan;Jesse D Bloom;

Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the {approx} 104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the proteins structure and function, and can be used to create a model that describes hemagglutinins evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenzas antigenic evolution.

2014-06-22

The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

10.1101/005041

Bargavi Thyagarajan;Jesse D Bloom;

Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the {approx} 104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the proteins structure and function, and can be used to create a model that describes hemagglutinins evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenzas antigenic evolution.

2014-06-23

Independent Theta Phase Coding Accounts for CA1 Population Sequences and Enables Flexible Remapping

10.1101/005066

Angus Chadwick;Mark C. W. van Rossum;Matthew F. Nolan;

Populations of hippocampal place cells encode an animals past, current and future location through sequences of action potentials generated within each cycle of the network theta rhythm. These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies. In contrast, we show that a model based on rate and phase coding in independent neurons is sufficient to explain the organization of CA1 population activity during theta states. We show that CA1 population activity can be described as an evolving traveling wave that exhibits phase coding, rate coding, spike sequences and that generates an emergent population theta rhythm. We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity. Our analysis suggests that independent coding enables flexible generation of sequential population activity within the duration of a single theta cycle.

2014-05-12

Evidences of a cytotoxic activity of S-adenosylmethionine on OCI-AML3 cells

10.1101/004978

Kathrin Aprile von Hohenstaufen;Irina Puoti;Marisa Meloni;Barbara De Servi;

BackgroundThe acute myeloid leukemia (AML) cell line OCI-AML3, carrying both NPM1 mutation A and the heterozygous DNMT3A R882C mutation, represents the model for in vitro studies on AML with mutated NPM11. AML with mutated NPM1 harbours a hypo-methylated profile distinct from those of the other AML subtypes2. This characteristic is probably related to the inhibitory effect of the mutant DNMT3A on the wild type protein3. S-adenosylmethionine (SAM) is a universal methyl donor acting as a coenzyme of DNMT3A. There are growing evidences of the antineoplastic effect of SAM in vitro and in murine models of gastric cancer, colon cancer and hepatocellular carcinoma, where SAM induces the downregulation of several oncogenes4-10. Moreover SAM upregulates the expression of DNMT enzymes in lung cancer cells11. In our knowledge there are no published data exploring the effect of SAM on the growth of OCI-AML3 cells and its ability to modulate DNMT3A activity in this cell line. Study design and methodsThe present data have been generated between August 2013 and April 2014 at the VITROSCREEN facilities in Milan-ITALY. We used a 3-(4,5-dimethylthiazol-2-yl)-2,5-dephenyl tetrazolium bromide (MTT) assay to assess the cytotoxic effect of SAM iodide (Sigma-Aldrich) on OCI-AML3 cells (DSMZ Leibniz Institut). We analyzed then the ability of SAM to induce apoptosis by Tali Image-Based Cytometer (green Annexin V - Alexa Fluor 488 for apoptotic cells, red propidium and green Annexin V-Alexa Fluor 488 for necrotic cells). ResultsThe MTT assays were performed after having treated the OCI-AML3 cells with various concentrations of the indicated drug for 24 hours. We observed no significant effects on cells viability using 0.5M, 10 M and 100 M of SAM (data not shown). In contrast, a dose dependent cytotoxic effect of SAM on OCI-AML3 cells was evident for concentrations equal or superior to 500 M, with an IC50 of 500 M (Figure 1). Since a Cmax of 211(SD 94)M after single intravenous infusion of SAM was previously reported in healthy voluntarees12, we decided to investigate the cytotoxic effect of SAM for concentrations close to 211 M using the MTT test. A significant dose dependent reduction of the cells viability was observed with SAM 200M (62,74% viable cells) and SAM 300M (53.32% viable cells), (Figure 2). The Apoptosis assay after 24 hours of treatment with SAM showed no differences in the percentages of apoptotic cells between the OCI-AML3 cells treated with SAM 300-500-2500 M and the untreated cells (data not shown). After 72 hours, only a minimal effect on the amount of apoptotic cells was obtained, while a clear dose dependent increase in the proportion of dead cells was noted (Figure 3), confirming the results of the aforementioned MTT tests. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/004978v1_fig1.gif" ALT="Figure 1"> View larger version (12K): [email protected]@23bd30org.highwire.dtl.DTLVardef@59a9c5org.highwire.dtl.DTLVardef@98e377_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO MTT assay after 24 h exposure of OCI-AML3 cells to SAM at 0.5 mM, 2.5 mM and 10 mM. CN= negative control, untreated cells. SAM=S-Adenosylmethionine. Percentages of viable cells are reported. Both SAM treated cells and CN were tested in triplicate samples. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/004978v1_fig2.gif" ALT="Figure 2"> View larger version (16K): [email protected]@17e1d00org.highwire.dtl.DTLVardef@a213c5org.highwire.dtl.DTLVardef@c01c13_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO MTT assay after 24 h exposure of OCI-AML3 cells to SAM at 200 M (0.2mM), 300 M, 400 M, 500 M, 1000 M, 2500M. CN=negative control, untreated cells. SAM=S-Adenosylmethionine. Percentages of viable cells are reported. Both SAM treated cells and CN were tested in triplicate samples. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/004978v1_fig3.gif" ALT="Figure 3"> View larger version (31K): [email protected]@1825707org.highwire.dtl.DTLVardef@1f99a6org.highwire.dtl.DTLVardef@a08020_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO Percentages of Living, Dead and Apoptotic OCI-AML3 cells assessed by Tali image based cytometer after 72 hours treatment with different concentrations of SAM, and after 72 hours in medium (CN=negative control). Both SAM treated cells and CN were tested in duplicate samples. C_FIG ConclusionsSAM showed remarkable in vitro cytotoxic activity on OCI-AML3 cells at concentrations similar to those achievable in humans after intravenous administration. SAM is not able to induce apoptosis of OCI-AML3 cells in vitro after 72 hours of treatment. However, the increase in the amount of dead cells after SAM treatment may be due to mechanisms other than apoptosis. In order to verify if the observed cytotoxicity was mediated by the enzymatic activity of DNMT3A, we planned to repeat the cytotoxicity assays after DNMT3A silencing. The in vivo antineoplastic effect of SAM could be assessed in NOD/SCID mice engrafted with OCI-AML3 cells. Authors contributionKAvH wrote the study rationale, designed the study, interpreted the data and wrote the article; IP revised the article, MM and BDS planned and interpreted the experiments and BDS performed the experiments.

2014-05-08

Cosi2 : An efficient simulator of exact and approximate coalescent with selection

10.1101/005090

Ilya Shlyakhter;Pardis C. Sabeti;Stephen F. Schaffner;

MotivationEfficient simulation of population genetic samples under a given demographic model is a prerequisite for many analyses. Coalescent theory provides an efficient framework for such simulations, but simulating longer regions and higher recombination rates remains challenging. Simulators based on a Markovian approximation to the coalescent scale well, but do not support simulation of selection. Gene conversion is not supported by any published coalescent simulators that support selection.\n\nResultsWe describe cosi2, an efficient simulator that supports both exact and approximate coalescent simulation with positive selection. cosi2 improves on the speed of existing exact simulators, and permits further speedup in approximate mode while retaining support for selection. cosi2 supports a wide range of demographic scenarios including recombination hot spots, gene conversion, population size changes, population structure and migration.\n\ncosi2 implements coalescent machinery efficiently by tracking only a small subset of the Ancestral Recombination Graph, sampling only relevant recombination events, and using augmented skip lists to represent tracked genetic segments. To preserve support for selection in approximate mode, the Markov approximation is implemented not by moving along the chromosome but by performing a standard backwards-in-time coalescent simulation while restricting coalescence to node pairs with overlapping or near-overlapping genetic material. We describe the algorithms used by cosi2 and present comparisons with existing selection simulators.\n\nAvailabilityA free C++ implementation of cosi2 is available at http://broadinstitute.org/[~]ilya/cosi2.\n\[email protected]

2014-05-14

When genomes collide: multiple modes of germline misregulation in a dysgenic syndrome of Drosophila virilis

10.1101/005124

Mauricio A. Galdos;Alexandra A. Erwin;Michelle L. Wickersheim;Chris C. Harrison;Kendra D. Marr;Justin Blumenstiel;

In sexually reproducing species the union of gametes that are not closely related can result in genomic incompatibility. Hybrid dysgenic syndromes represent a form of genomic incompatibility that can arise when transposable element (TE) abundance differs between two parents. When TEs lacking in the female parent are transmitted paternally, a lack of corresponding silencing small RNAs (piRNAs) transmitted through the female germline can lead to TE mobilization in progeny. The epigenetic nature of this phenomenon is demonstrated by the fact that genetically identical females of the reciprocal cross are normal. Here we show that in the hybrid dysgenic syndrome of Drosophila virilis, an excess of paternally inherited TE families leads not only to increased expression of these TEs, but also coincides with derepression of TEs in equal abundance within parents. Moreover, TE derepression is stable as flies age and associated with piRNA biogenesis defects for only some TEs. At the same time, TE activation is associated with a genome wide shift in the distribution of endogenous gene expression and an increase in abundance of off-target genic piRNAs. To identify regions of the maternal genome that most protect against dysgenesis, we performed an F3 backcross analysis. We find that pericentric regions play a dominant role in maternal protection. This F3 backcross approach additionally allowed us to clarify the properties of genic paramutation in D. virilis. Overall, results support a model in which early germline events in dysgenesis establish a chronic, stable state of mis-expression that is maintained through adulthood.\n\nSuch early events in the germline that are mediated by parent-of-origin effects may be important in determining patterns of gene expression in natural populations.\n\nAuthor SummaryTransposable elements (TE) are selfish elements that code for the function of copying themselves. More than half the human genome is comprised of such elements. Studies in the fruit flies Drosophila melanogaster and D. virilis have been important in demonstrating a role for RNA silencing by piwi-interacting RNAs (piRNAs) in protecting the genome against these harmful elements. These small RNAs are capable of recognizing TE mRNAs and mediating their destruction by Argonaute proteins. They are also transmitted by the female germline to offspring in order to maintain a stable genome across generations. When males carrying a particular TE family are crossed with females lacking the element, the mother is unable to provide genome defense via complementary piRNAs that target the element. This leads to excess TE activation in the germline and sterility. This phenomenon is known as hybrid dysgenesis. In this article we characterize the genomic landscape of TE destabilization that occurs in hybrid dysgenesis in D. virilis. Previous studies had demonstrated that multiple TEs mobilized during hybrid dysgenesis. We demonstrate that this mobilization of multiple TEs is associated with activation of additional TEs in the germline. In addition, we find that TE activation leads to the production of off-target genic piRNAs that cause reduced expression of highly expressed genes. Finally, we show that genic off-target effects of piRNA silencing can contribute to parent-of-origin effects on gene expression. Similar phenomena may influence patterns of gene expression in the germline of natural populations.

2014-05-13

A new insight for the screening of potential β-lactamase inhibitors

10.1101/005181

Vijai Singh;Dharmendra Kumar Chaudhary;

The {beta}-lactamase produces by Aeromonas hydrophila which enables to hydrolyze and inactivate {beta}- lactam ring of antibiotics. The homology modeling was used to generate the 3-D model of {beta}-lactamase by using known template 3-D structure. The stereochemical quality and torsion angle of 3-D model were validated. Total eleven effective drugs have been selected and targeted the active amino acid residues in {beta}-lactamase. The drugs were derivative of {beta}-lactam ring antibiotics and screening was made by docking. Out of 11 drugs, 3 drugs (Ampicillin, Astreonam and Sultamicillin) were found to be more potent on the basis of robust binding energy between protein-drug interactions. Additionally, homology of {beta}-lactamase of A. hydrophila resembled with other pathogenic bacteria that used for phylogeny analysis. These findings suggest a new insight for better understanding and useful for designing of novel potent drugs.

2014-05-14

A new insight for the screening of potential β-lactamase inhibitors

10.1101/005181

Vijai Singh;Dharmendra Kumar Chaudhary;

The {beta}-lactamase produces by Aeromonas hydrophila which enables to hydrolyze and inactivate {beta}- lactam ring of antibiotics. The homology modeling was used to generate the 3-D model of {beta}-lactamase by using known template 3-D structure. The stereochemical quality and torsion angle of 3-D model were validated. Total eleven effective drugs have been selected and targeted the active amino acid residues in {beta}-lactamase. The drugs were derivative of {beta}-lactam ring antibiotics and screening was made by docking. Out of 11 drugs, 3 drugs (Ampicillin, Astreonam and Sultamicillin) were found to be more potent on the basis of robust binding energy between protein-drug interactions. Additionally, homology of {beta}-lactamase of A. hydrophila resembled with other pathogenic bacteria that used for phylogeny analysis. These findings suggest a new insight for better understanding and useful for designing of novel potent drugs.

2014-05-15

qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots

10.1101/005165

Stephen D. Turner;

SummaryGenome-wide association studies (GWAS) have identified thousands of human trait-associated single nucleotide polymorphisms. Here, I describe a freely available R package for visualizing GWAS results using Q-Q and manhattan plots. The qqman package enables the flexible creation of manhattan plots, both genome-wide and for single chromosomes, with optional highlighting of SNPs of interest.\n\nAvailabilityqqman is released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=qqman). The source code is available on GitHub (https://github.com/stephenturner/qqman).\n\[email protected]

2014-05-14

RNA-seq gene profiling - a systematic empirical comparison

10.1101/005207

Nuno A Fonseca;John A Marioni;Alvis Brazma;

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the \"true\" expression levels?\n\nWe evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the ground truth in real RNAseq data sets, we used simulated data to assess the differences between the \"true\" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.

2014-05-14

RNA-seq gene profiling - a systematic empirical comparison

10.1101/005207

Nuno A Fonseca;John A Marioni;Alvis Brazma;

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the \"true\" expression levels?\n\nWe evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the ground truth in real RNAseq data sets, we used simulated data to assess the differences between the \"true\" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.

2014-05-29

RNA-seq gene profiling - a systematic empirical comparison

10.1101/005207

Nuno A Fonseca;John A Marioni;Alvis Brazma;

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the \"true\" expression levels?\n\nWe evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the ground truth in real RNAseq data sets, we used simulated data to assess the differences between the \"true\" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.

2014-07-16