scholarly journals How Much Information is Provided by Human Epigenomic Data? An Evolutionary View

2018 ◽  
Author(s):  
Brad Gulko ◽  
Adam Siepel
Keyword(s):  
Genetic Variation ◽  
Natural Selection ◽  
Evolutionary Model ◽  
Cell Types ◽  
Data Sets ◽  
Produce Cell ◽  
Neutral Drift ◽  
Fitness Consequences ◽  
Evolutionary View ◽  
Genomic Function

ABSTRACTHere, we ask the question, “How much information do available epigenomic data sets provide about human genomic function, individually or in combination?” We consider nine epigenomic and annotation features across 115 cell types and measure genomic function by using signatures of natural selection as a proxy. We measure information as the reduction in entropy under a probabilistic evolutionary model that describes genetic variation across ∼50 diverse humans and several nonhuman primates. We find that several genomic features yield more information in combination than they do individually, with DNase-seq displaying particularly strong synergy. Most of the entropy in human genetic variation, by far, reflects mutation and neutral drift; the genome-wide reduction in entropy due to selection is equivalent to only a small fraction of the storage requirements of a single human genome. Based on this framework, we produce cell-type-specific maps of the probability that a mutation at each nucleotide will have fitness consequences (FitCons scores). These scores are predictive of known functional elements and disease-associated variants, they reveal relationships among cell types, and they suggest that ∼8% of nucleotide sites are constrained by natural selection.

scholarly journals Cryptic variation and its manifestation in the Lower Trentonian Rafinesquina lineage (Ordovician, New York State)

1992 ◽  
Vol 6 ◽  
pp. 292-292
Author(s):  
Robert Titus
Keyword(s):  
New York ◽  
Genetic Variation ◽  
Natural Selection ◽  
New York State ◽  
Ecological Factors ◽  
Stabilizing Selection ◽  
Rapid Population Growth ◽  
York State ◽  
Cryptic Variation ◽  
Adaptive Peak

Species populations commonly carry a great deal of genetic variation which is not expressed in individual phenotypes. Cryptic variation can be carried in recessive alleles, in cases of heterosis, or where modifier genes inhibit expression of the hidden trait. Other genetic and ecological factors also allow cryptic variation. Stabilizing selection prevents the expression of hidden traits; normalizing selection weeds out the deviants and canalizing selection suppresses their traits. Together the two keep the species near the top of the adaptive peak. Cryptic variation balances a species' need to be well-adapted to its environment and also for it to maintain a reserve of variation for potential environmental change. Expression of cryptic traits is rare and is usually associated with times of greatly reduced natural selection and rapid population growth, when the lower slopes of the adaptive peak are exposed.A possible example of the manifestation of cryptic traits occurs within the lower Trentonian Rafinesquina lineage of New York State. The two most commonly reported species of the genus have been reappraised in terms of cryptic variation. Extensive collections of Rafinesquina “lennoxensis” reveal far more intergrading morphotypes than had hitherto been recognized. The form which Salmon (1942) described is broadly U-shaped with sulcate margins. It grades into very convex forms as well as sharply-defined or convexly geniculate types. Of great importance, all forms grade into the flat, U-shaped, alate R. trentonensis, which is, by far, the most common and widespread lower Trentonian member of the genus. The R. “lennoxensis” assemblage has a very narrow biostratigraphy, being confined to a few locations in the upper Napanee Limestone. This places it in a quiet, protected, low stress, lagoonal setting behind the barrier shoal facies of the Kings Falls Limestone.The R. “lennoxensis” assemblage does not constitute a natural biologic species; it is reinterpreted as an assemblage of phenodeviants occupying a low stress, low natural selection lagoon facies. All such forms should be included within R. trentonensis. Given the evolutionary plasticity of this genus, extensive cryptic variation is not surprising.

scholarly journals Evolution and evolvability: celebrating Darwin 200

2008 ◽  
Vol 5 (1) ◽  
pp. 44-46 ◽  
Author(s):  
John F.Y Brookfield
Keyword(s):  
Genetic Variation ◽  
Natural Selection ◽  
Time Scales ◽  
Evolutionary Change ◽  
Adaptive Mutation ◽  
Genetic Covariances ◽  
The Relationship ◽  
The Way

The concept of ‘evolvability’ is increasingly coming to dominate considerations of evolutionary change. There are, however, a number of different interpretations that have been put on the idea of evolvability, differing in the time scales over which the concept is applied. For some, evolvability characterizes the potential for future adaptive mutation and evolution. Others use evolvability to capture the nature of genetic variation as it exists in populations, particularly in terms of the genetic covariances between traits. In the latter use of the term, the applicability of the idea of evolvability as a measure of population's capacity to respond to natural selection rests on one, but not the only, view of the way in which we should envisage the process of natural selection. Perhaps the most potentially confusing aspects of the concept of evolvability are seen in the relationship between evolvability and robustness.

scholarly journals The genomics of adaptation

2012 ◽  
Vol 279 (1749) ◽  
pp. 5024-5028 ◽  
Author(s):  
Jacek Radwan ◽  
Wiesław Babik
Keyword(s):  
Genetic Variation ◽  
Natural Selection ◽  
Royal Society ◽  
Genetic Basis ◽  
Genomic Data ◽  
Evolutionary Change ◽  
Introductory Article ◽  
Technological Advances ◽  
A Genome ◽  
Genome Level

The amount and nature of genetic variation available to natural selection affect the rate, course and outcome of evolution. Consequently, the study of the genetic basis of adaptive evolutionary change has occupied biologists for decades, but progress has been hampered by the lack of resolution and the absence of a genome-level perspective. Technological advances in recent years should now allow us to answer many long-standing questions about the nature of adaptation. The data gathered so far are beginning to challenge some widespread views of the way in which natural selection operates at the genomic level. Papers in this Special Feature of Proceedings of the Royal Society B illustrate various aspects of the broad field of adaptation genomics. This introductory article sets up a context and, on the basis of a few selected examples, discusses how genomic data can advance our understanding of the process of adaptation.

scholarly journals Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

2009 ◽  
Vol 14 (9) ◽  
pp. 1054-1066 ◽  
Author(s):  
Keith A. Houck ◽  
David J. Dix ◽  
Richard S. Judson ◽  
Robert J. Kavlock ◽  
Jian Yang ◽  
...  
Keyword(s):  
Human Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Environmental Chemicals ◽  
Data Sets ◽  
Chemical Library ◽  
Data Set ◽  
Cell Systems ◽  
Disease Biology ◽  
Nfκb Pathway

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, leverage cellular regulatory networks to detect and distinguish chemicals with a broad range of target mechanisms and biological processes relevant to human toxicity. Here the authors use the BioMAP human cell systems to characterize effects relevant to human tissue and inflammatory disease biology following exposure to the 320 environmental chemicals in the Environmental Protection Agency’s (EPA’s) ToxCast phase I library. The ToxCast chemicals were assayed at 4 concentrations in 8 BioMAP cell systems, with a total of 87 assay endpoints resulting in more than 100,000 data points. Within the context of the BioMAP database, ToxCast compounds could be classified based on their ability to cause overt cytotoxicity in primary human cell types or according to toxicity mechanism class derived from comparisons to activity profiles of BioMAP reference compounds. ToxCast chemicals with similarity to inducers of mitochondrial dysfunction, cAMP elevators, inhibitors of tubulin function, inducers of endoplasmic reticulum stress, or NFκB pathway inhibitors were identified based on this BioMAP analysis. This data set is being combined with additional ToxCast data sets for development of predictive toxicity models at the EPA. ( Journal of Biomolecular Screening 2009:1054-1066)

scholarly journals Evidence of between-population differences in natural selection on extra-floral nectaries of the shrub Anemopaegma album (Bignoniaceae)

Botany ◽  
2016 ◽  
Vol 94 (3) ◽  
pp. 201-213
Author(s):  
Anselmo Nogueira ◽  
Pedro J. Rey ◽  
Julio M. Alcántara ◽  
Lúcia G. Lohmann
Keyword(s):  
Genetic Variation ◽  
Natural Selection ◽  
Evolutionary Ecology ◽  
Common Garden ◽  
Phenotypic Selection ◽  
Population Differences ◽  
Wild Populations ◽  
Neotropical Savanna ◽  
Foliar Damage ◽  
Floral Nectaries

Extra-floral nectaries (EFNs) are thought to represent protective adaptations against herbivory, but studies on the evolutionary ecology of EFNs have seldom been conducted. Here we investigate the patterns of natural selection and genetic variation in EFN traits in two wild populations of Anemopaegma album Mart. ex DC. (Bignoniaceae) that have been previously described as contrasting EFN – ant adapted localities in the Neotropical savanna (Cristália and Grão Mogol). In each population, four EFN descriptors, foliar damage, and reproductive success variables were measured per plant (100–120 plants per population). To estimate the heritability of EFN traits, we crossed reproductive plants in the field, and grew offspring plants in a common garden. The results showed that ant assemblages differed between populations, as did the range of foliar herbivory. Genetic variation and positive phenotypic selection in EFN abundance were only detected in the Cristália population, in which plants with more EFNs were more likely to reproduce. An evaluation of putative causal links conducted by path analysis corroborated the existence of phenotypic selection on EFNs, which was mediated by the herbivory process in the Cristália population. While EFNs could be currently under selection in Cristália, it is possible that past selection may have driven EFN traits to become locally adapted to the local ant assemblage in the Grão Mogol population.

scholarly journals Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

2019 ◽  
Vol 78 (8) ◽  
pp. 1127-1134 ◽  
Author(s):  
Paul Martin ◽  
James Ding ◽  
Kate Duffus ◽  
Vasanthi Priyadarshini Gaddi ◽  
Amanda McGovern ◽  
...  
Keyword(s):  
Rheumatic Diseases ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Healthcare Providers ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Causal Genes ◽  
Additional Cell

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

scholarly journals Pharmacologically targeting tribbles gene expression in colorectal cancer

2021 ◽  
Vol 31 (Supplement_2) ◽  
Author(s):  
Victor Yassuda ◽  
Ana Luísa De Sousa-Coelho
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cell Types ◽  
Disease Recurrence ◽  
Rosemary Extract ◽  
Acid Oxidation ◽  
Data Sets ◽  
Compensatory Mechanism ◽  
Dependent Manner ◽  
Pharmacological Potential

Abstract Background TRIB1, TRIB2 and TRIB3 belong to the mammalian Tribbles family of pseudokinases proteins. Several studies reported Tribbles oncogenic role in different types of cancer, including colorectal cancer (CRC). Though current CRC treatment can be curative, patients are in risk of disease recurrence, meaning novel pharmacological targets and strategies are required. Our goal was to analyze Tribbles gene expression in CRC in response to different drugs. Methods Tribbles transcript levels were obtained from GEO profiles database (NCBI). Gene data sets (GDS) were selected based on experimental drug treatment description. Statistical analysis was performed at GraphPadPrism. Results Compared to non-treated control, TRIB2 expression was ∼2-fold increased in colorectal adenocarcinoma samples from patients treated with cyclooxygenase-2 inhibitor celecoxib (GDS3384), though not statistically significant (P < 0.1). TRIB1 was unaltered and data for TRIB3 was not available. By contrast, all Tribbles showed differential expression after treatment of SW620 colon cancer cells with supercritical rosemary extract in progressive increasing doses (0, 30, 60, 100 μg/mL) (P < 0.01;GDS5416). While both TRIB1 and TRIB3 were moderately increased in a dose-dependent manner (∼18% and 13%, respectively), TRIB2 was maximally down-regulated by ∼15% after 60 μg/mL. Conclusions Although celecoxib exhibits antiproliferative effects in different cancer cell types, TRIB2 gene expression showed a trend to be induced after treatment, in contrast to several genes involved in fatty acid oxidation that were down-regulated, which could result from a compensatory mechanism based on a metabolic shift. Since TRIB1/TRIB3 and TRIB2 were oppositely modulated in response to rosemary extract, additional studies are needed to validate its specific pharmacological potential interest for CRC treatment.

scholarly journals HOT or not: examining the basis of high-occupancy target regions

2017 ◽  
Author(s):  
Katarzyna Wreczycka ◽  
Vedran Franke ◽  
Bora Uyar ◽  
Ricardo Wurmus ◽  
Altuna Akalin
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Data Sets ◽  
Gene Promoters ◽  
Quadruplex Dna ◽  
Golden Standard ◽  
Multiple Cell ◽  
Multiple Species

AbstractHigh-occupancy target (HOT) regions are the segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and the associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solemnly defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed “hyper-ChIPable” regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers and enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

scholarly journals MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data

2019 ◽  
Author(s):  
Elmer A. Fernández ◽  
Yamil D. Mahmoud ◽  
Florencia Veigas ◽  
Darío Rocha ◽  
Mónica Balzarini ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Therapy Response ◽  
Cell Types ◽  
Gene Signature ◽  
Response To Therapy ◽  
Support Vector ◽  
Data Sets ◽  
Cell Type ◽  
Before And After

AbstractRNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

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