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Research into the impact of selection during maize domestication has established that selection during domestication is linked with strongly reduced diversity in the non-transcribed region where regulatory sequences are generally found, but with more modestly reduced diversity in the transcriptional unit. An analysis of nucleotide polymorphism in teosinte branched1, a gene involved in maize evolution, indicates that selection during maize domestication was aimed at the non-transcribed region.

The divergence of Drosophila pseudoobscura and close relatives D. persimilis and D. pseudoobscura bogotana has been studied using comparative DNA sequence data from multiple nuclear loci. New data from the Hsp82 and Adh regions, in conjunction with existing data from Adh and the Period locus, are examined in the light of various models of speciation. The principal finding is that the three loci present very different histories, with Adh indicating large amounts of recent gene flow among the taxa, while little or no gene flow is apparent in the data from the other loci. The data were compared with predictions from several isolation models of divergence. These models include no gene flow, and they were found to be incompatible with the data. Instead the DNA data, taken together with other evidence, seem consistent with divergence models in which natural selection acts against gene flow at some loci more than at others. This family of models includes some sympatric and parapatric speciation models, as well as models of secondary contact and subsequent reinforcement of sexual isolation.

Thirty-five period locus sequences from Drosophila pseudoobscura and its siblings species, D. p. bogotana, D. persimilis, and D. miranda, were studied. A large amount of variation was found within D. pseudoobscura and D. persimilis, consistent with histories of large effective population sizes. D. p. bogotana, however, has a severe reduction in diversity. Combined analysis of per with two other loci, in both D. p. bogotana and D. pseudoobscura, strongly suggest this reduction is due to recent directional selection at or near per within D. p. bogotana. Since D. p. bogotana is highly variable and shares variation with D. pseudoobscura at other loci, the low level of variation at per within D. p. bogotana can not be explained by a small effective population size or by speciation via founder event. Both D. pseudoobscura and D. persimilis have considerable intraspecific gene flow. A large portion of one D. persimilis sequence appears to have arisen via introgression from D. pseudoobscura. The time of this event appears to be well after the initial separation of these two species. The estimated times since speciation are one mya for D. pseudoobscura and D. persimilis and 2 mya since the formation of D. miranda.

Abstract Background Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.

Abstract Background A large quantity of nitrogen (N) fertilizer is used for crop production to achieve high yields at a significant economic and environmental cost. Efforts have been directed to understanding the molecular basis of plant responses to N and identifying N-responsive genes in order to manipulate their expression, thus enabling plants to use N more efficiently. No studies have yet delineated these responses at the transcriptional level when plants are grown under chronic N stress and the understanding of regulatory elements involved in N response is very limited. Results To further our understanding of the response of plants to varying N levels, a growth system was developed where N was the growth-limiting factor. An Arabidopsis whole genome microarray was used to evaluate global gene expression under different N conditions. Differentially expressed genes under mild or severe chronic N stress were identified. Mild N stress triggered only a small set of genes significantly different at the transcriptional level, which are largely involved in various stress responses. Plant responses were much more pronounced under severe N stress, involving a large number of genes in many different biological processes. Differentially expressed genes were also identified in response to short- and long-term N availability increases. Putative N regulatory elements were determined along with several previously known motifs involved in the responses to N and carbon availability as well as plant stress. Conclusion Differentially expressed genes identified provide additional insights into the coordination of the complex N responses of plants and the components of the N response mechanism. Putative N regulatory elements were identified to reveal possible new components of the regulatory network for plant N responses. A better understanding of the complex regulatory network for plant N responses will help lead to strategies to improve N use efficiency.

Fundamental questions remain about the application of omics in environmental risk assessments, such as the consistency of data across laboratories. The objective of the present study was to determine the congruence of transcript data across 6 independent laboratories. Male fathead minnows were exposed to a measured concentration of 15.8 ng/L 17α-ethinylestradiol (EE2) for 96 h. Livers were divided equally and sent to the participating laboratories for transcriptomic analysis using the same fathead minnow microarray. Each laboratory was free to apply bioinformatics pipelines of its choice. There were 12 491 transcripts that were identified by one or more of the laboratories as responsive to EE2. Of these, 587 transcripts (4.7%) were detected by all laboratories. Mean overlap for differentially expressed genes among laboratories was approximately 50%, which improved to approximately 59.0% using a standardized analysis pipeline. The dynamic range of fold change estimates was variable between laboratories, but ranking transcripts by their relative fold difference resulted in a positive relationship for comparisons between any 2 laboratories (mean R2 > 0.9, p < 0.001). Ten estrogen-responsive genes encompassing a fold change range from dramatic (>20-fold; e.g., vitellogenin) to subtle (∼2-fold; i.e., block of proliferation 1) were identified as differentially expressed, suggesting that laboratories can consistently identify transcripts that are known a priori to be perturbed by a chemical stressor. Thus, attention should turn toward identifying core transcriptional networks using focused arrays for specific chemicals. In addition, agreed-on bioinformatics pipelines and the ranking of genes based on fold change (as opposed to p value) should be considered in environmental risk assessment. These recommendations are expected to improve comparisons across laboratories and advance the use of omics in regulations. Environ Toxicol Chem 2017;36:2593-2601. © 2017 SETAC [ABSTRACT FROM AUTHOR]